Read the following three research articles and complete written response to the readings. Write a page and a half synthesis of the three articles plus 1 discussion question per article.
The following factors will be considered in grading: relevance, accuracy, synthetization of the reading materials, degree to which the responses show understanding/comprehension of the material, and quality of writing.
· Questions must be original, thoughtful and not easily found in the readings.
· Follows APA Rules
· Use proper citations
· Use past tense when discussing the studies (the research was already conducted).
· Avoid the use of the following words: me, you, I, we, prove, proof
· Refer to the articles by their authors (year of publication) (not by the title of the article or the words first, second, or third)
· Do not just summarize the articles. Dig deeper!
***FOLLOW THE ATTACHED SAMPLE
Two Factor Model of ASD Symptoms
One of the key factors in determining whether an individual has Autism Spectrum Disorder (ASD) is in their social and communication skills. Individuals who are diagnosed with ASD have delayed joint attention, eye gazing, and other social interactions such as pointing (Swain et al., 2014).
Joint attention is an important social skill to master because it is a building block for developing theory of mind which, helps us to understand other’s perspectives. Korhonen et al. (2014) found that individuals with autism have impaired joint attention. However, some did not show impairment in joint attention, which lead to evidence that suggests there are different trajectories for joint attention. One suggestion as to why Korhonen et al. (2014) found mixed results, is that there is evidence that joint attention may not be directly linked to individuals with ASD since they were unable to find a difference in joint attention between ASD and developmentally delayed (DD) individuals. Another suggestion for the mixed results, is individual interest in the task vary. Research has found that while individualized studies are beneficial in detecting personal potential and abilities, it would be difficult to generalize the study in order to further research to ASD as a whole (Korhonen et al., 2014). In addition to joint attention, atypical gaze shifts is a distinguishing factor in individuals with ASD. Swain et al. (2014) found the main difference between typically developing (TD) and ASD individuals in the first 12 months of life is in gaze shifts. Individuals that were diagnosed with ASD earlier had lower scores on positive affect, joint attention, and gaze shifts, however those diagnosed later differed from typically developing (TD) only in gaze shifts. It is not until 24 months that later onset ASD individuals significantly differ from their TD peers, by displaying lower positive affect and gestures (Swain et al., 2014). These findings may lead to other ASD trajectories.
Another defining characteristic of ASD is the excess of restrictive patterns of interest and repetitive motor movements. These patterns and movements often impaired the individual from completing daily tasks. Like joint attention and gaze shifts, these repetitive movements and patterns of interest have different trajectories (Joseph et al., 2013). Joseph et al. (2013) found that individuals with high cognitive functioning ASD engage in more distinct and specific interests and less in repetitive motor movements than individuals with lower cognitive functioning ASD. Another finding showed that at the age of two, repetitive motor and play patterns were more common than compulsion. By the age of four all these behaviors increased however, repetitive use of specific objects was found to be less frequent in older children than younger children. This finding suggests that the ritualistic behaviors and motor movements may present themselves differently based on the age of the individual (Joseph et al., 2013).
Joseph et al. (2013), Korhornen et al. (2014), and Swain et al. (2014) all defined key characteristics of an ASD individual and explains the different trajectories of each characteristic. The difficulty with the trajectories is that it is specific to each individual, some symptoms may worsen while others remain stable. It is also difficult to generalize finding with small sample sizes (Joseph et al., 2013).
Discussion Questions:
1. Korhonen et al. (2014) did not use preference-based stimuli to look for joint attention and did not separate high- from low-functioning ASD individuals. Do you think that there could be a difference in level of motivation from each group? If so, how do you think this could change the results?
2. Swain et al. (2014) found that early and late onset of ASD did not differ in their social skills scores at the age of 12 months. If we know that their social skills do not differ then, is there another factor that would allow diagnosis of late onset ASD to be diagnosed at an earlier point in development?
3. Joseph et al. (2013) explains that it is difficult to assess the trajectories of ASD with a small sample size however, how do you think that their findings still help advance the research on ASD?
Detecting early signs of autism is essential
for timely diagnosis and initiation of effective inter-
ventions. Several research groups have initiated pro-
spective studies of high-risk populations including
infant siblings, to systematically collect data on early
signs within a longitudinal design. Despite the potential
advantages of prospective studies of young children at
high-risk for autism, there are also significant meth-
odological, ethical and practical challenges. This paper
outlines several of these challenges, including those
related to sampling (e.g., defining appropriate com-
parison groups), measurement and clinical implications
(e.g., addressing the needs of infants suspected of
having early signs). We suggest possible design and
implementation strategies to address these various
challenges, based on current research efforts in
the field and previous studies involving high-risk
populations.
Keywords Early identification Æ Screening Æ
Longitudinal studies Æ Prospective studies Æ Infant Æ
Autism Æ Child development Æ Siblings
Please note that the opinion and assertions contained herein
are the private opinions of the authors and are not to be con-
strued as official or as representing the views of the National
Institute of Child Health and Human Development, the
National Institute of Mental Health, or the National Institutes
of Health.
L. Zwaigenbaum (&)
Department of Paediatrics, McMaster Children’s Hospital
at McMaster University, PO Box 2000, Hamilton, Ontario
L8N 3Z5, Canada
e-mail: zwaigenb@mcmaster.ca
A. Thurm
Division of Pediatric Translational Research and Treatment
Development, National Institute of Mental Health,
Bethesda, MD, USA
W. Stone
Departments of Pediatrics and Psychology & Human
Development, Vanderbilt University, Nashville,
TN, USA
G. Baranek
Division of Occupational Science, University of North
Carolina at Chapel Hill, Chapel Hill, NC, USA
S. Bryson
Departments of Pediatrics and Psychology, Dalhousie
University, Halifax, NS, USA
J. Iverson
Department of Psychology, University of Pittsburgh,
Pittsburgh, PA, USA
A. Kau
Center for Developmental Biology and Perinatal Medicine,
National Institute of Child Health and Human
Development, Bethesda, MD, USA
A. Klin
Yale Child Study Centre, Yale University, New Haven, CT,
USA
C. Lord
Department of Psychology, University of Michigan, Ann
Arbor, MI, USA
R. Landa
Department of Psychiatry and Behavioral Sciences,
Kennedy Krieger Institute, Baltimore, MD, USA
J Autism Dev Disord (2007) 37:466–480
DOI 10.1007/s10803-006-0179-x
123
O R I G I N A L P A P E R
Studying the Emergence of Autism Spectrum Disorders
in High-risk Infants: Methodological and Practical Issues
Lonnie Zwaigenbaum Æ Audrey Thurm Æ Wendy Stone Æ Grace Baranek Æ
Susan Bryson Æ Jana Iverson Æ Alice Kau Æ Ami Klin Æ Cathy Lord Æ
Rebecca Landa Æ Sally Rogers Æ Marian Sigman
Published online: 4 August 2006
� Springer Science+Business Media, Inc. 2006
Introduction
Several reviews over the past decade have highlighted
the importance of early recognition and specialized
intervention for improving outcomes for children with
autism spectrum disorders (ASD) (Dawson & Oster-
ling, 1997; Rogers, 1996; Smith, Groen, & Wynn, 2000).
Although recent service registry (Croen, Grether,
Hoogstrate, & Selvin, 2002) and population-based data
(Yeargin-Alsopp, et al., 2003) suggest that more chil-
dren are being diagnosed prior to age 4 years than in
the past, a formal diagnosis may still lag years behind
the time when parents initially identify concerns
(Coonrod & Stone, 2004; Howlin & Moore, 1997;
Siegel, Pliner, Eschler, & Elliott, 1988). As a result,
interest has increased in identifying and raising
awareness regarding the characteristics of ASD present
at young ages (Bryson, Zwaigenbaum & Roberts, 2004;
Landa, 2003). In addition to improving outcomes,
earlier diagnosis allows parents the opportunity to
receive counseling regarding current estimates of
recurrence risk in autism, which they may take into
account in future family planning. Research to date
supports the conclusions that one can: (1) reliably
diagnose as young as 24 months (Lord, 1995; Stone
et al., 1999); and (2) observe the behavioral markers of
autism well before 24 months (e.g., Dahlgren & Gill-
berg, 1989; Ohta, Nagai, Hara, & Sasaki, 1987; Rogers
& DiLalla, 1990).
Most of the work aimed at identifying early signs of
ASD has been retrospective, focusing on early behav-
ioral evidence of the disorder in children who have
already received a diagnosis. The most common
methods used to gather information about earlier
behaviors have been retrospective reports from parents
and analysis of early home videotapes. Although
research using these approaches has supported clinical
efforts aimed at earlier detection, many questions
regarding early signs, their timing, and their underlying
developmental mechanisms remain. Prospective
research into the early development of ASD in
high-risk infants is an exciting new frontier, and can
potentially answering these questions more systemati-
cally, while avoiding some of the biases associated with
retrospective designs. In this paper, we outline the
theoretical advantages and general feasibility of pro-
spective studies of young children at high-risk for
ASD, and acknowledge and discuss the significant
methodological, ethical and practical challenges that
accompany these studies. Issues discussed include the
design of high-risk studies, selection of comparison
groups, measurement of developmental delay and
deviance, generalizability, and clinical interpretation of
findings.
Identifying Early Signs of Autism using
Retrospective Designs
Retrospective parental reports offer a unique window
into early behaviors of children with ASD, as parents
have the advantage of observing their children’s
behavior over time and across a variety of settings.
Investigators report a wide range of symptoms that are
more common in children with autism under the age of
24 months than similar-aged children with develop-
mental delays or mental retardation (DD). Early
symptoms associated with autism cross several devel-
opmental domains, including social behavior (Dahlgren
& Gillberg, 1989; De Giacomo & Fombonne, 1998;
Hoshino et al., 1982; Ohta et al., 1987; Young, Brewer,
& Pattison, 2003), communication (Dahlgren &
Gillberg, 1989; De Giacomo & Fombonne, 1998; Ohta
et al., 1987; Young et al., 2003), affective expression
(Dahlgren & Gillberg, 1989; De Giacomo &
Fombonne, 1998; Hoshino et al., 1982), and sensory
hypo- and hypersensitivities (Dahlgren & Gillberg,
1989; De Giacomo & Fombonne, 1998; Hoshino et al.,
1982). These findings have been very important in
guiding further research aimed at identifying early signs
of ASD. However, a number of factors limit parents’
ability to provide accurate descriptions of early
behaviors. First, a parent’s incidental observations
regarding the subtle social and communicative differ-
ences that characterize young children with autism may
be limited compared to systematic assessment by
trained clinicians (Stone, Hoffman, Lewis, & Ousley,
1994). Moreover, their tendency to use compensatory
strategies to elicit their child’s best behaviors (with or
without their awareness) may affect their behavioral
descriptions (Baranek, 1999). Retrospective parental
R. Landa
Department of Psychiatry and Behavioral Sciences, Johns
Hopkins School of Medicine, Baltimore, MD, USA
S. Rogers
Department of Psychiatry and Behavioral Sciences,
University of California at Davis, Sacramento, CA, USA
M. Sigman
Departments of Psychology and Psychiatry, University of
California, Los Angeles, CA, USA
J Autism Dev Disord (2007) 37:466–480 467
123
reports may be also be prone to errors and distortions of
recall, especially when one asks parents to remember
behaviors that occurred many years ago. In particular,
having already received a diagnosis of autism for their
child, parents may bias their reports toward behaviors
that are consistent with the diagnosis. A recent retro-
spective study overcame some of these problems by
gathering information about behaviors under
24 months from parents of preschoolers before they had
received a diagnosis (Wimpory, Hobson, Williams, &
Nash, 2000). However, limitations of this methodology
remain, as retrospective reports are not generally
informative on the issue of whether differences in early
social and communicative development are best char-
acterized by delayed emergence, reduced frequency, or
truly abnormal or deviant quality of fundamental skills
such as joint attention.
A second strategy for obtaining retrospective
information about characteristics of autism present
before 24 months is the analysis of early videotapes of
children made by their parents. This approach has
significant strengths relative to retrospective parental
reports: it allows the observation of behaviors as they
occur in familiar and natural settings, and enables
objective rating of behavior by unbiased observers.
However, this methodology is not without its limita-
tions, the foremost being that parents record these
tapes to preserve family memories, rather than to
document their child’s behavior across a variety of
settings. As a result, tapes from different families will
naturally vary as a function of the quality of the
recording, the activities and settings that were
recorded, and the length of time the child is visible.
Moreover, if children do not behave as expected (or
desired), parents may re-record taped segments until
they obtain the desired response. Efforts to standardize
tapes across families can be extremely difficult and
time intensive (Baranek, 1999). Moreover, most
studies employing home videotapes have used children
with typical development (TD) rather than those with
DD as comparison groups, which limits the extent of
our knowledge about autism-specific deficits. Behav-
iors found to differentiate children with ASD from
children with TD under 24 months by at least two
studies are: responding to name (Baranek, 1999;
Osterling & Dawson, 1994; Osterling, Dawson, &
Munson, 2002), looking at others (Adrien et al., 1993;
Maestro et al., 2002; Osterling & Dawson, 1994;
Osterling et al., 2002), smiling at others (Adrien et al.,
1993; Maestro et al., 2002), and motor stereotypies
(Adrien et al., 1993; Baranek, 1999). Only two studies
published to date compared behaviors of children with
ASD with those of children with DD younger than
24 months; these found that children with ASD were
less likely to respond to their name (Baranek, 1999;
Osterling et al., 2002) and to look at others (Osterling
et al., 2002). Notably, analysis of home videos also
highlights that there is may be a subgroup of ‘‘late
onset’’ children whose early behavioral symptoms are
less apparent (Osterling et al., 2002; Werner, Dawson,
Osterling, & Dinno, 2000).
Potential Advantages of Prospective Studies
Retrospective parental reports and analyses of home
videos can help guide the development of early iden-
tification and screening procedures (as argued by Fil-
ipek et al., 1999), but these procedures must ultimately
be validated empirically in prospective studies, with
sufficient follow-up of both screen positive and screen
negative children to allow estimates of sensitivity and
specificity. In fact, prospective studies of high-risk
infants (which, until recently, have been rare in autism)
may also identify novel behavioral (and biological)
markers that show the way forward in developing more
effective early identification and screening measures.
Prospective studies are not subject to recall biases, they
can be designed to examine specific constructs of
interest, and they can provide comparable data col-
lection points and methods across children. Perhaps,
most importantly, these studies allow collection of data
longitudinally across different ages, which can foster
our understanding of developmental trajectories and
the impact of early delays in one domain (e.g., social
orienting) on the subsequent development of another
(e.g., language).
High-risk samples have informed studies of other
neurodevelopmental and neuropsychiatric conditions,
including language/reading disorders (Carroll &
Snowling, 2004), attention deficit hyperactivity disor-
der (Faraone, Biederman, Mennin, Gershon, & Tsu-
ang, 1996), bipolar affective disorder (Chang, Steiner,
& Ketter, 2000; Geller, Tillman, Craney, & Bolhofner,
2004), and schizophrenia (Schubert & McNeil, 2004).
Prospective studies of siblings and offspring of affected
probands have generated significant insights regarding
premorbid development and predictors of illness in
these high-risk groups. For example, children with
schizophrenic parents have attention and verbal
memory deficits, gross motor delays, and dysfunction
of smooth-pursuit eye movements (Erlenmeyer-Kim-
ling, 2000; Schubert & McNeil, 2004), and children with
a parent or sibling with dyslexia have greater difficulty
with phonological processing than age-matched low-
risk controls, despite normal early language develop-
ment (Carroll & Snowling, 2004). Notably, these
468 J Autism Dev Disord (2007) 37:466–480
123
studies generally focus on group differences between
high- and low-risk children rather than the association
between early markers and outcome status, because of
insufficient power and/or follow-up. In contrast,
autism can be diagnosed in early childhood, so out-
comes can be determined after a relatively short
follow-up period. Hence, one can study autism pro-
spectively much more easily (i.e., with fewer resources
and with less risk of sample loss) than an adult-onset
disorder such as schizophrenia.
Prospective Studies in Autism: Siblings and other
High-Risk Groups
Several populations at increased risk of ASD that can
be identified in early childhood: children with early
signs of autism or developmental delays (DD) identi-
fied through population screening, children at
increased risk of autism due to specific medical diag-
noses or genetic anomalies, and the main focus of this
paper, infants with an older sibling with ASD.
At least two research groups have studied early
signs of autism in high-risk samples identified by
population screening. Charman et al., (1997) and
Swettenham et al., (1998), reported on a high-risk group
of children who failed the Checklist for Autism in
Toddlers (CHAT; Baron-Cohen, Allen, & Gillberg,
1992), a screening measure administered at 18 months
of age. Detailed assessment of social, communication
and play skills was completed at 20 months, and
diagnostic outcomes were assessed at age of three and a
half. Children subsequently diagnosed with autism were
compared to those subsequently diagnosed with devel-
opmental delay based to their 20-month skills. At this
early age, the children with autism spent less time
looking at adults during free play (Swettenham et al.,
1998), were less likely to look at the face of an adult
feigning distress (Charman et al., 1997), showed less
gaze switching between people and objects (Charman
et al., 1997; Swettenham et al., 1998), and showed less
imitation (Charman et al., 1997). Wetherby et al., (2004)
followed a group of children who had failed communi-
cation screening using the Communication and Sym-
bolic Behavior Scales Developmental Profile (CSBS
DP, Wetherby & Prizant, 2002). They obtained video-
tapes of the CSBS Behavior Sample at a mean age of
18–21 months for children who received later diagnoses
of autism, DD, or who were typically developing.
Specific features that differentiated children with
autism from the other two groups include social-
communication behaviors (e.g., reduced eye gaze,
coordination of gaze with other nonverbal behaviors,
directing attention, responding to name, and unusual
prosody) and repetitive body and object use. Notably,
the content of the initial screen (i.e., children are se-
lected based on a particular profile of early signs), may
introduce sampling biases, and the fact that data are
only collected from the point of first screening onward
limits the age range over which autism can be studied.
There are also children at increased risk for autism
due to medical risk factors, such as Fragile X syndrome
(Rogers, Wehner & Hagerman et al., 2001), specific
chromosome abnormalities (Xu, Zwaigenbaum, Szat-
mari & Scherer, 2004), tuberous sclerosis (Bolton &
Griffiths, 1997), and prenatal exposure to valproic acid
(Williams et al., 2001) or thalidomide (Stromland et al.,
2002). However, these specific risk factors are all rel-
atively rare and would be difficult to study in large
numbers, and may be associated with unique clinical
features that may not generalize to other children with
autism.
There is growing interest in studying infant siblings
of children with ASD, who are arguably the most
clearly defined high-risk group available. Notably,
Baron-Cohen et al., (2002) originally developed the
CHAT screening algorithm based on items that, at
18 months, were atypical in four siblings subsequently
diagnosed with autism. More recent reports by Pilow-
sky and colleagues (Pilowski, Yirmiya, Shalev, &
Gross-Tsur, 2003; Pilowski, Yirmiya, Doppelt, Gross-
Tsur, & Shalev, 2004) support the feasibility of study-
ing early development in siblings and Zwaigenbaum
et al., (2005) reported several behavioral markers
which, at 12 months, predict a subsequent diagnosis of
autism in a sibling sample. In addition, Landa & Gar-
rett-Mayer (2006) report developmental levels and
trajectories in that differentiate infant siblings later
diagnosed with autism spectrum disorder, beginning at
6 months of age. Autism is associated with the highest
relative risk in siblings, compared to general popula-
tion of all the neuropsychiatric disorders (Szatmari,
Jones, Zwaigenbaum & MacLean, 1998). Previous
studies found rates of autism in siblings of children
with autism range from 3% to 5%, which is at least
20 times higher than rates of autism in the general
population (Bailey, Phillips & Rutter, 1996; Simonoff,
1998; Szatmari et al., 1998). In fact, estimates of
recurrence risk (that is, the risk to later-born children)
may be as high as 8.6% when one child in the family
has autism, and 35% when two siblings have autism
(Ritvo et al., 1989). Notably, these risk estimates may
be somewhat conservative, as they come mainly from
studies conducted over 20 years ago, using more
restrictive diagnostic criteria (DSM-III).
The risk to relatives of individuals with autism also
extends beyond the traditional boundaries of the
J Autism Dev Disord (2007) 37:466–480 469
123
autistic spectrum (Bailey, Palferman, Heavey, & Le
Couteur, 1998). Family members have higher rates of
certain psychiatric and developmental disorders, com-
pared to individuals with no family history of autism
(Landa, Folstein & Isaacs, 1991; Landa et al., 1992;
Pickles et al., 2000; Piven, Palmer, Jacobi, Childress, &
Arndt, 1997; Smalley, McCracken, & Tanguay, 1995;
Yirmiya & Shaked, 2005). As well, because of the early
age of diagnosis of the proband, one can ascertain sib-
lings in early infancy or even prenatally, making it
possible to study early neurodevelopmental mecha-
nisms, and to partially avoid (or at least to systemati-
cally measure) the impact of potentially confounding
environmental factors. Infant sibling research offers
unique opportunities to study the neural origins and
developmental cascade that leads to autism, potentially
providing new insights into its neurobiology, improved
methods of early detection, and earlier opportunities for
intervention.
In August 2003, the National Alliance for Autism
Research (NAAR) and the National Institute of Child
Health and Human Development (NICHD) co-spon-
sored a workshop for researchers engaged in the study
of populations of young children at high-risk for autism,
particularly siblings of children with autism. Despite the
theoretical advantages and exciting opportunities
associated with this research design, there are clearly
significant methodological, ethical and practical chal-
lenges facing researchers studying young children at
high-risk for autism. In the remainder of this paper we
outline several of these challenges, including those
related to sampling (e.g., recruitment of adequately
sized samples, determining inclusion/exclusion criteria
for high-risk infants and appropriate comparison
groups), measurement (e.g., selection of constructs and
measures) and clinical implications (e.g., clinical man-
agement of infants who appear to have early signs of
ASD). We suggest possible design and implementation
solutions for these various challenges, based on current
research efforts in the field and previous studies
involving high-risk populations. These issues have
implications not only for research with infant siblings,
but also for research in other aspects of early charac-
terization and diagnosis of autism.
High-risk studies in other fields (e.g., schizophrenia,
dyslexia) have generally been designed to compare
siblings with controls on a group basis, without
knowing the ultimate outcomes of individual siblings
(Carroll & Snowling, 2004; Erlenmeyer-Kimling,
2000). Initial infant sibling studies of ASD by Pilowski
and colleagues (2003, 2004) also focus on group com-
parisons. However, if the main objective of a sibling
study is to identify early markers that are predictive of
a specific diagnosis, then individual outcomes become
important, and one must power the sample size with
reference to the expected number of participants who
will have the diagnosis of interest (i.e., not the total
number of infants enrolled).
The required sample size for these studies will
depend on the specific research question posed. A few
issues are considered for illustration. First, if one
defines the outcome of interest more broadly (e.g.,
language delay), there will be a larger number of sib-
lings with that outcome, potentially making it easier to
detect differences between ‘affected’ and ‘unaffected’
siblings. However, predictors of secondary outcomes
such as language delay may not generalize outside of an
autism sibling sample, limiting the clinical utility of such
findings. A second issue to consider is the strength of
the association between the predictor variables and
outcome under study (e.g., the sensitivity and specificity
of early markers for the subsequent diagnosis of aut-
ism), which will influence the power to detect a rela-
tionship. However, the investigator may sometimes
select predictor variables on a theoretical basis, so the
actual strength of the relation between predictor and
outcome variables may be difficult to estimate with
confidence. A third variable to consider is the number
of outcomes/variables being studied; for example,
contrasting siblings by more than two outcomes (e.g.,
ASD versus developmental delay versus typical devel-
opment), or examining the effects of stratification
variables (e.g., gender) within and across groups may be
of interest, but will require even larger samples sizes.
Due to limitations on numbers of infants born to
older siblings with autism within specified geographic
regions, studies may maximize their efforts to collect
data in a timely fashion by establishing collaborations
across multiple sites and utilizing a common set of
core assessment measures. Such collaborations accel-
erate the process of identifying early predictors of
outcome by increasing the collective sample size so
that investigators can address more refined questions
about outcomes and predictors. Although collabora-
tions between research groups require additional ef-
fort and resources to support the necessary steps of
ensuring consistency in methods and measures, as well
as inter-rater reliability for observations, these proce-
dures allow the examination of consistency of findings
across sites, ensure the fidelity of assessment
470 J Autism Dev Disord (2007) 37:466–480
123
measures, and facilitate future attempts to replicate
findings.
Inclusion/Exclusion Criteria
Decisions regarding inclusion/exclusion criteria for
siblings also depend on the goals of the study. One
major consideration is whether to include probands
and/or siblings with conditions associated with autism
(e.g., tuberous sclerosis, Fragile X syndrome) and those
with other medical risk factors that may predispose the
infant to developmental problems (e.g., low birth
weight, perinatal injuries). If the main goal is to study
early signs and neurodevelopmental mechanisms of
autism in high-risk infants, then there may be some
flexibility in whether to exclude probands and siblings
with known risk factors. Children with such risk factors
may differ developmentally from other children with
autism, an interesting and clinically relevant issue to
explore. However, if a major goal is to identify phe-
notypes and endophenotypes that ‘run true’ in families
for subsequent genetic linkage studies, or to estimate
recurrence rates associated with ‘idiopathic autism,’
then studies may need to exclude cases with known risk
factors.
Within a high-risk (or sibling) design, several decisions
need to be made, including enrollment age for siblings,
selection of comparison groups, and approach to out-
come assessment. Scientific and practical consider-
ations must guide each of these decisions.
Enrollment Age of Siblings
The main strength of the high-risk design is the
potential to study ASD earlier than would be possible
by ascertaining children at the time of diagnosis, which
rarely occurs before age 2 years. There may be
advantages to starting assessment of high-risk infants
either during the first or the second year of life,
depending on the particular focus of the study.
Although studying autism in the first year of life is
largely uncharted territory, this strategy may be an
optimal way to learn about atypical patterns of infant
development that underlie later manifestations of
autism. The few extant findings examining children in
the first and second year of life indicate increased
subtlety of impairments at earlier ages and a number of
measurement challenges. For instance, two studies that
focused specifically on children younger than
12 months (Baranek, 1999; Werner et al., 2000) found
that children with autism show reduced social orienting
compared to typically developing children, but in
general, find fewer differences between the two groups
than analyses of videos taken at 12 months or later. As
well, preliminary data from ongoing sibling studies find
that behavioral risk markers more readily distinguish
autism at 12 months than at 6 months (Zwaigenbaum
et al., 2005). Studying children during the first year of
life presents tremendous opportunities to examine
early neurodevelopmental mechanisms that may
determine later impairments and developmental tra-
jectories in autism (for example, social orienting and
gaze monitoring; Moore & Corkum, 1998; Phillips,
Wellman & Spelke, 2002). Moreover, preliminary
findings that atypical brain growth (Courchesne, Car-
per & Akshoomoff, 2003) may predate behavioral
differences in autism emphasize that studies that target
high-risk infants earlier in life may yield unique data on
early markers. Another advantage of enrolling siblings
at 6 months or younger is the potential to reduce the
problem of biased sampling (and inflated recurrence
risk estimates) resulting from over-referral of parents
who have behavioral concerns.
Prospective studies of toddlers at high-risk of autism
starting in the second year of life are also informative.
Recruitment at this age can include high-risk children
other than siblings (e.g., population screening on the
basis of delays in communication skills; Wetherby et al.,
2004), allowing comparison of children with ASD
across different ascertainment routes, helping to ensure
the generalizability of findings. As well, while it may be
easier to study basic developmental mechanisms in the
first year of life, a more substantial empirical basis exists
for studying behavioral markers and early signs of
autism in the second year. These studies may lead to the
development of new screening measures (or validation
of existing measures), and generate educational strat-
egies to help improve early detection of autism in the
general community, such as in the ‘First Words’ initia-
tive (Wetherby et al., 2004). Recruiting siblings during
the second year of life may also be less resource-
intensive than recruiting younger infants.
The optimal age interval to detect the onset of autistic
symptoms and/or regression remains an empirical
question. Research on typically-developing populations,
as well as research on children with developmental
disorders, indicates that there are ‘‘critical periods’’ for
development of skills typically delayed or absent in
autism—such as between the ages of 6 and 18 months
J Autism Dev Disord (2007) 37:466–480 471
123
when social-communicative behaviors such as joint
attention skills, pointing, and imitation are consolidating
(Corkum & Moore, 1998). Multiple assessments within
such critical period could be extremely informative for
the timing and developmental sequence of these
impairments. However, there are potential trade-offs
between the rich detail afforded by frequent assessment,
and the cost, burden on parents, and potential practice
effects on some standardized measures. Frequent
assessments may be most feasible using naturalistic
observations that do not lead to test-related learning,
such as videotaped maternal-infant interaction samples
to track social development (Hsu & Fogel, 2003), speech
samples or vocabulary checklists to track language
development (Tsao, Liu & Kuhl, 2004) or parent diaries
or report forms tracking the emergence of behaviors
such as gestures (Crais, Day, & Campbell, 2004). The use
of parent questionnaires or diaries and video or audio-
taped behavior samples from home can facilitate data
collection. One can reserve standardized assessment of
language, cognition and adaptive function for ‘‘land-
mark’’ evaluations at less frequent intervals, depending
on study design and the minimal allowable testing
interval on particular tests. Some studies might combine
microanalysis of the development and emergence of
early social-communication processes (i.e., frequent
quantitative and qualitative analyses of operationally
defined, spontaneously occurring behaviors) with
macroanalysis of developmental trajectories in broad
domains of functioning.
If one of the goals of a high-risk study is to identify
early autism-specific markers, then comparison groups
are essential to control for potential confounding
variables and to minimize potential sources of bias. If
early markers identified in high-risk samples are to be
useful in the general population community samples or
clinically referred samples (i.e., to guide first- and
second-level screening and surveillance), it is impor-
tant to know not only whether these markers can dis-
tinguish autism from typical development, but also
whether they distinguish autism from language delays
and/or other developmental delays. One should base
the selection of comparison groups and matching
variables in sibling studies on the populations to which
the research findings will be applied.
In some ways, subgroups of the sibling sample itself
are ‘‘built in’’ comparison groups of infants who will
have outcomes other than ASD. Based on previous
family studies in autism, we might anticipate that in
addition to the 5–8% of siblings who develop ASD,
approximately 10–20% will exhibit milder impair-
ments, including language delay (Bailey et al., 1998;
Folstein et al., 1999; Murphy et al., 2000), leaving about
70% to develop typically. Comparing siblings who
develop ASD, to siblings who do not, controls for two
important factors: (1) the potential impact of exposure
to an older sibling with ASD (and to related psycho-
social stressors on the family); and (2) the possible
expectation bias of increased risk of ASD on the part
of the examiner (i.e., it may be difficult to maintain
blinding to sibling status) or parent rater. However,
there are also important limitations to this approach,
not the least of which is the possibility of misclassifi-
cation error at the point of the initial outcome assess-
ment. For example, some siblings classified as
‘‘typically developing’’ based on standardized mea-
sures may in fact have mild impairments that may
become more apparent at a later age. As well, some
children who are classified as delayed or as having
symptoms of a ‘‘broader autism phenotype’’ may later
receive a diagnosis of an ASD (particularly Asperger’s
syndrome) later on. Misclassification errors will tend to
minimize differences between groups and reduce
power. Other groups of typically developing and
developmentally delayed children may also include
some who would be classified differently as they get
older, but this is more likely to be an issue for siblings
of children with ASD because of their genetic liability.
As well, siblings with developmental delays may not be
representative of other children with delays. In par-
ticular, although siblings of children with autism are
not known to be at higher risk of global cognitive delay
unless they also have an ASD (Fombonne, Bolton,
Prior, Jordan, & Rutter, 1997; Szatmari et al., 1993),
they may have specific language impairments (Dawson
et al., 2002; Landa & Garrett-Mayer, 2006).
Thus, in addition to siblings of children with autism
who do not develop an ASD, one should consider
additional comparison groups. For example, some
studies may benefit from having low-risk groups that
control for the effects of being a later-born child, such
as infant siblings of typically developing children with
no family history of ASD. Including groups to serve as
controls for the developmental delays that often
accompany ASD is also important to consider,
although the selection and recruitment of such groups
is a challenge (Szatmari, Zwaigenbaum & Bryson,
2004). With the exception of children with identified
syndromes (who are unlikely to be representative),
even seemingly high-risk populations, such as siblings
of children with developmental delay, may include a
relatively small proportion who will ultimately receive
a diagnosis of developmental delay and may not cover
472 J Autism Dev Disord (2007) 37:466–480
123
the full spectrum of delays that one might observe in an
unselected sample (Crow & Tolmie, 1998). One may
find delays of a broader range of severity among
infants referred to early intervention programs due to
constitutional and/or psychosocial risk factors (Allen,
1993), and among infants attending a neonatal follow-
up clinic due to prematurity (e.g., Bucher, Killer,
Ochsner, Vaihinger, & Fauchere, 2002). Alternatively,
if the study follows children starting at a sufficiently
advanced age, then one can utilize a comparison group
of children ascertained directly by developmental or
communication delays (for example, through popula-
tion screening; Wetherby et al., 2004). However, a
substantial proportion of children identified due to this
type of delay in the second year of life may ultimately
receive a diagnosis of ASD (Robins, Fein, Barton, &
Green, 2001), so group comparisons may not be valid
or robust until one follows samples to an age at which
diagnostic classification is relatively stable (i.e., at least
3 years of age).
Once one select comparison groups, one should
consider other potential confounds between risk sta-
tus and outcome measures as potential matching
variables (Jarrold & Brock, 2004; Szatmari et al.,
2004). Such confounds might include age, gender
(since autism and language delays are more prevalent
among boys than girls), and birth order (since early
infant behaviour may be influenced by exposure to
older siblings). One should also consider matching on
parental education and/or socioeconomic status.
Although neither factor is known to affect rates of
autism, each may influence rates of other relevant
outcomes such as developmental delays and behav-
ioral disorders.
Several ongoing studies of young infants use end-
points of at least 3 years of age, although investigators
may determine and communicate diagnoses to the
family before this time (Zwaigenbaum et al., 2005).
This approach is consistent with evidence that the
stability of autism spectrum diagnoses increases sig-
nificantly by this age (Lord & Risi, 2000). Ideally, the
diagnostician should be blind to the child’s group and
previous evaluation data to reduce expectation biases.
Diagnosis should also be based on expert assessment
using standardized measures (e.g., the Autism Diag-
nostic Interview—Revised and the Autism Diagnostic
Observation Schedule) and best clinical judgment
based on ICD-10 or DSM-IV-TR criteria. There is
currently very little published concerning the agree-
ment between the ADI-R and ADOS (either in
combination or singly) with clinical diagnosis based
on DSM-IV (de Bildt et al., 2004), although it is well-
established that diagnostic agreement in general cor-
relates positively with the experience of the clinician
(Stone et al., 1999; Volkmar et al., 1994). In that
regard, one should also consider the additional step of
having expert clinicians review all available clinical
data and then reach a consensus best estimate diag-
nosis (as is done in some genetic studies; see Ma-
cLean et al., 1999). However, we do not yet know
whether clinical experience with older preschool
children will ensure stability of autism diagnoses in
toddlers. At present, there is little data on the sensi-
tivity and specificity of measures such as the ADI-R
and ADOS in children under age 2–3 years, so the
interpretation of these measures requires careful
clinical judgment (Lord & Risi, 2000; Moore &
Goodson, 2003).
Given the hypothesis that high-risk infants have
increased rates of language disorders, impaired cogni-
tive abilities, atypical social behaviors and other fea-
tures of the broader autism phenotype, assessments
should measure development across multiple domains
over time in order to capture the breadth of outcomes.
A comprehensive developmental approach grounded
in a thorough intellectual ability assessment is neces-
sary, as one needs to consider constructs such as play,
imitation, language and social interaction in the con-
text of the young child’s cognitive abilities. Develop-
mental assessment should include measures of
expressive and receptive language, adaptive behavior
and overall cognitive profile (see Klin, Chawarska,
Rubin & Volkmar, 2004 for a review). Although it is
challenging to find cognitive tests that include sufficient
nonverbal as well as verbal components, and that one
can use across a reasonable developmental range
without floor or ceiling effects, such assessments will
allow for outcomes such as mental retardation and
specific language impairment to be distinguished from
autism spectrum disorders. One of the challenges at
the outset is that most available measures are designed
to detect quantitative delays in early development
(e.g., smaller vocabularies, lower age equivalent scores
in various areas of cognition) but not atypical or
qualitatively abnormal or deviant patterns of skill
development (e.g., splinter skills, atypical develop-
mental sequence) that may ultimately be more specific
J Autism Dev Disord (2007) 37:466–480 473
123
to autism. Data on developmental trajectories of lan-
guage and cognitive skills may ultimately be more
informative than profiles from any single point in time,
another advantage of studying autism in high-risk
samples using a longitudinal design.
Because the existing diagnostic criteria for autism
(APA, 2000) are not necessarily suitable for diagnosing
very young children, evaluation of young children for
signs of autism (or related communication or social
problems) must include assessment of underlying
developmental constructs. For example, early charac-
teristics of autism evident in children younger than
2 years may likely include subtle deficits such as vari-
able eye gaze, inconsistent joint attention skills,
reduced vocal and/or motor imitation, and repetitive or
abnormal use of objects (Zwaigenbaum et al., 2005).
These behaviors or skill deficits may be markers for
disrupted underlying mechanisms, such as attentional
control, executive functioning, preferential orientation
to social stimuli, social motivation, face processing and
auditory processing (Volkmar, Lord, Bailey, Schultz, &
Klin, 2004). Measures of some of these analogue skills
including joint attention (Mundy, Sigman & Kasari,
1990) and imitation (Rogers, 1999; Stone, Ousley &
Littleford, 1997) have become quite refined. In addi-
tion, some investigators have reported measures of
face processing (Dawson & Zanolli, 2003) and eye-
gaze tracking (Chawarska, Klin, & Volkmar, 2003) in
very young children. However, the field generally lacks
well-validated measurements for most neuropsycho-
logical processes in very young children. Although this
presents an initial challenge to prospective studies of
autism, high-risk samples may provide the necessary
developmental substrate to evaluate innovative mea-
sures focused on early impairments and underlying
mechanisms in autism. Moreover, longitudinal studies
that assess the persistence and developmental pro-
gression of atypical behaviors and skills deficits offer a
significant advantage over previous cross-sectional
research.
Early indicators of autism may present more as the
absence of expected behaviors rather than as the
presence of obvious behavioral aberrations. Measures
that ‘‘press’’ for social or communication behaviors
that are often delayed or deviant in children with
ASD would seem appropriate for assessing high-risk
infants. For example, the Autism Observation Scale
for Infants-AOSI; Zwaigenbaum et al., 2005),
recently developed for the purpose of assessing early
signs of ASD across a range of developmental
domains, adopts this approach. Similarly, the Com-
munication and Symbolic Behavior Scales-Develop-
mental Profile includes several specific play
interactions that press for early social communicative
behaviors, including measures of joint attention
(Wetherby & Prizant, 2002). However, a brief period
of observation in a research lab may not easily cap-
ture the range of contexts and facilitating/interfering
conditions that influence these behaviors in everyday
situations. As a result, one should obtain information
about the persistence, quality and frequency of social
responses from parental report as well as observation,
with special attention paid to how one elicits the
responses and how much parental prompting, sup-
ports, and accommodations are required. For in-
stance, in addition to level and type of
communication, examination of the rate of commu-
nicative behaviors during ‘‘typical’’ social situations
may be informative (Charman et al., 2005). One also
needs data on the quality and context of observed
behaviors to complement simple frequency counts.
Contributions of infant development experts may be
critical for identifying measures that capture the
variability of typical infant development with respect
to social-communicative behaviors.
Measurement of atypical behaviors in young children
is also challenging. The types of unusual behaviors
seen in very young children with autism, such as
seeking or avoiding specific types of sensory responses
and input and repetitive motor behaviors, are partic-
ularly difficult to measure because they vary in pre-
sentation within and across children. Stereotypic
motor behaviors may also be less frequent at very
young ages, at least by parental report (Stone et al.,
1994) and may be difficult to distinguish from the
normal rhythmic movements observed in typically
developing infants (Thelen, 1981a; b). What is pre-
dictive of autism may not simply be the type of
behavior, but rather, the persistence, quality, fre-
quency and contexts under which the behavior is ob-
served—but determining this will require careful
quantitative and qualitative analysis, and appropriate
comparison groups. There are very little normative
data on the development of sensory preferences in
typical infants against which to compare the sensory
behaviors of infants at increased risk for ASD. It is
essential that measures of repetitive behaviors and
sensory interests be normed in typically developing
infants so one can meaningfully interpret the signifi-
cance of findings in high-risk populations.
474 J Autism Dev Disord (2007) 37:466–480
123
Potential Differences between Participants and
Non-Participants
Given that investigators may ultimately use the findings
from prospective studies of high-risk samples to assist
with identification of early signs of autism in the general
population, it is important to consider which factors
may influence participation rates. First, specific con-
cerns may motivate parents to enroll their younger in-
fants. This selection factor does not necessarily imply
that early development in this group will differ from
that of other infants subsequently diagnosed with aut-
ism. However, if parents are more sensitive to atypical
development in one domain compared to another (e.g.,
verbal language versus motor imitation), this factor may
bias the phenotypic distribution of participating infants.
This bias is most explicit in high-risk studies that use
specific screening tools to identify their participants
(Wetherby et al., 2004), but may also be an issue in
sibling studies. Given that early concerns may influence
participation rates, one must interpret estimates of
recurrence risk from infant sibling studies cautiously.
Second, the characteristics of the proband (e.g., level of
function, severity of symptoms) may influence parents’
perceptions of risk and hence, likelihood of participat-
ing. Similarly, parents with other children or relatives
with autism or autism-related conditions may also per-
ceive greater risk in their infants. Finally, other family
characteristics can influence research participation rates
in general, such as socioeconomic status, parental edu-
cation, and family composition (e.g., single versus two-
parent family, number of siblings).
Potential Differences between Siblings who
Develop ASD and other Children with ASD
Children with ASD ascertained through an affected
sibling may differ from other children with ASD. For
example, differences in genetic factors, that is, genes
that lead to higher recurrence rates, may influence the
clinical expression of autism. Notably, a slightly higher
rate of the BAP occurs in extended relatives when
there are two affected children in a sibship (Szatmari
et al., 2000). Differences in early development may
also result from the very fact that there is already a
child in the family with a diagnosis of autism. For the
second affected child, this may lead to earlier recog-
nition of symptoms and initiation of intervention, as
well as differences in parent–child interactions, influ-
enced both by parents’ previous experience with aut-
ism and the added stress of parenting an older child
with special needs. Parents often raise the question as
to whether some behaviors may result from interac-
tions with the older sibling with autism. However, most
available data on early markers of autism in young
children point to the absence of typical social-commu-
nicative behaviors—which would be less influenced by
interaction with siblings—rather that the presence of
atypical, potentially learned behaviors (Baranek, 1999;
Dawson & Osterling, 1997; Rogers & DiLalla, 1990).
Notably, cross-sectional studies have failed to identify
differences in autistic symptoms or level of function
between children with autism who have a sibling with
autism, and children with autism who do not (Cuccaro
et al., 2003). Comparing developmental trajectories
unfolding into later stages of childhood of children
with ASD ascertained through sibling studies with
those of children referred early (e.g., under 2–3 years)
for diagnostic assessment may shed further light on
potential differences between the two groups.
Assessment and identification of possible early mark-
ers of autism have important clinical implications for
individual participants and their families. Discussing
and responding to clinical concerns are, inevitably,
major components (and major responsibilities) in the
day-to-day operation of prospective studies of high-risk
infants, and present challenging clinical and ethical
issues. First, what are clinically sensitive, yet scientifi-
cally rigorous, approaches to eliciting parents’ con-
cerns? How do we best collect information about
parental impressions across a broad range of domains
or test hypotheses about specific early signs without
creating concerns or raising parental anxiety? One
approach is to ask open-ended questions about par-
ticular developmental domains (e.g., ‘‘Describe your
child’s play interests.’’). This approach may yield richer
information than a checklist of atypical behaviors.
Opportunities to observe the child’s naturally occur-
ring behaviors and responses to experimentally
designed presses also reduce the potential burden on
parents to be the sole source of information on early
signs.
Second, how do researchers communicate concerns
that arise from their assessments? The involvement of
an experienced clinician is critical for this aspect of the
project. Providing feedback to parents regarding stan-
dardized measures of language, motor, and cognitive
development is relatively straightforward when the
J Autism Dev Disord (2007) 37:466–480 475
123
child’s performance is consistent with age expectations;
however, one requires clinical expertise to interpret
and communicate assessment results when delays are
found. It may also be possible to share some observa-
tions made during administration of experimental
measures. However, interpretation of the severity of
developmental delays or of performance on specific
experimental tasks in relation to future risk of autism is
much more difficult. At the outset of a study, the
relation between early findings and risk of autism is
generally unknown, and only descriptive feedback is
possible. On the other hand, as the number of children
who complete the study protocol to outcome assess-
ment increases, data accumulate regarding the predic-
tive validity of early markers. At what point is there an
ethical obligation to share this information with par-
ticipants? This issue should be given careful consider-
ation in the study development and design, and in some
situations may warrant ongoing consultation with an
independent ethics committee.
Third, how do researchers respond to concerns that
parents communicate spontaneously? Handling
parental concerns about their infants or toddlers
clearly requires clinical sensitivity and acumen. One
must acknowledge concerns and treat them with
appropriate seriousness, even if their implications for
course and/or prognosis are unknown.
Ethical standards dictate that researchers follow cur-
rent best practice in dealing with diagnostic issues in
high-risk samples. When children meet DSM-IV cri-
teria for ASD, the diagnosis must be communicated to
parents in a timely way to ensure that they can obtain
appropriate services for their child. In some cases a
clinical diagnosis is appropriate before one schedules
the child’s formal outcome assessment. This procedure
may have an impact on outcome assessment itself, to
the extent that early intervention accelerates skill
development and reduces symptoms. However, one
can still complete an independent (and optimally
blind) assessment of diagnosis (e.g., at age 3 years),
thus providing an opportunity to assess stability of
early diagnoses in this group. Moreover, if researchers
fail to communicate diagnoses when criteria are met,
parents may seek support elsewhere and elect to opt
out of studies. Selective drop-out of children with
diagnoses may prove to be a greater threat to longi-
tudinal research in high-risk samples than the effects of
early intervention.
Longitudinal studies of referred samples indicate
that the vast majority of children receiving an expert
clinical diagnosis of ASD at 24 months retain that
diagnosis in an independent assessment at age 3 years
(Lord, 1995; Stone et al., 1999), but there may still be
some diagnostic changes up until age 7 (Charman et al.,
2005). However, only a small percentage of children
with ASD are referred prior to 24 months and children
with the most severe symptoms may predominate, an
ascertainment bias that may inflate estimates of diag-
nostic stability. A high-risk sample may be more likely
to include children across the full range of ASD
severity, including those with milder or more subtle
symptoms.
There may even be instances in which a child
appears to meet DSM-IV criteria for ASD even earlier
than 2 years. However, there are currently few data on
the stability of diagnoses made prior to 2 years, and as
noted earlier, there are no guidelines on how to
interpret scores on standardized measures such as the
ADI-R and ADOS, or even how to interpret DSM-IV
criteria for children in this age group. Boundaries
between ‘‘early markers’’ (those atypical behaviors
which have a statistical association with a later diag-
nosis of autism) and ‘‘diagnostic markers’’ (atypical
behaviors which provide evidence that DSM-IV crite-
ria are currently met) are ill-defined. Although this
situation presents a clinical dilemma in current studies
of high-risk infants, prospective research in this area
provides a unique opportunity to develop diagnostic
criteria that are more developmentally appropriate for
this age group.
The current emphasis on avoiding delays in diag-
nosis places a strong focus on children with ASD who
are missed by early identification and screening efforts
(false negatives). However, particularly as we begin to
test the limits of our clinical experience regarding
assigning diagnosis to toddlers with strong evidence of
autism, we must also consider the significance of mis-
classification errors in the opposite direction (i.e., false
positives, children who do not retain a stable diagnosis
of autism or move in an out of ASD) (Charman et al.,
2005). Although children with other developmental
conditions may also benefit from early referral to
intervention services, clinical best practice requires
careful follow-up to at least an age where diagnostic
stability is better established, and sensitive but open
discussion at the time of diagnosis regarding possible
change in status over time.
Given the discrepancy between accelerating knowl-
edge concerning early behavioral markers for autism
and the lack of proven interventions for children
476 J Autism Dev Disord (2007) 37:466–480
123
under the age of 2 years, combined with the notion
that earlier intervention is highly desirable to maxi-
mize the chances of a positive outcome (Lord &
McGee, 2001), the process of referring families for
intervention is complex. To fulfill ethical require-
ments, informed consent must address what will occur
when study measures indicate that a child has a sig-
nificant problem, the criteria for which should be
specified a priori (Chen, Miller & Rosenstein, 2003).
Clinicians are obliged to refer children for treatment
when they believe it is clinically necessary for facili-
tating the child’s development as well as providing
support to the parents. Developmental services have a
responsibility to offer interventions targeting chil-
dren’s specific needs (mandated by law in the U.S.)
However, local providers of early intervention may
have limited experience in delivering interventions
specialized to the social-communicative needs of
children younger than age 2 years, indicating a critical
need for further research in this area. One should
carefully document any intervention received by
participants with respect to modality (targeted skills/
ideology), setting (home based versus clinic/center
based), and, critically, intensity (hours per week) to
try to factor such interventions into outcome analyses.
Notably, we currently lack efficacy data for inter-
ventions targeting early signs of autism in this age
group, so it will be difficult to determine whether the
interventions change developmental trajectories or
whether gains related to the natural unfolding of
developmental processes. Controlled clinical trials of
interventions that target the specific deficits of autism
yet are developmentally appropriate to young infants
and toddlers are essential to resolve this issue. How-
ever, until such data are available, the absence of a
clear-cut standard of care for at-risk children will
leave a significant degree of ambiguity regarding
appropriate and ethical referral decisions, a situation
which investigators note in other samples at high-risk
of psychopathology (Heinssen, Perkins, Appelbaum,
& Fenton, 2001, p. 572).
In summary, general recommendations for the field
with respect to high-risk research include the need to
pay critical attention to methodological rigor as well as
human subjects concerns and practicalities in engaging
families in research, retaining their research partici-
pation, and ethically considering appropriate parental
involvement and feedback. Specific recommendations
include a careful consideration of issues related to
recruitment and sampling, the need to follow infant
participants closely during ‘‘critical’’ age periods (6–
18 months), the need to consider current knowledge
limitations in making decisions about clinical concerns,
diagnoses, and referrals, and the need to use appro-
priate comparison groups.
Other recommendations include collaboration
across research groups to achieve adequate samples for
successful data analysis of siblings who develop autism.
Given the small sample sizes of families in any one
geographic area and the low recurrence risk estimates,
collaboration among research groups greatly expedite
studies examining the development of younger siblings
with autism. To facilitate productive collaboration,
research groups should attempt to use consistent
diagnostic methodologies as well as at least some
common core measures.
Another avenue for maximizing the efforts of
studying high-risk samples is to include researchers
from disciplines such as genetics, neurobiology,
developmental psychology as well as ethicists.
Although autism clinical researchers may lead these
studies, geneticists and neuroscientists could use
early phenotypic and endophenotypic data to narrow
their search for gene locations and brain mecha-
nisms. Contributions from experts in normative
development may enhance infant sibling studies by
providing guidance in developing measures suitable
for infants as well as evaluating variability in
behaviors and in specific skill development in the
first year of life. Ethicists may be necessary for
designing studies that maximize data collection while
ensuring participants and family members engaged in
such research have a favorable risk-benefit ratio
(Chen et al., 2003).
The methodological and clinical concerns that are
specific to research with samples at high-risk for the
development of autism continue to evolve, particularly
as one identifies and tests behavioral (and biological)
markers at younger ages. As research with infant sib-
lings begins to validate early manifestations of autism
empirically, and consequently early diagnostic mea-
surements improve, both research questions and design
will narrow in focus and guide the development of
more refined guidelines for such investigations.
We thank the National Alliance for Autism
Research (NAAR) and the National Institute of Child Health
and Human Development (NICHD) for supporting the work-
shop where we initially formulated the ideas outlined in this
paper, and to NAAR, NICHD and the National Institute of
Mental Health (NIMH) for supporting our ongoing collaborative
research. In particular, we thank Dr. Andy Shih and Dr. Eric
London at NAAR for their support and guidance. We also thank
J Autism Dev Disord (2007) 37:466–480 477
123
the investigators who have joined the ‘Baby Sibs’ Research
Consortium since the initial inception of this paper, including
Drs. Alice Carter, Leslie Carver, Kasia Chawarska, John
Constantino, Karen Dobkins, Deborah Fein, Daniel Menninger,
Helen Tager-Flusberg, and Nurit Yirmiya for their valuable in-
sights and outstanding commitment. We also thank the scientific
advisors to the consortium, including Drs. Anthony Bailey, Peter
Mundy, Peter Szatmari, Steve Warren and Marshalyn Yeargin-
Allsopp. Dr. Zwaigenbaum is supported by a New Investigator
Fellowship from the Canadian Institute of Health Research.
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480 J Autism Dev Disord (2007) 37:466–480
123
Abstract
Overview
Overview
Issues Related to Sampling
Sample Size
Issues Related to Study Design
Frequency of Assessments
Comparison Groups
Outcome Assessment
Issues Regarding Measures
Constructs for Measurement
Measures of Delay or Deviance
Measurement of Atypical Behaviors
Issues Related to Generalizability
Clinical Issues Related to following High-Risk Infants
Addressing Concerns
Clinical Diagnosis
Intervention Referrals
Summary and Future Directions
Acknowledgments
References
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39
EW RESEARCH
N
8
D
The Broader Autism Phenotype in Infancy:
When Does It Emerge?
Sally Ozonoff, PhD, Gregory S. Young, PhD, Ashleigh Belding, BA,
Monique Hill, MA, Alesha Hill, BA, Ted Hutman, PhD, Scott Johnson, PhD,
Meghan Miller, PhD, Sally J. Rogers, PhD, A.J. Schwichtenberg, PhD,
Marybeth Steinfeld, MD, Ana-Maria Iosif, PhD
Objective: This study had 3 goals, which were to examine the following: the frequency of
atypical development, consistent with the broader autism phenotype, in high-risk infant siblings
of children with autism spectrum disorder (ASD); the age at which atypical development is first
evident; and which developmental domains are affected. Method: A prospective longitudinal
design was used to compare 294 high-risk infants and 116 low-risk infants. Participants were
tested at 6, 12, 18, 24, and 36 months of age. At the final visit, outcome was classified as ASD,
Typical Development (TD), or Non-TD (defined as elevated Autism Diagnostic Observation
Schedule [ADOS] score, low Mullen Scale scores, or both). Results: Of the high-risk group,
28% were classified as Non-TD at 36 months of age. Growth curve models demonstrated that
the Non-TD group could not be distinguished from the other groups at 6 months of age,
but differed significantly from the Low-Risk TD group by 12 months on multiple measures.
The Non-TD group demonstrated atypical development in cognitive, motor, language,
and social domains, with differences particularly prominent in the social-communication
domain. Conclusions: These results demonstrate that features of atypical development, consis-
tent with the broader autism phenotype, are detectable by the first birthday and affect develop-
ment in multiple domains. This highlights the necessity for close developmental surveillance of
infant siblings of children with ASD, along with implementation of appropriate interventions as
needed. J. Am. Acad. Child Adolesc. Psychiatry, 2014;53(4):398–407. Key Words: autism spec-
trum disorder, broader autism phenotype, siblings, social-communication, infancy
he broader autism phenotype (BAP) is a
constellation of subclinical characteristics
T that are seen at elevated rates in family
members of children with autism spectrum dis-
order (ASD).1 It is generally agreed that the BAP
encompasses features related to the core diag-
nostic domains of ASD, such as language delays
and deficits, social difficulties, and rigidity of
personality or behavior.2,3 Most previous studies
have examined the BAP in parents and school-age
siblings of children with ASD2,3; few have inves-
tigated BAP features in infancy and toddlerhood,
This article is discussed in an editorial by Dr. John R. Pruett, Jr.
on page 392.
Clinical guidance is available at the end of this article.
Supplemental material cited in this article is available online.
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so it is not clear when these differences in
behavior first develop and can be detected.
For questions that require precise timing of
onset, prospective studies provide an optimal
experimental design, because they do not rely
solely on parent report, which can be subject to
recall errors and other biases. In the past decade,
prospective studies of high-risk infants have
proliferated. Most commonly, the individuals at
increased risk for ASD studied thus far are later-
born siblings of children with ASD. Such infant
sibling study designs often compare high-risk
samples to low-risk infants with no family his-
tory of ASD. Although several dozen such studies
have been published, most focus on describing
the early development and predictive early risk
signs of infants who ultimately develop ASD.4,5
Other infant sibling studies have reported differ-
ences between high- and low-risk groups in a
variety of domains, including eye contact, joint
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BROADER AUTISM PHENOTYPE IN INFANCY
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attention, and nonverbal reasoning, but did not
follow the infants long enough to know whether
these differences were early signs of ASD or
might instead index other types of atypical out-
comes, including the BAP.6-9
Only a few infant sibling studies have specif-
ically focused on describing early signs of the
BAP.10-15 These investigations follow participants
until age 3 years, determine which children
develop ASD, and remove them from the larger
high-risk group before analyses (because, by
definition, the BAP and ASD are mutually exclu-
sive). Several studies, most involving small sam-
ples, have found significant differences between
high-risk non-ASD groups and low-risk control
individuals early in life, on tasks of response to
joint attention at 14 months (n ¼ 8)10 and social
referencing at 18 months (n ¼ 30),11 as well as
on parent report measures of temperament as
early as 7 months (n ¼ 12).12 Early differences in
parent-reported temperament in high-risk siblings
without ASD have also been reported in a much
larger sample at 24 months of age (n ¼ 104).13
In a comprehensive study examining multiple
domains of development, 40 high-risk siblings
without ASD outcomes were, as a group, below
average in expressive and receptive language,
overall IQ, adaptive behavior, and social commu-
nication skills at 18 to 27 months.14 In addition,
parents reported social impairments on a ques-
tionnaire by 13 months of age. A recent large
study followed 170 high-risk children, none of
whom were diagnosed as having ASD at age
3 years.15 A cluster analysis identified a subgroup
(19% of the high-risk sample) that had elevated
scores on the Autism Observation Scale for Infants
at 12 months of age. At age 3, this cluster de-
monstrated lower scores than low-risk controls on
independent social-communication and cognitive
measures. Taken together, these and other studies
strongly suggest that behavioral and develop-
mental features consistent with the BAP emerge
early in life.
Most published sibling studies have been cross-
sectional and/or focused on whether group dif-
ferences are evident at a single age. Only 1 study
thus far has examined longitudinal trajectories
of development, following a cohort of 37 high-
risk children from 4 months to 7 years of age.16
At 7 years, the researchers split their high-risk
group into 2 subgroups, 1 group with BAP fea-
tures (40%) and 1 group without, and then
examined their cognitive and language trajec-
tories in the preschool years (4–54 months) using
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growth curve analysis. They found that language
scores were different for the BAP group as early
as 14 months, but that cognitive scores did not
differentiate the group from the low-risk controls
at any age. The current study took a similar
approach, examining development longitudinally
from 6 to 36 months in high- and low-risk infants
(n ¼ 294 and n ¼ 116, respectively) and looking
for the earliest inflection point at which the tra-
jectories diverge from one of typical to atypical
development. The current study is the largest
sample to date that examines BAP features longi-
tudinally. We focus on several domains of early
development: social-communication, language,
nonverbal cognitive, and fine motor abilities.
The studies reviewed above have taken 1 of
2 approaches when studying the BAP. Some have
studied all children in the high-risk group, after
excluding those with an ASD outcome, looking
for differences from low-risk infants.14 Others
have classified an “atypical” outcome group, us-
ing varying criteria at varying outcome ages, and
then examined whether this “atypical” subgroup
differs from low-risk controls at earlier ages than
when the groups were defined.10,12,16 This latter
approach is the one used in the current study. It is
clear that there is substantial heterogeneity within
the high-risk group; virtually all previous studies
find that atypical development or BAP-like fea-
tures are present in only a subset of siblings of
children with ASD.2,3,17 Therefore, studying all
high-risk siblings without ASD outcomes risks
the possibility of obscuring potential differences
that may be evident in a subgroup. Using a de-
finition similar to other recent investigations,10,12
we identified a group of high-risk children with
non-typical developmental outcomes at 36 months
of age. We then used growth curve analysis to
examine when non-typical development could
first be detected. We studied multiple areas of
development, extending more broadly than the
BAP (e.g., social-communication, but also cogni-
tion and motor skills), to examine in which do-
mains non-typical development was evident.
METHOD
Participants
The sample reported in this article was drawn from a
larger longitudinal study of infant siblings of children
with ASD (High-Risk group) or children with typical
development (Low-Risk group), recruited at 2 sites
(University of California, Davis [UC Davis] and Uni-
versity of California, Los Angeles [UCLA]) during 2
phases of grant funding (2003–2008 and 2008–2013).
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OZONOFF et al.
The sole inclusion criterion for the High-Risk group
was status as a younger sibling of a child with ASD.
Diagnosis of the affected older sibling was confirmed
by meeting ASD criteria on both the Autism Diagnostic
Observation Schedule (ADOS) and the Social
Communication Questionnaire (SCQ).18,19 Exclusion
criteria for the High-Risk group included birth before
36 weeks of gestation and a known genetic disorder
(e.g., fragile X syndrome) in the older affected sibling.
The primary inclusion criterion for the Low-Risk group
was status as a younger sibling of a child (or children)
with typical development. Low-risk status of all older
siblings was confirmed by an intake screening ques-
tionnaire and scores below the ASD range on the SCQ.
Exclusion criteria for the Low-Risk group were as fol-
lows: birth before 36 weeks of gestation; develop-
mental, learning, or medical conditions in any older
sibling; and ASD in any first-, second-, or third-degree
relative. All participants with complete data at the 36-
month outcome visit were included.
Participants were enrolled before 18 months of
age (age at enrollment: mean ¼ 6.7 months, SD ¼ 5.2
months; 76% were enrolled by 9 months or earlier).
Depending on age of study entry, data were collected
at up to 5 ages: 6, 12, 18, 24, and 36 months. At the
36-month visit, participants were classified into 1 of 3
algorithmically defined outcome groups: ASD, Typical
Development (TD), and Non-Typical Development
(Non-TD). Table 1 provides algorithmic group defini-
tions, which were developed by the Baby Siblings
Research Consortium, a network of researchers study-
ing very young children at risk for ASD (Chawarska
et al., unpublished data, November 2013).
Given this article’s focus on the BAP, which by
definition is a characteristic of family members of a
child with ASD, the small groups of Low-Risk partici-
pants with ASD (n ¼ 4) or Non-TD (n ¼ 27) outcomes
were not included in analyses. The final sample with
complete 36-month data included in the study were
51 participants classified with ASD (17.4% of the High-
Risk group; n ¼ 8 females), 83 with Non-TD outcomes
(28.2% of the High-Risk group; n ¼ 32 females), and
TABLE 1 Algorithmic Group Outcome Definitions
Outcome Classification
ASD At o
Mee
Typical Development Doe
No
No
ADO
Non-Typical Development Doe
Two
One
ADO
Note: ADOS ¼ Autism Diagnostic Observation Schedule; ASD ¼ autism spectru
specified.
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276 with TD outcomes, who were further stratified into
High-Risk TD (n ¼ 160; n ¼ 90 females) and Low-Risk
TD (n ¼ 116; n ¼ 53 females). Of the 83 participants in
the Non-TD sample, 66 were classified into this group
because of elevated ADOS alone, 9 were classified
because of low Mullen Scale scores alone (8 had at least
1 Mullen Scale score that was �2 SD below the mean,
and 1 had �2 Mullen Scale scores that were �1.5 SD
below mean), and 8 were classified as Non-TD because
of both elevated ADOS and low Mullen Scale scores
(7 had at least 1 Mullen Scale score that was �2 SD
below the mean, and 1 had �2 Mullen Scale scores that
were �1.5 SD below the mean).
Measures
The study was conducted under the approval of both
sites’ institutional review boards. Infants were assessed
by examiners who were unaware of group membership.
Autism Diagnostic Observation Schedule.18 This is a
semi-structured, standardized interaction and obser-
vation tool that measures symptoms of autism. It has
2 empirically derived cutoffs, 1 for ASD and 1 for
Autistic Disorder. Because data collection occurred
before the publication of newer ADOS algorithms, the
CommunicationþSocial Total algorithm score was
used.19 Psychometric studies report high interrater
reliability and agreement in diagnostic classification
(autism versus non-ASD). The ADOS was used to
confirm older sibling diagnosis and to determine infant
outcome at 36 months of age (Table 1).
Mullen Scales of Early Learning.20 This is a stan-
dardized developmental test for children from birth
to 68 months. Four subscales were administered: Fine
Motor, Visual Reception, Expressive Language, and
Receptive Language. Scores are expressed in raw score
points, which can also be converted to T-scores and
age equivalents using published normative data. An
overall score, the Early Learning Composite, is also
obtained. The Mullen Scale subscales have excellent
internal consistency (median ¼ 0.91) and test–retest
reliability (median ¼ 0.84). This test was used to
Criteria
r above the ASD cutoff of the ADOS and
ts DSM-IV-TR criteria for Autistic Disorder or PDD-NOS
s not meet criteria for ASD classification and
more than 1 Mullen Scale subtest �1.5 SD below mean and
Mullen Scale subtest �2 SD below mean and
S >3 points below ASD cutoff
s not meet criteria for ASD classification and
or more Mullen Scale subtests �1.5 SD below mean and/or
or more Mullen subtests �2 SD below mean and/or
S �3 points below ASD cutoff
m disorder; PDD-NOS ¼ pervasive developmental disorder not otherwise
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BROADER AUTISM PHENOTYPE IN INFANCY
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measure cognitive functioning at each visit and to
determine outcome status at 36 months. Ongoing
administration and scoring fidelity procedures were
implemented to ensure that there were minimal cross-
examiner and cross-site differences.
Examiner-Rated Social Engagement. At the end of the
session, examiners rated 3 behaviors using a 3-point
scale (1 ¼ rare, 2 ¼ occasional, 3 ¼ frequent), as fol-
lows: frequency of eye contact; frequency of shared
affect; and overall social responsiveness. These 3 scores
were summed to create a social engagement composite
score (ranging from 3 to 9). In a previous study, this
measure was able to distinguish infants with typical
versus atypical development by 12 months of age.21
Clinical Best Estimate Outcome Classification. At the
end of the 36-month visit, examiners classified each
child into 1 of 6 Clinical Best Estimate (CBE) categories:
ASD, BAP, Behavior Problems, Global Developmental
Delay, Speech–Language Problems, or Typical Devel-
opment. In contrast to the algorithmic groups (ASD,
TD, Non-TD) that were empirically determined for the
current analyses, the CBE classifications were clinically
defined. Children classified with ASD met DSM-IV-TR
criteria for autistic disorder or pervasive developmental
disorder not otherwise specified (PDD-NOS). Children
classified as BAP displayed social-communication diffi-
culties that were judged to be below the ASD threshold.
Children classified as having ADHD concerns displayed
high activity level, poor attention, or disruptive
behavior, beyond what would be expected for devel-
opmental level. Children classified clinically with Global
Developmental Delay had low scores across multiple
cognitive and motor domains. Children classified as
having Speech–Language Problems displayed immature
speech patterns or low language levels in isolation
(no accompanying social or cognitive difficulties). All
other participants were classified as having Typical
Development.
Statistical Analysis
Mixed-effects linear models were used to estimate
patterns of change in Mullen Scale raw scores and to
test whether group was related to the initial level or
rate of change in these variables.22 All core models
included fixed effects for group (ASD, Non-TD, High-
Risk TD, and Low-Risk TD), the linear effect of age
(centered at 6 months), and the interaction between
group and age. To account for the correlated nature of
the data, the core models included 2 random effects for
child-specific intercepts and slopes. Additional fixed
terms (for the quadratic effect of age, the interaction of
the quadratic effect of age with group, gender, phase,
site, etc.) were also added to the core model and tested.
These terms were retained in the models only if they
were significant. For the models with a significant
quadratic effect of age, we also included random effects
for the quadratic age. For some of those models, there
was little variability left in the intercepts, so only the
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child-specific slopes were retained. A similar modeling
strategy was used to analyze the Examiner-Rated So-
cial Engagement composite scores (with age centered at
6 months) and ADOS social-communication scores
(with age centered at 18 months). Further details on the
mixed-effects models are presented in Supplement 1,
available online.
All tests were 2-sided, with a ¼ 0.05. Residual ana-
lyses and graphical diagnostics determined that the
model assumptions were adequately met. Analyses were
implemented using PROC MIXED in SAS Version 9.3.23
RESULTS
Table 2, Table S1 (available online), and Figure 1
summarize the results of the mixed-effects models
for Mullen Scale raw scores. At baseline (6 months
of age), all 4 groups had comparable values on
all 4 scales. The Low-Risk TD group demon-
strated a sharp increase in raw scores with age
on all Mullen Scales. The High-Risk TD group
had significantly slower growth over time than
the Low-Risk TD group on the Expressive and
Receptive Language scales, but not on the Visual
Reception and Fine Motor scales. At 36 months,
the 2 TD groups had comparable Visual Recep-
tion and Fine Motor scores, but the High-Risk
TD group showed significantly lower levels of
Expressive Language (by 1.1 points) and Recep-
tive Language (by 1.7 points). The ASD group
showed a significantly slower rate of change than
both TD groups on all 4 scales and was signifi-
cantly different from both groups by 12 months
of age. Of primary interest for this article, the
Non-TD group’s performance was intermediate
between the ASD and both TD groups. The Non-
TD group had lower rates of growth than both
TD groups, resulting in significant differences
from them by 12 months of age on all scales
except Fine Motor. The differences from both TD
groups were modest at 12 months (differences
from Non-TD ranged from 0.3 to 1.5 points across
scales for Low-Risk TD and 0.2 to 1.1 points for
High-Risk TD) but amplified over time (at 36
months, differences from Non-TD ranged from
3.4 to 4.7 points in Low-Risk TD and from 2.8 to
3.5 points in High-Risk TD).
At 6 months of age, all 4 groups had similar
Examiner-Rated Social Engagement composite
scores (Table 2 and Figure 2). The 2 TD groups
exhibited significant growth over time, and the
ASD group showed a sharp decrease in scores
with age. The Non-TD group had a flat trajectory,
with significant differences from the Low-Risk
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TABLE 2 Parameter Estimates (SE) for Mixed-Effects Regression Models Predicting Mullen Scale Raw Scores, Examiner-Rated Social Engagement, and Autism Diagnostic
Observation Schedule (ADOS) Social-Communication Scores
Model Term
Mullen Scale
Examiner-Rated
Social Engagement
ADOS
Social-CommunicationEL RL VR FM
Estimated trajectory for Low-Risk TD group
Baseline 6.24 (0.15)*** 6.69 (0.22)*** 9.52 (0.18)*** 9.28 (0.18)*** 7.87 (0.18)*** 2.42 (0.26)***
Linear age effect (year) 11.42 (0.18)*** 14.81 (0.55) *** 12.84 (0.30) *** 12.98 (0.39)*** 1.13 (0.34)** e0.48 (0.82)
Quadratic age effect (year) months) — e1.49 (0.21)*** e0.28 (0.10)** e1.26 (0.16)*** e0.29 (0.11)* e0.08 (0.49)
Estimated difference between ASD and Low-Risk TD group
Baseline e0.30 (0.29) 0.82 (0.42) 0.35 (0.32) e0.35 (0.35) e0.23 (0.37) 8.46 (0.70)***
Linear age effect (year) e3.60 (0.34)*** e9.32 (1.02)*** e3.11 (0.38)*** e0.63 (0.73) e2.12 (0.59)*** e6.83 (2.51)**
Quadratic age effect (year) months) — 2.09 (0.39)*** — e0.67 (0.30)* e0.46 (0.20)* 5.94 (1.56)***
Estimated difference between Non-TD and Low-Risk TD groups
Baseline 0.31 (0.24) 0.04 (0.35) 0.03 (0.27) e0.13 (0.29) e0.37 (0.31) 2.24 (0.39)***
Linear age effect (year) e1.94 (0.28)*** e3.43 (.86)*** e1.42 (.32)*** e0.17 (0.61) e0.53 (0.51) e1.33 (1.23)
Quadratic age effect (year) months) — .61 (0.33) — e0.45 (0.25) e0.13 (0.17) 1.75 (0.73)*
Estimated difference between High-Risk TD and Low-Risk TD groups
Baseline 0.32 (0.21) 0.49 (0.31) 0.15 (0.23) 0.15 (0.26) e0.26 (0.29) 1.10 (0.34)**
Linear age effect (year) e0.56 (0.23)* e2.03 (0.73)** e0.22 (0.27) e0.67 (0.52) e0.38 (0.47) e2.02 (1.05)
Quadratic age effect (year) months) — 0.46 (0.27) — .15 (0.21) e0.17 (0.15) 1.01 (0.62)
Note: Baseline is 18 months for ADOS and 6 months for all other variables. ASD ¼ autism spectrum disorder; EL ¼ Expressive Language; FM ¼ Fine Motor; RL ¼ Receptive Language; SE ¼ standard error; TD ¼ typically
developing; VR ¼ Visual Reception.
*p < .05; **p < .01; ***p < .001.
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FIGURE 1 Estimated trajectories for Mullen Scales. ASD ¼ autism spectrum disorder; TD ¼ typically developing.
BROADER AUTISM PHENOTYPE IN INFANCY
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TD group evident starting at 12 months and
resulting in 36-month scores that were signi-
ficantly lower over time than both TD groups
(by w1 point) but higher than the ASD group
(by w2 points).
At 18 months (the first visit in which the ADOS
was administered), there were significant group
differences on the social-communication algo-
rithm score, with the Low-Risk TD group dem-
onstrating lower scores than the High-Risk TD
(by 1 point), Non-TD (by 2 points), and ASD (by
8 points) groups (Table 2 and Figure 2). The Low-
Risk TD group demonstrated a stable trajectory
over time, whereas the High-Risk TD group
exhibited a slight decrease over time. The Non-
TD group showed a significant quadratic effect
of age. At 36 months, the 2 TD groups had
comparable scores (1.5 and 1.9, respectively),
whereas the Non-TD and ASD group showed
significantly higher scores (estimated values 5.7
and 13.1, respectively). Again, as with the Mullen
Scale, the scores and longitudinal trajectories of
the Non-TD group fell intermediate between the
TD and ASD groups.
Table 3 depicts the correspondence between
the empirically derived algorithmic classifications
(ASD, TD, Non-TD) and clinical judgment (CBE
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outcome classification) at 36 months. The perfect
correspondence between the 2 classifications for
the ASD group is secondary to the algorithmic
definition, which requires a clinical diagnosis
of ASD. The Non-TD group had a significantly
higher rate of classifications of BAP, ADHD
concerns, Global Developmental Delay, and
Speech–Language Problems and significantly
lower rate of Typical Development classifications
than both the High-Risk and Low-Risk TD
groups (Fisher’s exact test, p < .001). The most
common clinical classification for the Non-TD
group was BAP, with more than one-third of
the sample falling in this category. Three Non-
TD participants received a CBE rating of ASD
but did not meet the algorithmic criteria (e.g.,
did not have an ADOS score over the ASD
cutoff), resulting in their classification as Non-
TD. Interestingly, almost 40% of the Non-TD
group was judged by examiners to have a CBE
outcome of typical development, despite the
elevated ADOS scores or lowered Mullen Scale
scores that classified them empirically in the Non-
TD group.
In secondary analyses, we added to the core
models and tested terms for gender, site, funding
phase, and, for those models with significant
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FIGURE 2 Estimated trajectories for Examiner-Rated Social Engagement composite and Autism Diagnostic Observation
Schedule (ADOS) social-communication algorithm score. ASD ¼ autism spectrum disorder; TD ¼ typically developing.
OZONOFF et al.
gender effects, the interactions between gender
and group and between gender and age. There
was no phase effect, and site was significant only
in the model predicting receptive language (the
UCLA sample scored 0.4 points higher than the
UC Davis sample, p < .05, but the difference was
so small that it is unlikely to be clinically mean-
ingful). Gender was a significant predictor for all
Mullen Scales except Receptive Language, with
girls demonstrating slightly higher Visual
Reception scores than boys (0.5 point, p < .05).
For Expressive Language and Fine Motor, there
was a significant gender-by-group interaction,
driven by girls in the ASD group, who scored
lower than boys on these scales, whereas girls
in the other 3 groups scored w0.5 point higher
than boys on the same scales. There were no
gender, phase, or site effects in the model pre-
dicting ADOS social-communication score. For
the Examiner-Rated Social Engagement compos-
ite, there was a significant gender-by-group in-
teraction, driven again by the girls in the ASD
group, who scored 2 points lower than the boys
(p < .001), whereas girls in the other 3 groups
scored similarly to boys of the same group. For
this variable, there was a phase effect, with phase
2 children scoring about 0.2 points higher than
phase 1 children (p ¼ .03). The interaction be-
tween gender and age was not significant in any
of the models considered.
DISCUSSION
This study focused on developmental aspects of
the BAP, exploring the frequency of non-typical
development in high-risk infant siblings, the
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age at which atypical development was first
evident, and which developmental domains were
affected. We found that 28% of the high-risk
cohort demonstrated atypical development (not
including ASD) at 36 months of age, as defined by
elevated ADOS scores (within 3 points of the ASD
cutoff), low Mullen Scale scores, or both. Working
backwards from this age, we used growth curve
models to determine when these differences
in development could first be detected. On the
Mullen Scales of Early Learning and the examiner
ratings of social engagement, the Non-TD group
was not distinguishable from any other group
at 6 months, but differed significantly from the
Low-Risk TD group by 12 months of age, devi-
ating from typical development as early as the
group with ASD. At 18 months, the earliest age at
which the ADOS was administered, the Non-TD
group was already obtaining significantly higher
scores than the Low-Risk TD group.
The aspects of atypical development that dis-
tinguished the Non-TD group from the Low-Risk
group occurred in all domains assessed in this
study (cognition, motor, language, and social
development) but were most prominent in the
social-communication domain. Of the Non-TD
group, 90% demonstrated social-communication
difficulties (as defined by an ADOS score within
3 points of the ASD cutoff), including reduced
eye contact, infrequent social initiations with
unfamiliar persons, repetitive vocalizations, and
delayed onset of gestures, speech, and play. Iso-
lated language and cognitive delays (e.g., low
Mullen Scale scores alone) were relatively rare,
seen in only 10% of the Non-TD group, as pre-
vious studies have also found.24 When such
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TABLE 3 Clinical Best Estimate Classifications at 36 Months by Algorithmic Group
Clinical Best Estimate, n (%)
ASD
(n ¼ 51)
Non-TD
(n ¼ 83)
High-Risk TD
(n ¼ 160)
Low-Risk TD
(n ¼ 116)
Autism spectrum disorder 51 (100) 3 (4) 0 (0) 0 (0)
Broader autism phenotype 0 (0) 29 (35) 10 (6) 0 (0)
ADHD concerns 0 (0) 8 (10) 7 (4) 2 (2)
Global developmental delay 0 (0) 5 (6) 2 (1) 0 (0)
Speechelanguage problems 0 (0) 6 (7) 14 (9) 3 (3)
Typical development 0 (0) 32 (39) 127 (79) 111 (96)
Note: ASD ¼ autism spectrum disorder; TD ¼ typically developing.
BROADER AUTISM PHENOTYPE IN INFANCY
Down
delays were evident, they occurred in combina-
tion with elevated ADOS scores. Thus, the vast
majority of the Non-TD group demonstrated the
kinds of social-communication features that have
been previously described in older siblings as
consistent with the BAP. Interestingly, almost
40% of the Non-TD group was given a CBE rating
of typical development by examiners, despite
such elevated ADOS scores. We plan to further
examine this subgroup to better understand what
may lead to a clinical judgment of typicality,
despite non-typical scores. An item analysis of the
ADOS, for example, may reveal that high scores
on certain items are not considered particularly
concerning by clinicians, leading to a CBE of
typical development, whereas high scores on
other items (e.g., eye contact) are judged as
consistent with the BAP.
One of the primary gaps in the literature
motivating this research was the paucity of
studies of BAP-like phenomena in very young
siblings, with most previous investigations con-
ducted on school-age siblings and parents. This
results in a need to “translate” the types of defi-
cits seen at older ages, and instruments used to
measure them, into those appropriate for earlier
stages of development. Some of this translation
was straightforward, when the same instrument
used with older siblings and parents could also
be used with this young age group (e.g., the
ADOS; the comprehensive review by Sucksmith
et al.3 includes a list of previous studies and
measures used). It was not clear at the start of
this study whether the Mullen Scales would
adequately index any cognitive delays that might
be apparent. The findings here demonstrate that
general developmental delays can occasionally be
seen in very young siblings and that the Mullen
Scales can detect them.
In future follow-up studies, as our sample
reaches school-age, we plan to examine what
proportion of the High-Risk children meet
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definitions of the BAP used previously in older
samples.1 Many definitions include behavioral
features not seen in infancy or measured by our
tasks, such as peer problems, pragmatic language
difficulties, rigid inflexible behavior, anxiety, and
depression. It is possible that the rate of atypical
development will increase over time, and that
some children in the High-Risk TD group who
did not show atypicalities at 36 months or did
not meet cutoffs for the Non-TD definition may
be identified with a BAP-like phenotype as they
are followed up longitudinally into the school
years. Previous longitudinal studies have, in fact,
reported a significant increase in the number of
high-risk siblings identified with BAP-related
difficulties at age 7 years compared to the pre-
school years.25-27
The results reported here are largely consistent
with a recently published study that used a dif-
ferent type of prospective design.28 This research
team analyzed the Avon Longitudinal Study of
Parents and Children (ALSPAC) cohort, a very
large, community-ascertained general population
sample that followed children from before birth
to age 11 years, obtaining parent reports of de-
velopment (including a measure of ASD traits)
at multiple ages. Bolton et al. found that parents
reported differences in development within the
first year of life that not only predicted later di-
agnoses of ASD, but also a wider, subthreshold
range of autistic-like behaviors potentially con-
sistent with the BAP.28
A question that often arises is whether siblings
like those in the Non-TD group, who have delays
that are sub-threshold to ASD, should receive
early intervention services or whether their de-
lays will lessen over time without treatment.
There are not, as yet, any well-controlled inter-
vention studies that can help to answer this
question, so we must turn to other sources. One
answer to the question comes from the law
involving early intervention services for children
Y
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Clinical Guidance
� Close to 50% of younger siblings of children with
ASD develop in an atypical fashion. In the current
study, 17% developed ASD, and another 28%
showed delays or deficits in other areas of
development or behavior.
� Differences in development are detectable using
standardized assessment instruments by 12 months
of age in many children.
� The most common development differences seen in
younger siblings of children with ASD are delays in
social-communication development (including
reduced eye contact, extreme shyness with unfamil-
iar persons, and delayed onset of gestures and
speech). Some younger siblings also show delays in
cognitive and motor abilities, as well as attentional
and behavioral problems.
� Close developmental surveillance of infant siblings
of children with ASD is necessary, along with
implementation of appropriate interventions as
needed.
OZONOFF et al.
under 3 years of age, the Individuals with Dis-
abilities Education Act (IDEA), Part C, which
states that young children with delays and those
who are at high risk for developmental delays are
entitled to assessments and intervention services.
Thus, good clinical practice suggests that when
children are showing atypical development, they
and their families should be provided with in-
formation about the child’s difficulties, clinical
reports when practical, and referrals to local Part
C service providers. The second response to this
question about early intervention for BAP-like
features comes from 2 long-term longitudinal
studies of infant siblings, both of which demon-
strated that children with early lagging trajec-
tories continue to experience challenges after the
preschool period and do not “catch up” to typi-
cally developing peers.16,28
Which types of intervention should be pro-
vided to this wide-ranging group of children?
Certainly, no single approach or modality can be
expected to fit a group whose difficulties range
from severe hyperactivity, to mild-to-moderate
intellectual impairment, to subthreshold symp-
toms of ASD. Intervention approaches need to be
chosen based on each child’s profile of strengths
and weaknesses and each family’s goals and
priorities. However, there are a range of choices
available to early intervention professionals from
a range of disciplines. Empirically supported,
manualized, parent-implemented interventions
for toddlers and preschoolers with behavior
disorders, general delayed development, social-
communicative symptoms related to autism, and
difficulties with expressive communication are
represented in the literature, and many of these
can be carried out by professionals from a variety
of disciplines.29-31
We will continue to follow our sample as they
reach school age, to examine whether develop-
mental difficulties identified at age 3 years per-
sist, and whether new difficulties (e.g., learning
disorders, anxiety) emerge over time. It is critical
to better understand the long-term functional
consequences of the early developmental pat-
terns identified in the current study. The ulti-
mate goal of this program of research is to
determine whether monitoring and identifica-
tion in the preschool years could be used to
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provide appropriate interventions that would
reduce the number of high-risk siblings who
display later difficulties. &
AL
y –
is
Accepted December 24, 2013.
Drs. Ozonoff, Young, Miller, Rogers, Steinfeld, and Iosif, and
Ms. Belding, Ms. M. Hill, and Ms. A. Hill are with the University of
CaliforniaeDavis. Drs. Hutman and Johnson are with the University of
CaliforniaeLos Angeles. Dr. Schwichtenberg is with Purdue University.
This study was supported by the National Institute of Mental Health
grants R01 MH0638398 (S.O.) and U54 MH068172 (Marian
Sigman, PhD [deceased]).
Drs. Iosif and Young served as the statistical experts for this research.
Editorial support for the preparation of this article was provided by
Diane Larzelere, BA, University of California-Davis. The authors thank
the children and families who participated in this longitudinal study.
Disclosure: Drs. Ozonoff, Young, Hutman, Johnson, Miller, Rogers,
Schwichtenberg, Steinfeld, and Iosif, and Ms. Belding, Ms. M. Hill
and Ms. A. Hill report no biomedical financial interests or potential
conflicts of interest.
Correspondence to Sally Ozonoff, PhD, MIND Institute, University of
California Davis Health System, 2825 50th Street, Sacramento CA
95817; e-mail: sally.ozonoff@ucdmc.ucdavis.edu
0890-8567/$36.00/ª2014 American Academy of Child and
Adolescent Psychiatry
http://dx.doi.org/10.1016/j.jaac.2013.12.020
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OZONOFF et al.
SUPPLEMENT 1
Mixed-Effects Model Details. Mixed-effects regres-
sion models were used to estimate individual
patterns of change in Mullen Scale raw scores,
Examiner-Rated Social Engagement Scores, and
Autism Diagnostic Observation Schedule (ADOS)
social-communication scores from 6 to 36 months,
and to test the effects of diagnosis and covariates
on the initial level and the rate of change in these
variables. Change in these variables was assessed
in the mixed-effects models with a term for age
(centered at baseline 6 months). The models as-
sume that each child’s individual path of growth
followed the mean path, except for child-specific
random effects that caused the initial level to be
higher or lower and the rate of change (linear,
quadratic) to be faster or slower.
The core set of models included fixed effects
for diagnosis, age (centered at baseline), and the
interaction between diagnosis and age. A second
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set of models also included terms for the quadratic
effect of age and the interaction of diagnosis with
the quadratic effect of age. These interaction terms
tested whether the rate of change in the variables
varied across diagnosis. The most general core
model included 3 random effects: a random
intercept and random slopes for both the linear
and the quadratic effect of age. These random ef-
fects (describing the between-child variation) were
assumed to follow a multivariate normal distri-
bution. We used an unstructured covariance ma-
trix, which is the most general structure, to model
the dependence between the random effects. To
model within-person variation, we assumed that
the observed measurements differed from the
child’s true trajectory by independent, identically
distributed errors at each visit. Separate variances
for this residual error (assessing the within-child
variance) were estimated in each group, and
tests to assess whether the within-child variances
differed across diagnosis were performed.
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TABLE S1 Estimated Trajectories (Estimate, 95% CI) From the Mixed-Effects Models Predicting Mullen Scale Raw
Scores, Examiner-Rated Social Engagement Scores, and Autism Diagnostic Observation Schedule (ADOS) Social-
Communication Scores
Age
Estimated Scores (95% Confidence Intervals)
ASD
(n ¼ 51)
Non-TD
(n ¼ 83)
High-Risk TD
(n ¼ 160)
Low-Risk TD
(n ¼ 116)
Mullen Expressive Language
6 mo 5.9 (5.5e6.4) 6.5 (6.2e6.9) 6.6 (6.3e6.8) 6.2 (5.9e6.5)
12 mo 9.8 (9.4e10.3) 11.3 (10.9e11.6)** 12.0 (11.7e12.2) 11.9 (11.7e12.2)**
18 mo 13.8 (13.2e14.3) 16.0 (15.6e16.4)*** 17.4 (17.1e17.7) 17.7 (17.3e18.0)***
24 mo 17.7 (16.9e18.4) 20.8 (20.2e21.3)*** 22.8 (22.4e23.3) 23.4 (22.9e23.9)***
36 mo 25.5 (24.2e26.8) 30.2 (29.2e31.2)*** 33.7 (33.0e34.4) 34.8 (34.0e35.6)***
Mullen Receptive Language
6 mo 7.5 (6.8e8.2) 6.7 (6.3e7.3) 7.2 (6.8e7.6) 6.7 (6.3e7.1)
12 mo 10.4 (9.8e11.0) 12.2 (11.8e12.6)*** 13.3 (13.0e13.6) 13.7 (13.4e14.1)***
18 mo 13.6 (12.8e14.5) 17.2 (16.6e17.9)*** 18.9 (18.5e19.4) 20.0 (19.5e20.5)***
24 mo 17.2 (16.1e18.1) 21.8 (21.1e22.6)*** 24.0 (23.5e24.6) 25.6 (24.9e26.2)***
36 mo 25.0 (23.5e26.5) 29.7 (28.6e30.8)*** 32.7 (31.9e33.5) 34.4 (33.4e35.3)***
Mullen Visual Reception
6 mo 9.9 (9.3e10.4) 9.5 (9.1e10.0) 9.6 (9.3e10.0) 9.5 (9.2e9.9)
12 mo 14.7 (14.2e15.1) 15.2 (14.9e15.5)** 15.9 (15.7e16.2) 15.9 (15.6e16.1)**
18 mo 19.3 (18.8e19.9) 20.7 (20.3e21.1)*** 22.0 (21.7e22.3) 22.1 (21.7e22.4)***
24 mo 23.8 (23.1e24.6) 26.1 (25.5e26.6)*** 28.0 (27.6e28.4) 28.2 (27.7e28.7)***
36 mo 32.4 (31.1e33.8) 36.4 (35.3e37.4)*** 39.5 (38.7e40.2) 39.9 (39.0e40.8)***
Mullen Fine Motor
6 mo 8.9 (8.4e9.5) 9.1 (8.7e9.6) 9.4 (9.1e9.8) 9.3 (8.9e9.6)
12 mo 14.6 (14.3e15.0) 15.1 (14.9e15.4)# 15.3 (15.1e15.5) 15.5 (15.2e15.7)#
18 mo 19.4 (18.9e19.8) 20.2 (19.9e20.6)** 20.6 (20.4e20.9) 21.0 (20.7e21.3)**
24 mo 23.1 (22.5e23.7) 24.5 (24.1e25.0)*** 25.4 (25.1e25.7) 25.9 (25.5e26.3)***
36 mo 27.7 (26.5e29.0) 30.5 (29.5e31.4)*** 33.3 (32.7e33.9) 33.8 (33.1e34.6)***
Examiner-Rated Social Engagement Composite Score
6 mo 7.6 (7.0e8.2) 7.5 (7.0e7.9) 7.6 (7.2e8.0) 7.9 (7.5e8.3)
12 mo 7.2 (6.9e7.5) 7.8 (7.5e8.0)*** 8.0 (7.7e8.2) 8.4 (8.1e8.6)***
18 mo 6.8 (6.5e7.1) 7.9 (7.7e8.2)*** 8.2 (8.0e8.4) 8.7 (8.5e8.9)***
24 mo 6.5 (6.2e6.9) 8.0 (7.7e8.3)*** 8.4 (8.2e8.7) 8.9 (8.6e9.0)***
36 mo 6.2 (5.8e6.6) 7.9 (7.7e8.2)*** 8.7 (8.5e8.9) 8.8 (8.6e9.0)***
ADOS Social-Communication Score
18 mo 10.9 (9.6e12.2) 4.7 (4.1e5.2)*** 3.5 (3.1e3.9) 2.4 (1.9e2.9)***
24 mo 8.7 (7.5e9.9) 4.2 (3.7e4.7)*** 2.5 (2.2e2.8) 2.2 (1.7e2.6)***
36 mo 13.1 (11.8e14.4) 5.7 (5.3e6.1)*** 1.9 (1.6e2.1) 1.5 (1.2e1.8)***
Note: ASD ¼ autism spectrum disorder; TD ¼ typically developing.
#p < .07; *p < .05; **p < .01; ***p < .001 (for comparing Non-TD and Low-Risk TD).
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Method
Participants
Measures
Autism Diagnostic Observation Schedule18
Mullen Scales of Early Learning20
Examiner-Rated Social Engagement
Clinical Best Estimate Outcome Classification
Statistical Analysis
Results
Discussion
References
Supplement 1
Mixed-Effects Model Details
Contents lists available at ScienceDirect
Neuroscience and Biobehavioral Reviews
journal homepage: www.elsevier.com/locate/neubiorev
Changing conceptualizations of regression: What prospective studies reveal
about the onset of autism spectrum disorder
Sally Ozonoffa,⁎, Ana-Maria Iosifb
a Department of Psychiatry and Behavioral Sciences, MIND Institute, University of California – Davis, 2825 50th Street, Sacramento CA, 95817, USA
b Department of Public Health Sciences, University of California – Davis, Medical Sciences 1C, Davis CA, 95616, USA
A R
T
I C L E I N F O
Keywords:
Autism spectrum disorder
Onset patterns
Regression
Prospective studies
A B S T R A C T
Until the last decade, studies of the timing of early symptom emergence in autism spectrum disorder (ASD) relied
upon retrospective methods. Recent investigations, however, are raising significant questions about the accuracy
and validity of such data. Questions about when and how behavioral signs of autism emerge may be better
answered through prospective studies, in which infants are enrolled near birth and followed longitudinally until
the age at which ASD can be confidently diagnosed or ruled out. This review summarizes the results of recent
studies that utilized prospective methods to study infants at high risk of developing ASD due to family history.
Collectively, prospective studies demonstrate that the onset of ASD involves declines in the rates of key social
and communication behaviors during the first years of life for most children. This corpus of literature suggests
that regressive onset patterns occur much more frequently than previously recognized and may be the rule rather
than the exception.
1. Introduction
The onset of behavioral signs of autism spectrum disorder (ASD) is
usually conceptualized as occurring in one of two ways: an early onset
pattern, in which children demonstrate delays and deviances in social
and communication development early in life, and a regressive pattern,
in which children develop largely as expected for some period and then
experience a substantial decline in or loss of previously developed
skills. While it was long believed that the majority of children with ASD
demonstrated an early onset pattern, more recent studies suggest that
regressive onset occurs more frequently than previously recognized
(Brignell et al., 2017; Hansen et al., 2008; Kern et al., 2015; Pickles
et al., 2009; Shumway et al., 2011; Thurm et al., 2014; for a review, see
meta-analysis by Barger et al., 2013). Studies occasionally also identify
a third onset pattern, that of developmental stagnation or plateau
(Shumway et al., 2011), that is characterized by intact early skills that
fail to progress or transform into more advanced developmental
achievements. This onset pattern is distinct from regression, in that the
child does not lose acquired skills, but instead fails to make expected
gains.
1.1. Methods for measuring onset patterns
The most common procedure for collecting information about the
timing of early symptoms is retrospective parent report. A number of
factors can influence report validity, including awareness of the child’s
eventual diagnosis and knowledge of developmental milestones. It has
long been understood that retrospective reports are subject to problems
of memory and interpretation (Finney, 1981; Henry et al., 1994; Pickles
et al., 1996), including in studies of ASD (Andrews et al., 2002). Mul-
tiple studies have documented the ways in which recall problems and
other biases can influence parent report. Changes in recall occur over
time, with past events often reported to occur more recently, closer to
the time of recollection, than they actually took place, an error called
forward telescoping (Loftus and Marburger, 1983). Studies of children
with ASD have demonstrated significant forward telescoping in parent
report of milestones, resulting in parents being less likely to report re-
gression and more likely to report early delays as their children grow
older (Hus et al., 2011; Lord et al., 2004). A recent study from our
research team (Ozonoff et al., 2018a) conducted longitudinal inter-
views with parents about onset of ASD symptoms when their child was
2–3 years old (Time 1) and approximately 6 years old (Time 2). Sig-
nificant forward telescoping was found in both age of regression and
age when milestones were achieved. The correspondence between Time
1 and Time 2 parent report of onset was low (kappa = .38). One-quarter
of the sample changed onset categories, most often due to parents not
recalling a regression at Time 2 that they had reported at Time 1.
Analysis of home movies of children later diagnosed with ASD is
https://doi.org/10.1016/j.neubiorev.2019.03.012
Received 24 September 2018; Received in revised form 12 February 2019; Accepted 14 March 2019
⁎ Corresponding author.
E-mail addresses: sozonoff@ucdavis.edu (S. Ozonoff), aiosif@ucdavis.edu (A.-M. Iosif).
Neuroscience and Biobehavioral Reviews 100 (2019) 296–
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another retrospective method used in research studies to study
symptom emergence (Goldberg et al., 2008; Palomo et al., 2006). Video
analysis may be a more objective procedure for documenting early
symptoms than parent recall (Werner and Dawson, 2005) but it is labor-
intensive and subject to other limitations, such as selective recording
(e.g., tendency of parents to film positive behaviors). In a study from
our team that compared classification of onset based on coding of fa-
mily movies to onset type as recalled by parents (Ozonoff et al., 2011a),
less than half of children whose home video displayed clear evidence of
a major decline in social and communication behavior were reported to
have had a regression by parents. Similarly, only 40% of participants
with clear evidence of early delays and little evidence of skill decline on
video were reported by parents to show an early onset pattern.
1.2. Prospective studies of onset
Questions about when and how behavioral signs of autism emerge
may be better answered through prospective investigations, in which
infants are recruited and enrolled near birth, prior to the advent of
parent concerns, and then followed longitudinally through the window
of developmental risk, until the age at which ASD can be confidently
diagnosed or ruled out, usually 36 months. A few large general popu-
lation cohorts have been studied prospectively to examine onset pat-
terns (Brignell et al., 2017; Havdahl et al., 2018) but this study design is
inefficient, since fewer than 2 in 100 participants will develop ASD
(Centers for Disease Control and Prevention, 2018), making it difficult
to achieve an appropriate sample size. Additionally, large prospective
cohort studies must, of necessity, rely upon parent questionnaires and
rarely provide the opportunity for in-person clinical assessments to
verify diagnosis or onset pattern.
For this reason, most prospective investigations utilize high-risk
samples in order to increase the number of ASD outcomes that are in-
formative for study. The most widely used high-risk group has been
later-born siblings of children with ASD, who are known to be at higher
ASD risk than the general population (Constantino et al., 2010). Most
investigations compare high-risk infants to lower-risk participants with
no known family history of ASD in first-, second-, and sometimes third-
degree relatives. This study design improves on retrospective methods
in a number of important ways. Serial comprehensive assessments, in
standardized testing contexts, are used to document the timing of
symptom emergence, thus avoiding reliance on potentially fallible
parent recall or non-representative home video. Assessments can utilize
a wide range of tools, including eye tracking, EEG, and imaging, al-
lowing broader investigations of symptom onset and testing of specific
hypotheses. And while most retrospective studies recruit samples
through clinics, which may influence the results by including more
severely affected children, infant sibling studies avoid such potential
biases by ascertaining participants via family history alone.
Several recent papers provide comprehensive reviews of the infant
sibling literature (Bölte et al., 2013; Jones et al., 2014; Pearson et al.,
2018; Szatmari et al., 2016). Here we focus on research reports of
greatest relevance to symptom emergence, specifically those that study
infants beginning in the first year of life on measures that are appro-
priate for examining potential skill decline over time. Using a variety of
different prospective methods, these studies have reported largely in-
tact early development, followed by developmental declines and onset
of symptoms around the first birthday and in the second year of life. For
example, Zwaigenbaum et al. (2005), using the Autism Observation
Scale for Infants (AOSI), reported no differences at 6 months between
infants subsequently diagnosed with ASD and both high- and low-risk
infants without ASD outcomes; significant group differences emerged at
12 months and increased over time. This pattern on the AOSI was later
replicated by an independent research team (Gammer et al., 2015).
Wan et al. (2013) found that infant-parent interaction quality at 6–10
months did not predict which children would be diagnosed with ASD at
age 3, but by 12–15 months, such variables were significantly
associated with diagnostic outcome. Similar findings of lack of early
group differences (or lack of early predictive ability), followed by later
divergence from typically developing infants, have been reported by
Landa and Garrett-Mayer (2006) using the Mullen Scales of Early
Learning, Rozga et al. (2011) in joint attention, Bedford et al. (2012) on
a gaze-following eye-tracking task, Elsabbagh et al. (2013) on a gap-
overlap attention task, and Wolff et al. (2014) studying repetitive be-
havior. In an incisive recent review that attempts to reconcile retro-
spective and prospective studies of regression and explore how study
design affects the likelihood of capturing regression, Pearson et al.
(2018) conclude that, among infants who later develop ASD, “the ma-
jority show declining fixation of eyes, gaze to faces, and social en-
gagement, from typical levels in early infancy (2–6 months) to sig-
nificantly reduced levels by 24–36 months (p. 14).”
2. Findings from the University of California Davis infant sibling
study
In our laboratory, we have taken the analytic approach of growth
curve modeling to examine directly the evidence of longitudinal de-
velopmental change in the first three years of life. Between 2003 and
2015, the UC Davis Infant Sibling Study recruited three cohorts of later-
born siblings, each composed of 50 low-risk and 100 high-risk infants.
Participants were tested as early as 6 months of age and then seen every
3 to 6 months until their 3rd birthday (up to 7 in-person evaluations).
They have since been followed into school age and tested at approxi-
mately three-year intervals. The oldest children from Cohort 1 are now
16 years of age and the retention rate is over 80%. At each infant and
preschool visit, a battery of age-appropriate standardized tests and
experimental tasks was administered that measured language, cogni-
tion, social, communication, motor, and many other domains.
Approximately 20% of the high-risk infants were later diagnosed with
ASD (Ozonoff et al., 2011b). Diagnoses of ASD were made at any point
that a child met criteria (mean age 24.2 months) but a full diagnostic
assessment was completed on all children, regardless of previous find-
ings, at 36 months by examiners unaware of family risk or prior as-
sessment results. In the following sections, we summarize several stu-
dies from these cohorts that consistently demonstrate declining
trajectories across a variety of different measures and developmental
domains.
The phenomenon of regression is defined by loss or significant de-
crease in already-acquired skills. Thus, a critical methodological issue
in prospective studies that wish to examine onset patterns is selection of
which behaviors to measure. They must be 1) developmentally appro-
priate across the full age window of risk and 2) robustly present, at high
frequency, in the first year of life. Such behaviors have the capacity to
decrease and are therefore of highest relevance to the study of onset
patterns. Measures that focus on socio-communicative behaviors that
have not yet emerged in the first year of life, such as joint attention,
imitation, and verbal communication, will be less useful for testing
hypotheses about declining capacities. The behaviors our laboratory
has focused on, including gaze to faces and eyes of others, shared affect,
and social interest/engagement, are well developed in the first year of
life (Inada et al., 2010) and therefore of highest relevance in the pro-
spective measurement of regression.
2.1. Behavioral coding of social-communication rates
Our first exploration of longitudinal change in early social and
communicative behaviors used video recordings of participants inter-
acting with examiners during structured developmental testing
(Ozonoff et al., 2010). Research assistants, unaware of family risk group
or diagnostic outcome, were trained to 90% reliability to detect three
behaviors: gaze to an adult’s face, smiles at an adult that were paired
with eye contact, and vocalizations directed at an adult that were ac-
companied by eye contact. Rate per minute of eye gaze, shared affect,
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and directed vocalizations of the 25 children in Cohort 1 with outcomes
of ASD were compared to those of 25 children who did not have ASD
outcomes randomly selected from the low-risk group. The two groups
behaved similarly at 6 months: frequencies of none of the three beha-
viors differed between the groups and effect sizes were in the small
range. Over time, the Low-Risk (LR) Non-ASD group had a significant
increase in social smiles and directed vocalizations, while maintaining
the same consistently high level of gaze to faces. In the ASD group, in
contrast, the rates of all three behaviors dramatically decreased over
time. Fig. 1A displays longitudinal trajectories of eye contact rate per
minute, showing comparable values between groups at 6 months, fol-
lowed by group differences that became statistically significant by 12
months and persisted and widened over time. These longitudinal de-
creases in the rates of key social and communicative behaviors provided
the first prospectively measured evidence consistent with a regressive
onset pattern.
We have since replicated these findings (see Fig. 1B) using the same
methods in an independent sample of 46 infants later diagnosed with
ASD from Cohorts 2 and 3 of our longitudinal project. In this analysis
(Gangi et al., in preparation), a third group, composed of high-risk (HR)
infants who did not have ASD outcomes, was also included. This group
was not different from the LR Non-ASD group in the frequency of gaze
to adult faces at any age and did not show any evidence of decline in
development, which was evident only in the participants developing
ASD, replicating our 2010 study.
2.2. Observer ratings of social engagement
Coding behavior frequencies from video is time-consuming, labor-
intensive, expensive, and not transferable to clinical contexts, so our
research program has also sought to establish whether declining tra-
jectories are evident using other methodological approaches. At the end
of each visit, examiners rate the frequency of eye contact, shared affect,
and overall social engagement (number of social initiations and social
responses) made by the infant throughout the session, across all tasks,
using a 3-point scale (1 = rare, 2 = occasional, 3 = frequent). These
scores are summed to create a composite that ranges from 3 to 9. As
reported in Ozonoff et al. (2010) and depicted in Fig. 2A, these ex-
aminer ratings of social engagement showed similar longitudinal pat-
terns to the social behaviors coded from video. There were no group
differences in the 6-month examiner ratings; however, while the LR
Non-ASD group showed a significant increase in social engagement
ratings over time, reaching close to the maximum score by 36 months,
the children in the ASD outcome group had a strong decline in social
engagement ratings over the same time period.
This finding was recently replicated in an independent group of 32
infants with ASD outcomes from Cohorts 2 and 3 of our sample
(Ozonoff et al., 2018b). We used the same examiner rating variable
(this time with an expanded 5-point scale) and compared the ASD group
to both a low-risk and a high-risk group without ASD. Again, all three
groups had comparable levels of social engagement based on examiner
scores at 6 months of age. The ASD group then demonstrated a decrease
in scores with age, while the HR Non-ASD group showed stable high
scores over time and the LR Non-ASD group demonstrated increasing
scores longitudinally. By 12 months, the two Non-ASD groups demon-
strated significantly higher rates of social engagement, as judged by
examiners, than the ASD group and these differences widened over
time, as can be seen in Fig. 2B. Along with our 2010 paper, these
findings demonstrate that the declines in the frequency of social and
communication behaviors detected through more labor-intensive video
coding methods are also detectable through much simpler methods that
would be feasible for broader use, such as brief observational ratings of
social engagement by clinical professionals.
2.3. Longitudinal parent ratings of social behavior
The question remained, however, whether such findings could be an
artifact or byproduct of the assessment context with an unfamiliar ex-
aminer. For both clinical use and future development of screening
methods, it is critical to also establish whether parent ratings are sen-
sitive to the developmental decline phenomenon we have reported. In a
recent study (Ozonoff et al., 2018b), we examined parent prospective
ratings of the same early-appearing socio-communicative behaviors
measured in the video and examiner ratings. Parents in our study
completed the Early Development Questionnaire (EDQ; Ozonoff et al.,
2005) prior to each visit. The EDQ consists of 45 questions about the
child’s current functioning in social, communication, and repetitive
behavior domains. Each item is rated on a 4-point frequency scale
(0=behavior never occurs, 3=behavior often occurs). Three items,
comparable in content to the video codes and examiner ratings, were
summed: item 1 (“my child looks at me during social interactions”),
item 4 (“my child smiles back at me when I smile at him/her”), and
item 13 (“when I call my child’s name, he/she looks at me right away”).
In addition to being parallel to the behaviors rated by examiners, these
items were selected because they represent early-appearing behaviors
that are relevant and developmentally appropriate across all ages of the
Fig. 1. Declining trajectories of gaze to eyes in children developing ASD, coded from a videotaped interaction with an examiner. Panel A: Cohort 1, Panel B: Cohorts 2
and 3.
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study (6–36 months). In contrast to other EDQ items that measure later-
developing skills (e.g., joint attention, language), the items selected for
the composite measure behaviors present in the first year of life (Inada
et al., 2010). As with the behaviors we selected for coding and examiner
observational ratings, it was critical that the behaviors rated by parents
have the potential to demonstrate decreases over time as ASD signs
emerge. The composite variable, quantifying parent report of the fre-
quency of key early social behaviors, had a potential range of 0 – 9. On
the parent-rated EDQ, there were again no group differences at 6
months. As with the other measures, the ASD group showed a decline in
levels of social engagement with age, while both the high-risk and low-
risk Non-ASD groups demonstrated gains in social engagement over
time. The ASD group’s scores were significantly lower than both Non-
ASD groups by 12 months and the differences increased with age, de-
monstrating the same declining trajectory as evident in the coded be-
havior and examiner ratings (see Fig. 3A).
Employing a similar approach, we replicated the ability of parent
report to capture the decline in social and communication development
using a standardized, normed measure (Parikh et al., 2018), the Infant-
Toddler Checklist (ITC), a 24-item parent questionnaire from the
Communication and Social Behavior Scales (CSBS; Wetherby and
Prizant, 2002). This instrument is normed from 6 to 24 months and
includes questions that span this developmental range, from early-ap-
pearing behaviors like social smiling to those that emerge at older ages,
such as spoken language and pretend play. We created a composite of
three items that represent behaviors typically present in the first year of
life (item 2: “when your child plays with toys, does he/she look at you
to see if you are watching?”; item 3: “does your child smile or laugh
while looking at you?”; item 19: “when you call your child’s name, does
he/she respond by looking or turning toward you?”). We then com-
pared growth trajectories in infants subsequently diagnosed with ASD
(n = 46) to the HR Non-ASD (n = 139) and LR Non-ASD groups
(n = 96). There were no group differences on the 3-item ITC composite
at 6 months of age; however, over time, the ASD group showed a de-
cline in scores, while the two Non-ASD groups demonstrated gains (see
Fig. 3B). This resulted in the ASD group having significantly lower
scores at 24 months than both comparison groups.
These studies from our lab show that children with ASD, as a group,
evidence declines in development from 6 to 36 months. Such declines
are seen only in the ASD group and not in comparison samples, even
those with elevated genetic risk or other developmental concerns.
Findings of declining trajectories have since been replicated by other
Fig. 2. Declining trajectories of social engagement in children developing ASD, as rated by examiners unaware of risk group or outcome. Panel A: Cohort 1, Panel B:
Cohorts 2 and 3.
Fig. 3. Declining trajectories of social engagement in children developing ASD, as rated by parents, Cohorts 2 and 3. Panel A: Early Development Questionnaire,
Panel B: Infant Toddler Checklist.
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299
independent research teams. Landa et al. (2013) examined growth
trajectories in infants later diagnosed with ASD and Non-ASD partici-
pants. Approximately half of the children with ASD, labeled the Early-
ASD group, demonstrated differences from the Non-ASD cases at 14
months but the other half (Later-ASD group) did not diverge from ty-
pical infants until 24 months. The Later-ASD group demonstrated a
steep decline in shared positive affect, as measured by the CSBS
(Wetherby and Prizant, 2002), between 14 and 24 months. Jones and
Klin (2013) conducted a prospective eye-tracking study with high- and
low-risk infants to assess attention to eyes. The authors reported that
very early in development (i.e., first two months of life), both low-risk
and high-risk infants displayed high levels of attention to eyes, with no
group differences. However, high-risk infants who were later diagnosed
with ASD began to demonstrate a steady decline in looking at eyes at
four months, reaching a level that was approximately half that of low-
risk infants by 24 months. What was most predictive of a later ASD
outcome was not the amount of visual fixation on eyes displayed at any
particular age, but the overall declining trajectory over time. This study
found that the majority of infants developing ASD demonstrated this
declining pattern.
2.4. Growth curve modeling approaches to determining onset classifications
In aggregate, the studies reviewed up to this point converge on the
conclusion that longitudinal decreases in key social behaviors are a
signature of the early emergence of ASD. But these data do not clarify
how widespread such phenomena are within ASD and whether the
group-level findings are driven by extreme outliers or characterize a
majority of young children developing ASD. In our lab, we have ap-
proached this issue analytically using multivariate Latent Class Analysis
(LCA; Muthen, 2004), permitting us to identify distinct subgroups of
children based on their longitudinal patterns on multiple measures of
social communication. This technique does not rely on preconceived
notions or poorly defined definitions of onset phenomena, but instead
uses statistical modeling to empirically derive the optimum number of
classes described by the patterns of performance demonstrated in the
measures.
Data from a recent paper (Ozonoff et al., 2018b) address the ques-
tion of how widespread the declining trajectories pattern is within a
group of 32 infants subsequently diagnosed with ASD. We employed
latent class growth models to examine potential within-group variation
in onset patterns, using both examiner ratings of social engagement and
parent ratings from the EDQ (see Sections 2.2 and 2.3 for instrument
descriptions). Best model fit for examiner ratings was a two-group so-
lution. Using their highest posterior group probability, the 32 partici-
pants were classified into two trajectories (see Fig. 4A, which also
presents the Low- and High-Risk Non-ASD groups as contrasts). Only a
small proportion of the ASD cases (n = 4; 13%) were assigned to an
Early Onset/No Regression group by the latent class analyses, based on
examiners prospectively reporting low levels of social behavior at all
ages. The vast majority (n = 28; 88%) were classified by these analyses
into a Regression group, in which examiners prospectively rated in-
itially high levels of social engagement that dropped significantly over
time.
The best fit for the parent EDQ 3-item composite in the latent class
models was a three-group solution: Group 1, an Early Onset trajectory,
Group 2, a Declining trajectory, and Group 3, an Improving trajectory
(see Fig. 4B). Parents prospectively reported low levels of social en-
gagement at all ages for Group 1, which again made up a small minority
of the sample (n = 4; 13% of the sample). The majority of the sample
(n = 22; 69%) was classified in Group 2; these children were pro-
spectively reported by parents to show high rates of social engagement
early in life, which significantly declined over time. Parents of children
in Group 3 (n = 6; 19%) prospectively reported low levels of skills at
early ages that then significantly increased over time.
2.5. Concordance between retrospective and prospective onset
classifications
In several of our studies, we have examined the correspondence
between prospectively- and retrospectively-defined onset patterns and
in each case have found them to be quite poor. For example, in our
initial paper (Ozonoff et al., 2010), we compared onset classifications
based upon coded frequencies of social and communicative behaviors
to onset classifications employing retrospective parent report on the
Autism Diagnostic Interview-Revised (ADI-R; Le Couteur et al., 2003).
Using prospective observational data, 86% of the ASD sample showed
decreasing rates of eye contact, social smiles, and vocalizations over
time, but by parental recall using the ADI-R, only 17% of the children
were classified as having regressive onset. In a more recent study
(Ozonoff et al., 2018b), 69% of parents rated their child in a manner
consistent with regression on a prospective questionnaire (the EDQ,
described in section 2.3), but only 29% rated that same child as losing
skills using a retrospective measure (the ADI-R). Parents were able to
implicitly identify the changes in their child’s development over time
when making ratings of the frequencies of current behaviors, but often
did not explicitly label these changes as skill loss or regression when
asked in a more categorical way. These results were particularly
striking since both relied upon parental observations of the child. Si-
milar findings of low concordance between retrospective and pro-
spective methods of defining onset were reported by Landa et al.
(2013). And a recent large general population study (Havdahl et al.,
2018) found similar under-reporting of losses based on a retrospective
parent interview. Of parents who prospectively reported a loss, defined
as rating certain social behaviors as present at 18 months but absent at
36 months, only a striking 2% of them recalled such a decline, or la-
beled it as a loss, when asked at age 3.
3. Conclusions and theoretical implications
A number of conclusions can be drawn from the collective body of
work reviewed in this paper.
3.1. Onset involves declining social development
ASD emerges over the first two years of life and is not present “from
the beginning of life” as stated by Kanner (1943, p. 242) in his seminal
paper. For many years, it was presumed that ASD signs were present,
but were just challenging to measure, from birth. Diagnostic criteria for
ASD were developed at a time when children with autism were rarely, if
ever, identified in infancy and thus many symptoms in the DSM and ICD
criteria, such as delays or deficits in gestures, language, imitation, and
pretense, are less relevant to the first year of life. As we have empha-
sized in this review, one key to understanding early symptom emer-
gence is to focus on very early-appearing social behaviors, those that
are robustly present in early infancy, such as social interest, shared
affect, gaze to faces and eyes, and response to name. When such a
methodological approach is taken, there is clear evidence, across mul-
tiple methods, replicated by independent research teams, of declining
social behavior over time, after a period of relatively typical develop-
ment. There is convergence across studies of a lack of group differences
from comparison samples without ASD before 9 months of age, fol-
lowed by statistically significant differences starting at 12 months that
widen over time. Logically, if certain skills are evident at typical rates at
an early point in development and then those same skills, defined and
measured the same way later in development, have substantially di-
minished, resulting in statistically significant differences from typical
infants, a loss or regression of some magnitude must have occurred.
This paper advocates for taking a dimensional approach and using
trajectories to identify patterns of onset. We are not arguing, however,
for a fundamental reconsideration of the use of the word “regression.”
The Merriam-Webster definition of the word regression is “a trend or
S. Ozonoff and A.-M. Iosif Neuroscience and Biobehavioral Reviews 100 (2019) 296–304
300
shift toward a lower or less perfect state, such as (a) progressive decline
of a manifestation of disease or (b) gradual loss of acquired skills.” This
aptly describes the loss of established skills (e.g., eye contact, response
to name, social interest) that occurs during the declines in social de-
velopment described in this paper.
3.2. Regression in ASD is the Rule, not the exception
The data summarized in this review suggest that a regressive pattern
of onset is much more common than previously thought, the rule rather
than the exception. While retrospective studies yielded regression es-
timates of 20–30%, prospective data put them much higher, in some
studies well over 80%. We and others (Jones et al., 2014; Ozonoff et al.,
2018b; Pearson et al., 2018) have suggested that the regressions re-
ported by parents retrospectively on measures like the ADI-R represent
just “the tip of the iceberg,” while prospective studies are able to cap-
ture earlier, more gradual, subtle changes that may be less noticeable in
real-time observation. A hypothesis deriving from this supposition is
that concordance between retrospective and prospective methods
should be most frequent when the regression occurs later, is more
drastic or severe, and involves loss of clearly defined skills like lan-
guage. No published studies have yet examined this question and it
would be a fruitful avenue for future investigation.
We propose that the way ASD starts, for all children, is through
declines in early social and communication abilities. This presents a
testable hypothesis: that all infants developing ASD lose some skills, but
at different ages, some of which may be harder to detect with current
measurement approaches than others. It may be difficult for parents to
perceive and describe changing patterns of development that occur over
many months during infancy, particularly when the period of normalcy
is fairly brief. Our team (Ozonoff et al., 2010, 2011a) and others
(Pearson et al., 2018; Rogers, 2009; Szatmari et al., 2016; Thurm et al.,
2014) have suggested that onset is better thought of dimensionally, as a
continuum of age when social and communication behaviors begin to
diverge or decline, rather than a dichotomy (regression v. early onset).
In a dimensional conceptualization of onset, at one end of the con-
tinuum lie children who display declines so early that they are difficult
to measure and symptoms appear to have always been present. At the
other end of the continuum are children who experience losses so late,
when more skills have been acquired and thus there are more skills to
lose, that the regression appears quite overt and dramatic. We propose
that variable timing of these processes across children leads to symp-
toms exceeding the threshold for diagnosis at different points in the first
3 years of life, resulting in a distributed curve of onset timing.
3.3. Simplex v. multiplex samples
An important question to consider is whether regression in infant
sibling samples is representative of regression in children with ASD who
are the first in their families to be diagnosed with the condition. If
symptoms emerge differently in multiplex and simplex families, then
the insights about onset afforded by prospective research may not be
applicable to the general population of children with ASD. For example,
perhaps children in multiplex families are more likely to experience a
regression than children from simplex families, accounting for the
higher rates of decline apparent in prospective studies. We have no
reason to believe this is the case. In fact, the rate of retrospectively-
reported regression in multiplex families has been reported to be similar
or lower than in simplex families (Boterberg et al., 2019b; Parr et al.,
2011), failing to account for the high rates apparent in infant sibling
studies, whose participants are, by definition, from multiplex families.
A related issue is that parents participating in infant sibling investiga-
tions have an older child with ASD. It is possible that these parents may
be different reporters than other parents, given their previous experi-
ence with ASD. This may make them more astute observers of devel-
opment than parents in the general population and therefore more
likely to recognize skill decline. This hypothesis, however, is not sup-
ported by the data presented earlier in this review in which parents in
multiplex families also under-report skill loss (Landa et al., 2013;
Ozonoff et al., 2010, 2018a, 2018b). Nevertheless, it is important to
keep these cautions in mind in interpreting the extant data on onset
patterns. Validating these results in different kinds of samples, such as
community-based epidemiological cohorts or other high-risk groups
like very preterm infants, will be critical.
3.4. Improving the measurement of onset
Collectively, the studies reviewed in this paper present significant
concerns about the accuracy of the most widely used methods of
measuring regression, that is, retrospective parent report, and argue
against their widespread use. The challenge currently faced by the field
is that there are no practical alternative strategies to parent report for
characterizing onset status. The time-intensive process and cost of home
videotape analysis is prohibitive for large samples. Future studies will
continue to rely on retrospective data, of necessity, since inclusion
criteria for most samples require a confirmed ASD diagnosis (i.e., not
Fig. 4. Latent classes of social engagement, demonstrating declining trajectories in the majority of children developing ASD, Cohorts 2 and 3. Panel A: Examiner
ratings, Panel B: Parent ratings.
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prospective data).
Several strategies have been proposed to improve reporting (Ayhan
and Isiksal, 2005). To minimize comprehension or interpretation pro-
blems, it is recommended that further specific information about the
behavior in question be provided. ASD screening instruments have
begun to incorporate video to improve accuracy (Marrus et al., 2018;
Smith et al., 2017) and this strategy could be adapted to improve re-
porting of onset patterns. For example, longitudinal video of a child
experiencing skill loss could be shown to parents to illustrate the kinds
of changes in behavior that define regression. To minimize recall pro-
blems, the simplest approach, and the one shown to have the best va-
lidity, is to ask respondents to consult relevant records prior to com-
pleting the interview (Ayhan and Isiksal, 2005). Parents could, for
example, review entries in baby books or journals or watch home video
of the child prior to the interview. Another approach is to link reporting
to key events in the respondent’s life by creating a detailed timeline and
context that assist recall of specific details (Loftus and Marburger,
1983). This method has already been used by Werner et al. (2005) to
improve recall of early development in ASD and it could be further
adapted for reporting about onset patterns. Whether these methods will
enhance the validity of parent report of onset remains to be seen and
would be a fruitful area of future study. For further discussion, see also
Boterberg et al. (2019a).
3.5. Validity of previous studies of regression
The studies reviewed in this paper call into serious question the
validity of previous studies of regression, which have, of necessity and
the lack of alternatives, relied upon retrospective measures. Refining
methods of studying the onset of ASD has the potential to transform
research programs on etiological factors that contribute to the devel-
opment of ASD by providing more precise and accurate measurements
of an important phenotype (Barbaresi, 2016; Thurm et al., 2018). A
better understanding of the inflection points at which development
diverges from a typical trajectory to an autism trajectory could be
highly informative to the search for risk factors. Better measures of
onset are urgently needed for etiologic studies, which have been hin-
dered already by the tremendous heterogeneity of the autism pheno-
type (Constantino and Charman, 2016). Many recent studies have ex-
amined whether onset types are associated with potential etiologic
factors and biological correlates, such as brain growth (Nordahl et al.,
2011; Valvo et al., 2016), seizures (Barger et al., 2017), vaccinations
(Goin-Kochel et al., 2016), gastrointestinal problems (Downs et al.,
2014; Richler et al., 2006), immunological function (Scott et al., 2017;
Wasilewska et al., 2012), and genetic and genomic variations (Goin-
Kochel et al., 2017; Gupta et al., 2017; Parr et al., 2011), including
mitochondrial and MeCP2 mutations (Shoffner et al., 2010;
Veeraragavan et al., 2016; Xi et al., 2007). So far, none of these factors
has been firmly associated with onset patterns. This may be due to the
errors that are likely to have occurred in the classifications of onset
done in these studies. Clearly, examining the biological underpinnings
of an imprecise measure is problematic.
In a review of autism genetics, one of the major priorities identified
for future research is the characterization of ASD subtypes to relate to
genetic variations (Geschwind, 2011). As more and more risk genes for
ASD are identified, the common molecular pathways that these genes
share are becoming understood, with some expressed early in neuro-
biological development and others later (Konopka et al., 2012). A twin
study (Hallmayer et al., 2011) suggested a greater role for environ-
mental factors in ASD than previously appreciated. A more precise
timing of first symptom emergence would enhance identification of
etiological factors and when they might operate, with potential im-
plications for intervention and prevention.
4. Clinical implications
Finally, the studies reviewed here provide hope and promise for
improvements in screening, early diagnosis, and treatment. Many pro-
spective studies (e.g., Bosl et al., 2018; Jones and Klin, 2013; Ozonoff
et al., 2010) used measures, such as behavioral coding, eye tracking,
and EEG, that are expensive, labor intensive, and not practical for
routine use. Studies reviewed in this paper, however, have demon-
strated that prospective parent report can identify declining trajectories
of development (Ozonoff et al., 2018b; Parikh et al., 2018), as long as
the instruments focus on early-appearing social behaviors, present in
the first year of life, that have the potential to demonstrate decreases
over time as ASD signs emerge. Brief rating scales of this type, ad-
ministered longitudinally at regular well-child health care visits, could
provide a clinically feasible and cost-effective screening tool capable of
detecting declines over time. We hypothesize that dynamic screening,
which utilizes longitudinal screenings over time and comparison of
scores across ages to identify declining trajectories, will improve
identification over static, cross-sectional screenings examining whether
a single score at a single age exceeds a cutoff. This approach has been
successfully used in identifying Rett syndrome (RTT), where head cir-
cumference is normal at birth, followed by deceleration of head growth
between 5 months and 4 years (Hagberg et al., 2001; Tarquinio et al.,
2012). Through the development of RTT-specific growth references
throughout early childhood, based on mapping head circumference
trajectories, diagnosis of RTT has been possible at earlier ages (Schultz
et al., 1993; Tarquinio et al., 2012). We (Ozonoff et al., 2010, 2018b)
and others (Landa et al., 2013; Pearson et al., 2018; Thomas et al.,
2009) have suggested that this kind of dimensional, trajectory-based
methodological approach, percentiling social and communication
milestones as we percentile other growth parameters, could be applied
to detect ASD early.
Prospective studies have repeatedly demonstrated that develop-
mental declines follow a period in the first year of life when socio-
communicative skills are largely intact. Such early intact skills can be
capitalized upon in treatment, presenting opportunities for preventive
intervention when the brain is rapidly developing and maximally
malleable. For many years, the holy grail has been finding a marker
present prior to symptom emergence, thus affording the possibility of
earlier, possibly preventative, treatment during the prodromal period. If
the prospective methods described in this paper can be harnessed to
identify infants at risk for ASD, during the decline of skills, rather than
after the decline was over, it might be possible to disrupt these trajec-
tories prior to the full onset of symptoms (Dawson, 2011). Children
could be provided immediate access to infant interventions (Fein et al.,
2016; Rogers et al., 2014), capitalizing on still-preserved skills and
harnessing the brain plasticity of early infancy to improve outcomes,
lessen disability, and perhaps, prevent the full disorder from devel-
oping.
Conflict of interest
The authors have no competing interests to declare.
Acknowledgments
This work was supported by NIH grants R01 MH068398 (Ozonoff)
and R01 MH099046 (Ozonoff). Thank you to Sofie Boterberg for her
reading of an earlier version of this manuscript. We are deeply grateful
to all the children and parents who participated in and showed sus-
tained commitment to our longitudinal program of research.
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Introduction
Methods for measuring onset patterns
Prospective studies of onset
Findings from the University of California Davis infant sibling study
Behavioral coding of social-communication rates
Observer ratings of social engagement
Longitudinal parent ratings of social behavior
Growth curve modeling approaches to determining onset classifications
Concordance between retrospective and prospective onset classifications
Conclusions and theoretical implications
Onset involves declining social development
Regression in ASD is the Rule, not the exception
Simplex v. multiplex samples
Improving the measurement of onset
Validity of previous studies of regression
Clinical implications
Conflict of interest
Acknowledgments
References
