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Toward an Interdisciplinary Understanding of Sensory Dysfunction in Autism Spectrum Disorder: An Integration of the Neural and Symptom Literatures


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Sensory processing differences have long been associated with autism spectrum disorder (ASD), and they have recently been added to the diagnostic criteria for the disorder. The focus on sensory processing in ASD research has increased substantially in the last decade. This research has been approached from two different perspectives: the first focuses on characterizing the symptoms that manifest in response to real world sensory stimulation, and the second focuses on the neural pathways and mechanisms underlying sensory processing. The purpose of this paper is to integrate the empirical literature on sensory processing in ASD from the last decade, including both studies characterizing sensory symptoms and those that investigate neural response to sensory stimuli. We begin with a discussion of definitions to clarify some of the inconsistencies in terminology that currently exist in the field. Next, the sensory symptoms literature is reviewed with a particular focus on developmental considerations and the relationship of sensory symptoms to other core features of the disorder. Then, the neuroscience literature is reviewed with a focus on methodological approaches and specific sensory modalities. Currently, these sensory symptoms and neuroscience perspectives are largely developing independently from each other leading to multiple, but separate, theories and methods, thus creating a multidisciplinary approach to sensory processing in ASD. In order to progress our understanding of sensory processing in ASD, it is now critical to integrate these two research perspectives and move towards an interdisciplinary approach. This will inevitably aid in a better understanding of the underlying biological basis of these symptoms and help realize the translational value through its application to early identification and treatment. The review ends with specific recommendations for future research to help bridge these two research perspectives in order to advance our understanding of sensory processing in ASD.
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published: 17 June 2016
doi: 10.3389/fnins.2016.00268
Frontiers in Neuroscience | 1June 2016 | Volume 10 | Article 268
Edited by:
Benjamin Gesundheit,
Cell El Ltd., Israel
Reviewed by:
Carolyn McCormick,
Brown University, USA
Shulamite A. Green,
University of California, Los Angeles,
Kimberly B. Schauder
Specialty section:
This article was submitted to
Child and Adolescent Psychiatry,
a section of the journal
Frontiers in Neuroscience
Received: 11 March 2016
Accepted: 27 May 2016
Published: 17 June 2016
Schauder KB and Bennetto L (2016)
Toward an Interdisciplinary
Understanding of Sensory
Dysfunction in Autism Spectrum
Disorder: An Integration of the Neural
and Symptom Literatures.
Front. Neurosci. 10:268.
doi: 10.3389/fnins.2016.00268
Toward an Interdisciplinary
Understanding of Sensory
Dysfunction in Autism Spectrum
Disorder: An Integration of the Neural
and Symptom Literatures
Kimberly B. Schauder *and Loisa Bennetto
Department of Clinical and Social Sciences in Psychology, University of Rochester, Rochester, NY, USA
Sensory processing differences have long been associated with autism spectrum
disorder (ASD), and they have recently been added to the diagnostic criteria for the
disorder. The focus on sensory processing in ASD research has increased substantially
in the last decade. This research has been approached from two different perspectives:
the first focuses on characterizing the symptoms that manifest in response to real world
sensory stimulation, and the second focuses on the neural pathways and mechanisms
underlying sensory processing. The purpose of this paper is to integrate the empirical
literature on sensory processing in ASD from the last decade, including both studies
characterizing sensory symptoms and those that investigate neural response to sensory
stimuli. We begin with a discussion of definitions to clarify some of the inconsistencies
in terminology that currently exist in the field. Next, the sensory symptoms literature is
reviewed with a particular focus on developmental considerations and the relationship
of sensory symptoms to other core features of the disorder. Then, the neuroscience
literature is reviewed with a focus on methodological approaches and specific sensory
modalities. Currently, these sensory symptoms and neuroscience perspectives are
largely developing independently from each other leading to multiple, but separate,
theories and methods, thus creating a multidisciplinary approach to sensory processing
in ASD. In order to progress our understanding of sensory processing in ASD, it is now
critical to integrate these two research perspectives and move toward an interdisciplinary
approach. This will inevitably aid in a better understanding of the underlying biological
basis of these symptoms and help realize the translational value through its application
to early identification and treatment. The review ends with specific recommendations for
future research to help bridge these two research perspectives in order to advance our
understanding of sensory processing in ASD.
Keywords: sensory processing, autism spectrum disorder, interdisciplinary approaches, sensory symptoms,
hyper-responsiveness, hypo-responsiveness
Schauder and Bennetto Sensory Dysfunction in ASD
Autism spectrum disorder (ASD) is characterized by deficits
in social communication and the presence of restricted and
repetitive behaviors, including sensory atypicalities. Sensory
processing abnormalities have been reported in ASD since
the earliest firsthand and clinical accounts (Kanner, 1943;
Cesaroni and Garber, 1991). Recent estimates suggest a high
prevalence of sensory symptoms, with reports ranging from
60 to 96% of children with ASD exhibiting some degree
of atypical responses to sensory stimuli (Dunn et al., 2002;
Baranek et al., 2006; Billstedt et al., 2007; Leekam et al., 2007;
Klintwall et al., 2011; Lane et al., 2011; Marco et al., 2011). The
updated diagnostic criteria for ASD in DSM-5 includes abnormal
sensory behaviors (American Psychological Association, 2013).
Sensory processing differences likely contribute to many of the
higher-order cognitive and social deficits associated with ASD
(Cascio et al., 2016), highlighting the broad potential impact
of atypical sensory processing. Understanding the mechanisms
through which these sensory symptoms manifest could help
parents, educators, clinicians, and individuals themselves attend
to the sensory environment and make adjustments accordingly
in the hopes of normalizing one’s sensory experiences and
alleviating any downstream effects of atypical responding to
sensory input. Over the last decade, there has been a significant
increase in research related to sensory processing in ASD, which
builds upon the initial accounts and research studies in this
domain (see Rogers and Ozonoff, 2005, for a review). This
accumulating research has been approached from two major
perspectives. The first focuses on the symptoms that manifest in
response to real world sensory stimulation. The second focuses
on the neural pathways and mechanisms underlying sensory
Currently, the symptom literature largely utilizes self- or
parent- report questionnaires and/or observational, lab-based
paradigms, in an attempt to characterize the observable reactions
that impact an individual on a daily basis. Current neuroscience
approaches measure the degree and timing of neural response.
Despite each field lending itself to the study of unique aspects of
sensory processing in ASD, there is a gap in our understanding
of how neural response to sensory input is related to symptoms.
Nonetheless, neural reactivity and processing patterns underlie
and give rise to the presence of sensory symptoms (Marco
et al., 2011), making it essential to understand how neural
processing contributes to sensory symptoms. Each field has
unique strengths, which have led to important contributions in
our understanding of sensory processing in ASD; however, they
each have a lot to learn from the other as we begin to bridge these
two fields to gain a more comprehensive picture of this newly
recognized diagnostic feature of ASD.
The purpose of this paper is to integrate the current
research perspectives and methodological approaches related
to sensory processing in ASD. Specifically, we will first
clarify important terminology that is currently used in the
study of sensory processing in ASD. Then, empirical studies
from the last decade that focus on non-social, unisensory
experiences, including those from both the sensory symptoms
and neuroscience perspectives, will be reviewed with the goal
of increased understanding of each respective field in order to
move toward an interdisciplinary approach to sensory processing
in ASD. Some recent studies have begun to integrate these
perspectives (Green et al., 2013, 2015; Cascio et al., 2015) and
provide a basis to further build upon. Furthermore, both of
these perspectives investigate various domains of ASD (e.g.,
sensory, cognitive, social, language), and ASD research as a
whole would benefit from integration of these perspectives.
However, given that sensory processing differences potentially
have broad downstream effects in higher-order domains (e.g.,
cognitive, social, language), it is essential to first bridge these
perspectives at the sensory level. Thus, although a systematic
review of all pieces of sensory processing in ASD is beyond
the scope of this paper, its goal is to provide a foundation for
shared understanding among disciplines investigating sensory
processing in ASD.
In order to integrate the literatures on sensory processing in ASD,
it is important to clarify the current terminology used across
fields. Recently, Cascio et al. (2016) highlighted the inconsistent
conceptualizations and definitions across fields related to sensory
processing in ASD and how this poses a challenge to cross-
discipline communication and collaboration. The definitions that
follow are an attempt to summarize and organize the existing
terms used to describe sensory processing in ASD (Table 1). Our
goal is to highlight the many components of sensory processing
that might exist and to highlight the nuanced differences between
these possible components of sensory processing.
Terminology confusion exists for two main reasons. First,
both the neural and symptoms literatures adopt some of
the same terms, but these terms oftentimes refer to very
different concepts. For example, behavior is a problematic
term because it is conceptualized differently across fields.
Namely, neuroscientists discuss behavior in terms of perceptual
decisions (e.g., detection or discrimination abilities; Weigelt
et al., 2012), whereas clinicians tend to focus on observable
reactions (e.g., a child covering his/her ears at the sound
of a blender; McCormick et al., 2016). The second point of
confusion regarding terminology surrounds the large range of
terms within the sensory symptom literature that are oftentimes
used interchangeably, but are arguably different constructs.
The discussion that follows highlights both aspects of this
Sensory processing is the umbrella term that refers to the
entire process from the brain registering sensory input from
the outside world to the individual generating a response
based on that input. Atypical neural responding to sensory
input is thought to impact responses at multiple levels,
including perceptual, physiological, and observable differences
(Marco et al., 2011;Figure 1). Clinically, atypical sensory
processing is manifested in inappropriate responses to sensory
input that involve emotional and behavioral disruptions,
and interfere with an individual’s daily functioning (Miller
et al., 2007). More generally, these disruptions can be
considered sensory symptoms. Although it is understood that
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Schauder and Bennetto Sensory Dysfunction in ASD
sensory symptoms are a result of how sensory information is
processed in the brain and the body (Cascio et al., 2016), the
translation from neural firing to sensory symptoms is a complex
Early models of sensory processing, developed to explain
behavior and plan clinical interventions, emphasized both
neurological thresholds and response patterns in the generation
of sensory reactions, or symptoms (Ayres, 1972; Dunn, 1997).
Neurological thresholds refer to the amount of sensory input
needed for the brain to register that input. Clinically, low
threshold to stimulation is thus conceptualized as an individual
requiring less sensory input to generate the typical response,
whereas high threshold to stimulation is conceptualized as an
individual requiring more sensory input to generate a typical
response. These thresholds are important because it follows that
typical levels of sensory input might then generate an atypical
response—over-reaction for low threshold to stimulation and
under-reaction for high threshold to stimulation. Researchers
who have adopted these models generally rely on questionnaires
to measure these constructs (e.g., Joosten and Bundy, 2010;
Reynolds et al., 2011). However, questionnaires are unable to
accurately measure an individual’s threshold to stimulation, and
thus rely on an assumption that an observable reaction accurately
captures the complexity of processing sensory input (see Sensory
Symptoms section for more information).
Neuroscientists measure this construct of neurological
threshold more directly, but have adopted different terminology.
At the neural level, hyper- and hypo-responsiveness1refer to
increased or decreased neural firing, respectively (e.g., Gomot
et al., 2008). Additionally, thresholds are determined by the
process of sensory gating and habituation. Sensory gating refers
to the brain’s inhibitory ability to filter out redundant or
unnecessary neural responses to irrelevant environmental stimuli
(Orekhova et al., 2008). Habituation refers to decreased neural
response to repetitive sensory stimulation (e.g., Guiraud et al.,
2011). At the perceptual level, sensitivity is determined by the
smallest stimulus intensity that is detectable, and is defined as
the inverse of a perceptual threshold (Engen, 1988). Adding to
the confusion, sensory symptom researchers define sensitivity as
a negative reaction in response to low threshold to stimulation
(Dunn, 1997). In addition, they refer to hyper-responsiveness as
the presence of an atypical response, such as covering one’s ears
in response to everyday noises, and hypo-responsiveness as the
absence of a typical response, such as failure to orient to salient
sounds (e.g., Ben-Sasson et al., 2009; McCormick et al., 2016).
Based on the theoretical conceptualization that these observable
components are a result of neurological thresholds, hyper- and
hypo-responsiveness have thus each become more of a single
construct than is theoretically warranted. Furthermore, because
of this parallel terminology, connections between neural firing
patterns and symptoms are often portrayed as overly simplistic.
The complexity of sensory processing is further highlighted
when incorporating the response pattern component, which
1Hyper-responsive and over-responsive are synonymous terms, as are hypo-
responsive and under-responsive. The term hyper-responsive and hypo-responsive
will be used exclusively in this paper.
suggests a wide range of possible symptoms that could result
from atypical neural registration and perception of sensory
stimuli. Sensory defensiveness is observed as a negative reaction
to a sensory stimulus that is not typically considered to be
aversive, such as repeatedly itching or screaming in reaction to
a tag in clothing (Baranek et al., 1997). Sensory avoidance is
observed as a resistance or unwillingness to interact with the
stimulus (Dunn, 1997). Poor registration is defined as decreased
ability to register sensory input and is oftentimes measured by
a lack of response (Dunn, 1997). Conversely, sensory orienting
is the direction of attention to a sensory stimulus, which is
assumed to be in response to successful registration of that
stimulus (Liss et al., 2006). Similar to the neuroscience definition,
habituation is defined as decreased response to repetitive sensory
stimulation; however, in this literature, it is measured by
orienting responses, often to repeated sounds (Baranek et al.,
2007). Sensory filtering, which has been predominately defined
in the auditory modality, refers to the ability to selectively
attend to relevant sensory information and exclude irrelevant
or distracting sensory information (Tomchek and Dunn, 2007).
Sensory seeking is defined as excessively seeking out sensory input
(Dunn, 1997; Liss et al., 2006).
Although these symptoms are uniquely defined, these
terms are sometimes being operationalized more broadly (e.g.,
defensiveness as high sensitivity or having a low threshold to
stimulation; Kern et al., 2006), rather than focusing exclusively
on the observable reaction. Furthermore, in some cases it
is unclear as to how these symptoms originate from neural
responding patterns. For example, sensory seeking was originally
conceptualized as a compensatory response in an individual
with high threshold to stimulation (Dunn, 1997), but has also
been conceptualized as a compensatory response to overarousal
(Liss et al., 2006), which would theoretically occur in individuals
with low threshold to stimulation. This example underscores
the importance of measuring both the neural response and the
symptom presentation and cautions against assuming that the
observable reaction accurately captures the complexity of sensory
In sum, the conceptualization of sensory processing as
a unitary construct is challenged by the broad array of
existing terms that each define specific components of this
complicated process. The inconsistent definitions across
disciplines, highlighted above, have contributed to confusion
within the study of sensory processing in ASD. At the neural
level, thresholds determine which sensory information is
registered in the brain. This then influences perceptual sensitivity
and bodily response, and leads to observable reactions and
symptoms (Figure 1). The literature defines and differentiates
between several possible symptom presentations (defensiveness,
avoidance, poor registration, poor habituation, poor filtering,
seeking), measured mainly at the level of observable reactions.
Organizing these terms and reconciling the manner in which
they are used in both the neural and symptom literatures
provides an important step toward clarifying the often overly
simplified connections between sensory symptoms and their
underlying neural patterns and promoting more collaborative
future research.
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Schauder and Bennetto Sensory Dysfunction in ASD
TABLE 1 | Terminology in sensory processing in ASD research.
Term Definition in symptom research Definition in neuroscience research
Sensory processing The process of the brain registering sensory input from the outside world and the individual generating a response based on that input
Symptom Atypical responses to sensory input that interfere with an
individual’s daily functioning
Behavior Observable reactions Ability to detect or discriminate, measured by perceptual decisions
Low threshold Requires less sensory input to generate a typical response
(referred to as low threshold to stimulation)
Requires less sensory input to generate a neural response
High threshold Requires more sensory input to generate a typical response
(referred to as high threshold to stimulation)
Requires more sensory input to generate a neural response
Hyper-responsiveness Presence of an atypical response, or over-reaction Increased neural responding
Hypo-responsiveness Absence of a typical response, or under-reaction Decreased neural responding
Sensory gating Inhibitory functioning at the neural level, which filters out redundant or
unnecessary neural responses to all other environmental stimuli
Sensitivity Negative reaction to sensory input Degree to which one is susceptible to perceiving small changes in
stimulus intensity; inverse of perceptual threshold
Habituation Decreased response of the individual to repetitive sensory
Decreased neural response to repetitive sensory stimulation
Defensiveness Negative reaction to sensory input
Avoidance Resistance or unwillingness to interact with sensory stimuli
Poor registration Decreased ability to register sensory input (typically measured by
lack of response)
Sensory orienting Directed attention to a sensory stimulus
Sensory Filtering Ability to process relevant sensory information and exclude
irrelevant/distracting sensory information
Sensory Seeking Excessively seeking out sensory input
These definitions are adopted from the literature, but they are fine-tuned to emphasize important differences between terms that are oftentimes used interchangeably.
In the past 10 years, sensory experiences of individuals with
ASD have been assessed via three methods: self-, parent-,
and teacher-report questionnaires; behavioral observations; and
autonomic measurement. Questionnaire measures are by far
the most commonly used, and have largely been successful in
clinical settings to generally discriminate typical from atypical
sensory processing. However, their ability to more sensitively
capture individual differences in sensory processing is less
clear for three main reasons. First, the inherent nature of
questionnaires limits their utility to assessing components of
sensory processing to those that can be observed. However, many
of the sensory questionnaire measures yield scores that imply
information about other components of sensory processing. For
example, Poor Registration, a factor score on the commonly used
Sensory Profile (Dunn, 1999), includes items such as “decreased
awareness of pain and temperature,” “doesn’t notice when people
come into the room,” and “does not seem to smell strong odors.”
Thus, all of these infer poor registration of the stimulus from an
Secondly, a common practice in sensory questionnaire
measures is to combine items that target different levels of
sensory processing. For example, the Hyperresponsiveness score
of the Sensory Experience Questionnaire (Baranek et al., 2006)
includes items such as “does your child notice sounds in the
environment before other people do?,” “is your child disturbed
by too much light inside or brightness outside?,” “does your child
react negatively or pull away when touched by a person?” Thus,
this score collapses the perceptual, emotional, and observable
responses across multiple sensory modalities, and possibly misses
important differences that may occur within more precisely
defined sensory processing levels and modalities. Finally, many
sensory questionnaires include some items evaluating social-
sensory experiences (e.g., does your child respond to his/her
name?; Dunn, 1999; Baranek et al., 2006), making it impossible to
disentangle the independent contributions of sensory and social
components. Given these limitations of questionnaire measures
in the assessment of sensory processing in ASD, the literature
that follows mostly captures a coarse picture of the observable
reactions component of sensory processing. To parallel this level
of inquiry, we will use the general terms of hyper- and hypo-
responsiveness in our discussions of findings and will clarify
specific aspects of those responding patterns when appropriate.
In the past few years, there have been some positive
advancements in the development and analysis of questionnaire
measures that provide promising avenues for future research.
Specifically, more focused questionnaires have been developed
to address some of the limitations outlined above. An example
is the Sensory Perception Quotient (Tavassoli et al., 2014a).
This questionnaire focuses on basic detection and discrimination
abilities and includes items that target specific sensory receptors
across a variety of sensory modalities (e.g., “I find it difficult to
see individual stars on a clear night” and “I would be the first
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Schauder and Bennetto Sensory Dysfunction in ASD
FIGURE 1 | Conceptual map of sensory processing using a common example of hyper-responsiveness to auditory stimulation. The first column outlines
various levels of analysis from brain to symptom. For the sake of simplicity, these are organized using a bottom-up conceptualization in a single pathway from brain to
symptom; however, in reality, there are likely several different pathways from brain to symptom, including bidirectional relationships between certain levels. The second
column provides an example of an appropriate method for each level of analysis. The third column provides one example of a possible finding related to auditory
hyper-responsiveness using each particular method.
to hear if there was a fly in the room”). Thus, this questionnaire
is still limited to observable responses, but begins to selectively
target the perceptual level of analysis. Another advancement
that has recently been applied to sensory questionnaires is
the use of cluster-based statistical analysis to identify sensory-
based subtypes within ASD. Subtype identification is motivated
by the known heterogeneity within ASD and has implications
for neurobiological studies that aim to link sensory features
with specific underlying mechanisms. Such studies have yielded
somewhat inconsistent subtypes (Lane et al., 2010, 2011, 2014;
Ausderau et al., 2014), which is possibly due to imprecise
measurement tools. Nonetheless, this subtype identification
approach is a positive step in our understanding of underlying
Other methods that assess sensory symptoms include lab-
based observational paradigms and autonomic measurements
to characterize physiological responses. Lab-based observational
paradigms are similar to questionnaire measures in that they
largely focus on observable reactions to real world sensory input
(e.g., toys, objects with certain sensory features). However, they
differ from questionnaire methods in that they rely on behavioral
coding in the lab to obtain a more controlled and objective
measure of these symptoms. Psychophysiological studies focus
on the body’s response to sensory stimulation, specifically looking
at functioning of the autonomic nervous system (ANS). Thus,
these studies provide objective measures of bodily states that have
been linked with emotional states. The following sections review
the sensory symptom literature that uses questionnaires, lab-
based observational coding paradigms, and psychophysiological
Questionnaire-Based Studies
Questionnaire-based studies on sensory processing in ASD,
detailed below, have culminated in two major conclusions: (1).
Individuals with ASD respond to sensory input in ways different
from the typical population, across a variety of modalities and
across the entire lifespan, and (2). Sensory processing differences
are related to a variety of the core and associated symptoms of
ASD and affect the quality of life in these individuals.
Sensory Symptoms Across Development
Some studies examined developmental trends in samples
spanning large age ranges. These studies showed atypicalities in
ASD across all sensory modalities that decrease with age (Kern
et al., 2006; Leekam et al., 2007). Additionally, symptoms in
different modalities (e.g., visual, auditory, tactile) were found
to be moderately correlated with each other across the lifespan,
but only correlated with general autism symptom severity in
childhood. (Kern et al., 2007). These findings support a general
disruption in sensory processing, with responses to stimuli in
each modality being related to each other, at least at the symptom
level. Additionally, these findings suggest a maturational process
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Schauder and Bennetto Sensory Dysfunction in ASD
that leads to a decrease in sensory symptoms with increasing age
that is independent from change in autism severity more globally.
Given these general developmental trends, studies investigating
narrower age ranges provide more detailed information at
important developmental stages.
Four studies have specifically looked at the profile of
sensory symptoms in infants and toddlers. Patterns of hyper-
responsiveness best differentiated those with ASD from those
with typical development (TD); this was seen both within the
modalities of touch, audition, and taste/smell (Wiggins et al.,
2009), and across low threshold patterns more globally including
both sensitivity and avoidance (Ben-Sasson et al., 2007). These
sensory symptoms presented irrespective of cognitive ability
and expressive language level (Ben-Sasson et al., 2007; Klintwall
et al., 2011). However, hypo-responsiveness best differentiated
toddlers with ASD from those with developmental delay and
TD, suggesting that a pattern of decreased responding may
be the most unique pattern in ASD (Baranek et al., 2006),
a finding confirmed by a meta-analytic review (Ben-Sasson
et al., 2009). A subgroup of toddlers with ASD had atypical
scores across multiple patterns, highlighting the comorbidity of
these symptoms even at this early developmental stage (Baranek
et al., 2006; Ben-Sasson et al., 2007). A study using high-
risk infants found that those who developed ASD had more
auditory symptoms and hypo-responsive patterns compared to
high-risk infants that do not develop ASD and low-risk infants
(Germani et al., 2014), suggesting that these may be risk factors
for developing ASD and making these symptoms potentially
important for early identification of the disorder.
In slightly older (3–6 years old) children, Tomchek and Dunn
(2007) reported similar findings of global hypo-responsiveness
as well as hyper-responsiveness in the auditory, tactile, and
taste/smell modalities. Also in line with the findings in infants
and toddlers, sensory symptoms in children do not seem to be
related to cognitive abilities (O’Donnell et al., 2012). However,
these sensory symptoms do seem to be related to language skills
and adaptive functioning in children (Tomchek et al., 2015).
McCormick et al. (2016) examined developmental trajectories
of sensory symptoms in children with ASD compared to those
with other developmental disorders and TD, and found that
children with ASD have elevated sensory symptoms from an
early age (2 years) that remain stable through the age of
8, and that hyper-responsiveness in the taste/smell modality
and poor auditory filtering best differentiated ASD from other
developmental disorders. In a large study of 3–10 year old
children, sensory symptoms across modalities were higher in
children with ASD and ADHD compared to those with TD
(Cheung and Siu, 2009). Although the ASD and ADHD groups
were not distinguishable overall, as age increased, the ASD group
showed decreases in symptoms while the ADHD group showed
stable or increased symptoms. Finally, two studies investigated
sensory symptoms in the home vs. school environments, and
demonstrated both shared and unique expression of sensory
symptoms across contexts (Brown and Dunn, 2010; Fernandez-
Andres et al., 2015).
In adolescents, two studies found less sensory seeking in ASD
(De La Marche et al., 2012; Stewart et al., 2016) with De La
Marche et al. (2012) also showing more hyper-responsiveness
(specifically in sensory avoidance) and Stewart et al. (2016) also
showing hypo-responsiveness (Low Registration). Using a small
sample of adults, Crane et al. (2009) found similar self-reported
symptoms in adults with ASD compared to TD adults, with the
most obvious differences in sensory avoidance. These sensory
scores were all correlated with IQ, suggesting that higher IQ in
adults may serve as a protective factor against persistent sensory
symptoms. Increased levels of self-reported hyper-responsiveness
in a large sample of adults with ASD have been shown using
the SensOR (Tavassoli et al., 2014b) and the Sensory Processing
Quotient (Tavassoli et al., 2014a), measures specifically targeting
hyper-responsive sensory symptoms.
Developmentally, the literature paints a picture of early hypo-
responsiveness, as well as hyper-responsiveness particularly in
the auditory, taste/smell, and touch modalities; these symptoms
pervasively affect individuals with ASD and remain stable
through at least 8 years of age. The pattern of sensory seeking
remains unclear, and may be due in part to the varying
conceptualizations of these symptoms; additional work is needed
to characterize this pattern of symptoms. Although sensory
symptoms appear to be unrelated to more global functioning
in toddlers, sensory symptoms begin to show relationships with
adaptive functioning and language skills in early childhood.
As individuals with ASD mature, hyper-responsive symptoms
best characterize adults. This could indicate that early hypo-
responsiveness leads to later global hyper-responsiveness or
alternatively, could be due to differences in parent- vs. self-
reporting strategies that are typically used in younger children
and adults, respectively. If the latter is the case, it is possible
that subjective and observed experiences differ, raising the
importance of collecting multiple types of data in the study of
sensory symptoms in ASD.
Sensory Symptoms and Their Relation to Other ASD
Symptoms and Challenges
Several studies have examined relationships between sensory
differences and a variety of the core and associated symptoms
of ASD, revealing important information about the functional
impact of sensory processing and its interference in day-to-day
life. Three studies have looked at the relationship between
sensory symptoms and general autism symptoms. In school-aged
children, general sensory symptoms measured by the Sensory
Processing Measure were related to autism severity measured
by the Gilliam Autism Rating Scale, 2nd Edition in both the
home and school environment (Sanz-Cervera et al., 2015). In
a similarly aged sample, Hilton et al. (2010) used the Sensory
Profile and identified that more proximal senses (touch, taste)
may be more related to social difficulties, measured by the Social
Responsiveness Scale in children with ASD. In adults both with
and without ASD, Tavassoli et al. (2014b) showed a relationship
between self-reported sensory hyper-responsiveness and autism
Other studies have looked at sensory processing in relation
to more specific types of functioning, such as school-related
difficulties and activity participation. In school-aged children
with ASD, Ashburner et al. (2008) found empirical support
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Schauder and Bennetto Sensory Dysfunction in ASD
for the theoretical links between atypical sensory responding
and difficulties in classroom settings. Specifically, they found
hypo-responsiveness and difficulties with auditory filtering to
explain about half of the variance in academic performance,
above and beyond IQ and general ASD symptoms. Additionally,
tactile hyper-responsiveness and auditory filtering deficits
explained 36% of the variance in inattention problems. Together,
these findings show the impact of sensory symptoms on
children’s ability to pay attention and perform successfully in
school. Different sensory responding profiles have also been
linked to different preferred activities; children with sensory
hyper-responsiveness participate less in social, school, and
extracurricular activities (Reynolds et al., 2011; Little et al., 2015),
while children with sensory hypo-responsiveness participate
more in activities outside the home, which the authors speculate
may be because they are more passive (Little et al., 2015). These
sensory responding and functional impairment relationships
also exist in younger children with ASD; preschoolers with
more significant sensory abnormalities also have more behavior
problems (O’Donnell et al., 2012).
Ben-Sasson et al. (2008) used cluster profiles in toddlers and
found that those with high hyper- and hypo-responsiveness
also had more negative emotions and higher levels of
anxiety and depression symptoms. These authors speculated
that internalizing disorders in ASD may develop from the
accumulated negative experiences with sensory input throughout
development. Sensory hyper-responsiveness, specifically, was
moderately correlated with anxiety in a very large sample of
children with ASD (Mazurek et al., 2013). Green et al. (2012)
longitudinally tested the relationship between sensory hyper-
responsiveness and anxiety in 149 toddlers with ASD. This study
confirmed the correlation between sensory hyper-responsiveness
and anxiety in ASD, and importantly established a directional
link from sensory hyper-responsiveness to anxiety. While sensory
hyper-responsiveness remained stable and predicted change
in anxiety over time, anxiety levels increased over time and
did not predict change in sensory hyper-responsiveness. These
findings suggest that sensory hyper-responsiveness may emerge
earlier than and exacerbate the presentation of anxiety, or
that these symptoms may have a common cause with different
developmental manifestations.
Sensory hyper-responsiveness has also been linked to
gastrointestinal (GI) problems (Mazurek et al., 2013), picky
eating (Cermak et al., 2010; Nadon et al., 2011), sleep problems
(Mazurek and Petroski, 2015), more externalizing behaviors, and
increased parenting stress and family impairment (Ben-Sasson
et al., 2013). Mazurek et al. (2013) proposed that sensory hyper-
responsiveness, anxiety, and GI problems may be explained by
shared neural mechanisms through which heightened stress leads
to physiological and cognitive symptoms of anxiety in addition to
experiencing particular stimuli as aversive. Individuals exhibiting
this trio of symptoms may represent a unique subgroup of ASD.
Furthermore, sensory hyper-responsiveness has been linked
with increased sleep problems through similar hypothesized
mechanisms (Mazurek and Petroski, 2015). Overall, this body
of work focusing specifically on sensory hyper-responsiveness
represents theoretically relevant and convincing evidence for the
link between sensory hyper-responsiveness and anxiety, GI, sleep,
and other problems in ASD. The observable nature of hyper-
responsive reactions likely contributes to the comprehensiveness
and replicability of this finding.
Several studies have examined the relationship between
sensory symptoms and restricted and repetitive behaviors.
Among children 5–18 years with intellectual disability, hyper-
responsiveness (sensory sensitivity and sensory avoidance)
distinguished those with ASD from those without ASD
(Joosten and Bundy, 2010). The authors suggest a mechanism
whereby hyper-responsiveness leads to increased anxiety,
which subsequently leads to engagement in low-level
repetitive behaviors to cope with that anxiety. Boyd et al.
(2009) investigated relationships between sensory responding
(measured by the Sensory Questionnaire), repetitive behaviors
(measured by the Repetitive Behavior Scale—Revised), and
executive functioning (measured by the Behavior Rating
Inventory of Executive Function) in 6–17 year olds with and
without ASD. No hypothesized relationships were found
between sensory responding and executive functioning; however,
sensory responding was related to two specific types of repetitive
behaviors: stereotypy and compulsions. This data did not
support the theoretical claim that executive dysfunction is
the shared mechanism for sensory symptoms and repetitive
behaviors in ASD, and the authors suggest that neurobiological,
rather than neurocognitive, mechanisms might better explain
this link. In other words, it is possible that the questionnaire
measure designed to target neurocognitive processes does not
accurately capture the neurobiological underpinnings that
give rise to these neurocognitive processes. Wigham et al.
(2015) investigated the relationship between sensory symptoms,
repetitive behaviors, and intolerance of uncertainty, finding
that the relationship between sensory responding and repetitive
behaviors (particularly sameness behaviors) was mediated
by intolerance of uncertainty, providing a specific cognitive
explanation for the relationship. These studies provide some
initial empirical evidence to support the conceptual relationship
between sensory processing and repetitive behaviors in ASD,
highlighting intolerance of uncertainty, but not executive
functioning, as a possible mediating factor.
In sum, questionnaire-based studies have repeatedly
confirmed that sensory processing is atypical in ASD at the level
of reported observable reactions. Specific aspects of sensory
processing difficulties, including hyper-responsiveness in the
modalities of touch, audition, and taste/smell and patterns of
general hypo-responsiveness, have most consistently emerged
in the questionnaire-based literature with other differences
being less consistently reported. Thus, patterns of both hyper-
and hypo- responsiveness in ASD exist at the group level, with
hyper-responsiveness further broken down at the level of sensory
modality. This may reflect greater differentiation of sensory
processing abilities in the hyper-responsiveness, vs. hypo-
responsiveness, pattern, but it likely reflects the better precision
of characterizing these symptoms given that they are defined by
the presence of an atypical reaction and thus easier to observe
and report. Additionally, the literature that specifically focuses
on hyper-responsiveness has provided a convincing link between
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these sensory symptoms and other challenging symptoms (e.g.,
anxiety, GI problems) that occur in ASD at a disproportional rate.
Nonetheless, improved measurement development and analysis
techniques will be necessary to enhance the sensitivity of these
questionnaires in order to understand individual differences in
sensory processing. In addition, the current literature provides
some evidence for differences in subjective and observed sensory
experiences based on self- and parent- report, respectively.
Self-report in ASD has been criticized because verbal and
cognitive deficits (e.g., insight) may limit accurate reporting
of symptoms (Ozsivadjian et al., 2012); however, these types
of questionnaires may be the only way to capture subjective
experiences. Parent-report questionnaires in ASD have been
criticized for relying on retrospective report, which can lead to
inaccurate reporting and recollection biases (Hoyle et al., 2001)
and can additionally be influenced by individual factors such as
parenting stress (Ooi et al., 2016). However, questionnaires allow
for observations across time and contexts and thus may be one of
the best ways to evaluate more generalizable observable reactions
to sensory input and their impact on day-to-day functioning.
Future measure development would benefit from focus on this
component rather than attempting to use questionnaires for
aspects of sensory processing that cannot be readily observed
(e.g., high threshold to stimulation).
Lab-Based Observational Coding Studies
Several lab-based observational coding paradigms have been
developed to assess sensory symptoms, including the Sensory
Processing Assessment (SPA), Tactile Defensiveness and
Discrimination Test—Revised (TDDT-R), Sensory Processing
Scales, and Sensory Challenge Protocol. The SPA Baranek
et al. (2007) and the TDDT-R were designed as play-based
assessments to observe sensory patterns in the lab. The SPA
has been validated in children 9 months to 6 years and the
TDDT-R has been validated in children 2–14 years; however,
these tools are still in development and thus clinical norms
have not been published. Additionally, the play-based nature of
these assessment tools limits their use to younger and/or lower
functioning individuals. The Sensory Processing Scales (Schoen
et al., 2014) consists of structured games that involve sensory
components (e.g., observe a spinning sparkle wheel; paint your
arm with a feather, brush, and rough sponge). Although not
developed for individuals with ASD specifically, it has now been
applied to high functioning children ages 4–16 years with and
without ASD (note: high functioning ASD typically refers to
individuals with at least low average cognitive ability). The play-
based assessment may also be appropriate for lower functioning
individuals, but remains to be tested in this population. The
Sensory Challenge Protocol (McIntosh et al., 1999; discussed in
the Psychophysiological Studies section) was designed to assess
physiological responses to the presentation of items with sensory
features (e.g., strobe light for visual, feather touching the face for
tactile). This protocol has been used in children ages 4–15 years,
but has been modified for use in younger children (McCormick
et al., 2014).
Baranek et al. (2007) utilized the SPA, which involves
presenting infants and young children with a variety of toys with
different sensory features and coding the resulting behaviors.
They found increased hyper-responsiveness (sensory aversion)
in children with ASD and developmental delay compared to
those with TD, and that across all three groups, these symptoms
decrease as both chronological and mental age increase. They
also found a deficit in sensory orienting in ASD at a mental
age of 6 months that normalized by a mental age of 5.5 years.
Baranek et al. (2013) specifically looked at sensory orienting
using the SPA, confirming the early orienting deficit in ASD and
extending this to be true for both social and non-social stimuli,
and to be related to joint attention. Foss-Feig et al. (2012) utilized
the TDDT-R, a structured behavioral observation paradigm, and
tactile specific scores on parent report questionnaires revealing
specific associations between tactile hypo-responsiveness and
both core features of autism: social communication impairments
and restricted/repetitive behaviors. Tavassoli et al. (2016) utilized
the Sensory Processing Scales in high functioning children
with and without ASD ages 4–15. Children with ASD showed
significantly greater sensory reactivity compared to controls,
and this reactivity was correlated with parent-reported sensory
symptoms across the entire sample.
In these studies, questionnaire measures and lab-based
observational coding measures were inconsistently correlated
with each other, suggesting that unique information can be
gleaned from each measure. This is likely due in part to the
limitations of each type of measure: observations from lab-based
paradigms may not be generalizable to real life settings and
questionnaire measures are prone to reporter biases that decrease
internal validity. Although additional paradigm development
work is needed, observational coding paradigms represent a more
structured and objective way to understand observable reactions
to the presentation of sensory input.
Several studies have combined questionnaire-based measures
with observational coding paradigms to create composite
scores for different sensory response patterns. This approach
adheres to the multi-method model and allows for a more
comprehensive assessment. One study using composite scores
found an association between hyper-responsiveness and
repetitive behaviors in both ASD and developmental delay
(Boyd et al., 2010) and another found an association between
hypo-responsiveness and sensory seeking symptoms with
various measures of social and communication deficits (Watson
et al., 2011), confirming the findings previously highlighted in
studies utilizing only questionnaires. Furthermore, in a study
looking at associations between sensory responding patterns and
temperament, Brock et al. (2012) found that increased sensory
symptoms across all three patterns (hyper-responsiveness,
hypo-responsiveness, sensory seeking) were associated with
increased withdrawal and negative mood. This multi-method
approach is commendable, but future studies should expand
on this approach to investigate both shared and unique
contributions of symptoms assessed using different methods.
Furthermore, hypo-responsiveness may be better understood by
methods that do not rely on observation and report given that
they are defined by the absence of a typical response and may
never be able to be accurately assessed by questionnaire or other
observational methods.
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Psychophysiological Studies
Psychophysiological studies focus on the body’s response to
sensory stimulation, specifically looking at functioning of
the ANS. The ANS functions through the sympathetic and
parasympathetic branches, which are responsible for fight or
flight responses and the regulation of those responses and
maintenance of homeostasis, respectively. This method provides
objective measures of the individual’s bodily responses to sensory
input that have been systematically linked to emotional states.
Three studies have used the Sensory Challenge Protocol while
measuring ANS responding and have yielded conflicting results.
Schoen et al. (2009) assessed children ages 4–15 years with
ASD, Sensory Modulation Disorder, and TD, measuring arousal
levels with electrodermal activity, a measure of sympathetic
activity. Children with ASD had typical habituation patterns,
but lower baseline arousal levels and lower reactivity, especially
to the first two stimuli within each modality, suggesting that
children with ASD may have a reduced ability to initially attend
to, and thus process, environmental stimuli. Interestingly, they
also found that parent-reported sensory symptoms were not
related to the physiological arousal levels, again pointing to
a divergence between lab- and questionnaire-based measures.
Schaaf et al. (2015) collected cardiac measures associated with
sympathetic and parasympathetic activity in children with and
without ASD ages 6–9 years. Respiratory sinus arrhythmia,
the measure of parasympathetic activity, was lower in ASD
in response to sensory stimulation. However, pre-ejection
period, the measure of sympathetic activity, did not differ
between groups. These findings suggest that parasympathetic
regulatory functions are specifically faulty in children with
ASD. Finally, Lane et al. (2012) tested the mediating role
of reactivity to sensory stimuli in the path from baseline
reactivity to anxiety symptoms in children ages 6–10 years with
ASD, attention-deficit hyperactivity disorder, or TD. Findings
suggest a direct relationship between parent-reported sensory
hyper-responsiveness and child-reported anxiety. Additionally,
they found a relationship between baseline reactivity and
anxiety fully mediated by reactivity to sensory stimuli, and a
relationship between baseline reactivity and habituation partially
mediated by reactivity to sensory stimuli. Together, these
findings suggest that the initial state of arousal influences
the degree of physiological response to sensation, which
then determines both the nervous system’s recovery ability
and symptoms of anxiety. One additional study used a
modified version of the Sensory Challenge Protocol in children
2–5 years with and without ASD and found no group
differences in psychophysiological responses to sensory stimuli
nor relationships between these responses and parent-reported
sensory symptoms (McCormick et al., 2014). In sum, the Sensory
Challenge Protocol represents an ecologically valid paradigm
(presenting real world objects with sensory features), and when
combined with psychophysiological measurement, provides
objective information about an individual’s emotional response
beyond the level of behavioral observations. Additional studies
using this paradigm are necessary to better understand the
currently conflicting results of sympathetic and parasympathetic
differences in response to sensory input in ASD.
Two additional studies have tried to relate ANS responses to
reported and/or coded observable responses to sensory input.
Woodard et al. (2012) presented sensory stimuli to a small
group of children ages 2–3 years with and without ASD.
They recorded heart rate during the presentations and coded
observable reactions. Although heart rate and behavioral codes
were only weakly correlated, both measures showed that toddlers
with ASD were more hyper-responsive compared to toddlers
with TD. Additionally, there was only one significant relationship
between scores on the Infant-Toddler Sensory Profile (parent
report questionnaire) and heart rate: hypo-responsiveness was
negatively associated with heart rate. These findings suggest that
questionnaire measures and behavioral ratings are at best a weak
indicator of autonomic activity and highlight the importance
of including multiple measures to examine sensory processing
in ASD. In a study of 5–19 year olds looking at ANS function
in ASD and its relation to sensory symptoms, Daluwatte et al.
(2015) found that lower pupil constriction amplitude, a measure
associated with parasympathetic activity, was related to increased
overall sensory symptoms in ASD. The authors conclude that
certain atypical sensory symptoms in ASD seem to be related to
reduced parasympathetic modulation.
Overall, these studies provide mixed evidence at younger ages
and consistent evidence at older ages of ANS dysfunction in ASD,
particularly in response to sensory stimulation, with some studies
pointing to deficits in sympathetic activity and others pointing
to specific deficits in the regulatory role of the parasympathetic
system. These studies begin to characterize the body’s response to
sensory input by using measures that tap the peripheral nervous
system. In this way, this collection of studies begins to bridge
the neural and symptom literatures, while including an objective
measure of the affective component associated with symptoms.
Summary of Sensory Symptoms Literature
In sum, our understanding of sensory symptoms in ASD
has improved somewhat in the past decade. Specifically,
larger, better-characterized, and narrower age range samples
have provided opportunities to use more advanced analytic
approaches and to characterize the development of sensory
symptoms. Additionally, the specific focus on hyper-
responsiveness taken by several studies has provided the
start to a more comprehensive understanding in that pattern
of sensory processing. Similarly, development of more specific
questionnaires and lab-based paradigms has advanced the
measurement tools available and begun to allow for a multi-
method approach (e.g., ability to create composite scores using
questionnaire and observational methods). Finally, an increased
focus on psychophysiological responses provides a new level of
understanding about the body’s underlying responses to sensory
The sensory symptoms literature presented above focuses on
the individual’s response to sensory input and highlights the
significant impact of these symptoms on individuals with ASD
and their families. The neuroscience literature that follows
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focuses on the brain’s functional response to sensory input. Thus,
studies of brain structure and connectivity patterns, both of
which are impacted in ASD and likely contribute to the functional
responding differences, are not evaluated here. However, the
relevant functional consequences of these differences will still
likely be captured.
By in large, neuroscience studies focus on a specific
sensory modality and employ a specific methodology based
on which characteristics of neural processing are of interest.
The two most commonly used methods in ASD research
are electroencephalography (EEG) and functional magnetic
resonance imaging (fMRI), with EEG studies focusing
primarily on the timing of neural responses and fMRI studies
focusing primarily on the location of neural responses.
Magnetoencephalography (MEG), which captures both timing
and location, has also more recently been utilized in ASD. These
studies will be reviewed by methodology used and by sensory
modality studied.
Electroencephalography (EEG)
Several studies use EEG to measure cortical reactivity to sensory
stimulation, focusing largely on individual components that
correspond to specific sensory events (event related potentials;
ERP). EEG is a relatively non-invasive approach that can
be used successfully across all ages and functioning levels.
Task demands are typically minimal and paradigms can be
short. Finally, EEG provides high temporal resolution at the
expense of spatial resolution. Given this strength of temporal
resolution, the following EEG studies highlight both bottom-
up and top-down influences on sensory processing, which
typically occur at earlier and later temporal stages of processing,
respectively. Bottom-up refers to the influence of purely sensory
information on processing, while top-down refers to higher-
order influences (e.g., attention) that interact with the incoming
sensory information.
The vast majority of EEG studies have targeted the auditory
modality. Generally, two paradigms have been used in
conjunction with EEG recordings: paired-clicks and auditory
oddball tasks. In paired-clicks paradigms, two auditory stimuli
are presented in succession allowing for a comparison in cortical
response to the first and second (repetitive) input. Typically,
there is a decrease in amplitude of these early components of
the auditory ERP to a repeated stimulus, reflecting inhibition
of the repetitive input, known as sensory gating. Oddball tasks
typically utilize three stimuli: a standard stimulus that is the
most commonly presented stimulus, a deviant stimulus that
is presented less frequently and varies in one dimension (e.g.,
frequency) from the standard stimulus, and a novel stimulus
that is also presented less frequently but varies greatly from the
standard stimulus. Typically, there are distinct cortical responses
to the deviant and novel stimuli reflecting appropriate change
and novelty detection.
Orekhova and Stroganova (2014) recently reviewed the
auditory ERP studies in ASD, which include participants across
a range of cognitive ability. Early auditory components include
the P50 and P100. Studies of sensory gating in ASD collectively
show intact P50 responses, but reduced reactivity of the P100 in
the right hemisphere. The later auditory ERP components, MMN
and P3a, are both involved in processing change events, with
MMN being critical for initial deviancy detection and P3a being
important for involuntary attention orienting and evaluation
preceding a response. The results for these later ERP components
in ASD are not entirely consistent, but overall, suggest intact
cortical change detection and evaluation when stimuli are within
the focus of attention, but show cortical hypo-responsiveness
when stimuli are outside the focus of attention, highlighting the
importance of top-down modulation of sensory processing in
ASD. Additionally, the authors suggest that age and cognitive
ability of participant samples may explain some of the discrepant
findings across studies.
Donkers et al. (2015) sought to relate auditory ERPs to sensory
symptom patterns in children with ASD ages 4–12 across a range
of cognitive ability. Utilizing an auditory oddball paradigm, they
found marginally smaller amplitudes of the early P1 and N2
ERPs to standard tones, smaller P3a amplitude to novel tones,
and longer P1 latency to deviant tones in ASD compared to
TD. Although no single ERP component predicted any sensory
symptom pattern, complex and conditional associations between
auditory ERPs and sensory symptom patterns were revealed,
highlighting both bottom-up (early sensory) and top-down
(attentional) influences on the severity of sensory symptoms
in ASD. Sensory symptoms were assessed broadly (i.e., across
all sensory modalities) and included both parent report and
behavioral observation measures to calculate composite scores of
sensory hyper-responsiveness, hypo-responsiveness, and seeking.
This attempt to relate neural responsiveness to symptoms is
commendable; however, the neural measure was exclusively in
the auditory modality while the symptoms were more broadly
assessed across multiple sensory modalities.
Whereas the majority of EEG studies have been in the auditory
modality, an increasing number of studies have used visual
paradigms. Findings regarding early visual processing are mixed.
Individuals with ASD across a range of cognitive ability
showed hyper-responsiveness to flashes of light, demonstrated
by stronger and quicker initial visual evoked responses and
a slower recovery (Isler et al., 2010). In a study investigating
cortical representation of the visual periphery in ASD, individuals
with high functioning ASD had larger early responses (increased
P1 amplitude) to checkerboard pattern stimuli presented in
the periphery (Frey et al., 2013), suggesting early hyper-
responsiveness and increased cortical representation of the visual
periphery in ASD. Visual evoked responses to grating stimuli
suggest a dissociation between hyper- and hypo-responsiveness
based on spatial frequency, with high and low spatial frequency
of a stimulus yielding increased and decreased responding,
respectively (Vlamings et al., 2010; Pei et al., 2014). Hypo-
responsiveness may be further restricted to the right hemisphere
(Pei et al., 2014). Two studies investigated later components
of visual processing by using visual oddball tasks in children
with and without ASD. Both found cortical hyper-responsiveness
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to visual change, regardless of whether it was an active
(Baruth et al., 2010) task with high functioning ASD or a
passive (Clery et al., 2013b) task with individuals with ASD
across a range of cognitive ability. These results suggest that
attention to visual input does not differentiate hyper- and hypo-
responsiveness in the same way that it seems to in the auditory
Milne et al. (2009) studied visual processing by examining
changes in EEG power, which provides an index of neural
synchronization. Participants with and without ASD across a
range of cognitive ability viewed grating stimuli of varying
spatial frequencies and were asked to press a button each time
they saw a zebra on a screen. Overall, they found earlier peak
latencies for the early C1 and P1 ERPs in ASD supporting faster
reactions to basic visual stimuli. After localizing the clusters that
accounted for the greatest amount of variance in EEG activity,
the authors found stronger power in ASD in cingulate gyrus
(reflecting greater attentional control), reduced effect of spatial
frequency in ASD in striate and extrastriate regions (reflecting
less neural specialization in networks recruited for basic visual
perception), and no differences in the parietal region. Together,
these findings suggest that early visual areas (e.g., primary visual
cortex) are not hyper-responsive in ASD, but rather that there
is reduced modulation of the networks involved in basic visual
perception that likely contributes to the disruption of perceptual
binding in ASD. There is also evidence of reduced synchrony of
visual areas between the right and left hemisphere (Isler et al.,
Only one study has used ERP to investigate the brain’s
functional response to non-social tactile stimulation in high
functioning children and adolescents with ASD, delivering air
puffs to participants’ finger tips while they were attending
to this stimulation (Cascio et al., 2015). Although no group
differences were seen in the ERP response to the stimulation, ERP
responses at different time points post-stimulus were related to
tactile hyper- and hypo- responsiveness as measured by parent
report. These findings suggest that earlier neural responses to
tactile stimulation are related to tactile hyper-responsiveness,
while slightly later neural responses are related to tactile
hypo-responsiveness and may involve higher-level processes
such as attention allocation and assignment of emotional
In sum, the EEG literature has revealed important differences
in the timing of response to auditory, visual, and tactile
input in ASD. Although many studies suggest atypicalities in
temporally later stages of sensory processing, earlier stages have
also been implicated. Additionally, both bottom-up and top-
down processes seem to be affected, with top-down processes
(e.g., attention) possibly differentiating impairment, at least
in the auditory modality. Importantly, each sensory modality
provides slightly different conclusions, providing merit to the
single sensory modality approach in the study of neural
mechanisms of sensory processing. Finally, existing attempts to
relate EEG measures to questionnaire measures serve as models
for integrating neural and symptom perspectives.
Functional Magnetic Resonance Imaging
Rather than measuring the electrical activity of neurons like in
EEG methods, fMRI is based on the indirect measurement of
brain activity through changes in blood flow. Predicated on the
assumption that increased blood flow (i.e., activation) to a neural
region is indicative of increased neural activity, fMRI studies
provide exceptional spatial resolution at the expense of temporal
resolution. Unlike EEG, which has been utilized across a variety
of ages and functioning levels, fMRI is typically used in older and
higher functioning ASD participants, who can remain still for
the duration of the paradigm and tolerate the MRI environment.
The studies described below use high functioning samples and
often include participants that span a broad age range. Although
the majority of the fMRI studies in ASD investigate higher
order cognitions (e.g., theory of mind, face processing, language
processing), several studies, outlined below, assess basic sensory
processing. These studies provide information about where in
the brain sensory processing differences exist in ASD. These
findings largely parallel the EEG findings, in that regions involved
in purely sensory processing and those involved in attentional
control both show atypical responding patterns in ASD.
Gomot et al. (2006, 2008) demonstrated the modulating role of
attention across two auditory oddball fMRI studies, one with
a passive listening task and one with an active task. Children
and adolescents with ASD showed lower brain activation in the
passive task and higher brain activation in an active task, in
both parietal and frontal areas in response to deviant and novel
stimuli. These results show both neural hypo-responsiveness
(Gomot et al., 2006) and hyper-responsiveness (Gomot et al.,
2008) to auditory change dependent on attention, consistent with
the ERP data of the MMN and P3a components (Orekhova and
Stroganova, 2014). Thus, the role of attention seems to be critical
in auditory change perception in ASD, with consistent findings
across EEG and fMRI studies. In a study of adolescents and
adults with ASD investigating non-social auditory complexity,
temporal but not spatial stimulus complexity was associated
with increased and decreased fMRI activity in ASD in primary
and other (anterolateral and posterior superior temporal gyrus)
auditory cortical regions, respectively (Samson et al., 2011).
In a small fMRI visual attention study looking at children with
ASD, their unaffected siblings, and controls (Belmonte et al.,
2010), children with ASD had decreased activation in attention
networks during the trials, but they and their unaffected siblings
showed delayed activation of these networks (immediately
following each trial). The unaffected siblings showed greater
delayed attention network activation suggesting a stronger
compensatory process that may be protective against developing
symptoms of ASD. Clery et al. (2013a) looked at visual change
detection using fMRI in a small group of adults with and without
ASD. Adults with ASD showed increased activation in visual
areas and decreased activation in frontal areas to deviant and
novel stimuli, consistent with the idea of increased sensory
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processing and reduced top-down modulation of sensory areas.
The anterior cingulate cortex, a region important for attention
switching and allocation of attentional resources, was also shown
to be hyper-responsive in ASD while processing visual change
events, offering an explanation of attention switching deficits as
a mechanism for altered visual change detection in ASD. Thus,
auditory change detection seems to be dependent on attention
engagement, and visual change detection may be dependent on
attention switching.
Ohta et al. (2012) investigated sensory (visual) filtering
by implementing an fMRI paradigm manipulating perceptual
load and presence of a distractor stimulus in adults with and
without ASD. In typical individuals, the degree of processing
of unattended stimuli is dependent on task load (e.g., greater
processing when task load is low), which reflects efficient
processing abilities. Results from this study showed no group
differences in activation in the fronto-parietal attention network
across conditions, but found that distractor-evoked activity in
visual cortex was modulated less by perceptual load in ASD.
These findings suggest a lack of flexible top-down regulation of
sensory processing.
Two fMRI studies have looked at responses to tactile stimulation
in ASD. Kaiser et al. (2016) investigated neural responses to touch
on the palm in children and adolescents with and without ASD,
and found increased response in primary somatosensory cortex
and insula in ASD, suggesting hyper-responsiveness to non-
social touch. Cascio et al. (2012) demonstrated increased fMRI
activation in attention areas in adults with ASD when presented
with aversive, but not pleasant, tactile stimulation. This study also
collected subjective ratings of roughness and pleasantness for the
same stimuli and found no correlations between these ratings
and the fMRI response, highlighting a disconnect between neural
responding and subjective experiences. Together, these studies
suggest neural hyper-responsiveness to basic tactile stimulation,
but there is inconsistency as to whether this hyper-responsiveness
is localized in sensory (Kaiser et al., 2016) or attention (Cascio
et al., 2012) areas.
Multiple Modalities
Two fMRI studies have made notable attempts to link neural
responding to sensory symptoms by investigating sensory and
limbic responses to mildly aversive sensory stimuli (Green
et al., 2013: auditory, visual, and audiovisual combined stimuli;
Green et al., 2015: auditory, tactile, and audiotactile combined
stimuli) in children and adolescents with and without ASD.
Across both studies, they found neural hyper-responsiveness in
ASD across the unisensory and multisensory conditions in both
sensory and limbic areas, including frontal regions, with the
strongest increases in neural responding during the multisensory
condition. In the multisensory conditions, signal increase in
several areas, including sensory, limbic, and frontal regions, was
positively associated with sensory hyper-responsiveness scores
on a composite variable generated from two parent report
questionnaires (Short Sensory Profile and SensOR) above and
beyond anxiety levels. Unfortunately, a similar analysis was
not reported in the unisensory conditions. Green et al. (2015)
conducted additional analyses looking at the specific role of
sensory hyper-responsiveness symptoms, finding that those with
ASD and high sensory hyper-responsiveness symptoms were
driving the effects of neural hyper-responsiveness, and that
those with ASD only (and low sensory hyper-responsiveness
symptoms) were similar to controls. Connectivity analyses
suggest that the hyper-responsiveness in primary sensory
areas observed in those with ASD and high hyper-responsive
symptoms may lead to hyper-responsiveness in limbic areas,
which may then over-engage frontal areas in an attempt to
regulate the response. Together, these two studies highlight the
value of investigating differences in neural responding patterns
based on sensory subgroups within ASD, the role of top-down
modulation, and the value of combining neural and symptom
In sum, there are many fewer fMRI studies investigating
neural response patterns to basic sensory stimuli compared to
the EEG literature. However, the few studies outlined above allow
for preliminary understanding of how the brain responds when
processing basic auditory, visual, and tactile stimuli. In higher
order regions, atypical responding patterns are consistently
reported in ASD; however, the evidence is mixed in that some
reports show hyper-responsiveness and other reports show hypo-
responsiveness. In purely sensory regions, atypicalities in ASD
are not always observed; however, when these atypicalities are
observed, evidence is overwhelmingly in the direction of neural
hyper-responsiveness, especially in the most simplistic tasks
(e.g., presenting sensory stimuli with no requirement of task
engagement). Recent advancements in this literature highlight
the value of integrating neural and symptom perspectives.
Variability in Responding to Sensory Input
in EEG and fMRI Studies
The studies reviewed above focus on the amount of responding
to a particular sensory stimulus collapsed across several stimulus
presentations. Another approach is to look at the variability
in responding across the different stimulus presentations to
determine the consistency or amount of “noise” in the neural
response. In fact, increased (or decreased; Davis and Plaisted-
Grant, 2015) neural noise as a heuristic theory of ASD has
generated a lot of attention recently (Dinstein et al., 2015).
Four neural studies provide empirical evidence for increased
variability in neural responding to basic sensory input, consistent
with such an account. Milne (2011) reanalyzed a subset of the
data from Milne et al. (2009) to investigate intra-participant
variability of the P1 (early sensory) cortical response (amplitude
and latency) and inter-trial phase coherence. Inter-trial phase
coherence measures the degree to which EEG activity is phase-
locked to a specific stimulus presentation across trials. They
found increased intra-participant variability in the children with
ASD across all three measures, pointing to evidence for increased
neural noise in ASD. Weinger et al. (2014) also reported increased
neural noise in a sample of children with ASD, as measured
by EEG in the visual modality. They presented checkerboard
pattern stimuli and found no group differences in amount of
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neural responding; however, they found decreased signal to noise
ratio and increased neural noise (greater variability in amplitude
across trials) in ASD.
Similar findings of increased neural noise exist across two
fMRI studies (Dinstein et al., 2012; Haigh et al., 2015) in adults
with ASD. Visual, auditory, or tactile stimuli were presented
while participants completed an unrelated task to divert attention
away from sensory input. There were no differences in amount
or location of neural activation to these stimuli; however, the
variance of responses was larger in ASD compared to controls.
This increased variance was specific to sensory regions during
stimulus presentation; it was not seen in other neural areas during
stimulus presentation or in sensory areas during rest. Overall,
these studies of variability in neural response do not support
theories of neural hyper- or hypo-responsiveness to simple
sensory input (at least when attention is diverted away), but
offer increased intra-participant neural variability as a consistent
marker of sensory processing dysfunction in ASD.
Magnetoencephalography (MEG)
Three studies of sensory processing in ASD have used MEG,
two in the auditory and one in the tactile modality. MEG
provides similar temporal resolution to EEG but with better
spatial resolution. Roberts et al. (2010) conducted an MEG
study on auditory processing in high functioning children with
and without ASD and found delayed M100 latency in ASD
across all four tone frequencies presented. There were no group
differences in the M50 response. Typical maturational processes
of the M100 response becoming earlier with age was observed
in the control group, but absent in the ASD group, suggesting
atypical maturation of the auditory cortex in ASD. Orekhova
et al. (2012) utilized a paired clicks paradigm (detailed above in
the EEG section) with MEG in children with and without ASD,
and found atypical P100 lateralization in children with ASD.
Children with ASD showed less right lateralization in response
to the auditory stimulus, and this was correlated with total
sensory problems measured by a parent questionnaire. The child
P100 is thought to be involved in arousal, spatial orienting, and
attention processing, all of which are typically right lateralized
functions. Atypical P100 lateralization seen in ASD may reflect
disrupted preattentive arousal, in which children with ASD rely
on non-optimal left hemisphere processing.
In a tactile MEG study with high functioning children
with and without ASD, Marco et al. (2012) applied a finger
tapping paradigm to investigate the timing and amplitude of
responses in primary somatosensory cortex. Children with ASD
had reduced responses in the slow, but not fast, rate version of
the paradigm, specifically in the left hemisphere. Additionally,
tactile sensitivity scores on the Sensory Profile correlated with
amount of neural response in primary somatosensory cortex
across the combined sample. An additional analysis separating
the groups by tactile sensitivity scores revealed more robust
neural differences between these groups in both the right and
left hemisphere, suggesting that these neural differences are more
closely related to individual differences in tactile functioning
than to ASD specifically. Together, these three studies point
to maturational and lateralization differences related to sensory
processing that may be present in ASD, and indicates the need
for further work using this approach.
Summary of Neuroscience Literature
In sum, the neural processing of sensory input in ASD literature
points to atypical neural processing of even the most basic
sensory stimuli that can be observed across a variety of
methodologies. EEG studies provide evidence for atypicalities
in ASD during both earlier and later sensory processing stages.
fMRI studies provide evidence for different spatial activation
patterns across areas of the brain responsible for these earlier
and later sensory processing stages. Emerging MEG evidence
points to maturational and lateralization differences related to
sensory processing that may be present in ASD. Higher-order
behaviors at the symptom level are complex and arise from
complicated interactions of these simpler processes. Although
we are far from understanding these complicated interactions,
this neural literature highlights the basic nature of some
of the differences associated with ASD. Additional research
building upon existing efforts to combine neural and symptom
perspectives is necessary to sort through the broad array of
findings outlined above.
Psychophysics is an approach that has been recently applied to
the study of sensory processing in ASD by both neuroscientists
and sensory symptom researchers. Psychophysical studies rely
on a decision related to a perceptual experience, and are
designed to closely model neural responding patterns. Thus,
this approach capitalizes on an intuitive intermediary between
the neural response to sensory input and the individual’s
observable reaction. Additionally, psychophysical studies allow
for the study of isolated features of real world stimuli that
can be conceptualized as the building blocks of higher-level
perception. For example, if studying motion perception within
the visual modality, one would present the most basic motion
stimulus (i.e., a moving grating pattern) and determine an
individual’s ability to perceive that stimulus in either a detection
task (e.g., press a button when you see the moving stimulus
on the screen) or a discrimination task (e.g., decide if the
stimulus is moving to the right or the left). Although a
review of this literature is beyond the scope of this paper,
examples of this approach applied to ASD include multiple
sensory modalities, including visual (e.g., Bertone et al., 2005),
auditory (e.g., Jones et al., 2009), and tactile (e.g., Cascio et al.,
Although these types of measurements lie in between neural
response and observable reactions, this method on its own has yet
to provide an integrative framework for understanding sensory
processing in ASD. This is likely because important links are
missing between neural responding, detection/discrimination
decisions, and observable reactions. In fact, a large research area
in the field of basic neuroscience seeks to understand how neural
firing translates to these types of decisions in humans generally
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(and primates more globally). Additionally, much remains to
be known about the link between these more basic perceptual
decisions and higher-order observable reactions. In particular,
these measurements largely ignore the affective component
and real-life impact that characterize sensory symptoms of
ASD. However, these tools may allow us to characterize
hypo-responsive symptoms in a way that questionnaires and
observational coding paradigms miss, because this category of
symptoms is defined by the absence of typical reactions.
Until 2005, sensory processing research in ASD was largely
focused on whether individuals with ASD exhibited atypical
sensory responses and if these sensory responding patterns could
be used to differentiate ASD from other developmental disorders
(e.g., intellectual disability, Fragile X syndrome). Because of these
motivations, sensory processing research was mostly descriptive
and one-dimensional. Additionally, this early research suffered
from studies with small, and often poorly characterized samples
resulting in uncertainty about the presence and uniqueness of
sensory processing difficulties in ASD. In the last decade, sensory
processing research in ASD has expanded significantly—in
the number of studies published, increased methodological
rigor of these studies, and diversification of approaches used.
This has led to a multidisciplinary understanding of sensory
processing in ASD. Currently, the field has a vast amount
of descriptive and emerging mechanistic information about
how individuals with ASD (and possible subgroups within
ASD) perceive and respond to sensory information differently.
However, this information has been generated from two very
different perspectives: clinical science and neuroscience. In
order to integrate this information into a cohesive picture
of how and why sensory processing differences manifest in
ASD and to be able to see the translational value in its
application to early identification and treatment, it is essential
for these two perspectives to communicate more effectively
and move toward an interdisciplinary understanding. Specific
recommendations are outlined below that will hopefully allow us
to embark on the next phase of sensory processing research in
What Can Sensory Symptom Researchers
Learn from Neuroscientists?
Greater Use of Modality-Specific Measurement
One of the difficulties with the current approach to studying
sensory symptoms in ASD is the use of measures that
collapse across auditory, visual, tactile, and other sensory
modalities. The neuroscience research has revealed important
differences between sensory processing modalities in ASD and
has highlighted the importance of precision and specificity
in conceptualizing atypical responses. The sensory symptom
literature has begun to address this by using subscale or factor
scores that isolate specific modalities. However, the use of more
modality-specific measures, both questionnaire and lab-based,
will improve sensitivity of these measures and hopefully help
begin to bridge knowledge from the neuroscience and clinical
Appropriate Selection of Measurement Based on
Pattern of Responding
In line with the above recommendation, the most appropriate
measurement tool may depend on the pattern of responding
of interest. For example, hyper-responsiveness may be best
investigated with questionnaires and lab-based observational
coding paradigms, if these measurement tools are enhanced.
However, hypo-responsiveness is difficult to capture with
observation in the lab or via parent questionnaires, given that
these symptoms are defined as the absence of typical responding.
Such symptoms may be more directly and accurately captured
using psychophysical approaches that rely on detection and/or
discrimination thresholds (i.e., the minimum amount of sensory
input needed for reliable perception).
What Can Neuroscientists Learn from
Sensory Symptoms Researchers?
Increased Focus on Developmental Change Over
The sensory symptoms literature has moved toward investigating
specific developmental periods, with a few longitudinal studies
that directly explore developmental effects, but the neuroscience
research lags behind in this manner. Although the symptoms
literature suggests little developmental change at the symptom
level in childhood, developmental differences do seem apparent
when one looks across the lifespan. Future neuroscience
studies should consider narrower age ranges and specific
consideration of developmental changes occurring at those ages,
as developmental changes may be more pronounced at the neural
level. Although there are some longitudinal studies investigating
structural neural changes in ASD, longitudinal studies examining
functional responding to sensory input are lacking.
Recognition and Investigation of the Heterogeneity
within Sensory Processing in ASD
The sensory symptom literature provides emerging evidence
for sensory-based subgroups in ASD. It will be beneficial for
neuroscientists to appreciate the heterogeneity within ASD,
rather than conceptualizing ASD as a single, homogenous
disorder. Given the sensitivity and precision of neuroscience
approaches, it is possible that these approaches will aid our
understanding of this heterogeneity and reveal meaningful
subgroups within the disorder.
Greater Emphasis on Top-Down Modulation of
Sensory Processing
From the sensory symptom literature, the modulation of sensory
input is most related to clinical problems. The current neural
literature also points to the importance of the modulatory role
of cognitive processes, such as attention, on sensory processing.
Thus, future studies should continue to examine these top-down
effects in an effort to better map the mechanisms involved in the
presentation of sensory symptoms in ASD.
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Schauder and Bennetto Sensory Dysfunction in ASD
General Recommendations
Exploration of Sensory Processing in Lower
Functioning Individuals with ASD
The sensory symptoms literature suggests that sensory difficulties
are present across functioning levels in ASD. However, the
presentation of these difficulties likely differs across various
levels of analysis. Current measures, particularly those with
the greatest sensitivity, are largely limited to high functioning
individuals with ASD, thus over-representing this population in
research. Although measure development for lower functioning
individuals is challenging, adaptation of measures for this
population presents an opportunity for creative collaboration
between fields.
Increased Understanding That the Relationship
Between Neural Hyper- and Hypo-Responsiveness
and Symptoms of Hyper- and Hypo-Responsiveness
is Highly Complex
It is tempting to assume that the relationship between neural
responding and observable reactions is simplistic, and many of
the current theories about sensory functioning in ASD adopt that
framework. This is especially true given the shared terminology
used to describe both responses. However, this assumption leads
to oversimplification, which hinders progress toward unraveling
the complex reality of these relationships. Each field is able to
provide information on specific aspects of sensory processing,
but researchers should be mindful of the limitations of each
Utilization of a Multi-Method Approach to Assess
Different Aspects of Sensory Symptoms
Several studies that have used multi-method approaches suggest
divergence among different aspects of sensory processing.
In fact, review of the literature suggests that each method
contributes to our understanding of sensory processing in a
unique way. Questionnaires and lab-based observational coding
paradigms can be best used to characterize observable reactions
to sensory input, most notably in the hyper-responsive pattern,
and the day-to-day impact on functioning; psychophysiological
measurements add additional information about the underlying
bodily response, including an emotional reaction component;
psychophysical methods characterize the perceptual responses
(detection/discrimination) that result from atypical neural
processing; and direct neural measures (EEG, fMRI, MEG)
provide the best information about underlying timing, degree,
and location of neural response to sensory input. An important
caveat to consider is that each of these methods is only as
useful as the measurement tool selected, and measurement
work, particularly for questionnaires, continues to be necessary.
However, by improving upon and then combining these
methodologies, we will protect against over-interpretation of
any single-method. Specifically, each method can contribute
knowledge about a specific and appropriate aspect of sensory
processing rather than attempt to make claims about sensory
processing as a whole. Then, upon combination of these different
methods, a more accurate understanding of sensory processing
in ASD can be achieved.
The interest in sensory processing in ASD has expanded
substantially in the last decade, as evidenced by an increased
number of studies using well-characterized samples with
sufficient sample sizes, the development of new measures and
paradigms, and the adoption of neuroscience approaches. At this
point, sensory processing should no longer be conceptualized as
a single construct that can be measured similarly by different
tools. Instead, each approach offers a unique contribution to
a piece of sensory processing, and if applied appropriately,
the understanding of sensory processing in ASD as a whole
can progress. It is our hope that this paper highlighted the
importance of sensory processing in ASD, explained the two
major research perspectives related to sensory processing in
ASD, and provided a framework for conceptualization of sensory
processing moving forward. Finally, by first understanding the
link between brain and symptoms within the sensory domain, we
can more successfully understand the relationship between brain
and symptoms in ASD more broadly.
KS conceived the idea and drafted the first version of the
paper. LB supervised the manuscript and reviewed the paper
for intellectual content. KS and LB revised the manuscript. Both
authors approved the final version.
KS and LB were supported in part by R01 DC009439.
The authors would like to acknowledge members of the Bennetto
Lab, Casey Zampella, Laura Soskey, Jessica Keith, and Paul Allen,
for helpful comments and discussion on the manuscript. The
authors would also like to thank Duje Tadin for reviewing a
previous version of this paper.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2016 Schauder and Bennetto. This is an open-access article distributed
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distribution or reproduction in other forums is permitted, provided the original
author(s) or licensor are credited and that the original publication in this journal
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Frontiers in Neuroscience | 18 June 2016 | Volume 10 | Article 268
... found in previous studies (Green et al., 2012;Mazurek et al., 2013). Interestingly, although there has been no reported evidence of an established link between sensory seeking and anxiety (Schauder & Bennetto, 2016), the current study found Q1 to be associated with anxiety. Slightly deviating from the sensory processing framework, this finding may be linked to the "over-arousal hypothesis" which theorizes sensory seeking as a compensatory behavior to distract individuals from aspects of their environment that provoke anxiety (Liss et al., 2006). ...
... These findings are contradictory to findings from the current study and may demonstrate a need for further research. However, it is important to note that previous studies focused on autistic toddlers (Ben-Sasson et al., 2009;Green et al., 2012) and as the profile of SF and anxiety in autistic individuals has been shown to change in adolescence and adulthood (Schauder & Bennetto, 2016;Uljarevic et al., 2020;van Steensel et al., 2011), it is plausible that the relationship between the two variables may change as well. ...
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Objectives Because atypical global neural connectivity has been documented in autistic youth, but only limited data are available regarding the association between generalized anxiety disorder (GAD), sensory features (SF), and neural connectivity between frontal and parietal brain regions, these links were investigated in a sample of male autistic children and adolescents. Methods Forty-one autistic males aged between 6 and 18 years and their mothers were recruited as volunteer participants from Queensland, Australia. Participants underwent 3 min of eyes-closed and 3 min of eyes-opened electroencephalography (EEG) under resting conditions. EEG connectivity was investigated using Granger causality between frontal and parietal regions in alpha (8–13 Hz) and beta (13–30 Hz) bands. Results There was a significant ( p < .01) positive correlation between SF and GAD. GAD was associated with some characteristics of SF in the sample population. Additionally, there was a significant ( p < .01) inverse correlation between directional frontoparietal connectivity and SF during the eyes-closed condition, specifically in relation to avoiding stimuli and sensitivity to the environment. Conclusions Reduced frontoparietal connectivity in association with higher anxiety and SF may demonstrate reduced relaxation due to greater sensitivity to sensory input.
... For example, previous studies have identified sensory stimuli as causes of outbursts for some individuals 5,7,14,15 , which could be related to underlying sensory processing difficulties shared by individuals across a wide range of diagnoses (e.g., refs. [16][17][18][19][20][21]. Thus, it would be unlikely for outbursts caused by sensory stimuli to manifest in ways which are entirely specific to diagnosis. ...
... The current literature has primarily focused on the symptomatology and aetiology of atypical sensory processing within the context of autism (e.g., ref. 17 ). Regarding individuals with other neurodevelopmental disorders, atypical sensory processing has been documented in children with ADHD 18 , across different genetic syndromes (e.g., ref. 19 ), and in children who have experienced maltreatment 20 . ...
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Emotional outbursts or temper outbursts are challenging behaviours commonly experienced by people with neurodevelopmental disorders and people who have experienced childhood adversity, which can negatively impact individuals and their families. Emotional outbursts may manifest in different situations via unique pathways distinguished by context-specific differences in the regulation and expression of emotions. Caregivers (N = 268) of young people (6–25 years) with emotional outbursts completed a bespoke caregiver-report questionnaire. Potential pathways were identified by examining the patterns of antecedents and setting events related to outbursts through factor and cluster analyses. Six contextual factors were derived from the Emotional Outburst Questionnaire. Based on these factors, the responses were classified into three clusters, which may represent potential pathways of emotional outbursts. The three clusters were characterized by the increased likelihood of outbursts: (1) across all setting events and triggers; (2) in safe setting events; (3) in unsafe setting events. These potential pathways may be related to: (1) differences in sensory processing; (2) masking of emotions in unsafe environments; (3) differences in safety perception. This framework supports a transdiagnostic account of emotional outbursts and may facilitate the development of pathway-specific intervention strategies.
... However, questionnaires can only inform about aspects of sensory processing at the level of observable reactions. Among sensory assessments that measure the brain's functional response to sensory stimuli, electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG) have been utilized to investigate the activity of various specific networks involved in sensory perception, as well as detect abnormalities in individuals with neurological disorders (Atagun et al., 2020;Bak et al., 2011;Demopoulos et al., 2017;Pierce et al., 2021;Schauder & Bennetto, 2016;Wang et al., 2014). In particular, discrimination tasks have been linked to GABAergic inhibition. ...
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Background Tactile processing plays a pivotal role in the early stages of human development; however, little is known about tactile function in young children. An understanding of how tactile processing changes with age from early childhood to adulthood is fundamental in understanding altered tactile experiences in neurodevelopmental disorders, such as autism spectrum disorder. Methods In this cross‐sectional study, 142 children and adults aged 3–23 years completed a vibrotactile testing battery consisting of 5 tasks, which rely on different cortical and cognitive mechanisms. The battery was designed to be suitable for testing in young children to investigate how tactile processing changes from early childhood to adulthood. Results Our results suggest a pattern of rapid, age‐related changes in tactile processing toward lower discrimination thresholds (lower discrimination thresholds = greater sensitivity) across early childhood, though we acknowledge limitations with cross‐sectional data. Differences in the rate of change across tasks were observed, with tactile performance reaching adult‐like levels at a younger age on some tasks compared to others. Conclusions While it is known that early childhood is a period of profound development including tactile processing, our data provides evidence for subtle differences in the developmental rate of the various underlying cortical, physical, and cognitive processes. Further, we are the first to show the feasibility of vibrotactile testing in early childhood (<6 years). The results of this work provide estimates of age‐related differences in performance, which could have important implications as a reference for investigating altered tactile processing in developmental disorders.
... Concerning ASD children, clinical studies [9,10], autobiographical narratives [11][12][13], and parents' reports [14,15] converge in indicating difficulties in regulating and organizing the type and intensity of behavioral reactivity to sensory inputs from the environment. Children with ASD often show the co-existence of more than one sensory pattern [16,17], with the highest prevalence of SUR, followed by SOR and sensation seeking [7]. These notions led in 2013 to the inclusion of both SOR and SUR among the DSM-5 diagnostic criteria for ASD (American Psychiatric Association 2013) [18], while sensation seeking was already included in DSM-IV within the rubric of unusual sensory interests [19]. ...
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Background: Sensory reactivity is considered one of the diagnostic criteria for Autism Spectrum Disorders (ASD) and has been associated with poorer functional outcomes, behavioral difficulties, and autism severity across the lifespan. The characterization of the sensory processing in ASD has thus become crucial to identify the sensory and motor features influencing the development of personal autonomy. Objectives: The present study has two aims: (1) to compare the sensory processing between school-aged children with ASD and typically developing peers (TD); (2) to evaluate whether, within the ASD sample, the cognitive level and reported sensory symptoms explain the scores exhibited at the Sensory Processing Measure (SPM-2). Methods: The SPM-2 test was administered to the parents of 105 children with ASD and 70 TD. The ASD group was further subdivided into two groups, namely high and low functioning based on their cognitive level (High Functioning (HF), IQ > 80; Low Functioning (LF), IQ < 80). Results: ASD children exhibited higher scores throughout the SPM-2 total score and its multiple subscales. Within ASD, while HF and LF children did not differ in terms of the SPM-2 total score, a significant difference was found for the hearing, social participation, and balance and motion subscales. Conclusions: Aside from classical knowledge that the ASD population suffers from sensory processing disorders, we revealed that different sensory patterns are associated with high or low cognitive functioning. Beyond its neurobiological interest, such knowledge may be of fundamental importance for individualizing psychoeducational interventions in preschool- and school-aged children and later developmental stages.
... Indeed, most existing clinical NDD measures focus on core symptom definition and have been developed for diagnostic characterization. They have limited utility as read-outs of specific mechanistic perturbations and are psychometrically often not suitable as outcome measures for intervention studies [64,65]. To overcome this, we have recently investigated how sensory reactivity problems, recently added as a core domain element for ASD in the DSM, may extend into problematic behavior or affective dysregulation and how disturbed E/I ratio homeostasis may be translated into clinical readout measures [63]. ...
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Pharmacological options for neurodevelopmental disorders are limited to symptom suppressing agents that do not target underlying pathophysiological mechanisms. Studies on specific genetic disorders causing neurodevelopmental disorders have elucidated pathophysiological mechanisms to develop more rational treatments. Here, we present our concerted multi-level strategy ‘BRAINMODEL’, focusing on excitation/inhibition ratio homeostasis across different levels of neuroscientific interrogation. The aim is to develop personalized treatment strategies by linking iPSC-based models and novel EEG measurements to patient report outcome measures in individual patients. We focus our strategy on chromatin- and SNAREopathies as examples of severe genetic neurodevelopmental disorders with an unmet need for rational interventions.
... Autistics have commonly been reported to experience hypo-(under-reactivity to sensory stimuli) and hyper-sensitivity ('overload' of sensory stimuli) in addition to "sensory seeking" behaviours (Bogdashina, 2003;Schauder & Bennetto, 2016). Self-report accounts of autistic individuals clearly show that their perception of the world is different from that of neurotypicals (Grandin and Duffy, 2008;Fleischmann, 2012). ...
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Purpose Global and local processing is part of human perceptual organisation, where global processing helps extract the “gist” of the visual information and local processing helps perceive the details. Individual differences in these two types of visual processing have been found in autism and ADHD (Attention-Deficit Hyperactivity Disorder). Virtual reality (VR) has become a more available method of research in the last few decades. No previous research has investigated perceptual differences using this technology. Design/methodology/approach The objective of the research is to threefold: (1) identify if there is association between ADHD and autistic traits and the performance on the Rey-Osterrieth complex figure (ROCF) task, (2) investigate practical effects of using VR drawing tools for research on perceptual experiences and (3) explore any perceptual differences brought out by the three-dimensional nature of the VR. The standard ROCF test was used as a baseline task to investigate the practical utility of using VR as an experimental platform. A total of 94 participants were tested. Findings Attention-to-detail, attention switching and imagination subscales of autism quotient (AQ) questionnaire were found to be predictors of organisational ROCF scores, whereas only the attention-to-detail subscale was predictive of perceptual ROCF scores. Originality/value The current study is an example of how classic psychological paradigms can be transferred into the virtual world. Further investigation of the distinct individual preferences in drawing tasks in VR could lead to a better understanding of individual differences in the processing of visuospatial information.
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Challenges with identifying and measuring anxiety in children and young people with an Autism Spectrum Disorder (ASD) have prompted studies examining the reliability of and agreement between different informants. In this study, agreement and factors influencing agreement (caregivers’ educational level and stress; child’s age, gender, verbal and performance IQ) between parent and child reports of anxiety symptoms was examined in a sample of 70 children with an ASD (66 boys; 9–16 years; mean age = 11.21, SD = 1.79 years). The participants completed the Spence Children’s Anxiety Scale (SCAS) - Child Version, while their parents completed the SCAS - Parent Version and the Parenting Stress Index. Children rated themselves as having significantly more anxiety symptoms compared to parental ratings of children’s anxiety. Parent and child reports of anxiety were significantly positively correlated for separation, social and generalized anxiety and for total anxiety scores with mostly medium effect sizes, but not for panic attack or obsessive-compulsive subscale scores. Higher parent-child agreement was found for anxiety symptoms associated with clearly observable behaviours. More agreement was associated with higher child verbal IQ and lower levels of parenting stress. Specifically, increased parental stress was associated with more discrepant caregiver-child reports of social anxiety symptoms. Our findings support the need for multi-informant data in order to capture a more comprehensive clinical picture of anxiety symptoms in children with an ASD and to consider the informants’ own stress and anxiety levels when obtaining caregivers’ perspectives.
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Sensory symptoms are prevalent in autism spectrum disorder but little is known about the early developmental patterns of these symptoms. This study examined the development of sensory symptoms and the relationship between sensory symptoms and adaptive functioning during early childhood. Three groups of children were followed across three time points from 2 to 8 years of age: autism spectrum disorder, developmental delay, and typical development. At each time point, parents filled out questionnaires regarding their child's sensory symptoms and adaptive functioning. At the initial time point, parents of children with autism spectrum disorder reported more sensory symptoms in their children than parents in the typical development group. Parents in the autism spectrum disorder group reported more sensory symptoms than parents in the developmental delay group within smell, taste, and auditory domains. While the typical development group decreased in reported sensory symptoms across the study period, the clinical groups demonstrated no significant change across assessment points. Sensory symptoms for all groups were not independently predictive of adaptive functioning when verbal mental age was also included in the model. The young age range at the initial assessment and pattern of results suggest that sensory symptoms are present early in the etiology of autism spectrum disorder and other developmental disorders and remain stable over time.
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The main objective of this study was to analyze in a sample of children with ASD the relationship between sensory processing, social participation and praxis impairments and some of the child's characteristics, such as non-verbal IQ, severity of ASD symptoms and the number of ADHD symptoms (inattention and hyperactivity/impulsivity), both in the home and main-classroom environments. Participants were the parents and teachers of 41 children with ASD from 5 to 8 years old (M = 6.09). They completed the Sensory Processing Measure (SPM) to evaluate sensory processing, social participation and praxis; the Gilliam Autism Rating Scale (GARS-2) to evaluate autism severity; and a set of items (the DSM-IV-TR criteria) to evaluate the number of inattention and hyperactivity/impulsivity symptoms in the child. Non-verbal IQ -– measured by the Raven's Coloured Progressive Matrices Test- – did not show a relationship with any of the SPM variables. The SPM variables were significant predictors of autism severity and had similar weights in the two environments. In the case of ADHD symptoms, the SPM variables had a greater weight in the home than in the classroom environment, and they were significant predictors of both inattention and hyperactivity/impulsivity – especially inattention- – only in the family context. The moderate association between inattention and auditory processing found in the main-classroom suggests the possible utility of certain measures aimed to simplify any classroom's acoustic environment.
Heightened interest in sensory function in persons with autism spectrum disorder (ASD) presents an unprecedented opportunity for impactful, interdisciplinary work between neuroscientists and clinical practitioners for whom sensory processing is a focus. In spite of this promise, and a number of overlapping perspectives on sensory function in persons with ASD, neuroscientists and clinical practitioners are faced with significant practical barriers to transcending disciplinary silos. These barriers include divergent goals, values, and approaches that shape each discipline, as well as different lexical conventions. This commentary is itself an interdisciplinary effort to describe the shared perspectives, and to conceptualize a framework that may guide future investigation in this area. We summarize progress to date and issue a call for clinical practitioners and neuroscientists to expand cross-disciplinary dialogue and to capitalize on the complementary strengths of each field to unveil the links between neural and behavioral manifestations of sensory differences in persons with ASD. Joining forces to face these challenges in a truly interdisciplinary way will lead to more clinically informed neuroscientific investigation of sensory function, and better translation of those findings to clinical practice. Likewise, a more coordinated effort may shed light not only on how current approaches to treating sensory processing differences affect brain and behavioral responses to sensory stimuli in individuals with ASD, but also on whether such approaches translate to gains in broader characteristics associated with ASD. It is our hope that such interdisciplinary undertakings will ultimately converge to improve assessment and interventions for persons with ASD. Autism Res 2016,. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.
G.T. Fechner originated psychophysics in the 1860s to describe mathematically the relationship between body and mind, the conscious experience of a sensation resulting from an external physical stimulus. Psychophysics had an important immediate impact on psychology, sensory physiology, and related fields, because it provided a means of measuring sensation which previously, like all other aspects of the mind, had been considered private and immeasurable. Knowledge of such variables is essential in human factors research on the compatibility of the environment and equipment with human sensory ability, and in evaluating human error. Historically, psychophysics promised to make possible a quantitative and scientific study of what was then described as “higher mental processes,” now a topic in cognitive science. It had a direct effect on Binet, for example, who developed the now universally used methods for measuring intelligence.
Little empirical data about the nature of tactile defensiveness and other types of sensory defensiveness are available. Clinicians speculate that these various phenomena are related as part of the more general construct of sensory defensiveness. Furthermore, although it is suspected that these behaviors are prevalent in persons with developmental disabilities, no estimates are currently available. This study used data from a 54-item survey about various kinds of stereotyped and unusual behaviors completed by a large sample of adults (n=158) and children (n=88) with developmental disabilities. Six items from the survey were selected that were thought to represent types of sensory defensiveness. Estimates of relative prevalence of these behaviors ranged from 3% to 30%. Developmental differences emerged; children in the sample displayed a higher prevalence of noise sensitivity and other, general sensitivity. Many of the items were significantly correlated with one another. An initial principle component analysis provided some evidence for a general factor of sensory defensiveness. A second principle component analysis with varimax rotation demonstrated two subtypes: “Auditory/Other Hypersensitivity” and “Tactile Defensiveness.” These findings elucidated the complexity of the structure of sensory defensiveness and have implications for occupational therapy assessment and treatment, particularly in the area of sensory integration theory.
Sensory processing differences in preschool-age children with autism spectrum disorder (ASD) affect their engagement in everyday activities, thereby influencing opportunities to practice and develop skills such as social communication and adaptive behavior. The purpose of this study was to investigate the extent to which specific sensory processing patterns relate to aspects of development (i.e., adaptive behavior, expressive and receptive language, fine and gross motor skills, social behavior) in a sample of preschool-age children with ASD (N = 400). A retrospective chart review was used to gather clinical data. Results suggest that sensory processing patterns differentially affect children's developmental skills and adaptive behavior. Certain sensory processing patterns predicted children's development of language, motor, and adaptive skills. These findings have clear implications for occupational therapy practice with young children with ASD. Practitioners should consider how sensory processing in ASD both supports and limits children's ability to engage in social communication and learning opportunities.
Sensory reactivity is a new DSM-5 criterion for autism spectrum disorder (ASD). The current study aims to validate a clinician-administered sensory observation in ASD, the Sensory Processing Scale Assessment (SPS). The SPS and the Short Sensory Profile (SSP) parent-report were used to measure sensory reactivity in children with ASD (n = 35) and typically developing children (n = 27). Sixty-five percent of children with ASD displayed sensory reactivity symptoms on the SPS and 81.1 % on the SSP. SPS scores significantly predicted SSP scores. We next identified the five SPS tasks that best differentiated groups. Our results indicate that a combination of parent-report and at least the five most differentiating observational tasks may be most sensitive in identifying the presence of sensory reactivity issues.
More than half of youth with autism spectrum disorders (ASDs) have sensory overresponsivity (SOR), an extreme negative reaction to sensory stimuli. However, little is known about the neurobiological basis of SOR, and there are few effective treatments. Understanding whether SOR is due to an initial heightened sensory response or to deficits in regulating emotional reactions to stimuli has important implications for intervention. To determine differences in brain responses, habituation, and connectivity during exposure to mildly aversive sensory stimuli in youth with ASDs and SOR compared with youth with ASDs without SOR and compared with typically developing control subjects. Functional magnetic resonance imaging was used to examine brain responses and habituation to mildly aversive auditory and tactile stimuli in 19 high-functioning youths with ASDs and 19 age- and IQ-matched, typically developing youths (age range, 9-17 years). Brain activity was related to parents' ratings of children's SOR symptoms. Functional connectivity between the amygdala and orbitofrontal cortex was compared between ASDs subgroups with and without SOR and typically developing controls without SOR. The study dates were March 2012 through February 2014. Relative increases in blood oxygen level-dependent signal response across the whole brain and within the amygdala during exposure to sensory stimuli compared with fixation, as well as correlation between blood oxygen level-dependent signal change in the amygdala and orbitofrontal cortex. The mean age in both groups was 14 years and the majority in both groups (16 of 19 each) were male. Compared with neurotypical control participants, participants with ASDs displayed stronger activation in primary sensory cortices and the amygdala (P < .05, corrected). This activity was positively correlated with SOR symptoms after controlling for anxiety. The ASDs with SOR subgroup had decreased neural habituation to stimuli in sensory cortices and the amygdala compared with groups without SOR. Youth with ASDs without SOR showed a pattern of amygdala downregulation, with negative connectivity between the amygdala and orbitofrontal cortex (thresholded at z > 1.70, P < .05). Results demonstrate that youth with ASDs and SOR show sensorilimbic hyperresponsivity to mildly aversive tactile and auditory stimuli, particularly to multiple modalities presented simultaneously, and show that this hyperresponsivity is due to failure to habituate. In addition, findings suggest that a subset of youth with ASDs can regulate their responses through prefrontal downregulation of amygdala activity. Implications for intervention include minimizing exposure to multiple sensory modalities and building coping strategies for regulating emotional response to stimuli.