Over‐Responsiveness and Greater Variability in Roughness Perception in Autism

Haigh, Sarah M.; Minshew, Nancy J.; Heeger, David J.; Dinstein, Ilan; Behrmann, Marlene

doi:10.1002/aur.1505

Cited by 27 publications

(21 citation statements)

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“…In contrast to TD individuals whose response characteristics tended toward the more normal Gaussian distribution, patterns of ASD individuals revealed statistical features that tended toward a more Exponential, “memoryless” distribution; Figure A. Unlike previous studies proposing higher variability in neural responses and sensory processing in ASD (e.g., Dinstein et al., ; Haigh, Minshew, Heeger, Dinstein, & Behrmann, ; Torres et al., ; Weinger, Zemon, Soorya, & Gordon, ), we discovered evidence for worse noise‐to‐signal at two distinct levels of functioning in adults with ASD, linking worse spontaneous movement‐based FF during the scan and worse response fluctuation‐based FF in RT latencies at the same time (Figure C). Finally, in our CEM imaging cohort, we linked differences in stochastic patterns in behavior during the scan to significant differences in interference‐specific neural coupling between bilateral anterior insula and IFS and prefrontal cortex in ASD (Figure B).…”

Section: Discussioncontrasting

confidence: 89%

Cortical interactions during the resolution of information processing demands in autism spectrum disorders

et al. 2016

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IntroductionOur flexible and adaptive interactions with the environment are guided by our individual representation of the physical world, estimated through sensation and evaluation of available information against prior knowledge. When linking sensory evidence with higher‐level expectations for action, the central nervous system (CNS) in typically developing (TD) individuals relies in part on distributed and interacting cortical regions to communicate neuronal signals flexibly across the brain. Increasing evidence suggests that the balance between levels of signal and noise during information processing may be disrupted in individuals with Autism Spectrum Disorders (ASD).MethodsParticipants with and without ASD performed a visuospatial interference task while undergoing functional Magnetic Resonance Imaging (fMRI). We empirically estimated parameters characterizing participants’ latencies and their subtle fluctuations (noise accumulation) over the 16‐min scan. We modeled hemodynamic activation and used seed‐based analyses of neural coupling to study dysfunction in interference‐specific connectivity in a subset of ASD participants who were nonparametrically matched to TD participants on age, male‐to‐female ratio, and magnitude of movement during the scan.ResultsStochastic patterns of response fluctuations reveal significantly higher noise‐to‐signal levels and a more random and noisy structure in ASD versus TD participants, and in particular ASD adults who have the greatest clinical autistic deficits. While individuals with ASD show an overall weaker modulation of interference‐specific functional connectivity relative to TD individuals, in particular between the seeds of Anterior Cingulate Cortex (ACC) and Inferior Parietal Sulcus (IPS) and the rest of the brain, we found that in ASD, higher uncertainty during the task is linked to increased interference‐specific coupling between bilateral anterior insula and prefrontal cortex.ConclusionsSubtle and informative differences in the structure of experiencing information exist between ASD and TD individuals. Our findings reveal in ASD an atypical capacity to apply previously perceived information in a manner optimal for adaptive functioning, plausibly revealing suboptimal message‐passing across the CNS.

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Section: Discussioncontrasting

confidence: 89%

Cortical interactions during the resolution of information processing demands in autism spectrum disorders

et al. 2016

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“…Given the statistical properties of spiking neural activity (79), this result also predicts greater trial-by-trial response variability in autism. Evidence supporting these predictions comes from imaging studies reporting larger stimulus-driven response amplitudes (80) or increased variance (47) in autism, as well as recent psychophysical findings (81). In one imaging study, increased stimulus-driven activity in auditory cortex associated with lowlevel feature processing was observed in subjects with autism compared to typically developing controls (82).…”

Section: Discussionmentioning

confidence: 91%

A computational perspective on autism

Rosenberg

Patterson

Angelaki

2015

Proc. Natl. Acad. Sci. U.S.A.

149

151

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Autism is a neurodevelopmental disorder that manifests as a heterogeneous set of social, cognitive, motor, and perceptual symptoms. This system-wide pervasiveness suggests that, rather than narrowly impacting individual systems such as affection or vision, autism may broadly alter neural computation. Here, we propose that alterations in nonlinear, canonical computations occurring throughout the brain may underlie the behavioral characteristics of autism. One such computation, called divisive normalization, balances a neuron's net excitation with inhibition reflecting the overall activity of the neuronal population. Through neural network simulations, we investigate how alterations in divisive normalization may give rise to autism symptomatology. Our findings show that a reduction in the amount of inhibition that occurs through divisive normalization can account for perceptual consequences of autism, consistent with the hypothesis of an increased ratio of neural excitation to inhibition (E/I) in the disorder. These results thus establish a bridge between an E/I imbalance and behavioral data on autism that is currently absent. Interestingly, our findings implicate the context-dependent, neuronal milieu as a key factor in autism symptomatology, with autism reflecting a less "social" neuronal population. Through a broader discussion of perceptual data, we further examine how altered divisive normalization may contribute to a wide array of the disorder's behavioral consequences. These analyses show how a computational framework can provide insights into the neural basis of autism and facilitate the generation of falsifiable hypotheses. A computational perspective on autism may help resolve debates within the field and aid in identifying physiological pathways to target in the treatment of the disorder.A utism is a neurodevelopmental disorder that is dramatically increasing in prevalence (Fig. 1). Recent data place the number of children being diagnosed with autism in the United States at 1 in 68, more than doubling in the last decade (1-4). The disorder is highly pervasive, affecting individuals at cognitive, motor, and perceptual levels. It is furthermore a "spectrum disorder," with symptoms that manifest in varying degrees across individuals. This heterogeneity presents significant challenges to establishing a comprehensive characterization of the disorder.Research investigating the genetic and molecular basis of autism implicates over 100 genes (5), many of which are involved in synaptic development and function (6-8). As such, one prominent hypothesis is that autism arises from a neurophysiological excitation-to-inhibition (E/I) imbalance (9, 10). However, the connection between an E/I imbalance and the behavioral characteristics of the disorder remains unclear. Considering the pervasive nature of autism, and the covariance of loosely related symptoms (11-14), one possibility is that an E/I imbalance widely affects neural computation, in turn giving rise to the broad behavioral symptoms recognized as autism.Here,...

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“…However, the picture in regards to anticipated links between neural and clinical measures of sensory function is quite complex. Such links are sometimes not observed [e.g., Woynaroski et al, ], are weaker than expected [e.g., Brandwein et al, ; Haigh, Minshew, Heeger, Dinstein, & Behrmann, ; Hardan et al, ], or are difficult to interpret conceptually, such as an association between sensory seeking behavior and visual accuracy [e.g., Stewart et al, ]. One possible explanation for the frequent failure to find theoretically based associations is that our present measurement system/s are problematic (i.e., are not quantifying constructs in a way that is reliable and/or valid).…”

Section: Inroads Challenges and Future Directionsmentioning

confidence: 87%

Toward an interdisciplinary approach to understanding sensory function in autism spectrum disorder

et al. 2016

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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.

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Over‐Responsiveness and Greater Variability in Roughness Perception in Autism

Cited by 27 publications

References 50 publications

Cortical interactions during the resolution of information processing demands in autism spectrum disorders

Cortical interactions during the resolution of information processing demands in autism spectrum disorders

A computational perspective on autism

Toward an interdisciplinary approach to understanding sensory function in autism spectrum disorder

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