A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders

Philip, Ruth C. M.; Dauvermann, Maria R.; Whalley, Heather C.; Baynham, Katie; Lawrie, Stephen M.; Stanfield, Andrew C.

doi:10.1016/j.neubiorev.2011.10.008

Cited by 331 publications

(285 citation statements)

References 140 publications

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“…Visual processing involves the striate cortex within the calcarine fissure, and the surrounding cortex, including the cuneus, the caudal portion of the precuneus, and the lingual gyrus. Findings of abnormalities in visual cortex in ASD are common (Barbeau et al, 2015;Samson et al, 2012;Philip et al, 2012;Green et al, 2013). Auditory processing involves Heschl's gyrus and the surrounding cortex within the superior temporal gyrus.…”

Section: Discussionmentioning

confidence: 99%

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

et al. 2017

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Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multiprotocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a wholePreprint submitted to NeuroImage September 4, 2017 brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.

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Section: Discussionmentioning

confidence: 99%

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

et al. 2017

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“…Subjects with autism did not present this incongruent > congruent effect, showing no difference in brain activation in the congruent or incongruent situations [13]. A recent meta-analysis on fMRI studies in autism highlighted that, when taking into account studies investigating activation during basic or complex social tasks, the brain region that is shown to be abnormally activated in subjects with autism is the STS [75].…”

Section: Visual Social Processing 1) Face Processing Studiesmentioning

confidence: 99%

“…Studies using fMRI have relied on different paradigms related to social processes to investigate differences in brain activity between subjects with autism and control subjects during performance of variable tasks [72].…”

Section: Activation Pet and Mri Studiesmentioning

confidence: 99%

“…The processing of faces in individuals with autism appears to be atypically organized in comparison to the network observed in control subjects. Activations in the fusiform face area (FFA) [74], and the occipital face area are commonly reported as reduced in individuals with ASD (for review, see [75]. Schultz and colleagues were the first to use fMRI to study face perception in autistic subjects.…”

Section: Visual Social Processing 1) Face Processing Studiesmentioning

confidence: 99%

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Autism, social cognition and superior temporal sulcus

Zilbovicius¹,

Saitovitch²,

Popa³

et al. 2013

OJPsych

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Results on brain imaging studies have led to a better understanding of the neural circuits involved in social cognition and its implication in autism spectrum disorders (ASD). It has been shown that the superior temporal sulcus (STS) is highly implicated in social processes, from perception of socially relevant information, such as body movements or eye gaze, to more complex social cognition processes. Furthermore, several multimodal brain imaging results point to anatomo-functional abnormalities in the STS in both children and adults with ASD. These results are highly consistent with social impairments in ASD, among which eye gaze perception is particularly relevant. Gaze abnormalities can now be objectively measured using eye-tracking methodology, leading to a better characterization of social perception impairments in autism. Moreover, these gaze abnormalities have been associated with STS abnormalities in ASD. Based on these results, our hypothesis is that anatomo-functional anomalies in the STS occurring early across brain development could constitute the first step in the cascade of neural dysfunction underlying autism. In the present work, we'll review recent data of STS contribution to normal social cognition and it's implication in autism.

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“…There are also indications for age bias. Thus, cognitive activation studies in autism have been focused strongly on adults rather than on children and adolescents with the disorder [22]. There are hardly any imaging studies in subjects with more severe autism, in spite of the fact about 40 % of all subject with autism are low functioning [23].…”

mentioning

confidence: 99%

Brain imaging: closing the gap between basic research and clinical application is urgently needed

Buitelaar

Coghill

2013

Eur Child Adolesc Psychiatry

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Brain imaging has revolutionized neural science over the past decades through the identification and description of the neural circuits involved in social cognition, cognitive control, memory, empathy, mood regulation and many other domains. The characterization of these neural circuits at the level of brain structure and function has deepened our understanding of the neural underpinnings of virtually all psychiatric disorders, from ADHD and autism to anxiety and mood disorders, and from schizophrenia to bipolar illness and Alzheimer's disease. One significant newly acquired insight is that psychiatric disorders may be understood best not so much by a neural lesion model focused on discovering specific brain regions where ADHD, autism and other disorders 'reside'. Rather, a disruption of optimal structural and functional connectivity between separate areas of the brain, alongside interference of well-coordinated and synchronized activity of various neural systems turn out to be a most valuable perspective for understanding the biological basis of psychiatric disorders. These may be conceptualized as 'systems disorders of the brain' [1][2][3][4][5]. Brain imaging has also started to add a developmental perspective by outlining how neural circuits develop over age and how this influences and interacts with maturation of cognitive functions and development of behavioral competences. As such, developmental psychopathology has been complemented by developmental neuroscience, i.e. the study of individual patterns of development of the structure and functions of the brain. This includes the study of the development of neurochemical systems, myelinization, grey/white matter growth, synaptic function, etc., as well as the study of the genetic and environmental causes of individual variability. Together with progress in identifying genetic risk factors for psychiatric illness, brain imaging, and brain imaging genetics confer the opportunity to provide mechanistic explanations about how genetic and environmental risk factors may affect neural circuits and cognitive systems, and how this may lead to clinical symptoms [6][7][8].The current issue provides four review papers on brain imaging that are relevant for child and adolescent psychiatry. Johnston et al. [9] discuss the clinical relevance of brain imaging for clinical prediction in psychiatry. In particular, the application of pattern detection technique such as support vector machines has already demonstrated its potential to predict diagnostic status and might further contribute to predictions on treatment response and course of the disorder. Rubia [10] reviews the functional MRI literature on the functional development of cognitive and motivational control, timing, and attention, as well as the development of resting state neural networks. She signals a shift with age from bottom-up processing regions to topdown control in cortico-cortical and cortico-subcortical pathways. Although psychological trauma is a frequent cause of psychopathology at all ages, research into...

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A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders

Cited by 331 publications

References 140 publications

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

Autism, social cognition and superior temporal sulcus

Brain imaging: closing the gap between basic research and clinical application is urgently needed

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