scholarly journals Brain activity during facial processing in autism spectrum disorder: an activation likelihood estimation (ALE) meta‐analysis of neuroimaging studies

2021 ◽  
Author(s):  
Cristiano Costa ◽  
Ioana Alina Cristea ◽  
Elisa Dal Bò ◽  
Caterina Melloni ◽  
Claudio Gentili
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Activity ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Activation Likelihood Estimation ◽  
Facial Processing

scholarly journals Brain activity during facial processing in autism spectrum disorder: an activation likelihood estimation (ALE) meta-analysis of neuroimaging studies

2020 ◽  
Author(s):  
Cristiano Costa ◽  
Ioana Alina Cristea ◽  
Elisa Dal Bò ◽  
Caterina Melloni ◽  
Claudio Gentili
Keyword(s):  
Autism Spectrum Disorder ◽  
Face Processing ◽  
Emotional Processing ◽  
Brain Activity ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Activation Likelihood Estimation ◽  
Primary Analysis

AbstractBackgroundThough aberrant face processing is a hallmark of autistic spectrum disorder (ASD), findings on accompanying brain activity are divergent. Therefore, we conducted an activation likelihood estimation (ALE) meta-analysis of studies examining brain activity during face processing.MethodsWe searched PubMed and PsycINFO using combinations of terms as ‘fMRI’, ‘Autism Spectrum Disorder’, ‘Face Perception’. Eligible studies reported on DSM-diagnosed ASD patients, compared to controls (HC), using face stimuli presented in fMRI and reporting whole-brain analysis coordinates. We compared two approaches: “convergence of differences” (primary analysis) using study-level coordinates from ASD vs. HC contrasts, and “differences in convergence” (secondary) pooling coordinates within each group separately, and contrasting the resultant ALE-maps.ResultsThirty-five studies (655 ASD and 668 HC) were included. Primary analysis identified a cluster in amygdala/parahippocampus where HC showed greater convergence of activation. Secondary analysis yielded no significant results.ConclusionsResults suggest that ASD dysfunction in face processing relies on structures involved in emotional processing rather than perception. We also demonstrate that the two ALE methodologies lead to divergent results.

scholarly journals A meta-analysis of areas of structural variation in grey matter in individuals with Autism Spectrum Disorder (ASD) in relation to gene expression of candidate ASD genes

2021 ◽  
Author(s):  
Alessia Camasio ◽  
Elisa Panzeri ◽  
Lorenzo Mancuso ◽  
Donato Liloia ◽  
Jordi Manuello ◽  
...  
Keyword(s):  
Gene Expression ◽  
Autism Spectrum Disorder ◽  
Structural Damage ◽  
Grey Matter ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
High Heritability ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of developmental pathologies with a strong genetic basis and high heritability. Although neuroimaging studies have indicated anatomical changes in grey matter (GM) morphometry, their associations with gene expression remain elusive. In the present study, we aim to understand how gene expression correlates with structural brain aberration in ASD and how it distributes in a functional network perspective. First, we performed an activation likelihood estimation (ALE) meta-analysis to determine GM alteration in the brain, then we selected genes from the SHANK, NRXN, NLGN family and MECP2, which have been implicated with ASD, particularly in regards to altered synaptic transmission. Gene expression maps were built. We then assessed the correlation between the gene expression maps and the GM alteration maps. We found that the default mode network regions were the most significantly correlated with gene expression of selected genes in both areas of GM decrease and increase. The dorsal attention and the cerebellar network regions are significantly correlated with ASD genes. Different networks, namely somatomotor, limbic and basal ganglia/thalamus network - were found in the increase; for each of these networks, however, only a few genes were significant. Our approach allowed to combine the well beaten path of genetic and brain imaging in a novel way, to specifically investigate the relation between gene expression and brain with structural damage, and individuate genes of interest for further investigation in specific functional networks.

scholarly journals Brain structure anomalies in autism spectrum disorder-a meta-analysis of VBM studies using anatomic likelihood estimation

2011 ◽  
Vol 33 (6) ◽  
pp. 1470-1489 ◽  
Author(s):  
Thomas Nickl-Jockschat ◽  
Ute Habel ◽  
Tanja Maria Michel ◽  
Janessa Manning ◽  
Angela R. Laird ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Structure ◽  
Meta Analysis ◽  
Likelihood Estimation ◽  
Autism Spectrum ◽  
Spectrum Disorder

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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