Handedness and depression: a meta-analysis across 87 studies

2021 ◽  
Author(s):  
Julian Packheiser ◽  
Judith Schmitz ◽  
Gesa Berretz ◽  
Lena Sophie Pfeifer ◽  
Clara Celina Stein ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Effect Size ◽  
Neurodevelopmental Disorders ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Precise Estimate ◽  
Brain Lateralization ◽  
Functional Brain ◽  
Depression Disorders

Alterations in functional brain lateralization, often indicated by an increased prevalence of left- and/or mixed-handedness, have been demonstrated in several psychiatric and neurodevelopmental disorders like schizophrenia or autism spectrum disorder. For depression, however, this relationship is largely unclear. While a few studies found evidence that handedness and depression are associated, both the effect size and the direction of this association remain elusive. Here, we collected data from 87 studies totaling 35,501 individuals diagnosed with depression disorders to provide a precise estimate of differences in left-, mixed- and non-right-handedness between depressed and healthy samples. We found no differences in left- (OR = 1.04, p = .384), mixed- (OR = 1.64, p = .060) or non-right-handedness (OR = 1.05, p = .309) between the two groups. We could thus find no link between handedness and depression on the meta-analytical level.

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

Understanding the Effect Size of Peer-Mediated Academic Instruction: A Meta-Analysis

2021 ◽  
pp. 108835762110233
Author(s):  
April Haas ◽  
Kimberly J. Vannest ◽  
Marcus C. Fuller ◽  
Jennifer B. Ganz
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Skills ◽  
Effect Size ◽  
Meta Analysis ◽  
Academic Skills ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Academic Instruction ◽  
Students With Autism ◽  
Moderate Effect

A deficit in social skills often makes teaching academics a second thought when instructing students with autism. Peer-mediated instruction has been well studied for increasing academic skills across various disabilities and social skills for students with autism spectrum disorder (ASD). The purpose of this study was to determine the effect size of peer-mediated academic instruction (PMAI) to increase academic skills for students with ASD. Results indicate studies demonstrate a weak to moderate effect when using PMAI to teach students with ASD. These results validate PMAI can be effective when teaching academic skills to students with ASD; however, more studies are needed to better understand for whom and under what conditions PMAI is most effective.

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.

Autism spectrum disorder and air pollution: a systematic review and meta-analysis

2021 ◽  
pp. 116856
Author(s):  
Frédéric Dutheil ◽  
Aurélie Comptour ◽  
Roxane Morlon ◽  
Martial Mermillod ◽  
Bruno Pereira ◽  
...  
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Air Pollution ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder
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