scholarly journals Significant Subgraph Mining for Neural Network Inference with Multiple Comparisons Correction

2021 ◽  
Author(s):  
Aaron Gutknecht ◽  
Michael Wibral
Keyword(s):  
Resting State ◽  
Power Analysis ◽  
Network Inference ◽  
Multiple Comparisons ◽  
Autism Spectrum ◽  
Experimental Designs ◽  
Extensive Investigation ◽  
Empirical Power ◽  
Network Comparison ◽  
Subgraph Mining

We describe how the recently introduced method of significant subgraph mining can be employed as a useful tool in network comparison. It is applicable whenever the goal is to compare two sets of unweighted graphs and to determine differences in the processes that generate them. We provide an extension of the method to dependent graph generating processes as the occur for example in within-subject experimental designs. Furthermore, we present an extensive investigation of error-statistical properties of the method in simulation using Erdos-Renyi models and in empirical data. In particular, we perform an empirical power analysis for transfer entropy networks inferred from resting state MEG data comparing autism spectrum patients with neurotypical controls. From this analysis one may estimate that the appropriate sample size for similar studies should be chosen in the order of n=60 per group or larger.

scholarly journals A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

2021 ◽  
Vol 11 (8) ◽  
pp. 3636
Author(s):  
Faria Zarin Subah ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
State Of The Art ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Bootstrap Analysis ◽  
Proposed Model ◽  
Art Methods ◽  
The Mean

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.

scholarly journals Sex Differences in Functional Connectivity Between Resting State Brain Networks in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Vânia Tavares ◽  
Luís Afonso Fernandes ◽  
Marília Antunes ◽  
Hugo Ferreira ◽  
Diana Prata
Keyword(s):  
Autism Spectrum Disorder ◽  
Sex Differences ◽  
Resting State ◽  
Brain Connectivity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Control Networks ◽  
Highly Sensitive ◽  
Female Specific ◽  
Main Effects

AbstractFunctional brain connectivity (FBC) has previously been examined in autism spectrum disorder (ASD) between-resting-state networks (RSNs) using a highly sensitive and reproducible hypothesis-free approach. However, results have been inconsistent and sex differences have only recently been taken into consideration using this approach. We estimated main effects of diagnosis and sex and a diagnosis by sex interaction on between-RSNs FBC in 83 ASD (40 females/43 males) and 85 typically developing controls (TC; 43 females/42 males). We found increased connectivity between the default mode (DM) and (a) the executive control networks in ASD (vs. TC); (b) the cerebellum networks in males (vs. females); and (c) female-specific altered connectivity involving visual, language and basal ganglia (BG) networks in ASD—in suggestive compatibility with ASD cognitive and neuroscientific theories.

scholarly journals Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

2015 ◽  
Vol 72 (8) ◽  
pp. 767 ◽  
Author(s):  
Leonardo Cerliani ◽  
Maarten Mennes ◽  
Rajat M. Thomas ◽  
Adriana Di Martino ◽  
Marc Thioux ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Resting State Networks

scholarly journals Transcriptional Gene Silencing of the Autism-Associated Long Noncoding RNA MSNP1AS in Human Neural Progenitor Cells

2016 ◽  
Vol 38 (5) ◽  
pp. 375-383 ◽  
Author(s):  
Jessica J. DeWitt ◽  
Patrick M. Hecht ◽  
Nicole Grepo ◽  
Brent Wilkinson ◽  
Oleg V. Evgrafov ◽  
...  
Keyword(s):  
Immune Response ◽  
Chromatin Remodeling ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Multiple Comparisons ◽  
Neural Progenitor ◽  
Autism Spectrum ◽  
Biological Processes ◽  
Altered Expression ◽  
Genome Wide

The long noncoding RNA MSNP1AS (moesin pseudogene 1, antisense) is a functional element that was previously associated with autism spectrum disorder (ASD) with genome-wide significance. Expression of MSNP1AS was increased 12-fold in the cerebral cortex of individuals with ASD and 22-fold in individuals with a genome-wide significantly associated ASD genetic marker on chromosome 5p14.1. Overexpression of MSNP1AS in human neuronal cells caused decreased expression of moesin protein, which is involved in neuronal process stability. In this study, we hypothesize that MSNP1AS knockdown impacts global transcriptome levels. We transfected the human neural progenitor cell line SK- N-SH with constructs that caused a 50% suppression of MSNP1AS expression. After 24 h, cells were harvested for total RNA isolation. Strand-specific RNA sequencing analysis indicated altered expression of 1,352 genes, including altered expression of 318 genes following correction for multiple comparisons. Expression of the OAS2 gene was increased >150-fold, a result that was validated by quantitative PCR. Gene ontology analysis of the 318 genes with altered expression following correction for multiple comparisons indicated that upregulated genes were significantly enriched for genes involved in immune response, and downregulated genes were significantly enriched for genes involved in chromatin remodeling. These data indicate multiple transcriptional and translational functions of MSNP1AS that impact ASD-relevant biological processes. Chromatin remodeling and immune response are biological processes implicated by genes with rare mutations associated with ASD. Our data suggest that the functional elements implicated by association of common genetic variants impact the same biological processes, suggesting a possible shared common molecular pathway of ASD.

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.

scholarly journals The Impact of Cannabidiol on Human Brain Function: A Systematic Review

2021 ◽  
Vol 11 ◽  
Author(s):  
Albert Batalla ◽  
Julian Bos ◽  
Amber Postma ◽  
Matthijs G. Bossong
Keyword(s):  
Systematic Review ◽  
Psychiatric Disorder ◽  
Human Brain ◽  
Healthy Volunteers ◽  
Resting State ◽  
Brain Function ◽  
Brain Activity ◽  
Autism Spectrum ◽  
Cognitive Tasks ◽  
Therapeutic Effects

Background: Accumulating evidence suggests that the non-intoxicating cannabinoid compound cannabidiol (CBD) may have antipsychotic and anxiolytic properties, and thus may be a promising new agent in the treatment of psychotic and anxiety disorders. However, the neurobiological substrates underlying the potential therapeutic effects of CBD are still unclear. The aim of this systematic review is to provide a detailed and up-to-date systematic literature overview of neuroimaging studies that investigated the acute impact of CBD on human brain function.Methods: Papers published until May 2020 were included from PubMed following a comprehensive search strategy and pre-determined set of criteria for article selection. We included studies that examined the effects of CBD on brain function of healthy volunteers and individuals diagnosed with a psychiatric disorder, comprising both the effects of CBD alone as well as in direct comparison to those induced by ∆9-tetrahydrocannabinol (THC), the main psychoactive component of Cannabis.Results: One-ninety four studies were identified, of which 17 met inclusion criteria. All studies investigated the acute effects of CBD on brain function during resting state or in the context of cognitive tasks. In healthy volunteers, acute CBD enhanced fronto-striatal resting state connectivity, both compared to placebo and THC. Furthermore, CBD modulated brain activity and had opposite effects when compared to THC following task-specific patterns during various cognitive paradigms, such as emotional processing (fronto-temporal), verbal memory (fronto-striatal), response inhibition (fronto-limbic-striatal), and auditory/visual processing (temporo-occipital). In individuals at clinical high risk for psychosis and patients with established psychosis, acute CBD showed intermediate brain activity compared to placebo and healthy controls during cognitive task performance. CBD modulated resting limbic activity in subjects with anxiety and metabolite levels in patients with autism spectrum disorders.Conclusion: Neuroimaging studies have shown that acute CBD induces significant alterations in brain activity and connectivity patterns during resting state and performance of cognitive tasks in both healthy volunteers and patients with a psychiatric disorder. This included modulation of functional networks relevant for psychiatric disorders, possibly reflecting CBD’s therapeutic effects. Future studies should consider replication of findings and enlarge the inclusion of psychiatric patients, combining longer-term CBD treatment with neuroimaging assessments.

scholarly journals Sex Differences in the Default Mode Network with Regard to Autism Spectrum Traits: A Resting State fMRI Study

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0143126 ◽  
Author(s):  
Minyoung Jung ◽  
Maria Mody ◽  
Daisuke N. Saito ◽  
Akemi Tomoda ◽  
Hidehiko Okazawa ◽  
...  
Keyword(s):  
Sex Differences ◽  
Default Mode Network ◽  
Resting State ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Default Mode ◽  
Fmri Study

Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI

2020 ◽  
pp. 169-186
Author(s):  
S. Vidhusha ◽  
A. Kavitha
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Resting State ◽  
Cognitive Task ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Control Group ◽  
Neural Connectivity ◽  
Activation Patterns ◽  
The Brain

Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.

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