scholarly journals The Importance of Anti-Correlations in Graph Theory Based Classification of Autism Spectrum Disorder

2019 ◽  
Author(s):  
Amirali Kazeminejad ◽  
Roberto C Sotero
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Networks ◽  
Model Performance ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Network Graph ◽  
Functional Connections ◽  
Functional Brain Networks

AbstractIn recent years, there has been a significant growth in the number of applications of machine learning (ML) techniques to the study and identification of neurological disorders. These methods rely heavily on what features are made available to the ML algorithm. Features such as graph theoretical metrics of resting-state fMRI-based brain networks have proven useful. However, the computation of functional brain networks relies on making an arbitrary choice about whether the obtained anti-correlations, representing the strengths of functional connections in the brain, should be discarded or not. In this study, we examine how this choice affects the performance of a support vector machine (SVM) model for classifying autism spectrum disorder. We extracted graph theoretical features using three different pipelines for constructing the functional network graph. These pipelines primarily used positive weights, negative weights (anti-correlations) and only the absolute value of weights of the correlation matrix derived from fMRI time-series. Our results suggest that in the presence of Global Signal Regression (GSR) the features extracted from anti-correlations play a major role in improving model performance. However, this does not undermine the importance of features from other pipelines.

scholarly journals Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics

Diagnostics ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. 51 ◽  
Author(s):  
Aitana Pascual-Belda ◽  
Antonio Díaz-Parra ◽  
David Moratal
Keyword(s):  
Autism Spectrum Disorder ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Typical Development ◽  
Functional Brain ◽  
Functional Brain Networks ◽  
Management Functions ◽  
The Brain

The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson’s correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.

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.

A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys

2021 ◽  
pp. 155005942110262
Author(s):  
Bo Chen
Keyword(s):  
Autism Spectrum Disorder ◽  
Activity Patterns ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Eigenvector Centrality ◽  
Information Process ◽  
Whole Brain ◽  
Functional Connections ◽  
Cortical Regions ◽  
Prefrontal Lobe

The abnormal cortices of autism spectrum disorder (ASD) brains are uncertain. However, the pathological alterations of ASD brains are distributed throughout interconnected cortical systems. Functional connections (FCs) methodology identifies cooperation and separation characteristics of information process in macroscopic cortical activity patterns under the context of network neuroscience. Embracing the graph theory concepts, this paper introduces eigenvector centrality index (EC score) ground on the FCs, and further develops a new framework for researching the dysfunctional cortex of ASD in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in EC-scores of 26 ASD boys and 28 matched healthy controls (HCs). For whole brain regional EC scores of ASD boys, orbitofrontal superior medial cortex, insula R, posterior cingulate gyrus L, and cerebellum 9 L are endowed with different EC scores significantly. In the brain subsystems level, EC scores of DMN, prefrontal lobe, and cerebellum are aberrant in the ASD boys. Generally, the EC scores display widespread distribution of diseased regions in ASD brains. Meanwhile, the discovered regions and subsystems, such as MPFC, AMYG, INS, prefrontal lobe, and DMN, are engaged in social processing. Meanwhile, the CBCL externalizing problem scores are associated with EC scores.

scholarly journals Fostering Emotion Recognition in Children with Autism Spectrum Disorder

2021 ◽  
Vol 5 (10) ◽  
pp. 57
Author(s):  
Vinícius Silva ◽  
Filomena Soares ◽  
João Sena Esteves ◽  
Cristina P. Santos ◽  
Ana Paula Pereira
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Interaction ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Social Communication ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Robotic Platform ◽  
Children With Asd

Facial expressions are of utmost importance in social interactions, allowing communicative prompts for a speaking turn and feedback. Nevertheless, not all have the ability to express themselves socially and emotionally in verbal and non-verbal communication. In particular, individuals with Autism Spectrum Disorder (ASD) are characterized by impairments in social communication, repetitive patterns of behaviour, and restricted activities or interests. In the literature, the use of robotic tools is reported to promote social interaction with children with ASD. The main goal of this work is to develop a system capable of automatic detecting emotions through facial expressions and interfacing them with a robotic platform (Zeno R50 Robokind® robotic platform, named ZECA) in order to allow social interaction with children with ASD. ZECA was used as a mediator in social communication activities. The experimental setup and methodology for a real-time facial expression (happiness, sadness, anger, surprise, fear, and neutral) recognition system was based on the Intel® RealSense™ 3D sensor and on facial features extraction and multiclass Support Vector Machine classifier. The results obtained allowed to infer that the proposed system is adequate in support sessions with children with ASD, giving a strong indication that it may be used in fostering emotion recognition and imitation skills.

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 Vocal markers of Autism Spectrum Disorder: assessing the generalizability of machine learning models

2021 ◽  
Author(s):  
Astrid Rybner ◽  
Emil Trenckner Jessen ◽  
Marie Damsgaard Mortensen ◽  
Stine Nyhus Larsen ◽  
Ruth Grossman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Model Performance ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Future Studies ◽  
Out Of Sample ◽  
Biobehavioral Markers ◽  
Similar Task ◽  
Machine Learning Models

Background: Machine learning (ML) approaches show increasing promise to identify vocal markers of Autism Spectrum Disorder (ASD). Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected in diverse settings such as using a different speech task or a different language. Aim: In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. Methods: We re-train a promising published ML model of vocal markers of ASD on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on i) different participants from the same study, performing the same task; ii) the same participants, performing a different (but similar) task; iii) a different study with participants speaking a different language, performing the same type of task. Results: While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to new similar tasks and not at all to new languages. The ML pipeline is openly shared. Conclusion: Generalizability of ML models of vocal markers - and more generally biobehavioral markers - of ASD is an issue. We outline three recommendations researchers could take in order to be more explicit about generalizability and improve it in future studies.

scholarly journals Decreased homotopic interhemispheric functional connectivity in children with autism spectrum disorder

2020 ◽  
Author(s):  
Shuxia Yao ◽  
Menghan Zhou ◽  
Yuan Zhang ◽  
Feng Zhou ◽  
Qianqian Zhang ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Symptom Severity ◽  
Visual Processing ◽  
Brain Networks ◽  
Cingulate Cortex ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Volume Changes ◽  
Processing Network

AbstractWhile a number of functional and structural changes occur in large-scale brain networks in autism spectrum disorder (ASD), reduced interhemispheric resting state functional connectivity (rsFC) between homotopic regions may be of particular importance as a biomarker. ASD is an early-onset developmental disorder and neural alterations are often age-dependent, reflecting dysregulated developmental trajectories, although no studies have investigated whether homotopic interhemispheric rsFC alterations occur in ASD children. The present study conducted a voxel-based homotopic interhemispheric rsFC analysis in 146 SD and 175 typically developing children under age 10 and examined associations with symptom severity in the Autism Brain Imaging Data Exchange datasets. Given the role of corpus callosum (CC) in interhemispheric connectivity and reported CC volume changes in ASD we additionally examined whether there were parallel volumetric changes in ASD children. Results demonstrated decreased homotopic rsFC in ASD children in the medial prefrontal cortex, precuneus and posterior cingulate cortex of the default mode network (DMN), the dorsal anterior cingulate cortex of the salience network, the precentral gyrus and inferior parietal lobule of the mirror neuron system, the lingual, fusiform and inferior occipital gyri of the visual processing network and thalamus. Symptom severity was associated with homotopic rsFC in regions in the DMN and visual processing network. There were no significant CC volume changes in ASD children. The present study shows that reduced homotopic interhemispheric rsFC in brain networks in ASD adults/adolescents is already present in children of 5-10 years old and further supports their potential use as a general ASD biomarker.

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