Age- and Severity-Specific Deep Learning Models for Autism Spectrum Disorder Classification Using Functional Connectivity Measures

Jain, Vaibhav; Rakshe, Chetan Tanaji; Sengar, Sandeep Singh; Murugappan, M.; Ronickom, Jac Fredo Agastinose

doi:10.1007/s13369-023-08560-8

Cited by 3 publications

(1 citation statement)

References 67 publications

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“…Tong et al combined contrastive learning and sparse canonical correlation analysis to identify the dimensions of resting-state electroencephalographic (EEG) connectivity in ASD, which were significantly correlated with social and communication deficits (SCDs) and restricted and repetitive behaviors (RRBs) [14]. Jain et al explored the impact of brain FC patterns on the diagnosis of ASD using deep neural networks (DNNs) and developed proper diagnostic models to address ASD heterogeneity [15]. Although FC is based on the assumption that neural signal fluctuations have a time-invariant nature, the spontaneous fluctuations in brain activity and the dynamics of brain networks should be noted [16,17].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Qian,

Yang,

Cai

et al. 2024

Brain Sciences

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Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain’s intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.

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Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Qian,

Yang,

Cai

et al. 2024

Brain Sciences

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Functional Connectivity and Graph Embedding-Based Domain Adaptation for Autism Classification from Multi-site Data

Singh,

Shukla,

Gore

2024

Arab J Sci Eng

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Exploring Autism Spectrum Disorder: A Comparative Study of Traditional Classifiers and Deep Learning Classifiers to Analyze Functional Connectivity Measures from a Multicenter Dataset

Mainas,

Golosio,

Retico

et al. 2024

Applied Sciences

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The investigation of functional magnetic resonance imaging (fMRI) data with traditional machine learning (ML) and deep learning (DL) classifiers has been widely used to study autism spectrum disorders (ASDs). This condition is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for the development of effective treatments. This study compares traditional ML and DL classifiers in the analysis of tabular data, in particular, functional connectivity measures obtained from the time series of a public multicenter dataset, and evaluates whether the features that contribute most to the classification task vary depending on the classifier used. Specifically, Support Vector Machine (SVM) classifiers, with both linear and radial basis function (RBF) kernels, and Extreme Gradient Boosting (XGBoost) classifiers are compared against the TabNet classifier (a DL architecture customized for tabular data analysis) and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. Among the latter, SVMs outperform the other classifiers with an AUC of around 75%, whereas the best performances of TabNet and MLP reach 65% and 71% at most, respectively. Furthermore, the analysis of the feature importance showed that the brain regions that contribute the most to the classification task are those primarily responsible for sensory and spatial perception, as well as attention modulation, which is known to be altered in ASDs.

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Age- and Severity-Specific Deep Learning Models for Autism Spectrum Disorder Classification Using Functional Connectivity Measures

Cited by 3 publications

References 67 publications

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study

Functional Connectivity and Graph Embedding-Based Domain Adaptation for Autism Classification from Multi-site Data

Exploring Autism Spectrum Disorder: A Comparative Study of Traditional Classifiers and Deep Learning Classifiers to Analyze Functional Connectivity Measures from a Multicenter Dataset

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