Multimodal Kernel-based discriminant correlation analysis data-fusion approach: an automated autism spectrum disorder diagnostic system.

Autism spectrum disorder (ASD) diagnostic systems, based on association of multimodal tools such as combination of Electroencephalogram (EEG) and eye-tracking, have emerged as an analytical to provide objective biomarkers. However, the existing feature-redundancy-based systems have lacked in providing knowledge of fusion approaches and robust feature-set. The present paper aims to reduce disorder homogeneity by proposing a multimodal diagnostic system which can incorporate multimodal data. The paper has collected simultaneous-data from three modalities (laptop-performance tool, EEG machine, and Eye-tracker) fused the recorded computational, neural and visual data. The multimodal features are analyzed via proposed multimodal Kernel-based discriminant correlation analysis (MKDCA) fusion approach and classified using state-of-the-art machine-learning classifiers. The proposed framework has considered the distinct cardinality of the feature vectors and fused the group structure among multiple samples after ranking them in increasing order. As per the results, the proposed multimodal system provided fused feature set of 11 influential features out of total 39 features. The SVM classifier has diagnosed ASD with 92% testing accuracy and 0.988 AUC(ROC). The proposed automated fusion-based system has the potential to classify disorder by reducing the disorder heterogeneity and stratifying ASD individuals into homogeneous sub-groups. In future, the correlation of reduced feature set with ASD clinical symptoms accounted by screening scales can provide clinical relevance of proposed model.

Brain Functional Network Topology in Autism Spectrum Disorder: A Novel Weighted Hierarchical Complexity Metric for Electroencephalogram.

Recent complex network analysis reflected the brain network as a modular network with small-world architecture in Autism Spectrum Disorder (ASD). Network hierarchy, which can provide important information to comment on brain networks, especially in ASD, has not yet been fully explored. The present work proposes a Weighted Hierarchical Complexity (WHC) metric to study network topology using the node degree concept. To do so, brain networks have been constructed using a visibility algorithm. To ensure proper mapping of network characteristics by the proposed metric, it is statistically compared to other network measures of brain connectivity related to integration, segregation and centrality. Further, for automated ASD classification, these network metrics were fed to explainable machine learning algorithms and the results revealed that brain regions tend to hierarchically coordinate in ASD, but the hierarchical architecture is attenuated after a few steps compared to networks in Typically Developing individuals (TDs). The value of WHC (0.55) reveals architecture up to three levels (four-degree nodes) with an abundance of 2-degree hubs in ASD indicating high intra-connectivity compared to TDs (WHC=0.78; four-level spread). The explainable Support Vector Machine (SVM)-classifier model highlighted the role of WHC in classifying ASD with 98.76% of accuracy. The graph-theory metrics ensured that weaker long-range connections and stronger intra-connections are markers of ASD. Thus, it becomes evident that whole-brain architecture can be characterised by a chain-like hierarchical modular structure representing atypical brain topology as in ASD.

Analysis of simultaneous visual and complex neural dynamics during cognitive learning to diagnose ASD.

The interactions between gaze processing and neural activities mediate cognition. The present paper aims to identify the involvement of visual and neural dynamics in shaping the cognitive behavior in Autism Spectrum Disorder (ASD). Electroencephalogram (EEG) and Eye-tracker signals of ASD and Typically Developing (TD) are recorded while performing two difficulty levels of a maze-based experimental task. During task, the performance metrics, complex neural measures extracted from EEG data using Visibility Graph (VG) algorithm and visual measures extracted from eye-tracker data are analyzed and compared. For both task levels, the cognition processing is examined via performance metrics (reaction-time and poor accuracy), gaze measures (saccade, fixation duration and blinkrate) and VG-based metrics (average weighted degree, clustering coefficient, path length, global efficiency, mutual information). An engagement in cognitive processing in ASD is revealed statistically by high reaction time, poor accuracy, increased fixation duration, raised saccadic amplitude, higher blink rate, reduced average weighted degree, global efficiency, mutual information as well as higher eigenvector centrality and path length. Over the course of repetitive trials, the cognitive improvement is although poor in ASD compared to TDs, the reconfigurations of visual and neural network dynamics revealed activation of Cognitive Learning (CL) in ASD. Furthermore, the correlation of gaze-EEG measures reveal that independent brain region functioning is not impaired but declined mutual interaction of brain regions causes cognitive deficit in ASD. And correlation of EEG-gaze measures with clinical severity measured by Autism Diagnostic Observation Schedule(ADOS) suggest that visual-neural activities reveals social behavior/cognition in ASD. Thus, visual and neural dynamics together support the revelation of the cognitive behavior in ASD.

Conditional entropy approach to analyze cognitive dynamics in autism spectrum disorder.

OBJECTIVE: Preliminary evidence has documented functional connectivity during the cognitive task in Autism Spectrum Disorder (ASD). However, evidence of effective neural connectivity with respect to information flow between different brain regions during complex tasks is missing. The present paper aims to provide insights into the cognition-based neural dynamics reflecting information exchange in brain network under cognitive load in ASD. METHODS: Twenty-two individuals with ASD (8-18 years) and 18 Typically Developing (TD; 6-17 years) individuals participated in the cognitive task of differentiating risky from neutral stimuli. The Conditional Entropy (CE) technique is applied upon task-activated Electroencephalogram (EEG) to measure the causal influence of the activity of brain's one Region of interest (ROI) over another. RESULTS: A higher CE in frontal ROI and left hemisphere reflected atypical brain complexity in ASD. The absence of causal effect, poor Coupling Strength (CS; measured using CE) and hemisphere lateralization is responsible for lower cognition in ASD. However, the persistent information exchange during the task reflects the existence of certain alternative paths when other direct paths remained disconnected due to cognitive impairment. The Support Vector Machine (SVM) classifier showed that CE can identify the atypical information exchange with an accuracy of 96.89% and area under curve = 0.987. DISCUSSION: The statistical results reflect a significant change in the information flow between different ROIs in ASD. A correlation of CS and behavioral domain suggests that the cognitive decline could be predicted from the connectivity patterns. Thus, CS could be a potential biomarker to identify cognitive status at a higher discrimination rate in ASD.

Multiplex temporal measures reflecting neural underpinnings of brain functional connectivity under cognitive load in Autism Spectrum Disorder.

Methods: Thirty individuals with ASD (11-18years) and thirty Typically Developing (TD) individuals (11-16years) were recruited to perform the mental task. The participants were instructed to flip the shown geometric images mentally and mark their response on a scale. The task-related multivariate EEG activations were analyzed using multiplex temporal Visibility Graphs (VGs) to compute local and global brain network functional connectivity dynamics.Results: With cognitive load (0-back to the 2-back task), the behavioral performance (d' index and Reaction Time) has reduced in ASD. The brain network has become more segregated and less integrated, reflecting more involvement of intra-regions over inter-regions in ASD. The frequent rerouting in hubs measured by Eigenvector Centrality (EC) indicated the progression of brain trajectory towards ASD. Overall, the neural mechanisms involving hyperactive response in frontal regions, frequent rewiring, and strengthened brain connectivity as a result of learning-induced performance reflected the adaptation to cognitive demands in ASD.Discussion: The correlation between complex graph measures and behavioral domain further reflected that neural metrics could predict the behavioral performance of the individuals in the task. In the future, modeling functional connectivity-based markers have the potential to reflect the brain trajectory alterations, which can detect ASD even before the behavioral manifestations become apparent.