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.
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http://dx.doi.org/10.1007/s13246-021-01045-8 | DOI Listing |
Elife
January 2025
Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Cognitive flexibility requires both the encoding of task-relevant and the ignoring of task-irrelevant stimuli. While the neural coding of task-relevant stimuli is increasingly well understood, the mechanisms for ignoring task-irrelevant stimuli remain poorly understood. Here, we study how task performance and biological constraints jointly determine the coding of relevant and irrelevant stimuli in neural circuits.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Department of Electronics Engineering, Pusan National University, Busan, South Korea.
The amount of information contained in speech signals is a fundamental concern of speech-based technologies and is particularly relevant in speech perception. Measuring the mutual information of actual speech signals is non-trivial, and quantitative measurements have not been extensively conducted to date. Recent advancements in machine learning have made it possible to directly measure mutual information using data.
View Article and Find Full Text PDFJ Neurophysiol
February 2025
Centre for Sensorimotor Performance, School of Human Movement and Nutrition SciencesThe University of QueenslandBrisbaneQueenslandAustralia.
Purposeful movement often requires selection of a particular action from a range of alternatives, but how does the brain represent potential actions so that they can be compared for selection, and how are motor commands generated if movement is initiated before the final goal is identified? According to one hypothesis, the brain averages partially prepared motor plans to generate movement when there is goal uncertainty. This is consistent with the idea that motor decision-making unfolds through competition between internal representations of alternative actions. An alternative hypothesis holds that only one movement, which is optimized for task performance, is prepared for execution at any time.
View Article and Find Full Text PDFHum Brain Mapp
February 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, and Emory, Atlanta, Georgia, USA.
Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags.
View Article and Find Full Text PDFHum Brain Mapp
February 2025
Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
In contrast to blood-oxygenation level-dependent (BOLD) functional MRI (fMRI), which relies on changes in blood flow and oxygenation levels to infer brain activity, diffusion fMRI (DfMRI) investigates brain dynamics by monitoring alterations in the apparent diffusion coefficient (ADC) of water. These ADC changes may arise from fluctuations in neuronal morphology, providing a distinctive perspective on neural activity. The potential of ADC as an fMRI contrast (ADC-fMRI) lies in its capacity to reveal neural activity independently of neurovascular coupling, thus yielding complementary insights into brain function.
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