A Network Theory Investigation into the Altered Resting State Functional Connectivity in Attention-Deficit Hyperactivity Disorder

• URL: http://arxiv.org/abs/2212.02402v1
• Date: Wed, 23 Nov 2022 00:35:16 GMT
• Title: A Network Theory Investigation into the Altered Resting State Functional Connectivity in Attention-Deficit Hyperactivity Disorder
• Authors: Sadi Md. Redwan, Md Palash Uddin, Muhammad Imran Sharif, and Anwaar Ulhaq
• Abstract summary: fMRI allows researchers to study healthy and pathological brains while they perform various neuropsychological functions. Recent neuroimaging research has seen an increase in modeling and analyzing brain activity in terms of a graph or network. The purpose of this study is to look into the abnormalities in resting brain functions in adults with Attention Deficit Hyperactivity Disorder (ADHD)
• Score: 1.3416169841532526
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the last two decades, functional magnetic resonance imaging (fMRI) has emerged as one of the most effective technologies in clinical research of the human brain. fMRI allows researchers to study healthy and pathological brains while they perform various neuropsychological functions. Beyond task-related activations, the human brain has some intrinsic activity at a task-negative (resting) state that surprisingly consumes a lot of energy to support communication among neurons. Recent neuroimaging research has also seen an increase in modeling and analyzing brain activity in terms of a graph or network. Since graph models facilitate a systems-theoretic explanation of the brain, they have become increasingly relevant with advances in network science and the popularization of complex systems theory. The purpose of this study is to look into the abnormalities in resting brain functions in adults with Attention Deficit Hyperactivity Disorder (ADHD). The primary goal is to investigate resting-state functional connectivity (FC), which can be construed as a significant temporal coincidence in blood-oxygen-level dependent (BOLD) signals between functionally related brain regions in the absence of any stimulus or task. When compared to healthy controls, ADHD patients have lower average connectivity in the Supramarginal Gyrus and Superior Parietal Lobule, but higher connectivity in the Lateral Occipital Cortex and Inferior Temporal Gyrus. We also hypothesize that the network organization of default mode and dorsal attention regions is abnormal in ADHD patients.

Related papers

• Brain-like Functional Organization within Large Language Models [58.93629121400745]
  The human brain has long inspired the pursuit of artificial intelligence (AI) Recent neuroimaging studies provide compelling evidence of alignment between the computational representation of artificial neural networks (ANNs) and the neural responses of the human brain to stimuli. In this study, we bridge this gap by directly coupling sub-groups of artificial neurons with functional brain networks (FBNs) This framework links the AN sub-groups to FBNs, enabling the delineation of brain-like functional organization within large language models (LLMs)
  arXiv  Detail & Related papers  (2024-10-25T13:15:17Z)
• HyperBrain: Anomaly Detection for Temporal Hypergraph Brain Networks [19.484750476697833]
  HyperBrain is an unsupervised anomaly detection framework for temporal hypergraph brain networks. It uses a novel customized temporal walk (BrainWalk) and neural encodings to detect abnormal co-activations among brain regions. Results obtained from HyperBrain are consistent with clinical research on these brain disorders.
  arXiv  Detail & Related papers  (2024-10-02T23:20:13Z)
• Large Language Model-based FMRI Encoding of Language Functions for Subjects with Neurocognitive Disorder [53.575426835313536]
  This paper explores language-related functional changes in older NCD adults using LLM-based fMRI encoding and brain scores. We analyze the correlation between brain scores and cognitive scores at both whole-brain and language-related ROI levels. Our findings reveal that higher cognitive abilities correspond to better brain scores, with correlations peaking in the middle temporal gyrus.
  arXiv  Detail & Related papers  (2024-07-15T01:09:08Z)
• Graph Neural Networks for Brain Graph Learning: A Survey [53.74244221027981]
  Graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs.
  arXiv  Detail & Related papers  (2024-06-01T02:47:39Z)
• BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations [67.79256149583108]
  We propose a novel model called BrainODE to achieve continuous modeling of dynamic brain signals. By learning latent initial values and neural ODE functions from irregular time series, BrainODE effectively reconstructs brain signals at any time point.
  arXiv  Detail & Related papers  (2024-04-30T10:53:30Z)
• Extracting the Multiscale Causal Backbone of Brain Dynamics [9.905883167156393]
  We propose the multiscale causal backbone (MCB) of brain dynamics. Our approach leverages recent advances in multiscale causal structure learning. Thanks to its multiscale nature, causal dynamics are driven by brain regions associated with high-level cognitive functions.
  arXiv  Detail & Related papers  (2023-10-31T19:47:11Z)
• Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks [53.257804915263165]
  Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. Key evidence from neuroimaging data for pathological commonness remains unrevealed. We build a deep learning model, using multi-site functional magnetic resonance imaging data, for classifying 5 different brain disorders from healthy controls.
  arXiv  Detail & Related papers  (2023-02-23T09:22:05Z)
• Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity [48.75665245214903]
  We propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling.
  arXiv  Detail & Related papers  (2022-09-22T04:17:57Z)
• Classification of ADHD Patients by Kernel Hierarchical Extreme Learning Machine [4.168157981135698]
  We consider the dynamics of brain functional connectivity, modeling a functional brain dynamics model from medical imaging. In this paper, we consider comparisons of fMRI imaging data on 23 ADHD and 45 NC children. Our experimental methods achieved better classification results than existing methods.
  arXiv  Detail & Related papers  (2022-02-18T01:32:55Z)
• Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis [11.85489505372321]
  We train a-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. St-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals.
  arXiv  Detail & Related papers  (2020-03-24T01:56:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.