Exploiting the Brain's Network Structure for Automatic Identification of
ADHD Subjects
- URL: http://arxiv.org/abs/2306.09239v1
- Date: Thu, 15 Jun 2023 16:22:57 GMT
- Title: Exploiting the Brain's Network Structure for Automatic Identification of
ADHD Subjects
- Authors: Soumyabrata Dey, Ravishankar Rao, Mubarak Shah
- Abstract summary: We show that the brain can be modeled as a functional network, and certain properties of the networks differ in ADHD subjects from control subjects.
We train our classifier with 776 subjects and test on 171 subjects provided by The Neuro Bureau for the ADHD-200 challenge.
- Score: 70.37277191524755
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Attention Deficit Hyperactive Disorder (ADHD) is a common behavioral problem
affecting children. In this work, we investigate the automatic classification
of ADHD subjects using the resting state Functional Magnetic Resonance Imaging
(fMRI) sequences of the brain. We show that the brain can be modeled as a
functional network, and certain properties of the networks differ in ADHD
subjects from control subjects. We compute the pairwise correlation of brain
voxels' activity over the time frame of the experimental protocol which helps
to model the function of a brain as a network. Different network features are
computed for each of the voxels constructing the network. The concatenation of
the network features of all the voxels in a brain serves as the feature vector.
Feature vectors from a set of subjects are then used to train a PCA-LDA
(principal component analysis-linear discriminant analysis) based classifier.
We hypothesized that ADHD-related differences lie in some specific regions of
the brain and using features only from those regions is sufficient to
discriminate ADHD and control subjects. We propose a method to create a brain
mask that includes the useful regions only and demonstrate that using the
feature from the masked regions improves classification accuracy on the test
data set. We train our classifier with 776 subjects and test on 171 subjects
provided by The Neuro Bureau for the ADHD-200 challenge. We demonstrate the
utility of graph-motif features, specifically the maps that represent the
frequency of participation of voxels in network cycles of length 3. The best
classification performance (69.59%) is achieved using 3-cycle map features with
masking. Our proposed approach holds promise in being able to diagnose and
understand the disorder.
Related papers
- Multi-Head Graph Convolutional Network for Structural Connectome
Classification [8.658134276685404]
We propose a machine-learning model inspired by graph convolutional networks (GCNs)
The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes.
To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification.
arXiv Detail & Related papers (2023-05-02T15:04:30Z) - 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) - Investigating Brain Connectivity with Graph Neural Networks and
GNNExplainer [0.0]
We have made a step toward an in-depth examination of functional connectivity during a dichotic listening task via deep learning.
We propose a graph neural network-based framework within which we represent EEG data as signals in the graph domain.
arXiv Detail & Related papers (2022-06-04T07:47:13Z) - Functional2Structural: Cross-Modality Brain Networks Representation
Learning [55.24969686433101]
Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases.
We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder.
We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
arXiv Detail & Related papers (2022-05-06T03:45:36Z) - 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) - MHATC: Autism Spectrum Disorder identification utilizing multi-head
attention encoder along with temporal consolidation modules [11.344829880346353]
Resting-state fMRI is commonly used for diagnosing Autism Spectrum Disorder (ASD) by using network-based functional connectivity.
We propose a novel deep learning architecture (MHATC) consisting of multi-head attention and temporal consolidation modules for classifying an individual as a patient of ASD.
arXiv Detail & Related papers (2021-12-27T07:50:16Z) - Overcoming the Domain Gap in Neural Action Representations [60.47807856873544]
3D pose data can now be reliably extracted from multi-view video sequences without manual intervention.
We propose to use it to guide the encoding of neural action representations together with a set of neural and behavioral augmentations.
To reduce the domain gap, during training, we swap neural and behavioral data across animals that seem to be performing similar actions.
arXiv Detail & Related papers (2021-12-02T12:45:46Z) - Leveraging Human Selective Attention for Medical Image Analysis with
Limited Training Data [72.1187887376849]
The selective attention mechanism helps the cognition system focus on task-relevant visual clues by ignoring the presence of distractors.
We propose a framework to leverage gaze for medical image analysis tasks with small training data.
Our method is demonstrated to achieve superior performance on both 3D tumor segmentation and 2D chest X-ray classification tasks.
arXiv Detail & Related papers (2021-12-02T07:55:25Z) - Identifying Autism Spectrum Disorder Based on Individual-Aware
Down-Sampling and Multi-Modal Learning [4.310840361752551]
We propose a novel feature extraction method for fMRI that can learn a personalized lowe-resolution representation of the entire brain networking.
The present model has achieved a mean classification accuracy of 85.95% and a mean AUC of 0.92, which is better than the state-of-the-art methods.
arXiv Detail & Related papers (2021-09-19T14:22:55Z) - Emotional EEG Classification using Connectivity Features and
Convolutional Neural Networks [81.74442855155843]
We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification.
The level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.
arXiv Detail & Related papers (2021-01-18T13:28:08Z)
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.