Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes for Detection of Autism Spectrum Disorder

• URL: http://arxiv.org/abs/2211.07360v1
• Date: Sun, 30 Oct 2022 01:43:17 GMT
• Title: Spatio-Temporal Attention in Multi-Granular Brain Chronnectomes for Detection of Autism Spectrum Disorder
• Authors: James Orme-Rogers and Ajitesh Srivastava
• Abstract summary: Graph-based learning techniques have demonstrated impressive results on resting-state functional magnetic resonance imaging (rs-fMRI) data. IMAGIN achieves a 5-fold cross-validation accuracy of 79.25%, which surpasses the current state-of-the-art by 1.5%.
• Score: 5.908259551646475
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The traditional methods for detecting autism spectrum disorder (ASD) are expensive, subjective, and time-consuming, often taking years for a diagnosis, with many children growing well into adolescence and even adulthood before finally confirming the disorder. Recently, graph-based learning techniques have demonstrated impressive results on resting-state functional magnetic resonance imaging (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE). We introduce IMAGIN, a multI-granular, Multi-Atlas spatio-temporal attention Graph Isomorphism Network, which we use to learn graph representations of dynamic functional brain connectivity (chronnectome), as opposed to static connectivity (connectome). The experimental results demonstrate that IMAGIN achieves a 5-fold cross-validation accuracy of 79.25%, which surpasses the current state-of-the-art by 1.5%. In addition, analysis of the spatial and temporal attention scores provides further validation for the neural basis of autism.

Related papers

• Diagnosis and Pathogenic Analysis of Autism Spectrum Disorder Using Fused Brain Connection Graph [14.00990852115585]
  We propose a model for diagnosing Autism spectrum disorder (ASD) using multimodal magnetic resonance imaging (MRI) data. Our approach integrates brain connectivity data fromDTI and functional MRI, employing graph neural networks (GNNs) for fused graph classification. We analyze network node centrality, calculating degree, subgraph, and eigenvector centralities on a bimodal fused brain graph to identify pathological regions linked to ASD.
  arXiv  Detail & Related papers  (2024-09-22T01:23:46Z)
• Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor [15.39635888144281]
  We resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance.
  arXiv  Detail & Related papers  (2023-07-12T22:08:22Z)
• 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)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• 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)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• Early Autism Spectrum Disorders Diagnosis Using Eye-Tracking Technology [62.997667081978825]
  Lack of money, absence of qualified specialists, and low level of trust to the correction methods are the main issues that affect the in-time diagnoses of ASD. Our team developed the algorithm that will be able to predict the chances of ASD according to the information from the gaze activity of the child.
  arXiv  Detail & Related papers  (2020-08-21T20:22:55Z)
• A Convolutional Neural Network for gaze preference detection: A potential tool for diagnostics of autism spectrum disorder in children [0.0]
  We propose a convolutional neural network (CNN) algorithm for gaze prediction using images extracted from a one-minute stimulus video. Our model achieved a high accuracy rate and robustness for prediction of gaze direction with independent persons.
  arXiv  Detail & Related papers  (2020-07-28T18:47:21Z)
• 4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification [69.62333053044712]
  We propose a 4D convolutional deep learning approach for ASD classification where we jointly learn from spatial and temporal data. We employ 4D neural networks and convolutional-recurrent models which outperform a previous approach with an F1-score of 0.71 compared to an F1-score of 0.65.
  arXiv  Detail & Related papers  (2020-04-21T17:19:06Z)

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.