Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learning

• URL: http://arxiv.org/abs/2109.09129v2
• Date: Tue, 21 Sep 2021 02:36:21 GMT
• Title: Identifying Autism Spectrum Disorder Based on Individual-Aware Down-Sampling and Multi-Modal Learning
• Authors: Li Pan, Jundong Liu, Mingqin Shi, Chi Wah Wong, Kei Hang Katie Chan
• Abstract summary: 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.
• Score: 4.310840361752551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autism Spectrum Disorder(ASD) is a set of neurodevelopmental conditions that affect patients' social abilities. In recent years, deep learning methods have been employed to detect ASD through functional MRI (fMRI). However, existing approaches solely concentrated on the abnormal brain functional connections but ignored the importance of regional activities. Due to this biased prior knowledge, previous diagnosis models suffered from inter-site heterogeneity and inter-individual phenotypical differences. To address this issue, we propose a novel feature extraction method for fMRI that can learn a personalized lowe-resolution representation of the entire brain networking regarding both the functional connections and regional activities. First, we abstract the brain imaging as a graph structure, where nodes represent brain areas and edges denote functional connections, and downsample it to a sparse network by hierarchical graph pooling. Subsequently, by assigning each subject with the extracted features and building edges through inter-individual non-imaging characteristics, we build a population graph. The non-identically distributed node features are further recalibrated to node embeddings learned by graph convolutional networks. By these means, our framework can extract features directly and efficiently from the entire fMRI and be aware of implicit inter-individual differences. We have evaluated our framework on the ABIDE-I dataset with 10-fold cross-validation. 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.

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)
• Classification of developmental and brain disorders via graph convolutional aggregation [6.6356049194991815]
  We introduce an aggregator normalization graph convolutional network by leveraging aggregation in graph sampling. The proposed model learns discriminative graph node representations by incorporating both imaging and non-imaging features into the graph nodes and edges. We benchmark our model against several recent baseline methods on two large datasets, Autism Brain Imaging Data Exchange (ABIDE) and Alzheimer's Disease Neuroimaging Initiative (ADNI)
  arXiv  Detail & Related papers  (2023-11-13T14:36:29Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification [31.668499876984487]
  We propose a specificity-aware graph learning framework for rs-fMRI analysis and automated brain disorder identification. At each client, our model consists of a shared and a personalized branch, where parameters of the shared branch are sent to the server while those of the personalized branch remain local. Experimental results on two fMRI datasets with a total of 1,218 subjects suggest SFGL outperforms several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-08-20T15:55:45Z)
• Exploiting the Brain's Network Structure for Automatic Identification of ADHD Subjects [70.37277191524755]
  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.
  arXiv  Detail & Related papers  (2023-06-15T16:22:57Z)
• 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)
• BrainFormer: A Hybrid CNN-Transformer Model for Brain fMRI Data Classification [31.83866719445596]
  BrainFormer is a general hybrid Transformer architecture for brain disease classification with single fMRI volume. BrainFormer is constructed by modeling the local cues within each voxel with 3D convolutions. We evaluate BrainFormer on five independently acquired datasets including ABIDE, ADNI, MPILMBB, ADHD-200 and ECHO.
  arXiv  Detail & Related papers  (2022-08-05T07:54:10Z)
• 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)
• Domain Invariant Model with Graph Convolutional Network for Mammogram Classification [49.691629817104925]
  We propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN) We first propose a Bayesian network, which explicitly decomposes the latent variables into disease-related and other disease-irrelevant parts that are provable to be disentangled from each other. To better capture the macroscopic features, we leverage the observed clinical attributes as a goal for reconstruction, via Graph Convolutional Network (GCN)
  arXiv  Detail & Related papers  (2022-04-21T08:23:44Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)
• Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks [0.1160208922584163]
  We train a convolutional neural network (CNN) with the largest multi-source, functional MRI (fMRI) connectomic dataset ever compiled. Our study finds that deep learning models that distinguish ASD from TD controls focus broadly on temporal and cerebellar connections.
  arXiv  Detail & Related papers  (2020-02-14T17:28:16Z)

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.