Multigroup recognition of dementia patients with dynamic brain connectivity under multimodal cortex parcellation

Wang, Bo-Cheng; Li, Lei; Peng, Long; Jiang, Zhuolin; Dai, Kexuan; Xie, Qingguo; Cao, Yue; Yu, Dingguo

doi:10.1016/j.bspc.2022.103725

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…Lin et al ( 2022 ) constructed dFCNs based on the sliding window strategy from resting state fMRI (rs-fMRI) data and extracted advanced features of dFCNs to classify brain disease by the proposed convolutional recurrent neural network. Wang B. et al ( 2022 ) constructed dFCNs based on human connectivity project multimodal partitioning. Different from static FCN, dFCN can reveal more useful information for distinguishing between patients with brain diseases and healthy subjects.…”

Section: Features Extracted From Fmri Datamentioning

confidence: 99%

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

Teng,

Mi,

Shi

et al. 2023

Front. Neurosci.

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Brain diseases, including neurodegenerative diseases and neuropsychiatric diseases, have long plagued the lives of the affected populations and caused a huge burden on public health. Functional magnetic resonance imaging (fMRI) is an excellent neuroimaging technology for measuring brain activity, which provides new insight for clinicians to help diagnose brain diseases. In recent years, machine learning methods have displayed superior performance in diagnosing brain diseases compared to conventional methods, attracting great attention from researchers. This paper reviews the representative research of machine learning methods in brain disease diagnosis based on fMRI data in the recent three years, focusing on the most frequent four active brain disease studies, including Alzheimer's disease/mild cognitive impairment, autism spectrum disorders, schizophrenia, and Parkinson's disease. We summarize these 55 articles from multiple perspectives, including the effect of the size of subjects, extracted features, feature selection methods, classification models, validation methods, and corresponding accuracies. Finally, we analyze these articles and introduce future research directions to provide neuroimaging scientists and researchers in the interdisciplinary fields of computing and medicine with new ideas for AI-aided brain disease diagnosis.

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Section: Features Extracted From Fmri Datamentioning

confidence: 99%

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

Teng,

Mi,

Shi

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Our investigation included several graph theory measures as supplementary features for disease classification. These measures, namely graph strength, clustering coefficient, local efficiency, page rank centrality, betweenness centrality, eigenvector, flow coefficient, and k-coreness centrality, were calculated based on binary or weighted graphs after implementing a sparsity threshold (Wang B. et al, 2022). The ideal sparse brain graphs were constructed by optimizing the global brain efficiency, and the graph theory features extracted from the corresponding taskspecific brain nodes.…”

Section: Graph Theory Measures the Connectomementioning

confidence: 99%

TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network

Lang,

Yang,

2023

Front. Neurosci.

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Neuropsychiatric disorder (ND) is often accompanied by abnormal functional connectivity (FC) patterns in specific task contexts. The distinctive task-specific FC patterns can provide valuable features for ND classification models using deep learning. However, most previous studies rely solely on the whole-brain FC matrix without considering the prior knowledge of task-specific FC patterns. Insight by the decoding studies on brain-behavior relationship, we develop TSP-GNN, which extracts task-specific prior (TSP) connectome patterns and employs graph neural network (GNN) for disease classification. TSP-GNN was validated using publicly available datasets. Our results demonstrate that different ND types show distinct task-specific connectivity patterns. Compared with the whole-brain node characteristics, utilizing task-specific nodes enhances the accuracy of ND classification. TSP-GNN comprises the first attempt to incorporate prior task-specific connectome patterns and the power of deep learning. This study elucidates the association between brain dysfunction and specific cognitive processes, offering valuable insights into the cognitive mechanism of neuropsychiatric disease.

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Enhanced brain efficiency network by integrating the new causality with fMRI and its application for Alzheimer’s Disease study

Wang

2023

Biomedical Signal Processing and Control

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Multigroup recognition of dementia patients with dynamic brain connectivity under multimodal cortex parcellation

Cited by 4 publications

References 43 publications

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

Brain disease research based on functional magnetic resonance imaging data and machine learning: a review

TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network

Enhanced brain efficiency network by integrating the new causality with fMRI and its application for Alzheimer’s Disease study

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