Multigroup recognition of dementia patients with dynamic brain connectivity under multimodal cortex parcellation. (July 2022)

• Record Type: Journal Article
• Authors: Wang, Bocheng; Li, Lei; Peng, Long; Jiang, Zhuolin; Dai, Kexuan; Xie, Qi; Cao, Yue; Yu, Dingguo
• Is Part Of: Biomedical signal processing and control. Volume 76(2022)
• Publication Date: 2022-07
• Subjects: HCP MMP -- Dynamic Connectivity -- Alzheimer's Disease -- Resting-State Network -- Deep Learning -- Graph-based Analysis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103725
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A DBCP method was proposed to explore the spatiotemporal characteristics of brain. Enhanced classification capability in dynamic connectivity with HCP MMP. DMN and DAN showed predominant responsibility for different stages of dementia. Abstract: Objective: To accurately predict Alzheimer's disease (AD) in its early stage of cognitive impairment is crucial to clinical diagnosis and intervention. However, there is no consensus over which parts of brain areas are responsible for cognitive decline due to the incompatible and single-modal-based parcellation methods employed by researchers. Methods: A novel dynamic brain connectivity processing method (DBCP) is proposed based on the human connectome project multimodal parcellation (HCP MMP) to explore the spatial–temporal characteristics of the brain in different stages of mild cognitive impairment (MCI) and Alzheimer's disease. First, dynamic connectivity under HCP MMP is constructed to divide the whole fMRI time series into hundreds of segmentations. Then, graph-based topological measures are calculated, followed by statistical outlier examinations implemented by the K-means method. Results: A superior performance (accuracy = 86%, recall = 87%, precision = 86%, F1-score = 86%) in the four groups (healthy control vs. early MCI vs. late MCI vs. AD) recognition is achieved by training an effective but uncomplicated deep learning model. Conclusion: Dynamic connectivity within the most fine-grained multimodal human cortex parcellation can reveal more useful details to distinguish brain dysfunctional patients compared with static connectivity or single modal based parcellation, and the proposed method can suppress the outliers well among fragmented fMRI signals. Significance: Providing more evidence on the primary responsibility of DMN and DAN for cognitive impairment of the brain, 64 cortex regions with significant topological alterations are suggested as the most prominent and fine-grained biomarker for further longitudinal AD studies.