Deep-learning-optimized microstate network analysis for early Parkinson's disease with mild cognitive impairment.

ABSTRACT: Graph-theory-based topological impairment of the whole-brain network has been verified to be one of the characteristics of mild cognitive impairment (MCI). However, two major challenges impede the further understanding of topological features for the personalized functional connectivity network of early Parkinson's disease (ePD) with MCI. The uncertain of characteristic frequency band reflecting the abnormality of ePD-MCI and the setting of fixed length of sliding window at a second level in the construction of conventional brain network both limit a deeper exploration of network characteristics for ePD-MCI. Thus, a convolutional neural network is constructed first and the gradient-weighted class activation mapping method is used to determine the characteristic frequency band of the ePD-MC

SUBMITTER: Zhang L 

PROVIDER: S-EPMC11564620 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature