Abstract
Mild cognitive impairment (MCI) is generally considered to be a key indicator for predicting the early progression of Alzheimer’s disease (AD). Currently, the brain connection (BC) estimated by fMRI data has been validated to be an effective diagnostic biomarker for MCI. Existing studies mainly focused on the single connection pattern for the neuro-disease diagnosis. Thus, such approaches are commonly insufficient to reveal the underlying changes between groups of MCI patients and normal controls (NCs), thereby limiting their performance. In this context, the information associated with multiple patterns (e.g., functional connectivity or effective connectivity) from single-mode data are considered for the MCI diagnosis. In this paper, we provide a novel multiple connection pattern combination (MCPC) approach to combine different patterns based on the kernel combination trick to identify MCI from NCs. In particular, sixty-three MCI cases and sixty-four NC cases from the ADNI dataset are conducted for the validation of the proposed MCPC method. The proposed method achieves 87.40% classification accuracy and significantly outperforms methods that use a single pattern.
Highlights
As the most concerning neurodegenerative disease, Alzheimer’s disease (AD) comes to be the most common causes of dementia (Gaugler et al, 2016)
We attempt to improve the performance of mild cognitive impairment (MCI) identification by single-mode data by generating multi-view information
We utilized the information associated with multiple brain connection patterns, which are derived from the functional magnetic resonance imaging (fMRI) data
Summary
As the most concerning neurodegenerative disease, Alzheimer’s disease (AD) comes to be the most common causes of dementia (Gaugler et al, 2016). AD can seriously interfere with patient’s daily lives, and eventually lead to deaths. A natural ambition is to delay the progression of AD during its early stages via pharmacological and behavioural interventions. Mild cognitive impairment (MCI) is often considered an early indicator of potential progression to AD (Wee et al, 2012). 10–15% of patients with MCI progress to AD per year (Misra et al, 2009). The accurate diagnosis of MCI has attracted considerable attention
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