Abstract
Functional brain network (FBN) provides an effective biomarker for understanding brain activation patterns and a diagnostic criterion for neurodegenerative diseases detections. Unfortunately, it remains challenges to estimate the biologically meaningful or discriminative FBNs accurately, because of the poor quality of functional magnetic resonance imaging data or our limited understanding of human brain. In this study, a novel FBN estimation model based on group similarity prior was proposed. In particular, we extended the FBN estimation model to tensor form and incorporated the tensor trace-norm regularizer to formulate the group similarity constraint. To verify the proposed method, we conducted experiments on identifying mild cognitive impairments (MCIs) from normal controls (NCs) based on the estimated FBNs. Experimental results illustrated that our method is effective in modeling FBNs. Consequently, we achieved 91.97% classification accuracy, outperforming the state-of-the-art methods. The post hoc analysis further demonstrated that more biologically meaningful functional brain connections were obtained using our proposed method.
Highlights
As a neurodegenerative disorder, Alzheimer’s disease (AD) is one of the most common causes of dementia (Wee et al, 2012)
137 participants including 68 mild cognitive impairments (MCIs) and 69 normal controls (NCs) were adopted in this experiment, which was similar to a previous study (Zhou et al, 2018)
By projecting brain regions with significant brain network functional connectivity differences and graph theory metrics to subnetworks, we found that the differences between MCI patients and NCs were distributed mainly in the Default mode network (DMN), dorsal
Summary
Alzheimer’s disease (AD) is one of the most common causes of dementia (Wee et al, 2012). According to a recent report (Bain et al, 2008), the incidence of AD doublets every 5 years after age 60. AD seriously interferes with patients’ daily life, affects their memory and ability to communicate, and eventually causes their deaths. There is no effective treatment for AD far. Mild cognitive impairment (MCI) is often considered as a critical time window and treatment period for the prediction or delaying the conversion in AD (Wee et al, 2012). In some recent statistical studies, nearly 10–15% patients with MCI develop probable AD each year
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