Abstract
Most previous imaging studies have used traditional Pearson correlation analysis to construct brain networks. This approach fails to adequately and completely account for the interaction between adjacent brain regions. In this study, we used the L1-norm linear regression model to test the small-world attributes of the brain networks of three groups of patients, namely, those with mild cognitive impairment (MCI), Alzheimer's disease (AD), and healthy controls (HCs); we attempted to identify the method that may detect minor differences in MCI and AD patients. Twenty-four AD patients, 33 MCI patients, and 27 HC elderly subjects were subjected to functional MRI (fMRI). We applied traditional Pearson correlation and the L1-norm to construct the brain networks and then tested the small-world attributes by calculating the following parameters: clustering coefficient (Cp), path length (Lp), global efficiency (Eg), and local efficiency (Eloc). As expected, L1 could detect slight changes, mainly in MCI patients expressing higher Cp and Eloc; however, no statistical differences were found between MCI patients and HCs in terms of Cp, Lp, Eg, and Eloc, using Pearson correlation. Compared with HCs, AD patients expressed a lower Cp, Eloc, and Lp and an increased Eg using both connectivity metrics. The statistical differences between the groups indicated the brain networks constructed by the L1-norm were more sensitive to detect slight small-world network changes in early stages of AD.
Highlights
The human brain network has been proven to possess smallworld properties that confer several advantages [1, 2] including high local and global efficiency (Eloc and Eg, respectively) in information communication [3], optimal synchronization of neural activity among different brain regions via central hubs, and most importantly, protection of the brain from random failure through redundant densely neighbored connections and from targeted attacks under disease conditions, due to high resilience conferred by high centrality and clustering [4]
We focused on studying the differences in parameters of small-world networks constructed by both Pearson correlation and L1-norm regression; the differences were evaluated between the normal subjects and patients with Alzheimer’s disease (AD) and Mild cognitive impairment (MCI), with the purpose of providing some clues to the understanding of these conditions
We used the L1-norm regularization and Pearson correlation to construct brain functional networks, and our results revealed that the small-world properties of the networks in MCI and AD patients were disrupted compared to healthy controls (HCs)
Summary
The human brain network has been proven to possess smallworld properties that confer several advantages [1, 2] including high local and global efficiency (Eloc and Eg, respectively) in information communication [3], optimal synchronization of neural activity among different brain regions via central hubs, and most importantly, protection of the brain from random failure through redundant densely neighbored connections and from targeted attacks under disease conditions, due to high resilience conferred by high centrality and clustering [4]. Characterizing the underlying architecture of brain networks may contribute comprehensive insights into the pathogenesis of network dysfunctional mechanisms in AD [12]
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