Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease

Abstract

Functional connectivity networks (FCNs) using resting-state functional magnetic resonance imaging (rs-fMRI) have been applied to the analysis and diagnosis of brain disease, such as Alzheimer's disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI). Different from conventional studies focusing on static descriptions on functional connectivity (FC) between brain regions in rs-fMRI, recent studies have resorted to dynamic connectivity networks (DCNs) to characterize the dynamic changes of FC, since dynamic changes of FC may indicate changes in macroscopic neural activity patterns in cognitive and behavioral aspects. However, most of the existing studies only investigate the temporal properties of DCNs (e.g., temporal variability of FC between specific brain regions), ignoring the important spatial properties of the network (e.g., spatial variability of FC associated with a specific brain region). Also, emerging evidence on FCNs has suggested that, besides temporal variability, there is significant spatial variability of activity foci over time. Hence, integrating both temporal and spatial properties of DCNs can intuitively promote the performance of connectivity-network-based learning methods. In this paper, we first define a new measure to characterize the spatial variability of DCNs, and then propose a novel learning framework to integrate both temporal and spatial variabilities of DCNs for automatic brain disease diagnosis. Specifically, we first construct DCNs from the rs-fMRI time series at successive non-overlapping time windows. Then, we characterize the spatial variability of a specific brain region by computing the correlation of functional sequences (i.e., the changing profile of FC between a pair of brain regions within all time windows) associated with this region. Furthermore, we extract both temporal variabilities and spatial variabilities from DCNs as features, and integrate them for classification by using manifold regularized multi-task feature learning and multi-kernel learning techniques. Results on 149 subjects with baseline rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) suggest that our method can not only improve the classification performance in comparison with state-of-the-art methods, but also provide insights into the spatio-temporal interaction patterns of brain activity and their changes in brain disorders.

Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach

“…Previous studies on resting-state functional connectivity were mainly based on the temporal correlation between regional blood oxygen level-dependent (BOLD) time courses, barring an implicit assumption that functional connectivity is temporal stationary (Sporns, 2011;Jie et al, 2018). As a matter of fact, a number of researches have revealed that functional connectivity may experience a dynamic change over time (Calhoun et al, 2014), which, to a certain extent, might be attributed to the neuronal origin and related to the cognitive and vigilance state variations (Chang et al, 2013;Thompson et al, 2013;Jie et al, 2018). By measuring timevarying functional connectivity among brain regions, dynamic functional connectivity (DFC) analysis furnishes a more detailed description of interactions in the brain.…”

Extensive functional repertoire underpins complex behaviours: insights from Parkinson’s disease

“…Hence, the behavioural outcomes that rely on such efficient reconfigurations may be impaired, which may result in specific symptoms. Notably, accounting for temporal variability of brain activity has improved the diagnostic classification of neurodegenerative diseases, suggesting that additional information can indeed be disclosed with such approaches (22,23).…”

Decoding visual network‐related dynamic functional connectivity for eyes‐open and eyes‐closed using machine learning

“…Notably, the dynamic FC were more sensitive to brain disorders than static FC. 20,21 Dynamic FC were time-resolved bivariate measures. 22 Time-varying features could be obtained from dynamic FC.…”