Selective impairment of hippocampus and posterior hub areas in Alzheimer’s disease: an MEG-based multiplex network study

Abstract

Although frequency-specific network analyses have shown that functional brain networks are altered in patients with Alzheimer's disease, the relationships between these frequency-specific network alterations remain largely unknown. Multiplex network analysis is a novel network approach to study complex systems consisting of subsystems with different types of connectivity patterns. In this study, we used magnetoencephalography to integrate five frequency-band specific brain networks in a multiplex framework. Previous structural and functional brain network studies have consistently shown that hub brain areas are selectively disrupted in Alzheimer's disease. Accordingly, we hypothesized that hub regions in the multiplex brain networks are selectively targeted in patients with Alzheimer's disease in comparison to healthy control subjects. Eyes-closed resting-state magnetoencephalography recordings from 27 patients with Alzheimer's disease (60.6 ± 5.4 years, 12 females) and 26 controls (61.8 ± 5.5 years, 14 females) were projected onto atlas-based regions of interest using beamforming. Subsequently, source-space time series for both 78 cortical and 12 subcortical regions were reconstructed in five frequency bands (delta, theta, alpha 1, alpha 2 and beta band). Multiplex brain networks were constructed by integrating frequency-specific magnetoencephalography networks. Functional connections between all pairs of regions of interests were quantified using a phase-based coupling metric, the phase lag index. Several multiplex hub and heterogeneity metrics were computed to capture both overall importance of each brain area and heterogeneity of the connectivity patterns across frequency-specific layers. Different nodal centrality metrics showed consistently that several hub regions, particularly left hippocampus, posterior parts of the default mode network and occipital regions, were vulnerable in patients with Alzheimer's disease compared to control subjects. Of note, these detected vulnerable hubs in Alzheimer's disease were absent in each individual frequency-specific network, thus showing the value of integrating the networks. The connectivity patterns of these vulnerable hub regions in the patients were heterogeneously distributed across layers. Perturbed cognitive function and abnormal cerebrospinal fluid amyloid-β42 levels correlated positively with the vulnerability of the hub regions in patients with Alzheimer's disease. Our analysis therefore demonstrates that the magnetoencephalography-based multiplex brain networks contain important information that cannot be revealed by frequency-specific brain networks. Furthermore, this indicates that functional networks obtained in different frequency bands do not act as independent entities. Overall, our multiplex network study provides an effective framework to integrate the frequency-specific networks with different frequency patterns and reveal neuropathological mechanism of hub disruption in Alzheimer's disease.

Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data

“…Finally, in this project we focused on the ability of ComBat harmonization to preserve age-related associations with several network connectivity and efficiency measures. However, previous studies (Bullmore and Sporns, 2012; Stam, 2014; Fornito et al, 2015; Yu et al, 2016, 2017) have shown that functional brain network organizations are highly correlated with other demographic (e.g. gender, educational level), clinical phenotypes (e.g.…”

“…Finally, in this project we focused on the ability of ComBat harmonization to preserve age-related associations with several network connectivity and efficiency measures. However, previous studies (Bullmore & Sporns, 2012;Fornito, Zalesky, & Breakspear, 2015;Stam, 2014;Yu et al, 2016Yu et al, , 2017 have shown that functional brain network organizations are highly correlated with other demographic (e.g., gender, educational level), clinical phenotypes (e.g., disease severity for neurological disorders), and pathological biomarkers (e.g., amyloid-b 42 and tau proteins in Alzheimer's disease). In particular, the EMBARC functional dataset was originally designed to study the potential differences on fMRI measurements between MDD patients and healthy controls (Greenberg et al, 2015;Trivedi et al, 2016;Webb et al, 2016).…”

Neural oscillations and connectivity characterizing the state of tonic experimental pain in humans

“…We, however, found an increased dwPLI-based global clustering coefficient in the alpha frequency band, showing an increased functional segregation during tonic pain. Such significant changes of electrophysiological global network measures have been observed in different neurological and psychiatric disorders such as Alzheimer's disease (Stam, Jones, Nolte, Breakspear, & Scheltens, 2007;Yu et al, 2017), multiple sclerosis, and epilepsy (Stam, 2014). Although such changes have not directly been shown in chronic pain so far, fMRI studies reporting a global reorganization of the brain in chronic pain (Mano et al, 2018;Mansour et al, 2016) [Bradley et al, 2017]).…”

Multilayer network modeling of integrated biological systems

“…Anatomical and functional information has been integrated to gain insights about the macro-scale topology of the Macaque monkey [26] and the human brain [27]. Temporal [28,29] and multi-frequency [30,31,32,33] decompositions of human brain activity, followed by their successive integration into multilayer networks, have been used to improve our understanding of brain function in cognitive tasks and brain diseases, such as Alzheimer's, Parkinson's or Schizophrenia.…”