IC‐03‐03: Cognitive Complaints in Older Adults at Risk For Alzheimer’s Disease are Associated with Altered Resting State Networks

Abstract

Introduction: Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting-state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole-brain functional connectivity (FC) in relation to neurocognitive variables. Methods: Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278-region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score).Results: Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP-DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN-pattern, characterized by increased FC within all resting-state networks, was negatively associated with self CCI. VIS-pattern, characterized by an increase in visual resting-state network, was negatively associated with CCI self or informant scores. FP-DMNpattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory. Discussion: Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization.

Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

“…The EEG and fMRI patterns are co-expressed over subjects and quantified by subject-specific mixing weights. This result corroborates previous findings extracting the same robust ICN-FG components from different fMRI datasets (Amico et al, 2017; Contreras et al, 2017).…”

“…SI3 in SI). The fMRI IC weights of the component are in line with previous findings extracting VIS-SM independent components from different fMRI datasets (Amico et al, 2017; Contreras et al, 2017).…”

Brain functional network integrity sustains cognitive function despite atrophy in presymptomatic genetic frontotemporal dementia

“…Graph-theoretic quantification of network organisation confirms the relevance of modularity and efficiency to function in FTD [16]. Conversely, the loss of neural systems’ modularity mirrors the loss of functional specialization with age [45] and dementia [44]. Here, we show the significance of the maintenance of this functional network organisation, with a progressively stronger correlation with cognitive performance as seemingly healthy adults approach the age of expected onset of FTD.…”

“…This balance of within- and between-network connectivity is characteristic of segregated and specialized network organization of brain systems. Such functional segregation varies with physiological ageing [17,18,43], with cognitive function [18] and in individuals at risk for Alzheimer’s disease [44]. Graph-theoretic quantification of network organisation confirms the relevance of modularity and efficiency to function in FTD [16].…”

Alterations in resting-state network dynamics along the Alzheimer’s disease continuum: a combined MEG-PET/MR approach

“…Spontaneous BOLD signals typically covary between different brain areas, which is thought to reflect large-scale resting-state functional networks (i.e., the so-called resting-state networks (RSNs)) whose anatomical architecture is close to task-based functional networks 28,29 . Among changes in RSNs uncovered by rsfMRI, evidence for AD-related alterations of DMN resting-state functional connectivity (rsFC) appears as a promising in-vivo marker of AD and its preclinical/predementia stages [30][31][32] . Still, the use of fMRI along the AD continuum suffers from two important pitfalls.…”