Alterations in resting-state network dynamics along the Alzheimer’s disease continuum

Abstract

Human brain activity is intrinsically organized into resting-state networks (RSNs) that transiently activate or deactivate at the sub-second timescale. Few neuroimaging studies have addressed how Alzheimer's disease (AD) affects these fast temporal brain dynamics, and how they relate to the cognitive, structural and metabolic abnormalities characterizing AD. We aimed at closing this gap by investigating both brain structure and function using magnetoencephalography (MEG) and hybrid positron emission tomography-magnetic resonance (PET/MR) in 10 healthy elders, 10 patients with subjective cognitive decline (SCD), 10 patients with amnestic mild cognitive impairment (aMCI) and 10 patients with typical Alzheimer’s disease with dementia (AD). The fast activation/deactivation state dynamics of RSNs were assessed using hidden Markov modeling (HMM) of power envelope fluctuations at rest measured with MEG. Correlations were sought between temporal properties of HMM states and participants' cognitive test scores, whole hippocampal grey matter volume and regional brain glucose metabolism. The posterior default-mode network (DMN) was less often activated and for shorter durations in AD patients than matched healthy elders. No significant difference was found in patients with SCD or aMCI. The time spent by participants in the activated posterior DMN state did not correlate significantly with cognitive scores, nor with the whole hippocampal volume. However, it correlated positively with the regional glucose consumption in the right dorsolateral prefrontal cortex (DLPFC). AD patients present alterations of posterior DMN power activation dynamics at rest that identify an additional electrophysiological correlate of AD-related synaptic and neural dysfunction. The right DLPFC may play a causal role in the activation of the posterior DMN, possibly linked to the occurrence of mind wandering episodes. As such, these data might suggest a neural correlate of the decrease in mind wandering episodes reported in pathological aging.

Age of onset modulates resting‐state brain network dynamics in Friedreich Ataxia

“…The HMM of MEG source power envelopes was used to investigate FRDA‐related alterations in fast brain network dynamics on the whole 5 min of resting state recording of each participant. The methodology was adapted from Coquelet et al (2020) and Puttaert et al (2020).…”

“…This approach was already used to demonstrate a link between the temporal stability of RSN activations and the level of ZDHHC9 gene expression in subjects with ZDHHC9 mutations causing X‐linked intellectual disability (Hawkins et al, 2020). It was also able to discriminate healthy elders from AD patients, whose spontaneous synchronization in the default mode network (DMN) was less likely and less stable, probably due to a reduction in “static” (i.e., over longer timescales) DMN functional integration that is a core feature of AD (Puttaert et al, 2020; Sitnikova et al, 2018). Although FRDA‐related alterations of brain rsFC over long timescales (i.e., minute long) have been previously characterized (Naeije et al, 2020c), data on potential alterations of fast RSNs dynamics are, to the best of our knowledge, not available.…”

Decreased Alpha Peak Frequency Is Linked to Episodic Memory Impairment in Pathological Aging

“…The comprehensive clinical and neuropsychological evaluation has been described in detail elsewhere ( Puttaert et al, 2020 ).…”

“…Subtractive voxel-based analysis of FDG-PET data were also performed as previously done in previous studies from our group ( De Tiege et al, 2004 ; De Tiège et al, 2008b ; Puttaert et al, 2020 ). We used SPM12 (see text footnote 1, Wellcome Trust Centre for Neuroimaging, London, United Kingdom) to construct general linear models (GLMs) of the preprocessed FDG-PET data of healthy elders and patients with altered FCSRT performance taken as separate groups.…”

The New Therapeutics in Alzheimer’s Disease Longitudinal Cohort study (NTAD): study protocol

“…While PET imaging now offers ligands that indirectly quantify synaptic density, electroencephalography (EEG) and magnetoencephalography (MEG) measure neurophysiological properties that depend on synaptic integrity and function within local and large-scale brain networks. MEG can identify synaptic and local circuit impairments [23,24] and their impact on network dynamics in Alzheimer's Disease, [25][26][27][28] frontotemporal dementia, [24,[29][30][31][32] and Lewy-body disease. [33] MEG and EEG therefore have potential to support and de-risk clinical trials of novel compounds.…”