Distinct BOLD variability changes in the default mode and salience networks in Alzheimer’s disease spectrum and associations with cognitive decline

Abstract

However, it remains largely unknown how network-specific and frequency-specific variability changes along the Alzheimer's disease (AD) spectrum and relates to cognitive decline. We hypothesized that cognitive impairment was related to distinct BOLD variability alterations in two brain networks with reciprocal relationship, i.e., the AD-specific default mode network (DMN) and the salience network (SN). We examined variability of resting-state fMRI data at two characteristic slow frequency-bands of slow4 (0.027-0.073 Hz) and slow5 (0.01-0.027 Hz) in 96 AD, 98 amnestic mild cognitive impairment (aMCI), and 48 age-matched healthy controls (HC) using two commonly used pre-processing pipelines. Cognition was measured with a neuropsychological assessment battery. Using both global signal regression (GSR) and independent component analysis (ICA), results generally showed a reciprocal DMN-SN variability balance in aMCI (vs. AD and/or HC), although there were distinct frequency-specific variability patterns in association with different pre-processing approaches. Importantly, lower slow4 posterior-DMN variability correlated with poorer baseline cognition/smaller hippocampus and predicted faster cognitive decline in all patients using both GSR and ICA. Altogether, our findings suggest that reciprocal DMN-SN variability balance in aMCI might represent an early signature in neurodegeneration and cognitive decline along the AD spectrum.Alzheimer's disease (AD) is the major cause of dementia, and increasing attention has been focused on early disease detection/prevention. Therefore, studying brain changes along the AD disease continuum is important, i.e., from normal aging to the prodromal stage (amnestic mild cognitive impairment, aMCI) and finally to dementia stage. Using resting-state functional connectivity methods 1-5 that quantifies the temporal synchrony between brain regions, both AD and aMCI have been found to target large-scale networks, including reduced After ICA-based denoising, there was a main effect of group mainly in the posterior DMN including the precuneus/posterior cingulate cortex and VN (cuneus), and the SN (insula) ( Supplementary Table 1). Group comparisons replicated the GSR findings that aMCI had higher variability in the posterior DMN compared with AD (precuneus; Fig. 1B, bottom panel) and HC (angular gyrus ; Fig. 1A, bottom panel), as well as lower SN variability compared with HC (insula ; Fig. 1C, bottom panel), although the latter did not survive the cluster-level Scientific RepoRtS | (2020) 10:6457 | https://doi.Furthermore, we examined whether the variability related to global cognition decline over time, defined as the difference between baseline and year 2 (year 2 minus baseline).For all correlation analyses, we focused on the brain clusters showing group differences between aMCI and HC or between AD and HC (including clusters surviving the voxel-level threshold consistently using both GSR and ICA-based denoising) (n = 3 for slow4, and n = 6 for slow5 for both data denoising methods). Correlati...

Changes in Brain Function Networks in Patients With Amnestic Mild Cognitive Impairment: A Resting-State fMRI Study

“…Yi et al (54) reported that DMN is the most important network to distinguish between aMCI with and without amyloid-beta (Aβ) protein deposition. A reciprocal variability balance between DMN and SN was observed in aMCI (55). The findings of the present and previous studies imply that atypical activation of SN and subsequent activation of ECN are the compensatory mechanisms responsible for the decreased function of DMN under MCI conditions, in which SN has to obtain more resources to maintain brain function.…”

Neural Correlates of Cognitive Dysfunctions in Cervical Spondylotic Myelopathy Patients: A Resting-State fMRI Study

“…Accordingly, numerous researchers conducted their study based on variance and found that the capacity to understand and predict several important phenomena can improve dramatically ( 50 – 52 ). Therefore, recently, a lot of studies have used the variability of neural signals (e.g., variance of signals or standard deviation of signals) as a powerful tool for investigating brain function in healthy adults and the sick ( 21 , 41 , 53 , 54 ). They established that the variability of BOLD signals varies across the entire brain and lifespan, its physiological significance indicator ( 55 ).…”

“…To assess brain neural activity, we calculated the BOLD signal variability, which is an established method of quantifiably evaluating BOLD level fluctuations (32)(33)(34)(35)(36). Moreover, BOLD variability has been shown to provide brain functional architecture in aging adults and the cognitive declined diseases (21)(22)(23)(37)(38)(39)(40)(41). We used functional connectivity analysis and graph theoretical analysis to better investigate brain network changes in CSM patients.…”

Application of Structural and Functional Connectome Mismatch for Classification and Individualized Therapy in Alzheimer Disease

“…These areas include the precuneus/posterior cingulate cortex, medial prefrontal cortex (MPFC), and medial, lateral, and inferior parietal cortex, and its activity holds potential as a non-invasive biomarker of incipient AD (45). Researchers have demonstrated the disconnection or decreased functional connectivity within/between DMN and other networks, which contribute to a cognition decline (46). Regardless of the mechanism, functional data seems less consistent than structural data most in the DMN.…”