Resting-state functional MRI signal fluctuations are correlated with brain amyloid-β deposition

Scheel, Norman; Tarumi, Takashi; Tomoto, Tsubasa; Cullum, Munro; Zhang, R; Zhu, Daoben

doi:10.1101/2021.04.22.21255924

Cited by 4 publications

(3 citation statements)

References 71 publications

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“…Similarly, more recent evidence reported differences in FC dynamic states dwell time between AD patients and control groups and further observed lower dynamic FC variability in Alzheimer patients 43 and in relationship with transient changes in cognition typical of early stages of neurodegenerative disease. 44 In line with our results, recent work has also suggested that early amyloid deposition in the brain reduces variations of rs-fMRI BOLD signal 45 and FC 41 over time in cognitively unimpaired individuals.…”

Section: Discussionsupporting

confidence: 92%

Eigenvector centrality dynamics are related to Alzheimer’s disease pathological changes in non-demented individuals

Lorenzini

Ingala

Collij

et al. 2023

Brain Communications

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Amyloid-β accumulation starts in highly connected brain regions and is associated with functional connectivity alterations in the early stages of Alzheimer’s disease. This regional vulnerability is related to the high neuronal activity and strong fluctuations typical of these regions. Recently, dynamic functional connectivity was introduced to investigate changes in functional network organization over time. High dynamic functional connectivity variations indicate increased regional flexibility to participate in multiple subnetworks, promoting functional integration. Currently only a limited number of studies have explored the temporal dynamics of functional connectivity in the pre-dementia stages of Alzheimer’s disease. We study the associations between abnormal cerebrospinal fluid amyloid and both static and dynamic properties of functional hubs, using eigenvector centrality, and their relationship with cognitive performance, in 701 non-demented participants from the European Prevention of Alzheimer’s Dementia (EPAD) cohort. Voxel-wise eigenvector centrality was computed for the whole functional magnetic resonance imaging time series (static), and within a sliding window (dynamic). Differences in static eigenvector centrality between amyloid positive (A+) and negative (A-) participants and amyloid-tau (AT) groups were found in a general linear model. Dynamic eigenvector centrality standard deviation and range were compared between groups within clusters of significant static eigenvector centrality differences, and within 10 canonical resting-state networks. The effect of the interaction between amyloid status and cognitive performance on dynamic eigenvector centrality variability was also evaluated with linear models. Models were corrected for age, sex and education level. Lower static centrality was found in A + participants in posterior brain areas including a parietal and an occipital cluster; higher static centrality was found in a medio-frontal cluster. Lower eigenvector centrality variability (standard deviation) occurred in A + participants in the frontal cluster. The default mode network and the dorsal visual networks of A + participants had lower dynamic eigenvector centrality variability. Centrality variability in the Default Mode Network and dorsal visual networks were associated with cognitive performance in the A- and A + groups, with lower variability being observed in A + participants with good cognitive scores. Our results support the role and timing of eigenvector centrality alterations in very early stages of Alzheimer’s disease and show that centrality variability over time adds relevant information on the dynamic patterns that cause static eigenvector centrality alterations. We propose that dynamic eigenvector centrality is an early biomarker of the interplay between early Alzheimer’s disease pathology and cognitive decline.

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Section: Discussionsupporting

confidence: 92%

Eigenvector centrality dynamics are related to Alzheimer’s disease pathological changes in non-demented individuals

Lorenzini

Ingala

Collij

et al. 2023

Brain Communications

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“…Previous research had proved that DMN connectivity dysfunction was associated with amyloid burden in the cognitively normal elderly (Sheline et al, 2010;Mormino et al, 2011;Wang et al, 2013b;Elman et al, 2016;Palmqvist et al, 2017). Moreover, in the Scheel et al study, a robust negative correlation between ALFF signals and the amyloid load was predominantly in the DMN and visual network, in the amnesic MCI group (Scheel et al, 2021). The negative correlation shown in the ACC for both SCD and T2DM groups may illustrate the vulnerability of the ACC to amyloid pathology.…”

Section: Partial Correlation In T Dm and Scd Groups: Cortex Id Roi-basedmentioning

confidence: 76%

The fractional amplitude of low-frequency fluctuations signals related to amyloid uptake in high-risk populations—A pilot fMRI study

Bao

Shea

Chiu

et al. 2022

Front. Aging Neurosci.

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BackgroundPatients with type 2 diabetes mellitus (T2DM) and subjective cognitive decline (SCD) have a higher risk to develop Alzheimer's Disease (AD). Resting-state-functional magnetic resonance imaging (rs-fMRI) was used to document neurological involvement in the two groups from the aspect of brain dysfunction. Accumulation of amyloid-β (Aβ) starts decades ago before the onset of clinical symptoms and may already have been associated with brain function in high-risk populations. However, this study aims to compare the patterns of fractional amplitude of low-frequency fluctuations (fALFF) maps between cognitively normal high-risk groups (SCD and T2DM) and healthy elderly and evaluate the association between regional amyloid deposition and local fALFF signals in certain cortical regions.Materials and methodsA total of 18 T2DM, 11 SCD, and 18 healthy elderlies were included in this study. The differences in the fALFF maps were compared between HC and high-risk groups. Regional amyloid deposition and local fALFF signals were obtained and further correlated in two high-risk groups.ResultsCompared to HC, the altered fALFF signals of regions were shown in SCD such as the left posterior cerebellum, left putamen, and cingulate gyrus. The T2DM group illustrated altered neural activity in the superior temporal gyrus, supplementary motor area, and precentral gyrus. The correlation between fALFF signals and amyloid deposition was negative in the left anterior cingulate cortex for both groups. In the T2DM group, a positive correlation was shown in the right occipital lobe and left mesial temporal lobe.ConclusionThe altered fALFF signals were demonstrated in high-risk groups compared to HC. Very early amyloid deposition in SCD and T2DM groups was observed to affect the neural activity mainly involved in the default mode network (DMN).

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“…An alternative to GSR is to regress out the average signals derived only from the white matter (WM) and cerebrospinal fluid (CSF), where neuronal contributions are thought to be negligible ( Bartoň et al, 2019 ). The WM-CSF regression approach found considerable application ( Satterthwaite et al, 2013 ; Parkes et al, 2018 ; He et al, 2020 ; Scheel et al, n.d. ). However, there is evidence that WM also contains information about brain function ( Ding et al, 2013 ; Mazerolle et al, 2013 ; Peer et al, 2017 ; Wang et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging

Golestani,

Chen

2024

Front. Neurosci.

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IntroductionPhysiological nuisance contributions by cardiac and respiratory signals have a significant impact on resting-state fMRI data quality. As these physiological signals are often not recorded, data-driven denoising methods are commonly used to estimate and remove physiological noise from fMRI data. To investigate the efficacy of these denoising methods, one of the first steps is to accurately capture the cardiac and respiratory signals, which requires acquiring fMRI data with high temporal resolution.MethodsIn this study, we used such high-temporal resolution fMRI data to evaluate the effectiveness of several data-driven denoising methods, including global-signal regression (GSR), white matter and cerebrospinal fluid regression (WM-CSF), anatomical (aCompCor) and temporal CompCor (tCompCor), ICA-AROMA. Our analysis focused on the consequence of changes in low-frequency, cardiac and respiratory signal power, as well as age-related differences in terms of functional connectivity (fcMRI).ResultsOur results confirm that the ICA-AROMA and GSR removed the most physiological noise but also more low-frequency signals. These methods are also associated with substantially lower age-related fcMRI differences. On the other hand, aCompCor and tCompCor appear to be better at removing high-frequency physiological signals but not low-frequency signal power. These methods are also associated with relatively higher age-related fcMRI differences, whether driven by neuronal signal or residual artifact. These results were reproduced in data downsampled to represent conventional fMRI sampling frequency. Lastly, methods differ in performance depending on the age group.DiscussionWhile this study cautions direct comparisons of fcMRI results based on different denoising methods in the study of aging, it also enhances the understanding of different denoising methods in broader fcMRI applications.

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Resting-state functional MRI signal fluctuations are correlated with brain amyloid-β deposition

Cited by 4 publications

References 71 publications

Eigenvector centrality dynamics are related to Alzheimer’s disease pathological changes in non-demented individuals

Eigenvector centrality dynamics are related to Alzheimer’s disease pathological changes in non-demented individuals

The fractional amplitude of low-frequency fluctuations signals related to amyloid uptake in high-risk populations—A pilot fMRI study

Comparing data-driven physiological denoising approaches for resting-state fMRI: implications for the study of aging

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