BOLD and EEG Signal Variability at Rest Differently Relate to Aging in the Human Brain

Abstract

Variability of neural activity is regarded as a crucial feature of healthy brain function, and several neuroimaging approaches have been employed to assess it noninvasively. Studies on the variability of both evoked brain response and spontaneous brain signals have shown remarkable changes with aging but it is unclear if the different measures of brain signal variability -identified with either hemodynamic or electrophysiological methods -reflect the same underlying physiology. In this study, we aimed to explore age differences of spontaneous brain signal variability with two different imaging modalities (EEG, fMRI) in healthy younger (25±3 years, N=135) and older (67±4 years, N=54) adults. Consistent with the previous studies, we found lower blood oxygenation level dependent (BOLD) variability in the older subjects as well as less signal variability in the amplitude of low-frequency oscillations (1-12 Hz), measured in source space. These age-related reductions were mostly observed in the areas that overlap with the default mode network. Moreover, age-related increases of variability in the amplitude of beta-band frequency EEG oscillations (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25) were seen predominantly in fronto-temporal and sensorimotor brain regions. There were significant sex differences in BOLD and EEG signal variability in various brain regions, but no significant interactions between age and sex were observed. Further, both univariate and multivariate correlation analyses revealed no significant associations between these two variability measures. In summary, we show that both BOLD and EEG signal variability reflect aging-related processes but are likely to be dominated by different physiological origins, which relate differentially to age and sex.

The effects of age on resting‐state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

“…This is suggestive of an indirect association between RSFA and GMV introduced by cardiovascular effects on brain-wide atrophy (Gu et al, 2019;Srinivasa et al, 2016) and other non-morphological confounds in T1-weighted data (Bhogal et al, 2017;Ge et al, 2017;Tardif et al, 2017). The lack of evidence for an association between age-related effects on RSFA and brain atrophy after adjusting for cardiovascular health is consistent with previous reports using direct physiological measures of neural activity (MEG and EEG): no age-related associations between RSFA and neuronal indices were detected (Kumral et al, 2020;Tsvetanov et al, 2015). Furthermore, potential age-related associations between RSFA and cognitive function are fully explained by cerebrovascular risk factors, such as WMH burden (Millar et al, 2020).…”

“…We demonstrate that the effects of age on RSFA can be sufficiently captured by the joint consideration of cardiovascular (based on ECG, BP, WMH, and BMI measures) and cerebrovascular factors (CBF from ASL). Variance in brain atrophy (GM volume Figure 6) and neuronal activity (Kumral et al, 2020;Tsvetanov et al, 2015) do not explain unique relationship between RSFA and age. This means that RSFA is a suitable measure for differentiating between vascular and neuronal influences on task-based BOLD signal.…”

EEG Power spectra and subcortical pathology in chronic disorders of consciousness

“…total power and θ/δ components). It is also noteworthy that in our data combination of EEG and MRI data is additive in terms of enhancing discriminability across groups, strengthening the idea that multimodal approaches are a desirable way of assaying different – and complementary – aspects of DOC pathology (Coleman, Bekinschtein, Monti, Owen, & Pickard, 2009; Schiff, 2006) and, more broadly, brain function and health (Kumral et al, 2020; Nentwich et al, 2020), particularly in the context of deriving predictive models from brain data (Engemann et al, 2020). Despite the good concordance between behavior-based diagnosis and the classification based on demographic, brain atrophy, and EEG variables, the two approaches still disagree over one-third of the cases.…”

Separating vascular and neuronal effects of age on fMRI BOLD signals

“…This could explain why Tsvetanov and colleagues [12] found that age differences in RSFA are either fully or partly mediated by heart rate variability. In contrast, these authors further found no evidence that neural variability (as measured by magnetoencephalography (MEG)) mediated the age effects of RSFA [12]; these findings were further supported by EEG-based neural estimates [205]. However, while age-related differences in RSFA may not reflect neuronal signals, the use of either somatic vascular measures or cerebrovascular measures explained only part of the age-related differences in RSFA.…”