Evaluating the Sensitivity of Resting-State BOLD Variability to Age and Cognition after Controlling for Motion and Cardiovascular Influences: A Network-Based Approach

Millar, Peter R.; Petersen, Steven E.; Ances, Beau M.; Gordon, Brian A.; Benzinger, Tammie L.S.; Morris, John C.; Balota, David A.

doi:10.1093/cercor/bhaa138

Cited by 24 publications

(35 citation statements)

References 63 publications

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“…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). Taken together these findings suggest that the age‐related differences in BOLD signal variability at resting state are unlikely to be of neuronal origin beyond the effects of age on various types of vascular signals.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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Accurate identification of brain function is necessary to understand neurocognitive aging, and thereby promote health and well-being. Many studies of neurocognitive aging have investigated brain function with the blood-oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging. However, the BOLD signal is a composite of neural and vascular signals, which are differentially affected by aging. It is, therefore, essential to distinguish the age effects on vascular versus neural function. The BOLD signal variability at rest (known as resting state fluctuation amplitude, RSFA), is a safe, scalable, and robust means to calibrate vascular responsivity, as an alternative to breath-holding and hypercapnia. However, the use of RSFA for normalization of BOLD imaging assumes that age differences in RSFA reflecting only vascular factors, rather than age-related differences in neural function (activity) or neuronal loss (atrophy). Previous studies indicate that two vascular factors, cardiovascular health (CVH) and cerebrovascular function, are insufficient when used alone to fully explain age-related differences in RSFA. It remains possible that their joint consideration is required to fully capture age differences in RSFA. We tested the hypothesis that RSFA no longer varies with age after adjusting for a combination of cardiovascular and cerebrovascular measures. We also tested the hypothesis that RSFA variation with age is not associated with atrophy. We used data from the population-based, lifespan Cam-CAN cohort. After controlling for cardiovascular and cerebrovascular estimates alone, the residual variance in RSFA across individuals was significantly associated with age. However, when controlling for both cardiovascular and cerebrovascular estimates, the variance in RSFA was no longer associated with age. Grey matter volumes did not explain age differences in RSFA, after controlling for CVH. The results were consistent between voxel-level analysis and independent component analysis. Our findings indicate that cardiovascular and cerebrovascular signals are together sufficient predictors of age differences in RSFA. We suggest that RSFA can be used to separate vascular from neuronal factors, to characterize neurocognitive aging. We discuss the implications and make recommendations for the use of RSFA in the research of aging. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Discussionmentioning

confidence: 99%

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

et al. 2020

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“…Although useful and interesting in their own right ( Tsvetanov et al. 2015 , 2021 ; Millar et al. 2020 ), externally measured vascular and metabolic risk factors that affect cerebral vasculature and associated responses are insufficient for resolving these issues, especially when cross-sectional comparisons and mediation models are used ( Nyberg et al.…”

Section: Discussionmentioning

confidence: 99%

Lost Dynamics and the Dynamics of Loss: Longitudinal Compression of Brain Signal Variability is Coupled with Declines in Functional Integration and Cognitive Performance

et al. 2021

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Reduced moment-to-moment blood oxygen level-dependent (BOLD) signal variability has been consistently linked to advanced age and poorer cognitive performance, showing potential as a functional marker of brain aging. To date, however, this promise has rested exclusively on cross-sectional comparisons. In a sample of 74 healthy adults, we provide the first longitudinal evidence linking individual differences in BOLD variability, age, and performance across multiple cognitive domains over an average period of 2.5 years. As expected, those expressing greater loss of BOLD variability also exhibited greater decline in cognition. The fronto-striato-thalamic system emerged as a core neural substrate for these change–change associations. Preservation of signal variability within regions of the fronto-striato-thalamic system also cohered with preservation of functional integration across regions of this system, suggesting that longitudinal maintenance of “local” dynamics may require across-region communication. We therefore propose this neural system as a primary target in future longitudinal studies on the neural substrates of cognitive aging. Given that longitudinal change–change associations between brain and cognition are notoriously difficult to detect, the presence of such an association within a relatively short follow-up period bolsters the promise of brain signal variability as a viable, experimentally sensitive probe for studying individual differences in human cognitive aging.

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“…Indeed, one study has reported strong correlations between resting-state and task-driven estimates of BOLD variability (Grady & Garrett, 2018); however, these relationships were demonstrated in relatively small samples of 15 older and 20 younger adults. Furthermore, estimates of BOLD variability show fair test-retest reliability over 3-year intervals, at levels comparable to or greater than estimates of functional connectivity (Millar, Petersen, et al, 2020).…”

Section: Introductionmentioning

confidence: 86%

“…Although widespread patterns of age-related reductions in BOLD variability have been observed spanning multiple functional networks, some regional age-related increases in variability have been reported in the same studies. Importantly, these extreme-group (i.e., college-aged students vs. community-dwelling individuals over 60 years old) difference patterns have been replicated in large, continuous aging samples as well (Millar, Petersen, et al, 2020;Nomi, Bolt, Ezie, Uddin, & Heller, 2017;Hu, Chao, Zhang, Ide, & Li, 2014).…”

Section: Introductionmentioning

confidence: 87%

“…Moreover, there is growing interest in potential vascular influences on agerelated cognitive decline (for a review, see Abdelkarim et al, 2019;Wåhlin & Nyberg, 2019;O'Brien et al, 2003). Indeed, one recent report demonstrated that relationships between resting-state BOLD variability and a global cognitive composite were eliminated after correcting for similar CVH measures (Millar, Petersen, et al, 2020). However, these relationships have not been examined within specific cognitive domains, so it is unclear whether potential cardiovascular factors may be sensitive to domain-general or domain-specific processing.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Cognitive Relationships with Resting-State and Task-driven Blood Oxygen Level-Dependent Variability

Millar

Ances

Gordon

et al. 2021

Journal of Cognitive Neuroscience

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Recent functional magnetic resonance imaging studies have reported that moment-to-moment variability in the blood oxygen level-dependent (BOLD) signal is positively associated with task performance and, thus, may reflect a behaviorally sensitive signal. However, it is not clear whether resting-state and task-driven BOLD variability are differentially related to cognition, as they may be driven by distinct sources of variance in the BOLD signal. Moreover, other studies have suggested that age differences in resting-state BOLD variability may be particularly sensitive to individual differences in cardiovascular, rather than neural, factors. In this study, we tested relationships between measures of behavioral task performance and BOLD variability during both resting-state and task-driven runs of a Stroop and an animacy judgment task in a large, well-characterized sample of cognitively normal middle-aged to older adults. Resting-state BOLD variability was related to composite measures of global cognition and attentional control, but these relationships were eliminated after correction for age or cardiovascular estimates. In contrast, task-driven BOLD variability was related to attentional control measured both inside and outside the scanner, and importantly, these relationships persisted after correction for age and cardiovascular measures. Overall, these results suggest that BOLD variability is a behaviorally sensitive signal. However, resting-state and task-driven estimates of BOLD variability may differ in the degree to which they are sensitive to age-related, cardiovascular, and neural mechanisms.

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Evaluating the Sensitivity of Resting-State BOLD Variability to Age and Cognition after Controlling for Motion and Cardiovascular Influences: A Network-Based Approach

Cited by 24 publications

References 63 publications

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

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

Lost Dynamics and the Dynamics of Loss: Longitudinal Compression of Brain Signal Variability is Coupled with Declines in Functional Integration and Cognitive Performance

Evaluating Cognitive Relationships with Resting-State and Task-driven Blood Oxygen Level-Dependent Variability

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