Aging and the Spectral Properties of Brain Hemodynamics
Ki Yun ParkiD, Abraham Z. SnyderiD, Manu S. GoyaliD, Timothy O. LaumanniD, John J. LeeiD, Babatunde Adeyemo, Nicholas V. Metcalf, Andrei G. VlassenkoiD, Joshua S. ShimonyiD, Eric C. LeuthardtiD
Abstract
Cerebral glucose metabolism (CMRGlc) systematically decreases with advancing age. We sought to identify correlates of decreased CMRGlc in the spectral properties of fMRI signals imaged in the task-free state. We analyzed lifespan resting-state fMRI data acquired in 455 healthy adults (ages 18-87 years) and cerebral metabolic data acquired in a separate cohort of 94 healthy adults (ages 25-45 years, 65-85 years). We characterized the spectral properties of the fMRI data in terms of the relative predominance of slow vs. fast activity using the spectral slope (SS) measure. We found that the relative proportion of fast activity increases with advancing age (SS flattening) across most cortical regions. The regional distribution of spectral slope was topographically correlated with CMRGlc in young adults. Notably, whereas most older adults maintained a youthful pattern of SS topography, a distinct subset of older adults significantly diverged from the youthful pattern. This subset of older adults also diverged from the youthful pattern of CMRGlc metabolism. This divergent pattern was associated with T2-weighted signal changes in frontal lobe white matter, an independent marker of small vessel disease. These findings suggest that BOLD signal spectral slope flattening may represent a biomarker of age-associated neurometabolic pathology. Significance StatementAging is associated with a decline in cerebral glucose metabolism (CMRGlc). Here, we identified correlates of CMRGlc in the spectral properties of resting-state fMRI data using spectral slope (SS), which quantifies the relative predominance of slow vs. fast activity. We found that SS flattening with advancing age is most prominent in regions characterized by high CMRGlc in youth. A subset of older adults who diverged from the youthful pattern of SS topography also exhibited evidence of frontal lobe white matter pathology. These findings suggest that the spectral properties of rs-fMRI data provide mechanistic insights into age-related neuropathology. In particular, spectral slope measures may serve as early indicators of age-related neurometabolic decline and potentially identify individuals in whom intervention is indicated.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 2
- FWCI
- 1.15
- Field percentile
- top 24%
- Organism
- Assay
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Neurometabolic timecourse of healthy aging
Tao Gong, Steve C. N. Hui, … Richard A.E. Edden·NeuroImagePeer-reviewed·Nov 7, 2022
- Regional glia-related metabolite levels are higher at older age and scale negatively with visuo-spatial working memory: A cross-sectional proton MR spectroscopy study at 7 tesla on normal cognitive ageing
Anna Lind, Carl‐Johan Boraxbekk, … Anouk Marsman·Journal of NeurosciencePeer-reviewed·Sep 29, 2020
- Temporal and Spatial Scales of Human Resting-state Cortical Activity Across the Lifespan
John Bero, Colin Humphries, … Daeyeol Lee·Journal of NeurosciencePeer-reviewed·Nov 17, 2025
- Lower Brain Glucose Metabolism in Normal Ageing is Predominantly Frontal and Temporal: A Systematic Review and Pooled Effect Size and Activation Likelihood Estimates Meta-Analyses.
Hamish A Deery, Robert Di Paolo, … Sharna D. Jamadar·Human Brain MappingPeer-reviewed·Oct 21, 2022
- Investigating the effects of healthy cognitive aging on brain functional connectivity using 4.7 T resting-state functional Magnetic Resonance Imaging.
Stanislau Hrybouski, Ivor Cribben, … Nikolai V. Malykhin·Brain Structure and FunctionPeer-reviewed·Feb 18, 2021
- The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors
Kamen A. Tsvetanov, Richard N. Henson, … James Benedict Rowe·PsychophysiologyPeer-reviewed·Nov 18, 2020