Partitioning Neural and Vascular contributions to fMRI-BOLD variability in aging
Poster No:
941
Submission Type:
Late-Breaking Abstract Submission
Authors:
Vicente Medel1, Daniel Franco-O'Byrne1, Ana Castro-Laguardia1, Josefina Cruzat1, Carlos Coronel-Oliveros2, Gabriel Wainstein3, Cecilia Gonzalez-Campo4, James Shine5, Agustín Ibáñez6, Enzo Tagliazucchi1
Institutions:
1Universidad Adolfo Ibáñez, Santiago, RM, 2Trinity College Dublin, Dublin, Dublin, 3University of Sydney, Camperdown, NSW, 4Universidad de San Andres, Buenos Aires, Buenos Aires, 5The University of Sydney, Sydney, NSW, 6Universidad Adolfo Ibáñez, Santiago, Santiago
First Author:
Co-Author(s):
Introduction:
Aging is accompanied by complex changes in brain function, structure, and metabolism, yet the specific contributions of neural and vascular processes to these alterations remain unclear. BOLD fMRI signal variability has emerged as a key marker of brain function, reflecting both neuronal and hemodynamic dynamics. However, its interpretation in aging is confounded by changes in the neurovascular system, particularly in the hemodynamic response function (HRF). In this study, we investigate the aging LEMON database (N=200, EEG and fMRI) to partitionate neural and vascular contributions to BOLD variability changes across aging, framing these findings within the broader context of neurovascular, metabolic and circuit-level alterations across aging.
Methods:
Data were obtained from the LEMON database, including structural MRI, resting-state fMRI, and EEG recordings from younger and older adults.
Structural MRI: Gray matter atrophy was assessed using FreeSurfer, and white matter hyperintensities (WMHs) were mapped with the Lesion Segmentation Toolbox (LST, SPM).
Resting-State fMRI: BOLD variability was quantified as the standard deviation of the BOLD signal. The rsHRF toolbox was used to estimate and deconvolve the hemodynamic response function (HRF) to dissociate neural and vascular contributions.
EEG: Data were preprocessed and source-reconsstructed with MNE-Python. 1/f slope and spectral parametrization were computed to investigate circuit-level neurophysiological aging.
Statistical analyses examined the interplay between structural atrophy, neurovascular function, and electrophysiological changes across aging through General Linear Model effect at each parcel (Desikan-Killiany atlas).
Structural MRI: Gray matter atrophy was assessed using FreeSurfer, and white matter hyperintensities (WMHs) were mapped with the Lesion Segmentation Toolbox (LST, SPM).
Resting-State fMRI: BOLD variability was quantified as the standard deviation of the BOLD signal. The rsHRF toolbox was used to estimate and deconvolve the hemodynamic response function (HRF) to dissociate neural and vascular contributions.
EEG: Data were preprocessed and source-reconsstructed with MNE-Python. 1/f slope and spectral parametrization were computed to investigate circuit-level neurophysiological aging.
Statistical analyses examined the interplay between structural atrophy, neurovascular function, and electrophysiological changes across aging through General Linear Model effect at each parcel (Desikan-Killiany atlas).
Results:
Before HRF deconvolution, age was significantly correlated with BOLD signal variability (p < 0.001). However, after deconvolving the neural BOLD signal, this correlation was no longer significant (p = 0.1), suggesting that age-related changes in BOLD variability are largely driven by vascular rather than neural factors.
HRF parametrization analysis revealed that HRF peak height correlated with WMH burden and the EEG 1/f slope, but not with BOLD variability after deconvolution, indicating a stronger association between vascular integrity and electrophysiological markers than with BOLD neural fluctuations.
Finally, GLM models showed that the effect of age on HRF (at the parcel level, Desikan-Killiany atlas) was correlated with the effect of age on gray matter atrophy, an association that remained significant after spin-test correction. This highlights a link between neurovascular function and structural brain aging. We did not find this effect on deconvolved BOLD signal variability.
HRF parametrization analysis revealed that HRF peak height correlated with WMH burden and the EEG 1/f slope, but not with BOLD variability after deconvolution, indicating a stronger association between vascular integrity and electrophysiological markers than with BOLD neural fluctuations.
Finally, GLM models showed that the effect of age on HRF (at the parcel level, Desikan-Killiany atlas) was correlated with the effect of age on gray matter atrophy, an association that remained significant after spin-test correction. This highlights a link between neurovascular function and structural brain aging. We did not find this effect on deconvolved BOLD signal variability.
·Hemodynamic Response Function Deconvolution affects BOLD signal variability correlation with age. HRF peak height is heterogeneous across space.
Conclusions:
Our findings demonstrate that age-related changes in BOLD signal variability are primarily driven by vascular factors, as evidenced by the loss of correlation after HRF deconvolution. However, this does not mean that vascular factors are not dynamic and heterogeneous. The strong association between HRF peak height, WMH burden, and the EEG 1/f slope suggests that vascular health and circuit-level neurophysiological changes are key modulators of the aging brain, hinting a feedback metabolic modulation at the neurovascular union. Moreover, the correlation between age-related HRF alterations and gray matter atrophy underscores the interplay between neurovascular and structural aging.
These results have important implications for whole-brain computational modeling of aging, particularly in models that transform neuronal firing rates into BOLD signals, such as the Balloon-Windkessel model. Standard models often assume a homogeneous HRF across brain regions, but our findings emphasize the need for a modulated HRF model that accounts for spatial and individual variability in neurovascular function. Incorporating such adjustments could improve the accuracy of large-scale simulations of brain dynamics and aging-related network alterations.
These results have important implications for whole-brain computational modeling of aging, particularly in models that transform neuronal firing rates into BOLD signals, such as the Balloon-Windkessel model. Standard models often assume a homogeneous HRF across brain regions, but our findings emphasize the need for a modulated HRF model that accounts for spatial and individual variability in neurovascular function. Incorporating such adjustments could improve the accuracy of large-scale simulations of brain dynamics and aging-related network alterations.
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics 2
Physiology, Metabolism and Neurotransmission Other
Keywords:
Aging
Cerebrovascular Disease
Data analysis
FUNCTIONAL MRI
NORMAL HUMAN
STRUCTURAL MRI
White Matter
1|2Indicates the priority used for review
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