Revealing the Spatial Pattern of Brain Hemodynamic Sensitivity to Healthy Aging through Sparse Dynamic Causal Model

• Published in: The Journal of neuroscience Vol. 45; no. 1; p. e1940232024
• Main Authors: Baron, Giorgia, Silvestri, Erica, Benozzo, Danilo, Chiuso, Alessandro, Bertoldo, Alessandra
• Format: Journal Article
• Language: English
• Published: United States Society for Neuroscience 01.01.2025
• Subjects: Adolescent; Adult; Age; Aged; Aging; Aging - physiology; Brain; Brain - blood supply; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Connectome - methods; Female; Females; Functional magnetic resonance imaging; Healthy Aging - physiology; Hemispheric laterality; Hemodynamic responses; Hemodynamics; Hemodynamics - physiology; Heterogeneity; Humans; Image processing; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Middle Aged; Models, Neurological; Neuroimaging; Neurovascular Coupling - physiology; Prediction models; Regression models; Response functions; Sensitivity; Sensitivity analysis; Spatial heterogeneity; Young Adult; hemodynamic response function; dynamic causal modelling; aging
• ISSN: 0270-6474; 1529-2401; 1529-2401
• DOI: 10.1523/JNEUROSCI.1940-23.2024

Age-related changes in the BOLD response could reflect neurovascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a sparse dynamic causal model (sDCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model. sDCM was applied to the resting-state functional magnetic resonance imaging data of a cohort of 126 healthy individuals in a wide age range (31 females), providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model predicting the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects (173 females) selected from the Human Connectome Project in Aging and Development cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant–corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between certain right-hemisphere hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable.