Search bioRxiv⌕ Search

Biology subjects

Giulietti, G.

Publications and source records attributed to Giulietti, G..

2 recordsLinked to original sources

Cerebrovascular response dynamics to hypercapnia in healthy aging

Cerebrovascular dysfunction is an early and underrecognized contributor to cognitive decline. Standard measures such as cerebrovascular reactivity (CVR) during hypercapnia capture only the amplitude of flow responses, providing limited insight into the timing of vascular adaptation. Temporal features, such as delay (onset latency) and time constant (rate of adjustment), together with gain (response amplitude) may serve as more sensitive indicators of vascular health, but cannot be directly obtained from conventional imaging. Here, we investigated cerebral blood flow (CBF), cerebral blood volume (CBV), and blood oxygenation level dependent (BOLD) signal dynamics during hypercapnic challenge in healthy aging. Using a physiologically validated computational model, we estimated delay, time constant, and gain by optimizing the mapping of end-tidal gases to their arterial counterparts in a region-of-interest framework. Once parametrized using CBF, the model successfully predicted CBV and BOLD responses in independent experimental sessions. Across subjects, aging was associated with widespread heterogeneous region-specific changes in delay and substantial reductions in gain and time constant, indicating that cerebrovascular responses become weaker and less adaptable with age. These results demonstrate that calibrated simulations have the ability to track vascular aging, allowing the extraction of parameters that may represent novel biomarkers of cerebrovascular dysfunction. Unlike conventional CVR, temporal hemodynamic parameters capture the dynamics of vascular adaptation, providing a complementary dimension for early detection and therapeutic monitoring in aging and disease.

neuroscience↗

Non-neuronal signal fluctuations in Alzheimer's disease and in mild cognitive impairment

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) permits the investigation neural activity thanks to the neurovascular coupling mechanism. However, neural activity accounts for only a portion of the observed BOLD signal fluctuations, as the vasculature integrates multiple physiological inputs that contribute to the response. Research focusing on isolating the vascular components of the BOLD signal revealed that markers of cerebrovascular health, such as cerebrovascular reactivity (CVR), serve as valuable biomarkers for neurodegenerative diseases. This study examines the relationship between vascular metrics and noise in a cohort comprising individuals with Alzheimers disease (AD), mild cognitive impairment (MCI), and healthy controls (HC). Vascular responses were assessed using three functional contrasts during a hypercapnic challenge: arterial spin labeling (ASL) to measure cerebral blood flow (CBF) reactivity, vascular space occupancy (VASO) to quantify cerebral blood volume (CBV) reactivity, and BOLD imaging. Noise metrics were derived from multi-echo BOLD resting-state data by isolating the TE-independent components of the signal. Mean correlation coefficients for noise vs ASL-CVR are: (-0.12 {+/-} 0.06) for HC, (-0.14 {+/-} 0.08) for MCI, (-0.11 {+/-} 0.05) for AD. Mean correlation coefficients for noise vs BOLD-CVR are: (0.25 {+/-} 0.11) for HC, (0.24 {+/-} 0.07) for MCI, (0.23 {+/-} 0.11) for AD. Mean correlation coefficients for noise vs VASO-CVR are: (0.13 {+/-} 0.10) for HC, (0.13 {+/-} 0.07) for MCI, (0.12 {+/-} 0.12) for AD. These results suggest that TE-independent noise relates to the three vascular contrasts to varying extents and directions, with no significant differences across groups. Further analysis within specific functional networks revealed group differences in specific networks. The observed cortical correlations between noise and vascular features provide important insights into brain function and the progression of neurodegenerative diseases, offering a potential avenue to disentangle vascular and neural contributions in brain network and connectivity studies.

neuroscience↗