Biology subjects

Giove, F.

Publications and source records attributed to Giove, F..

6 recordsLinked to original sources

bioRxiv2025

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↗

bioRxiv2025

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↗

bioRxiv2025

MOTUM: a system for Motion Online Tracking Under MRI

Attempts to implement realistic body-environment interactions during functional magnetic resonance imaging (fMRI) experiments have developed expensive, hardly reproducible, and task-specific setups. Here, we introduce MOTUM (Motion Online Tracking Under MRI), a novel system that combines real-time kinematic tracking with immersive virtual reality, enabling participants to perform naturalistic movements inside the scanner. As a proof-of-concept, we tested MOTUM during a reach-to-grasp task with and without visual feedback of ones hand (N = 7). The system achieved high-fidelity motion tracking, induced an intense immersive experience, evoked expected sensorimotor brain activations, and maintained high fMRI data quality. Standard fMRI control metrics were below the critical threshold in 99% of volumes, indicating that participants arm movements had minimal impact on head motion and data quality. While hand movements had little to no effect on brain activity, arm movements resulted in sparse spurious correlations that were easily controlled for. Critically, MOTUM allowed us to extract rich kinematic indices and link them directly to brain activity on a trial-by-trial basis. Parametric modulation analyses revealed that natural variations in movement dynamics significantly influenced neural responses in parietal, frontal, and occipital regions. In sum, MOTUM is a robust method to study motor control and beyond, enabling a new class of fMRI experiments that bridge ecological realism and experimental control, pushing current neuroimaging research towards real-life neuroscience.

neuroscience↗

bioRxiv2024

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subjects sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1{+/-}6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44[≤]r[≤]0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21[≥]r[≥]-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20[≥]r[≥]-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39[≤]r[≤]0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22[≥]r[≥]-0.25). Linear mixture of sex and age explained 26{+/-}10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33{+/-}11% when body height was added into the mixture model. Age itself explained only 2{+/-}2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.

neuroscience↗

bioRxiv2021

Perception affects the brain's metabolic response to sensory stimulation

Processing of incoming sensory stimulation triggers an increase of cerebral perfusion and blood oxygenation (neurovascular response) as well as an alteration of the metabolic neurochemical profile (neurometabolic response). Here we show that perceived and unperceived isoluminant chromatic flickering stimuli designed to have similar neurovascular responses as measured by blood oxygenation level dependent functional MRI (BOLD-fMRI) in primary visual cortex (V1) have markedly different neurometabolic responses as measured by functional MRS. In particular, a significant regional buildup of lactate, an index of aerobic glycolysis, and glutamate, an index of malate-aspartate shuttle, occurred in V1 only when the flickering is perceived, without any relation with behavioral or physiological variables. Wheras the BOLD-fMRI signal in V1, a proxy for input to V1, was insensitive to flickering perception by design, the BOLD-fMRI signal in secondary visual areas was larger during perceived than unperceived flickering indicating increased output from V1. These results indicate that the upregulation of energy metabolism induced by visual stimulation depends on the type of information processing taking place in V1, and that 1H-fMRS provides unique information about local input/output balance that is not measured by BOLD-fMRI. Significance statementVisual perception has a measurable metabolic effect in the primary visual cortex (V1). Here we show that the upregulation of energy metabolism induced by isoluminant chromatic flickering depends on subjective visual perception. Within V1, perceived and unperceived stimuli that are contrast-matched to elicit similar blood-oxygenation level-dependent fMRI response are associated with clearly distinct neurochemical profiles. Specifically, regional accumulations of lactate (an index of aerobic glycolysis) and glutamate (an index of malate-aspartate shuttle) only develop during perceived stimuli, together with a larger activation of secondary visual areas. Our results imply a dissociation between metabolic and functional response, and indicate that that the upregulation of energy metabolism induced by visual stimulation depends on the type of information processing taking place in V1.

neuroscience↗

bioRxiv2020

Automated skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 seconds and no pre-processing requirements. We evaluated the performance of our network in the presence of skip connections and recently proposed framing connections, finding the simplest network to be the most effective. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1,782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net.

bioinformatics↗