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Gonzalez Campo, C.

Publications and source records attributed to Gonzalez Campo, C..

2 recordsLinked to original sources

Cardiometabolic state links neurovascular burden with brain structure and function across age: evidence from EEG and MRI

Aging affects brain structure and function alongside metabolic and vascular processes leading to energetic impairments. While local neurometabolic dysfunction in aging is well-documented, the influence of systemic cardiometabolic and vascular markers on brain structure and function remains less understood. We examine the link between cardiometabolic dysfunction (measured by an allostatic load index) and neurovascular burden (measured by white matter hyperintensities) with brain changes, including ventricular and hippocampal volume, as well as EEG activity, across age. Analyzing data from 196 healthy individuals across age (20-75 years), we found a significant positive correlation between allostatic load index and white-matter hyperintensities, irrespective of age. White-matter hyperintensities are also positively linked with ventricular enlargement, but not hippocampal atrophy. The allostatic load index mediated the relationship between white-matter hyperintensities and ventricular volume. Regarding brain function, changes in the spectral aperiodic exponent but not periodic alpha power were linked to white-matter hyperintensities and the allostatic load index. Such index mediated the relationship between spectral aperiodic exponent and white-matter hyperintensities. Thus, findings suggest that the cardiometabolic state, as measured by an allostatic load index, plays a crucial role in brain health across age, particularly influencing ventricular enlargement and increased aperiodic activity.

neuroscience↗

Model-based whole-brain perturbational landscape of neurodegenerative diseases

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimers disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.

neuroscience↗