Dataset Information

Cortical and subcortical brain networks predict prevailing heart rate.

ABSTRACT: Resting heart rate may confer risk for cardiovascular disease (CVD) and other adverse cardiovascular events. While the brainstem's autonomic control over heart rate is well established, less is known about the regulatory role of higher-level cortical and subcortical brain regions, especially in humans. The present study sought to characterize the brain networks that predict variation in prevailing heart rate in otherwise healthy adults. We used machine learning approaches designed for complex, high-dimensional datasets, to predict variation in instantaneous heart period (the inter-heartbeat-interval) from whole brain hemodynamic signals measured by fMRI. Task-based and resting-state fMRI, as well as peripheral physiological recordings, were taken from two datasets that included extensive repeated measurements within individuals. Our models reliably predicted instantaneous heart period from whole brain fMRI data both within and across individuals, with prediction accuracies being highest when measured within-participants. We found that a network of cortical and subcortical brain regions, many linked to psychological stress, were reliable predictors of variation in heart period. This adds to evidence on brain-heart interactions and constitutes an incremental step towards developing clinically-applicable biomarkers of brain contributions to CVD risk.

SUBMITTER: Sentis AI

PROVIDER: S-EPMC10802251 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Cortical and subcortical brain networks predict prevailing heart rate.

Sentis Amy Isabella AI Rasero Javier J Gianaros Peter J PJ Verstynen Timothy D TD

bioRxiv : the preprint server for biology 20240107

Resting heart rate may confer risk for cardiovascular disease (CVD) and other adverse cardiovascular events. While the brainstem's autonomic control over heart rate is well established, less is known about the regulatory role of higher-level cortical and subcortical brain regions, especially in humans. The present study sought to characterize the brain networks that predict variation in prevailing heart rate in otherwise healthy adults. We used machine learning approaches designed for complex, h ...[more]

PMID: 38260308

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Project description:Generalized tonic-clonic seizures are among the most dramatic physiological events in the nervous system. The brain regions involved during partial seizures with secondary generalization have not been thoroughly investigated in humans. We used single photon emission computed tomography (SPECT) to image cerebral blood flow (CBF) changes in 59 secondarily generalized seizures from 53 patients. Images were analysed using statistical parametric mapping to detect cortical and subcortical regions most commonly affected in three different time periods: (i) during the partial seizure phase prior to generalization; (ii) during the generalization period; and (iii) post-ictally. We found that in the pre-generalization period, there were focal CBF increases in the temporal lobe on group analysis, reflecting the most common region of partial seizure onset. During generalization, individual patients had focal CBF increases in variable regions of the cerebral cortex. Group analysis during generalization revealed that the most consistent increase occurred in the superior medial cerebellum, thalamus and basal ganglia. Post-ictally, there was a marked progressive CBF increase in the cerebellum which spread to involve the bilateral lateral cerebellar hemispheres, as well as CBF increases in the midbrain and basal ganglia. CBF decreases were seen in the fronto-parietal association cortex, precuneus and cingulate gyrus during and following seizures, similar to the 'default mode' regions reported previously to show decreased activity in seizures and in normal behavioural tasks. Analysis of patient behaviour during and following seizures showed impaired consciousness at the time of SPECT tracer injections. Correlation analysis across patients demonstrated that cerebellar CBF increases were related to increases in the upper brainstem and thalamus, and to decreases in the fronto-parietal association cortex. These results reveal a network of cortical and subcortical structures that are most consistently involved in secondarily generalized tonic-clonic seizures. Abnormal increased activity in subcortical structures (cerebellum, basal ganglia, brainstem and thalamus), along with decreased activity in the association cortex may be crucial for motor manifestations and for impaired consciousness in tonic-clonic seizures. Understanding the networks involved in generalized tonic-clonic seizures can provide insights into mechanisms of behavioural changes, and may elucidate targets for improved therapies.

Dataset Information

Cortical and subcortical brain networks predict prevailing heart rate.

Publications

Cortical and subcortical brain networks predict prevailing heart rate.

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