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P#202

Central Autonomic Network association with cardiac autonomic sleep-wake rhythm in healthy adults.

Agustina Wainsztein

  • CABA,
  • Argentina
  • Agustina E. Wainsztein ¹
  • , Vicente Camacho-Téllez ¹
  • , Carolina Abulafia ¹
  • , Mirta Villarreal ¹
  • , Daniel Vigo ¹
  • , Ximena Goldberg ²
  • , Carles Soriano-Mas ³
  • , Salvador M. Guinjoan ¹
  • , Mariana Castro ¹
  • 1 Grupo INAAC, FLENI-CONICET Neurosciences Institute, Buenos Aires, Argentina
  • 2 Parc Taulí University Hospital, Universitat Autònoma de Barcelona, Spain
  • 3 Bellvitge Biomedical Research Institute-IDIBELL Barcelona, Spain

BACKGROUND: Resting heart rate variability (HRV) has been shown as an index of inhibitory control, adaptability and health. The Central Autonomic Network (CAN) comprises brain regions also involved in behavioral and emotional processes. Evidence on the relationship of cortical thickness of CAN components and HRV sleep-wake cycle is scarce. We investigated neural correlates of HRV chronobiological patterns in healthy individuals. METHODS: Thirteen participants (10 female; mean age: 36 ±12) underwent 24hr-HRV recordings following structural MRI. Cortical and subcortical volumetry were performed (Freesurfer). Sleep-wake HRV measures (i.e, HF and RMSSD) were calculated and correlated with cortical thickness of CAN regions. Self-reported mental health measures were examined. RESULTS: Cortical thickness of right anterior cingulate (ACC) and insula, bilateral precentral and inferior frontal gyrus (IFG) areas, were positively correlated with sleep-HF and RMSSD (p<.05). Increased diurnal-HF and RMSSD were associated with greater orbitofrontal, and right ACC and insula respectively (p<.05). CONCLUSIONS: Greater cortical thickness of CAN regions may be associated with increased sleep and wake cardiac parasympathetic control in healthy adults. HRV chronobiological patterns could serve as an index of preserved neural architecture and functioning subsequently leading to emotional well-being. Future research should aim to identify early neurophysiological biomarkers of psychopathology.

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