Linking brain structure, cognition, and sleep: insights from clinical data

Author:

Wei Ruoqi1234ORCID,Ganglberger Wolfgang2356ORCID,Sun Haoqi235,Hadar Peter  N7,Gollub Randy  L8910,Pieper Steve11,Billot Benjamin12,Au Rhoda13,Eugenio Iglesias Juan9101114,Cash Sydney S7,Kim Soriul15,Shin Chol1516,Brandon Westover M235,Joseph Thomas Robert1310

Affiliation:

1. Division of Pulmonary Critical Care & Sleep Medicine, Department of Medicine, Beth Israel Deaconess Medical Center , Boston, MA , USA

2. McCance Center for Brain Health, Massachusetts General Hospital , Boston, MA , USA

3. Division of Sleep Medicine, Harvard Medical School , Boston, Massachusetts , USA

4. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida , Gainesville, FL , USA

5. Department of Neurology, Beth Israel Deaconess Medical Center , Boston, MA , USA

6. Sleep and Health Zurich, University of Zurich , Zurich , Switzerland

7. Department of Neurology, Massachusetts General Hospital , Boston, MA , USA

8. Department of Psychiatry, Massachusetts General Hospital , Boston, MA , USA

9. Department of Radiology, Massachusetts General Hospital , Boston, MA , USA

10. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Boston, MA , USA

11. Isomics, Inc. Cambridge , MA , USA

12. Computer Science and Artificial Intelligence Lab , MIT, Boston, MA , USA

13. Anatomy& Neurobiology, Neurology, Medicine and Epidemiology, Boston University Chobanian & Avedisian School of Medicine and School of Public Health, Boston University , Boston, MA , USA

14. Center for Medical Image Computing, University College London , London , UK

15. Institute of Human Genomic Study, College of Medicine, Kore University , Seoul , Republic of Korea

16. Biomedical Research Center, Korea University Ansan Hospital , Ansan , Republic of Korea

Abstract

Abstract Study Objectives To use relatively noisy routinely collected clinical data (brain magnetic resonance imaging (MRI) data, clinical polysomnography (PSG) recordings, and neuropsychological testing), to investigate hypothesis-driven and data-driven relationships between brain physiology, structure, and cognition. Methods We analyzed data from patients with clinical PSG, brain MRI, and neuropsychological evaluations. SynthSeg, a neural network-based tool, provided high-quality segmentations despite noise. A priori hypotheses explored associations between brain function (measured by PSG) and brain structure (measured by MRI). Associations with cognitive scores and dementia status were studied. An exploratory data-driven approach investigated age-structure-physiology-cognition links. Results Six hundred and twenty-three patients with sleep PSG and brain MRI data were included in this study; 160 with cognitive evaluations. Three hundred and forty-two participants (55%) were female, and age interquartile range was 52 to 69 years. Thirty-six individuals were diagnosed with dementia, 71 with mild cognitive impairment, and 326 with major depression. One hundred and fifteen individuals were evaluated for insomnia and 138 participants had an apnea–hypopnea index equal to or greater than 15. Total PSG delta power correlated positively with frontal lobe/thalamic volumes, and sleep spindle density with thalamic volume. rapid eye movement (REM) duration and amygdala volume were positively associated with cognition. Patients with dementia showed significant differences in five brain structure volumes. REM duration, spindle, and slow-oscillation features had strong associations with cognition and brain structure volumes. PSG and MRI features in combination predicted chronological age (R2 = 0.67) and cognition (R2 = 0.40). Conclusions Routine clinical data holds extended value in understanding and even clinically using brain-sleep-cognition relationships.

Funder

NIH

NSF

Publisher

Oxford University Press (OUP)

Subject

Physiology (medical),Neurology (clinical)

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