Associations of Brain Entropy Estimated by Resting State fMRI With Physiological Indices, Body Mass Index, and Cognition

• Published in: Journal of magnetic resonance imaging Vol. 59; no. 5; pp. 1697 - 1707
• Main Authors: Del Mauro, Gianpaolo, Wang, Ze
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.05.2024
• Subjects: Adult; Blood pressure; Body Mass Index; Body size; Brain; Brain mapping; Brain Mapping - methods; Cognition; Cognition & reasoning; Connectome; Cortex (parietal); Data processing; Datasets; Entropy; Field strength; Functional magnetic resonance imaging; Heart rate; Humans; Information processing; Limbic system; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mathematical models; Multiple regression models; Neuroimaging; Physiological effects; Physiology; Regression analysis; Respiration; Respiratory rate; Retrospective Studies; Sensorimotor system; Statistical analysis; Statistical tests; Thresholds; blood pressure; cognition; brain entropy; resting state fMRI; body mass index; respiratory rate
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.28948

In recent years, resting-state fMRI (rsfMRI)-based brain entropy (BEN) has gained increasing interest as a tool to characterize brain activity. While previous studies indicate that BEN is correlated with cognition, it remains unclear whether BEN is influenced by other factors that typically affect brain activity measured by fMRI. To investigate the relationship between BEN and physiological indices, including respiratory rate (RR), heart rate (HR), systolic blood pressure (s-BP), and body mass index (BMI), and to investigate whether and to what extent the relationship between BEN and cognition is influenced by physiological variables. Retrospective. One thousand two hundred six healthy subjects (mean age: 28.83 ± 3.69 years; 550 male) with rsfMRI datasets selected from the Human Connectome Project (HCP). Multiband echo planar imaging (EPI) sequence at 3.0 Tesla. Neurocognitive, physical health (RR, HR, s-BP, BMI), and rsfMRI data were retrieved from the HCP datasets. Neurocognition was measured through the total cognition composite (TCC) score provided by HCP. BEN maps were calculated from rsfMRI data. Multiple regression models, p -family wise error (FWE) < 0.05 and p -FWE < 0.05 were considered statistically significant. BEN was negatively associated with RR (T-thresholds ranging from 4.75 to 4.8; r-threshold = |0.15|) and positively associated with s-BP and BMI (T-thresholds ranging from 4.75 to 4.8; r-threshold = |0.15|) in areas overlapping with the default mode network. After controlling the physiological effects, BEN still showed regional associations with TCC, including negative associations (T-thresholds = 3.09; r-threshold = |0.1|) in the fronto-parietal cortex and positive associations (T-thresholds = 3.09; r-threshold = |0.1|) in the sensorimotor system (motor network and the limbic system). RR negatively affects rsfMRI-derived BEN, while s-BP and BMI positively affect BEN. The positive associations between BEN and cognition in the motor network and the limbic system might indicate a facilitation of information processing in the sensorimotor system. 3 TECHNICAL EFFICACY: Stage 3.