Correlation between subcutaneous adipose tissue of the head and body mass index: Implications for functional neuroimaging.


Journal

Human movement science
ISSN: 1872-7646
Titre abrégé: Hum Mov Sci
Pays: Netherlands
ID NLM: 8300127

Informations de publication

Date de publication:
Oct 2022
Historique:
received: 25 07 2022
revised: 17 08 2022
accepted: 19 08 2022
pubmed: 31 8 2022
medline: 5 10 2022
entrez: 30 8 2022
Statut: ppublish

Résumé

High body mass index (BMI) is generally assumed to represent overall amounts of body adipose tissue (fat). Increased adipose tissue amounts in persons with increased BMI has been cited as a barrier to assessment of body tissues such as muscle. Significant increases in the amount of adipose tissue between the dermal layer and the skull may result in high electrical impedance and/or increased light diffusion causing a lower signal to noise ratio during use of neuroimaging tools such as electroencepholography (EEG), transcranial direct current stimulation (tDCS), and functional near infrared spectroscopy (fNIRS). Investigating how subcutaneous adipose tissue in the head region increases with respect to total body fat percentage and BMI is an important step in developing mathematical corrections in neuroimaging measurements as BMI increases, as recommended in other measurement modalities such as electromyography (EMG). We hypothesized that percentage of subcutaneous adipose tissue in the head region would increase with respect to both total body fat percentage and BMI. A statistically significant increase in subcutaneous head fat percentage occurred with increased BMI and total body fat percentage. The data investigated in this study indicate that participant age, sex, and BMI are important features to consider in model corrections during data signal processing and analyses for subcutaneous head fat in neuroimaging approaches. The data in this project serve to provide physiological justification for this practice along with regression analyses to be considered for physiologically-based signal to noise correction algorithms.

Identifiants

pubmed: 36041254
pii: S0167-9457(22)00077-X
doi: 10.1016/j.humov.2022.102997
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

102997

Informations de copyright

Copyright © 2022 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest None.

Auteurs

Stacey L Gorniak (SL)

Department of Health and Human Performance, University of Houston, Houston, TX 77204, United States of America. Electronic address: sgorniak@uh.edu.

Hao Meng (H)

Department of Health and Human Performance, University of Houston, Houston, TX 77204, United States of America.

Luca Pollonini (L)

Department of Engineering Technology, University of Houston, Houston, TX 77204, United States of America.

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Classifications MeSH