Peter R Boord1, Chris J Rennie, Leanne M Williams. 1. The Brain Dynamics Center, Westmead Millennium Institute, Westmead Hospital and Western Clinical School, University of Sydney, Westmead Hospital, Westmead, NSW 2145, Australia. peter_boord@brainresource.com
Abstract
AIMS: This study investigated the relationship between electroencephalograph (EEG) power and basal metabolic rate (BMR) over the human lifespan, to better understand the mechanisms involved in the decline of neural activity with age. METHODS: Eyes-open EEG power was calculated in standard frequency bands and averaged across recording sites in 1831 healthy subjects aged 6 to 86 years, from the Brain Resource International Database. In a subset of 175 subjects, structural MRI scans were also undertaken to determine the role of grey matter. Cerebral metabolic rate (CMR) was estimated using two models of EEG power, based on: (1) normalization of BMR by total body mass, and (2) scaling by cortical grey matter. RESULTS: Regression analysis revealed a linear relationship between the CMR estimates and EEG power under both models. In the full sample, CMR explained 65% of the variance in delta power, and 53% of the variance in theta power over the age span. DISCUSSION: The results demonstrate that the large EEG signals in early childhood are associated with a higher BMR during that age. INTEGRATIVE SIGNIFICANCE: The use of cross-modal measurements in this study highlights the utility of capturing data in an integrative framework to reveal fundamental physiological relationships.
AIMS: This study investigated the relationship between electroencephalograph (EEG) power and basal metabolic rate (BMR) over the human lifespan, to better understand the mechanisms involved in the decline of neural activity with age. METHODS: Eyes-open EEG power was calculated in standard frequency bands and averaged across recording sites in 1831 healthy subjects aged 6 to 86 years, from the Brain Resource International Database. In a subset of 175 subjects, structural MRI scans were also undertaken to determine the role of grey matter. Cerebral metabolic rate (CMR) was estimated using two models of EEG power, based on: (1) normalization of BMR by total body mass, and (2) scaling by cortical grey matter. RESULTS: Regression analysis revealed a linear relationship between the CMR estimates and EEG power under both models. In the full sample, CMR explained 65% of the variance in delta power, and 53% of the variance in theta power over the age span. DISCUSSION: The results demonstrate that the large EEG signals in early childhood are associated with a higher BMR during that age. INTEGRATIVE SIGNIFICANCE: The use of cross-modal measurements in this study highlights the utility of capturing data in an integrative framework to reveal fundamental physiological relationships.
Authors: Chella Kamarajan; Ashwini K Pandey; David B Chorlian; Niklas Manz; Arthur T Stimus; Lance O Bauer; Victor M Hesselbrock; Marc A Schuckit; Samuel Kuperman; John Kramer; Bernice Porjesz Journal: Int J Psychophysiol Date: 2015-09-18 Impact factor: 2.997
Authors: Eduardo López-Caneda; Fernando Cadaveira; Angeles Correas; Alberto Crego; Fernando Maestú; Socorro Rodríguez Holguín Journal: Front Behav Neurosci Date: 2017-09-14 Impact factor: 3.558
Authors: Chella Kamarajan; Ashwini K Pandey; David B Chorlian; Niklas Manz; Arthur T Stimus; Andrey P Anokhin; Lance O Bauer; Samuel Kuperman; John Kramer; Kathleen K Bucholz; Marc A Schuckit; Victor M Hesselbrock; Bernice Porjesz Journal: PLoS One Date: 2015-11-18 Impact factor: 3.240