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P300, grey matter volume and individual characteristics correlates in healthy elderly

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We investigated whether P300-ERP and cognitive test performance differ for age, sex,and education in two groups of healthy elderly, and verified whether any correlationsexist between P300 amplitude and latency and gray matter volume using whole brainvoxel-by-voxel-based mapping, controlling for age, education, sex and Total IntracranialVolume (TIV). We used 32 channel electroencephalograms (EEG) to record the P300responses and 3T Magnetic Resonance Imaging (MRI) to determine gray matter volume.We recruited 36 native-Dutch speaking healthy older subjects, equally divided in twosub-groups of 52–64 and 65–76 years old, administered a battery of cognitive testsand recorded their demographics, EEGs and task performance; additionally, 16 adultsfrom the second sub-group underwent an MRI scan. We found significant differencesbetween age groups in their cognitive tests performance, P300 amplitudes for the frontaland parietal electrodes for the most difficult task, and P300 latencies for frontal, centraland parietal electrodes for all three tasks difficulty levels. Interesting, sex and educationaffected cognitive and P300 results. Higher education was related to higher accuracy,and P300 amplitudes and shorter latencies. Moreover, females exhibited higher P300amplitudes and shorter latencies, and better cognitive tasks performance comparedto males. Additionally, for the 16 adults underwent to MRI scan, we found positivecorrelations between P300 characteristics in frontal, central and parietal areas and graymatter volume, controlling for demographic variables and TIV, but also showing that age,sex, and education correlate with gray matter volume. These findings provide supportthat age, sex, and education affect an individual’s cognitive, neurophysiological andstructural characteristics, and therefore motivate the need to further investigate thesein relation to P300 responses and gray matter volume in healthy elderly.
Tijdschrift: Frontiers in Aging Neuroscience
ISSN: 1663-4365
Volume: 11
Pagina's: 1 - 13
Jaar van publicatie:2019