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Bakhtiari A, Petersen J, Urdanibia-Centelles O, Ghazi MM, Fagerlund B, Mortensen EL, Osler M, Lauritzen M, Benedek K. Power and distribution of evoked gamma oscillations in brain aging and cognitive performance. GeroScience 2023:10.1007/s11357-023-00749-x. [PMID: 36763241 PMCID: PMC10400513 DOI: 10.1007/s11357-023-00749-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 01/31/2023] [Indexed: 02/11/2023] Open
Abstract
AIMS Gamma oscillations (≈25-100 Hz) are believed to play an essential role in cognition, and aberrant gamma oscillations occur in brain aging and neurodegeneration. This study examined age-related changes in visually evoked gamma oscillations at two different time points 5 years apart and tested the hypothesis that the power of gamma oscillations correlated to cognitive skills. METHODS The cohort consists of elderly males belonging to the Metropolit 1953 Danish Male Birth Cohort (first visit, N=124; second visit, N=88) over a 5-year period from 63 to 68 years of age. Cognitive functions were assessed using a neuropsychological test battery measuring global cognition, intelligence, memory, and processing speed. The power of steady-state visual evoked potentials (SSVEP) was measured at 8 Hz (alpha) and 36 Hz (gamma) frequencies using EEG scalp electrodes. RESULTS Over the 5-year period cognitive performance remained relatively stable while the power of visually evoked gamma oscillations shifted from posterior to anterior brain regions with increasing age. A higher-than-average cognitive score was correlated with higher gamma power in parieto-occipital areas and lower in frontocentral areas, i.e., preserved distribution of the evoked activity. CONCLUSIONS Our data reveal that the distribution of visually evoked gamma activity becomes distributed with age. Preserved posterior-occipital gamma power in participants with a high level of cognitive performance is consistent with a close association between the ability to produce gamma oscillations and cognition. The data may contribute to our understanding of the mechanisms that link evoked gamma activity and cognition in the aging brain.
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Affiliation(s)
- Aftab Bakhtiari
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. .,Center for Healthy Aging, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. .,Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Jacob Petersen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Olalla Urdanibia-Centelles
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Mostafa Mehdipour Ghazi
- Pioneer Centre for Artificial Intelligence, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Birgitte Fagerlund
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark.,Child and Adolescent Mental Health Center, Copenhagen University Hospital - Mental Health Services CPH, Copenhagen, Denmark
| | | | - Merete Osler
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark.,Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark
| | - Martin Lauritzen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.,Center for Healthy Aging, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Krisztina Benedek
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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Floriano A, Delisle-Rodriguez D, Diez PF, Bastos-Filho TF. Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 184:105271. [PMID: 31881401 DOI: 10.1016/j.cmpb.2019.105271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 11/21/2019] [Accepted: 12/10/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Recently, a promising Brain-Computer Interface based on Steady-State Visual Evoked Potential (SSVEP-BCI) was proposed, which composed of two stimuli presented together in the center of the subject's field of view, but at different depth planes (Depth-of-Field setup). Thus, users were easily able to select one of them by shifting their eye focus. However, in that work, EEG signals were collected through electrodes placed on occipital and parietal regions (hair-covered areas), which demanded a long preparation time. Also, that work used low-frequency stimuli, which can produce visual fatigue and increase the risk of photosensitive epileptic seizures. In order to improve the practicality and visual comfort, this work proposes a BCI based on Depth-of-Field using the high-frequency SSVEP response measured from below-the-hairline areas (behind-the-ears). METHODS Two high-frequency stimuli (31 Hz and 32 Hz) were used in a Depth-of-Field setup to study the SSVEP response from behind-the-ears (TP9 and TP10). Multivariate Spectral F-test (MSFT) method was used to verify the elicited response. Afterwards, a BCI was proposed to command a mobile robot in a virtual reality environment. The commands were recognized through Temporally Local Multivariate Synchronization Index (TMSI) method. RESULTS The data analysis reveal that the focused stimuli elicit distinguishable SSVEP response when measured from hairless areas, in spite of the fact that the non-focused stimulus is also present in the field of view. Also, our BCI shows a satisfactory result, reaching average accuracy of 91.6% and Information Transfer Rate (ITR) of 5.3 bits/min. CONCLUSION These findings contribute to the development of more safe and practical BCI.
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Affiliation(s)
- Alan Floriano
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil.
| | - Denis Delisle-Rodriguez
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria, Brazil.
| | - Pablo F Diez
- Gabinete de Tecnologia Medica (GATEME), Facultad de Ingenieria, Universidad Nacional de San Juan, San Juan, Argentina.
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