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Dziego CA, Bornkessel-Schlesewsky I, Schlesewsky M, Sinha R, Immink MA, Cross ZR. Augmenting complex and dynamic performance through mindfulness-based cognitive training: An evaluation of training adherence, trait mindfulness, personality and resting-state EEG. PLoS One 2024; 19:e0292501. [PMID: 38768220 PMCID: PMC11104625 DOI: 10.1371/journal.pone.0292501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 05/03/2024] [Indexed: 05/22/2024] Open
Abstract
Human performance applications of mindfulness-based training have demonstrated its utility in enhancing cognitive functioning. Previous studies have illustrated how these interventions can improve performance on traditional cognitive tests, however, little investigation has explored the extent to which mindfulness-based training can optimise performance in more dynamic and complex contexts. Further, from a neuroscientific perspective, the underlying mechanisms responsible for performance enhancements remain largely undescribed. With this in mind, the following study aimed to investigate how a short-term mindfulness intervention (one week) augments performance on a dynamic and complex task (target motion analyst task; TMA) in young, healthy adults (n = 40, age range = 18-38). Linear mixed effect modelling revealed that increased adherence to the web-based mindfulness-based training regime (ranging from 0-21 sessions) was associated with improved performance in the second testing session of the TMA task, controlling for baseline performance. Analyses of resting-state electroencephalographic (EEG) metrics demonstrated no change across testing sessions. Investigations of additional individual factors demonstrated that enhancements associated with training adherence remained relatively consistent across varying levels of participants' resting-state EEG metrics, personality measures (i.e., trait mindfulness, neuroticism, conscientiousness), self-reported enjoyment and timing of intervention adherence. Our results thus indicate that mindfulness-based cognitive training leads to performance enhancements in distantly related tasks, irrespective of several individual differences. We also revealed nuances in the magnitude of cognitive enhancements contingent on the timing of adherence, regardless of total volume of training. Overall, our findings suggest that mindfulness-based training could be used in a myriad of settings to elicit transferable performance enhancements.
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Affiliation(s)
- Chloe A. Dziego
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
| | - Ina Bornkessel-Schlesewsky
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
| | - Matthias Schlesewsky
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
| | - Ruchi Sinha
- Centre for Workplace Excellence, University of South Australia, Adelaide, South Australia
| | - Maarten A. Immink
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
- Sport, Health, Activity, Performance and Exercise (SHAPE) Research Centre, Flinders University, Adelaide, Australia
| | - Zachariah R. Cross
- Cognitive Neuroscience Laboratory, Australian Research Centre for Interactive and Virtual Environments, University of South Australia, Adelaide, Australia
- Department of Medical Social Sciences, Northwestern Feinberg School of Medicine, Chicago, Illinois, United States of America
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Sonkusare S, Ding Q, Zhang Y, Wang L, Gong H, Mandali A, Manssuer L, Zhao YJ, Pan Y, Zhang C, Li D, Sun B, Voon V. Power signatures of habenular neuronal signals in patients with bipolar or unipolar depressive disorders correlate with their disease severity. Transl Psychiatry 2022; 12:72. [PMID: 35194027 PMCID: PMC8863838 DOI: 10.1038/s41398-022-01830-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 01/17/2022] [Accepted: 01/28/2022] [Indexed: 01/03/2023] Open
Abstract
The habenula is an epithalamic structure implicated in negative reward mechanisms and plays a downstream modulatory role in regulation of dopaminergic and serotonergic functions. Human and animal studies show its hyperactivity in depression which is curtailed by the antidepressant response of ketamine. Deep brain stimulation of habenula (DBS) for major depression have also shown promising results. However, direct neuronal activity of habenula in human studies have rarely been reported. Here, in a cross-sectional design, we acquired both spontaneous resting state and emotional task-induced neuronal recordings from habenula from treatment resistant depressed patients undergoing DBS surgery. We first characterise the aperiodic component (1/f slope) of the power spectrum, interpreted to signify excitation-inhibition balance, in resting and task state. This aperiodicity for left habenula correlated between rest and task and which was significantly positively correlated with depression severity. Time-frequency responses to the emotional picture viewing task show condition differences in beta and gamma frequencies for left habenula and alpha for right habenula. Notably, alpha activity for right habenula was negatively correlated with depression severity. Overall, from direct habenular recordings, we thus show findings convergent with depression models of aberrant excitatory glutamatergic output of the habenula driving inhibition of monoaminergic systems.
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Affiliation(s)
- Saurabh Sonkusare
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom ,grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ,grid.8547.e0000 0001 0125 2443Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Qiong Ding
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingying Zhang
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linbin Wang
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hengfen Gong
- grid.24516.340000000123704535Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
| | - Alekhya Mandali
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Luis Manssuer
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom ,grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China ,grid.8547.e0000 0001 0125 2443Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yi-Jie Zhao
- grid.8547.e0000 0001 0125 2443Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Yixin Pan
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chencheng Zhang
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dianyou Li
- grid.16821.3c0000 0004 0368 8293Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Valerie Voon
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom. .,Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. .,Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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Boubakeur MR, Wang G. Self-Relative Evaluation Framework for EEG-Based Biometric Systems. SENSORS (BASEL, SWITZERLAND) 2021; 21:2097. [PMID: 33802708 PMCID: PMC8002517 DOI: 10.3390/s21062097] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/14/2021] [Accepted: 03/15/2021] [Indexed: 11/16/2022]
Abstract
In recent years, electroencephalogram (EEG) signals have been used as a biometric modality, and EEG-based biometric systems have received increasing attention. However, due to the sensitive nature of EEG signals, the extraction of identity information through processing techniques may lead to some loss in the extracted identity information. This may impact the distinctiveness between subjects in the system. In this context, we propose a new self-relative evaluation framework for EEG-based biometric systems. The proposed framework aims at selecting a more accurate identity information when the biometric system is open to the enrollment of novel subjects. The experiments were conducted on publicly available EEG datasets collected from 108 subjects in a resting state with closed eyes. The results show that the openness condition is useful for selecting more accurate identity information.
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Affiliation(s)
- Meriem Romaissa Boubakeur
- Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
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