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Guo F, Wang C, Tao G, Ma H, Zhang J, Wang Y. A longitudinal study on the impact of high-altitude hypoxia on perceptual processes. Psychophysiology 2024; 61:e14548. [PMID: 38385977 DOI: 10.1111/psyp.14548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024]
Abstract
This study aimed to explore the neural mechanisms underlying high-altitude (HA) adaptation and deadaptation in perceptual processes in lowlanders. Eighteen healthy lowlanders were administered a facial S1-S2 matching task that included incomplete face (S1) and complete face (S2) photographs combined with ERP technology. Participants were tested at four time points: shortly before they departed the HA (Test 1), twenty-five days after entering the HA (Test 2), and one week (Test 3) and one month (Test 4) after returning to the lowlands. Compared with those at sea level (SL), shorter reaction times (RTs), shorter latencies of P1 and N170, and larger amplitudes of complete face N170 were found in HAs. After returning to SL, compared with that of HA, the amplitude of the incomplete face P1 was smaller after one week, and the complete face was smaller after one month. The right hemisphere N170 amplitude was greater after entering HA and one week after returning to SL than at baseline, but it returned to baseline after one month. Taken together, the current findings suggest that HA adaptation increases visual cortex excitation to accelerate perceptual processing. More mental resources are recruited during the configural encoding stage of complete faces after HA exposure. The perceptual processes affected by HA exposure are reversible after returning to SL, but the low-level processing stage differs between incomplete and complete faces due to neural compensation mechanisms. The configural encoding stage in the right hemisphere is affected by HA exposure and requires more than one week but less than one month to recover to baseline.
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Affiliation(s)
- Fumei Guo
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Changming Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Getong Tao
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Hailin Ma
- Plateau Brain Science Research Center, Tibet University/South China Normal University, Guangzhou/Tibet, China
| | - Jiaxing Zhang
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, China
| | - Yan Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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Su R, Jia S, Zhang N, Wang Y, Li H, Zhang D, Ma H, Su Y. The effects of long-term high-altitude exposure on cognition: A meta-analysis. Neurosci Biobehav Rev 2024; 161:105682. [PMID: 38642865 DOI: 10.1016/j.neubiorev.2024.105682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Revised: 04/14/2024] [Accepted: 04/16/2024] [Indexed: 04/22/2024]
Abstract
Long-term high altitudes (HA) exposure's impact on cognition has yielded inconsistent findings in previous research. To address this, we conducted a meta-analysis of 49 studies (6191 individuals) to comprehensively evaluate this effect. Moderating factors such as cognitive task type, altitude (1500-2500 m, 2500-4000 m, and above 4000 m), residential type (chronic and lifelong), adaptation level and demographic factors were analyzed. Cognitive tasks were classified into eight categories: perceptual processes, psychomotor function, long-term memory, working memory, inhibitory control, problem-solving, language, and others. Results revealed a moderate negative effect of HA on cognitive performance (g = -.40, SE =.18, 95% CI = -.76 to -.05). Psychomotor function and long-term memory notably experience the most pronounced decline, while working memory and language skills show moderate decreases due to HA exposure. However, perceptual processes, inhibitory control, and problem-solving abilities remain unaffected. Moreover, residing at altitudes above 4000 m and being a HA immigrant are associated with significant cognitive impairment. In summary, our findings indicate a selective adaptation of cognitive performance to HA conditions.
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Affiliation(s)
- Rui Su
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China; Key Laboratory of High Altitudes Brain Science and Environmental Acclimation, Tibet University, Lhasa 85000, China
| | - Shurong Jia
- Key Laboratory of High Altitudes Brain Science and Environmental Acclimation, Tibet University, Lhasa 85000, China
| | - Niannian Zhang
- Key Laboratory of High Altitudes Brain Science and Environmental Acclimation, Tibet University, Lhasa 85000, China
| | - Yiyi Wang
- Department of Psychology, University of Chicago, Chicago, IL 60637, United States
| | - Hao Li
- Key Laboratory of High Altitudes Brain Science and Environmental Acclimation, Tibet University, Lhasa 85000, China
| | - Delong Zhang
- Key Laboratory of High Altitudes Brain Science and Environmental Acclimation, Tibet University, Lhasa 85000, China; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Hailin Ma
- Key Laboratory of High Altitudes Brain Science and Environmental Acclimation, Tibet University, Lhasa 85000, China
| | - Yanjie Su
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China.
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Ahuja H, Badhwar S, Edgell H, Litoiu M, Sergio LE. Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME. Front Hum Neurosci 2024; 18:1359162. [PMID: 38638805 PMCID: PMC11024369 DOI: 10.3389/fnhum.2024.1359162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/15/2024] [Indexed: 04/20/2024] Open
Abstract
The COVID-19 pandemic has affected millions worldwide, giving rise to long-term symptoms known as post-acute sequelae of SARS-CoV-2 (PASC) infection, colloquially referred to as long COVID. With an increasing number of people experiencing these symptoms, early intervention is crucial. In this study, we introduce a novel method to detect the likelihood of PASC or Myalgic Encephalomyelitis (ME) using a wearable four-channel headband that collects Electroencephalogram (EEG) data. The raw EEG signals are processed using Continuous Wavelet Transform (CWT) to form a spectrogram-like matrix, which serves as input for various machine learning and deep learning models. We employ models such as CONVLSTM (Convolutional Long Short-Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional Long short-term memory). Additionally, we test the dataset on traditional machine learning models for comparative analysis. Our results show that the best-performing model, CNN-LSTM, achieved an accuracy of 83%. In addition to the original spectrogram data, we generated synthetic spectrograms using Wasserstein Generative Adversarial Networks (WGANs) to augment our dataset. These synthetic spectrograms contributed to the training phase, addressing challenges such as limited data volume and patient privacy. Impressively, the model trained on synthetic data achieved an average accuracy of 93%, significantly outperforming the original model. These results demonstrate the feasibility and effectiveness of our proposed method in detecting the effects of PASC and ME, paving the way for early identification and management of the condition. The proposed approach holds significant potential for various practical applications, particularly in the clinical domain. It can be utilized for evaluating the current condition of individuals with PASC or ME, and monitoring the recovery process of those with PASC, or the efficacy of any interventions in the PASC and ME populations. By implementing this technique, healthcare professionals can facilitate more effective management of chronic PASC or ME effects, ensuring timely intervention and improving the quality of life for those experiencing these conditions.
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Affiliation(s)
- Harit Ahuja
- School of Information Technology, York University, Toronto, ON, Canada
| | - Smriti Badhwar
- School of Kinesiology and Health Science, York University, Toronto, ON, Canada
| | - Heather Edgell
- School of Kinesiology and Health Science, York University, Toronto, ON, Canada
| | - Marin Litoiu
- School of Information Technology, York University, Toronto, ON, Canada
- Lassonde School of Engineering, York University, Toronto, ON, Canada
| | - Lauren E. Sergio
- School of Information Technology, York University, Toronto, ON, Canada
- School of Kinesiology and Health Science, York University, Toronto, ON, Canada
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Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network. Bioengineering (Basel) 2023; 10:bioengineering10030361. [PMID: 36978752 PMCID: PMC10044910 DOI: 10.3390/bioengineering10030361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/11/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023] Open
Abstract
In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners’ cognitive load in real time using wireless portable EEG systems.
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