1
|
Pei G, Yang R, Shi Z, Guo G, Wang S, Liu M, Qiu Y, Wu J, Go R, Han Y, Yan T. Enhancing Working Memory Based on Mismatch Negativity Neurofeedback in Subjective Cognitive Decline Patients: A Preliminary Study. Front Aging Neurosci 2020; 12:263. [PMID: 33132892 PMCID: PMC7550626 DOI: 10.3389/fnagi.2020.00263] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 08/03/2020] [Indexed: 01/16/2023] Open
Abstract
Mismatch negativity (MMN) is suitable for studies of preattentive auditory discriminability and the auditory memory trace. Subjective cognitive decline (SCD) is an ideal target for early therapeutic intervention because SCD occurs at preclinical stages many years before the onset of Alzheimer’s disease (AD). According to a novel lifespan-based model of dementia risk, hearing loss is considered the greatest potentially modifiable risk factor of dementia among nine health and lifestyle factors, and hearing impairment is associated with cognitive decline. Therefore, we propose a neurofeedback training based on MMN, which is an objective index of auditory discriminability, to regulate sensory ability and memory as a non-pharmacological intervention (NPI) in SCD patients. Seventeen subjects meeting the standardized clinical evaluations for SCD received neurofeedback training. The auditory frequency discrimination test, the visual digital N-back (1-, 2-, and 3-back), auditory digital N-back (1-, 2-, and 3-back), and auditory tone N-back (1-, 2-, and 3-back) tasks were used pre- and post-training in all SCD patients. The intervention schedule comprised five 60-min training sessions over 2 weeks. The results indicate that the subjects who received neurofeedback training had successfully improved the amplitude of MMN at the parietal electrode (Pz). A slight decrease in the threshold of auditory frequency discrimination was observed after neurofeedback training. Notably, after neurofeedback training, the working memory (WM) performance was significantly enhanced in the auditory tone 3-back test. Moreover, improvements in the accuracy of all WM tests relative to the baseline were observed, although the changes were not significant. To the best of our knowledge, our preliminary study is the first to investigate the effects of MMN neurofeedback training on WM in SCD patients, and our results suggest that MMN neurofeedback may represent an effective treatment for intervention in SCD patients and the elderly with aging memory decline.
Collapse
Affiliation(s)
- Guangying Pei
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Ruoshui Yang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Zhongyan Shi
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Guoxin Guo
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Shujie Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Miaomiao Liu
- Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan
| | - Yuxiang Qiu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jinglong Wu
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China.,Faculty of Engineering, Okayama University, Okayama, Japan
| | - Ritsu Go
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
| |
Collapse
|
2
|
Lin Y, Liu B, Liu Z, Gao X. EEG gamma-band activity during audiovisual speech comprehension in different noise environments. Cogn Neurodyn 2015; 9:389-98. [PMID: 26157512 DOI: 10.1007/s11571-015-9333-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Revised: 01/15/2015] [Accepted: 01/23/2015] [Indexed: 10/24/2022] Open
Abstract
The presence of cross-modal stochastic resonance in different noise environments has been proved in previous behavioral and event-related potential studies, while it was still unclear whether the gamma-band oscillation study was another evidence of cross-modal stochastic resonance. The multisensory gain of gamma-band activity between the audiovisual (AV) and auditory-only conditions in different noise environments was analyzed. Videos of face motion articulating words concordant with different levels of pink noise were used as stimuli. Signal-to-noise ratios (SNRs) of 0, -4, -8, -12 and -16 dB were selected to measure the speech recognition accuracy and EEG activity for 20 healthy subjects. The power and phase of EEG gamma-band oscillations increased in a time window of 50-90 ms. The multisensory gains of evoked and total activity, as well as phase-locking factor, were greatest at the -12 dB SNR, which were consistent with the behavioral result. The multisensory gain of gamma-band activity showed an inverted U-shaped curve as a function of SNR. This finding confirmed the presence of cross-modal stochastic resonance. In addition, there was a significant correlation between evoked activity and phase-locking factor of gamma-band at five different SNRs. Gamma-band oscillation was believed to play a role in the rapid processing and information linkage strengthening of AV modalities in the early stage of cognitive processes.
Collapse
Affiliation(s)
- Yanfei Lin
- School of Medicine, Tsinghua University, Beijing, 100084 People's Republic of China ; School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081 People's Republic of China
| | - Baolin Liu
- School of Computer Science and Technology, Tianjin University, Tianjin, 300072 People's Republic of China
| | - Zhiwen Liu
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081 People's Republic of China
| | - Xiaorong Gao
- School of Medicine, Tsinghua University, Beijing, 100084 People's Republic of China
| |
Collapse
|
3
|
Wu W, Wu C, Gao S, Liu B, Li Y, Gao X. Bayesian estimation of ERP components from multicondition and multichannel EEG. Neuroimage 2014; 88:319-39. [DOI: 10.1016/j.neuroimage.2013.11.028] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2013] [Revised: 11/11/2013] [Accepted: 11/14/2013] [Indexed: 11/28/2022] Open
|