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Noble SC, Woods E, Ward T, Ringwood JV. Accelerating P300-based neurofeedback training for attention enhancement using iterative learning control: a randomised controlled trial. J Neural Eng 2024; 21:026006. [PMID: 38394680 DOI: 10.1088/1741-2552/ad2c9e] [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: 07/26/2023] [Accepted: 02/23/2024] [Indexed: 02/25/2024]
Abstract
Objective. Neurofeedback (NFB) training through brain-computer interfacing has demonstrated efficacy in treating neurological deficits and diseases, and enhancing cognitive abilities in healthy individuals. It was previously shown that event-related potential (ERP)-based NFB training using a P300 speller can improve attention in healthy adults by incrementally increasing the difficulty of the spelling task. This study aims to assess the impact of task difficulty adaptation on ERP-based attention training in healthy adults. To achieve this, we introduce a novel adaptation employing iterative learning control (ILC) and compare it against an existing method and a control group with random task difficulty variation.Approach. The study involved 45 healthy participants in a single-blind, three-arm randomised controlled trial. Each group underwent one NFB training session, using different methods to adapt task difficulty in a P300 spelling task: two groups with personalised difficulty adjustments (our proposed ILC and an existing approach) and one group with random difficulty. Cognitive performance was evaluated before and after the training session using a visual spatial attention task and we gathered participant feedback through questionnaires.Main results. All groups demonstrated a significant performance improvement in the spatial attention task post-training, with an average increase of 12.63%. Notably, the group using the proposed iterative learning controller achieved a 22% increase in P300 amplitude during training and a 17% reduction in post-training alpha power, all while significantly accelerating the training process compared to other groups.Significance. Our results suggest that ERP-based NFB training using a P300 speller effectively enhances attention in healthy adults, with significant improvements observed after a single session. Personalised task difficulty adaptation using ILC not only accelerates the training but also enhances ERPs during the training. Accelerating NFB training, while maintaining its effectiveness, is vital for its acceptability by both end-users and clinicians.
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Affiliation(s)
- S-C Noble
- Department of Electronic Engineering, Maynooth University, Maynooth, Ireland
| | - E Woods
- Discipline of Physiology, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - T Ward
- Insight SFI Research Centre for Data Analytics, Dublin City University, Dublin, Ireland
| | - J V Ringwood
- Department of Electronic Engineering, Maynooth University, Maynooth, Ireland
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Paban V, Mheich A, Spieser L, Sacher M. A multidimensional model of memory complaints in older individuals and the associated hub regions. Front Aging Neurosci 2023; 15:1324309. [PMID: 38187362 PMCID: PMC10771290 DOI: 10.3389/fnagi.2023.1324309] [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: 10/19/2023] [Accepted: 11/27/2023] [Indexed: 01/09/2024] Open
Abstract
Memory complaints are highly prevalent among middle-aged and older adults, and they are frequently reported in individuals experiencing subjective cognitive decline (SCD). SCD has received increasing attention due to its implications for the early detection of dementia. This study aims to advance our comprehension of individuals with SCD by elucidating potential cognitive/psychologic-contributing factors and characterizing cerebral hubs within the brain network. To identify these potential contributing factors, a structural equation modeling approach was employed to investigate the relationships between various factors, such as metacognitive beliefs, personality, anxiety, depression, self-esteem, and resilience, and memory complaints. Our findings revealed that self-esteem and conscientiousness significantly influenced memory complaints. At the cerebral level, analysis of delta and theta electroencephalographic frequency bands recorded during rest was conducted to identify hub regions using a local centrality metric known as betweenness centrality. Notably, our study demonstrated that certain brain regions undergo changes in their hub roles in response to the pathology of SCD. Specifically, the inferior temporal gyrus and the left orbitofrontal area transition into hubs, while the dorsolateral prefrontal cortex and the middle temporal gyrus lose their hub function in the presence of SCD. This rewiring of the neural network may be interpreted as a compensatory response employed by the brain in response to SCD, wherein functional connectivity is maintained or restored by reallocating resources to other regions.
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Affiliation(s)
- Véronique Paban
- Aix-Marseille Université, CNRS, LNC (Laboratoire de Neurosciences Cognitives–UMR 7291), Marseille, France
| | - A. Mheich
- CHUV-Centre Hospitalier Universitaire Vaudois, Service des Troubles du Spectre de l’Autisme et Apparentés, Lausanne University Hospital, Lausanne, Switzerland
| | - L. Spieser
- Aix-Marseille Université, CNRS, LNC (Laboratoire de Neurosciences Cognitives–UMR 7291), Marseille, France
| | - M. Sacher
- University of Toulouse Jean-Jaurès, CNRS, LCLLE (Laboratoire Cognition, Langues, Langage, Ergonomie–UMR 5263), Toulouse, France
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Liu Q, Shi Z, Wang K, Liu T, Funahashi S, Wu J, Zhang J. Treatment Enhances Betweenness Centrality of Fronto-Parietal Network in Parkinson’s Patients. Front Comput Neurosci 2022; 16:891384. [PMID: 35720771 PMCID: PMC9204483 DOI: 10.3389/fncom.2022.891384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/05/2022] [Indexed: 11/18/2022] Open
Abstract
Previous studies have demonstrated a close relationship between early Parkinson’s disease and functional network abnormalities. However, the pattern of brain changes in the early stages of Parkinson’s disease has not been confirmed, which has important implications for the study of clinical indicators of Parkinson’s disease. Therefore, we investigated the functional connectivity before and after treatment in patients with early Parkinson’s disease, and further investigated the relationship between some topological properties and clinicopathological indicators. We included resting state-fMRI (rs-fMRI) data from 27 patients with early Parkinson’s disease aged 50–75 years from the Parkinson’s Disease Progression Markers Initiative (PPMI). The results showed that the functional connectivity of 6 networks, cerebellum network (CBN), cingulo_opercular network (CON), default network (DMN), fronto-parietal network (FPN), occipital network (OCC), and sensorimotor network (SMN), was significantly changed. Compared to before treatment, the main functional connections were concentrated in the CBN after treatment. In addition, the coefficients of these nodes have also changed. For betweenness centrality (BC), the FPN showed a significant improvement in treatment (p < 0.001). In conclusion, the alteration of functional networks in early Parkinson’s patients is critical for clarifying the mechanisms of early diagnosis of the disease.
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Affiliation(s)
- Qing Liu
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - ZhongYan Shi
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Kexin Wang
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Tiantian Liu
- Laboratory for Brain Science and Neurotechnology, School of Life Sciences, Beijing Institute of Technology, Beijing, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China
| | - Jinglong Wu
- Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- *Correspondence: Jinglong Wu,
| | - Jian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, China
- Jian Zhang,
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Wang J, Wang K, Liu T, Wang L, Suo D, Xie Y, Funahashi S, Wu J, Pei G. Abnormal Dynamic Functional Networks in Subjective Cognitive Decline and Alzheimer's Disease. Front Comput Neurosci 2022; 16:885126. [PMID: 35586480 PMCID: PMC9108158 DOI: 10.3389/fncom.2022.885126] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Subjective cognitive decline (SCD) is considered to be the preclinical stage of Alzheimer's disease (AD) and has the potential for the early diagnosis and intervention of AD. It was implicated that CSF-tau, which increases very early in the disease process in AD, has a high sensitivity and specificity to differentiate AD from normal aging, and the highly connected brain regions behaved more tau burden in patients with AD. Thus, a highly connected state measured by dynamic functional connectivity may serve as the early changes of AD. In this study, forty-five normal controls (NC), thirty-six individuals with SCD, and thirty-five patients with AD were enrolled to obtain the resting-state functional magnetic resonance imaging scanning. Sliding windows, Pearson correlation, and clustering analysis were combined to investigate the different levels of information transformation states. Three states, namely, the low state, the middle state, and the high state, were characterized based on the strength of functional connectivity between each pair of brain regions. For the global dynamic functional connectivity analysis, statistically significant differences were found among groups in the three states, and the functional connectivity in the middle state was positively correlated with cognitive scales. Furthermore, the whole brain was parcellated into four networks, namely, default mode network (DMN), cognitive control network (CCN), sensorimotor network (SMN), and occipital-cerebellum network (OCN). For the local network analysis, statistically significant differences in CCN for low state and SMN for middle state and high state were found in normal controls and patients with AD. Meanwhile, the differences were also found in normal controls and individuals with SCD. In addition, the functional connectivity in SMN for high state was positively correlated with cognitive scales. Converging results showed the changes in dynamic functional states in individuals with SCD and patients with AD. In addition, the changes were mainly in the high strength of the functional connectivity state.
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Affiliation(s)
- Jue Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Kexin Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Tiantian Liu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Li Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Dingjie Suo
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yunyan Xie
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Shintaro Funahashi
- Kokoro Research Center, Kyoto University, Kyoto, Japan
- Laboratory of Cognitive Brain Science, Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan
| | - Jinglong Wu
- Research Center for Medical Artificial Intelligence, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China
- *Correspondence: Jinglong Wu
| | - Guangying Pei
- School of Life Science, Beijing Institute of Technology, Beijing, China
- Guangying Pei
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Jiang Y, Jessee W, Hoyng S, Borhani S, Liu Z, Zhao X, Price LK, High W, Suhl J, Cerel-Suhl S. Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work? Front Aging Neurosci 2022; 14:780817. [PMID: 35418848 PMCID: PMC8995767 DOI: 10.3389/fnagi.2022.780817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 02/08/2022] [Indexed: 09/19/2023] Open
Abstract
Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.
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Affiliation(s)
- Yang Jiang
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - William Jessee
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Stevie Hoyng
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Soheil Borhani
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Ziming Liu
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Lacey K. Price
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
| | - Walter High
- New Mexico Veteran Affairs Medical Center, Albuquerque, NM, United States
| | - Jeremiah Suhl
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
| | - Sylvia Cerel-Suhl
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
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