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Li J, Li X, Chen F, Li W, Chen J, Zhang B. Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings. Rev Neurosci 2024; 35:373-386. [PMID: 38157429 DOI: 10.1515/revneuro-2023-0098] [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: 08/28/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024]
Abstract
Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.
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Affiliation(s)
- Jing Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Xin Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Futao Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Weiping Li
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Jiu Chen
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
| | - Bing Zhang
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, 210008, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, Jiangsu, 210008, China
- Medical Imaging Center, The Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210008, China
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, Jiangsu, 210008, China
- Institute of Brain Science, Nanjing University, Nanjing, Jiangsu, 210008, China
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Niu Y, Chen X, Chen Y, Yao Z, Chen X, Liu Z, Meng X, Liu Y, Zhao Z, Fan H. A gender recognition method based on EEG microstates. Comput Biol Med 2024; 173:108366. [PMID: 38554661 DOI: 10.1016/j.compbiomed.2024.108366] [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: 09/23/2023] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND Gender carries important information related to male and female characteristics, and a large number of studies have attempted to use physiological measurement methods for gender classification. Although previous studies have shown that there exist statistical differences in some Electroencephalographic (EEG) microstate parameters between males and females, it is still unknown that whether these microstate parameters can be used as potential biomarkers for gender classification based on machine learning. METHODS We used two independent resting-state EEG datasets: the first dataset included 74 females and matched 74 males, and the second one included 42 males and matched 42 females. EEG microstate analysis based on modified k-means clustering method was applied, and temporal parameter and nonlinear characteristics (sample entropy and Lempel-Ziv complexity) of EEG microstate sequences were extracted to compare between males and females. More importantly, these microstate temporal parameters and complexity were tried to train six machine learning methods for gender classification. RESULTS We obtained five common microstates for each dataset and each group. Compared with the male group, the female group has significantly higher temporal parameters of microstate B, C, E and lower temporal parameters of microstate A and D, and higher complexity of microstate sequence. When using combination of microstate temporal parameters and complexity or only microstate temporal parameters as classification features in an independent test set (the second dataset), we achieved 95.2% classification accuracy. CONCLUSION Our research findings indicate that the dynamics of microstate have considerable Gender-specific alteration. EEG microstates can be used as neurophysiological biomarkers for gender classification.
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Affiliation(s)
- Yanxiang Niu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Xin Chen
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Yuansen Chen
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Zixuan Yao
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Xuemei Chen
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Ziquan Liu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Xiangyan Meng
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China
| | - Yanqing Liu
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China.
| | - Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
| | - Haojun Fan
- Institute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China; Wenzhou Safety (Emergency) Institute, Tianjin University, 325000, Wenzhou, China.
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Zhou Y, Gong L, Yang Y, Tan L, Ruan L, Chen X, Luo H, Ruan J. Spatio-temporal dynamics of resting-state brain networks are associated with migraine disability. J Headache Pain 2023; 24:13. [PMID: 36800935 PMCID: PMC9940435 DOI: 10.1186/s10194-023-01551-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/13/2023] [Indexed: 02/21/2023] Open
Abstract
OBJECTIVE The changes in resting-state functional networks and their correlations with clinical traits remain to be clarified in migraine. Here we aim to investigate the brain spatio-temporal dynamics of resting-state networks and their possible correlations with the clinical traits in migraine. METHODS Twenty Four migraine patients without aura and 26 healthy controls (HC) were enrolled. Each included subject underwent a resting-state EEG and echo planar imaging examination. The disability of migraine patients was evaluated by Migraine Disability Assessment (MIDAS). After data acquisition, EEG microstates (Ms) combining functional connectivity (FC) analysis based on Schafer 400-seven network atlas were performed. Then, the correlation between obtained parameters and clinical traits was investigated. RESULTS Compared with HC group, the brain temporal dynamics depicted by microstates showed significantly increased activity in functional networks involving MsB and decreased activity in functional networks involving MsD; The spatial dynamics were featured by decreased intra-network FC within the executive control network( ECN) and inter-network FC between dorsal attention network (DAN) and ECN (P < 0.05); Moreover, correlation analysis showed that the MIDAS score was positively correlated with the coverage and duration of MsC, and negatively correlated with the occurrence of MsA; The FC within default mode network (DMN), and the inter-FC of ECN- visual network (VN), ECN- limbic network, VN-limbic network was negatively correlated with MIDAS. However, the FC of DMN-ECN was positively correlated with MIDAS; Furthermore, significant interactions between the temporal and spatial dynamics were also obtained. CONCLUSIONS Our study confirmed the notion that altered spatio-temporal dynamics exist in migraine patients during resting-state. And the temporal dynamics, the spatial changes and the clinical traits such as migraine disability interact with each other. The spatio-temporal dynamics obtained from EEG microstate and fMRI FC analyses may be potential biomarkers for migraine and with a huge potential to change future clinical practice in migraine.
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Affiliation(s)
- Yan Zhou
- Department of Neurology, Jianyang People's Hospital, Jianyang, 641400, China
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Liusheng Gong
- Department of Neurology, Jianyang People's Hospital, Jianyang, 641400, China
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
| | - Yushu Yang
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Linjie Tan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Lili Ruan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Xiu Chen
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Hua Luo
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China
| | - Jianghai Ruan
- Department of Neurology, The Affiliated Hospital of Southwest Medical University, Luzhou, 646000, China.
- Laboratory of Neurological Diseases and Brain Function, Luzhou, 646000, China.
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