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Dou S, Liu X, Deng Y, Chen Y, Song P, Wen T, Han B. Lightweight and wearable magnetoencephalography system based on spatially-grid constrained coils and compact magnetically shielded room. Neuroimage 2024; 300:120842. [PMID: 39304094 DOI: 10.1016/j.neuroimage.2024.120842] [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: 05/27/2024] [Revised: 09/05/2024] [Accepted: 09/06/2024] [Indexed: 09/22/2024] Open
Abstract
Magnetoencephalography based on optically pumped magnetometers can passively detect the ultra-weak brain magnetic field signals, which has significant clinical application prospects for the diagnosis and treatment of cerebral disorders. This paper proposes a brain magnetic signal measurement method on the basis of the active-passive coupling magnetic shielding strategy and helmet-mounted detection array, which has lower cost and comparable performance over the existing ones. We first utilized the spatially-grid constrained coils and biplanar coils with proportion-integration-differentiation controller with tracking differentiator to ensure a near-zero and stable magnetic field environment with large uniform region. Subsequently, we implemented the brain magnetic signal measurement with the subject randomly moving fingers through tapping a keyboard and with the condition of opening and closing the eyes. Effectively induced brain magnetic signals were detected at the motor functional area and occipital lobe area in the two experiments, respectively. The proposed method will contribute to the development of functional brain imaging.
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Affiliation(s)
- Shuai Dou
- School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100191, China; Ningbo Institute of Technology, Beihang University, Ningbo 315800, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing 100191, China
| | - Xikai Liu
- Ningbo Institute of Technology, Beihang University, Ningbo 315800, China; Zhejiang Engineering Research Center of Precision Electromagnetic Control Technology and Equipment, Ningbo 315800, China.
| | - Ya Deng
- Ningbo Institute of Technology, Beihang University, Ningbo 315800, China; Zhejiang Engineering Research Center of Precision Electromagnetic Control Technology and Equipment, Ningbo 315800, China.
| | - Yimin Chen
- School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100191, China
| | - Pengfei Song
- Ningbo Institute of Technology, Beihang University, Ningbo 315800, China; Zhejiang Engineering Research Center of Precision Electromagnetic Control Technology and Equipment, Ningbo 315800, China
| | - Tong Wen
- School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100191, China; Ningbo Institute of Technology, Beihang University, Ningbo 315800, China
| | - Bangcheng Han
- School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing 100191, China; Institute of Large-scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing 100191, China
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Balaji SS, Parhi KK. Seizure onset zone (SOZ) identification using effective brain connectivity of epileptogenic networks. J Neural Eng 2024; 21:036053. [PMID: 38885675 DOI: 10.1088/1741-2552/ad5938] [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: 01/17/2024] [Accepted: 06/17/2024] [Indexed: 06/20/2024]
Abstract
Objective. To demonstrate the capability of utilizing graph feature-based supervised machine learning (ML) algorithm on intracranial electroencephalogram recordings for the identification of seizure onset zones (SOZs) in individuals with drug-resistant epilepsy.Approach. Utilizing three model-free measures of effective connectivity (EC)-directed information, mutual information-guided Granger causality index (MI-GCI), and frequency-domain convergent cross-mapping (FD-CCM) - directed graphs are generated. Graph centrality measures at different sparsity are used as the classifier's features.Main results. The centrality features achieve high accuracies exceeding 90% in distinguishing SOZ electrodes from non-SOZ electrodes. Notably, a sparse graph representation with just ten features and simple ML models effectively achieves such performance. The study identifies FD-CCM centrality measures as particularly significant, with a mean AUC of 0.93, outperforming prior literature. The FD-CCM-based graph modeling also highlights elevated centrality measures among SOZ electrodes, emphasizing heightened activity relative to non-SOZ electrodes during ictogenesis.Significance. This research not only underscores the efficacy of automated SOZ identification but also illuminates the potential of specific EC measures in enhancing discriminative power within the context of epilepsy research.
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Affiliation(s)
- Sai Sanjay Balaji
- University of Minnesota, Department of Electrical & Computer Engineering, Minneapolis, MN, United States of America
| | - Keshab K Parhi
- University of Minnesota, Department of Electrical & Computer Engineering, Minneapolis, MN, United States of America
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Bai D, Yao W, Wang S, Wang J. Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms. ENTROPY 2022; 24:e24030314. [PMID: 35327825 PMCID: PMC8946927 DOI: 10.3390/e24030314] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 02/19/2022] [Accepted: 02/20/2022] [Indexed: 12/27/2022]
Abstract
Schizophrenia is a neuropsychiatric disease that affects the nonlinear dynamics of brain activity. The primary objective of this study was to explore the complexity of magnetoencephalograms (MEG) in patients with schizophrenia. We combined a multiscale method and weighted permutation entropy to characterize MEG signals from 19 schizophrenia patients and 16 healthy controls. When the scale was larger than 42, the MEG signals of schizophrenia patients were significantly more complex than those of healthy controls (p<0.004). The difference in complexity between patients with schizophrenia and the controls was strongest in the frontal and occipital areas (p<0.001), and there was almost no difference in the central area. In addition, the results showed that the dynamic range of MEG complexity is wider in healthy individuals than in people with schizophrenia. Overall, the multiscale weighted permutation entropy method reliably quantified the complexity of MEG from schizophrenia patients, contributing to the development of potential magnetoencephalographic biomarkers for schizophrenia.
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Affiliation(s)
- Dengxuan Bai
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
| | - Wenpo Yao
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- Correspondence: (W.Y.); (J.W.)
| | - Shuwang Wang
- School of Electronic Information, Nanjing Vocational College of Information Technolog, Nanjing 210023, China;
| | - Jun Wang
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- Correspondence: (W.Y.); (J.W.)
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Azizi S, Hier DB, Wunsch DC. Schizophrenia Classification Using Resting State EEG Functional Connectivity: Source Level Outperforms Sensor Level. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1770-1773. [PMID: 34891630 DOI: 10.1109/embc46164.2021.9630713] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Disrupted functional and structural connectivity measures have been used to distinguish schizophrenia patients from healthy controls. Classification methods based on functional connectivity derived from EEG signals are limited by the volume conduction problem. Recorded time series at scalp electrodes capture a mixture of common sources signals, resulting in spurious connections. We have transformed sensor level resting state EEG times series to source level EEG signals utilizing a source reconstruction method. Functional connectivity networks were calculated by computing phase lag values between brain regions at both the sensor and source level. Brain complex network analysis was used to extract features and the best features were selected by a feature selection method. A logistic regression classifier was used to distinguish schizophrenia patients from healthy controls at five different frequency bands. The best classifier performance was based on connectivity measures derived from the source space and the theta band.The transformation of scalp EEG signals to source signals combined with functional connectivity analysis may provide superior features for machine learning applications.
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Bai D, Yao W, Lv Z, Yan W, Wang J. Multiscale multidimensional recurrence quantitative analysis for analysing MEG signals in patients with schizophrenia. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102586] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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[Effect of blood glucose on quantitative electroencephalography parameters in preterm infants]. ZHONGGUO DANG DAI ER KE ZA ZHI = CHINESE JOURNAL OF CONTEMPORARY PEDIATRICS 2020; 22. [PMID: 33059802 PMCID: PMC7569000 DOI: 10.7499/j.issn.1008-8830.2005046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
OBJECTIVE To study the value of quantitative electroencephalography (qEEG) in evaluating the effect of blood glucose on the brain function of preterm infants. METHODS The preterm infants who were admitted to the Department of Neonatology, The Third Xiangya Hospital of Central South University, from January to December 2019 were enrolled. According to the level of blood glucose, they were divided into group 1 (blood glucose <4.95 mmol/L), group 2 (blood glucose 4.95 to <6.60 mmol/L), group 3 (blood glucose 6.60 to <8.55 mmol/L), and group 4 (blood glucose ≥8.55 mmol/L). The changes in qEEG parameters were compared between groups, and a correlation analysis was performed for blood glucose and qEEG parameters. RESULTS A total of 39 preterm infants were enrolled (84 blood glucose measurements). Compared with group 4, the other three groups had significant increases in the total spectral power of each brain region and the absolute power of each frequency band in the frontal and occipital regions (P<0.05). The total spectral power, δ/θ ratio, and (δ+θ)/(α+β) ratio of each brain region were negatively correlated with blood glucose level, while the relative power of θ frequency band was positively correlated with blood glucose level (P<0.05). CONCLUSIONS With the change in blood glucose, there are significant changes in the total spectral power of each brain region, the power of each frequency band, and the frequency spectrum composition on qEEG in preterm infants. qEEG may therefore become an important tool to monitor the effect of abnormal blood glucose on brain function in preterm infants.
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EEG signal classification of imagined speech based on Riemannian distance of correntropy spectral density. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101899] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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Parhi KK, Zhang Z. Discriminative Ratio of Spectral Power and Relative Power Features Derived via Frequency-Domain Model Ratio With Application to Seizure Prediction. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2019; 13:645-657. [PMID: 31095498 DOI: 10.1109/tbcas.2019.2917184] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The ratio of spectral power in two different bands and relative band power have been shown to be sometimes more discriminative features than the spectral power in a specific band for binary classification of a time series for seizure prediction. However, why and which ratio of spectral power and relative power features are better discriminators than a band power have not been understood. While general answers to why and which are difficult, this paper partially addresses the answer to these questions. Using auto-regressive modeling, this paper, for the first time, theoretically explains that for high signal-to-noise ratio (SNR) cases, the ratio features may sometime amplify the discriminability of one of the two states in a time series, as compared with a band power. This paper, also for the first time, introduces a novel frequency-domain model ratio (FDMR) that can be used to select the two frequency bands. The FDMR computes the ratio of the frequency responses of the two auto-regressive model filters that correspond to two different states. It is shown that the ratio implicitly cancels the effect of change of variance of the white noise that is input to the auto-regressive model in a non-stationary environment for high SNR conditions. It is also shown that under certain sufficient but not necessary conditions, the ratio of the spectral power and the relative band power, i.e., the band power divided by the total power spectral density, can be better discriminators than band power. Synthesized data and scalp EEG data from the MIT Physionet for patient-specific seizure prediction are used to explain why the ratios of spectral power obtained by a ranking algorithm in the prior literature satisfy the sufficient conditions for amplification of the ratio feature derived in this paper.
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Sen B, Chu SH, Parhi KK. Ranking Regions, Edges and Classifying Tasks in Functional Brain Graphs by Sub-Graph Entropy. Sci Rep 2019; 9:7628. [PMID: 31110317 PMCID: PMC6527859 DOI: 10.1038/s41598-019-44103-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Accepted: 05/09/2019] [Indexed: 01/27/2023] Open
Abstract
This paper considers analysis of human brain networks or graphs constructed from time-series collected from functional magnetic resonance imaging (fMRI). In the network of time-series, the nodes describe the regions and the edge weights correspond to the absolute values of correlation coefficients of the time-series of the two nodes associated with the edges. The paper introduces a novel information-theoretic metric, referred as sub-graph entropy, to measure uncertainty associated with a sub-graph. Nodes and edges constitute two special cases of sub-graph structures. Node and edge entropies are used in this paper to rank regions and edges in a functional brain network. The paper analyzes task-fMRI data collected from 475 subjects in the Human Connectome Project (HCP) study for gambling and emotion tasks. The proposed approach is used to rank regions and edges associated with these tasks. The differential node (edge) entropy metric is defined as the difference of the node (edge) entropy corresponding to two different networks belonging to two different classes. Differential entropy of nodes and edges are used to rank top regions and edges associated with the two classes of data. Using top node and edge entropy features separately, two-class classifiers are designed using support vector machine (SVM) with radial basis function (RBF) kernel and leave-one-out method to classify time-series for emotion task vs. no-task, gambling task vs. no-task and emotion task vs. gambling task. Using node entropies, the SVM classifier achieves classification accuracies of 0.96, 0.97 and 0.98, respectively. Using edge entropies, the classifier achieves classification accuracies of 0.91, 0.96 and 0.94, respectively.
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Affiliation(s)
- Bhaskar Sen
- Department of Electrical and Computer Engineering, University of Minnesota - Twin Cities, Minneapolis, USA
| | - Shu-Hsien Chu
- Department of Electrical and Computer Engineering, University of Minnesota - Twin Cities, Minneapolis, USA
| | - Keshab K Parhi
- Department of Electrical and Computer Engineering, University of Minnesota - Twin Cities, Minneapolis, USA.
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