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Szirmai D, Zabihi A, Kói T, Hegyi P, Wenning AS, Engh MA, Molnár Z, Csukly G, Horváth AA. EEG connectivity and network analyses predict outcome in patients with disorders of consciousness - A systematic review and meta-analysis. Heliyon 2024; 10:e31277. [PMID: 38826755 PMCID: PMC11141356 DOI: 10.1016/j.heliyon.2024.e31277] [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: 11/07/2023] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 06/04/2024] Open
Abstract
Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60-0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70-0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70-0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.
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Affiliation(s)
- Danuta Szirmai
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
| | - Arashk Zabihi
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
| | - Tamás Kói
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
- Mathematical Institute, Department of Stochastics, Budapest University of Technology and Economics, Budapest, Hungary (Műegyetem rkp. 3, Budapest, H-1111, Hungary
| | - Péter Hegyi
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
- Institute of Pancreatic Diseases, Semmelweis University, Budapest, Hungary (Tömő u. 25-29, Budapest, H-1083, Hungary
- Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary (Szigeti út 12., Pécs, H-7624, Hungary
| | - Alexander Schulze Wenning
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
| | - Marie Anne Engh
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
| | - Zsolt Molnár
- Department of Anesthesiology and Intensive Therapy, Semmelweis University, Budapest, Hungary (Üllői út 78., Budapest, H-1082, Hungary
- Department of Anesthesiology and Intensive Therapy, Poznan University of Medical Sciences, Poznan, Poland (49 Przybyszewskiego St, Poznan, Poland, 60-355, Poland
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary (Balassa u. 6, Budapest, H-1083, Hungary
| | - András Attila Horváth
- Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
- Neurocognitive Research Center, National Institute of Mental Health, Neurology, Neurosurgery, Budapest, Hungary (Amerikai út 57., Budapest, H-1145, Hungary
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary (Üllői út 26., Budapest, H-1085, Hungary
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Kim YW, Kim S, Jin MJ, Im CH, Lee SH. The Importance of Low-frequency Alpha (8-10 Hz) Waves and Default Mode Network in Behavioral Inhibition. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2024; 22:53-66. [PMID: 38247412 PMCID: PMC10811390 DOI: 10.9758/cpn.22.1035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/13/2023] [Accepted: 02/17/2023] [Indexed: 01/23/2024]
Abstract
Objective : Alpha wave of electroencephalography (EEG) is known to be related to behavioral inhibition. Both the alpha wave and default mode network (DMN) are predominantly activated during resting-state. To study the mechanisms of the trait inhibition, this research investigating the relations among alpha wave, DMN and behavioral inhibition in resting-state. Methods : We explored the relationship among behavioral inhibition, resting-state alpha power, and DMN. Resting-state EEG, behavioral inhibition/behavioral activation scale (BIS/BAS), Barratt impulsivity scale, and no-go accuracy were assessed in 104 healthy individuals. Three groups (i.e., participants with low/middle/high band power) were formed based on the relative power of each total-alpha, low-alpha (LA), and high-alpha band. Source-reconstructed EEG and functional network measures of 25 DMN regions were calculated. Results : Significant differences and correlations were found based on LA band power alone. The high LA group had significantly greater BIS, clustering coefficient, efficiency, and strength, and significantly lower path length than low/middle LA group. BIS score showed a significant correlation with functional network measures of DMN. Conclusion : Our study revealed that LA power is related to behavioral inhibition and functional network measures of DMN of LA band appear to represent significant inhibitory function.
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Affiliation(s)
- Yong-Wook Kim
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Korea
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Sungkean Kim
- Department of Human-Computer Interaction, Hanyang University, Ansan, Korea
| | - Min Jin Jin
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Korea
- Institute of General Education, Kongju National University, Gongju, Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, Korea
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Korea
- Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
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Zheng X, Wang B, Liu H, Wu W, Sun J, Fang W, Jiang R, Hu Y, Jin C, Wei X, Chen SSC. Diagnosis of Alzheimer's disease via resting-state EEG: integration of spectrum, complexity, and synchronization signal features. Front Aging Neurosci 2023; 15:1288295. [PMID: 38020761 PMCID: PMC10661409 DOI: 10.3389/fnagi.2023.1288295] [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: 09/04/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Background Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 70% of total dementia cases with a prevalence of more than 55 million people. Electroencephalogram (EEG) has become a suitable, accurate, and highly sensitive biomarker for the identification and diagnosis of AD. Methods In this study, a public database of EEG resting state-closed eye recordings containing 36 AD subjects and 29 normal subjects was used. And then, three types of signal features of resting-state EEG, i.e., spectrum, complexity, and synchronization, were performed by applying various signal processing and statistical methods, to obtain a total of 18 features for each signal epoch. Next, the supervised machine learning classification algorithms of decision trees, random forests, and support vector machine (SVM) were compared in categorizing processed EEG signal features of AD and normal cases with leave-one-person-out cross-validation. Results The results showed that compared to normal cases, the major change in EEG characteristics in AD cases was an EEG slowing, a reduced complexity, and a decrease in synchrony. The proposed methodology achieved a relatively high classification accuracy of 95.65, 95.86, and 88.54% between AD and normal cases for decision trees, random forests, and SVM, respectively, showing that the integration of spectrum, complexity, and synchronization features for EEG signals can enhance the performance of identifying AD and normal subjects. Conclusion This study recommended the integration of EEG features of spectrum, complexity, and synchronization for aiding the diagnosis of AD.
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Affiliation(s)
- Xiaowei Zheng
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
- School of Mathematics, Northwest University, Xian, China
- Medical Big Data Research Center, Northwest University, Xi'an, China
| | - Bozhi Wang
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Hao Liu
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Wencan Wu
- School of Mathematics, Northwest University, Xian, China
| | - Jiamin Sun
- School of Mathematics, Northwest University, Xian, China
| | - Wei Fang
- School of Mathematics, Northwest University, Xian, China
| | - Rundong Jiang
- School of Mathematics, Northwest University, Xian, China
| | - Yajie Hu
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Cheng Jin
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
| | - Xin Wei
- Expert Workstation in Sichuan Province, Chengdu Jincheng College, Chengdu, China
- School of Humanities and Education, Xi'an Eurasia University, Xi'an, China
- Institute of Social Psychology, Xi'an Jiaotong University, Xi'an, China
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Sun H, Jin J, Daly I, Huang Y, Zhao X, Wang X, Cichocki A. Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems. J Neurosci Methods 2023; 399:109969. [PMID: 37683772 DOI: 10.1016/j.jneumeth.2023.109969] [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: 05/26/2023] [Revised: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 09/10/2023]
Abstract
Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.
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Affiliation(s)
- Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China; Shenzhen Research Institute of East China University of Science and Technology, Shen Zhen 518063, China.
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom
| | - Yitao Huang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xueqing Zhao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- RIKEN Brain Science Institute, Wako 351-0198, Japan; Nicolaus Copernicus University (UMK), 87-100 Torun, Poland
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Rodríguez-González V, Núñez P, Gómez C, Shigihara Y, Hoshi H, Tola-Arribas MÁ, Cano M, Guerrero Á, García-Azorín D, Hornero R, Poza J. Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses. Neuroimage 2023; 280:120332. [PMID: 37619796 DOI: 10.1016/j.neuroimage.2023.120332] [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: 04/02/2023] [Revised: 07/05/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.
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Affiliation(s)
- Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain.
| | - Pablo Núñez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Coma Science Group, GIGA-Consciousness, University of Liège, Liège, Belgium
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain
| | | | | | - Miguel Ángel Tola-Arribas
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Mónica Cano
- Servicio de Neurología. Hospital Universitario Río Hortega, Valladolid, Spain
| | - Ángel Guerrero
- Hospital Clínico Universitario, Valladolid, Spain; Department of Medicine, University of Valladolid, Spain
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III (CIBER-BBN), Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
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Kumar WS, Ray S. Healthy ageing and cognitive impairment alter EEG functional connectivity in distinct frequency bands. Eur J Neurosci 2023; 58:3432-3449. [PMID: 37559505 DOI: 10.1111/ejn.16114] [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: 05/11/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/11/2023]
Abstract
Functional connectivity (FC) indicates the interdependencies between brain signals recorded from spatially distinct locations in different frequency bands, which is modulated by cognitive tasks and is known to change with ageing and cognitive disorders. Recently, the power of narrow-band gamma oscillations induced by visual gratings have been shown to reduce with both healthy ageing and in subjects with mild cognitive impairment (MCI). However, the impact of ageing/MCI on stimulus-induced gamma FC has not been well studied. We recorded electroencephalogram (EEG) from a large cohort (N = 229) of elderly subjects (>49 years) while they viewed large cartesian gratings to induce gamma oscillations and studied changes in alpha and gamma FC with healthy ageing (N = 218) and MCI (N = 11). Surprisingly, we found distinct differences across age and MCI groups in power and FC. With healthy ageing, alpha power did not change but FC decreased significantly. MCI reduced gamma but not alpha FC significantly compared with age and gender matched controls, even when power was matched between the two groups. Overall, our results suggest distinct effects of ageing and disease on EEG power and FC, suggesting different mechanisms underlying ageing and cognitive disorders.
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Affiliation(s)
| | - Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bengaluru, India
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Wu Z, Tang X, Wu J, Huang J, Shen J, Hong H. Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism. Med Biol Eng Comput 2023; 61:2391-2404. [PMID: 37095297 DOI: 10.1007/s11517-023-02840-z] [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: 10/10/2022] [Accepted: 04/13/2023] [Indexed: 04/26/2023]
Abstract
Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented on bulky and heavy computing devices that are inconvenient for physical actions. To date, the application of deep-learning techniques in independent portable brain-computer-interface (BCI) devices has not been extensively explored. In this study, we proposed a high-accuracy MI EEG decoder by incorporating spatial-attention mechanism into convolution neural network (CNN), and deployed it on fully integrated single-chip microcontroller unit (MCU). After the CNN model was trained on workstation computer using GigaDB MI datasets (52 subjects), its parameters were then extracted and converted to build deep-learning architecture interpreter on MCU. For comparison, EEG-Inception model was also trained using the same dataset, and was deployed on MCU. The results indicate that our deep-learning model can independently decode imaginary left-/right-hand motions. The mean accuracy of the proposed compact CNN reaches 96.75 ± 2.41% (8 channels: Frontocentral3 (FC3), FC4, Central1 (C1), C2, Central-Parietal1 (CP1), CP2, C3, and C4), versus 76.96 ± 19.08% of EEG-Inception (6 channels: FC3, FC4, C1, C2, CP1, and CP2). To the best of our knowledge, this is the first portable deep-learning decoder for MI EEG signals. The findings demonstrate high-accuracy deep-learning decoding of MI EEG in a portable mode, which has great implications for hand-disabled patients. Our portable system can be used for developing artificial-intelligent wearable BCI devices, as it is less computationally expensive and convenient for real-life application.
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Affiliation(s)
- Zhanxiong Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
| | - Xudong Tang
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jinhui Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jiye Huang
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Jian Shen
- Neurosurgery Department, The First Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Hui Hong
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
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Huang SS, Yu YH, Chen HH, Hung CC, Wang YT, Chang CH, Peng SJ, Kuo PH. Functional connectivity analysis on electroencephalography signals reveals potential biomarkers for treatment response in major depression. BMC Psychiatry 2023; 23:554. [PMID: 37528355 PMCID: PMC10394892 DOI: 10.1186/s12888-023-04958-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/13/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND The treatment efficacy varies across individual patients with major depressive disorder (MDD). It lacks robust electroencephalography (EEG) markers for an antidepressant-responsive phenotype. METHOD This is an observational study enrolling 28 patients with MDD and 33 healthy controls (mean age of 40.7 years, and 71.4% were women). Patients underwent EEG exams at baseline (week0) and week1, while controls' EEG recordings were acquired only at week0. A resting eye-closing EEG segment was analyzed for functional connectivity (FC). Four parameters were used in FC analysis: (1) node strength (NS), (2) global efficiency (GE), (3) clustering coefficient (CC), and (4) betweenness centrality (BC). RESULTS We found that controls had higher values in delta wave in the indices of NS, GE, BC, and CC than MDD patients at baseline. After treatment with antidepressants, patients' FC indices improved significantly, including GE, mean CC, and mean NS in the delta wave. The FC in the alpha and beta bands of the responders were higher than those of the non-responders. CONCLUSIONS The FC of the MDD patients at baseline without treatment was worse than that of controls. After treatment, the FC improved and was close to the values of controls. Responders showed better FC in the high-frequency bands than non-responders, and this feature exists in both pre-treatment and post-treatment EEG.
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Affiliation(s)
- Shiau-Shian Huang
- Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Yu-Hsiang Yu
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - His-Han Chen
- Department of Psychiatry, Yang Ji Mental Hospital, Keelung, Taiwan
| | - Chia-Chun Hung
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Yao-Ting Wang
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Chieh Hsin Chang
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Syu-Jyun Peng
- Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan.
- Psychiatric Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
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Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach. Brain Sci 2022; 13:brainsci13010071. [PMID: 36672052 PMCID: PMC9856795 DOI: 10.3390/brainsci13010071] [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: 11/25/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome. METHODS We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy. RESULTS Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified. CONCLUSIONS The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT. SIGNIFICANCE The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.
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Chung YG, Jeon Y, Kim RG, Cho A, Kim H, Hwang H, Choi J, Kim KJ. Variations of Resting-State EEG-Based Functional Networks in Brain Maturation From Early Childhood to Adolescence. J Clin Neurol 2022; 18:581-593. [PMID: 36062776 PMCID: PMC9444558 DOI: 10.3988/jcn.2022.18.5.581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 02/08/2022] [Accepted: 02/08/2022] [Indexed: 11/23/2022] Open
Abstract
Background and Purpose Alterations in human brain functional networks with maturation have been explored extensively in numerous electroencephalography (EEG) and functional magnetic resonance imaging studies. It is known that the age-related changes in the functional networks occurring prior to adulthood deviate from ordinary trajectories of network-based brain maturation across the adult lifespan. Methods This study investigated the longitudinal evolution of resting-state EEG-based functional networks from early childhood to adolescence among 212 pediatric patients (age 12.2±3.5 years, range 4.4–17.9) in 6 frequency bands using 8 types of functional connectivity measures in the amplitude, frequency, and phase domains. Results Electrophysiological aspects of network-based pediatric brain maturation were characterized by increases in both functional segregation and integration up to middle adolescence. EEG oscillations in the upper alpha band reflected the age-related increases in mean node strengths and mean clustering coefficients and a decrease in the characteristic path lengths better than did those in the other frequency bands, especially for the phase-domain functional connectivity. The frequency-band-specific age-related changes in the global network metrics were influenced more by volume-conduction effects than by the domain specificity of the functional connectivity measures. Conclusions We believe that this is the first study to reveal EEG-based functional network properties during preadult brain maturation based on various functional connectivity measures. The findings potentially have clinical applications in the diagnosis and treatment of age-related brain disorders.
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Affiliation(s)
- Yoon Gi Chung
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Yonghoon Jeon
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Ryeo Gyeong Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Anna Cho
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Hunmin Kim
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
| | - Hee Hwang
- Department of Pediatrics, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jieun Choi
- Department of Pediatrics, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea
| | - Ki Joong Kim
- Department of Pediatrics, Seoul National University Children's Hospital, Seoul National University College of Medicine, Seoul, Korea
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11
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Sanchez-Reolid R, Martinez-Saez MC, Garcia-Martinez B, Fernandez-Aguilar L, Segura LR, Latorre JM, Fernandez-Caballero A. Emotion Classification from EEG with a Low-Cost BCI Versus a High-End Equipment. Int J Neural Syst 2022; 32:2250041. [DOI: 10.1142/s0129065722500411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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12
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A Weighted Error Distance Metrics (WEDM) for Performance Evaluation on Multiple Change-Point (MCP) Detection in Synthetic Time Series. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6187110. [PMID: 35371237 PMCID: PMC8970941 DOI: 10.1155/2022/6187110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 12/16/2021] [Accepted: 01/22/2022] [Indexed: 11/18/2022]
Abstract
Change-point detection (CPD) is to find abrupt changes in time-series data. Various computational algorithms have been developed for CPD applications. To compare the different CPD models, many performance metrics have been introduced to evaluate the algorithms. Each of the previous evaluation methods measures the different aspects of the methods. Based on the existing weighted error distance (WED) method on single change-point (CP) detection, a novel WED metrics (WEDM) was proposed to evaluate the overall performance of a CPD model across not only repetitive tests on single CP detection, but also successive tests on multiple change-point (MCP) detection on synthetic time series under the random slide window (RSW) and fixed slide window (FSW) frameworks. In the proposed WEDM method, a concept of normalized error distance was introduced that allows comparisons of the distance between the estimated change-point (eCP) position and the target change point (tCP) in the synthetic time series. In the successive MCPs detection, the proposed WEDM method first divides the original time-series sample into a series of data segments in terms of the assigned tCPs set and then calculates a normalized error distance (NED) value for each segment. Next, our WEDM presents the frequency and WED distribution of the resultant eCPs from all data segments in the normalized positive-error distance (NPED) and the normalized negative-error distance (NNED) intervals in the same coordinates. Last, the mean WED (MWED) and MWTD (1-MWED) were obtained and then dealt with as important performance evaluation indexes. Based on the synthetic datasets in the Matlab platform, repetitive tests on single CP detection were executed by using different CPD models, including ternary search tree (TST), binary search tree (BST), Kolmogorov–Smirnov (KS) tests, t-tests (T), and singular spectrum analysis (SSA) algorithms. Meanwhile, successive tests on MCPs detection were implemented under the fixed slide window (FSW) and random slide window (RSW) frameworks. These CPD models mentioned above were evaluated in terms of our WED metrics, together with supplementary indexes for evaluating the convergence of different CPD models, including rates of hit, miss, error, and computing time, respectively. The experimental results showed the value of this WEDM method.
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13
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Pusarla AN, Singh BA, Tripathi CS. Learning DenseNet features from EEG based spectrograms for subject independent emotion recognition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103485] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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14
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Physical Exercise Effects on University Students’ Attention: An EEG Analysis Approach. ELECTRONICS 2022. [DOI: 10.3390/electronics11050770] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Physically active breaks (AB) are currently being proposed as an interesting tool to improve students’ attention. Reviews and meta-analyses confirm their effect on attention, but also warned about the sparse evidence based on vigilance and university students. Therefore, this pilot study aimed to (a) determine the effects of AB in comparison with passive breaks on university students’ vigilance and (b) to validate an analysis model based on machine learning algorithms in conjunction with a multiparametric model based on electroencephalography (EEG) signal features. Through a counterbalanced within-subject experimental study, six university students (two female; mean age = 25.67, STD = 3.61) had their vigilance performances (i.e., response time in Psycho-Motor Vigilance Task) and EEG measured, before and after a lecture with an AB and another lecture with a passive break. A multiparametric model based on the spectral power, signal entropy and response time has been developed. Furthermore, this model, together with different machine learning algorithms, shows that for the taken signals there are significant differences after the AB lesson, implying an improvement in attention. These differences are most noticeable with the SVM with RBF kernel and ANNs with F1-score of 85% and 88%, respectively. In conclusion, results showed that students performed better on vigilance after the lecture with AB. Although limited, the evidence found could help researchers to be more accurate in their EEG analyses and lecturers and teachers to improve their students’ attentions in a proper way.
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15
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Wang K, Zhang Y, Zhu Y, Luo Y. Associations between cortical activation and network interaction during sleep. Behav Brain Res 2022; 422:113751. [PMID: 35038462 DOI: 10.1016/j.bbr.2022.113751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 01/03/2022] [Accepted: 01/12/2022] [Indexed: 11/02/2022]
Abstract
Cortical activation and network interaction, two characterizations of the cortical states, are separately studied in most previous studies. To further clarify the underlying mechanism, the association between these two indicators during sleep was investigated in this study. Twenty healthy individuals were enrolled and all of them underwent overnight polysomnography (PSG) recording. The relative spectral powers and the phase transfer entropy (PTE) of various frequency components were extracted from 6 electroencephalographic (EEG) channels, to assess the cortical activation and network interaction, respectively. Pearson correlation coefficient was employed to estimate their associations. The results suggested that there was a negative correlation between spectral power and phase transfer entropy in δ and α frequency bands during sleep. As the sleep deepened, an increased negative correlation in the δ frequency band was noted, but the negative correlation became less extreme in the α frequency band. The extremum of the correlation coefficient was noted in δ of N3, and α of Wake. Overall, this study provides a connection between these two cortical activity assessments, especially reveals the variable characteristics of different frequency components, which is conducive to better understand sleep state.
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Affiliation(s)
- Kejie Wang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yangting Zhang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yongpeng Zhu
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yuxi Luo
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China; Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, Sun Yat-Sen University, Guangzhou, China.
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16
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Griffa A, Legdeur N, Badissi M, van den Heuvel MP, Stam CJ, Visser PJ, Hillebrand A. Magnetoencephalography Brain Signatures Relate to Cognition and Cognitive Reserve in the Oldest-Old: The EMIF-AD 90 + Study. Front Aging Neurosci 2021; 13:746373. [PMID: 34899269 PMCID: PMC8656941 DOI: 10.3389/fnagi.2021.746373] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/01/2021] [Indexed: 11/25/2022] Open
Abstract
The oldest-old subjects represent the fastest growing segment of society and are at high risk for dementia with a prevalence of up to 40%. Lifestyle factors, such as lifelong participation in cognitive and leisure activities, may contribute to individual cognitive reserve and reduce the risk for cognitive impairments. However, the neural bases underlying cognitive functioning and cognitive reserve in this age range are still poorly understood. Here, we investigate spectral and functional connectivity features obtained from resting-state MEG recordings in a cohort of 35 cognitively normal (92.2 ± 1.8 years old, 19 women) and 11 cognitively impaired (90.9 ± 1.9 years old, 1 woman) oldest-old participants, in relation to cognitive traits and cognitive reserve. The latter was approximated with a self-reported scale on lifelong engagement in cognitively demanding activities. Cognitively impaired oldest-old participants had slower cortical rhythms in frontal, parietal and default mode network regions compared to the cognitively normal subjects. These alterations mainly concerned the theta and beta band and partially explained inter-subject variability of episodic memory scores. Moreover, a distinct spectral pattern characterized by higher relative power in the alpha band was specifically associated with higher cognitive reserve while taking into account the effect of age and education level. Finally, stronger functional connectivity in the alpha and beta band were weakly associated with better cognitive performances in the whole group of subjects, although functional connectivity effects were less prominent than the spectral ones. Our results shed new light on the neural underpinnings of cognitive functioning in the oldest-old population and indicate that cognitive performance and cognitive reserve may have distinct spectral electrophysiological substrates.
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Affiliation(s)
- Alessandra Griffa
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Center of Neuroprosthetics, Institute of Bioengineering, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.,Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Nienke Legdeur
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Maryam Badissi
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Martijn P van den Heuvel
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neuroscience and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Pieter Jelle Visser
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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17
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Rodriguez-Gonzalez V, Pablo VGD, Gomez C, Shigihara Y, Hoshi H, Hornero R, Tola-Arribas MA, Cano M, Poza J. High Frequential Resolution Networks: Considerations on a New Functional Brain Connectivity Framework. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:722-725. [PMID: 34891393 DOI: 10.1109/embc46164.2021.9630196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Connectivity analyses are widely used to assess the interaction brain networks. This type of analyses is usually conducted considering the well-known classical frequency bands: delta, theta, alpha, beta, and gamma. However, this parcellation of the frequency content can bias the analyses, since it does not consider the between-subject variability or the particular idiosyncrasies of the connectivity patterns that occur within a band. In this study, we addressed these limitations by introducing the High Frequential Resolution Networks (HFRNs). HFRNs were constructed, using a narrow-bandwidth FIR bank filter of 1 Hz bandwidth, for two different connectivity metrics (Amplitude Envelope Correlation, AEC, and Phase Lag index, PLI) and for 3 different databases of MEG and EEG recordings. Results showed a noticeable similarity between the frequential evolution of PLI, AEC, and the Power Spectral Density (PSD) from MEG and EEG signals. Nonetheless, some technical remarks should be considered: (i) results at the gamma band should exclude the frequency range around 50 Hz due to abnormal connectivity patterns, consequence of the previously applied 50 Hz notch-filter; (ii) HFRNs patterns barely vary with the connection distance; and (iii) a low sampling frequency can exert a remarkable influence on HFRNs. To conclude, we proposed a new framework to perform connectivity analyses that allow to further analyze the frequency-based distribution of brain networks.
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18
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Lian J, Wang K, Luo Y. Investigation of Sleep-Dependent Activation-Interaction Association Network. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5991-5994. [PMID: 34892483 DOI: 10.1109/embc46164.2021.9629635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The cortical activation and the interaction between cortical regions were considered to exist a strong correlation in recent neuroscience researches. However, such association during sleep was still unclear. The aim of the present work was to further investigate this association according to an activation-interaction association network. This study included 24 healthy individuals and all of them underwent overnight polysomnography. The absolute spectral powers of three frequency bands and the phase transfer entropy were extracted from six electroencephalogram channels. For each frequency band and sleep stage, activation-interaction association networks were built and correlation analysis was conducted by using Pearson correlation test. Results revealed the evident association between features derived from the two approaches during sleep, and as the sleep deepened, these correlation values attenuated in the alpha band, whereas the inversion happened in the delta band. This study exposed more detailed information of cortical activity during sleep, which will facilitate us to conduct research from a more comprehensive perspective, helping us make a more appropriate evaluation and explanation.
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19
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van der Velde B, White T, Kemner C. The emergence of a theta social brain network during infancy. Neuroimage 2021; 240:118298. [PMID: 34171501 DOI: 10.1016/j.neuroimage.2021.118298] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 05/28/2021] [Accepted: 06/20/2021] [Indexed: 11/17/2022] Open
Abstract
Infants' socio-cognitive ability develops dramatically during the first year of life. From the perspective of ontogeny, the early development of social behavior allows for parent-child attachment, which in turn enhances survival. Thus, it is theorized that the development of social behavior, driven by social brain networks, forms the core of developmental acquisitions during this period. Further, understanding the maturation within the neural networks during social development is crucial to obtain a better grasp of the development of social developmental disorders. Therefore, we performed a longitudinal study in 854 infants measured at around 5 and 10 months to map the development of functional networks in the brain when infants were processing social and non-social videos. Using EEG, we focused on the frequency bands most commonly connected to social behavior: theta and alpha. We found that alpha networks remained relatively stable over the first year of life and showed no selectivity for social versus non-social stimuli, theta networks, showed strong global reconfigurations. The development of the theta networks progressed from a parietal occipital network in early infancy to a frontoparietal network towards the end of the first year of life. This reconfiguration coincided with selectivity for social versus non-social stimuli, with infants approaching the end of their first year of life showing increased synchronicity of theta communication when watching social videos versus non-social videos. Our findings provide strong evidence for the involvement of a frontoparietal theta network in the development of the social brain.
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Affiliation(s)
- Bauke van der Velde
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands.
| | - Tonya White
- Department of Radiology and Nuclear Medicine, Erasmus Universiteit, Rotterdam, the Netherlands; Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, Netherlands
| | - Chantal Kemner
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands
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20
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Maffei A, Sessa P. Time-resolved connectivity reveals the “how” and “when” of brain networks reconfiguration during face processing. NEUROIMAGE: REPORTS 2021. [DOI: 10.1016/j.ynirp.2021.100022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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21
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Koutlis C, Kimiskidis VK, Kugiumtzis D. Comparison of Causality Network Estimation in the Sensor and Source Space: Simulation and Application on EEG. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:706487. [PMID: 36925583 PMCID: PMC10013050 DOI: 10.3389/fnetp.2021.706487] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/09/2021] [Indexed: 11/13/2022]
Abstract
The usage of methods for the estimation of the true underlying connectivity among the observed variables of a system is increasing, especially in the domain of neuroscience. Granger causality and similar concepts are employed for the estimation of the brain network from electroencephalogram (EEG) data. Also source localization techniques, such as the standardized low resolution electromagnetic tomography (sLORETA), are widely used for obtaining more reliable data in the source space. In this work, connectivity structures are estimated in the sensor and in the source space making use of the sLORETA transformation for simulated and for EEG data with episodes of spontaneous epileptiform discharges (ED). From the comparative simulation study on high-dimensional coupled stochastic and deterministic systems originating in the sensor space, we conclude that the structure of the estimated causality networks differs in the sensor space and in the source space. Moreover, different network types, such as random, small-world and scale-free, can be better discriminated on the basis of the data in the original sensor space than on the transformed data in the source space. Similarly, in EEG epochs containing epileptiform discharges, the discriminative ability of network topological indices was significantly better in the sensor compared to the source level. In conclusion, causality networks constructed at the sensor and source level, for both simulated and empirical data, exhibit significant structural differences. These observations indicate that further studies are warranted in order to clarify the exact relationship between data registered in the sensor and source space.
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Affiliation(s)
- Christos Koutlis
- Information Technologies Institute, Centre of Research and Technology Hellas, Thessaloniki, Greece
| | - Vasilios K Kimiskidis
- 1st Department of Neurology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitris Kugiumtzis
- Division of Electronics and Computing, Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
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22
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Nentwich M, Ai L, Madsen J, Telesford QK, Haufe S, Milham MP, Parra LC. Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI. Neuroimage 2020; 218:117001. [PMID: 32492509 PMCID: PMC7457369 DOI: 10.1016/j.neuroimage.2020.117001] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 05/21/2020] [Accepted: 05/26/2020] [Indexed: 02/07/2023] Open
Abstract
A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain ''functional connectivity'' -- the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest effect sizes are found for age and sex for both fMRI and EEG. We replicate previous findings of an association of Intelligence Quotient (IQ) and Attention Deficit Hyperactivity Disorder (ADHD) with the pattern of fMRI functional connectivity. We also find an association with socioeconomic status, anxiety and the Child Behavior Checklist Score. For EEG we find a significant connectivity-phenotype relationship with IQ. The actual spatial patterns of functional connectivity are quite different between fMRI and source-space EEG. However, within EEG we observe clusters of functional connectivity that are consistent across frequency bands. Additionally we analyzed reproducibility of functional connectivity. We compare connectivity obtained with different tasks, including resting state, a video and a visual flicker task. For both EEG and fMRI the variation between tasks was smaller than the variability observed between subjects. We also found an increase of reliability with increasing frequency of the EEG, and increased sampling duration. We conclude that, while the patterns of functional connectivity are distinct between fMRI and phase-coupling of EEG, they are nonetheless similar in their robustness to the task, and similar in that idiosyncratic patterns of connectivity predict individual phenotypes.
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Affiliation(s)
- Maximilian Nentwich
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Lei Ai
- Center for the Developing Brain, The Child Mind Institute, New York, NY, USA
| | - Jens Madsen
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Qawi K Telesford
- Center for Biomedical Imaging and Neuromodulation, The Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Stefan Haufe
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Michael P Milham
- Center for the Developing Brain, The Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, The Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Lucas C Parra
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA.
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23
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Griffa A, Van De Ville D, Herrmann FR, Allali G. Neural circuits of idiopathic Normal Pressure Hydrocephalus: A perspective review of brain connectivity and symptoms meta-analysis. Neurosci Biobehav Rev 2020; 112:452-471. [PMID: 32088348 DOI: 10.1016/j.neubiorev.2020.02.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Revised: 01/09/2020] [Accepted: 02/17/2020] [Indexed: 12/13/2022]
Abstract
Idiopathic normal pressure hydrocephalus (iNPH) is a prevalent reversible neurological disorder characterized by impaired locomotion, cognition and urinary control with ventriculomegaly. Symptoms can be relieved with cerebrospinal fluid drainage, which makes iNPH the leading cause of reversible dementia. Because of a limited understanding of pathophysiological mechanisms, unspecific symptoms and the high prevalence of comorbidity (i.e. Alzheimer's disease), iNPH is largely underdiagnosed. For these reasons, there is an urgent need for developing noninvasive quantitative biomarkers for iNPH diagnosis and prognosis. Structural and functional changes of brain circuits in relation to symptoms and treatment response are expected to deliver major advances in this direction. We review structural and functional brain connectivity findings in iNPH and complement those findings with iNPH symptom meta-analyses in healthy populations. Our goal is to reinforce our conceptualization of iNPH as to brain network mechanisms and foster the development of new hypotheses for future research and treatment options.
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Affiliation(s)
- Alessandra Griffa
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL), Lausanne, Switzerland.
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland.
| | - Gilles Allali
- Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Faculty of Medicine, University of Geneva, Geneva, Switzerland; Department of Neurology, Division of Cognitive & Motor Aging, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY, USA.
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24
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Exploring the Correlation Between M/EEG Source–Space and fMRI Networks at Rest. Brain Topogr 2020; 33:151-160. [DOI: 10.1007/s10548-020-00753-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 01/23/2020] [Indexed: 11/26/2022]
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