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Dong L, Yang R, Xie A, Wang X, Feng Z, Li F, Ren J, Li J, Yao D. Transforming of scalp EEGs with different channel locations by REST for comparative study. Brain Res Bull 2024; 217:111064. [PMID: 39232993 DOI: 10.1016/j.brainresbull.2024.111064] [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: 05/20/2024] [Revised: 08/11/2024] [Accepted: 08/30/2024] [Indexed: 09/06/2024]
Abstract
OBJECTIVE The diversity of electrode placement systems brought the problem of channel location harmonization in large-scale electroencephalography (EEG) applications to the forefront. Therefore, our goal was to resolve this problem by introducing and assessing the reference electrode standardization technique (REST) to transform EEGs into a common electrode distribution with computational zero reference at infinity offline. METHODS Simulation and eye-closed resting-state EEG datasets were used to investigate the performance of REST for EEG signals and power configurations. RESULTS REST produced small errors (the root mean square error (RMSE): 0.2936-0.4583; absolute errors: 0.2343-0.3657) and high correlations (>0.9) between the estimated signals and true ones. The comparison of configuration similarities in power among various electrode distributions revealed that REST induced infinity reference could maintain a perfect performance similar (>0.9) to that of true one. CONCLUSION These results demonstrated that REST transformation could be adopted to resolve the channel location harmonization problem in large-scale EEG applications.
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Affiliation(s)
- Li Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Runchen Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ao Xie
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinrui Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zongwen Feng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Junru Ren
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Jianfu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China.
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Lopez Naranjo C, Razzaq FA, Li M, Wang Y, Bosch‐Bayard JF, Lindquist MA, Gonzalez Mitjans A, Garcia R, Rabinowitz AG, Anderson SG, Chiarenza GA, Calzada‐Reyes A, Virues‐Alba T, Galler JR, Minati L, Bringas Vega ML, Valdes‐Sosa PA. EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition. Hum Brain Mapp 2024; 45:e26698. [PMID: 38726908 PMCID: PMC11082925 DOI: 10.1002/hbm.26698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate "mediators". Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency-resolved functional connectivity measures as a mediator using the full EEG cross-spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive-definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a "compressed CST." The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix-valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well-studied 50+-year Barbados Nutrition Study dataset by comparing EEGs collected in school-age children (5-11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains.
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Affiliation(s)
- Carlos Lopez Naranjo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Fuleah Abdul Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Hangzhou Dianzi UniversityZhejiangHangzhouChina
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | | | | | - Anisleidy Gonzalez Mitjans
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Montreal Neurological Institute‐HospitalMcGill UniversityMontrealQuebecCanada
| | - Ronaldo Garcia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | | | - Simon G. Anderson
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health ResearchUniversity of the West IndiesCave HillBarbados
| | - Giuseppe A. Chiarenza
- Centro Internazionale Disturbi di Apprendimento, Attenzione, Iperattività (CIDAAI)MilanItaly
| | | | | | - Janina R. Galler
- Division of Pediatric Gastroenterology and NutritionMassachusetts General Hospital for ChildrenBostonMassachusettsUSA
| | - Ludovico Minati
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Center for Mind/Brain Science (CIMeC)University of TrentoTrentoItaly
| | - Maria L. Bringas Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Cuban Center for NeuroscienceLa HabanaCuba
| | - Pedro A. Valdes‐Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Cuban Center for NeuroscienceLa HabanaCuba
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Kang JH, Bae JH, Jeon YJ. Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review. Bioengineering (Basel) 2024; 11:418. [PMID: 38790286 PMCID: PMC11118246 DOI: 10.3390/bioengineering11050418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024] Open
Abstract
The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
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Affiliation(s)
- Jae-Hwan Kang
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Jang-Han Bae
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
| | - Young-Ju Jeon
- Digital Health Research Division, Korea Institute of Oriental Medicine, Daejeon 34054, Republic of Korea; (J.-H.K.); (J.-H.B.)
- Aging Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon 34141, Republic of Korea
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4
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Areces-Gonzalez A, Paz-Linares D, Riaz U, Wang Y, Li M, Razzaq FA, Bosch-Bayard JF, Gonzalez-Moreira E, Ontivero-Ortega M, Galan-Garcia L, Martínez-Montes E, Minati L, Valdes-Sosa MJ, Bringas-Vega ML, Valdes-Sosa PA. CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics. Front Neurosci 2024; 18:1237245. [PMID: 38680452 PMCID: PMC11047451 DOI: 10.3389/fnins.2024.1237245] [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: 06/09/2023] [Accepted: 02/22/2024] [Indexed: 05/01/2024] Open
Abstract
We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.
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Affiliation(s)
- Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University “Hermanos Saiz Montes de Oca” of Pinar del Río, Pinar del Rio, Cuba
| | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Usama Riaz
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Hangzhou Dianzi University, Hangzhou, Zhejiang, China
| | - Fuleah A. Razzaq
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jorge F. Bosch-Bayard
- McGill Centre for Integrative Neurosciences MCIN, LudmerCentre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Eduardo Gonzalez-Moreira
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | | | | | | | - Marlis Ontivero-Ortega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | | | | | - Ludovico Minati
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
| | | | - Maria L. Bringas-Vega
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
| | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Sciences Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neurosciences Center, Havana, Cuba
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5
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Razzaq FA, Calzada-Reyes A, Tang Q, Guo Y, Rabinowitz AG, Bosch-Bayard J, Galan-Garcia L, Virues-Alba T, Suarez-Murias C, Miranda I, Riaz U, Bernardo Lagomasino V, Bryce C, Anderson SG, Galler JR, Bringas-Vega ML, Valdes-Sosa PA. Spectral quantitative and semi-quantitative EEG provide complementary information on the life-long effects of early childhood malnutrition on cognitive decline. Front Neurosci 2023; 17:1149102. [PMID: 37781256 PMCID: PMC10540225 DOI: 10.3389/fnins.2023.1149102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 07/18/2023] [Indexed: 10/03/2023] Open
Abstract
Objective This study compares the complementary information from semi-quantitative EEG (sqEEG) and spectral quantitative EEG (spectral-qEEG) to detect the life-long effects of early childhood malnutrition on the brain. Methods Resting-state EEGs (N = 202) from the Barbados Nutrition Study (BNS) were used to examine the effects of protein-energy malnutrition (PEM) on childhood and middle adulthood outcomes. sqEEG analysis was performed on Grand Total EEG (GTE) protocol, and a single latent variable, the semi-quantitative Neurophysiological State (sqNPS) was extracted. A univariate linear mixed-effects (LME) model tested the dependence of sqNPS and nutritional group. sqEEG was compared with scores on the Montreal Cognitive Assessment (MoCA). Stable sparse classifiers (SSC) also measured the predictive power of sqEEG, spectral-qEEG, and a combination of both. Multivariate LME was applied to assess each EEG modality separately and combined under longitudinal settings. Results The univariate LME showed highly significant differences between previously malnourished and control groups (p < 0.001); age (p = 0.01) was also significant, with no interaction between group and age detected. Childhood sqNPS (p = 0.02) and adulthood sqNPS (p = 0.003) predicted MoCA scores in adulthood. The SSC demonstrated that spectral-qEEG combined with sqEEG had the highest predictive power (mean AUC 0.92 ± 0.005). Finally, multivariate LME showed that the combined spectral-qEEG+sqEEG models had the highest log-likelihood (-479.7). Conclusion This research has extended our prior work with spectral-qEEG and the long-term impact of early childhood malnutrition on the brain. Our findings showed that sqNPS was significantly linked to accelerated cognitive aging at 45-51 years of age. While sqNPS and spectral-qEEG produced comparable results, our study indicated that combining sqNPS and spectral-qEEG yielded better performance than either method alone, suggesting that a multimodal approach could be advantageous for future investigations. Significance Based on our findings, a semi-quantitative approach utilizing GTE could be a valuable diagnostic tool for detecting the lasting impacts of childhood malnutrition. Notably, sqEEG has not been previously explored or reported as a biomarker for assessing the longitudinal effects of malnutrition. Furthermore, our observations suggest that sqEEG offers unique features and information not captured by spectral quantitative EEG analysis and could lead to its improvement.
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Affiliation(s)
- Fuleah A. Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Qin Tang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | - Yanbo Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | | | | | | | | | | | - Ileana Miranda
- National Center for Animal and Plant Health, CENSA, San José de las Lajas, Mayabeque, Cuba
| | - Usama Riaz
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Cyralene Bryce
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
| | - Simon G. Anderson
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
| | - Janina R. Galler
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Cave Hill, Barbados
- Division of Pediatric Gastroenterology and Nutrition, MassGeneral Hospital for Children, Boston, MA, United States
| | - Maria L. Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
| | - Pedro A. Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformatics, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, La Habana, Cuba
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Jaramillo-Jimenez A, Tovar-Rios DA, Ospina JA, Mantilla-Ramos YJ, Loaiza-López D, Henao Isaza V, Zapata Saldarriaga LM, Cadavid Castro V, Suarez-Revelo JX, Bocanegra Y, Lopera F, Pineda-Salazar DA, Tobón Quintero CA, Ochoa-Gomez JF, Borda MG, Aarsland D, Bonanni L, Brønnick K. Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis. Clin Neurophysiol 2023; 151:28-40. [PMID: 37146531 DOI: 10.1016/j.clinph.2023.03.363] [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: 09/25/2022] [Revised: 02/18/2023] [Accepted: 03/27/2023] [Indexed: 05/07/2023]
Abstract
OBJECTIVE This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. METHODS We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature. RESULTS For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs. CONCLUSIONS Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD. SIGNIFICANCE Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.
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Affiliation(s)
- Alberto Jaramillo-Jimenez
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación SINAPSIS, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia.
| | - Diego A Tovar-Rios
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Universidad del Valle, Grupo de Investigación en Estadística Aplicada - INFERIR, Faculty of Engineering, Santiago de Cali, Colombia; Universidad del Valle, Prevención y Control de la Enfermedad Crónica - PRECEC, Faculty of Health, Santiago de Cali, Colombia
| | - Johann Alexis Ospina
- Facultad de Ciencias Básicas, Universidad Autónoma de Occidente, Santiago de Cali, Colombia
| | - Yorguin-Jose Mantilla-Ramos
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Daniel Loaiza-López
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Verónica Henao Isaza
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Luisa María Zapata Saldarriaga
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Valeria Cadavid Castro
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Semillero de Investigación NeuroCo, Universidad de Antioquia, School of Medicine & School of Engenieering. Medellín, Colombia
| | - Jazmin Ximena Suarez-Revelo
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Yamile Bocanegra
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Francisco Lopera
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - David Antonio Pineda-Salazar
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Carlos Andrés Tobón Quintero
- Grupo de Neurociencias de Antioquia, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia; Área Investigación e Innovación, Hospital Alma Mater de Antioquia. Medellín, Colombia
| | - John Fredy Ochoa-Gomez
- Grupo Neuropsicología y Conducta, Universidad de Antioquia, School of Medicine. Medellín, Colombia
| | - Miguel Germán Borda
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Semillero de Neurociencias y Envejecimiento, Pontificia Universidad Javeriana, Ageing Institute, Medical School. Bogotá, Colombia
| | - Dag Aarsland
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway; Department of Old Age Psychiatry, Institute of Psychiatry, Psychology, and Neuroscience, King's College London. London, UK
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, G. d'Annunzio University. Chieti, Italy
| | - Kolbjørn Brønnick
- Centre for Age-Related Medicine (SESAM), Stavanger University Hospital. Stavanger, Norway; Faculty of Health Sciences, University of Stavanger. Stavanger, Norway
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Prado P, Mejía JA, Sainz‐Ballesteros A, Birba A, Moguilner S, Herzog R, Otero M, Cuadros J, Z‐Rivera L, O'Byrne DF, Parra M, Ibáñez A. Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12455. [PMID: 37424962 PMCID: PMC10329259 DOI: 10.1002/dad2.12455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 06/05/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023]
Abstract
Introduction Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers. Methods We implemented an automatic processing pipeline incorporating electrode layout integrations, patient-control normalizations, and multi-metric EEG source space connectomics analyses. Results Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z-score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long-range connections, and diminished short-range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia. Discussion Harmonized multi-metric analysis of EEG source space connectivity can address data heterogeneities in multi-centric studies, representing a powerful tool for accurately characterizing dementia.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Escuela de FonoaudiologíaFacultad de Odontología y Ciencias de la RehabilitaciónUniversidad San SebastiánSantiagoChile
| | - Jhony A. Mejía
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Departamento de Ingeniería BiomédicaUniversidad de Los AndesBogotáColombia
- Memory and Aging ClinicUniversity of CaliforniaSan FranciscoUnited States
| | - Agustín Sainz‐Ballesteros
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
- Instituto Universitario de NeurocienciaUniversidad de La LagunaTenerifeSpain
- Facultad de PsicologíaUniversidad de La LagunaTenerifeSpain
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonUnited States
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Fundación para el Estudio de la Conciencia Humana (EcoH)Santiago de ChileChile
| | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y DiseñoUniversidad San SebastiánSantiagoChile
- Centro BASAL Ciencia & Vida; Facultad de Ingeniería y TecnologíaUniversidad San SebastiánSantiago de ChileChile
| | - Jhosmary Cuadros
- Advanced Center for Electrical and Electronic Engineering (AC3E)Universidad Técnica Federico Santa MaríaValparaísoChile
| | - Lucía Z‐Rivera
- Advanced Center for Electrical and Electronic Engineering (AC3E)Universidad Técnica Federico Santa MaríaValparaísoChile
| | - Daniel Franco O'Byrne
- Center for Social and Cognitive Neuroscience (CSCN)School of PsychologyUniversidad Adolfo IbáñezSantiagoChile
| | - Mario Parra
- School of Psychological Sciences and HealthUniversity of StrathclydeGlasgowUK
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
- Center for Social and Cognitive Neuroscience (CSCN)School of PsychologyUniversidad Adolfo IbáñezSantiagoChile
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
- Global Brain Health Institute (GBHI)University of California San FranciscoCalifornia and Trinity College DublinDublinIreland
- Trinity College Dublin (TCD)DublinIreland
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8
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Kurbatskaya A, Jaramillo-Jimenez A, Ochoa-Gomez JF, Bronnick K, Fernandez-Quilez A. Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083565 DOI: 10.1109/embc40787.2023.10340700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and β) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically-validated frequency bands, and feature selection. Following, we assess the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.Clinical relevance- We present an end-to-end pipeline to extract clinically relevant features from rs-EEG recordings that can facilitate the analysis and detection of PD. Further, we provide an ML system that shows a good performance in detecting PD, even in the presence of centers with different acquisition protocols. Our results show the relevance of harmonizing features and provide a good starting point for future studies focusing on rs-EEG analysis and multi-center data.
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Normative Structure of Resting-State EEG in Bipolar Derivations for Daily Clinical Practice: A Pilot Study. Brain Sci 2023; 13:brainsci13020167. [PMID: 36831710 PMCID: PMC9953767 DOI: 10.3390/brainsci13020167] [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: 12/29/2022] [Revised: 01/12/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
We used numerical methods to define the normative structure of resting-state EEG (rsEEG) in a pilot study of 37 healthy subjects (10-74 years old), using a double-banana bipolar montage. Artifact-free 120-200 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum was calculated and used to compute the area for delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands and was log-transformed. Furthermore, Shannon's spectral entropy (SSE) and coherence by bands were computed. Finally, we also calculated the main frequency and amplitude of the posterior dominant rhythm. According to the age-dependent distribution of the bands, we divided the patients in the following three groups: younger than 20; between 21 and 50; and older than 51 years old. The distribution of bands and coherence was different for the three groups depending on the brain lobes. We described the normative equations for the three age groups and for every brain lobe. We showed the feasibility of a normative structure of rsEEG picked up with a double-banana montage.
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Savostyanov A, Vergunov E, Saprygin A, Lebedkin D. Validation of a face image assessment technology to study the dynamics of human functional states in the EEG resting-state paradigm. Vavilovskii Zhurnal Genet Selektsii 2022; 26:765-772. [PMID: 36694716 PMCID: PMC9837164 DOI: 10.18699/vjgb-22-92] [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: 09/13/2022] [Revised: 11/17/2022] [Accepted: 11/17/2022] [Indexed: 01/06/2023] Open
Abstract
The article presents the results of a study aimed at finding covariates to account for the activity of implicit cognitive processes in conditions of functional rest of the subjects and during them being presented their own or someone else's face in a joint analysis of EEG experiment data. The proposed approach is based on the analysis of the dynamics of the facial muscles of the subject recorded on video. The pilot study involved 18 healthy volunteers. In the experiment, the subjects were sitting in front of a computer screen and performed the following task: sequentially closed their eyes (three trials of 2 minutes each) and opened them (three trials of the same duration between periods of closed eyes) when the screen was either empty or when it was showing a video recording of their own face or the face of an unfamiliar person of the same gender as the participant. EEG, ECG and a video of the face were recorded for all subjects. In the work a separate subtask of the study was also addressed: validating a technique for assessing the dynamics of the subjects' facial muscle activity using the recorded videos of the "eyes open" trials to obtain covariates that can be included in subsequent processing along with EEG correlates in neurocognitive experiments with a paradigm that does not involve the performance of active cognitive tasks ("resting-state conditions"). It was shown that the subject's gender, stimulus type (screen empty or showing own/other face), trial number are accompanied by differences in facial activity and can be used as study-specific covariates. It was concluded that the analysis of the dynamics of facial activity based on video recording of "eyes open" trials can be used as an additional method in neurocognitive research to study implicit cognitive processes associated with the perception of oneself and other, in the functional rest paradigm.
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Affiliation(s)
- A.N. Savostyanov
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, RussiaScientific Research Institute of Neurosciences and Medicine, Novosibirsk, RussiaInstitute for the Humanities of Novosibirsk State University, Novosibirsk, Russia
| | - E.G. Vergunov
- Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
| | - A.E. Saprygin
- Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, Novosibirsk, RussiaScientific Research Institute of Neurosciences and Medicine, Novosibirsk, Russia
| | - D.A. Lebedkin
- Scientific Research Institute of Neurosciences and Medicine, Novosibirsk, RussiaInstitute for the Humanities of Novosibirsk State University, Novosibirsk, Russia
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11
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Bringas-Vega ML, Michel CM, Saxena S, White T, Valdes-Sosa PA. Neuroimaging and global health. Neuroimage 2022; 260:119458. [PMID: 35820584 DOI: 10.1016/j.neuroimage.2022.119458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 11/26/2022] Open
Affiliation(s)
- Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Institute, University of Electronic Sciences and Technology of China UESTC, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Christoph M Michel
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland; CIBM Center for Biomedical Imaging, Lausanne, Geneva, Switzerland
| | - Shekar Saxena
- Department of Global Health and Population, Harvard T H Chan School of Public Health, Harvard University, Boston, USA
| | - Tonya White
- Department of Child and Adolescent Psychiatry & Department of Radiology and Nuclear Medicine, Erasmus University Rotterdam, the Netherlands
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Institute, University of Electronic Sciences and Technology of China UESTC, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba.
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