Source space connectomics of neurodegeneration: One-metric approach does not fit all
Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive character...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | Neurobiology of Disease |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S096999612300061X |
| _version_ | 1797867395765764096 |
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| author | Pavel Prado Sebastian Moguilner Jhony A. Mejía Agustín Sainz-Ballesteros Mónica Otero Agustina Birba Hernando Santamaria-Garcia Agustina Legaz Sol Fittipaldi Josephine Cruzat Enzo Tagliazucchi Mario Parra Rubén Herzog Agustín Ibáñez |
| author_facet | Pavel Prado Sebastian Moguilner Jhony A. Mejía Agustín Sainz-Ballesteros Mónica Otero Agustina Birba Hernando Santamaria-Garcia Agustina Legaz Sol Fittipaldi Josephine Cruzat Enzo Tagliazucchi Mario Parra Rubén Herzog Agustín Ibáñez |
| author_sort | Pavel Prado |
| collection | DOAJ |
| description | Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patients<HCs) involving convergent temporo-parieto-occipital regions in AD, and fronto-temporo-parietal areas in bvFTD. Hyperconnectivity (patients>HCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration. |
| first_indexed | 2024-04-09T23:40:37Z |
| format | Article |
| id | doaj.art-83cdcb5a0bbc4321a6a7fdaa2bdc7ee5 |
| institution | Directory Open Access Journal |
| issn | 1095-953X |
| language | English |
| last_indexed | 2024-04-09T23:40:37Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neurobiology of Disease |
| spelling | doaj.art-83cdcb5a0bbc4321a6a7fdaa2bdc7ee52023-03-19T04:37:07ZengElsevierNeurobiology of Disease1095-953X2023-04-01179106047Source space connectomics of neurodegeneration: One-metric approach does not fit allPavel Prado0Sebastian Moguilner1Jhony A. Mejía2Agustín Sainz-Ballesteros3Mónica Otero4Agustina Birba5Hernando Santamaria-Garcia6Agustina Legaz7Sol Fittipaldi8Josephine Cruzat9Enzo Tagliazucchi10Mario Parra11Rubén Herzog12Agustín Ibáñez13Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, ChileLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, ArgentinaLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Ingeniería Biomédica, Universidad de Los Andes, Bogotá, ColombiaLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, ChileFacultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile; Centro BASAL Ciencia & Vida, Universidad San Sebastián, Santiago, ChileLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, ArgentinaPhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Global Brain Health Institute, University of California San Francisco, San Francisco, California; Global Brain Health Institute, Trinity College Dublin, Dublin, IrelandCognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; National Scientific and Technical Research Council, Buenos Aires, ArgentinaLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland; National Scientific and Technical Research Council, Buenos Aires, ArgentinaLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, ChileLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Física, Universidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA -CONICET), Buenos Aires, ArgentinaSchool of Psychological Sciences and Health, University of Strathclyde, Glasgow, United KingdomLatin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), Chile.Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Trinity College Dublin (TCD), Dublin, Ireland; Corresponding author at: Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Peñalolén, Santiago de Chile, Chile.Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patients<HCs) involving convergent temporo-parieto-occipital regions in AD, and fronto-temporo-parietal areas in bvFTD. Hyperconnectivity (patients>HCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.http://www.sciencedirect.com/science/article/pii/S096999612300061XComposite connectivity metricConnectomicsDementia biomarkerEEG source-spaceMulti-feature machine learning classification |
| spellingShingle | Pavel Prado Sebastian Moguilner Jhony A. Mejía Agustín Sainz-Ballesteros Mónica Otero Agustina Birba Hernando Santamaria-Garcia Agustina Legaz Sol Fittipaldi Josephine Cruzat Enzo Tagliazucchi Mario Parra Rubén Herzog Agustín Ibáñez Source space connectomics of neurodegeneration: One-metric approach does not fit all Neurobiology of Disease Composite connectivity metric Connectomics Dementia biomarker EEG source-space Multi-feature machine learning classification |
| title | Source space connectomics of neurodegeneration: One-metric approach does not fit all |
| title_full | Source space connectomics of neurodegeneration: One-metric approach does not fit all |
| title_fullStr | Source space connectomics of neurodegeneration: One-metric approach does not fit all |
| title_full_unstemmed | Source space connectomics of neurodegeneration: One-metric approach does not fit all |
| title_short | Source space connectomics of neurodegeneration: One-metric approach does not fit all |
| title_sort | source space connectomics of neurodegeneration one metric approach does not fit all |
| topic | Composite connectivity metric Connectomics Dementia biomarker EEG source-space Multi-feature machine learning classification |
| url | http://www.sciencedirect.com/science/article/pii/S096999612300061X |
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