Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization

Pavel Prado; Jhony A. Mejía; Agustín Sainz-Ballesteros; Agustina Birba; Sebastian Moguilner; Rubén Herzog; Mónica Otero; Jhosmary Cuadros; Lucía Z-Rivera; Daniel Franco O'Byrne; Mario Parra Rodriguez; Agustín Ibáñez

doi:10.1002/dad2.12455

Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization

Pavel Prado
, Jhony A. Mejía
, Agustín Sainz-Ballesteros
, Agustina Birba
, Sebastian Moguilner
, Rubén Herzog
, Mónica Otero
, Jhosmary Cuadros
, Lucía Z-Rivera
, Daniel Franco O'Byrne
, Mario Parra Rodriguez
, Agustín Ibáñez

Psychology

Research output: Contribution to journal › Article › peer-review

28 Citations (Scopus)

29 Downloads (Pure)

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.

Original language	English
Article number	e12455
Journal	Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
Volume	15
Issue number	3
Early online date	8 Jul 2023
DOIs	https://doi.org/10.1002/dad2.12455
Publication status	Published - 30 Jul 2023

Funding

The authors thank the ReDLat participants and their families for their valuable time and commitment to our study. AI is supported by Takeda Grant CW2680521; CONICET; FONCYT‐PICT (2017‐1818, 2017‐1820); ANID/FONDECYT Regular (1210195, 1210176, 1220995); ANID/FONDAP (15150012); ANID/PIA/ANILLOS ACT210096; ANID/FONDEF ID20I10152, ID22I10029; and the Multi‐Partner Consortium to Expand Dementia Research in Latin America (ReDLat), funded by the National Institutes of Aging of the National Institutes of Health under award number R01AG057234, an Alzheimer's Association grant (SG‐20‐725707‐ReDLat), the Rainwater Foundation, and the Global Brain Health Institute. SF is an Atlantic Fellow for Equity in Brain Health at the Global Brain Health Institute (GBHI) and is supported with funding from GBHI, BrainLat, ANID/FONDEF ID22I10029, and CONICET. MO is supported by ANID/FONDECYT Postdoctorado 3210508. The content is solely the responsibility of the authors and does not represent the official views of these institutions. The authors thank the ReDLat participants and their families for their valuable time and commitment to our study. AI is supported by Takeda Grant CW2680521; CONICET; FONCYT-PICT (2017-1818, 2017-1820); ANID/FONDECYT Regular (1210195, 1210176, 1220995); ANID/FONDAP (15150012); ANID/PIA/ANILLOS ACT210096; ANID/FONDEF ID20I10152, ID22I10029; and the Multi-Partner Consortium to Expand Dementia Research in Latin America (ReDLat), funded by the National Institutes of Aging of the National Institutes of Health under award number R01AG057234, an Alzheimer's Association grant (SG-20-725707-ReDLat), the Rainwater Foundation, and the Global Brain Health Institute. SF is an Atlantic Fellow for Equity in Brain Health at the Global Brain Health Institute (GBHI) and is supported with funding from GBHI, BrainLat, ANID/FONDEF ID22I10029, and CONICET. MO is supported by ANID/FONDECYT Postdoctorado 3210508. The content is solely the responsibility of the authors and does not represent the official views of these institutions. Open access funding provided by IReL. Agustín Ibáñez is partially supported by grants of Takeda CW2680521; FONCYT PICT (2017‐1818, 2017‐1820); ANID/FONDECYT Regular (1210195, 1210176, 1220995); ANID/FONDAP (15150012); FONDEF ID20I10152, ID22I10029; ANID/PIA/ANILLOS ACT210096; and the Multi‐Partner Consortium to Expand Dementia Research in Latin America (ReDLat), funded by the National Institutes of Aging of the National Institutes of Health under award number R01AG057234, an Alzheimer's Association grant (SG‐20‐725707‐ReDLat), the Rainwater Foundation, and the Global Brain Health Institute (GBHI). Pavel Prado, Jhony A. Mejía, Agustín Sainz‐Ballesteros, Agustina Birba, Sebastian Moguilner, Rubén Herzog, Mónica Otero, Jhosmary Cuadros, Lucía Zepeda, Daniel Franco‐O'Byrne, and Mario Parra have nothing to disclose. The content of the article is solely the responsibility of the authors and does not represent the official views of these institutions. The authors thank the ReDLat participants and their families for their commitment to our study. Author disclosures are available in the supporting information .

Keywords

AD
automatic harmonization
bvFTD
dementia classification
EEG
inverse solution methods
multi-centric studies
whole-brain functional connectivity

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10.1002/dad2.12455Licence: CC BY-NC 4.0

Prado-etal-DADM-2023-Harmonized-multi-metric-and-multi-centric-assessment-of-EEG-source-spaceFinal published version, 5.53 MBLicence: CC BY-NC 4.0

Cite this

Prado, P., Mejía, J. A., Sainz-Ballesteros, A., Birba, A., Moguilner, S., Herzog, R., Otero, M., Cuadros, J., Z-Rivera, L., O'Byrne, D. F., Parra Rodriguez, M., & Ibáñez, A. (2023). Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 15(3), Article e12455. https://doi.org/10.1002/dad2.12455

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title = "Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization",

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.",

keywords = "AD, automatic harmonization, bvFTD, dementia classification, EEG, inverse solution methods, multi-centric studies, whole-brain functional connectivity",

author = "Pavel Prado and Mej{\'i}a, \{Jhony A.\} and Agust{\'i}n Sainz-Ballesteros and Agustina Birba and Sebastian Moguilner and Rub{\'e}n Herzog and M{\'o}nica Otero and Jhosmary Cuadros and Luc{\'i}a Z-Rivera and O'Byrne, \{Daniel Franco\} and \{Parra Rodriguez\}, Mario and Agust{\'i}n Ib{\'a}{\~n}ez",

year = "2023",

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doi = "10.1002/dad2.12455",

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journal = "Alzheimer's \& Dementia: Diagnosis, Assessment \& Disease Monitoring",

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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 Rodriguez, M & Ibáñez, A 2023, 'Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization', Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, vol. 15, no. 3, e12455. https://doi.org/10.1002/dad2.12455

TY - JOUR

T1 - Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization

AU - Prado, Pavel

AU - Mejía, Jhony A.

AU - Sainz-Ballesteros, Agustín

AU - Birba, Agustina

AU - Moguilner, Sebastian

AU - Herzog, Rubén

AU - Otero, Mónica

AU - Cuadros, Jhosmary

AU - Z-Rivera, Lucía

AU - O'Byrne, Daniel Franco

AU - Parra Rodriguez, Mario

AU - Ibáñez, Agustín

PY - 2023/7/30

Y1 - 2023/7/30

N2 - 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.

AB - 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.

KW - AD

KW - automatic harmonization

KW - bvFTD

KW - dementia classification

KW - EEG

KW - inverse solution methods

KW - multi-centric studies

KW - whole-brain functional connectivity

U2 - 10.1002/dad2.12455

DO - 10.1002/dad2.12455

M3 - Article

SN - 2352-8729

VL - 15

JO - Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

JF - Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

IS - 3

M1 - e12455

ER -

Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization

Abstract

Funding

Keywords

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