Personalized connectivity-guided DLPFC-TMS for depression: Advancing computational feasibility, precision and reproducibility.


Journal

Human brain mapping
ISSN: 1097-0193
Titre abrégé: Hum Brain Mapp
Pays: United States
ID NLM: 9419065

Informations de publication

Date de publication:
09 2021
Historique:
revised: 16 11 2020
received: 06 05 2020
accepted: 13 12 2020
pubmed: 6 2 2021
medline: 19 3 2022
entrez: 5 2 2021
Statut: ppublish

Résumé

Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) is an established treatment for refractory depression, however, therapeutic outcomes vary. Mounting evidence suggests that clinical response relates to functional connectivity with the subgenual cingulate cortex (SGC) at the precise DLPFC stimulation site. Critically, SGC-related network architecture shows considerable interindividual variation across the spatial extent of the DLPFC, indicating that connectivity-based target personalization could potentially be necessary to improve treatment outcomes. However, to date accurate personalization has not appeared feasible, with recent work indicating that the intraindividual reproducibility of optimal targets is limited to 3.5 cm. Here we developed reliable and accurate methodologies to compute individualized connectivity-guided stimulation targets. In resting-state functional MRI scans acquired across 1,000 healthy adults, we demonstrate that, using this approach, personalized targets can be reliably and robustly pinpointed, with a median accuracy of ~2 mm between scans repeated across separate days. These targets remained highly stable, even after 1 year, with a median intraindividual distance between coordinates of only 2.7 mm. Interindividual spatial variation in personalized targets exceeded intraindividual variation by a factor of up to 6.85, suggesting that personalized targets did not trivially converge to a group-average site. Moreover, personalized targets were heritable, suggesting that connectivity-guided rTMS personalization is stable over time and under genetic control. This computational framework provides capacity for personalized connectivity-guided TMS targets to be robustly computed with high precision and has the flexibly to advance research in other basic research and clinical applications.

Identifiants

pubmed: 33544411
doi: 10.1002/hbm.25330
pmc: PMC8357003
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4155-4172

Subventions

Organisme : NIMH NIH HHS
ID : U54 MH091657
Pays : United States

Commentaires et corrections

Type : CommentIn

Informations de copyright

© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

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Auteurs

Robin F H Cash (RFH)

Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.
Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.

Luca Cocchi (L)

Clinical Brain Networks Group, QIMR Berghofer, Brisbane, Queensland, Australia.

Jinglei Lv (J)

Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.
Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.
School of Biomedical Engineering, The University of Sydney, Camperdown, New South Wales, Australia.

Yumeng Wu (Y)

Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.

Paul B Fitzgerald (PB)

Epworth Centre for Innovation and Mental Health, Epworth Healthcare and the Monash University Central Clinical School, Camberwell, Victoria, Australia.

Andrew Zalesky (A)

Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.
Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.

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