Precision Network Modeling of TMS Across Individuals Suggests Therapeutic Targets and Potential for Improvement.


Journal

medRxiv : the preprint server for health sciences
Titre abrégé: medRxiv
Pays: United States
ID NLM: 101767986

Informations de publication

Date de publication:
16 Aug 2024
Historique:
medline: 26 8 2024
pubmed: 26 8 2024
entrez: 26 8 2024
Statut: epublish

Résumé

Higher-order cognitive and affective functions are supported by large-scale networks in the brain. Dysfunction in different networks is proposed to associate with distinct symptoms in neuro-psychiatric disorders. However, the specific networks targeted by current clinical transcranial magnetic stimulation (TMS) approaches are unclear. While standard-of-care TMS relies on scalp-based landmarks, recent FDA-approved TMS protocols use individualized functional connectivity with the subgenual anterior cingulate cortex (sgACC) to optimize TMS targeting. Leveraging previous work on precision network estimation and recent advances in network-level TMS targeting, we demonstrate that clinical TMS approaches target different functional networks between individuals. Homotopic scalp positions (left F3 and right F4) target different networks within and across individuals, and right F4 generally favors a right-lateralized control network. We also modeled the impact of targeting the dorsolateral prefrontal cortex (dlPFC) zone anticorrelated with the sgACC and found that the individual-specific anticorrelated region variably targets a network coupled to reward circuitry. Combining individualized, precision network mapping and electric field (E-field) modeling, we further illustrate how modeling can be deployed to prospectively target distinct closely localized association networks in the dlPFC with meaningful spatial selectivity and E-field intensity. Lastly, our findings emphasize differences between selectivity and maximal intensity, highlighting the need to consider both metrics in precision TMS efforts.

Identifiants

pubmed: 39185539
doi: 10.1101/2024.08.15.24311994
pmc: PMC11343249
pii:
doi:

Types de publication

Journal Article Preprint

Langues

eng

Auteurs

Classifications MeSH