Bayesian Optimization of Neurostimulation (BOONStim)
BACKGROUND: Transcranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to identify TMS targets are needed.
OBJECTIVE: The current study presents the development and validation of the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline.
METHODS: BOONStim uses Bayesian optimization for individualized TMS targeting, automating interoperability between surface-based fMRI analytic tools and TMS electric field modeling. Bayesian optimization performance was evaluated in a sample dataset (N=10) using standard circular and functional connectivity-defined targets, and compared to grid optimization.
RESULTS: Bayesian optimization converged to similar levels of total electric field stimulation across targets in under 30 iterations, converging within a 5% error of the maxima detected by grid optimization, and requiring less time.
CONCLUSIONS: BOONStim is a scalable and configurable user-friendly pipeline for individualized TMS targeting with quick turnaround.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
bioRxiv : the preprint server for biology - (2024) vom: 28. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Oliver, Lindsay D [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 01.04.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2024.03.08.584169 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM370487672 |
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| 100 | 1 | |a Oliver, Lindsay D |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Bayesian Optimization of Neurostimulation (BOONStim) |
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| 500 | |a Date Revised 01.04.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a BACKGROUND: Transcranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to identify TMS targets are needed | ||
| 520 | |a OBJECTIVE: The current study presents the development and validation of the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline | ||
| 520 | |a METHODS: BOONStim uses Bayesian optimization for individualized TMS targeting, automating interoperability between surface-based fMRI analytic tools and TMS electric field modeling. Bayesian optimization performance was evaluated in a sample dataset (N=10) using standard circular and functional connectivity-defined targets, and compared to grid optimization | ||
| 520 | |a RESULTS: Bayesian optimization converged to similar levels of total electric field stimulation across targets in under 30 iterations, converging within a 5% error of the maxima detected by grid optimization, and requiring less time | ||
| 520 | |a CONCLUSIONS: BOONStim is a scalable and configurable user-friendly pipeline for individualized TMS targeting with quick turnaround | ||
| 650 | 4 | |a Preprint | |
| 700 | 1 | |a Jeyachandra, Jerrold |e verfasserin |4 aut | |
| 700 | 1 | |a Dickie, Erin W |e verfasserin |4 aut | |
| 700 | 1 | |a Hawco, Colin |e verfasserin |4 aut | |
| 700 | 1 | |a Mansour, Salim |e verfasserin |4 aut | |
| 700 | 1 | |a Hare, Stephanie M |e verfasserin |4 aut | |
| 700 | 1 | |a Buchanan, Robert W |e verfasserin |4 aut | |
| 700 | 1 | |a Malhotra, Anil K |e verfasserin |4 aut | |
| 700 | 1 | |a Blumberger, Daniel M |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Zhi-De |e verfasserin |4 aut | |
| 700 | 1 | |a Voineskos, Aristotle N |e verfasserin |4 aut | |
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