TY - JOUR
T1 - Basis profile curve identification to understand electrical stimulation effects in human brain networks
AU - Miller, Kai J.
AU - Müller, Klaus Robert
AU - Hermes, Dora
N1 - Funding Information:
Funding:KJMwassupportedbytheVanWagenen Fellowship,theBrainResearchFoundationwitha Fay/FrankSeedGrant,andtheBrain&Behavior ResearchFoundationwithaNARSADYoung InvestigatorGrant.Thisworkwasalsosupported byNIH-NCATSCTSAKL2TR002379(KJM).DH wassupportedbytheNIH-NIMHCRCNS R01MH122258-01.Manuscriptcontentsaresolely theresponsibilityoftheauthorsanddonot necessarilyrepresenttheofficialviewsoftheNIH. KRMwassupportedinpartbytheInstituteof Information&CommunicationsTechnology Planning&Evaluation(IITP)grantfundedbythe KoreaGovernment(No.2017-0-00451, DevelopmentofBCIbasedBrainandCognitive ComputingTechnologyforRecognizingUser’s IntentionsusingDeepLearning)and(No.2019-0-00079,ArtificialIntelligenceGraduateSchool Program,KoreaUniversity),andbytheGerman MinistryforEducationandResearch(BMBF)under Grants01IS14013A-E,01GQ1115,01GQ0850, 01IS18025A,031L0207Dand01IS18037A;the GermanResearchFoundation(DFG)underGrant Math+,EXC2046/1,ProjectID390685689.The fundershadnoroleinstudydesign,datacollection andanalysis,decisiontopublish,orpreparationof themanuscript.
Funding Information:
KJM was supported by the Van Wagenen Fellowship, the Brain Research Foundation with a Fay/Frank Seed Grant, and the Brain & Behavior Research Foundation with a NARSAD Young Investigator Grant. This work was also supported by NIH-NCATS CTSA KL2 TR002379 (KJM). DH was supported by the NIH-NIMH CRCNS R01MH122258-01. Manuscript contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH. KRM was supported in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User's Intentions using Deep Learning) and (No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University), and by the German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, 031L0207D and 01IS18037A; the German Research Foundation (DFG) under Grant Math+, EXC 2046/1, Project ID 390685689. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful for the participation of the patient in the study, and for the assistance of the staff in Saint Marys Hospital at Mayo Clinic, Rochester, MN. Greg Worrell, Pradeep Udayavar Shenoy, Amin Nourmohammadi, and Harvey Huang generously provided helpful discussion and commentary during the drafting of the manuscript.
Publisher Copyright:
© 2021 Miller et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2021/9
Y1 - 2021/9
N2 - Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.
AB - Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique “basis profile curves” (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.
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U2 - 10.1371/journal.pcbi.1008710
DO - 10.1371/journal.pcbi.1008710
M3 - Article
C2 - 34473701
AN - SCOPUS:85114443187
VL - 17
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 9
M1 - e1008710
ER -