Four methods of brain pattern analyses of fMRI signals associated with wrist extension versus wrist flexion studied for potential use in future motor learning BCI

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dc.contributor.authorRavindran, Aniruddh
dc.contributor.authorRieke, Jake D.
dc.contributor.authorZapata, Jose Daniel Alcantara
dc.contributor.authorWhite, Keith D.
dc.contributor.authorMatarasso, Avi
dc.contributor.authorYusufali, M. Minhal
dc.contributor.authorRana, Mohit
dc.contributor.authorGunduz, Aysegul
dc.contributor.authorModarres, Mo
dc.contributor.authorSitaram, Ranganatha
dc.contributor.authorDaly, Janis J.
dc.date.accessioned2025-01-20T22:10:23Z
dc.date.available2025-01-20T22:10:23Z
dc.date.issued2021
dc.description.abstractObjective
dc.description.abstractIn stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults.
dc.description.abstractApproach
dc.description.abstractWe studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) 'Winner Take All'; and 4) Relative Dominance.
dc.description.abstractResults
dc.description.abstractWith these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects.
dc.description.abstractSignificance
dc.description.abstractWe encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data.
dc.fuente.origenWOS
dc.identifier.doi10.1371/journal.pone.0254338
dc.identifier.issn1932-6203
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0254338
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/94370
dc.identifier.wosidWOS:000686474200034
dc.issue.numero8
dc.language.isoen
dc.revistaPlos one
dc.rightsacceso restringido
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleFour methods of brain pattern analyses of fMRI signals associated with wrist extension versus wrist flexion studied for potential use in future motor learning BCI
dc.typeartículo
dc.volumen16
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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