Personalized brain stimulation for effective neurointervention across participants
| Autor: | Evelyn H. Kroesbergen, Sanne H. G. van der Ven, Michael A. Osborne, Nienke E. R. van Bueren, James G. Sheffield, Thomas Reed, Roi Cohen Kadosh, Vu Nguyen |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
Computer science Physiology Social Sciences computer.software_genre Transcranial Direct Current Stimulation 0302 clinical medicine Medicine and Health Sciences Psychology Biology (General) Function (engineering) media_common Transcranial alternating current stimulation Clinical Neurophysiology Brain Mapping Ecology Applied Mathematics Simulation and Modeling 05 social sciences Bayesian optimization Brain Cognition Electroencephalography 3. Good health Electrophysiology Bioassays and Physiological Analysis Computational Theory and Mathematics Brain Electrophysiology Modeling and Simulation Current strength Physical Sciences Sensory Perception Female Algorithms Research Article Optimization Imaging Techniques QH301-705.5 media_common.quotation_subject Neurophysiology Learning and Plasticity Surgical and Invasive Medical Procedures Neuroimaging Machine learning Research and Analysis Methods 050105 experimental psychology 03 medical and health sciences Cellular and Molecular Neuroscience Perception Genetics Humans 0501 psychology and cognitive sciences Multiplication Transcranial Alternating Current Stimulation Baseline (configuration management) Transcranial Stimulation Molecular Biology Ecology Evolution Behavior and Systematics Arithmetic Functional Electrical Stimulation business.industry Electrophysiological Techniques Cognitive Psychology Biology and Life Sciences Bayes Theorem Brain stimulation Cognitive Science Artificial intelligence Clinical Medicine business computer 030217 neurology & neurosurgery Mathematics Neuroscience |
| Zdroj: | PLoS Computational Biology, Vol 17, Iss 9, p e1008886 (2021) Plos Computational Biology, 17, 9 PLoS Computational Biology Plos Computational Biology, 17 |
| ISSN: | 1553-7358 |
| Popis: | Accumulating evidence from human-based research has highlighted that the prevalent one-size-fits-all approach for neural and behavioral interventions is inefficient. This approach can benefit one individual, but be ineffective or even detrimental for another. Studying the efficacy of the large range of different parameters for different individuals is costly, time-consuming and requires a large sample size that makes such research impractical and hinders effective interventions. Here an active machine learning technique is presented across participants—personalized Bayesian optimization (pBO)—that searches available parameter combinations to optimize an intervention as a function of an individual’s ability. This novel technique was utilized to identify transcranial alternating current stimulation (tACS) frequency and current strength combinations most likely to improve arithmetic performance, based on a subject’s baseline arithmetic abilities. The pBO was performed across all subjects tested, building a model of subject performance, capable of recommending parameters for future subjects based on their baseline arithmetic ability. pBO successfully searches, learns, and recommends parameters for an effective neurointervention as supported by behavioral, simulation, and neural data. The application of pBO in human-based research opens up new avenues for personalized and more effective interventions, as well as discoveries of protocols for treatment and translation to other clinical and non-clinical domains. Author summary The common one-size-fits-all approach used in biological and behavioral research has shown to be inefficient. This is especially the case in the field of brain stimulation, where many different combinations of stimulation parameters (i.e., frequency and current strength of the applied current) can be used for restorative or enhancement purposes, in clinical and non-clinical populations, respectively. Even intervention protocols that have reported to be effective for certain individuals can be detrimental for others. Here we present an active machine learning method, personalized Bayesian optimization (pBO) that successfully searches, learns, and recommends neurostimulation parameters across individuals. Based on an individual’s baseline cognitive ability, the pBO identifies specific combinations of transcranial alternating current stimulation parameters, which are most likely to improve cognitive performance, in which case arithmetic problem solving. This timely approach provides a possible solution for the pressing need for personalization in different disciplines including medicine, psychology, and education. |
| Databáze: | OpenAIRE |
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