Machine learning classifiers for electrode selection in the design of closed-loop neuromodulation devices for episodic memory improvement.
Cereb Cortex
; 33(13): 8150-8163, 2023 06 20.
Article
en En
| MEDLINE
| ID: mdl-36997155
ABSTRACT
Successful neuromodulation approaches to alter episodic memory require closed-loop stimulation predicated on the effective classification of brain states. The practical implementation of such strategies requires prior decisions regarding electrode implantation locations. Using a data-driven approach, we employ support vector machine (SVM) classifiers to identify high-yield brain targets on a large data set of 75 human intracranial electroencephalogram subjects performing the free recall (FR) task. Further, we address whether the conserved brain regions provide effective classification in an alternate (associative) memory paradigm along with FR, as well as testing unsupervised classification methods that may be a useful adjunct to clinical device implementation. Finally, we use random forest models to classify functional brain states, differentiating encoding versus retrieval versus non-memory behavior such as rest and mathematical processing. We then test how regions that exhibit good classification for the likelihood of recall success in the SVM models overlap with regions that differentiate functional brain states in the random forest models. Finally, we lay out how these data may be used in the design of neuromodulation devices.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Encéfalo
/
Electrodos
/
Electroencefalografía
/
Memoria Episódica
/
Máquina de Vectores de Soporte
/
Bosques Aleatorios
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Cereb Cortex
Asunto de la revista:
CEREBRO
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos