Artículo
Consistent gradient of performance and decoding of stimulus type and valence from local and network activity
Hesse Rizzi, Eugenia Fátima
; Mikulan, Ezequiel Pablo
; Sitt, Jacobo Diego
; García, María del Carmen; Silva, Walter
; Ciraolo, Carlos; Vaucheret Paz, Esteban Fabian; Raimondo, Federico
; Baglivo, Fabricio Hugo; Gonzalez Adolfi, Federico
; Herrera, Eduar; Bekinschtein, Tristán Andrés
; Petroni, Agustín; Lew, Sergio Eduardo; Sedeño, Lucas
; García, Adolfo Martín
; Ibañez, Agustin Mariano
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Fecha de publicación:
02/2019
Editorial:
Institute of Electrical and Electronics Engineers
Revista:
Ieee Transactions On Neural Systems And Rehabilitation Engineering
ISSN:
1534-4320
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
The individual differences approach focuses on the variation of behavioral and neural signatures across subjects. In this context, we searched for intracranial neural markers of performance in three individuals with distinct behavioral patterns (efficient, borderline, and inefficient) in a dual-valence task assessing facial and lexical emotion recognition. First, we performed a preliminary study to replicate well-established evoked responses in relevant brain regions. Then, we examined time series data and network connectivity, combined with multivariate pattern analyses and machine learning, to explore electrophysiological differences in resting-state versus task-related activity across subjects. Next, using the same methodological approach, we assessed the neural decoding of performance for different dimensions of the task. The classification of time series data mirrored the behavioral gradient across subjects for stimulus type but not for valence. However, network-based measures reflected the subjects' hierarchical profiles for both stimulus types and valence. Therefore, this measure serves as a sensitive marker for capturing distributed processes such as emotional valence discrimination, which relies on an extended set of regions. Network measures combined with classification methods may offer useful insights to study single subjects and understand inter-individual performance variability. Promisingly, this approach could eventually be extrapolated to other neuroscientific techniques.
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Articulos(INCYT)
Articulos de INSTITUTO DE NEUROCIENCIAS COGNITIVAS Y TRASLACIONAL
Articulos de INSTITUTO DE NEUROCIENCIAS COGNITIVAS Y TRASLACIONAL
Articulos(SEDE CENTRAL)
Articulos de SEDE CENTRAL
Articulos de SEDE CENTRAL
Citación
Hesse Rizzi, Eugenia Fátima; Mikulan, Ezequiel Pablo; Sitt, Jacobo Diego; García, María del Carmen; Silva, Walter; et al.; Consistent gradient of performance and decoding of stimulus type and valence from local and network activity; Institute of Electrical and Electronics Engineers; Ieee Transactions On Neural Systems And Rehabilitation Engineering; 27; 4; 2-2019; 619-629
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