Graph measures represent an optimal way to investigate neural networks’ organization, yet their application is still limited in developmental samples. To uncover the organization of 7-month-old infants’ functional brain networks during an emotional perception task, we combined a decoding technique (i.e., Principal Component Regression) to graph metrics computation. Nodes’ Within Module Degree Z Score (WMDZ) was computed as a measure of modular organization, and we decoded networks’ functional organizations across EEG alpha and theta bands in response to static and dynamic facial expressions of emotions. We found that infants’ brain topological activity differentiates between static and dynamic emotional faces due to the involvement of visual streams and sensorimotor areas, as often observed in adults. Moreover, network invariances point toward an already present rudimental network structure tuned to face processing already at 7-months of age. Overall, our results affirm the fruitfulness of the application of graph measures in developmental samples, due to their flexibility and the wealth of information they provide over infants’ networks functional organization.

Polver, S., Quadrelli, E., Turati, C., & Bulf, H. (2022). Decoding functional brain networks through graph measures in infancy: The case of emotional faces. BIOLOGICAL PSYCHOLOGY, 170(April 2022) [10.1016/j.biopsycho.2022.108292].

Decoding functional brain networks through graph measures in infancy: The case of emotional faces

Polver S.
;
Quadrelli E.
;
Turati C.
;
Bulf H.
2022

Abstract

Graph measures represent an optimal way to investigate neural networks’ organization, yet their application is still limited in developmental samples. To uncover the organization of 7-month-old infants’ functional brain networks during an emotional perception task, we combined a decoding technique (i.e., Principal Component Regression) to graph metrics computation. Nodes’ Within Module Degree Z Score (WMDZ) was computed as a measure of modular organization, and we decoded networks’ functional organizations across EEG alpha and theta bands in response to static and dynamic facial expressions of emotions. We found that infants’ brain topological activity differentiates between static and dynamic emotional faces due to the involvement of visual streams and sensorimotor areas, as often observed in adults. Moreover, network invariances point toward an already present rudimental network structure tuned to face processing already at 7-months of age. Overall, our results affirm the fruitfulness of the application of graph measures in developmental samples, due to their flexibility and the wealth of information they provide over infants’ networks functional organization.
Articolo in rivista - Articolo scientifico
Decoding; EEG; Emotional faces; Graph theory; Infants;
English
Polver, S., Quadrelli, E., Turati, C., & Bulf, H. (2022). Decoding functional brain networks through graph measures in infancy: The case of emotional faces. BIOLOGICAL PSYCHOLOGY, 170(April 2022) [10.1016/j.biopsycho.2022.108292].
Polver, S; Quadrelli, E; Turati, C; Bulf, H
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/358684
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