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Neuroimage. 2015 Apr 15;110:182-93. doi: 10.1016/j.neuroimage.2014.12.085. Epub 2015 Jan 22.

Multiple brain networks underpinning word learning from fluent speech revealed by independent component analysis.

Author information

1
Cognition and Brain Plasticity Unit, Bellvitge Research Biomedical Institute (IDIBELL), Hospitalet de Llobregat, 08907 Barcelona, Spain; Dept. of Basic Psychology, University of Barcelona, 08035 Barcelona, Spain; Institut du Cerveau et de la Moelle épinière, ICM, PICNIC Lab, 75013 Paris, France.
2
Cognition and Brain Plasticity Unit, Bellvitge Research Biomedical Institute (IDIBELL), Hospitalet de Llobregat, 08907 Barcelona, Spain; Dept. of Basic Psychology, University of Barcelona, 08035 Barcelona, Spain.
3
Department of Neurology, University of Lübeck, Lübeck, Germany; CNS-LAB, International Neuroscience Institute (INI), Hannover, Germany.
4
Department of Neurology, University of Lübeck, Lübeck, Germany.
5
INSERM U955, Equipe 1, Neuropsychologie Interventionnelle, IMRB, Créteil, France; Ecole Normale Superieure, Departement d'Etudes Cognitives, Paris, France.
6
Cognition and Brain Plasticity Unit, Bellvitge Research Biomedical Institute (IDIBELL), Hospitalet de Llobregat, 08907 Barcelona, Spain; Dept. of Basic Psychology, University of Barcelona, 08035 Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
7
Cognition and Brain Plasticity Unit, Bellvitge Research Biomedical Institute (IDIBELL), Hospitalet de Llobregat, 08907 Barcelona, Spain; Dept. of Basic Psychology, University of Barcelona, 08035 Barcelona, Spain; Ecole Normale Superieure, Departement d'Etudes Cognitives, Paris, France; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.

Abstract

Although neuroimaging studies using standard subtraction-based analysis from functional magnetic resonance imaging (fMRI) have suggested that frontal and temporal regions are involved in word learning from fluent speech, the possible contribution of different brain networks during this type of learning is still largely unknown. Indeed, univariate fMRI analyses cannot identify the full extent of distributed networks that are engaged by a complex task such as word learning. Here we used Independent Component Analysis (ICA) to characterize the different brain networks subserving word learning from an artificial language speech stream. Results were replicated in a second cohort of participants with a different linguistic background. Four spatially independent networks were associated with the task in both cohorts: (i) a dorsal Auditory-Premotor network; (ii) a dorsal Sensory-Motor network; (iii) a dorsal Fronto-Parietal network; and (iv) a ventral Fronto-Temporal network. The level of engagement of these networks varied through the learning period with only the dorsal Auditory-Premotor network being engaged across all blocks. In addition, the connectivity strength of this network in the second block of the learning phase correlated with the individual variability in word learning performance. These findings suggest that: (i) word learning relies on segregated connectivity patterns involving dorsal and ventral networks; and (ii) specifically, the dorsal auditory-premotor network connectivity strength is directly correlated with word learning performance.

KEYWORDS:

Dorsal-stream; Functional connectivity; ICA; Ventral stream; Word-learning

[Indexed for MEDLINE]

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