The Artists’ Brain: A Data Fusion Approach to Characterize the Neural Bases of Professional Visual Artists

Document Type

Article

Source of Publication

bioRxiv

Publication Date

1-2-2025

Abstract

Artistic creativity relies on complex perceptual, cognitive and motoric functions, yet the specific neural characterization of being an artist remain incompletely understood. To fill this gap in the literature, the present study aims to characterize the gray matter (GM) and white matter (WM) contributions to professional visual artists as compared to non-artists controls. The MRI brain scans of 12 professional artists and 12 matched non-artists were analyzed via an unsupervised machine learning method known as Transposed Independent Vector Analysis (tIVA) to detect joint GM-WM networks. Two independent networks were found. The first network (IC2), more expressed in artists, included increased GM-WM concentration in regions associated with the Default Mode Network (DMN), Executive Control Network (ECN), and sensorimotor networks possibly related with augmented cognitive and ideational control, and increased perceptual-motor integration skills critical for creative tasks. The second network (IC8), less expressed in artists, included decreased GM-WM density in regions related to the Salience Network, such as the Anterior Cingulate Cortex, suggesting attentional regulation processes that may not be as central to visual artists. In sum, these results suggest that artists may rely on specialized brain networks, reflecting unique neural adaptations in individuals with pronounced creativity and extensive creative training.

Disciplines

Arts and Humanities

Keywords

art, artist, creativity, neuroscience, data fusion, machine learning

Indexed in Scopus

no

Open Access

yes

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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