The Artists' Brain: A Data Fusion Approach To Characterize The Neural Bases Of Professional Visual Artists

Document Type

Article

Source of Publication

Psychology Of Aesthetics Creativity And The Arts

Publication Date

1-12-2026

Abstract

Although everyone has the capacity to draw, only some develop the expertise to produce professional art. Despite extensive creativity research, surprisingly little is known about how years of visual artistic training reshape the neural architecture that distinguishes professional artists from nonartist. To address this gap, we applied joint independent component analysis to detect structural (gray matter volume, white matter fractional anisotropy), and functional (resting-state regional homogeneity), neuroimaging data from 12 professional visual artists and 12 matched controls. This multimodal approach identified a joint gray matter-resting-state regional homogeneity-fractional anisotropy component (Independent Component 2) that significantly distinguished artists from controls (p = .020, d = 1.028). Compared to controls, artists showed coordinated neural adaptations including increased GM in parietal, temporal, frontal regions, and posterior cingulate cortex; enhanced white matter integrity in anterior thalamic radiations, corticospinal tracts, and association fibers; and increased functional homogeneity in basal ganglia and cerebellar structures. Notably, Independent Component 2 expression correlated with higher visual imagery vividness, linking neural adaptations to cognitive abilities fundamental to artistic creation. Taken together, these results highlight the involvement of canonical creativity networks (default mode network-executive control network) while also extending them to include domain-specific adaptations in cerebellar, sensorimotor, and subcortical systems. Despite these advances, replication with larger samples is necessary.

ISSN

1931-3896

Publisher

American Psychological Association (APA)

Disciplines

Life Sciences

Keywords

artist, creativity, neuroscience, data fusion, machine learning

Indexed in Scopus

no

Open Access

no

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