Shared and distinct neural signatures of feature and spatial attention

• Published in: NeuroImage (Orlando, Fla.) Vol. 317; p. 121296
• Main Authors: Yang, Anmin, Tian, Jinhua, Wang, Wenbo, Zhou, Liqin, Zhou, Ke
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.08.2025; Elsevier Limited; Elsevier
• Subjects: Adult; Attention; Attention - physiology; Between-subject whole-brain machine learning approach; Brain - physiology; Brain Mapping - methods; Brain research; Brain signature; Cognition & reasoning; Cognitive ability; Feature attention; Female; Humans; Machine Learning; Magnetic Resonance Imaging - methods; Male; Nerve Net - diagnostic imaging; Nerve Net - physiology; Neuroimaging; Neurons; Radiology/Diagnostic Imaging; Space Perception - physiology; Spatial attention; Visual perception; Visual Perception - physiology; Young Adult; Beijing China; China; Between-subject whole-brain machine learning approach; Feature attention; Spatial attention; Brain signature
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2025.121296

The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent activation during various visual attention tasks. However, these studies have been limited by small sample sizes and methodological constraints inherent in univariate analysis. Here, we utilized a between-subject whole-brain machine learning approach with a large sample size (N=235) to investigate the neural signatures of FA (FAS) and SA (SAS). Both FAS and SAS showed cross-task predictive capabilities, though inter-task prediction was weaker than intra-task prediction, suggesting both shared and distinct mechanisms. Specifically, the frontoparietal network exhibited the highest predictive performance for FA, while the visual network excelled in predicting SA, highlighting their respective prominence in the two attention processes. Moreover, both signatures demonstrated distributed representations across large-scale brain networks, as each cluster within the signatures was sufficient for predicting FA and SA, but none of them were deemed necessary for either FA or SA. Our study challenges traditional network-centric models of attention, emphasizing distributed brain functioning in attention, and provides comprehensive evidence for shared and distinct neural mechanisms underlying FA and SA. •We identified neural signatures for feature attention (FAS) and spatial attention (SAS) using a between-subject whole-brain machine learning approach with a large sample size of 235 participants.•FA and SA exhibited both shared and distinct neural components across whole-brain, network, cluster and voxel levels, revealing the intricate interactions within attentional networks.•The clusters associated with FAS and SAS were sufficient for predicting their respective attention types, but not were individually necessary, supporting the notion of a distributed neural representation for both forms of attention.