Exploring Neural Mechanisms of Visual Working Memory for Real-World Stimuli Categories: Insights From the Fusiform...

To dissect the neural mechanisms underlying visual working memory (VWM) processing of real-world stimuli (Body, Face, Place, Tool).

This study leveraged task-fMRI data from the Human Connectome Project (HCP) n-back paradigm. The Neurofunctional Integration and Specificity Analysis (NISA) framework was proposed to synergistically combine Representational Similarity Analysis (RSA) and multivoxel machine learning classification and regression, enabling distinct characterization of visual perception and VWM processes. Functional connectivity (FC) patterns of NISA-selected regions of interest were further integrated with transcriptomic data to probe molecular substrates.

Bilateral fusiform gyrus (FFG) voxel patterns showed maximal stimulus representation fidelity (r = -0.43 to -0.42, q < 1.05 × 10^-130), with 80% 4-class category decoding accuracy and significant behavioral prediction (r = 0.42 to, 0.56, q < 2.85 × 10^-7). Transcriptomic decoding revealed associations between bilateral FFG FC profiles and genes implicated in mental and psychiatric disorders (q < 0.05).

The FFG operates as a dual-process hub, concurrently mediating visual perceptual categorization and working memory maintenance. Its synergistic excitation-inhibition in FFG may optimize the behavior performance through dynamic resource allocation, while FC-transcriptome coupling further revealed gene networks implicated in cognitive vulnerability across VWM categories.