VisNIRMIR

VisNIRMIR is a vision-integrated non-invasive magnetic resonance imaging research framework designed to map the neural correlates of visual processing using combined MRI modalities. It seeks to fuse high-resolution structural imaging with functional and diffusion data while presenting controlled visual stimuli to participants and tracking eye movements. The goal is to obtain spatially precise maps of visual pathways and cortical responses in a non-invasive setting.

Core components include a visual stimulus delivery and eye-tracking subsystem, a magnetic resonance imaging suite capable of structural MRI, functional MRI, and diffusion-weighted imaging, and a multimodal data integration platform. The platform aligns stimuli timing with MRI data acquisition, synchronizes eye-tracking data, and performs cross-modal registration. Analyses typically involve retinotopic mapping, functional localization of visual areas, connectivity assessments, and machine learning-based decoding of stimulus-evoked activity.

Methods emphasize safety and comfort, with MRI-compatible displays and optics, artifact correction, and real-time quality monitoring. Data fusion techniques integrate electrophysiological proxies from vision tasks with MRI signals to enhance spatial specificity and interpretability.

Development and research activity around VisNIRMIR began in the late 2010s, with pilot studies reported by multidisciplinary teams exploring the feasibility of simultaneous visual stimulation and MRI-based imaging. Since then, various labs have demonstrated prototype workflows and published methodological papers, contributing toward standardized protocols and open-source software tools.

Applications span basic neuroscience, clinical research on visual disorders, and explorations of brain-computer interface concepts. VisNIRMIR facilitates retinotopic mapping, investigation of visual cortical hierarchies, and assessment of neural plasticity in response to visual training or disease.

Challenges include the technical complexity of synchronizing devices in the MRI environment, managing motion and physiological noise, ensuring patient safety and comfort, and the need for robust data analysis pipelines. Cost and accessibility remain considerations for wider adoption.