This study assessed the cross-modal agreement between spin-exchange relaxation-free magnetoencephalography (SERF-MEG) and conventional visual evoked potentials (VEPs) in patients with optic nerve injury using comparable pattern-reversal visual stimulation paradigms. Forty-five patients with optic neuritis (ON), ischemic optic neuropathy (ION), and traumatic optic neuropathy (TON) were enrolled, and a paired-eye design was applied to assess interocular electrophysiological changes between the affected and fellow eyes. Both modalities indicated consistent impairment of visual pathway function, reflected by reduced response amplitudes and prolonged latencies. For latency measures, MEG-derived M100 showed moderate directional concordance and a significant correlation with VEP P100, whereas amplitude measures demonstrated similar directional changes but limited quantitative correlations across modalities. Compared with the MaxP2P method, the channel-averaged method displayed higher stability and cross-modal concordance. These findings show that SERF-MEG can capture clinically relevant visual pathway impairment and may complement conventional VEP by providing additional information on cortical response patterns. Overall, SERF-MEG shows potential as an objective functional biomarker for neuro-ophthalmic assessment of optic nerve injury.

This study aimed to characterize cortical dysfunction and frequency-specific network reorganization following optic nerve injury using spin-exchange relaxation-free magnetoencephalography (SERF-MEG), and to assess the potential of MEG-derived multiscale features as sensitive functional biomarkers for clinical evaluation. In this prospective case–control study, SERF-MEG recordings were acquired during a pattern-reversal visual stimulation paradigm. Time-domain evoked components (M100/M135), global electrophysiological indices, energy-based metrics, and alpha- and beta-band phase-based functional connectivity were extracted. Network topology was quantified using graph-theoretical measures, including global and local efficiency, clustering coefficient, and assortativity. Group-level differences between patients and healthy controls were statistically analyzed. Patients showed significantly reduced M100/M135 amplitudes, prolonged M100 latency, and a lower early-component energy ratio. Functional connectivity was significantly decreased in the alpha and beta bands, accompanied by reduced global and local efficiency, mean strength, and clustering coefficient. Seed-based analyses revealed reduced connectivity predominantly in occipito-parietal and occipito-temporal pathways. SERF-MEG provides sensitive identification of cortical- and network-level functional impairments following optic nerve damage. MEG has significant clinical potential for disease diagnosis and therapy monitoring, providing a novel objective assessment tool for neuro-ophthalmological disorders.

Visual evoked potential (VEP) is widely used to assess optic nerve function, but its ability to differentiate optic neuropathies remains limited. This study investigated the diagnostic value of spin-exchange relaxation-free magnetoencephalography (SERF-MEG), alone and in combination with VEP, using machine learning. A total of 142 eyes, including 71 healthy controls (HC), 34 eyes with optic neuritis (ON), and 37 eyes with ischemic optic neuropathy (ION), were enrolled. Three feature sets were constructed from VEP, SERF-MEG, and their combination. Repeated stratified 5-fold nested cross-validation was used to evaluate nine supervised machine learning algorithms, and SHAP analysis was used to evaluate the interpretability of the models. The multilayer perceptron model obtained an AUC of 0.792 and an MCC of 0.470, whereas the combination VEP–MEG model performed the best in differentiating HC from optic neuropathies. The MEG model fared better than the VEP and combined models for distinguishing ON from ION, with logistic regression obtaining the greatest AUC of 0.847. Cortical network measurements and visually evoked response properties were found to contribute the most to model prediction, according to SHAP analysis. These results imply that SERF-MEG may enhance the diagnosis and differential diagnosis of optic neuropathies by offering supplementary neurophysiological data beyond traditional VEP.