Editorial: Innovative imaging in neurological disorders: bridging engineering and medicine

Abstract

a) Neuro-AI Integration: By introducing a new AI framework for understanding temporal structure in silent image sequences in CATS (Context-Aware Temporal synthesis) that working without motion cues or audio by using curvature-aware alignment, symmetry-enforced attention and sematic memory. CATS shows achievement up to 15% relative improvement in egocentric video understanding, stable regime separation and accuracy gains on anomalous diffusion dynamic by capturing intrinsic temporal structure rather than dataset-specific cues (Rokaya et al., 2026).To assess brain activities, they have been using magnetoencephalography (MEG), which is a high temporal and spatial resolution neuroimaging technique. In a deep learning pipeline study of brain development and structure-function relations in children with unilateral cerebral palsy (uCP), automated measurement of segmenting lesions and quantifying lesion-free brain volume using T1+FLAIR MRI were explored. Results demonstrate that lesion-free volume correlate with motor and visual outcomes, and the thalamic volume has special predictive potentials (Simarro et al., 2025).T1-relaxation times as ALS early diagnostic marker study by T1-mapping along corticospinal tracts can differentiate ALS patients from control group. ALS patients group shows significant elevation T1 values in corticospinal tracts regions and associated with lower ALS Functional Rating Scale revised (ALSFRS-R) (Dierksen et al., 2025).A pilot study tested a new rehabilitation approach for patients have post-stroke hemiparesis and unstable gait. Instead of stimulation of brain regions separately, the researchers combined two types of non-invasive brain stimulation at the same time: transcranial direct current stimulation (tDCS) to the supplementary motor area (SMA), which involved rhythm control and postural preparation and transcranial alternating current stimulation (tACS) to the primary motor cortex (M1), which timed precisely to each patient's gait cycle. In this study, a total of group. Both groups completed 15 treadmill-based gait training sessions over three weeks, with peripheral nerve stimulation to assist ankle movement. The real stimulation group showed significant reduced gait variability with better balance scores compared to the control group. Furthermore, improvement in gait variability was strongly correlated with improvement in balance. Therefore, simultaneous stimulation SMA and M1 appears safe, and effective for improving gait stability after stroke (Yamashita et al., 2025).Despite remarkable progress in advanced neurological imaging, several challenges stand in the routine practice way. As many cutting-edge imaging techniques remain limited to research due to training requirements, or limited availability. In addition, imaging protocol variability, hardware, and interpretation continue to limit large scale clinical applications. Moreover, high-resolution and multimodal imaging generate massive datasets that require sophisticated analysis tools and welltrained personnel. However, AI-driven interpretation holds huge promise, it must be validated, regulated, and merged into existing systems to be trustworthy.The merging of engineering innovation and neurological medicine has an important role in reshaping clinical practice. Modern imaging tools are giving clinicians the ability to detect diseases earlier, track progression more precisely and take a decision with greater confidence. These advances are changing how we think about neurological disorders: not as static conditions but as dynamic processes that can be monitored, predicted, and potentially altered. As imaging continues to evolve, it will play a central role in bridging the long-standing divide between what clinicians can observe and what patients experience. The future of neurology will depend on how effectively we integrate these tools into real world care.