Neurology 2026

Stroke is the second leading cause of death and a significant cause of disability worldwide. Its incidence is increasing because of the aging population. A key challenge in stroke rehabilitation is identifying an individual’s recovery potential to make personalized neurorehabilitation decisions, avoiding a “one-size-fits-all” approach because stroke is a vascular disease with highly heterogeneous effects on injury and recovery. The recent advances in artificial intelligence technologies will be introduced in the talk for generating intelligent computational models that enable precision prediction of post- stroke outcomes and precision rehabilitation. Post-stroke outcomes, e.g., motor function, can be predicted using structural and functional biomarkers of the descending corticomotor pathway, typically measured using magnetic resonance imaging and transcranial magnetic stimulation, respectively. However, the precise structural determinants of intact corticomotor function are unknown. Identifying structure–function links in the corticomotor pathway could provide valuable insight into the mechanisms of post-stroke motor impairment. This talk will introduce the supervised machine learning approach to classify upper limb motor evoked potential status using MRI metrics obtained early after stroke. Based on the intelligent post-stroke recovery prediction, the optimal multi-target electromagnetic nerve stimulation system will be introduced in this talk for achieving personalized precision rehabilitation. Temporal interference magnetic stimulation can stimulate deep targets without activating superficial non-target areas. However, at present, the stimulation target of this technology is single, and it is challenging to realize the coordinated stimulation of multiple brain regions, which limits its application in the modulation of multiple nodes in the brain network. This talk will introduce a multi-target temporal interference magnetic stimulation system with array coils. Mmulti-target temporal interference magnetic stimulation with array coils can simultaneously stimulate multiple network nodes in the brain region and realize accurate stimulation of multiple targets in the brain area.Stroke is the second leading cause of death and a significant cause of disability worldwide. Its incidence is increasing because of the aging population. A key challenge in stroke rehabilitation is identifying an individual’s recovery potential to make personalized neurorehabilitation decisions, avoiding a “one-size-fits-all” approach because stroke is a vascular disease with highly heterogeneous effects on injury and recovery. The recent advances in artificial intelligence technologies will be introduced in the talk for generating intelligent computational models that enable precision prediction of post- stroke outcomes and precision rehabilitation. Post-stroke outcomes, e.g., motor function, can be predicted using structural and functional biomarkers of the descending corticomotor pathway, typically measured using magnetic resonance imaging and transcranial magnetic stimulation, respectively. However, the precise structural determinants of intact corticomotor function are unknown. Identifying structure–function links in the corticomotor pathway could provide valuable insight into the mechanisms of post-stroke motor impairment. This talk will introduce the supervised machine learning approach to classify upper limb motor evoked potential status using MRI metrics obtained early after stroke. Based on the intelligent post-stroke recovery prediction, the optimal multi-target electromagnetic nerve stimulation system will be introduced in this talk for achieving personalized precision rehabilitation. Temporal interference magnetic stimulation can stimulate deep targets without activating superficial non-target areas. However, at present, the stimulation target of this technology is single, and it is challenging to realize the coordinated stimulation of multiple brain regions, which limits its application in the modulation of multiple nodes in the brain network. This talk will introduce a multi-target temporal interference magnetic stimulation system with array coils. Mmulti-target temporal interference magnetic stimulation with array coils can simultaneously stimulate multiple network nodes in the brain region and realize accurate stimulation of multiple targets in the brain area.

Alan Wang is a principal investigator and Associate Professor at The University of Auckland. He has more than ten years of research experience in bioengineering informatics and integrated medicine, especially in advancing the role of medical informatics in health care. His research interests include bioengineering, data informatics, neurocomputing, and biomedical statistics and simulation. He has developed medical data analytics methods for mobile health and personalized diagnosis and prognosis based on intelligent computing theories. He has experience analyzing huge cohorts of patient data with applications of early diagnosis, disease understanding, and effective treatment of patients with different disorders. He serves as an Editorial Board Member and an Active Reviewer for several international journals