Post-stroke delta waves and microstructural integrity: a multimodal diffusion-electroencephalography study

Stroke represents the second leading cause of death and the primary cause of long-term disability and cognitive decline worldwide. While its focal impact on the lesion site is considerable, it does not fully account for the complexity of its consequences. In recent years, increasing attention has been directed toward the investigation of stroke-related effects on brain regions that are not directly lesioned but anatomically connected to the ischemic site. These areas often exhibit functional and structural abnormalities as a consequence of disconnection, reflecting the brain’s organization as an integrated network and ultimately contributing to cognitive and behavioral deficits. In this context, the term diaschisis highlights how focal structural injury can compromise the maintenance of long-range communication between brain areas, reducing interhemispheric homotopic integration and interhemispheric segregation. From a structural perspective, quantitative microstructural parameters derived from diffusion MRI (dMRI) provide the most accessible in vivo proxy for investigating tissue biology, structural disruption, and cellular integrity being able to capture progressive and irreversible post-stroke degeneration of neurons and myelin, clearly distinguishing patients from healthy individuals. In parallel, the association between structural lesions and brain rhythm slowing has been recognized since 1937, when William Grey Walter first reported a link between brain damage and pronounced slowing in EEG recordings, particularly in the delta frequency range (0.5–4 Hz) even in awake individuals. This phenomenon has since been exploited as a diagnostic marker for lesion detection and progression monitoring. Quantitative electroencephalography (qEEG) is now increasingly recognized as a valuable, non-invasive tool to investigate post-stroke electrophysiological changes thanks to brain rhythm slowing—accompanied by a decrease of power in alpha-band activity—and the emergence of sleep-like dynamics, characterized by bistable cortical states that resemble features of physiological sleep. However, the relationship between the emergence of slow waves and the underlying microstructural integrity remains incompletely understood. This study investigates the association between local post-stroke electrophysiological activity and spatially corresponding microstructural alterations derived from dMRI. To the best of our knowledge, we propose for the first time a multimodal integration model that bridges biological, physiological and behavioral domains, offering a more comprehensive perspective on stroke-related brain dysfunction. This framework thereby emerges as a promising approach to contribute to the identification of predictive markers for recovery capturing both pathophysiological and compensatory signals with the ambition to promote a paradigm shift in neurorehabilitation: from symptom-centric strategies to personalized and mechanistically informed interventions.