Antonio Luigi Bisogno

Post-stroke rehabilitation is a complex process influenced by several neurophysiological factors. The recovery is traditionally predicted based on initial

Mechanical thrombectomy effectively restores blood flow in patients with acute ischaemic stroke caused by large vessel occlusion. While mechanical thrombectomy has improved functional outcomes, 35%–60% of patients still experience residual disabilities. Typically, patients are selected for mechanical thrombectomy based on degree of hypoperfusion around the core measured on a vascular atlas. This study had two aims: (i) to evaluate the prognostic value of lesion topography onto functional outcome at 3 months post-mechanical thrombectomy, when the lesion is localized either onto a vascular atlas or large-scale, functional or structural, network atlases; and (2) to examine patterns of post-stroke structural and functional disconnection significantly related to the most common stroke functional outcome scale, i.e. the modified Rankin scale at 3 months post-event. A retrospective analysis was conducted on 70 acute stroke patients who underwent mechanical thrombectomy at the Padua University Hospital (January 2018–June 2022). Inclusion criteria involved first ever ischaemic strokes with anterior circulation large vessel occlusion. Imaging data from sub-acute structural MRI and CT scans were used to estimate indirect structural and functional disconnections. Outcome measures included the modified Rankin Scale at 3 months, with prediction analysis performed using Lasso regression across vascular, grey matter and white matter atlases. Three-month modified Rankin Scale was best predicted using Yeo's functional atlas (R2 = 0.382), followed by the functional white matter atlas (R2 = 0.338); the vascular atlas yielded the weakest prediction (R2 = 0.146). Lesion damage to the corticospinal tract and corona radiata was significantly associated with the modified Rankin Scale. Functional disconnection significantly correlated with disability, particularly in sensorimotor, dorsal attention (DAN) and visual networks. Structural disconnections in the corticospinal tract, corpus callosum, corona radiata, thalamic radiation and left inferior and superior longitudinal fasciculus were also associated with poor functional outcome. This study demonstrates that lesion topography embedded in a network framework provides a more robust prediction of functional outcome. These findings emphasize the importance of understanding network alterations to enhance recovery prediction and optimize treatment strategies for stroke patients. Further research should explore the integration of network-based assessments in clinical practice for evaluating revascularization treatment eligibility.

Post-stroke rehabilitation is a complex process influenced by several neurophysio-logical factors. The recovery is traditionally predicted based on initial impairment using linear models. The Proportional Recovery Rule (PRR), developed on the Fugl-Meyer scale, has even been proposed as a therapeutic target. In this framework, patients are classified as “fitters” or “non-fitters”, though this distinction depends on the methodology used. Additionally, issues like mathematical coupling and ceiling effects on clinical scales could raise concerns about the validity of these models. To overcome these issues, Repeated Spectral Clustering (RSC) was used to identify recovery patterns based on NIHSS. We selected 201 patients from the WAKE-UP trail, all moderately impaired at onset and still impaired at 22/36 hours. Clustering was performed using a similarity matrix based on pairwise absolute differences between recovery ratios, calculated from 22/36 hours to 90 days post-stroke. Cluster differences were tested with prognostic factors, including lesion volume, side, treatment, and the Heidelberg scale. The PRR was fit to the cohort for comparison with clustering results. The linear fit reproduced findings consistent with the literature, such as a correlation of ρ ( x , Δ) = . 73 and an average recovery ratio of 70% for the “fitters”. RSC grouped patients into six recovery clusters: C ₀ (full recovery), C ₁ (above average), C ₂ and C ₃ (average, PRR-aligned), C ₄ (below average), and C ₅ (deterioration). NIHSS scores in most patients declined non-proportionally. Lesion volume was not significantly different across clusters, while left-sided strokes were higher in low recovery clusters. Patients with a recovery ratio ≥ 0.3 within two weeks mostly fell into favorable clusters ( C ₀ – C ₃ ), covering ≈ 90% of such cases. The identified clusters provide a refined view of stroke recovery following wake-up stroke. Clustering better captures patient similarities, enabling the assessment of neurophysiological differences between groups and supporting tailored interventions.

Purpose: Stroke is a leading cause of death and disability. The subsequent dysfunctions relate to brain lesion location. The complex relationship between behavioral symptoms and lesion location is essential for care and rehabilitation, and useful to understand the healthy brain. Such complexity eludes linear methods. Differently from previous works, this study clusters patients by recurrent symptoms profiles, then retrieves their neural correlates. Methods: Prevalence, severity and associations of symptoms affecting patients are condensed in a similarity measure between their profiles. The computation specifically adheres to the ordinal nature of NIHSS data. A network model links patients with strength proportional to the behavioral similarity. The network nodes are then classified through a novel spectral clustering variation. Lesions from each cluster’s patients are used in a voxel-wise analysis to statically validate the findings. Results: The behavioral clusters express symptoms co-occurring in accordance with the literature on behavioral deficits covariance. Moreover, they show coherent groups of brain lesions in the associated CT and MRI scans. The anatomical position of the significant voxels found are aligned with the literature. Even when lesion density maps overlap, the significant voxels are well separated. Conclusion: The presented workflow offers concrete, clinically useful correlations between patients profiles, individual patients-to-group associations, in-group co-occurrences of symptoms, and brain lesion correlates. The underlying mathematical assumptions, and the unsupervised machine learning techniques offer statistically robust results and are being further applied to other multimodal biomedical data.

Lorenzo Pini, Antonio Luigi Bisogno, Alessandro Salvalaggio, Gordon L Shulman, Maurizio Corbetta; The correlation of behavioural deficits post-stroke: a trivial