Connectomic stroke lesion measures provide no benefit over basic spatial lesion features in the prognosis of global stroke outcome measures.

The prediction of stroke outcome from imaging markers could be used to guide individualized therapeutic approaches. We aimed to find the best imaging marker to predict the global functional impact of a stroke lesion among low- to high-level connectomic measures-indirect estimations of structural connectivity, graph representations, or brain modes-as well as spatial lesion features. This observational study retrospectively analysed clinical routine data from patients with acute first-ever ischaemic stroke. We traced lesions in diffusion-weighted MRI and computed 21 topographic or connectomic measures, including (i) tract-wise, voxel-wise and interregional white matter disconnection that were indirectly estimated by reference to healthy connectome data; (ii) interregional network structure by graph measures; and (iii) brain modes, which represent elementary interactions between grey matter regions. We used all features to predict stroke severity [National Institutes of Health Stroke Scale (NIHSS) 24 h] or classify poor functional outcome (mRS 3 months ≥ 2) in a nested cross-validation with high-dimensional machine-learning models. For comparison to specific, granular post-stroke cognitive deficits, we replicated the modelling procedures in another sample for selective attention and phonemic word fluency. The study included 755 patients [mean age = 66.9 ± 15.3 years; NIHSS 24 h median (IQR) = 2 (1; 5); mRS 3 months = 1 (0; 2)]. For both measures, simple spatial lesion features (NIHSS 24 h: ² = 0.395 ± 0.059; mRS: accuracy = 65.62% ± 3.45, positive predictive value = 0.72 ± 0.13; negative predictive value = 0.64 ± 0.04) outperformed connectomic measures (all < 0.0007), even though the predictions of the best measures in each category were numerically close. Control analyses on specific cognitive deficits in a sample of 182 patients found connectomic measures to be equal or even superior to spatial lesion features. Connectomic stroke imaging markers provide no benefit in the prediction of acute stroke severity and functional outcome at 3 months. Spatial lesion imaging features seem to effectively capture the global neurological perturbation caused by a stroke lesion and could provide a basis for personalized prediction algorithms. On the other hand, connectomic stroke imaging markers may be warranted when modelling specific post-stroke cognitive deficits.