The structural connectome and motor recovery after stroke: predicting natural recovery

Abstract

Stroke patients vary considerably in terms of outcomes: some patients present 'natural' recovery proportional to their initial impairment (fitters), while others do not (non-fitters). Thus, a key challenge in stroke rehabilitation is to identify individual recovery potential to make personalized decisions for neuro-rehabilitation, obviating the 'one-size-fits-all' approach. This goal requires (i) the prediction of individual courses of recovery in the acute stage; and (ii) an understanding of underlying neuronal network mechanisms. 'Natural' recovery is especially variable in severely impaired patients, underscoring the special clinical importance of prediction for this subgroup. Fractional anisotropy connectomes based on individual tractography of 92 patients were analysed 2 weeks after stroke (TA) and their changes to 3 months after stroke (TC - TA). Motor impairment was assessed using the Fugl-Meyer Upper Extremity (FMUE) scale. Support vector machine classifiers were trained to separate patients with natural recovery from patients without natural recovery based on their whole-brain structural connectomes and to define their respective underlying network patterns, focusing on severely impaired patients (FMUE < 20). Prediction accuracies were cross-validated internally, in one independent dataset and generalized in two independent datasets. The initial connectome 2 weeks after stroke was capable of segregating fitters from non-fitters, most importantly among severely impaired patients (TA: accuracy = 0.92, precision = 0.93). Secondary analyses studying recovery-relevant network characteristics based on the selected features revealed (i) relevant differences between networks contributing to recovery at 2 weeks and network changes over time (TC - TA); and (ii) network properties specific to severely impaired patients. Important features included the parietofrontal motor network including the intraparietal sulcus, premotor and primary motor cortices and beyond them also attentional, somatosensory or multimodal areas (e.g. the insula), strongly underscoring the importance of whole-brain connectome analyses for better predicting and understanding recovery from stroke. Computational approaches based on structural connectomes allowed the individual prediction of natural recovery 2 weeks after stroke onset, especially in the difficult to predict group of severely impaired patients, and identified the relevant underlying neuronal networks. This information will permit patients to be stratified into different recovery groups in clinical settings and will pave the way towards personalized precision neurorehabilitative treatment.

Keywords: connectivity; diffusion; recovery; stroke; structural.

Figure 1

Fitters and non-fitters of proportional recovery. All patients in the SEOUL dataset are shown by their observed and predicted proportional recovery of FMUE. Non-fitters (red) are identified by the clustering showing less natural recovery than predicted [0.7 (66 − FMUE_TA) + 0.4] compared to the fitters (blue). For details, see the ‘Materials and methods’ section and Supplementary Table 1.

Figure 2

SVM features of all patients for connectomes 2 weeks after stroke as well as changes observed up to 3 months. Brain areas of the connectome in which connectivity positively (red) or negatively (blue) correlates with the likelihood of the patient being a fitter. The feature weights of all connections were summed for each area of the parcellation, and a z-transform was applied. Here, only those areas exceeding 1 SD are shown. For all results, see Supplementary Table 4. The figure shows the results for the SVM including all subjects in the SEOUL dataset with the connectomes at 2 weeks after stroke (TA, top), as well as connectome changes to 3 months (TC − TA, bottom). The results are presented on the inflated FreeSurfer brain as well as the MNI standard brain, with z-coordinates given. Darker grey areas on the inflated FreeSurfer brain represent sulci, whereas lighter grey areas represent gyri. AH = affected hemisphere; SC = central sulcus; SFS = superior frontal sulcus; SIP = intraparietal sulcus.

Figure 3

Permutation results, severe patients. The areas shown are those for which connectivity was specifically important for the distinction between fitters and non-fitters in the severely impaired patient group, as revealed by the permutation analyses for 2 weeks (top) as well as white matter change up to 3 months (bottom). The colour indicates how many connections for each area showed specificity. Areas with at least two specific connections are shown; all areas are plotted on the inflated FreeSurfer brain as well as the MNI standard brain with z-coordinates given. See Supplementary Table 7 for the full results. AH = affected hemisphere.

Figure 4

White matter changes. Comparison of the FA status of each connection relevant for recovery prediction (features) at 2 weeks compared to 3 months by Student’s t-test reveals predominantly t-scores that show a decrease in FA (red). Blue colours indicate an increase in FA over time. Lacking correction for multiple comparisons, these results must be considered with caution. Numbers and colour codes represent the t-score. (A) Summed t-scores of features for each area are plotted on the inflated FreeSurfer cortex as well as the standard MNI brain with z-coordinates given. (B) Summed t-scores for weights that show a decrease or an increase in FA, separated between positive and negative features. AH = affected hemisphere; FA = fractional anisotropy; NEG = connections being negative features; POS = connections being positive features.