Recent findings have demonstrated that stroke lesions affect neural communication in the entire brain. However, it is less clear whether network interactions are also relevant for plasticity and repair. This study investigated whether the coherence of neural oscillations at language or motor nodes is associated with future clinical improvement. Twenty-four stroke patients underwent high-density EEG recordings and standardized motor and language tests at 2-3 weeks (T0) and 3 months (T1) after stroke onset. In addition, EEG and motor assessments were obtained from a second population of 18 stroke patients. The graph theoretical measure of weighted node degree at language and motor areas was computed as the sum of absolute imaginary coherence with all other brain regions and compared to the amount of clinical improvement from T0 to T1. At T0, beta-band weighted node degree at the ipsilesional motor cortex was linearly correlated with better subsequent motor improvement, while beta-band weighted node degree at Broca's area was correlated with better language improvement. Clinical recovery was further associated with contralesional theta-band weighted node degree. These correlations were each specific to the corresponding brain area and independent of initial clinical severity, age, and lesion size. Findings were reproduced in the second stroke group. Conversely, later coherence increases occurring between T0 and T1 were associated with less clinical improvement. Improvement of language and motor functions after stroke is therefore associated with inter-regional synchronization of neural oscillations in the first weeks after stroke. A better understanding of network mechanisms of plasticity may lead to new prognostic biomarkers and therapeutic targets.See Ward (doi:10.1093/brain/awv265) for a scientific commentary on this article.

Reliable predictors of motor improvement in individual patients after stroke are scarce. Acute determination of upper limb Fugl-Meyer assessment (FMA) appears to have predictive value.1,2 This approach predicts that patients will improve approximately 70% of the difference between the maximum upper extremity FMA score and the score first tested for a given individual (recoverytypical). However, a significant subset of patients improves much less than predicted (recovery-atypical). Alternative models using other techniques like diffusion tensor imaging (DTI) 3,4 also fail to predict recovery in some patients. Here, we show that a combination of FMA and DTI obtained in the first weeks after stroke accurately discriminate between recovery-typical and recovery-atypical patients. In addition, we identify an alternative set of model parameters required for predictions in the recovery-atypical subgroup.We examined 25 patients (mean age 61 years, 11 female) with first ischemic stroke. Patients had a mean NIH Stroke Scale score of 11 (table e-1 on the Neurology ® Web site at Neurology.org). FMA 5 and DTI (appendix e-1) were obtained 2 weeks and 3 months after stroke onset. The FMA was performed by a trained occupational therapist blinded to the DTI results. Corticospinal tract (CST) asymmetry was calculated from the mean fractional anisotropy (FA) of the CST 3 :As with previous observations, 3,4 patients with greater CST asymmetry at 2 weeks had proportionally more severe motor deficits, both 2 weeks and 3 months after stroke onset (Spearman r , 20.8, p , 0.0001, figure 1, A and B). However, CST asymmetry was not significantly related to FMA improvement from 2 weeks to 3 months ( figure 1C). These findings were also confirmed when FA ispilesional /FA contralesional ratios were used as predictors rather than CST asymmetry.As expected, a majority of patients followed the FMA-based model predictions ( figure 1D; filled symbols), while a subgroup of patients exhibited recovery-atypical profiles ( figure 1D; unfilled circles). In order to further characterize the 2 patient subgroups, we computed the model residuals as the difference between the predicted FMA improvement and the observed FMA improvement. The residuals clearly differentiate recovery-typical ( figure 1E; filled bars) from recoveryatypical patients (unfilled bars). All recovery-atypical Ethan R. Buch, PhD Sviatlana Rizk, PhD Pierre Nicolo, MS Leonardo G. Cohen, MD Armin Schnider, MD Adrian G. Guggisberg, MD Figure 1 Fugl-Meyer assessment (FMA) and diffusion tensor imaging (DTI) predictors of motor improvementCorticospinal tract (CST) asymmetry at 2 weeks correlated with the severity of motor deficits at (A) 2 weeks and (B) 3 months after stroke, but not with (C) the change between these 2 time points. (D) The model DFMA 5 0.7*(66-FMA intial ), represented by a continuous line, predicted future FMA improvement in most patients (filled symbols), but there was a significant recovery-atypical subset (n 5 9; unfilled circles). (E) Recovery-atypical patients were def...

Objective. Several training programs have been developed in the past to restore motor functions after stroke. Their efficacy strongly relies on the possibility to assess individual levels of impairment and recovery rate. However, commonly used clinical scales rely mainly on subjective functional assessments and are not able to provide a complete description of patients’ neuro-biomechanical status. Therefore, current clinical tests should be integrated with specific physiological measurements, i.e. kinematic, muscular, and brain activities, to obtain a deep understanding of patients’ condition and of its evolution through time and rehabilitative intervention. Approach. We proposed a multivariate approach for motor control assessment that simultaneously measures kinematic, muscle and brain activity and combines the main physiological variables extracted from these signals using principal component analysis (PCA). We tested it in a group of six sub-acute stroke subjects evaluated extensively before and after a four-week training, using an upper-limb exoskeleton while performing a reaching task, along with brain and muscle measurements. Main results. After training, all subjects exhibited clinical improvements correlating with changes in kinematics, muscle synergies, and spinal maps. Movements were smoother and faster, while muscle synergies increased in numbers and became more similar to those of the healthy controls. These findings were coupled with changes in cortical oscillations depicted by EEG-topographies. When combining these physiological variables using PCA, we found that (i) patients’ kinematic and spinal maps parameters improved continuously during the four assessments; (ii) muscle coordination augmented mainly during treatment, and (iii) brain oscillations recovered mostly pre-treatment as a consequence of short-term subacute changes. Significance. Although these are preliminary results, the proposed approach has the potential of identifying significant biomarkers for patient stratification as well as for the design of more effective rehabilitation protocols.