Decoding post-stroke motor function from structural brain imaging

AbstractClinical research based on neuroimaging data has benefited from machine learning methods, which have the ability to provide individualized predictions and to account for the interaction among units of information in the brain. Application of machine learning in structural imaging to investigate diseases that involve brain injury presents an additional challenge, especially in conditions like stroke, due to the high variability across patients regarding characteristics of the lesions. Extracting data from anatomical images in a way that translates brain damage information into features to be used as input to learning algorithms is still an open question. One of the most common approaches to capture regional information from brain injury is to obtain the lesion load per region (i.e. the proportion of voxels in anatomical structures that are considered to be damaged). However, no systematic evaluation has yet been performed to compare this approach with using patterns of voxels (i.e. considering each voxel as a single feature). In this paper we compared both approaches applying Gaussian Process Regression to decode motor scores in 50 chronic stroke patients based solely on data derived from structural MRI. For both approaches we compared different ways to delimit anatomical areas: regions of interest from an anatomical atlas, the corticospinal tract, a mask obtained from fMRI analysis with a motor task in healthy controls and regions selected using lesion-symptom mapping. Our analysis showed that extracting features through patterns of voxels that represent lesion probability produced better results than quantifying the lesion load per region. In particular, from the different ways to delimit anatomical areas compared, the best performance was obtained with a combination of a range of cortical and subcortical motor areas as well as the corticospinal tract. These results will inform the appropriate methodology for predicting long term motor outcomes from early post-stroke structural brain imaging

Optimising rehabilitation and recovery after a stroke

Stroke can cause significant disability and impact quality of life. Multidisciplinary neurorehabilitation that meets individual needs can help to optimise recovery. Rehabilitation is essential for best quality care but should start early, be ongoing and involve effective teamwork. We describe current stroke rehabilitation processes, from the hyperacute setting through to inpatient and community rehabilitation, to long-term care and report on which UK quality care standards are (or are not) being met. We also examine the gap between what stroke rehabilitation is recommended and what is being delivered, and suggest areas for further improvement

Relationship between intensity and recovery in post-stroke rehabilitation: a retrospective analysis.

Work in animal models suggests high-intensity rehabilitation-based training that starts soon after stroke is the most effective approach to promote recovery.1 In humans, the interaction between treatment onset and intensity remains unclear.2 It has been suggested that reducing daily treatment duration below 3 hours at the acute and subacute stages leads to a poorer prognosis,3 while there may also be an upper bound beyond which high-intensity motor rehabilitation at the acute stage might lead to unwanted side effects.4 Designing optimal rehabilitation treatment programmes for stroke patients will not be possible until we understand ‘how much’, ‘when’ and ‘what’ treatment should be delivered.2 In this retrospective analysis, we assessed patients’ responsiveness to high-intensity and low-intensity rehabilitation protocols across different stages of chronicity post-stroke to address the ‘how much’ and ‘when’ questions.This study was supported by the cRGS project under the grant agreement H2020-EU, ID: 840052, and by the RGS@home project from H2020-EU, EIT Health, ID: 19 277

The impact of brain lesions on tDCS-induced electric fields

Transcranial direct current stimulation (tDCS) can enhance motor and language rehabilitation after stroke. Though brain lesions distort tDCS-induced electric field (E-field), systematic accounts remain limited. Using electric field modelling, we investigated the effect of 630 synthetic lesions on E-field magnitude in the region of interest (ROI). Models were conducted for two tDCS montages targeting either primary motor cortex (M1) or Broca's area (BA44). Absolute E-field magnitude in the ROI differed by up to 42% compared to the non-lesioned brain depending on lesion size, lesion-ROI distance, and lesion conductivity value. Lesion location determined the sign of this difference: lesions in-line with the predominant direction of current increased E-field magnitude in the ROI, whereas lesions located in the opposite direction decreased E-field magnitude. We further explored how individualised tDCS can control lesion-induced effects on E-field. Lesions affected the individualised electrode configuration needed to maximise E-field magnitude in the ROI, but this effect was negligible when prioritising the maximisation of radial inward current. Lesions distorting tDCS-induced E-field, is likely to exacerbate inter-individual variability in E-field magnitude. Individualising electrode configuration and stimulator output can minimise lesion-induced variability but requires improved estimates of lesion conductivity. Individualised tDCS is critical to overcome E-field variability in lesioned brains

Identifying Primordial Substructure in NGC 2264

We present new Spitzer Space Telescope observations of the young cluster NGC2264. Observations at 24 micron with the Multiband Imaging Photometer has enabled us to identify the most highly embedded and youngest objects in NGC2264. This letter reports on one particular region of NGC2264 where bright 24 micron sources are spatially configured in curious linear structures with quasi-uniform separations. The majority of these sources (~60% are found to be protostellar in nature with Class I spectral energy distributions. Comparison of their spatial distribution with sub-millimeter data from Wolf-Chase (2003) and millimeter data from Peretto et al. (2005) shows a close correlation between the dust filaments and the linear spatial configurations of the protostars, indicating that star formation is occurring primarily within dense dusty filaments. Finally, the quasi-uniform separations of the protostars are found to be comparable in magnitude to the expected Jeans length suggesting thermal fragmentation of the dense filamentary material.Comment: Accepted for publication in ApJL, 5 pages, 4 figures. Color version available from the following webpages: http://cfa-www.harvard.edu/~pteixeir/ and http://cfa-www.harvard.edu/~clada

Real-time auditory feedback may reduce abnormal movements in patients with chronic stroke

Purpose The current pilot study assesses the use of real-time auditory feedback to help reduce abnormal movements during an active reaching task in patients with chronic stroke. Materials and methods 20 patients with chronic stroke completed the study with full datasets (age: M = 53 SD = 14; sex: male = 75%; time since stroke in months: M = 34, SD = 33). Patients undertook 100 repetitions of an active reaching task while listening to self-selected music which automatically muted when abnormal movement was detected, determined by thresholds set by clinical therapists. A within-subject design with two conditions (with auditory feedback vs. without auditory feedback) presented in a randomised counterbalanced order was used. The dependent variable was the duration of abnormal movement as a proportion of trial duration. Results A significant reduction in the duration of abnormal movement was observed when patients received auditory feedback, F(1,18) = 9.424, p = 0.007, with a large effect size (partial η2 = 0.344). Conclusions Patients with chronic stroke can make use of real-time auditory feedback to increase the proportion of time they spend in optimal movement patterns. The approach provides a motivating framework that encourages high dose with a key focus on quality of movement

Computational modelling of movement-related beta-oscillatory dynamics in human motor cortex

Oscillatory activity in the beta range, in human primary motor cortex (M1), shows interesting dynamics that are tied to behaviour and change systematically in disease. To investigate the pathophysiology underlying these changes, we must first understand how changes in beta activity are caused in healthy subjects. We therefore adapted a canonical (repeatable) microcircuit model used in dynamic causal modelling (DCM) previously used to model induced responses in visual cortex. We adapted this model to accommodate cytoarchitectural differences between visual and motor cortex. Using biologically plausible connections, we used Bayesian model selection to identify the best model of measured MEG data from 11 young healthy participants, performing a simple handgrip task. We found that the canonical M1 model had substantially more model evidence than the generic canonical microcircuit model when explaining measured MEG data. The canonical M1 model reproduced measured dynamics in humans at rest, in a manner consistent with equivalent studies performed in mice. Furthermore, the changes in excitability (self-inhibition) necessary to explain beta suppression during handgrip were consistent with the attenuation of sensory precision implied by predictive coding. These results establish the face validity of a model that can be used to explore the laminar interactions that underlie beta-oscillatory dynamics in humans in vivo. Our canonical M1 model may be useful for characterising the synaptic mechanisms that mediate pathophysiological beta dynamics associated with movement disorders, such as stroke or Parkinson's disease

Functional strength training versus movement performance therapy for upper limb motor recovery early after stroke: a RCT

Background: Not all stroke survivors respond to the same form of physical therapy in the same way early after stroke. The response is variable and a detailed understanding of the interaction between specific physical therapies and neural structure and function is needed. Objectives: To determine if upper limb recovery is enhanced more by functional strength training (FST) than by movement performance therapy (MPT), to identify the differences in the neural correlates of response to (1) FST and (2) MPT and to determine whether or not pretreatment neural characteristics can predict recovery in response to (1) FST and (2) MPT. Design: Randomised, controlled, observer-blind, multicentre trial with embedded explanatory investigations. An independent facility used computer-generated randomisation for participants’ group allocation. Setting: In-patient rehabilitation, participants’ homes, university movement analysis facilities and NHS or university neuroimaging departments in the UK. Participants: People who were between 2 and 60 days after stroke in the territory of the anterior cerebral circulation, with some voluntary muscle contraction in the more affected upper limb but not full function. Interventions: Routine rehabilitation [conventional physical therapy (CPT)] plus either MPT or FST in equal doses during a 6-week intervention phase. FST was progressive resistive exercise provided during training of functional tasks. MPT was therapist ‘hands-on’ sensory input and guidance for production of smooth and accurate movement. Main outcomes: Action Research Arm Test (ARAT) score for clinical efficacy. Neural measures were made of corticocortical [fractional anisotropy (FA) from corpus callosum midline], corticospinal connectivity (asymmetry of corticospinal tracts FA) and resting motor threshold of paretic biceps brachii (pBB) and extensor carpi radialis muscles (derived from transcranial magnetic stimulation). Analysis: Change in ARAT scores were analysed using analysis of covariance models adjusted for baseline variables and randomisation strata. Correlation coefficients were calculated between change in neural measures and change in ARAT score per group and for the whole sample. An interaction term was calculated for each baseline neural measure and ARAT score change from baseline to outcome. Results: A total of 288 participants were randomised [mean age 72.2 (standard deviation 12.5) years; mean ARAT score of 25.5 (18.2); n = 283]. For the 240 participants with ARAT measurements at baseline and outcome, the mean change scores were FST + CPT = 9.70 (11.72) and MPT + CPT = 7.90 (9.18). The group difference did not reach statistical significance (least squares mean difference 1.35, 95% confidence interval –1.20 to 3.90; p = 0.298). Correlations between ARAT change scores and baseline neural values ranged from –0.147 (p = 0.385) for whole-sample corticospinal connectivity (n = 37) to 0.199 (p = 0.320) for MPT + CPT resting motor threshold pBB (n = 27). No statistically significant interaction effects were found between baseline neural variables and change in ARAT score. There were no differences between groups in adverse events. Limitations: The number of participants in the embedded explanatory investigation was lower than expected. Conclusions: The small difference in upper limb improvement in response to FST and MPT did not reach statistical significance. Baseline neural measures neither correlated with upper limb recovery nor predicted therapy response. Future work: Needs to continue investigation of the variability of response to specific physical therapies in people early after stroke. Trial registration: Current Controlled Trials ISRCTN19090862 and National Research Ethics Service reference number 11/EE/0524. Funding: This project was funded by the Efficacy and Mechanism Evaluation programme, a Medical Research Council and National Institute for Health Research partnership