Predict Motor Outcome Research Articles

Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model

Background and aimsPreterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonance imaging (MRI) acquired between 29 and 35 weeks postmenstrual age (PMA) using a deep learning convolutional neural network (CNN) model. MethodsSeventy-seven very preterm infants (born <31 weeks gestational age (GA)) in a prospective longitudinal cohort underwent diffusion MR imaging (3T Siemens Trio; 64 directions, b ​= ​2000 ​s/mm2). Motor outcome at 2 years corrected age (CA) was measured by Neuro-Sensory Motor Developmental Assessment (NSMDA). Scores were dichotomised into normal (functional score: 0, normal; n ​= ​48) and abnormal scores (functional score: 1–5, mild-profound; n ​= ​29). MRIs were pre-processed to reduce artefacts, upsampled to 1.25 ​mm isotropic resolution and maps of fractional anisotropy (FA) were estimated. Patches extracted from each image were used as inputs to train a CNN, wherein each image patch predicted either normal or abnormal outcome. In a postprocessing step, an image was classified as predicting abnormal outcome if at least 27% (determined by a grid search to maximise the model performance) of its patches predicted abnormal outcome. Otherwise, it was considered as normal. Ten-fold cross-validation was used to estimate performance. Finally, heatmaps of model predictions for patches in abnormal scans were generated to explore the locations associated with abnormal outcome. ResultsFor the identification of infants with abnormal motor outcome based on the FA data from early MRI, we achieved mean sensitivity 70% (standard deviation SD 19%), mean specificity 74% (SD 39%), mean AUC (area under the receiver operating characteristic curve) 72% (SD 14%), mean F1 score of 68% (SD 13%) and mean accuracy 73% (SD 19%) on an unseen test data set. Patch-based prediction heatmaps showed that the patches around the motor cortex and somatosensory regions were most frequently identified by the model with high precision (74%) as a location associated with abnormal outcome. Part of the cerebellum, and occipital and frontal lobes were also highly associated with abnormal NSMDA/motor outcome. Discussion/conclusionThis study established the potential of an early brain MRI-based deep learning CNN model to identify preterm infants at risk of a later motor impairment and to identify brain regions predictive of adverse outcome. Results suggest that predictions can be made from FA maps of diffusion MRIs well before term equivalent age (TEA) without any prior knowledge of which MRI features to extract and associated feature extraction steps. This method, therefore, is suitable for any case of brain condition/abnormality. Future studies should be conducted on a larger cohort to re-validate the robustness and effectiveness of these models.

A Tractography-Based Grading Scale of Brain Arteriovenous Malformations Close to the Corticospinal Tract to Predict Motor Outcome After Surgery.

Background: Surgical decision-making for brain arteriovenous malformations (AVMs) close to the corticospinal tract (CST) is always challenging. The purpose of this study was to develop a tractography-based grading scale to improve preoperative risk prediction and patient selection.Methods: We analyzed a consecutive, surgically treated series of 90 patients with AVMs within a 10-mm range from the CST demonstrated by preoperative diffusion tensor tractography. Poor motor outcome was defined as persistent postoperative limb weakness. We examined the predictive ability of nidus-to-CST distance (NCD), the closest CST level (CCL), deep perforating artery supply, as well as variables of the supplemented Spetzler-Martin grading system. Three logistic models were derived from different multivariable logistic regression analyses, of which the most predictive model was selected to construct a prediction grading scale. Receiver operating characteristic analysis was conducted to test the predictive accuracy of the grading scale.Results: Twenty-one (23.3%) patients experienced persistent postoperative limb weakness after a mean 2.7-year follow-up. The most predictive logistic model showed NCD (P = 0.001), CCL (P = 0.017), patient age (P = 0.004), and AVM diffuseness (P = 0.021) were independent predictors for poor motor outcome. We constructed the CLAD grading scale incorporating these predictors. The predictive accuracy of the CLAD grade was better compared with the supplemented Spetzler-Martin grade (area under curve = 0.84 vs. 0.68, P = 0.023).Conclusions: Both NCD and CCL predict motor outcome after resection of AVMs close to the CST. We propose the CLAD grading scale as an effective risk-prediction tool in surgical decision-making.Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT01758211 and NCT02868008

Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review.

Stroke is a leading cause of disability worldwide. Motor impairments occur in most of the patients with stroke in the acute phase and contribute substantially to disability. Diffusion tensor imaging (DTI) biomarkers such as fractional anisotropy (FA) measured at an early phase after stroke have emerged as potential predictors of motor recovery. In this narrative review, we: (1) review key concepts of diffusion MRI (dMRI); (2) present an overview of state-of-art methodological aspects of data collection, analysis and reporting; and (3) critically review challenges of DTI in stroke as well as results of studies that investigated the correlation between DTI metrics within the corticospinal tract and motor outcomes at different stages after stroke. We reviewed studies published between January, 2008 and December, 2018, that reported correlations between DTI metrics collected within the first 24 h (hyperacute), 2–7 days (acute), and >7–90 days (early subacute) after stroke. Nineteen studies were included. Our review shows that there is no consensus about gold standards for DTI data collection or processing. We found great methodological differences across studies that evaluated DTI metrics within the corticospinal tract. Despite heterogeneity in stroke lesions and analysis approaches, the majority of studies reported significant correlations between DTI biomarkers and motor impairments. It remains to be determined whether DTI results could enhance the predictive value of motor disability models based on clinical and neurophysiological variables.

Predicting Motor Outcome in Acute Intracerebral Hemorrhage.

Predicting motor outcome following intracerebral hemorrhage is challenging. We tested whether the combination of clinical scores and DTI-based assessment of corticospinal tract damage within the first 12 hours of symptom onset after intracerebral hemorrhage predicts motor outcome at 3 months. We prospectively studied patients with motor deficits secondary to primary intracerebral hemorrhage within the first 12 hours of symptom onset. Patients underwent multimodal MR imaging including DTI. We assessed intracerebral hemorrhage and perihematomal edema location and volume, and corticospinal tract involvement. The corticospinal tract was considered affected when the tractogram passed through the intracerebral hemorrhage or/and the perihematomal edema. We also calculated affected corticospinal tract-to-unaffected corticospinal tract ratios for fractional anisotropy, mean diffusivity, and axial and radial diffusivities. Motor impairment was graded by the motor subindex scores of the modified NIHSS. Motor outcome at 3 months was classified as good (modified NIHSS 0-3) or poor (modified NIHSS 4-8). Of 62 patients, 43 were included. At admission, the median NIHSS score was 13 (interquartile range = 8-17), and the median modified NIHSS score was 5 (interquartile range = 2-8). At 3 months, 13 (30.23%) had poor motor outcome. Significant independent predictors of motor outcome were NIHSS and modified NIHSS at admission, posterior limb of the internal capsule involvement by intracerebral hemorrhage at admission, intracerebral hemorrhage volume at admission, 72-hour NIHSS, and 72-hour modified NIHSS. The sensitivity, specificity, and positive and negative predictive values for poor motor outcome at 3 months by a combined modified NIHSS of >6 and posterior limb of the internal capsule involvement in the first 12 hours from symptom onset were 84%, 79%, 65%, and 92%, respectively (area under the curve = 0.89; 95% CI, 0.78-1). Combined assessment of motor function and posterior limb of the internal capsule damage during acute intracerebral hemorrhage accurately predicts motor outcome.

Artificial Neural Network-Based Prediction of Outcome in Parkinson's Disease Patients Using DaTscan SPECT Imaging Features.

Quantitative analysis of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images can enhance diagnostic confidence and improve their potential as a biomarker to monitor the progression of Parkinson's disease (PD). In the present work, we aim to predict motor outcome from baseline DAT SPECT imaging radiomic features and clinical measures using machine learning techniques. We designed and trained artificial neural networks (ANNs) to analyze the data from 69 patients within the Parkinson's Progressive Marker Initiative (PPMI) database. The task was to predict the unified PD rating scale (UPDRS) part III motor score in year 4 from 92 imaging features extracted on 12 different regions as well as 6 non-imaging measures at baseline (year 0). We first performed univariate screening (including theadjustment for false discovery) to select 4 regions each having 10 features with significant performance in classifying year 4 motor outcome into two classes of patients (divided by the UPDRS III threshold of 30). The leave-one-out strategy was then applied to train and test the ANNs for individual and combinations of features. The prediction statistics were calculated from 100 rounds of experiments, and theaccuracy in appropriate prediction (classification of year 4 outcome) was quantified. Out ofthe baseline non-imaging features, only the UPDRS III (at year 0) was predictive, while multiple imaging features depicted significance. The different selected features reached a predictive accuracy of 70% if used individually. Combining the top imaging features from the selected regions significantly improved the prediction accuracy to 75% (p < 0.01). The combination of imaging features with the year 0 UPDRS III score also improved the prediction accuracy to 75%. This study demonstrated the added predictive value of radiomic features extracted from DAT SPECT images in serving as a biomarker for PD progression tracking.

Abstract TP509: Patterns of Injury in Children With Perinatal Arterial Ischemic Stroke and Infantile Spasms

Perinatal arterial ischemic stroke (AIS) occurs in 1 in 4000 live births and is the most common stroke in children. Consequences include seizures and a spectrum of cognitive and motor disability. Infantile spasms (IS) is an epileptic encephalopathy of infancy with an incidence of 2 in 10,000 live births. Approximately 5% of IS is caused by perinatal AIS. Patterns of ischemic injury that may predispose infants to IS and predict treatment response have not yet been identified. This retrospective case series of infants with AIS and IS provides detailed descriptions of ischemic distribution, seizure presentation, treatment and outcomes. Inclusion criteria were: term birth, ischemic stroke or encephalomalacia in an arterial distribution identified or presumed to have occurred in the perinatal period, a diagnosis of infantile spasms. Patients with a watershed pattern of injury were excluded. The modified pediatric ASPECTS was used to qualify and quantify the type and distribution of stroke. Areas of injury were identified on MRI and scored from T2/Flair or DWI sequences. Eleven patients with AIS and IS were identified. Of nine who fit inclusion criteria, all had MCA territory involvement with 6/9 having basal ganglia injury. Five had ischemia identified retrospectively after developing IS and four presented as neonates. The highest ASPECTS (bilateral deep MCA) was associated with the worst outcome in motor function and epilepsy control. However, the second-highest ASPECTS (unilateral MCA, bilateral ACA) had mild motor deficits and no seizure recurrence after IS resolution. The three lowest ASPECTS (&lt;10) involved unilateral cortical MCA strokes sparing the deep structures, and also had the best motor outcome and seizure control. The co-occurrence of perinatal AIS and IS often results in significant neurologic disability. Although there was no defined pattern of regional homogeneity, there was a trend towards basal ganglia injury with progression of epilepsy after IS. This study highlights that size of ischemic injury may be less important than location as a predictor of motor outcome and seizure intractability. Future research will focus on identifying areas of injury that may confer increased risk of IS compared to stroke patients who remain seizure-free.

Thalamic and ventricular volumes predict motor response to deep brain stimulation for Parkinson's disease

BackgroundBrain atrophy frequently occurs with Parkinson's disease (PD) and relates to increased motor symptoms of PD. The predictive value of neuroimaging-based measures of global and regional brain volume on motor outcomes in deep brain stimulation (DBS) remains unclear but potentially could improve patient selection and targeting. ObjectivesTo determine the predictive value of preoperative volumetric MRI measures of cortical and subcortical brain volume on motor outcomes of subthalamic nucleus (STN) DBS in PD. MethodsPreoperative T1 3D MP-RAGE structural brain MRI images were analyzed for each participant to determine subcortical, ventricular, and cortical volume and thickness. Change in Unified Parkinson's Disease Rating Scale (UPDRS) scores for subsection 3, representing motor outcomes, was computed preoperatively and postoperatively following DBS programming in 86 participants. A multiple linear regression analysis was performed to investigate the relationship between volumetric data and the effect of DBS on UPDRS 3 scores. ResultsLarger ventricular and smaller thalamic volumes predicted significantly less improvement of UPDRS 3 scores after STN DBS. ConclusionsOur findings demonstrate in PD that regional brain volumes, in particular thalamic and ventricular volumes, predict motor outcomes after DBS. Differences in regional brain volumes may alter electrode targeting, reflect a specific disease trait such as postoperative progression of subclinical dementia, or directly interfere with the action of DBS.

Factors affecting the motor outcome after corrective surgery for acquired comitant esotropia.

To determine the possible predictors of the motor outcome after corrective surgery for acquired comitant esotropia. The study included 40 patients with acquired comitant esotropia. After evaluation of the visual, sensory, and motor status; cycloplegic refraction; fundus examination; and spectacle prescription, all patients underwent corrective strabismus surgery. A successful motor outcome was defined as 0 to 10 prism diopters of horizontal tropia. Statistical analysis of the results was done using the chi-square tests. At the sixth postoperative month, 90% achieved a successful motor outcome, while 2.5% had a consecutive exotropia >10 prism diopters, and 7.5% had a residual esotropia >10 prism diopters. A statistically significant relationship existed between the motor outcome and the following: preoperative results of the Worth four-dot test at 6 m (p = .011) and 0.33 m (p = .009), preoperative distant (p = .016) and near (p = .017) angles of esotropia with glasses, postoperative near angles of deviation at 1 week (p = .005), 1 month (p < .001), 6 weeks (p < .001), 3 months (p = .001), and 6 months (p < .001) following surgery, postoperative distant angles of deviation at 6 weeks (p < .001) and 3 months (p = .03) following surgery, 6 week (p = .01) and 6 month (p = .036) postoperative results of the Worth four-dot test at 0.33 m, and the 6-month postoperative sensory outcome (p = .006). Preoperative and postoperative sensory and motor functions are predictors of the 6-month postoperative motor outcome.

The use of kinematic robotic indices to predict motor outcome in upper limb treatment of stroke patients

The main objective of total knee arthroplasty is to relieve pain, restore normal knee function, and improve gait stability. Significant flexion contractures can severely impair function after surgery. The purpose of this study is to evaluate the efficacy of implementing a continuous proximal sciatic nerve block in conjunction with aggressive physical therapy to treat patients with persistent flexion contractures that were recalcitrant to rehabilitation efforts following primary total knee arthroplasty (TKA).From December 2012 to January 2016, the following subjects were enrolled in this study: 20 patients (15 females and 5 males aged between 62 and 78 years old; median age = 65.7 y) with flexion contractures ranging from 15° to 25° (19.2°±5.6°) that persisted for at least 1.5 months following total knee arthroplasty and showed no significant improvement in response to conventional therapeutic methods. Demographic data, the passive range of motion, flexion contracture, pain score during stretching, and the Hospital for Special Surgery knee score were recorded. A portable motion analyzer was used to obtain the corresponding gait parameters from the flexion contractures group and control group. Repeated measurement ANOVA was used to compare the clinical results.In combination with 2 to 4 (2.5 ± 1.3) months of aggressive knee stretching exercises, 16 out of 18 knees achieved full extension, and 2 out of 18 improved to within 5° of the full extension. An average of the 12 to 48 (26.6 ± 10.7) month follow-up showed that this improved range of motion was maintained for all the corresponding patients, and that there were no reoccurrences of deformity. The mean Hospital for Special Surgery knee scores improved from 61.2 to 93.2 points (p < 0.001). After six months of continuous proximal sciatic nerve blockage, all gait parameters for the flexion contractures group exhibited significant improvement.A continuous proximal sciatic nerve block could be a useful adjunct to a physical therapy regimen for patients with knee flexion contractures, especially for patients with difficult-to-treat cases of knee flexion contracture that are recalcitrant to conservative therapy.

Implementing biomarkers to predict motor recovery after stroke.

There is growing interest in using biomarkers to predict motor recovery and outcomes after stroke. The PREP2 algorithm combines clinical assessment with biomarkers in an algorithm, to predict upper limb functional outcomes for individual patients. To date, PREP2 is the first algorithm to be tested in clinical practice, and other biomarker-based algorithms are likely to follow. This review considers how algorithms to predict motor recovery and outcomes after stroke might be implemented in clinical practice. There are two tasks: first the prediction information needs to be obtained, and then it needs to be used. The barriers and facilitators of implementation are likely to differ for these tasks. We identify specific elements of the Consolidated Framework for Implementation Research that are relevant to each of these two tasks, using the PREP2 algorithm as an example. These include the characteristics of the predictors and algorithm, the clinical setting and its staff, and the healthcare environment. Active, theoretically underpinned implementation strategies are needed to ensure that biomarkers are successfully used in clinical practice for predicting motor outcomes after stroke, and should be considered in parallel with biomarker development.

Early functional MRI activation predicts motor outcome after ischemic stroke: a longitudinal, multimodal study.

An accurate prediction of long term outcome after stroke is urgently required to provide early individualized neurorehabilitation. This study aimed to examine the added value of early neuroimaging measures and identify the best approaches for predicting motor outcome after stroke. This prospective study involved 34 first-ever ischemic stroke patients (time since stroke: 1-14 days) with upper limb impairment. All patients underwent baseline multimodal assessments that included clinical (age, motor impairment), neurophysiological (motor-evoked potentials, MEP) and neuroimaging (diffusion tensor imaging and motor task-based fMRI) measures, and also underwent reassessment 3 months after stroke. Bivariate analysis and multivariate linear regression models were used to predict the motor scores (Fugl-Meyer assessment, FMA) at 3 months post-stroke. With bivariate analysis, better motor outcome significantly correlated with (1) less initial motor impairment and disability, (2) less corticospinal tract injury, (3) the initial presence of MEPs, (4) stronger baseline motor fMRI activations. In multivariate analysis, incorporating neuroimaging data improved the predictive accuracy relative to only clinical and neurophysiological assessments. Baseline fMRI activation in SMA was an independent predictor of motor outcome after stroke. A multimodal model incorporating fMRI and clinical measures best predicted the motor outcome following stroke. fMRI measures obtained early after stroke provided independent prediction of long-term motor outcome.

Pre-therapy Neural State of Bilateral Motor and Premotor Cortices Predicts Therapy Gain After Subcortical Stroke: A Pilot Study.

The aim of the study was to examine whether neural state of spared motor and premotor cortices captured before a therapy predicts therapy-related motor gains in chronic subcortical stroke. Ten survivors, presenting chronic moderate upper limb impairment, underwent proton magnetic resonance spectroscopy, magnetic resonance imaging, clinical, and kinematics assessments before a 4-wk impairment-oriented training. Clinical/kinematics assessments were repeated after therapy, and motor gain was defined as positive values of clinical upper limb/elbow motion changes and negative values of trunk motion changes. Candidate predictors were N-acetylaspartate-neuronal marker, glutamate-glutamine-indicator of glutamatergic neurotransmission, and myo-inositol-glial marker, measured bilaterally within the upper limb territory in motor and premotor (premotor cortex, supplementary motor area) cortices. Traditional predictors (age, stroke length, pre-therapy upper limb clinical impairment, infarct volume) were also investigated. Poor motor gain was associated with lower glutamate-glutamine levels in ipsilesional primary motor cortex and premotor cortex (r = 0.77, P = 0.01 and r = 0.78, P = 0.008, respectively), lower N-acetylaspartate in ipsilesional premotor cortex (r = 0.69, P = 0.02), higher glutamate-glutamine in contralesional primary motor cortex (r = -0.68, P = 0.03), and lower glutamate-glutamine in contralesional supplementary motor area (r = 0.64, P = 0.04). These predictors outperformed myo-inositol metrics and traditional predictors (P ≈ 0.05-1.0). Glutamatergic state of bilateral motor and premotor cortices and neuronal state of ipsilesional premotor cortex may be important for predicting motor outcome in the context of a restorative therapy.

Motor Outcomes After Surgical Resection of Lesions Involving the Motor Pathway: APrognostic Evaluation Scale.

Preservation of function is essential in surgical resection of lesions involving the motor pathways. The aim of this work is to evaluate the effect of anatomic features of lesions located in the motor pathway on neurologic and functional outcome. We propose an evaluation score to assess the risk of postoperative worsening. A total of 92 patients suffering from lesions involving the motor pathways have been studied for what concerns motor functions (muscular strength scale [MRC]) after surgical treatment. Patient-related, surgery-related, and lesion-related data were recorded to identify relations with motor outcomes. Cortical surfacing of the lesion and amount of millimeters of interface between the lesion and corticospinal tract have been investigated with preoperative magnetic resonance imaging sequences and tractographic reconstructions. Means of the MRC Scale preoperative and 7days postoperative were 3.6 ± 2.23 and 3.8 ± 2.28 for the upper limbs, and 4.1 ± 2.31 and 4.4 ± 2.33 for the lower limbs, respectively. Patients that were stable or improved at 7 days from surgery were 82.6%. The anatomic features investigated have been summed up in a single score (envelopment/surfacing [ES]). In cases of ES classes V and VI, a total cumulative predicted worsening rate was 24.2% and 14.8%, respectively. The risk of worsening was 1.682 times greater for the higher ES classes (IV-VI). The anatomic features of lesions are crucial in the prediction of motor outcome. The new proposed score (ES score) is useful to stratify motor function prognosis.

Abstract 14: Effects of Lesion Laterality on Post-Stroke Motor Performance: An ENIGMA Stroke Recovery Analysis

The laterality of the lesioned hemisphere is often overlooked in stroke recovery research due to small sample sizes. Here, we used a well-powered dataset from ENIGMA Stroke Recovery (a consortium that harmonizes post-stroke MRIs and behavioral data worldwide; http://enigma.usc.edu) to analyze the effects of left (LHL) versus right (RHL) hemisphere lesions on motor performance. Given the different functional roles of each hemisphere, we hypothesized that the LHL group should show better motor performance, and, consequently, different brain-behavior relationships, compared to the RHL group. Data from over 2000 stroke patients across 20 sites worldwide has been committed. To date, structural T1-weighted MRIs from n=343 (10 sites) have been analyzed (LHL n=174; RHL n=169). ENIGMA protocols extracted volumes of subcortical regions of interest and provided quality control. Regression analyses examined brain volumes as predictors of motor outcomes. Motor scores were combined across scales/sites, with each score expressed as a percentage of the maximum score. Covariates (e.g., age, sex, intracranial volume) and manually marked lesion effects were also modeled. Statistical significance was assessed nonparametrically by permutation. As anticipated, the LHL group had better motor performance compared to the RHL group (t(1,341)=3.07, p=0.0023). In addition, while the combined LHL+RHL analyses showed significant associations between motor scores and volumes of the basal ganglia/lateral ventricles, separate group analyses showed strong associations for the LHL group, but only one association for the RHL group (Table 1). This may suggest that motor recovery following RH lesions is more heterogeneous or relies more on cortical regions/networks that were not assessed here. While further research is needed, these results suggest that laterality of the lesioned hemisphere affects neural patterns related to motor recovery and should be carefully examined.

Erratum to "Are we armed with the right data? Pooled individual data review of biomarkers in people with severe upper limb impairment after stroke".

[This corrects the article DOI: 10.1016/j.nicl.2016.09.015.].

Parietal operculum and motor cortex activities predict motor recovery in moderate to severe stroke

While motor recovery following mild stroke has been extensively studied with neuroimaging, mechanisms of recovery after moderate to severe strokes of the types that are often the focus for novel restorative therapies remain obscure. We used fMRI to: 1) characterize reorganization occurring after moderate to severe subacute stroke, 2) identify brain regions associated with motor recovery and 3) to test whether brain activity associated with passive movement measured in the subacute period could predict motor outcome six months later.Because many patients with large strokes involving sensorimotor regions cannot engage in voluntary movement, we used passive flexion-extension of the paretic wrist to compare 21 patients with subacute ischemic stroke to 24 healthy controls one month after stroke. Clinical motor outcome was assessed with Fugl-Meyer motor scores (motor-FMS) six months later. Multiple regression, with predictors including baseline (one-month) motor-FMS and sensorimotor network regional activity (ROI) measures, was used to determine optimal variable selection for motor outcome prediction. Sensorimotor network ROIs were derived from a meta-analysis of arm voluntary movement tasks. Bootstrapping with 1000 replications was used for internal model validation.During passive movement, both control and patient groups exhibited activity increases in multiple bilateral sensorimotor network regions, including the primary motor (MI), premotor and supplementary motor areas (SMA), cerebellar cortex, putamen, thalamus, insula, Brodmann area (BA) 44 and parietal operculum (OP1-OP4). Compared to controls, patients showed: 1) lower task-related activity in ipsilesional MI, SMA and contralesional cerebellum (lobules V-VI) and 2) higher activity in contralesional MI, superior temporal gyrus and OP1-OP4. Using multiple regression, we found that the combination of baseline motor-FMS, activity in ipsilesional MI (BA4a), putamen and ipsilesional OP1 predicted motor outcome measured 6 months later (adjusted-R2 = 0.85; bootstrap p < 0.001). Baseline motor-FMS alone predicted only 54% of the variance. When baseline motor-FMS was removed, the combination of increased activity in ipsilesional MI-BA4a, ipsilesional thalamus, contralesional mid-cingulum, contralesional OP4 and decreased activity in ipsilesional OP1, predicted better motor outcome (djusted-R2 = 0.96; bootstrap p < 0.001).In subacute stroke, fMRI brain activity related to passive movement measured in a sensorimotor network defined by activity during voluntary movement predicted motor recovery better than baseline motor-FMS alone. Furthermore, fMRI sensorimotor network activity measures considered alone allowed excellent clinical recovery prediction and may provide reliable biomarkers for assessing new therapies in clinical trial contexts. Our findings suggest that neural reorganization related to motor recovery from moderate to severe stroke results from balanced changes in ipsilesional MI (BA4a) and a set of phylogenetically more archaic sensorimotor regions in the ventral sensorimotor trend, in which OP1 and OP4 processes may complement the ipsilesional dorsal motor cortex in achieving compensatory sensorimotor recovery.

Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images.

No disease modifying therapies for Parkinson's disease (PD) have been found effective to date. To properly power clinical trials for discovery of such therapies, the ability to predict outcome in PD is critical, and there is a significant need for discovery of prognostic biomarkers of PD. Dopamine transporter (DAT) SPECT imaging is widely used for diagnostic purposes in PD. In the present work, we aimed to evaluate whether longitudinal DAT SPECT imaging can significantly improve prediction of outcome in PD patients. In particular, we investigated whether radiomics analysis of DAT SPECT images, in addition to use of conventional non-imaging and imaging measures, could be used to predict motor severity at year 4 in PD subjects. We selected 64 PD subjects (38 male, 26 female; age at baseline (year 0): 61.9 ± 7.3, range [46,78]) from the Parkinson's Progressive Marker Initiative (PPMI) database. Inclusion criteria included (i) having had at least 2 SPECT scans at years 0 and 1 acquired on a similar scanner, (ii) having undergone a high-resolution 3 T MRI scan, and (iii) having motor assessment (MDS-UPDRS-III) available in year 4 used as outcome measure. Image analysis included automatic region-of-interest (ROI) extraction on MRI images, registration of SPECT images onto the corresponding MRI images, and extraction of radiomic features. Non-imaging predictors included demographics, disease duration as well as motor and non-motor clinical measures in years 0 and 1. The image predictors included 92 radiomic features extracted from the caudate, putamen, and ventral striatum of DAT SPECT images at years 0 and 1 to quantify heterogeneity and texture in uptake. Random forest (RF) analysis with 5000 trees was used to combine both non-imaging and imaging variables to predict motor outcome (UPDRS-III: 27.3 ± 14.7, range [3,77]). The RF prediction was evaluated using leave-one-out cross-validation. Our results demonstrated that addition of radiomic features to conventional measures significantly improved (p < 0.001) prediction of outcome, reducing the absolute error of predicting MDS-UPDRS-III from 9.00 ± 0.88 to 4.12 ± 0.43. This shows that radiomics analysis of DAT SPECT images has a significant potential towards development of effective prognostic biomarkers in PD.

Diffusion tensor imaging predicts motor outcome in children with acquired brain injury.

Rehabilitation in children with acquired brain injury is a challenging endeavour. There is a large variability in motor recovery between patients, and a need to optimize therapies by exploiting cerebral plasticity and recovery mechanisms. This retrospective study aims to identify tract-based markers that could serve as predictors of functional outcome following rehabilitation. Twenty-nine children with traumatic brain injury (n=14) or stroke (n=15) underwent a 3T Magnetic Resonance Imaging (MRI) measurement, including Diffusion Tensor Imaging (DTI) between admission to the Hospital and onset of rehabilitation therapy at the Rehabilitation Centre. The Functional Independence Measure for Children (WeeFIM) was routinely applied at admission and discharge from the Rehabilitation Centre. Distinguishing between children with good versus poor functional independence was performed using ROC-analysis. A non-parametric partial correlation analysis between the DTI and WeeFIM motor scores was performed with age, time in rehabilitation, and time of MRI scan after injury as covariates. Mean fractional anisotropy (FA) from the DTI in the ipsilesional corticospinal-tract provided the highest predictive accuracy (sensitivity=95%, specificity=78%, Youden Index=0.73, Area under the curve=0.9), in comparison to the lesion volume or other clinical variables. Mean FA of the ipsilesional corticospinal-tract correlated positively with the WeeFIM discharge motor scores (ρ=0.547, p=0.004). Prediction was poorer for the lesion volume or Glasgow Coma Scale. The results suggest that DTI data could improve the prediction of functional outcome after rehabilitation in children and adolescents with stroke or traumatic brain injury. Specifically, mean FA shows the highest predictive accuracy in comparison to lesion volume or clinical scales.

Are we armed with the right data? Pooled individual data review of biomarkers in people with severe upper limb impairment after stroke.

To build an understanding of the neurobiology underpinning arm recovery in people with severe arm impairment due to stroke, we conducted a pooled individual data systematic review to: 1) characterize brain biomarkers; 2) determine relationship(s) between biomarkers and motor outcome; and 3) establish relationship(s) between biomarkers and motor recovery. Three electronic databases were searched up to October 2, 2015. Eligible studies included adults with severe arm impairment after stroke. Descriptive statistics were calculated to characterize brain biomarkers, and pooling of individual patient data was performed using mixed-effects linear regression to examine relationships between brain biomarkers and motor outcome and recovery. Thirty-eight articles including individual data from 372 people with severe arm impairment were analysed. The majority of individuals were in the chronic (> 6 months) phase post stroke (51%) and had a subcortical stroke (49%). The presence of a motor evoked potential (indexed by transcranial magnetic stimulation) was the only biomarker related to better motor outcome (p = 0.02). There was no relationship between motor outcome and stroke volume (cm3), location (cortical, subcortical, mixed) or side (left vs. right), and corticospinal tract asymmetry index (extracted from diffusion weighted imaging). Only one study had longitudinal data, thus no data pooling was possible to address change over time (preventing our third objective). Based on the available evidence, motor evoked potentials at rest were the only biomarker that predicted motor outcome in individuals with severe arm impairment following stroke. Given that few biomarkers emerged, this review highlights the need to move beyond currently known biomarkers and identify new indices with sufficient variability and sensitivity to guide recovery models in individuals with severe motor impairments following stroke. PROSPERO: CRD42015026107.

Predicting motor outcomes with 3 month prone hip angles in premature infants.

This study used kinematic analysis to identify a reliable and rapid assessment method for abnormal patterns of motor development in preterm infants. In a retrospective analysis, we examined video of n= 35 preterm infants at 3mo corrected age (CA) who had concurrent Test of Infant Motor Performance (TIMP) scores. Hyperflexion at the hip produces common gait anomalies seen in children with CP, therefore we analyzed hip angle in the prone head lift position at 3 months CA. Magnetic Resonance Spectroscopy (MRS) was performed at term equivalent (n= 23) and Bayley-III neurodevelopmental tests were performed at 1 year (n= 28). We correlated hip angles with TIMP and Bayley-III scores, and MRS neuronal metabolites. Hip angle positively correlated with TIMP at 3 months (r= 0.642, p≤ 0.001), but not with Bayley-III at 1 year (r= 0.122, p= 0.529). Hip angle correlated negatively with myo-inositol (mI) ratios in frontal white matter tracts (mI/Cr r= -0.520, p= 0.011). These results suggest prone hip angle may be a quantitative proxy for the 42-item TIMP at 3 months, and that hypertonicity in the hip flexor musculature is a manifestation of white matter metabolic abnormalities (elevated mI ratios) that may indicate occult white matter injury.