Non-invasive Neuromodulation in Motor Rehabilitation after Stroke

In this thesis, we aimed to integrate recent insights on motor learning, stroke recovery and neuromodulation with the ultimate goal to improve upper limb rehabilitation after stroke

Predicting Upper Limb Motor Impairment Recovery after Stroke: A Mixture Model

Objective: Spontaneous recovery is an important determinant of upper extremity recovery after stroke and has been described by the 70% proportional recovery rule for the Fugl–Meyer motor upper extremity (FM-UE) scale. However, this rule is criticized for overestimating the predictability of FM-UE recovery. Our objectives were to develop a longitudinal mixture model of FM-UE recovery, identify FM-UE recovery subgroups, and internally validate the model predictions. Methods: We developed an exponential recovery function with the following parameters: subgroup assignment probability, proportional recovery coefficient rk, time constant in weeks τk, and distribution of the initial FM-UE scores. We fitted the model to FM-UE measurements of 412 first-ever ischemic stroke patients and cross-validated endpoint predictions and FM-UE recovery cluster assignment. Results: The model distinguished 5 subgroups with different recovery parameters (r1 = 0.09, τ1 = 5.3, r2 = 0.46, τ2 = 10.1, r3 = 0.86, τ3 = 9.8, r4 = 0.89, τ4 = 2.7, r5 = 0.93, τ5 = 1.2). Endpoint FM-UE was predicted with a median absolute error of 4.8 (interquartile range [IQR] = 1.3–12.8) at 1 week poststroke and 4.2 (IQR = 1.3–9.8) at 2 weeks. Overall accuracy of assignment to the poor (subgroup 1), moderate (subgroups 2 and 3), and good (subgroups 4 and 5) FM-UE recovery clusters was 0.79 (95% equal-tailed interval [ETI] = 0.78–0.80) at 1 week poststroke and 0.81 (95% ETI = 0.80–0.82) at 2 weeks. Interpretation: FM-UE recovery reflects different subgroups, each with its own recovery profile. Cross-validation indicates that FM-UE endpoints and FM-UE recovery clusters can be well predicted. Results will contribute to the understanding of upper limb recovery patterns in the first 6 months after stroke. ANN NEUROL 2020

Population-wide cerebellar growth models of children and adolescents

In the past, the cerebellum has been best known for its crucial role in motor function. However, increasingly more findings highlight the importance of cerebellar contributions in cognitive functions and neurodevelopment. Using a total of 7240 neuroimaging scans from 4862 individuals, we describe and provide detailed, openly available models of cerebellar development in childhood and adolescence (age range: 6–17 years), an important time period for brain development and onset of neuropsychiatric disorders. Next to a traditionally used anatomical parcellation of the cerebellum, we generated growth models based on a recently proposed functional parcellation. In both, we find an anterior-posterior growth gradient mirroring the age-related improvements of underlying behavior and function, which is analogous to cerebral maturation patterns and offers evidence for directly related cerebello-cortical developmental trajectories. Finally, we illustrate how the current approach can be used to detect cerebellar abnormalities in clinical samples.</p

TMS motor mapping: Comparing the absolute reliability of digital reconstruction methods to the golden standard

Background: Changes in transcranial magnetic stimulation motor map parameters can be used to quantify plasticity in the human motor cortex. The golden standard uses a counting analysis of motor evoked potentials (MEPs) acquired with a predefined grid. Recently, digital reconstruction methods have been proposed, allowing MEPs to be acquired with a faster pseudorandom procedure. However, the reliability of these reconstruction methods has never been compared to the golden standard. Objective: To compare the absolute reliability of the reconstruction methods with the golden standard. Methods: In 21 healthy subjects, both grid and pseudorandom acquisition were performed twice on the first day and once on the second day. The standard error of measurement was calculated for the counting analysis and the digital reconstructions. Results: The standard error of measurement was at least equal using digital reconstructions. Conclusion: Pseudorandom acquisition and digital reconstruction can be used in intervention studies without sacrificing reliability

Cerebellar transcranial direct current stimulation interacts with BDNF Val66Met in motor learning

Background: Cerebellar transcranial direct current stimulation has been reported to enhance motor associative learning and motor adaptation, holding promise for clinical application in patients with movement disorders. However, behavioral benefits from cerebellar tDCS have been inconsistent. Objective: Identifying determinants of treatment success is necessary. BDNF Val66Met is a candidate determinant, because the polymorphism is associated with motor skill learning and BDNF is thought to mediate tDCS effects. Methods: We undertook two cerebellar tDCS studies in subjects genotyped for BDNF Val66Met. Subjects performed an eyeblink conditioning task and received sham, anodal or cathodal tDCS (N = 117, between-subjects design) or a vestibulo-ocular reflex adaptation task and received sham and anodal tDCS (N = 51 subjects, within-subjects design). Performance was quantified as a learning parameter from 0 to 100%. We investigated (1) the distribution of the learning parameter with mixture modeling presented as the mean (M), standard deviation (S) and proportion (P) of the groups, and (2) the role of BDNF Val66Met and cerebellar tDCS using linear regression presented as the regression coefficients (B) and odds ratios (OR) with equally-tailed intervals (ETIs). Results: For the eyeblink conditioning task, we found distinct groups of learners (MLearner = 67.2%; SLearner = 14.7%; PLearner = 61.6%) and non-learners (MNon-learner = 14.2%; SNon-learner = 8.0%; PNon-learner = 38.4%). Carriers of the BDNF Val66Met polymorphism were more likely to be learners (OR = 2.7 [1.2 6.2]). Within the group of learners, anodal tDCS supported eyeblink conditioning in BDNF Val66Met non-carriers (B = 11.9% 95%ETI = [0.8 23.0]%), but not in carriers (B = 1.0% 95%ETI = [-10.2 12.1]%). For the vestibulo-ocular reflex adaptation task, we found no effect of BDNF Val66Met (B = −2.0% 95%ETI = [-8.7 4.7]%) or anodal tDCS in either carriers (B = 3.4% 95%ETI = [-3.2 9.5]%) or non-carriers (B = 0.6% 95%ETI = [-3.4 4.8]%). Finally, we performed additional saccade and visuomotor adaptation experiments (N = 72) to investigate the general role of BDNF Val66Met in cerebellum-dependent learning and found no difference between carriers and non-carriers for both saccade (B = 1.0% 95%ETI = [-8.6 10.6]%) and visuomotor adaptation (B = 2.7% 95%ETI = [-2.5 7.9]%). Conclusions: The specific role for BDNF Val66Met in eyeblink conditioning, but not vestibulo-ocular reflex adaptation, saccade adaptation or visuomotor adaptation could be related to dominance of the role of simple spike suppression of cerebellar Purkinje cells with a high baseline firing frequency in eyeblink conditioning. Susceptibility of non-carriers to anodal tDCS in eyeblink conditioning might be explained by a relatively larger effect of tDCS-induced subthreshold depolarization in this group, which might increase the spontaneous firing frequency up to the level of that of the carriers

Computerised patient-specific prediction of the recovery profile of upper limb capacity within stroke services: The next step

Introduction: Predicting upper limb capacity recovery is important to set treatment goals, select therapies and plan discharge. We introduce a prediction model of the patient-specific profile of upper limb capacity recovery up to 6 months poststroke by incorporating all serially assessed clinical information from patients. Methods: Model input was recovery profile of 450 patients with a first-ever ischaemic hemispheric stroke measured using the Action Research Arm Test (ARAT). Subjects received at least three assessment sessions, starting within the first week until 6 months poststroke. We developed mixed-effects models that are able to deal with one or multiple measurements per subject, measured at non-fixed time points. The prediction accuracy of the different models was established by a fivefold cross-validation procedure. Results: A model with only ARAT time course, finger extension and shoulder abduction performed as good as models with more covariates. For the final model, cross-validation prediction errors at 6 months poststroke decreased as the number of measurements per subject increased, from a median error of 8.4 points on the ARAT (Q1-Q3:1.7-28.1) when one measurement early poststroke was used, to 2.3 (Q1-Q3:1-7.2) for seven measurements. An online version of the recovery model was developed that can be linked to data acquisition environments. Conclusio

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Movement state noise and output noise relate to visuomotor adaptation rate in an optimal way

Human movement relies on noisy processes in neurons, muscle cells and sensory cells. Therefore, movements are variable and can never be exactly reproduced. The nervous system seems to exploit this movement noise for motor learning and specifically motor adaptation. However, a positive relation between movement noise and motor adaptation has not been consistently found in motor adaptation literature. Possibly, noise is comprised of distinct processes which contribute to motor adaptation in different ways. In Kalman filter theory, motor adaptation rate is calculated optimally from state noise and output noise, with state noise and adaptation rate positively correlated and output noise and adaptation rate negatively correlated. Therefore, if people learn (close) optimally from error, we would expect a similar relation. To investigate the relation between state noise, output noise and adaptation rate, we performed a visuomotor reaching adaptation experiment with a baseline and a perturbation block in 69 subjects. State noise, output noise and adaptation rate in the baseline and perturbation block were extracting using Bayesian fitting of a trial-to-trial state-space model. We found that adaptation rate in the perturbation block correlates positively with baseline state noise (r=0.27; 95%HDI=[0.05 0.50]) and negatively with baseline output noise (r= 0.41; 95%HDI=[ 0.63 0.16]). In addition, the steady-state Kalman gain calculated from baseline state and output noise correlated positively with adaptation rate in the perturbation block (r = 0.31; 95%HDI = [0.09 0.54]). Therefore, noise can be viewed both as a supporting factor for motor adaptation (state noise) and as a noise factor hampering optimal performance (output noise), and in order to understand the relationship of noise to learning, one must decompose noise into its constituent components

BDNF Val66Met but not transcranial direct current stimulation affects motor learning after stroke

BACKGROUND: tDCS is a non-invasive neuromodulation technique that has been reported to improve motor skill learning after stroke. However, the contribution of tDCS to motor skill learning has only been investigated in a small number of studies. In addition, it is unclear if tDCS effects are mediated by activity-dependent BDNF release and dependent on timing of tDCS relative to training. OBJECTIVE: Investigate the role of activity-dependent BDNF release and timing of tDCS relative to training in motor skill learning. METHODS: Double-blind, between-subjects randomized controlled trial of circuit tracing task improvement (ΔMotor skill) in 80 chronic stroke patients who underwent tDCS and were genotyped for BDNF Val66Met. Patients received either short-lasting tDCS (20 min) during training (short-lasting online group), long-lasting tDCS (10 min-25 min break - 10 min) one day before training (long-lasting offline group), short-lasting tDCS one day before training (short-lasting offline group), or sham tDCS. ΔMotor skill was defined as the skill difference on the circuit tracing task between day one and day nine of the study. RESULTS: Having at least one BDNF Met allele was found to diminish ΔMotor skill (βBDNF,Met = -0.217 95%HDI = [-0.431 -0.0116]), indicating activity-dependent BDNF release is important for motor skill learning after stroke. However, none of the tDCS protocols affected ΔMotor skill (βShort-lasting,online = 0.0908 95%HDI = [-0.227 0.403]; βLong-lasting,offline = 0.0242 95%HDI = [-0.292 0.349]; βShort-lasting,offline = -0.108 95%HDI = [-0.433 0.210]). CONCLUSION: BDNF Val66Met is a determinant of motor skill learning after stroke and could be important for prognostic models. tDCS does not modulate motor skill learning in our study and might be less effective than previously assumed. TRIAL REGISTRATION: ClinicalTrials.gov NCT02399540