Faculty Bibliography

Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T₁-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P < 0.004). We tested subacute (≤90 days) and chronic (≥180 days) stroke subgroups separately, with exploratory analyses in early stroke (≤21 days) and across all time. Sub-analyses in chronic stroke were also performed based on class of sensorimotor deficits (impairment, activity limitations) and side of lesioned hemisphere. Worse sensorimotor behaviour was associated with a smaller ipsilesional thalamic volume in both early (n = 179; d = 0.68) and subacute (n = 274, d = 0.46) stroke. In chronic stroke (n = 404), worse sensorimotor behaviour was associated with smaller ipsilesional putamen (d = 0.52) and nucleus accumbens (d = 0.39) volumes, and a larger ipsilesional lateral ventricle (d = -0.42). Worse chronic sensorimotor impairment specifically (measured by the Fugl-Meyer Assessment; n = 256) was associated with smaller ipsilesional putamen (d = 0.72) and larger lateral ventricle (d = -0.41) volumes, while several measures of activity limitations (n = 116) showed no significant relationships. In the full cohort across all time (n = 828), sensorimotor behaviour was associated with the volumes of the ipsilesional nucleus accumbens (d = 0.23), putamen (d = 0.33), thalamus (d = 0.33) and lateral ventricle (d = -0.23). We demonstrate significant relationships between post-stroke sensorimotor behaviour and reduced volumes of deep grey matter structures that were spared by stroke, which differ by time and class of sensorimotor measure. These findings provide additional insight into how different cortico-thalamo-striatal circuits support post-stroke sensorimotor outcomes.

Introduction: Activities of daily living (ADLs) are performed through a sequence of fundamental units of motion, called primitives. We previously observed that during ADLs, one upper extremity (UE) may engage two objects simultaneously, such as turning on a faucet while holding a toothbrush. These dual-object primitives (DOPs) may demand increased neural resources, as they likely entail the simultaneous execution of two motor plans. Skilled movement by the nondominant healthy UE or the paretic UE has also been found to require increased neural activity. We posited that performance of DOPs would exceed the neural resources available to the nondominant or paretic side, reducing their performance on these sides. We also predicted that the frequency of DOP performance by the paretic UE would relate to its degree of motor impairment.
Method(s): We studied 19 right-hand dominant healthy subjects (10M:9F; 62.0 +/- 13.6 years) and 43 premorbidly right-hand dominant stroke subjects (23M:20F; 24L:19R paretic; 57.5 +/- 14.5 years; 5.7 +/- 6.5 years post stroke). We evaluated subjects on the UE Fugl-Meyer Assessment (FMA) and videotaped their performance of a feeding and toothbrushing task. We analyzed the videos to extract the incidence and count of DOP performance by each UE. To control for dominance and paresis, we normalized DOP counts to the total number of primitives performed by the UE. We used two-tailed Fisher's Exact tests to compare the incidence of DOPs performed by each UE, and Spearman's correlation to examine the relationship between FMA score and DOP frequency.
Result(s): In healthy subjects, the incidence of DOPs was lower on the nondominant than dominant side (12/19 vs. 19/19; p<0.01). In stroke subjects, the incidence of DOPs was lower on the paretic than nonparetic side (19/43 vs. 43/43; p<0.01). The laterality of paresis did not affect whether that UE would perform DOPs (11/19 dominant paretic vs. 8/24 nondominant paretic; p=0.132). In stroke subjects, lower FMA scores were related to a lower frequency of DOP performance on their paretic UE (rho=0.368, p=0.015).
Discussion(s): Our results suggest that UE laterality and impairment may impact DOP performance in healthy and stroke subjects, respectively. DOPs were less commonly performed by the nondominant UE and the paretic UE, and worse impairment was associated with lower DOP performance. We speculate that engaging two objects simultaneously requires additional neural resources that are unavailable to the nondominant or injured motor network. It is conceivable that the return of DOP performance by the paretic UE may track with the availability of a recovered neural substrate.

BACKGROUND AND PURPOSE/OBJECTIVE:The brain stem is a complex configuration of small nuclei and pathways for motor, sensory, and autonomic control that are essential for life, yet internal brain stem anatomy is difficult to characterize in living subjects. We hypothesized that the 3D fast gray matter acquisition T1 inversion recovery sequence, which uses a short inversion time to suppress signal from white matter, could improve contrast resolution of brain stem pathways and nuclei with 3T MR imaging. MATERIALS AND METHODS/METHODS:-space to reduce motion; total scan time = 58 minutes). One subject returned for an additional 5-average study that was combined with a previous session to create a highest quality atlas for anatomic assignments. A 1-mm isotropic resolution, 12-minute version, proved successful in a patient with a prior infarct. RESULTS:The fast gray matter acquisition T1 inversion recovery sequence generated excellent contrast resolution of small brain stem pathways in all 3 planes for all 10 subjects. Several nuclei could be resolved directly by image contrast alone or indirectly located due to bordering visualized structures (eg, locus coeruleus and pedunculopontine nucleus). CONCLUSIONS:The fast gray matter acquisition T1 inversion recovery sequence has the potential to provide imaging correlates to clinical conditions that affect the brain stem, improve neurosurgical navigation, validate diffusion tractography of the brain stem, and generate a 3D atlas for automatic parcellation of specific brain stem structures.

Background. After stroke, recovery of movement in proximal and distal upper extremity (UE) muscles appears to follow different time courses, suggesting differences in their neural substrates. Objective. We sought to determine if presence or absence of motor evoked potentials (MEPs) differentially influences recovery of volitional contraction and strength in an arm muscle versus an intrinsic hand muscle. We also related MEP status to recovery of proximal and distal interjoint coordination and movement fractionation, as measured by the Fugl-Meyer Assessment (FMA). Methods. In 45 subjects in the year following ischemic stroke, we tracked the relationship between corticospinal tract (CST) integrity and behavioral recovery in the biceps (BIC) and first dorsal interosseous (FDI) muscle. We used transcranial magnetic stimulation to probe CST integrity, indicated by MEPs, in BIC and FDI. We used electromyography, dynamometry, and UE FMA subscores to assess muscle-specific contraction, strength, and inter-joint coordination, respectively. Results. Presence of MEPs resulted in higher likelihood of muscle contraction, greater strength, and higher FMA scores. Without MEPs, BICs could more often volitionally contract, were less weak, and had steeper strength recovery curves than FDIs; in contrast, FMA recovery curves plateaued below normal levels for both the arm and hand. Conclusions. There are shared and separate substrates for paretic UE recovery. CST integrity is necessary for interjoint coordination in both segments and for overall recovery. In its absence, alternative pathways may assist recovery of volitional contraction and strength, particularly in BIC. These findings suggest that more targeted approaches might be needed to optimize UE recovery.

Introduction: Rehabilitation training after stroke commonly focuses on practicing activities of daily living (ADLs), comprised of functional movements and, more fundamentally, functional primitives. Animal models have demonstrated extensive motor recovery if many functional movements are trained early after stroke. In humans, the optimal rehabilitation dose to maximize recovery is not known, in part because a tool to precisely but pragmatically measure rehabilitation does not currently exist. We are building a measurement tool that can objectively decompose ADLs into their constituent primitives. We report here developments in the first important step of building this tool-the automatic identification of functional primitives that constitute various ADLs.
Method(s): 32 stroke subjects (gender: 18F/14M; paretic side: 14R/18L; age: 56.2 +/- 13.54 years; time since stroke: 6.7 +/- 7.57 years; mean FuglMeyer score: 44.21 +/- 14.26) performed a battery of 9 ADLs in an inpatient gym. Participants wore 9 inertial measurement units (IMUs) on their cervical spine, thoracic spine, pelvis, and bilateral hands, forearms, and arms. The IMU system generated linear accelerations, orientations, quaternions, and joint angles at 100 Hz. Human coders used synchronously recorded video to segment each activity into its constituent primitives: reach, transport, stabilize, reposition, and idle. This segmentation step also assigned primitive labels to the IMU data. Using labeled IMU data, we trained a sequence-to-sequence convolutional neural network (CNN) in 21 subjects and tested it in 11 subjects. Subjects were chosen randomly and were balanced for paretic side. The model had 14 convolutional layers with batch normalization between each layer to reduce the covariate shift. Data windows of 1 s (with a slide of 0.25 s) were fed into the CNN. Using a softmax activation function, the final layer of the CNN generated the probability of the data sample being each primitive. The winning probability was chosen as the label name. To measure the classification accuracy (positive predictive value, PPV) of the approach, we compared the CNNgenerated label against the human-generated label for all data windows.
Result(s): Our approach had an average classification accuracy of 64% for identifying the five primitives. Its lowest accuracy was in identifying reaches (PPV 37%), which were commonly confused with transports. It was moderately accurate in identifying repositions (PPV 46%), which were also confused with transports. The approach performed well in identifying idles (PPV 67%), stabilizations (PPV 62%), and transports (PPV 60%).
Discussion(s): We present a novel approach for classifying functional primitives embedded in ADLs, an important step toward dose quantitation in rehabilitation. Though classification performance was modest, the approach performs well above chance (PPV 20%), affirming its plausibility for use in stroke patients. Future work will test other deep network architectures and data augmentation techniques to improve classification performance

Background: Functional upper extremity (UE) motion enables humans to execute activities of daily living (ADLs). There currently exists no universal language to systematically characterize this type of motion or its fundamental building blocks, called functional primitives. Without a standardized classification approach, pooling mechanistic knowledge and unpacking rehabilitation content will remain challenging. Methods: We created a taxonomy to characterize functional UE motions occurring during ADLs, classifying them by motion presence, temporal cyclicity, upper body effector, and contact type. We identified five functional primitives by their phenotype and purpose: reach, reposition, transport, stabilize, and idle. The taxonomy was assessed for its validity and interrater reliability in right-paretic chronic stroke patients performing a selection of ADL tasks. We applied the taxonomy to identify the primitive content and motion characteristics of these tasks, and to evaluate the influence of impairment level on these outcomes. Results: The taxonomy could account for all motions in the sampled activities. Interrater reliability was high for primitive identification (Cohen's kappa = 0.95-0.99). Using the taxonomy, the ADL tasks were found to be composed primarily of transport and stabilize primitives mainly executed with discrete, proximal motions. Compared to mildly impaired patients, moderately impaired patients used more repeated reaches and axial-proximal UE motion to execute the tasks. Conclusions: The proposed taxonomy yields objective, quantitative data on human functional UE motion. This new method could facilitate the decomposition and quantification of UE rehabilitation, the characterization of functional abnormality after stroke, and the mechanistic examination of shared behavior in motor studies.

Recent advances in wearable sensor technology and machine learning (ML) have allowed for the seamless and objective study of human motion in clinical applications, including Parkinson's disease, and stroke. Using ML to identify salient patterns in sensor data has the potential for widespread application in neurological disorders, so understanding how to develop this approach for one's area of inquiry is vital. We previously proposed an approach that combined wearable inertial measurement units (IMUs) and ML to classify motions made by stroke patients. However, our approach had computational and practical limitations. We address these limitations here in the form of a primer, presenting how to optimize a sensor-ML approach for clinical implementation. First, we demonstrate how to identify the ML algorithm that maximizes classification performance and pragmatic implementation. Second, we demonstrate how to identify the motion capture approach that maximizes classification performance but reduces cost. We used previously collected motion data from chronic stroke patients wearing off-the-shelf IMUs during a rehabilitation-like activity. To identify the optimal ML algorithm, we compared the classification performance, computational complexity, and tuning requirements of four off-the-shelf algorithms. To identify the optimal motion capture approach, we compared the classification performance of various sensor configurations (number and location on the body) and sensor type (IMUs vs. accelerometers). Of the algorithms tested, linear discriminant analysis had the highest classification performance, low computational complexity, and modest tuning requirements. Of the sensor configurations tested, seven sensors on the paretic arm and trunk led to the highest classification performance, and IMUs outperformed accelerometers. Overall, we present a refined sensor-ML approach that maximizes both classification performance and pragmatic implementation. In addition, with this primer, we showcase important considerations for appraising off-the-shelf algorithms and sensors for quantitative motion assessment.

PURPOSE OF REVIEW/OBJECTIVE:Repetitive transcranial magnetic stimulation (rTMS) noninvasively modulates brain excitability in humans and influences mediators of plasticity in animals. When applied in humans in the months to years after stroke, potentiation of motor recovery has been limited. Recently, investigators have shifted rTMS administration into the early weeks following stroke, when injury-induced plasticity could be maximally engaged. This article provides an overview of basic mechanisms of rTMS, consideration of its interaction with various forms of neuroplasticity, and a summary of the highest quality clinical evidence for rTMS given early after stroke. RECENT FINDINGS/RESULTS:Studies of repetitive magnetic stimulation in vitro and in vivo have found modulation of excitatory and inhibitory neurotransmission and induction of cellular mechanisms supporting plasticity. A handful of clinical studies have shown sustained improvements in grip strength and UE motor impairment when rTMS is delivered in the first weeks after stroke. Though in its infancy, recent research suggests a plasticity-enhancing influence and modest motor recovery potentiation when rTMS is delivered early after stroke.

Following a stroke, mirror movements are unintended movements that appear in the non-paretic hand when the paretic hand voluntarily moves. Mirror movements have previously been linked to overactivation of sensorimotor areas in the non-lesioned hemisphere. In this study, we hypothesized that mirror movements might instead have a subcortical origin, and are the by-product of subcortical motor pathways upregulating their contributions to the paretic hand. To test this idea, we first characterized the time course of mirroring in 53 first-time stroke patients, and compared it to the time course of activities in sensorimotor areas of the lesioned and non-lesioned hemispheres (measured using functional MRI). Mirroring in the non-paretic hand was exaggerated early after stroke (Week 2), but progressively diminished over the year with a time course that parallelled individuation deficits in the paretic hand. We found no evidence of cortical overactivation that could explain the time course changes in behaviour, contrary to the cortical model of mirroring. Consistent with a subcortical origin of mirroring, we predicted that subcortical contributions should broadly recruit fingers in the non-paretic hand, reflecting the limited capacity of subcortical pathways in providing individuated finger control. We therefore characterized finger recruitment patterns in the non-paretic hand during mirroring. During mirroring, non-paretic fingers were broadly recruited, with mirrored forces in homologous fingers being only slightly larger (1.76 times) than those in non-homologous fingers. Throughout recovery, the pattern of finger recruitment during mirroring for patients looked like a scaled version of the corresponding control mirroring pattern, suggesting that the system that is responsible for mirroring in controls is upregulated after stroke. Together, our results suggest that post-stroke mirror movements in the non-paretic hand, like enslaved movements in the paretic hand, are caused by the upregulation of a bilaterally organized subcortical system.