Faculty Bibliography

Introduction: Quantifying upper extremity (UE) motor impairment after stroke is impractical, limiting our ability to tailor rehabilitation training in real time. The current gold-standard measure of impairment, the Fugl-Meyer Assessment (FMA), is time-consuming and requires a trained assessor. The FMA furthermore does not assess functional motions in real-world contexts, which is exactly where we aim our rehabilitation interventions. Here, we took initial steps to develop an approach to automatically quantify UE motor impairment during functional task performance.
Method(s): We studied 51 chronic stroke patients (28F:23M; 57.7 (21.3-84.3) years old; 28L:23R paretic; FMA 43.1 (8-65)).We recorded upper body motion with 9 inertial measurement units (IMUs) while patients performed the FMA and a functional task (moving an object on a horizontal 8-target array). We trained a long short-term memory (LSTM) deep learning model to estimate FMA scores from the recorded motion (training set n=40; test set n=11; 4 LSTM layers with between-layer batch normalization; IMU data windows of 4s with slide of 1s). LSTM-generated impairment scores were computed from FMA motions or from functional motions. To ascertain the accuracy of the approach, we calculated the root mean square error (RMSE) and the Spearman correlation coefficient (rho) between the LSTM scores and the FMA scores from a trained expert. We also examined whether the performance of particular classes of functional primitives (i.e. reach, transport, or reposition) would be sufficient to accurately estimate impairment.
Result(s): Using motions from the FMA performance, our approach estimated FMA scores within 1.1 points of a trained assessor. Using motions from the functional task performance, our approach estimated FMA scores within 1.6 points. Correlation values between the FMA scores and LSTM scores were rho = 0.98 for FMA motions and rho = 0.96 for functional motions. Among the three functional primitives, reaches were the most informative for estimating the impairment scores (RMSE: 1.9 points), followed by transports (RMSE: 2.1 points), and repositions (RMSE: 2.8 points).
Discussion(s): We present a new approach that uses sensor-based motion capture and deep learning to automatically estimate UE motor impairment. This approach has high accuracy and shows high concurrent validity with the FMA, even when it assesses unrelated functional motions. Thus, it may be possible to directly measure impairment from performance of real-world functional tasks, which the FMA does not offer. Estimating impairment during stroke rehabilitation would enable clinicians to tailor treatment strategy in real time.

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.

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.

Background: The corticoreticulospinal tract (CReST) is a major descending motor pathway in humans, but little is known about its relative innervation of proximal versus distal upper extremity (UE) muscles. In addition, CReST is believed to reorganize after corticospinal injury, but changes in its projections to different paretic muscles remain unknown. Here, we used transcranial magnetic stimulation (TMS) to probe the functional connectivity of the contralesional CReST to an arm muscle (biceps (BIC)) and an intrinsic hand muscle (first dorsal interosseous (FDI)) in healthy and stroke subjects.
Method(s): In this cross-sectional observational study, we examined 15 healthy (F: 7; mean age: 54 (44-81) years; mean UE Fugl-Meyer Assessment (FMA) score: 65 (63-66)) and 16 chronic stroke subjects (F: 10; mean age 62 (44-85) years; mean UE FMA score: 49 (23-64); mean time since stroke: 5 (0.5-14.4) years). We applied TMS to the contralesional hemisphere (assigned in healthy subjects) to elicit ipsilateral motor evoked potentials (iMEPs). We measured contralesional CReST functional connectivity (iMEP presence/absence) and projection strength (iMEP size; mV*ms) to the paretic BIC and FDI. We also measured paretic muscle maximum voluntary contraction and segmental FMA subscores. We examined differences in CReST projections between muscles and subject groups using Fisher's exact tests and general linear mixed models, and examined neurophysiologicalbehavioral relationships with Pearson's and Spearman's correlations.
Result(s): The contralesional CReST made functional connections to both muscles of most subjects (iMEP presence/absence: healthy BIC 14/1, healthy FDI 15/0; stroke BIC 11/5, stroke FDI 15/1). CReST functional connectivity did not differ between muscles in either healthy or stroke subjects (all p>0.172), and did not differ between subject groups for either muscle (all p=1.0). However, CReST projection strength for the muscles diverged between subject groups, manifesting as larger iMEPs in FDIs than BICs in healthy subjects (1.9 mV*ms, p=0.042) and larger iMEPs in BICs than FDIs in stroke subjects (1.0 mV*ms, p=0.042). Muscle iMEP sizes did not significantly differ between healthy and stroke subjects. Muscle strength related to iMEP size in only the paretic BIC of stroke subjects (r(6)=0.853, p=0.007). There was no relationship between FMA subscores and iMEP size for either muscle in either subject group.
Conclusion(s): Our findings indicate that the contralesional CReST has readily identifiable connections to the paretic BIC and FDI. In healthy subjects, the identification of a stronger CReST projection strength to the FDI challenges the notion of a proximal innervation bias by the reticulospinal tract. The shift in projection strength to the BIC after stroke reinforces the concept that the CReST reorganizes after CST injury, with circumscribed behavioral relevance. To confirm a recovery role of the CReST, a longitudinal observation of recovering behavior relating to changing CReST neurophysiology is required.

Introduction: With the corticospinal tract (CST), the corticoreticulospinal tract (CReST) is a major descending motor pathway with widespread bilateral innervation. In animals, CST damage causes a loss of motor control and prompts reorganization in the CReST, possibly with stronger connectivity to arm flexors (e.g. biceps (BIC)) than finger abductors (e.g. first dorsal interosseous (FDI)). CReST reorganization may also contribute to widespread muscle co-activations (i.e. abnormal synergy expression) in the paretic upper extremity (UE). Here, we posited that CReST reorganization after stroke targets the BIC more than the FDI in humans. We predicted that CReST activity, manifesting as abnormal synergy expression, would be more strongly evoked by skilled arm flexion than finger abduction in stroke patients.
Method(s): We studied the paretic UE of 14 chronic stroke patients (F: 8; mean age: 64 (44-85) years; mean post-stroke time: 5 (0.5-14.4) years) and the matched UE of 14 healthy controls (F: 6; mean age: 55 (36-81) years). Subjects used their arm or index finger to move an onscreen cursor through an arc-shaped channel while the remainder of the UE was restrained.We recorded effector kinematics with an infrared camera and electromyographic (EMG) signals from triceps (TRI), deltoid (DLT), BIC, extensor digitorum, flexor carpi radialis (FCR), flexor digitorum superficialis (FDS), and FDI. To quantify movement error, we calculated the average radial distance between the cursor path and the outer channel edge. To quantify abnormal muscle synergies, we applied a non-negative matrix factorization algorithm to the EMG data to identify muscle synergies and calculated the similarity of the synergy vectors between patients and controls; higher similarity scores indicate more normal synergy patterns. We calculated muscle co-activations using correlations between EMG signals of each muscle-pair. We examined group differences with independent t-tests and control-synergy relationships with correlations.
Result(s): Movement errors were higher in patients than controls for the arm (p<0.01) and trended higher for the finger (p=0.074). In the arm, movement errors were inversely related to synergy similarity scores (p<0.01). Higher errors also related to greater FDI-FCR, BIC-TRI, BIC-DLT, and TRI-DLT coactivation (all p<0.05). In the finger, movement errors were unrelated to synergy similarity scores. Lower movement errors related to greater FDSTRI co-activation (p<0.05).
Discussion(s): In the arm, we found that as motor control worsened, the expression of abnormal synergies increased, indicating that CReST activation may increase with loss of CST function. Muscle co-activation was widespread in the UE, in keeping with CReST's multilevel spinal branching. We did not find a relationship between motor control and synergy expression with finger movement, although the long-range co-contraction between the FDS and TRI may speak to a CST-driven stabilizing strategy. Our findings strengthen the notion that CReST reorganization after stroke may preferentially target the arm flexor and its synergies.

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.

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.