Design of the WHIP-PD study: a phase II, twelve-month, dual-site, randomized controlled trial evaluating the effects of a cognitive-behavioral approach for promoting enhanced walking activity using mobile health technology in people with Parkinson-disease.

BACKGROUND: Parkinson disease (PD) is a debilitating and chronic neurodegenerative disease resulting in ambulation difficulties. Natural walking activity often declines early in disease progression despite the relative stability of motor impairments. In this study, we propose a paradigm shift with a "connected behavioral approach" that targets real-world walking using cognitive-behavioral training and mobile health (mHealth) technology. METHODS/DESIGN: The Walking and mHealth to Increase Participation in Parkinson Disease (WHIP-PD) study is a twelve-month, dual site, two-arm, randomized controlled trial recruiting 148 participants with early to mid-stage PD. Participants will be randomly assigned to connected behavioral or active control conditions. Both conditions will include a customized program of goal-oriented walking, walking-enhancing strengthening exercises, and eight in-person visits with a physical therapist. Participants in the connected behavioral condition also will (1) receive cognitive-behavioral training to promote self-efficacy for routine walking behavior and (2) use a mHealth software application to manage their program and communicate remotely with their physical therapist. Active control participants will receive no cognitive-behavioral training and manage their program on paper. Evaluations will occur at baseline, three-, six-, and twelve-months and include walking assessments, self-efficacy questionnaires, and seven days of activity monitoring. Primary outcomes will include the change between baseline and twelve months in overall amount of walking activity (mean number of steps per day) and amount of moderate intensity walking activity (mean number of minutes per day in which > 100 steps were accumulated). Secondary outcomes will include change in walking capacity as measured by the six-minute walk test and ten-meter walk test. We also will examine if self-efficacy mediates change in amount of walking activity and if change in amount of walking activity mediates change in walking capacity. DISCUSSION: We expect this study to show the connected behavioral approach will be more effective than the active control condition in increasing the amount and intensity of real-world walking activity and improving walking capacity. Determining effective physical activity interventions for persons with PD is important for preserving mobility and essential for maintaining quality of life. Clinical trials registration NCT03517371, May 7, 2018. TRIAL REGISTRATION: ClinicalTrials.gov: NCT03517371. Date of registration: May 7, 2018. Protocol version: Original.

A Single, Continuously Applied Control Policy for Modeling Reaching Movements with and without Perturbation.

It has been debated whether kinematic features, such as the number of peaks or decomposed submovements in a velocity profile, indicate the number of discrete motor impulses or result from a continuous control process. The debate is particularly relevant for tasks involving target perturbation, which can alter movement kinematics. To simulate such tasks, finite-horizon models require two preset movement durations to compute two control policies before and after the perturbation. Another model employs infinite- and finite-horizon formulations to determine, respectively, movement durations and control policies, which are updated every time step. We adopted an infinite-horizon optimal feedback control model that, unlike previous approaches, does not preset movement durations or use multiple control policies. It contains both control-dependent and independent noises in system dynamics, state-dependent and independent noises in sensory feedbacks, and different delays and noise levels for visual and proprioceptive feedbacks. We analytically derived an optimal solution that can be applied continuously to move an effector toward a target regardless of whether, when, or where the target jumps. This single policy produces different numbers of peaks and "submovements" in velocity profiles for different conditions and trials. Movements that are slower or perturbed later appear to have more submovements. The model is also consistent with the observation that subjects can perform the perturbation task even without detecting the target jump or seeing their hands during reaching. Finally, because the model incorporates Weber's law via a state representation relative to the target, it explains why initial and terminal visual feedback are, respectively, less and more effective in improving end-point accuracy. Our work suggests that the number of peaks or submovements in a velocity profile does not necessarily reflect the number of motor impulses and that the difference between initial and terminal feedback does not necessarily imply a transition between open- and closed-loop strategies.

Persistent residual errors in motor adaptation tasks: reversion to baseline and exploratory escape.

When movements are perturbed in adaptation tasks, humans and other animals show incomplete compensation, tolerating small but sustained residual errors that persist despite repeated trials. State-space models explain this residual asymptotic error as interplay between learning from error and reversion to baseline, a form of forgetting. Previous work using zero-error-clamp trials has shown that reversion to baseline is not obligatory and can be overcome by manipulating feedback. We posited that novel error-clamp trials, in which feedback is constrained but has nonzero error and variance, might serve as a contextual cue for recruitment of other learning mechanisms that would then close the residual error. When error clamps were nonzero and had zero variance, human subjects changed their learning policy, using exploration in response to the residual error, despite their willingness to sustain such an error during the training block. In contrast, when the distribution of feedback in clamp trials was naturalistic, with persistent mean error but also with variance, a state-space model accounted for behavior in clamps, even in the absence of task success. Therefore, when the distribution of errors matched those during training, state-space models captured behavior during both adaptation and error-clamp trials because error-based learning dominated; when the distribution of feedback was altered, other forms of learning were triggered that did not follow the state-space model dynamics exhibited during training. The residual error during adaptation appears attributable to an error-dependent learning process that has the property of reversion toward baseline and that can suppress other forms of learning.

Glucocerebrosidase activity in Parkinson's disease with and without GBA mutations.

Glucocerebrosidase (GBA) mutations have been associated with Parkinson's disease in numerous studies. However, it is unknown whether the increased risk of Parkinson's disease in GBA carriers is due to a loss of glucocerebrosidase enzymatic activity. We measured glucocerebrosidase enzymatic activity in dried blood spots in patients with Parkinson's disease (n = 517) and controls (n = 252) with and without GBA mutations. Participants were recruited from Columbia University, New York, and fully sequenced for GBA mutations and genotyped for the LRRK2 G2019S mutation, the most common autosomal dominant mutation in the Ashkenazi Jewish population. Glucocerebrosidase enzymatic activity in dried blood spots was measured by a mass spectrometry-based assay and compared among participants categorized by GBA mutation status and Parkinson's disease diagnosis. Parkinson's disease patients were more likely than controls to carry the LRRK2 G2019S mutation (n = 39, 7.5% versus n = 2, 0.8%, P < 0.001) and GBA mutations or variants (seven homozygotes and compound heterozygotes and 81 heterozygotes, 17.0% versus 17 heterozygotes, 6.7%, P < 0.001). GBA homozygotes/compound heterozygotes had lower enzymatic activity than GBA heterozygotes (0.85 µmol/l/h versus 7.88 µmol/l/h, P < 0.001), and GBA heterozygotes had lower enzymatic activity than GBA and LRRK2 non-carriers (7.88 µmol/l/h versus 11.93 µmol/l/h, P < 0.001). Glucocerebrosidase activity was reduced in heterozygotes compared to non-carriers when each mutation was compared independently (N370S, P < 0.001; L444P, P < 0.001; 84GG, P = 0.003; R496H, P = 0.018) and also reduced in GBA variants associated with Parkinson's risk but not with Gaucher disease (E326K, P = 0.009; T369M, P < 0.001). When all patients with Parkinson's disease were considered, they had lower mean glucocerebrosidase enzymatic activity than controls (11.14 µmol/l/h versus 11.85 µmol/l/h, P = 0.011). Difference compared to controls persisted in patients with idiopathic Parkinson's disease (after exclusion of all GBA and LRRK2 carriers; 11.53 µmol/l/h, versus 12.11 µmol/l/h, P = 0.036) and after adjustment for age and gender (P = 0.012). Interestingly, LRRK2 G2019S carriers (n = 36), most of whom had Parkinson's disease, had higher enzymatic activity than non-carriers (13.69 µmol/l/h versus 11.93 µmol/l/h, P = 0.002). In patients with idiopathic Parkinson's, higher glucocerebrosidase enzymatic activity was associated with longer disease duration (P = 0.002) in adjusted models, suggesting a milder disease course. We conclude that lower glucocerebrosidase enzymatic activity is strongly associated with GBA mutations, and modestly with idiopathic Parkinson's disease. The association of lower glucocerebrosidase activity in both GBA mutation carriers and Parkinson's patients without GBA mutations suggests that loss of glucocerebrosidase function contributes to the pathogenesis of Parkinson's disease. High glucocerebrosidase enzymatic activity in LRRK2 G2019S carriers may reflect a distinct pathogenic mechanism. Taken together, these data suggest that glucocerebrosidase enzymatic activity could be a modifiable therapeutic target.

Robotic therapy for chronic stroke: general recovery of impairment or improved task-specific skill?

There is a great need to develop new approaches for rehabilitation of the upper limb after stroke. Robotic therapy is a promising form of neurorehabilitation that can be delivered in higher doses than conventional therapy. Here we sought to determine whether the reported effects of robotic therapy, which have been based on clinical measures of impairment and function, are accompanied by improved motor control. Patients with chronic hemiparesis were trained for 3 wk, 3 days a week, with titrated assistive robotic therapy in two and three dimensions. Motor control improvements (i.e., skill) in both arms were assessed with a separate untrained visually guided reaching task. We devised a novel PCA-based analysis of arm trajectories that is sensitive to changes in the quality of entire movement trajectories without needing to prespecify particular kinematic features. Robotic therapy led to skill improvements in the contralesional arm. These changes were not accompanied by changes in clinical measures of impairment or function. There are two possible interpretations of these results. One is that robotic therapy only leads to small task-specific improvements in motor control via normal skill-learning mechanisms. The other is that kinematic assays are more sensitive than clinical measures to a small general improvement in motor control.

The neural correlates of learned motor acuity.

We recently defined a component of motor skill learning as "motor acuity," quantified as a shift in the speed-accuracy trade-off function for a task. These shifts are primarily driven by reductions in movement variability. To determine the neural correlates of improvement in motor acuity, we devised a motor task compatible with magnetic resonance brain imaging that required subjects to make finely controlled wrist movements under visual guidance. Subjects were imaged on day 1 and day 5 while they performed this task and were trained outside the scanner on intervening days 2, 3, and 4. The potential confound of performance changes between days 1 and 5 was avoided by constraining movement time to a fixed duration. After training, subjects showed a marked increase in success rate and a reduction in trial-by-trial variability for the trained task but not for an untrained control task, without changes in mean trajectory. The decrease in variability for the trained task was associated with increased activation in contralateral primary motor and premotor cortical areas and in ipsilateral cerebellum. A global nonlocalizing multivariate analysis confirmed that learning was associated with increased overall brain activation. We suggest that motor acuity is acquired through increases in the number of neurons recruited in contralateral motor cortical areas and in ipsilateral cerebellum, which could reflect increased signal-to-noise ratio in motor output and improved state estimation for feedback corrections, respectively.

Corticospinal and corticoreticulospinal projections benefit motor behaviors in chronic stroke.

After corticospinal tract (CST) stroke, several motor deficits in the upper extremity (UE) emerge, including diminished muscle strength, motor control, and muscle individuation. Both the ipsilesional CST and contralesional corticoreticulospinal tract (CReST) innervate the paretic UE and may have different innervation patterns for the proximal and distal UE segments. These patterns may underpin distinct pathway relationships to separable motor behaviors. In this cross-sectional study of 15 chronic stroke patients and 28 healthy subjects, we examined two key questions: (1) whether segmental motor behaviors differentially relate to ipsilesional CST and contralesional CReST projection strengths, and (2) whether motor behaviors segmentally differ in the paretic UE. We measured strength, motor control, and muscle individuation in a proximal (biceps, BIC) and distal muscle (first dorsal interosseous, FDI) of the paretic UE. We measured the projection strengths of the ipsilesional CST and contralesional CReST to these muscles using transcranial magnetic stimulation (TMS). Stroke subjects had abnormal motor control and muscle individuation despite strength comparable to healthy subjects. In stroke subjects, stronger ipsilesional CST projections were linked to superior motor control in both UE segments, whereas stronger contralesional CReST projections were linked to superior muscle strength and individuation in both UE segments. Notably, both pathways also shared associations with behaviors in the proximal segment. Motor control deficits were segmentally comparable, but muscle individuation was worse for distal motor performance. These results suggest that each pathway has specialized contributions to chronic motor behaviors but also work together, with varying levels of success in supporting chronic deficits. Key points summary: Individuals with chronic stroke typically have deficits in strength, motor control, and muscle individuation in their paretic upper extremity (UE). It remains unclear how these altered behaviors relate to descending motor pathways and whether they differ by proximal and distal UE segment.In this study, we used transcranial magnetic stimulation (TMS) to examine projection strengths of the ipsilesional corticospinal tract (CST) and contralesional corticoreticulospinal tract (CReST) with respect to quantitated motor behaviors in chronic stroke.We found that stronger ipsilesional CST projections were associated with better motor control in both UE segments, whereas stronger contralesional CReST projections were associated with better strength and individuation in both UE segments. In addition, projections of both pathways shared associations with motor behaviors in the proximal UE segment.We also found that deficits in strength and motor control were comparable across UE segments, but muscle individuation was worse with controlled movement in the distal UE segment.These results suggest that the CST and CReST have specialized contributions to chronic motor behaviors and also work together, although with different degrees of efficacy.

Overcoming motor "forgetting" through reinforcement of learned actions.

The human motor system rapidly adapts to systematic perturbations but the adapted behavior seems to be forgotten equally rapidly. The reason for this forgetting is unclear, as is how to overcome it to promote long-term learning. Here we show that adapted behavior can be stabilized by a period of binary feedback about success and failure in the absence of vector error feedback. We examined the time course of decay after adaptation to a visuomotor rotation through a visual error-clamp condition--trials in which subjects received false visual feedback showing perfect directional performance, regardless of the movements they actually made. Exposure to this error-clamp following initial visuomotor adaptation led to a rapid reversion to baseline behavior. In contrast, exposure to binary feedback after initial adaptation turned the adapted state into a new baseline, to which subjects reverted after transient exposure to another visuomotor rotation. When both binary feedback and vector error were present, some subjects exhibited rapid decay to the original baseline, while others persisted in the new baseline. We propose that learning can be decomposed into two components--a fast-learning, fast-forgetting adaptation process that is sensitive to vector errors and insensitive to task success, and a second process driven by success that learns more slowly but is less susceptible to forgetting. These two learning systems may be recruited to different degrees across individuals. Understanding this competitive balance and exploiting the long-term retention properties of learning through reinforcement is likely to be essential for successful neuro-rehabilitation.

Mutations in SLC20A2 are a major cause of familial idiopathic basal ganglia calcification.

Familial idiopathic basal ganglia calcification (IBGC) or Fahr's disease is a rare neurodegenerative disorder characterized by calcium deposits in the basal ganglia and other brain regions, which is associated with neuropsychiatric and motor symptoms. Familial IBGC is genetically heterogeneous and typically transmitted in an autosomal dominant fashion. We performed a mutational analysis of SLC20A2, the first gene found to cause IBGC, to assess its genetic contribution to familial IBGC. We recruited 218 subjects from 29 IBGC-affected families of varied ancestry and collected medical history, neurological exam, and head CT scans to characterize each patient's disease status. We screened our patient cohort for mutations in SLC20A2. Twelve novel (nonsense, deletions, missense, and splice site) potentially pathogenic variants, one synonymous variant, and one previously reported mutation were identified in 13 families. Variants predicted to be deleterious cosegregated with disease in five families. Three families showed nonsegregation with clinical disease of such variants, but retrospective review of clinical and neuroimaging data strongly suggested previous misclassification. Overall, mutations in SLC20A2 account for as many as 41% of our familial IBGC cases. Our screen in a large series expands the catalog of SLC20A2 mutations identified to date and demonstrates that mutations in SLC20A2 are a major cause of familial IBGC. Non-perfect segregation patterns of predicted deleterious variants highlight the challenges of phenotypic assessment in this condition with highly variable clinical presentation.

Parkinson disease phenotype in Ashkenazi Jews with and without LRRK2 G2019S mutations.

The phenotype of Parkinson's disease (PD) in patients with and without leucine-rich repeat kinase 2 (LRRK2) G2019S mutations reportedly is similar; however, large, uniformly evaluated series are lacking. The objective of this study was to characterize the clinical phenotype of Ashkenazi Jewish (AJ) PD carriers of the LRRK2 G2019S mutation. We studied 553 AJ PD patients, including 65 patients who were previously reported, from three sites (two in New York and one in Tel-Aviv). Glucocerebrosidase (GBA) mutation carriers were excluded. Evaluations included the Montreal Cognitive Assessment (MoCA), the Unified Parkinson's Disease Rating Scale (UPDRS), the Geriatric Depression Scale (GDS) and the Non-Motor Symptoms (NMS) questionnaire. Regression models were constructed to test the association between clinical and demographic features and LRRK2 status (outcome) in 488 newly recruited participants. LRRK2 G2019S carriers (n = 97) and non-carriers (n = 391) were similar in age and age at onset of PD. Carriers had longer disease duration (8.6 years vs. 6.1 years; P < 0.001), were more likely to be women (51.5% vs. 37.9%; P = 0.015), and more often reported first symptoms in the lower extremities (40.0% vs. 19.2%; P < 0.001). In logistic models that were adjusted for age, disease duration, sex, education, and site, carriers were more likely to have lower extremity onset (P < 0.001), postural instability and gait difficulty (PIGD) (P = 0.043), and a persistent levodopa response for >5 years (P = 0.042). Performance on the UPDRS, MoCA, GDS, and NMS did not differ by mutation status. PD in AJ LRRK2 G2019S mutation carriers is similar to idiopathic PD but is characterized by more frequent lower extremity involvement at onset and PIGD without the associated cognitive impairment.

Movement duration, Fitts's law, and an infinite-horizon optimal feedback control model for biological motor systems.

Optimization models explain many aspects of biological goal-directed movements. However, most such models use a finite-horizon formulation, which requires a prefixed movement duration to define a cost function and solve the optimization problem. To predict movement duration, these models have to be run multiple times with different prefixed durations until an appropriate duration is found by trial and error. The constrained minimum time model directly predicts movement duration; however, it does not consider sensory feedback and is thus applicable only to open-loop movements. To address these problems, we analyzed and simulated an infinite-horizon optimal feedback control model, with linear plants, that contains both control-dependent and control-independent noise and optimizes steady-state accuracy and energetic costs per unit time. The model applies the steady-state estimator and controller continuously to guide an effector to, and keep it at, target position. As such, it integrates movement control and posture maintenance without artificially dividing them with a precise, prefixed time boundary. Movement pace is determined by the model parameters, and the duration is an emergent property with trial-to-trial variability. By considering the mean duration, we derived both the log and power forms of Fitts's law as different approximations of the model. Moreover, the model reproduces typically observed velocity profiles and occasional transient overshoots. For unbiased sensory feedback, the effector reaches the target without bias, in contrast to finite-horizon models that systematically undershoot target when energetic cost is considered. Finally, the model does not involve backward and forward sweeps in time, its stability is easily checked, and the same solution applies to movements of different initial conditions and distances. We argue that biological systems could use steady-state solutions as default control mechanisms and might seek additional optimization of transient costs when justified or demanded by task or context.

Using Wearable Sensors to Assess Freezing of Gait in the Real World.

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) that remains difficult to assess. Wearable movement sensors and associated algorithms can be used to quantify FOG in laboratory settings, but the utility of such methods for real world use is unclear. We aimed to determine the suitability of our wearable sensor-based FOG assessment method for real world use by assessing its performance during in-clinic simulated real world activities. Accuracy of the sensor-based method during simulated real-world tasks was calculated using expert rated video as the gold standard. To determine feasibility for unsupervised home use, we also determined correlations between the percent of active time spent freezing (%ATSF) during unsupervised home use and in-clinic activities. Nineteen people with PD and FOG participated in this study. Results from our sensor-based method demonstrated an accuracy above 90% compared to gold-standard expert review during simulated real-world tasks. Additionally, %ATSF from our sensor-based method during unsupervised home use correlated strongly with %ATSF from our sensor-based method during in-clinic simulated real-world activities (ρ = 0.73). Accuracy values and correlation patterns suggest our method may be useful for FOG assessment in the real world.

How is a motor skill learned? Change and invariance at the levels of task success and trajectory control.

The public pays large sums of money to watch skilled motor performance. Notably, however, in recent decades motor skill learning (performance improvement beyond baseline levels) has received less experimental attention than motor adaptation (return to baseline performance in the setting of an external perturbation). Motor skill can be assessed at the levels of task success and movement quality, but the link between these levels remains poorly understood. We devised a motor skill task that required visually guided curved movements of the wrist without a perturbation, and we defined skill learning at the task level as a change in the speed-accuracy trade-off function (SAF). Practice in restricted speed ranges led to a global shift of the SAF. We asked how the SAF shift maps onto changes in trajectory kinematics, to establish a link between task-level performance and fine motor control. Although there were small changes in mean trajectory, improved performance largely consisted of reduction in trial-to-trial variability and increase in movement smoothness. We found evidence for improved feedback control, which could explain the reduction in variability but does not preclude other explanations such as an increased signal-to-noise ratio in cortical representations. Interestingly, submovement structure remained learning invariant. The global generalization of the SAF across a wide range of difficulty suggests that skill for this task is represented in a temporally scalable network. We propose that motor skill acquisition can be characterized as a slow reduction in movement variability, which is distinct from faster model-based learning that reduces systematic error in adaptation paradigms.

Essential tremor with ubiquitinated intranuclear inclusions and cerebellar degeneration.

BACKGROUND: Essential tremor (ET), a progressive, age-associated disease, is one of the most common neurological disorders. Yet until recently, there had been few postmortem examinations so that the full range of pathological changes associated with this disease has not been catalogued. OBJECTIVES: We report a patient with ET who had a pattern of pathological change which to our knowledge has not previously been reported in ET or another neurological disease. METHODS: Clinical-pathological case report. RESULTS: The patient had adult-onset, non-familial, kinetic arm tremor that gradually worsened. Voice and head tremors were also present. The clinical diagnosis was ET. She died at age 102. On postmortem examination, there was severe segmental loss of Purkinje cells, Bergmann gliosis and numerous torpedoes in the cerebellum. The other outstanding change was the presence of neurons in the cerebral cortex and hippocampus that contained an ubiquitinated, nuclear inclusion. These inclusions were not detected in Luxol fast blue/hematoxylin and eosin-stained sections. CONCLUSIONS: This ET patient had a pattern of pathological change that has not been reported previously. This case further reinforces the view that ET is likely to be a heterogeneous family of degenerative diseases whose underlying pathological anatomy involves the cerebellum.

Transductive Learning Models for Accurate Ambulatory Gait Analysis in Elderly Residents of Assisted Living Facilities.

Instrumented footwear represents a promising technology for spatiotemporal gait analysis in out-of-the-lab conditions. However, moderate accuracy impacts this technology's ability to capture subtle, but clinically meaningful, changes in gait patterns that may indicate adverse outcomes or underlying neurological conditions. This limitation hampers the use of instrumented footwear to aid functional assessments and clinical decision making. This paper introduces new transductive-learning inference models that substantially reduce measurement errors relative to conventional data processing techniques, without requiring subject-specific labelled data. The proposed models use subject-optimized input features and hyperparameters to adjust the spatiotemporal gait metrics (i.e., stride time, length, and velocity, swing time, and double support time) obtained with conventional techniques, resulting in computationally simpler models compared to end-to-end machine learning approaches. Model validity and reliability were evaluated against a gold-standard electronic walkway during a clinical gait performance test (6-minute walk test) administered to N = 95 senior residents of assisted living facilities with diverse levels of gait and balance impairments. Average reductions in absolute errors relative to conventional techniques were -42.0% and -33.5% for spatial and gait-phase parameters, respectively, indicating the potential of transductive learning models for improving the accuracy of instrumented footwear for ambulatory gait analysis.

Learning not to generalize: modular adaptation of visuomotor gain.

When a new sensorimotor mapping is learned through practice, learning commonly transfers to unpracticed regions of task space, that is, generalization ensues. Does generalization reflect fixed properties of movement representations in the nervous system and thereby limit what visuomotor mappings can and cannot be learned? Or does what needs to be learned determine the shape of generalization? We used the broad generalization properties of visuomotor gain adaptation to address these questions. Adaptation to a single gain for reaching movements is known to generalize broadly across movement directions. By training subjects on two different gains in two directions, we set up a potential conflict between generalization patterns: if generalization of gain adaptation indicates fixed properties of movement amplitude encoding, then learning two different gains in different directions should not be possible. Conversely, if generalization is flexible, then it should be possible to learn two gains. We found that subjects were able to learn two gains simultaneously, although more slowly than when they adapted to a single gain. Analysis of the resulting double-gain generalization patterns, however, unexpectedly revealed that generalization around each training direction did not arise de novo, but could be explained by a weighted combination of single-gain generalization patterns, in which the weighting takes into account the relative angular separation between training directions. Our findings therefore demonstrate that the mappings to each training target can be fully learned through reweighting of single-gain generalization patterns and not through a categorical alteration of these functions. These results are consistent with a modular decomposition approach to visuomotor adaptation, in which a complex mapping results from a combination of simpler mappings in a "mixture-of-experts" architecture.

Gait Cycle Validation and Segmentation Using Inertial Sensors.

In this paper, we develop an algorithm to automatically validate and segment a gait cycle in real time into three gait events, namely midstance, toe-off, and heel-strike, using inertial sensors. We first use the physical models of sensor data obtained from a foot-mounted inertial system to differentiate stationary and moving segments of the sensor data. Next, we develop an optimization routine called sparsity-assisted wavelet denoising (SAWD), which simultaneously combines linear time invariant filters, orthogonal multiresolution representations such as wavelets, and sparsity-based methods, to generate a sparse template of the moving segments of the gyroscope measurements in the sagittal plane for valid gait cycles. Thereafter, to validate any moving segment as a gait cycle, we compute the root-mean-square error between the generated sparse template and the sparse representation of the moving segment of the gyroscope data in the sagittal plane obtained using SAWD. Finally, we find the local minima for the stationary and moving segments of a valid gait cycle to detect the gait events. We compare our proposed method with existing methods, for a fixed threshold, using real data obtained from three groups, namely controls, participants with Parkinson disease, and geriatric participants. Our proposed method demonstrates an average F1 score of 87.78% across all groups for a fixed sampling rate, and an average F1 score of 92.44% across all Parkinson disease participants for a variable sampling rate.

Generalization of motor learning depends on the history of prior action.

Generalization of motor learning refers to our ability to apply what has been learned in one context to other contexts. When generalization is beneficial, it is termed transfer, and when it is detrimental, it is termed interference. Insight into the mechanism of generalization may be acquired from understanding why training transfers in some contexts but not others. However, identifying relevant contextual cues has proven surprisingly difficult, perhaps because the search has mainly been for cues that are explicit. We hypothesized instead that a relevant contextual cue is an implicit memory of action with a particular body part. To test this hypothesis we considered a task in which participants learned to control motion of a cursor under visuomotor rotation in two contexts: by moving their hand through motion of their shoulder and elbow, or through motion of their wrist. Use of these contextual cues led to three observations: First, in naive participants, learning in the wrist context was much faster than in the arm context. Second, generalization was asymmetric so that arm training benefited subsequent wrist training, but not vice versa. Third, in people who had prior wrist training, generalization from the arm to the wrist was blocked. That is, prior wrist training appeared to prevent both the interference and transfer that subsequent arm training should have caused. To explain the data, we posited that the learner collected statistics of contextual history: all upper arm movements also move the hand, but occasionally we move our hands without moving the upper arm. In a Bayesian framework, history of limb segment use strongly affects parameter uncertainty, which is a measure of the covariance of the contextual cues. This simple Bayesian prior dictated a generalization pattern that largely reproduced all three findings. For motor learning, generalization depends on context, which is determined by the statistics of how we have previously used the various parts of our limbs.

Predicting Cognitive Improvement in Normal Pressure Hydrocephalus Patients Using Preoperative Neuropsychological Testing and Cerebrospinal Fluid Biomarkers.

BACKGROUND: Though it is well known that normal pressure hydrocephalus (NPH) patients can cognitively improve after ventriculoperitoneal shunting (VPS), one of the major dilemmas in NPH is the ability to prospectively predict which patients will improve. OBJECTIVE: To prospectively assess preoperative predictors of postshunt cognitive improvement. METHODS: This was a prospective observational cohort including 52 consecutive patients with approximately 1-yr follow-up. Patients underwent neuropsychological testing at baseline, postlumbar drainage, and postshunt. Cerebrospinal fluid (CSF) biomarkers and cortical biopsies were also collected to examine their relationship with postshunt cognitive improvement. RESULTS: Rey Auditory Verbal Learning Test-L (RAVLT-L) was the only neuropsychological test to demonstrate statistically significant improvement both postlumbar drain and postshunt. Improvement on the RAVLT-L postlumbar drain predicted improvement on the RAVLT-L postshunt. Patients with biopsies demonstrating Aß+ Tau+ had lower ventricular CSF Aß42 and higher lumbar CSF pTau compared to Aß- Tau- patients. A receiver operating curve analysis using lumbar pTau predicted Aß+ Tau+ biopsy status but was not related to neuropsychological test outcome. CONCLUSION: The RAVLT can be a useful preoperative predictor of postoperative cognitive improvement, and thus, we recommend using the RAVLT to evaluate NPH patients. CSF biomarkers could not be related to neuropsychological test outcome. Future research in a larger patient sample will help determine the prospective utility of CSF biomarkers in the evaluation of NPH patients.

Why don't we move faster? Parkinson's disease, movement vigor, and implicit motivation.

People generally select a similar speed for a given motor task, such as reaching for a cup. One well established determinant of movement time is the speed-accuracy trade-off: movement time increases with the accuracy requirement. A second possible determinant is the energetic cost of making a movement. Parkinson's disease (PD), a condition characterized by generalized movement slowing (bradykinesia), provides the opportunity to directly explore this second possibility. We compared reaching movements of patients with PD with those of control subjects in a speed-accuracy trade-off task comprising conditions of increasing difficulty. Subjects completed as many trials as necessary to make 20 movements within a required speed range (trials to criterion, N(c)). Difficulty was reflected in endpoint accuracy and N(c). Patients were as accurate as control subjects in all conditions (i.e., PD did not affect the speed-accuracy trade-off). However, N(c) was consistently higher in patients, indicating reluctance to move fast although accuracy was not compromised. Specifically, the dependence of N(c) on movement energy cost (slope S(N)) was steeper in patients than in control subjects. This difference in S(N) suggests that bradykinesia represents an implicit decision not to move fast because of a shift in the cost/benefit ratio of the energy expenditure needed to move at normal speed. S(N) was less steep, but statistically significant, in control subjects, which demonstrates a role for energetic cost in the normal control of movement speed. We propose that, analogous to the established role of dopamine in explicit reward-seeking behavior, the dopaminergic projection to the striatum provides a signal for implicit "motor motivation."