Parkinson Disease Genetics Extended to African and Hispanic Ancestries in the VA Million Veteran Program.

Background and Objectives: Nearly all genetic analyses of Parkinson disease (PD) have been in populations of European ancestry. We sought to test the ability of a machine learning method to extract accurate PD diagnoses from an electronic medical record (EMR) system, to see whether genetic variants identified in European populations generalize to individuals of African and Hispanic ancestries, and to compare the rates of PD across ancestries. Methods: A machine learning method using natural language processing was applied to EMRs of US veterans participating in the VA Million Veteran Program (MVP) to identify individuals with PD. These putative cases were vetted via blind chart review by a movement disorder specialist. A polygenic risk score (PRS) of 90 established genetic variants whose genotypes were imputed from a customized Axiom Biobank Array was evaluated in different case groups. Results: The EMR prediction scores had a distinct trimodal distribution, with 97% of the high group and only 30% of the middle group having a credible diagnosis of PD. Using the 3,542 cases from the high group matched 4:1 to controls, the PRS was highly predictive in individuals of European ancestry (n = 3,137 cases; OR = 1.82; p = 8.01E-48), and nearly identical effect sizes were seen in individuals of African (n = 184; OR = 2.07; p = 3.4E-4) and Hispanic ancestries (n = 221; OR = 2.13; p = 3.9E-6). The PRS was much less predictive for the 2,757 European ancestry cases who had an ICD code for PD but for whom the machine learning method had a lower confidence in their diagnosis. No novel ancestry-specific genetic variants were identified. Individuals with African ancestry had one-quarter the rate of PD compared with European or Hispanic ancestries aged 60-70 years and one half the rate in the 70-80 years age range. African American cases had a higher proportion of their DNA originating in Europe compared with African American controls. Discussion: Machine learning can reliably classify PD using data from a large EMR. Larger studies of non-European populations are required to confirm the generalizability of PD risk variants identified in populations of European ancestry and the increased risk coming from a higher proportion of European DNA in African Americans.

Using kinematics to re-define the pull test as a quantitative biomarker of the postural response in normal pressure hydrocephalus patients.

Quantitative biomarkers are needed for the diagnosis, monitoring and therapeutic assessment of postural instability in movement disorder patients. The goal of this study was to create a practical, objective measure of postural instability using kinematic measurements of the pull test. Twenty-one patients with normal pressure hydrocephalus and 20 age-matched control subjects were fitted with inertial measurement units and underwent 10-20 pull tests of varying intensities performed by a trained clinician. Kinematic data were extracted for each pull test and aggregated. Patients participated in 103 sessions for a total of 1555 trials while controls participated in 20 sessions for a total of 299 trials. Patients were separated into groups by MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) pull test score. The center of mass velocity profile easily distinguished between patient groups such that score increases correlated with decreases in peak velocity and later peak velocity onset. All patients except those scored as "3" demonstrated an increase in step length and decrease in reaction time with increasing pull intensity. Groups were distinguished by differences in the relationship of step length to pull intensity (slope) and their overall step length or reaction time regardless of pull intensity (y-intercept). NPH patients scored as "normal" on the MDS-UPDRS scale were kinematically indistinguishable from age-matched control subjects during a standardized perturbation, but could be distinguished from controls by their response to a range of pull intensities. An instrumented, purposefully varied pull test produces kinematic metrics useful for distinguishing clinically meaningful differences within hydrocephalus patients as well as distinguishing these patients from healthy, control subjects.

Overcoming Long-Term Variability in Local Field Potentials Using an Adaptive Decoder.

Long-term variability remains one of the major hurdles in using intracortical recordings like local field potentials for brain computer interfaces (BCI). Practical neural decoders need to overcome time instability of neural signals to estimate subject behavior accurately and faithfully over the long term. This paper presents a novel decoder that 1) characterizes each behavioral task (i.e., different movement directions under different force conditions) with multiple neural patterns and 2) adapts to the long-term variations in neural features by identifying the stable neural patterns. This adaptation can be performed in both an unsupervised and a semisupervised learning framework requiring minimal feedback from the user. To achieve generalization over time, the proposed decoder uses redundant sparse regression models that adapt to day-to-day variations in neural patterns. While this update requires no explicit feedback from the BCI user, any feedback (explicit or derived) to the BCI improves its performance. With this adaptive decoder, we investigated the effects of long-term neural modulation especially when subjects encountered new external forces against movement. The proposed decoder predicted eight hand-movement directions with an accuracy of 95% over two weeks (when there was no external forces); and 85% in later acquisition sessions spanning up to 42 days (when the monkeys countered external field forces). Since the decoder can operate with or without manual intervention, it could alleviate user frustration associated with BCI.

Trial-to-trial Adaptation: Parsing out the Roles of Cerebellum and BG in Predictive Motor Timing.

We previously demonstrated that predictive motor timing (i.e., timing requiring visuomotor coordination in anticipation of a future event, such as catching or batting a ball) is impaired in patients with spinocerebellar ataxia (SCA) types 6 and 8 relative to healthy controls. Specifically, SCA patients had difficulties postponing their motor response while estimating the target kinematics. This behavioral difference relied on the activation of both cerebellum and striatum in healthy controls, but not in cerebellar patients, despite both groups activating certain parts of cerebellum during the task. However, the role of these two key structures in the dynamic adaptation of the motor timing to target kinematic properties remained unexplored. In the current paper, we analyzed these data with the aim of characterizing the trial-by-trial changes in brain activation. We found that in healthy controls alone, and in comparison with SCA patients, the activation in bilateral striatum was exclusively associated with past successes and that in the left putamen, with maintaining a successful performance across successive trials. In healthy controls, relative to SCA patients, a larger network was involved in maintaining a successful trial-by-trial strategy; this included cerebellum and fronto-parieto-temporo-occipital regions that are typically part of attentional network and action monitoring. Cerebellum was also part of a network of regions activated when healthy participants postponed their motor response from one trial to the next; SCA patients showed reduced activation relative to healthy controls in both cerebellum and striatum in the same contrast. These findings support the idea that cerebellum and striatum play complementary roles in the trial-by-trial adaptation in predictive motor timing. In addition to expanding our knowledge of brain structures involved in time processing, our results have implications for the understanding of BG disorders, such as Parkinson disease where feedback processing or reward learning is affected.

Dynamic reorganization of neural activity in motor cortex during new sequence production.

Although previous studies have shown that primary motor cortex (M1) neurons are modulated during the performance of a sequence of movements, it is not known how this neural activity in the M1 reorganizes during new learning of sequence-dependent motor skills. Here we trained monkeys to move to each of four spatial targets to produce multiple distinct sequences of movements in which the spatial organization of the targets determined uniquely the serial order of the movements. After the monkeys memorized the sequences, we changed one element of these over-practised sequences and the subjects were required to learn the new sequence through trial and error. When one element in an over-learned four-element sequence was changed, the sequence-specific neural activity was totally disrupted, but relatively minor changes in the direction-specific activity were observed. The data suggest that sequential motor skills are represented within M1 in the context of the complete sequential behavior rather than as a series of single consecutive movements; and sequence-specific neurons in the M1 are involved in new learning of sequence by using memorized knowledge to acquire complex motor skill efficiently.

Proceedings of the workshop on Cerebellum, Basal Ganglia and Cortical Connections Unmasked in Health and Disorder held in Brno, Czech Republic, October 17th, 2013.

The proceedings of the workshop synthesize the experimental, preclinical, and clinical data suggesting that the cerebellum, basal ganglia (BG), and their connections play an important role in pathophysiology of various movement disorders (like Parkinson's disease and atypical parkinsonian syndromes) or neurodevelopmental disorders (like autism). The contributions from individual distinguished speakers cover the neuroanatomical research of complex networks, neuroimaging data showing that the cerebellum and BG are connected to a wide range of other central nervous system structures involved in movement control. Especially, the cerebellum plays a more complex role in how the brain functions than previously thought.

The olivo-cerebellar system as a neural clock.

The cerebellum, and the olivo-cerebellar system in particular, may be the central mechanism of a neural clock that provides a rhythmic neural signal used to time motor and cognitive processes. Several independent lines of evidence support this hypothesis. First, the resting membrane potential of neurons in the inferior olive oscillates at ~10 Hz and the neural input from the olive leads to rhythmic complex spikes in cerebellum Purkinje cells. Second, the repeating modular microstructure of the cerebellum is ideally suited for performing computations underlying a basic neural process such as timing. Third, damage to the cerebellum leads to deficits in the perception of time and in the production of timed movements. Fourth, functional imaging studies in human subjects have shown activation of the inferior olive specifically during time perception. However, additional data on the exact role of rhythmic cerebellar activity during basis motor and sensory processing will be necessary before the hypothesis that the cerebellum is a neural clock is more widely accepted.

Long-term decoding of arm movement using Spatial Distribution of Neural Patterns.

Day to day variability and non-stationarity caused by changes in subject motivation, learning and behavior pose a challenge in using local field potentials (LFP) for practical Brain Computer Interfaces. Pattern recognition algorithms require that the features possess little to no variation from the training to test data. As such models developed on one day fail to represent the characteristics on the other day. This paper provides a solution in the form of adaptive spatial features. We propose an algorithm to capture the local spatial variability of LFP patterns and provide accurate long-term decoding. This algorithm achieved more than 95% decoding of eight movement directions two weeks after its initial training.

Source localization techniques for direction decoding from local field potentials.

Local Field Potential (LFP) recordings are one type of intracortical recordings, (besides Single Unit Activity) that can help decode movement direction successfully. In the longterm however, using LFPs for decoding presents some major challenges like inherent instability and non-stationarity. Our approach to overcome this challenge bases around the hypothesis that each task has a signature source-location pattern. The methodology involves introduction of source localization, and tracking of sources over a period of time that enables us to decode movement direction in an eight-direction center-out-reach-task. We establish that such tracking can be used for long term decoding, with preliminary results indicating consistent patterns. In fact, tracking task related source locations render up to 66% accuracy in decoding movement direction one week after the decoding model was learnt.

Robust movement direction decoders from local field potentials using spatio-temporal qualitative patterns.

A major drawback of using Local Field Potentials (LFP) for Brain Computer Interface (BCI) is their inherent instability and non-stationarity. Specifically, even when a well-trained subject performs the same task over a period of time, the neural data observed are unstable. To overcome this problem in decoding movement direction, this paper proposes the use of qualitative information in the form of spatial patterns of inter-channel ranking of multi-channel LFP recordings. The quality of the decoding was further refined by concentrating on the statistical distributions of the top powered channels. Decoding of movement direction was performed using Support Vector Machines (SVM) to construct decoders, instead of the traditional spatial patterns. Our algorithm provides a decoding power of up to 74% on average over a period of two weeks, compared with the state-of-the-art methods in the literature that yield only 33%. Furthermore, it provides 62.5% direction decoding in novel motor environments, compared with 29.5% with conventional methods. Finally, a comparison with the traditional methods and other surveyed literature is presented.

Role of olivocerebellar system in timing without awareness.

The timing of events can be implicit or without awareness yet critical for task performance. However, the neural correlates of implicit timing are unknown. One system that has long been implicated in event timing is the olivocerebellar system, which originates exclusively from the inferior olive. By using event-related functional MRI in human subjects and a specially designed behavioral task, we examined the effect of the subjects' awareness of changes in stimulus timing on the olivocerebellar system response. Subjects were scanned while observing changes in stimulus timing that were presented near each subject's detection threshold such that subjects were aware of such changes in only approximately half the trials. The inferior olive and multiple areas within the cerebellar cortex showed a robust response to time changes regardless of whether the subjects were aware of these changes. Our findings provide support to the proposed role of the olivocerebellar system in encoding temporal information and further suggest that this system can operate independently of awareness and mediate implicit timing in a multitude of perceptual and motor operations, including classical conditioning and implicit learning.

A subspace approach to learning recurrent features from brain activity.

This paper introduces a novel technique to address the instability and time variability challenges associated with brain activity recorded on different days. A critical challenge when working with brain signal activity is the variability in their characteristics when the signals are collected in different sessions separated by a day or more. Such variability is due to the acute and chronic responses of the brain tissue after implantation, variations as the subject learns to optimize performance, physiological changes in a subject due to prior activity or rest periods and environmental conditions. We propose a novel approach to tackle signal variability by focusing on learning subspaces which are recurrent over time. Furthermore, we illustrate how we can use projections on those subspaces to improve classification for an application such as brain-machine interface (BMI). In this paper, we illustrate the merits of finding recurrent subspaces in the context of movement direction decoding using local field potential (LFP). We introduce two methods for using the learned subspaces in movement direction decoding and show a decoding power improvement from 76% to 88% for a particularly unstable subject and consistent decoding across subjects.

The neural substrate of predictive motor timing in spinocerebellar ataxia.

The neural mechanisms involved in motor timing are subcortical, involving mainly cerebellum and basal ganglia. However, the role played by these structures in predictive motor timing is not well understood. Unlike motor timing, which is often tested using rhythm production tasks, predictive motor timing requires visuo-motor coordination in anticipation of a future event, and it is evident in behaviors such as catching a ball or shooting a moving target. We examined the role of the cerebellum and striatum in predictive motor timing in a target interception task in healthy (n = 12) individuals and in subjects (n = 9) with spinocerebellar ataxia types 6 and 8. The performance of the healthy subjects was better than that of the spinocerebellar ataxia. Successful performance in both groups was associated with increased activity in the cerebellum (right dentate nucleus, left uvula (lobule V), and lobule VI), thalamus, and in several cortical areas. The superior performance in the controls was related to activation in thalamus, putamen (lentiform nucleus) and cerebellum (right dentate nucleus and culmen-lobule IV), which were not activated either in the spinocerebellar subjects or within a subgroup of controls who performed poorly. Both the cerebellum and the basal ganglia are necessary for the predictive motor timing. The degeneration of the cerebellum associated with spinocerebellar types 6 and 8 appears to lead to quantitative rather than qualitative deficits in temporal processing. The lack of any areas with greater activity in the spinocerebellar group than in controls suggests that limited functional reorganization occurs in this condition.

Time robust movement direction decoding in Local Field Potentials using channel ranking.

Movement direction for Brain Machine Interface (BMI) can be decoded successfully using Local Field Potentials (LFP) and Single Unit Activity (SUA). A major challenge when dealing with the intra-cortical recordings is to develop decoders that are robust in time. In this paper we present for the first time a technique that uses the qualitative information derived from multiple LFP channels rather than the absolute power of the recorded signals. In this novel method, we use a power based inter-channel ranking system to define the quality of a channel in multi-channel LFP. This representation enables us to bypass the problems associated with the dynamic ranges of absolute power. We also introduce a parameter based ranking system that provides the same rank to channels that have comparable powers. We show that using our algorithms, we can develop models that provide stable decoding of eight movement directions with an average efficiency of above 56% over a period of two weeks. Moreover, the decoding power using this method is 46% at the end of two weeks versus the 13% using the traditional approaches. We also applied these models to decoding movements performed in a force field and again achieved significantly higher decoding power than the existing methods.

Selection of optimal programming contacts based on local field potential recordings from subthalamic nucleus in patients with Parkinson's disease.

BACKGROUND: In the United States, the most commonly used surgical treatment for patients with Parkinson's disease is the implantation of deep brain stimulation (DBS) electrodes within the subthalamic nucleus. However, DBS device programming remains difficult and is a possible source of decreased efficacy. OBJECTIVE: We investigated the relationship between local field potential (LFP) activities in the subthalamic nucleus and the therapeutic response to programming. METHODS: We recorded LFPs with macroelectrodes placed unilaterally for DBS in 4 PD patients, 3 weeks after implantation, before the start of log-term DBS. Power-frequency spectra were calculated for each of 7 possible electrode contacts or contact pairs, over multiple 5- to 10-minute quiet waking epochs and over 30-second epochs during hand movements. Subsequently, DBS devices were programmed, with testing to determine which electrode contacts or contact pairs demonstrated optimal therapeutic efficacy. RESULTS: For each patient, the contact pair found to provide optimal efficacy was associated with the highest energy in the beta (13-32 Hz) and gamma (48-220 Hz) bands during postoperative LFP recordings at rest and during hand movements. Activities in other frequency bands did not show significant correlations between LFP power and optimal electrode contacts. CONCLUSION: Postoperative subband analysis of LFP recordings in beta and gamma frequency ranges may be used to select optimal electrode contacts. These results indicate that LFP recordings from implanted DBS electrodes can provide important clues to guide the optimization of DBS therapy in individual patients.

Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: an EEG and fMRI study of motor imagery and movements.

Similar to the occipital alpha rhythm, electroencephalographic (EEG) signals in the alpha- and beta-frequency bands can be suppressed by movement or motor imagery and have thus been thought to represent the "idling state" of the sensorimotor cortex. A negative correlation between spontaneous alpha EEG and blood-oxygen-level-dependent (BOLD) signals has been reported in combined EEG and fMRI (functional Magnetic Resonance Imaging) experiments when subjects stayed at the resting state or alternated between the resting state and a task. However, the precise nature of the task-induced alpha modulation remains elusive. It was not clear whether alpha/beta rhythm suppressions may co-vary with BOLD when conducting tasks involving varying activations of the cortex. Here, we quantified the task-evoked responses of BOLD and alpha/beta-band power of EEG directly in the cortical source domain, by using source imaging technology, and examined their covariation across task conditions in a mixed block and event-related design. In this study, 13 subjects performed tasks of right-hand, right-foot or left-hand movement and motor imagery when EEG and fMRI data were separately collected. Task-induced increase of BOLD signal and decrease of EEG amplitudes in alpha and beta bands were shown to be co-localized at the somatotopic sensorimotor cortex. At the corresponding regions, the reciprocal changes of the two signals co-varied in the magnitudes across imagination and movement conditions. The spatial correspondence and negative covariation between the two measurements were further shown to exist at somatotopic brain regions associated with different body parts. These results suggest an inverse functional coupling relationship between task-induced changes of BOLD and low-frequency EEG signals.

High accuracy decoding of movement target direction in non-human primates based on common spatial patterns of local field potentials.

BACKGROUND: The current development of brain-machine interface technology is limited, among other factors, by concerns about the long-term stability of single- and multi-unit neural signals. In addition, the understanding of the relation between potentially more stable neural signals, such as local field potentials, and motor behavior is still in its early stages. METHODOLOGY/PRINCIPAL FINDINGS: We tested the hypothesis that spatial correlation patterns of neural data can be used to decode movement target direction. In particular, we examined local field potentials (LFP), which are thought to be more stable over time than single unit activity (SUA). Using LFP recordings from chronically implanted electrodes in the dorsal premotor and primary motor cortex of non-human primates trained to make arm movements in different directions, we made the following observations: (i) it is possible to decode movement target direction with high fidelity from the spatial correlation patterns of neural activity in both primary motor (M1) and dorsal premotor cortex (PMd); (ii) the decoding accuracy of LFP was similar to the decoding accuracy obtained with the set of SUA recorded simultaneously; (iii) directional information varied with the LFP frequency sub-band, being greater in low (0.3-4 Hz) and high (48-200 Hz) frequency bands than in intermediate bands; (iv) the amount of directional information was similar in M1 and PMd; (v) reliable decoding was achieved well in advance of movement onset; and (vi) LFP were relatively stable over a period of one week. CONCLUSIONS/SIGNIFICANCE: The results demonstrate that the spatial correlation patterns of LFP signals can be used to decode movement target direction. This finding suggests that parameters of movement, such as target direction, have a stable spatial distribution within primary motor and dorsal premotor cortex, which may be used for brain-machine interfaces.

Overcoming measurement time variability in brain machine interface.

We introduce a subspace learning approach for multi-channel Local Field Potentials (LFP), and demonstrate its application in movement direction decoding for 8 directions movement. We show that the subspace learning method can effectively address the issue of signal instability across recording sessions by extracting recurrent features from the data. We present results for movement direction decoding, where we trained on two recording sessions, and evaluated decoding performance on a third session. We combine our method with a classifier based on Error-Correcting Output Codes (ECOC) and Common Spatial Patterns (CSP) and found improvement in Decoding Power (DP) from 76% to 88% for a subject known to have strong inter-session variability. Furthermore, we saw an increase from 86% to 90% DP with another subject which exhibited significantly less variability.

Offline decoding of end-point forces using neural ensembles: application to a brain-machine interface.

Brain-machine interfaces (BMIs) hold a lot of promise for restoring some level of motor function to patients with neuronal disease or injury. Current BMI approaches fall into two broad categories--those that decode discrete properties of limb movement (such as movement direction and movement intent) and those that decode continuous variables (such as position and velocity). However, to enable the prosthetic devices to be useful for common everyday tasks, precise control of the forces applied by the end-point of the prosthesis (e.g., the hand) is also essential. Here, we used linear regression and Kalman filter methods to show that neural activity recorded from the motor cortex of the monkey during movements in a force field can be used to decode the end-point forces applied by the subject successfully and with high fidelity. Furthermore, the models exhibit some generalization to novel task conditions. We also demonstrate how the simultaneous prediction of kinematics and kinetics can be easily achieved using the same framework, without any degradation in decoding quality. Our results represent a useful extension of the current BMI technology, making dynamic control of a prosthetic device a distinct possibility in the near future.

Differential effect of reward and punishment on procedural learning.

Reward and punishment are potent modulators of associative learning in instrumental and classical conditioning. However, the effect of reward and punishment on procedural learning is not known. The striatum is known to be an important locus of reward-related neural signals and part of the neural substrate of procedural learning. Here, using an implicit motor learning task, we show that reward leads to enhancement of learning in human subjects, whereas punishment is associated only with improvement in motor performance. Furthermore, these behavioral effects have distinct neural substrates with the learning effect of reward being mediated through the dorsal striatum and the performance effect of punishment through the insula. Our results suggest that reward and punishment engage separate motivational systems with distinctive behavioral effects and neural substrates.