Parkinson's disease patients show impaired corrective grasp control and eye-hand coupling when reaching to grasp virtual objects

Striatal Dopamine Contributions to Skilled Motor Learning

Coordinated multijoint limb and digit movements-"manual dexterity"-underlie both specialized skills (e.g., playing the piano) and more mundane tasks (e.g., tying shoelaces). Impairments in dexterous skill cause significant disability, as occurs with motor cortical injury, Parkinson's disease, and a range of other pathologies. Clinical observations, as well as basic investigations, suggest that corticostriatal circuits play a critical role in learning and performing dexterous skills. Furthermore, dopaminergic signaling in these regions is implicated in synaptic plasticity and motor learning. Nonetheless, the role of striatal dopamine signaling in skilled motor learning remains poorly understood. Here, we use fiber photometry paired with a genetically encoded dopamine sensor to investigate striatal dopamine release in both male and female mice as they learn and perform a skilled reaching task. Dopamine rapidly increases during a skilled reach and peaks near pellet consumption. In the dorsolateral striatum, dopamine dynamics are faster than in the dorsomedial and ventral striatum. Across training, as reaching performance improves, dopamine signaling shifts from pellet consumption to cues that predict pellet availability, particularly in medial and ventral areas of the striatum. Furthermore, performance prediction errors are present across the striatum, with reduced dopamine release after an unsuccessful reach. These findings show that dopamine dynamics during skilled motor behaviors change with learning and are differentially regulated across striatal subregions.

Striatal dopamine contributions to skilled motor learning

Coordinated multi-joint limb and digit movements - "manual dexterity" - underlie both specialized skills (e.g., playing the piano) and more mundane tasks (e.g., tying shoelaces). Impairments in dexterous skill cause significant disability, as occurs with motor cortical injury, Parkinson's Disease, and a range of other pathologies. Clinical observations, as well as basic investigations, suggest that cortico-striatal circuits play a critical role in learning and performing dexterous skills. Furthermore, dopaminergic signaling in these regions is implicated in synaptic plasticity and motor learning. Nonetheless, the role of striatal dopamine signaling in skilled motor learning remains poorly understood. Here, we use fiber photometry paired with a genetically encoded dopamine sensor to investigate striatal dopamine release as mice learn and perform a skilled reaching task. Dopamine rapidly increases during a skilled reach and peaks near pellet consumption. In dorsolateral striatum, dopamine dynamics are faster than in dorsomedial and ventral striatum. Across training, as reaching performance improves, dopamine signaling shifts from pellet consumption to cues that predict pellet availability, particularly in medial and ventral areas of striatum. Furthermore, performance prediction errors are present across the striatum, with reduced dopamine release after an unsuccessful reach. These findings show that dopamine dynamics during skilled motor behaviors change with learning and are differentially regulated across striatal subregions.

The Effects of Subthalamic Nucleus Deep Brain Stimulation and Retention Delay on Memory-Guided Reaching Performance in People with Parkinson's Disease

BACKGROUND

Subthalamic nucleus deep brain stimulation (STN-DBS) improves intensive aspects of movement (velocity) in people with Parkinson's disease (PD) but impairs the more cognitively demanding coordinative aspects of movement (error). We extended these findings by evaluating STN-DBS induced changes in intensive and coordinative aspects of movement during a memory-guided reaching task with varying retention delays.

OBJECTIVE

We evaluated the effect of STN-DBS on motor control during a memory-guided reaching task with short and long retention delays in participants with PD and compared performance to healthy controls (HC).

METHODS

Eleven participants with PD completed the motor section of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS III) and performed a memory-guided reaching task under four different STN-DBS conditions (DBS-OFF, DBS-RIGHT, DBS-LEFT, and DBS-BOTH) and two retention delays (0.5 s and 5 s). An additional 13 HC completed the memory-guided reaching task.

RESULTS

Unilateral and bilateral STN-DBS improved the MDS-UPDRS III scores. In the memory-guided reaching task, both unilateral and bilateral STN-DBS increased the intensive aspects of movement (amplitude and velocity) in the direction toward HC but impaired coordinative aspects of movement (error) away from the HC. Furthermore, movement time was decreased but reaction time was unaffected by STN-DBS. Shorter retention delays increased amplitude and velocity, decreased movement times, and decreased error, but increased reaction times in the participants with PD. There were no interactions between STN-DBS condition and retention delay.

CONCLUSION

STN-DBS may affect cognitive-motor functioning by altering activity throughout cortico-basal ganglia networks and the oscillatory activity subserving them.

Medication adversely impacts visually-guided eye movements in Parkinson's disease

OBJECTIVE

We examined whether previous inconsistent findings about the effect of anti-Parkinsonian medication on visually-guided saccades (VGS) were due to the use of different paradigms, which change the timing of fixation offset and target onset, or different target eccentricities.

METHODS

Thirty-three participants with Parkinson's disease (PD) completed the VGS tasks OFF and ON medication, along with 13 healthy controls. Performance on 3 paradigms (gap, step, and overlap) and 2 target eccentricities was recorded. We used mixed models to determine the effect of medication, paradigm, and target eccentricity on saccade latency, gain, and peak velocity.

RESULTS

First, we confirmed known paradigm effects on latency, and target eccentricity effects on gain and peak velocity in participants with PD. Second, latency was positively associated with OFF medication Movement Disorders Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) motor score in PD. Third, medication prolonged latency for the larger target eccentricity across the 3 paradigms, while decreasing gain and peak velocity in the step paradigm across target eccentricities.

CONCLUSIONS

Medication adversely affected and was not therapeutically beneficial for VGS. Previous inconsistencies may have resulted from chosen target eccentricity.

SIGNIFICANCE

The negative medication effect on VGS may be clinically significant, as many activities in daily life require oculomotor control, inhibitory control, and visually-guided shifts of attention.

Reaching and Grasping Movements in Parkinson's Disease: A Review

Parkinson's disease (PD) is known to affect the brain motor circuits involving the basal ganglia (BG) and to induce, among other signs, general slowness and paucity of movements. In upper limb movements, PD patients show a systematic prolongation of movement duration while maintaining a sufficient level of endpoint accuracy. PD appears to cause impairments not only in movement execution, but also in movement initiation and planning, as revealed by abnormal preparatory activity of motor-related brain areas. Grasping movement is affected as well, particularly in the coordination of the hand aperture with the transport phase. In the last fifty years, numerous behavioral studies attempted to clarify the mechanisms underlying these anomalies, speculating on the plausible role that the BG-thalamo-cortical circuitry may play in normal and pathological motor control. Still, many questions remain open, especially concerning the management of the speed-accuracy tradeoff and the online feedback control. In this review, we summarize the literature results on reaching and grasping in parkinsonian patients. We analyze the relevant hypotheses on the origins of dysfunction, by focusing on the motor control aspects involved in the different movement phases and the corresponding role played by the BG. We conclude with an insight into the innovative stimulation techniques and computational models recently proposed, which might be helpful in further clarifying the mechanisms through which PD affects reaching and grasping movements.

Sensor Network for Analyzing Upper Body Strategies in Parkinson's Disease versus Normative Kinematic Patterns

In rehabilitation, the upper limb function is generally assessed using clinical scales and functional motor tests. Although the Box and Block Test (BBT) is commonly used for its simplicity and ease of execution, it does not provide a quantitative measure of movement quality. This study proposes the integration of an ecological Inertial Measurement Units (IMUs) system for analysis of the upper body kinematics during the execution of a targeted version of BBT, by able-bodied persons with subjects with Parkinson's disease (PD). Joint angle parameters (mean angle and range of execution) and hand trajectory kinematic indices (mean velocity, mean acceleration, and dimensionless jerk) were calculated from the data acquired by a network of seven IMUs. The sensors were applied on the trunk, head, and upper limb in order to characterize the motor strategy used during the execution of BBT. Statistics revealed significant differences (p < 0.05) between the two groups, showing compensatory strategies in subjects with PD. The proposed IMU-based targeted BBT protocol allows to assess the upper limb function during manual dexterity tasks and could be used in the future for assessing the efficacy of rehabilitative treatments.

Visual losses in early-onset and late-onset Parkinson's disease

Patients with Parkinson's disease (PD) manifest visual losses. However, it is not known whether these losses are equivalent in both early-onset (EOPD) and late-onset (LOPD) patients. We evaluated contrast sensitivity and color vision in EOPD and LOPD patients and in age-matched controls. Losses occurred in both patient groups but were more pronounced in EOPD, consistent with the notion that non-motor symptoms are affected by age of symptom onset. More studies of visual function in EOPD and LOPD patients are needed to understand how aging is related to the pathophysiology of non-motor PD symptomatology. This would permit earlier diagnosis and, perhaps, better management of the disease.

Activities of daily living in Parkinson's disease: Time/gender perspective

OBJECTIVE

The objectives of this study were to explore the changes in the activities of daily living (ADL) in persons with Parkinson's disease (pwPD) over time and to investigate possible differences in ADL performance between men and women with PD.

MATERIALS & METHODS

One hundred twenty-nine persons (76 men) with a clinically established PD self-assessed their ADL performance from the time of diagnosis up to 8 years follow-up using the ADL taxonomy. Other demographic and clinical data (motor state, cognition, depression) were also collected and subjected to further analysis.

RESULTS

Nine of 12 domains in the ADL taxonomy showed a change over time (Eating and Drinking [P = .009], Mobility [P < .001], Toilet activities [P = .031], Dressing [P < .001], Personal hygiene [P < .001], Communication [P < .001], Cooking [P = .001], Shopping [P < .001] and Cleaning [P < .001]). In addition to time, two domains, (Shopping [P = .007] and Cleaning [P = .027]) also showed an effect of gender with worse scores in women. The nine ADL domains showing effect of time, showed temporary improvement at 12 months follow-up, most probably due to dopaminergic medication. All nine domains deteriorated at later follow-up.

CONCLUSIONS

As expected, there was deterioration in self-assessed performance in the majority od ADL domains over time. Women assessed their ADLs worse in two domains (Shopping and Cleaning) probably reflecting a general gender-related activity pattern rather than being a PD-specific finding.

Toward Precision Psychiatry: Statistical Platform for the Personalized Characterization of Natural Behaviors

There is a critical need for new analytics to personalize behavioral data analysis across different fields, including kinesiology, sports science, and behavioral neuroscience. Specifically, to better translate and integrate basic research into patient care, we need to radically transform the methods by which we describe and interpret movement data. Here, we show that hidden in the “noise,” smoothed out by averaging movement kinematics data, lies a wealth of information that selectively differentiates neurological and mental disorders such as Parkinson’s disease, deafferentation, autism spectrum disorders, and schizophrenia from typically developing and typically aging controls. In this report, we quantify the continuous forward-and-back pointing movements of participants from a large heterogeneous cohort comprising typical and pathological cases. We empirically estimate the statistical parameters of the probability distributions for each individual in the cohort and report the parameter ranges for each clinical group after characterization of healthy developing and aging groups. We coin this newly proposed platform for individualized behavioral analyses “precision phenotyping” to distinguish it from the type of observational–behavioral phenotyping prevalent in clinical studies or from the “one-size-fits-all” model in basic movement science. We further propose the use of this platform as a unifying statistical framework to characterize brain disorders of known etiology in relation to idiopathic neurological disorders with similar phenotypic manifestations.

Muscle artifacts in single trial EEG data distinguish patients with Parkinson's disease from healthy individuals

Parkinson's disease (PD) is known to lead to marked alterations in cortical-basal ganglia activity that may be amenable to serve as a biomarker for PD diagnosis. Using non-linear delay differential equations (DDE) for classification of PD patients on and off dopaminergic therapy (PD-on, PD-off, respectively) from healthy age-matched controls (CO), we show that 1 second of quasi-resting state clean and raw electroencephalogram (EEG) data can be used to classify CO from PD-on/off based on the area under the receiver operating characteristic curve (AROC). Raw EEG is shown to classify more robustly (AROC=0.59-0.86) than clean EEG data (AROC=0.57-0.72). Decomposition of the raw data into stereotypical and non-stereotypical artifacts provides evidence that increased classification of raw EEG time series originates from muscle artifacts. Thus, non-linear feature extraction and classification of raw EEG data in a low dimensional feature space is a potential biomarker for Parkinson's disease.

Motor output variability, deafferentation, and putative deficits in kinesthetic reafference in Parkinson's disease

Parkinson’s disease (PD) is a neurodegenerative disorder defined by motor impairments that include rigidity, systemic slowdown of movement (bradykinesia), postural problems, and tremor. While the progressive decline in motor output functions is well documented, less understood are impairments linked to the continuous kinesthetic sensation emerging from the flow of motions. There is growing evidence in recent years that kinesthetic problems are also part of the symptoms of PD, but objective methods to readily quantify continuously unfolding motions across different contexts have been lacking. Here we present evidence from a deafferented subject (IW) and a new statistical platform that enables new analyses of motor output variability measured as a continuous flow of kinesthetic reafferent input. Systematic increasing similarities between the patterns of motor output variability in IW and the participants with increasing degrees of PD severity suggest potential deficits in kinesthetic sensing in PD. We propose that these deficits may result from persistent, noisy, and random motor patterns as the disorder progresses. The stochastic signatures from the unfolding motions revealed levels of noise in the motor output fluctuations of these patients bound to decrease the kinesthetic signal’s bandwidth. The results are interpreted in light of the concept of kinesthetic reafference ( Von Holst and Mittelstaedt, 1950). In this context, noisy motor output variability from voluntary movements in PD leads to a returning stream of noisy afference caused, in turn, by those faulty movements themselves. Faulty efferent output re-enters the CNS as corrupted sensory motor input. We find here that severity level in PD leads to the persistence of such patterns, thus bringing the statistical signatures of the subjects with PD systematically closer to those of the subject without proprioception.

Loudness perception and speech intensity control in Parkinson's disease

The aim of this study was to examine loudness perception in individuals with hypophonia and Parkinson's disease. The participants included 17 individuals with hypophonia related to Parkinson's disease (PD) and 25 age-equivalent controls. The three loudness perception tasks included a magnitude estimation procedure involving a sentence spoken at 60, 65, 70, 75 and 80 dB SPL, an imitation task involving a sentence spoken at 60, 65, 70, 75 and 80 dB SPL, and a magnitude production procedure involving the production of a sentence at five different loudness levels (habitual, two and four times louder and two and four times quieter). The participants with PD produced a significantly different pattern and used a more restricted range than the controls in their perception of speech loudness, imitation of speech intensity, and self-generated estimates of speech loudness. The results support a speech loudness perception deficit in PD involving an abnormal perception of externally generated and self-generated speech intensity.

LEARNING OUTCOMES

Readers will recognize that individuals with hypophonia related to Parkinson's disease may demonstrate a speech loudness perception deficit involving the abnormal perception of externally generated and self-generated speech intensity.

Delay differential analysis of electroencephalographic data

We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.

Kinematics of the Reach-to-Grasp Movement in Vascular Parkinsonism: A Comparison with Idiopathic Parkinson's Disease Patients

The performance of patients with vascular parkinsonism (VPD) on a reach-to-grasp task was compared with that of patients affected by idiopathic Parkinson’s disease (IPD) and age-matched control subjects. The aim of the study was to determine how patients with VPD and IPD compare at the level of the kinematic organization of prehensile actions. We examined how subjects concurrently executed the transport and grasp components of reach-to-grasp movements when grasping differently sized objects. When comparing both VPD and IPD groups to control subjects, all patients showed longer movement duration and smaller hand opening, reflecting bradykinesia and hypometria, respectively. Furthermore, for all patients, the onset of the manipulation component was delayed with respect to the onset of the transport component. However, for patients with VPD this delay was significantly smaller than that found for the IPD group. It is proposed that this reflects a deficit – which is moderate for VPD as compared to IPD patients – in the simultaneous (or sequential) implementation of different segments of a complex movement. Altogether these findings suggest that kinematic analysis of reach-to-grasp movement has the ability to provide potential instruments to characterize different forms of parkinsonism.

Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders

Traditional approaches for neurological rehabilitation of patients affected with movement disorders, such as Parkinson's disease (PD), dystonia, and essential tremor (ET) consist mainly of oral medication, physical therapy, and botulinum toxin injections. Recently, the more invasive method of deep brain stimulation (DBS) showed significant improvement of the physical symptoms associated with these disorders. In the past several years, the adoption of feedback control theory helped DBS protocols to take into account the progressive and dynamic nature of these neurological movement disorders that had largely been ignored so far. As a result, a more efficient and effective management of PD cardinal symptoms has emerged. In this paper, we review closed-loop systems for rehabilitation of movement disorders, focusing on PD, for which several invasive and noninvasive methods have been developed during the last decade, reducing the complications and side effects associated with traditional rehabilitation approaches and paving the way for tailored individual therapeutics. We then present a novel, transformative, noninvasive closed-loop framework based on force neurofeedback and discuss several future developments of closed-loop systems that might bring us closer to individualized solutions for neurological rehabilitation of movement disorders.

Scaling and coordination deficits during dynamic object manipulation in Parkinson's disease

The ability to reach for and dynamically manipulate objects in a dexterous fashion requires scaling and coordination of arm, hand, and fingertip forces during reach and grasp components of this behavior. The neural substrates underlying dynamic object manipulation are not well understood. Insight into the role of basal ganglia-thalamocortical circuits in object manipulation can come from the study of patients with Parkinson's disease (PD). We hypothesized that scaling and coordination aspects of motor control are differentially affected by this disorder. We asked 20 PD patients and 23 age-matched control subjects to reach for, grasp, and lift virtual objects along prescribed paths. The movements were subdivided into two types, intensive (scaling) and coordinative, by detecting their underlying self-similarity. PD patients off medication were significantly impaired relative to control subjects for both aspects of movement. Intensive deficits, reduced peak speed and aperture, were seen during the reach. Coordinative deficits were observed during the reach, namely, the relative position along the trajectory at which peak speed and aperture were achieved, and during the lift, when objects tilted with respect to the gravitational axis. These results suggest that basal ganglia-thalamocortical circuits may play an important role in fine motor coordination. Dopaminergic therapy significantly improved intensive but not coordinative aspects of movements. These findings are consistent with a framework in which tonic levels of dopamine in the dorsal striatum encode the energetic cost of a movement, thereby improving intensive or scaling aspects of movement. However, repletion of brain dopamine levels does not restore finely coordinated movement.

An EEMD-IVA framework for concurrent multidimensional EEG and unidimensional kinematic data analysis

Joint blind source separation (JBSS) is a means to extract common sources simultaneously found across multiple datasets, e.g., electroencephalogram (EEG) and kinematic data jointly recorded during reaching movements. Existing JBSS approaches are designed to handle multidimensional datasets, yet to our knowledge, there is no existing means to examine common components that may be found across a unidimensional dataset and a multidimensional one. In this paper, we propose a simple, yet effective method to achieve the goal of JBSS when concurrent multidimensional EEG and unidimensional kinematic datasets are available, by combining ensemble empirical mode decomposition (EEMD) with independent vector analysis (IVA). We demonstrate the performance of the proposed method through numerical simulations and application to data collected from reaching movements in Parkinson's disease. The proposed method is a promising JBSS tool for real-world biomedical signal processing applications.

Non-linear dynamical analysis of EEG time series distinguishes patients with Parkinson's disease from healthy individuals

The pathophysiology of Parkinson’s disease (PD) is known to involve altered patterns of neuronal firing and synchronization in cortical-basal ganglia circuits. One window into the nature of the aberrant temporal dynamics in the cerebral cortex of PD patients can come from analysis of the patients electroencephalography (EEG). Rather than using spectral-based methods, we used data models based on delay differential equations (DDE) as non-linear time-domain classification tools to analyze EEG recordings from PD patients on and off dopaminergic therapy and healthy individuals. Two sets of 50 1-s segments of 64-channel EEG activity were recorded from nine PD patients on and off medication and nine age-matched controls. The 64 EEG channels were grouped into 10 clusters covering frontal, central, parietal, and occipital brain regions for analysis. DDE models were fitted to individual trials, and model coefficients and error were used as features for classification. The best models were selected using repeated random sub-sampling validation and classification performance was measured using the area under the ROC curve A′. In a companion paper, we show that DDEs can uncover hidden dynamical structure from short segments of simulated time series of known dynamical systems in high noise regimes. Using the same method for finding the best models, we found here that even short segments of EEG data in PD patients and controls contained dynamical structure, and moreover, that PD patients exhibited a greater dynamic range than controls. DDE model output on the means from one set of 50 trials provided nearly complete separation of PD patients off medication from controls: across brain regions, the area under the receiver-operating characteristic curves, A′, varied from 0.95 to 1.0. For distinguishing PD patients on vs. off medication, classification performance A′ ranged from 0.86 to 1.0 across brain regions. Moreover, the generalizability of the model to the second set of 50 trials was excellent, with A′ ranging from 0.81 to 0.94 across brain regions for controls vs. PD off medication, and from 0.62 to 0.82 for PD on medication vs. off. Finally, model features significantly predicted individual patients’ motor severity, as assessed with standard clinical rating scales.