Task-Related Reorganization of Cognitive Network in Parkinson's Disease Using Electrophysiology.

BACKGROUND: Cognitive deficits in Parkinson's disease (PD) patients are well described, however, their underlying neural mechanisms as assessed by electrophysiology are not clear. OBJECTIVES: To reveal specific neural network alterations during the performance of cognitive tasks in PD patients using electroencephalography (EEG). METHODS: Ninety participants, 60 PD patients and 30 controls underwent EEG recording while performing a GO/NOGO task. Source localization of 16 regions of interest known to play a pivotal role in GO/NOGO task was performed to assess power density and connectivity within this cognitive network. The connectivity matrices were evaluated using a graph-theory approach that included measures of cluster-coefficient, degree, and global-efficiency. A mixed-model analysis, corrected for age and levodopa equivalent daily dose was performed to examine neural changes between PD patients and controls. RESULTS: PD patients performed worse in the GO/NOGO task (P < 0.001). The power density was higher in δ and θ bands, but lower in α and ß bands in PD patients compared to controls (interaction group × band: P < 0.001), indicating a general slowness within the network. Patients had more connections within the network (P < 0.034) than controls and these were used for graph-theory analysis. Differences between groups in graph-theory measures were found only in cluster-coefficient, which was higher in PD compared to controls (interaction group × band: P < 0.001). CONCLUSIONS: Cognitive deficits in PD are underlined by alterations at the brain network level, including higher δ and θ activity, lower α and ß activity, increased connectivity, and segregated network organization. These findings may have important implications on future adaptive deep brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Paroxysmal Slow-Wave Events Are Uncommon in Parkinson's Disease.

Background: Parkinson's disease (PD) is currently considered to be a multisystem neurodegenerative disease that involves cognitive alterations. EEG slowing has been associated with cognitive decline in various neurological diseases, such as PD, Alzheimer's disease (AD), and epilepsy, indicating cortical involvement. A novel method revealed that this EEG slowing is composed of paroxysmal slow-wave events (PSWE) in AD and epilepsy, but in PD it has not been tested yet. Therefore, this study aimed to examine the presence of PSWE in PD as a biomarker for cortical involvement. Methods: 31 PD patients, 28 healthy controls, and 18 juvenile myoclonic epilepsy (JME) patients (served as positive control), underwent four minutes of resting-state EEG. Spectral analyses were performed to identify PSWEs in nine brain regions. Mixed-model analysis was used to compare between groups and brain regions. The correlation between PSWEs and PD duration was examined using Spearman's test. Results: No significant differences in the number of PSWEs were observed between PD patients and controls (p > 0.478) in all brain regions. In contrast, JME patients showed a higher number of PSWEs than healthy controls in specific brain regions (p < 0.023). Specifically in the PD group, we found that a higher number of PSWEs correlated with longer disease duration. Conclusions: This study is the first to examine the temporal characteristics of EEG slowing in PD by measuring the occurrence of PSWEs. Our findings indicate that PD patients who are cognitively intact do not have electrographic manifestations of cortical involvement. However, the correlation between PSWEs and disease duration may support future studies of repeated EEG recordings along the disease course to detect early signs of cortical involvement in PD.

Stopping is not an option: the evolution of unstoppable motion elements (primitives).

Stopping performance is known to depend on low-level motion features, such as movement velocity. It is not known, however, whether it is also subject to high-level motion constraints. Here, we report results of 15 subjects instructed to connect four target points depicted on a digitizing tablet and stop "as rapidly as possible" upon hearing a "stop" cue (tone). Four subjects connected target points with straight paths, whereas 11 subjects generated movements corresponding to coarticulation between adjacent movement components. For the noncoarticulating and coarticulating subjects, stopping performance was not correlated or only weakly correlated with motion velocity, respectively. The generation of a straight, point-to-point movement or a smooth, curved trajectory was not disturbed by the occurrence of a stop cue. Overall, the results indicate that stopping performance is subject to high-level motion constraints, such as the completion of a geometrical plan, and that globally planned movements, once started, must run to completion, providing evidence for the definition of a motion primitive as an unstoppable motion element.

Impaired Inhibitory Control During Walking in Parkinson's Disease Patients: An EEG Study.

BACKGROUND: The performance on a visual Go/NoGo (VGNG) task during walking has been used to evaluate the effect of gait on response inhibition in young and older adults; however, no work has yet included Parkinson's disease (PD) patients for whom such changes may be even more enhanced. OBJECTIVE: In this study, we aimed to explore the effect of gait on automatic and cognitive inhibitory control phases in PD patients and the associated changes in neural activity and compared them with young and older adults. METHODS: 30 PD patients, 30 older adults, and 11 young adults performed a visual Go/NoGo task in a sitting position and during walking on a treadmill while their EEG activity and gait were recorded. Brain electrical activity was evaluated by the amplitude, latency, and scalp distribution of N2 and P300 event related potentials. Mix model analysis was used to examine group and condition effects on task performance and brain activity. RESULTS: The VGNG accuracy rates in PD patients during walking were lower than in young and older adults (F = 5.619, p = 0.006). For all groups, N2 latency during walking was significantly longer than during sitting (p = 0.013). In addition, P300 latency was significantly longer in PD patients (p < 0.001) and older adults (p = 0.032) during walking compared to sitting and during 'NoGo' trials compared with 'Go' trials. Moreover, the young adults showed the smallest number of electrodes for which a significant differential activation between sit to walk was observed, while PD patients showed the largest with N2 being more strongly manifested in bilateral parietal electrodes during walking and in frontocentral electrodes while seated. CONCLUSION: The results show that response inhibition during walking is impaired in older subjects and PD patients and that increased cognitive load during dual-task walking relates to significant change in scalp electrical activity, mainly in parietal and frontocentral channels.

Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG current source dipoles.

Objective. Growing evidence suggests that electroencephalography (EEG) electrode (sensor) potential time series (PTS) of slow cortical potentials (SCPs) hold motor neural correlates that can be used for motion trajectory prediction, commonly by multiple linear regression (mLR). It is not yet known whether arm-joint trajectories can be reliably decoded from current sources, computed from sensor data, from which brain areas they can be decoded and using which neural features.Approach. In this study, the PTS of 44 sensors were fed into sLORETA source localization software to compute current source activity in 30 regions of interest (ROIs) found in a recent meta-analysis to be engaged in action execution, motor imagery and motor preparation. The current sources PTS and band-power time series (BTS) in several frequency bands and time lags were used to predict actual and imagined trajectories in 3D space of the three velocity components of the hand, elbow and shoulder of nine subjects using an mLR model.Main results. For all arm joints and movement types, current source SCPs PTS contributed most to trajectory reconstruction with time lags 150, 116 and 84 ms providing the highest contribution, and current source BTS in any of the tested frequency bands was not informative. Person's correlation coefficient (r) averaged across movement types, arm joints and velocity components using source data was slightly lower than using sensor data (r= 0.25 andr= 0.28, respectively). For each ROI, the three current source dipoles had different contribution to the reconstruction of each of the three velocity components.Significance. Overall, our results demonstrate the feasibility of predicting of actual and imagined 3D trajectories of all arm joints from current sources, computed from scalp EEG. These findings may be used by developers of a future BCI as a validated set of contributing ROIs.

Changes in the EEG spectral power during dual-task walking with aging and Parkinson's disease: initial findings using Event-Related Spectral Perturbation analysis.

BACKGROUND: The ability to maintain adequate motor-cognitive performance under increasing task demands depends on the regulation and coordination of neural resources. Studies have shown that such resources diminish with aging and disease. EEG spectral analysis is a method that has the potential to provide insight into neural alterations affecting motor-cognitive performance. The aim of this study was to assess changes in spectral analysis during dual-task walking in aging and disease METHODS: 10 young adults, ten older adults, and ten patients with Parkinson's disease (PD) completed an auditory oddball task while standing and while walking on a treadmill. Spectral power within four frequency bandwidths, delta (< 4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz), was calculated using Event-Related Spectral Perturbation (ERSP) analyses and compared between single task and dual task and between groups. RESULTS: Differences in ERSP were found in all groups between the single and dual-task conditions. In response to dual-task walking, beta increased in all groups (p < 0.026), delta decreased in young adults (p = 0.03) and patients with PD (0.015) while theta increased in young adults (p = 0.028) but decreased in older adults (p = 0.02) and patients with PD (p = 0.015). Differences were seen between the young, the older adults, and the patients with PD. CONCLUSIONS: These findings are the first to show changes in the power of different frequency bands during dual-task walking with aging and disease. These specific brain modulations may reflect deficits in readiness and allocation of attention that may be responsible for the deficits in dual-task performance.

Reconstruction of hand, elbow and shoulder actual and imagined trajectories in 3D space using EEG slow cortical potentials.

OBJECTIVE: The ability to decode kinematics of imagined movement from neural activity is essential for the development of prosthetic devices that can aid motor-disabled persons. To date, non-invasive recording methods, including electroencephalogram (EEG) were used to decode actual and imagined hand trajectory to control neuromotor prostheses, commonly by applying multi-dimensional linear regression (mLR) models to adjust the two temporal signals-neural signal and limb kinematics. It is still debated, however, whether the EEG signal, in general, and slow cortical potentials (SCPs), in specific, hold motor neural correlates. Moreover, it has not yet been tested whether the trajectory of proximal arm joints, i.e. shoulder, can also be reconstructed and if decoding performance is dependent on movement speed and/or position variance. APPROACH: We predicted hand, elbow and shoulder trajectories in 3D space in time series of both movement types (actual and imagined) of seven subjects using an mLR model, commonly applied for motion trajectory prediction (MTP) and used source localization to detect and compare between brain areas activated during actual and imagined movements for each arm joint. MAIN RESULTS: For all arm joints and movement types, SCPs contributed the most to trajectory reconstruction, and decoding accuracy peaked using neural signals preceding kinematics by 120-210 ms. The average (across subjects) Pearson's correlation coefficient between predicted and actual trajectories ranged 0.24-0.49, 0.41-0.48 and 0.18-0.40 for the hand, elbow and shoulder, respectively, and was significantly higher than chance level (p  < 0.01) for all subjects. For the imagined movements, reconstruction accuracy ranged between 0.09-0.23, 0.20-0.27 and 0.11-0.18 for the hand, elbow and shoulder, respectively, and was significantly higher than chance level (p  < 0.05) for all or some of the arm joints. The model performance was positively correlated with movement speed and negatively correlated with position variance. Source localization suggested that the neural circuits engaged in motor imagery are more diffuse and bilateral; motor imagery was, when compared to movement execution, more associated with recruiting premotor regions and a large area of the left parietal cortex. SIGNIFICANCE: Our results demonstrate the feasibility of predicting 3D imagined trajectories of all arm joints from scalp EEG and imply the existence of movement related neural correlates in slow cortical potentials.

Methods for Gait Analysis During Obstacle Avoidance Task.

In this study, we present algorithms developed for gait analysis, but suitable for many other signal processing tasks. A novel general-purpose algorithm for extremum estimation of quasi-periodic noisy signals is proposed. This algorithm is both flexible and robust, and allows custom adjustments to detect a predetermined wave pattern while being immune to signal noise and variability. A method for signal segmentation was also developed for analyzing kinematic data recorded while performing on obstacle avoidance task. The segmentation allows detecting preparation and recovery phases related to obstacle avoidance. A simple kernel-based clustering method was used for classification of unsupervised data containing features of steps within the walking trial and discriminating abnormal from regular steps. Moreover, a novel algorithm for missing data approximation and adaptive signal filtering is also presented. This algorithm allows restoring faulty data with high accuracy based on the surrounding information. In addition, a predictive machine learning technique is proposed for supervised multiclass labeling with non-standard label structure.

Analyzing movement trajectories using a Markov bi-clustering method.

In this study we treat scribbling motion as a compositional system in which a limited set of elementary strokes are capable of concatenating amongst themselves in an endless number of combinations, thus producing an unlimited repertoire of complex constructs. We broke the continuous scribblings into small units and then calculated the Markovian transition matrix between the trajectory clusters. The Markov states are grouped in a way that minimizes the loss of mutual information between adjacent strokes. The grouping algorithm is based on a novel markov-state bi-clustering algorithm derived from the Information-Bottleneck principle. This approach hierarchically decomposes scribblings into increasingly finer elements. We illustrate the usefulness of this approach by applying it to human scribbling.

A new method to record and control for 2D-movement kinematics during functional magnetic resonance imaging (fMRI).

The recording of movement kinematics during functional magnetic resonance imaging (fMRI) experiments is complicated due to technical constraints of the imaging environment. Nevertheless, to study the functions of brain areas related to motor control, reliable and accurate records of movement trajectories and speed profiles are needed. We present a method designed to record and characterize the kinematic properties of drawing- and handwriting-like forearm movements during fMRI studies by recording pen stroke trajectories. The recording system consists of a translucent plastic board, a plastic pen containing fiber optics and a halogen light power source, a CCD camera, a video monitor and a PC with a video grabber card. Control experiments using a commercially available digitizer tablet have demonstrated the reliability of the data recorded during fMRI. Since the movement tracking signal is purely optical, there is no interaction with the MR (echoplanar) images. Thus, the method allows to obtain movement records with high spatial and temporal resolution which are suitable for the kinematic analysis of hand movements in fMRI studies.

Decoding Imagined 3D Arm Movement Trajectories From EEG to Control Two Virtual Arms-A Pilot Study.

Background: Realization of online control of an artificial or virtual arm using information decoded from EEG normally occurs by classifying different activation states or voluntary modulation of the sensorimotor activity linked to different overt actions of the subject. However, using a more natural control scheme, such as decoding the trajectory of imagined 3D arm movements to move a prosthetic, robotic, or virtual arm has been reported in a limited amount of studies, all using offline feed-forward control schemes. Objective: In this study, we report the first attempt to realize online control of two virtual arms generating movements toward three targets/arm in 3D space. The 3D trajectory of imagined arm movements was decoded from power spectral density of mu, low beta, high beta, and low gamma EEG oscillations using multiple linear regression. The analysis was performed on a dataset recorded from three subjects in seven sessions wherein each session comprised three experimental blocks: an offline calibration block and two online feedback blocks. Target classification accuracy using predicted trajectories of the virtual arms was computed and compared with results of a filter-bank common spatial patterns (FBCSP) based multi-class classification method involving mutual information (MI) selection and linear discriminant analysis (LDA) modules. Main Results: Target classification accuracy from predicted trajectory of imagined 3D arm movements in the offline runs for two subjects (mean 45%, std 5%) was significantly higher (p < 0.05) than chance level (33.3%). Nevertheless, the accuracy during real-time control of the virtual arms using the trajectory decoded directly from EEG was in the range of chance level (33.3%). However, the results of two subjects show that false-positive feedback may increase the accuracy in closed-loop. The FBCSP based multi-class classification method distinguished imagined movements of left and right arm with reasonable accuracy for two of the three subjects (mean 70%, std 5% compared to 50% chance level). However, classification of the imagined arm movement toward three targets was not successful with the FBCSP classifier as the achieved accuracy (mean 33%, std 5%) was similar to the chance level (33.3%). Sub-optimal components of the multi-session experimental paradigm were identified, and an improved paradigm proposed.

Decoding Imagined 3D Hand Movement Trajectories From EEG: Evidence to Support the Use of Mu, Beta, and Low Gamma Oscillations.

Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements i.e., executed arm movements. Decoding of observed or imagined 3D movements has been demonstrated with limited success and only reported in a few studies. MTP studies normally use EEG potentials filtered in the low delta (~1 Hz) band for reconstructing the trajectory of an executed or an imagined/observed movement. In contrast to MTP, multiclass classification based sensorimotor rhythm brain-computer interfaces aim to classify movements using the power spectral density of mu (8-12 Hz) and beta (12-28 Hz) bands. Approach: We investigated if replacing the standard potentials time-series input with a power spectral density based bandpower time-series improves trajectory decoding accuracy of kinesthetically imagined 3D hand movement tasks (i.e., imagined 3D trajectory of the hand joint) and whether imagined 3D hand movements kinematics are encoded also in mu and beta bands. Twelve naïve subjects were asked to generate or imagine generating pointing movements with their right dominant arm to four targets distributed in 3D space in synchrony with an auditory cue (beep). Main results: Using the bandpower time-series based model, the highest decoding accuracy for motor execution was observed in mu and beta bands whilst for imagined movements the low gamma (28-40 Hz) band was also observed to improve decoding accuracy for some subjects. Moreover, for both (executed and imagined) movements, the bandpower time-series model with mu, beta, and low gamma bands produced significantly higher reconstruction accuracy than the commonly used potential time-series model and delta oscillations. Significance: Contrary to many studies that investigated only executed hand movements and recommend using delta oscillations for decoding directional information of a single limb joint, our findings suggest that motor kinematics for imagined movements are reflected mostly in power spectral density of mu, beta and low gamma bands, and that these bands may be most informative for decoding 3D trajectories of imagined limb movements.

Block design enhances classification of 3D reach targets from electroencephalographic signals.

To date, decoding accuracy of actual or imagined pointing movements to targets in 3D space from electroencephalographic (EEG) signals has remained modest. The reason may pertain to the fact that these movements activate essentially the same neural networks. In this study, we aimed at testing whether repetitive pointing movements to each of the targets promotes the development of segregated neural patterns, resulting in enhanced decoding accuracy. Six human subjects generated slow or fast repetitive pointing movements with their right dominant arm to one of five targets distributed in 3D space, followed by repetitive imagery of movements to the same target or to a different target. Nine naive subjects generated both repetitive and non-repetitive slow actual movements to each of the five targets to test the effect of block design on decoding accuracy. In order to assure that base line drift and low frequency motion artifacts do not contaminate the data, the data were high-pass filtered in 4-30Hz, leaving out the delta and gamma band. For the repetitive trials, the model decoded target location with 81% accuracy, which is significantly higher than chance level. The average decoding rate of target location was only 30% for the non-repetitive trials, which is not significantly different than chance level. A subset of electrodes, mainly over the contralateral sensorimotor areas, was found to provide most of the discriminative features for all tested conditions. Time proximity between trained and tested blocks was found to enhance decoding accuracy of target location both by target non-specific and specific mechanisms. Our findings suggest that movement repetition promotes the development of distinct neural patterns, presumably by the formation of target-specific kinesthetic memory.

The activity in the contralateral primary motor cortex, dorsal premotor and supplementary motor area is modulated by performance gains.

There is growing experimental evidence that the engagement of different brain areas in a given motor task may change with practice, although the specific brain activity patterns underlying different stages of learning, as defined by kinematic or dynamic performance indices, are not well understood. Here we studied the change in activation in motor areas during practice on sequences of handwriting-like trajectories, connecting four target points on a digitizing table "as rapidly and as accurately as possible" while lying inside an fMRI scanner. Analysis of the subjects' pooled kinematic and imaging data, acquired at the beginning, middle, and end of the training period, revealed no correlation between the amount of activation in the contralateral M1, PM (dorsal and ventral), supplementary motor area (SMA), preSMA, and Posterior Parietal Cortex (PPC) and the amount of practice per-se. Single trial analysis has revealed that the correlation between the amount of activation in the contralateral M1 and trial mean velocity was partially modulated by performance gains related effects, such as increased hand motion smoothness. Furthermore, it was found that the amount of activation in the contralateral preSMA increased when subjects shifted from generating straight point-to-point trajectories to their spatiotemporal concatenation into a smooth, curved trajectory. Altogether, our results indicate that the amount of activation in the contralateral M1, PMd, and preSMA during the learning of movement sequences is correlated with performance gains and that high level motion features (e.g., motion smoothness) may modulate, or even mask correlations between activity changes and low-level motion attributes (e.g., trial mean velocity).

The point of no return in planar hand movements: an indication of the existence of high level motion primitives.

Previous psychophysical studies have sought to determine whether the processes of movement engagement and termination are dissociable, whether stopping an action is a generic process, and whether there is a point in time in which the generation of a planned action is inevitable ("point of no return"). It is not clear yet, however, whether the action of stopping is merely a manifestation of low level, dynamic constraints, or whether it is also subject to a high level, kinematic plan. In the present study, stopping performance was studied while nine subjects, who generated free scribbling movements looking for the location of an invisible circular target, were requested unexpectedly to impede movement. Temporal analysis of the data shows that in 87% of the movements subsequent to the 'stop' cue, the tangential motion velocity profile was not a decelerating function of the time but rather exhibited a complex pattern comprised of one or more velocity peaks, implying an unstoppable motion element. Furthermore, geometrical analysis shows that the figural properties of the path generated after the 'stop' cue were part of a repetitive geometrical pattern and that the probability of completing a pattern after the 'stop' cue was correlated with the relative advance in the geometrical plan rather than the amount of time that had elapsed from the pattern initiation. Altogether, these findings suggest that the "point of no return" phenomenon in humans may also reflect a high level kinematic plan and could serve as a new operative definition of motion primitives.

The acquisition and implementation of the smoothness maximization motion strategy is dependent on spatial accuracy demands.

We recently showed that extensive training on a sequence of planar hand trajectories passing through several targets resulted in the co-articulation of movement components and in the formation of new movement elements (primitives) (Sosnik et al. in Exp Brain Res 156(4):422-438, 2004). Reduction in movement duration was accompanied by the gradual replacing of a piecewise combination of rectilinear trajectories with a single, longer curved one, the latter affording the maximization of movement smoothness ("global motion planning"). The results from transfer experiments, conducted by the end of the last training session, have suggested that the participants have acquired movement elements whose attributes were solely dictated by the figural (i.e., geometrical) form of the path, rather than by both path geometry and its time derivatives. Here we show that the acquired movement generation strategy ("global motion planning") was not specific to the trained configuration or total movement duration. Performance gain (i.e., movement smoothness, defined by the fit of the data to the behavior, predicted by the "global planning" model) transferred to non-trained configurations in which the targets were spatially co-aligned or when participants were instructed to perform the task in a definite amount of time. Surprisingly, stringent accuracy demands, in transfer conditions, resulted not only in an increased movement duration but also in reverting to the straight trajectories (loss of co-articulation), implying that the performance gain was dependent on accuracy constraints. Only 28.5% of the participants (two out of seven) who were trained in the absence of visual feedback from the hand (dark condition) co-articulated by the end of the last training session compared to 75% (six out of eight) who were trained in the light, and none of them has acquired a geometrical motion primitive. Furthermore, six naive participants who trained in dark condition on large size targets have all co-articulated by the end of the last training session, still, none of them has acquired a geometrical motion primitive. Taken together, our results indicate that the acquisition of a geometrical motion primitive is dependent on the existence of visual feedback from the hand and that the implementation of the smoothness-maximization motion strategy is dependent on spatial accuracy demands. These findings imply that the specific features of the training experience (i.e., temporal or spatial task demands) determine the attributes of an acquired motion planning strategy and primitive.

When practice leads to co-articulation: the evolution of geometrically defined movement primitives.

The skilled generation of motor sequences involves the appropriate choice, ordering and timing of a sequence of simple, stereotyped movement elements. Nevertheless, a given movement element within a well-rehearsed sequence can be modified through interaction with its neighboring elements (co-articulation). We show that extensive training on a sequence of planar hand trajectories passing through several targets resulted in the co-articulation of movement components, and in the formation of new movement elements (primitives). Reduction in movement duration was accompanied by the gradual replacement of straight trajectories by longer curved ones, the latter affording the maximization of movement smoothness. Surprisingly, the curved trajectories were generated even when new target configurations were introduced, i.e., when target distances were scaled, movement direction reversed or when different start and end positions were used, indicating the acquisition of geometrically defined movement elements. However, the new trajectories were not shared by the untrained hand. Altogether, our results suggest that novel movement elements can be acquired through extensive training in adults.