A Fully-Immersive Virtual Reality Setup to Study Gait Modulation

Cortical networks of parkinsonian gait: a metabolic and functional connectivity study

OBJECTIVE

Locomotion is an automated voluntary movement sustained by coordinated neural synchronization across a distributed brain network. The cerebral cortex is central for adapting the locomotion pattern to the environment and alterations of cortical network dynamics can lead to gait impairments. Gait problems are a common symptom with a still unclear pathophysiology and represent an unmet therapeutical need in Parkinson's disease. Little is known about the cortical network dynamics of locomotor control in these patients.

METHODS

We studied the cortical basis of parkinsonian gait by combining metabolic brain imaging with high-density EEG recordings and kinematic measurements performed at rest and during unperturbed overground walking.

RESULTS

We found significant changes in functional connectivity between frontal, sensorimotor, and visuomotor cortical areas during walking as compared to resting. Specifically, hypokinetic gait was associated with poor information flow from the supplementary motor area (SMA) to precuneus and from calcarine to lingual gyrus, as well as high information flow from calcarine to cuneus.

INTERPRETATION

Our findings support a role for visuomotor integration processes in PD-related hypokinetic gait and suggest that reinforcing visual information may act as a compensatory strategy to allow SMA-mediated feedforward locomotor control in PD.

Case report: Improvement of gait with adaptive deep brain stimulation in a patient with Parkinson's disease

Gait disturbance is a common and severe symptom of Parkinson's disease that severely impairs quality of life. Current treatments provide only partial benefits with wide variability in outcomes. Also, deep brain stimulation of the subthalamic nucleus (STN-DBS), a mainstay treatment for bradykinetic-rigid symptoms and parkinsonian tremor, is poorly effective on gait. We applied a novel DBS paradigm, adjusting the current amplitude linearly with respect to subthalamic beta power (adaptive DBS), in one parkinsonian patient with gait impairment and chronically stimulated with conventional DBS. We studied the kinematics of gait and gait initiation (anticipatory postural adjustments) as well as subthalamic beta oscillations with both conventional and adaptive DBS. With adaptive DBS, the patient showed a consistent and long-lasting improvement in walking while retaining benefits on other disease-related symptoms. We suggest that adaptive DBS can benefit gait in Parkinson's disease possibly by avoiding overstimulation and dysfunctional entrainment of the supraspinal locomotor network.

Neuroanatomical Predictors of Transcranial Direct Current Stimulation (tDCS)-Induced Modifications in Neurocognitive Task Performance in Typically Developing Individuals

Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation technique gaining more attention in neurodevelopmental disorders (NDDs). Due to the phenotypic heterogeneity of NDDs, tDCS is unlikely to be equally effective in all individuals. The present study aimed to establish neuroanatomical markers in typically developing (TD) individuals that may be used for the prediction of individual responses to tDCS. Fifty-seven male and female children received 2 mA anodal and sham tDCS, targeting the left dorsolateral prefrontal cortex (DLPFCleft), right inferior frontal gyrus, and bilateral temporoparietal junction. Response to tDCS was assessed based on task performance differences between anodal and sham tDCS in different neurocognitive tasks (N-back, flanker, Mooney faces detection, attentional emotional recognition task). Measures of cortical thickness (CT) and surface area (SA) were derived from 3 Tesla structural MRI scans. Associations between neuroanatomy and task performance were assessed using general linear models (GLM). Machine learning (ML) algorithms were employed to predict responses to tDCS. Vertex-wise estimates of SA were more closely linked to differences in task performance than measures of CT. Across ML algorithms, highest accuracies were observed for the prediction of N-back task performance differences following stimulation of the DLPFCleft, where 65% of behavioral variance was explained by variability in SA. Lower accuracies were observed for all other tasks and stimulated regions. This suggests that it may be possible to predict individual responses to tDCS for some behavioral measures and target regions. In the future, these models might be extended to predict treatment outcome in individuals with NDDs.

Using virtual reality to induce multi-trial inattentional blindness despite trial-by-trial measures of awareness

Unconscious processing has been widely examined using diverse and well-controlled methodologies. However, the extent to which these findings are relevant to real-life instances of information processing without awareness is limited. Here, we present a novel inattentional blindness (IB) paradigm in virtual reality (VR). In three experiments, we managed to repeatedly induce IB while participants foveally viewed salient stimuli for prolonged durations. The effectiveness of this paradigm demonstrates the close relationship between top-down attention and subjective experience. Thus, this method provides an ecologically valid setup to examine processing without awareness.

Gait-related beta-gamma phase amplitude coupling in the subthalamic nucleus of parkinsonian patients

Analysis of coupling between the phases and amplitudes of neural oscillations has gained increasing attention as an important mechanism for large-scale brain network dynamics. In Parkinson's disease (PD), preliminary evidence indicates abnormal beta-phase coupling to gamma-amplitude in different brain areas, including the subthalamic nucleus (STN). We analyzed bilateral STN local field potentials (LFPs) in eight subjects with PD chronically implanted with deep brain stimulation electrodes during upright quiet standing and unperturbed walking. Phase-amplitude coupling (PAC) was computed using the Kullback-Liebler method, based on the modulation index. Neurophysiological recordings were correlated with clinical and kinematic measurements and individual molecular brain imaging studies ([123I]FP-CIT and single-photon emission computed tomography). We showed a dopamine-related increase in subthalamic beta-gamma PAC from standing to walking. Patients with poor PAC modulation and low PAC during walking spent significantly more time in the stance and double support phase of the gait cycle. Our results provide new insights into the subthalamic contribution to human gait and suggest cross-frequency coupling as a gateway mechanism to convey patient-specific information of motor control for human locomotion.

Dynamic evaluation of spine kinematics in individuals with Parkinson's disease and freezing of gait

BACKGROUND

Freezing of gait (FoG) is an episodic failure of gait exposing people with Parkinson's disease (PD) to a high risk of falling. Despite growing evidence of the interconnection between impaired trunk control and FoG, a detailed description of spinal kinematics during walking is still lacking in this population.

RESEARCH QUESTION

Do spinal alterations impact gait performance in individuals with PD and FoG?

METHODS

We analyzed kinematic data of 47 PD participants suffering (PD-FOG, N = 24) or not suffering from FoG (PD-NFOG, N = 23) and 15 healthy controls (HCO) during quiet standing and unperturbed walking. We estimated the main spinal variables (i.e., spinal length, lordosis and kyphosis angles, trunk inclination), the pelvis angles, and the shoulder-pelvis angles during gait and standing. We studied differences across conditions and groups and the relationships between postural and gait parameters using linear regression methods.

RESULTS

During standing and walking, both PD groups showed increased trunk inclination and decreased lordosis angle with respect to HCO, as well as a decreased range in variation of kyphosis angle, pelvic obliquity, and shoulder-pelvis angles. Only PD-FOG participants showed reduced range of lordosis angle and spinal length compared to HCO. PD-FOG individuals were also not able to straighten their spine during walking compared to standing. Stride length and velocity were decreased in both patient groups compared to HCO, while swing duration was reduced only in the PD-FOG group. In individuals with FoG, trunk inclination and lordosis angle showed moderate but significant positive correlations with all gait alterations.

SIGNIFICANCE

Spine alterations impacted gait performance in individuals with PD suffering from FoG. Excessive trunk inclination and poor mastering of the lordosis spinal region may create an unfavourable postural precondition for forward walking. Physical therapy should target combined spinal and stepping alterations in these individuals.

Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease

Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Differences in gait parameters when crossing real versus projected everyday life obstacles in healthy children and adolescents

Practicing complex everyday life walking activities is challenging in paediatric neurorehabilitation, although it would prepare patients more comprehensively for the requirements of daily life. Floor projections allow simulation and training of such situations in therapy. Twenty healthy youths aged 6-18 years stepped over a tree trunk and balanced over kerbstones in a real and projected condition. Spatiotemporal and kinematic parameters of the two conditions were compared by equivalence analysis, using the medians of the differences between the two conditions with their bootstrapped 95% confidence intervals. Velocity, step and stride length, step width, and single support time were generally equivalent between the two conditions. Knee and hip joint angles and toe clearance decreased substantially during the execution phase of the projected tree trunk condition. The largest differences were found at the end of the execution phase in both tasks for the ankle joints. As spatiotemporal parameters were equivalent between the conditions, floor projections seem suitable to train precise foot placement. However, differences in knee and hip joint kinematics and toe clearance revealed that floor projections are not applicable for obstacles with a vertical extension. Therefore, exercises aiming at knee and hip flexion improvement should favourably be trained with real objects.

Gait Event Prediction Using Surface Electromyography in Parkinsonian Patients

Gait disturbances are common manifestations of Parkinson's disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine learning approach to approximate IMU angular velocity profiles and subsequently gait events using electromyographic (EMG) channels during overground walking in patients with PD. We recorded six parkinsonian patients while they walked for at least three minutes. Patient-agnostic regression models were trained on temporally embedded EMG time series of different combinations of up to five leg muscles bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement of <50 ms), low numbers of missed events (<2%), and next to no false-positive event detections (<0.1%). Swing and stance phases could thus be determined with high fidelity (median F1-score of ~0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction, which allows the simultaneous acquisition of an electromyographic signal to be performed. This gait analysis approach has the potential to make additional measurement devices such as IMUs and force plates less essential, thereby reducing financial and preparation overheads and discomfort factors in gait studies.