Postural Instability And Gait Disorder Research Articles

Static and Dynamic Functional Network Connectivity inParkinson's Disease Patients With Postural Instability andGait Disorder.

The exact cause of the parkinsonism gait remains uncertain. We first focus on understanding the underlying neurological reasons for these symptoms through the examination of both static functional network connectivity (SFNC) and dynamic functional network connectivity (DFNC). We recruited 64 postural instability and gait disorder-dominated Parkinson's disease (PIGD-PD) patients, 31 non-PIGD-PD (nPIGD-PD) patients, and 54 healthy controls (HC) from Nanjing Brain Hospital. The GIFT software identified five distinct independent components: the basal ganglia (BG), cerebellum (CB), sensory networks (SMN), default mode network (DMN), and central executive network (CEN). We conducted a comparison between the SFNC and DFNC of the five networks and analyzed their correlations with postural instability and gait disorder (PIGD) symptoms. Compared with nPIGD-PD patients, the PIGD-PD patients demonstrated reduced connectivity between CEN and DMN while spending less mean dwell time (MDT) in state 4. This is characterized by strong connections. Compared with HC, PIGD-PD patients exhibited enhanced connectivity in the SFNC between CB and CEN, as well as the network between CB and DMN. Patients with PIGD-PD spent more MDT in state 1, which is characterized by few connections, and less MDT in state 4. In state 3, there was an increase in the functional connectivity between the CB and DMN in patients with PIGD-PD. The nPIGD patients showed increased SFNC connectivity between CB and DMN compared to HC. These patients spent more MDT in state 1 and less in state 4. The MDT and fractional windows of state 2 showed a positive link with PIGD scores. Patients with PIGD-PD exhibit a higher likelihood of experiencing reduced brain connectivity and impaired information processing. The enhanced connection between the cerebellum and DMN networks is considered a type of dynamic compensation.

Impaired Gait, Postural Instability, and Rigidity in Relation to CB1 Receptor Availability in Parkinson's Disease.

In Parkinson's disease (PD), postural instability and gait disorder (PIGD) symptoms are associated with a worse prognosis for an unknown reason. The objective was to explore the relationship between cannabinoid receptor type 1 (CB1R) availability and motor symptoms in PD with [18F]FMPEP-d2 positron emission tomography (PET). Fifteen individuals with PD underwent [18F]FMPEP-d2 PET to measure cerebral CB1R availability. The Unified Parkinson's Disease Rating Scale motor part (UPDRS-III) was used to evaluate the motor symptoms. A negative correlation was observed between [18F]FMPEP-d2 VT and PIGD score (P = 0.002) as well as rigidity subscore (P < 0.001). Both clusters covered widespread areas of both hemispheres. In contrast, tremor or bradykinesia did not correlate to [18F]FMPEP-d2 VT. Gait, postural instability, and rigidity in PD are associated with decreased CB1R availability, unlike tremor or bradykinesia, suggesting that the endocannabinoid system has a role in the pathophysiology of different motor symptoms in PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Application of sensory nerve quantitative tests to analyze the subtypes of motor disorders in Parkinson's disease.

This study investigated the sensory nerve function in people with different subtypes of Parkinson's disease (PD), which included the tremor-dominant (TD) group (n = 30), postural instability and gait disorder (PIGD) group (n = 33), and healthy-controls (HC) group (n = 33). Sural nerve's current perception threshold (CPT) and pain tolerance threshold (PTT) in both feet were measured at different frequencies. Results were evaluated using the mini-mental state examination (MMSE), Hoehn Yahr scale (H-Y) , and 3-meter timed-up-and-go-test (TUGT). The MMSE scores of the TD and HC groups were higher than those of the PIGD group (TD < HC). The 3-meter TUGT scores of the PIGD group were higher than theTD and HC groups (TD > HC). The PIGD patients experienced a significantly shorter disease duration and higher H-Y score than the TD patients ( P < 0.05). The values of 2 KHz CPT of left-side (CPTL), 2KHz CPT of right-side (CPTR), and 5 Hz CPTR in the PIGD group were significantly higher compared to the TD and HC groups ( P < 0.05, Bonferroni correction). Additionally, the values of 250 Hz CPTL, 5 Hz CPTL, 250 Hz CPTR, 2 kHz PTT of left-side (PTTL), 250 Hz PTTL, and 5 Hz PTTL in the PIGD group were significantly elevated relative to the TD group ( P < 0.05, Bonferroni correction). Distinctive current threshold perception and PTT of the sural nerve can be observed in patients with varying PD subtypes, and sensory nerve conduction threshold electrical diagnostic testing can detect these discrepancies in sensory nerve function.

Evoked potential response in patients with idiopathic Parkinson’s disease and atypical parkinsonian syndromes: A comparative study

Patients with idiopathic Parkinson&amp;rsquo;s disease (IPD) and atypical parkinsonian syndromes (APSs) suffer from a range of disorders, especially in balance and locomotion, which necessitate visual, auditory, and somatosensory inputs. In this study, IPD patients, APS patients, and healthy controls (HCs) (n = 50 per group) underwent a series of assessments for visual evoked potentials (VEP), brainstem auditory evoked response (BAER), and short-latency somatosensory evoked potentials (SSEP). Results showed that VEP P100 latency was prolonged in multiple system atrophy-cerebellar type (MSA-C), multiple system atrophy-parkinsonian type (MSA-P), and corticobasal ganglionic degeneration (CBD) patients. The latency of peaks III and V was prolonged in IPD, MSA-C, dementia with Lewy bodies (DLB), and Parkinson&amp;rsquo;s disease dementia (PDD). BAER I-III and I-V interpeak latency were prolonged in IPD, DLB, and PDD, whereas BAER I-III, III-V, and I-V interpeak latencies were increased and the V/I amplitude ratio was decreased in MSA-C. The central sensory conduction time (N20-N13) was increased in MSA-P and MSA-C in SSEP. IPD patients had prolonged VEP P100 latency (P &lt; 0.001), lower VEP N75-P100 amplitude (P &lt; 0.001), prolonged BAER I, II, II, IV, V peak latencies (P &lt; 0.001), I-III, I-V interpeak latencies (P &lt; 0.001), lower BAER V/I amplitude ratio (P &lt; 0.001), and prolonged SSEP N13, N20, central sensory conduction time (N20-N13) (P &lt; 0.001) than HCs. IPD patients also had prolonged BAER I, II, II, IV, V peak latencies (P &lt; 0.001), prolonged I-III, I-V interpeak latencies (P &lt; 0.001), and shorter SSEP N13, N20, central sensory conduction time (N20-N13) (P &lt; 0.001) than APS patients. Moreover, APS patients had prolonged VEP P100 and N145 latencies (P &lt; 0.001) and decreased N75-N145 amplitude (P &lt; 0.001) compared to HCs. APS patients also had prolonged BAER II, II, IV, V peak latencies (P &lt; 0.001), prolonged I-III, III-V, I-V interpeak latencies (P &lt; 0.001), decreased V/I amplitude ratio, and prolonged SSEP N13, N20, central sensory conduction time (N20-N13) (P &lt; 0.001) than HCs. Postural instability and gait disorder (PIGD) IPD had significantly prolonged BAER III, V peak latencies (P &lt; 0.05), and prolonged III-V interpeak latencies (P &lt; 0.05) compared to tremor-dominant IPD. Overall, the IPD and APS patients had significant VEP, BAER, and SSEP abnormalities of demyelination and axonal variety in the visual, auditory, and somatosensory pathways. The changes were also correlated with the disease duration and severity. Although the diseases are predominantly motor disorders with significant non-motor components, these electrophysiological abnormalities might open a new avenue to assess the non-motor symptoms.

Histogram analysis of diffusion kurtosis imaging of deep brain nuclei in Parkinson's disease with different motor subtypes

To evaluate the diagnostic and differential efficacy of diffusion kurtosis imaging (DKI) histogram analysis for different motor subtypes of Parkinson's disease (PD). Seventy PD patients including 40 with postural instability and gait disorder (PIGD) and 30 with tremor-dominant (TD) and 36 healthy controls (HC) were enrolled prospectively and underwent MRI examinations. The regions of interest (ROI) in the deep brain nuclei were delineated and features were extracted on the map of mean kurtosis (MK), axial kurtosis (Ka), and radial kurtosis (Kr), respectively. The differences in histogram features between PD patients and HC and between patients with PIGD and TD were compared. The areas under the curve (AUCs) were calculated to evaluate the diagnostic efficacy of all histogram features. The correlations between histogram features and clinical indicators were evaluated. Some DKI histogram features were significantly different between PD patients and HC, and also different between patients with PIGD and TD (all p<0.05). MK of the substantia nigra pars reticulate (SNprkurtosis), Ka of the substantia nigra pars compacta (SNpc) 50 percentile (SNpcP50), and Kr of SNpc 90th percentile showed the highest AUC for distinguishing patients with PIGD from HC. MK-SNpc 10th percentile, Ka-SNpc 25th percentile, and Kr of the head of the caudate nucleus (CN) 90th percentile had the highest AUC for distinguishing patients with TD from HC. MK of the putamen 10th percentile combined with Ka of the bilateral red nucleus RNkurtosis yielded the highest diagnostic performance with an AUC of 0.762 for distinguishing patients with PIGD from TD. Certain DKI histogram features were correlated with Hoehn-Yahr (H&Y) stage, Mini Mental State Examination (MMSE) score, tremor score, and PIGD score (all p<0.05). DKI histogram analysis was useful to diagnose and discriminate different motor subtypes of PD. Certain DKI histogram features correlated with clinical indicators.

Phenotyping Motor-subtypes of Parkinsonism from Full-body Kinematics using Machine Learning (P6-11.007)

<h3>Objective:</h3> The purpose of this study is to use whole-body kinematics data during walking to accurately identify motor subtypes of Parkinson’s disease (PD) and parkinsonism. <h3>Background:</h3> Two major motor subtypes of PD have been described, Postural Instability and Gait Disorder (PIGD) and Tremor Dominant (TD), which differ in the disease prognosis. Prior studies have explored quantifying motor subtypes via gait analysis, which lacks data for whole-body movements that are required when analyzing an entire gait cycle. In this study, we measure whole-body kinematics while walking, which is then analyzed to classify PIGD vs. TD subtypes using machine learning (ML) models. <h3>Design/Methods:</h3> The whole-body kinematic marker time series were collected from 57 patients (including 5 patients with atypical parkinsonism) while walking in off medication state using a motion capture system from 2015 to 2017. From all marker kinematics data, we extracted various features beyond gait-related features, including time series and spectral features. Then, ML models were used to classify the extracted features into the corresponding motor subtypes assessed with Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). <h3>Results:</h3> Compared to the clinically assessed motor subtypes, our model classified whole-body kinematics while walking with a 97.0% F1 score, which significantly outperformed the gait-based analysis which showed a 55.8% F1 score. Both upper and lower body movements were found to be useful indicators to distinguish between PIGD and TD. <h3>Conclusions:</h3> Our findings strongly supported our hypothesis that accurately quantifying motor subtypes requires more than gait-related or lower-body movement features that are typically used in previous work. This feature analysis may lead to novel hypotheses for fine-grained upper-body motion phenotypes related to motor subtypes. More importantly, our techniques can help monitor changes in motor subtypes for guiding appropriate intervention through objective quantification of walking movements. <b>Disclosure:</b> The institution of an immediate family member of Mr. Gong has received research support from National Science Foundation. Dr. Kwon has nothing to disclose. Prof. Clifford has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Alivecor. Prof. Clifford has stock in Mindchild Medical. Prof. Clifford has received intellectual property interests from a discovery or technology relating to health care. Prof. Clifford has received publishing royalties from a publication relating to health care. Dr. Esper has received research support from Centers for Disease Control and Prevention. Dr. Esper has received publishing royalties from a publication relating to health care. Dr. Factor has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Acorda. Dr. Factor has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Sunovion. Dr. Factor has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Alterity. Dr. Factor has received personal compensation in the range of $10,000-$49,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Lundbeck. Dr. Factor has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Biogen. The institution of Dr. Factor has received research support from Impax. The institution of Dr. Factor has received research support from Biohaven. The institution of Dr. Factor has received research support from Sunovion. The institution of Dr. Factor has received research support from Vaccinex. The institution of Dr. Factor has received research support from Voyager. The institution of Dr. Factor has received research support from Neurocrine. The institution of Dr. Factor has received research support from US World Meds. The institution of Dr. Factor has received research support from Addex. The institution of Dr. Factor has received research support from Prelinia. The institution of Dr. Factor has received research support from Medtronics. The institution of Dr. Factor has received research support from Boston Scientific. Dr. Factor has received publishing royalties from a publication relating to health care. Dr. Factor has received publishing royalties from a publication relating to health care. Dr. Factor has received publishing royalties from a publication relating to health care. Dr. Factor has received publishing royalties from a publication relating to health care. Dr. McKay has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Biocircuit Technologies. The institution of Dr. McKay has received research support from NIH. The institution of Dr. McKay has received research support from the McCamish Foundation.

Aberrant inter-network functional connectivity in drug-naive Parkinson's disease patients with tremor dominant and postural instability and gait difficulty.

Background: Insight into neural mechanisms of tremor dominant (TD) and postural instability and gait disorder (PIGD) subtypes in Parkinson's disease (PD) is vital for understanding pathophysiological hypotheses underlying this phenotype. However, network disturbances and their correlation with motor subtypes of PD remain unclear. We aimed to investigate the alterations of intra- and inter-network functional connectivity (FC) in drug-naive PD patients with different motor subtypes. Methods: Resting-state functional magnetic resonance imaging was performed on 25 drug-naive PD patients with TD (PD-TD) and 40 drug-naive PD patients with PIGD (PD-PIGD), and 37 healthy controls (HCs) underwent. The following networks were extracted using independent component analysis: sensorimotor network (SMN), left executive control network (LECN), right executive control network, anterior salience network (aSN), posterior salience network (pSN), ventral attention network (VAN), dorsal attention network (DAN), default mode network (DMN), visual network, and auditory network (AN). We measured FC values within and between these networks. Results: There were no detectable variations in intra-network FC. PD-PIGD group demonstrated lower FC between aSN and pSN, as well as between VAN and DMN, in contrast to PD-TD group. Particularly, the FC strength between VAN and DMN was positively correlated with TD and tremor scores, and the best fitting classification models of TD and PIGD subtypes were based on the FC between aSN and pSN. Compared with HCs, both PD-TD and PD-PIGD patients displayed decreased FC between two SMN subnetworks, while PD-TD patients exhibited increased FC between the SMN subnetwork and pSN, and between LECN and VAN. Furthermore, PD-PIGD patients demonstrated decreased FC between the SMN subnetwork and AN. Conclusions: The altered FC between aSN and pSN can be an imaging marker to distinguish PD-TD from PD-PIGD. We for the first time disclosed that the PD-TD patients compensated by increasing attention resources and the PD-PIGD patients displayed reduced FC between SMN and AN. Our findings provide a basis for identification and precision treatment of PD motor subtypes.

Brain network characteristics and cognitive performance in motor subtypes of Parkinson's disease: A resting state fMRI study

IntroductionParkinson's disease (PD) is a heterogeneous disorder with great variability in motor and non-motor manifestations. It is hypothesized that different motor subtypes are characterized by different neuropsychiatric and cognitive symptoms, but the underlying correlates in cerebral connectivity remain unknown. Our aim is to compare brain network connectivity between the postural instability and gait disorder (PIGD) and tremor-dominant (TD) subtypes, using both a within- and between-network analysis. MethodsThis cross-sectional resting-state fMRI study includes 81 PD patients, 54 belonging to the PIGD and 27 to the TD subgroup. Group-level spatial maps were created using independent component analysis. Differences in functional connectivity were investigated using dual regression analysis and inter-network connectivity analysis. An additional voxel-based morphometry analysis was performed to examine if results were influenced by grey matter atrophy. ResultsThe PIGD subgroup scored worse than the TD subgroup on all cognitive domains. Resting-state fMRI network analyses suggested that the connection between the visual and sensorimotor network is a potential differentiator between PIGD and TD subgroups. However, after correcting for dopaminergic medication use these results were not significant anymore. There was no between-group difference in grey matter volume. ConclusionDespite clear motor and cognitive differences between the PIGD and TD subtypes, no significant differences were found in network connectivity. Methodological challenges, substantial symptom heterogeneity and many involved variables make analyses and hypothesis building around PD subtypes highly complex. More sensitive visualisation methods combined with machine learning approaches may be required in the search for characteristic underpinnings of PD subtypes.

Detecting motor symptom fluctuations in Parkinson’s disease with generative adversarial networks

Parkinson’s disease is a neurodegenerative disorder characterized by several motor symptoms that develop gradually: tremor, bradykinesia, limb rigidity, and gait and balance problems. While there is no cure, levodopa therapy has been shown to mitigate symptoms. A patient on levodopa experiences cycles in the severity of their symptoms, characterized by an ON state—when the drug is active—and an OFF state—when symptoms worsen as the drug wears off. The longitudinal progression of the disease is monitored using episodic assessments performed by trained physicians in the clinic, such as the Unified Parkinson’s Disease Rating Scale (UPDRS). Lately, there has been an effort in the field to develop continuous, objective measures of motor symptoms based on wearable sensors and other remote monitoring devices. In this work, we present an effort towards such a solution that uses a single wearable inertial sensor to automatically assess the postural instability and gait disorder (PIGD) of a Parkinson’s disease patient. Sensor data was collected from two independent studies of subjects performing the UPDRS test and then used to train and validate a convolutional neural network model. Given the typical limited size of such studies we also employed the use of generative adversarial networks to improve the performance of deep-learning models that usually require larger amounts of data for training. We show that for a 2-min walk test, our method’s predicted PIGD scores can be used to identify a patient’s ON/OFF states better than a physician evaluated on the same criteria. This result paves the way for more reliable, continuous tracking of Parkinson’s disease symptoms.

Cerebro-cerebellar motor networks in clinical subtypes of Parkinson’s disease

Parkinson’s disease (PD) patients can be classified in tremor-dominant (TD) and postural-instability-and-gait-disorder (PIGD) motor subtypes. PIGD represents a more aggressive form of the disease that TD patients have a potentiality of converting into. This study investigated functional alterations within the cerebro-cerebellar system in PD-TD and PD-PIGD patients using stepwise functional connectivity (SFC) analysis and identified neuroimaging features that predict TD to PIGD conversion. Thirty-two PD-TD, 26 PD-PIGD patients and 60 healthy controls performed clinical/cognitive evaluations and resting-state functional MRI (fMRI). Four-year clinical follow-up data were available for 28 PD-TD patients, who were classified in 10 converters (cTD-PD) and 18 non-converters (ncTD-PD) to PIGD. The cerebellar seed-region was identified using a fMRI motor task. SFC analysis, characterizing regions that connect brain areas to the cerebellar seed at different levels of link-step distances, evaluated similar and divergent alterations in PD-TD and PD-PIGD. The discriminatory power of clinical data and/or SFC in distinguishing cPD-TD from ncPD-TD patients was assessed using ROC curve analysis. Compared to PD-TD, PD-PIGD patients showed decreased SFC in temporal lobe and occipital lobes and increased SFC in cerebellar cortex and ponto-medullary junction. Considering the subtype-conversion analysis, cPD-TD patients were characterized by increased SFC in temporal and occipital lobes and in cerebellum and ponto-medullary junction relative to ncPD-TD group. Combining clinical and SFC data, ROC curves provided the highest classification power to identify conversion to PIGD. These findings provide novel insights into the pathophysiology underlying different PD motor phenotypes and a potential tool for early characterization of PD-TD patients at risk of conversion to PIGD.

Association of Plasma and Electroencephalography Markers With Motor Subtypes of Parkinson's Disease.

ObjectiveThe aim of this study was to investigate the correlations of plasma neurodegenerative proteins and electroencephalography (EEG) dynamic functional network (DFN) parameters with disease progression in early Parkinson’s disease (PD) with different motor subtypes, including tremor-dominant (TD) and postural instability and gait disorder (PIGD).MethodsIn our study, 33 patients with PD (21 TD and 12 PIGD) and 33 healthy controls (HCs) were enrolled. Plasma neurofilament light chain (NfL), α-synuclein (α-syn), total-tau (t-tau), β-amyloid 42 (Aβ42), and β-amyloid 40 (Aβ40) levels were measured using an ultrasensitive single-molecule array (Simoa) immunoassay. All the patients with PD underwent EEG quantified by DFN analysis. The motor and non-motor performances were evaluated by a series of clinical assessments. Subsequently, a correlation analysis of plasma biomarkers and EEG measures with clinical scales was conducted.ResultsIn the TD group, plasma NfL exhibited a significant association with MDS-UPDRS III and Montreal Cognitive Assessment (MoCA). A higher Aβ42/40 level was significantly related to a decrease in Hamilton Depression Rating Scale (HAMD) and Hamilton Anxiety Rating Scale (HAMA) in the PIGD group. In terms of the correlation between EEG characteristic parameters and clinical outcomes, trapping time (TT) delta was positively correlated with MDS-UPDRS III and MoCA scores in the TD group, especially in the prefrontal and frontal regions. For other non-motor symptoms, there were significant direct associations of kPLI theta with HAMD and HAMA, especially in the prefrontal region, and kPLI gamma was particularly correlated with Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire (RBDSQ) scores in the prefrontal, frontal, and parietal regions in the TD group. Furthermore, there was a significant positive correlation between plasma t-tau and kPLI, and pairwise correlations were found among plasma NfL, theta TT, and MoCA scores in the TD group.ConclusionThese results provide evidence that plasma neurodegenerative proteins and EEG measures have great potential in predicting the disease progression of PD subtypes, especially for the TD subtype. A combination of these two kinds of markers may have a superposition effect on monitoring and estimating the prognosis of PD subtypes and deserves further research in larger, follow-up PD cohorts.

Subthalamic low beta bursts differ in Parkinson’s disease phenotypes

ObjectiveParkinson’s disease (PD) patients may be categorized into tremor-dominant (TD) and postural-instability and gait disorder (PIGD) motor phenotypes, but the dynamical aspects of subthalamic nucleus local field potentials (STN-LFP) and the neural correlates of this phenotypical classification remain unclear. Methods35 STN-LFP (20 PIGD and 15 TD) were investigated through continuous wavelet transform and machine-learning-based methods. The beta oscillation – the main band associated with motor impairment in PD – dynamics was characterized through beta burst parameters across phenotypes and burst intervals under specific proposed criteria for optimal burst threshold definition. ResultsLow-frequency (13–22 Hz) beta burst probability was the best predictor for PD phenotypes (75% accuracy). PIGD patients presented higher average burst duration (p = 0.018), while TD patients exhibited higher burst probability (p = 0.014). Categorization into shorter and longer than 400 ms bursts led to significant interaction between burst length categories and the phenotypes (p < 0.050) as revealed by mixed-effects models. Long burst durations and short bursts probability positively correlated, respectively, with rigidity-bradykinesia (p = 0.029) and tremor (p = 0.038) scores. ConclusionsSubthalamic low-frequency beta bursts differed between TD and PIGD phenotypes and correlated with motor symptoms. SignificanceThese findings improve the PD phenotypes’ electrophysiological characterization and may define new criteria for adaptive deep brain stimulation.

Long-Term Cognitive Decline Related to the Motor Phenotype in Parkinson's Disease.

Parkinson's disease (PD) is associated with various non-motor symptoms, including cognitive deterioration. Here, we used data from the DEMPARK/LANDSCAPE cohort to describe the association between progression of cognitive profiles and the PD motor phenotypes: postural instability and gait disorder (PIGD), tremor-dominant (TR-D), and not-determined (ND). Demographic, clinical, and neuropsychological six-year longitudinal data of 711 PD-patients were included (age: M = 67.57; 67.4% males). We computed z-transformed composite scores for a priori defined cognitive domains. Analyses were controlled for age, gender, education, and disease duration. To minimize missing data and drop-outs, three-year follow-up data of 442 PD-patients was assessed with regard to the specific role of motor phenotype on cognitive decline using linear mixed modelling (age: M = 66.10; 68.6% males). Our study showed that in the course of the disease motor symptoms increased while MMSE and PANDA remained stable in all subgroups. After three-year follow-up, significant decline of overall cognitive performance for PIGD-patients were present and we found differences for motor phenotypes in attention (β= -0.08, SE = 0.003, p < 0.006) and memory functions showing that PIGD-patients deteriorate per months by -0.006 compared to the ND-group (SE = 0.003, p = 0.046). Furthermore, PIGD-patients experienced more often difficulties in daily living. Over a period of three years, we identified distinct neuropsychological progression patterns with respect to different PD motor phenotypes, with early executive deficits yielding to a more amnestic profile in the later course. Here, in particular PIGD-patients worsened over time compared to TR-D and ND-patients, highlighting the greater risk of dementia for this motor phenotype.

Correlation of Dopaminergic Denervation and the Progression of Autonomic Dysfunctions in Different Clinical Subtypes of Parkinson's Disease.

Background Autonomic dysfunctions occur in the early stage of Parkinson's disease (PD) and impact the quality of life during the progression of the disease. In this study, we evaluated the serial progression of autonomic dysfunctions between different subtypes of a prospective PD cohort. Materials and Methods From the Parkinson's Progression Markers Initiative (PPMI) database, 325 PD patients (age: 61.2 ± 9.7, M : F = 215 : 110) were enrolled. Patients were subgrouped into tremor-dominant (TD), indeterminate, and postural instability and gait disorder (PIGD) subtypes. The progression of autonomic dysfunctions and dopaminergic denervation from I-123 FP-CIT SPECT images of each group were analyzed and compared at baseline, 12 months, 24 months, and 48 months of follow-up periods. Results The SCOPA-AUT score of the indeterminate subtype was significantly higher than that of the TD subtype (P < 0.05) at baseline and was significantly higher than that of both TD and PIGD subtypes (P < 0.05) at 48 months. The indeterminate subtype had the most significant correlation between the aggravation of dopaminergic denervation in I-123 FP-CIT SPECT images and the increase of SCOPA-AUT scores during 48 months of follow-up (r = 0.56, P < 0.01). Conclusions Autonomic dysfunctions were most severe in the indeterminate subtype throughout the 48 months of the follow-up period, with a significant correlation with dopaminergic denervation. We suggest a positive relationship between dopaminergic denervation and autonomic dysfunctions of the indeterminate subtype, beginning from the early stage of PD.

A Cross-Sectional Comprehensive Assessment of the Profile and Burden of Non-motor Symptoms in Relation to Motor Phenotype in the Nigeria Parkinson Disease Registry Cohort.

Data on non-motor symptoms (NMS) in black Africans with Parkinson's disease (PD) are sparse. To describe the profile of NMS in the Nigeria PD Registry (NPDR) cohort and explore the relationship between NMS and PD motor phenotype. We conducted a cross-sectional study of the frequency and burden of NMS, based on the non-motor symptoms scale (NMSS) and the Chaudhuri method respectively in our cohort. Baseline demographics, disease characteristics (Hoehn and Yahr stage, MDS-UPDRS total score and Part III motor score), motor phenotype (based on Stebbin et al's algorithm), and levodopa equivalent daily dose (LEDD) were documented. Data are presented for 825 PD whose mean age at study was 63.7 ± 10.1 years, female sex-221 [26.8%] while median PD duration was 36 months. PD phenotypes included tremor-dominant 466 (56.5%), postural instability and gait disorder (PIGD) 259 (31.4%), and indeterminate 100 (12.1%). 82.6% were on treatment (median LEDD of 500 mg/24 hours). 804 (97.5%) endorsed at least 1 NMS. The median NMSS score was 26.0 while subscores for urinary and sexual function domains were significantly higher in males (P< 0.05). PIGD-PD had more frequent NMS and higher frequency of severe/very severe NMSS burden (P= 0.000 for both). Nocturia and fatigue were the most prevalent NMS overall and across motor subtypes. PIGD phenotype and total UPDRS scores were the independent determinants of NMSS scores (P= 0.000). The profile and burden of NMS, and association with motor subtype in our black African cohort is largely similar to descriptions from other populations.

Motor cerebro-cerebellar networks breakdown among different subtypes of Parkinson’s disease

Parkinson’s disease (PD) patients are classified as tremor-dominant (TD) and postural instability and gait disorder (PIGD) phenotypes. The aim of this study was to investigate functional alterations within motor circuits of the cerebro-cerebellar system in PD-TD and PD-PIGD groups using stepwise functional connectivity (SFC) method.

Does Subthalamic Deep Brain Stimulation Impact Asymmetry and Dyscoordination of Gait in Parkinson’s Disease?

Background. Subthalamic deep brain stimulation (STN-DBS) is an effective treatment for selected Parkinson’s disease (PD) patients. Gait characteristics are often altered after surgery, but quantitative therapeutic effects are poorly described. Objective. The goal of this study was to systematically investigate modifications in asymmetry and dyscoordination of gait 6 months postoperatively in patients with PD and compare the outcomes with preoperative baseline and to asymptomatic controls without PD. Methods. A convenience sample of thirty-two patients with PD (19 with postural instability and gait disorder (PIGD) type and 13 with tremor dominant disease) and 51 asymptomatic controls participated. Parkinson patients were tested prior to the surgery in both OFF and ON medication states, and 6-months postoperatively in the ON stimulation condition. Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) I to IV and medication were compared to preoperative conditions. Asymmetry ratios, phase coordination index, and walking speed were assessed. Results. MDS-UPDRS I to IV at 6 months improved significantly, and levodopa equivalent daily dosages significantly decreased. STN-DBS increased step time asymmetry (hedges’ g effect sizes [95% confidence interval] between pre- and post-surgery: .27 [-.13, .73]) and phase coordination index (.29 [-.08, .67]). These effects were higher in the PIGD subgroup than the tremor dominant (step time asymmetry: .38 [-.06, .90] vs .09 [-.83, 1.0] and phase coordination index: .39 [-.04, .84] vs .13 [-.76, .96]). Conclusions. This study provides objective evidence of how STN-DBS increases asymmetry and dyscoordination of gait in patients with PD and suggests motor subtypes‐associated differences in the treatment response.

Spectral characteristics of subthalamic nucleus local field potentials in Parkinson's disease: Phenotype and movement matter.

Parkinson's disease (PD) is clinically heterogeneous across patients and may be classified in three motor phenotypes: tremor dominant (TD), postural instability and gait disorder (PIGD), and undetermined. Despite the significant clinical characterization of motor phenotypes, little is known about how electrophysiological data, particularly subthalamic nucleus local field potentials (STN-LFP), differ between TD and PIGD patients. This is relevant since increased STN-LFP bandpower at α-β range (8-35Hz) is considered a potential PD biomarker and, therefore, a critical setpoint to drive adaptive deep brain stimulation. Acknowledging STN-LFP differences between phenotypes, mainly in rest and movement states, would better fit DBS to clinical and motor demands. We studied this issue through spectral analyses on 35 STN-LFP in TD and PIGD patients during rest and movement. We demonstrated that higher β2 activity (22-35Hz) was observed in PIGD only during rest. Additionally, bandpower differences between rest and movement occurred at the α-β range, but with different patterns as per phenotypes: movement-induced desynchronization concerned lower frequencies in TD (10-20Hz) and higher frequencies in PIGD patients (21-28Hz). Finally, when supervised learning algorithms were employed aiming to discriminate PD phenotypes based on STN-LFP bandpower features, movement information had improved the classification accuracy, achieving peak performances when TD and PIGD movement-induced desynchronization ranges were considered. These results suggest that STN-LFP β-band encodes phenotype-movement dependent information in PD patients.

Serum NFL levels predict progression of motor impairment and reduction in putamen dopamine transporter binding ratios in de novo Parkinson's disease: An 8-year longitudinal study

Neurofilament light chain (NFL) level in biofluids is a sensitive measure of axonal damage and a promising biomarker in neurodegenerative diseases. In Parkinson's disease (PD), NFL can distinguish PD from other parkinsonian disorders, and NFL concentration is associated with disease severity, risk of progression, and survival. To determine whether serum NFL at baseline in de novo PD predicts motor decline, differentially impacts specific motor features, predicts cognitive decline, and predicts loss of dopamine terminals, here we evaluated 376 de novo PD patients from the PPMI database and analyzed the effect of baseline serum NFL levels on progression over eight years of motor impairment measured with the UPDRS, cognitive function measured with the MoCA, and putamen dopamine transporter (DAT) binding ratio measured with DaTscan. In longitudinal mixed effects models that controlled for age, gender, disease duration, and levodopa equivalent drug dose, higher levels of serum NFL at baseline were associated with greater increases of UPDRS-III and total UPDRS scores, with greater worsening of postural instability and gait disorder (PIGD) scores but not tremor scores over time. In contrast, baseline serum NFL was not associated with significant progression of MoCA scores in this de novo PD cohort. Higher baseline serum NFL was associated with greater reduction of putamen DAT binding ratio over time. Together, these findings show that baseline serum NFL levels predict the rate of motor decline, the accumulation of PIGD clinical features, and the progression of dopamine transporter loss in the early stage of PD.

Association of Serum Indirect Bilirubin Concentrations with Motor Subtypes of Parkinson’s Disease

Introduction: We aimed to investigate the change of serum indirect bilirubin (IBIL) concentrations in patients with Parkinson’s disease (PD) and whether IBIL concentrations were associated with the motor subtypes of PD. Methods: A case-control study was performed to evaluate differences in bilirubin concentrations between 78 PD subjects and 78 controls. Venous blood samples were collected, and total bilirubin (TBIL), direct bilirubin (DBIL), and IBIL concentrations were analyzed between PD subjects and controls. PD patients were classified into three motor subtypes: tremor-dominant (TD), intermediate (I), and postural instability and gait disorder (PIGD). It was evaluated whether there were differences in IBIL concentrations between the different motor subtypes and between motor subtypes and controls. Results: PD patients had lower IBIL concentrations compared to controls (6.51 ± 4.03 vs. 10.82 ± 4.61, p< 0.001). There was no significant difference in IBIL concentrations between PD males and PD females (6.66 ± 3.64 vs. 6.22 ± 4.79, p =0.655). IBIL concentrations had negative relationships with levodopa-equivalent daily dose (LEDD) (R = –0.452, p < 0.001) and positive relationships with tremor score (R = 0.360, p = 0.001). IBIL concentrations were significantly lower for PIGD than for TD subtype (4.88 ± 4.03 vs. 9.00 ± 4.15, p< 0.001). The lower IBIL concentrations in PD compared to controls were mainly driven by the PIGD patients. Conclusions: PD subjects showed lower levels of IBIL compared to controls. Higher IBIL levels were associated with TD motor subtype in PD, which could be related to the antioxidative properties of IBIL.