Mild Parkinson's Disease Research Articles

Neural effects of multisensory dance training in Parkinson's disease: evidence from a longitudinal neuroimaging single case study.

Dance is associated with beneficial outcomes in motor and non-motor domains in Parkinson's disease (PD) and regular participation may help delay symptom progression in mild PD. However, little is known about the neurobiological mechanisms of dance interventions for PD. The present case study explored potential neuroplastic changes in a 69-year-old male with mild PD participating in regular dance classes over 29 weeks. Functional MRI was performed at four timepoints (pre-training, 11 weeks, 18 weeks, 29 weeks), where the individual imagined a dance choreography while listening to the corresponding music. Neural activity was compared between dance-imagery and fixation blocks at each timepoint. Analysis of functionally defined regions revealed significant blood-oxygen-level-dependent (BOLD) signal activation in the supplementary motor area, right and left superior temporal gyri and left and right insula, with modulation of these regions observed over the training period except for the left insula. The results suggest the potential for dance to induce neuroplastic changes in people with PD in regions associated with motor planning and learning, auditory processing, rhythm, emotion, and multisensory integration. The findings are consistent with dance being a multimodal therapeutic activity that could provide long-term benefits for people with PD.

The effects of Baduanjin on fine motor skills in mild and moderate Parkinson’s disease: A randomized controlled trial

BackgroundFine motor impairment is common in Parkinson’s disease (PD), which reduces patients’ quality of life. There are few suitable targeted treatments. We conducted a clinical trial to determine whether Baduanjin Qigong exercise would increase fine motor skills in PD patients. MethodsSixty PD patients (Hoehn-Yahr stage 1–4) with hand fine motor impairment were randomly assigned to the Baduanjin group and the physical activity group. Baduanjin group practiced Baduanjin exercise five times weekly for 40 min (warm-up 5 min, Baduanjin 30 min, cool-down 5 min). The usual physical activity groups maintained their habit of usual physical activities. The participants underwent assessments in the “ON” medication state at baseline and 4-week follow-up time points. The Purdue Pegboard Test (PPT) was used as the primary outcome to assess manual dexterity. The secondary outcomes included the Movement Disorders Society-Unified Parkinson’s Disease Rating Scale, part III (MDS-UPDRS III), and the Parkinson’s disease questionnaire (PDQ-39). ResultsThe results of PPT revealed the Baduanjin group showed statistically significant improvement in the “non-dominant hand” and “assembly” scores compared to the usual physical activity group (P < 0.05), but with no significant difference in “dominant hand” and “both hands” (P > 0.05). Additionally, the Baduanjin group showed better performance in the PDQ-39 (P < 0.05). ConclusionOur study concludes that a 4-week Baduanjin exercise is effective in improving fine motor function and quality of life in patients with mild and moderate PD. The results suggest a promising intervention to be implemented in community or home settings for managing fine motor impairment in PD.

Secondary analysis reveals gut microbiota differences in patients with Parkinson’s disease and/or cognitive impairment

Background: Parkinson’s disease (PD) is a neurodegenerative disorder, and the main clinical characteristics are bradykinesia and muscle stiffness. Cognitive impairment (CI) is a prevalent non-motor manifestation observed in individuals with PD. According to disease severity, it can be divided into PD with mild cognitive impairment (MCI) and PD dementia. CI in PD patients may precede motor symptoms, and the gut microbiota plays an important role in PD pathogenesis. Therefore, gut microbiota may be one of the diagnostic targets for PD-CI. Methods: This study compared the gut microbiota of 43 PD-CI patients [Montreal Cognitive Assessment (MoCA) score &lt; 26] and 38 PD patients without CI (MoCA ≥ 26). Patients’ neuropsychological conditions, depression scale, and brain structure scanned by magnetic resonance imaging (MRI) were also recorded. The fecal metagenomic datasets of patients with PD, PD-CI, and CI only were retrieved from public databases for reanalysis to explore the relationship between PD, CI, and gut microbiota. Results: We found that the cortical thickness and the volume of the hippocampus, gray matter, and thalamus were significantly reduced among patients with PD-CI compared to PD without CI (P &lt; 0.05). Moreover, the gut microbiome in patients with PD-CI had fewer short-chain fatty acid (SCFA) producing bacteria and more pathogenic bacteria. There were also alterations in patterns of metabolic pathway-encoding genes. Additionally, PD affected gut microbiota more than CI. Conclusion: CI may aggravate the severity of PD, but it did not drastically alter subjects’ gut microbiota. This study reveals the relationship between gut microbiota, PD, and CI.

Transcranial Temporal Interference Stimulation of the Right Globus Pallidus in Parkinson's Disease.

Invasive deep brain stimulation (DBS) has been shown to be effective in treating patients with Parkinson's disease (PD), yet its clinical use is limited to patients at the advanced stage of the disease. Transcranial temporal interference stimulation (tTIS) may be a novel nonneurosurgical and safer alternative, yet its therapeutic potential remains unexplored. This pilot study aims to examine the feasibility and safety of tTIS targeting the right globus pallidus internus (GPi) for motor symptoms in patients with PD. Twelve participants with mild PD completed this randomized, double-blind, and sham-controlled experiment. Each of them received either 20-minute or sham tTIS of the right GPi. Before and immediately after the stimulation, participants completed the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) in the "medication-on" state to assess the motor symptoms. The blinding efficacy and side effects were also assessed. tTIS was well tolerated by participants, with only mild, transient adverse effects reported. tTIS significantly reduced MDS-UPDRS-III scores by 6.64 points (14.7%), particularly in bradykinesia (23.5%) and tremor (15.3%). The left side showed more significant alleviation in motor symptoms, particularly bradykinesia, compared to the right side. Participants with severer bradykinesia and tremor before stimulation experienced greater improvement after tTIS. This pilot study suggests that the tTIS, as a novel noninvasive DBS approach, is feasible and safe for alleviating motor symptoms in mild PD, especially bradykinesia and tremor. Future larger-scale and more definitive studies are needed to confirm the benefits. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Fractional Anisotropy is a More Sensitive Diagnostic Biomarker Than Mean Kurtosis for Patients with Parkinson Disease with Cognitive Dysfunction: A Diffusional Kurtosis Map Tract-Based Spatial Statistics Study.

There is heterogeneity of white matter damage in Parkinson's disease patients with different cognitive states. Our aim was to find sensitive diffusional kurtosis imaging biomarkers to differentiate the white matter damage pattern of mild cognitive impairment and dementia. Nineteen patients with Parkinson disease with mild cognitive impairment and 18 patients with Parkinson disease with dementia were prospectively enrolled. All participants underwent MR examination with 3D-T1-weighted image and diffusional kurtosis imaging sequences. Demographic data were compared between the 2 groups. Voxelwise statistical analyses of diffusional kurtosis imaging parameters were performed using tract-based spatial statistics. The receiver operator characteristic curve of significantly different metrics was graphed. The correlation of significantly different metrics with global cognitive status was analyzed. Compared with the Parkinson disease with mild cognitive impairment group, the fractional anisotropy and mean kurtosis values decreased in 4 independent clusters in the forceps minor, forceps major, inferior fronto-occipital fasciculus, and the inferior and superior longitudinal fasciculus in patients with Parkinson disease with dementia; the mean diffusivity decreased in 1 cluster in the forceps minor. The fractional anisotropy value in the inferior fronto-occipital fasciculus and inferior longitudinal fasciculus would be the diffusional kurtosis imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and patients with Parkinson disease with dementia, with the best diagnostic efficiency of 0.853. The fractional anisotropy values in the forceps minor (β = 84.20, P < .001) and years of education (β = 0.38, P = .014) were positively correlated with the Montreal Cognitive Assessment. The diffusional kurtosis imaging-derived fractional anisotropy and mean kurtosis can detect the different white matter damage patterns of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia. Fractional anisotropy is more sensitive than mean kurtosis in the differential diagnosis; fractional anisotropy derived from diffusional kurtosis imaging could become a promising imaging marker for the differential diagnosis of Parkinson disease with mild cognitive impairment and Parkinson disease with dementia.

Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.

Effects of ankle isokinetic training on muscle strength and balance amongst older women with mild Parkinson's disease: A randomised trial.

Patients with Parkinson's disease frequently experience problems with balance and gait. This study examined the influences of regular ankle isokinetic muscle strength training on muscle strength and balance ability amongst elderly women with mild Parkinson's disease. A total of 55elderly women with mild Parkinson's disease were randomly divided into the experimental group (n= 28) and the control group (n= 27) for 16weeks. The experimental group was trained for 16weeks by using isokinetic muscle strength training. After 16weeks of training in the experimental group, the peak torque (PT) of ankle (average of the left and right sides) dorsiflexion, plantarflexion, eversion and inversion, and the standing balance index revealed that the left and right stability and overall stability indexes significantly increased by 25.9%, 43.5%, 62.7%, 82.8%, 37.1% and 32.2%, respectively (p< 0.05). The experimental group exhibited significantly increased PT of ankle dorsiflexion, plantarflexion, eversion and inversion. Moreover, the left and right stability and overall stability indexes were significantly improved compared with those of the control group (p< 0.05). This study found a linear regression relationship between the PT of ankle inversion and overall stability index. This study determined that ankle isokinetic muscle strength training improved ankle dorsiflexion, plantarflexion, eversion and inversion muscle strength, and balance ability amongst elderly women with mild Parkinson's disease. However, it did not improve stability in the anterior and posterior directions. Ankle inversion muscle strength is more likely to affect overall balance.

Effects on cerebral blood flow after single doses of the β2 agonist, clenbuterol, in healthy volunteers and patients with mild cognitive impairment or Parkinson's disease.

Cerebral hypometabolism occurs years prior to a diagnosis of neurodegenerative diseases and coincides with reduced cerebral perfusion and declining noradrenergic transmission from the locus coeruleus. In pre-clinical models, β-adrenoceptor (β-AR) agonists increase cerebrocortical glucose metabolism, and may have therapeutic potential for neurodegenerative diseases. This study investigated the safety and effects on regional cerebral blood flow (rCBF) of the oral, brain-penetrant β2-AR agonist, clenbuterol, in healthy volunteers (HV) and patients with mild cognitive impairment (MCI) or Parkinson's disease (PD). This study evaluated the safety and effects on cerebral activity of the oral, brain-penetrant, β2-AR agonist clenbuterol (20-160 μg) in healthy volunteers and patients with MCI or PD. Regional CBF, which is tightly coupled to glucose metabolism, was measured by arterial spin labelling MRI in 32 subjects (25 HV and 8 MCI or PD) across five cohorts. In some cohorts, low doses of nadolol (1-5mg), a β-AR antagonist with minimal brain penetration, were administered with clenbuterol to control peripheral β2-AR responses. Significant, dose-dependent increases in rCBF were seen in multiple brain regions, including hippocampus, amygdala and thalamus, following the administration of clenbuterol to HVs (mean changes from baseline in hippocampal rCBF of -1.7%, 7.3%, 22.9%, 28.4% 3h after 20, 40, 80 and 160 μg clenbuterol, respectively). In patients with MCI or PD, increases in rCBF following 80 μg clenbuterol were observed both without and with 5mg nadolol (in hippocampus, 18.6%/13.7% without/with nadolol). Clenbuterol was safe and well-tolerated in all subjects; known side effects of β2-agonists, including increased heart rate and tremor, were mild in intensity and were blocked by low-dose nadolol. The effects of clenbuterol on rCBF were evident both in the absence and presence of low-dose nadolol, suggesting central nervous system (CNS) involvement. Concomitant inhibition of the peripheral effects of clenbuterol by nadolol confirms that meaningful β2-AR antagonism in the periphery was achieved without interrupting the central effects of clenbuterol on rCBF.

Brain Connectivity Networks Constructed Using MRI for Predicting Patterns of Atrophy Progression in Parkinson Disease.

Background Whether connectome mapping of structural and functional connectivity across the brain could be used to predict patterns of atrophy progression in patients with mild Parkinson disease (PD) has not been well studied. Purpose To assess the structural and functional connectivity of brain regions in healthy controls and its relationship with the spread of gray matter (GM) atrophy in patients with mild PD. Materials and Methods This prospective study included participants with mild PD and controls recruited from a single center between January 2012 and December 2023. Participants with PD underwent three-dimensional T1-weighted brain MRI, and the extent of regional GM atrophy was determined at baseline and every year for 3 years. The structural and functional brain connectome was constructed using diffusion tensor imaging and resting-state functional MRI in healthy controls. Disease exposure (DE) indexes-indexes of the pathology of each brain region-were defined as a function of the structural or functional connectivity of all the connected regions in the healthy connectome and the severity of atrophy of the connected regions in participants with PD. Partial correlations were tested between structural and functional DE indexes of each GM region at 1- or 2-year follow-up and atrophy progression at 2- or 3-year follow-up. Prediction models of atrophy at 2- or 3-year follow-up were constructed using exhaustive feature selection. Results A total of 86 participants with mild PD (mean age at MRI, 60 years ± 8 [SD]; 48 male) and 60 healthy controls (mean age at MRI, 62 years ± 9; 31 female) were included. DE indexes at 1 and 2 years were correlated with atrophy at 2 and 3 years (r range, 0.22-0.33; P value range, .002-.04). Models including DE indexes predicted GM atrophy accumulation over 3 years in the right caudate nucleus and some frontal, parietal, and temporal brain regions (R2 range, 0.40-0.61; all P < .001). Conclusion The structural and functional organization of the brain connectome plays a role in atrophy progression in the early stages of PD. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Yamada in this issue.

Using sustained vowels to identify patients with mild Parkinson's disease in a Chinese dataset.

Parkinson's disease (PD) is the second most common neurodegenerative disease and affects millions of people. Accurate diagnosis and subsequent treatment in the early stages can slow down disease progression. However, making an accurate diagnosis of PD at an early stage is challenging. Previous studies have revealed that even for movement disorder specialists, it was difficult to differentiate patients with PD from healthy individuals until the average modified Hoehn-Yahr staging (mH&Y) reached 1.8. Recent researches have shown that dysarthria provides good indicators for computer-assisted diagnosis of patients with PD. However, few studies have focused on diagnosing patients with PD in the early stages, specifically those with mH&Y ≤ 1.5. We used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets. Our results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75. The results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.

The effect of chronotropic incompetence on physiologic responses during progressive exercise in people with Parkinson’s disease

PurposeHeart rate (HR) response is likely to vary in people with Parkinson’s disease (PD), particularly for those with chronotropic incompetence (CI). This study explores the impact of CI on HR and metabolic responses during cardiopulmonary exercise test (CPET) in people with PD, and its implications for exercise intensity prescription.MethodsTwenty-eight participants with mild PD and seventeen healthy controls underwent CPET to identify the presence or absence of CI. HR and metabolic responses were measured at submaximal (first (VT1) and second (VT2) ventilatory thresholds), and at peak exercise. Main outcome measures were HR, oxygen consumption (VO2), and changes in HR responses (HR/WR slope) to an increase in exercise demand.ResultsCI was present in 13 (46%) PD participants (PDCI), who during CPET, exhibited blunted HR responses compared to controls and PD non-CI beyond 60% of maximal workload (p ≤ 0.05). PDCI presented a significantly lower HR at VT2, and peak exercise compared to PD non-CI and controls (p ≤ 0.001). VO2 was significantly lower in PDCI than PD non-CI and controls at VT2 (p = 0.003 and p = 0.036, respectively) and at peak exercise (p = 0.001 and p = 0.023, respectively).ConclusionAlthough poorly understood, the presence of CI in PD and its effect on HR and metabolic responses during incremental exercise is significant and important to consider when programming aerobic exercises.

Eye movement function captured via an electronic tablet informs on cognition and disease severity in Parkinson’s disease

Studying the oculomotor system provides a unique window to assess brain health and function in various clinical populations. Although the use of detailed oculomotor parameters in clinical research has been limited due to the scalability of the required equipment, the development of novel tablet-based technologies has created opportunities for fast, easy, cost-effective, and reliable eye tracking. Oculomotor measures captured via a mobile tablet-based technology have previously been shown to reliably discriminate between Parkinson’s Disease (PD) patients and healthy controls. Here we further investigate the use of oculomotor measures from tablet-based eye-tracking to inform on various cognitive abilities and disease severity in PD patients. When combined using partial least square regression, the extracted oculomotor parameters can explain up to 71% of the variance in cognitive test scores (e.g. Trail Making Test). Moreover, using a receiver operating characteristics (ROC) analysis we show that eye-tracking parameters can be used in a support vector classifier to discriminate between individuals with mild PD from those with moderate PD (based on UPDRS cut-off scores) with an accuracy of 90%. Taken together, our findings highlight the potential usefulness of mobile tablet-based technology to rapidly scale eye-tracking use and usefulness in both research and clinical settings by informing on disease stage and cognitive outcomes.

Responsiveness of respiratory function in Parkinson's Disease to an integrative exercise programme: A prospective cohort study.

Respiratory disorders are the most common cause of death in Parkinson's Disease (PD). Conflicting data exist on the aetiology of respiratory dysfunction in PD and few studies examine the effects of exercise-based interventions on respiratory measures. This study was conducted to better understand respiratory dysfunction in PD and to identify measures of dysfunction responsive to an integrative exercise programme. The objectives were to compare baseline respiratory measures with matched, published population norms and to examine immediate and longer-term effects of a 12-week integrated exercise programme on these measures. Twenty-three people with mild PD (median Hoehn & Yahr = 2) self-selected to participate in this exploratory prospective cohort study. Evaluation of participants occurred at three time points: at baseline; following the 12-week exercise programme and at 4-month follow-up. Outcome measures included: Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), FEV1/FVC ratio, Peak Expiratory Flow (PEF), Inspiratory Muscle Strength (MIP), Expiratory Muscle Strength (MEP), Peak Cough Flow (PCF), and Cardiovascular Fitness measures of estimated VO2 max and 6-Minute Walk Test (6MWT). Compared to published norms, participants had impaired cough, reduced respiratory muscle strength, FEV, FVC, PEF and cardiovascular fitness. Post exercise intervention, statistically significant improvements were noted in MEP, cardiovascular fitness, and PEF. However only gains in PEF were maintained at 4-month follow-up. Significant respiratory dysfunction exists, even in the early stages of PD. Metrics of respiratory muscle strength, peak expiratory flow and cardiovascular fitness appear responsive to an integrative exercise programme.

Oral Microbiota and Porphyromonas gingivalis Kgp Genotypes Altered in Parkinson's Disease with Mild Cognitive Impairment.

Cognitive impairment (CI) is a common complication of the non-motor symptoms in Parkinson's disease (PD), including PD with mild cognitive impairment (PD-MCI) and PD dementia. Recent studies reported the oral dysbiosis in PD and CI, respectively. Porphyromonas gingivalis (P. gingivalis), a pathogen of oral dysbiosis, plays an important role in PD, whose lysine-gingipain (Kgp) could lead to AD-type pathologies. No previous study investigated the composition of oral microbiota and role of P. gingivalis in PD-MCI. This study aimed to investigate the differences of oral microbiota composition, P. gingivalis copy number, and Kgp genotypes among PD-MCI, PD with normal cognition (PD-NC) and periodontal status-matched control (PC) groups. The oral bacteria composition, the copy number of P. gingivalis, and the Kgp genotypes in gingival crevicular fluid from PD-MCI, PD-NC, and PC were analyzed using 16S ribosomal RNA sequencing, quantitative real-time PCR, and MseI restriction. We found that the structures of oral microbiota in PD-MCI group were significantly different compared to that in PD-NC and PC group. The relative abundances of Prevotella, Lactobacillus, Megasphaera, Atopobium, and Howardella were negatively correlated with cognitive score. Moreover, there was a significant difference of Kgp genotypes among the three groups. The predominant Kgp genotypes of P. gingivalis in the PD-MCI group were primarily Kgp II, whereas in the PD-NC group, it was mainly Kgp I. The Kgp II correlated with lower MMSE and MoCA scores, which suggested that Kgp genotypes II is related to cognitive impairment in PD.

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson's Disease Progression Levels through Analysis of Vocal Acoustic Patterns.

Speech impairments often emerge as one of the primary indicators of Parkinson's disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.

Neuropsychiatric symptoms and brain morphology in patients with mild cognitive impairment, cerebrovascular disease and Parkinson disease: A cross sectional and longitudinal study.

Neuropsychiatric symptoms (NPS) increase risk of developing dementia and are linked to various neurodegenerative conditions, including mild cognitive impairment (MCI due to Alzheimer's disease [AD]), cerebrovascular disease (CVD), and Parkinson's disease (PD). We explored the structural neural correlates of NPS cross-sectionally and longitudinally across various neurodegenerative diagnoses. The study included individuals with MCI due to AD, (n=74), CVD (n=143), and PD (n=137) at baseline, and at 2-years follow-up (MCI due to AD, n=37, CVD n=103, and PD n=84). We assessed the severity of NPS using the Neuropsychiatric Inventory Questionnaire. For brain structure we included cortical thickness and subcortical volume of predefined regions of interest associated with corticolimbic and frontal-executive circuits. Cross-sectional analysis revealed significant negative correlations between appetite with both circuits in the MCI and CVD groups, while apathy was associated with these circuits in both the MCI and PD groups. Longitudinally, changes in apathy scores in the MCI group were negatively linked to the changes of the frontal-executive circuit. In the CVD group, changes in agitation and nighttime behavior were negatively associated with the corticolimbic and frontal-executive circuits, respectively. In the PD group, changes in disinhibition and apathy were positively associated with the corticolimbic and frontal-executive circuits, respectively. The observed correlations suggest that underlying pathological changes in the brain may contribute to alterations in neural activity associated with MBI. Notably, the difference between cross-sectional and longitudinal results indicates the necessity of conducting longitudinal studies for reproducible findings and drawing robust inferences.

Brain network centrality and connectivity are associated with clinical subtypes and disease progression in Parkinson's disease.

To investigate brain network centrality and connectivity alterations in different Parkinson's disease (PD) clinical subtypes using resting-state functional magnetic resonance imaging (RS-fMRI), and to explore the correlation between baseline connectivity changes and the clinical progression. Ninety-two PD patients were enrolled at baseline, alongside 38 age- and sex-matched healthy controls. Of these, 85 PD patients underwent longitudinal assessments with a mean of 2.75 ± 0.59 years. Two-step cluster analysis integrating comprehensive motor and non-motor manifestations was performed to define PD subtypes. Degree centrality (DC) and secondary seed-based functional connectivity (FC) were applied to identify brain network centrality and connectivity changes among groups. Regression analysis was used to explore the correlation between baseline connectivity changes and clinical progression. Cluster analysis identified two main PD subtypes: mild PD and moderate PD. Two different subtypes within the mild PD were further identified: mild motor-predominant PD and mild-diffuse PD. Accordingly, the disrupted DC and seed-based FC in the left inferior frontal orbital gyrus and left superior occipital gyrus were severe in moderate PD. The DC and seed-based FC alterations in the right gyrus rectus and right postcentral gyrus were more severe in mild-diffuse PD than in mild motor-predominant PD. Moreover, disrupted DC were associated with clinical manifestations at baseline in patients with PD and predicted motor aspects progression over time. Our study suggested that brain network centrality and connectivity changes were different among PD subtypes. RS-fMRI holds promise to provide an objective assessment of subtype-related connectivity changes and predict disease progression in PD.

Characterization of Walking in Mild Parkinson's Disease: Reliability, Validity and Discriminant Ability of the Six-Minute Walk Test Instrumented with a Single Inertial Sensor.

Although the 6-Minute Walk Test (6MWT) is among the recommended clinical tools to assess gait impairments in individuals with Parkinson's disease (PD), its standard clinical outcome consists only of the distance walked in 6 min. Integrating a single Inertial Measurement Unit (IMU) could provide additional quantitative and objective information about gait quality complementing standard clinical outcome. This study aims to evaluate the test-retest reliability, validity and discriminant ability of gait parameters obtained by a single IMU during the 6MWT in subjects with mild PD. Twenty-two people with mild PD and ten healthy persons performed the 6MWT wearing an IMU placed on the lower trunk. Features belonging to rhythm and pace, variability, regularity, jerkiness, intensity, dynamic instability and symmetry domains were computed. Test-retest reliability was evaluated through the Intraclass Correlation Coefficient (ICC), while concurrent validity was determined by Spearman's coefficient. Mann-Whitney U test and the Area Under the receiver operating characteristic Curve (AUC) were then applied to assess the discriminant ability of reliable and valid parameters. Results showed an overall high reliability (ICC ≥ 0.75) and multiple significant correlations with clinical scales in all domains. Several features exhibited significant alterations compared to healthy controls. Our findings suggested that the 6MWT instrumented with a single IMU can provide reliable and valid information about gait features in individuals with PD. This offers objective details about gait quality and the possibility of being integrated into clinical evaluations to better define walking rehabilitation strategies in a quick and easy way.

Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment.

Cognitive impairment (CI) is a significant non-motor symptoms in Parkinson's disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD-normal cognition [NC]) to stages with Mild CI (PD-MCI) and PD dementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting-state functional connectivity (FC) among three CI stages of PD. We enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra- and inter-network FC analysis in conjunction with comprehensive clinical cognitive assessment. Our findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter-intra-network FC in differentiating between PD-MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN, DMN, and salience network (SN) in the discriminating PD-NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD. We propose that resting-state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD-NC to MCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages.

Experience of Physical Rehabilitation in Empowering Individuals Living with Parkinson’s Disease: A Qualitative Study Protocol

ntroduction: Life expectancy is good, patients live with certain problems (tremors, difficulty in activities of daily living, cognitive impairment, lack of balance etc). Although mainstay treatment requires pharmacological treatment and regular physiotherapy. So, the first-hand experience of physiotherapy matters in these individuals. Need of the study: The present protocol will explore the experience of individuals with Parkinson’s Disease (PD). It will help in exploring the patients’ physical and emotional experiences as they return to the community, their barriers, facilitators and also gaps they experience in their rehabilitation journey. Aim: To explore the experience of physical rehabilitation in empowering individuals with Parkinson’s Disease (PD). Materials and Methods: An open-ended interview guide was formed and validated by multidisciplinary experts. Both male and female above the age of 40 years, undergoing rehabilitation for atleast 6 months and having mild and moderate PD (Hoehn and Yahr stage I-III). No depression and anxiety is assessed on Hospital Anxiety and Depression scale (HADS); HADS-depression score=9, HAD-anxiety=8 will be recruited. It will have 17 open ended questions regarding the experiences after Physical Rehabilitation, Cognitive impairment’s, Activities of Daily Living (ADLs), Freezing, Balance, Facilitators, Barriers. Semi-structured interviews will be conducted and transcribed verbatim into English language, then entered into Qualitative Data Analysis (QDA) miner software for analysis. The interview guide is validated by the experts and obtained an Itemlevel Content Validity Index (I-CVI) of all items/questions of 0.84-1 while Scale-level Content Validity Index (S-CVI) by Average method and Universal agreement is 0.914 and 0.647, respectively. Results will be depicted in form of word clouds and code tree.