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Parkinson's Disease (PD) exhibits heterogeneous cognitive deficits that may represent different cognitive phenotypes. While previous studies have described them in a "macro" manner, only one study has applied Network Analysis (NA) in PD. NA represents a model to explore relationships between cognitive abilities, aiding in understanding cognitive phenotypes. This study aims to verify whether the cognitive system undergoes reorganization in PD with Mild Cognitive Impairment (PD-MCI) patients. To explore this, a Level II cognitive assessment was administered to 275 PD patients, who were classified into two diagnostic categories: PD-Cognitive Unimpaired (CU) (n = 171) and PD-MCI (n = 104). NA was applied to construct Gaussian Graphical Models for each diagnostic group, where nodes represent cognitive tests and demographic factors, and edges represent their interconnections. The NA revealed substantial differences between the cognitive networks of PD-CU and PD-MCI patients. Specifically, the network of PD-MCI patients appears less sparse, with some weakened relationships between nodes. Overall, the results support the presence of a cognitive reorganization in PD-MCI patients, potentially indicating a functional compensation mechanism. In conclusion, this study enhances the understanding of the cognitive mechanisms underlying cognitive decline in patients with PD., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Background: Sleep fragmentation is a persistent problem throughout the course of Parkinson's disease (PD). However, the related neurophysiological patterns and the underlying mechanisms remained unclear., Method: We recorded subthalamic nucleus (STN) local field potentials (LFPs) using deep brain stimulation (DBS) with real-time wireless recording capacity from 13 patients with PD undergoing a one-night polysomnography recording, 1 month after DBS surgery before initial programming and when the patients were off-medication. The STN LFP features that characterised different sleep stages, correlated with arousal and sleep fragmentation index, and preceded stage transitions during N2 and REM sleep were analysed., Results: Both beta and low gamma oscillations in non-rapid eye movement (NREM) sleep increased with the severity of sleep disturbance (arousal index (ArI)-beta NREM : r=0.9, p=0.0001, sleep fragmentation index (SFI)-beta NREM : r=0.6, p=0.0301; SFI-gamma NREM : r=0.6, p=0.0324). We next examined the low-to-high power ratio (LHPR), which was the power ratio of theta oscillations to beta and low gamma oscillations, and found it to be an indicator of sleep fragmentation (ArI-LHPR NREM : r=-0.8, p=0.0053; ArI-LHPR REM : r=-0.6, p=0.0373; SFI-LHPR NREM : r=-0.7, p=0.0204; SFI-LHPR REM : r=-0.6, p=0.0428). In addition, long beta bursts (>0.25 s) during NREM stage 2 were found preceding the completion of transition to stages with more cortical activities (towards Wake/N1/REM compared with towards N3 (p<0.01)) and negatively correlated with STN spindles, which were detected in STN LFPs with peak frequency distinguishable from long beta bursts (STN spindle: 11.5 Hz, STN long beta bursts: 23.8 Hz), in occupation during NREM sleep (β=-0.24, p<0.001)., Conclusion: Features of STN LFPs help explain neurophysiological mechanisms underlying sleep fragmentations in PD, which can inform new intervention for sleep dysfunction., Trial Registration Number: NCT02937727., Competing Interests: Competing interests: Luming Li and Hongwei Hao serve on the scientific advisory board for Beijing Pins Medical Co., Ltd and are listed as inventors in issued patents and patent applications on the deep brain stimulator used in this work., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Deep brain stimulation is a well-established treatment for improving motor symptoms in Parkinson's disease. However, persistent non-motor symptoms, such as excessive daytime sleepiness, remain a significant challenge and necessitate further investigation. In this study, we conducted repeated measurements of daytime sleepiness using a modified multiple sleep latency test in a healthy monkey (macaca fascicularis), which was later rendered parkinsonian through MPTP administration. Deep brain stimulation targeting the lateral hypothalamic area revealed frequency-dependent modulation of both sleepiness level and core body temperature. High-frequency stimulation (80 Hz) increased sleepiness in the healthy state, while low-frequency stimulation (20 Hz) promoted wakefulness in the parkinsonian state. These findings suggest a promising therapeutic approach for addressing sleep/wake disturbances, not only in Parkinson's disease but also in other severe sleep disorders., Competing Interests: Declarations Competing interests S.Chabardès declares that he has received consultation fees from Medtronic and Boston Scientific in the last 5 years. The other authors declare no competing interests. We used AI to improve readability and language of the work. Ethics approval In accordance with the Grenoble Alpes University, Grenoble Institut of Neurosciences (B3851610008) policies and with French legislation, experiments were performed in compliance with the European Community Council Directive of 2010 (2010/63/UE) for care of laboratory animals and in accordance with the ARRIVE guidelines. All procedures were reviewed and validated by the local ethic committee n˚004 and were authorized by the Direction Départementale des Services Vétérinaires de l’Isère – Ministère de l’Agriculture et de la Pêche, France., (© 2024. The Author(s).)

Background: Current diagnosis and monitoring of Parkinson's disease (PD) is based on subjective clinical assessments. Objective measures of motor functioning could support clinical acumen. Computer vision (CV) technology is a promising contactless technique but requires further validation., Aim: To investigate the performance of CV analysis of clinic-based videos of finger-tapping. Our goals were (i) to distinguish PD from healthy controls (HC), when compared to human raters, (ii) to measure the severity of bradykinesia, and (iii) detect ON/OFF medication state., Methods: Videos of thirty-one persons with PD and forty-nine HC were collected during clinical outpatient visits. Videos were analysed using CV to produce speed, amplitude, rhythm and composite bradykinesia measures. All videos were independently rated by three raters using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Modified Bradykinesia Rating Scale (MBRS). Twenty video pairs were conducted in ON and OFF states. Classification accuracy for PD/HC state and ON/OFF state were measured using the Area under Receiver Operating characteristic curve and a confusion matrix. CV and clinical measures were correlated using Spearman coefficients., Results: CV classified disease state with higher accuracy than clinical raters (91 % sensitivity; 97 % specificity). CV measures of bradykinesia correlated significantly with clinical ratings: R = 0.740 for MDS-UPDRS, 0.715 for MBRS speed, 0.714 for amplitude and 0.504 for rhythm. CV classified ON/OFF state as accurately as clinical raters., Discussion: CV can provide a valid, objective and contactless bradykinesia assessment based on clinically collected videos, which offers promise as a new clinical outcome, including in remote settings., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest related to the contents of this manuscript., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background and Purpose: High-density surface electromyography (HD-sEMG) allows non-invasive analysis of motor unit (MU) activity and recruitment. This study examines MU recruitment features as well as action potential using HD-sEMG in the suprahyoid muscles of patients with Parkinson's disease (PD) and assesses alterations caused by cervical percutaneous interferential current stimulation., Methods: Electrical activity of the suprahyoid muscles during tongue elevation was measured using HD-sEMG. Maximum voluntary contraction (MVC) was measured, then the modified entropy, coefficient of variation, correlation coefficient, and root mean square (RMS) were examined. For comparison, HD-sEMG was also conducted on healthy controls. In addition, this study assessed the impact of cervical percutaneous interferential current stimulation on HD-sEMG in patients with PD (Hoehn-Yahr stages 2-4) over 8 weeks., Results: Twenty-two patients with PD (mean age 71.7 ± 6.0, 7 females) and 14 healthy controls (mean age 74.9 ± 6.1, 7 females) were included. The RMS at 60 % MVC was significantly lower in patients with PD than in healthy controls (p = 0.007). After cervical percutaneous interferential current stimulation, the correlation coefficients at 40 % and 60 % MVC significantly decreased (p = 0.040 and p = 0.048, respectively), and the RMS at 60 % MVC significantly increased (p = 0.035)., Conclusion: HD-sEMG of the suprahyoid muscles showed characteristic findings in patients with PD and was able to detect subtle changes due to electrical stimulation intervention. Thus, HD-sEMG could be a promising tool for evaluating swallowing-related muscle conditions and interventions., Competing Interests: Declaration of competing interest HM received honoraria from Eisai, Shionogi, Otsuka Pharmaceutical, and Sumitomo Pharma. The other authors declare no competing interests., (Copyright © 2024. Published by Elsevier B.V.)

Parkinsonian tremor (PT) and Essential tremor (ET) exist as upper limb tremors in clinical practice. Notably, their types of trembling share similar presentations and overlapping frequencies. To enhance objectivity and efficiency in the diagnosis of these two diseases, there is a pressing need for more objective tremor classification procedures. This study proposes a novel multimodal fusion network based on a cross-attention mechanism (MFCA-Net) to automate the classification of upper limb tremors between PTs and ETs. To this end, 140 patients with PTs and ETs were recruited, and acceleration and surface electromyography (sEMG) signals were collected from the forearm during tremor episodes. To comprehensively capture the global and local features of input signals, a multiscale convolution in MFCA-Net was designed. Furthermore, the cross-attention mechanism was applied to fuse the features of the two input signals. The results demonstrate that the final classification accuracy exceeded 97.18% when MFCA-Net was used. Compared with the single acceleration signal and single sEMG signal inputs, the recognition accuracies increased by 18.91% and 10.04%, respectively. Therefore, the proposed MFCA-Net in this study serves as an objective and potential tool for assisting clinicians in the diagnosis of PT and ET patients., Competing Interests: Declarations Competing interests The authors declare no competing interests. Ethics statement The study involving human participants was reviewed and approved by the Ruijin Hospital Ethics Committee of Shanghai Jiao Tong University School of Medicine (No. 2023 − 359). Patients/participants provided written informed consent to participate in this study. All the experiments were performed in accordance with the relevant guidelines and regulations., (© 2024. The Author(s).)

Parkinson's disease (PD) is characterized by motor and non-motor symptoms. Cognitive decline is crucial in disease progression and affect quality of life; however, their underlying mechanisms in PD remain unclear. We explored the relationship between cognitive impairment and functional connectivity (FC) using resting-state functional magnetic resonance imaging in 26 patients with sporadic PD, focusing on the changes in FC between on and off states. Cognitive function was assessed using the Mini-Mental State Examination (MMSE) score. The correlation between MMSE scores and changes in FC values during on and off states was assessed using Pearson's correlation coefficient. The correlation between changes in FC during the on and off periods and cognitive function differed for each cognitive function item. MMSE memory scores were positively correlated with FC between the brainstem and the left cerebral hemisphere. MMSE attention scores were positively correlated with FC between the bilateral thalamus and frontal lobes and negatively correlated with FC between the left cerebral hemispheres. These findings may facilitate our understanding of the neural correlates underlying cognitive impairment in PD and help develop treatment strategies to preserve cognitive function., (© 2024. The Author(s).)

Purpose: Respiratory-swallow coordination (RSC) frequently changes in people with Parkinson's disease (PwPD). Little is known about how these changes relate to impairments in swallowing safety (penetration and aspiration) and efficiency (pharyngeal residue). Therefore, the aims of this study were to assess the relationships between RSC, pharyngeal residue, penetration, and aspiration in PwPD., Method: Twenty-four PwPD were recruited to undergo simultaneous assessment of RSC, swallowing safety, and swallowing efficiency. RSC was assessed using respiratory inductive plethysmography and nasal airflow and included measurements of respiratory pause duration, respiratory phase patterning, and lung volume during swallowing. Swallowing safety and efficiency were assessed using flexible endoscopic evaluation of swallowing, analyzed using the Visual Analysis of Swallowing Efficiency and Safety, and included measurements of pharyngeal residue, penetration, and aspiration. All data were blindly analyzed, with 20% of the data repeated for interrater reliability assessment. Multilevel statistical models were used to examine the relationships between RSC and swallowing., Results: A total of 812 swallows were analyzed from 24 participants. Only 33.4% of swallows exhibited the typical exhale-swallow-exhale pattern. Additionally, 95% of participants exhibited abnormal swallow function. More severe hypopharyngeal residue ratings were associated with inhaling before the swallow compared to exhaling before the swallow. Additionally, more severe events of penetration and aspiration were associated with (a) inhaling before the swallow compared to exhaling before the swallow, (b) inhaling after the swallow compared to exhaling after the swallow, and (c) longer swallow-related respiratory pause durations. Inhaling after the swallow exhibited the strongest relationship with impairments in swallowing safety when compared to all other RSC variables., Conclusions: RSC exhibited significant relationships with pharyngeal residue, penetration, and aspiration in these PwPD. Clinicians should attend to RSC when assessing swallowing in PwPD. Future research is needed to examine if training an exhale-swallow-exhale pattern can be used to improve disordered swallowing in PwPD., Open Science Form: https://doi.org/10.23641/asha.27211770.

The ability to learn novel motor skills is essential for patients with Parkinson's disease (PD) to regain activities of daily living. However, the underlying mechanisms of motor learning in PD remain unclear. To identify motor features that are distinctively manifested in PD during motor learning, we quantified a rich set of variables reflecting various aspects of the learning process in a virtual throwing task. While the performance outcome improved similarly over 3 days of practice for both PD patients and age-matched controls, further analysis revealed distinct learning processes between the two groups. PD patients initially performed with a slow release velocity and gradually increased it as practice progressed, whereas the control group began with an unnecessarily rapid release velocity, which they later stabilized at a lower value. Performance characteristics related to the timing of ball release and the inter-release interval did not show significant group differences, although they were modulated across practice in both groups. After one week, both groups retained the performance outcomes and underlying kinematics developed over practice. This study underscores the importance of analyzing the multi-faceted learning process to characterize motor skill learning in PD. The findings may provide insights into PD pathophysiology and inform rehabilitation strategies., (© 2024. The Author(s).)

Slow gait speed and disrupted brain network connectivity are common in patients with Parkinson's disease (PD). This study aimed to clarify the relationship between gait speed and clinical characteristics in PD, and explore the underlying brain network mechanisms. Forty-two PD patients and 20 healthy controls (HC) were recruited. Statistical independent component analysis and correlation analysis were employed to investigate underlying neural mechanisms and relationships. PD patients exhibited significantly slower gait speed, which showed a significant negative correlation with postural instability and gait disturbance scores. Network connectivity analysis revealed decreased intranetwork functional connectivity (FC) within visual network (VN) and cerebellum network (CN), but increased internetwork FC between CN and both sensorimotor network (SMN) and frontoparietal network (FPN) in PD patients compared to HC. The slow gait speed PD subgroup demonstrated increased intranetwork FC within SMN and VN, along with decreased FC between VN and both FPN and default mode network. Correlation analyses revealed negative correlation between gait speed and FC of CN and positive correlation to FC of CN-SMN. Our study identified relationships between gait speed and clinical characteristics, and corresponding network connectivity alterations in PD patients, providing insights into the neural mechanisms underlying gait impairments in PD., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

This study investigated the association between cognitive function and facial emotion recognition (FER) in patients with Parkinson's disease (PD) and mild cognitive impairment (PD-MCI). We enrolled 126 participants from Taiwan, including 63 patients with idiopathic PD and 63 matched healthy controls. The PD group was divided into two groups: those with normal cognitive function (PD-NC) and those with MCI (PD-MCI). Participants underwent a modality emotion recognition test and comprehensive cognitive assessment. Our findings reveal that patients with PD-MCI exhibit significantly impaired FER, especially in recognizing "disgust," compared with patients with PD-NC and healthy adults (P = .001). This deficit correlates with executive function, attention, memory, and visuospatial abilities. Attention mediates the relationship between executive function and "disgust" FER. The findings highlight how patients with PD-MCI are specifically challenged when recognizing "disgust" and suggest that cognitive training focusing on cognitive flexibility and attention may improve their FER abilities. This study contributes to our understanding of the nuanced relationship between cognitive dysfunction and FER in patients with PD-MCI, emphasizing the need for targeted interventions., (© The Author(s) 2024. Published by Oxford University Press.)

Compensatory mechanisms in Parkinson's disease are defined as the changes that the brain uses to adapt to neurodegeneration and progressive dopamine reduction. Motor compensation in early Parkinson's disease could, in part, be responsible for a unilateral onset of clinical motor signs despite the presence of bilateral nigrostriatal degeneration. Although several mechanisms have been proposed for compensatory adaptations in Parkinson's disease, the underlying pathophysiology is unclear. Here, we investigate motor compensation in Parkinson's disease by investigating the relationship between clinical signs, dopamine transporter imaging data and neurophysiological measures of the primary motor cortex (M1), using transcranial magnetic stimulation in presymptomatic and symptomatic hemispheres of patients. In this cross-sectional, multicentre study, we screened 82 individuals with Parkinson's disease. Patients were evaluated clinically in their medication OFF state using standardized scales. Sixteen Parkinson's disease patients with bilateral dopamine transporter deficit in the putamina but unilateral symptoms were included. Twenty-eight sex- and age-matched healthy controls were also investigated. In all participants, we tested cortical excitability using single- and paired-pulse techniques, interhemispheric inhibition and cortical plasticity with paired associative stimulation. Data were analysed with ANOVAs, multiple linear regression and logistic regression models. Individual coefficients of motor compensation were defined in patients based on clinical and imaging data, i.e. the motor compensation coefficient. The motor compensation coefficient includes an asymmetry score to balance motor and dopamine transporter data between the two hemispheres, in addition to a hemispheric ratio accounting for the relative mismatch between the magnitude of motor signs and dopaminergic deficit. In patients, corticospinal excitability and plasticity were higher in the presymptomatic compared with the symptomatic M1. Also, interhemispheric inhibition from the presymptomatic to the symptomatic M1 was reduced. Lower putamen binding was associated with higher plasticity and reduced interhemispheric inhibition in the presymptomatic hemisphere. The motor compensation coefficient distinguished the presymptomatic from the symptomatic hemisphere. Finally, in the presymptomatic hemisphere, a higher motor compensation coefficient was associated with lower corticospinal excitability and interhemispheric inhibition and with higher plasticity. In conclusion, the present study suggests that motor compensation involves M1-striatal networks and intercortical connections becoming more effective with progressive loss of dopaminergic terminals in the putamen. The balance between these motor networks seems to be driven by cortical plasticity., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Control of actions allows adaptive, goal-directed behaviour. The basal ganglia, including the subthalamic nucleus, are thought to play a central role in dynamically controlling actions through recurrent negative feedback loops with the cerebral cortex. Here, we summarize recent translational studies that used deep brain stimulation to record neural activity from and apply electrical stimulation to the subthalamic nucleus in people with Parkinson's disease. These studies have elucidated spatial, spectral and temporal features of the neural mechanisms underlying the controlled delay of actions in cortico-subthalamic networks and demonstrated their causal effects on behaviour in distinct processing windows. While these mechanisms have been conceptualized as control signals for suppressing impulsive response tendencies in conflict tasks and as decision threshold adjustments in value-based and perceptual decisions, we propose a common framework linking decision-making, cognition and movement. Within this framework, subthalamic deep brain stimulation can lead to suboptimal choices by reducing the time that patients take for deliberation before committing to an action. However, clinical studies have consistently shown that the occurrence of impulse control disorders is reduced, not increased, after subthalamic deep brain stimulation surgery. This apparent contradiction can be reconciled when recognizing the multifaceted nature of impulsivity, its underlying mechanisms and modulation by treatment. While subthalamic deep brain stimulation renders patients susceptible to making decisions without proper forethought, this can be disentangled from effects related to dopamine comprising sensitivity to benefits versus costs, reward delay aversion and learning from outcomes. Alterations in these dopamine-mediated mechanisms are thought to underlie the development of impulse control disorders and can be relatively spared with reduced dopaminergic medication after subthalamic deep brain stimulation. Together, results from studies using deep brain stimulation as an experimental tool have improved our understanding of action control in the human brain and have important implications for treatment of patients with neurological disorders., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Aims: 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)., Methods: 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., Results: 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., Conclusion: 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., (© 2024 The Author(s). CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic models to deep learning methods remains understudied. This study compares sequential modeling techniques to identify cognitive progression in individuals with and without PD. Using data from the Parkinson's Progression Marker Initiative, shallow Markov, deep recurrent (long short-term memory [LSTM]), and nonrecurrent (temporal fusion transformer [TFT]) models were compared to predict cognitive status over time. Cognitive status was categorized into normal cognition (NC), mild cognitive impairment (MCI), and dementia. Predictions were made annually for up to 3 years using clinical data, including demographics, cognitive assessments, PD severity, and medical history. Each approach was evaluated using inverse probability weighted (IPW-) F1 scores. An ensemble method combined outputs from the Markov, LSTM, and TFT models. The dataset included 917 individuals (53% PD; 30% at risk for PD; 17% Healthy Controls). The TFT model outperformed others across all annual periods (IPW-F1 = 0.468) compared to the Markov (IPW-F1 = 0.349) and LSTM (IPW-F1 = 0.414) models, with improved performance using an ensemble approach (IPW-F1 = 0.502). For MCI and dementia predictions, which were rarer occurrences compared to NC status (ratios: 50:8:1), the TFT model consistently outperformed competing models, achieving IPW-F1 scores of 0.496 and 0.533 for MCI and dementia, respectively. In conclusion, sequential deep learning models like TFT, which mitigate long-term memory loss and can interpret complex, high-dimensional data, perform best overall in predicting clinically important cognitive transitions. These methods should be further explored for predicting degenerative conditions., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Purpose: Using regional homogeneity (ReHo) and functional connectivity (FC) to assess alterations in brain function and their potential relation to different Hoehn and Yahr (H&Y) stages in Parkinson's disease (PD)., Materials and Methods: 66 patients with PD and 57 age- and sex-matched healthy control (HC) participants were recruited. All subjects underwent clinical assessments and resting-state functional magnetic resonance imaging (rs-fMRI) scanning. We analyzed alterations in regional brain activity using ReHo analyses in all subjects and further explored their relationship to disease severity. Finally, the brain region significantly associated with disease severity was used as a seed point to analyze the FC changes between it and other brain regions in the whole brain., Results: Compared with HC participants, PD patients showed a significant decrease ReHo in the sensorimotor network (bilateral precentral and postcentral gyrus). The ReHo value of the left precentral gyrus in PD patients decreased with increasing H&Y stage and correlated negatively with Unified Parkinson's Disease Rating Scale (UPDRS) III scores. Further, FC analysis of the left precentral gyrus as a region of interest showed that functional activity between the left precentral gyrus and sensorimotor network, default network, and visual network was decreased., Conclusion: The left precentral gyrus plays an important role in the pathophysiological mechanisms of PD patients, and this finding further highlights the potential of the primary motor cortex (M1) as a non-invasive therapeutic target for neuromodulation in PD patients., Competing Interests: Declaration of Competing Interest The authors have not published, posted, or submitted any related papers from the same study. All authors have read and approved the submitted manuscript. The authors declare no competing interests., (Copyright © 2024. Published by Elsevier Inc.)

Speech is a complex behavior that can be used to study unique contributions of the basal ganglia to motor control in the human brain. Computational models suggest that the basal ganglia encode either the phonetic content or the sequence of speech elements. To explore this question, we investigated the relationship between phoneme and sequence features of a spoken syllable triplet and the firing rate of subthalamic nucleus (STN) neurons recorded during the implantation of deep brain stimulation (DBS) electrodes in individuals with Parkinson's disease. Patients repeated aloud a random sequence of three consonant-vowel (CV) syllables in response to audio cues. Single-unit extracellular potentials were sampled from the sensorimotor STN; a total of 227 unit recordings were obtained from the left STN of 25 subjects (4 females). Of these, 113 (50%) units showed significant task-related increased firing and 53 (23%) showed decreased firing ( t test relative to inter-trial period baseline, P < 0.05). Linear regression analysis revealed that both populations of STN neurons encode phoneme and sequence features of produced speech. Maximal phoneme encoding occurred at the time of phoneme production, suggesting efference copy- or sensory-related processing, rather than speech motor planning (-50 ms and +175 ms relative to CV transition for consonant and vowel encoding, respectively). These findings demonstrate that involvement of the basal ganglia in speaking includes separate single unit representations of speech sequencing and phoneme selection in the STN. NEW & NOTEWORTHY Speech is a unique human behavior that requires dynamic execution of precisely timed and coordinated movements, resulting in intelligible vocalizations. Here, we demonstrate that activity of individual neurons in the subthalamic nucleus (STN) of the basal ganglia encode syllable sequence order and phoneme identity during a speech production task. These findings advance our understanding of neural substrates of human speech and shed light on potential involvement of the STN in complex human behaviors.

Methods: This study assessed data from two cohorts of patients with alpha-synucleinopathies (University of Brescia and University of Rome Tor-Vergata cohorts). Consecutive participants with video-polysomnography-confirmed iRBD, Parkinson's disease (PD), dementia with Lewy bodies (DLB) and controls underwent neurological, clinical and 123 I-FP-CIT SPECT imaging assessments. Individuals with iRBD were longitudinally monitored to collect clinical phenoconversion to PD or DLB. The main outcome was to identify whole brain 123 I-FP-CIT SPECT measures reflecting monoaminergic deficits in each clinical group as compared to controls., Results: The cohort (n = 184) included 45 patients with iRBD, 47 PD, 42 DLB and 50 age-matched controls. Individuals with iRBD were categorized as RBD-DAT- (n = 32) and RBD-DAT+ (n = 13), according to nigrostriatal assessment used in clinical practice. Compared to controls, RBD-DAT- showed an early involvement of the left insula, which increased in RBD-DAT+, and was present in patients with Parkinson's disease and dementia with Lewy bodies. Longitudinal cox regression analyses revealed a higher risk of phenoconversion in individuals with iRBD and insular monoaminergic deficits [HR = 3.387; CI 95%: 1.18-10.27]., Interpretation: In this study, altered insular monoaminergic binding in iRBD was associated with phenoconversion to DLB or PD. These findings may provide a helpful stratification approach for future pharmacological or non-pharmacological interventions., (© 2024 The Author(s). Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Background: Stimulation of a specific site in the dorsolateral subthalamic nucleus (STN) was recently associated with slower motor progression in Parkinson's Disease (PD), based on the deep brain stimulation (DBS) in early-stage PD pilot clinical trial. Here, subject-level visualizations are presented of this early-stage PD dataset to further describe the relationship between active contacts and motor progression. This study also evaluates whether stimulation of the sweet spot and connectivity model associated with slower motor progression is also associated with improvements in long-term motor outcomes in patients with advanced-stage PD., Methods: Active contacts of the early-stage PD cohort (N = 14) were analyzed alongside the degree of two-year motor progression. Sweet spot and connectivity models derived from the early-stage PD cohort were then used to determine how well they can estimate the variance in long-term motor outcomes in an independent STN-DBS cohort of advanced-stage PD patients (N = 29)., Results: In early-stage PD, proximity of stimulation to the dorsolateral STN was associated with slower motor progression. In advanced-stage PD, stimulation proximity to the early PD connectivity model and sweet spot were associated with better long-term motor outcomes (R = 0.60, P < 0.001; R = 0.37, P = 0.046, respectively)., Conclusions: Results suggest stimulation of a specific site in the dorsolateral STN is associated with both slower motor progression and long-term motor improvements in PD., Competing Interests: Declarations Conflict of Interest MH receives funding from the National Institutes on Aging K01AG066971 and The Consolidated Anti-Aging Foundation. MH is a shareholder of Arena Therapeutics, a company focused on advancing research of DBS for the treatment of patients recently diagnosed with PD. DI receives funding from the National Institute of Neurological Disorders and Stroke (1K23NS131592-01) and from Teva Branded Pharmaceutical Products, R&D. DC is a shareholder of Arena Therapeutics, a company focused on advancing research of DBS for the treatment of patients recently diagnosed with PD. AH was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, 424778381—TRR 295), Deutsches Zentrum für Luft- und Raumfahrt (DynaSti grant within the EU Joint Programme Neurodegenerative Disease Research, JPND), the National Institutes of Health (R01 13478451, 1R01NS127892-01, 2R01 MH113929 & UM1NS132358) as well as the New Venture Fund (FFOR Seed Grant). AH reports lecture fees for Boston Scientific and is a consultant for FxNeuromodulation and Abbott. There are no additional disclosures to report for NR, KP, EA, SS, FTP, PH, TLD. Ethical approval This study was approved by the Vanderbilt Institutional Review Board. All subjects provided written informed consent to participate. We confirm that we have read the Journal’s position on issues involved in ethical publication and affirm that this work is consistent with those guidelines., (© 2024. The Author(s).)

Background: Sleep macro and microstructural features have a relevant role for cognition. Although alterations in sleep macrostructure have been reported in persons with neurodegenerative disorders, including Parkinson's disease (PD), it is unknown whether there is a relationship between alterations in microstructure (sleep spindles) and global cognitive deficits in this disease., Objective: To explore the association between the macro and microstructure of sleep (sleep spindles) and the general cognitive state in persons with PD., Methods: Thirty-three patients with idiopathic PD underwent a one-night polysomnography (PSG) and a global cognitive assessment using the Montreal Cognitive Assessment (MoCA) test. PSG-based macrostructural sleep values and quantification and spectral estimation of sleep spindles were obtained., Results: We found increases in total sleep time, latency to rapid eye movement (REM) sleep, and percentage of N1 stage, as well as a decrease in percentage of REM sleep and sleep efficiency compared to values reported in healthy adults. Compared to expected values, a decrease in the number of sleep spindles was found at frontal regions. Participants with cognitive impairment showed an even lower count of sleep spindles, as well as an increase in the amplitude of underlying sigma (12-16 Hz) waves (fast spindles). When exploring MoCA subdomains, we found a consistent relationship between the number and amplitude of sleep spindles and attention capacity., Conclusions: Decreased number and increased amplitude of sleep spindles are linked to cognitive impairment in persons with PD, especially in attention capacity. Therefore, sleep spindles characteristics could serve as prognostic indicators of cognitive deterioration in PD., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Freezing of gait (FOG) is a disabling motor symptom prevalent in patients with Parkinson's disease (PD); however, its pathophysiological mechanisms are poorly understood. This study aimed to investigate whole-brain functional connectivity (FC) pattern alterations in PD patients with FOG. A total of 18 PD patients, 10 with FOG (PD-FOG) and 8 without FOG (PD-nFOG), and 10 healthy controls were enrolled. High-resolution 3D T1-weighted and resting-state functional MRI (rs-fMRI) data were obtained from all participants. The groups' internetwork connectivity differences were explored with rs-fMRI FC using seed-based analysis and graph theory. Multiple linear regression analysis estimated the relationship between FC changes and clinical measurements. Rs-fMRI analysis demonstrated alterations in FC in various brain regions between the three groups. Freezing of Gait Questionnaire severity was correlated with decreased brain functional connection between Vermis12 and the left temporal occipital fusiform cortex (r = -0.82, P < .001). Graph theory topological metrics indicated a decreased clustering coefficient in the right superior temporal gyrus in the PD-nFOG group. PD-FOG patients exhibited a compensatory increase in connectivity between the left inferior frontal gyrus language network and the postcentral gyrus compared to PD-nFOG patients. Further, the decreased connection between Vermis 12 and the left temporal occipital fusiform cortex may serve as a potential neuroimaging biomarker for tracking PD-FOG and distinguishing between PD subtypes., (© 2024 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Objective: Parkinson's disease (PD) may affect not only motor functions, but also cognitive processes such as attention. While past research has examined PD impact on spatial attention, it has not addressed how the key functions of attentional orienting and alerting in PD are mediated by cueing format, an ecologically relevant parameter. We assessed how exogenous and endogenous orienting cue modes affect PD patients' visuospatial attention expressed as dorsal attention network orienting benefits, ventral attention network reorienting costs, and alerting abilities., Method: Ninety PD patients and 72 healthy comparison participants performed a spatial attention task in an engaging game format which required selection of a target location without prior cueing, or with temporal, valid spatial, or invalid spatial exogenous or endogenous cueing., Results: PD patients differed from healthy participants only in response time benefits in orienting under endogenous probabilistically predictive cue processing. They did not exhibit greater reorienting costs, differences in inhibition of return, or alerting deficits, irrespective of modes of cueing., Conclusion: These results suggest that fundamental orienting and alerting functions might be intact in PD, with challenges emerging only if additional cognitive processes, including those related to motor preparation, are required to utilize cue information. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Background: Freezing of gait is one of the most disturbing motor symptoms of Parkinson's disease (PD). However, the effective connectivity between key brain hubs that are associated with the pathophysiological mechanism of freezing of gait remains elusive., Objective: The aim of this study was to identify effective connectivity underlying freezing of gait., Methods: This study applied spectral dynamic causal modeling (DCM) of resting-state functional magnetic resonance imaging in dedicated regions of interest determined using a data-driven approach., Results: Abnormally increased functional connectivity between the bilateral dorsolateral prefrontal cortex (DLPFC) and the bilateral mesencephalic locomotor region (MLR) was identified in freezers compared with nonfreezers. Subsequently, spectral DCM analysis revealed that increased top-down excitatory effective connectivity from the left DLPFC to bilateral MLR and an independent self-inhibitory connectivity within the left DLPFC in freezers versus nonfreezers (>99% posterior probability) were inversely associated with the severity of freezing of gait. The lateralization of these effective connectivity patterns was not attributable to the initial dopaminergic deficit nor to structural changes in these regions., Conclusions: We have identified novel effective connectivity and an independent self-inhibitory connectivity underlying freezing of gait. Our findings imply that modulating the effective connectivity between the left DLPFC and MLR through neurostimulation or other interventions could be a target for reducing freezing of gait in PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron loss, leading to motor and non-motor symptoms. Early detection before symptom onset is crucial but challenging. This study presents a framework integrating circuit modeling, non-equilibrium dynamics, and optimization to understand PD pathogenesis and enable precision interventions. Neuronal firing patterns, particularly oscillatory activity, play a critical role in PD pathology. The basal ganglia network, specifically the subthalamic nucleus-external globus pallidus (STN-GPe) circuitry, exhibits abnormal activity associated with motor dysfunction. The framework leverages the non-equilibrium landscape and flux theory to identify key connections generating pathological activity, providing insights into disease progression and potential intervention points. The intricate STN-GPe interplay is highlighted, shedding light on compensatory mechanisms within this circuitry may initially counteract changes but later contribute to pathological alterations as disease progresses. The framework addresses the need for comprehensive evaluation methods to assess intervention outcomes. Cross-correlations between state variables provide superior early warning signals compared to traditional indicators relying on critical slowing down. By elucidating compensatory mechanisms and circuit dynamics, the framework contributes to improved management, early detection, risk assessment, and potential prevention/delay of PD development. This pioneering research paves the way for precision medicine in neurodegenerative disorders., (© 2024 The Author(s). Advanced Science published by Wiley‐VCH GmbH.)

Introduction: To apply susceptibility separation on quantitative susceptibility mapping (QSM) images of Parkinson's disease (PD) patients to obtain more accurate images and gain pathophysiological insights., Methods: This retrospective study included subjects who underwent head MRI, including QSM between March 2016 and March 2018. Patients with PD were categorized as having mild cognitive impairment (PD-MCI), or normal cognition (PD-CN); healthy controls (HC) were also enrolled. Susceptibility separation generated positive (QSM+) and negative susceptibility (QSM-) labels. Voxel-based whole-brain susceptibility and atlas-based susceptibility were compared among groups on white matter. Correlations between susceptibility and Montreal Cognitive Assessment (MoCA) scores were analyzed., Results: Overall, 65 subjects (mean age 72 years ±6, 35 men) were included. White-matter regions with significant (P < 0.05) group differences were found for QSM+ (HC > PD-MCI, PD-CN > PD-MCI) and QSM- (PD-MCI > HC, PD-MCI > PD-CN). In the atlas-based analyses, PD-MCI exhibited lower QSM + values (vs. HC; P = 0.002, vs. PD-CN; P = 0.001), and higher QSM-values (vs. HC; P = 0.02, vs. PD-CN; P = 0.03) in the genu of corpus callosum (gCC). QSM+ and QSM-showed significant positive and negative correlations with MoCA (P < 0.05). In the gCC, partial correlation analyses revealed a positive correlation between QSM+ and MoCA (R = 0.458, P < 0.001) and a negative correlation between QSM- and MoCA (R = -0.316, P = 0.01)., Conclusion: QSM utilizing susceptibility separation is valuable for assessing white matter in PD patients, where nerve fiber loss potentially influences cognitive function., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Contemporary research to better understand free-living fall risk assessment in Parkinson's disease (PD) often relies on the use of wearable inertial-based measurement units (IMUs) to quantify useful temporal and spatial gait characteristics (e.g., step time, step length). Although use of IMUs is useful to understand some intrinsic PD fall-risk factors, their use alone is limited as they do not provide information on extrinsic factors (e.g., obstacles). Here, we update on the use of ergonomic wearable video-based eye-tracking glasses coupled with AI-based computer vision methodologies to provide information efficiently and ethically in free-living home-based environments to better understand IMU-based data in a small group of people with PD. The use of video and AI within PD research can be seen as an evolutionary step to improve methods to understand fall risk more comprehensively., Competing Interests: Declaration of competing interest The authors declare that they have no competing interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Reinforcement learning, crucial for behavior in dynamic environments, is driven by rewards and punishments, modulated by dopamine (DA) changes. This study explores the dopaminergic system's influence on learning, particularly in Parkinson's disease (PD), where medication leads to impaired adaptability. Highlighting the role of tonic DA in signaling the valence of actions, this research investigates how DA affects response vigor and decision-making in PD. DA not only influences reward and punishment learning but also indicates the cognitive effort level and risk propensity in actions, which are essential for understanding and managing PD symptoms. In this work, we adapt our existing neurocomputational model of basal ganglia (BG) to simulate two reversal learning tasks proposed by Cools et al. We first optimized a Hebb rule for both probabilistic and deterministic reversal learning, conducted a sensitivity analysis (SA) on parameters related to DA effect, and compared performances between three groups: PD-ON, PD-OFF, and control subjects. In our deterministic task simulation, we explored switch error rates after unexpected task switches and found a U-shaped relationship between tonic DA levels and switch error frequency. Through SA, we classify these three groups. Then, assuming that the valence of the stimulus affects the tonic levels of DA, we were able to reproduce the results by Cools et al. As for the probabilistic task simulation, our results are in line with clinical data, showing similar trends with PD-ON, characterized by higher tonic DA levels that are correlated with increased difficulty in both acquisition and reversal tasks. Our study proposes a new hypothesis: valence, signaled by tonic DA levels, influences learning in PD, confirming the uncorrelation between phasic and tonic DA changes. This hypothesis challenges existing paradigms and opens new avenues for understanding cognitive processes in PD, particularly in reversal learning tasks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

This study advances the automation of Parkinson's disease (PD) diagnosis by analyzing speech characteristics, leveraging a comprehensive approach that integrates a voting-based machine learning model. Given the growing prevalence of PD, especially among the elderly population, continuous and efficient diagnosis is of paramount importance. Conventional monitoring methods suffer from limitations related to time, cost, and accessibility, underscoring the need for the development of automated diagnostic tools. In this paper, we present a robust model for classifying speech patterns in Korean PD patients, addressing a significant research gap. Our model employs straightforward preprocessing techniques and a voting-based machine learning approach, demonstrating superior performance, particularly when training data is limited. Furthermore, we emphasize the effectiveness of the eGeMAPSv2 feature set in PD analysis and introduce new features that substantially enhance classification accuracy. The proposed model, achieving an accuracy of 84.73 % and an area under the ROC (AUC) score of 92.18 % on a dataset comprising 100 Korean PD patients and 100 healthy controls, offers a practical solution for automated diagnosis applications, such as smartphone apps. Future research endeavors will concentrate on enhancing the model's performance and delving deeper into the relationship between high-importance features and PD., Competing Interests: Declaration of competing interest We, the authors of the manuscript titled "Machine Learning-Based Classification of Parkinson's Disease Using Acoustic Features: Insights from Multilingual Speech Tasks," declare that we have no conflicts of interest or financial interests that could be construed as influencing the research presented in this paper. We confirm that the research presented in this manuscript was conducted impartially, and we have not received any financial support or incentives that might create a conflict of interest with the results and conclusions of this research. Our primary aim is to contribute to the field of Parkinson's disease diagnosis using machine learning and acoustic features, and we have adhered to ethical standards and best practices throughout the research process. If, in the future, any potential conflicts of interest arise related to this work, we commit to promptly disclosing them to the relevant parties. We have submitted this manuscript to "Computers in Biology and Medicine" with the understanding that the information provided in this Declaration of Interest Statement accurately represents our current affiliations and interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Introduction: Hearing impairments in Parkinson's Disease (PD) have received limited attention in the past, possibly because PD patients often report no perceived hearing disability, yet negative consequences of hearing impairment might aggravate communication difficulties and social withdrawal., Objective: Our aim was to investigate functional hearing (speech in noise recognition) in PD and evaluate its relationship to neuropsychiatric symptoms, cognition and quality of life., Methods: Participants with PD were recruited in a tertiary movement disorder clinic. Demographic, audiological, neuropsychiatric and quality of life data were collected. Participants underwent pure tone audiometry (PTA) and Hearing in Noise test (HINT) as a part of their audiological evaluation., Results: A total of 29 participants (mean age: 65.8±8.3 years, M:F= 1.6:1, mean disease duration 5.2 ± 4.0 years) completed the study. All assessments were done in the ON state. 19/29 (65.5 %) participants had normal tone audiometry for age; functional hearing loss, however, was present in 17/29 (58.6 %) according to the HINT. 65 % (11/17) of the affected participants had a disease duration of <4 years. The majority (72.4 %) with poor functional hearing did not perceive any hearing impairment. Hearing deficits did not correlate with non-motor symptoms (NMS), including cognition or other quality of life measures., Conclusions: Functional hearing loss is common in PD, often presents early in the disease and the majority of PD patients are unaware of their functional hearing loss. Its potential impact on cognition, communication and quality of life requires further investigation and tailored treatment., Competing Interests: Declaration of Competing Interest None, (Copyright © 2024. Published by Elsevier B.V.)

Introduction: Past studies suggested that Parkinson's disease (PD) patients who engage in physical activity (PA) after diagnosis have slower motor progression. Here, we examine the influence of lifetime PA prior to PD onset on motor, cognitive, and overall functional decline among PD patients., Methods: For 495 participants in the Parkinson's Environment and Gene (PEG) studies, we collected PA-related measures through interviews and quantified these using metabolic equivalents (MET) scores. PD progression was defined as time to a Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) conversion to ≥ 35 points, Hoehn and Yahr (H&Y) ≥ 3, and a 4-point decline in Mini-Mental State Examination (MMSE). We used Cox frailty models to estimate hazard ratios and inverse probability weights to account for heterogeneity by enrollment wave and censoring., Results: For PD patients reporting the highest lifetime strenuous MET-h/wk (highest quartile), we estimated a lower HR for time-to-UPDRS-III-conversion (Q4 vs. Q1: HR = 0.56, 95 % CI = [0.36, 0.87]). Additionally, having engaged in any competitive sport also reduced the risk of reaching a UPDRS-III ≥ 35 points (low vs. none: HR = 0.61, 95 % CI = [0.44, 0.86]; high vs. none: HR = 0.63; 95 % CI = [0.44,0.86]); high levels of sports activities also affected progression on the H&Y scale (high vs. none: HR = 0.73; 95 % CI = [0.46,1.00]). Lifetime PA measures did not affect time-to-MMSE decline., Conclusion: Our study suggests that PD patients who engaged in higher levels of lifetime strenuous PA and competitive sports prior to PD diagnosis experience slower motor and overall functional decline, suggesting that lifetime PA may contribute to a physical reserve advantageous for PD patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Objective: To assess correlative strengths of quantitative electroencephalography (qEEG) and visual rating scale EEG features on cognitive outcomes in only autopsied cases from the Arizona Study of Neurodegenerative Disorders (AZSAND). We hypothesized that autopsy proven Parkinson Disease will show distinct EEG features from Alzheimer's Disease prior to dementia (mild cognitive impairment)., Background: Cognitive decline is debilitating across neurodegenerative diseases. Resting-state EEG analysis, including spectral power across frequency bins (qEEG), has shown significant associations with neurodegenerative disease classification and cognitive status, with autopsy confirmed diagnosis relatively lacking., Methods: Biannual EEG was analyzed from autopsied cases in AZSAND who had at least one rsEEG (>1 min eyes closed±eyes open). Analysis included global relative spectral power and a previously described visual rating scale (VRS). Linear mixed regression was performed for neuropsychological assessment and testing within 2 years of death (n = 236, 594 EEG exams) in a mixed linear regression model., Results: The cohort included cases with final clinicopathologic diagnoses of Parkinson's disease (n = 73), Alzheimer disease (n = 65), and tauopathy not otherwise specified (n = 56). A VRS score of 3 diffuse or frequent generalized slowing) over the study duration was associated with an increase in consensus diagnosis cognitive worsening at 4.9 (3.1) years (HR 2.02, CI 1.05-3.87). Increases in global theta power% and VRS were the most consistently associated with large regression coefficients inversely with cognitive performance measures., Conclusion: Resting-state EEG analysis was meaningfully related to cognitive performance measures in a community-based autopsy cohort. EEG deserves further study and use as a cognitive biomarker., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Eye movement dysfunction is one of the non-motor symptoms of Parkinson's disease (PD). An accurate analysis method for eye movement is an effective way to gain a deeper understanding of the nervous system function of PD patients. However, currently, there are only a few assistive methods available to help physicians conveniently and consistently assess patients suspected of having PD. To solve this problem, we proposed a novel visual behavioral analysis method using eye tracking to evaluate eye movement dysfunction in PD patients automatically. This method first provided a physician task simulation to induce PD-related eye movements in Virtual Reality (VR). Subsequently, we extracted eye movement features from recorded eye videos and applied a machine learning algorithm to establish a PD diagnostic model. Then, we collected eye movement data from 66 participants (including 22 healthy controls and 44 PD patients) in a VR environment for training and testing during visual tasks. Finally, on this relatively small dataset, the results reveal that the Support Vector Machine (SVM) algorithm has better classification potential., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) and globus pallidus internus (GPi) is an accepted therapy for Parkinson's disease (PD) with disabling motor complications. For elderly patients with poorer cognition and postural instability, GPi has been proposed as the preferable DBS target based on expert opinion, arguing GPi-DBS may be less complicated by depression, apathy, worsened verbal fluency, and executive dysfunction, resulting in greater improvement in quality of life (QoL). However, data supporting such patient-tailored approach are lacking., Objectives: The aims were to analyze whether the DBS target influences QoL in a PD cohort and a matched subgroup of frail patients with poor cognitive status and reduced postural stability, and whether other factors affect the QoL outcomes., Methods: In this retrospective study, we analyzed a single-center cohort of 138 PD patients who received bilateral STN-DBS (117) or GPi-DBS (21) using the mentioned approach for target selection. All patients underwent standardized clinical evaluations of motor- and nonmotor signs as well as QoL before and 1 year after surgery., Results: DBS of both targets improved motor signs, dyskinesias, and pain. QoL improved without significant difference between the targets, but with a trend for greater improvement across all QoL domains in favor of the STN, even in an STN subgroup matched to the GPi group., Conclusion: Our results contradict the prevailing belief that GPi-DBS is superior in frail PD patients with cognitive decline and postural instability, questioning the proposed patient-tailored approach of DBS target selection. Further studies are needed for a data-driven approach., (© 2024 The Author(s). Movement Disorders Clinical Practice published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Background: A randomized trial suggested that reducing left-sided subthalamic stimulation amplitude could improve axial dysfunction., Objectives: To explore open-label tolerability and associations between trial outcomes and asymmetry data., Methods: We collected adverse events in trial participants treated with open-label lateralized settings for ≥3 months. We explored associations between trial outcomes, location of stimulation and motor asymmetry., Results: 14/17 participants tolerated unilateral amplitude reduction (left-sided = 10, right-sided = 4). Two hundred eighty-four left-sided and 1113 right-sided stimulated voxels were associated with faster gait velocity, 81 left-sided and 22 right-sided stimulated voxels were associated with slower gait velocity. Amplitude reduction contralateral to shorter step length was associated with 2.4-point reduction in axial MDS-UPDRS. Reduction contralateral to longer step length was associated with 10-point increase in MDS-UPDRS., Conclusions: Left-sided amplitude reduction is potentially more tolerable than right-sided amplitude reduction. Right-sided more than left-sided stimulation could be associated with faster gait velocity. Shortened step length might reflect contralateral overstimulation., (© 2024 The Author(s). Movement Disorders Clinical Practice published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Introduction: Patients with Parkinson's Disease (PD) commonly experience Olfactory Dysfunction (OD). Our exploratory study examined hippocampal volumetric and resting-state functional magnetic resonance imaging (rs-fMRI) variations in a Healthy Control (HC) group versus a cognitively normal PD group, further categorized into PD with No/Mild Hyposmia (PD-N/MH) and PD with Severe Hyposmia (PD-SH)., Methods: We calculated participants' relative Total Hippocampal Volume (rTHV) and performed Spearman's partial correlations, controlled for age and gender, to examine the correlation between rTHV and olfactory performance assessed by the Odor Stick Identification Test for the Japanese (OSIT-J) score. Mann-Whitney U tests assessed rTHV differences across groups and subgroups, rejecting the null hypothesis for p < 0.05. Furthermore, a seed-based rs-fMRI analysis compared hippocampal connectivity differences using a one-way ANCOVA covariate model with controls for age and gender., Results: Spearman's partial correlations indicated a moderate positive correlation between rTHV and OSIT-J in the whole study population (ρ = 0.406; p = 0.007), PD group (ρ = 0.493; p = 0.008), and PD-N/MH subgroup (ρ = 0.617; p = 0.025). Mann-Whitney U tests demonstrated lower rTHV in PD-SH subgroup compared to both HC group (p = 0.013) and PD-N/MH subgroup (p = 0.029). Seed-to-voxel rsfMRI analysis revealed reduced hippocampal connectivity in PD-SH subjects compared to HC subjects with a single cluster of voxels., Conclusions: Although the design of the study do not allow to make firm conclusions, it is reasonable to speculate that the progressive involvement of the hippocampus in PD patients is associated with the progression of OD., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Recent imaging studies identified a brain network associated with clinical improvement following deep brain stimulation (DBS) in Parkinson's disease (PD), the PD response network., Objectives: This study aimed to assess the impact of neuromodulation on PD motor symptoms by targeting this network noninvasively using multifocal transcranial direct current stimulation (tDCS)., Methods: In a prospective, randomized, double-blinded, crossover trial, 21 PD patients (mean age 59.7 years, mean Hoehn & Yahr [H&Y] 2.4) received multifocal tDCS targeting the a-priori network. Twenty-minute sessions of tDCS and sham were administered on 2 days in randomized order. Movement Disorder Society-Unified Parkinson's Disease Rating Scale-Part III (MDS-UPDRS-III) scores were assessed., Results: Before intervention, MDS-UPDRS-III scores were comparable in both conditions (stimulation days: 37.38 (standard deviation [SD] = 12.50, confidence interval [CI] = 32.04, 42.73) vs. sham days: 36.95 (SD = 13.94, CI = 30.99, 42.91), P = 0.63). Active stimulation resulted in a reduction by 3.6 points (9.7%) to 33.76 (SD = 11.19, CI = 28.98, 38.55) points, whereas no relevant change was observed after sham stimulation (36.43 [SD = 14.15, CI = 30.38, 42.48], average improvement: 0.5 [1.4%]). Repeated-measures analysis of variance (ANOVA) confirmed significance (main effect of time: F (1,20) =4.35, P < 0.05). Tukey's post hoc tests indicated MDS-UPDRS-III improvement after active stimulation (t [20] = 2.9, P = 0.03) but not after sham (t [20] = 0.42, P > 0.05). In a subset of patients that underwent DBS surgery later, their DBS response correlated with tDCS effects (R = 0.55, P (1)  = 0.04)., Conclusion: Noninvasive, multifocal tDCS targeting a DBS-derived network significantly improved PD motor symptoms. Despite a small effect size, this study provides proof of principle for the successful noninvasive neuromodulation of an invasively identified network. Future studies should investigate repeated tDCS sessions and their utility for screening before DBS surgery. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Functional movement disorder (FMD) is a common manifestation of functional neurological disorder. FMD can occur alongside other neurological conditions, but especially in patients with established Parkinson's disease (PD). An interesting observation emerging across cohort studies and case series is that FMD can precede the diagnosis of PD, suggesting that FMD may itself be a prodromal symptom of neurodegeneration. Such a notion would have significant clinical implications for the assessment and management of people with FMD, particularly with respect to decisions around the use of auxiliary investigations, counselling, and follow-up. In this Viewpoint we review the evidence concerning the temporal relationship between FMD and PD. We discuss the potential explanations and mechanisms for FMD as a prodromal symptom of PD, and highlight clinical considerations and important outstanding questions in the field. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Background: Exercise potentially improves gait, balance, and habitual physical activity in Parkinson's disease (PD). However, given the heterogeneous nature of the disease, it is likely that people respond differently to exercise interventions. Factors determining responsiveness to exercise interventions remain unclear., Objectives: To address this uncertainty, we explored the responsiveness to our highly challenging balance and gait intervention (HiBalance) in people with PD., Methods: Thirty-nine participants with mild-moderate PD who underwent the HiBalance intervention from our randomized controlled trial were included. We defined response in three domains: (1) balance based on Mini-BESTest, (2) gait based on gait velocity, and (3) physical activity based on accelerometry-derived steps per day. In each domain, we explored three responsiveness levels: high, low, or non-responders according to the change from pre- to post-intervention. Separate Random Forests for each responder domain classified these responsiveness levels and identified variable importance., Results: Only the Random Forest for the balance domain classified all responsiveness levels above the chance level indicated by a Cohen's kappa of "slight" agreement. Variable importance differed among the responsiveness levels. Slow gait velocity indicated high responders in the balance domain but showed low probabilities for low and non-responders. For low and non-responders, fall history or no falls, respectively, were more important., Conclusions: Among three responder domains and responsiveness levels, we could moderately classify responders in the balance domain, but not for the gait or physical activity domain. This can guide inclusion criteria for balance-targeted, personalized intervention studies in people with PD., (© 2024 The Author(s). Movement Disorders Clinical Practice published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Age is a primary risk factor for Parkinson's disease (PD); however, the effects of aging on the Parkinsonian brain remain poorly understood, particularly for deep brain structures. We investigated intraoperative micro-electrode recordings from the subthalamic nucleus (STN) of PD patients aged between 42 and 76 years. Age was associated with decreased oscillatory beta power and non-oscillatory high-frequency power, independent of PD-related variables. Single unit firing and burst rates were also reduced, whereas the coefficient of variation and the structure of burst activity were unchanged. Phase synchronization (debiased weighed phase lag index [dWPLI]) between sites was pronounced in the beta band between electrodes in the superficial STN but was unaffected by age. Our results show that aging is associated with reduced neuronal activity without changes to its temporal structure. We speculate that the loss of activity in the STN may mediate the relationship between PD and age., (© 2024 The Author(s). European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

Annotated electroencephalogram (EEG) data is the prerequisite for artificial intelligence-driven EEG autoanalysis. However, the scarcity of annotated data due to its high-cost and the resulted insufficient training limits the development of EEG autoanalysis. Generative self-supervised learning, represented by masked autoencoder, offers potential but struggles with non-Euclidean structures. To alleviate these challenges, this work proposes a self-supervised graph masked autoencoder for EEG representation learning, named GMAEEG. Concretely, a pretrained model is enriched with temporal and spatial representations through a masked signal reconstruction pretext task. A learnable dynamic adjacency matrix, initialized with prior knowledge, adapts to brain characteristics. Downstream tasks are achieved by finetuning pretrained parameters, with the adjacency matrix transferred based on task functional similarity. Experimental results demonstrate that with emotion recognition as the pretext task, GMAEEG reaches superior performance on various downstream tasks, including emotion, major depressive disorder, Parkinson's disease, and pain recognition. This study is the first to tailor the masked autoencoder specifically for EEG representation learning considering its non-Euclidean characteristics. Further, graph connection analysis based on GMAEEG may provide insights for future clinical studies.

Olfactory dysfunction is an early sign of such neurodegenerative diseases as Parkinson's (PD) and Alzheimer's (AD), and is often present in Mild Cognitive Impairment (MCI), a precursor of AD. Understanding neuro-temporal relationships, i.e., functional connectivity, between olfactory eloquent structures in such disorders, could shed light on their basic pathophysiology. To this end, we employed region-based analyses using resting-state functional magnetic resonance imaging (rs-fMRI) obtained from cognitively normal (CN), MCI, and PD patients with cognitive impairment (PD-CogImp). Using machine learning (linear and ensemble learning), we determined whether the identified functional patterns could classify abnormal function from normal function. Olfaction, as measured by objective testing, was found to be most strongly associated with diagnostic status, emphasizing the fundamental association of this primary sensory system with these conditions. Consistently lower functional connectivity was observed in the PD-CogImp cohort compared to the CN cohort among all identified brain regions. Differences were also found between PD-CogImp and MCI at the level of the orbitofrontal and cingulate cortices. MCI and CN subjects had different functional connectivity between the posterior orbitofrontal cortex and thalamus. Regardless of study group, males showed significantly higher connectivity than females in connections involving the orbitofrontal cortex. The logistic regression model trained using the top discriminatory features revealed that caudate was the most involved olfaction-related brain structure (accuracy = 0.88, Area under the Receiver operator characteristic curve of 0.90). In aggregate, our study demonstrates that resting functional connectivity among olfactory eloquent structures has potential value in better understanding the pathophysiology of several neurodegenerative diseases., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The work elucidates the importance of accurate Parkinson's disease classification within medical diagnostics and introduces a novel framework for achieving this goal. Specifically, the study focuses on enhancing disease identification accuracy utilizing boosting methods. A standout contribution of this work lies in the utilization of a light gradient boosting machine (LGBM) coupled with hyperparameter tuning through grid search optimization (GSO) on the Parkinson's disease dataset derived from speech recording signals. In addition, the Synthetic Minority Over-sampling Technique (SMOTE) has also been employed as a pre-processing technique to balance the dataset, enhancing the robustness and reliability of the analysis. This approach is a novel addition to the study and underscores its potential to enhance disease identification accuracy. The datasets employed in this work include both gender-specific and combined cases, utilizing several distinctive feature subsets including baseline, Mel-frequency cepstral coefficients (MFCC), time-frequency, wavelet transform (WT), vocal fold, and tunable-Q-factor wavelet transform (TQWT). Comparative analyses against state-of-the-art boosting methods, such as AdaBoost and XG-Boost, reveal the superior performance of our proposed approach across diverse datasets and metrics. Notably, on the male cohort dataset, our method achieves exceptional results, demonstrating an accuracy of 0.98, precision of 1.00, sensitivity of 0.97, F1-Score of 0.98, and specificity of 1.00 when utilizing all features with GSO-LGBM. In comparison to AdaBoost and XGBoost, the proposed framework utilizing LGBM demonstrates superior accuracy, achieving an average improvement of 5% in classification accuracy across all feature subsets and datasets. These findings underscore the potential of the proposed methodology to enhance disease identification accuracy and provide valuable insights for further advancements in medical diagnostics., (© 2024. International Federation for Medical and Biological Engineering.)

Objective: Constipation frequently occurs in patients with Parkinson's disease (PD) and may be related to cognitive and emotional disorders. The aim of this study is to investigate the clinical and brain functional characteristics of patients with PD presenting with constipation., Methods: The motor and non-motor symptoms of patients with PD were evaluated, and a resting-state functional magnetic resonance imaging (RS-fMRI) study was conducted based on propensity score matching. Alterations in brain function were analyzed using regional homogeneity (ReHo) and functional connectivity (FC)., Results: Compared with patients without constipation (PD-NC group), patients with constipation (PD-C group) had more serious motor and non-motor symptoms (including cognitive and emotional disorders along with visual hallucinations). Further, emotional and cognitive disorders were correlated with the occurrence of constipation in patients with PD. Compared with the PD-NC group, the PD-C group showed a reduced ReHo of the right insula and bilateral orbitofrontal cortex (OFC), increased ReHo of the left postcentral gyrus, and enhanced FC between the right OFC and the left middle temporal gyrus (MTG) and middle occipital gyrus (MOG). Additionally, the activity of the OFC and insula was significantly correlated with the constipation, mood, and cognitive levels of patients with PD., Conclusions: Constipation in patients with PD is closely related to emotional and cognitive impairments, abnormal activity and FC of brain regions such as the right insula and bilateral OFC may play an important role in this., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot's gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase., (© 2024. International Federation for Medical and Biological Engineering.)

Parkinson's disease (PD) is the second most frequent neurodegenerative disorder, affecting millions of people and rapidly increasing over the last decades. Even though there is no intervention yet to stop the neurodegenerative pathology, many efficient treatment methods are available, including for patients with advanced PD. Neuroplasticity is a fundamental property of the human brain to adapt both to external changes and internal insults and pathological processes. In this paper we examine the current knowledge and concepts concerning changes at network level, cellular level and molecular level as parts of the neuroplastic response to protein aggregation pathology, synapse loss and neuronal loss in PD. We analyse the beneficial, compensatory effects, such as augmentation of nigral neurons efficacy, as well as negative, maladaptive effects, such as levodopa-induced dyskinesia. Effects of physical activity and different treatments on neuroplasticity are considered and the opportunity of biomarkers identification and use is discussed., (© 2024. The Author(s).)

The progressive loss of motor function in the brain is a hallmark of Parkinson's disease (PD). Electroencephalogram (EEG) signals are commonly used for early diagnosis since they are associated with a brain disorder. This work aims to find a better way to represent electroencephalography (EEG) signals and enhance the classification accuracy of individuals with Parkinson's disease using EEG signals. In this paper, we present two hybrid deep neural networks (DNN) that combine convolutional neural networks with long short-term memory to diagnose Parkinson's disease using EEG signals, that is, through the establishment of parallel and series combined models. The deep CNN network is utilized to acquire the structural features of ECG signals and extract meaningful information from them, after which the signals are sent via a long short-term memory network to extract the features' context dependency. The proposed architecture was able to achieve 97.6% specificity, 97.1% sensitivity, and 98.6% accuracy for a parallel model and 99.1% specificity, 98.5% sensitivity, and 99.7% accuracy for a series model, both in 3-class classification (PD patients with medication, PD patients without medication and healthy).

Purpose: Preliminary data suggest that gait abnormalities in Parkinson disease (PD) may be associated with sympathetic cardiac denervation. No kinematic gait studies were performed to confirm this observation. We aimed to correlate spatiotemporal kinematic gait parameters with cardiac sympathetic denervation as determined by cardiac [ 11 C]HED PET in PD., Methods: Retrospective database analysis of 27 PD patients with cardiac sympathetic denervation. All patients underwent spatiotemporal kinematic gait assessment (medication 'off' state), cardiac [ 11 C]HED and dopaminergic brain [ 11 C]DTBZ PET scans. We employed a hierarchical regression approach to examine associations between the extent of cardiac denervation, dopaminergic nigrostriatal neurodegeneration, and three gait parameters - velocity, step length and cadence., Results: More extensive cardiac denervation was associated with slower velocity (estimate: -1.034, 95% CI [-1.65, -0.42], p = 0.002), shorter step length (estimate: -0.818, 95% CI [-1.43, -0.21], p = 0.011) and lower cadence (estimate: -0.752, 95% CI [-1.28, -0.23], p = 0.007) explaining alone 30% (Adjusted-R²: 0.297), 20% (Adjusted-R²: 0.202) and 23% (Adjusted-R²: 0.227) of the variability, respecivetly. These associations remained independent of striatal dopaminergic impairment and confounding factors such as age, Hoehn and Yahr (HY) stages, peripheral neuropathy, cognition, and autonomic symptoms. In contrast, striatal dopaminergic denervation was significantly associated with step length (estimate: 0.883, 95% CI [0.29, 1.48], p = 0.005), explaining about 24% of the variability but was dependent of HY stage., Conclusions: More severe cardiac noradrenergic denervation was associated with lower gait velocity, independent of striatal dopaminergic denervation and HY stage, impacting both step length and cadence. These results suggest independent contributions of the peripheral autonomic system degeneration on gait dynsfunction in PD., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Automated sleep detection in movement disorders may allow monitoring sleep, potentially guiding adaptive deep brain stimulation (DBS)., Objectives: The aims were to compare wake-versus-sleep status (WSS) local field potentials (LFP) in a home environment and develop biomarkers of WSS in Parkinson's disease (PD), essential tremor (ET), and Tourette's syndrome (TS) patients., Methods: Five PD, 2 ET, and 1 TS patient were implanted with Medtronic Percept (3 STN [subthalamic nucleus], 3 GPi [globus pallidus interna], and 2 ventral intermediate nucleus). Over five to seven nights, β-band (12.5-30 Hz) and/or α-band (7-12 Hz) LFP power spectral densities were recorded. Wearable actigraphs tracked sleep., Results: From sleep to wake, PD LFP β-power increased in STN and decreased in GPi, and α-power increased in both. Machine learning classifiers were trained. For PD, the highest WSS accuracy was 93% (F1 = 0.93), 86% across all patients (F1 = 0.86). The maximum accuracy was 86% for ET and 89% for TS., Conclusion: Chronic intracranial narrowband recordings can accurately identify sleep in various movement disorders and targets in this proof-of-concept study. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Objective: Multitasking is an essential part of everyday functioning often not formally assessed by traditional neuropsychological tests. Although individuals with Parkinson's disease (PD) experience both motor and cognitive difficulties, previous research has demonstrated more pronounced functional difficulties with the presence of mild cognitive impairment (PD-MCI). The current study compared individuals with PD-MCI, PD with normal cognition (PD-NC), and healthy controls on a naturalistic task of multitasking, the Day Out Task (DOT). Method: Participants were 38 healthy older adults (HOA), 23 individuals with PD-NC, and 15 individuals with PD-MCI. Participants completed a battery of neuropsychological tasks and the DOT. Informants also completed a self-reported questionnaire of participants' everyday executive functioning. Results: Compared to PD-NC and HOA, participants with PD-MCI were less accurate and efficient and took longer to complete the DOT. After controlling for motor performance, only DOT accuracy remained worse, with poorer accuracy resulted from more subtasks being left incomplete or being completed inaccurately by the PD-MCI group. DOT sequencing was a significant predictor of informant reported everyday dysexecutive symptoms. Conclusions: The findings highlight that individuals with PD-MCI are likely to experience difficulties completing complex everyday tasks due to both motor and cognitive impairments. Clinicians may therefore recommend strategies to support efficiency and accuracy in complex tasks of everyday functioning in treatment considerations.

Objective: Cognitive impairment in Parkinson's disease (PD) can show a very heterogeneous trajectory among patients. Here, we explored the mechanisms involved in the expression and prediction of different cognitive phenotypes over 4 years., Methods: In 2 independent cohorts (total n = 475), we performed a cluster analysis to identify trajectories of cognitive progression. Baseline and longitudinal level II neuropsychological assessments were conducted, and baseline structural magnetic resonance imaging, resting electroencephalogram and neurofilament light chain plasma quantification were carried out. Linear mixed-effects models were used to study longitudinal changes. Risk of mild cognitive impairment and dementia were estimated using multivariable hazard regression. Spectral power density from the electroencephalogram at baseline and source localization were computed., Results: Two cognitive trajectories were identified. Cluster 1 presented stability (PD-Stable) over time, whereas cluster 2 showed progressive cognitive decline (PD-Progressors). The PD-Progressors group showed an increased risk for evolving to PD mild cognitive impairment (HR 2.09; 95% CI 1.11-3.95) and a marked risk for dementia (HR 4.87; 95% CI 1.34-17.76), associated with progressive worsening in posterior-cortical-dependent cognitive processes. Both clusters showed equivalent clinical and sociodemographic characteristics, structural magnetic resonance imaging, and neurofilament light chain levels at baseline. Conversely, the PD-Progressors group showed a fronto-temporo-occipital and parietal slow-wave power density increase, that was in turn related to worsening at 2 and 4 years of follow-up in different cognitive measures., Interpretation: In the absence of differences in baseline cognitive function and typical markers of neurodegeneration, the further development of an aggressive cognitive decline in PD is associated with increased slow-wave power density and with a different profile of worsening in several posterior-cortical-dependent tasks. ANN NEUROL 2024;96:981-993., (© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)