Understanding REM Sleep Behavior Disorder through Functional MRI: A Systematic Review.

Neuroimaging studies in rapid eye movement sleep behavior disorder (RBD) can inform fundamental questions about the pathogenesis of Parkinson's disease (PD). Across modalities, functional magnetic resonance imaging (fMRI) may be better suited to identify changes between neural networks in the earliest stages of Lewy body diseases when structural changes may be subtle or absent. This review synthesizes the findings from all fMRI studies of RBD to gain further insight into the pathophysiology and progression of Lewy body diseases. A total of 32 studies were identified using a systematic review conducted according to PRISMA guidelines between January 2000 to February 2024 for original fMRI studies in patients with either isolated RBD (iRBD) or RBD secondary to PD. Common functional alterations were detectable in iRBD patients compared with healthy controls across brainstem nuclei, basal ganglia, frontal and occipital lobes, and whole brain network measures. Patients with established PD and RBD demonstrated decreased functional connectivity across the whole brain and brainstem nuclei, but increased functional connectivity in the cerebellum and frontal lobe compared with those PD patients without RBD. Finally, longitudinal changes in resting state functional connectivity were found to track with disease progression. Currently, fMRI studies in RBD have demonstrated early signatures of neurodegeneration across both motor and non-motor pathways. Although more work is needed, such findings have the potential to inform our understanding of disease, help to distinguish between prodromal PD and prodromal dementia with Lewy bodies, and support the development of fMRI-based outcome measures of phenoconversion and progression in future disease modifying trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Prediction of motor and non-motor Parkinson's disease symptoms using serum lipidomics and machine learning: a 2-year study.

Identifying biological factors which contribute to the clinical progression of heterogeneous motor and non-motor phenotypes in Parkinson's disease may help to better understand the disease process. Several lipid-related genetic risk factors for Parkinson's disease have been identified, and the serum lipid signature of Parkinson's disease patients is significantly distinguishable from controls. However, the extent to which lipid profiles are associated with clinical outcomes remains unclear. Untargeted high-performance liquid chromatography-tandem mass spectrometry identified >900 serum lipids in Parkinson's disease subjects at baseline (n = 122), and the potential for machine learning models using these lipids to predict motor and non-motor clinical scores after 2 years (n = 67) was assessed. Machine learning models performed best when baseline serum lipids were used to predict the 2-year future Unified Parkinson's disease rating scale part three (UPDRS III) and Geriatric Depression Scale scores (both normalised root mean square error = 0.7). Feature analysis of machine learning models indicated that species of lysophosphatidylethanolamine, phosphatidylcholine, platelet-activating factor, sphingomyelin, diacylglycerol and triacylglycerol were top predictors of both motor and non-motor scores. Serum lipids were overall more important predictors of clinical outcomes than subject sex, age and mutation status of the Parkinson's disease risk gene LRRK2. Furthermore, lipids were found to better predict clinical scales than a panel of 27 serum cytokines previously measured in this cohort (The Michael J. Fox Foundation LRRK2 Clinical Cohort Consortium). These results suggest that lipid changes may be associated with clinical phenotypes in Parkinson's disease.

Effect of dopamine on limbic network connectivity at rest in Parkinson's disease patients with freezing of gait.

Background: Freezing of gait (FOG) in Parkinson's disease (PD) has a poorly understood pathophysiology, which hinders treatment development. Recent work showed a dysfunctional fronto-striato-limbic circuitry at rest in PD freezers compared to non-freezers in the dopamine "OFF" state. While other studies found that dopaminergic replacement therapy alters functional brain organization in PD, the specific effect of dopamine medication on fronto-striato-limbic functional connectivity in freezers remains unclear. Objective: To evaluate how dopamine therapy alters resting state functional connectivity (rsFC) of the fronto-striato-limbic circuitry in PD freezers, and whether the degree of connectivity change is related to freezing severity and anxiety. Methods: Twenty-three PD FOG patients underwent MRI at rest (rsfMRI) in their clinically defined "OFF" and "ON" dopaminergic medication states. A seed-to-seed based analysis was performed between a priori defined limbic circuitry ROIs. Functional connectivity was compared between OFF and ON states. A secondary correlation analyses evaluated the relationship between Hospital Anxiety and Depression Scale (HADS)-Anxiety) and FOG Questionnaire with changes in rsFC from OFF to ON. Results: PD freezers' OFF compared to ON showed increased functional coupling between the right hippocampus and right caudate nucleus, and between the left putamen and left posterior parietal cortex (PPC). A negative association was found between HADS-Anxiety and the rsFC change from OFF to ON between the left amygdala and left prefrontal cortex, and left putamen and left PPC. Conclusion: These findings suggest that dopaminergic medication partially modulates the frontoparietal-limbic-striatal circuitry in PD freezers, and that the influence of medication on the amygdala, may be related to clinical anxiety in freezer.

Efficacy of biologically-directed daylight therapy on sleep and circadian rhythm in Parkinson's disease: a randomised, double-blind, parallel-group, active-controlled, phase 2 clinical trial.

Background: New non-pharmacological treatments for improving non-motor symptoms in Parkinson's disease (PD) are urgently needed. Previous light therapies for modifying sleep behaviour lacked standardised protocols and were not personalised for an individual patient chronotype. We aimed to assess the efficacy of a biologically-directed light therapy in PD that targets retinal inputs to the circadian system on sleep, as well as other non-motor and motor functions. Methods: In this randomised, double-blind, parallel-group, active-controlled trial at the Queensland University of Technology, Australia, participants with mild to moderate PD were computer randomised (1:1) to receive one of two light therapies that had the same photometric luminance and visual appearance to allow blinding of investigators and participants to the intervention. One of these biologically-directed lights matched natural daylight (Day Mel), which is known to stimulate melanopsin cells. The light therapy of the other treatment arm of the study, specifically supplemented the stimulation of retinal melanopsin cells (Enhanced Mel), targeting deficits to the circadian system. Both lights were administered 30 min per day over 4-weeks and personalised to an individual patient's chronotype, while monitoring environmental light exposure with actigraphy. Co-primary endpoints were a change from baseline in mean sleep macrostructure (polysomnography, PSG) and an endocrine biomarker of circadian phase (dim light melatonin secretion onset, DLMO) at weeks 4 and 6. Participants data were analysed using an intention to treat principle. All endpoints were evaluated by applying a mixed model analysis. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12621000077864. Findings: Between February 4, 2021 and August 8, 2022, 144 participants with PD were consecutively screened, 60 enrolled and randomly assigned to a light intervention. There was no significant difference in co-primary outcomes between randomised groups overall or at any individual timepoint during follow-up. The mean (95% CI) for PSG, N3% was 24.15 (19.82-28.48) for Day Mel (n = 23) and 19.34 (15.20-23.47) for the Enhanced Mel group (n = 25) in week 4 (p = 0.12); and 21.13 (16.99-25.28) for Day Mel (n = 26) and 18.48 (14.34-22.62) for the Enhanced Mel group (n = 25) in week 6, (p = 0.37). The mean (95% CI) DLMO (decimal time) was 19.82 (19.20-20.44) for Day Mel (n = 22) and 19.44 (18.85-20.04) for the Enhanced Mel group (n = 24) in week 4 (p = 0.38); and 19.90 (19.27-20.53) for Day Mel (n = 23) and 19.04 (18.44-19.64) for the Enhanced Mel group (n = 25) in week 6 (p = 0.05). However, both the controlled daylight (Day Mel) and the enhanced melanopsin (Enhanced Mel) interventions demonstrated significant improvement in primary PSG sleep macrostructure. The restorative deep sleep phase (PSG, N3) significantly improved at week 6 in both groups [model-based mean difference to baseline (95% CI): -3.87 (-6.91 to -0.83), p = 0.04]. There was a phase-advance in DLMO in both groups which did not reach statistical significance between groups at any time-point. There were no safety concerns or severe adverse events related to the intervention. Interpretation: Both the controlled daylight and melanopsin booster light showed efficacy in improving measures of restorative deep sleep in people with mild to moderate PD. That there was no significant difference between the two intervention groups may be due to the early disease stage. The findings suggest that controlled indoor daylight that is personalised to the individuals' chronotype could be effective for improving sleep in early to moderate PD, and further studies evaluating controlled daylight interventions are now required utilising this standardised approach, including in advanced PD. Funding: The Michael J Fox Foundation for Parkinson's Research, Shake IT Up Australia, National Health and Medical Research Council, and Australian Research Council.

Abnormal higher-order network interactions in Parkinson's disease visual hallucinations.

Visual hallucinations in Parkinson's disease can be viewed from a systems-level perspective, whereby dysfunctional communication between brain networks responsible for perception predisposes a person to hallucinate. To this end, abnormal functional interactions between higher-order and primary sensory networks have been implicated in the pathophysiology of visual hallucinations in Parkinson's disease, however the precise signatures remain to be determined. Dimensionality reduction techniques offer a novel means for simplifying the interpretation of multidimensional brain imaging data, identifying hierarchical patterns in the data that are driven by both within- and between-functional network changes. Here, we applied two complementary non-linear dimensionality reduction techniques-diffusion-map embedding and t-distributed stochastic neighbour embedding (t-SNE)-to resting state functional MRI data, in order to characterize the altered functional hierarchy associated with susceptibility to visual hallucinations. Our study involved 77 people with Parkinson's disease (31 with hallucinations; 46 without hallucinations) and 19 age-matched healthy control subjects. In patients with visual hallucinations, we found compression of the unimodal-heteromodal gradient consistent with increased functional integration between sensory and higher order networks. This was mirrored in a traditional functional connectivity analysis, which showed increased connectivity between the visual and default mode networks in the hallucinating group. Together, these results suggest a route by which higher-order regions may have excessive influence over earlier sensory processes, as proposed by theoretical models of hallucinations across disorders. By contrast, the t-SNE analysis identified distinct alterations in prefrontal regions, suggesting an additional layer of complexity in the functional brain network abnormalities implicated in hallucinations, which was not apparent in traditional functional connectivity analyses. Together, the results confirm abnormal brain organization associated with the hallucinating phenotype in Parkinson's disease and highlight the utility of applying convergent dimensionality reduction techniques to investigate complex clinical symptoms. In addition, the patterns we describe in Parkinson's disease converge with those seen in other conditions, suggesting that reduced hierarchical differentiation across sensory-perceptual systems may be a common transdiagnostic vulnerability in neuropsychiatric disorders with perceptual disturbances.

Predicting neurodegeneration from sleep related biofluid changes.

Sleep-wake disturbances are common in neurodegenerative diseases and may occur years before the clinical diagnosis, potentially either representing an early stage of the disease itself or acting as a pathophysiological driver. Therefore, discovering biomarkers that identify individuals with sleep-wake disturbances who are at risk of developing neurodegenerative diseases will allow early diagnosis and intervention. Given the association between sleep and neurodegeneration, the most frequently analyzed fluid biomarkers in people with sleep-wake disturbances to date include those directly associated with neurodegeneration itself, such as neurofilament light chain, phosphorylated tau, amyloid-beta and alpha-synuclein. Abnormalities in these biomarkers in patients with sleep-wake disturbances are considered as evidence of an underlying neurodegenerative process. Levels of hormonal sleep-related biomarkers such as melatonin, cortisol and orexin are often abnormal in patients with clinical neurodegenerative diseases, but their relationships with the more standard neurodegenerative biomarkers remain unclear. Similarly, it is unclear whether other chronobiological/circadian biomarkers, such as disrupted clock gene expression, are causal factors or a consequence of neurodegeneration. Current data would suggest that a combination of fluid biomarkers may identify sleep-wake disturbances that are most predictive for the risk of developing neurodegenerative disease with more optimal sensitivity and specificity.

Targeting sleep and the circadian system as a novel treatment strategy for Parkinson's disease.

There is a growing appreciation of the wide range of sleep-wake disturbances that occur frequently in Parkinson's disease. These are known to be associated with a range of motor and non-motor symptoms and significantly impact not only on the quality of life of the patient, but also on their bed partner. The underlying causes for fragmented sleep and daytime somnolence are no doubt multifactorial but there is clear evidence for circadian disruption in Parkinson's disease. This appears to be occurring not only as a result of the neuropathological changes that occur across a distributed neural network, but even down to the cellular level. Such observations indicate that circadian changes may in fact be a driver of neurodegeneration, as well as a cause for some of the sleep-wake symptoms observed in Parkinson's disease. Thus, efforts are now required to evaluate approaches including the prescription of precision medicine to modulate photoreceptor activation ratios that reflect daylight inputs to the circadian pacemaker, the use of small molecules to target clock genes, the manipulation of orexin pathways that could help restore the circadian system, to offer novel symptomatic and novel disease modifying strategies.

The clinical phenotype of psychiatric-onset prodromal dementia with Lewy bodies: a scoping review.

BACKGROUND: Recent consensus research criteria have identified a 'psychiatric onset' form of prodromal dementia with Lewy bodies (DLB) characterised by prominent late-onset psychiatric symptoms. Although recognised as important to raise the index of diagnostic suspicion, evidence regarding this cohort was deemed too limited to impose formal criteria. We reviewed the published literature on psychiatric-onset DLB to identify key clinical characteristics and evidence gaps to progress our understanding of this entity. METHODS: Medline, PubMed and Embase were searched for relevant articles containing longitudinal follow-up of patients initially presenting with a psychiatric illness who subsequently developed DLB according to the diagnostic criteria available at the time. RESULTS: Two cohort studies (18 and 21 patients) along with 12 case series (13 cases) were identified totalling 52 patients (63% female). Initial psychiatric presentation occurred at a mean of 63 years (range 53-88), with depression being the most frequently reported psychiatric presentation (88%). Psychotic presentations were less common on presentation (11%) but became more prevalent throughout the prodromal period before the diagnosis of DLB (83%). Relapses of the psychiatric disease were common occurring in 94% (32/34) of patients. Parkinsonism, cognitive fluctuations, visual hallucinations, and REM sleep behaviour disorder were uncommonly reported at initial presentation (3.8%). CONCLUSIONS: Psychiatric-onset DLB is characterized by a female predominant relapsing-remitting psychiatric illness presenting with affective symptoms but later developing psychotic features prior to the onset of DLB. Additional prospective studies including other neurodegenerative cohorts with harmonised assessments are required to inform definitive diagnostic criteria for this condition.

Fighting Against the Clock: Circadian Disruption and Parkinson's Disease.

Circadian disruption is being increasingly recognized as a critical factor in the development and progression of Parkinson's disease (PD). This review aims to provide an in-depth overview of the relationship between circadian disruption and PD by exploring the molecular, cellular, and behavioral aspects of this interaction. This review will include a comprehensive understanding of how the clock gene system and transcription-translation feedback loops function and how they are diminished in PD. The article also discusses the role of clock genes in the regulation of circadian rhythms, as well as the impact of clock gene dysregulation on mitochondrial function, oxidative stress, and neuroinflammation, including the microbiota-gut-brain axis, which have all been proposed as being crucial mechanisms in the pathophysiology of PD. Finally, this review highlights potential therapeutic strategies targeting the clock gene system and circadian rhythm for the treatment of PD.

Isolated rapid eye movement sleep behaviour disorder (iRBD) in the Island Study Linking Ageing and Neurodegenerative Disease (ISLAND) Sleep Study: protocol and baseline characteristics.

Isolated rapid eye movement (REM) sleep behaviour disorder (iRBD) is a sleep disorder that is characterised by dream enactment episodes during REM sleep. It is the strongest known predictor of α-synuclein-related neurodegenerative disease (αNDD), such that >80% of people with iRBD will eventually develop Parkinson's disease, dementia with Lewy bodies, or multiple system atrophy in later life. More research is needed to understand the trajectory of phenoconversion to each αNDD. Only five 'gold standard' prevalence studies of iRBD in older adults have been undertaken previously, with estimates ranging from 0.74% to 2.01%. The diagnostic recommendations for video-polysomnography (vPSG) to confirm iRBD makes prevalence studies challenging, as vPSG is often unavailable to large cohorts. In Australia, there have been no iRBD prevalence studies, and little is known about the cognitive and motor profiles of Australian people with iRBD. The Island Study Linking Ageing and Neurodegenerative Disease (ISLAND) Sleep Study will investigate the prevalence of iRBD in Tasmania, an island state of Australia, using validated questionnaires and home-based vPSG. It will also explore several cognitive, motor, olfactory, autonomic, visual, tactile, and sleep profiles in people with iRBD to better understand which characteristics influence the progression of iRBD to αNDD. This paper details the ISLAND Sleep Study protocol and presents preliminary baseline results.

Partial endorsement of: "Video-polysomnography procedures for diagnosis of rapid eye movement sleep behavior disorder (RBD) and the identification of its prodromal stages: Guidelines from the International RBD Study Group" by the World Sleep Society.

Updated guidelines for the video-polysomnography (vPSG) procedures for diagnosing rapid eye movement sleep behavior disorder (RBD) and the identification of its prodromal stages have recently been proposed by the Neurophysiology Working Group of the International RBD Study Group (IRBDSG). These guidelines were selected for review by a World Sleep Society (WSS) Parasomnias Task Force and the WSS International Sleep Medicine Guidelines Committee. A survey was completed by sleep society leaders and prominent sleep clinicians and researchers in 31 WSS member countries across six continents, focused on sleep technologist training and certification; extent of public/private health insurance coverage for the vPSG evaluation of RBD; extent of hospital-based sleep-technologist-attended overnight vPSG studies; availability of video during PSG studies; and sufficient specification of PSG machines to record and analyze REM sleep without atonia. The findings from this survey indicated that most health systems and medical communities across WSS member countries would not be capable of implementing the proposed more stringent guidelines, which would then strongly interfere with the diagnosis of RBD in a large portion of patients who would not be able to receive the required (often repeated) vPSG evaluation. Therefore, the WSS can only partially endorse the updated guidelines and concludes that the current International Classification of Sleep Disorders-3rd edition diagnostic criteria for RBD should still be retained as the standard reference for the diagnosis of RBD, and that further discussion across all members of the IRBDSG should take place to ensure the feasibility of any future proposed changes.

Cognitive and Motor Decline in Dementia with Lewy Bodies and Parkinson's Disease Dementia.

Background: There is a need to better understand the rate of cognitive and motor decline of Dementia with Lewy bodies (DLB) and Parkinson's disease Dementia (PDD). Objectives: To compare the rate of cognitive and motor decline in patients with DLB and PDD from the E-DLB Consortium and the Parkinson's Incidence Cohorts Collaboration (PICC) Cohorts. Methods: The annual change in MMSE and MDS-UPDRS part III was estimated using linear mixed regression models in patients with at least one follow-up (DLB n = 837 and PDD n = 157). Results: When adjusting for confounders, we found no difference in the annual change in MMSE between DLB and PDD (-1.8 [95% CI -2.3, -1.3] vs. -1.9 [95% CI -2.6, -1.2] [P = 0.74]). MDS-UPDRS part III showed nearly identical annual changes (DLB 4.8 [95% CI 2.1, 7.5]) (PDD 4.8 [95% CI 2.7, 6.9], [P = 0.98]). Conclusions: DLB and PDD showed similar rates of cognitive and motor decline. This is relevant for future clinical trial designs.

A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis.

Psychotic symptoms occur in a majority of schizophrenia patients and in ~50% of all Parkinson's disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little is known, however, about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as in schizophrenia and PD. The present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals in at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms. SCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD, PDN and Con-PD, as well as PDN and PDP, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC ~0.80) of FEP and Con-Psy, and fair performance (AUC ~0.72) when differentiating PDP from Con-PD. Importantly, the best performance was found in partly the same networks, including the thalamus. Alterations within selected SCNs may be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

Multi-Level Adversarial Spatio-Temporal Learning for Footstep Pressure Based FoG Detection.

Freezing of gait (FoG) is one of the most common symptoms of Parkinson's disease, which is a neurodegenerative disorder of the central nervous system impacting millions of people around the world. To address the pressing need to improve the quality of treatment for FoG, devising a computer-aided detection and quantification tool for FoG has been increasingly important. As a non-invasive technique for collecting motion patterns, the footstep pressure sequences obtained from pressure sensitive gait mats provide a great opportunity for evaluating FoG in the clinic and potentially in the home environment. In this study, FoG detection is formulated as a sequential modelling task and a novel deep learning architecture, namely Adversarial Spatio-temporal Network (ASTN), is proposed to learn FoG patterns across multiple levels. ASTN introduces a novel adversarial training scheme with a multi-level subject discriminator to obtain subject-independent FoG representations, which helps to reduce the over-fitting risk due to the high inter-subject variance. As a result, robust FoG detection can be achieved for unseen subjects. The proposed scheme also sheds light on improving subject-level clinical studies from other scenarios as it can be integrated with many existing deep architectures. To the best of our knowledge, this is one of the first studies of footstep pressure-based FoG detection and the approach of utilizing ASTN is the first deep neural network architecture in pursuit of subject-independent representations. In our experiments on 393 trials collected from 21 subjects, the proposed ASTN achieved an AUC 0.85, clearly outperforming conventional learning methods.

Understanding visual hallucinations: A new synthesis.

Despite decades of research, we do not definitively know how people sometimes see things that are not there. Eight models of complex visual hallucinations have been published since 2000, including Deafferentation, Reality Monitoring, Perception and Attention Deficit, Activation, Input, and Modulation, Hodological, Attentional Networks, Active Inference, and Thalamocortical Dysrhythmia Default Mode Network Decoupling. Each was derived from different understandings of brain organisation. To reduce this variability, representatives from each research group agreed an integrated Visual Hallucination Framework that is consistent with current theories of veridical and hallucinatory vision. The Framework delineates cognitive systems relevant to hallucinations. It allows a systematic, consistent, investigation of relationships between the phenomenology of visual hallucinations and changes in underpinning cognitive structures. The episodic nature of hallucinations highlights separate factors associated with the onset, persistence, and end of specific hallucinations suggesting a complex relationship between state and trait markers of hallucination risk. In addition to a harmonised interpretation of existing evidence, the Framework highlights new avenues of research, and potentially, new approaches to treating distressing hallucinations.

Subthalamic Nucleus Activity during Cognitive Load and Gait Dysfunction in Parkinson's Disease.

BACKGROUND: Gait freezing is a common, disabling symptom of Parkinson's disease characterized by sudden motor arrest during walking. Adaptive deep brain stimulation devices that detect freezing and deliver real-time, symptom-specific stimulation are a potential treatment strategy. Real-time alterations in subthalamic nucleus firing patterns have been demonstrated with lower limb freezing, however, whether similar abnormal signatures occur with freezing provoked by cognitive load, is unknown. METHODS: We obtained subthalamic nucleus microelectrode recordings from eight Parkinson's disease patients performing a validated virtual reality gait task, requiring responses to on-screen cognitive cues while maintaining motor output. RESULTS: Signal analysis during 15 trials containing freezing or significant motor output slowing precipitated by dual-tasking demonstrated reduced θ frequency (3-8 Hz) firing compared to 18 unaffected trials. CONCLUSIONS: These preliminary results reveal a potential neurobiological basis for the interplay between cognitive factors and gait disturbances including freezing in Parkinson's disease, informing development of adaptive deep brain stimulation protocols. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Higher Order Polynomial Transformer for Fine-Grained Freezing of Gait Detection.

Freezing of Gait (FoG) is a common symptom of Parkinson's disease (PD), manifesting as a brief, episodic absence, or marked reduction in walking, despite a patient's intention to move. Clinical assessment of FoG events from manual observations by experts is both time-consuming and highly subjective. Therefore, machine learning-based FoG identification methods would be desirable. In this article, we address this task as a fine-grained human action recognition problem based on vision inputs. A novel deep learning architecture, namely, higher order polynomial transformer (HP-Transformer), is proposed to incorporate pose and appearance feature sequences to formulate fine-grained FoG patterns. In particular, a higher order self-attention mechanism is proposed based on higher order polynomials. To this end, linear, bilinear, and trilinear transformers are formulated in pursuit of discriminative fine-grained representations. These representations are treated as multiple streams and further fused by a cross-order fusion strategy for FoG detection. Comprehensive experiments on a large in-house dataset collected during clinical assessments demonstrate the effectiveness of the proposed method, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 is achieved for detecting FoG.

Expert Consensus on the Use of On-Demand Treatments for OFF Episodes in Parkinson's Disease: A Modified Delphi Panel.

Background: On-demand treatments can treat OFF episodes in Parkinson's disease, however, there is limited information regarding when to prescribe them. Objective: Develop expert consensus to determine appropriate clinical factors for considering on-demand treatments. Methods: Using a RAND/UCLA modified Delphi panel method, a panel developed consensus on the use of on-demand treatments for OFF episodes. Results: The panel agreed on-demand treatments were appropriate when OFF episodes were associated with greater functional impact and interfered with basic daily activities. The panel also agreed on-demand treatment may be appropriate for patients with morning akinesia and/or delayed ON of first levodopa dose and >1 type of OFF episode (eg, early morning OFF or wearing OFF regardless of frequency). Conclusions: Experts agreed on-demand treatment is appropriate for many patients with OFF episodes. The greater the functional impact of OFF episodes, the more likely experts agreed that on-demand treatment is appropriate to prescribe.