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Parkinson disease (PD) is a progressive disorder characterized by dopaminergic neurodegeneration in the brain. The development of parkinsonism is preceded by a long prodromal phase, and >50% of dopaminergic neurons can be lost from the substantia nigra by the time of the initial diagnosis. Therefore, validation of in vivo imaging biomarkers for early diagnosis and monitoring of disease progression is essential for future therapeutic developments. PET and single-photon emission CT targeting the presynaptic terminals of dopaminergic neurons can be used for early diagnosis by detecting axonal degeneration in the striatum. However, these techniques poorly differentiate atypical parkinsonian syndromes from PD, and their availability is limited in clinical settings. Advanced MRI in which pathological changes in the substantia nigra are visualized with diffusion, iron-sensitive susceptibility and neuromelanin-sensitive sequences potentially represents a more accessible imaging tool. Although these techniques can visualize the classic degenerative changes in PD, they might be insufficient for phenotyping or prognostication of heterogeneous aspects of PD resulting from extranigral pathologies. The retina is an emerging imaging target owing to its pathological involvement early in PD, which correlates with brain pathology. Retinal optical coherence tomography (OCT) is a non-invasive technique to visualize structural changes in the retina. Progressive parafoveal thinning and fovea avascular zone remodelling, as revealed by OCT, provide potential biomarkers for early diagnosis and prognostication in PD. As we discuss in this Review, multimodal imaging of the substantia nigra and retina is a promising tool to aid diagnosis and management of PD.In vivo imaging biomarkers for early diagnosis and monitoring of Parkinson disease (PD) are important for the development of new therapies. The authors review recent advances in brain and retinal imaging in PD, focusing particularly on multimodal approaches with applications at the prodromal stage.

Anti-NMDA receptor (NMDAR) encephalitis is an autoimmune disorder in which the use of immunotherapy and the long-term outcome have not been defined. We aimed to assess the presentation of the disease, the spectrum of symptoms, immunotherapies used, timing of improvement, and long-term outcome. In this multi-institutional observational study, we tested for the presence of NMDAR antibodies in serum or CSF samples of patients with encephalitis between Jan 1, 2007, and Jan 1, 2012. All patients who tested positive for NMDAR antibodies were included in the study; patients were assessed at symptom onset and at months 4, 8, 12, 18, and 24, by use of the modified Rankin scale (mRS). Treatment included first-line immunotherapy (steroids, intravenous immunoglobulin, plasmapheresis), second-line immunotherapy (rituximab, cyclophosphamide), and tumour removal. Predictors of outcome were determined at the Universities of Pennsylvania (PA, USA) and Barcelona (Spain) by use of a generalised linear mixed model with binary distribution. We enrolled 577 patients (median age 21 years, range 8 months to 85 years), 211 of whom were children (<18 years). Treatment effects and outcome were assessable in 501 (median follow-up 24 months, range 4–186): 472 (94%) underwent first-line immunotherapy or tumour removal, resulting in improvement within 4 weeks in 251 (53%). Of 221 patients who did not improve with first-line treatment, 125 (57%) received second-line immunotherapy that resulted in a better outcome (mRS 0–2) than those who did not (odds ratio [OR] 2·69, CI 1·24–5·80; p=0·012). During the first 24 months, 394 of 501 patients achieved a good outcome (mRS 0–2; median 6 months, IQR 2–12) and 30 died. At 24 months’ follow-up, 203 (81%) of 252 patients had good outcome. Outcomes continued to improve for up to 18 months after symptom onset. Predictors of good outcome were early treatment (0·62, 0·50–0·76; p<0·0001) and no admission to an intensive care unit (0·12, 0·06–0·22; p<0·0001). 45 patients had one or multiple relapses (representing a 12% risk within 2 years); 46 (67%) of 69 relapses were less severe than initial episodes (p<0·0001). In 177 children, predictors of good outcome and the magnitude of effect of second-line immunotherapy were similar to those of the entire cohort. Most patients with anti-NMDAR encephalitis respond to immunotherapy. Second-line immunotherapy is usually effective when first-line treatments fail. In this cohort, the recovery of some patients took up to 18 months. The Dutch Cancer Society, the National Institutes of Health, the McKnight Neuroscience of Brain Disorders award, The Fondo de Investigaciones Sanitarias, and Fundació la Marató de TV3.

Non-motor symptoms are common in Parkinson’s disease (PD) and they represent a major source of disease burden. Several non-motor manifestations, such as rapid eye movement sleep behavior disorder, olfactory loss, gastrointestinal abnormalities, visual alterations, cognitive and mood disorders, are known to precede the onset of motor signs. Nonetheless, the mechanisms mediating these alterations are poorly understood and probably involve several neurotransmitter systems. The dysregulation of GABAergic system has received little attention in PD, although the spectrum of non-motor symptoms might be linked to this pathway. This Mini Review aims to provide up-to-date information about the involvement of the GABAergic system for explaining non-motor manifestations in early stages of PD. Therefore, special attention is paid to the clinical data derived from patients with isolated REM sleep behavior disorder or drug-naïve patients with PD, as they represent prodromal and early stages of the disease, respectively. This, in combination with animal studies, might help us to understand how the disturbance of the GABAergic system is related to non-motor manifestations of PD.

Characterizing the effect of age and sex on macular retinal layer thicknesses and foveal pit morphology is crucial to differentiating between natural and disease-related changes. We applied advanced image analysis techniques to optical coherence tomography (OCT) to: 1) enhance the spatial description of age and sex effects, and 2) create a detailed open database of normative retinal layer thickness maps and foveal pit shapes. The maculae of 444 healthy subjects (age range 21–88) were imaged with OCT. Using computational spatial data analysis, thickness maps were obtained for retinal layers and averaged into 400 (20 x 20) sectors. Additionally, the geometry of the foveal pit was radially analyzed by computing the central foveal thickness, rim height, rim radius, and mean slope. The effect of age and sex on these parameters was analyzed with multiple regression mixed-effects models. We observed that the overall age-related decrease of the total retinal thickness (TRT) (-1.1% per 10 years) was mainly driven by the ganglion cell-inner plexiform layer (GCIPL) (-2.4% per 10 years). Both TRT and GCIPL thinning patterns were homogeneous across the macula when using percentual measurements. Although the male retina was 4.1 μm thicker on average, the greatest differences were mainly present for the inner retinal layers in the inner macular ring (up to 4% higher TRT than in the central macula). There was an age-related decrease in the rim height (1.0% per 10 years) and males had a higher rim height, shorter rim radius, and steeper mean slope. Importantly, the radial analysis revealed that these changes are present and relatively uniform across angular directions. These findings demonstrate the capacity of advanced analysis of OCT images to enhance the description of the macula. This, together with the created dataset, could aid the development of more accurate diagnosis models for macular pathologies.

Non-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening.

Objective This study aimed to analyze the association of autonomic dysfunction with cognition, depression, apathy, and fatigue in Lewy body disease (LBD). Methods We included 61 patients [49 with idiopathic Parkinson’s disease, 7 with dementia with Lewy bodies, and 5 E46K-SNCA mutation carriers] and 22 healthy controls. All participants underwent a comprehensive battery of neuropsychological and clinical measures, autonomic symptom assessment with the SCOPA-AUT, analysis of non-invasive hemodynamic parameters during deep breathing, the Valsalva maneuver, and a 20-min tilt test, and electrochemical skin conductance measurement at rest (Sudoscan). Student’s t tests were used to assess group differences, and bivariate correlations and stepwise linear regressions to explore associations between autonomic function, cognition, depression, apathy, and fatigue. Results Compared to controls, patients who had significant impairment ( p  < 0.05) in cognition, higher depression, apathy, and fatigue, more autonomic symptoms and objective autonomic dysfunction, reduced deep breathing heart rate variability [expiratory-to-inspiratory (E/I) ratio], prolonged pressure recovery time, and lower blood pressure in Valsalva late phase II and phase IV, while 24.1% had orthostatic hypotension in the tilt test. Autonomic parameters significantly correlated with cognitive and neuropsychiatric outcomes, systolic blood pressure during the Valsalva maneuver predicting apathy and depression. The E/I ratio was the main predictor of cognitive performance (17.6% for verbal fluency to 32.8% for visual memory). Conclusion Cardiovascular autonomic dysfunction is associated with cognitive and neuropsychiatric impairment in LBD, heart rate variability during deep breathing and systolic blood pressure changes during the Valsalva procedure are the main predictors of neuropsychological performance and depression/apathy symptoms, respectively.

Background Cognitive decline has been reported in premanifest and manifest Huntington’s disease but reliable biomarkers are lacking. Inner retinal layer thickness seems to be a good biomarker of cognition in other neurodegenerative diseases. Objective To explore the relationship between optical coherence tomography-derived metrics and global cognition in Huntington’s Disease. Methods Thirty-six patients with Huntington’s disease (16 premanifest and 20 manifest) and 36 controls matched by age, sex, smoking status, and hypertension status underwent macular volumetric and peripapillary optical coherence tomography scans. Disease duration, motor status, global cognition and CAG repeats were recorded in patients. Group differences in imaging parameters and their association with clinical outcomes were analyzed using linear mixed-effect models. Results Premanifest and manifest Huntington’s disease patients presented thinner retinal external limiting membrane-Bruch’s membrane complex, and manifest patients had thinner temporal peripapillary retinal nerve fiber layer compared to controls. In manifest Huntington’s disease, macular thickness was significantly associated with MoCA scores, inner nuclear layer showing the largest regression coefficients. This relationship was consistent after adjusting for age, sex, and education and p-value correction with False Discovery Rate. None of the retinal variables were related to Unified Huntington’s Disease Rating Scale score, disease duration, or disease burden. Premanifest patients did not show a significant association between OCT-derived parameters and clinical outcomes in corrected models. Conclusions In line with other neurodegenerative diseases, OCT is a potential biomarker of cognitive status in manifest HD. Future prospective studies are needed to evaluate OCT as a potential surrogate marker of cognitive decline in HD.

Telerehabilitation in neurological and cardiological diseases is an alternative rehabilitation that improves the quality of life and health conditions of patients and enhances the accessibility to health care. However, despite the reported benefits of telerehabilitation, it is necessary to study its impact on the healthcare system. The systematic review aims to investigate the costs and results of telerehabilitation in neurological and cardiological diseases. MEDLINE and EMBASE databases were searched from 2005 to 2021, for studies that assess the costs and results of telerehabilitation compared to traditional rehabilitation (center-based programs) in neurological and cardiological diseases. A narrative synthesis of results was carried out. A total of 8 studies (865 participants) of 430 records were included. Three studies were related to the costs and results of telerehabilitation in neurological diseases (specifically in stroke). In total, five studies assessed telerehabilitation in cardiological diseases (chronic heart failure, coronary heart disease, acute coronary syndrome, and cardiovascular diseases). The duration of the telerehabilitation ranged from 6 to 48 weeks. The studies included cost-analysis, cost-benefit, cost-effectiveness, or cost-utility. In total, four studies found significant cost/savings per person between $565.66 and $2,352.00 ( < 0.05). In contrast, most studies found differences in costs and clinical effects between the telerehabilitation performed and the rehabilitation performed at the clinic. Just one study found quality-adjusted life years (QALY) significant differences between groups [Incremental cost-effectiveness ratio (ICER) per QALY ($-21,666.41/QALY). Telerehabilitation is an excellent alternative to traditional center rehabilitation, which increases the accessibility to rehabilitation to more people, either due to the geographical situation of the patients or the limitations of the health systems. Telerehabilitation seems to be as clinical and cost-effective as traditional rehabilitation, even if, generally, telerehabilitation is less costly. More research is needed to evaluate health-related quality of life and cost-effectiveness in other neurological diseases. [https://figshare.com/articles/journal_ contribution/Review_Protocol_Costs_and_effects_of_Telerehabilitation_in_ Neurological_and_Cardiological_Diseases_A_Systematic_Review/19619838], identifier [19619838].

Parkinson’s disease (PD) is a neurodegenerative disorder marked by motor and cognitive impairments. The early prediction of cognitive deterioration in PD is crucial. This work aims to predict the change in the Montreal Cognitive Assessment (MoCA) at years 4 and 5 from baseline in the Parkinson’s Progression Markers Initiative database. The predictors included demographic and clinical variables: motor and non-motor symptoms from the baseline visit and change scores from baseline to the first-year follow-up. Various regression models were compared, and SHAP (SHapley Additive exPlanations) values were used to assess domain importance, while model coefficients evaluated variable importance. The LASSOLARS algorithm outperforms other models, achieving lowest the MAE, 1.55±0.23 and 1.56±0.19, for the fourth- and fifth-year predictions, respectively. Moreover, when trained to predict the average MoCA score change across both time points, its performance improved, reducing its MAE by 19%. Baseline MoCA scores and MoCA deterioration over the first-year were the most influential predictors of PD (highest model coefficients). However, the cumulative effect of other cognitive variables also contributed significantly. This study demonstrates that mid-term cognitive deterioration in PD can be accurately predicted from patients’ baseline cognitive performance and short-term cognitive deterioration, along with a few easily measurable clinical measurements.

Visual hallucinations (VH) are present in up to 75% of Parkinson’s disease (PD) patients. However, their neural bases and participation of the visual system in VH are not well-understood in PD. Seventy-four participants, 12 PD with VH (PDVH), 35 PD without VH (PDnoVH) and 27 controls underwent a battery of primary visual function and visual cognition tests, retinal optical coherence tomography and structural and resting-state functional brain MRI. We quantified cortical thickness with Freesurfer and functional connectivity (FC) of Visual (VIS), Fronto-Parietal (FP), Ventral Attention (VAN) and Dorsal Attention (DAN) networks with CONN toolbox. Group comparisons were performed with MANCOVA. Area Under the Curve (AUC) was computed to assess the ability of visual variables to differentiate PDVH and PDnoVH. There were no significant PDVH vs PDnoVH differences in disease duration, motor manifestations, general cognition or dopamine agonist therapy (DA) use. Compared to PDnoVH and HC, and regardless of DA use, PDVH showed significantly reduced contrast sensitivity, visuoperceptive and visuospatial abilities, increased retina photoreceptor layer thickness, reduced cortical thickness mostly in right visual associative areas, decreased between-network VIS–VAN and VAN–DAN connectivity and increased within-network DAN connectivity. The combination of clinical and imaging variables that best discriminated PDVH and PDnoVH (highest AUC), where within-network DAN FC, photoreceptor layer thickness and cube analysis test from Visual Object and Space Perception Battery (accuracy of 81.8%). Compared to PDnoVH, PDVH have specific functional and structural abnormalities within the visual system, which can be quantified non-invasively and could potentially constitute biomarkers for VH in PD.