ANN for Parkinson’s Disease Prediction

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%

Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes

KOMPLEKSOWE METODY UCZENIA MASZYNOWEGO I UCZENIA GŁĘBOKIEGO DO KLASYFIKACJI CHOROBY PARKINSONA I OCENY JEJ NASILENIA

In this study, we aimed to adopt a comprehensive approach to categorize and assess the severity of Parkinson's disease by leveraging techniques from both machine learning and deep learning. We thoroughly evaluated the effectiveness of various models, including XGBoost, Random Forest, Multi-Layer Perceptron (MLP), and Recurrent Neural Network (RNN), utilizing classification metrics. We generated detailed reports to facilitate a comprehensive comparative analysis of these models. Notably, XGBoost demonstrated the highest precision at 97.4%. Additionally, we took a step further by developing a Gated Recurrent Unit (GRU) model with the purpose of combining predictions from alternative models. We assessed its ability to predict the severity of the ailment. To quantify the precision levels of the models in disease classification, we calculated severity percentages. Furthermore, we created a Receiver Operating Characteristic (ROC) curve for the GRU model, simplifying the evaluation of its capability to distinguish among various severity levels. This comprehensive approach contributes to a more accurate and detailed understanding of Parkinson's disease severity assessment.W tym badaniu naszym celem było przyjęcie kompleksowego podejścia do kategoryzacji i oceny ciężkości choroby Parkinsona poprzez wykorzystanie technik zarówno uczenia maszynowego, jak i głębokiego uczenia. Dokładnie oceniliśmy skuteczność różnych modeli, w tym XGBoost, Random Forest, Multi-Layer Perceptron (MLP) i Recurrent Neural Network (RNN), wykorzystując wskaźniki klasyfikacji. Wygenerowaliśmy szczegółowe raporty, aby ułatwić kompleksową analizę porównawczą tych modeli. Warto zauważyć, że XGBoost wykazał najwyższą precyzję na poziomie 97,4%. Ponadto poszliśmy o krok dalej, opracowując model Gated Recurrent Unit (GRU) w celu połączenia przewidywań z alternatywnych modeli. Oceniliśmy jego zdolność do przewidywania nasilenia dolegliwości. Aby określić ilościowo poziomy dokładności modeli w klasyfikacji chorób, obliczyliśmy wartości procentowe nasilenia. Ponadto stworzyliśmy krzywą charakterystyki operacyjnej odbiornika (ROC) dla modelu GRU, upraszczając ocenę jego zdolności do rozróżniania różnych poziomów nasilenia. To kompleksowe podejście przyczynia się do dokładniejszego i bardziej szczegółowego zrozumienia oceny ciężkości choroby Parkinsona

Clinical and dopaminergic imaging characteristics of the FARPRESTO cohort of trial-ready idiopathic rapid eye movement sleep behavior patients

Introduction: Idiopathic/isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is considered the prodromal stage of alpha-synucleinopathies. Thus, iRBD patients are the ideal target for disease-modifying therapy. The risk FActoRs PREdictive of phenoconversion in iRBD Italian STudy (FARPRESTO) is an ongoing Italian database aimed at identifying risk factors of phenoconversion, and eventually to ease clinical trial enrollment of well-characterized subjects.Methods: Polysomnography-confirmed iRBD patients were retrospectively and prospectively enrolled. Baseline harmonized clinical and nigrostriatal functioning data were collected at baseline. Nigrostriatal functioning was evaluated by dopamine transporter-single-photon emission computed tomography (DaT-SPECT) and categorized with visual semi-quantification. Longitudinal data were evaluated to assess phenoconversion. Cox regressions were applied to calculate hazard ratios.Results: 365 patients were enrolled, and 289 patients with follow-up (age 67.7 & PLUSMN; 7.3 years, 237 males, mean follow-up 40 & PLUSMN; 37 months) were included in this study. At follow-up, 97 iRBD patients (33.6%) phenoconverted to an overt synucleinopathy. Older age, motor and cognitive impairment, constipation, urinary and sexual dysfunction, depression, and visual semi-quantification of nigrostriatal functioning predicted phenoconversion. The remaining 268 patients are in follow-up within the FARPRESTO project.Conclusions: Clinical data (older age, motor and cognitive impairment, constipation, urinary and sexual dysfunction, depression) predicted phenoconversion in this multicenter, longitudinal, observational study. A standardized visual approach for semi-quantification of DaT-SPECT is proposed as a practical risk factor for phenoconversion in iRBD patients. Of note, non-converted and newly diagnosed iRBD patients, who represent a trial-ready cohort for upcoming disease-modification trials, are currently being enrolled and followed in the FARPRESTO study. New data are expected to allow better risk characterization

Identification of prodromal presentations of Parkinson's disease among primary care outpatients in Germany

Background: This study aimed to identify clinical features that predate the diagnosis of PD in a primary care setting. Methods: This retrospective case-control study was based on data from the Disease Analyzer database (IQVIA) and included 17,702 patients with Parkinson's disease and 17,702 non-PD controls matched for age, sex, and index year. We analyzed the prevalence of 15 defined diagnoses and symptoms documented within 2 years, ≥2 to <5, and ≥5 to <10 years prior to the index date in patients with and without PD. Logistic regression analyses were conducted to assess the association between PD and the predefined diagnoses. Results: The prevalence of motor, neuropsychiatric and autonomic features was higher in those with a later diagnosis of Parkinson's disease than controls for all three periods except for rigidity in the ≥2 to <5 and ≥5 to <10-year periods and erectile dysfunction in the most recent period before diagnosis. The clinical presentation recorded in the greatest percentage of patients was depression, followed by dizziness, insomnia, and constipation, but these were also common in the control population. The odds ratios were highest for increase in tremor, followed by balance impairment and memory problems, particularly in the latest period before diagnosis, and by constipation particularly in the earliest period examined. Conclusion: The prodromal features of PD could be identified in this large primary care database in Germany with similar results to those found in previous database studies despite differences in methodologies and systems

Making it count : novel behavioural tasks to quantify symptoms of dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a neurodegenerative disease and a common cause of dementia in the elderly. The primary pathology of DLB is the mis-folding of the α-synuclein protein, classifying DLB as a synucleinopathy. However, concomitant pathologies are commonly found in post-mortem examination of DLB patients that may complicate diagnosis. Furthermore, DLB is a relatively new disease, first discovered in 1976, while the first official diagnostic criteria released in 1996. Consequently, the diagnostic criteria for DLB have evolved as more is learnt about the clinical and neuropathological profile. Synucleinopathies are also known to be heterogeneous, with no single symptom or biomarker present in all DLB cases. Instead, combinations of common symptoms lead to a diagnosis of probable DLB. Two of the most prominent and debilitating symptoms of DLB are visual hallucinations and cognitive fluctuations. Visual hallucinations (VH) in DLB patients are typically vivid, well-formed percepts and are a major cause of patient and caregiver stress as well as a risk factor for the patient being placed into professional care. Cognitive fluctuations (CF) involve a cycling change in attention and alertness and may occur on a daily or monthly basis, while drops in awareness may last seconds or hours. Currently, the only tools to measure cognitive fluctuations or visual hallucinations are scales or questionnaires that rely on responses from the patient or informant. Furthermore, severity of the symptom is then ranked on an arbitrary ranking system. While this method has advantages in a clinical setting, the subjective nature of the scales combined with the ranking of scores results in a loss of sensitivity. In a research setting, especially imaging or clinical trials, objective measures that are sensitive to changes in symptom severity are highly valued. This allows researchers to assess the relationship between behavioural and fMRI data and clinicians to observe subtle changes in severity. Furthermore, the measures need to be easy to conduct as patients are often severely impaired. The aim of this thesis is to test cognitive function using three paradigms that are novel to DLB patients: Sustained Attention Response Task (SART), the Mental Rotation (MR) task and the Bistable Percept Paradigm (BPP). Overall, this thesis provided the groundwork needed before these three tasks can be utilised in a clinical or research setting. Moreover, as each task was accessible to DLB patients and provided a measure associated with VH or CF, they may prove useful for future neuroimaging/neuropsychological studies

2008 Progress Report on Brain Research

Highlights new research on various disorders, nervous system injuries, neuroethics, neuroimmunology, pain, sense and body function, stem cells and neurogenesis, and thought and memory. Includes essays on arts and cognition and on deep brain stimulation

Clinical phenotypes and constipation severity in Parkinson’s disease: Relation to Prevotella species

Background:&nbsp;The gut microbiome is speculated to play a crucial role in its pathogenesis of Parkinson’s disease as a triggering factor. Recent hypotheses suggested that&nbsp;Prevotella species&nbsp;regulate gut permeability, exert a neuroprotective effect, and interestingly, has been suspected to be deficient in PD patients, and so may play a role in this disease.&nbsp;Aim:&nbsp;This study was designed to compare between PD patients and their healthy controls as regards relative&nbsp;Prevotella&nbsp;abundance, prevalence of&nbsp;Prevotella-dominant Enterotype, and constipation severity. Also, to correlate&nbsp;Prevotella&nbsp;changes with the clinical phenotypes and &nbsp;severity of motor and non-motor symptoms of PD.&nbsp;Methods:&nbsp;Twenty-five PD cases were enrolled in this study and cross-matched to 25 healthy subjects representing the control group. Overall NMS severity was assessed using the Non-Motor Symptoms Scale (NMSS). Quantitative SYBR green Real Time PCR was performed for the identification and quantitation of&nbsp;Prevotella&nbsp;in stool.&nbsp;Results:&nbsp;Prevotella&nbsp;relative abundance was 4-fold decreased in cases when compared to controls with PIGD phenotype showing the lowest abundance, however the difference was not statistically significance.&nbsp;Prevotella-dominant Enterotype was less presented in cases compared to controls, the result was statistically significant. Severe and very severe constipation grades presented 64% of cases group Vs 12% of control group. There was statistically significant positive correlation between total constipation score and UPDRS total score and motor symptoms phenotypes.&nbsp;Conclusion:&nbsp;Relative low&nbsp;Prevotella abundance&nbsp;in PD patients appears to be related to severe phenotypes of the disease; PIGD and mixed phenotypes. Severe constipation was more presented in PD cases which may be considered &nbsp;as a preclinical biomarker for PD