Firing Alterations of Neurons in Alzheimer's Disease: Are They Merely a Consequence of Pathogenesis or a Pivotal Component of Disease Progression?

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder, yet its underlying causes remain elusive. The conventional perspective on disease pathogenesis attributes alterations in neuronal excitability to molecular changes resulting in synaptic dysfunction. Early hyperexcitability is succeeded by a progressive cessation of electrical activity in neurons, with amyloid beta (Aß) oligomers and tau protein hyperphosphorylation identified as the initial events leading to hyperactivity. In addition to these key proteins, voltage-gated sodium and potassium channels play a decisive role in the altered electrical properties of neurons in AD. Impaired synaptic function and reduced neuronal plasticity contribute to a vicious cycle, resulting in a reduction in the number of synapses and synaptic proteins, impacting their transportation inside the neuron. An understanding of these neurophysiological alterations, combined with abnormalities in the morphology of brain cells, emerges as a crucial avenue for new treatment investigations. This review aims to delve into the detailed exploration of electrical neuronal alterations observed in different AD models affecting single neurons and neuronal networks.

Cell replacement therapy with stem cells in multiple sclerosis, a systematic review.

Multiple sclerosis (MS) is a chronic inflammatory, autoimmune, and neurodegenerative disease of the central nervous system (CNS), characterized by demyelination and axonal loss. It is induced by attack of autoreactive lymphocytes on the myelin sheath and endogenous remyelination failure, eventually leading to accumulation of neurological disability. Disease-modifying agents can successfully address inflammatory relapses, but have low efficacy in progressive forms of MS, and cannot stop the progressive neurodegenerative process. Thus, the stem cell replacement therapy approach, which aims to overcome CNS cell loss and remyelination failure, is considered a promising alternative treatment. Although the mechanisms behind the beneficial effects of stem cell transplantation are not yet fully understood, neurotrophic support, immunomodulation, and cell replacement appear to play an important role, leading to a multifaceted fight against the pathology of the disease. The present systematic review is focusing on the efficacy of stem cells to migrate at the lesion sites of the CNS and develop functional oligodendrocytes remyelinating axons. While most studies confirm the improvement of neurological deficits after the administration of different stem cell types, many critical issues need to be clarified before they can be efficiently introduced into clinical practice.

A Paradigm Shift in the Management of Patients with Parkinson's Disease.

BACKGROUND: Technological evolution leads to the constant enhancement of monitoring systems and recording symptoms of diverse disorders. SUMMARY: For Parkinson's disease, wearable devices empowered with machine learning analysis are the main modules for objective measurements. Software and hardware improvements have led to the development of reliable systems that can detect symptoms accurately and be implicated in the follow-up and treatment decisions. KEY MESSAGES: Among many different devices developed so far, the most promising ones are those that can record symptoms from all extremities and the trunk, in the home environment during the activities of daily living, assess gait impairment accurately, and be suitable for a long-term follow-up of the patients. Such wearable systems pave the way for a paradigm shift in the management of patients with Parkinson's disease.

Lawton's Instrumental Activities of Daily Living for Greek-Speaking Adults with Cognitive Impairment: A Psychometric Evaluation Study with Additional Receiver Operating Characteristic Curve Analysis.

One of the components of a dementia diagnosis is the assessment of functional abilities. These abilities are measured via screeners, such as the Instrumental Activities of Daily Living (IADL) scale. The IADL scale is a valid tool that has been adapted in many languages. This study aimed to provide a cut-off point and validate the Greek version of the IADL scale in populations with cognitive impairment. IADL data were collected from 132 individuals: 24 PD patients, 24 Parkinson's disease dementia (PDD) patients, and 24 AD patients. The remaining 60 participants were cognitive healthy adults (CHAs). The CHA group and the PD group served as the cognitively unimpaired group (CUG), while the PDD and AD groups served as the cognitively impaired group (CIG). Additionally, the MMSE, the AMTS, the Clock Drawing Test CDT, the Arizona Battery for Communication Disorders of Dementia (ABCD), the NPI, and the GDS-15 were administered to the participants. Statistically significant differences in the IADL scores were exhibited between all subgroups. The IADL scale showed high internal consistency (Cronbach's alpha = 0.890). A threshold equal to 6.00 (AUC = 0.888, p < 0.001) was estimated between the CUG and the CIG. Significant positive correlations were observed between IADL and MMSE (r = 0.764, p < 0.001), IADL and AMTS (r = 0.724, p < 0.001), IADL and ABCD (r = 0.702, p < 0.001), and IADL and CDT (r = 0.627, p < 0.001) results. Given the obtained results, the IADL scale is a valid tool for clinical use with high reliability and sensitivity. Also, the IADL scale is a valuable instrument for screening functional abilities associated with cognitive impairment.

Toward objective monitoring of Parkinson's disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor®.

Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms. As disease progresses, fluctuations in the response to levodopa treatment may develop, along with emergence of freezing of gait (FoG) and levodopa induced dyskinesia (LiD). The optimal management of the motor symptoms and their complications, depends, principally, on the consistent detection of their course, leading to improved treatment decisions. During the last few years, wearable devices have started to be used in the clinical practice for monitoring patients' PD-related motor symptoms, during their daily activities. This work describes the results of 2 multi-site clinical studies (PDNST001 and PDNST002) designed to validate the performance and the wearability of a new wearable monitoring device, the PDMonitor®, in the detection of PD-related motor symptoms. For the studies, 65 patients with Parkinson's disease and 28 healthy individuals (controls) were recruited. Specifically, during the Phase I of the first study, participants used the monitoring device for 2-6 h in a clinic while neurologists assessed the exhibited parkinsonian symptoms every half hour using the Unified Parkinson's Disease Rating Scale (UPDRS) Part III, as well as the Abnormal Involuntary Movement Scale (AIMS) for dyskinesia severity assessment. The goal of Phase I was data gathering. On the other hand, during the Phase II of the first study, as well as during the second study (PDNST002), day-to-day variability was evaluated, with patients in the former and with control subjects in the latter. In both cases, the device was used for a number of days, with the subjects being unsupervised and free to perform any kind of daily activities. The monitoring device produced estimations of the severity of the majority of PD-related motor symptoms and their fluctuations. Statistical analysis demonstrated that the accuracy in the detection of symptoms and the correlation between their severity and the expert evaluations were high. As a result, the studies confirmed the effectiveness of the system as a continuous telemonitoring solution, easy to be used to facilitate decision-making for the treatment of patients with Parkinson's disease.

Cellular Localization of Orexin 1 Receptor in Human Hypothalamus and Morphological Analysis of Neurons Expressing the Receptor.

The orexin system is related to food behavior, energy balance, wakefulness and the reward system. It consists of the neuropeptides orexin A and B, and their receptors, orexin 1 receptor (OX1R) and orexin 2 receptor (OX2R). OX1R has selective affinity for orexin A, and is implicated in multiple functions, such as reward, emotions, and autonomic regulation. This study provides information about the OX1R distribution in human hypothalamus. The human hypothalamus, despite its small size, demonstrates a remarkable complexity in terms of cell populations and cellular morphology. Numerous studies have focused on various neurotransmitters and neuropeptides in the hypothalamus, both in animals and humans, however, there is limited experimental data on the morphological characteristics of neurons. The immunohistochemical analysis of the human hypothalamus revealed that OX1R is mainly found in the lateral hypothalamic area, the lateral preoptic nucleus, the supraoptic nucleus, the dorsomedial nucleus, the ventromedial nucleus, and the paraventricular nucleus. The rest of the hypothalamic nuclei do not express the receptor, except for a very low number of neurons in the mammillary bodies. After identifying the nuclei and neuronal groups that were immunopositive for OX1R, a morphological and morphometric analysis of those neurons was conducted using the Golgi method. The analysis revealed that the neurons in the lateral hypothalamic area were uniform in terms of their morphological characteristics, often forming small groups of three to four neurons. A high proportion of neurons in this area (over 80%) expressed the OX1R, with particularly high expression in the lateral tuberal nucleus (over 95% of neurons). These results were analyzed, and shown to represent, at the cellular level, the distribution of OX1R, and we discuss the regulatory role of orexin A in the intra-hypothalamic areas, such as its special role in the plasticity of neurons, as well as in neuronal networks of the human hypothalamus.

Clinical Evaluation in Parkinson's Disease: Is the Golden Standard Shiny Enough?

Parkinson's disease (PD) has become the second most common neurodegenerative condition following Alzheimer's disease (AD), exhibiting high prevalence and incident rates. Current care strategies for PD patients include brief appointments, which are sparsely allocated, at outpatient clinics, where, in the best case scenario, expert neurologists evaluate disease progression using established rating scales and patient-reported questionnaires, which have interpretability issues and are subject to recall bias. In this context, artificial-intelligence-driven telehealth solutions, such as wearable devices, have the potential to improve patient care and support physicians to manage PD more effectively by monitoring patients in their familiar environment in an objective manner. In this study, we evaluate the validity of in-office clinical assessment using the MDS-UPDRS rating scale compared to home monitoring. Elaborating the results for 20 patients with Parkinson's disease, we observed moderate to strong correlations for most symptoms (bradykinesia, rest tremor, gait impairment, and freezing of gait), as well as for fluctuating conditions (dyskinesia and OFF). In addition, we identified for the first time the existence of an index capable of remotely measuring patients' quality of life. In summary, an in-office examination is only partially representative of most PD symptoms and cannot accurately capture daytime fluctuations and patients' quality of life.

Treatment of acquired transthyretin amyloidosis in domino liver transplantation.

BACKGROUND: Domino liver transplantation (DLT) has been commonly used during the last two decades to partly meet the high need for liver transplants. However, the recipients of grafts from patients with noncirrhotic inherited metabolic disorders may ultimately develop metabolic syndrome, and management is usually intricate, being complicated by the underlying initial disorder, other comorbidities, and post-transplantation conditions. CASE: We report here the management and the outcome in a patient with acquired transthyretin amyloidosis after DLT and significant comorbidities. Final treatment with a transthyretin gene silencing agent, patisiran, was well tolerated and resulted in remission of the aggravating neurological deficits in a follow-up period of 2 years. CONCLUSIONS: The case presented here supports the concept that patisiran can target the hepatocytes producing the mutated transthyretin in acquired transthyretin amyloidosis, as efficiently as in hereditary transthyretin amyloidosis (hATTR), and can be used to treat patients with transthyretin amyloidosis after DLT.

Segmentation and volume quantification of MR Images for the detection and monitoring multiple sclerosis progression.

Multiple Sclerosis (MS) lesions detection and disease's progression monitoring at the same time, play an important role. The purpose of this research is to demonstrate a method for detecting MS plaques and volume estimation from MR Images for monitoring the progression of the disease and the brain atrophy caused. In the proposed research, a clustering-based method is utilized in order to delineate MS plaques in brain, based on anatomical information, brain geometry and lesion features. In addition to volumetric information concerning lesions and whole brain volume, volume quantification is employed to estimate MS atrophy by measuring Brain Parenchymal Fraction (BPF). In the present study, Fluid Attenuated Inversion Recovery (FLAIR) images were utilized for the detection of MS lesions and BPF evaluation, while Tl-weighted MR Images utilized in volume estimation. 30 MS patients were included in a dataset consisted of 3D FLAIR and T1-weighted MR images in order to evaluate the proposed technique. MRI scans performed in two different clinical visits, a baseline and a visit after 6 months. The results extracted in segmentation of MS lesions in terms of sensitivity is 73.80 %. The BPF at baseline estimated to 0.82 ± 0.01, and at 1stfollow up, 0.83 ± 0.01. Finally, the brain volume loss between baseline and after 6 months is 0.4%.

Multiple Sclerosis Severity Estimation and Progression Prediction Based on Machine Learning Techniques.

The aim of the study is to address the Multiple Sclerosis (MS) severity estimation problem based on EDSS score and the prediction of the disease's progression with the application of Machine Learning (ML) approaches. Several ML techniques are implemented. The data are provided by the Neurology Clinic of the University Hospital of Ioannina and were collected in the framework of the ProMiSi project. The features recorded are grouped into: general demographic information, MS clinical related data, results of special tests, treatment, and comorbidities. The records from 30 patients are utilized and are recorded in three time points. The ML methods provided quite high results with 94.87% accuracy for the MS severity estimation and 83.33% for the disease's progression prediction.

Validation of the Greek version of the Abbreviated Mental Test Score: Preliminary findings for cognitively impaired patients of different etiology.

BACKGROUND: Screening people's cognitive skills have been proven essential for reference to full assessment. These methods include short scales, such as the Abbreviated Mental Test Score (AMTS). The AMTS is a valid 10-item questionnaire that has been translated into many languages, but not in Greek yet. The aim of this study is the validation of the Greek version of the AMTS with an additional estimation of its cutoff scores. METHODS: About 132 individuals [60 controls and 72 patients (24 with Parkinson's disease (PD), 24 with Parkinson's disease dementia (PDD), and 24 with Alzheimer's disease (AD)] participated in this study. All participants besides the AMTS completed the Mini Mental State Examination (MMSE), the Tuokko's Clock Drawing Test (CDT), the Instrumental Activities of Daily Living (IADL), the Arizona Battery for Communication Disorders of Dementia (ABCD), the Hellenic versions of the Neuropsychiatric Inventory (NPI), and the Geriatric Depression Scale (GDS-15). RESULTS: Statistically significant differences were found between all subgroups for the AMTS. The AMTS showed high internal consistency (Cronbach alpha = 0.819 and coefficient omega ω = 0.814). A threshold equal to 6.50 (AUC: 0.908, p = 0.000) between groups with and without cognitive impairment was calculated. The AMTS was significantly correlated with the CDT, IADL, and MMSE. CONCLUSION: The proposed version of the AMTS can distinguish between groups with and without cognitive impairment. Additionally, the AMTS is found to be clinically valid having high reliability and classification accuracy. Conclusively, it is a valuable instrument for screening different types of cognitively impaired patients.

ROC Analysis Cut-off Points of Hellenic Voice Handicap Index for Neurogenic Voice Disorders Patients: an Exploratory Study.

OBJECTIVE: The inclusion of subjective methods for evaluating Voice Disorders is proven an essential factor for diagnosis as these methods include self-reported questionnaires (eg, Voice Handicap Index-VHI) for everyday clinical practice. In turn, by obtaining cut-off scores of self-perceived questionnaires intended for assessment procedures of different voice disorders (eg, patients with neurological problems), the clinicians might be helped toward finding their patients' needs leading to better monitoring, and treatment suggestions. Consequently, the purpose of this study was to estimate the cut-off scores for the Greek VHI relevant to patients with neurogenic voice disorders. METHODS: Ninety subjects participated in this research. Sixty-six of them served as the control group while the remaining 24 patients exhibited Neurogenic Voice Disorders (eg, spasmodic dysphonia or vocal fold paralysis). They filled in the VHI and the Voice Evaluation Template. All participants were examined with the use of video laryngeal endoscopy and stroboscopy. RESULTS: The analysis revealed higher medians in all domains (of the VHI) for the patients compared to the control group. The cut-off points were estimated at the values of 24.50 (Total Score-AUC 0.932, P = 0.000), 9.00 (Functional Domain-AUC 0.917, P = 0.000), 10.00 (Physical Domain-AUC 0.948, P = 0.000), and 9.00 (Emotional Domain-AUC 0.830, P = 0.000). CONCLUSION: The estimated cut-off scores are in agreement with previous studies. These scores could probably used to enhance therapeutic monitoring of patients who suffer from neurogenic voice disorders. This study underlines the importance of considering different cutoff points for individuals with voice disorders due to diverse neurogenic etiologies.

Early-Onset Frontotemporal Dementia-Related Semantic Variant of Primary Progressive Aphasia: Multimodal Evaluation With Brain Perfusion SPECT, SPECT/MRI Coregistration, and MRI Volumetry.

ABSTRACT: Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by cortical and subcortical atrophies, with early involvement of the hippocampus and amygdala. A 58-year-old man with clinical presentation of primary progressive aphasia-particularly its svPPA (semantic variant)-and bilateral asymmetric (left-predominant) anterior temporal lobe atrophy on MRI was referred for brain perfusion SPECT. This revealed bilateral hypoperfusion of the anterior temporal lobe (sustained by software-fused SPECT/MRI), pointing toward FTD rather than Alzheimer disease. Furthermore, voxel-based MRI volumetric analysis confirmed bilateral atrophy affecting the hippocampus and amygdala. Combining SPECT with MRI was supportive of the early-onset FTD-related svPPA diagnosis.

New treatment approaches for Alzheimer's disease: preclinical studies and clinical trials centered on antidiabetic drugs.

INTRODUCTION: Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) represent two major chronic diseases that affect a large percentage of the population and share common pathogenetic mechanisms, including oxidative stress and inflammation. Considering their common mechanistic aspects, and given the current lack of effective therapies for AD, accumulating research has focused on the therapeutic potential of antidiabetic drugs in the treatment or prevention of AD. AREAS COVERED: This review examines the latest preclinical and clinical evidence on the potential of antidiabetic drugs as candidates for AD treatment. Numerous approved drugs for T2DM, including insulin, metformin, glucagon-like peptide-1 receptor agonists (GLP-1 RA), and sodium glucose cotransporter 2 inhibitors (SGLT2i), are in the spotlight and may constitute novel approaches for AD treatment. EXPERT OPINION: Among other pharmacologic agents, GLP-1 RA and SGLT2i have so far exhibited promising results as novel treatment approaches for AD, while current research has centered on deciphering their action on the central nervous system (CNS). Further investigation is crucial to reveal the most effective pharmacological agents and their optimal combinations, maximize their beneficial effects on neurons, and find ways to increase their distribution to the CNS.

Configurable Offline Sensor Placement Identification for a Medical Device Monitoring Parkinson's Disease.

Sensor placement identification in body sensor networks is an important feature, which could render such a system more robust, transparent to the user, and easy to wear for long term data collection. It can be considered an active measure to avoid the misuse of a sensing system, specifically as these platforms become more ubiquitous and, apart from their research orientation, start to enter industries, such as fitness and health. In this work we discuss the offline, fixed class, sensor placement identification method implemented in PDMonitor®, a medical device for long-term Parkinson's disease monitoring at home. We analyze the stepwise procedure used to accurately identify the wearables depending on how many are used, from two to five, given five predefined body positions. Finally, we present the results of evaluating the method in 88 subjects, 61 Parkinson's disease patients and 27 healthy subjects, when the overall average accuracy reached 99.1%.

Education on palliative care for Parkinson patients: development of the "Best care for people with late-stage Parkinson's disease" curriculum toolkit.

BACKGROUND: Palliative care education among all stakeholders involved in the care of patients with late-stage Parkinson's disease is not adequate. In fact, there are many unmet educational and training needs as confirmed with a targeted, narrative literature review. METHODS: To address these needs we have developed the "Best Care for People with Late-Stage Parkinson's Disease" curriculum toolkit. The toolkit is based on recommendations and guidelines for training clinicians and other healthcare professionals involved in palliative care, educational material developed in recent research efforts for patients and caregivers with PD and consensus meetings of leading experts in the field. The final version of the proposed toolkit was drafted after an evaluation by external experts with an online survey, the feedback of which was statistically analysed with the chi-square test of independence to assess experts' views on the relevance and importance of the topics. A sentiment analysis was also done to complement statistics and assess the experts positive and negative sentiments for the curriculum topics based on their free text feedback. RESULTS: The toolkit is compliant with Kern's foundational framework for curriculum development, recently adapted to online learning. The statistical analysis of the online survey, aiming at toolkit evaluation from external experts (27 in total), confirms that all but one (nutrition in advanced Parkinson's disease) topics included, as well as their objectives and content, are highly relevant and useful. CONCLUSIONS: In this paper, the methods for the development of the toolkit, its stepwise evolution, as well as the toolkit implementation as a Massive Open Online Course (MOOC), are presented. The "Best Care for People with Late-Stage Parkinson' s disease" curriculum toolkit can provide high-quality and equitable education, delivered by an interdisciplinary team of educators. The toolkit can improve communication about palliative care in neurological conditions at international and multidisciplinary level. It can also offer continuing medical education for healthcare providers.

Accurate Monitoring of Parkinson's Disease Symptoms With a Wearable Device During COVID-19 Pandemic.

BACKGROUND: Accurate assessment of symptoms in Parkinson's disease (PD) is essential for optimal treatment decisions. During the past few years, different monitoring modalities have started to be used in the everyday clinical practice, mainly for the evaluation of motor symptoms. However, monitoring technologies for PD have not yet gained wide acceptance among physicians, patients, and caregivers. The COVID-19 pandemic disrupted the patients' access to healthcare, bringing to the forefront the need for wearable sensors, which provide effective remote symptoms' evaluation and follow-up. CASE REPORT: We report two cases with PD, whose symptoms were monitored with a new wearable CE-marked system (PDMonitor®), enabling appropriate treatment modifications. CONCLUSION: Objective assessment of the patient's motor symptoms in his daily home environment is essential for an accurate monitoring in PD and enhances treatment decisions.

The Tuokko version of the Clock Drawing Test: A validation study in the Greek population.

INTRODUCTION: The present study aims to be the first to validate the Tuokko version of the Clock Drawing Test (CDT) and estimate its cutoff score after its translation into the Greek language and administration in the Greek population. METHODS: One hundred and thirty-two individuals participated in this study [60 with Good Cognitive Health (GCH), 24 with Parkinson's Disease (PD), 24 with Parkinson's Disease Dementia (PDD) and 24 with Alzheimer's Disease (AD)]. The CDT was administered to all participants. Additionally, the cognitive and mental status of the sample were estimated through the use of the Mini Mental State Examination (MMSE), Abbreviated Mental Test Score (AMTS), Arizona Battery for Communication Disorders of Dementia (ABCD), Instrumental Activities of Daily Living (IADL), the Neuropsychiatric Inventory (H-NPI) and the Geriatric Depression Scale -15 (GDS-15). RESULTS: Statistically significant differences were found between all groups on the CDT, with AD patients having lower scores than all subgroups in the study. The CDT showed a high internal consistency (Cronbach's alpha = 0.832). The ROC analysis provided a cutoff point equal to 4.00 (AUC: 0.821, p < 0.001) between the Cognitively Unimpaired Group (CUG: GCH and PD group) and the Cognitively Impaired Group (CIG: PPD and AD patients), 5.00 (AUC: 0.845, p < 0.001) between the GCH group and the PDD group, and 4.00 (AUC: 0.780, p < 0.001) between the GCH group and the AD group. Finally, the cutoff point between the PD group and the PDD group was 4.00 (AUC: 0.896, p < 0.005), and 3.00 (AUC: 0.899, p < 0.001) between the PD group and the AD group. Significant positive Pearson's correlations were observed between CDT and MMSE (r = 0.808, p < 0.001), CDT and AMTS (r = 0.688, p < 0.001), CDT and ABCD (r = 0.770, p < 0.001), CDT and the ABCD Visuospatial Construction subdomain (r = 0.880, p < 0.001); while a negative correlation was found between CDT and IADL (r = -0.627, p < 0.001) between the CUG and the CIG groups. CONCLUSION: Given the results obtained, the CDT appears to be a clinically valid screening instrument for the assessment of visuospatial abilities, with high reliability in Greek populations with cognitive impairment.

Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson's Disease.

Freezing of Gait (FoG) is a common symptom in Parkinson's Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.

Decision Support for Medication Change of Parkinson's Disease Patients.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is a degenerative disorder of the central nervous system for which currently there is no cure. Its treatment requires long-term, interdisciplinary disease management, and usage of typical medications, including levodopa, dopamine agonists, and enzymes, such as MAO-B inhibitors. The key goal of disease management is to prolong patients' independence and keep their quality of life. Due to the different combinations of motor and non-motor symptoms from which PD patients suffer, in addition to existing comorbidities, the change of medications and their combinations is difficult and patient-specific. To help physicians, we developed two decision support models for PD management, which suggest how to change the medication treatment. METHODS: The models were developed using DEX methodology, which integrates the qualitative multi-criteria decision modelling with rule-based expert systems. The two DEX models differ in the way the decision rules were defined. In the first model, the decision rules are based on the interviews with neurologists (DEX expert model), and in the second model, they are formed from a database of past medication change decisions (DEX data model). We assessed both models on the Parkinson's Progression Markers Initiative (PPMI) and on a questionnaire answered by 17 neurologists from 4 European countries using accuracy measure and the Jaccard index. RESULTS: Both models include 15 sub-models that address possible medication treatment changes based on the given patients' current state. In particular, the models incorporate current state changes in patients' motor symptoms (dyskinesia intensity, dyskinesia duration, OFF duration), mental problems (impulsivity, cognition, hallucinations and paranoia), epidemiologic data (patient's age, activity level) and comorbidities (cardiovascular problems, hypertension and low blood pressure). The highest accuracy of the developed sub-models for 15 medication treatment changes ranges from 69.31 to 99.06 %. CONCLUSIONS: Results show that the DEX expert model is superior to the DEX data model. The results indicate that the constructed models are sufficiently adequate and thus fit for the purpose of making "second-opinion" suggestions to decision support users.