Intercellular Spread of Protein Aggregates in Neurodegenerative Disease.

Most neurodegenerative diseases are characterized by the accumulation of protein aggregates, some of which are toxic to cells. Mounting evidence demonstrates that in several diseases, protein aggregates can pass from neuron to neuron along connected networks, although the role of this spreading phenomenon in disease pathogenesis is not completely understood. Here we briefly review the molecular and histopathological features of protein aggregation in neurodegenerative disease, we summarize the evidence for release of proteins from donor cells into the extracellular space, and we highlight some other mechanisms by which protein aggregates might be transmitted to recipient cells. We also discuss the evidence that supports a role for spreading of protein aggregates in neurodegenerative disease pathogenesis and some limitations of this model. Finally, we consider potential therapeutic strategies to target spreading of protein aggregates in the treatment of neurodegenerative diseases.

Peryton: a manual collection of experimentally supported microbe-disease associations.

We present Peryton (https://dianalab.e-ce.uth.gr/peryton/), a database of experimentally supported microbe-disease associations. Its first version constitutes a novel resource hosting more than 7900 entries linking 43 diseases with 1396 microorganisms. Peryton's content is exclusively sustained by manual curation of biomedical articles. Diseases and microorganisms are provided in a systematic, standardized manner using reference resources to create database dictionaries. Information about the experimental design, study cohorts and the applied high- or low-throughput techniques is meticulously annotated and catered to users. Several functionalities are provided to enhance user experience and enable ingenious use of Peryton. One or more microorganisms and/or diseases can be queried at the same time. Advanced filtering options and direct text-based filtering of results enable refinement of returned information and the conducting of tailored queries suitable to different research questions. Peryton also provides interactive visualizations to effectively capture different aspects of its content and results can be directly downloaded for local storage and downstream analyses. Peryton will serve as a valuable source, enabling scientists of microbe-related disease fields to form novel hypotheses but, equally importantly, to assist in cross-validation of findings.

TCRD and Pharos 2021: mining the human proteome for disease biology.

In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.

Estimating the prevalence of Niemann-Pick disease type C (NPC) in the United States.

BACKGROUND: Niemann-Pick Disease Type C (NPC) is an ultra-rare progressive neurodegenerative disease caused by autosomal recessive mutations in the NPC1 or NPC2 genes that lead to premature death, with most individuals dying between 10 and 25 years of age. NPC can present at any age and many individuals with NPC may be misdiagnosed or undiagnosed. A key challenge with recognizing NPC is the heterogeneous and nonspecific clinical presentation. Currently, there are no approved treatments for NPC in the United States; miglustat, an FDA-approved treatment for Gaucher disease, is used off-label for NPC and GM1 gangliosidosis. OBJECTIVES: To estimate the number of people in the United States that 1) have an NPC diagnosis 2) have an NPC diagnosis and/or are treated off-label with miglustat for NPC and 3) are likely to have NPC. METHODS: For the first two objectives, patients were identified using the Symphony Integrated DataVerse database (Oct 2015-Jan 2020). To identify the number of people with NPC for Objective 1, cases of NPC were defined as any patients with an ICD-10 code of E75.242 (NPC) during the study period. Objective 2 expands upon Objective 1, including (a) patients from Objective 1 and (b) patients with documented miglustat use (NDC 43975-0310 or 10,148-0201) who did not have any claim with Gaucher disease (ICD-10 E75.22) or GM1 gangliosidosis (ICD-10 E75.1) during the study period. For the third objective, published NPC incidence (1 per 89,000 live births) and expected mortality estimates were applied to the 2018 United States birth rate (11.6 per 1000) and population size (326.7 million). RESULTS: A total of 308 million unique individuals were represented in the database. Of these, 294 individuals had an NPC diagnosis, yielding an identified NPC prevalence of 0.95 per million people in the United States. 305 individuals were diagnosed with NPC and/or were treated with miglustat without having a diagnosis for either Gaucher or GM1 gangliosidosis, yielding an NPC diagnosed or treated prevalence of 0.99 per million people in the United States. Based on the published literature, there are an estimated 42 new NPC cases per year. Applying this number to the distribution of NPC type (based on age of neurologic symptom onset) and corresponding mortality estimates generates an estimated 943 prevalent cases of NPC, or 2.9 cases of NPC per million people in the United States. CONCLUSIONS: NPC is an ultra-rare, progressive neurodegenerative disease with approximately 1 per million people in the United States diagnosed with or treated off-label for NPC. Given that NPC is often misdiagnosed or undiagnosed, the estimated prevalence from the epidemiology calculations (2.9 per million) approximates the number of NPC cases if disease awareness, screening and diagnosis efforts were enhanced.

Classification Accuracy of Transcranial Magnetic Stimulation for the Diagnosis of Neurodegenerative Dementias.

OBJECTIVE: Transcranial magnetic stimulation (TMS) has been suggested as a reliable, noninvasive, and inexpensive tool for the diagnosis of neurodegenerative dementias. In this study, we assessed the classification performance of TMS parameters in the differential diagnosis of common neurodegenerative disorders, including Alzheimer disease (AD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). METHODS: We performed a multicenter study enrolling patients referred to 4 dementia centers in Italy, in accordance with the Standards for Reporting of Diagnostic Accuracy. All patients underwent TMS assessment at recruitment (index test), with application of reference clinical criteria, to predict different neurodegenerative disorders. The investigators who performed the index test were masked to the results of the reference test and all other investigations. We trained and tested a random forest classifier using 5-fold cross-validation. The primary outcome measures were the classification accuracy, precision, recall, and F1 score of TMS in differentiating each neurodegenerative disorder. RESULTS: A total of 694 participants were included, namely 273 patients diagnosed as AD, 67 as DLB, and 207 as FTD, and 147 healthy controls (HC). A series of 3 binary classifiers was employed, and the prediction model exhibited high classification accuracy (ranging from 0.89 to 0.92), high precision (0.86-0.92), high recall (0.93-0.98), and high F1 scores (0.89-0.95) in differentiating each neurodegenerative disorder. INTERPRETATION: TMS is a noninvasive procedure that reliably and selectively distinguishes AD, DLB, FTD, and HC, representing a useful additional screening tool to be used in clinical practice. Ann Neurol 2020;87:394-404.

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model.

Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aß42), Aß40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aß40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.

An application of machine learning with feature selection to improve diagnosis and classification of neurodegenerative disorders.

BACKGROUND: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machine learning techniques play a key role. However, the amount of health data acquired from digital machines has high dimensionality and not all data acquired from digital machines are relevant for a particular disease. Primary Progressive Aphasia (PPA) is a neurodegenerative syndrome including several specific diseases, and it is a good model to implement machine learning analyses. In this work, we applied five feature selection algorithms to identify the set of relevant features from 18F-fluorodeoxyglucose positron emission tomography images of the main areas affected by PPA from patient records. On the other hand, we carried out classification and clustering algorithms before and after the feature selection process to contrast both results with those obtained in a previous work. We aimed to find the best classifier and the more relevant features from the WEKA tool to propose further a framework for automatic help on diagnosis. Dataset contains data from 150 FDG-PET imaging studies of 91 patients with a clinic prognosis of PPA, which were examined twice, and 28 controls. Our method comprises six different stages: (i) feature extraction, (ii) expertise knowledge supervision (iii) classification process, (iv) comparing classification results for feature selection, (v) clustering process after feature selection, and (vi) comparing clustering results with those obtained in a previous work. RESULTS: Experimental tests confirmed clustering results from a previous work. Although classification results for some algorithms are not decisive for reducing features precisely, Principal Components Analisys (PCA) results exhibited similar or even better performances when compared to those obtained with all features. CONCLUSIONS: Although reducing the dimensionality does not means a general improvement, the set of features is almost halved and results are better or quite similar. Finally, it is interesting how these results expose a finer grain classification of patients according to the neuroanatomy of their disease.

Clinical and neuropathological differences between Parkinson's disease, Parkinson's disease dementia and dementia with Lewy bodies - current issues and future directions.

Lewy body diseases share clinical, pathological, genetic and biochemical signatures, and are regarded as a highly heterogeneous group of neurodegenerative disorders. Inclusive of Parkinson's disease (PD), Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB), controversy still exists as to whether they should be considered as separate disease entities or as part of the same disease continuum. Here we discuss emerging knowledge relating to both clinical, and neuropathological differences and consider current biomarker strategies as we try to improve our diagnostic capabilities and design of disease modifying therapeutics for this group of debilitating neurodegenerative disorders. This article is part of the Special Issue "Synuclein".

Are comorbidities compatible with a molecular pathological classification of neurodegenerative diseases?

PURPOSE OF REVIEW: The purpose of this review is to provide an update on comorbidities in neurodegenerative conditions. The term comorbidity is used here to distinguish cases with overlapping pathogenic mechanisms, which includes combinations of neurodegenerative proteinopathies from cases with multimorbidity, which is defined as concomitant brain and systemic disorders with different pathogenic mechanisms. RECENT FINDINGS: Comorbid proteinopathies are more frequent in both sporadic and hereditary neurodegenerative diseases than previously assumed. The most frequent additional proteinopathies are related to Alzheimer's disease, Lewy body disorder, and limbic predominant transactive response DNA-binding protein 43 proteinopathy, however, different forms of tau pathologies are also increasingly recognized. In addition to ageing, synergistic interaction of proteins, common disease pathways, and the influence of genetic variations are discussed as possible pathogenic players. SUMMARY: Comorbid proteinopathies might influence the clinical course and have implications for biomarker and therapeutic development. As pure forms of proteinopathies are still observed, the notion of current molecular classification is justified. This corroborates elucidation of various pathogenic pathways leading to neurodegeneration. Assuming that single proteins and associated pathways are targeted in therapy trials, efforts are needed to better stratify patients and to select pure proteinopathy forms lacking unfavorable genetic constellations. Otherwise combined therapeutic strategies might be necessary for comorbid proteinopathies.

An efficient Gait Dynamics classification method for Neurodegenerative Diseases using Brain signals.

Neurons of the human brain are primarily affected by the Huntington's disease (HD), Amyotrophic Lateral Sclerosis (ALS), Parkinson's disease and so on. Classification of these neurodegenerative diseases (NDD) is clinically important to analyze the destruction of nerve cells. Early diagnosis of NDD'S helps in saving the human life. Based on the report of previous studies, motor impairment or human gait cycle is largely affected by the clinical symptoms of NDD. Accurate diagnosis of various neurodegenerative diseases in correct time is very important for early diagnosis of the disease. Diseases can be diagnosed earlier by means of characterizing the gait cycle. In this work, a gait dynamics classification method is proposed for determining the neurodegenerative diseases from the brain signals using multilevel feature extraction method. From force sensitive resistors, the left and right feet signals recorded in 60 one minute are included in the input database. It is obtained through fixing 16 healthy subjects, 13 ALS, 20 HD, and 15 PD. Using six levels of Discrete Wavelet Transform (DWT), the features are determined by means of decomposing the raw signal. Ultimately, the pathological gait signals are classified through exploiting three multilevel feature extraction techniques named as, (Detrended Fluctuation Analysis (DFA), Positive, Negative Peak Histogram Analysis (PNPHA) (proposed Method) and Statistical Temporal parameter Analysis (STA)). Experimental outcomes proved that the gait dynamics are successively distinguished between NDD and group of healthy controls using the proposed method.

New classification of tauopathies.

Tauopathies are a group of neurodegenerative diseases characterized by pathological intracellular deposits of the protein tau. Isoform composition, morphology and anatomical distribution of cellular tau-immunoreactivities are defining distinct tauopathies as molecular pathological disease entities. The clinical spectrum of tauopathies includes syndromes with primary motor symptoms and with primary cognitive dysfunction. The traditional syndrome-based classification is currently being complemented by a molecular-pathological classification. While the syndrome-based classification is helpful to select symptomatic therapies, and to generate clinical working hypotheses about underlying etiologies, the molecular-pathological classification is most important for the development and application of molecularly tailored disease-modifying therapies.

[Networks involved in motor cognition : Physiology and pathophysiology of apraxia]. / Netzwerke für motorische Kognition : Physiologie und Pathophysiologie der Apraxie.

Apraxia is an umbrella term for different disorders of higher motor abilities that are not explained by elementary sensorimotor deficits (e. g. paresis or ataxia). Characteristic features of apraxia that are easy to recognize in clinical practice are difficulties in pantomimed or actual use of tools as well as in imitation of meaningless gestures. Apraxia is bilateral, explaining the cognitive motor disorders and occurs frequently (but not exclusively) after left hemispheric lesions, as well as in neurodegenerative diseases, such as corticobasal syndrome and Alzheimer's disease. Apraxic deficits can seriously impair activities of daily living, which is why the appropriate diagnosis is of great relevance. At the functional anatomical level, different cognitive motor skills rely on at least partly different brain networks, namely, a ventral processing pathway for semantic components, such as tool-action associations, a ventro-dorsal pathway for sensorimotor representations of learnt motor acts, as well as a dorso-dorsal pathway for on-line motor control and, probably, imitation of meaningless gestures. While these networks partially overlap with language-relevant regions, more clear cut dissociations are found between apraxia deficits and disorders of spatial attention. In addition to behavioral interventions, noninvasive neuromodulation approaches, as well as human-computer interface assistance systems are a growing focus of interest for the treatment of apraxia.

Molecular Pathological Classification of Neurodegenerative Diseases: Turning towards Precision Medicine.

Neurodegenerative diseases (NDDs) are characterized by selective dysfunction and loss of neurons associated with pathologically altered proteins that deposit in the human brain but also in peripheral organs. These proteins and their biochemical modifications can be potentially targeted for therapy or used as biomarkers. Despite a plethora of modifications demonstrated for different neurodegeneration-related proteins, such as amyloid-ß, prion protein, tau, α-synuclein, TAR DNA-binding protein 43 (TDP-43), or fused in sarcoma protein (FUS), molecular classification of NDDs relies on detailed morphological evaluation of protein deposits, their distribution in the brain, and their correlation to clinical symptoms together with specific genetic alterations. A further facet of the neuropathology-based classification is the fact that many protein deposits show a hierarchical involvement of brain regions. This has been shown for Alzheimer and Parkinson disease and some forms of tauopathies and TDP-43 proteinopathies. The present paper aims to summarize current molecular classification of NDDs, focusing on the most relevant biochemical and morphological aspects. Since the combination of proteinopathies is frequent, definition of novel clusters of patients with NDDs needs to be considered in the era of precision medicine. Optimally, neuropathological categorizing of NDDs should be translated into in vivo detectable biomarkers to support better prediction of prognosis and stratification of patients for therapy trials.

Classifications of neurogenetic diseases: An increasingly complex problem.

Neurodegenerative disorders represent a wide group of diseases affecting the central and/or peripheral nervous system. Many of these disorders were described in the 19th century, but our genetic knowledge of them is recent (over the past 25 years). However, the continual discovery of disease-causing gene mutations has led to difficulties in the classification of these diseases. For this reason, our present proposals for updating and simplifying the classification of some of these conditions (Charcot-Marie-Tooth diseases, distal hereditary motor neuropathies, hereditary sensory and autonomic neuropathies, hereditary spastic ataxias, hereditary spastic paraplegias and hereditary spastic ataxias) are expounded here.

Reduced telomere length in neurodegenerative disorders may suggest shared biology.

Early cell death is a feature of neurodegenerative disorders. Telomere shortening is related to premature cellular senescence and could be a marker for cellular pathology in neurological diseases. Relative telomere length in dementia (N=70), Huntington's disease (N=35), ataxia telangiectasia (N=9), and age-group matched control samples (N=105) was measured as relative telomere copy/single copy gene ratios. Individuals with Huntington's disease had the lowest relative telomere copy/single copy gene ratio (0.21), followed by ataxia telangiectasia (0.31) and dementia (0.48). The younger control group had the highest relative telomere copy/single copy gene ratio (1.07). The reduced telomere length could be indicative of shared biological pathways across these disorders contributing to cellular senescence.

A disease-specific metabolic brain network associated with corticobasal degeneration.

Corticobasal degeneration is an uncommon parkinsonian variant condition that is diagnosed mainly on clinical examination. To facilitate the differential diagnosis of this disorder, we used metabolic brain imaging to characterize a specific network that can be used to discriminate corticobasal degeneration from other atypical parkinsonian syndromes. Ten non-demented patients (eight females/two males; age 73.9 ± 5.7 years) underwent metabolic brain imaging with (18)F-fluorodeoxyglucose positron emission tomography for atypical parkinsonism. These individuals were diagnosed clinically with probable corticobasal degeneration. This diagnosis was confirmed in the three subjects who additionally underwent post-mortem examination. Ten age-matched healthy subjects (five females/five males; age 71.7 ± 6.7 years) served as controls for the imaging studies. Spatial covariance analysis was applied to scan data from the combined group to identify a significant corticobasal degeneration-related metabolic pattern that discriminated (P < 0.001) the patients from the healthy control group. This pattern was characterized by bilateral, asymmetric metabolic reductions involving frontal and parietal cortex, thalamus, and caudate nucleus. These pattern-related changes were greater in magnitude in the cerebral hemisphere opposite the more clinically affected body side. The presence of this corticobasal degeneration-related metabolic topography was confirmed in two independent testing sets of patient and control scans, with elevated pattern expression (P < 0.001) in both disease groups relative to corresponding normal values. We next determined whether prospectively computed expression values for this pattern accurately discriminated corticobasal degeneration from multiple system atrophy and progressive supranuclear palsy (the two most common atypical parkinsonian syndromes) on a single case basis. Based upon this measure, corticobasal degeneration was successfully distinguished from multiple system atrophy (P < 0.001) but not progressive supranuclear palsy, presumably because of the overlap (∼ 24%) that existed between the corticobasal degeneration- and the progressive supranuclear palsy-related metabolic topographies. Nonetheless, excellent discrimination between these disease entities was achieved by computing hemispheric asymmetry scores for the corticobasal degeneration-related pattern on a prospective single scan basis. Indeed, a logistic algorithm based on the asymmetry scores combined with separately computed expression values for a previously validated progressive supranuclear palsy-related pattern provided excellent specificity (corticobasal degeneration: 92.7%; progressive supranuclear palsy: 94.1%) in classifying 58 testing subjects. In conclusion, corticobasal degeneration is associated with a reproducible disease-related metabolic covariance pattern that may help to distinguish this disorder from other atypical parkinsonian syndromes.

Self-awareness in neurodegenerative disease relies on neural structures mediating reward-driven attention.

Accurate self-awareness is essential for adapting one's tasks and goals to one's actual abilities. Patients with neurodegenerative diseases, particularly those with right frontal involvement, often present with poor self-awareness of their functional limitations that may exacerbate their already jeopardized decision-making and behaviour. We studied the structural neuroanatomical basis for impaired self-awareness among patients with neurodegenerative disease and healthy older adults. One hundred and twenty-four participants (78 patients with neurodegenerative diseases including Alzheimer's disease, behavioural variant frontotemporal dementia, right-temporal frontotemporal dementia, semantic variant and non-fluent variant primary progressive aphasia, and 46 healthy controls) described themselves on the Patient Competency Rating Scale, rating observable functioning across four domains (daily living activities, cognitive, emotional control, interpersonal). All participants underwent structural magnetic resonance imaging. Informants also described subjects' functioning on the same scale. Self-awareness was measured by comparing self and informant ratings. Group differences in discrepancy scores were analysed using general linear models, controlling for age, sex and disease severity. Compared with controls, patients with behavioural variant frontotemporal dementia overestimated their functioning in all domains, patients with Alzheimer's disease overestimated cognitive and emotional functioning, patients with right-temporal frontotemporal dementia overestimated interpersonal functioning, and patients with non-fluent aphasia overestimated emotional and interpersonal functioning. Patients with semantic variant aphasia did not overestimate functioning on any domain. To examine the neuroanatomic correlates of impaired self-awareness, discrepancy scores were correlated with brain volume using voxel-based morphometry. To identify the unique neural correlates of overlooking versus exaggerating deficits, overestimation and underestimation scores were analysed separately, controlling for age, sex, total intracranial volume and extent of actual functional decline. Atrophy related to overestimating one's functioning included bilateral, right greater than left frontal and subcortical regions, including dorsal superior and middle frontal gyri, lateral and medial orbitofrontal gyri, right anterior insula, putamen, thalamus, and caudate, and midbrain and pons. Thus, our patients' tendency to under-represent their functional decline was related to degeneration of domain-general dorsal frontal regions involved in attention, as well as orbitofrontal and subcortical regions likely involved in assigning a reward value to self-related processing and maintaining accurate self-knowledge. The anatomic correlates of underestimation (right rostral anterior cingulate cortex, uncorrected significance level) were distinct from overestimation and had a substantially smaller effect size. This suggests that underestimation or 'tarnishing' may be influenced by non-structural neurobiological and sociocultural factors, and should not be considered to be on a continuum with overestimation or 'polishing' of functional capacity, which appears to be more directly mediated by neural circuit dysfunction.

CNN-Based Neurodegenerative Disease Classification Using QR-Represented Gait Data.

PURPOSE: The primary aim of this study is to develop an effective and reliable diagnostic system for neurodegenerative diseases by utilizing gait data transformed into QR codes and classified using convolutional neural networks (CNNs). The objective of this method is to enhance the precision of diagnosing neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD), through the introduction of a novel approach to analyze gait patterns. METHODS: The research evaluates the CNN-based classification approach using QR-represented gait data to address the diagnostic challenges associated with neurodegenerative diseases. The gait data of subjects were converted into QR codes, which were then classified using a CNN deep learning model. The dataset includes recordings from patients with Parkinson's disease (n = 15), Huntington's disease (n = 20), and amyotrophic lateral sclerosis (n = 13), and from 16 healthy controls. RESULTS: The accuracy rates obtained through 10-fold cross-validation were as follows: 94.86% for NDD versus control, 95.81% for PD versus control, 93.56% for HD versus control, 97.65% for ALS versus control, and 84.65% for PD versus HD versus ALS versus control. These results demonstrate the potential of the proposed system in distinguishing between different neurodegenerative diseases and control groups. CONCLUSION: The results indicate that the designed system may serve as a complementary tool for the diagnosis of neurodegenerative diseases, particularly in individuals who already present with varying degrees of motor impairment. Further validation and research are needed to establish its wider applicability.

Lewy pathology and neurodegeneration in premotor Parkinson's disease.

The foremost motor manifestations of Parkinson's disease are resting tremor, cogwheel rigidity, hypokinesia/bradykinesia, and postural instability. Epidemiological and clinical data reveal that a wide variety of additional complaints (nonmotor symptoms), also considerably impar patients' quality of life parallel to the chronic-progressive neurodegenerative disorder. This article reviews the neuropathology and anatomy of Lewy pathology-related neurodegeneration in relation to selected nonmotro and prodromal dysfunctions.