Cellular and pathological heterogeneity of primary tauopathies.

Microtubule-associated protein tau is abnormally aggregated in neuronal and glial cells in a range of neurodegenerative diseases that are collectively referred to as tauopathies. Multiple studies have suggested that pathological tau species may act as a seed that promotes aggregation of endogenous tau in naïve cells and contributes to propagation of tau pathology. While they share pathological tau aggregation as a common feature, tauopathies are distinct from one another with respect to predominant tau isoforms that accumulate and the selective vulnerability of brain regions and cell types that have tau inclusions. For instance, primary tauopathies present with glial tau pathology, while it is mostly neuronal in Alzheimer's disease (AD). Also, morphologies of tau inclusions can greatly vary even within the same cell type, suggesting distinct mechanisms or distinct tau conformers in each tauopathy. Neuropathological heterogeneity across tauopathies challenges our understanding of pathophysiology behind tau seeding and aggregation, as well as our efforts to develop effective therapeutic strategies for AD and other tauopathies. In this review, we describe diverse neuropathological features of tau inclusions in neurodegenerative tauopathies and discuss what has been learned from experimental studies with mouse models, advanced transcriptomics, and cryo-electron microscopy (cryo-EM) on the biology underlying cell type-specific tau pathology.

An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms.

Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are needed. Here we describe spectral phenotyping, a new kind of biomarker that makes disease predictions based on chemical rather than biological endpoints in cells. Spectral phenotyping uses Fourier Transform Infrared (FTIR) spectromicroscopy to produce an absorbance signature as a rapid physiological indicator of disease state. FTIR spectromicroscopy has over the past been used in differential diagnoses of manifest disease. Here, we report that the unique FTIR chemical signature accurately predicts disease class in mouse with high probability in the absence of brain pathology. In human cells, the FTIR biomarker accurately predicts neurodegenerative disease class using fibroblasts as surrogate cells.

A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers.

We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.

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.

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.

Development of a Brief Screen to Detect Cognitive Impairment in Older Adults: The QuickSort.

BACKGROUND: Sorting tests detect cognitive decline in older adults who have a neurodegenerative disorder, such as Alzheimer's and Parkinson's disease. Although equally effective at detecting impairment as other cognitive screens (e.g. Mini-Mental State Examination (MMSE)), sorting tests are not commonly used in this context. This study examines the QuickSort, which is a new brief sorting test that is designed to screen older adults for cognitive impairment. DESIGN: Observational cohort study. SETTING: General community and inpatients, Australia. PARTICIPANTS: Older (≥60 years) community-dwelling adults (n = 187) and inpatients referred for neuropsychological assessment (n = 78). A normative subsample (n = 115), screened for cognitive and psychological disorders, was formed from the community sample. MEASUREMENTS: Participants were administered the QuickSort, MMSE, Frontal Assessment Battery (FAB), and Depression Anxiety and Stress Scale-21. The QuickSort requires people to sort nine stimuli by color, shape, and number, and to explain the basis for their correct sorts. Sorting (range = 0-12), Explanation (range = 0-6), and Total (range = 0-18) scores were calculated for the QuickSort. RESULTS: The Cognitively Healthy subsample completed the QuickSort within 2 minutes, 50% had errorless performance, and 95% had Total scores of 10 or greater. The likelihood of community-dwelling older adults and inpatients (n = 260) being impaired on either the MMSE or FAB, or both, increased by a factor of 3.75 for QuickSort Total scores of less than 10 and reduced by a factor of 0.23 for scores of 10 or greater. CONCLUSION: The QuickSort provides a quick, reliable, and valid alternative to lengthier cognitive screens (e.g., MMSE and FAB) when screening older adults for cognitive impairment. The QuickSort performance of an older adult can be compared with a cognitively healthy normative sample and used to estimate the likelihood they will be impaired on either the MMSE or FAB, or both. Clinicians can also use evidence-based modeling to customize the QuickSort for their setting.

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.

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.

Is it accurate to classify ALS as a neuromuscular disorder?

INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a fatal disorder characterized by the progressive loss of upper and lower motor neurons. ALS has traditionally been classified within the domain of neuromuscular diseases, which are a unique spectrum of disorders that predominantly affect the peripheral nervous system. However, over the past decades compounding evidence has emerged that there is extensive involvement of the central nervous system. Therefore, one can question whether it remains accurate to classify ALS as a neuromuscular disorder. AREAS COVERED: In this review, the authors sought to discuss current approaches toward disease classification and how we should classify ALS based on novel insights from clinical, imaging, pathophysiological, neuropathological and genetic studies. EXPERT OPINION: ALS exhibits the cardinal features of a neurodegenerative disease. Therefore, classifying ALS as a neuromuscular disease in the strict sense has become untenable. Diagnosing ALS however does require significant neuromuscular expertise and therefore neuromuscular specialists remain best equipped to evaluate this category of patients. Designating motor neuron diseases as a separate category in the ICD-11 is justified and adequately deals with this issue. However, to drive effective therapy development the fields of motor neuron disease and neurodegenerative disorders must come together.

Defining and predicting transdiagnostic categories of neurodegenerative disease.

The prevalence of concomitant proteinopathies and heterogeneous clinical symptoms in neurodegenerative diseases hinders the identification of individuals who might be candidates for a particular intervention. Here, by applying an unsupervised clustering algorithm to post-mortem histopathological data from 895 patients with degeneration in the central nervous system, we show that six non-overlapping disease clusters can simultaneously account for tau neurofibrillary tangles, α-synuclein inclusions, neuritic plaques, inclusions of the transcriptional repressor TDP-43, angiopathy, neuron loss and gliosis. We also show that membership to the six transdiagnostic disease clusters, which explains more variance in cognitive phenotypes than can be explained by individual diagnoses, can be accurately predicted from scores of the Mini-Mental Status Exam, protein levels in cerebrospinal fluid, and genotype at the APOE and MAPT loci, via cross-validated multiple logistic regression. This combination of unsupervised and supervised data-driven tools provides a framework that could be used to identify latent disease subtypes in other areas of medicine.

Latent atrophy factors related to phenotypical variants of posterior cortical atrophy.

OBJECTIVE: To determine whether atrophy relates to phenotypical variants of posterior cortical atrophy (PCA) recently proposed in clinical criteria (i.e., dorsal, ventral, dominant-parietal, and caudal) we assessed associations between latent atrophy factors and cognition. METHODS: We employed a data-driven Bayesian modeling framework based on latent Dirichlet allocation to identify latent atrophy factors in a multicenter cohort of 119 individuals with PCA (age 64 ± 7 years, 38% male, Mini-Mental State Examination 21 ± 5, 71% ß-amyloid positive, 29% ß-amyloid status unknown). The model uses standardized gray matter density images as input (adjusted for age, sex, intracranial volume, MRI scanner field strength, and whole-brain gray matter volume) and provides voxelwise probabilistic maps for a predetermined number of atrophy factors, allowing every individual to express each factor to a degree without a priori classification. Individual factor expressions were correlated to 4 PCA-specific cognitive domains (object perception, space perception, nonvisual/parietal functions, and primary visual processing) using general linear models. RESULTS: The model revealed 4 distinct yet partially overlapping atrophy factors: right-dorsal, right-ventral, left-ventral, and limbic. We found that object perception and primary visual processing were associated with atrophy that predominantly reflects the right-ventral factor. Furthermore, space perception was associated with atrophy that predominantly represents the right-dorsal and right-ventral factors. However, individual participant profiles revealed that the large majority expressed multiple atrophy factors and had mixed clinical profiles with impairments across multiple domains, rather than displaying a discrete clinical-radiologic phenotype. CONCLUSION: Our results indicate that specific brain behavior networks are vulnerable in PCA, but most individuals display a constellation of affected brain regions and symptoms, indicating that classification into 4 mutually exclusive variants is unlikely to be clinically useful.

Compounds that extend longevity are protective in neurodegenerative diseases and provide a novel treatment strategy for these devastating disorders.

While aging is the greatest risk factor for the development of neurodegenerative disease, the role of aging in these diseases is poorly understood. In the inherited forms of these diseases, the disease-causing mutation is present from birth but symptoms appear decades later. This indicates that these mutations are well tolerated in younger individuals but not in older adults. Based on this observation, we hypothesized that changes taking place during normal aging make the cells in the brain (and elsewhere) susceptible to the disease-causing mutations. If so, then delaying some of these age-related changes may be beneficial in the treatment of neurodegenerative disease. In this review, we examine the effects of five compounds that have been shown to extend longevity (metformin, rapamycin, resveratrol, N-acetyl-l-cysteine, curcumin) in four of the most common neurodegenerative diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis). While not all investigations observe a beneficial effect of these compounds, there are multiple studies that show a protective effect of each of these lifespan-extending compounds in animal models of neurodegenerative disease. Combined with genetic studies, this suggests the possibility that targeting the aging process may be an effective strategy to treat neurodegenerative disease.

Analysis of Inter-Chromosomal Distribution of Disease-Related Genes in Human Genome.

Many studies have shown that the spatial distribution of genes within a single chromosome exhibits distinct patterns. However, little is known about the characteristics of inter-chromosomal distribution of genes (including protein-coding genes, processed transcripts and pseudogenes) in different genomes. In this study, we explored these issues using the available genomic data of both human and model organisms. Moreover, we also analyzed the distribution pattern of protein-coding genes that have been associated with 14 common diseases and the insert/deletion mutations and single nucleotide polymorphisms detected by whole genome sequencing in an acute promyelocyte leukemia patient. We obtained the following novel findings. Firstly, inter-chromosomal distribution of genes displays a nonstochastic pattern and the gene densities in different chromosomes are heterogeneous. This kind of heterogeneity is observed in genomes of both lower and higher species. Secondly, protein-coding genes involved in certain biological processes tend to be enriched in one or a few chromosomes. Our findings have added new insights into our understanding of the spatial distribution of genome and disease- related genes across chromosomes. These results could be useful in improving the efficiency of disease-associated gene screening studies by targeting specific chromosomes.

The Amyloid, Tau, and Neurodegeneration (A/T/N) Classification Applied to a Clinical Research Cohort with Long-Term Follow-Up.

BACKGROUND: The unbiased amyloid, tau, and neurodegeneration (A/T/N) classification is designed to characterize individuals in the Alzheimer continuum and is currently little explored in clinical cohorts. OBJECTIVE: A retrospective comparison of the A/T/N classification system with the results of a two-year clinical study, with extended follow-up up to 10 years after inclusion. METHODS: Patients (n = 102) clinically diagnosed as Alzheimer's disease (AD) with dementia or amnestic mild cognitive impairment (MCI), and 61 cognitively healthy control individuals were included. Baseline cerebrospinal fluid core biomarkers for AD (Aß42, phosphorylated tau, and total tau) were applied to the A/T/N classification using the final clinical diagnosis at extended follow-up as the gold standard. RESULTS: A + T + N+ was a strong predictor for AD dementia, even among cognitively healthy individuals. Amnestic MCI was heterogenous, considering both clinical outcome and distribution within A/T/N. Some individuals with amnestic MCI progressed to clinical AD dementia within all four major A/T/N groups. The highest proportion of progression was among triple positive cases, but progression was also common in individuals with suspected non-Alzheimer pathophysiology (A-T + N+), and those with triple negative status. A-T-N- individuals who were cognitively healthy overwhelmingly remained cognitively intact over time, but in amnestic MCI the clinical outcome was heterogenous, including AD dementia, other dementias, and recovery. CONCLUSION: The A/T/N framework accentuates biomarkers over clinical status. However, when selecting individuals for research, a combination of the two may be necessary since the prognostic value of the A/T/N framework depends on clinical status.

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.

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.

Molecular pathology of neurodegenerative diseases: principles and practice.

Neurodegenerative diseases are characterised by selective dysfunction and progressive loss of synapses and neurons associated with pathologically altered proteins that deposit primarily in the human brain and spinal cord. Recent discoveries have identified a spectrum of distinct immunohistochemically and biochemically detectable proteins, which serve as a basis for protein-based disease classification. Diagnostic criteria have been updated and disease staging procedures have been proposed. These are based on novel concepts which recognise that (1) most of these proteins follow a sequential distribution pattern in the brain suggesting a seeding mechanism and cell-to-cell propagation; (2) some of the neurodegeneration-associated proteins can be detected in peripheral organs; and (3) concomitant presence of neurodegeneration-associated proteins is more the rule than the exception. These concepts, together with the fact that the clinical symptoms do not unequivocally reflect the molecular pathological background, place the neuropathological examination at the centre of requirements for an accurate diagnosis. The need for quality control in biomarker development, clinical and neuroimaging studies, and evaluation of therapy trials, as well as an increasing demand for the general public to better understand human brain disorders, underlines the importance for a renaissance of postmortem neuropathological studies at this time. This review summarises recent advances in neuropathological diagnosis and reports novel aspects of relevance for general pathological practice.

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.

Predicting long-term cognitive and neuropathological consequences of moderate to severe traumatic brain injury: Review and theoretical framework.

Prognostic modeling in moderate to severe traumatic brain injury (TBI) has historically focused primarily on the projection of crude outcomes such as the risk of mortality and disability. Initial work in this area has perpetuated the notion that prognosis after moderate to severe TBI can be measured as a single, static, and dichotomous outcome. However, more recent conceptualizations describe moderate to severe TBI as the initiation of a chronic disease state with high levels of inter-individual variability in terms of symptom manifestation and disease progression. Unfortunately, existing prognostic models provide limited insight into the extent of chronic cognitive and neurodegenerative changes experienced by moderate to severe TBI survivors. Though prior research has identified a variety of acute factors that appear to influence post-injury cognitive and neuropathological outcomes, an empirically supported framework for prognostic modeling of these injury-distal outcomes does not exist. The current review considers the literature on an expanded array of empirically supported predictors (both premorbid and injury-related) in association with long-term sequelae of moderate to severe TBI. We also provide a theoretical framework and statistical approach for prognostic modeling in moderate to severe TBI in order to unify efforts across research groups and facilitate important progress in this research area.

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".