Classification Of Neurodegenerative Diseases Research Articles

Automated detection and classification of neurodegenerative diseases using time-frequency based methods and artificial neural network classifier

Automated detection and classification of neurodegenerative diseases using time-frequency based methods and artificial neural network classifier

Tensor Decomposition of Foot Pressure for Classification of Neurodegenerative Diseases

Tensor Decomposition of Foot Pressure for Classification of Neurodegenerative Diseases

Optimizing Neurodegenerative Disease Classification with Canny Segmentation and Voting Classifier: An Imbalanced Dataset Study

This study explores the efficacy of a Voting Classifier, combining Logistic Regression, Random Forest, and Gaussian Naive Bayes, in the classification of neurodegenerative diseases, focusing on Alzheimer's Disease (AD), Parkinson’s Disease (PD), and control groups. Utilizing a dataset pre-processed with Canny segmentation and Hu Moments feature extraction, the research aimed to address the challenges posed by imbalanced datasets in medical image classification. The classifier's performance was evaluated through a 5-fold cross-validation approach, with metrics including accuracy, precision, recall, and F1-Score. The results revealed a consistent recall rate of approximately 46% across all folds, indicating the model's effectiveness in identifying cases of neurodegenerative diseases. However, the precision and F1-Score were notably lower, averaging around 22% and 29%, respectively, underscoring the difficulties in achieving accurate classification in imbalanced datasets. The study contributes to the understanding of machine learning applications in medical diagnostics, specifically in the challenging context of neurodegenerative disease classification. It highlights the potential of using advanced image processing techniques combined with machine learning ensembles in enhancing diagnostic accuracy. However, it also draws attention to the inherent challenges in such approaches, particularly regarding precision in imbalanced datasets. Recommendations for future research include exploring data balancing techniques, alternative feature extraction methods, and different machine learning algorithms to improve the precision and overall performance. Additionally, applying the model to a broader and more diverse dataset could provide more generalizable and robust findings. This study is significant for researchers and practitioners in medical imaging and machine learning, offering insights into the complexities and potential of automated disease classification

Uncovering the distinct macro-scale anatomy of dysexecutive and behavioural degenerative diseases.

There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer's disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer's disease pathology (behavioural Alzheimer's disease) and 28 had amnestic Alzheimer's disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as 'eigenbrains'. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer's disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer's disease, then amnestic Alzheimer's disease. Dysexecutive Alzheimer's disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer's disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer's disease, temporo-parietal areas in amnestic Alzheimer's disease and frontotemporal areas in bvFTD and behavioural Alzheimer's disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer's disease patient for behavioural Alzheimer's disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network

Building upon the recent advances and successes in the application of deep learning to the medical field, we propose in this work a new approach to detect and classify early-stage Alzheimer patients using online handwriting (HW) loop patterns. To cope with the lack of training data prevalent in the tasks of classification of neuro-degenerative diseases from behavioral data, we investigate several data augmentation techniques. In this respect, compared to the traditional data augmentation techniques proposed for HW-based Parkinson detection, we investigate a variant of Generative Adversarial Networks (GANs), DoppelGANger, especially tailored for times series and hence suitable for synthesizing realistic online handwriting sequences. Based on a 1D-Convolutional Neural Network (1D-CNN) to perform Alzheimer classification, we show, on a real dataset related to HW and Alzheimer, that our DoppelGANger-based augmentation model allow the CNN to significantly outperform both the current state of the art and the other data augmentation techniques.

Assessment of perfusion deficit with early phases of [18F]PI-2620 tau-PET versus [18F]flutemetamol-amyloid-PET recordings

PurposeCharacteristic features of amyloid-PET (A), tau-PET (T), and FDG-PET (N) can serve for the A/T/N classification of neurodegenerative diseases. Recent studies showed that the early, perfusion-weighted phases of amyloid- or tau-PET recordings serve to detect cerebrometabolic deficits equally to FDG-PET, therefore providing a surrogate of neuronal injury. As such, two channels of diagnostic information can be obtained in the setting of a single PET scan. However, there has hitherto been no comparison of early-phase amyloid- and tau-PET as surrogates for deficits in perfusion/metabolism. Therefore, we undertook to compare [18F]flutemetamol-amyloid-PET and [18F]PI-2620 tau-PET as “one-stop shop” dual purpose tracers for the detection of neurodegenerative disease.MethodsWe obtained early-phase PET recordings with [18F]PI-2620 (0.5–2.5 min p.i.) and [18F]flutemetamol (0–10 min p.i.) in 64 patients with suspected neurodegenerative disease. We contrasted global mean normalized images (SUVr) in the patients with a normal cohort of 15 volunteers without evidence of increased pathology to β-amyloid- and tau-PET examinations. Regional group differences of tracer uptake (z-scores) of 246 Brainnetome volumes of interest were calculated for both tracers, and the correlations of the z-scores were evaluated using Pearson’s correlation coefficient. Lobar compartments, regions with significant neuronal injury (z-scores < − 3), and patients with different neurodegenerative disease entities (e.g., Alzheimer’s disease or 4R-tauopathies) served for subgroup analysis. Additionally, we used partial regression to correlate regional perfusion alterations with clinical scores in cognition tests.ResultsThe z-scores of perfusion-weighted images of both tracers showed high correlations across the brain, especially in the frontal and parietal lobes, which were the brain regions with pronounced perfusion deficit in the patient group (R = 0.83 ± 0.08; range, 0.61–0.95). Z-scores of individual patients correlated well by region (R = 0.57 ± 0.15; range, 0.16–0.90), notably when significant perfusion deficits were present (R = 0.66 ± 0.15; range, 0.28–0.90).ConclusionThe early perfusion phases of [18F]PI-2620 tau- and [18F]flutemetamol-amyloid-PET are roughly equivalent indices of perfusion defect indicative of regional and lobar neuronal injury in patients with various neurodegenerative diseases. As such, either tracer may serve for two diagnostic channels by assessment of amyloid/tau status and neuronal activity.

Latent space projection of brain FDG‐PET creates a powerful classifier for neurodegenerative diseases

AbstractBackgroundFluorodeoxyglucose positron emission tomography (FDG‐PET) is an established modality for differential diagnosis of dementia. Deriving phenotypic signatures from FDG‐PET via machine learning is challenging due to the high dimensional nature of brain images relative to the generally small number of labeled examples available for training, the class imbalance among those labels, and the cooccurrence of multiple pathologies. In this study, we developed a multi‐class, multi‐label framework to address these challenges.MethodA database of clinically acquired PET/CT images from 3,000 unique patients was used to develop a latent space model using matrix decomposition. This model was then applied to images from a separate cohort of Mayo Clinic Alzheimer’s Disease Research Center participants (n=1,745) labeled as cognitively unimpaired (CU) (n=1,436) or with the following potentially co‐occurring phenotypes: Alzheimer’s disease (AD) (n=165), Lewy body dementia (DLB) (n=92), behavioral variant frontotemporal dementia (bvFTD) (n=43), semantic (svPPA) (n=10) and logopenic (lvPPA) (n=13) variant PPA, and posterior cortical atrophy (PCA) (n=17). A k‐nearest neighbors classifier that is robust to these imbalanced and overlapping labels was then trained on these examples. The resulting classifier was evaluated by area under receiver‐operator characteristic curve (ROC‐AUC) via leave one out cross validation, using clinical diagnosis as the gold standard.ResultROC curves and AUC scores for each phenotype are illustrated in Fig. 1a. Because the classifier is based on a k‐nearest neighbors connectivity matrix, it has a convenient graphical representation, where images are nodes and edges are drawn between an image and its set of nearest neighbors in latent space. A self‐organizing force directed graph constructed in this way is illustrated in Fig 1b, highlighting the strong separation of CU and degenerative images, as well as the segregation of each phenotype within the neurodegenerative region of the graph.ConclusionIn this study, we developed a machine learning framework for classification of neurodegenerative disease based on k‐nearest neighbor analysis in a low dimensional latent space projection of FDG‐PET images. By leveraging low‐dimensional representations and k‐nearest neighbors analysis, this framework is robust in multi‐class, multi‐label tasks with strong class imbalance and provides a highly interpretable graphical representation.

Latent space projection of brain FDG‐PET creates a powerful classifier for neurodegenerative diseases

AbstractBackgroundFluorodeoxyglucose positron emission tomography (FDG‐PET) is an established modality for differential diagnosis of dementia. Deriving phenotypic signatures from FDG‐PET via machine learning is challenging due to the high dimensional nature of brain images relative to the generally small number of labeled examples available for training, the class imbalance among those labels, and the cooccurrence of multiple pathologies. In this study, we developed a multi‐class, multi‐label framework to address these challenges.MethodA database of clinically acquired PET/CT images from 3,000 unique patients was used to develop a latent space model using matrix decomposition. This model was then applied to images from a separate cohort of Mayo Clinic Alzheimer’s Disease Research Center participants (n=1,745) labeled as cognitively unimpaired (CU) (n=1,436) or with the following potentially co‐occurring phenotypes: Alzheimer’s disease (AD) (n=165), Lewy body dementia (DLB) (n=92), behavioral variant frontotemporal dementia (bvFTD) (n=43), semantic (svPPA) (n=10) and logopenic (lvPPA) (n=13) variant PPA, and posterior cortical atrophy (PCA) (n=17). A k‐nearest neighbors classifier that is robust to these imbalanced and overlapping labels was then trained on these examples. The resulting classifier was evaluated by area under receiver‐operator characteristic curve (ROC‐AUC) via leave one out cross validation, using clinical diagnosis as the gold standard.ResultROC curves and AUC scores for each phenotype are illustrated in Fig. 1a. Because the classifier is based on a k‐nearest neighbors connectivity matrix, it has a convenient graphical representation, where images are nodes and edges are drawn between an image and its set of nearest neighbors in latent space. A self‐organizing force directed graph constructed in this way is illustrated in Fig 1b, highlighting the strong separation of CU and degenerative images, as well as the segregation of each phenotype within the neurodegenerative region of the graph.ConclusionIn this study, we developed a machine learning framework for classification of neurodegenerative disease based on k‐nearest neighbor analysis in a low dimensional latent space projection of FDG‐PET images. By leveraging low‐dimensional representations and k‐nearest neighbors analysis, this framework is robust in multi‐class, multi‐label tasks with strong class imbalance and provides a highly interpretable graphical representation.

Interpretable Classification of Tauopathies with a Convolutional Neural Network Pipeline Using Transfer Learning and Validation against Post-Mortem Clinical Cases of Alzheimer's Disease and Progressive Supranuclear Palsy.

Neurodegenerative diseases, tauopathies, constitute a serious global health problem. The etiology of these diseases is unclear and an increase in their incidence has been projected in the next 30 years. Therefore, the study of the molecular mechanisms that might stop these neurodegenerative processes is very relevant. Classification of neurodegenerative diseases using Machine and Deep Learning algorithms has been widely studied for medical imaging such as Magnetic Resonance Imaging. However, post-mortem immunofluorescence imaging studies of the brains of patients have not yet been used for this purpose. These studies may represent a valuable tool for monitoring aberrant chemical changes or pathological post-translational modifications of the Tau polypeptide. We propose a Convolutional Neural Network pipeline for the classification of Tau pathology of Alzheimer's disease and Progressive Supranuclear Palsy by analyzing post-mortem immunofluorescence images with different Tau biomarkers performed with models generated with the architecture ResNet-IFT using Transfer Learning. These models' outputs were interpreted with interpretability algorithms such as Guided Grad-CAM and Occlusion Analysis. To determine the best classifier, four different architectures were tested. We demonstrated that our design was able to classify diseases with an accuracy of 98.41% on average whilst providing an interpretation concerning the proper classification involving different structural patterns in the immunoreactivity of the Tau protein in NFTs present in the brains of patients with Progressive Supranuclear Palsy and Alzheimer's disease.

Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer's Disease.

The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, Friedreich's ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder

Background and Objectives18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson’s disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer’s disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. MethodsWe applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. ResultsThe GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman’s rho =0.62, P=0.004). ConclusionIn this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages.

Neurodegenerative diseases-Caps: a capsule network based early screening system for the classification of neurodegenerative diseases.

The online version contains supplementary material available at 10.1007/s11571-022-09787-1.

Gut-Brain Axis as a Pathological and Therapeutic Target for Neurodegenerative Disorders.

Human lifestyle and dietary behaviors contribute to disease onset and progression. Neurodegenerative diseases (NDDs), considered multifactorial disorders, have been associated with changes in the gut microbiome. NDDs display pathologies that alter brain functions with a tendency to worsen over time. NDDs are a worldwide health problem; in the US alone, 12 million Americans will suffer from NDDs by 2030. While etiology may vary, the gut microbiome serves as a key element underlying NDD development and prognosis. In particular, an inflammation-associated microbiome plagues NDDs. Conversely, sequestration of this inflammatory microbiome by a correction in the dysbiotic state of the gut may render therapeutic effects on NDDs. To this end, treatment with short-chain fatty acid-producing bacteria, the main metabolites responsible for maintaining gut homeostasis, ameliorates the inflammatory microbiome. This intimate pathological link between the gut and NDDs suggests that the gut-brain axis (GBA) acts as an underexplored area for developing therapies for NDDs. Traditionally, the classification of NDDs depends on their clinical presentation, mostly manifesting as extrapyramidal and pyramidal movement disorders, with neuropathological evaluation at autopsy as the gold standard for diagnosis. In this review, we highlight the evolving notion that GBA stands as an equally sensitive pathological marker of NDDs, particularly in Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and chronic stroke. Additionally, GBA represents a potent therapeutic target for treating NDDs.

Neuroprotective treatment of idiopathic, genetic and atypical Parkinson's disease with alpha-synuclein-Pathology

Kernpunkt der Klassifikation neurodegenerativer Erkrankungen ist der histopathologische Nachweis von Ablagerungen bestimmter Proteine im Gehirn. Hierbei unterscheiden sich die verschiedenen Krankheitsentitäten sowohl hinsichtlich der Art der nachweisbaren Proteine als auch hinsichtlich der Konfiguration und Lokalisation der entsprechenden Proteinaggregate. Gemeinsames Kernmerkmal der als Synukleinopathien zusammengefassten Erkrankungen sind Ablagerungen des Proteins α‑Synuklein (ASYN). Die bekanntesten Erkrankungen dieses Spektrums sind die Parkinson-Krankheit (PK) mit neuronalem Nachweis von Lewy-Körperchen, die Demenz vom Lewy-Körper-Typ (DLK) mit zusätzlichem Nachweis von β‑Amyloid-Ablagerungen sowie die seltene Multisystematrophie (MSA) mit glialem Nachweis sog. Papp-Lantos-Körperchen. Da neben der diagnostischen mittlerweile auch die zentrale pathophysiologische Bedeutung des ASYN erwiesen ist, fokussiert sich die Entwicklung neuer Therapien aktuell auf die Beeinflussung der toxischen Wirkung dieses Proteins. Die verschiedenen Therapiekonzepte lassen sich grob in sechs Gruppen zusammenfassen: 1. die Verringerung der ASYN-Expression (Antisense-Therapie), 2. die Verhinderung der Bildung toxischer ASYN-Aggregate (Antiaggregativa, Chelatoren), 3. das Auflösen bzw. die Beseitigung intra- oder extrazellulärer toxischer ASYN-Aggregate (aktive und passive Immuntherapie, Antiaggregativa), 4. die Verstärkung zellulärer Abräummechanismen (Autophagie, lysosomale Mikrophagie) zur Beseitigung toxischer Formen von α‑Synuklein, 5. die Modulation neuroinflammatorischer Prozesse sowie 6. neuroprotektive Strategien. In diesem Artikel fassen wir die aktuellen Therapieentwicklungen zusammen und geben einen Ausblick auf vielversprechende zukünftige Therapieansätze.

How an Infection of Sheep Revealed Prion Mechanisms in Alzheimer's Disease and Other Neurodegenerative Disorders.

Although it is not yet universally accepted that all neurodegenerative diseases (NDs) are prion disorders, there is little disagreement that Alzheimer’s disease (AD), Parkinson’s disease, frontotemporal dementia (FTD), and other NDs are a consequence of protein misfolding, aggregation, and spread. This widely accepted perspective arose from the prion hypothesis, which resulted from investigations on scrapie, a common transmissible disease of sheep and goats. The prion hypothesis argued that the causative infectious agent of scrapie was a novel proteinaceous pathogen devoid of functional nucleic acids and distinct from viruses, viroids, and bacteria. At the time, it seemed impossible that an infectious agent like the one causing scrapie could replicate and exist as diverse microbiological strains without nucleic acids. However, aggregates of a misfolded host-encoded protein, designated the prion protein (PrP), were shown to be the cause of scrapie as well as Creutzfeldt–Jakob disease (CJD) and Gerstmann–Sträussler–Scheinker syndrome (GSS), which are similar NDs in humans. This review discusses historical research on diseases caused by PrP misfolding, emphasizing principles of pathogenesis that were later found to be core features of other NDs. For example, the discovery that familial prion diseases can be caused by mutations in PrP was important for understanding prion replication and disease susceptibility not only for rare PrP diseases but also for far more common NDs involving other proteins. We compare diseases caused by misfolding and aggregation of APP-derived Aβ peptides, tau, and α-synuclein with PrP prion disorders and argue for the classification of NDs caused by misfolding of these proteins as prion diseases. Deciphering the molecular pathogenesis of NDs as prion-mediated has provided new approaches for finding therapies for these intractable, invariably fatal disorders and has revolutionized the field.

The Amygdala in Neurodegeneration

The amygdala is a key anatomic structure that has multiple different nuclei and is involved in several critical aspects of cognition and systemic functions. Several different neurodegenerative diseases have major pathological effects on distinct amygdala nuclei. This presentation will describe the classic and characteristic anatomic distributions in the amygdala of “pure” Alzheimer disease and “pure” Lewy body disease, as well as “normal aging”. In addition, data will be presented on how these classic distributions are altered in either “mixed dementias” or in some atypical forms of neurodegeneration. Amygdala pathology will also be illustrated in several other neurodegenerative diseases. The implications of the differing anatomic distributions in different neurodegenerative diseases will be discussed.LEARNING OBJECTIVESThis presentation will enable the learner to:Recognize key anatomic divisions of the amygdala 1.Describe how different neurodegenerative diseases affect the amygdala2.Consider how anatomic specificity of protein aggregation is important in the classification of neurodegenerative diseases

A data science approach for reliable classification of neuro-degenerative diseases using gait patterns

Neuro-degenerative diseases (NDD) continue to increase globally and have significant impact on health, developmental and financial fronts. Recent studies have shown that gait impairment as one of the earliest signs of the disease. However, classification of multiple types of NDD becomes more challenging because of the high overlapping symptoms specifically at early stages. This paper entails a composite of signal processing and machine intelligence algorithms to process the gait data captured through multi-sensors for a reliable classification different types of NDD. The captured dataset used in this research consisted of 60 patients’ records representing three different types of NDD. Our simulation results indicated that the proposed approach outperformed existing works in this domain. The proposed work might help the mitigation plans for NDD, reliable monitoring of the disease progression and can assist the evaluation of possible therapy and treatments that would benefit the individuals, associated families, society and healthcare services.

Classification of Neurodegenerative Diseases via Topological Motion Analysis—A Comparison Study for Multiple Gait Fluctuations

Neurodegenerative diseases are common progressive nervous system disorders that show intricate clinical patterns. The gait fluctuations reflect the physiology and pathologic alterations in the locomotor control system. Using gait fluctuations for disease state evaluation is an essential way for clinical trials and healthcare monitoring. The classification of gait fluctuations helps improve the life quality and enhance clinical diagnosis ability in neuro-degenerative patients. In this work, we firstly embed the time series of multiple gait fluctuations into the phase space. Then we use persistent homology to extract the topological signatures of barcodes. Together with a random forest classifier, we proposed a topological motion analysis (TMA) framework to analyze the gait fluctuations. Further, we proposed a comprehensive comparison study using the TMA framework in the neuro-degenerative classification tasks for stance-, stride-, and swing-based gait fluctuations. In the tasks of comparing amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Parkinson's disease (PD) to the healthy control (HC) group, the best-achieved AUC scores were 0.9135, 0.9906 and 0.9667 respectively, which show the effectiveness of TMA framework. In summary, our study proposed a TMA framework towards gait fluctuations classification in the neuro-degenerative analysis tasks. The proposed method shows promising clinical application value in earlier interventions and state monitoring for neurodegenerative patients.

Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data.

Recently, a hypergraph constructed from functional magnetic resonance imaging (fMRI) was utilized to explore brain functional connectivity networks (FCNs) for the classification of neurodegenerative diseases. Each edge of a hypergraph (called hyperedge) can connect any number of brain regions-of-interest (ROIs) instead of only two ROIs, and thus characterizes high-order relations among multiple ROIs that cannot be uncovered by a simple graph in the traditional graph based FCN construction methods. Unlike the existing hypergraph based methods where all hyperedges are assumed to have equal weights and only certain topological features are extracted from the hypergraphs, we propose a hypergraph learning based method for FCN construction in this paper. Specifically, we first generate hyperedges from fMRI time series based on sparse representation, then employ hypergraph learning to adaptively learn hyperedge weights, and finally define a hypergraph similarity matrix to represent the FCN. In our proposed method, weighting hyperedges results in better discriminative FCNs across subjects, and the defined hypergraph similarity matrix can better reveal the overall structure of brain network than using those hypergraph topological features. Moreover, we propose a multi-hypergraph learning based method by integrating multi-paradigm fMRI data, where the hyperedge weights associated with each fMRI paradigm are jointly learned and then a unified hypergraph similarity matrix is computed to represent the FCN. We validate the effectiveness of the proposed method on the Philadelphia Neurodevelopmental Cohort dataset for the classification of individuals' learning ability from three paradigms of fMRI data. Experimental results demonstrate that our proposed approach outperforms the traditional graph based methods (i.e., Pearson's correlation and partial correlation with the graphical Lasso) and the existing unweighted hypergraph based methods, which sheds light on how to optimize estimation of FCNs for cognitive and behavioral study.

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

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. 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. 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.