Disease-related Patterns Research Articles

Co-pathologies modify hippocampal protein accumulation patterns in neurodegenerative diseases.

Limited research has extensively analyzed neurodegenerative disease-related protein deposition patterns in the hippocampus. This study examined the distribution of proteins in hippocampal subregions across major neurodegenerative diseases and explored their relation to each other. The area density of phosphorylated tau (p-tau), amyloid beta (Aβ), α-synuclein, and phosphorylated TDP-43 protein deposits together with pyramidal cell density in each hippocampal subregion, including CA1-4, prosubiculum (ProS), and subiculum was assessed in 166 cases encompassing various neurodegenerative diseases. Alzheimer's disease-associated p-tau predominated in ProS, Aβ in the CA1, and Lewy body-related α-synuclein in the CA2. The area density of protein deposits increased with the pathological stage until a peak, then decreased in cases with high pathology stages along with pyramidal cell density. Comorbid protein pathology influenced protein deposition patterns. This comprehensive evaluation reveals characteristic neurodegenerative disease-related protein accumulation patterns in hippocampal subregions modified by co-pathologies. Alzheimer's disease-related phosphorylated tau predominates in the prosubiculum. Amyloid beta predominates in the CA1 and Lewy body-related α-synuclein in the CA2. The area density of protein deposition increases with the disease stage up to a peak. In the high pathology stage, protein deposition and pyramidal cell density decreases. Comorbid protein pathology affects the pattern of protein accumulation.

Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques.

Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characterizing the complex abnormality patterns has been challenging. In this work, we used resting-state functional magnetic resonance imaging (fMRI) data to measure functional connectivity between 55 schizophrenia patients and 63 healthy controls across 246 regions of interest (ROIs) and extracted the disease-related connectivity patterns using energy landscape (EL) analysis. EL analysis captures the complexity of brain function in schizophrenia by focusing on functional brain state stability and region-specific dynamics. Age, sex, and smoker demographics between patients and controls were not significantly different. However, significant patient and control differences were found for the brief psychiatric rating scale (BPRS), auditory perceptual trait and state (APTS), visual perceptual trait and state (VPTS), working memory score, and processing speed score. We found that the brains of individuals with schizophrenia have abnormal energy landscape patterns between the right and left rostral lingual gyrus, and between the left lateral and orbital area in 12/47 regions. The results demonstrate the potential of the proposed imaging analysis workflow to identify potential connectivity biomarkers by indexing specific clinical features in schizophrenia patients.

Development and initial validation of the Cognitive Change Scale (CCS)

Background: Cognitive impairment is common in neurologic diseases. Precise measurement of cognitive change over time is necessary for isolating disease-related patterns from normal age-related decline. Existing measures of subjective cognition, however, focus on present status. There is, to our knowledge, no currently available self-report measure of cognitive change. We therefore developed the Cognitive Change Scale (CCS), which assesses perceived cognitive change in neurologic populations. Methods: A systematic mixed-methods process was followed for the scale design and validation. Associations of CCS responses to demographics, mood, and fatigue were examined in 131 persons with multiple sclerosis. A total of 46 participants also completed a cognitive test battery. Correlations of test scores with CCS responses were calculated. Results: The 17-item CCS showed good reliability and validity. Results of exploratory and confirmatory factor analyses supported a four-factor structure, with items reflecting change in (1) general cognition, (2) language and executive function, (3) external feedback, and (4) use of coping strategies. Positive relationships of CCS scores with fatigue, depression, and anxiety were observed. Correlations of CCS scores with cognitive test performance did not reach significance. Conclusion: The CCS may be a useful cognitive outcome tool for treatment trials in neurologic populations.

Uncovering Disease Mechanisms through AI and Visual Computing: A Multi-Scale Approach to Biological Data Interpretation

This research focuses on uncovering complex disease mechanisms by employing artificial intelligence (AI) and visual computing to interpret multi-scale biological data. By analyzing data from molecular to organ-level systems, this study aims to bridge the gap between cellular insights and clinical applications, offering new perspectives on diseases such as cancer, neurodegenerative disorders, and cardiovascular diseases. Through a comprehensive approach to data integration, the project compiles omics and imaging data to enable holistic analysis across biological scales. The use of hierarchical neural networks, graph neural networks (GNNs), and convolutional neural networks (CNNs) allows for multi-dimensional modeling and the extraction of disease-related patterns. The findings are validated with clinical data to support actionable insights, laying the groundwork for personalized disease models and precision therapeutic strategies. Expected outcomes include breakthroughs in disease understanding, advancements in precision medicine, and the establishment of a robust AI framework that will contribute to future biomedical applications. This multidisciplinary approach promises to transform diagnostics, prognosis, and treatment, facilitating a deeper understanding of disease mechanisms and their translation into clinical practice. The integration of multi-scale data enables a nuanced view of disease, capturing interactions that may be obscured in single-level analyses. By employing advanced visual computing techniques, the project highlights structural abnormalities within tissue samples, linking cellular changes to systemic disease pathways. This approach not only aids in identifying novel biomarkers but also supports the development of individualized treatment models that predict patient-specific responses. With validation from clinical datasets, the AI framework is poised to enhance clinical decision-making, offering insights tailored to the unique biological profiles of patients. The project’s multi-scale methodology serves as a foundation for future biomedical research, setting a new standard for AI-driven disease analysis.

Huntington's Disease-Related Mortality Patterns: A Two-Decade Analysis of Mortality Trends in the United States, from 1999-2019.

Huntington's disease (HD) is an autosomal dominant, progressive neurodegenerative disorder debilitating mainly in adults. This study aimed to assess the trends in HD-related mortality regarding various demographic factors. Death certificates from the CDC WONDER were studied from 1999 to 2019, for HD-related mortality in adults aged 25 + years. Age-adjusted Mortality Rate (AAMR) per 100,000 persons and Annual Percentage Change (APC) were calculated and stratified by year, age groups, gender, race/ethnicity, state, census region, urbanization, and place of death. Between 1999 to 2019, 22,595 deaths occurred in adults due to HD. The AAMR increased from 0.43 to 0.54 during this period (APC = 0.50; 95% CI: 0.18 to 0.84). Old adults (65-85 + years) had the highest overall AAMR, followed by middle-aged adults (45-64 years) and young adults (25-44 years) (AAMR old: 1.01 vs. AAMR middle-age: 0.68 vs. AAMR young: 0.16). Men had slightly greater overall AAMRs than women (AAMR men: 0.54 vs. AAMR women: 0.48). When stratified by race, non-Hispanic (NH) Whites had significantly higher mortality rates than NH African Americans (AAMR NH White: 0.61 vs. NH African American: 0.35), while the AAMR were lowest in Hispanic/Latino (0.28). The AAMRs also showed variation by region (overall AAMR: Midwest: 0.63, Northeast: 0.47, West: 0.48, South: 0.46), and non-metropolitan areas had higher HD-related AAMR (0.66) than metropolitan areas (0.47). HD-related mortality in US adults has increased since 1999. Reflecting on the variations in trends observed, new strategies are required to optimize the quality of care in long-term care facilities.

A method for the synchronization of inertial sensor signals and local field potentials from deep brain stimulation systems

Objective. Recent innovative neurostimulators allow recording local field potentials (LFPs) while performing motor tasks monitored by wearable sensors. Inertial sensors can provide quantitative measures of motor impairment in people with subthalamic nucleus deep brain stimulation. To the best of our knowledge, there is no validated method to synchronize inertial sensors and neurostimulators without an additional device. This study aims to define a new synchronization method to analyze disease-related brain activity patterns during specific motor tasks and evaluate how LFPs are affected by stimulation and medication. Approach. Fourteen male subjects treated with subthalamic nucleus deep brain stimulation were recruited to perform motor tasks in four different medication and stimulation conditions. In each condition, a synchronization protocol was performed consisting of taps on the implanted neurostimulator, which produces artifacts in the LFPs that a nearby inertial sensor can simultaneously record. Main results. In 64% of the recruited subjects, induced artifacts were detected at least in one condition. Among those subjects, 83% of the recordings could be synchronized offline analyzing LFPs and wearables data. The remaining recordings were synchronized by video analysis. Significance. The proposed synchronization method does not require an external system (e.g., TENS electrodes) and can be easily integrated into clinical practice. The procedure is simple and can be carried out in a short time. A proper and simple synchronization will also be useful to analyze subthalamic neural activity in the presence of specific events (e.g., freezing of gait events) to identify predictive biomarkers.

DBScope as a versatile computational toolbox for the visualization and analysis of sensing data from deep brain stimulation

Different neurostimulators for deep brain stimulation (DBS) come already with the ability to chronically sense local field potentials during stimulation. This invaluable new data has the potential to increase our understanding of disease-related brain activity patterns, their temporal evolution, and their modulation in response to therapies. It also gives the opportunity to unveil new electrophysiological biomarkers and ultimately bring adaptive stimulation therapies closer to clinical practice. Unfortunately, there are still very limited options on how to visualize, analyze, and exploit the full potential of the sensing data from these new DBS neurostimulators. To answer this need, we developed a free open-source toolbox, named DBScope, that imports data from neurostimulation devices and can be operated in two ways: via user interface and programmatically, as a library of functions. In this way, it can be used by both clinicians during clinical sessions (for instance, to visually inspect data from the current or previous in-clinic visits), and by researchers in their research pipelines (e.g., for pre-processing, feature extraction and biomarker search). All in all, the DBScope toolbox is set to facilitate the clinical decision-making process and the identification of clinically relevant biomarkers. The toolbox is already being used in clinical and research environments, and it is freely available to download at GitHub (where it is also fully documented).

Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis.

To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.

Associations Between Dietary Patterns and Kidney Health Assessed in the Population-Based CHRIS Study Using Reduced Rank Regression

ObjectiveWhile diet plays a key role in CKD management, the potential for diet to impact CKD prevention in the general population is less clear. Using a priori knowledge, we derived disease-related dietary patterns (DPs) through reduced rank regression (RRR) and investigated associations with kidney function, separately focusing on generally healthy individuals and those with self-reported kidney diseases, hypertension or diabetes mellitus. Methods8,686 participants from the population-based Cooperative Health Research in South Tyrol (CHRIS) study were split into a group free of kidney disease, hypertension and diabetes (n=6,133) and a group with any of the three conditions (n=2,553). Diet was assessed through the self-administered GA2LEN food frequency questionnaire (FFQ) and DPs were derived through RRR selecting FFQ-derived sodium, potassium, phosphorus and protein intake as mediators. Outcomes were creatinine-based estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (UACR), CKD and microalbuminuria. Multiple linear and logistic models were used to assess associations between RRR-based DPs and kidney outcomes separately in the two analytic groups. ResultsWe identified three DPs, where high adherence reflected high levels of all nutrients (DP1), high potassium-phosphorus and low protein-sodium levels (DP2), and low potassium-sodium and high protein-phosphorus levels (DP3), respectively. We observed heterogeneous associations with kidney outcomes, varying by analytic group and sex. Kidney outcomes were much more strongly associated with DPs than with single nutrients. ConclusionRRR is a feasible approach to estimate disease-related DPs and explore the combined effects of nutrients on kidney health. Heterogeneous associations across kidney outcomes suggest possible specificity to kidney function or damage. In individuals reporting kidney disease, hypertension or diabetes, specific dietary habits were associated with better kidney health, indicating that disease-specific dietary interventions can be effective for disease control.

Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia.

This study aims to identify dynamic patterns within the spatiotemporal feature space that are specific to nonpsychotic major depression (NPMD), psychotic major depression (PMD), and schizophrenia (SCZ). The study also evaluates the effectiveness of machine learning algorithms based on these network manifestations in differentiating individuals with NPMD, PMD, and SCZ. A total of 579 participants were recruited, including 152 patients with NPMD, 45 patients with PMD, 185 patients with SCZ, and 197 healthy controls (HCs). A dynamic functional connectivity (DFC) approach was employed to estimate the principal FC states within each diagnostic group. Incremental proportions of data (ranging from 10% to 100%) within each diagnostic group were used for variability testing. DFC metrics, such as proportion, mean duration, and transition number, were examined among the four diagnostic groups to identify disease-related neural activity patterns. These patterns were then used to train a two-layer classifier for the four groups (HC, NPMD, PMD, and SCZ). The four principal brain states (i.e., states 1,2,3, and 4) identified by the DFC approach were highly representative within and across diagnostic groups. Between-group comparisons revealed significant differences in network metrics of state 2 and state 3, within delta, theta, and gamma frequency bands, between healthy individuals and patients in each diagnostic group (p < 0.01, FDR corrected). Moreover, the identified key dynamic network metrics achieved an accuracy of 73.1 ± 2.8% in the four-way classification of HC, NPMD, PMD, and SCZ, outperforming the static functional connectivity (SFC) approach (p < 0.001). These findings suggest that the proposed DFC approach can identify dynamic network biomarkers at the single-subject level. These biomarkers have the potential to accurately differentiate individual subjects among various diagnostic groups of psychiatric disorders or healthy controls. This work may contribute to the development of a valuable EEG-based diagnostic tool with enhanced accuracy and assistive capabilities.

Disease-related data patterns in cerebrospinal fluid diagnostics: medical quality versus analytical quantity.

Cerebrospinal fluid (CSF) diagnostics is characterized by the biologically relevant combination of analytes in order to obtain disease-related data patterns that enable medically relevant interpretations. The necessary change in knowledge bases such as barrier function as a diffusion/CSF flow model and immunological networks of B-cell clones and pleiotropic cytokines is considered. The biophysical and biological principles for data combination are demonstrated using examples from neuroimmunological and dementia diagnostics. In contrast to current developments in clinical chemistry and laboratory medicine, CSF diagnostics is moving away from mega-automated systems with a constantly growing number of individual analyses toward a CSF report that integrates all patient data. Medical training in data sample interpretation in the inter-laboratory test systems ("EQA schemes") has become increasingly important. However, the results for CSF diagnostics (EQAS from INSTAND) indicate a crucially misguided trend. The separate analysis of CSF and serum in different, non-matched assays and extreme batch variations systematically lead to misinterpretations, which are the responsibility of the test providers. The questionable role of expensive accreditation procedures and the associated false quality expectations are discussed. New concepts that reintegrate the medical expertise of the clinical chemist must be emphasized along with the positive side effect of reducing costs in the healthcare system.

Six and twelve-month respiratory outcomes in a cohort of severe and critical COVID-19 survivors: A prospective monocentric study in Latin America.

Severe COVID-19 can result in long-term sequelae known as "chronic COVID," characterized by a wide range of persistent physical and mental symptoms. Chest imaging and pulmonary function test alterations have been observed in recovered patients. Most studies focus on up to a 3-month follow-up after symptom onset or hospital discharge, with few reports on long-term follow-up and limited evidence regarding disease progression in Latin America. This study aims to describe the clinical characteristics and changes in pulmonary function, imaging, and quality of life in severe and critical COVID-19 patients requiring ICU admission in a high-complexity hospital in Latin America. A prospective cohort of survivors underwent clinical, radiological, pulmonary function, and quality of life assessments 6 and 12 months post-discharge. One hundred twelve patients were included, all of whom attended the 6-month follow-up, and 99 returned for the 12-month follow-up. Most subjects had no previous respiratory symptoms or significant medical history. At the end of the follow-up period, 74% of the patients showed interstitial infiltrates in chest tomography and a higher frequency of fibroatelectatic tracts and parenchymal bands. Pulmonary function tests returned to normal ranges, except for carbon monoxide diffusion, but no altered scores were reported in the questionnaires. Despite residual radiological findings, most parameters studied in severe and critical COVID-19 survivors improved over the 12-month follow-up period. Regardless of the imaging abnormalities, the improvement in variables such as symptomatic relief and normal pulmonary function suggests that these alterations are transient. Carbon monoxide diffusion did not normalize by the end of the follow-up, which is consistent with the abnormalities reported in multiple studies, indicating a potential disease-related pattern.

Predicting disease-related MRI patterns of multiple sclerosis through GAN-based image editing

IntroductionMultiple sclerosis (MS) is a complex neurodegenerative disorder that affects the brain and spinal cord. In this study, we applied a deep learning-based approach using the StyleGAN model to explore patterns related to MS and predict disease progression in magnetic resonance images (MRI). MethodsWe trained the StyleGAN model unsupervised using T1-weighted GRE MR images and diffusion-based ADC maps of MS patients and healthy controls. We then used the trained model to resample MR images from real input data and modified them by manipulations in the latent space to simulate MS progression. We analyzed the resulting simulation-related patterns mimicking disease progression by comparing the intensity profiles of the original and manipulated images and determined the brain parenchymal fraction (BPF). ResultsOur results show that MS progression can be simulated by manipulating MR images in the latent space, as evidenced by brain volume loss on both T1-weighted and ADC maps and increasing lesion extent on ADC maps. ConclusionOverall, this study demonstrates the potential of the StyleGAN model in medical imaging to study image markers and to shed more light on the relationship between brain atrophy and MS progression through corresponding manipulations in the latent space.

Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture.

Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders. A significant barrier to usage of these signals in diagnosis and treatment is the lack of consistent relationships between GI myoelectric features and function. A potential cause of this issue is the use of arbitrary classification criteria, such as percentage of power in tachygastric and bradygastric frequency bands. Here we applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, we recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimensional residual convolutional network, followed by a fully connected layer, with a decision based on a sigmoidal output. For this 2-class problem, accuracy was 90%, sensitivity (feeding detection) was 90%, and specificity (baseline detection) was 89%. By comparison, approaches using hand-crafted features (e.g., SVM, random forest, and logistic regression) produced an accuracy from 54% to 82%, sensitivity from 46% to 84% and specificity from 66% to 80%. These results suggest that automatic feature extraction and deep neural networks could be useful to assess GI function for comparing baseline to an active functional GI state, such as feeding. In future testing, the current approach could be applied to determine normal and disease-related GI myoelectric patterns to diagnosis and assess patients with GI disease.

Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer’s disease-related pattern

BackgroundAlzheimer’s disease-related pattern (ADRP) is a metabolic brain biomarker of Alzheimer’s disease (AD). While ADRP is being introduced into research, the effect of the size of the identification cohort and the effect of the resolution of identification and validation images on ADRP’s performance need to be clarified.Methods240 2-[18F]fluoro-2-deoxy-d-glucose positron emission tomography images [120 AD/120 cognitive normals (CN)] were selected from the Alzheimer's disease neuroimaging initiative database. A total of 200 images (100 AD/100 CN) were used to identify different versions of ADRP using a scaled subprofile model/principal component analysis. For this purpose, five identification groups were randomly selected 25 times. The identification groups differed in the number of images (20 AD/20 CN, 30 AD/30 CN, 40 AD/40 CN, 60 AD/60 CN, and 80 AD/80 CN) and image resolutions (6, 8, 10, 12, 15 and 20 mm). A total of 750 ADRPs were identified and validated through the area under the curve (AUC) values on the remaining 20 AD/20 CN with six different image resolutions.ResultsADRP’s performance for the differentiation between AD patients and CN demonstrated only a marginal average AUC increase, when the number of subjects in the identification group increases (AUC increase for about 0.03 from 20 AD/20 CN to 80 AD/80 CN). However, the average of the lowest five AUC values increased with the increasing number of participants (AUC increase for about 0.07 from 20 AD/20 CN to 30 AD/30 CN and for an additional 0.02 from 30 AD/30 CN to 40 AD/40 CN). The resolution of the identification images affects ADRP’s diagnostic performance only marginally in the range from 8 to 15 mm. ADRP’s performance stayed optimal even when applied to validation images of resolution differing from the identification images.ConclusionsWhile small (20 AD/20 CN images) identification cohorts may be adequate in a favorable selection of cases, larger cohorts (at least 30 AD/30 CN images) shall be preferred to overcome possible/random biological differences and improve ADRP’s diagnostic performance. ADRP’s performance stays stable even when applied to the validation images with a resolution different than the resolution of the identification ones.

Language patterns in Japanese patients with Alzheimer disease: A machine learning approach.

The authors applied natural language processing and machine learning to explore the disease-related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro-American languages. The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add-on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part-of-speech and dependency was tagged and counted to create features such as tag-frequency and tag-to-tag transition-frequency. Each feature's importance was computed during the 100-fold repeated random subsampling validation and averaged. The model resulted in an accuracy of 0.84 (SD=0.06), and an area under the curve of 0.90 (SD=0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part-of-speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words-related features were lower among the patients, whereas those with stagnation-related features were higher. The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical-semantic decline known as 'empty speech', which is regarded as a characteristic of AD.

Parkinson’s Disease-Related Brain Metabolic Pattern Is Expressed in Schizophrenia Patients during Neuroleptic Drug-Induced Parkinsonism

Drug-induced parkinsonism (DIP) is a frequent parkinsonian syndrome that appears as a result of pharmacotherapy for the management of psychosis. It could substantially hamper treatment and therefore its diagnosis has a direct influence on treatment effectiveness. Although of such high importance, there is a lack of systematic research for developing neuroimaging-based criteria for DIP diagnostics for such patients. Therefore, the current study was aimed at applying a metabolic brain imaging approach using the 18F-FDG positron emission tomography and spatial covariance analysis to reveal possible candidates for DIP markers. As a result, we demonstrated, to our knowledge, the first attempt at the application of the Parkinson's Disease-Related Pattern (PDRP) as a metabolic signature of parkinsonism for the assessment of PDRP expression for schizophrenia patients with DIP. As a result, we observed significant differences in PDRP expression between the control group and the groups with PD and DIP patients. Similar differences in PDRP expression were also found when the non-DIP schizophrenia patients were compared with the PD group. Therefore, our findings made it possible to conclude that PDRP is a promising tool for the development of clinically relevant criteria for the estimation of the risk of developing DIP.

The Oral Microbiota in Health and Disease: An Overview of Molecular Findings.

Culture-independent nucleic acid technologies have been extensively applied to the analysis of oral bacterial communities associated with healthy and diseased conditions. These methods have confirmed and substantially expanded the findings from culture studies to reveal the oral microbial inhabitants and candidate pathogens associated with the major oral diseases. Over 1000 bacterial distinct species-level taxa have been identified in the oral cavity and studies using next-generation DNA sequencing approaches indicate that the breadth of bacterial diversity is even much larger. Nucleic acid technologies have also been helpful in profiling bacterial communities and identifying disease-related patterns. This chapter provides an overview of the diversity and taxonomy of oral bacteria associated with health and disease.

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.