scholarly journals Frontotemporal Dementia: A Clinical Review

2019 ◽  
Vol 39 (02) ◽  
pp. 251-263 ◽  
Author(s):  
Harri Sivasathiaseelan ◽  
Charles Marshall ◽  
Jennifer Agustus ◽  
Elia Benhamou ◽  
Rebecca Bond ◽  
...  
Keyword(s):  
Neural Network ◽  
Frontotemporal Dementia ◽  
Clinical Symptoms ◽  
Cellular Level ◽  
Diverse Group ◽  
Disease Modification ◽  
Selective Targeting ◽  
Young Onset Dementia ◽  
Presymptomatic Diagnosis ◽  
Frontotemporal Dementias

AbstractFrontotemporal dementias are a clinically, neuroanatomically, and pathologically diverse group of diseases that collectively constitute an important cause of young-onset dementia. Clinically, frontotemporal dementias characteristically strike capacities that define us as individuals, presenting broadly as disorders of social behavior or language. Neurobiologically, these diseases can be regarded as “molecular nexopathies,” a paradigm for selective targeting and destruction of brain networks by pathogenic proteins. Mutations in three major genes collectively account for a substantial proportion of behavioral presentations, with far-reaching implications for the lives of families but also potential opportunities for presymptomatic diagnosis and intervention. Predicting molecular pathology from clinical and radiological phenotypes remains challenging; however, certain patterns have been identified, and genetically mediated forms of frontotemporal dementia have spearheaded this enterprise. Here we present a clinical roadmap for diagnosis and assessment of the frontotemporal dementias, motivated by our emerging understanding of the mechanisms by which pathogenic protein effects at the cellular level translate to abnormal neural network physiology and ultimately, complex clinical symptoms. We conclude by outlining principles of management and prospects for disease modification.

scholarly journals Molecular characterization of Polychromophilus parasites of Scotophilus kuhlii bats in Thailand

Parasitology ◽  
2020 ◽  
pp. 1-5
Author(s):  
Chatree Chumnandee ◽  
Nawarat Pha-obnga ◽  
Oskar Werb ◽  
Kai Matuschewski ◽  
Juliane Schaer
Keyword(s):  
Blood Cells ◽  
Clinical Symptoms ◽  
Temperate Climate ◽  
Diverse Group ◽  
Malaria Parasites ◽  
Climate Zones ◽  
Haemosporidian Parasites ◽  
South And Southeast Asia ◽  
Molecular Investigation

Abstract Parasites of the haemosporidian genus Polychromophilus have exclusively been described in bats. These parasites belong to the diverse group of malaria parasites, and Polychromophilus presents the only haemosporidian taxon that infects mammalian hosts in tropical as well as in temperate climate zones. This study provides the first information of Polychromophilus parasites in the lesser Asiatic yellow bat (Scotophilus kuhlii) in Thailand, a common vespertilionid bat species distributed in South and Southeast Asia. The gametocyte blood stages of the parasites could not be assigned to a described morphospecies and molecular analysis revealed that these parasites might represent a distinct Polychromophilus species. In contrast to Plasmodium species, Polychromophilus parasites do not multiply in red blood cells and, thus, do not cause the clinical symptoms of malaria. Parasitological and molecular investigation of haemosporidian parasites of wildlife, such as the neglected genus Polychromophilus, will contribute to a better understanding of the evolution of malaria parasites.

A wide-spectrum coordination model of schizophrenia

2003 ◽  
Vol 26 (1) ◽  
pp. 84-85
Author(s):  
Hendrik Pieter Barendregt
Keyword(s):  
Molecular Level ◽  
Clinical Symptoms ◽  
Wide Spectrum ◽  
Cellular Level ◽  
Cognitive Level ◽  
Levels Of Abstraction ◽  
Coordination Model ◽  
Target Article ◽  
Four Levels ◽  
Important Notion

AbstractThe target article presents a model for schizophrenia extending four levels of abstraction: molecules, cells, cognition, and syndrome. An important notion in the model is that of coordination, applicable to both the level of cells and of cognition. The molecular level provides an “implementation” of the coordination at the cellular level, which in turn underlies the coordination at the cognitive level, giving rise to the clinical symptoms.

BP Neural Network Model for early Diagnosis of Kawasaki Disease

2012 ◽  
Vol 468-471 ◽  
pp. 723-726 ◽  
Author(s):  
Jiang Huang ◽  
Jian Feng Chen
Keyword(s):  
Neural Network ◽  
Kawasaki Disease ◽  
Early Diagnosis ◽  
Infectious Diseases ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Clinical Symptoms ◽  
Laboratory Data ◽  
Bp Neural Network Model

In order to diagnose Kawasaki Disease during early phase, clinical symptoms (temperature, rash, conjunctival injection, erythema of thelips, and oral mucosal changes) and laboratory data (white blood cell, neutrophil, platelet, high sensitive c-reactive protein, and erythrocyte sedimentation rate) of 138 children with Kawasaki disease or infectious diseases were used to develop a BP neural network model. 90 random cases were trained using MATLAB software for setting up the BP neural network model. The other 48 cases were analyzed to predict Kawasaki disease using this model. Results showed that the predict accuracy in patients with Kawasaki disease and children with infectious diseases are 95.6% and 88%, respectively. Our result indicates that the BP neural network model is likely to provide an accurate test for early diagnosis of Kawasaki disease.

scholarly journals Discovering and interpreting transcriptomic drivers of imaging traits using neural networks

2020 ◽  
Vol 36 (11) ◽  
pp. 3537-3548
Author(s):  
Nova F Smedley ◽  
Suzie El-Saden ◽  
William Hsu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Tumor Imaging ◽  
Tissue Level ◽  
Cellular Level ◽  
Linear Mapping ◽  
Supplementary Information ◽  
Clinical Value ◽  
Cancer Heterogeneity ◽  
Level Information

Abstract Motivation Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and cellular-level information from genomics are needed. However, these ‘radiogenomic’ studies often use linear or shallow models, depend on feature selection, or consider one gene at a time to map images to genes. Moreover, no study has systematically attempted to understand the molecular basis of imaging traits based on the interpretation of what the neural network has learned. These studies are thus limited in their ability to understand the transcriptomic drivers of imaging traits, which could provide additional context for determining clinical outcomes. Results We present a neural network-based approach that takes high-dimensional gene expression data as input and performs non-linear mapping to an imaging trait. To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks. In glioblastoma patients, our models outperformed comparable classifiers (>0.10 AUC) and our interpretation methods were validated using a similar model to identify known relationships between genes and molecular subtypes. We found that tumor imaging traits had specific transcription patterns, e.g. edema and genes related to cellular invasion, and 10 radiogenomic traits were significantly predictive of survival. We demonstrate that neural networks can model transcriptomic heterogeneity to reflect differences in imaging and can be used to derive radiogenomic traits with clinical value. Availability and implementation https://github.com/novasmedley/deepRadiogenomics. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Perspective on Accelerated Aging Caused by the Genetic Deficiency of the Metabolic Protein, OPA1

2021 ◽  
Vol 12 ◽  
Author(s):  
Irina Erchova ◽  
Shanshan Sun ◽  
Marcela Votruba
Keyword(s):  
Normal Development ◽  
Clinical Symptoms ◽  
Accelerated Aging ◽  
Cellular Level ◽  
Cellular Aging ◽  
Protective Mechanisms ◽  
Metabolic Protein ◽  
Cellular Defenses ◽  
Mutant Cells ◽  
Early Recruitment

Autosomal Dominant Optic Atrophy (ADOA) is an ophthalmological condition associated primarily with mutations in the OPA1 gene. It has variable onset, sometimes juvenile, but in other patients, the disease does not manifest until adult middle age despite the presence of a pathological mutation. Thus, individuals carrying mutations are considered healthy before the onset of clinical symptoms. Our research, nonetheless, indicates that on the cellular level pathology is evident from birth and mutant cells are different from controls. We argue that the adaptation and early recruitment of cytoprotective responses allows normal development and functioning but leads to an exhaustion of cellular reserves, leading to premature cellular aging, especially in neurons and skeletal muscle cells. The appearance of clinical symptoms, thus, indicates the overwhelming of natural cellular defenses and break-down of native protective mechanisms.

scholarly journals Right Anterior Temporal Degeneration, Emotions, and Loss of Nonverbal Semantics: Guidelines for Diagnosis of the Emotional Semantic Variant Frontotemporal Dementia

2021 ◽  
Author(s):  
Kyan Younes ◽  
Valentina Borghesani ◽  
Maxime Montembeault ◽  
Salvatore Spina ◽  
Ariane E Welch ◽  
...  
Keyword(s):  
Frontotemporal Dementia ◽  
Right Hemisphere ◽  
Clinical Symptoms ◽  
Neuropsychological Testing ◽  
Primary Progressive Aphasia ◽  
Structural Mri ◽  
Underlying Mechanism ◽  
Semantic Knowledge ◽  
Clinical Syndrome ◽  
Semantic Variant

Anterior temporal lobe (ATL) degeneration is caused by a pathological process that has a focal onset in the left or right hemisphere. Patients with left-lateralized ATL atrophy typically meet criteria for semantic variant primary progressive aphasia (PPA), a clinical syndrome characterized by loss of verbal semantic knowledge. There is less consensus regarding the symptoms that emerge when atrophy targets the right ATL (rATL), but previous studies have emphasized prosopagnosia as well as alterations in emotion, memory, behavior, and semantic knowledge, symptoms that often lead to a diagnosis of behavioral variant frontotemporal dementia (bvFTD). The goal of the present study was to characterize the cognitive and socioemotional deficits of patients with rATL degeneration in order to refine current conceptualizations of the rATL clinical syndrome. We identified individuals clinically diagnosed as bvFTD or PPA in our cohort of patients prospectively evaluated for FTD-spectrum disorders. We selected patients who also underwent structural MRI and a comprehensive, multidisciplinary evaluation (n = 478). Based on structural MRI atrophy index, individuals with predominant, rATL atrophy (n = 46) were identified and patients with co-occurrence of significant frontal atrophy were excluded. Nineteen patients with rATL degeneration had undergone autopsy. We used the clinical histories to identify early symptoms and examined the cognitive, socioemotional, genetic, and pathological profiles of patients with rATL degeneration. In patients with rATL degeneration, the most common early clinical symptoms were loss of empathy (27%), person-specific semantic knowledge (23%), and complex compulsions (18%). On socioemotional testing and informant-reported measures, patients exhibited diminished interpersonal warmth, empathy, and emotional theory of mind. Neuropsychological testing revealed deficits in identifying famous people and discriminating facial affect despite preserved face perception. FTLD-TDP was the most frequent pathological correlate of rATL degeneration (84%), followed by Pick type (10%), a subtype of FTLD-tau. Our results indicate that patients with early, rATL-predominant degeneration present with a behavioral syndrome that results from loss of empathy for others. The underlying mechanism is a progressive loss of semantic knowledge for concepts of social-emotional relevance. We herein refer to this syndrome as emotional semantic variant frontotemporal dementia. We propose novel diagnostic criteria for this rATL syndrome in order to facilitate early identification in clinical and research settings. This classification is relevant because, if appropriately diagnosed, these patients most often have FTLD-TDP Type-C pathology.

Frontotemporal dementia is a complex disorder with a wide spectrum of clinical symptoms. Personalized medicine and gene therapy are promising strategies for treatment

2021 ◽  
Author(s):  
Moataz Dowaidar
Keyword(s):  
Frontotemporal Dementia ◽  
Clinical Symptoms ◽  
Wide Spectrum ◽  
High Sensitivity ◽  
Ubiquitin Proteasome System ◽  
Assessment Instruments ◽  
Disease Modifying Drugs ◽  
Mutation Carriers ◽  
Definition Of ◽  
Tau Aggregation

Frontotemporal dementia is an uncommon and complex sickness with a wide spectrum of clinical symptoms, making drug development for the condition challenging. Frontal and temporal lobe responsibilities, as well as the level to which these activities are affected in certain diseases, contribute to clinical heterogeneity. Unfortunately, the available data on symptomatic treatments is drawn from limited case studies and RCTs, which include persons with the same FTD diagnosis. Advances in the identification of new FTD treatments demand the construction of huge clinical networks that are built on collaborative, multicenter research. One of the primary drawbacks of the present studies is the variety of the assessment instruments utilized, such as the FBI, Cambridge Behavioral Inventory, NPI, and BEHAVE-AD, some of which were not originally established for FTD but were first validated for other kinds of dementia, such as AD. The FBI was shown to have high sensitivity and specificity in discriminating between bvFTD and non-FTD, whereas the NPI score did not.The new definition of Alzheimer's disease was changed from a clinical diagnostic to a biomarker-based diagnostic, although we are still a long way from providing a scientific description of FTD. In the future, there might be an increased likelihood of recognizing symptoms or signs in clinical studies because of a revision to the criteria of FTD.Treatment techniques should be customized to individual patient subgroups or mutation carriers. Currently, as a treatment for FTD-tau, antisense oligonucleotide inhibition of MAPT, tau phosphorylation inhibition, microtubule stability, and inhibition of tau aggregation are all being investigated. It is possible to inhibit TDP-43 aggregation, enhance progranulin levels, activate the autophagy–lysosome system, or modify the ubiquitin-proteasome system in FTD-TDP. Despite these breakthroughs, the etiology and genetics of FTD remain unclear. Additional genetic pathways and loci associated with immune dysregulation and inflammation have recently been discovered, furthering the search for possible treatment targets. Also, knowing who the mutation carriers are early on allows researchers to test disease-modifying drugs earlier, when cognitive problems may still be reversible.Research gaps in the etiology and clinical characterization of FTD should be resolved, as well as the production of precise biomarkers and specialized evaluation methodologies. Desharnais and colleagues have created a checklist to assist standardize future FTD clinical trials and boost the probability of successful findings.

Methods for dynamic study of the biological neural network at the cellular level using computer processing

2021 ◽  
Vol 3 ◽  
Author(s):  
A.V. Medievsky ◽  
◽  
A.G. Zotin ◽  
K.V. Simonov ◽  
A.S. Kruglyakov
Keyword(s):  
Neural Network ◽  
Cellular Level ◽  
Computational Method ◽  
Color Coding ◽  
Low Contrast ◽  
Biological Neural Network ◽  
Fundamental Knowledge ◽  
The Brain ◽  
Contrast Image ◽  
Specific Neuron

The study of the principles of formation and development of the structure of the brain is necessary to replenish fundamental knowledge both in the field of neurophysiology and in medicine. A detailed description of all the features of the brain will allow you to choose the most effective therapy method, or check the effectiveness of the drugs being developed. The basis for creating a model of a biological neural network is a map of nerve cells and their connections. To obtain it, it is necessary to carry out microscopy of the cell culture. This will produce a low-contrast image. The study of these images is a difficult task therefore a computational method for processing images based on the Shearlet transform algorithm with contrast using color coding has been developed, designed to improve the process of creating a neural network model. To assess the functional characteristics of each cell a modified version of the MEA method is proposed. The new version will have movable microelectrodes capable of homing to the desired coordinates in accordance with the data from the analyzed microscopic images and interacting with a specific neuron. The contact of a microelectrode with a single cell allows one to study its individual adhesions with minimal noise from the excitation of neighboring cells.

Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: applied to GENFI study

2018 ◽  
Author(s):  
Claire Cury ◽  
Stanley Durrleman ◽  
David Cash ◽  
Marco Lorenzi ◽  
Jennifer M Nicholas ◽  
...  
Keyword(s):  
Frontotemporal Dementia ◽  
Clinical Symptoms ◽  
Brain Structures ◽  
Spatiotemporal Analysis ◽  
Cortical Atrophy ◽  
Imaging Biomarkers ◽  
Mri Scans ◽  
Healthy Control ◽  
Diffeomorphic Deformation ◽  
Shape Changes

AbstractBrain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer’s disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure.In this paper we propose a spatiotemporal analysis pipeline based Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects.We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease.

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