scholarly journals Mathematical analysis of the influence of brain metabolism on the BOLD signal in Alzheimer’s disease

2017 ◽  
Vol 38 (2) ◽  
pp. 304-316 ◽  
Author(s):  
Felix Winter ◽  
Catrin Bludszuweit-Philipp ◽  
Olaf Wolkenhauer
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State Fmri ◽  
Blood Oxygen Level Dependent ◽  
Peak Height ◽  
Brain Dysfunction ◽  
Patient Specific ◽  
Theoretic Approach ◽  
Hemodynamic Response Function ◽  
Clinical Tool

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) is a standard clinical tool for the detection of brain activation. In Alzheimer’s disease (AD), task-related and resting state fMRI have been used to detect brain dysfunction. It has been shown that the shape of the BOLD response is affected in early AD. To correctly interpret these changes, the mechanisms responsible for the observed behaviour need to be known. The parameters of the canonical hemodynamic response function (HRF) commonly used in the analysis of fMRI data have no direct biological interpretation and cannot be used to answer this question. We here present a model that allows relating AD-specific changes in the BOLD shape to changes in the underlying energy metabolism. According to our findings, the classic view that differences in the BOLD shape are only attributed to changes in strength and duration of the stimulus does not hold. Instead, peak height, peak timing and full width at half maximum are sensitive to changes in the reaction rate of several metabolic reactions. Our systems-theoretic approach allows the use of patient-specific clinical data to predict dementia-driven changes in the HRF, which can be used to improve the results of fMRI analyses in AD patients.

scholarly journals Exploring Alzheimer’s Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures

Biomolecules ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 503
Author(s):  
Hila Dagan ◽  
Efrat Flashner-Abramson ◽  
Swetha Vasudevan ◽  
Maria R. Jubran ◽  
Ehud Cohen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Phenotypic Variability ◽  
Brain Regions ◽  
Patient Specific ◽  
Transcriptional Networks ◽  
Molecular Heterogeneity ◽  
Theoretic Approach ◽  
Molecular Variability ◽  
Depth Analysis

Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1–3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.

scholarly journals Evolutionary optimization in classification of early-MCI patients from healthy controls using graph measures of resting-state fMRI

2021 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Evolutionary Algorithms ◽  
Resting State ◽  
Optimization Methods ◽  
Resting State Fmri ◽  
Optimization Method ◽  
Effective Parameters

AbstractsIdentifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer’s disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer’s disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.

Diagnosis of Alzheimer’s disease using resting-state fMRI and graph theory

2018 ◽  
Vol 26 (6) ◽  
pp. 921-931 ◽  
Author(s):  
Mahtab Mohammadpoor Faskhodi ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Resting State ◽  
Resting State Fmri

Structural, Functional, and Molecular Neuroimaging Biomarkers for Alzheimer’s Disease

2013 ◽  
pp. 821-825
Author(s):  
James B. Brewer ◽  
Jorge Sepulcre ◽  
Keith A. Johnson
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Cognitive Impairment ◽  
Neurodegenerative Disorders ◽  
Brain Dysfunction ◽  
Imaging Biomarkers ◽  
Neurocognitive Evaluation ◽  
Neuroimaging Biomarkers

Advances in quantitative structural, functional, and molecular neuroimaging have provided new tools for objective, in vivo, assessment of critical aspects of Alzheimer’s disease and other neurodegenerative disorders. Measures of brain atrophy or brain dysfunction, coupled with measures of disease-linked pathology, might complement the history, physical and neurocognitive evaluation of patients and thereby improve predictive prognosis, especially at early stages of cognitive impairment where neurodegenerative etiology is less certain. Such imaging biomarkers are currently used in nearly all clinical trials of therapeutic agents for Alzheimer’s disease and are increasingly incorporated into clinical practice. In this chapter, imaging biomarkers are introduced and discussed to familiarize the reader with their potential research and clinical uses.

scholarly journals COMPLEMENT CONTRIBUTES TO ALZHEIMER’S DISEASE-INDUCED SYNAPSE DECLINE IN THE MURINE RETINA

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S967-S967
Author(s):  
Fenge Li ◽  
Danye Jiang ◽  
Melanie Samuel
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Horizontal Cell ◽  
Brain Dysfunction ◽  
Cell Integrity ◽  
Rt Pcr ◽  
Aged Patients ◽  
Complement Component C3 ◽  
Cell Processes ◽  
Ad Mouse Model

Abstract Alzheimer's disease (AD) is among the most debilitating form of cognitive impairment in aged patients. Synapse deficits are thought to be a central trigger of neural miswiring and brain dysfunction in AD. However, the pathways that control synapse connectivity remain largely unknown. The retina is an easily accessible system with two distinct synapse layers and three cellular layers comprised of distinct neural types. In this study, we leveraged this system to assess synapse and cell integrity in the APPNLGF amyloid-beta AD mouse model. We showed that the expression of the complement component C3 is significantly increased in APPNLGF retina synapses, and that there is a significant decline of several synapse-associated markers by RT-PCR. These mice also display disorganized horizontal cell processes and visual function deficits. These results suggest that complement may drive AD-related changes in the synaptic and functional properties of the retina, which could serve as assessable preclinical biomarkers for AD. In ongoing studies, we are testing whether and how complement regulates synapse refinement and shapes retina synapse specificity in AD.

Automated MRI-Based Deep Learning Model for Detection of Alzheimer’s Disease Process

2020 ◽  
Vol 30 (06) ◽  
pp. 2050032
Author(s):  
Wei Feng ◽  
Nicholas Van Halm-Lutterodt ◽  
Hao Tang ◽  
Andrew Mecum ◽  
Mohamed Kamal Mesregah ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Disease Process ◽  
Disease Classification ◽  
Support Vector ◽  
Imaging Data ◽  
Clinical Tool ◽  
3D Cnn

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI). The advanced deep learning method, a novel brain imaging technique, was applied in this study to evaluate its contribution to improving the diagnostic accuracy of AD. Three-dimensional convolutional neural networks (3D-CNNs) were applied with magnetic resonance imaging (MRI) to execute binary and ternary disease classification models. The dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used to compare the deep learning performances across 3D-CNN, 3D-CNN-support vector machine (SVM) and two-dimensional (2D)-CNN models. The outcomes of accuracy with ternary classification for 2D-CNN, 3D-CNN and 3D-CNN-SVM were [Formula: see text]%, [Formula: see text]% and [Formula: see text]% respectively. The 3D-CNN-SVM yielded a ternary classification accuracy of 93.71%, 96.82% and 96.73% for NC, MCI and AD diagnoses, respectively. Furthermore, 3D-CNN-SVM showed the best performance for binary classification. Our study indicated that ‘NC versus MCI’ showed accuracy, sensitivity and specificity of 98.90%, 98.90% and 98.80%; ‘NC versus AD’ showed accuracy, sensitivity and specificity of 99.10%, 99.80% and 98.40%; and ‘MCI versus AD’ showed accuracy, sensitivity and specificity of 89.40%, 86.70% and 84.00%, respectively. This study clearly demonstrates that 3D-CNN-SVM yields better performance with MRI compared to currently utilized deep learning methods. In addition, 3D-CNN-SVM proved to be efficient without having to manually perform any prior feature extraction and is totally independent of the variability of imaging protocols and scanners. This suggests that it can potentially be exploited by untrained operators and extended to virtual patient imaging data. Furthermore, owing to the safety, noninvasiveness and nonirradiative properties of the MRI modality, 3D-CNN-SMV may serve as an effective screening option for AD in the general population. This study holds value in distinguishing AD and MCI subjects from normal controls and to improve value-based care of patients in clinical practice.

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