scholarly journals FMRI Functional Connectivity Evaluation in Alzheimer’s Stages: Linear and Non-Linear Approaches

2021 ◽  
Author(s):  
Hessam Ahmadi ◽  
Emad Fatemizadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Permutation Test ◽  
Pearson Correlation ◽  
Functional Integration ◽  
Clustering Coefficient ◽  
Kernel Trick ◽  
Non Linear ◽  
The Brain

Abstract Neuroimaging data analysis reveals the underlying interactions in the brain. It is essential, yet controversial, to choose a proper tool to manifest brain functional connectivity. In this regard, researchers have not reached a definitive conclusion between the linear and non-linear approaches, as both have pros and cons. In this study, to evaluate this concern, the functional Magnetic Resonance Imaging (fMRI) data of different stages of Alzheimer’s disease are investigated. In the linear approach, the Pearson Correlation Coefficient (PCC) is employed as a common technique to generate brain functional graphs. On the other hand, for non-linear approaches, two methods including Distance Correlation (DC) and the kernel trick are utilized. By the use of the three mentioned routines and graph theory, functional brain networks of all stages of Alzheimer’s disease (AD) are constructed and then sparsed. Afterwards, graph global measures are calculated over the networks and a non-parametric permutation test is conducted. Results reveal that the non-linear approaches have more potential to discriminate groups in all stages of AD. Moreover, the kernel trick method is more powerful in comparison to the DC technique. Nevertheless, AD degenerates the brain functional graphs more at the beginning stages of the disease. At the first phase, both functional integration and segregation of the brain degrades, and as AD progressed brain functional segregation further declines. The most distinguishable feature in all stages is the clustering coefficient that reflects brain functional segregation.

scholarly journals 3265 Analysis of High-Dimensional Patient Data in Characterizing Alzheimer’s Disease Progression

2019 ◽  
Vol 3 (s1) ◽  
pp. 3-3
Author(s):  
Daniel Baer ◽  
Andrew B. Lawson ◽  
Brandon Vaughan ◽  
Jane E. Joseph
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Cognitive Decline ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Eigenvector Centrality ◽  
Research Hypothesis ◽  
The Brain ◽  
Over Time

OBJECTIVES/SPECIFIC AIMS: Our research hypothesis is that resting state fMRI (rsfMRI) data can be used to identify regions of the brain which are associated with cognitive decline in patients – thereby providing a tool by which to characterize AD progression in patients. METHODS/STUDY POPULATION: We used data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to analyze Mini-Mental State Examination (MMSE) questionnaire scores from 14 patients diagnosed with AD at two measurement occasions. RsfMRI data was available at the first of these occasions for these patients. These rsfMRI data were summarized into 264 node-based graph theory measures of clustering coefficient and eigenvector centrality. To address our research hypothesis, we modeled changes in patient MMSE scores over time as a function of these rsfMRI data, controlling for relevant confounding factors. This model accounted for the high-dimensionality of our predictor data, the longitudinal nature of the outcome, and our desire to identify a subset of regions in the brain most associated with the MMSE outcome. RESULTS/ANTICIPATED RESULTS: The use of either the clustering coefficient or eigenvector centrality rsfMRI predictors in modeling MMSE scores for patients over time resulted in the identification of different subsets of brain regions associated with cognitive decline. This suggests that these predictors capture different information on patient propensity for cognitive decline. Further work is warranted to validate these results on a larger sample of ADNI patients. DISCUSSION/SIGNIFICANCE OF IMPACT: We conclude that different rsfMRI graph theory measures capture different aspects of cognitive function and decline in patients, which could be a future consideration in clinical practice.

scholarly journals A Tensor-Based Framework for rs-fMRI Classification and Functional Connectivity Construction

2020 ◽  
Vol 14 ◽  
Author(s):  
Ali Noroozi ◽  
Mansoor Rezghi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Extraction Methods ◽  
Classification Performance ◽  
Brain Diseases ◽  
Brain Disorder ◽  
Brain Images ◽  
The Brain ◽  
Normal Controls

Recently, machine learning methods have gained lots of attention from researchers seeking to analyze brain images such as Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to obtain a deeper understanding of the brain and such related diseases, for example, Alzheimer's disease. Finding the common patterns caused by a brain disorder through analysis of the functional connectivity (FC) network along with discriminating brain diseases from normal controls have long been the two principal goals in studying rs-fMRI data. The majority of FC extraction methods calculate the FC matrix for each subject and then use simple techniques to combine them and obtain a general FC matrix. In addition, the state-of-the-art classification techniques for finding subjects with brain disorders also rely on calculating an FC for each subject, vectorizing, and feeding them to the classifier. Considering these problems and based on multi-dimensional nature of the data, we have come up with a novel tensor framework in which a general FC matrix is obtained without the need to construct an FC matrix for each sample. This framework also allows us to reduce the dimensionality and create a novel discriminant function that rather than using FCs works directly with each sample, avoids vectorization in any step, and uses the test data in the training process without forcing any prior knowledge of its label into the classifier. Extensive experiments using the ADNI dataset demonstrate that our proposed framework effectively boosts the fMRI classification performance and reveals novel connectivity patterns in Alzheimer's disease at its early stages.

scholarly journals Diagnosis of Alzheimer’s Disease Using Brain Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Ramesh Kumar Lama ◽  
Goo-Rak Kwon
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Feature Selection Method ◽  
Brain Network ◽  
Classification Performance ◽  
Support Vector ◽  
Early Event ◽  
Learning Machine ◽  
The Brain

Recent studies suggest the brain functional connectivity impairment is the early event occurred in case of Alzheimer’s disease (AD) as well as mild cognitive impairment (MCI). We model the brain as a graph based network to study these impairment. In this paper, we present a new diagnosis approach using graph theory based features from functional magnetic resonance (fMR) images to discriminate AD, MCI, and healthy control (HC) subjects using different classification techniques. These techniques include linear support vector machine (LSVM), and regularized extreme learning machine (RELM). We used pairwise Pearson’s correlation-based functional connectivity to construct the brain network. We compare the classification performance of brain network using Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Node2vec graph embedding approach is employed to convert graph features to feature vectors. Experimental results show that the SVM with LASSO feature selection method generates better classification accuracy compared to other classification technique.

scholarly journals Decoupling of regional neural activity and inter-regional functional connectivity in Alzheimer’s Disease – a simultaneous PET/MRI study

2021 ◽  
Author(s):  
Somayeh Maleki Balajoo ◽  
Farzaneh Rahmani ◽  
Reza Khosrowabadi ◽  
Chun Meng ◽  
Simon B. Eickhoff ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Glucose Metabolism ◽  
Neural Activity ◽  
Fdg Pet ◽  
Disease Status ◽  
Blood Oxygenation ◽  
Clustering Coefficient ◽  
Regional Metabolism

Abstract Purpose: Alzheimer’s disease (AD) and mild cognitive impairment (MCI), a syndrome at-risk for AD, are characterized by both aberrant regional neural activity and disrupted inter-regional functional connectivity (FC). It is, however, not clear how aberrant regional neural activity and inter-regional FC interact across MCI and AD. Thus, we investigated the interplay between regional neural activity and inter-regional topological measures of FC in MCI and AD using simultaneous PET/MR measurement.Methods: We scanned 19 patients with MCI, 33 patients with AD, and 26 healthy individuals by simultaneous FDG-PET/resting-state fMRI. First, we assessed regional glucose metabolism identified through FDG-PET (as a proxy of regional neural activity), and inter-regional FC topology through clustering coefficient and degree centrality (as surrogates of local segregation and global connectivity, respectively, based on fMRI blood oxygenation). Next, we examined the potential moderating effect of disease status (MCI or AD) on the link between regional metabolism and inter-regional FC topology using hierarchical moderated multiple regression analysis.Results: Not only regional metabolism and inter-regional FC metrics were disrupted in in MCI and AD patients, but also AD significantly alters coupling between regional metabolism and inter-regional FC, particularly in the right inferior temporal, supplementary motor area and planum temporal areas, where AD moderated the effect of their regional glucose metabolism on predicting their inter-reginal FC. Conclusion: Our findings demonstrated that AD decouples the association between regional neural activity and functional segregation.

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

GeroPsych ◽  
2012 ◽  
Vol 25 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Katja Franke ◽  
Christian Gaser
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Brain Aging ◽  
Elderly Subjects ◽  
Additional Increase ◽  
Longitudinal Changes ◽  
Novel Method ◽  
The Brain

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

The Weak Combined Magnetic Fields Reduce the Brain β-Amyloid in an Animal Model of Sporadic Alzheimer's Disease

PIERS Online ◽  
2009 ◽  
Vol 5 (4) ◽  
pp. 311-315 ◽  
Author(s):  
Natalia V. Bobkova ◽  
Vadim V. Novikov ◽  
Natalia I. Medvinskaya ◽  
Irina Yu. Aleksandrova ◽  
Eugenii E. Fesenko
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Animal Model ◽  
Magnetic Fields ◽  
Sporadic Alzheimer’S Disease ◽  
Combined Magnetic Fields ◽  
The Brain

SPECIFIC PROPERTIES OF TUBULIN IN THE PREFRONTAL CORTEX IN CONTROL, SCHIZOPHRENIA AND VASCULAR DEMENTIA

2020 ◽  
pp. 65-71
Author(s):  
Burbaeva G.Sh. ◽  
Androsova L.V. ◽  
Vorobyeva E.A. ◽  
Savushkina O.K.
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prefrontal Cortex ◽  
Vascular Dementia ◽  
Binding Activity ◽  
Nephelometric Method ◽  
Rate Of Polymerization ◽  
Mental Diseases ◽  
And Control ◽  
The Brain

The aim of the study was to evaluate the rate of polymerization of tubulin into microtubules and determine the level of colchicine binding (colchicine-binding activity of tubulin) in the prefrontal cortex in schizophrenia, vascular dementia (VD) and control. Colchicine-binding activity of tubulin was determined by Sherlinе in tubulin-enriched extracts of proteins from the samples. Measurement of light scattering during the polymerization of the tubulin was carried out using the nephelometric method at a wavelength of 450-550 nm. There was a significant decrease in colchicine-binding activity and the rate of tubulin polymerization in the prefrontal cortex in both diseases, and in VD to a greater extent than in schizophrenia. The obtained results suggest that not only in Alzheimer's disease, but also in other mental diseases such as schizophrenia and VD, there is a decrease in the level of tubulin in the prefrontal cortex of the brain, although to a lesser extent than in Alzheimer's disease, and consequently the amount of microtubules.

Electromagnetic field in Alzheimer’s disease: a literature review of recent preclinical and clinical studies

2020 ◽  
Vol 17 ◽  
Author(s):  
Reem Habib Mohamad Ali Ahmad ◽  
Marc Fakhoury ◽  
Nada Lawand
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
In Vivo Studies ◽  
Key Factors ◽  
Potential Benefits ◽  
Preclinical And Clinical Studies ◽  
The Brain

: Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the progressive loss of neurons leading to cognitive and memory decay. The main signs of AD include the irregular extracellular accumulation of amyloidbeta (Aβ) protein in the brain and the hyper-phosphorylation of tau protein inside neurons. Changes in Aβ expression or aggregation are considered key factors in the pathophysiology of sporadic and early-onset AD and correlate with the cognitive decline seen in patients with AD. Despite decades of research, current approaches in the treatment of AD are only symptomatic in nature and are not effective in slowing or reversing the course of the disease. Encouragingly, recent evidence revealed that exposure to electromagnetic fields (EMF) can delay the development of AD and improve memory. This review paper discusses findings from in vitro and in vivo studies that investigate the link between EMF and AD at the cellular and behavioural level, and highlights the potential benefits of EMF as an innovative approach for the treatment of AD.

Integrated Biomarkers for Depression in Alzheimer’s Disease: A Critical Review

2017 ◽  
Vol 14 (4) ◽  
pp. 441-452 ◽  
Author(s):  
Sofia Wenzler ◽  
Christian Knochel ◽  
Ceylan Balaban ◽  
Dominik Kraft ◽  
Juliane Kopf ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Affect Regulation ◽  
Current Evidence ◽  
Diagnostic Process ◽  
Neuropsychiatric Manifestation ◽  
The Brain ◽  
Control Functional

Depression is a common neuropsychiatric manifestation among Alzheimer’s disease (AD) patients. It may compromise everyday activities and lead to a faster cognitive decline as well as worse quality of life. The identification of promising biomarkers may therefore help to timely initiate and improve the treatment of preclinical and clinical states of AD, and to improve the long-term functional outcome. In this narrative review, we report studies that investigated biomarkers for AD-related depression. Genetic findings state AD-related depression as a rather complex, multifactorial trait with relevant environmental and inherited contributors. However, one specific set of genes, the brain derived neurotrophic factor (BDNF), specifically the Val66Met polymorphism, may play a crucial role in AD-related depression. Regarding neuroimaging markers, the most promising findings reveal structural impairments in the cortico-subcortical networks that are related to affect regulation and reward / aversion control. Functional imaging studies reveal abnormalities in predominantly frontal and temporal regions. Furthermore, CSF based biomarkers are seen as potentially promising for the diagnostic process showing abnormalities in metabolic pathways that contribute to AD-related depression. However, there is a need for standardization of methodological issues and for replication of current evidence with larger cohorts and prospective studies.

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