Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.

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Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2019-0058 ◽

2020 ◽

Vol 19 (3) ◽

Author(s):

Yunlong Nie ◽

Eugene Opoku ◽

Laila Yasmin ◽

Yin Song ◽

Jie Wang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Default Mode Network ◽

Resting State ◽

Brain Connectivity ◽

Parametric Bootstrap ◽

Imaging Genetics ◽

Nucleotide Polymorphisms ◽

Mixed Effect ◽

Default Mode

AbstractWe conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer’s disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.

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Investigation of the default mode network of the brain during alzheimer's disease by minimum spanning tree

2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU) ◽

10.1109/siu.2011.5929791 ◽

2011 ◽

Author(s):

Koray Ciftci

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Default Mode Network ◽

Spanning Tree ◽

Minimum Spanning Tree ◽

Default Mode ◽

The Brain

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Subtypes of functional brain connectivity as early markers of neurodegeneration in Alzheimer’s disease

10.1101/195164 ◽

2017 ◽

Cited By ~ 1

Author(s):

Pierre Orban ◽

Angela Tam ◽

Sebastian Urchs ◽

Melissa Savard ◽

Cécile Madjar ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Older Adults ◽

Alzheimer's Disease ◽

Brain Connectivity ◽

Brain Network ◽

List Type ◽

Healthy Older Adults ◽

Functional Brain ◽

Default Mode ◽

Functional Brain Network

HighlightsReliable functional brain network subtypes accompany cognitive impairment in ADSymptom-related subtypes exist in the default-mode, limbic and salience networksA limbic subtype is associated with a familial risk of AD in healthy older adultsLimbic subtypes also associate with beta amyloid deposition and ApoE4In BriefWe found reliable subtypes of functional brain connectivity networks in older adults, associated with AD-related clinical symptoms in patients as well as several AD risk factors/biomarkers in asymptomatic individuals.SummaryThe heterogeneity of brain degeneration has not been investigated yet for functional brain network connectivity, a promising biomarker of Alzheimer’s disease. We coupled cluster analysis with resting-state functional magnetic resonance imaging to discover connectivity subtypes in healthy older adults and patients with cognitive disorders related to Alzheimer’s disease, noting associations between subtypes and cognitive symptoms in the default-mode, limbic and salience networks. In an independent asymptomatic cohort with a family history of Alzheimer’s dementia, the connectivity subtypes had good test-retest reliability across all tested networks. We found that a limbic subtype was overrepresented in these individuals, which was previously associated with symptoms. Other limbic subtypes showed associations with cerebrospinal fluid Aβ1-42levels and ApoE4 genotype. Our results demonstrate the existence of reliable subtypes of functional brain networks in older adults and support future investigations in limbic connectivity subtypes as early biomarkers of Alzheimer’s degeneration.

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EARLIER DETECTION OF ALZHEIMER’S DISEASE USING IMAGE PROCESSING AND MACHINE LEARNING ALGORITHMS WITH GRAPH THEORY

International Journal of Computer Science and Mobile Computing ◽

10.47760/ijcsmc.2021.v10i08.006 ◽

2021 ◽

Vol 10 (8) ◽

pp. 36-40

Author(s):

D.J. Samatha Naidu ◽

G. Anand Kumar Reddy

Keyword(s):

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Graph Theory ◽

Thinking Skills ◽

Brain Network ◽

Machine Learning Algorithms ◽

Brain Disorder ◽

Automated Method ◽

The Brain

Alzheimer’s disease is one of the brain disease which is irreversible, progressive brain disorder that slowly destroys memory and thinking skills and, eventually, the ability to carry out the simplest tasks. There is no cure for Alzheimer’s disease but we prevent it’s by early detection. In existing work, limited with Alzheimer’s are irreversible, effect on daily activities, high memory loss and reducing the size of brain, etc. previous works focused on 2D and 3D formats now we considering 4D images. In proposed work, this work aims to present an automated method that assists in the diagnosis of Alzheimer’s disease supports the monitoring of the progression of the disease. The study of brain network based on resting-state functional Magnetic Resonance Imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize various aspects of the brain network by calculating measures the accuracy of different machine learning methods and different features to classify Cognitively Normal (C.N) individuals from Alzheimer’s Disease (A.D) and to predict longitudinal outcomes in participants with Mild Cognitive Impairment (MCI).

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Acute cognitive deficits after traumatic brain injury predict Alzheimer's disease-like degradation of the human default mode network

10.1101/2020.08.01.20166561 ◽

2020 ◽

Author(s):

Andrei Irimia ◽

Alexander S Maher ◽

Nikhil N Chaudhari ◽

Nahian F Chowdhury ◽

Elliot B Jacobs

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Traumatic Brain Injury ◽

Brain Injury ◽

Cognitive Deficits ◽

Default Mode Network ◽

Brain Connectivity ◽

The Elderly ◽

Post Injury ◽

Default Mode

Traumatic brain injury (TBI) and Alzheimer's disease (AD) are prominent neurological conditions whose neural and cognitive commonalities are poorly understood. The extent of TBI-related neurophysiological abnormalities has been hypothesized to reflect AD-like Neurodegeneration because TBI can increase vulnerability to AD. However, it remains challenging to prognosticate AD risk partly because the functional relationship between acute posttraumatic sequelae and chronic AD-like degradation remains elusive. Here, functional magnetic resonance imaging (fMRI), network theory, and machine learning (ML) are leveraged to study the extent to which geriatric mild TBI (mTBI) can lead to AD-like alteration of resting-state activity in the default mode network (DMN). This network is found to contain modules whose extent of AD-like, posttraumatic degradation can be accurately prognosticated based on the acute cognitive deficits of geriatric mTBI patients with cerebral microbleeds. Aside from establishing a predictive physiological association between geriatric mTBI, cognitive impairment, and AD-like functional degradation, these findings advance the goal of acutely forecasting mTBI patients' chronic deviations from normality along AD-like functional trajectories. The association of geriatric mTBI with AD-like changes in functional brain connectivity as early as ~6 months post-injury carries substantial implications for public health because TBI has relatively high prevalence in the elderly.

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