Multi-Scale Graph Theoretical Analysis of Resting-State fMRI for Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls

• URL: http://arxiv.org/abs/2409.04072v3
• Date: Mon, 22 Sep 2025 21:49:35 GMT
• Title: Multi-Scale Graph Theoretical Analysis of Resting-State fMRI for Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls
• Authors: Ali Khazaee, Abdolreza Mohammadi, Ruairi O'Reilly,
• Abstract summary: Alzheimer's disease (AD) is a neurodegenerative disorder marked by memory loss and cognitive decline.<n>Traditional methods, such as Pearson's correlation, have been used to calculate association matrices.<n>We introduce a novel method that integrates discrete wavelet transform (DWT) and graph theory to model the dynamic behavior of brain networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Alzheimer's disease (AD) is a neurodegenerative disorder marked by memory loss and cognitive decline, making early detection vital for timely intervention. However, early diagnosis is challenging due to the heterogeneous presentation of symptoms. Resting-state functional magnetic resonance imaging (rs-fMRI) captures spontaneous brain activity and functional connectivity, which are known to be disrupted in AD and mild cognitive impairment (MCI). Traditional methods, such as Pearson's correlation, have been used to calculate association matrices, but these approaches often overlook the dynamic and non-stationary nature of brain activity. In this study, we introduce a novel method that integrates discrete wavelet transform (DWT) and graph theory to model the dynamic behavior of brain networks. Our approach captures the time-frequency representation of brain activity, allowing for a more nuanced analysis of the underlying network dynamics. Machine learning was employed to automate the discrimination of different stages of AD based on learned patterns from brain network at different frequency bands. We applied our method to a dataset of rs-fMRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, demonstrating its potential as an early diagnostic tool for AD and for monitoring disease progression. Our statistical analysis identifies specific brain regions and connections that are affected in AD and MCI, at different frequency bands, offering deeper insights into the disease's impact on brain function.

Related papers

• Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective [89.99666725996975]
  The brain age gap prediction (BAGP) models estimate the difference between a person's predicted brain age from data and their chronological age.<n>This tutorial article provides an overview of BAGP and introduces a principled framework for this application based on recent advancements in graph signal processing (GSP)<n>VNNs offer strong theoretical grounding and operational interpretability, enabling robust estimation of brain age gap predictions.
  arXiv  Detail & Related papers  (2025-10-14T17:44:45Z)
• Uncovering Alzheimer's Disease Progression via SDE-based Spatio-Temporal Graph Deep Learning on Longitudinal Brain Networks [18.54013631358448]
  We develop a neural network framework to predict future Alzheimer's disease (AD) progression.<n>Our framework effectively learns sparse regional and connective importance probabilities.<n>Our findings highlight the potential of-temporal graph-based learning for early, individualized prediction of AD progression.
  arXiv  Detail & Related papers  (2025-09-26T01:02:34Z)
• Cross-modal Causal Intervention for Alzheimer's Disease Prediction [12.485088483891843]
  We propose a visual-language causal intervention framework named Alzheimer's Disease Prediction with Cross-modal Causal Intervention.<n>Our framework implicitly eliminates confounders through causal intervention.<n> Experimental results demonstrate the outstanding performance of our method in distinguishing CN/MCI/AD cases.
  arXiv  Detail & Related papers  (2025-07-18T14:21:24Z)
• Naturalistic Language-related Movie-Watching fMRI Task for Detecting Neurocognitive Decline and Disorder [60.84344168388442]
  Language-related functional magnetic resonance imaging (fMRI) may be a promising approach for detecting cognitive decline and early NCD.<n>We examined the effectiveness of this task among 97 non-demented Chinese older adults from Hong Kong.<n>The study demonstrated the potential of the naturalistic language-related fMRI task for early detection of aging-related cognitive decline and NCD.
  arXiv  Detail & Related papers  (2025-06-10T16:58:47Z)
• ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network Embedding [1.6044444452278062]
  We propose BrainTWT, a novel dynamic network embedding approach that captures temporal evolution of the brain connectivity over time. The experimental evaluation, utilizing the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrates that BrainTWT outperforms baseline methods in ASD classification.
  arXiv  Detail & Related papers  (2025-03-16T05:44:11Z)
• Deep Learning for Early Alzheimer Disease Detection with MRI Scans [1.9806397201363817]
  Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis. We perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency.
  arXiv  Detail & Related papers  (2025-01-17T07:30:16Z)
• BrainMAP: Learning Multiple Activation Pathways in Brain Networks [77.15180533984947]
  We introduce a novel framework BrainMAP to learn Multiple Activation Pathways in Brain networks. Our framework enables explanatory analyses of crucial brain regions involved in tasks.
  arXiv  Detail & Related papers  (2024-12-23T09:13:35Z)
• Generative forecasting of brain activity enhances Alzheimer's classification and interpretation [16.09844316281377]
  Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor neural activity. Deep learning has shown promise in capturing these representations. In this study, we focus on time series forecasting of independent component networks derived from rs-fMRI as a form of data augmentation.
  arXiv  Detail & Related papers  (2024-10-30T23:51:31Z)
• Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging [1.074960192271861]
  This study introduces a novel brain-aware readout layer (BA readout layer) for Graph Neural Networks (GNNs) By clustering brain regions based on functional connectivity and node embedding, this layer improves the GNN's capability to capture complex brain network characteristics. Our results show that GNNs with the BA readout layer significantly outperform traditional models in predicting the Preclinical Alzheimer's Cognitive Composite (PACC) score.
  arXiv  Detail & Related papers  (2024-10-03T05:04:45Z)
• Toward Robust Early Detection of Alzheimer's Disease via an Integrated Multimodal Learning Approach [5.9091823080038814]
  Alzheimer's Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes. This study introduces an advanced multimodal classification model that integrates clinical, cognitive, neuroimaging, and EEG data.
  arXiv  Detail & Related papers  (2024-08-29T08:26:00Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• BrainODE: Dynamic Brain Signal Analysis via Graph-Aided Neural Ordinary Differential Equations [67.79256149583108]
  We propose a novel model called BrainODE to achieve continuous modeling of dynamic brain signals. By learning latent initial values and neural ODE functions from irregular time series, BrainODE effectively reconstructs brain signals at any time point.
  arXiv  Detail & Related papers  (2024-04-30T10:53:30Z)
• Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification [42.82118108887965]
  Building comprehensive brain connectomes has proved to be fundamental importance in resting-state fMRI (rs-fMRI) analysis. We model the brain network as a directed acyclic graph (DAG) to discover direct causal connections between brain regions. We propose Spatial-Temporal DAG Convolutional Network (ST-DAGCN) to jointly infer effective connectivity and classify rs-fMRI time series.
  arXiv  Detail & Related papers  (2023-12-16T04:31:51Z)
• UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training [66.16134293168535]
  We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain. Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
  arXiv  Detail & Related papers  (2023-09-13T09:22:49Z)
• Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks [53.257804915263165]
  Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. Key evidence from neuroimaging data for pathological commonness remains unrevealed. We build a deep learning model, using multi-site functional magnetic resonance imaging data, for classifying 5 different brain disorders from healthy controls.
  arXiv  Detail & Related papers  (2023-02-23T09:22:05Z)
• A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification [5.563162319586206]
  Recent applications of pattern recognition techniques on brain connectome classification using functional connectivity (FC) are shifting towards acknowledging aspects of brain connectivity across time. In this paper, a deep non-temporalal variation Bayes framework is proposed to learn to identify autism spectrum disorder (ASD) in human participants. The framework incorporates a spatial-aware recurrent neural network with an attention-based message passing scheme to capture richtemporal patterns across dynamic FC networks.
  arXiv  Detail & Related papers  (2023-02-14T18:42:17Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.