Integrating Causal Inference with Graph Neural Networks for Alzheimer's Disease Analysis

• URL: http://arxiv.org/abs/2511.14922v1
• Date: Tue, 18 Nov 2025 21:22:24 GMT
• Title: Integrating Causal Inference with Graph Neural Networks for Alzheimer's Disease Analysis
• Authors: Pranay Kumar Peddi, Dhrubajyoti Ghosh,
• Abstract summary: Causal-GCN is an interventional graph convolutional framework that integrates do-calculus-based back-door adjustment.<n>Applying to 484 subjects from the ADNI cohort, Causal-GCN achieves performance comparable to baseline GNNs.
• Score: 0.17188280334580194
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep graph learning has advanced Alzheimer's (AD) disease classification from MRI, but most models remain correlational, confounding demographic and genetic factors with disease specific features. We present Causal-GCN, an interventional graph convolutional framework that integrates do-calculus-based back-door adjustment to identify brain regions exerting stable causal influence on AD progression. Each subject's MRI is represented as a structural connectome where nodes denote cortical and subcortical regions and edges encode anatomical connectivity. Confounders such as age, sec, and APOE4 genotype are summarized via principal components and included in the causal adjustment set. After training, interventions on individual regions are simulated by serving their incoming edges and altering node features to estimate average causal effects on disease probability. Applied to 484 subjects from the ADNI cohort, Causal-GCN achieves performance comparable to baseline GNNs while providing interpretable causal effect rankings that highlight posterior, cingulate, and insular hubs consistent with established AD neuropathology.

Related papers

• R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression [63.97617759805451]
  Early detection of Alzheimer's disease requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility.<n>We introduce R-GenIMA, an interpretable multimodal large language model that couples a novel ROI-wise vision transformer with genetic prompting.<n>R-GenIMA achieves state-of-the-art performance in four-way classification across normal cognition, subjective memory concerns, mild cognitive impairment, and AD.
  arXiv  Detail & Related papers  (2025-12-22T02:54:10Z)
• 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)
• Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations [57.054499278843856]
  Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies.<n>Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs.<n>We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data.
  arXiv  Detail & Related papers  (2025-10-05T12:35:01Z)
• 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)
• 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)
• Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification [5.135525907581342]
  We propose a novel framework called CGB (Causal Graphs for Brains) for brain disease classification/detection.<n>CGB unveils causal relationships between ROIs that bring vital information to enhance brain disease classification performance.<n>Our experiments show that CGB outperforms state-of-the-art methods in classification tasks on brain disease datasets.
  arXiv  Detail & Related papers  (2025-05-30T10:50:45Z)
• GKAN: Explainable Diagnosis of Alzheimer's Disease Using Graph Neural Network with Kolmogorov-Arnold Networks [0.6282459656801734]
  We propose a novel single-modal framework that integrates Kolmogorov-Arnold Networks (KAN) into Graph Convolutional Networks (GCNs) to enhance both diagnostic accuracy and interpretability.<n>GCN-KAN outperforms traditional GCNs by 4-8% in classification accuracy while providing interpretable insights into key brain regions associated with Alzheimer's Disease (AD)<n>This approach offers a robust and explainable tool for early AD diagnosis.
  arXiv  Detail & Related papers  (2025-04-01T16:31:00Z)
• Graph Theory and GNNs to Unravel the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progression [0.0]
  We proposed and evaluated a graph-based framework to assess variations in Alzheimer's disease (AD) neuropathologies. Our framework focuses on classic (cAD) and rapid (rpAD) progression forms. Results suggest a unique neuropathological network organization for each AD variant.
  arXiv  Detail & Related papers  (2024-03-01T16:16:51Z)
• Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer's Disease using EEG Data [9.601125513491835]
  We propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a correlation-based measure of power spectral density similarity.
  arXiv  Detail & Related papers  (2023-04-12T14:13:09Z)
• DBGDGM: Dynamic Brain Graph Deep Generative Model [63.23390833353625]
  Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data. It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction. Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs. We propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
  arXiv  Detail & Related papers  (2023-01-26T20:45:30Z)
• 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)
• Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis [11.85489505372321]
  We train a-temporal graph convolutional network (ST-GCN) on short sub-sequences of the BOLD time series to model the non-stationary nature of functional connectivity. St-GCN is significantly more accurate than common approaches in predicting gender and age based on BOLD signals.
  arXiv  Detail & Related papers  (2020-03-24T01:56:50Z)
• 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.