Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review

• URL: http://arxiv.org/abs/2405.00577v1
• Date: Wed, 1 May 2024 15:29:55 GMT
• Title: Discovering robust biomarkers of neurological disorders from functional MRI using graph neural networks: A Review
• Authors: Yi Hao Chan, Deepank Girish, Sukrit Gupta, Jing Xia, Chockalingam Kasi, Yinan He, Conghao Wang, Jagath C. Rajapakse,
• Abstract summary: We provide an overview of how GNN and model explainability techniques have been applied on fMRI datasets for disorder prediction tasks. We find that while most studies have performant models, salient features highlighted in these studies vary greatly across studies on the same disorder. We suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of these potential biomarkers.
• Score: 4.799269666410891
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. In this review, we provide an overview of how GNN and model explainability techniques have been applied on fMRI datasets for disorder prediction tasks, with a particular emphasis on the robustness of biomarkers produced for neurodegenerative diseases and neuropsychiatric disorders. We found that while most studies have performant models, salient features highlighted in these studies vary greatly across studies on the same disorder and little has been done to evaluate their robustness. To address these issues, we suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of these potential biomarkers. We further highlight gaps in the existing literature and put together a prediction-attribution-evaluation framework that could set the foundations for future research on improving the robustness of potential biomarkers discovered via GNNs.

Related papers

• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer [32.26944736442376]
  Graph neural networks (GNNs) have been the dominant deep learning model for analyzing graph-structured data. The root of the challenges is the unique graph structure of biological signaling pathways. We present a novel GNN model architecture, named PathFormer, which integrates signaling network, priori knowledge and omics data to rank biomarkers and predict disease diagnosis.
  arXiv  Detail & Related papers  (2024-02-11T18:23:54Z)
• Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies [50.11449968854487]
  Genome-wide association studies (GWAS) are used to identify relationships between genetic variations and specific traits. Representation learning for imaging genetics is largely under-explored due to the unique challenges posed by GWAS. We introduce a trans-modal learning framework Genetic InfoMax (GIM) to address the specific challenges of GWAS.
  arXiv  Detail & Related papers  (2023-09-26T03:59:21Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning? [0.0]
  We introduce unsupervised graph representations that explicitly map the neural mechanisms of a core aspect of ASD. First results from functional-near infrared spectroscopy data indicate potential predictive capacities of a task-agnostic, interpretable graph representation.
  arXiv  Detail & Related papers  (2022-08-17T07:10:57Z)
• Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers [0.293168019422713]
  We show how a deep conditional generative model can be used to perturb local imaging features in baseline images that are pertinent to subject-specific future disease evolution. Our model produces counterfactuals with changes in imaging features that reflect established clinical markers predictive of future MRI lesional activity at the population level.
  arXiv  Detail & Related papers  (2022-08-03T18:58:45Z)
• Quantifying the Reproducibility of Graph Neural Networks using Multigraph Brain Data [0.0]
  Graph neural networks (GNNs) have witnessed an unprecedented proliferation in tackling several problems in computer vision, computer-aided diagnosis, and related fields. While prior studies have focused on boosting the model accuracy, quantifying the most discriminative features identified by GNNs is still an intact problem that yields concerns about their reliability in clinical applications in particular. We propose for the first time, a framework for GNN assessment via the most discriminative features (i.e., biomarkers) shared between different models. To ascertain the soundness of our framework, the assessment embraces variations of different factors such as training strategies and
  arXiv  Detail & Related papers  (2021-09-06T05:31:02Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)
• Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis [29.489129970039873]
  A promising approach to identify the salient regions is using Graph Neural Networks (GNNs) We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders. We apply the PR-GNN framework on a Biopoint Autism Spectral Disorder (ASD) fMRI dataset.
  arXiv  Detail & Related papers  (2020-07-29T04:19:36Z)
• 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)

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.