Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis

• URL: http://arxiv.org/abs/2409.18967v1
• Date: Wed, 11 Sep 2024 08:02:57 GMT
• Title: Brain Network Diffusion-Driven fMRI Connectivity Augmentation for Enhanced Autism Spectrum Disorder Diagnosis
• Authors: Haokai Zhao, Haowei Lou, Lina Yao, Yu Zhang,
• Abstract summary: Due to the high cost of fMRI data acquisition and labeling, the amount of fMRI data is usually small. With the rise of generative models, especially diffusion models, the ability to generate realistic samples close to the real data distribution has been widely used for data augmentations.
• Score: 12.677178802864029
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Functional magnetic resonance imaging (fMRI) is an emerging neuroimaging modality that is commonly modeled as networks of Regions of Interest (ROIs) and their connections, named functional connectivity, for understanding the brain functions and mental disorders. However, due to the high cost of fMRI data acquisition and labeling, the amount of fMRI data is usually small, which largely limits the performance of recognition models. With the rise of generative models, especially diffusion models, the ability to generate realistic samples close to the real data distribution has been widely used for data augmentations. In this work, we present a transformer-based latent diffusion model for functional connectivity generation and demonstrate the effectiveness of the diffusion model as an augmentation tool for fMRI functional connectivity. Furthermore, extended experiments are conducted to provide detailed analysis of the generation quality and interpretations for the learned feature pattern. Our code will be made public upon acceptance.

Related papers

• A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds [49.34500499203579]
  We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics. We generate high-quality synthetic fMRI data based on user-supplied demographics.
  arXiv  Detail & Related papers  (2024-05-13T17:49:20Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network [5.945843237682432]
  Cross-modal transformer generative adversarial network (CT-GAN) is proposed to fuse functional and structural information. By analyzing the generated connectivity features, the proposed model can identify AD-related brain connections. Evaluations on the public ADNI dataset show that the proposed CT-GAN can dramatically improve prediction performance and detect AD-related brain regions effectively.
  arXiv  Detail & Related papers  (2023-09-28T07:06:42Z)
• DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI [15.40111168345568]
  It is challenging to bridge the reliable non-linear mapping relations between structural connectivity (SC) and functional magnetic resonance imaging (fMRI) A novel diffusision generative adversarial network-based fMRI-to-SC model is proposed to predict SC from brain fMRI in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-09-28T06:55:50Z)
• Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
  Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
  arXiv  Detail & Related papers  (2023-08-24T01:30:18Z)
• Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI [9.916963496386089]
  A novel hierarchical structural-functional connectivity fusing (HSCF) model is proposed to construct brain structural-functional connectivity matrices. Results from a wide range of tests performed on the public Alzheimer's Disease Neuroimaging Initiative database show that the proposed model performs better than competing approaches.
  arXiv  Detail & Related papers  (2023-06-16T05:22:25Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Cross-Modal Causal Intervention for Medical Report Generation [109.83549148448469]
  Medical report generation (MRG) is essential for computer-aided diagnosis and medication guidance. Due to the spurious correlations within image-text data induced by visual and linguistic biases, it is challenging to generate accurate reports reliably describing lesion areas. We propose a novel Visual-Linguistic Causal Intervention (VLCI) framework for MRG, which consists of a visual deconfounding module (VDM) and a linguistic deconfounding module (LDM)
  arXiv  Detail & Related papers  (2023-03-16T07:23:55Z)
• Characterization Multimodal Connectivity of Brain Network by Hypergraph GAN for Alzheimer's Disease Analysis [30.99183477161096]
  multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis. We propose a novel Hypergraph Generative Adversarial Networks(HGGAN) to generate multimodal connectivity of Brain Network from rs-fMRI combination with DTI.
  arXiv  Detail & Related papers  (2021-07-21T09:02:29Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)

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.