Learning Robust Hierarchical Patterns of Human Brain across Many fMRI Studies

• URL: http://arxiv.org/abs/2105.06535v1
• Date: Thu, 13 May 2021 20:10:00 GMT
• Title: Learning Robust Hierarchical Patterns of Human Brain across Many fMRI Studies
• Authors: Dushyant Sahoo, Christos Davatzikos
• Abstract summary: Resting-state fMRI has been shown to provide surrogate biomarkers for the analysis of various diseases. To improve the statistical power of biomarkers and the understanding mechanism of the brain, pooling of multi-center studies has become increasingly popular. But pooling the data from multiple sites introduces variations due to hardware, software, and environment.
• Score: 2.451910407959205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Resting-state fMRI has been shown to provide surrogate biomarkers for the analysis of various diseases. In addition, fMRI data helps in understanding the brain's functional working during resting state and task-induced activity. To improve the statistical power of biomarkers and the understanding mechanism of the brain, pooling of multi-center studies has become increasingly popular. But pooling the data from multiple sites introduces variations due to hardware, software, and environment. In this paper, we look at the estimation problem of hierarchical Sparsity Connectivity Patterns (hSCPs) in fMRI data acquired on multiple sites. We introduce a simple yet effective matrix factorization based formulation to reduce site-related effects while preserving biologically relevant variations. We leverage adversarial learning in the unsupervised regime to improve the reproducibility of the components. Experiments on simulated datasets display that the proposed method can estimate components with improved accuracy and reproducibility. We also demonstrate the improved reproducibility of the components while preserving age-related variation on a real dataset compiled from multiple sites.

Related papers

• A Confounding Factors-Inhibition Adversarial Learning Framework for Multi-site fMRI Mental Disorder Identification [22.749962294853187]
  We propose a novel multi-site adversarial learning network (MSalNET) for fMRI-based mental disorder detection. The proposed method was evaluated on two multi-site fMRI datasets, i.e., Autism Brain Imaging Data Exchange (ABIDE) and ADHD-200.
  arXiv  Detail & Related papers  (2025-04-12T10:58:19Z)
• MVICAD2: Multi-View Independent Component Analysis with Delays and Dilations [61.59658203704757]
  We propose Multi-View Independent Component Analysis with Delays and Dilations (MVICAD2), which allows sources to differ across subjects in both temporal delays and dilations. We present a model with identifiable sources, derive an approximation of its likelihood in closed form, and use regularization and optimization techniques to enhance performance.
  arXiv  Detail & Related papers  (2025-01-13T15:47:02Z)
• Biological Brain Age Estimation using Sex-Aware Adversarial Variational Autoencoder with Multimodal Neuroimages [8.610253537046692]
  We propose a novel framework for brain age estimation utilizing a sex-aware adversarial variational autoencoder (SA-AVAE) We decompose the latent space into modality-specific codes and shared codes to represent complementary and common information across modalities. We incorporate sex information into the learned latent code, enabling the model to capture sex-specific aging patterns for brain age estimation.
  arXiv  Detail & Related papers  (2024-12-07T12:10:29Z)
• Self-Supervised Pre-training Tasks for an fMRI Time-series Transformer in Autism Detection [3.665816629105171]
  Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that encompasses a wide variety of symptoms and degrees of impairment. We have developed a transformer-based self-supervised framework that directly analyzes time-series fMRI data without computing functional connectivity. We show that randomly masking entire ROIs gives better model performance than randomly masking time points in the pre-training step.
  arXiv  Detail & Related papers  (2024-09-18T20:29:23Z)
• Knowledge-Guided Prompt Learning for Lifespan Brain MR Image Segmentation [53.70131202548981]
  We present a two-step segmentation framework employing Knowledge-Guided Prompt Learning (KGPL) for brain MRI. Specifically, we first pre-train segmentation models on large-scale datasets with sub-optimal labels. The introduction of knowledge-wise prompts captures semantic relationships between anatomical variability and biological processes.
  arXiv  Detail & Related papers  (2024-07-31T04:32:43Z)
• FORESEE: Multimodal and Multi-view Representation Learning for Robust Prediction of Cancer Survival [3.4686401890974197]
  We propose a new end-to-end framework, FORESEE, for robustly predicting patient survival by mining multimodal information. Cross-fusion transformer effectively utilizes features at the cellular level, tissue level, and tumor heterogeneity level to correlate prognosis. The hybrid attention encoder (HAE) uses the denoising contextual attention module to obtain the contextual relationship features. We also propose an asymmetrically masked triplet masked autoencoder to reconstruct lost information within modalities.
  arXiv  Detail & Related papers  (2024-05-13T12:39:08Z)
• Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study [1.621204680136386]
  Parkinson's disease (PD) is a common neurodegenerative disorder with a poorly understood physiopathology and no established biomarkers for the diagnosis of early stages and for prediction of disease progression. Several neuroimaging biomarkers have been studied recently, but these are susceptible to several sources of variability related for instance to cohort selection or image analysis. This study is part of a larger project investigating the replicability of potential neuroimaging biomarkers of PD.
  arXiv  Detail & Related papers  (2024-02-20T13:42:50Z)
• fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding [54.17776744076334]
  We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
  arXiv  Detail & Related papers  (2023-11-01T07:24:22Z)
• 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)
• Robust Hierarchical Patterns for identifying MDD patients: A Multisite Study [3.4561220135252264]
  We look at hierarchical Sparse Connectivity Patterns (h SCPs) as biomarkers for major depressive disorder (MDD) We propose a novel model based on h SCPs to predict MDD patients from functional connectivity matrices extracted from resting-state fMRI data. Our results show the impact of diversity on prediction performance. Our model can reduce diversity and improve the predictive and generalizing capability of the components.
  arXiv  Detail & Related papers  (2022-02-22T19:40:32Z)
• Extraction of Hierarchical Functional Connectivity Components in human brain using Adversarial Learning [2.451910407959205]
  Inter-scanner variations and other confounding factors pose a challenge to the robust estimation of functionally-interpretable brain networks. The paper aims to use current advancements in adversarial learning to estimate interpretable hierarchical patterns in the human brain using rsfMRI data.
  arXiv  Detail & Related papers  (2021-04-20T21:38:55Z)
• Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning [62.17532253489087]
  Deep learning methods have been shown to produce superior performance on MR image reconstruction. These methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. We propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy.
  arXiv  Detail & Related papers  (2021-03-03T03:04:40Z)
• 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)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)
• Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results [13.615292855384729]
  To train a high-quality deep learning model, the aggregation of a significant amount of patient information is required. Due to the need to protect the privacy of patient data, it is hard to assemble a central database from multiple institutions. Federated learning allows for population-level models to be trained without centralizing entities' data.
  arXiv  Detail & Related papers  (2020-01-16T04:49:33Z)

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.