Discovering Dynamic Effective Connectome of Brain with Bayesian Dynamic DAG Learning

• URL: http://arxiv.org/abs/2309.07080v3
• Date: Sat, 9 Mar 2024 17:56:38 GMT
• Title: Discovering Dynamic Effective Connectome of Brain with Bayesian Dynamic DAG Learning
• Authors: Abdolmahdi Bagheri, Mohammad Pasande, Kevin Bello, Babak Nadjar Araabi, Alireza Akhondi-Asl
• Abstract summary: We introduce Dynamic DAG learning with M-matrices Acyclicity characterization (BDyMA) method to address the challenges in discovering Dynamic Effective Connectome (DEC) We show that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and traditional methods.
• Score: 6.955540664243645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization (BDyMA) method to address the challenges in discovering DEC. The presented dynamic causal model enables us to discover direct feedback loop edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and traditional methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process.

Related papers

• LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data [13.390666123493409]
  LOCAL is a highly efficient, easy-to-implement, and constraint-free method for recovering dynamic causal structures. ACML generates causal masks using learnable priority vectors and the Gumbel-Sigmoid function. DGPL transforms causal learning into decomposed matrix products, capturing the dynamic causal structure of high-dimensional data.
  arXiv  Detail & Related papers  (2024-10-25T10:48:41Z)
• Enhancing EEG Signal Generation through a Hybrid Approach Integrating Reinforcement Learning and Diffusion Models [6.102274021710727]
  This study introduces an innovative approach to the synthesis of Electroencephalogram (EEG) signals by integrating diffusion models with reinforcement learning. Our methodology enhances the generation of EEG signals with detailed temporal and spectral features, enriching the authenticity and diversity of synthetic datasets.
  arXiv  Detail & Related papers  (2024-09-14T07:22:31Z)
• Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition [20.655367200006076]
  This study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals. The graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks.
  arXiv  Detail & Related papers  (2023-10-22T03:47:11Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• Discovering Dynamic Causal Space for DAG Structure Learning [64.763763417533]
  We propose a dynamic causal space for DAG structure learning, coined CASPER. It integrates the graph structure into the score function as a new measure in the causal space to faithfully reflect the causal distance between estimated and ground truth DAG.
  arXiv  Detail & Related papers  (2023-06-05T12:20:40Z)
• Brain Effective Connectome based on fMRI and DTI Data: Bayesian Causal Learning and Assessment [1.9874264019909988]
  We introduce two Bayesian causal discovery frameworks -- the Bayesian GOLEM (BGOLEM) and Bayesian FGES (BFGES) We show that our Bayesian methods achieve higher accuracy than traditional methods on fMRI data. Overall, our study's numerical and graphical results highlight the potential for these frameworks to advance our understanding of brain function and organization significantly.
  arXiv  Detail & Related papers  (2023-02-10T22:47:06Z)
• Federated Causal Discovery [74.37739054932733]
  This paper develops a gradient-based learning framework named DAG-Shared Federated Causal Discovery (DS-FCD) It can learn the causal graph without directly touching local data and naturally handle the data heterogeneity. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method.
  arXiv  Detail & Related papers  (2021-12-07T08:04:12Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• Dynamic Bottleneck for Robust Self-Supervised Exploration [84.78836146128236]
  We propose a Dynamic Bottleneck (DB) model, which attains a dynamics-relevant representation based on the information-bottleneck principle. Based on the DB model, we further propose DB-bonus, which encourages the agent to explore state-action pairs with high information gain. Our experiments show that exploration with DB bonus outperforms several state-of-the-art exploration methods in noisy environments.
  arXiv  Detail & Related papers  (2021-10-20T19:17:05Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)

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.