Joint estimation of multiple Granger causal networks: Inference of group-level brain connectivity

• URL: http://arxiv.org/abs/2105.07196v1
• Date: Sat, 15 May 2021 10:29:02 GMT
• Title: Joint estimation of multiple Granger causal networks: Inference of group-level brain connectivity
• Authors: Parinthorn Manomaisaowapak and Jitkomut Songsiri
• Abstract summary: This paper considers joint learning of multiple Granger graphical models to discover underlying differential Granger causality structures across multiple time series. Our methods were also applied to available resting-state fMRI time series from the ADHD-200 data sets to learn the differences of causality mechanisms.
• Score: 8.122270502556374
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper considers joint learning of multiple sparse Granger graphical models to discover underlying common and differential Granger causality (GC) structures across multiple time series. This can be applied to drawing group-level brain connectivity inferences from a homogeneous group of subjects or discovering network differences among groups of signals collected under heterogeneous conditions. By recognizing that the GC of a single multivariate time series can be characterized by common zeros of vector autoregressive (VAR) lag coefficients, a group sparse prior is included in joint regularized least-squares estimations of multiple VAR models. Group-norm regularizations based on group- and fused-lasso penalties encourage a decomposition of multiple networks into a common GC structure, with other remaining parts defined in individual-specific networks. Prior information about sparseness and sparsity patterns of desired GC networks are incorporated as relative weights, while a non-convex group norm in the penalty is proposed to enhance the accuracy of network estimation in low-sample settings. Extensive numerical results on simulations illustrated our method's improvements over existing sparse estimation approaches on GC network sparsity recovery. Our methods were also applied to available resting-state fMRI time series from the ADHD-200 data sets to learn the differences of causality mechanisms, called effective brain connectivity, between adolescents with ADHD and typically developing children. Our analysis revealed that parts of the causality differences between the two groups often resided in the orbitofrontal region and areas associated with the limbic system, which agreed with clinical findings and data-driven results in previous studies.

Related papers

• Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Data [21.697069894721448]
  We present a theoretically-grounded method that infers Granger causal structure and identifies unknown targets by leveraging heterogeneous interventional time series data. Our method outperforms several robust baseline methods in learning Granger causal structure from interventional time series data.
  arXiv  Detail & Related papers  (2024-06-14T21:36:00Z)
• Causal K-Means Clustering [5.087519744951637]
  Causal k-Means Clustering harnesses the widely-used k-means clustering algorithm to uncover the unknown subgroup structure. We present a plug-in estimator which is simple and readily implementable using off-the-shelf algorithms. Our proposed methods are especially useful for modern outcome-wide studies with multiple treatment levels.
  arXiv  Detail & Related papers  (2024-05-05T23:59:51Z)
• Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks [60.09912912343705]
  This study explores the benefits of integrating the novel clustered federated learning (CFL) approach with non-independent and identically distributed (non-IID) datasets. A detailed theoretical analysis of the generalization gap that measures the degree of non-IID in the data distribution is presented. Solutions to address the challenges posed by non-IID conditions are proposed with the analysis of the properties.
  arXiv  Detail & Related papers  (2024-03-05T17:49:09Z)
• Deep Learning-based Group Causal Inference in Multivariate Time-series [8.073449277052495]
  Causal inference in a nonlinear system of multivariate timeseries is instrumental in disentangling the intricate web of relationships among variables. In this work, we test model invariance by group-level interventions on the trained deep networks to infer causal direction in groups of variables.
  arXiv  Detail & Related papers  (2024-01-16T14:19:28Z)
• Fundamental limits of community detection from multi-view data: multi-layer, dynamic and partially labeled block models [7.778975741303385]
  We study community detection in multi-view data in modern network analysis. We characterize the mutual information between the data and the latent parameters. We introduce iterative algorithms based on Approximate Message Passing for community detection.
  arXiv  Detail & Related papers  (2024-01-16T07:13:32Z)
• Population-Based Evolutionary Gaming for Unsupervised Person Re-identification [26.279581599246224]
  Unsupervised person re-identification has achieved great success through the self-improvement of individual neural networks. We develop a population-based evolutionary gaming (PEG) framework in which a population of diverse neural networks is trained concurrently through selection, reproduction, mutation, and population mutual learning. PEG produces new state-of-the-art accuracy for person re-identification, indicating the great potential of population-based network cooperative training for unsupervised learning.
  arXiv  Detail & Related papers  (2023-06-08T14:33:41Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• The Group Loss++: A deeper look into group loss for deep metric learning [65.19665861268574]
  Group Loss is a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group. We show state-of-the-art results on clustering and image retrieval on four datasets, and present competitive results on two person re-identification datasets.
  arXiv  Detail & Related papers  (2022-04-04T14:09:58Z)
• Effect Identification in Cluster Causal Diagrams [51.42809552422494]
  We introduce a new type of graphical model called cluster causal diagrams (for short, C-DAGs) C-DAGs allow for the partial specification of relationships among variables based on limited prior knowledge. We develop the foundations and machinery for valid causal inferences over C-DAGs.
  arXiv  Detail & Related papers  (2022-02-22T21:27:31Z)
• Exact Recovery in the General Hypergraph Stochastic Block Model [92.28929858529679]
  This paper investigates fundamental limits of exact recovery in the general d-uniform hypergraph block model (d-HSBM) We show that there exists a sharp threshold such that exact recovery is achievable above the threshold and impossible below it.
  arXiv  Detail & Related papers  (2021-05-11T03:39:08Z)
• Amortized Probabilistic Detection of Communities in Graphs [49.46170819501234]
  We propose a simple framework for amortized community detection. We combine the expressive power of GNNs with recent methods for amortized clustering. We evaluate several models from our framework on synthetic and real datasets.
  arXiv  Detail & Related papers  (2020-10-29T16:18:48Z)

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.