# Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

arXiv: https://arxiv.org/abs/2202.02393 · HTML: https://mlsanity.com/paper/2202.02393/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 64 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Attention is not Explanation](https://mlsanity.com/paper/1902.10186/) (2019)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Attention is not not Explanation](https://mlsanity.com/paper/1908.04626/) (2019)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Inductive Representation Learning on Temporal Graphs](https://mlsanity.com/paper/2002.07962/) (2020)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Modeling polypharmacy side effects with graph convolutional networks](https://mlsanity.com/paper/1802.00543/) (2018)
- [Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease](https://mlsanity.com/paper/1806.01738/) (2018)
- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Differentiable Graph Module (DGM) for Graph Convolutional Networks](https://mlsanity.com/paper/2002.04999/) (2020)
- [Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis](https://mlsanity.com/paper/2003.10613/) (2020)
- [Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis](https://mlsanity.com/paper/2001.03690/) (2020)
- [Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks](https://mlsanity.com/paper/1703.02161/) (2017)
- [Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example](https://mlsanity.com/paper/1611.06066/) (2016)
- [Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity](https://mlsanity.com/paper/1806.01764/) (2018)
- [Whole MILC: generalizing learned dynamics across tasks, datasets, and populations](https://mlsanity.com/paper/2007.16041/) (2020)
- [IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction](https://mlsanity.com/paper/2103.15587/) (2021)
- [A deep learning model for data-driven discovery of functional connectivity](https://mlsanity.com/paper/2112.04013/) (2021)
- [Learnt dynamics generalizes across tasks, datasets, and populations](https://mlsanity.com/paper/1912.03130/) (2019)
- [Evolution and Analysis of Embodied Spiking Neural Networks Reveals Task-Specific Clusters of Effective Networks](https://mlsanity.com/paper/1704.04199/) (2017)
- [Brain dynamics via Cumulative Auto-Regressive Self-Attention](https://mlsanity.com/paper/2111.01271/) (2021)

## Built on by (most cited first, 2 of 2)

- [Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications](https://mlsanity.com/paper/2204.00323/) (2022)
- [Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey](https://mlsanity.com/paper/2307.09615/) (2023)

## Abstract (arXiv)

Recently, methods that represent data as a graph, such as graph neural networks (GNNs) have been successfully used to learn data representations and structures to solve classification and link prediction problems. The applications of such methods are vast and diverse, but most of the current work relies on the assumption of a static graph. This assumption does not hold for many highly dynamic systems, where the underlying connectivity structure is non-stationary and is mostly unobserved. Using a static model in these situations may result in sub-optimal performance. In contrast, modeling changes in graph structure with time can provide information about the system whose applications go beyond classification. Most work of this type does not learn effective connectivity and focuses on cross-correlation between nodes to generate undirected graphs. An undirected graph is unable to capture direction of an interaction which is vital in many fields, including neuroscience. To bridge this gap, we developed dynamic effective connectivity estimation via neural network training (DECENNT), a novel model to learn an interpretable directed and dynamic graph induced by the downstream classification/prediction task. DECENNT outperforms state-of-the-art (SOTA) methods on five different tasks and infers interpretable task-specific dynamic graphs. The dynamic graphs inferred from functional neuroimaging data align well with the existing literature and provide additional information. Additionally, the temporal attention module of DECENNT identifies time-intervals crucial for predictive downstream task from multivariate time series data.