A Deep Learning Model for Data-Driven Discovery of Functional Connectivity

Mahmood, Usman; Fu, Zening; Calhoun, Vince D.; Plis, Sergey M.

doi:10.3390/a14030075

Cited by 16 publications

(10 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We show three different results, one for each of the paper contributions. We compare our results with SOTA DL methods [6], [15], [22], [23], [26], [32], [47], [52]- [55] depending on the task, and ML methods such as support vector machine (SVM) and logistic regression (LR). To be fair to the other papers, we report directly from the results mentioned in the papers.…”

Section: Resultsmentioning

confidence: 99%

“…The representations can then be used for node classification, graph classification, or predicting edges between nodes by using an existing true graph structure or learning the graph [5], [7]- [14]. For any of the mentioned tasks, most of the existing work (classification, link prediction) has been done on static graphs, e.g., [6], creates a static graph based on representation and phenotype infor-mation of subjects, [15] learns a static graph between brain regions, [13] learns a static graph in an interacting system. In reality, many fields (social networks, brain connectivity, traffic data, speech) are dynamically changing and cannot be completely represented using a static graph.…”

Section: Introductionmentioning

confidence: 99%

“…This assumption is improbable in many different datasets across many fields. 2) The semi or unsupervised methods developed to learn the graph structure (link-prediction) create embeddings/representations based on the learned graph structure, and use these embeddings to either predict future embeddings or perform classification tasks where loss is the error in prediction or classification [13], [15], [17]. The problem with this approach is that the embeddings are used for prediction/classification.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

Mahmood,

Fu,

Calhoun

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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 suboptimal 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 crosscorrelation 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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

Mahmood,

Fu,

Calhoun

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…(c) BRAINNETTF vs. neural network models on learnable brain networks. Unlike classical GNNs, FBNETGEN [35], DGM [38] and BrainNetGNN [45] hold a similar idea, which is to apply GNNs based on a learnable graph. FBNETGEN achieves SOTA performance on the ABCD dataset for biological sex prediction, and the learnable graphs can be seen as a type of attention score.…”

Section: Performance Analysis (Rq1)mentioning

confidence: 99%

Brain Network Transformer

Kan¹,

Dai²,

Cui³

et al. 2022

Preprint

View full text Add to dashboard Cite

Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain network analysis. Driven by the unique properties of data, we model brain networks as graphs with nodes of fixed size and order, which allows us to (1) use connection profiles as node features to provide natural and low-cost positional information and (2) learn pairwise connection strengths among ROIs with efficient attention weights across individuals that are predictive towards downstream analysis tasks. Moreover, we propose an ORTHONORMAL CLUSTERING READOUT operation based on selfsupervised soft clustering and orthonormal projection. This design accounts for the underlying functional modules that determine similar behaviors among groups of ROIs, leading to distinguishable cluster-aware node embeddings and informative graph embeddings. Finally, we re-standardize the evaluation pipeline on the only one publicly available large-scale brain network dataset of ABIDE, to enable meaningful comparison of different models. Experiment results show clear improvements of our proposed BRAIN NETWORK TRANSFORMER on both the public ABIDE and our restricted ABCD datasets. The implementation is available at https://github.com/Wayfear/BrainNetworkTransformer. Nowadays Transformer-based models have led a tremendous success in various downstream tasks across fields including natural language processing [56, 17] and computer vision [20, 10, 55]. Recent efforts have also emerged to apply Transformer-based designs to graph representation learning. GAT [57] firstly adapts the attention mechanism to graph neural networks (GNNs) but only considers the local structures of neighboring nodes. Graph Transformer [21] injects edge information into the attention mechanism and leverages the eigenvectors of each node as positional embeddings. SAN [40] further enhances the positional embeddings by considering both eigenvalues and eigenvectors and improves the attention mechanism by extending the attention from local to global structures.36th Conference on Neural Information Processing Systems (NeurIPS 2022).

show abstract

“…Moreover, the statistical correlations are not specific to the downstream prediction tasks. Recently, there are some works that attempt to generate the brain networks and predict for the downstream tasks jointly [17], [23], [27]. But they are mainly based on structure similarity from GNNs [17] or attention weights [23], [27], which still cannot well characterize the complex relationships among different regions.…”

Section: A Fmri-based Brain Network Analysismentioning

confidence: 99%

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

Yu¹,

Kan²,

Cui³

et al. 2022

Preprint

View full text Add to dashboard Cite

Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopted for fMRI analysis with superior performance. Unfortunately, traditional functional brain networks are mainly constructed based on similarities among region of interests (ROI), which are noisy and agnostic to the downstream prediction tasks and can lead to inferior results for GNN-based models. To better adapt GNNs for fMRI analysis, we propose TBDS, an end-toend framework based on Task-aware Brain connectivity DAG (short for Directed Acyclic Graph) Structure generation for fMRI analysis. The key component of TBDS is the brain network generator which adopts a DAG learning approach to transform the raw time-series into task-aware brain connectivities. Besides, we design an additional contrastive regularization to inject taskspecific knowledge during the brain network generation process. Comprehensive experiments on two fMRI datasets, namely Adolescent Brain Cognitive Development (ABCD) and Philadelphia Neuroimaging Cohort (PNC) datasets demonstrate the efficacy of TBDS. In addition, the generated brain networks also highlight the prediction-related brain regions and thus provide unique interpretations of the prediction results. Our implementation will be published upon acceptance 1 .

show abstract

A Deep Learning Model for Data-Driven Discovery of Functional Connectivity

Cited by 16 publications

References 29 publications

Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series

Brain Network Transformer

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

Contact Info

Product

Resources

About