Stochastic encoding of graphs in deep learning allows for complex analysis of gender classification in resting-state and task functional brain networks from the UK Biobank

• URL: http://arxiv.org/abs/2002.10936v2
• Date: Wed, 27 May 2020 16:20:28 GMT
• Title: Stochastic encoding of graphs in deep learning allows for complex analysis of gender classification in resting-state and task functional brain networks from the UK Biobank
• Authors: Matthew Leming, John Suckling
• Abstract summary: We introduce a encoding method in an ensemble of CNNs to classify functional connectomes by gender. We measure the salience of three brain networks involved in task- and resting-states, and their interaction.
• Score: 0.13706331473063876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. While visualization techniques have been developed to interpret CNNs, bias inherent in the method of encoding abstract input data, as well as the natural variance of deep learning models, detract from the accuracy of these techniques. We introduce a stochastic encoding method in an ensemble of CNNs to classify functional connectomes by gender. We applied our method to resting-state and task data from the UK BioBank, using two visualization techniques to measure the salience of three brain networks involved in task- and resting-states, and their interaction. To regress confounding factors such as head motion, age, and intracranial volume, we introduced a multivariate balancing algorithm to ensure equal distributions of such covariates between classes in our data. We achieved a final AUROC of 0.8459. We found that resting-state data classifies more accurately than task data, with the inner salience network playing the most important role of the three networks overall in classification of resting-state data and connections to the central executive network in task data.

Related papers

• Coding schemes in neural networks learning classification tasks [52.22978725954347]
  We investigate fully-connected, wide neural networks learning classification tasks. We show that the networks acquire strong, data-dependent features. Surprisingly, the nature of the internal representations depends crucially on the neuronal nonlinearity.
  arXiv  Detail & Related papers  (2024-06-24T14:50:05Z)
• Graph Neural Networks for Learning Equivariant Representations of Neural Networks [55.04145324152541]
  We propose to represent neural networks as computational graphs of parameters. Our approach enables a single model to encode neural computational graphs with diverse architectures. We showcase the effectiveness of our method on a wide range of tasks, including classification and editing of implicit neural representations.
  arXiv  Detail & Related papers  (2024-03-18T18:01:01Z)
• Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data [54.55126643084341]
  Existing deep learning solutions suffer from three major limitations. We introduce FedGmTE-Net++, a federated graph-based multi-trajectory evolution network. Using the power of federation, we aggregate local learnings among diverse hospitals with limited datasets.
  arXiv  Detail & Related papers  (2024-01-01T10:20:01Z)
• Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution [30.186917337606477]
  We introduce a sparse multitask learning framework for motor imagery (MI) and motor execution (ME) tasks. Given a dual-task CNN model for MI-ME classification, we apply a saliency-based sparsification approach to prune superfluous connections. Our results indicate that this tailored sparsity can mitigate the overfitting problem and improve the test performance with small amount of data.
  arXiv  Detail & Related papers  (2023-12-10T09:06:16Z)
• Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks [19.639533220155965]
  This paper explores the application of convolutional neural networks for the purpose of multimodal deception detection. We use a dataset built by interviewing 104 subjects about two topics, with one truthful and one falsified response from each subject about each topic.
  arXiv  Detail & Related papers  (2023-11-18T02:44:33Z)
• Homological Convolutional Neural Networks [4.615338063719135]
  We propose a novel deep learning architecture that exploits the data structural organization through topologically constrained network representations. We test our model on 18 benchmark datasets against 5 classic machine learning and 3 deep learning models.
  arXiv  Detail & Related papers  (2023-08-26T08:48:51Z)
• Graph Neural Operators for Classification of Spatial Transcriptomics Data [1.408706290287121]
  We propose a study incorporating various graph neural network approaches to validate the efficacy of applying neural operators towards prediction of brain regions in mouse brain tissue samples. We were able to achieve an F1 score of nearly 72% for the graph neural operator approach which outperformed all baseline and other graph network approaches.
  arXiv  Detail & Related papers  (2023-02-01T18:32:06Z)
• Decomposing neural networks as mappings of correlation functions [57.52754806616669]
  We study the mapping between probability distributions implemented by a deep feed-forward network. We identify essential statistics in the data, as well as different information representations that can be used by neural networks.
  arXiv  Detail & Related papers  (2022-02-10T09:30:31Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks [81.74442855155843]
  We introduce a new classification system that utilizes brain connectivity with a CNN and validate its effectiveness via the emotional video classification. The level of concentration of the brain connectivity related to the emotional property of the target video is correlated with classification performance.
  arXiv  Detail & Related papers  (2021-01-18T13:28:08Z)

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.