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Found 33 papers, 7 papers with code
Hierarchical Spatio-Temporal State-Space Modeling for fMRI Analysis
Recent advances in deep learning structured state space models, especially the Mamba architecture, have demonstrated remarkable performance improvements while maintaining linear complexity.
Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images
We achieve this by implementing a diversified attention mechanism known as Spatial Sequence Attention (SSA) which is designed to extract and emphasize significant feature representations from structural MRI (sMRI).
DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks
Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes.
Unmasking Efficiency: Learning Salient Sparse Models in Non-IID Federated Learning
In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication.
Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli.
Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs
The cross-modal synthesis between structural magnetic resonance imaging (sMRI) and functional network connectivity (FNC) is a relatively unexplored area in medical imaging, especially with respect to schizophrenia.
Learning low-dimensional dynamics from whole-brain data improves task capture
We evaluate our approach on various task-fMRI datasets, including motor, working memory, and relational processing tasks, and demonstrate that it outperforms widely used dimensionality reduction techniques in how well the latent timeseries relates to behavioral sub-tasks, such as left-hand or right-hand tapping.
SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training
Federated learning (FL) enables the training of a model leveraging decentralized data in client sites while preserving privacy by not collecting data.
Coupled CP tensor decomposition with shared and distinct components for multi-task fMRI data fusion
Discovering components that are shared in multiple datasets, next to dataset-specific features, has great potential for studying the relationships between different subjects or tasks in functional Magnetic Resonance Imaging (fMRI) data.
MultiCrossViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data
Vision Transformer (ViT) is a pioneering deep learning framework that can address real-world computer vision issues, such as image classification and object recognition.
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs
To analyze communication, the brain is often split up into anatomical regions that each perform certain computations.
Prediction of Gender from Longitudinal MRI data via Deep Learning on Adolescent Data Reveals Unique Patterns Associated with Brain Structure and Change over a Two-year Period
Prior work has demonstrated that deep learning models that take advantage of the data's 3D structure can outperform standard machine learning on several learning tasks.
Pipeline-Invariant Representation Learning for Neuroimaging
We next propose two pipeline-invariant representation learning methodologies, MPSL and PXL, to improve robustness in classification performance and to capture similar neural network representations.
Explainable AI (XAI) in Biomedical Signal and Image Processing: Promises and Challenges
This paper aims at providing an overview on XAI in biomedical data processing and points to an upcoming Special Issue on Deep Learning in Biomedical Image and Signal Processing of the IEEE Signal Processing Magazine that is going to appear in March 2022.
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data
Our approach is evaluated on data with multiple motor sub-tasks to assess whether the model captures disentangled latent factors that correspond to each sub-task.
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series
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.
A deep learning model for data-driven discovery of functional connectivity
Functional connectivity (FC) studies have demonstrated the overarching value of studying the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.
Multi network InfoMax: A pre-training method involving graph convolutional networks
Since almost every DL model is an ensemble of multiple networks, we take our high-level embeddings from two different networks of a model --a convolutional and a graph network--.
Brain dynamics via Cumulative Auto-Regressive Self-Attention
The supervised training of the model as a classifier between patients and controls results in a model that generates directed connectivity graphs and highlights the components of the time-series that are predictive for each subject.
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures
The VAE is trained on voxelwise rs-fMRI data and performs non-linear dimensionality reduction that retains meaningful information.
Fusing multimodal neuroimaging data with a variational autoencoder
Neuroimaging studies often involve the collection of multiple data modalities.
Attend to connect: end-to-end brain functional connectivity estimation
Functional connectivity (FC) studies have demonstrated the benefits of investigating the brain and its disorders through the undirected weighted graph of fMRI correlation matrix.
Improved Differentially Private Decentralized Source Separation for fMRI Data
In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting.
Tracing Network Evolution Using the PARAFAC2 Model
For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood.
Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR
We propose the use of a multimodal multi-layer perceptron model to enhance the predictive power of structural and functional magnetic resonance imaging (sMRI and fMRI) combined.
Almost instant brain atlas segmentation for large-scale studies
Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than 50 regions.
Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics
In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel method for detecting higher order interactions among biologically relevant mulit-view data.
FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics
Here we propose a way of applying the FDR concept to sparse CCA, and a method to control the FDR.
End-to-end learning of brain tissue segmentation from imperfect labeling
Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes.
Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks
We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI).
Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel Canonical Correlation Analysis
Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data.
Rate-Agnostic (Causal) Structure Learning
That is, these algorithms all learn causal structure without assuming any particular relation between the measurement and system timescales; they are thus rate-agnostic.
Iterative Refinement of the Approximate Posterior for Directed Belief Networks
Variational methods that rely on a recognition network to approximate the posterior of directed graphical models offer better inference and learning than previous methods.
