Search Results for author:
Found 49 papers, 14 papers with code
MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding
Consequently, MoRE-Brain marks a substantial advance towards more generalizable and interpretable fMRI-based visual decoding.
Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis
Gaining insights into the structural and functional mechanisms of the brain has been a longstanding focus in neuroscience research, particularly in the context of understanding and treating neuropsychiatric disorders such as Schizophrenia (SZ).
Physics-Guided Multi-View Graph Neural Network for Schizophrenia Classification via Structural-Functional Coupling
Clinical studies reveal disruptions in brain structural connectivity (SC) and functional connectivity (FC) in neuropsychiatric disorders such as schizophrenia (SZ).
4D Multimodal Co-attention Fusion Network with Latent Contrastive Alignment for Alzheimer's Diagnosis
Multimodal neuroimaging provides complementary structural and functional insights into both human brain organization and disease-related dynamics.
Functional Correspondences in the Human and Marmoset Visual Cortex During Movie Watching: Insights from Correlation, Redundancy, and Synergy
The world of beauty is deeply connected to the visual cortex, as perception often begins with vision in both humans and marmosets.
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis
Resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity networks (FCNs) have become critical for understanding neurological disorders.
A Deep Spatio-Temporal Architecture for Dynamic Effective Connectivity Network Analysis Based on Dynamic Causal Discovery
Dynamic effective connectivity networks (dECNs) reveal the changing directed brain activity and the dynamic causal influences among brain regions, which facilitate the identification of individual differences and enhance the understanding of human brain.
The Dynamics of Triple Interactions in Resting fMRI: Insights into Psychotic Disorders
We first estimated dynamic triple interactions using resting-state fMRI.
Correlation of Correlation Networks: High-Order Interactions in the Topology of Brain Networks
Moreover, after applying topological network analysis to the correlation of correlation networks, we observed that some high-order interaction hubs predominantly occurred in primary and high-level cognitive areas, such as the visual and fronto-parietal regions.
Generative forecasting of brain activity enhances Alzheimer's classification and interpretation
Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience.
Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks
Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data.
Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification
Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive impairments, abnormalities in brain structure, function, and genetic factors.
Generalized Dynamic Brain Functional Connectivity Based on Random Convolutions
In this study, we propose a generalized approach to dynamics via a multi-dimensional random convolution (RandCon) DFC method that is able to effectively capture time-varying DFC at arbitrary time scales by extracting different local features from fMRI time series using a number of multi-dimensional random convolution kernels without the need for learning kernel weights.
An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease
% in two distinct tasks, dealing with also missing data.\\ \textbf{Approach:} We propose a multimodal DL-based classification framework where a generative module employing Cycle Generative Adversarial Networks was introduced in the latent space for imputing missing data (a common issue of multimodal approaches).
Multimodal MRI Accurately Identifies Amyloid Status in Unbalanced Cohorts in Alzheimer's Disease Continuum
Amyloid-$\beta$ (A$\beta$) plaques in conjunction with hyperphosphorylated tau proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease.
Spectral Introspection Identifies Group Training Dynamics in Deep Neural Networks for Neuroimaging
Neural networks, whice have had a profound effect on how researchers study complex phenomena, do so through a complex, nonlinear mathematical structure which can be difficult for human researchers to interpret.
A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds
We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics.
Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer's Disease Biomarkers
Additionally, the T1 images generated by our model showed a similar pattern of atrophy in the hippocampal and other temporal regions of Alzheimer's patients.
Multiscale Neuroimaging Features for the Identification of Medication Class and Non-Responders in Mood Disorder Treatment
In the clinical treatment of mood disorders, the complex behavioral symptoms presented by patients and variability of patient response to particular medication classes can create difficulties in providing fast and reliable treatment when standard diagnostic and prescription methods are used.
Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse
Our understanding of learning dynamics of deep neural networks (DNNs) remains incomplete.
Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis
The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high-performing deep learning models.
Constrained Independent Vector Analysis with Reference for Multi-Subject fMRI Analysis
Independent vector analysis (IVA) generalizes ICA to multiple datasets, i. e., to multi-subject data, and in addition to higher-order statistical information in ICA, it leverages the statistical dependence across the datasets as an additional type of statistical diversity.
Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks
The human brain has a complex, intricate functional architecture.
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities.
Predictive Sparse Manifold Transform
PSMT incorporates two layers where the first sparse coding layer represents the input sequence as sparse coefficients over an overcomplete dictionary and the second manifold learning layer learns a geometric embedding space that captures topological similarity and dynamic temporal linearity in sparse coefficients.
Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey
Secondly, we discuss how multiple recent neuroimaging studies leveraged model interpretability to capture anatomical and functional brain alterations most relevant to model predictions.
New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning
In this paper, we present a new method that leverages ICA and DL for the identification of directly interpretable patterns to discriminate between the HC and Sz groups.
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings.
Latent Similarity Identifies Important Functional Connections for Phenotype Prediction
Significance: We propose a novel algorithm for small sample, high feature dimension datasets and use it to identify connections in task fMRI data.
Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest.
An AO-ADMM approach to constraining PARAFAC2 on all modes
We also apply our model to two real-world datasets from neuroscience and chemometrics, and show that constraining the evolving mode improves the interpretability of the extracted patterns.
Algorithm-Agnostic Explainability for Unsupervised Clustering
The methods are (1) easy to implement and (2) broadly applicable across clustering algorithms, which could make them highly impactful.
Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data
We show that self-supervised models are not as robust as expected based on their results in natural imaging benchmarks and can be outperformed by supervised learning with dropout.
Peering Beyond the Gradient Veil with Distributed Auto Differentiation
We introduce an innovative, communication-friendly approach for training distributed DNNs, which capitalizes on the outer-product structure of the gradient as revealed by the mechanics of auto-differentiation.
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions.
On self-supervised multi-modal representation learning: An application to Alzheimer's disease
In this paper, we introduce a way to exhaustively consider multimodal architectures for contrastive self-supervised fusion of fMRI and MRI of AD patients and controls.
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease
Sensory input from multiple sources is crucial for robust and coherent human perception.
Distance Correlation Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies
Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders.
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC).
Interpretable multimodal fusion networks reveal mechanisms of brain cognition
Moreover, the estimated activation maps are class-specific, and the captured cross-data associations are interest/label related, which further facilitates class-specific analysis and biological mechanism analysis.
Causal inference of brain connectivity from fMRI with $ψ$-Learning Incorporated Linear non-Gaussian Acyclic Model ($ψ$-LiNGAM)
A popular definition of FC is by statistical associations between measured brain regions.
A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence
Our network analysis revealed the development of emotion-related intra- and inter- modular connectivity and pinpointed several emotion-related hubs.
Causality based Feature Fusion for Brain Neuro-Developmental Analysis
Second, we used causality values as the weight for the directional connectivity between brain regions.
Meta-modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia
Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality.
Multidataset Independent Subspace Analysis with Application to Multimodal Fusion
In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce.
Prediction of Progression to Alzheimer's disease with Deep InfoMax
Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging.
Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia
Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity.
Deep learning for neuroimaging: a validation study
In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data.
Block Coordinate Descent for Sparse NMF
However, present algorithms designed for optimizing the mixed norm L$_1$/L$_2$ are slow and other formulations for sparse NMF have been proposed such as those based on L$_1$ and L$_0$ norms.
