Modeling the Drosophila connectome using semiparametric spectral methods.
problem Understanding the structure and function of the Drosophila mushroom body network.
method Semiparametric spectral modeling, latent structure model (LSM), Gaussian mixture modeling (GMM), adjacency spectral embedding (ASE).
result Captures latent connectome structure and elucidates neuronal properties.
Paper proposes a new method for brain disease classification using connectome data.
problem Challenges in classifying brain diseases due to small sample size and high dimensionality.
method Simultaneous approximate diagonalization of adjacency matrices to compute stable eigenstructures.
result The method outperforms simple baselines and state-of-the-art approaches for Alzheimer's disease detection.
Machine learning applied to MRI connectome data for disease prediction and subnetwork analysis.
problem Predicting clinical outcomes and analyzing brain subnetworks from MRI connectome data.
method Review of machine learning approaches adapted to connectome data's unique properties.
result Machine learning models can improve disease prediction and subnetwork analysis in connectome data.
Study shows structural variability in white matter bundles influences brain network function.
problem Understanding how structural variability in white matter bundles affects brain network function.
method Developed a method to measure local integrity of white matter bundles and used statistical approaches to analyze data.
result Variability in the local connectome correlates with variability in functional brain dynamics.
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation …
Low-rank method improves brain network estimates for small samples.
problem Estimating mean of brain networks from small samples.
method Low-rank approximation with dimension selection and diagonal augmentation.
result Low-rank methods outperform standard sample mean, especially for small sample sizes.
DCNs mimic neuronal networks for improved neural classification.
problem Lack of topological similarity between DNNs and biological neural networks.
method Developed DCNs with topologies inspired by real-world neuronal networks.
result High classification accuracy achieved by DCNs.
Develops a new method to analyze brain networks for cognitive traits.
problem Challenges in summarizing and relating brain connectomes to human traits.
method Graph Auto-Encoding (GATE) model using deep learning.
result GATE improves prediction accuracy and efficiency over existing methods.
Neural connectomics has begun producing massive amounts of data, necessitating new analysis methods to discover the biological and computational structure. It has long been assumed that discovering neuron types and their relation to microcircuitry is crucial to understanding neural function. Here we developed a nonpara…
New brain atlas method improves classification accuracy.
problem Creating accurate brain atlases from connectomes.
method Connectivity-based hierarchical clustering and consensus aggregation.
result Consensus parcellation outperforms existing atlases in classification tasks.
Unsupervised framework captures acquisition variability in structural connectomes.
problem Acquisition differences across sites, scanners, and protocols complicate structural connectome analysis.
method An unsupervised framework using architectural annealing to balance discrete and continuous latent variables.
result Architectural annealing produces stronger site learning than baseline models.
Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to strong interest in using neuroimaging methods to accurately predict disorder status. In this work, we are specifically interested in a multivariate approach that uses features derived from …
Financial markets modeled like brain networks using dMNC.
problem Understanding latent dynamics in financial markets.
method Biologically inspired framework using dMNC.
result Structural persistence, regime shifts, and early warning signals identified.
A new neural network model improves fMRI classification.
problem Classifying brain states using fMRI data.
method Developed a connectome-convolutional neural network (CCNN) for fMRI functional connectivity classification.
result CCNN outperforms single metric classifiers and can adapt to various connectivity descriptors.
MergeNet detects morphological errors in 3D neuron segmentations.
problem High incidence of merge errors in deep learning 3D connectomics.
method Unsupervised training of MergeNet on various datasets.
result MergeNet can detect morphological errors in neuronal shapes.
New network learns non-parametric invariances from data.
problem Modeling non-parametric invariances in data.
method Introduces PRC-NPTN networks with permanent random connectomes.
result Improves generalization and outperforms existing methods.
R-PLS improves analysis of brain functional connectivity matrices.
problem Improving analysis of functional connectivity matrices in brain imaging.
method Introducing R-PLS, a generalization of PLS for symmetric positive definite matrices.
result R-PLS identifies key functional connections in brain imaging datasets.
BrainSurfCNN predicts task contrasts from resting-state fingerprints, improving accuracy over baseline.
problem Predicting task-evoked activity from resting-state functional connectivity.
method Surface-based convolutional neural network (BrainSurfCNN) with reconstructive-contrastive loss.
result Significantly improved accuracy in predicting task contrasts over baseline.
New methods improve brain data analysis from fMRI datasets.
problem Simplified brain models from correlational values are insufficient.
method Deep learning and geometric deep learning techniques.
result Improved predictive spatio-temporal brain data representation.
Researchers compare brain connectomes using geodesic distance on manifold for twin pairs.
problem Assessing functional similarity in brain networks between monozygotic and dizygotic twins.
method Using fMRI data, the researchers compared functional networks between mono- and dizygotic twin pairs by measuring similarity with geodesic distance on graph Laplacians.
result Functional networks are more similar in monozygotic twins compared to dizygotic twins, and similarity is higher for task-relevant networks.
Python package for SPD matrix distances, reproducible and extensible.
problem Computing distances between SPD matrices for various applications.
method Unified, extensible framework supporting multiple SPD metrics.
result Reproducible and accessible SPD matrix comparison tool.
This paper develops a method to learn lower-dimensional submanifolds of brain connectomes.
problem Learning lower-dimensional representations of manifold-valued data, especially brain connectomes.
method Riemannian variational autoencoder with intrinsic generative model.
result The method can learn weighted submanifolds of manifold-valued data.
Deep CNN classifies EEG-based brain connectivity in schizophrenia.
problem Classifying neuropsychiatric disorders using EEG connectivity.
method Multi-domain connectome CNN framework integrating time and frequency-domain metrics.
result MDC-CNN achieves 93.06% accuracy in schizophrenia classification. Efficiently clusters nodes in Gaussian graphical models from data.
problem Clustering nodes in Gaussian graphical models directly from data.
method Clusters nodes based on the similarity of their network neighborhoods defined by partial correlations. Uses matrix factors for limited data.
result Demonstrates improved clustering of nodes in Gaussian graphical models.
A new framework optimizes fMRI and behavioral data for better understanding of Autism.
problem Linking complex fMRI data to behavioral measures is challenging.
method Coupled manifold optimization framework projecting fMRI onto a shared manifold and mapping to behavioral measures.
result Framework outperforms traditional methods in predicting clinical severity of Autism.
New biomarkers for autism detected from R-fMRI data.
problem Challenges in extracting functional biomarkers from multi-site R-fMRI data for complex neuropsychiatric disorders.
method Developed pipelines to extract participant-specific connectomes from functionally-defined brain areas, compared across participants, and predicted neuropsychiatric status.
result 67% prediction accuracy on ABIDE dataset, significantly better than previous results.
Ensemble learning improves rs-fMRI predictions using 3D CNNs.
problem Improving specificity and sensitivity of rs-fMRI measurements through better parcellation schemes.
method Ensemble learning with 3D CNNs to combine predictions from different parcellations.
result Ensemble learning with 3D CNNs outperforms traditional methods in rs-fMRI classification and regression tasks.
Guided spectral embedding highlights C. elegans neural network.
problem Understanding the C. elegans neural network structure and function.
method A new guided spectral embedding method that maximizes energy concentration and minimizes modified embedded distance, using a given importance weighting of nodes.
result The guided approach provides more biological insights, distinguishing somatic positions and processing functions of cells.
Based on graphic lambda calculus, we propose a program for a new model of asynchronous distributed computing, inspired from Hewitt Actor Model, as well as several investigation paths, concerning how one may graft lambda calculus and knot diagrammatics.
Two embedding methods in spectral graph clustering yield different but valid groupings.
problem Clustering vertices of a graph without true groupings.
method Spectral graph clustering using Laplacian or Adjacency spectral embedding.
result Laplacian embedding captures left hemisphere/right hemisphere structure, while adjacency embedding captures gray matter/white matter structure.
There has been an explosion of interest in functional Magnetic Resonance Imaging (MRI) during the past two decades. Naturally, this has been accompanied by many major advances in the understanding of the human connectome. These advances have served to pose novel challenges as well as open new avenues for research. One …
Framework integrates brain connectivity data for clinical predictions.
problem Predicting clinical outcomes from brain connectivity data.
method Structurally-regularized Dynamic Dictionary Learning (sr-DDL) and LSTM-ANN block.
result Framework outperforms state-of-the-art approaches in clinical outcome prediction.
Bayesian method reconstructs neural network memories from connectivity.
problem Reconstructing memories from neural network connectivity.
method Bayesian inference using statistical physics principles.
result Algorithm successfully reconstructs stored patterns from synaptic connectivity.
Paper proposes a novel method to estimate differential networks using additional knowledge.
problem Estimating differential statistical dependency networks in high-dimensional data with limited samples.
method Integrates various sources of knowledge beyond data samples to improve differential network estimation.
result Achieves sharp asymptotic convergence rate and improved differential network estimation.
Proposes methods to recover labels from shuffled networks using graph averages.
problem Recovering labels from a shuffled network using graph averages.
method Cluster networks into classes, then match the new graph to cluster-averages, minimizing the graph matching objective function.
result Higher fidelity matching performance when clustering networks into different classes.
Unified model combines neural networks and dictionary learning for clinical predictions from brain data.
problem Predicting clinical severity from brain imaging data.
method Combines neural networks with dictionary learning to model patient-specific and shared features.
result Unified model outperforms state-of-the-art methods in predicting clinical severity.
URerF learns geodesic distances in noisy manifolds.
problem Learning geodesic distances in noisy high-dimensional data.
method Unsupervised random forest (URerF) with Bayesian Information Criterion.
result URerF outperforms other methods in estimating geodesic distances on noisy data.
Develops a new tensor model for clustering with degree correction.
problem Clustering with unknown degree heterogeneity in multiway data.
method Degree-corrected tensor block model with estimation guarantees.
result Demonstrates an intrinsic statistical-to-computational gap for tensors of order three or greater.
Paper introduces tractographic feature to predict stroke outcomes.
problem Predicting stroke outcomes using lesion volume alone is limited.
method Tractographic feature combining lesion and connectome data.
result Tractographic feature outperforms stroke volume in predicting mRS grades.
Survey on statistical inference methods for random dot product graphs.
problem Statistical inference on random dot product graphs.
method Spectral embeddings of adjacency and Laplacian matrices.
result Consistency and asymptotic normality of spectral embeddings.
Unified model learns joint and individual features from brain imaging data.
problem Integrating structural and functional connectivity data for behavioral phenotypes.
method Cross-Modal Joint-Individual Variational Network (CM-JIVNet) with multi-head attention fusion.
result CM-JIVNet outperforms in cross-modal reconstruction and behavioral trait prediction.
This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity. Our method improves the state-of-the-art reconstruction meth…
A fast and scalable deep learning model for analyzing fMRI data.
problem Challenges in modeling and analyzing large-scale fMRI data.
method Distributed deep Convolutional Autoencoder model leveraging multiple GPUs and Apache Spark.
result Efficient and scalable model for extracting hierarchical neuroscientific information from fMRI big data.
A new mathematical approach detects frequency-based alterations in brain networks.
problem Understanding disease-relevant brain alterations through network analysis.
method Proposes a novel connectome harmonic analysis framework using common harmonic waves learned from Stiefel manifolds.
result Identifies more significant and reproducible network dysfunction patterns in Alzheimer's disease.
GFA model uncovers brain-behavior associations in incomplete data sets.
problem Incomplete data sets and lack of robust statistical inferences.
method Hierarchical Bayesian model that handles missing data and models modality-specific associations.
result GFA identified four relevant shared factors and predicted non-imaging measures from brain connectivity.
In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs. We present a formulation o…
New method uses manifold learning to infer latent positions of 1D submanifolds in random dot product graphs.
problem Inference on latent positions of unknown 1D submanifolds in RDPGs.
method Apply Isomap for manifold learning to estimate arc lengths on the unknown submanifold.
result Test statistics based on Isomap converge to known submanifold power as auxiliary vertices increase.
Study predicts gender from brain FC at multiple scales using deep learning and Bayesian methods.
problem Predicting gender from brain functional connectivity.
method Deep learning and Bayesian deep learning applied to brain FC data from 1003 healthy adults.
result Bayesian deep learning provides accurate predictions and uncertainty information.