Deep learning aids in autism diagnosis and rehabilitation using neuroimaging data.
problem Challenges in automated detection and rehabilitation of ASD using neuroimaging data.
method Deep learning techniques applied to neuroimaging data for ASD diagnosis and rehabilitation.
result Deep learning improves accuracy in ASD diagnosis and rehabilitation.
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate…
Study generates synthetic fNIRS data and applies machine learning for improved neuroimaging.
problem Scarcity of high-quality fNIRS neuroimaging datasets.
method Synthetic data generation using Monte Carlo simulations and machine learning.
result Improved accuracy and efficiency of fNIRS tomography.
STORE model handles tensor responses in neuroimaging, achieving efficient and accurate estimation.
problem Handling tensor responses in neuroimaging analysis with sparse structures.
method Sparse TensOr REsponse (STORE) regression model with alternating updating algorithm.
result Established estimation error bounds and fast estimation error rates for tensor dimensions.
InVA models image outcomes from multiple modalities, outperforming standard VAEs.
problem Understanding relationships across multiple imaging modalities in neuroimaging.
method Integrative Variational Autoencoder (InVA) framework for image-on-image regression.
result InVA accurately predicts PET scans from structural MRI, outperforming conventional models.
Machine learning methods improve neuroimaging analysis of brain signals.
problem Traditional neuroimaging methods treat brain signals in isolation; ML methods examine complex relationships.
method Multivariate pattern analysis for high-dimensional signals.
result Machine learning enhances neuroimaging for cognitive state detection and clinical diagnosis.
HBR improves normative modeling of neuroimaging data across multiple sites.
problem Dealing with nuisance variation in neuroimaging data across different sites.
method Hierarchical Bayesian regression (HBR) for multi-site normative modeling.
result HBR provides more accurate normative ranges compared to existing methods.
Graphs improve brain activity decoding from fMRI data.
problem Decoding brain activity from fMRI data.
method Dimensionality reduction techniques based on graph representations of the brain.
result Mixed graphs using both geometric structure and functional connectivity offer the best performance.
Bayesian method for estimating functional graphical models from neuroimaging data.
problem Estimating dependence structures from functional data in neuroscience.
method Fully Bayesian regularization scheme, including direct Bayesian analog of functional graphical lasso and graphical horseshoe.
result Insight into brain compensation after traumatic brain injury.
Efficiently analyzes multidimensional functional data using separable basis functions.
problem Curse of dimensionality in traditional functional data analysis.
method Marginal product basis systems for multidimensional data, tensor decomposition, differential operator-based penalties.
result Efficient estimation of multidimensional functional data representations.
Two stopping criteria proposed for real-time fMRI experiments.
problem Developing accurate stopping criteria for real-time fMRI experiments.
method Empirical study of two proposed stopping criteria.
result Performance of two stopping criteria empirically studied.
Canonical correlation analysis (CCA) is a valuable method for interpreting cross-covariance across related datasets of different dimensionality. There are many potential applications of CCA to neuroimaging data analysis. For instance, CCA can be used for finding functional similarities across fMRI datasets collected fr…
Paper compares classical stats and statistical learning in neuroimaging.
problem Confusion between classical hypothesis testing and data-guided model estimation in neuroimaging.
method Examines commonalities and differences between classical statistics and statistical learning.
result Clarifies the conceptual implications of these methods in neuroimaging analysis.
This paper reviews cross-validation methods for brain decoding in neuroimaging.
problem Evaluating and tuning brain decoding models using cross-validation.
method Reviewed cross-validation procedures, including leave-one-out and repeated random splits, and discussed nested cross-validation.
result Popular leave-one-out method leads to unstable and biased estimates; repeated random splits are preferred.
Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexi…
A new geometric framework embeds correlation matrices into Euclidean space for scalable brain network analysis.
problem Inefficient and unstable analysis of functional brain networks in high-dimensional contexts.
method Diffeomorphic transformations to embed correlation matrices into Euclidean space, preserving manifold properties.
result Improved computational speed and enhanced accuracy compared to conventional manifold-based approaches.
The paper argues that encoding and decoding models in neuroimaging need to distinguish between causal and anti-causal relations.
problem Insufficient distinction between encoding- and decoding models in neuroimaging.
method Theoretical justification for distinguishing causal and anti-causal relations.
result Causal inference is essential for interpretation in neuroimaging.
Study reveals XAI methods fail in neuroimaging, suggesting domain-specific adaptation.
problem Systematic failures of XAI methods in neuroimaging applications.
method Systematic comparison of XAI methods on 45,000 structural brain MRIs using a novel validation framework.
result Two widely used XAI methods (GradCAM and Layer-wise Relevance Propagation) fail to accurately explain neuroimaging data.
Advances in Python and data standards boost reproducibility in neuroimaging.
problem Lack of reproducibility in scientific research.
method Open data sharing resources, data standards, open-source Python, and software engineering advances.
result Reproducibility in neuroimaging has improved with new tools and practices.
Deep learning improves AD diagnosis and prognosis from neuroimaging data.
problem Early detection and accurate classification of Alzheimer's disease.
method Deep learning models applied to neuroimaging data for AD diagnosis and prognosis.
result Deep learning models can achieve high accuracy in AD diagnosis and prognosis.
A new model for dynamic covariance recovery in neuroimaging data.
problem Estimating time-varying covariances in high-dimensional neuroimaging data.
method Nonconvex factorization into sparse spatial and smooth temporal components, combined with spectral initialization and gradient descent.
result The proposed method achieves linear convergence and superior performance compared to existing approaches.
New model improves detection of brain disorder variations.
problem Modeling brain disorder heterogeneity in neuroimaging data.
method Tensor-variate Gaussian process in a Bayesian mixed-effects model with Kronecker algebra and low-rank approximation.
result Improved detection sensitivity compared to mass-univariate and classifier approaches.
Method predicts brain regions for neuroimaging phenotypes.
problem Predicting phenotypes from brain networks without static community structure.
method Supervised community detection using block-structured regularization and ADMM optimization.
result The method identifies task-specific brain regions that improve phenotype prediction.
NEURO-DRAM improves neuroimaging classification accuracy.
problem Improper use of traditional computer vision models in neuroimaging.
method 3D recurrent visual attention model trained with reinforcement learning.
result NEURO-DRAM achieves state-of-the-art accuracy in Alzheimer's disease prediction.
We speed up factor analysis on large neuroimaging datasets.
problem Processing large multi-subject neuroimaging datasets efficiently.
method Optimized multi-subject factor analysis methods for parallel processing.
result Strong scaling up to 5.5x with 1024 nodes and 32,768 cores.
Deep learning models outperform MRI-based methods for MCI to AD conversion prediction.
problem Predicting the conversion of mild cognitive impairments to Alzheimer's Disease.
method Applied deep learning and machine learning algorithms on neuroimaging and clinical data.
result XGBoost algorithm trained on clinical and embedding data provided the best results with an accuracy of 0.76 and AUC of 0.86.
BPt is a Python library for ML with neuroimaging data.
problem Analyzing large neuroimaging datasets using machine learning.
method Unified framework of ML tools for tabulated and neuroimaging data.
result Unified ML tools for neuroimaging and tabulated data.
fastHDMI improves neuroimaging variable selection in high-dimensional data.
problem Efficient variable screening in high-dimensional neuroimaging datasets.
method Three mutual information estimation methods implemented in fastHDMI.
result FFTKDE-based method superior for continuous nonlinear outcomes.
DeepFDR uses deep learning for better FDR control in neuroimaging data.
problem Spatial dependence among voxel-based tests in neuroimaging data.
method DeepFDR leverages unsupervised deep learning-based image segmentation.
result DeepFDR outperforms existing methods in FDR control and computational efficiency.
New method quantifies biases in neuroimaging datasets.
problem Pooling neuroimaging datasets can introduce biases.
method Causal inference to quantify confounding factors.
result Simple pooling often leads to biased training data.
A new framework for brain mapping using statistical agnostic methods.
problem Estimating brain connectivity with limited data and controlling false positives.
method Statistical Agnostic Mapping (SAM) based on concentration inequalities.
result Relieves instability and provides less conservative p-value correction.
New method handles missing data in multimodal brain imaging.
problem Missing data in multimodal brain imaging.
method Full Information Linked ICA (FI-LICA) algorithm.
result FI-LICA outperforms current practices in classification and prediction.
Improves neuroimaging model precision and speed using Nesterov's smoothing.
problem High-dimensional brain image analysis for clinical diagnosis with structured sparsity.
method Proposes CONESTA, a first-order continuation algorithm that automatically adjusts smoothing parameters.
result Significantly outperforms state-of-the-art solvers in convergence speed and precision.
New method aligns brain surfaces based on functional signatures.
problem Inter-individual variability in neuroimaging data.
method Fused Unbalanced Gromov-Wasserstein (FUGW) based on Optimal Transport.
result FUGW significantly increases between-subject correlation of activity.
Traditional voxel-level multiple testing procedures in neuroimaging, mostly p-value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power. We extend the local-significance-index based procedure originally developed for the hidden Markov chain models, whic…
New method clusters neurons with similar connectivity profiles.
problem Accurately determining which neurons have similar neurological tasks.
method Proposes clustered Gaussian graphical model and symmetric convex clustering penalty.
result Demonstrates effectiveness of the approach on synthetic and real-world data.
The study clarifies causal interpretations in neuroimaging models.
problem Unclear causal interpretations in encoding and decoding models.
method Investigation of causal statements in encoding and decoding models.
result Only encoding models in stimulus-based settings support unambiguous causal interpretations.
Tool models nonlinear brain changes across Alzheimer's spectrum.
problem Limited GLM methods for nonlinear neuroimaging analysis.
method Voxelwise nonlinear regression for brain atrophy modeling.
result Distinct nonlinear brain atrophy patterns in Alzheimer's disease.
Deep normative modeling of clinical neuroimaging data improves diagnostic performance.
problem Modeling variation of neuroimaging measures across individuals for psychiatric disorders.
method Proposes a deep normative modeling framework based on neural processes (NPs) for spatially structured mixed-effect modeling of neuroimaging data.
result Substantial improvements in novelty detection performance for certain diagnostic problems.
Brain decoding involves the determination of a subject's cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemp…
Functional neuroimaging can measure the brain?s response to an external stimulus. It is used to perform brain mapping: identifying from these observations the brain regions involved. This problem can be cast into a linear supervised learning task where the neuroimaging data are used as predictors for the stimulus. Brai…
Integrative analysis of patient health records and neuroimages using MemGCN.
problem Combining EHR and neuroimaging data for disease understanding.
method Memory-Based Graph Convolution Network (MemGCN) framework.
result Superior classification performance in Parkinson's Disease cases versus controls.
fcHMRF-LIS controls FDR in neuroimaging data, improving power and scalability.
problem Complex spatial dependencies and high variability in FDR control methods for neuroimaging data.
method fcHMRF-LIS integrates LIS-based testing with fcHMRF to model spatial structures efficiently.
result fcHMRF-LIS achieves accurate FDR control, lower FNR, and higher true positives compared to existing methods.
Deep Boltzmann Machines analyze NeuroSynth's text data for brain function insights.
problem Analyzing text data from NeuroSynth for understanding brain function.
method Unsupervised analysis of NeuroSynth's text corpus using Deep Boltzmann Machines.
result DBMs learn embeddings with a clear semantic structure, facilitating machine learning.
New method optimizes noise estimation alongside regression coefficients for multimodal neuroimaging data.
problem Heteroscedastic regression models with different noise levels across data sources.
method Generalized Concomitant Multi-Task Lasso for jointly estimating regression coefficients and noise covariance.
result Improved prediction and support identification with correct noise covariance estimation.
Unified normative modeling for neuroimaging phenotypes using denoising diffusion models.
problem Discarding multivariate dependence in neuroimaging pipelines.
method Denoising diffusion probabilistic models (DDPMs) with FiLM and SAINT backbones.
result Unified multivariate normative modeling with better calibration and dependence preservation.
CNNs adapted for brain images predict age with improved accuracy.
problem Applying CNNs to brain images without considering their 3D structure.
method Two modifications to existing CNN architectures tailored for brain images.
result Achieved a mean absolute error (MAE) of 1.4 years compared to 1.6 years for a baseline.
Proposes FDR-corrected sparse CCA for neuroimaging and genomics.
problem High-dimensional datasets in neuroimaging and genomics make false discoveries a concern.
method FDR-corrected sparse canonical correlation analysis (CCA) for high-dimensional settings.
result The proposed method controls the FDR of canonical vectors in high-dimensional settings.