Paper defines and quantifies interpretability of brain decoding maps.
problem Difficulty in interpreting brain maps derived from multivariate classifiers.
method Theoretical definition of interpretability, decomposition into reproducibility and representativeness, heuristic method for approximating interpretability, multi-objective criterion for model selection.
result Optimizing hyper-parameters based on proposed criterion yields more informative brain maps.
NeuroQuery synthesizes brain mapping evidence across diverse concepts.
problem Lack of comprehensive meta-analysis of human brain mapping across different mental processes and mechanisms.
method A multivariate model that predicts the spatial distribution of neurological observations given text describing an experiment, cognitive process, or disease.
result Captures relationships and neural correlates of 7,547 neuroscience terms across 13,459 neuroimaging publications.
This study shows how EEG can be used to generate fMRI data.
problem Mapping fMRI from EEG signals.
method Deep learning approaches (Autoencoders, GANs, Pairwise Learning).
result Feasibility of EEG to fMRI brain image mappings.
Inferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (a.k.a.} decoding) have proven to be useful to capture mu…
Bayesian model clusters brain activity time series.
problem Heterogeneous multivariate time series in brain imaging.
method Group-based Bayesian mixture of smoothing splines with covariate effects.
result Distinct brain activity patterns identified.
New method learns complex brain signal patterns from EEG/MEG data.
problem Complex waveforms in brain signals not captured by linear filters.
method Multivariate convolutional sparse coding (CSC) algorithm.
result Reveals non-sinusoidal mu-shaped patterns in brain signals.
Deep neural networks map brain lesions to deficits for better brain function understanding.
problem Mapping the functional brain organization from pathological lesions.
method Deep generative neural network architectures, specifically variational convolutional volumetric auto-encoders.
result Our model outperforms established methods in lesion-deficit inference across various scenarios.
Torus graphs analyze multivariate phase coupling among brain signals.
problem Identifying coordinated phase changes across multiple brain regions.
method Torus graphs based on full exponential family with pairwise interactions.
result Torus graphs accurately identify conditional associations in multivariate phase data.
New method for estimating functional Gaussian graphical models for multivariate data.
problem Challenges in extending Gaussian graphical models to multivariate functional data due to compact covariance operators.
method Introducing partial separability for multivariate functional data, leading to a novel Karhunen-Loève expansion and efficient estimation through the joint graphical lasso.
result A well-defined functional Gaussian graphical model that can be identified with a sequence of finite-dimensional graphical models, each of identical fixed dimension.
Information mapping is a popular application of Multivoxel Pattern Analysis (MVPA) to fMRI. Information maps are constructed using the so called searchlight method, where the spherical multivoxel neighborhood of every voxel (i.e., a searchlight) in the brain is evaluated for the presence of task-relevant response patte…
New method extracts brain age from MRI sequences over time.
problem Lack of ground-truth labels in longitudinal neuroimaging data.
method Combines factor disentanglement with self-supervised learning.
result Extracts brain age information from MRI sequences.
Improved ROCKET algorithm for brain activity classification.
problem Classifying multivariate time series data from brain activity.
method Detach-Rocket Ensemble, leveraging pruning and ensemble methods.
result Competitive classification accuracy and interpretable channel relevance.
Inverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some cognitive variables related to brain activation maps, this approach aims at decoding brain activity. Inverse inference ta…
This paper introduces MOCM for better fMRI analysis stability.
problem Stable performance of MVP models on new fMRI datasets.
method Integrated objective function and multi-objective optimization approach.
result Superior performance compared to other techniques.
Bayesian methods struggle with large-scale brain connectivity estimation.
problem Estimating reliable whole-brain connectivity from limited data.
method Comparison of three Bayesian estimation methods for multivariate Ornstein-Uhlenbeck model.
result Bayesian method scales poorly with network size and requires more samples for similar accuracy.
The functional and structural representation of the brain as a complex network is marked by the fact that the comparison of noisy and intrinsically correlated high-dimensional structures between experimental conditions or groups shuns typical mass univariate methods. Furthermore most network estimation methods cannot d…
Study uses simulation-based inference to decode brain activity from synthetic stimuli.
problem Reversing the process of brain activity emulation to recover stimuli or their properties.
method Pairing brain emulator with LLMs to learn a probabilistic mapping from brain maps to stimulus parameters.
result LLMs can serve as controllable stimulus generators and parameters can be recovered from brain maps.
Improved Schizophrenia diagnosis using brain signal features with limited observations.
problem Ambulatory diagnoses of neuronal diseases with limited brain signal data.
method Pairwise distance learning approach using Siamese neural network and cosine contrastive loss.
result Improved accuracy and sensitivity in Schizophrenia diagnosis (+10pp).
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.
CorrCA identifies reliable dimensions in multivariate data across repetitions.
problem Finding consistent dimensions in multivariate data across trials, subjects, or raters.
method Maximizes the ratio of between-repetition to within-repetition covariance.
result CorrCA leads to repeat-reliability maximization and is equivalent to Linear Discriminant Analysis for zero-mean signals.
Spontaneous brain activity, as observed in functional neuroimaging, has been shown to display reproducible structure that expresses brain architecture and carries markers of brain pathologies. An important view of modern neuroscience is that such large-scale structure of coherent activity reflects modularity properties…
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…
MarmoNet automates analysis of marmoset brain axonal projections.
problem Automatically detect and segment axonal tracer signals in noisy, cluttered images.
method Uses machine learning, specifically CNNs and image registration, to process and map axonal projections.
result Automated pipeline extracts and maps axonal projections robustly.
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.
A graph-based method detects abnormal brain connections in functional MRI data.
problem Detecting abnormal brain connections in functional MRI data.
method High-order Graph Auto-Encoder (GAE) with hypersphere distribution for functional data analysis.
result Identifies correlations between affected brain regions and their simultaneous occurrence over time.
Deep learning predicts brain age from raw MRI data with high accuracy and reliability.
problem Predicting brain age from neuroimaging data to assess individual differences in brain aging.
method Convolutional Neural Networks (CNN) applied to raw T1-weighted MRI data.
result Brain-predicted age is a reliable and heritable biomarker of brain aging.
Proposes a new Sliced-Wasserstein distance for covariance matrices in M/EEG signals.
problem Efficiently dealing with distributions of covariance matrices in M/EEG multivariate time series.
method Defines a Sliced-Wasserstein distance for symmetric positive definite matrices and applies it to brain-age prediction and Brain Computer Interface applications.
result Demonstrates computational efficiency and strong theoretical guarantees for the proposed distance.
Deep learning models brain deformations based on atrophy and growth data.
problem Simulating brain deformations due to atrophy and growth.
method Differentiable biomechanical model using deep learning.
result Trained model can rapidly simulate new brain deformations with minimal residuals.
We introduce a model for causal structure learning from multivariate functional data, even when graphs have cycles.
problem Discovering causal relationships from multivariate functional data with cycles.
method Functional linear structural equation model with a low-dimensional causal embedded space.
result The proposed model is causally identifiable under standard assumptions.
New framework uses EEG to detect brain atrophy in AD, validated on large AD trial.
problem Diagnosis of Alzheimer's disease relies on subjective clinical interpretations.
method Combines Riemannian tangent space mapping and elastic net regression.
result Developed brain atrophy markers validated on large AD trial.
Paper presents a brain tumor segmentation method using NGMM and 3D FVF.
problem Automatic brain tumor segmentation from MRIs is challenging.
method Normalized Gaussian Bayesian classifier and 3D Fluid Vector Flow algorithm.
result The method successfully segments brain tumors from MRI images.
Novel model for decoding visual stimuli in human brains.
problem Challenges in MVP techniques, including noise and sparsity, and the cost of brain studies.
method Automatic detection of active regions, new Gaussian smoothing method, combining fMRI data sets.
result Superior performance compared to state-of-the-art methods.
ICAM creates interpretable feature attribution maps for brain images.
problem Challenges in predicting class relevance from brain images due to heterogeneity and background variation.
method A VAE-GAN framework for disentangling class relevance from background features.
result FA maps generated by ICAM outperform baseline methods and support phenotype variation exploration.
Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies. Due to the nature of the individual experiments, based on eliciting neural response from a small number of stimuli, this link is incomplete, and unidirectional from the causal point of view. To come to conclusions on t…
Stochastic encoding improves gender classification of brain networks from UK Biobank data.
problem Complexity and bias in interpreting deep learning models of brain connectivity.
method Stochastic encoding in ensemble of CNNs, multivariate balancing algorithm.
result AUROC of 0.8459, with resting-state data more accurate than task data.
New framework estimates graph from multimodal functional data.
problem Estimating graph from joint multimodal functional data.
method Integrative framework using partial correlation operator.
result Estimator converges to stationary point with quantifiable error.
Deep learning improves error detection in intracranial EEG.
problem Improving error detection in intracranial EEG.
method Employed convolutional neural networks (CNNs) for classification and characterization of error-related brain responses.
result CNNs outperformed traditional methods in classifying and decoding errors in intracranial EEG.
New model infers brain connectivity using nonlinear SVARMs.
problem Inferring effective brain network connectivity.
method Kernel-based nonlinear structural vector autoregressive models (SVARMs).
result Unveils previously unknown causal links between brain regions.
BrainCast predicts whole-brain fMRI time series from short scans.
problem Short scans reduce fMRI data quality and statistical power.
method Spatio-temporal forecasting framework for fMRI time series.
result BrainCast improves fMRI time series quality and prediction.
Study uses deep learning with attention to predict fetal brain gestational age.
problem Accurately predicting fetal brain gestational age for early diagnosis.
method Attention-based deep learning model combining multi-view MRI data.
result Age prediction performance with R2 = 0.94 using multi-view MRI and attention.
New approach models brain dynamics using coupled van der Pol oscillators and LSTM.
problem Capturing nonlinear dynamics in brain calcium imaging data.
method Proposes a new approach combining van der Pol oscillators and LSTM for modeling brain activity.
result Shows improved accuracy and interpretability compared to LSTM and hybrid VDP-LSTM approach.
We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tu…
Develops methods for selecting and estimating smooth functional coefficients in high-dimensional multivariate functional data.
problem Functional predictor selection and estimation of smooth functional coefficients in high-dimensional multivariate functional data.
method Functional group-sparse regression methods in a generic Hilbert space of infinite dimension.
result Consistency of estimation and selection (oracle property) under infinite-dimensional Hilbert spaces.
The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (l2 penalty) or scattered (l1 penalty) predictive pattern with a very limited…
Novel tRSA combines geometry and topology for brain and model analysis.
problem Traditional RSA overlooks topological information in neural representations.
method Topological RSA (tRSA) using nonlinear monotonic transforms.
result Robust model comparisons and novel insights into neural computation.
New method predicts AD progression using MEG brain networks.
problem Early diagnosis and prediction of Alzheimer's disease progression.
method MG2G, a deep learning method that maps brain networks into a latent space.
result MG2G detects subtle brain connectivity patterns and predicts AD progression.
Overcomplete representations and dictionary learning algorithms kept attracting a growing interest in the machine learning community. This paper addresses the emerging problem of comparing multivariate overcomplete representations. Despite a recurrent need to rely on a distance for learning or assessing multivariate ov…
A new method quantifies uncertainty in brain injury simulations.
problem High computational cost and high-dimensional inputs/outputs limit traditional UQ methods for biofidelic head models.
method Two-stage, data-driven manifold learning framework using Gaussian kernel-density estimation, diffusion maps, and Grassmannian diffusion maps.
result Surrogate models reduce computational cost while providing highly accurate approximations of the computational model.