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Found 49 papers, 22 papers with code
Variable Importance in High-Dimensional Settings Requires Grouping
Furthermore, as handling groups with high cardinality (such as a set of observations of a given modality) are both time-consuming and resource-intensive, we also introduce a new stacking approach extending the DNN architecture with sub-linear layers adapted to the group structure.
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.
Probing Brain Context-Sensitivity with Masked-Attention Generation
Two fundamental questions in neurolinguistics concerns the brain regions that integrate information beyond the lexical level, and the size of their window of integration.
Information-Restricted Neural Language Models Reveal Different Brain Regions' Sensitivity to Semantics, Syntax and Context
A fundamental question in neurolinguistics concerns the brain regions involved in syntactic and semantic processing during speech comprehension, both at the lexical (word processing) and supra-lexical levels (sentence and discourse processing).
Neural Language Models are not Born Equal to Fit Brain Data, but Training Helps
Neural Language Models (NLMs) have made tremendous advances during the last years, achieving impressive performance on various linguistic tasks.
Aligning individual brains with Fused Unbalanced Gromov-Wasserstein
We demonstrate that FUGW is well-suited for whole-brain landmark-free alignment.
A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension
Identifying the relevant variables for a classification model with correct confidence levels is a central but difficult task in high-dimension.
Shared Independent Component Analysis for Multi-Subject Neuroimaging
While ShICA-J is based on second-order statistics, we further propose to leverage non-Gaussianity of the components using a maximum-likelihood method, ShICA-ML, that is both more accurate and more costly.
Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction
High-quality data accumulation is now becoming ubiquitous in the health domain.
Functional Magnetic Resonance Imaging data augmentation through conditional ICA
Advances in computational cognitive neuroimaging research are related to the availability of large amounts of labeled brain imaging data, but such data are scarce and expensive to generate.
Spatially relaxed inference on high-dimensional linear models
This calls for a reformulation of the statistical inference problem, that takes into account the underlying spatial structure: if covariates are locally correlated, it is acceptable to detect them up to a given spatial uncertainty.
Adaptive Multi-View ICA: Estimation of noise levels for optimal inference
By contrast, we propose Adaptive multiView ICA (AVICA), a noisy ICA model where each view is a linear mixture of shared independent sources with additive noise on the sources.
An empirical evaluation of functional alignment using inter-subject decoding
Taking inter-subject decoding accuracy as a quantification of inter-subject similarity, our results support the use of functional alignment to improve inter-subject comparisons in the face of variable structure-function organization.
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso
To deal with this, we adapt the desparsified Lasso estimator ---an estimator tailored for high dimensional linear model that asymptotically follows a Gaussian distribution under sparsity and moderate feature correlation assumptions--- to temporal data corrupted with autocorrelated noise.
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso
To deal with this, we adapt the desparsified Lasso estimator -- an estimator tailored for high dimensional linear model that asymptotically follows a Gaussian distribution under sparsity and moderate feature correlation assumptions -- to temporal data corrupted with autocorrelated noise.
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA
Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization.
Fine-grain atlases of functional modes for fMRI analysis
We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12, 334 brain responses, standard GLM analysis of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2, 500 individuals, data compression and meta-analysis over more than 15, 000 statistical maps.
Aggregation of Multiple Knockoffs
We develop an extension of the Knockoff Inference procedure, introduced by Barber and Candes (2015).
NeuroQuery: comprehensive meta-analysis of human brain mapping
Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms.
Multi-subject MEG/EEG source imaging with sparse multi-task regression
Magnetoencephalography and electroencephalography (M/EEG) are non-invasive modalities that measure the weak electromagnetic fields generated by neural activity.
Fast shared response model for fMRI data
The shared response model provides a simple but effective framework to analyse fMRI data of subjects exposed to naturalistic stimuli.
Group level MEG/EEG source imaging via optimal transport: minimum Wasserstein estimates
Inferring the location of the current sources that generated these magnetic fields is an ill-posed inverse problem known as source imaging.
Extracting representations of cognition across neuroimaging studies improves brain decoding
Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework.
Approximate message-passing for convex optimization with non-separable penalties
We introduce an iterative optimization scheme for convex objectives consisting of a linear loss and a non-separable penalty, based on the expectation-consistent approximation and the vector approximate message-passing (VAMP) algorithm.
Optimizing deep video representation to match brain activity
The comparison of observed brain activity with the statistics generated by artificial intelligence systems is useful to probe brain functional organization under ecological conditions.
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
In many applications where collecting data is expensive, for example neuroscience or medical imaging, the sample size is typically small compared to the feature dimension.
Text to brain: predicting the spatial distribution of neuroimaging observations from text reports
In this paper, we propose to mine brain medical publications to learn the spatial distribution associated with anatomical terms.
Learning Neural Representations of Human Cognition across Many fMRI Studies
Cognitive neuroscience is enjoying rapid increase in extensive public brain-imaging datasets.
Stochastic Subsampling for Factorizing Huge Matrices
We present a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns.
Learning brain regions via large-scale online structured sparse dictionary learning
We propose a multivariate online dictionary-learning method for obtaining decompositions of brain images with structured and sparse components (aka atoms).
Subsampled online matrix factorization with convergence guarantees
We present a matrix factorization algorithm that scales to input matrices that are large in both dimensions (i. e., that contains morethan 1TB of data).
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
These R-fMRI pipelines build participant-specific connectomes from functionally-defined brain areas.
Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signals
Our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis.
Social-sparsity brain decoders: faster spatial sparsity
Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions.
Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its predictive power.
Dictionary Learning for Massive Matrix Factorization
Sparse matrix factorization is a popular tool to obtain interpretable data decompositions, which are also effective to perform data completion or denoising.
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Compressed Online Dictionary Learning for Fast fMRI Decomposition
We present a method for fast resting-state fMRI spatial decomposi-tions of very large datasets, based on the reduction of the temporal dimension before applying dictionary learning on concatenated individual records from groups of subjects.
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data
Imaging neuroscience links human behavior to aspects of brain biology in ever-increasing datasets.
Fast clustering for scalable statistical analysis on structured images
The use of brain images as markers for diseases or behavioral differences is challenged by the small effects size and the ensuing lack of power, an issue that has incited researchers to rely more systematically on large cohorts.
Machine Learning for Neuroimaging with Scikit-Learn
Statistical machine learning methods are increasingly used for neuroimaging data analysis.
Region segmentation for sparse decompositions: better brain parcellations from rest fMRI
Functional Magnetic Resonance Images acquired during resting-state provide information about the functional organization of the brain through measuring correlations between brain areas.
Data-driven HRF estimation for encoding and decoding models
We develop a method for the joint estimation of activation and HRF using a rank constraint causing the estimated HRF to be equal across events/conditions, yet permitting it to be different across voxels.
Mapping paradigm ontologies to and from the brain
Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies.
Mapping cognitive ontologies to and from the brain
Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies.
Second order scattering descriptors predict fMRI activity due to visual textures
Second layer scattering descriptors are known to provide good classification performance on natural quasi-stationary processes such as visual textures due to their sensitivity to higher order moments and continuity with respect to small deformations.
HRF estimation improves sensitivity of fMRI encoding and decoding models
Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) signal.
Scikit-learn: Machine Learning in Python
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Brain covariance selection: better individual functional connectivity models using population prior
We describe subject-level brain functional connectivity structure as a multivariate Gaussian process and introduce a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population.
Discriminative Network Models of Schizophrenia
Schizophrenia is a complex psychiatric disorder that has eluded a characterization in terms of local abnormalities of brain activity, and is hypothesized to affect the collective, ``emergent working of the brain.
