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Found 14 papers, 0 papers with code
Deep multivariate autoencoder for capturing complexity in Brain Structure and Behaviour Relationships
Through rigorous validation, including 5 sample analyses for out-of-sample analysis, our results demonstrate that the multivariate autoencoder model outperforms traditional methods in capturing and generalizing correlations between brain and behavior beyond the training sample.
Robust and highly scalable estimation of directional couplings from time-shifted signals
The estimation of directed couplings between the nodes of a network from indirect measurements is a central methodological challenge in scientific fields such as neuroscience, systems biology and economics.
PAVI: Plate-Amortized Variational Inference
Given observed data and a probabilistic generative model, Bayesian inference searches for the distribution of the model's parameters that could have yielded the data.
PAVI: Plate-Amortized Variational Inference
Given some observed data and a probabilistic generative model, Bayesian inference aims at obtaining the distribution of a model's latent parameters that could have yielded the data.
Scalable Query Answering under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach
Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances.
Inverting brain grey matter models with likelihood-free inference: a tool for trustable cytoarchitecture measurements
Solving the problem of relating the dMRI signal with cytoarchitectural characteristics calls for the definition of a mathematical model that describes brain tissue via a handful of physiologically-relevant parameters and an algorithm for inverting the model.
Inferring the Localization of White-Matter Tracts using Diffusion Driven Label Fusion
Inferring which pathways are affected by a brain lesion is key for both pre and post-treatment planning.
ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models
Frequently, population studies feature pyramidally-organized data represented using Hierarchical Bayesian Models (HBM) enriched with plates.
Complex Coordinate-Based Meta-Analysis with Probabilistic Programming
We demonstrate results for two-term conjunctive queries, both on simulated meta-analysis databases and on the widely-used Neurosynth database.
PEP: Parameter Ensembling by Perturbation
In most experiments, PEP provides a small improvement in performance, and, in some cases, a substantial improvement in empirical calibration.
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.
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.
Probabilistic Diffeomorphic Registration: Representing Uncertainty
The Bayesian posterior distribution over the deformations aligning a moving and a fixed image is approximated via a variational formulation.
Deformable Registration of Feature-Endowed Point Sets Based on Tensor Fields
Our framework relies on a dense tensor field representation which we implement sparsely as a kernel mixture of tensor fields.
