HGF Toolbox v4.10

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HGF Toolbox v4.0

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PhysIO Toolbox

The PhysIO Toolbox can be downloaded as part of the TAPAS software collection of the TNU.

Example_FContrast_RETROICOR_SingleSubj The general purpose of this toolbox is the model-based physiological noise correction of fMRI data using peripheral measures of respiration and cardiac pulsation. A comprehensive description of the toolbox can be found in the remainder of this article and in the accompanying quick start manual and handbook.

Current Version: r671 (February 2nd, 2015)
(Code | Examples | GettingStarted | Handbook) New features include:

  • Compatibility tested for SPM12, small bugfixes Batch Dependencies
  • Cleaner Batch Interface with grouped sub-menus (cfg_choice)
  • New model: ‘none’ to just read out physiological raw data and preprocess, without noise modelling
  • Philips: Scan-timing via gradient log now automatized (gradient_log_auto)
  • Siemens: Tics-Logfile read-in (proprietary, needs Siemens-agreement)
  • All peak detections (cardiac/respiratory) now via auto_matched algorithm
  • Adapt plots/saving for Matlab R2014b

. . . → Read More: PhysIO Toolbox

HGF Toolbox v3.0

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HGF Toolbox v2.1

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Variational Bayesian linear regression

The conceptual and practical limitations of classical multiple linear regression models can be resolved naturally in a Bayesian framework. Unless based on an overly simplistic parameterization, however, exact inference in Bayesian regression models is analytically intractable. This problem can be overcome using methods for approximate inference.

This MATLAB toolbox implements variational inference for a fully Bayesian multiple linear regression model, including Bayesian model selection and prediction of unseen data points on the basis of the posterior predictive density.

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Mixed-effects inference for classification group studies

Multivariate classification algorithms are powerful tools for predicting cognitive or pathophysiological states from neuroimaging data. However, meaningful inference requires models that explicitly account for fixed-effects (within-subjects) and random-effects (across-subjects) variance components. While models of this sort are standard practice in mass-univariate analyses of fMRI data, they have not yet received much attention in the decoding literature.

This software closes this gap by providing full Bayesian mixed-effects inference for multivariate classification studies.

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