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README.md

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# Computational Neuroimaging Toolbox
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![](https://img.shields.io/github/issues/ccnmaastricht/CNI_toolbox)
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![](https://img.shields.io/github/forks/ccnmaastricht/CNI_toolbox)
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![](https://img.shields.io/github/stars/ccnmaastricht/CNI_toolbox)
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![](https://img.shields.io/github/license/ccnmaastricht/CNI_toolbox)
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The Computational Neuroimaging Toolbox is a MATLAB toolbox for estimating input-referred models. Specifically, the toolbox contains tools for Fourier analyses of phase-encoded stimuli, population receptive field mapping, estimating parameters of generic (user-defined) input-referred models as well as performing ridge regression.
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This code is hosted at https://github.com/ccnmaastricht/CNI_toolbox
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- h_stimulus: height of stimulus images in pixels
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optional inputs are
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- hrf : either a column vector containing a single hemodynamic
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- hrf : either a column vector containing a single hemodynamic
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response used for every voxel;
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or a matrix with a unique hemodynamic response along
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its columns for each voxel.
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By default the canonical two-gamma hemodynamic response
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By default the canonical two-gamma hemodynamic response
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function is generated internally based on the scan parameters.
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This class has the following functions
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- pRF.create_timecourses();
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- results = pRF.mapping(data);
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Use help pRF.function to get more detailed help on any specific function
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Use help pRF.function to get more detailed help on any specific function
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(e.g. help pRF.mapping)
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typical workflow:
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- n_slices : number of slices
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optional inputs are
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- hrf : either a column vector containing a single hemodynamic
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- hrf : either a column vector containing a single hemodynamic
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response used for every voxel;
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or a matrix with a unique hemodynamic response along
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its columns for each voxel.
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By default the canonical two-gamma hemodynamic response
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By default the canonical two-gamma hemodynamic response
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function is generated internally based on the scan parameters.
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This class has the following functions
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1. rrt = RRT(params);
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2. rrt.set_design(X);
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3. rrt.optimize_lambda(data,range);
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4. results = rrt.perform_ridge(data);
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4. results = rrt.perform_ridge(data);

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