Multivariate Pattern Analysis in Python

# Hypearalignment Tutorial Data: Faces and Objects in Ventral Temporal Cortex (fMRI)

This is a block-design fMRI dataset from a study on face and object representation in human ventral temporal cortex. It consists of 10 subjects with 8 runs per subject. In each run, the subjects passively viewed greyscale images from seven face & object categories. Each image was shown for 500ms and was followed by a 1500ms inter-stimulus interval. Full-brain fMRI data were recorded with a volume repetition time of 2.5s, thus, a stimulus block was covered by roughly 16 volumes. For a complete description of the experimental design, fMRI acquisition parameters, and preprocessing steps, and previously obtained results see the references below.

This tutorial dataset is based on data from a study published by Haxby et al. (2011). The datasets have been preprocessed to a degree that should allow people without prior fMRI experience to perform meaningful analyses. Moreover, it should not require further preprocessing with external tools.

All preprocessing has been performed using tools from AFNI & PyMVPA. Specifically, the 4D fMRI timeseries has been preprocessed as described in :ref: `Haxby et al. (2011)` and aligned to the standard MNI brain. A Ventral Temporal Cortex mask in MNI space is applied to the data.

## Terms Of Use

The original authors of Haxby et al. (2011) hold the copyright of this dataset and made it available under the terms of the Creative Commons Attribution-Share Alike 3.0 license.

## Download

A single compressed hdf5 is available at:

> http://data.pymvpa.org/datasets/hyperalignment_tutorial_data

## Content

hyperalignment_tutorial_data.hdf5.gz
  The list of datasets for 10 subjects stored as a compressed hdf5 file. Each dataset contains category & run labels.
hyperalignment_tutorial_data_2.4.hdf5.gz
  This file contains the same data as the first one, but it can be opened on systems that have no NiBabel installed. Loading this file requires PyMVPA version 2.4 or later.

## Instructions

    >>> from mvpa2.suite import *
    >>> filepath = os.path.join(pymvpa_datadbroot, 'hyperalignment_tutorial_data',
    ...                         "hyperalignment_tutorial_data.hdf5.gz")
    >>> datasets = h5load(filepath)
    >>> print len(datasets)
    10
    >>> print datasets[0]
    <Dataset: 56x3509@float32, <sa: chunks,targets,time_coords,time_indices>, <fa: voxel_indices>, <a: imghdr,imgtype,mapper,voxel_dim,voxel_eldim>>

## References

Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M. & Ramadge, P. J. (2011) <HGC+11>. A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex. Neuron, 72, 404–416. DOI: http://dx.doi.org/10.1016/j.neuron.2011.08.026