Multivariate Pattern Analysis in Python

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# Haxby et al. (2001): 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 6 subjects with 12 runs per subject. In each run, the subjects passively viewed greyscale images of eight object categories, grouped in 24s blocks separated by rest periods. 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 9 volumes. This dataset has been repeatedly reanalyzed. For a complete description of the experimental design, fMRI acquisition parameters, and previously obtained results see the references below.

## Terms Of Use

The original authors of Haxby et al. (2001) hold the copyright of this dataset and made it available under the terms of the Creative Commons Attribution-Share Alike 3.0 license.

## Download

Separate tarballs for each subject are available at:

> http://data.pymvpa.org/datasets/haxby2001

## Subject-tarball content

anat.nii.gz
  High resolution anatomical image. For subject 6 there is no anatomical image available.
bold.nii.gz
  4D fMRI timeseries image. (1452 volumes with 40 x 64 x 64 voxels, corresponding to a voxel size of 3.5 x 3.75 x 3.75 mm and a volume repetition time of 2.5 seconds). The timeseries contains all 12 runs of the original experiment, concatenated in a single file. Please note, that the timeseries signal is not detrended.
mask*.nii.gz
  Various masks in functional space provided by the original authors. “vt” refers to “ventral temporal”, “face” and “house” masks are GLM contrast based localizer maps.
labels.txt
  A two-column text file with the stimulation condition and the corresponding experimental run for each volume in the timeseries image. Labels are given in literal form (e.g. ‘face’).

Note

Data for the run 9 (chunk 8) of subject 5 was corrupted and therefore should not be used for the analyses. In the ‘labels.txt’ file all samples in that chunk are marked as ‘rest’ condition. (Acknowledgement goes to MS Al-Rawi who reminded us about this non-disclosed ‘feature’ of the dataset)

## Instructions

    >>> from mvpa2.suite import *
    >>> subjpath = os.path.join(pymvpa_datadbroot, 'haxby2001', 'subj1')
    >>> attrs = SampleAttributes(os.path.join(subjpath, 'labels.txt'),
    ...                          header=True)
    >>> ds = fmri_dataset(samples=os.path.join(subjpath, 'bold.nii.gz'),
    ...                   targets=attrs.labels, chunks=attrs.chunks,
    ...                   mask=os.path.join(subjpath, 'mask4_vt.nii.gz'))
    >>> print ds
    <Dataset: 1452x577@int16, <sa: chunks,targets,time_coords,time_indices>, <fa: voxel_indices>, <a: imghdr,imgtype,mapper,voxel_dim,voxel_eldim>>

## References

Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J., and Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430.

Hanson, S., Matsuka, T., and Haxby, J. (2004). Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001). revisited: is there a “face” area? NeuroImage 23, 156–166.

O’Toole, A. J., Jiang, F., Abdi, H., & Haxby, J. V. (2005). Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience, 17, 580–590.

Hanke, M., Halchenko, Y.O., Sederberg, P.B., Olivetti, E., Fründ, I., Rieger, J.W., Herrmann, C.S., Haxby, J.V., Hanson, S. and Pollmann, S (2009). PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3.

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