To download data go here.
In this challenge, we use T1-weighted structural brain Magnetic Resonance Imaging (MRI) scans and clinical data from large-scale neuroimaging studies. Until the challenge workshop (which will be held at MIT on September 18, 2014), we will keep any further details about the images, clinical labels and studies, a secret. This is to keep the focus on the machine learning tools and not the clinical context.
To remove the complexity of having to deal with processing the image data, we provide standardized (summary) features automatically extracted from the MRI scans. These morphological summary features were computed with FreeSurfer (*see below), a freely distributed brain MRI processing software package. In total, we provide 184 features per MRI scan, which include volumes of cortical and sub-cortical structures such as the caudate and average thickness measurements within cortical regions, such as the precuneus. Following common practice, the volume measurements have been normalized with intracranial volume (ICV) to account for variations in head size. For a detailed description of the morphological features, please refer to FeatureList.txt file (available with challenge data) and the FreeSurfer webpages.
For those participants interested in employing their own image processing pipelines, we also provide the original anonymized brain MRI scans, in nifti format. These data can be utilized to study the influence of different image processing tools on prediction accuracy. Thus, we recommend the participants who are willing to try out different image processing tools, to provide predictions based on the summary features as well. This way, we can disentangle the influences of the image processing and machine learning steps on final prediction accuracy.
*FreeSurfer Image Processing Pipeline
We used FreeSurfer (freesurfer.nmr.mgh.harvard.edu) (Fischl, 2012) -version 5.1 – a freely available, widely used and extensively validated brain MRI analysis software package - to process the structural brain MRI scans and compute morphological measurements. The FreeSurfer pipeline is fully automatic and includes steps to compute a representation of the cortical surface between white and gray matter, a representation of the pial surface (Dale et al., 1999; Fischl et al., 1999a), and a segmentation of white matter regions; to perform skull stripping, B1 bias field correction, nonlinear registration of the cortical surface of an individual with a stereotaxic atlas (Fischl et al., 1999b), labeling of regions of the cortical surface (Fischl et al., 2004), and labeling of sub-cortical brain structures (Fischl et al., 2002). Furthermore, for each MRI scan, FreeSurfer automatically computes subject-specific thickness measurements across the entire cortical mantle and within anatomically defined cortical regions of interest (ROIs), volume estimates of a wide range of sub-cortical structures and estimates of the intra-cranial volume (ICV) and measures of image quality, such as white-matter signal to noise ratio (WM-SNR), which is computed based on the noise level (standard deviation of intensities) within the white matter.
Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis: I. Segmentation and surface reconstruction. Neuroimage 9, 179-194.
Fischl, B., 2012. FreeSurfer. Neuroimage 62, 774-781.
Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., 2002. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341-355.
Fischl, B., Sereno, M.I., Dale, A.M., 1999a. Cortical surface-based analysis: II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195-207.
Fischl, B., Sereno, M.I., Tootell, R.B., Dale, A.M., 1999b. High-resolution intersubject averaging and a coordinate system for the cortical surface. Human brain mapping 8, 272-284.
Fischl, B., Van Der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D., 2004. Automatically parcellating the human cerebral cortex. Cerebral cortex 14, 11-22.