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A set of utilities for working with HCP-style fMRI data with nilearn.

Project description


This package contains utilities to use Human Connectome Project (HCP) data and HCP-like data (e.g. obtained from legacy data using ciftify) as well as corresponding parcellations with nilearn and other Python tools.

The HCP data differs from conventional volumetric fMRI data which records the BOLD signal from each voxel in a 3D volume in that the signal from the cortical surface is treated as a folded two dimensional surface, and hence the data is associated with vertices of a predefined surface mesh, while the subcortical structures are described volumetrically using voxels.

The CIFTI (more precisely CIFTI-2) file format encompasses both the cortical 2D surface data as well as the subcortical volume data. However, only the voxels associated with relevant subcortical structures are kept. Thus these data are quite richly structured. Although the standard Python tools for dealing with fMRI data like nibabel can read both the CIFTI-2 files containing the fMRI signals and the GIFTI files containing the surface mesh definitions, there is not much that one could do further out-of-the-box, in particular visualization using nilearn or processing parcellated data using e.g. machine learning tools which work exclusively with numpy arrays. The goal of this package is to ease the interoperability of HCP data and these standard Python tools.

brain image

The utilities mainly deal with plotting surface data, accessing the predefined subcortical structures as well as using various parcellations and identifying connected components on the cortical surface. Various helper functions aid e.g. in mapping the HCP fMRI cortical data to surface vertices for visualization etc. The functions work directly with numpy arrays of shape Tx91282 or 91282 for fMRI data, with T being the number of time frames, while 91282 is the standard HCP dimensionality for the 3T cortical surface and subcortical data.


Find the documentation at


Make sure that you have the following packages installed (e.g. using miniconda)

nibabel, nilearn, numpy, scikit-learn, matplotlib, pandas, scipy

Then install with

pip install hcp_utils

upgrade with

pip install --upgrade hcp_utils

External data

Surface meshes

The default surface meshes for 3D visualization come from the group average of the Human Connectome Project (HCP) 1200 Subjects (S1200) data release (March 2017) processed using HCP pipelines. They can be obtained on BALSA:

These group average files are redistributed under the HCP Open Access Data Use Terms with the acknowledgment:

"Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University."


When using the included parcellations, please cite the relevant papers, which include full details.

The Glasser MMP1.0 Parcellation: Glasser, Matthew F., Timothy S. Coalson, Emma C. Robinson, Carl D. Hacker, John Harwell, Essa Yacoub, Kamil Ugurbil, et al. 2016. “A Multi-Modal Parcellation of Human Cerebral Cortex.” Nature 536 (7615): 171–78. (see in particular the details in Supplementary Neuroanatomical Results).

Yeo 7 or (17) Network Parcellation: Yeo, B. T. Thomas, Fenna M. Krienen, Jorge Sepulcre, Mert R. Sabuncu, Danial Lashkari, Marisa Hollinshead, Joshua L. Roffman, et al. 2011. “The Organization of the Human Cerebral Cortex Estimated by Intrinsic Functional Connectivity.” Journal of Neurophysiology 106 (3): 1125–65.

The Cole-Anticevic Brain-wide Network Partition: Ji JL*, Spronk M*, Kulkarni K, Repovs G, Anticevic A**, Cole MW** (2019). "Mapping the human brain's cortical-subcortical functional network organization". NeuroImage. 185:35–57. doi:10.1016/j.neuroimage.2018.10.006 [* = equal contribution; ** = senior authors] (also available as an open access bioRxiv preprint: and

This package was initiated as a tool within the project "Bio-inspired artificial neural networks" funded by the Foundation for Polish Science (FNP).

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