The Cortical Explorer – A Web-based user-interface for the exploration of the human cerebral cortex

Due to the immense complexity of the brain’s neural network, research into the brain is performed at a hierarchy of scales. One common holistic approach is to model the brain, at a coarse scale, as consisting of a relatively small number of functionally specialised regions, connected in a network, known as the connectome. Recent advances in this field have led to a state-of-the-art mapping of the cortical connectome (Glasser et al, Nature 2016). This model (shown below) has the potential to vastly increase our understanding of the mechanisms that underpin an array of complex brain functions, and neurological diseases by providing a cyto-architecturally consistent reference framework through which different brains can be compared and contrasted.

The Human Connectome Project’s Multimodal Parcellation of the Human Cerebral Cortex (MMP v1)


However, typically only those with specialised neuroanatomical training understand what these different regions of the cortex actually do. This acts as a significant barrier for effective development of neuroimaging technology as most methods researchers do not understand the constraints or limitations of the data they are working work. Further, while the human brain is something that captures public imagination, as yet no tools exist to make this highly complex data accessible enough to feed that interest.

For this reason Samuel Budd, final year Meng student at Imperial College developed the Connectome Viewer (shown below, and online here). This is a web-based, interactive, 3D viewer for exploring (at this time) the HCP parcellation, although in principle any cortical imaging data can be used.

connectome viewer

The viewer allows users to interactively explore the  HCP’s Multi-modal Parcellation (MMP). Clicking on different regions brings up access to a wealth of meta-data summarising directly the extensive neuroanatomical supplementary results section of (Glasser et. al, 2016, Table 1). In this is described information pertaining to whether the selected area has been previously reported in previously studies, including links to these studies. ‘Sections’ refers to which broad neuroanatomical sections of the brain the region belongs to, where these sections reference how the brain was subdivided up during manual annotation (to break the data up into areas with consistent, folding, functional or cytoarchitectural  properties). ‘Top connections’ is designed to allow the user to see which other regions have the strongest functional connections to the selected regions. (note currently this is estimated from partial correlation analysis on functional timeseries from a small sub-set of the data, and is likely not optimal).

The explorer offers several different schemes for viewing the data, for example the regions connected (or in a common section) to the primary motor cortex can be viewed either by exploding (moving) the areas, or recolouring (to fade out unrelated regions)


Screen Shot 2017-08-05 at 08.10.18
Exploding regions
fading out regions; the choice of moving/recolouring is controlled by the tab highlighted by the blue arrow

Related studies can be accessed by clicking on the links in the meta-data tab:



Labels to all regions can be turned on and off using the bottom left tab shown here:


To view regions on the medial wall, left or right hemispheres can be turned off


The whole model can be rotated by clicking and dragging at points away from the centre of the model (see red arrow)


Currently beta views of other imaging data are available by clicking on more (top left, next to ‘Cortical Explorer’.

Cortical folding patterns viewed from the ‘more’ tab

In future it will be possible to view these averaged within the  parcellations as shown for the Nature paper.

Screen Shot 2017-08-05 at 08.36.37
Cortical imaging data averaged within the parcellated connectome (snapshot from Figure 4 of Glasser et al, Nature 2016

There is still considerable scope to add more features to the viewer; current priorities are incorporating true cortical thickness values into the depth of the mesh model for different regions, allowing cortical imaging data to be viewed on top of the parcellations, and user uploads of data in native cifti/gifti formats. Future work may explore representations of meta-data, or machine learning analysis on the full HCP data-base. Suggestions are very much welcome.

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