Mapping Cross-Scale Brain Data Using Inter-Atlas Connectivity Transformation (IntACT)

Presented During:

Poster No:


Submission Type:

Abstract Submission 


Gleb Bezgin1, Randy McIntosh2, Alan Evans3


1Montreal Neurological Institute, Montreal, Quebec, 2University of Toronto, Toronto, Ontario, 3McGill University, Montreal, Montreal

First Author:

Gleb Bezgin  
Montreal Neurological Institute
Montreal, Quebec


Randy McIntosh  
University of Toronto
Toronto, Ontario
Alan Evans  
McGill University
Montreal, Montreal


Combining information from neuroimaging, histology and axonal tract tracing data across species allows neuroscientists to gain better understanding of brain structure on macroscopic and mesoscopic scales. We introduce Inter-Atlas Connectivity Transformation (IntACT), a user-friendly tool to combine spatial brain data from different modalities at different scales of resolution.


IntACT has been implemented in Matlab; it constitutes a suite of tools with ConJUNGtion (Bezgin et al., 2012), implemented in Java to query, visualise and analyse data from the CoCoMac database (Bakker et al., 2012; Stephan et al., 2001), and provide resulting matrices for simulation using The Virtual Brain (TVB) platform (Sanz Leon et al., 2013). IntACT makes use of macaque and human templates from the Connectome Workbench and its predecessor Caret (Van Essen, 2012).


IntACT features an intuitive interface providing the following functionalities: 1) using weighted vertex-based representation, map connectivity data from one atlas (or several) to another; 2) for a set of standard macaque or human brain coordinates, compute probabilities of connections between given locations; 3) for a seed region coordinate, derive a probability distribution for its connectivity with the rest of the cortex (Fig.1). The IntACT user interface (Fig.2) provides each of these functionalities, together with information on laminar patterns of connectivity derived from CoCoMac (Fig.2, bottom middle). By means of interspecies mappings provided by Caret, the homological human locations are rendered on the MNI152 template (bottom left), and high-resolution histological sections from BigBrain (Amunts et al., 2013) provide further information on the locations of interest, together with their estimated laminar profiles (Wagstyl et al., 2018; Fig.2, right).
Supporting Image: fig1.png
   ·Figure 1.
Supporting Image: fig2.png
   ·Figure 2.


IntACT readily allows neuroscientists to combine data across modalities, species and parcellation schemes. Combining realistic estimates of connectivity, as well as laminar patterns and profiles thereof, can be particularly of use in simulation models aiming to bridge macro- and mesoscopic scales of resolution (Schmidt et al., 2018).

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural)

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Cortical Anatomy and Brain Mapping 2

Neuroinformatics and Data Sharing:

Brain Atlases
Informatics Other 1


Computational Neuroscience
Cortical Layers

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.


Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Not applicable

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Structural MRI
Postmortem anatomy
Computational modeling

For human MRI, what field strength scanner do you use?


Provide references using author date format

Amunts, K. (2013), 'BigBrain: an Ultrahigh-resolution 3D Human Brain Model', Science, vol. 340, no. 6139, pp. 1472-1475.
Bakker, R. (2012), 'CoCoMac 2.0 and the Future of Tract Tracing Databases', Frontiers in Neuroinformatics, vol. 6, no. 30.
Bezgin, G. (2012), ' Hundreds of brain maps in one atlas: registering coordinate-independent primate neuro-anatomical data to a standard brain', Neuroimage, vol. 62, no. 1, pp. 67-76.
Sanz Leon, P. (2013), 'The Virtual Brain: a Simulator of Primate Brain Network Dynamics', Frontiers in Neuroinformatics, vol. 7, no. 10.
Schmidt, M. (2018), ' A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas', PLoS Computational Biology, vol. 14, no. 10.
Stephan, K.E. (2001), 'Advanced Database Methodology for the Collation of Connectivity data on the Macaque brain (CoCoMac)', Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 356, pp. 1159-1186.
Van Essen, D.C. (2012), ' Cortical cartography and Caret software', Neuroimage, vol. 62, no. 2, pp. 757-764.
Wagstyl, K. (2018), ' Mapping Cortical Laminar Structure in the 3D BigBrain', Cerebral Cortex, vol. 28, no. 7, pp. 2551-2562.