The instrumentation and equipment used to image and record integrative brain activity generates vast amounts of digital data that present data storage and analysis issues for researchers.

The Centre’s Neuroinformatics Program, led by Dr Pulin Gong and Prof Wojtek Goscinski, supported the Centre’s neuroscience research in three areas:


The provision of access to data processing, advanced analysis and visualisation resources.


By partnering with the Multi-modal Australian ScienceS Imaging and Visualisation Environment (MASSIVE), our researchers had access to large data storage, fast data processing, and advanced visualisation systems to efficiently manage and process the computationally intensive brain datasets, via the Cloud.

Human Connectome Project

In collaboration with MASSIVE, we created an Australian mirror for data collected by the Human Connectome Project (HCP). This ambitious project continues to be one of the world’s largest brain imaging studies to map the networks underlying brain function and their relationship to genetics. Our mirror site hosted HCP brain imaging data to allow researchers to conduct their own brain functional network analyses.

Research tools and data

Our research tools and data repository housed experimental data and computational models, providing brain researchers around Australia access to these important resources. We provided 100TB of storage space and used GitLab, a Cloud repository, to help manage complex projects.


Supporting the development of software and analysis tools to be shared with the general public.

Marmoset Brain Connectivity Atlas

The marmoset brain connectivity atlas is a systematic, publicly available digital repository for data on the connections between different cortical areas, in a primate species.


NeuroPatt is a MATLAB toolbox to automatically detect, analyse and visualise spatiotemporal patterns in neural population activity, developed by Dr Pulin Gong’s group at the University of Sydney.


Spikenet provides a computational and mathematical platform for studying the working mechanisms of cortical microcircuits.


NFTsim (Neural Field Theory simulator) is written in C++ and implements streamlined standard methods to solve hyperbolic partial differential equations such as the damped 2D wave equation; time stepping methods to solve ordinary differential equations; and procedures for delay differential equations.


Matlab toolbox to deconvolve BOLD-fMRI data. It produces the underlying spatiotemporal neural and hemodynamic activity


Real-time brain states tracking system and corticothalamic neural field parameter estimation

catch22 – CAnonical Time-series CHaracteristics

catch22 is a collection of 22 time-series features coded in C that can be run from Python, R, Matlab, and Julia. The catch22 features are a high-performing subset of the over 7000 features in hctsa. Features were selected based on their classification performance across a collection of 93 real-world time-series classification problems.


Conda environment to use for EEG, MEG and physiological data analysis in python.


Composite vein imaging for susceptibility-based magnetic resonance imaging.

A set of tools for generating composite vein images from SWI and QSM images.

ICF – Iterative Cylindrical Fitting

Improved Quantification of Cerebral Vein Oxygenation Using Partial Volume Correction

ShMRF- Shape-based Markov Random Field

A tool designed to segment blood vessels from MRI images.


Building partnerships with international neuroinformatics initiatives.


The Centre partnered with the International Neuroinformatics Coordinating Facility (INCF) and served as the Australian (and a Governing) Node. Operating across four continents, the INCF develops collaborative neuroinformatics infrastructure and promotes the sharing of data and computing resources to the international research community.