fMRI Data Modelling


The Brain-Like Artificial Intelligence (BLAI) is pioneered by Prof.Nikola Kasabov and here it is applied to a specific application.

The project introduces a new methodology and a system for dynamic learning, visualisation and classification of functional Magnetic Resonance Imaging (fMRI) as Spatio-Temporal Brain Data (STBD). The method is based on an evolving spatio-temporal data machine (eSTDM) of evolving spiking neural networks (SNN) exemplified by the NeuCube architecture. The method consists of several steps: mapping spatial coordinates of fMRI data into a 3D SNN cube that represents a brain template; input data transformation into trains of spikes; deep, unsupervised learning in the 3D SNN cube of spatio-temporal patterns from data; supervised learning in an evolving SNN classifier; parameter optimisation; 3D visualisation and model interpretation. The learned connections in the SNN cube represent dynamic spatio-temporal relationships derived from the fMRI data. They can reveal new information about the underpinning brain functions under different conditions. The proposed methodology allows for the first time to analyse dynamic functional and structural connectivity of a learned SNN model from fMRI data. This can be used for a better understanding of brain activities and also for on-line generation of appropriate neuro-feedback to subjects for improved brain functions. The proposed NeuCube-based methodology offers also a superior classification accuracy when compared with traditional AI and statistical methods. The created NeuCube-based models of fMRI data are directly and efficiently implementable on high performance and low energy consumption neuromorphic platforms for real–time applications.

FIGURE 1. A block diagram of the NeuCube SNN model for fMRI data encoding, mapping, learning, classification and visualisation.

Related Papers and Benchmarking

The proposed methods and systems, when compared with traditional statistical and machine learning methods, showed superior results in the following aspects:

  1. An efficient neuromorphic approach, which offers better interpretation of the created models from one of the most complex STBD—fMRI data;
  2. Better classification accuracy when compared with traditional AI and statistical methods (see table below from Kasabov, Doborjeh, & Doborjeh, 2017);
  3. Reveals trajectories of functional spatio-temporal associations between areas of the brain (none of the traditional methods can reveal such interactions);
  4. Better visualisation of the created models, with a possible use of VR;
  5. Enabling new information and knowledge discovery through meaningful interpretation of the models.
TABLE. Comparison of classification accuracy of picture (class C1) versus sentence (class C2) data obtained by using NeuCube (50% of the data used for training and 50% used for testing in a cross validation mode) and traditional machine learning methods (obtained via NeuCom, The experiment is done on a total number of 80 samples (40 samples per class).
Accuracy (%)
F -Score

See also some of the related papers:

Kasabov, N. K., Doborjeh, M. G., & Doborjeh, Z. G. (2017). Mapping, learning, visualization, classification, and understanding of fMRI Data in the NeuCube evolving spatiotemporal data machine of spiking neural networks. IEEE transactions on neural networks and learning systems, 28(4), 887-899.

Kasabov, N., Zhou, L., Doborjeh, M. G., Doborjeh, Z. G., & Yang, J. (2016). New Algorithms for encoding, learning and classification of fmri data in a spiking neural network architecture: a case on modelling and understanding of dynamic cognitive processes.IEEE Transactions on Cognitive and Developmental Systems.

fMRI Data

Data can be download here.

R&D System

For this project, an R&D system has been developed based on NeuCube. The system can be obtained subject to licensing agreement.


The developer of this project is:

Maryam Gholami Doborjeh