FMRI

Title: Modelling Cognition in a Brain-like Spiking Neural Network Architecture: A Case on fMRI Data

One trial for Fig 1

Figure 1: Evolution of neurons’ activation degrees and the deep learning architecture formed in the SNNcube when the subject was watching a picture.

one trial for fig 2

Figure 2: Evolution of neurons’ activation degrees and the deep learning architecture formed in the SNNcube when the subject was reading a sentence


Modelling and Classification of FMRI data using the NeuCube Spiking Neural Network Architecture

Benchmark STAR/PLUS fMRI dataset is available at http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-81/www/ [1], [2]. During fMRI data acquisition, subjects were shown a picture and a sentence, and instructed to press a button to indicate whether the sentence correctly described the picture. Particular intervals of this data which are related to reading sentences (affirmative or negative sentences) were analysed using benchmark LIBSVM https://www.csie.ntu.edu.tw/~cjlin/libsvm/ and the data classification results published in [3]. Similarly, I used this fMRI data for illustration of the NeuCube classification of fMRI data corresponding to different sentence polarities. The results are reported as follows:

MethodSessions and selected voxels for classificationC1 (affirmative)C2 (negative)Total
NeuCubeSession I: 20 voxels selected using SNR80%100%90%
Session II: 20 pre-selected voxels from RDLPFC region90%80%85%
Session III: 20 pre-selected voxels from LDLPFC region90%80%85%
SVM [3]Session I: classification based on the LDLPFC’s voxels64%68%66%
classification based on the RDLPFC’s voxels65%69%67%

The feasibility analysis of the NeuCube architecture is not only limited to a higher classification accuracy, but a better visualisation and interpretation of the SNN models trained on the fMRI data as shown in Fig. 1. NeuCube platform is available free at http://www.kedri.aut.ac.nz/neucube.

Connectivity of SNNc

Fig 1. The initial (A) and final (B) connectivity of a SNNc after training with two different data sets, related correspondingly to: affirmative sentence versus negative sentence. The final connectivity is also shown as a 2D projection (C). Positive connections are shown in blue and negative – in red.

References

[1]

M. Just, “StarPlus fMRI data,” [Online]. Available: http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-81/www/. [Accessed 13 07 2014].

[2]

[3]

F. Pereira, “E-print Network,” 13 02 2002. [Online]. Available: http://www.osti.gov/eprints/topicpages/documents/record/181/3791737.html. [Accessed 2014 07 13].

M. Behroozi and M. R. Daliri, “RDLPFC area of the brain encodes sentence polarity: a study using fMRI,” Brain imaging and behavior, pp. 1-12, 2014.