Abstract
A large-scale model of brain dynamics, as it is manifested in functional neuroimaging data, is presented in this study. The model is built around a general trainable network of Hopf oscillators, the dynamics of which are described in the complex domain. It was shown earlier that when a pair of Hopf oscillators are coupled by power coupling with a complex coupling strength, it is possible to stabilize the normal phase difference at a value related to the angle of the complex coupling strength. In the present model, the magnitudes of the complex coupling weights are set using the Structural Connectivity information obtained from Diffusion Tensor Imaging (DTI). The complex-valued outputs of the oscillator network are transformed by a complex-valued feedforward network with a single hidden layer. The entire model is trained in 2 stages: in the \(1^{st} \) stage, the intrinsic frequencies of the oscillators in the oscillator network are trained, whereas in the \(2^{nd}\) stage, the weights of the feedforward network are trained using the complex backpropagation algorithm. The Functional Connectivity Matrix (FCM) obtained from the network’s output is compared with empirical Functional Connectivity Matrix, a comparison that resulted in a correlation of 0.99 averaged over 5 subjects.
Supported by DBT,Govt. of India, Centre of Computational System and Dynamics, IIT Madras.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Franck, A., et al.: Assessment of cerebrovascular dysfunction after traumatic brain injury with fMRI and fNIRS. NeuroImage: Clin. 25, 102086 (2020)
Griffeth, V.E.M., Buxton, R.B.: A theoretical framework for estimating cerebral oxygen metabolism changes using the calibrated-BOLD method: modeling the effects of blood volume distribution, hematocrit, oxygen extraction fraction, and tissue signal properties on the BOLD signal. Neuroimage 58(1), 198–212 (2011)
Cakan, C., Jajcay, N., Obermayer, K.: neurolib: a simulation framework for whole-brain neural mass modeling. Cogn. Comput. 1–21 (2021)
Marrelec, G., Messé, A., Giron, A., Rudrauf, D.: Functional connectivity’s degenerate view of brain computation. PLoS Comput. Biol. 12(10), e1005031 (2016)
Biswas, D., Pallikkulath, S., Chakravarthy, V.S.: A complex-valued oscillatory neural network for storage and retrieval of multidimensional aperiodic signals. Front. Comput. Neurosci. 15, 38 (2021)
Georgiou, G.M., Koutsougeras, C.: Complex domain backpropagation. IEEE Trans. Circuits Syst. II: Analog Digital Sig. Process. 39(5), 330–334 (1992)
Menon, S.S., Krishnamurthy, K.: A comparison of static and dynamic functional connectivities for identifying subjects and biological sex using intrinsic individual brain connectivity. Sci. Rep. 9(1), 1–11 (2019)
Biswal, B., Zerrin Yetkin, F., Haughton, V.M., Hyde, J.S.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Resonan. Med. 34(4), 537–541 (1995)
Pathak, A., Roy, D., Banerjee, A.: Whole-brain network models: from physics to bedside. Front. Comput. Neurosci. 16 (2022)
Deco, G., Kringelbach, M.L., Jirsa, V.K., et al.: The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core. Sci. Rep. 7, 3095 (2017)
Surampudi, S.G., Naik, S., Surampudi, R.B., Jirsa, V.K., Sharma, A., Roy, D.: Multiple kernel learning model for relating structural and functional connectivity in the brain. Sci. Rep. 8(1), 1–14 (2018)
Iravani, B., Arshamian, A., Fransson, P., Kaboodvand, N.: Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy. Neuroimage 231, 117844 (2021)
Hahn, G., et al.: Signature of consciousness in brain-wide synchronization patterns of monkey and human fMRI signals. Neuroimage 226, 117470 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bandyopadhyay, A., Ghosh, S., Biswas, D., Surampudi, R.B., Chakravarthy, V.S. (2023). A Phenomenological Deep Oscillatory Neural Network Model to Capture the Whole Brain Dynamics in Terms of BOLD Signal. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13624. Springer, Cham. https://doi.org/10.1007/978-3-031-30108-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-30108-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-30107-0
Online ISBN: 978-3-031-30108-7
eBook Packages: Computer ScienceComputer Science (R0)