A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling
Abstract We present a general, trainable oscillatory neural network as a large-scale model of brain dynamics. The model has a cascade of two stages - an oscillatory stage and a complex-valued feedforward stage - for modelling the relationship between structural connectivity and functional connectivi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-43547-3 |
| _version_ | 1797560060701835264 |
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| author | Anirban Bandyopadhyay Sayan Ghosh Dipayan Biswas V. Srinivasa Chakravarthy Raju S. Bapi |
| author_facet | Anirban Bandyopadhyay Sayan Ghosh Dipayan Biswas V. Srinivasa Chakravarthy Raju S. Bapi |
| author_sort | Anirban Bandyopadhyay |
| collection | DOAJ |
| description | Abstract We present a general, trainable oscillatory neural network as a large-scale model of brain dynamics. The model has a cascade of two stages - an oscillatory stage and a complex-valued feedforward stage - for modelling the relationship between structural connectivity and functional connectivity from neuroimaging data under resting brain conditions. Earlier works of large-scale brain dynamics that used Hopf oscillators used linear coupling of oscillators. A distinctive feature of the proposed model employs a novel form of coupling known as power coupling. Oscillatory networks based on power coupling can accurately model arbitrary multi-dimensional signals. Training the lateral connections in the oscillator layer is done by a modified form of Hebbian learning, whereas a variation of the complex backpropagation algorithm does training in the second stage. The proposed model can not only model the empirical functional connectivity with remarkable accuracy (correlation coefficient between simulated and empirical functional connectivity- 0.99) but also identify default mode network regions. In addition, we also inspected how structural loss in the brain can cause significant aberration in simulated functional connectivity and functional connectivity dynamics; and how it can be restored with optimized model parameters by an in silico perturbational study. |
| first_indexed | 2024-03-10T17:55:07Z |
| format | Article |
| id | doaj.art-a9498b81b7f24cd084f99b1192044d4f |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:55:07Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-a9498b81b7f24cd084f99b1192044d4f2023-11-20T09:14:41ZengNature PortfolioScientific Reports2045-23222023-10-0113111310.1038/s41598-023-43547-3A phenomenological model of whole brain dynamics using a network of neural oscillators with power-couplingAnirban Bandyopadhyay0Sayan Ghosh1Dipayan Biswas2V. Srinivasa Chakravarthy3Raju S. Bapi4Indian Institue of Technology Madras, BiotechnologyIndian Institue of Technology Madras, BiotechnologyIndian Institue of Technology Madras, BiotechnologyIndian Institue of Technology Madras, BiotechnologyIIIT Hyderabad, BiotechnologyAbstract We present a general, trainable oscillatory neural network as a large-scale model of brain dynamics. The model has a cascade of two stages - an oscillatory stage and a complex-valued feedforward stage - for modelling the relationship between structural connectivity and functional connectivity from neuroimaging data under resting brain conditions. Earlier works of large-scale brain dynamics that used Hopf oscillators used linear coupling of oscillators. A distinctive feature of the proposed model employs a novel form of coupling known as power coupling. Oscillatory networks based on power coupling can accurately model arbitrary multi-dimensional signals. Training the lateral connections in the oscillator layer is done by a modified form of Hebbian learning, whereas a variation of the complex backpropagation algorithm does training in the second stage. The proposed model can not only model the empirical functional connectivity with remarkable accuracy (correlation coefficient between simulated and empirical functional connectivity- 0.99) but also identify default mode network regions. In addition, we also inspected how structural loss in the brain can cause significant aberration in simulated functional connectivity and functional connectivity dynamics; and how it can be restored with optimized model parameters by an in silico perturbational study.https://doi.org/10.1038/s41598-023-43547-3 |
| spellingShingle | Anirban Bandyopadhyay Sayan Ghosh Dipayan Biswas V. Srinivasa Chakravarthy Raju S. Bapi A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling Scientific Reports |
| title | A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling |
| title_full | A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling |
| title_fullStr | A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling |
| title_full_unstemmed | A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling |
| title_short | A phenomenological model of whole brain dynamics using a network of neural oscillators with power-coupling |
| title_sort | phenomenological model of whole brain dynamics using a network of neural oscillators with power coupling |
| url | https://doi.org/10.1038/s41598-023-43547-3 |
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