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Temporal Multiple Kernel Learning (tMKL) model for predicting resting state FC via characterizing fMRI connectivity dynamics

Sriniwas Govinda Surampudi, Joyneel Misra, Gustavo Deco, Raju Bapi Surampudi, Avinash Sharma, Dipanjan Roy
doi: https://doi.org/10.1101/367276
Sriniwas Govinda Surampudi
1Center for Visual Information Technology, Kohli Center on Intelligent Systems, International Institute of Information Technology Hyderabad, Hyderabad, 500032, India
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Joyneel Misra
1Center for Visual Information Technology, Kohli Center on Intelligent Systems, International Institute of Information Technology Hyderabad, Hyderabad, 500032, India
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Gustavo Deco
4Center for Brain and Cognition, Dept. of Technology and Information, Universitat Pompeu Fabra, Carrer Tanger, 122-140, 08018, Barcelona, Spain
5Institució Catalana de la Recerca i Estudis Avançats, Universitat Barcelona, Passeig Lluís Companys 23, 08010 Barcelona, Spain
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Raju Bapi Surampudi
2School of Computer and Information Sciences, UoH, Hyderabad - 500046, India
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Avinash Sharma
1Center for Visual Information Technology, Kohli Center on Intelligent Systems, International Institute of Information Technology Hyderabad, Hyderabad, 500032, India
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Dipanjan Roy
3Cognitive Brain Dynamics Lab, NBRC, Manesar, Gurgaon, Haryana - 122051, India
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Abstract

Over the last decade there has been growing interest in understanding the brain activity in the absence of any task or stimulus captured by the resting-state functional magnetic resonance imaging (rsfMRI). These resting state patterns are not static, but exhibit complex spatio-temporal dynamics. In the recent years substantial effort has been put to characterize different FC configurations while brain states makes transitions over time. The dynamics governing this transitions and their relationship with stationary functional connectivity remains elusive. Over the last years a multitude of methods has been proposed to discover and characterize FC dynamics and one of the most accepted method is sliding window approach. Moreover, as these FC configurations are observed to be cyclically repeating in time there was further motivation to use of a generic clustering scheme to identify latent states of dynamics. We discover the underlying lower-dimensional manifold of the temporal structure which is further parameterized as a set of local density distributions, or latent transient states. We propose an innovative method that learns parameters specific to these latent states using a graph-theoretic model (temporal Multiple Kernel Learning, tMKL) and finally predicts the grand average functional connectivity (FC) of the unseen subjects by leveraging a state transition Markov model. tMKL thus learns a mapping between the underlying anatomical network and the temporal structure. Training and testing were done using the rs-fMRI data of 46 healthy participants and the results establish the viability of the proposed solution. Parameters of the model are learned via state-specific optimization formulations and yet the model performs at par or better than state-of-the-art models for predicting the grand average FC. Moreover, the model shows sensitivity towards subject-specific anatomy. The proposed model performs significantly better than the established models of predicting resting state functional connectivity based on whole-brain dynamic mean-field model, single diffusion kernel model and another version of multiple kernel learning model. In summary, We provide a novel solution that does not make strong assumption about underlying data and is generally applicable to resting or task data to learn subject specific state transitions and successful characterization of SC-dFC-FC relationship through an unifying framework.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 11, 2018.
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Temporal Multiple Kernel Learning (tMKL) model for predicting resting state FC via characterizing fMRI connectivity dynamics
Sriniwas Govinda Surampudi, Joyneel Misra, Gustavo Deco, Raju Bapi Surampudi, Avinash Sharma, Dipanjan Roy
bioRxiv 367276; doi: https://doi.org/10.1101/367276
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Temporal Multiple Kernel Learning (tMKL) model for predicting resting state FC via characterizing fMRI connectivity dynamics
Sriniwas Govinda Surampudi, Joyneel Misra, Gustavo Deco, Raju Bapi Surampudi, Avinash Sharma, Dipanjan Roy
bioRxiv 367276; doi: https://doi.org/10.1101/367276

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