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Classification of Focused Perturbations Using Time-Variant Functional Connectivity with rs-fmri

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Applications of Computational Intelligence (ColCACI 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1746))

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Abstract

Focal disturbances in the cerebral activity modulated by transcranial magnetic stimulation (TMS) produce alterations in brain connectivity at the global level. Those effects can be studied using time-varying functional connectivity (TVFC) based on functional magnetic resonance imaging at rest (rs-fMRI). The characteristics of these alterations could be modeled using machine learning algorithms for patient classification. This study used hidden Markov models (HMM) to evaluate temporal variations in functional connectivity after stimulation of two different brain areas (Frontal and Occipital). We modeled the dynamics of 15 resting-state networks in 12 states by calculating the fractional occupancy, mean lifetime, and interval time of each state. We then compared the difference between fMRI sessions, PRE, and POST-stimulus, observing significant differences for both conditions, especially after frontal stimulation. Finally, generative models based on HMM were trained, to classify PRE-stimulus and Occipital stimulus with an accuracy of 83%, PRE-stimulus and Frontal stimulus with an accuracy of 85%, and Occipital and Frontal stimulus with 65% accuracy. This finding could be extended to the characterization of pathologies where local disturbances have a global impact on functional connectivity, such as Epilepsy.

J. Arias-Londoño—started this work at the Antioquia University and finished it supported by a María Zambrano grant from the Universidad Politécnica de Madrid, Spain.

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Notes

  1. 1.

    https://openneuro.org/datasets/ds001927/versions/2.0.2.

  2. 2.

    C-PAC https://fcp-indi.github.io/.

  3. 3.

    Available at https://pypi.org/project/hmmlearn.

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Authors and Affiliations

  1. Universidad de Antioquia, Medellín, Colombia

    Catalina Bustamante

  2. G. de Investigación en Imágenes Médicas SURA, Medellín, Colombia

    Catalina Bustamante & Gabriel Castrillón

  3. Universidad Politécnica de Madrid, Madrid, Spain

    Julián Arias-Londoño

Authors
  1. Catalina Bustamante
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  2. Gabriel Castrillón
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  3. Julián Arias-Londoño
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Corresponding author

Correspondence to Catalina Bustamante .

Editor information

Editors and Affiliations

  1. Universidad del Rosario, Bogotá, Colombia

    Alvaro David Orjuela-Cañón

  2. Universidad Autónoma de Occidente, Cali, Colombia

    Jesus Lopez

  3. Universidad Politécnica de Madrid, Madrid, Spain

    Julian David Arias-Londoño

  4. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

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Bustamante, C., Castrillón, G., Arias-Londoño, J. (2023). Classification of Focused Perturbations Using Time-Variant Functional Connectivity with rs-fmri. In: Orjuela-Cañón, A.D., Lopez, J., Arias-Londoño, J.D., Figueroa-García, J.C. (eds) Applications of Computational Intelligence. ColCACI 2022. Communications in Computer and Information Science, vol 1746. Springer, Cham. https://doi.org/10.1007/978-3-031-29783-0_2

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  • DOI: https://doi.org/10.1007/978-3-031-29783-0_2

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  • Print ISBN: 978-3-031-29782-3

  • Online ISBN: 978-3-031-29783-0

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