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Machine learning identifies “rsfMRI epilepsy networks” in temporal lobe epilepsy

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Abstract

Objectives

Experimental models have provided compelling evidence for the existence of neural networks in temporal lobe epilepsy (TLE). To identify and validate the possible existence of resting-state “epilepsy networks,” we used machine learning methods on resting-state functional magnetic resonance imaging (rsfMRI) data from 42 individuals with TLE.

Methods

Probabilistic independent component analysis (PICA) was applied to rsfMRI data from 132 subjects (42 TLE patients + 90 healthy controls) and 88 independent components (ICs) were obtained following standard procedures. Elastic net-selected features were used as inputs to support vector machine (SVM). The strengths of the top 10 networks were correlated with clinical features to obtain “rsfMRI epilepsy networks.”

Results

SVM could classify individuals with epilepsy with 97.5% accuracy (sensitivity = 100%, specificity = 94.4%). Ten networks with the highest ranking were found in the frontal, perisylvian, cingulo-insular, posterior-quadrant, thalamic, cerebello-thalamic, and temporo-thalamic regions. The posterior-quadrant, cerebello-thalamic, thalamic, medial-visual, and perisylvian networks revealed significant correlation (r > 0.40) with age at onset of seizures, the frequency of seizures, duration of illness, and a number of anti-epileptic drugs.

Conclusions

IC-derived rsfMRI networks contain epilepsy-related networks and machine learning methods are useful in identifying these networks in vivo. Increased network strength with disease progression in these “rsfMRI epilepsy networks” could reflect epileptogenesis in TLE.

Key Points

• ICA of resting-state fMRI carries disease-specific information about epilepsy.

• Machine learning can classify these components with 97.5% accuracy.

• “Subject-specific epilepsy networks” could quantify “epileptogenesis” in vivo.

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Abbreviations

CA1-CA4:

Cornus amonis

FD:

Fascia dentata

FDR:

False discovery rate

HGMV:

Hippocampal gray matter volume

ICA:

Independent component analysis

ICs:

Independent components

ML:

Machine learning

MTS:

Mesial temporal sclerosis

PICA:

Probabilistic independent component analysis

ROI:

Region of interest

rsfMRI:

Resting-state functional magnetic resonance imaging

SUB:

Subiculum

SVM:

Support vector machine

TLE:

Temporal lobe epilepsy

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Acknowledgements

We acknowledge the Department of Science and Technology, Government of India for providing the 3T MRI scanner for research. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We also acknowledge research fellows Mr. Aditya Jayashankar and Mr. Sunil K. Khokhar for their help in analysis.

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

  1. Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India

    Rose Dawn Bharath, Rajanikant Panda & Sujas Bhardwaj

  2. Advance Brain Imaging Facility, Cognitive Neuroscience Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India

    Rose Dawn Bharath, Rajanikant Panda & Sujas Bhardwaj

  3. Coma Science Group, GIGA-Consciousness, Universitè de Liège, Liège, Belgium

    Rajanikant Panda

  4. Department of Computer Science, Indian Institute of Technology Delhi, New Delhi, Delhi, 110016, India

    Jeetu Raj

  5. Neurology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India

    Sujas Bhardwaj, Sanjib Sinha, Ganne Chaitanya, Kenchaiah Raghavendra & Ravindranadh C. Mundlamuri

  6. Department of Neurology, Thomas Jefferson University, Philadelphia, PA, USA

    Ganne Chaitanya

  7. Neurosurgery, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India

    Arivazhagan Arimappamagan, Malla Bhaskara Rao & Parthasarthy Satishchandra

  8. Neuropsychology, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India

    Jamuna Rajeshwaran

  9. Biostatistics, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka, 560029, India

    Kandavel Thennarasu

  10. Systems Science and Informatics Unit, Indian Statistical Institute, Bangalore, Karnataka, 560059, India

    Kaushik K. Majumdar

  11. Department of Electrical Engineering, Indian Institute of Technology Delhi, (IIT-D), New Delhi, Delhi, 110016, India

    Tapan K. Gandhi

Authors
  1. Rose Dawn Bharath
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Corresponding author

Correspondence to Tapan K. Gandhi.

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Guarantor

The scientific guarantor of this publication is Dr. Rose Dawn Bharath, Additional Professor, Neuroimaging and Interventional Radiology, NIMHANS, Bengaluru-29, India.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

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Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

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Bharath, R.D., Panda, R., Raj, J. et al. Machine learning identifies “rsfMRI epilepsy networks” in temporal lobe epilepsy. Eur Radiol 29, 3496–3505 (2019). https://doi.org/10.1007/s00330-019-5997-2

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