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

  • Rose Dawn Bharath
  • Rajanikant Panda
  • Jeetu Raj
  • Sujas Bhardwaj
  • Sanjib Sinha
  • Ganne Chaitanya
  • Kenchaiah Raghavendra
  • Ravindranadh C. Mundlamuri
  • Arivazhagan Arimappamagan
  • Malla Bhaskara Rao
  • Jamuna Rajeshwaran
  • Kandavel Thennarasu
  • Kaushik K. Majumdar
  • Parthasarthy Satishchandra
  • Tapan K. GandhiEmail author



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.


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.”


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.


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.


Temporal lobe epilepsy Magnetic resonance imaging Support vector machine Seizures 



Cornus amonis


Fascia dentata


False discovery rate


Hippocampal gray matter volume


Independent component analysis


Independent components


Machine learning


Mesial temporal sclerosis


Probabilistic independent component analysis


Region of interest


Resting-state functional magnetic resonance imaging




Support vector machine


Temporal lobe epilepsy



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.


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

Compliance with ethical standards


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.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.


• Prospective

• Case-control study

• Performed at one institution

Supplementary material

330_2019_5997_MOESM1_ESM.docx (1 mb)
ESM 1 (DOCX 1024 kb)


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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • Rose Dawn Bharath
    • 1
    • 2
  • Rajanikant Panda
    • 1
    • 2
    • 3
  • Jeetu Raj
    • 4
  • Sujas Bhardwaj
    • 1
    • 2
    • 5
  • Sanjib Sinha
    • 5
  • Ganne Chaitanya
    • 5
    • 6
  • Kenchaiah Raghavendra
    • 5
  • Ravindranadh C. Mundlamuri
    • 5
  • Arivazhagan Arimappamagan
    • 7
  • Malla Bhaskara Rao
    • 7
  • Jamuna Rajeshwaran
    • 8
  • Kandavel Thennarasu
    • 9
  • Kaushik K. Majumdar
    • 10
  • Parthasarthy Satishchandra
    • 7
  • Tapan K. Gandhi
    • 11
    Email author
  1. 1.Neuroimaging and Interventional RadiologyNational Institute of Mental Health and Neuro SciencesBangaloreIndia
  2. 2.Advance Brain Imaging Facility, Cognitive Neuroscience CentreNational Institute of Mental Health and Neuro SciencesBangaloreIndia
  3. 3.Coma Science Group, GIGA-ConsciousnessUniversitè de LiègeLiègeBelgium
  4. 4.Department of Computer ScienceIndian Institute of Technology DelhiNew DelhiIndia
  5. 5.NeurologyNational Institute of Mental Health and Neuro SciencesBangaloreIndia
  6. 6.Department of NeurologyThomas Jefferson UniversityPhiladelphiaUSA
  7. 7.NeurosurgeryNational Institute of Mental Health and Neuro SciencesBangaloreIndia
  8. 8.NeuropsychologyNational Institute of Mental Health and Neuro SciencesBangaloreIndia
  9. 9.BiostatisticsNational Institute of Mental Health and Neuro SciencesBangaloreIndia
  10. 10.Systems Science and Informatics UnitIndian Statistical InstituteBangaloreIndia
  11. 11.Department of Electrical EngineeringIndian Institute of Technology Delhi, (IIT-D)New DelhiIndia

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