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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
Neuro

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.

Keywords

Temporal lobe epilepsy Magnetic resonance imaging Support vector machine Seizures 

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

Notes

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.

Compliance with ethical standards

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.

Informed consent

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

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Prospective

• Case-control study

• Performed at one institution

Supplementary material

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

References

  1. 1.
    Chiang S, Haneef Z (2014) Graph theory findings in the pathophysiology of temporal lobe epilepsy. Clin Neurophysiol 125:1295–1305CrossRefGoogle Scholar
  2. 2.
    Liao W, Zhang Z, Pan Z et al (2010) Altered functional connectivity and small-world in mesial temporal lobe epilepsy. PLoS One 5:e8525CrossRefGoogle Scholar
  3. 3.
    Vlooswijk MC, Vaessen MJ, Jansen JF et al (2011) Loss of network efficiency associated with cognitive decline in chronic epilepsy. Neurology 77:938–944CrossRefGoogle Scholar
  4. 4.
    Liao W, Zhang Z, Pan Z et al (2011) Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI. Hum Brain Mapp 32:883–895CrossRefGoogle Scholar
  5. 5.
    Widjaja E, Zamyadi M, Raybaud C, Snead OC, Smith ML (2013) Abnormal functional network connectivity among resting-state networks in children with frontal lobe epilepsy. AJNR Am J Neuroradiol 34:2386–2392CrossRefGoogle Scholar
  6. 6.
    Zhang Z, Lu G, Zhong Y et al (2009) Impaired perceptual networks in temporal lobe epilepsy revealed by resting fMRI. J Neurol 256:1705–1713CrossRefGoogle Scholar
  7. 7.
    Waites AB, Briellmann RS, Saling MM, Abbott DF, Jackson GD (2006) Functional connectivity networks are disrupted in left temporal lobe epilepsy. Ann Neurol 59:335–343CrossRefGoogle Scholar
  8. 8.
    Luo C, Li Q, Xia Y et al (2012) Resting state basal ganglia network in idiopathic generalized epilepsy. Hum Brain Mapp 33:1279–1294CrossRefGoogle Scholar
  9. 9.
    Damoiseaux JS, Rombouts SA, Barkhof F et al (2006) Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 103:13848–13853CrossRefGoogle Scholar
  10. 10.
    Beckmann CF, Smith SM (2005) Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25:294–311CrossRefGoogle Scholar
  11. 11.
    Smith SM, Jenkinson M, Woolrich MW et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl 1):S208–S219CrossRefGoogle Scholar
  12. 12.
    Cerliani L, Thomas RM, Aquino D, Contarino V, Bizzi A (2017) Disentangling subgroups of participants recruiting shared as well as different brain regions for the execution of the verb generation task: a data-driven fMRI study. Cortex 86:247–259CrossRefGoogle Scholar
  13. 13.
    Li S, Tian J, Li M et al (2018) Altered resting state connectivity in right side frontoparietal network in primary insomnia patients. Eur Radiol 28:664–672CrossRefGoogle Scholar
  14. 14.
    Panda R, Bharath RD, Upadhyay N, Mangalore S, Chennu S, Rao SL (2016) Temporal dynamics of the default mode network characterize meditation-induced alterations in consciousness. Front Hum Neurosci 10:372CrossRefGoogle Scholar
  15. 15.
    Schölvinck ML, Maier A, Ye FQ, Duyn JH, Leopold DA (2010) Neural basis of global resting-state fMRI activity. Proc Natl Acad Sci U S A 107:10238–10243CrossRefGoogle Scholar
  16. 16.
    Rodionov R, De Martino F, Laufs H et al (2007) Independent component analysis of interictal fMRI in focal epilepsy: comparison with general linear model-based EEG-correlated fMRI. Neuroimage 38:488–500CrossRefGoogle Scholar
  17. 17.
    Simon P (2013) Too big to ignore: the business case for big data. John Wiley & Sons, Inc. New JerseyGoogle Scholar
  18. 18.
    Arbabshirani MR, Plis S, Sui J, Calhoun VD (2017) Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage 145:137–165CrossRefGoogle Scholar
  19. 19.
    Tognin S, Pettersson-Yeo W, Valli I et al (2013) Using structural neuroimaging to make quantitative predictions of symptom progression in individuals at ultra-high risk for psychosis. Front Psychiatry 4:187PubMedGoogle Scholar
  20. 20.
    van der Burgh HK, Schmidt R, Westeneng HJ, de Reus MA, van den Berg LH, van den Heuvel MP (2017) Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis. Neuroimage Clin 13:361–369CrossRefGoogle Scholar
  21. 21.
    Chen CP, Keown CL, Jahedi A et al (2015) Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. Neuroimage Clin 8:238–245CrossRefGoogle Scholar
  22. 22.
    Kaufmann T, Skåtun KC, Alnaes D et al (2015) Disintegration of sensorimotor brain networks in schizophrenia. Schizophr Bull 41:1326–1335CrossRefGoogle Scholar
  23. 23.
    Ryali S, Chen T, Supekar K, Menon V (2012) Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty. Neuroimage 59:3852–3861CrossRefGoogle Scholar
  24. 24.
    Ng B, Vahdat A, Hamarneh G, Abugharbieh R (2010) Generalized sparse classifiers for decoding cognitive states in fMRI. In: Wang F, Yan P, Suzuki K, Shen D (eds) Machine Learning in Medical Imaging. Lecture Notes in Computer Science, vol 6357. Springer, BerlinGoogle Scholar
  25. 25.
    Sochat V, Supekar K, Bustillo J, Calhoun V, Turner JA, Rubin DL (2014) A robust classifier to distinguish noise from fMRI independent components. PLoS One 9:e95493CrossRefGoogle Scholar
  26. 26.
    Beckmann CF, DeLuca M, Devlin JT, Smith SM (2005) Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond Ser B Biol Sci 360:1001–1013CrossRefGoogle Scholar
  27. 27.
    Beckmann CF, Mackay CE, Filippini N, Smith SM (2009) Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. Neuroimage 47:S148CrossRefGoogle Scholar
  28. 28.
    Chollet F (2015) Keras: deep learning library for theano and tensorflow. Available via https://keras.io
  29. 29.
    Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Statist Soc B 67:301–320CrossRefGoogle Scholar
  30. 30.
    Barrat A, Barthélemy M, Pastor-Satorras R, Vespignani A (2004) The architecture of complex weighted networks. Proc Natl Acad Sci U S A 101:3747–3752CrossRefGoogle Scholar
  31. 31.
    Suppa P, Anker U, Spies L et al (2015) Fully automated atlas-based hippocampal volumetry for detection of Alzheimer’s disease in a memory clinic setting. J Alzheimers Dis 44:183–193CrossRefGoogle Scholar
  32. 32.
    Eickhoff SB, Stephan KE, Mohlberg H et al (2005) A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 25:1325–1335CrossRefGoogle Scholar
  33. 33.
    Smith SM, Fox PT, Miller KL et al (2009) Correspondence of the brain's functional architecture during activation and rest. Proc Natl Acad Sci U S A 106:13040–13045CrossRefGoogle Scholar
  34. 34.
    Barba C, Rheims S, Minotti L et al (2016) Reply: temporal plus epilepsy is a major determinant of temporal lobe surgery failures. Brain 139:e36CrossRefGoogle Scholar
  35. 35.
    Kelly RE Jr, Alexopoulos GS, Wang Z et al (2010) Visual inspection of independent components: defining a procedure for artifact removal from fMRI data. J Neurosci Methods 189:233–245CrossRefGoogle Scholar
  36. 36.
    Griffanti L, Douaud G, Bijsterbosch J et al (2017) Hand classification of fMRI ICA noise components. Neuroimage 154:188–205CrossRefGoogle Scholar
  37. 37.
    Feigin A, Kaplitt MG, Tang C et al (2007) Modulation of metabolic brain networks after subthalamic gene therapy for Parkinson’s disease. Proc Natl Acad Sci U S A 104:19559–19564CrossRefGoogle Scholar
  38. 38.
    Hillary FG, Rajtmajer SM, Roman CA et al (2014) The rich get richer: brain injury elicits hyperconnectivity in core subnetworks. PLoS One 9:e104021CrossRefGoogle Scholar
  39. 39.
    Pitkänen A, Sutula TP (2002) Is epilepsy a progressive disorder? Prospects for new therapeutic approaches in temporal-lobe epilepsy. Lancet Neurol 1:173–181CrossRefGoogle Scholar
  40. 40.
    Scharfman HE (2007) The neurobiology of epilepsy. Curr Neurol Neurosci Rep 7:348–354CrossRefGoogle Scholar
  41. 41.
    Dyhrfjeld-Johnsen J, Santhakumar V, Morgan RJ, Huerta R, Tsimring L, Soltesz I (2007) Topological determinants of epileptogenesis in large-scale structural and functional models of the dentate gyrus derived from experimental data. J Neurophysiol 97:1566–1587CrossRefGoogle Scholar
  42. 42.
    Salinsky M, Kanter R, Dasheiff RM (1987) Effectiveness of multiple EEGs in supporting the diagnosis of epilepsy: an operational curve. Epilepsia 28:331–334CrossRefGoogle Scholar
  43. 43.
    Javidan M (2012) Electroencephalography in mesial temporal lobe epilepsy: a review. Epilepsy Res Treat 2012:637430PubMedPubMedCentralGoogle Scholar
  44. 44.
    Fergus P, Hussain A, Hignett D, Al-Jumeily D, Abdel-Aziz K, Hamdan H (2016) A machine learning system for automated whole-brain seizure detection. Appl Comput Inf 12:70–89Google Scholar
  45. 45.
    Focke NK, Yogarajah M, Symms MR, Gruber O, Paulus W, Duncan JS (2012) Automated MR image classification in temporal lobe epilepsy. Neuroimage 59:356–362CrossRefGoogle Scholar
  46. 46.
    Chiang S, Levin HS, Haneef Z (2015) Computer-automated focus lateralization of temporal lobe epilepsy using fMRI. J Magn Reson Imaging 41:1689–1694CrossRefGoogle Scholar

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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