[en] 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.
Disciplines :
Neurology
Author, co-author :
Bharath, Rose dawn; National Institute of Mental Health and Neuro Sciences
Panda, Rajanikant ; Université de Liège - ULiège > GIGA Consciousness - Coma Science Group
Raj, Jeetu; Indian Institute of Technology Delhi
Bhardwaj, Sujas; National Institute of Mental Health and Neuro Sciences
Sinha, Sanjib; National Institute of Mental Health and Neuro Sciences
Chaitanya, Ganne; National Institute of Mental Health and Neuro Sciences
Raghavendra, Kenchaiah; National Institute of Mental Health and Neuro Sciences
Mundlamuri, Ravindranadh C; National Institute of Mental Health and Neuro Sciences
Arimappamagan, Arivazhagan; National Institute of Mental Health and Neuro Sciences
Rao, Malla Bhaskara; National Institute of Mental Health and Neuro Sciences
Rajeshwaran, Jamuna; National Institute of Mental Health and Neuro Sciences
Thennarasu, Kandavel; National Institute of Mental Health and Neuro Sciences
Majumdari, Kaushik K; Indian Statistical Institute
Satishchandra, Parthasarthy; National Institute of Mental Health and Neuro Sciences
Gandhi, Tapan K.; Indian Institute of Technology Delhi
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