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RESTING-STATE FMRI ANALYSIS AS A PREDICTOR OF THE SUCCESS OF EPILEPSY SURGERY

RESTING-STATE FMRI ANALYSIS AS A PREDICTOR OF THE SUCCESS OF EPILEPSY SURGERY . By Carly Rosen. Hypothesis: the degree to which the resected epileptogenic region is functionally connected to the other hemisphere should predict seizure freedom after surgery. Background Information.

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RESTING-STATE FMRI ANALYSIS AS A PREDICTOR OF THE SUCCESS OF EPILEPSY SURGERY

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  1. RESTING-STATE FMRI ANALYSIS AS A PREDICTOR OF THE SUCCESS OF EPILEPSY SURGERY By Carly Rosen Hypothesis: the degree to which the resected epileptogenic region is functionally connected to the other hemisphere should predict seizure freedom after surgery.

  2. Background Information • Epilepsy is a neurological disorder that characterized by recurrent seizures. It is estimated to affect over 70 million people worldwide. • Surgical resection of the epileptogenic zone (EZ) is considered a standard of care for patients with seizures that cannot be controlled with antiepileptic drugs. This condition is known as intractable epilepsy. • The success of this epilepsy surgery is highly variable (30-80%). • There is some evidence that patients that continue to have seizures after epilepsy surgery have less lateralized functional connectivity than seizure-free patients.

  3. Functional connectivity analysis uses functional magnetic resonance imaging (fMRI) data to identify networks of brain activity. • The blood-oxygenation level dependent (BOLD) response is a correlate of brain activity. • Resting state fMRI (rsfMRI) connectivity analysis is typically used to define networks of functional areas. • rsfMRI may be used to identify the extent of epileptogenic tissue as well as predict cognitive changes after resection.

  4. Methods • Prior to implantation of intracranial electrodes, structural and functional MRIs were acquired from 13 intractable epilepsy patients. • After implantation, the EZ is determined through electrocorticography (ECoG) analysis and is targeted for resection. • The preoperative structural MRI (Fig. 1) is compared to the postoperative MRI (Fig. 2) in order to determine the resected area • A seed derived from the resection masks is used for computing functional connectivity Implantation of intracranial grid and strip electrodes

  5. Defining the Resection Zone Fig. 1 Fig. 2

  6. Engel Epilepsy SurgeryOutcome Scale • Class I: free of disabling seizures • Class II: rare disabling seizures • Class III: worthwhile improvement • Class IV: no worthwhile improvement* *no class IV patients are included in this study (Engel, Jerome. Surgical Treatment of the Epilepsies. New York: Raven, 1987.)

  7. Qualitative Analysis Inflated cortical surfaces of both hemispheres are shown for 3 different patients with similar resections. Resection areas are shown in white. Engel Class I

  8. The heat map shows regions of positive and negative BOLD signal correlation with the mean BOLD time series of the resected area. Regions with a strong positive correlation suggest connectivity to the resected EZ. Left temporal lobectomy and left amygdalo-hippocampectomy Engel Class II Left lateral temporal lobectomy and left amygdalo-hippocampectomy Engel Class II

  9. In this study the connectivity of the resected EZ to the remainder of the resected hemisphere is compared to the connectivity of the EZ to opposite hemisphere Engel Class III Left temporal lobectomy and left amygdalo-hippocampectomy

  10. Functional Connectivity Results

  11. Conclusion Raw correlation rho=-0.127, p=0.340 Absolute correlation rho=0.006, p=0.508 • Statistical analysis depicts that the data is not significant enough to support the idea that lateral functional connectivity analysis can predict epilepsy surgery outcome. • The negative raw correlation rho value is more consistent with the hypothesis than the positive absolute rho value. • This study may need to include more subjects or control for variability in resection area to increase statistical power and decrease inter-subject variability.

  12. References Constable, R. T., Scheinost, D., Finn, E., Hampson, M., Winstanley, F. S., Spencer, D. D., et al. (2012). Potential Use and Challenges of Functional Connectivity Mapping in Intractable Epilepsy. Frontiers in Neurology, 4(39), 1-11 Kuzniecky, R., & Devinsky, O. (2007). Surgery Insight: Surgical Management Of Epilepsy. Nature Clinical Practice Neurology, 3(12), 673-681. Negishi, M., Martuzzi, R., Novotny, E. J., Spencer, D. D., & Constable, R. T. (2011). Functional MRI Connectivity As A Predictor Of The Surgical Outcome Of Epilepsy. Epilepsia, 52(9), 1733-1740.

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