RESTING-STATE FMRI ANALYSIS AS A PREDICTOR OF

THE SUCCESS OF EPILEPSY SURGERY

Hypothesis: the degree to which the resected epileptogenic region is functionally connected to the

other hemisphere should predict seizure freedom after surgery.

By Carly Rosen

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.

• 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.

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.

Implantation of intracranial grid and strip electrodes

• 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

Defining the Resection Zone

Fig. 1

Fig. 2

Engel Epilepsy Surgery Outcome 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.)

Qualitative AnalysisInflated cortical surfaces of both hemispheres are shown for 3 different patients with similar resections. Resection areas are shown in white.

Engel Class I

Engel Class II

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.

Engel Class III

• 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

Functional Connectivity Results

Engel I Engel 2 Engel 3

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Raw Value Data (considers anti-correlation)

Absolute Value Data

Degr

ee o

f Lat

eral

ized

Func

tiona

l Con

necti

vity

Correlation values of each hemisphere to the resected zone are obtained for

every subject

The connectivity value of the nonresected hemisphere is

subtracted from the resected hemisphere value to determine

lateralization

The values of the subjects in each Engel

Outcome category are averaged and recorded in the

graph.

Conclusion

• 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.

Raw correlation rho=-0.127, p=0.340Absolute correlation rho=0.006, p=0.508

ReferencesConstable, 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), 1-11Kuzniecky, 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.