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  • Poster presentation
  • Open Access

Predicting surgical outcome in intractable epilepsy using a computational model of seizure initiation

  • 1Email author,
  • 1,
  • 2,
  • 3 and
  • 2
BMC Neuroscience201516 (Suppl 1) :P230

https://doi.org/10.1186/1471-2202-16-S1-P230

  • Published:

Keywords

  • Cortical Tissue
  • Seizure Onset
  • Seizure Focus
  • Intractable Epilepsy
  • Focal Epilepsy

A third of patients with epilepsy are refractory to anti-epileptic drug treatment. For some of these patients with focal epilepsy, better seizure control can be achieved by surgical treatment in which the seizure focus is localized and resected while avoiding crucial cortical tissues. However, approximately 30% of the patients continue to have seizures even after surgery. In other words, reliable criteria for patient's outcome prediction are absent. Computational models with appropriate parameter setting and patients specific connectivity allows an exciting opportunity to make predictions based on the model dynamics.

In this study, non-seizure (inter-ictal) epoch of electrographic recording has been used to calculate the functional synchrony between different cortical regions. This synchrony measure was then used as the connectivity parameter in a computational model of transitions to a seizure like state. Hypothesizing that the network synchrony plays an important role in determining the likelihood of surgical success, we retrospectively analyzed 19 patients having intractable epilepsy, who underwent surgical treatment to achieve seizure freedom. All data were collected confirming to ethical guidelines and under protocols monitored by the local Institutional Review Boards according to NIH guidelines.

Building upon the computational model in [1], the regions which were more likely to transit into a seizure like state were delineated. It was found that these regions are correlated with those identified by clinicians as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. The likelihood of a surgical success was calculated in silico by iteratively increasing the area of resection and the surgical outcomes were successfully predicted for 14 out of 19 patients.

The methods presented here may aid clinicians to delineate the seizure focus. Moreover, it may facilitate neurosurgeons in predicting the likelihood of a surgical success and to investigate alternative cortical tissues to operate on if the seizure focus is in the eloquent cortex.
Table 1

Prediction of surgical outcomes

No.

Age group

at onset

Age group

at surgery

Surgical Resection

Outcome

(Engel Class)

Predicted Outcome

1

21-30

21-30

Right Temporal lobe

Seizure Free (II)

Good outcome

2

41-50

41-50

Right Temporal lobe

Seizure Free (I)

Good outcome

3

21-30

21-30

Left Cingulate

Seizure Free (I)

Bad outcome

4

41-50

41-50

Left Temporal

Seizure Free (I)

Good outcome

5

11-20

11-20

Right Parietal

Seizure Free (I)

Good outcome

6

51-60

51-60

Amygdalohippocampectomy

Left Medial Frontal Lobe

Seizure Free (I)

Bad outcome

7

11-20

11-20

Right anterior-superior frontocortical

Right Temporal Lobe, Amygdalohippocampectomy

Seizure Free (I)

Good outcome

8

11-20

11-20

Left occipital brain lobe

Seizure Free (I)

Bad outcome

9

31-40

31-40

Right frontal lobe

Seizure Free (I)

Good outcome

10

1-10

1-10

Left lateral frontal cortex,

Left anterior frontal cortex

Mesial left frontal cortex

Seizure Free (I)

Bad outcome

11

21-30

21-30

Left FrontoTemporal

Not Seizure Free

Bad outcome

12

31-40

31-40

Right Temporo-Occipital Region

Not Seizure Free (IV)

Bad outcome

13

21-30

21-30

Right Temporal Lobe

Not Seizure Free (IV)

Bad outcome

14

11-20

11-20

Left Anterior Temporal Lobe

Amygdalohippocampectomy

Not Seizure Free (V)

Good outcome

15

1-10

1-10

Left Parietal Lobe

Not Seizure Free (IV)

Bad outcome

16

1-10

1-10

Right Frontal Lobe

Not Seizure Free (IV)

Bad outcome

17

31-40

31-40

Left Temporal

Not Seizure Free (V)

Bad outcome

18

21-30

21-30

Left Temporal Lobe

Not Seizure Free (V)

Bad outcome

19

1-10

1-10

Left Frontal Lesion

Not Seizure Free (V)

Bad outcome

Declarations

Acknowledgements

This work is funded in part by MOE Academic Research Funding Tier 1 grant M4010982.040.

Authors’ Affiliations

(1)
School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore
(2)
School of Computing Science, Newcastle University, Newcastle upon Tyne, UK
(3)
Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

References

  1. Nishant Sinha, Dauwels Justin, Wang Yujiang, Cash Sydney, Taylor Peter: An in silico approach for pre-surgical evaluation of an epileptic cortex. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE. 2014, 4884-4887.Google Scholar

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