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Advanced Neuroimaging for Modern Epilepsy Surgery

in 37 TLE patients with varying delays in tracer injection. The authors found significant ictal hyperperfusion in the ipsilateral temporal lobe and hypoperfusion in the ipsilateral orbitofrontal, bilateral superior frontal, contralateral cerebellar, and ipsilateral striatum regions.47

Future controlled

studies with improved temporal resolution with SPECT injection and scanning are needed to elucidate the dynamics of the seizure network.

One disadvantage of SPECT is the qualitative nature of image interpretation, which is subject to interpreter bias. One way to circumvent this problem is to create automated comparison of the baseline interictal SPECT with subtraction ictal SPECT co-registered to MRI (SISCOM). Implementation of this analysis method showed high predictive value when the hyperperfused area was completely resected.48–52

This result

has been confirmed in a large cohort of patients with non-localizing MRI and EEGs, where the presence of focal SPECT hypermetabolism has the highest odds ratio prediction of seizure-free outcome.35

The clinical value

and unique ability to capture dynamic network processes during ictal activity make SPECT an attractive imaging modality in the detection and study of epilepsy.


MEG was first introduced in 1968 and works by detecting magnetic fields generated by neuronal activity using arrays of superconducting quantum interference devices (SQUIDs). MEG is a non-invasive neuroimaging method that can be combined with MRI to generate magnetic source imaging (MSI) to localize epileptogenic zones (see Figure 5). MEG has the advantage over conventional EEG because magnetic signals can pass through skull and other tissues without significant distortion. In addition, MEG spikes are usually shorter in duration with a steeper ascending slope than EEG spikes, leading to a larger signal-to-noise ratio and greater source localization.53,54 One disadvantage of MEG, however, is that it only detects tangential components of a current source. Therefore, MEG selectively measures sulcal activity whereas EEG can detect both sulcal and cortical activity. Another major disadvantage is that MEG is mostly restricted to the detection of interictal spiking, as it is not practical or feasible to continuously monitor patients in the MEG device.

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In a prospective blinded study, Sutherling and colleagues found MSI to yield non-redundant information in 33 % of 69 patients with suspected neocortical epilepsy; this information consisted of additional areas to sample intracranial EEG or modifications of the surgical decision.55

The use of MEG in the clinical setting has largely been limited to mapping of the eloquent cortex for surgery; its diagnostic yield in detecting focal seizure foci remains uncertain. In the largest series, consisting of 455 epilepsy patients, 131/455 (28.8 %) underwent surgical treatment and MEG succeeded in identifying the epileptogenic zone in 70 % of patients and lobar localization in 89 % of patients. MEG supplied additional information in 5 % of patients and crucial information for the final decision for surgery in 10 % of patients.35 Interictal MEG used in the pre-surgical evaluation of patients with non-localizing lesions has shown general agreement with invasive EEG recording, with a sensitivity of 58–64 % and specificity of 79–88 %.35,36

Chang and colleagues demonstrated that

the improved spatial resolution of MEG could localize the epileptogenic source in patients with EEG-confirmed secondary bilateral synchrony that otherwise appeared as generalized.56

Altogether, these studies

and suggest that MEG/MSI has an important role in guiding the decision-making process for invasive monitoring and surgical resection for epilepsy surgery, especially when other localizing evidence is lacking or discordant.


With the aid of these neuroimaging tools, epilepsy surgeons are no longer operating on ‘invisible’ lesions. Ultrahigh-resolution MRI will be able to provide greater anatomic and structural detail that will reveal more information regarding the pathophysiology of epilepsy. The realm of functional imaging will also continue to grow as we discover more specific molecular tracers that will not only map the functional connectivity of epileptogenic networks but will also confer greater prognostic value in predicting seizure outcome. In the future, MEG may become a routine part of pre-surgical evaluation for epileptogenic foci localization and functional mapping. These techniques, among other emerging imaging methods, will undoubtedly push the field of epilepsy surgery to new frontiers. n

12. Jack CR, Jr, Sharbrough FW, Cascino GD, et al., Magnetic resonance image-based hippocampal volumetry: correlation with outcome after temporal lobectomy, Ann Neurol, 1992;31:138–46.

13. Bell ML, Rao S, So EL, et al., Epilepsy surgery outcomes in temporal lobe epilepsy with a normal MRI, Epilepsia, 2009;50:2053–60.

14. Immonen A, Jutila L, Muraja-Murro A, et al., Long-term epilepsy surgery outcomes in patients with MRI-negative temporal lobe epilepsy, Epilepsia, 2010;51:2260–9.

15. Cukiert A, Burattini JA, Mariani PP, et al., Outcome after cortico-amygdalo-hippocampectomy in patients with temporal lobe epilepsy and normal MRI, Seizure, 2010;19:319–23.

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17. Bernasconi N, Duchesne S, Janke A, et al., Whole-brain voxel-based statistical analysis of gray matter and white matter in temporal lobe epilepsy, Neuroimage, 2004;23:717–23.

18. Riederer F, Lanzenberger R, Kaya M, et al., Network atrophy in temporal lobe epilepsy: a voxel-based morphometry study, Neurology, 2008;71:419–25.

19. Bonilha L, Halford JJ, Rorden C, et al., Automated MRI analysis for identification of hippocampal atrophy in temporal lobe epilepsy, Epilepsia, 2009;50:228–33.

20. Keller SS, Roberts N, Voxel-based morphometry of temporal lobe epilepsy: an introduction and review of the literature, Epilepsia, 2008;49:741–57.

21. Richardson M, Current themes in neuroimaging of epilepsy: brain networks, dynamic phenomena, and clinical relevance, Clin Neurophysiol, 2010;121:1153–75.

22. Strandberg M, Larsson EM, Backman S, Kallen K, Pre-surgical epilepsy evaluation using 3T MRI. Do surface coils provide additional information?, Epileptic Disord, 2008;10:83–92.

23. Duyn JH, van Gelderen P, Li TQ, et al., High-field MRI of brain cortical substructure based on signal phase, Proc Natl Acad Sci U S A, 2007;104:11796–801.

24. Henry TR, Chupin M, Lehericy S, et al., Hippocampal sclerosis in temporal lobe epilepsy: findings at 7 T(1), Radiology, 2011;261:199–209.

25. Barkovich AJ, Raybaud CA, Malformations of cortical development, Neuroimaging Clin N Am, 2004;14:401–23.

26. Barkovich AJ, Raybaud CA, Neuroimaging in disorders of cortical development, Neuroimaging Clin N Am, 2004;14:231–54, viii.

27. Woo CL, Chuang SH, Becker LE, et al., Radiologic-pathologic correlation in focal cortical dysplasia and hemimegalencephaly in 18 children, Pediatr Neurol, 2001;25:295–303.

28. Chang EF, Wang DD, Barkovich AJ, et al., Predictors of seizure freedom after surgery for malformations of cortical development, Ann Neurol, 2011;70:151–62.

29. Kim YH, Kang HC, Kim DS, et al., Neuroimaging in identifying focal cortical dysplasia and prognostic factors in pediatric and adolescent epilepsy surgery, Epilepsia, 2011;52:722–7.

30. Thesen T, Quinn BT, Carlson C, et al., Detection of epileptogenic cortical malformations with surface-based MRI morphometry, PLoS One, 2011;6:e16430.

31. la Fougere C, Rominger A, Forster S, et al., PET and SPECT in epilepsy: a critical review, Epilepsy Behav, 2009;15:50–5.

32. Arnold S, Schlaug G, Niemann H, et al., Topography of interictal glucose hypometabolism in unilateral mesiotemporal epilepsy,


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