From anatomic standardization analysis of perfusion SPECT data to perfusion pattern modeling: evidence of functional networks in healthy subjects and temporal lobe epilepsy patients

Limbic networks: clinical perspective

Limbic epilepsy refers to a condition that consists of epileptic seizures that originate in or preferentially involve the limbic system. The majority of cases are medically refractory, necessitating surgical resection when possible. However, even resection of structures thought to be responsible for seizure generation may not leave a patient seizure free. While mesial temporal lobe limbic structures are centrally involved, there is growing evidence that the epileptogenic network consists of a broader area, involving structures outside of the temporal lobe and the limbic system. Information on structural, functional, and metabolic connectivity in patients with limbic epilepsy is available from a large body of studies employing methods such as MRI, EEG, MEG, fMRI, PET, and SPECT scanning, implicating the involvement of various brain regions in the epileptogenic network. To date, there are no consistent and conclusive findings to define the exact boundaries of this network, but it is possible that in the future studies of network connectivity in the individual patient may allow more tailored treatment and prognosis in terms of surgical resection.

Quantitative multi-compartmental SPECT image analysis for lateralization of temporal lobe epilepsy

This study assesses the utility of compartmental analysis of SPECT data in lateralizing ictal onset in cases of a putative mesial temporal lobe epilepsy (mTLE). An institutional archival review provided 46 patients (18M, 28F) operated for a putative mTLE who achieved an Engel class Ia postoperative outcome. This established the standard to assure a true ictal origin. Ictal and interictal SPECT images were separately coregistered to T1-weighted (T1W) magnetic resonance (MR) image using a rigid transformation and the intensities matched with an l(1) norm minimization technique. The T1W MR image was segmented into separate structures using an atlas-based automatic segmentation technique with the hippocampi manually segmented to improve accuracy. Mean ictal-interictal intensity difference values were calculated for select subcortical structures and the accuracy of lateralization evaluated using a linear classifier. Hippocampal SPECT analysis yielded the highest lateralization accuracy (91%) followed by the amygdala (87%), putamen (67%) and thalamus (61%). Comparative FLAIR and volumetric analyses yielded 89% and 78% accuracies, respectively. A multi-modality analysis did not generate a higher accuracy (89%). A quantitative anatomically compartmented approach to SPECT analysis yields a particularly high lateralization accuracy in the case of mTLE comparable to that of quantitative FLAIR MR imaging. Hippocampal segmentation in this regard correlates well with ictal origin and shows good reliability in the preoperative analysis.

[On the best strategies on the best results for surgery of frontal epilepsy]

Frontal lobe epilepsy surgery is the second most common surgery performed for drug-resistant partial epilepsy. We investigated the longitudinal outcome in a cohort of patients investigated since 1990 with SEEG and modern diagnostic techniques. We reviewed 105 patients who underwent surgery between 1990 and 2005 (mean follow-up, six years; range: one to 17 years) and analyzed the year-per-year follow-up according to Engel's classification. Favorable outcome (Class I) was observed for 70% and this result was stable at least five years after surgery. More than 90% of patients with lesion-related epilepsies (focal cortical dysplasia and dysembryoplastic neuroepithelial tumors) became seizure-free. Less than 50% of patients classified as having cryptogenic epilepsy (defined as normal imaging and neuropathology on surgical specimen) had a favorable outcome. Permanent neurological sequelae were subtle and rare, especially after surgery for dysplasia in eloquent cortex (primary motor cortex). Our data indicate that frontal surgery is a successful treatment in patients when electrophysiological and morphological investigations demonstrate a well-defined epileptogenic zone or lesion to be surgically resected. Progress in electrophysiological and brain-imaging techniques will further improve the selection of frontal lobe epilepsy surgery candidates.

Source analysis of epileptic discharges using multiple signal classification analysis

Multiple signal classification is an alternative to the traditional dipole fitting source analysis methods. Our aim was to assess the clinical usefulness of this algorithm and to compare the localization of the epileptiform electroencephalography discharges with the regions of altered cerebral blood flow in 10 patients with complex partial seizures undergoing preoperative investigation. We performed multiple signal classification analysis of ictal and interictal discharges, and registered single-photon emission computed tomography. Localization of the ictal, but not the interictal discharges, as determined by multiple signal classification analysis was consistent with the regions showing perfusion changes on the single-photon emission computed tomography. Multiple signal classification analysis is a promising tool in localizing foci in patients with complex partial seizures and may contribute to the preoperative evaluation.

A new improved version of the realistic digital brain phantom

Image analysis methods must be tested and evaluated within a controlled environment. Simulations can be an extremely helpful tool for validation because ground truth is known. We created the digital brain phantom that is at the heart of our publicly available database of realistic simulated magnetic resonance image (MRI) volumes known as BrainWeb. Even though the digital phantom had l mm(3) isotropic voxel size and a small number of tissue classes, the BrainWeb database has been used in more than one hundred peer-reviewed publications validating different image processing methods. In this paper, we describe the next step in the natural evolution of BrainWeb: the creation of digital brain phantom II that includes three major improvements over the original phantom. First, the realism of the phantom, and the resulting simulations, was improved by modeling more tissue classes to include blood vessels, bone marrow and dura mater classes. In addition. a more realistic skull class was created. The latter is particularly useful for SPECT, PET and CT simulations for which bone attenuation has an important effect. Second, the phantom was improved by an eight-fold reduction in voxel volume to 0.125 mm(3). Third, the method used to create the new phantom was modified not only to take into account the segmentation of these new structures, but also to take advantage of many more automated procedures now available. The overall process has reduced subjectivity and manual intervention when compared to the original phantom, and the process may be easily applied to create phantoms from other subjects. MRI simulations are shown to illustrate the difference between the previous and the new improved digital brain phantom II. Example PET and SPECT simulations are also presented.

Realistic Simulated MRI and SPECT Databases

This paper describes the construction of simulated SPECT and MRI databases that account for realistic anatomical and functional variability. The data is used as a gold-standard to evaluate four SPECT/MRI similarity-based registration methods. Simulation realism was accounted for using accurate physical models of data generation and acquisition. MRI and SPECT simulations were generated from three subjects to take into account inter-subject anatomical variability. Functional SPECT data were computed from six functional models of brain perfusion. Previous models of normal perfusion and ictal perfusion observed in Mesial Temporal Lobe Epilepsy (MTLE) were considered to generate functional variability. We studied the impact noise and intensity non-uniformity in MRI simulations and SPECT scatter correction may have on registration accuracy. We quantified the amount of registration error caused by anatomical and functional variability. Registration involving ictal data was less accurate than registration involving normal data. MR intensity nonuniformity was the main factor decreasing registration accuracy. The proposed simulated database is promising to evaluate many functional neuroimaging methods, involving MRI and SPECT data.