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

Predictive value of metabolic and perfusion changes outside the seizure onset zone for postoperative outcome in patients with refractory focal epilepsy

The value of functional molecular changes outside the seizure onset zone as independent predictive factors of surgical outcome has been scarcely evaluated. The aim of this retrospective study was to evaluate relative metabolic and perfusion changes outside the seizure onset zone as predictors of postoperative outcome in patients with unifocal refractory focal epilepsy. Eighty-six unifocal epilepsy patients who underwent ¹⁸F-FDG PET prior to surgery were included. Ictal and interictal perfusion SPECT was available in 65 patients. Good postoperative outcome was defined as the International League against Epilepsy class 1. Using univariate statistical analysis, the predictive ability of volume-of-interest based relative metabolism/perfusion for outcome classification was quantified by AUC ROC-curve, using composite, unilateral cortical (frontal, orbitofrontal, temporal, parietal, occipital) and central volumes-of-interest. The results were cross-validated, and a false discovery rate (FDR) correction was applied. As a secondary objective, a subgroup analysis was performed on temporal lobe epilepsy patients (N = 64). Increased relative ictal perfusion in the contralateral central volume-of-interest was significantly associated with the good surgical outcome both in the total population (AUC 0.79, p_FDR = 0.009) and the temporal lobe epilepsy subgroup (AUC 0.80, p_FDR = 0.028). No other significant associations between functional molecular changes and postoperative outcome were found. Increased relative ictal perfusion in the contralateral central region significantly predicted outcome after epilepsy surgery in patients with refractory focal epilepsy. We postulate that these relative perfusion changes could be an expression of better preoperative neuronal network integration and centralization in the contralateral central structures, which is suggested to be associated with better postoperative outcome.

[Optimized multi-scale entropy to localize epileptogenic hemisphere of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging]

Entropy model is widely used in epileptic electroencephalogram (EEG) analysis, but there are few reports on how to objectively select the parameters to compute the entropy model in the analysis of resting-state functional magnetic resonance imaging (rfMRI). Therefore, an optimization algorithm to confirm the parameters in multi-scale entropy (MSE) model was proposed, and the location of epileptogenic hemisphere was taken as an example to test the optimization effect by supervised machine learning. The rfMRI data of 20 temporal lobe epilepsy (TLE) patients with hippocampal sclerosis, positive on structural magnetic resonance imaging, were divided into left and right groups. Then, the parameters in MSE model were optimized by the receiver operating characteristic curves (ROC) and area under ROC curve (AUC) values in sensitivity analysis, and the entropy value of the brain regions with statistically significant difference between the groups were taken as sensitive features to epileptogenic hemisphere lateral. The optimized entropy values of these bio-marker brain areas were considered as feature vectors input into the support vector machine (SVM). Finally, combining optimized MSE model with SVM could accurately distinguish epileptogenic hemisphere in TLE at an average accuracy rate of 95%, which was higher than the current level. The results show that the MSE model parameter optimization algorithm can accurately extract the functional imaging markers sensitive to the epileptogenic hemisphere, and achieve the purpose of objectively selecting the parameters for MSE in rfMRI, which provides the basis for the application of entropy in advanced technology detection.

Prospective Quantitative Neuroimaging Analysis of Putative Temporal Lobe Epilepsy

Purpose: A prospective study of individual and combined quantitative imaging applications for lateralizing epileptogenicity was performed in a cohort of consecutive patients with a putative diagnosis of mesial temporal lobe epilepsy (mTLE). Methods: Quantitative metrics were applied to MRI and nuclear medicine imaging studies as part of a comprehensive presurgical investigation. The neuroimaging analytics were conducted remotely to remove bias. All quantitative lateralizing tools were trained using a separate dataset. Outcomes were determined after 2 years. Of those treated, some underwent resection, and others were implanted with a responsive neurostimulation (RNS) device. Results: Forty-eight consecutive cases underwent evaluation using nine attributes of individual or combinations of neuroimaging modalities: 1) hippocampal volume, 2) FLAIR signal, 3) PET profile, 4) multistructural analysis (MSA), 5) multimodal model analysis (MMM), 6) DTI uncertainty analysis, 7) DTI connectivity, and 9) fMRI connectivity. Of the 24 patients undergoing resection, MSA, MMM, and PET proved most effective in predicting an Engel class 1 outcome (>80% accuracy). Both hippocampal volume and FLAIR signal analysis showed 76% and 69% concordance with an Engel class 1 outcome, respectively. Conclusion: Quantitative multimodal neuroimaging in the context of a putative mTLE aids in declaring laterality. The degree to which there is disagreement among the various quantitative neuroimaging metrics will judge whether epileptogenicity can be confined sufficiently to a particular temporal lobe to warrant further study and choice of therapy. Prediction models will improve with continued exploration of combined optimal neuroimaging metrics.

Neuroimaging methods in Epilepsy of Temporal Origin

Background:

Temporal Lobe Epilepsy (TLE) comprises the most common form of symptomatic refractory focal epilepsy in adults. Accurate lateralization and localization of the epileptogenic focus are a significant prerequisite for determining surgical candidacy once the patient has been deemed medically intractable. Structural MR imaging, clinical, electrophysiological, and neurophysiological data have an established role in the localization of the epileptogenic foci. Nevertheless, hippocampal sclerosis cannot be detected on MR images in more than 30% of patients with TLE, and the presurgical assessment remains controversial. </P><P> Discussion: In the last years, advanced MR imaging techniques, such as 1H-MRS, DWI, DTI, DSCI, and fMRI, may provide valuable additional information regarding the physiological and metabolic characterization of brain tissue. MR imaging has shifted towards functional and molecular imaging, thus, promising to improve the accuracy regarding the lateralization and the localization of the epileptogenic focus. Additionally, nuclear medicine studies, such as SPECT and PET imaging modalities, have become an asset for the decoding of brain function and activity, and can be diagnostically helpful as well, since they provide valuable data regarding the altered metabolic activity of the seizure foci.

Conclusion:

Overall, advanced MRI, SPECT, and PET imaging techniques are increasingly becoming an essential part of TLE diagnostics, when the epileptogenic area is not identified on structural MRI or when structural MRI, clinical, and electrophysiological findings are not in concordance.

Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy

PURPOSE

This study systematically investigates the predictive power of volumetric imaging feature sets extracted from select neuroanatomical sites in lateralizing the epileptogenic focus in mesial temporal lobe epilepsy (mTLE) patients.

METHODS

A cohort of 68 unilateral mTLE patients who had achieved an Engel class I outcome postsurgically was studied retrospectively. The volumes of multiple brain structures were extracted from preoperative magnetic resonance (MR) images in each. The MR image data set consisted of 54 patients with imaging evidence for hippocampal sclerosis (HS-P) and 14 patients without (HS-N). Data mining techniques (i.e., feature extraction, feature selection, machine learning classifiers) were applied to provide measures of the relative contributions of structures and their correlations with one another. After removing redundant correlated structures, a minimum set of structures was determined as a marker for mTLE lateralization.

RESULTS

Using a logistic regression classifier, the volumes of both hippocampus and amygdala showed correct lateralization rates of 94.1%. This reflected about 11.7% improvement in accuracy relative to using hippocampal volume alone. The addition of thalamic volume increased the lateralization rate to 98.5%. This ternary-structural marker provided a 100% and 92.9% mTLE lateralization accuracy, respectively, for the HS-P and HS-N groups.

CONCLUSIONS

The proposed tristructural MR imaging biomarker provides greater lateralization accuracy relative to single- and double-structural biomarkers and thus, may play a more effective role in the surgical decision-making process. Also, lateralization of the patients with insignificant atrophy of hippocampus by the proposed method supports the notion of associated structural changes involving the amygdala and thalamus.

Contribution of Quantitative Amygdalar MR FLAIR Signal Analysis for Lateralization of Mesial Temporal Lobe Epilepsy

BACKGROUND AND PURPOSE

This study evaluates the contribution of an automated amygdalar fluid-attenuated inversion recovery (FLAIR) signal analysis for the lateralization of mesial temporal lobe epilepsy (mTLE).

METHODS

Sixty-nine patients (27 M, 42 F) who had undergone surgery and achieved an Engel class Ia postoperative outcome were identified as a pure cohort of mTLE cases. Forty-six nonepileptic subjects comprised the control group. The amygdala was segmented in T1-weighted images using an atlas-based segmentation. The right/left ratios of amygdalar FLAIR mean and standard deviation were calculated for each subject. A linear classifier (ie, discriminator line) was designed for lateralization using the FLAIR features and a boundary domain, within which lateralization was assumed to be less definitive, was established using the same features from control subjects. Hippocampal FLAIR and volume analysis was performed for comparison.

RESULTS

With the boundary domain in place, lateralization accuracy was found to be 70% with hippocampal FLAIR and 67% with hippocampal volume. Taking amygdalar analysis into account, 22% of cases that were found to have uncertain lateralization by hippocampal FLAIR analysis were confidently lateralized by amygdalar FLAIR. No misclassified case was found outside the amygdalar FLAIR boundary domain.

CONCLUSIONS

Amygdalar FLAIR analysis provides an additional metric by which to establish mTLE in those cases where hippocampal FLAIR and volume analysis have failed to provide lateralizing information.

Technical Note: Development of a 3D printed subresolution sandwich phantom for validation of brain SPECT analysis

PURPOSE

To make an adaptable, head shaped radionuclide phantom to simulate molecular imaging of the brain using clinical acquisition and reconstruction protocols. This will allow the characterization and correction of scanner characteristics, and improve the accuracy of clinical image analysis, including the application of databases of normal subjects.

METHODS

A fused deposition modeling 3D printer was used to create a head shaped phantom made up of transaxial slabs, derived from a simulated MRI dataset. The attenuation of the printed polylactide (PLA), measured by means of the Hounsfield unit on CT scanning, was set to match that of the brain by adjusting the proportion of plastic filament and air (fill ratio). Transmission measurements were made to verify the attenuation of the printed slabs. The radionuclide distribution within the phantom was created by adding (99m)Tc pertechnetate to the ink cartridge of a paper printer and printing images of gray and white matter anatomy, segmented from the same MRI data. The complete subresolution sandwich phantom was assembled from alternate 3D printed slabs and radioactive paper sheets, and then imaged on a dual headed gamma camera to simulate an HMPAO SPECT scan.

RESULTS

Reconstructions of phantom scans successfully used automated ellipse fitting to apply attenuation correction. This removed the variability inherent in manual application of attenuation correction and registration inherent in existing cylindrical phantom designs. The resulting images were assessed visually and by count profiles and found to be similar to those from an existing elliptical PMMA phantom.

CONCLUSIONS

The authors have demonstrated the ability to create physically realistic HMPAO SPECT simulations using a novel head-shaped 3D printed subresolution sandwich method phantom. The phantom can be used to validate all neurological SPECT imaging applications. A simple modification of the phantom design to use thinner slabs would make it suitable for use in PET.

Lateralization of temporal lobe epilepsy by imaging-based response-driven multinomial multivariate models

We have developed response-driven multinomial models, based on multivariate imaging features, to lateralize the epileptogenicity in temporal lobe epilepsy (TLE) patients. To this end, volumetrics and statistical quantities of FLAIR intensity and normalized ictal-interictal SPECT intensity on left and right hippocampi were extracted from preoperative images of forty-five retrospective TLE patients with surgical outcome of Engel class l. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Among univariate response models, the response model with SPECT attributes and response model with mean FLAIR attributes achieved the lowest fit deviance (65.1±0.2 and 65.5±0.3, respectively). They resulted in the highest probability of detection (0.82) and lowest probability of false alarm (0.02) for the epileptogenic side. The multivariate response model with incorporating all volumetrics, mean and standard deviation FLAIR, and SPECT attributes achieved a significantly lower fit deviance than other response models (11.9±0.1, p <; 0.001). It reached probability of detection of 1 with no false alarms. We were able to correctly lateralize the fifteen TLE patients who had undergone phase II intracranial monitoring. Therefore, the phase II intracranial monitoring might have been avoided for this set of patients. Based on this lateralization response model, the side of epileptogenicity was also detected for all thirty patients who had preceded to resection with only phase I of EEG monitoring. In conclusion, the proposed multinomial multivariate response-driven model for lateralization of epileptogenicity in TLE patients can help in decision-making prior to surgical resection and may reduce the need for implantation of intracranial monitoring electrodes.

Lateralization of temporal lobe epilepsy by multimodal multinomial hippocampal response-driven models

PURPOSE

Multiple modalities are used in determining laterality in mesial temporal lobe epilepsy (mTLE). It is unclear how much different imaging modalities should be weighted in decision-making. The purpose of this study is to develop response-driven multimodal multinomial models for lateralization of epileptogenicity in mTLE patients based upon imaging features in order to maximize the accuracy of noninvasive studies.

METHODS AND MATERIALS

The volumes, means and standard deviations of FLAIR intensity and means of normalized ictal-interictal SPECT intensity of the left and right hippocampi were extracted from preoperative images of a retrospective cohort of 45 mTLE patients with Engel class I surgical outcomes, as well as images of a cohort of 20 control, nonepileptic subjects. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Based on the Bayesian model averaging (BMA) theorem, response models were developed as compositions of independent univariate models.

RESULTS

A BMA model composed of posterior probabilities of univariate response models of hippocampal volumes, means and standard deviations of FLAIR intensity, and means of SPECT intensity with the estimated weighting coefficients of 0.28, 0.32, 0.09, and 0.31, respectively, as well as a multivariate response model incorporating all mentioned attributes, demonstrated complete reliability by achieving a probability of detection of one with no false alarms to establish proper laterality in all mTLE patients.

CONCLUSION

The proposed multinomial multivariate response-driven model provides a reliable lateralization of mesial temporal epileptogenicity including those patients who require phase II assessment.

Time-shift homotopic connectivity in mesial temporal lobe epilepsy

BACKGROUND AND PURPOSE

Voxel-mirrored intrinsic functional connectivity allows the depiction of interhemispheric homotopic connections in the human brain, whereas time-shift intrinsic functional connectivity allows the detection of the extent of brain injury by measuring hemodynamic properties. We combined time-shift voxel-mirrored homotopic connectivity analyses to investigate the alterations in homotopic connectivity in mesial temporal lobe epilepsy and assessed the value of applying this approach to epilepsy lateralization and the prediction of surgical outcomes in mesial temporal lobe epilepsy.

MATERIALS AND METHODS

Resting-state functional MR imaging data were acquired from patients with unilateral mesial temporal lobe epilepsy (n=62) (31 left- and 31 right-side) and healthy controls (n=33). Dynamic interhemispheric homotopic architecture seeding from each hemisphere was individually calculated by 0, 1, 2, and 3 repetition time time-shift voxel-mirrored homotopic connectivity. Voxel-mirrored homotopic connectivity maps were compared between the patient and control groups by using 1-way ANOVA for each time-shift condition, separately. Group comparisons were further performed on the laterality of voxel-mirrored homotopic connectivity in each time-shift condition. Finally, we correlated the interhemispheric homotopic connection to the surgical outcomes in a portion of the patients (n=20).

RESULTS

The patients with mesial temporal lobe epilepsy showed decreased homotopic connectivity in the mesial temporal structures, temporal pole, and striatum. Alterations of the bihemispheric homotopic connectivity were lateralized along with delays in the time-shift in mesial temporal lobe epilepsy. The patients with unsuccessful surgical outcomes presented larger interhemispheric voxel-mirrored homotopic connectivity differences.

CONCLUSIONS

This study showed whole patterns of dynamic alterations of interhemispheric homotopic connectivity in mesial temporal lobe epilepsy, extending the knowledge of abnormalities in interhemispheric connectivity in this condition. Time-shift voxel-mirrored homotopic connectivity has the potential for lateralization of unilateral mesial temporal lobe epilepsy and may have the capability of predicting surgical outcomes in this condition.

Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles

In medical domains with low tolerance for invalid predictions, classification confidence is highly important and traditional performance measures such as overall accuracy cannot provide adequate insight into classifications reliability. In this paper, a confident-prediction rate (CPR) which measures the upper limit of confident predictions has been proposed based on receiver operating characteristic (ROC) curves. It has been shown that heterogeneous ensemble of classifiers improves this measure. This ensemble approach has been applied to lateralization of focal epileptogenicity in temporal lobe epilepsy (TLE) and prediction of surgical outcomes. A goal of this study is to reduce extraoperative electrocorticography (eECoG) requirement which is the practice of using electrodes placed directly on the exposed surface of the brain. We have shown that such goal is achievable with application of data mining techniques. Furthermore, all TLE surgical operations do not result in complete relief from seizures and it is not always possible for human experts to identify such unsuccessful cases prior to surgery. This study demonstrates the capability of data mining techniques in prediction of undesirable outcome for a portion of such cases.