Opinion texts summarization based on texts concepts with multi-objective pruning approach.

Considering the huge volume of opinion texts published on various social networks, it is extremely difficult to peruse and use these texts. The automatic creation of summaries can be a significant help for the users of such texts. The current paper employs manifold learning to mitigate the challenges of the complexity and high dimensionality of opinion texts and the K-Means algorithm for clustering. Furthermore, summarization based on the concepts of the texts can improve the performance of the summarization system. The proposed method is unsupervised extractive, and summarization is performed based on the concepts of the texts using the multi-objective pruning approach. The main parameters utilized to perform multi-objective pruning include relevancy, redundancy, and coverage. The simulation results show that the proposed method outperformed the MOOTweetSumm method while providing an improvement of 11% in terms of the ROGUE-1 measure and an improvement of 9% in terms of the ROGUE-L measure.

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

[This corrects the article DOI: 10.1371/journal.pone.0199137.].

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.

DTI-based response-driven modeling of mTLE laterality.

PURPOSE: To develop lateralization models for distinguishing between unilateral and bilateral mesial temporal lobe epilepsy (mTLE) and determining laterality in cases of unilateral mTLE. BACKGROUND: mTLE is the most common form of medically refractory focal epilepsy. Many mTLE patients fail to demonstrate an unambiguous unilateral ictal onset. Intracranial EEG (icEEG) monitoring can be performed to establish whether the ictal origin is unilateral or truly bilateral with independent bitemporal ictal origin. However, because of the expense and risk of intracranial electrode placement, much research has been done to determine if the need for icEEG can be obviated with noninvasive neuroimaging methods, such as diffusion tensor imaging (DTI). METHODS: Fractional anisotropy (FA) was used to quantify microstructural changes reflected in the diffusivity properties of the corpus callosum, cingulum, and fornix, in a retrospective cohort of 31 patients confirmed to have unilateral (n = 24) or bilateral (n = 7) mTLE. All unilateral mTLE patients underwent resection with an Engel class I outcome. Eleven were reported to have hippocampal sclerosis on pathological analysis; nine had undergone prior icEEG. The bilateral mTLE patients had undergone icEEG demonstrating independent epileptiform activity in both right and left hemispheres. Twenty-three nonepileptic subjects were included as controls. RESULTS: In cases of right mTLE, FA showed significant differences from control in all callosal subregions, in both left and right superior cingulate subregions, and in forniceal crura. Comparison of right and left mTLE cases showed significant differences in FA of callosal genu, rostral body, and splenium and the right posteroinferior and superior cingulate subregions. In cases of left mTLE, FA showed significant differences from control only in the callosal isthmus. Significant differences in FA were identified when cases of right mTLE were compared with bilateral mTLE cases in the rostral and midbody callosal subregions and isthmus. Based on 11 FA measurements in the cingulate, callosal and forniceal subregions, a response-driven lateralization model successfully differentiated all cases (n = 54) into groups of unilateral right (n = 12), unilateral left (n = 12), and bilateral mTLE (n = 7), and nonepileptic control (23). CONCLUSION: The proposed response-driven DTI biomarker is intended to lessen diagnostic ambiguity of laterality in cases of mTLE and help optimize selection of surgical candidates. Application of this model shows promise in reducing the need for invasive icEEG in prospective cases.

MEG Coherence and DTI Connectivity in mTLE.

Magnetoencephalography (MEG) is a noninvasive imaging method for localization of focal epileptiform activity in patients with epilepsy. Diffusion tensor imaging (DTI) is a noninvasive imaging method for measuring the diffusion properties of the underlying white matter tracts through which epileptiform activity is propagated. This study investigates the relationship between the cerebral functional abnormalities quantified by MEG coherence and structural abnormalities quantified by DTI in mesial temporal lobe epilepsy (mTLE). Resting state MEG data was analyzed using MEG coherence source imaging (MEG-CSI) method to determine the coherence in 54 anatomical sites in 17 adult mTLE patients with surgical resection and Engel class I outcome, and 17 age- and gender- matched controls. DTI tractography identified the fiber tracts passing through these same anatomical sites of the same subjects. Then, DTI nodal degree and laterality index were calculated and compared with the corresponding MEG coherence and laterality index. MEG coherence laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in insular cortex and both lateral orbitofrontal and superior temporal gyri (p < 0.017). Likewise, DTI nodal degree laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in gyrus rectus, insular cortex, precuneus and superior temporal gyrus (p < 0.017). In insular cortex, MEG coherence laterality correlated with DTI nodal degree laterality ([Formula: see text] in the cases of mTLE. None of these anatomical sites showed statistically significant differences in coherence laterality between right and left sides of the controls. Coherence laterality was in agreement with the declared side of epileptogenicity in insular cortex (in 82 % of patients) and both lateral orbitofrontal (88 %) and superior temporal gyri (88 %). Nodal degree laterality was also in agreement with the declared side of epileptogenicity in gyrus rectus (in 88 % of patients), insular cortex (71 %), precuneus (82 %) and superior temporal gyrus (94 %). Combining all significant laterality indices improved the lateralization accuracy to 94 % and 100 % for the coherence and nodal degree laterality indices, respectively. The associated variations in diffusion properties of fiber tracts quantified by DTI and coherence measures quantified by MEG with respect to epileptogenicity possibly reflect the chronic microstructural cerebral changes associated with functional interictal activity. The proposed methodology for using MEG and DTI to investigate diffusion abnormalities related to focal epileptogenicity and propagation may provide a further means of noninvasive lateralization.

Application of MEG coherence in lateralization of mTLE.

Magnetoencephalography (MEG) is a noninvasive imaging method for localization of focal epileptiform activity in patients with epilepsy. This study investigates the cerebral functional abnormalities quantified by MEG coherence laterality in mesial temporal lobe epilepsy (mTLE). Resting state MEG data was analyzed using MEG coherence source imaging (MEG-CSI) method to determine the coherence in 54 anatomical sites in 12 adult mTLE patients and 12 age- and gender-matched controls. MEG coherence laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in insular cortex and both lateral orbitofrontal and superior temporal gyri (p<;0.025). None of these anatomical sites showed statistically significant differences in coherence laterality between right and left sides of controls. Coherence laterality was in agreement with the declared side of epileptogenicity in insular cortex (in 75% of patients) and both lateral orbitofrontal (83%) and superior temporal gyri (84%). Combining all significant laterality indices improved the lateralization accuracy to 92%. The proposed methodology for using MEG to investigate the abnormalities related to focal epileptogenicity and propagation can provide a further means of noninvasive lateralization.

Application of DTI connectivity in lateralization of mTLE.

Diffusion tensor imaging (DTI) is a noninvasive imaging method for measuring the diffusion properties of the underlying white matter tracts through which epileptiform activity is propagated. This study investigates the structural abnormalities quantified by DTI in mesial temporal lobe epilepsy (mTLE). Fiber tracts passing through 54 anatomical sites in 12 adult mTLE patients and 12 age- and gender-matched controls were identified using DTI tractography. DTI nodal degree (ND) and laterality index were then calculated. ND laterality, after Bonferroni adjustment, showed significant differences for right versus left mTLE in gyrus rectus, insular cortex, precuneus and superior temporal gyrus (p<;0.025). None of these anatomical sites showed statistically significant differences in ND laterality between right and left sides of the controls. Laterality models determined by logistic regression on the ND laterality data agreed with the side of epileptogenicity as it pertained to the gyrus rectus, insular cortex, precuneus and superior temporal gyrus for 89%, 72%, 83% and 92% of the patients, respectively. Combining the laterality measures in these four anatomical sites improved the results further with correct lateralization of 100% for all patients. The proposed methodology for using DTI connectivity to investigate diffusion abnormalities related to focal epileptogenicity and propagation can provide a further means of noninvasive lateralization.

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.

Support Vector Machine with nonlinear-kernel optimization for lateralization of epileptogenic hippocampus in MR images.

Surgical treatment is suggested for seizure control in medically intractable epilepsy patients. Detailed pre-surgical evaluation and lateralization using Magnetic Resonance Images (MRI) is expected to result in a successful surgical outcome. In this study, an optimized pattern recognition approach is proposed for lateralization of mesial Temporal Lobe Epilepsy (mTLE) patients using asymmetry of imaging indices of hippocampus. T1-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) images of 76 symptomatic mTLE patients are considered. First, hippocampus is segmented using automatic and manual segmentation methods; then, volumetric and intensity features are extracted from the MR images. A nonlinear Support Vector Machine (SVM) with optimized Gaussian Radial Basis Function (GRBF) kernel is used to classify the imaging features. Using leave-one-out cross validation, this method results in a correct lateralization rate of 82%, a probability of detection for the left side of 0.90 (with false alarm probability of 0.04) and a probability of detection for the right side of 0.69 (with zero false alarm probability). The lateralization results are compared to linear SVM, multi-layer perceptron Artificial Neural Network (ANN), and volumetry and FLAIR asymmetry analysis. This lateralization method is suggested for pre-surgical evaluation using MRI before surgical treatment in mTLE patients. It achieves a more correct lateralization rate and fewer false positives.

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.