Neuroimaging methods in Epilepsy of Temporal Origin

Differentiation of Epileptic Brain Abnormalities among Neurological Patients at Taif Region Using MRI

This study was conducted to assess the prevalence of epilepsy among different age groups and gender of neurological patients in the Taif region and define the most common brain lesion, affecting epileptic patients living in the Taif city using MRI. Data from 150 patients who were clinically diagnosed with epilepsy and had brain MRIs were analyzed using SPSS. Statistical significance was considered when the p value is 0.05. The percentage of epilepsy was generally higher in males than in females in the Taif city, and seizures were different between the studied age groups. However, epilepsy was more pronounced in females than in males at certain age groups. Moreover, white matter lesions were most commonly found in the studied group (27.7%), followed by focal lesions, edema, and stroke with equal percentages (16.9%) and less commonly with congenital diseases (12%) and atrophic changes (9.6%). Epilepsy was more pronounced in females than in males at certain age groups. White matter lesions were identified as the most common lesion, presenting in epilepsy patients in the Taif city.

Brain but not serum BDNF levels are associated with structural alterations in the hippocampal regions in patients with drug-resistant mesial temporal lobe epilepsy

Mesial temporal lobe epilepsy is the most common type of focal epilepsy, imposing a significant burden on the health care system worldwide. Approximately one-third of patients with this disease who do not adequately respond to pharmacotherapy are considered drug-resistant subjects. Despite having some clues of how such epileptic activity and resistance to therapy emerge, coming mainly from preclinical models, we still witness a scarcity of human data. To narrow this gap, in this study, we aimed to estimate the relationship between hippocampal and serum levels of brain-derived neurotrophic factor (BDNF), one of the main and most widely studied neurotrophins, and hippocampal subfield volumes in patients with drug-resistant mesial temporal epilepsy undergoing neurosurgical treatment. We found that hippocampal (but not serum) BDNF levels were negatively correlated with the contralateral volumes of the CA1 and CA4 subfields, presubiculum, subiculum, dentate gyrus, and molecular layer of the hippocampus. Taken together, these findings are generally in accordance with existing data, arguing for a proepileptic nature of BDNF effects in the hippocampus and related brain structures.

Temporal lobe epilepsy lateralisation and surgical outcome prediction using diffusion imaging

OBJECTIVE

We sought to augment the presurgical workup of medically refractory temporal lobe epilepsy by creating a supervised machine learning technique that uses diffusion-weighted imaging to classify patient-specific seizure onset laterality and surgical outcome.

METHODS

151 subjects were included in this analysis: 62 patients (aged 18-68 years, 36 women) and 89 healthy controls (aged 18-71 years, 47 women). We created a supervised machine learning technique that uses diffusion-weighted metrics to classify subject groups. Specifically, we sought to classify patients versus healthy controls, unilateral versus bilateral temporal lobe epilepsy, left versus right temporal lobe epilepsy and seizure-free versus not seizure-free surgical outcome. We then reduced the dimensionality of derived features with community detection for ease of interpretation.

RESULTS

We classified the subject groups in withheld testing data sets with a cross-fold average testing areas under the receiver operating characteristic curve of 0.745 for patients versus healthy controls, 1.000 for unilateral versus bilateral seizure onset, 0.662 for left versus right seizure onset, 0.800 for left-sided seizure-free vsersu not seizure-free surgical outcome and 0.775 for right-sided seizure-free versus not seizure-free surgical outcome.

CONCLUSIONS

This technique classifies important clinical decisions in the presurgical workup of temporal lobe epilepsy by generating discerning white-matter features. We believe that this work augments existing network connectivity findings in the field by further elucidating important white-matter pathology in temporal lobe epilepsy. We hope that this work contributes to recent efforts aimed at using diffusion imaging as an augmentation to the presurgical workup of this devastating neurological disorder.

Application value of molecular imaging technology in epilepsy

Epilepsy is a common neurological disease with various seizure types, complicated etiologies, and unclear mechanisms. Its diagnosis mainly relies on clinical history, but an electroencephalogram is also a crucial auxiliary examination. Recently, brain imaging technology has gained increasing attention in the diagnosis of epilepsy, and conventional magnetic resonance imaging can detect epileptic foci in some patients with epilepsy. However, the results of brain magnetic resonance imaging are normal in some patients. New molecular imaging has gradually developed in recent years and has been applied in the diagnosis of epilepsy, leading to enhanced lesion detection rates. However, the application of these technologies in epilepsy patients with negative brain magnetic resonance must be clarified. Thus, we reviewed the relevant literature and summarized the information to improve the understanding of the molecular imaging application value of epilepsy.

High-Resolution EEG Source Localization in Segmentation-Free Head Models Based on Finite-Difference Method and Matching Pursuit Algorithm

Electroencephalogram (EEG) is a method to monitor electrophysiological activity on the scalp, which represents the macroscopic activity of the brain. However, it is challenging to identify EEG source regions inside the brain based on data measured by a scalp-attached network of electrodes. The accuracy of EEG source localization significantly depends on the type of head modeling and inverse problem solver. In this study, we adopted different models with a resolution of 0.5 mm to account for thin tissues/fluids, such as the cerebrospinal fluid (CSF) and dura. In particular, a spatially dependent conductivity (segmentation-free) model created using deep learning was developed and used for more realist representation of electrical conductivity. We then adopted a multi-grid-based finite-difference method (FDM) for forward problem analysis and a sparse-based algorithm to solve the inverse problem. This enabled us to perform efficient source localization using high-resolution model with a reasonable computational cost. Results indicated that the abrupt spatial change in conductivity, inherent in conventional segmentation-based head models, may trigger source localization error accumulation. The accurate modeling of the CSF, whose conductivity is the highest in the head, was an important factor affecting localization accuracy. Moreover, computational experiments with different noise levels and electrode setups demonstrate the robustness of the proposed method with segmentation-free head model.

The Contribution of Rat Studies to Current Knowledge of Major Depressive Disorder: Results From Citation Analysis

Major depressive disorder (MDD) is the most severe depression type and one of the leading causes of morbidity worldwide. Animal models are widely used to understand MDD etiology, pathogenesis, and treatment, but the efficacy of this research for patients has barely been systematically evaluated. Such evaluation is important given the resource consumption and ethical concerns incurred by animal use. We used the citation tracking facilities within Web of Science and Scopus to locate citations of original research papers on rats related to MDD published prior to 2013—to allow adequate time for citations—identified in PubMed and Scopus by relevant search terms. Resulting citations were thematically coded in eight categories, and descriptive statistics were calculated. 178 publications describing relevant rat studies were identified. They were cited 8,712 times. More than half (4,633) of their citations were by other animal studies. 794 (less than 10%) were by human medical papers. Citation analysis indicates that rat model research has contributed very little to the contemporary clinical understanding of MDD. This suggests a misuse of limited funding hence supporting a change in allocation of research and development funds targeting this disorder to maximise benefits for patients.