Maturation of the human brain and epilepsy

Brain Age Prediction Based on Quantitative Susceptibility Mapping Using the Segmentation Transformer

The process of brain aging is intricate, encompassing significant structural and functional changes, including myelination and iron deposition in the brain. Brain age could act as a quantitative marker to evaluate the degree of the individual's brain evolution. Quantitative susceptibility mapping (QSM) is sensitive to variations in magnetically responsive substances such as iron and myelin, making it a favorable tool for estimating brain age. In this study, we introduce an innovative 3D convolutional network named Segmentation-Transformer-Age-Network (STAN) to predict brain age based on QSM data. STAN employs a two-stage network architecture. The first-stage network learns to extract informative features from the QSM data through segmentation training, while the second-stage network predicts brain age by integrating the global and local features. We collected QSM images from 712 healthy participants, with 548 for training and 164 for testing. The results demonstrate that the proposed method achieved a high accuracy brain age prediction with a mean absolute error (MAE) of 4.124 years and a coefficient of determination (R2) of 0.933. Furthermore, the gaps between the predicted brain age and the chronological age of Parkinson's disease patients were significantly higher than those of healthy subjects (P<0.01). We thus believe that using QSM-based predicted brain age offers a more reliable and accurate phenotype, with the potentiality to serve as a biomarker to explore the process of advanced brain aging.

Development and validation of a nomogram for the early prediction of drug resistance in children with epilepsy

BACKGROUND AND PURPOSE

This study aimed to effectively identify children with drug-resistant epilepsy (DRE) in the early stage of epilepsy, and take personalized interventions, to improve patients' prognosis, reduce serious comorbidity, and save social resources. Herein, we developed and validated a nomogram prediction model for children with DRE.

METHODS

The training set was patients with epilepsy who visited the Children's Hospital of Soochow University (Suzhou Industrial Park, Jiangsu Province, China) between January 2015 and December 2017. The independent risk factors for DRE were screened by univariate and multivariate logistic regression analyses using SPSS21 software. The nomogram was designed according to the regression coefficient. The nomogram was validated in the training and validation sets. Internal validation was conducted using bootstrapping analyses. We also externally validated this instrument in patients with epilepsy from the Children's Hospital of Soochow University (Gusu District, Jiangsu Province, China) and Yancheng Maternal and Child Health Hospital between January 2018 and December 2018. The nomogram's performance was assessed by concordance (C-index), calibration curves, as well as GiViTI calibration belts.

RESULTS

Multivariate logistic regression analysis of 679 children with epilepsy from the Children's Hospital of Soochow University (Suzhou Industrial Park, Jiangsu Province, China) showed that onset age<1, status epilepticus (SE), focal seizure, > 20 pre-treatment seizures, clear etiology (caused by genetic, structural, metabolic, or infectious), development and epileptic encephalopathy (DEE), and neurological abnormalities were all independent risk factors for DRE. The AUC of 0.92 for the training set compared to that of 0.91 for the validation set suggested a good discrimination ability of the prediction model. The C-index was 0.92 and 0.91 in the training and validation sets. Additionally, both good calibration curves and GiViTI calibration belts (P-value: 0.849 and 0.291, respectively) demonstrated that the predicted risks had strong consistency with the observed outcomes, suggesting that the prediction model in both groups was perfectly calibrated.

CONCLUSION

A nomogram prediction model for DRE was developed, with good discrimination and calibration in the training set and the validation set. Furthermore, the model demonstrated great accuracy, consistency, and prediction ability. Therefore, the nomogram prediction model can aid in the timely identification of DRE in children.

White matter brain aging in relationship to schizophrenia and its cognitive deficit

We hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used a machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. A feature selection procedure was first employed to choose fractional anisotropy values in 34 of 43 white fiber tracts. Using these features, a machine learning model was trained based on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age > 30 group in patients (p < 0.001) but not in age ≤ 30 group (p = 0.364). Δage in patients was significantly and negatively associated with both working memory (β = -0.176, p = 0.007) and processing speed (β = -0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from the third decade of life; the increase of Δage in SZs is associated with the declined neurocognitive performance.

Genetic disorders with central nervous system white matter abnormalities: An update

Several genetic disorders have variable degree of central nervous system white matter abnormalities. We retrieved and reviewed 422 genetic conditions with prominent and consistent involvement of white matter from the literature. We herein describe the current definitions, classification systems, clinical spectrum, neuroimaging findings, genomics, and molecular mechanisms of these conditions. Though diagnosis for most of these disorders relies mainly on genomic tests, specifically exome sequencing, we collate several clinical and neuroimaging findings still relevant in diagnosis of clinically recognizable disorders. We also review the current understanding of pathophysiology and therapeutics of these disorders.

Modeling poststroke epilepsy and preclinical development of drugs for poststroke epilepsy

Stroke is a severe clinical issue for global public health, representing the third leading cause of death and a major cause of disability in developed countries. Progresses in the pharmacological treatment of the acute stroke have given rise to a significant decrease in its mortality rate. However, as a result, there has been an increasing number of stroke survivors living with disability worldwide. Poststroke epilepsy (PSE) is a common clinical complication following stroke. Seizures can arise in close temporal association with stroke damage and/or after a variably longer interval. Overall, PSE have a good prognosis; in fact, its responding rate to antiepileptic drugs (AEDs) is higher than other types of epilepsy. However, regarding pharmacological treatment, some issues are still unresolved. To this aim, a deeper understanding of mechanisms underlying the transformation of infarcted tissue into an epileptic focus or better from a nonepileptic brain to an epileptic brain is also mandatory for PSE. However, studying epileptogenesis in patients with PSE clearly has several limitations and difficulties; therefore, modeling PSE is crucial. Until now, different experimental models have been used to study the etiopathology of cerebrovascular stroke with or without infarction, but few studies focused on poststroke epileptogenesis and PSE. In this review, we show a brief overview on the features emerging from preclinical research into experimental PSE, which could affect the discovery of biomarkers and therapy strategies for poststroke epileptogenesis. This article is part of the Special Issue "Seizures & Stroke".

Gray Matter Age Prediction as a Biomarker for Risk of Dementia

The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model to predict brain age trained on 3,688 dementia-free participants of the Rotterdam Study (mean age 66 ± 11 y, 55% women). Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for age, sex, intracranial volume, GM volume, hippocampal volume, white matter hyperintensities, years of education, and APOE ε4 allele carriership. Additionally, we computed the attention maps, which shows which regions are important for age prediction. Logistic regression and Cox proportional hazard models showed that the age gap was significantly related to incident dementia (odds ratio [OR] = 1.11 and 95% confidence intervals [CI] = 1.05-1.16; hazard ratio [HR] = 1.11, and 95% CI = 1.06-1.15, respectively). Attention maps indicated that GM density around the amygdala and hippocampi primarily drove the age estimation. We showed that the gap between predicted and chronological brain age is a biomarker, complimentary to those that are known, associated with risk of dementia, and could possibly be used for early-stage dementia risk screening.

Epigenetic Suppression of GADs Expression is Involved in Temporal Lobe Epilepsy and Pilocarpine-Induced Mice Epilepsy

Recent studies have shown that histone acetylation is involved with the regulation of enzyme glutamate decarboxylases (GADs), including GAD67 and GAD65. Here, we investigated the histone acetylation modifications of GADs in the pathogenesis of epilepsy and explored the therapeutic effect of a novel second-generation histone deacetylase inhibitor (HDACi) JNJ-26481585 in epilepsy animals. We revealed the suppression of GADs protein and mRNA level, and histone hypoacetylation in patients with temporal lobe epilepsy and pilocarpine-induced epilepsy mice model. Double-immunofluorescence also indicated that the hypoacetyl-H3 was located in hippocampal GAD67/GAD65 positive neurons in epilepsy mice. JNJ-26481585 significantly reversed the decrease of the GAD67/GAD65 both protein and mRNA levels, and the histone hypoacetylation of GABAergic neurons in epilepsy mice. Meanwhile, single-cell real-time PCR performed in GFP-GAD67/GAD65 transgenic mice demonstrated that JNJ-26481585 induced increase of GAD67/GAD65 mRNA level in GABAergic neurons. Furthermore, JNJ-26481585 significantly alleviated the epileptic seizures in mice model. Together, our findings demonstrate inhibition of GADs gene via histone acetylation plays an important role in the pathgenesis of epilepsy, and suggest JNJ-26481585 as a promising therapeutic strategy for epilepsy.

Development of epilepsy after ischaemic stroke

For about 30% of patients with epilepsy the cause is unknown. Even in patients with a known risk factor for epilepsy, such as ischaemic stroke, only a subpopulation of patients develops epilepsy. Factors that contribute to the risk for epileptogenesis in a given individual generally remain unknown. Studies in the past decade on epilepsy in patients with ischaemic stroke suggest that, in addition to the primary ischaemic injury, existing difficult-to-detect microscale changes in blood vessels and white matter present as epileptogenic pathologies. Injury severity, location and type of pathological changes, genetic factors, and pre-injury and post-injury exposure to non-genetic factors (ie, the exposome) can divide patients with ischaemic stroke into different endophenotypes with a variable risk for epileptogenesis. These data provide guidance for animal modelling of post-stroke epilepsy, and for laboratory experiments to explore with increased specificity the molecular 'mechanisms, biomarkers, and treatment targets of post-stroke epilepsy in different circumstances, with the aim of modifying epileptogenesis after ischaemic stroke in individual patients without compromising recovery.

Autism and Epilepsy: Exploring the Relationship Using Experimental Models

The common co-occurrence of autism and epilepsy suggests that certain neurobiological mechanisms are shared between these disorders. In particular, the profusion of novel genetic mutations being discovered in autism and epilepsy points to abnormalities in synapse formation and function that alter the balance between neuronal excitation and inhibition. Animal models can be informative in sorting out the medical and behavioral complexities in autism and epilepsy and the relationship between them. As mechanistic information accrues, it is anticipated that mutation- and pathway-specific targeted treatments can be developed.

Neonatal Seizures: Impact on Neurodevelopmental Outcomes

Neonatal period is the most vulnerable time for the occurrence of seizures, and neonatal seizures often pose a clinical challenge both for their acute management and frequency of associated long-term co-morbidities. Etiologies of neonatal seizures are known to play a primary role in the anti-epileptic drug responsiveness and the long-term sequelae. Recent studies have suggested that burden of acute recurrent seizures in neonates may also impact chronic outcomes independent of the etiology. However, not many studies, either clinical or pre-clinical, have addressed the long-term outcomes of neonatal seizures in an etiology-specific manner. In this review, we briefly review the available clinical and pre-clinical research for long-term outcomes following neonatal seizures. As the most frequent cause of acquired neonatal seizures, we focus on the studies evaluating long-term effects of HIE-seizures with the goal to evaluate (1) what parameters evaluated during acute stages of neonatal seizures can reliably be used to predict long-term outcomes? and (2) what available clinical and pre-clinical data are available help determine importance of etiology vs. seizure burdens in long-term sequelae.

Seizure susceptibility in immature brain due to lack of COX-2-induced PGF2α

The immature brain is prone to seizure; however, the mechanism underlying this vulnerability has not been clarified. Febrile seizure is common in young children, and the use of non-steroidal anti-inflammatory drugs for febrile seizure is not recommended. In previous studies, we established that prostaglandin (PG) F2α, a product of cyclooxygenase (COX), acts as an endogenous anticonvulsant in the adult mouse. Therefore, we assumed that COX-2 activity was involved with seizure susceptibility in early life. In the present study, immature mice (postnatal day 9) were far more prone to kainic acid (KA)-induced seizures than mature mice (after postnatal day 35). Seizure activity began later in immature mice, but was more severe and was unaffected by a potent COX inhibitor, indomethacin; in contrast, indomethacin aggravated seizure activity in mature mice. Immature mouse brains exhibited little basal COX-2 expression and little KA-induced COX-2 induction, while KA-induced COX-2 expression and PGF2α release were prominent in mature brains. During brain development, COX expression was increased and glycosylated in an age-dependent manner, which was necessary for COX enzyme activity. Intracisternal PGF2α administration also reduced KA-induced seizure activity and mortality. Taken together, low COX activity and the resulting deficiency of PGF2α may be an essential cause of increased seizure susceptibility in the immature brain.