Metabolic alterations of the dorsolateral prefrontal cortex in sleep-related hypermotor epilepsy: A proton magnetic resonance spectroscopy study

Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease

Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Global trends in research of glutamate in epilepsy during past two decades: A bibliometric analysis

Epilepsy affects more than 70 million people in the world. It is characterized by recurrent spontaneous seizures, and it is related to many neurological, cognitive, and psychosocial consequences. Glutamate neurotransmitter dysfunction has essential functions in the pathophysiology of epilepsy. In this work, bibliometric analysis was conducted to explore the trends, frontiers, and hotspots of the global scientific output of glutamate in epilepsy research in the past 20 years. The Science Citation Index Expanded of the Web of Science Core Collection (WoSCC) was searched to obtain information on publications and records published between 2002 and 2021. VOSviewer and CiteSpace were used to conduct bibliometric and visual analyses on the overall distribution of annual output, major countries, active institutions, journals, authors, commonly cited literature, and keywords. The impact and quality of the papers were assessed using the global citation score (GCS). Four thousand eight hundred ninety-one publications were retrieved in total. During the past two decades, the number of publications (Np) associated with glutamate in epilepsy has risen yearly. The United States has published the most papers; its H-index and number of citations are also the highest. The League of European Research Universities (LERU) was the most productive institution. In 2016, the total score of the paper written by Zhang Y was 854, ranking first. The keywords that appear most frequently are “epilepsy,” “glutamate,” “temporal lobe epilepsy (TLE),” “hippocampus,” and “seizures.” This study showed that although the publications related to epileptic glutamate fluctuated slightly, the Np increased overall. The United States is a great creator and influential country in this field. The first three authors are Eid, T., Aronica, E., and Smolders, I. “spectrum,” “animal model,” “inflammation,” “mutation,” “dysfunction,” and “prefrontal cortex” are increasing research hotspots. By recognizing the most critical indicators (researchers, countries, research institutes, and journals of glutamate release in epilepsy research), the research hotspot of glutamate in epilepsy could help countries, scholars, and policymakers in this field enhance their understanding of the role of glutamate in epilepsy and make decisions.

Imaging Genetics in Epilepsy: Current Knowledge and New Perspectives

Epilepsy is a neurological network disease with genetics playing a much greater role than was previously appreciated. Unfortunately, the relationship between genetic basis and imaging phenotype is by no means simple. Imaging genetics integrates multidimensional datasets within a unified framework, providing a unique opportunity to pursue a global vision for epilepsy. This review delineates the current knowledge of underlying genetic mechanisms for brain networks in different epilepsy syndromes, particularly from a neural developmental perspective. Further, endophenotypes and their potential value are discussed. Finally, we highlight current challenges and provide perspectives for the future development of imaging genetics in epilepsy.

1H-MRS reveals metabolic alterations in generalized tonic-clonic seizures before and after treatment

AIMS

To explore the possible metabolic alterations of bilateral dorsolateral prefrontal cortices (DLPFC) of generalized tonic-clonic seizures (GTCS) patients before and after antiepileptic drugs treatment as compared with healthy controls (HCs) using proton magnetic resonance spectroscopy (1H-MRS).

METHODS

We included 23 newly diagnosed and unmedicated GTCS patients and 23 sex- and age-matched HCs. Metabolites including N-acetyl aspartate (NAA), myo-inositol (Ins), choline (Cho), creatine (Cr), and glutamate + glutamine (Glu + Gln, Glx) concentrations were quantified by using LCModel software and then corrected for the partial volume effect of cerebrospinal fluid.

RESULTS

The results demonstrated that metabolite concentrations were not equal between the left and the right DLPFC. Compared with HC, NAA of the left DLPFC and Cr of the right DLPFC were significantly lower in pre-treatment patients. Self-controlled study revealed that the patients' NAA of the left DLPFC increased while their Cr of the right DLPFC decreased after treatment. Correlation analysis showed a negative correlation between the duration of medication and the pre- and post-treatment difference of Cr.

CONCLUSION

These findings may shed a light on the metabolic mechanism of GTCS and the neurobiochemical mechanisms of AEDs.

Social cognition in children and adolescents with epilepsy: A meta-analysis

Many studies have investigated impairments in two key domains of social cognition (theory of mind [ToM] and facial emotion recognition [FER]) in children and adolescents with epilepsy. However, inconsistent conclusions were found. Our objective was to characterize social cognition performance of children and adolescents with epilepsy. A literature search was conducted using Web of Science, PubMed, and Embase databases. The article retrieval, screening, quality assessment (Newcastle-Ottawa-Scale), and data extraction were performed independently by two investigators. A random-effects model was used to examine estimates. The meta-analysis included 19 studies, with a combined sample of 623 children and adolescents with epilepsy (mean [SD] age, 12.13 [2.62] years; 46.1% female) and 677 healthy controls [HCs]) (mean [SD] age, 11.48 [2.71] years; 50.7% female). The results revealed that relative to HCs, children and adolescents with epilepsy exhibited deficits in ToM (g = -1.08, 95% CI [-1.38, -0.78], p < 0.001, the number of studies [k] = 13), FER (g = -0.98, 95% CI [-1.33, -0.64], p < 0.001, k = 12), and ToM subcomponents (cognitive ToM: g = -1.04, 95% CI [-1.35, -0.72], p < 0.001, k = 12] and affective ToM: g = -0.73, 95% CI [-1.12, -0.34], p < 0.001, k = 8). In addition, there were no statistically significant differences in social cognition deficits between children and adolescents with focal epilepsy and generalized epilepsy. Meta-regressions confirmed the robustness of the results. These quantitative results further deepen our understanding of the two core domains of social cognition in children and adolescents with epilepsy and may assist in the development of cognitive interventions for this patient population. Systematic review registration: https://inplasy.com/inplasy-2022-3-0011/, identifier INPLASY202230011.