Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach.

Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed 116 TLE patients compared with 51 healthy controls. Employing microstate analysis, we assessed brain dynamic disparities between TLE patients and healthy controls, as well as between drug-resistant epilepsy (DRE) and drug-sensitive epilepsy (DSE) patients. We constructed dynamic functional connectivity networks based on microstates and quantified their spatial and temporal variability. Utilizing these brain network features, we developed machine learning models to discriminate between TLE patients and healthy controls, and between DRE and DSE patients. Temporal dynamics in TLE patients exhibited significant acceleration compared to healthy controls, along with heightened synchronization and instability in brain networks. Moreover, DRE patients displayed notably lower spatial variability in certain parts of microstate B, E and F dynamic functional connectivity networks, while temporal variability in certain parts of microstate E and G dynamic functional connectivity networks was markedly higher in DRE patients compared to DSE patients. The machine learning model based on these spatiotemporal metrics effectively differentiated TLE patients from healthy controls and discerned DRE from DSE patients. The accelerated microstate dynamics and disrupted microstate sequences observed in TLE patients mirror highly unstable intrinsic brain dynamics, potentially underlying abnormal discharges. Additionally, the presence of highly synchronized and unstable activities in brain networks of DRE patients signifies the establishment of stable epileptogenic networks, contributing to the poor responsiveness to antiseizure medications. The model based on spatiotemporal metrics demonstrated robust predictive performance, accurately distinguishing both TLE patients from healthy controls and DRE patients from DSE patients.

Solute carrier transporter disease and developmental and epileptic encephalopathy.

The International League Against Epilepsy officially revised its classification in 2017, which amended "epileptic encephalopathy" to "developmental and epileptic encephalopathy". With the development of genetic testing technology, an increasing number of genes that cause developmental and epileptic encephalopathies are being identified. Among these, solute transporter dysfunction is part of the etiology of developmental and epileptic encephalopathies. Solute carrier transporters play an essential physiological function in the human body, and their dysfunction is associated with various human diseases. Therefore, in-depth studies of developmental and epileptic encephalopathies caused by solute carrier transporter dysfunction can help develop new therapeutic modalities to facilitate the treatment of refractory epilepsy and improve patient prognosis. In this article, the concept of transporter protein disorders is first proposed, and nine developmental and epileptic encephalopathies caused by solute carrier transporter dysfunction are described in detail in terms of pathogenesis, clinical manifestations, ancillary tests, and precise treatment to provide ideas for the precise treatment of epilepsy.

Genetic variations in GABA metabolism and epilepsy.

Epilepsy is a paroxysmal brain disorder that results from an imbalance between neuronal excitation and inhibition. Gamma-aminobutyric acid (GABA) is the most important inhibitory neurotransmitter in the brain and plays an important role in the occurrence and development of epilepsy. Abnormalities in all aspects of GABA metabolism, including GABA synthesis, transport, genes encoding GABA receptors, and GABA inactivation, may lead to epilepsy. GABRA1, GABRA2, GABRA5, GABRB1, GABRB2, GABRB3, GABRG2 and GABBR2 are genes that encode GABA receptors and are commonly associated with epilepsy. Mutations of these genes lead to a variety of epilepsy syndromes with different clinical phenotypes, primarily by down regulating receptor expression and reducing the amplitude of GABA-evoked potentials. GABA is metabolized by GABA transaminase and succinate semi aldehyde dehydrogenase, which are encoded by the ABAT and ALDH5A1 genes, respectively. Mutations of these genes result in symptoms related to deficiency of GABA transaminase and succinate semi aldehyde dehydrogenase, such as epilepsy and cognitive impairment. Most of the variation in genes associated with GABA metabolism are accompanied by developmental disorders. This review focuses on advances in understanding the relationship between genetic variation in GABA metabolism and epilepsy to establish a basis for the accurate diagnosis and treatment of epilepsy.