Axonal structure underlies white matter functionality and plays a major role in brain connectivity. The current literature on the axonal structure is based on the analysis of two-dimensional (2D) cross-sections, which, as we demonstrate, is precarious. To be able to quantify three-dimensional (3D) axonal morphology, we developed a novel pipeline, called ACSON (AutomatiC 3D Segmentation and morphometry Of axoNs), for automated 3D segmentation and morphometric analysis of the white matter ultrastructure. The automated pipeline eliminates the need for time-consuming manual segmentation of 3D datasets. ACSON segments myelin, myelinated and unmyelinated axons, mitochondria, cells and vacuoles, and analyzes the morphology of myelinated axons. We applied the pipeline to serial block-face scanning electron microscopy images of the corpus callosum of sham-operated (n = 2) and brain injured (n = 3) rats 5 months after the injury. The 3D morphometry showed that cross-sections of myelinated axons were elliptic rather than circular, and their diameter varied substantially along their longitudinal axis. It also showed a significant reduction in the myelinated axon diameter of the ipsilateral corpus callosum of rats 5 months after brain injury, indicating ongoing axonal alterations even at this chronic time-point.

Summary: For several decades, both in vitro and in vivo models of seizures and epilepsy have been employed to unravel the molecular and cellular mechanisms underlying the occurrence of spontaneous recurrent seizures (SRS)—the defining hallmark of the epileptic brain. However, despite great advances in our understanding of seizure genesis, investigators have yet to develop reliable biomarkers and surrogate markers of the epileptogenic process. Sadly, the pathogenic mechanisms that produce the epileptic condition, especially after precipitating events such as head trauma, inflammation, or prolonged febrile convulsions, are poorly understood. A major challenge has been the inherent complexity and heterogeneity of known epileptic syndromes and the differential genetic susceptibilities exhibited by patients at risk. Therefore, it is unlikely that there is only one fundamental pathophysiologic mechanism shared by all the epilepsies. Identification of antiepileptogenesis targets has been an overarching goal over the last decade, as current anticonvulsant medications appear to influence only the acute process of ictogenesis. Clearly, there is an urgent need to develop novel therapeutic interventions that are disease modifying—therapies that either completely or partially prevent the emergence of SRS. An important secondary goal is to develop new treatments that can also lessen the burden of epilepsy comorbidities (e.g., cognitive impairment, mood disorders) by preventing or reducing the deleterious changes during the epileptogenic process. This review summarizes novel antiepileptogenesis targets that were critically discussed at the XIth Workshop on the Neurobiology of Epilepsy (WONOEP XI) meeting in Grottaferrata, Italy. Further, emerging neurometabolic links among several target mechanisms and highlights of the panel discussion are presented.

Tracing the entirety of ultrastructures in large three-dimensional electron microscopy (3D-EM) images of the brain tissue requires automated segmentation techniques. Current segmentation techniques use deep convolutional neural networks (DCNNs) and rely on high-contrast cellular membranes and high-resolution EM volumes. On the other hand, segmenting low-resolution, large EM volumes requires methods to account for severe membrane discontinuities inescapable. Therefore, we developed DeepACSON, which performs DCNN-based semantic segmentation and shape-decomposition-based instance segmentation. DeepACSON instance segmentation uses the tubularity of myelinated axons and decomposes under-segmented myelinated axons into their constituent axons. We applied DeepACSON to ten EM volumes of rats after sham-operation or traumatic brain injury, segmenting hundreds of thousands of long-span myelinated axons, thousands of cell nuclei, and millions of mitochondria with excellent evaluation scores. DeepACSON quantified the morphology and spatial aspects of white matter ultrastructures, capturing nanoscopic morphological alterations five months after the injury.