Disturbances of the early stages of neurogenesis lead to irreversible changes in the structure of the mature brain and its functional impairment, including increased excitability, which may be the basis for drug-resistant epilepsy. The range of possible clinical symptoms is as wide as the different stages of disturbed neurogenesis may be. In this study, the authors used a quadruple model of brain dysplasia by comparing structural and functional disorders in animals whose neurogenesis was disturbed with a single dose of 1 Gy of gamma rays at one of the four stages of neurogenesis, i.e. Thereafter, pilocarpine was administered and significant differences in susceptibility to seizure behavioral symptoms were detected depending on the degree of brain dysplasia. Before, during and after the seizures significant correlations were found between the density of parvalbumin-immunopositive neurons located in the cerebral cortex and the intensity of behavioral seizure symptoms or increases in the power of particular EEG bands. Neurons expressing calretinin or NPY showed also dysplasia-related increases without, however, correlations with parameters of seizure intensity. The results point to significant roles of parvalbumin-expressing interneurons, and also to expression of NPY - an endogenous anticonvulsant and neuroprotectant reducing susceptibility to seizures and supporting neuronal survival. This article is protected by copyright. All rights reserved.

Read the original article on Pubmed

The scientists here describe and validate Smile-GAN, a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration. Applying this framework to longitudinal data revealed two distinct progression pathways. Measures of expression of these patterns predicted the pathway and rate of future neurodegeneration. Pattern expression offered complementary performance to amyloid/tau in predicting clinical progression. These deep-learning derived biomarkers offer potential for precision diagnostics and targeted clinical trial recruitment.

Read the original article on Pubmed


Please, help us continue to provide valuable information: