Sharaev, M. | Search bioRxiv

bioRxiv2023

White matter lipidome alterations in the schizophrenia brain

Numerous brain imaging studies have reported white matter alterations in schizophrenia, but the lipidome analysis of the corresponding tissue remains incomplete. In this study, we investigated the lipidome composition of six subcortical white matter regions corresponding to major axonal tracks in both control subjects and schizophrenia patients. All six regions exhibited a consistent pattern of quantitative lipidome alterations in schizophrenia, affecting specific lipid classes. These alterations partly involved myelin-forming lipid classes, particularly sphingolipids, with the extent of alterations reflecting the myelin changes previously reported in structural brain imaging studies. The other part of the schizophrenia-associated alterations, which showed a significant decrease in the disorder, involved lipid classes abundant in mitochondria. A similar significant decrease was also observed in the mitochondria-enriched membrane fraction isolated from the white matter of individuals with schizophrenia. This suggests a substantial reduction in the number of mitochondria in subcortical white matter in schizophrenia, a hypothesis supported by quantitative mitochondria staining.

neuroscience↗

bioRxiv2021

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE)

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye-movements and muscle artifacts from electroencephalography (EEG). Due to other possible EEG contaminations, a rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts involvement. Noteworthy, as also revealed by our study, experts opinion about the nature of a component often disagrees highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm not only to remove artifacts but also to find specific patterns in EEG signals using ICA decomposition based on accumulated experts knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on estimation of IC time-series, IC amplitude topography and spectral power distribution. The platform allows supervised ML model training and re-training on available data subsamples for better performance in specific tasks (i.e. movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model shows that it can be used not only for detection of noisy IC but also for automatic identifications of components related to the functional brain oscillations such as alpha and mu-rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will be used for continuous improvement of ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.

neuroscience↗