Unveiling the neurolipidome of obsessive-compulsive disorder: A scoping review navigating future diagnostic and therapeutic applications.

Obsessive-Compulsive Disorder (OCD) poses a multifaceted challenge in psychiatry, with various subtypes and severities greatly impacting well-being. Recent scientific attention has turned towards lipid metabolism, particularly the neurolipidome, in response to clinical demands for cost-effective diagnostics and therapies. This scoping review integrates recent animal, translational, and clinical studies to explore impaired neurolipid metabolism mechanisms in OCD's pathogenesis, aiming to enhance future diagnostics and therapeutics. Five key neurolipids - endocannabinoids, lipid peroxidation, phospholipids, cholesterol, and fatty acids - were identified as relevant. While the endocannabinoid system shows promise in animal models, its clinical application remains limited. Conversely, lipid peroxidation and disruptions in phospholipid metabolism exhibit significant impacts on OCD's pathophysiology based on robust clinical data. However, the role of cholesterol and fatty acids remains inconclusive. The review emphasises the importance of translational research in linking preclinical findings to real-world applications, highlighting the potential of the neurolipidome as a potential biomarker for OCD detection and monitoring. Further research is essential for advancing OCD understanding and treatment modalities.

Neurolipidomic insights into anxiety disorders: Uncovering lipid dynamics for potential therapeutic advances.

Anxiety disorders constitute a spectrum of psychological conditions affecting millions of individuals worldwide, imposing a significant health burden. Historically, the development of anxiolytic medications has been largely focused on neurotransmitter function and modulation. However, in recent years, neurolipids emerged as a prime target for understanding psychiatric pathogenesis and developing novel medications. Neurolipids influence various neural activities such as neurotransmission and cellular functioning, as well as maintaining cell membrane integrity. Therefore, this review aims to elucidate the alterations in neurolipids associated with an anxious mental state and explore their potential as targets of novel anxiolytic medications. Existing evidence tentatively associates dysregulated neurolipid levels with the etiopathology of anxiety disorders. Notably, preclinical investigations suggest that several neurolipids, including endocannabinoids and polyunsaturated fatty acids, may hold promise as potential pharmacological targets. Overall, the current literature tentatively suggests the involvement of lipids in the pathogenesis of anxiety disorders, hinting at potential prospects for future pharmacological interventions.

LayNii: A software suite for layer-fMRI.

High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed opensource and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain 'layerification' and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data.