Fasting, ketogenic, and anti-inflammatory diets in multiple sclerosis: a randomized controlled trial with 18-month follow-up.

Background: Multiple sclerosis (MS) is the most common inflammatory disease of the central nervous system in young adulthood leading to disability and early retirement. Ketone-based diets improve the disease course in MS animal models and health outcomes in different pilot studies of neurodegenerative diseases.

Methods: We enrolled 105 individuals with relapsing-remitting MS (RRMS) in an 18-month, randomized, controlled study, and randomized them into (1) standard healthy diet (SD) as recommended by the German Nutrition Society, (2) fasting diet (FD) with 7-day fasts every 6 months with intermittent fasting at 6 of 7 days a week or (3) ketogenic diet (KD) with 20–40 g carbohydrates per day.

An Explainable AI Exploration of the Machine Learning Classification of Neoplastic Intracerebral Hemorrhage from Non-Contrast CT.

Intracerebral hemorrhage (ICH) associated with primary and metastatic brain tumors presents a significant challenge in neuro-oncology due to the substantial risk of complications [...

Neurobehavioral mechanisms of fear and anxiety in multiple sclerosis.

Background: Anxiety is a common yet often underdiagnosed and undertreated comorbidity in multiple sclerosis (MS). While altered fear processing is a hallmark of anxiety in other populations, its neurobehavioral mechanisms in MS remain poorly understood. This study investigates the extent to which neurobehavioral mechanisms of fear generalization contribute to anxiety in MS.

Investigation of the association of serum GFAP and NfL with brain and upper cervical MRI volumes in AQP4-IgG-positive NMOSD and MOGAD.

Background: Serum glial fibrillary acidic protein (sGFAP) is associated with disease activity in aquaporin-4-immunoglobulin G-seropositive neuromyelitis optica spectrum disorders (AQP4-IgG+NMOSD). Serum neurofilament light chain (sNfL) is a biomarker for neuroaxonal damage. However, the association of sGFAP and sNfL with magnetic resonance imaging (MRI) volumes in AQP4-IgG+NMOSD is unclear.

Automated MRI protocoling in neuroradiology in the era of large language models.

Purpose: This study investigates the automation of MRI protocoling, a routine task in radiology, using large language models (LLMs), comparing an open-source (LLama 3.1 405B) and a proprietary model (GPT-4o) with and without retrieval-augmented generation (RAG), a method for incorporating domain-specific knowledge.

Material And Methods: This retrospective study included MRI studies conducted between January and December 2023, along with institution-specific protocol assignment guidelines.

Deep Learning Model for Automated Segmentation of Orbital Structures in MRI Images.

Background: Magnetic resonance imaging (MRI) is a crucial tool for visualizing orbital structures and detecting eye pathologies. However, manual segmentation of orbital anatomy is challenging due to the complexity and variability of the structures. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNNs), offer promising solutions for automated segmentation in medical imaging.

Radiological signs of hypoxic-ischaemic encephalopathy on head computed tomography for prediction of poor functional outcome after cardiac arrest - a prospective observational cohort study.

Introduction: Qualitative assessment of hypoxic ischaemic encephalopathy on computed tomography (CT) after cardiac arrest is limited by interrater agreement. We explored how qualitative assessment can be improved.

Methods: In-depth analysis of radiological items evaluated in a prospective sub-study within the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial examining unconscious patients with CT > 48 h ≤ 7 days.

Inconsistency of AI in intracranial aneurysm detection with varying dose and image reconstruction.

Scanner-related changes in data quality are common in medical imaging, yet monitoring their impact on diagnostic AI performance remains challenging. In this study, we performed standardized consistency testing of an FDA-cleared and CE-marked AI for triage and notification of intracranial aneurysms across changes in image data quality caused by dose and image reconstruction. Our assessment was based on repeated examinations of a head CT phantom designed for AI evaluation, replicating a patient with three intracranial aneurysms in the anterior, middle and posterior circulation.

Brain Atrophy Is Associated with Hematoma Expansion in Intracerebral Hemorrhage, Depending on Coagulation Status.

This study aimed to research the potential association between brain atrophy and hematoma expansion (HE) in intracerebral hemorrhage (ICH). A retrospective analysis was conducted using data from patients with primary ICH in our stroke database. ICH volumes from initial and follow-up CT scans were manually segmented.

Macromolecule Modelling for Improved Metabolite Quantification Using Short Echo Time Brain H-MRS at 3 T and 7 T: The PRaMM Model.

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints. Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities.

Endovascular Simulator Training and Shadowing in Interventional Radiology: A Comparison of Two Teaching Methods in the Curricular Training of Medical Students.

Purpose: To identify the impact of endovascular simulator training and shadowing in interventional radiology on medical students' self-assessed IR knowledge. Moreover, the sequence of the teaching methods and its influence on the self-assessed IR knowledge is investigated.

Materials And Methods: A total of 19 fourth-year medical students participated in this study.

Clinical and imaging manifestations of intracerebral hemorrhage in brain tumors and metastatic lesions: a comprehensive overview.

Purpose: This observational study aims to provide a detailed clinical and imaging characterization/workup of acute intracerebral hemorrhage (ICH) due to either an underlying metastasis (mICH) or brain tumor (tICH) lesion.

Methods: We conducted a retrospective, single-center study, evaluating patients presenting with occult ICH on initial CT imaging, classified as tICH or mICH on follow-up MRI imaging according to the H-Atomic classification. Demographic, clinical and radiological data were reviewed.

Deep learning-enabled detection of hypoxic-ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.

Objective: To establish a deep learning model for the detection of hypoxic-ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format.

Methods: 168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.

Similarities in the Electrographic Patterns of Delayed Cerebral Infarction and Brain Death After Aneurysmal and Traumatic Subarachnoid Hemorrhage.

While subarachnoid hemorrhage is the second most common hemorrhagic stroke in epidemiologic studies, the recent DISCHARGE-1 trial has shown that in reality, three-quarters of focal brain damage after subarachnoid hemorrhage is ischemic. Two-fifths of these ischemic infarctions occur early and three-fifths are delayed. The vast majority are cortical infarcts whose pathomorphology corresponds to anemic infarcts.

Multiscale networks in multiple sclerosis.

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls.

Cortical matrix remodeling as a hallmark of relapsing-remitting neuroinflammation in MR elastography and quantitative MRI.

Multiple sclerosis (MS) is a chronic neuroinflammatory disease that involves both white and gray matter. Although gray matter damage is a major contributor to disability in MS patients, conventional clinical magnetic resonance imaging (MRI) fails to accurately detect gray matter pathology and establish a clear correlation with clinical symptoms. Using magnetic resonance elastography (MRE), we previously reported global brain softening in MS and experimental autoimmune encephalomyelitis (EAE).

Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

Background: Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity.

Methods: We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells.

Macromolecule modelling for improved metabolite quantification using short echo time brain H MRS at 3 T and 7 T: The PRaMM Model.

Purpose: To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints.

Methods: Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities.

Diffusion-weighted magnetic resonance imaging in early central retinal artery occlusion.

Introduction: Restricted retinal diffusion (RDR) has recently been recognized as a frequent finding on standard diffusion-weighted imaging (DWI) in central retinal artery occlusion (CRAO). However, data on early DWI signal evolution are missing.

Patients And Methods: Consecutive CRAO patients with DWI performed within 24 h after onset of visual impairment were included in a bicentric, retrospective cross-sectional study.

Spreading depolarization and angiographic spasm are separate mediators of delayed infarcts.

In DISCHARGE-1, a recent Phase III diagnostic trial in aneurysmal subarachnoid haemorrhage patients, spreading depolarization variables were found to be an independent real-time biomarker of delayed cerebral ischaemia. We here investigated based on prospectively collected data from DISCHARGE-1 whether delayed infarcts in the anterior, middle, or posterior cerebral artery territories correlate with (i) extravascular blood volumes; (ii) predefined spreading depolarization variables, or proximal vasospasm assessed by either (iii) digital subtraction angiography or (iv) transcranial Doppler-sonography; and whether spreading depolarizations and/or vasospasm are mediators between extravascular blood and delayed infarcts. Relationships between variable groups were analysed using Spearman correlations in 136 patients.