Cortical lesions impact cognitive decline in multiple sclerosis via volume loss of nonlesional cortex.

Objective: To assess the interrelationship between cortical lesions and cortical thinning and volume loss in people with multiple sclerosis within cortical networks, and how this relates to future cognition.

Methods: In this longitudinal study, 230 people with multiple sclerosis and 60 healthy controls underwent 3 Tesla MRI at baseline and neuropsychological assessment at baseline and 5-year follow-up. Cortical regions (N = 212) were divided into seven functional networks.

In vivo evidence for cell body loss in cortical lesions in people with multiple sclerosis.

Objective: To quantify alterations in soma and neurite density imaging measures within and surrounding cortical lesions in people with multiple sclerosis using in vivo high-gradient diffusion MRI.

Methods: In this cross-sectional study, 41 people with multiple sclerosis and 34 age- and sex-matched healthy controls underwent 3 T high-gradient diffusion MRI. Cortical lesions were segmented on artificial intelligence-enabled double inversion recovery images.

9-HODE associates with thalamic atrophy and predicts white matter damage in multiple sclerosis.

Background: Multiple sclerosis (MS) is characterized by extensive tissue damage leading to a range of complex symptoms, including physical disability and cognitive dysfunction. Recent work has indicated the clinical relevance of bioactive lipid mediators (LMs), which are known to orchestrate inflammation and its resolution and are deregulated in MS. However, it is unknown whether LM profiles relate to white matter (WM) damage.

Digital outcome measures are associated with brain atrophy in patients with multiple sclerosis.

Background: Digital monitoring of people with multiple sclerosis (PwMS) using smartphone-based monitoring tools is a promising method to assess disease activity and progression.

Objective: To study cross-sectional and longitudinal associations between active and passive digital monitoring parameters and MRI volume measures in PwMS.

Methods: In this prospective study, 92 PwMS were included.

Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.

Background: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies.

Objective: To evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal.

Methods: We used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years.

Subtypes and location of (juxta)cortical lesions relate to cognitive dysfunction in people with multiple sclerosis.

Background: Cortical lesion subtypes' occurrence and distribution across networks may shed light on cognitive impairment (CI) in multiple sclerosis (MS).

Methods: In 332 people with MS, lesions were classified as intracortical, leukocortical or juxtacortical based on artificially generated double inversion-recovery images.

Results: CI-related leukocortical lesion count increases were greatest within sensorimotor and cognitive networks ( < 0.

The cognitive relevance of non-lesional damage to cortical networks in people with multiple sclerosis.

Background: Cognitive impairment, a common and debilitating symptom in people with multiple sclerosis (MS), is especially related to cortical damage. However, the impact of regional cortical damage remains poorly understood. Our aim was to evaluate structural (network) integrity in lesional and non-lesional cortex in people with MS, and its relationship with cognitive dysfunction.

Exploring the effects of extended interval dosing of natalizumab and drug concentrations on brain atrophy in multiple sclerosis.

Background: Extended interval dosing (EID) of natalizumab treatment is increasingly used in multiple sclerosis. Besides the clear anti-inflammatory effect, natalizumab is considered to have neuroprotective properties as well.

Objectives: This study aimed to study the longitudinal effects of EID compared to standard interval dosing (SID) and natalizumab drug concentrations on brain atrophy.

Serum glial fibrillary acidic protein in natalizumab-treated relapsing-remitting multiple sclerosis: An alternative to neurofilament light.

Background: There is a need in Relapsing-Remitting Multiple Sclerosis (RRMS) treatment for biomarkers that monitor neuroinflammation, neurodegeneration, treatment response, and disease progression despite treatment.

Objective: To assess the value of serum glial fibrillary acidic protein (sGFAP) as a biomarker for clinical disease progression and brain volume measurements in natalizumab-treated RRMS patients.

Methods: sGFAP and neurofilament light (sNfL) were measured in an observational cohort of natalizumab-treated RRMS patients at baseline, +3, +12, and +24 months and at the last sample follow-up (median 5.

Reliability of brain atrophy measurements in multiple sclerosis using MRI: an assessment of six freely available software packages for cross-sectional analyses.

Purpose: Volume measurement using MRI is important to assess brain atrophy in multiple sclerosis (MS). However, differences between scanners, acquisition protocols, and analysis software introduce unwanted variability of volumes. To quantify theses effects, we compared within-scanner repeatability and between-scanner reproducibility of three different MR scanners for six brain segmentation methods.

Feasibility of detecting atrophy relevant for disability and cognition in multiple sclerosis using 3D-FLAIR.

Background And Objectives: Disability and cognitive impairment are known to be related to brain atrophy in multiple sclerosis (MS), but 3D-T1 imaging required for brain volumetrics is often unavailable in clinical protocols, unlike 3D-FLAIR. Here our aim was to investigate whether brain volumes derived from 3D-FLAIR images result in similar associations with disability and cognition in MS as do those derived from 3D-T1 images.

Methods: 3T-MRI scans of 329 MS patients and 76 healthy controls were included in this cross-sectional study.

Automatic segmentation and quantification of the optic nerve on MRI using a 3D U-Net.

Purpose: Pathological conditions associated with the optic nerve (ON) can cause structural changes in the nerve. Quantifying these changes could provide further understanding of disease mechanisms. We aim to develop a framework that automatically segments the ON separately from its surrounding cerebrospinal fluid (CSF) on magnetic resonance imaging (MRI) and quantifies the diameter and cross-sectional area along the entire length of the nerve.

Association of volumetric MRI measures and disability in MS patients of the same age: Descriptions from a birth year cohort.

Background And Objectives: Although MRI-based markers of neuroinflammation have proven crucial for the diagnosis of multiple sclerosis (MS), predicting clinical progression with inflammation remains difficult. Neurodegenerative markers such as brain volume loss show stronger clinical (predictive) correlations, but also harbor age-related variation that must be disentangled from disease duration. In this study we investigated how clinical disability is related to volumetric MRI measures in a cohort of MS patients and healthy controls (HC) of the same age: Project Y.

Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN.

Unlabelled: BACKGROUND : Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN).