Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods.

Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL + DiReCT.

Transient brain structure changes after high phenylalanine exposure in adults with phenylketonuria.

Phenylketonuria is a rare metabolic disease resulting from a deficiency of the enzyme phenylalanine hydroxylase. Recent cross-sectional evidence suggests that early-treated adults with phenylketonuria exhibit alterations in cortical grey matter compared to healthy peers. However, the effects of high phenylalanine exposure on brain structure in adulthood need to be further elucidated.

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study.

Objective: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current corticocentric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural magnetic resonance imaging in 1602 adults with epilepsy and 1022 healthy controls across 22 sites from the global ENIGMA-Epilepsy working group.

Methods: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions.

Patterns of subregional cerebellar atrophy across epilepsy syndromes: An ENIGMA-Epilepsy study.

Objective: The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current cortico-centric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural MRI in 1,602 adults with epilepsy and 1,022 healthy controls across twenty-two sites from the global ENIGMA-Epilepsy working group.

Methods: A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions.

Case report: Psychosis and catatonia in an adolescent patient with adipsic hypernatremia and autoantibodies against the subfornical organ.

This is the first description of a patient in which adipsic hypernatremia, a rare autoimmune encephalitis, presented in combination with complex psychiatric symptomatology, including psychosis and catatonia. Adipsic hypernatremia is characterized by autoantibodies against the thirst center of the brain. These autoantibodies cause inflammation and apoptosis in key regions of water homeostasis, leading to lack of thirst and highly increased serum sodium.

Clinical Evaluation of a Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis.

Objective: To evaluate the influence of quantitative reports (QReports) on the radiological assessment of hippocampal sclerosis (HS) from MRI of patients with epilepsy in a setting mimicking clinical reality.

Methods: The study included 40 patients with epilepsy, among them 20 with structural abnormalities in the mesial temporal lobe (13 with HS). Six raters blinded to the diagnosis assessed the 3T MRI in two rounds, first using MRI only and later with both MRI and the QReport.

Growing importance of brain morphometry analysis in the clinical routine: The hidden impact of MR sequence parameters.

Volumetric assessment based on structural MRI is increasingly recognized as an auxiliary tool to visual reading, also in examinations acquired in the clinical routine. However, MRI acquisition parameters can significantly influence these measures, which must be considered when interpreting the results on an individual patient level. This Technical Note shall demonstrate the problem.

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results.

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g.

Large-scale transient peri-ictal perfusion magnetic resonance imaging abnormalities detected by quantitative image analysis.

Epileptic seizures require a rapid and safe diagnosis to minimize the time from onset to adequate treatment. Some epileptic seizures can be diagnosed clinically with the respective expertise. For more subtle seizures, imaging is mandatory to rule out treatable structural lesions and potentially life-threatening conditions.

Multiple sclerosis as a model to investigate SARS-CoV-2 effect on brain atrophy.

Introduction: Data on structural brain changes after infection with SARS-CoV-2 is sparse. We postulate multiple sclerosis as a model to study the effects of SARS-CoV-2 on brain atrophy due to the unique availability of longitudinal imaging data in this patient group, enabling assessment of intraindividual brain atrophy rates.

Methods: Global and regional cortical gray matter volumes were derived from structural MRIs using FreeSurfer.

Reliable brain morphometry from contrast-enhanced T1w-MRI in patients with multiple sclerosis.

Brain morphometry is usually based on non-enhanced (pre-contrast) T1-weighted MRI. However, such dedicated protocols are sometimes missing in clinical examinations. Instead, an image with a contrast agent is often available.

Hippocampal volume in patients with bilateral and unilateral peripheral vestibular dysfunction.

Previous studies have found that peripheral vestibular dysfunction is associated with altered volumes in different brain structures, especially in the hippocampus. However, published evidence is conflicting. Based on previous findings, we compared hippocampal volume, as well as supramarginal, superior temporal, and postcentral gyrus in a sample of 55 patients with different conditions of peripheral vestibular dysfunction (bilateral, chronic unilateral, acute unilateral) to 39 age- and sex-matched healthy controls.

Cortical thickness and its relationship to cognitive performance and metabolic control in adults with phenylketonuria.

Despite good control of phenylalanine (Phe) levels during childhood and adolescence, adults with phenylketonuria (PKU) often show abnormalities in the white matter of the brain, which have been associated with poorer cognitive performance. However, whether such a relationship exists with cortical gray matter is still unknown. Therefore, we investigated cortical thickness and surface area in adults with early-treated PKU and their relationship to cognitive functions and metabolic control.

A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI.

Purpose: Hippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method.

Somatotopy of cervical dystonia in motor-cerebellar networks: Evidence from resting state fMRI.

Introduction: Cervical dystonia is the most frequent form of isolated focal dystonia. It is often associated with a dysfunction in brain networks, mostly affecting the basal ganglia, the cerebellum, and the somatosensory cortex. However, it is unclear if such a dysfunction is somato-specific to the brain areas containing the representation of the affected body part, and may thereby account for the focal expression of cervical dystonia.

Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation.

Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique to derive such measures from non-surface-based volumetric tissue maps. ANTs provides an open-source method for estimating cortical thickness, derived by applying DiReCT to an atlas-based segmentation.

The ENIGMA-Epilepsy working group: Mapping disease from large data sets.

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care.

Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning.

Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical routine, where time is often an issue. We propose a deep learning-based approach to predict the volumes of anatomically delineated subcortical regions of interest (ROI), and mean thicknesses and curvatures of cortical parcellations directly from T1-weighted MRI.

Divide and Conquer: Stratifying Training Data by Tumor Grade Improves Deep Learning-Based Brain Tumor Segmentation.

It is a general assumption in deep learning that more training data leads to better performance, and that models will learn to generalize well across heterogeneous input data as long as that variety is represented in the training set. Segmentation of brain tumors is a well-investigated topic in medical image computing, owing primarily to the availability of a large publicly-available dataset arising from the long-running yearly Multimodal Brain Tumor Segmentation (BraTS) challenge. Research efforts and publications addressing this dataset focus predominantly on technical improvements of model architectures and less on properties of the underlying data.

Acute cardiovascular protective effects of corticosteroids are mediated by non-transcriptional activation of endothelial nitric oxide synthase.

Corticosteroids have been shown to exert beneficial effects in the treatment of acute myocardial infarction, but the precise mechanisms underlying their protective effects are unknown. Here we show that high-dose corticosteroids exert cardiovascular protection through a novel mechanism involving the rapid, non-transcriptional activation of endothelial nitric oxide synthase (eNOS). Binding of corticosteroids to the glucocorticoid receptor (GR) stimulated phosphatidylinositol 3-kinase and protein kinase Akt, leading to eNOS activation and nitric oxide dependent vasorelaxation.