wulms/bidsconvertr: Minor bugfixes for publication

Converts DICOM data to NIfTI and finally to BID

Increased thalamic glutamate/glutamine levels in migraineurs

Abstract Background Increased cortical excitability has been hypothesized to play a critical role in various neurological disorders, such as restless legs syndrome, epilepsy and migraine. Particularly for migraine, local hyperexcitability has been reported. Levels of regional excitatory and inhibitory neurotransmitters are related to cortical excitability and hence may play a role in the origin of the disease. Consequently, a mismatch of the excitatory-inhibitory neurotransmitter network might contribute to local hyperexcitability and the onset of migraine attacks. In this study we sought to assess local levels of glutamate / glutamine (GLX) and gamma-aminobutyric acid (GABA) in the occipital cortex and right thalamus of migraineurs and healthy subjects. Methods We measured interictally local biochemical concentrations in the occipital lobe and the right thalamus in patients with migraine (without aura) and healthy controls (HCs) using proton magnetic resonance spectroscopy at 3 T. GLX levels were acquired using PRESS and GABA levels using the GABA-sensitive editing sequence MEGA-PRESS. Regional GLX and GABA levels were compared between groups. Results Statistical analyses revealed significantly increased GLX levels in both the primary occipital cortex and thalamus. However, we found no group differences in GABA levels for these two regions. Correlation analyses within the migraine group revealed no significant correlations between pain intensity and levels of GLX or GABA in either of the two brain regions. Conclusions Further research is needed to investigate the role of GABA/GLX ratios in greater depth and to measure changes in neurotransmitter levels over time, i.e. during migraine attacks and interictally

Cerebral MRI in a prospective cohort study on depression and atherosclerosis: the BiDirect sample, processing pipelines, and analysis tools

Abstract Background The use of cerebral magnetic resonance imaging (MRI) in observational studies has increased exponentially in recent years, making it critical to provide details about the study sample, image processing, and extracted imaging markers to validate and replicate study results. This article reviews the cerebral MRI dataset from the now-completed BiDirect cohort study, as an update and extension of the feasibility report published after the first two examination time points. Methods We report the sample and flow of participants spanning four study sessions and twelve years. In addition, we provide details on the acquisition protocol; the processing pipelines, including standardization and quality control methods; and the analytical tools used and markers available. Results All data were collected from 2010 to 2021 at a single site in Münster, Germany, starting with a population of 2,257 participants at baseline in 3 different cohorts: a population-based cohort (n = 911 at baseline, 672 with MRI data), patients diagnosed with depression (n = 999, 736 with MRI data), and patients with manifest cardiovascular disease (n = 347, 52 with MRI data). During the study period, a total of 4,315 MRI sessions were performed, and over 535 participants underwent MRI at all 4 time points. Conclusions Images were converted to Brain Imaging Data Structure (a standard for organizing and describing neuroimaging data) and analyzed using common tools, such as CAT12, FSL, Freesurfer, and BIANCA to extract imaging biomarkers. The BiDirect study comprises a thoroughly phenotyped study population with structural and functional MRI data. Relevance statement The BiDirect Study includes a population-based sample and two patient-based samples whose MRI data can help answer numerous neuropsychiatric and cardiovascular research questions. Key points • The BiDirect study included characterized patient- and population-based cohorts with MRI data. • Data were standardized to Brain Imaging Data Structure and processed with commonly available software. • MRI data and markers are available upon request. Graphical Abstrac

The relationship between Alzheimer's-related brain atrophy patterns and sleep macro-architecture

Introduction Sleep is increasingly recognized as a major risk factor for neurodegenerative disorders such as Alzheimer's disease (AD). Methods Using an magnetic resonance imaging (MRI)–based AD score based on clinical data from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) case-control cohort, we investigated the associations between polysomnography-based sleep macro-architecture and AD-related brain atrophy patterns in 712 pre-symptomatic, healthy subjects from the population-based Study of Health in Pomerania. Results We identified a robust inverse association between slow-wave sleep and the AD marker (estimate: −0.019; 95% confidence interval: −0.03 to −0.0076; false discovery rate [FDR] = 0.0041), as well as with gray matter (GM) thicknesses in typical individual cortical AD-signature regions. No effects were identified regarding rapid eye movement or non–rapid eye movement (NREM) stage 2 sleep, and NREM stage 1 was positively associated with GM thickness, mainly in the prefrontal cortical regions. Discussion There is a cross-sectional relationship between AD-related neurodegenerative patterns and the proportion of sleep spent in slow-wave sleep

Predicting brain-age from raw T(1)-weighted magnetic resonance imaging data using 3D convolutional neural networks

Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging. Current approaches rely on preparing the data with multiple preprocessing steps, such as registering voxels to a standardized brain atlas, which yields a significant computational overhead, hampers widespread usage and results in the predicted brain-age to be sensitive to preprocessing parameters. Here we describe a 3D Convolutional Neural Network (CNN) based on the ResNet architecture being trained on raw, non-registered T(1)-weighted MRI data of N=10,691 samples from the German National Cohort and additionally applied and validated in N=2,173 samples from three independent studies using transfer learning. For comparison, state-of-the-art models using preprocessed neuroimaging data are trained and validated on the same samples. The 3D CNN using raw neuroimaging data predicts age with a mean average deviation of 2.84 years, outperforming the state-of-the-art brain-age models using preprocessed data. Since our approach is invariant to preprocessing software and parameter choices, it enables faster, more robust and more accurate brain-age modeling