Biology subjects

Korbmacher, M.

Publications and source records attributed to Korbmacher, M..

6 recordsLinked to original sources

bioRxiv2023

Brain asymmetries from midlife to old adulthood and hemispheric brain age

The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N =48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.

neuroscience↗

bioRxiv2023

The intra-individual reliability of 1H-MRS measurement in the anterior cingulate cortex across one year

Magnetic resonance spectroscopy (MRS) is the primary method that can measure the levels of metabolites in the brain in vivo. To achieve its potential in clinical usage, the reliability of the measurement requires further articulation. Although there are many studies that investigate the reliability of gamma-aminobutyric acid (GABA), comparatively few studies have investigated the reliability of other brain metabolites, such as glutamate (Glu), N-acetyl-aspartate (NAA), creatine (Cr), phosphocreatine (PCr), or myo-inositol (mI), which all play a significant role in brain development and functions. In addition, previous studies which predominately used only two measurements (two datapoints) failed to provide the details of the time effect (e.g., time-of-day) on MRS measurement within subjects. Therefore, in this study, MRS data located in the anterior cingulate cortex (ACC) were repeatedly recorded across one year leading to at least 25 sessions for each subject with the aim of exploring the variability of other metabolites by using the index coefficient of variability (CV); the smaller the CV, the more reliable the measurements. We found that the metabolites of NAA, tNAA, and tCr showed the smallest CVs (between 1.61 and 4.90 %), and the metabolites of Glu, Glx, mI, and tCho showed modest CVs (between 4.26 and 7.89 %). Furthermore, we found that the concentration reference of the ratio to water with tissue correction results in smaller CVs compared to the ratio to tCr. In addition, we did not find any time-of-day effect on the MRS measurements. Collectively, the results of this study indicate that the MRS measurement is reasonably reliable in quantifying the levels of metabolites. Key pointsThe MRS measurement is reliable within subject. The ratio to water provides more reliable results compared to the ratio to tCr. The ratio to water is recommended as the internal concentration reference.

neuroscience↗

bioRxiv2023

The Bergen Breakfast Scanning Club dataset: a deep brain imaging dataset

Populational brain imaging methods based on group averages provide valuable insights into the general functions of the brain. However, they often overlook the inherent inter- and intra-subject variability, limiting our understanding of individual differences. To address this limitation, researchers have turned to big datasets and deep brain imaging datasets. Big datasets enable the exploration of inter-subject variations, while deep brain imaging datasets, involving repeated scanning of multiple subjects over time, offer detailed insights into intra-subject variability. Despite the availability of numerous big datasets, the number of deep brain imaging datasets remains limited. In this article, we present a deep brain imaging dataset derived from the Bergen Breakfast Scanning Club (BBSC) project. The dataset comprises data collected from three subjects who underwent repeated scanning over the course of approximately one year. Specifically, three types of data chunks were collected: behavioral data, functional brain data, and structural brain data. Functional brain images, encompassing magnetic resonance spectroscopy (MRS) and resting-state functional magnetic resonance imaging (fMRI), along with their anatomical reference T1-weighted brain images, were collected twice a week during the data collection period. In total, 38, 40, and 25 sessions of functional data were acquired for subjects 1, 2, and 3, respectively. On the other hand, structural brain images, including T2-weighted brain images, diffusion-weighted images (DWI), and fluid-attenuated inversion recovery (FLAIR) images, were obtained once a month. A total of 10, 9, and 6 sessions were collected for subjects 1, 2, and 3, respectively. The primary objective of this article is to provide a comprehensive description of the data acquisition protocol employed in the BBSC project, as well as detailed insights into the preprocessing steps applied to the acquired data.

neuroscience↗

bioRxiv2023

Brain age predictions in longitudinal data reveal the importance of scan quality and field strength

IntroductionBrain age, the estimation of a persons age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for for the same individual at various time points and validate our findings with age-matched healthy controls. MethodsWe used densly sampled T1-weighted MRI data from four individuals (from two datasets) to observe how brain age corresponds to age and is influenced by acquision and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pre-trained deep learning model. ResultsWe find small within-subject correlations between age and brain age. We also find evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data, and inconclusive effects of scan quality. ConclusionThe absence of maturation effects for the age range in the presented sample, brain age model-bias (including training age distribution and field strength) and model error are potential reasons for small relationships between age and brain age in longitudinal data. Future brain age models should account for differences in field strength and intra-individual differences.

neuroscience↗

bioRxiv2022

Male and Female Brain Coherence Models of Cognitive Performance and Psychopathology

Finding reliable imaging biomarkers of mental illness has been a major challenge, on a par with the quest for biomarkers of the male versus the female brain, as the two types of imaging inform one another. We explored the hypothesis that the degree of coherence (internal isomorphism) between brain volumes of the left versus the right hemisphere for patients with psychopathological conditions follows the brain coherence pattern of the healthy male or healthy female. We developed the distance index (DI) as a biomarker of brain coherence and compared it with three ad hoc coherence measures. We found that only DI could reliably distinguish males from females and patients from controls. Also, cortical regions with highest DI scores were swapped between males and females across groups following male/female models of psychopathology. Furthermore, although indices were similar in predicting cognitive performance, DI provided a more proportionate prediction pattern across diagnosis groups, and more robust interactions with males/females. These findings highlight the importance of brain coherence, particularly measured by DI, for phenotyping sex and mental illness.

neuroscience↗

bioRxiv2022

Brain-wide associations between white matter and agehighlight the role of fornix microstructure in brain age

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age- associations of WM features from different diffusion approaches, we analysed UK Biobank diffusion Magnetic Resonance Imaging (dMRI) data across midlife and older age (N = 35,749, 44.6 to 82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

neuroscience↗