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Korbmacher, M.

Publications and source records attributed to Korbmacher, M..

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A normative reference for large-scale human brain dynamics across the lifespan

Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed the study of static brain structure and connectivity, an equivalent normative framework for intrinsic brain dynamics has been lacking. This gap has limited our ability to characterize individual variability, development, ageing, and mental health conditions at scale. Here, we establish a population-level normative reference for large-scale human brain dynamics using resting-state fMRI data from more than 10,000 individuals spanning the lifespan and 91 scanning sites. We derive a compact set of recurring brain-state configurations that are reproducible across scanners and acquisition paradigms and that generalize to previously unseen cohorts. Anchoring these dynamic states to normative lifespan models enables the quantification of individual deviations relative to population reference distributions. We show that intrinsic brain dynamics undergo systematic reorganization across development and ageing, with pronounced changes before early adulthood and more gradual modulation thereafter. Applying this framework across multiple mental health conditions reveals disorder-specific and highly heterogeneous deviations in brain dynamics that are not captured by static neuroimaging measures. Robust transfer to independent cohorts and longitudinal analyses demonstrate that normative brain dynamics can be reliably assessed out of distribution. These results delineate a population-scale dynamic architecture of the human brain and extend normative brain mapping from static phenotypes to the temporal domain, providing a reference framework for studying brain function across the lifespan in health and disease.

neuroscience↗

Seizure-induced Transient Disruptive Changes in Brain Microstructure

A leading hypothesis for the mechanisms of electroconvulsive therapy (ECT) posits an initial disruptive effect followed by enhanced neuroplasticity. However, direct evidence of the presumed early disruptive changes, potentially driven by seizures, remains limited. This study examined longitudinal changes in multishell diffusion MRI-derived metrics in 25 individuals with depression, with scans acquired two hours before and after their first ECT session. Follow-up scans were collected within 14 days and 6 months after the last ECT session. To control for potential confounding effects of anesthesia and repeated measurements, two additional groups were included: 16 individuals undergoing short-acting anesthesia and 16 healthy controls without interventions. A multicompartment model was applied to explore extracellular free water and microstructures in the intracellular/extracellular compartments. Whole-brain voxel-wise analyses identified a pattern consistent with vasogenic edema (e.g., increased extracellular free water) in widespread bilateral brain regions following a single ECT session. Increases in brain tissue free water were significantly correlated with electrical stimulus charge (r=0.51, p=0.01) but not with post-ictal reorientation time (r=0.11, p=0.92). These changes were not observed in either control group. Follow-up assessments confirmed that the alterations in tissue free water resolved within 14 days. A single ECT-induced seizure induces transient, reversible changes consistent with vasogenic edema, rather than irreversible cellular injury typically associated with cytotoxic edema. These reversible changes may represent an initial disruptive phase that facilitates subsequent adaptive brain responses, including neuroplasticity and network reorganization underlying the therapeutic effects of ECT.

neuroscience↗

White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life

Advanced diffusion magnetic resonance imaging (dMRI) allows one to probe and assess brain white matter (WM) organization and microstructure in vivo. Various dMRI models with different theoretical and practical assumptions have been developed, representing partly overlapping characteristics of the underlying brain biology with potentially complemen-tary value in the cognitive and clinical neurosciences. To which degree the different dMRI metrics relate to clinically relevant geno-and phenotypes is still debated. Hence, we inves-tigate how tract-based and whole WM skeleton parameters from different single- and multi-compartment dMRI approaches associate with clinically relevant and white matter-related phenotypes (sex, age, pulse pressure (PP), body-mass-index (BMI), brain asymmetry) and genetic markers in the UK Biobank (UKB, n=52, 140) and the Adolescent Brain Cognitive Development (ABCD) Study (n=5, 844). Multi-compartment dMRI approaches provided the strongest WM associations with age, and unique insights into brain asymmetry. Kurto-sis was most indicative of PP and BMI. WM-based sex classifications and polygenic score associations for common psychiatric disorders and Alzheimers disease were similar across diffusion approaches. We conclude that WM microstructure is differentially associated with clinically relevant pheno- and genotypes at different points in life. Multi-compartment dMRI approaches, and particularly the examined Bayesian approach, provide additional information to conventional approaches in such examinations.

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Cross-sectional brain age assessments are limited in predicting future brain change

The concept of brain age (BA) describes an integrative imaging marker of brain health, often suggested to reflect ageing processes. However, the degree to which cross-sectional MRI features, including BA, reflect past, ongoing and future brain changes across different tissue types from macro-to microstructure remains controversial (Vidal-Pineiro et al. 2021). Here, we advance these findings by using multimodal imaging data of 39, 325 UK Biobank participants, aged 44 - 82 years at baseline and 2, 520 follow-ups within 1.12 - 6.90 years. In concordance with the original findings, we find insufficient evidence that BA reflects the rate of brain ageing. However, modality-specific differences in brain ages reflected the state of the brain, highlighting diffusion and multimodal MRI brain age as potentially useful cross-sectional markers.

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FreeSurfer version-shuffling can boost brain age predictions

Abstract / Key pointsO_LIThe influence of FreeSurfer version-dependent variability in reconstructed cortical features on brain age predictions is average small when varying training and test splits from the same data. C_LIO_LIFreeSurfer version differences can lead to some variability in brain age dependent on the choice of algorithm and individual differences in brain morphometry, highlighting the advantage of repeated random train-test splitting. C_LIO_LIShuffling of differently processed FreeSurfer data dependent on the FreeSurfer version increases performance and generalizability of the brain age prediction model. C_LI

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The within-subject stability of cortical thickness, surface area, and brain volumes across one year

T1-weighted (T1w) imaging is widely used to examine brain structure based on image-derived phenotypes (IDPs) such as cortical thickness, surface area, and brain volumes. The reliability of these IDPs has been extensively explored, mainly focusing on the inter-subject variations, whereas the stability of the within-subject variations has often been overlooked. Additionally, how environmental factors such as time of day and daylight hours impact the structural brain is poorly understood. Therefore, we aimed to address the stability of T1w-derived phenotypes and explore the effecting factors including processing stream, brain region size, time of day, daylight hours, and head movement. Three subjects in their late 20s, early 30s, and early 40s were scanned repeatedly on the same scanner over one year, from which a densely sampled dataset was acquired with 38, 40, and 25 sessions for subjects 1, 2, and 3, respectively. The temporal stability was evaluated using within-subject percentage change and coefficients of variation (CV). The effects of time of day and daylight hours were assessed by fitting general linear models, aggregating the effect size with meta-analysis, and equivalent analysis. The longitudinal processing stream generates more stable results than the cross-sectional processing stream, hence the following results are all derived from the longitudinal processing stream. First and foremost, most IDPs demonstrated percentage changes within 5% and CVs within 2% for almost all brain regions, indicating high stability. Interestingly, we found that the brain region size negatively correlates with the CVs. Specifically, several small brain regions, including the temporal pole, frontal pole, pericalcarine, entorhinal cortex, and accumbens area, showed low stability. In addition, cortical thickness change was strongly and positively correlated with that of volume change while being negatively correlated with change in surface area, illustrating their distinct roles in brain anatomy. Moreover, the time of day could be ignored when evaluating the total surface area and total cortical brain volume but not the average cortical thickness and total subcortical brain volume. Furthermore, daylight hours could be left out when evaluating IDPs since there was no appreciable effect of daylight hours on the IDPs stability. Lastly, apparent head motion causes cortical thickness and volume underestimated and surface area overestimated.

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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.

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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.

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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.

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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.

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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.

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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.

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