Biology subjects

Beyer, F.

Publications and source records attributed to Beyer, F..

At least 19 recordsLinked to original sources

bioRxiv2025

A multimodal human-computer interaction dataset for neurocognitive user state evaluation

We introduce the Simulated Environment for Neurocognitive State Evaluation (SENSE-42), a multimodal dataset collected during user interactions with desktop computers. It is designed for studying spontaneous fluctuations in the neurocognitive state related to the tonic alertness of computer users, with recordings from 42 participants over 2-hour sessions. Within a simulated desktop environment, participants performed real-world routine tasks, including application switching, file management, typing, and web browsing. High-resolution data were recorded across physiological (electroencephalography, electrocardiography, respiration) and subjective modalities of alertness. At five-minute intervals, alertness state was reported using seven questions, addressing sleepiness (Karolinska Sleepiness Scale), mental and temporal demand, perceived performance, effort and frustration (NASA Task Load Index), as well as attentiveness. Behavioural data included keyboard, mouse and webcam inputs. Demographic information, experience metrics, habits, and preferences of computer usage were collected. In addition, individual differences in sleep quality were evaluated using the Pittsburgh Sleep Quality Index and the Epworth Sleepiness Scale. The SENSE-42 dataset can contribute to future research in user state monitoring, behavioural analysis and physiological computing.

neuroscience↗

bioRxiv2025

Retinal nerve fibre layer thickness reflects characteristics of brain grey and white matter

The retina is a relatively accessible part of the central nervous system compared to the brain. Using high resolution optical imaging we investigated the relationship between retinal thickness, obtained with optical coherence tomography, and structural features of the brain obtained with magnetic resonance imaging. In a population-based sample of over 500 subjects, we hypothesized: (i) that there are structural associations between circumpapillary retinal nerve fibre layer thickness and brain grey matter density and white matter microstructural properties in visual information processing areas, and specifically contralateral associations for nasal retinal fibers, and (ii) that retinal findings reflect broader changes in brain grey and white matter related to cardiovascular risk factors. In support of the first hypothesis, we showed associations of circumpapillary retinal nerve fibre layer thickness with visual cortex grey matter density and with optic radiation fractional anisotropy. These correlations were stronger for the right eye, possibly reflecting right ocular dominancy. Regarding the second hypothesis, while we confirmed the broad impact of cardiovascular risk factors such as body mass index, diabetes, and hypertension on brain structure, we didnt find (adequate) significant partial correlations between circumpapillary retinal nerve fibre layer thickness and cardiovascular risk factors to support the hypothesis. As such, we couldnt confirm that circumpapillary retinal nerve fibre layer thickness is associated with the impact of cardiovascular risk factors on the brain structure. However, when the effects of cardiovascular risk factors were accounted for statistically, circumpapillary retinal nerve fibre layer thickness (particularly on the right side) was associated with fractional anisotropy of limbic system tracts, i.e., the fornix and stria terminalis including hippocampus and amygdala. To further explore the structural associations between eye and brain, in terms of a possible common underlying pathology related to cardiovascular risk factors and progressive neurodegenerative diseases on the central nervous system, longitudinal and interventional studies are necessary.

neuroscience↗

bioRxiv2024

The utility of explainable A.I. for MRI analysis: Relating model predictions to neuroimaging features of the aging brain

IntroductionDeep learning models highly accurately predict brain-age from MRI but their explanatory capacity is limited. Explainable A.I. (XAI) methods can identify relevant voxels contributing to model estimates, yet, they do not reveal which biological features these voxels represent. In this study, we closed this gap by relating voxel-based contributions to brain-age estimates, extracted with XAI, to human-interpretable structural features of the aging brain. MethodsTo this end, we associated participant-level XAI-based relevance maps extracted from two ensembles of 3D-convolutional neural networks (3D-CNN) that were trained on T1-weighted and fluid attenuated inversion recovery images of 2016 participants (age range 18-82 years), respectively, with regional cortical and subcortical gray matter volume and thickness, perivascular spaces (PVS) and water diffusion-based fractional anisotropy of main white matter tracts. ResultsWe found that all neuroimaging markers of brain aging, except for PVS, were highly correlated with the XAI-based relevance maps. Overall, the strongest correlation was found between ventricular volume and relevance (r = 0.69), and by feature, temporal-parietal cortical thickness and volume, cerebellar gray matter volume and frontal-occipital white matter tracts showed the strongest correlations with XAI-based relevance. ConclusionOur ensembles of 3D-CNNs took into account a plethora of known aging processes in the brain to perform age prediction. Some age-associated features like PVS were not consistently considered by the models, and the cerebellum was more important than expected. Taken together, we highlight the ability of end-to-end deep learning models combined with XAI to reveal biologically relevant, multi-feature relationships in the brain.

neuroscience↗

bioRxiv2024

Gut-specific H3R signaling orchestrates microglia-dependent resolution of peripheral inflammation

Chronic inflammatory diseases, like rheumatoid arthritis (RA) have been described to cause central nervous system (CNS) activation. Less is known about environmental factors that enable the CNS to suppress peripheral inflammation in RA. Here, we identified gut microbiota-derived histamine as such factor. We show that low levels of histamine activate the enteric nervous system, increase inhibitory neurotransmitter concentrations in the spinal cord and restore homeostatic microglia, thereby reducing inflammation in the joints. Selective histamine 3 receptor (H3R) signaling in the intestine is critical for this effect, as systemic and intrathecal application did not show effects. Microglia depletion or pharmacological silencing of local nerve fibers impaired oral H3R agonist-induced pro-resolving effects on arthritis. Moreover, therapeutic supplementation of the SCFA propionate identified one way to expand local intestinal histamine concentrations in mice and humans. Thus, we define a gut-CNS-joint axis pathway where microbiota-derived histamine initiates the resolution of arthritis via the CNS. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=109 HEIGHT=200 SRC="FIGDIR/small/603031v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@c217b8org.highwire.dtl.DTLVardef@a340ceorg.highwire.dtl.DTLVardef@1f3c1d8org.highwire.dtl.DTLVardef@3b6fc9_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIGut microbiota-derived histamine activates enteric neurons via H3R C_LIO_LILocal intestinal H3R activation induces shift to homeostatic microglia in the spinal cord C_LIO_LICNS controlled decrease in endothelial leakiness resolves synovial inflammation C_LI

immunology↗

bioRxiv2022

Longitudinal 7T MRI reveals volumetric changes in subregions of human medial temporal lobe to sex hormone fluctuations

The hippocampus and surrounding medial temporal lobe (MTL) are critical for memory processes, with local atrophy linked to memory deficits. Animal work shows that MTL subregions densely express sex hormone receptors and exhibit rapid structural changes synchronized with hormone fluctuations. Such transient effects in humans have thus far not been shown. By combining a dense-sampling protocol, ultra-high field neuroimaging and individually-derived segmentation analysis, we demonstrate how estradiol and progesterone fluctuations affect MTL subregion volumes across the human menstrual cycle. Twenty-seven healthy women (19-34 years) underwent 7T MRI at six timepoints to acquire T1-weighted and T2-weighted images. Linear mixed-effects modeling showed positive associations between estradiol and parahippocampal cortex volume, progesterone and subiculum and perirhinal Area 35 volumes, and an estradiol*progesterone interaction with CA1 volume. We confirmed volumetric changes were not driven by hormone-related water (cerebral spinal fluid) or blood-flow (pulsed arterial spin labeling) changes. These findings suggest that sex hormones alter structural brain plasticity in subregions that are differentially sensitive to hormones. Mapping how endogenous endocrine factors shape adult brain structure has critical implications for womens health during the reproductive years as well as later in life, such as increased dementia risk following perimenopause, a period of pronounced sex hormone fluctuations.

neuroscience↗

bioRxiv2021

Dietary and serum tyrosine, white matter microstructure and inter-individual variability in executive functions in overweight adults: relation to sex/gender and age

Tyrosine (tyr), the precursor of the neurotransmitter dopamine, is known to modulate cognitive functions including executive attention. Tyr supplementation is suggested to influence dopamine-modulated cognitive performance. However, results are inconclusive, regarding the presence or strength and also the direction of the association between tyr and cognitive function. This pre-registered cross-sectional analysis investigates whether diet-associated serum tyr relates to executive attention performance, and whether this relationship is moderated by differences in white matter microstructure. 59 healthy, overweight, young to middle-aged adults (20F, 28.3 {+/-} 6.6 years, BMI: 27.3 {+/-} 1.5 kg/m2) drawn from a longitudinal study reported dietary habits, donated blood and completed diffusion-weighted brain magnetic resonance imaging and the attention network test. Main analyses were performed using linear regressions and non-parametric voxel-wise inference testing. Confirmatory analyses did neither support an association between dietary and serum tyr nor a relationship between relative serum tyr/large neutral amino acids (LNAA) levels or white matter microstructure and executive attention performance. However, exploratory analyses revealed higher tyr intake, higher serum tyr and better executive attention performance in the male sex/gender group. In addition, older age was associated with higher dietary tyr intake and lower fractional anisotropy in a widespread cluster across the brain. Finally, a positive association between relative serum tyr/LNAA and executive attention performance was found in the male sex/gender group when accounting for age effects. Our analysis advances the field of dopamine-modulated cognitive functions by revealing sex/gender and age differences which might be diet-related. Longitudinal or intervention studies and larger sample sizes are needed to provide more reliable evidence for links between tyr and executive attention.

neuroscience↗

bioRxiv2021

Investigating the genetic and environmental basis of head micromovements during MRI

IntroductionHead motion during magnetic resonance imaging is heritable. Further, it shares phenotypical and genetic variance with body mass index (BMI) and impulsivity. Yet, to what extent this trait is related to single genetic variants and physiological or behavioral features is unknown. We investigated the genetic basis of head motion in a meta-analysis of genome-wide association studies. Further, we tested whether physiological or psychological measures, such as respiratory rate or impulsivity, mediated the relationship between BMI and head motion. MethodsWe conducted a genome-wide association meta-analysis for mean and maximal framewise head displacement (FD) in seven population neuroimaging cohorts (UK Biobank, LIFE-Adult, Rotterdam Study cohort 1-3, Austrian Stroke Prevention Family Study, Study of Health in Pomerania; total N = 35.109). We performed a pre-registered analysis to test whether respiratory rate, respiratory volume, self-reported impulsivity and heart rate mediated the relationship between BMI and mean FD in LIFE-Adult. ResultsNo variant reached genome-wide significance for neither mean nor maximal FD. Neither physiological nor psychological measures mediated the relationship between BMI and head motion. ConclusionBased on these findings from a large meta-GWAS and pre-registered follow-up study, we conclude that the previously reported genetic correlation between BMI and head motion relies on polygenic variation, and that neither psychological nor simple physiological parameters explain a substantial amount of variance in the association of BMI and head motion. Future imaging studies should thus rigorously control for head motion at acquisition and during preprocessing.

neuroscience↗

bioRxiv2021

Structural covariance of amygdala subregions is associated with trait aggression and endogenous testosterone in healthy individuals

Many studies point toward volume reductions in the amygdala as a potential neurostructural marker for trait aggression. However, most of these findings stem from clinical samples, rendering unclear whether the findings generalize to non-clinical populations. Furthermore, the notion of neural networks suggests that interregional correlations in grey matter volume (i.e., structural covariance) can explain individual differences in aggressive behavior beyond local univariate associations. Here, we tested whether structural covariance between amygdala subregions and the rest of the brain is associated with self-reported aggression in a large sample of healthy young students (n=263; 51% women). Salivary testosterone concentrations were measured for a subset of n=76 participants (45% women), allowing us to investigate the influence of endogenous testosterone on structural covariance. Aggressive individuals showed enhanced covariance between superficial amygdala (SFA) and dorsal anterior insula (dAI), but lower covariance between laterobasal amygdala (LBA) and dorsolateral prefrontal cortex (dlPFC). These structural patterns overlap with functional networks involved in the genesis and regulation of aggressive behavior, respectively. With increasing endogenous testosterone, we observed stronger structural covariance between centromedial amygdala (CMA) and medial prefrontal cortex in men and between CMA and orbitofrontal cortex in women. These results speak for structural covariance of amygdala subregions as a robust correlate of trait aggression in healthy individuals. Moreover, regions that showed structural covariance with the amygdala modulated by either testosterone or aggression did not overlap, speaking for a more complex role of testosterone in human social behavior rather than the simple assumption that testosterone only increases aggressiveness.

neuroscience↗

bioRxiv2021

Towards the Interpretability of Deep Learning Models for Human Neuroimaging

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n=2637, 18-82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37-3.86 years). We find that BA estimates capture aging at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected aging reflected cardiovascular risk factors and accelerated aging was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-aging in healthy and at-risk individuals throughout adulthood.

neuroscience↗

bioRxiv2021

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here, we built an interactive resource to benchmark brain morphology, www.brainchart.io, derived from any current or future sample of magnetic resonance imaging (MRI) data. With the goal of basing these reference charts on the largest and most inclusive dataset available, we aggregated 123,984 MRI scans from 101,457 participants aged from 115 days post-conception through 100 postnatal years, across more than 100 primary research studies. Cerebrum tissue volumes and other global or regional MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3; showed high stability of individual centile scores over longitudinal assessments; and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared to non-centiled MRI phenotypes, and provided a standardised measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In sum, brain charts are an essential first step towards robust quantification of individual deviations from normative trajectories in multiple, commonly-used neuroimaging phenotypes. Our collaborative study proves the principle that brain charts are achievable on a global scale over the entire lifespan, and applicable to analysis of diverse developmental and clinical effects on human brain structure. Furthermore, we provide open resources to support future advances towards adoption of brain charts as standards for quantitative benchmarking of typical or atypical brain MRI scans.

neuroscience↗

bioRxiv2021

Structural brain connectivity in children with developmental dyscalculia

Developmental dyscalculia (DD) is a neurodevelopmental disorder specific to arithmetic learning even with normal intelligence and age-appropriate education. Difficulties often persist from childhood through adulthood. Underlying neurobiological mechanisms of DD, however, are poorly understood. This study aimed to identify possible structural connectivity alterations in DD. We evaluated 10 children with pure DD (11.3 {+/-} 0.7 years) and 16 typically developing (TD) peers (11.2 {+/-} 0.6 years) using diffusion tensor imaging. We first assessed white matter microstructure with tract-based spatial statistics. Then we used probabilistic tractography to evaluate tract lengths and probabilistic connectivity maps in specific regions. At whole brain level, we found no significant microstructural differences in white matter between children with DD and TD peers. Also, seed-based connectivity probabilities did not differ between groups. However, we did find significant differences in regions-of-interest tracts which had previously been related to math ability in children. The major findings of our study were reduced white matter coherence and shorter tract lengths of the left superior longitudinal/arcuate fasciculus and left anterior thalamic radiation in the DD group. Furthermore, lower white matter coherence and shorter pathways corresponded with the lower math performance as a result of the correlation analyses. These results from regional analyses indicate that learning, memory and language-related pathways in the left hemisphere might underlie DD.

neuroscience↗

bioRxiv2020

Relationship between Regional White Matter Hyperintensities and Alpha Oscillations in Older Adults

Aging is associated with increased white matter hyperintensities (WMHs) and with the alterations of alpha oscillations (7-13 Hz). However, a crucial question remains, whether changes in alpha oscillations relate to aging per se or whether this relationship is mediated by age-related neuropathology like WMHs. Using a large cohort of cognitively healthy older adults (N=907, 60-80 years), we assessed relative alpha power, alpha peak frequency, and long-range temporal correlations (LRTC) from resting-state EEG. We further associated these parameters with voxel-wise WMHs from 3T MRI. We found that a higher prevalence of WMHs in the superior and posterior corona radiata as well as in the thalamic radiation was related to elevated alpha power, with the strongest association in the bilateral occipital cortex. In contrast, we observed no significant relation of the WMHs probability with alpha peak frequency and LRTC. Finally, higher age was associated with elevated alpha power via total WMH volume. Although an increase in alpha oscillations due to WMH can have a compensatory nature, we rather suggest that an elevated alpha power is a consequence of WMH affecting a spatial organization of alpha sources.

neuroscience↗

bioRxiv2020

Estimating the effect of a scanner upgrade on measures of grey matter structure for longitudinal designs

1Longitudinal imaging studies are crucial for advancing the understanding of brain development over the lifespan. Thus, more and more studies acquire imaging data at multiple time points or with long follow-up intervals. In these studies changes to magnetic resonance imaging (MRI) scanners often become inevitable which may decrease the reliability of the MRI assessments and introduce biases. We therefore investigated the difference between MRI scanners with subsequent versions (3 Tesla Siemens Verio vs. Skyra fit) on the cortical and subcortical measures of grey matter in 116 healthy, young adults using the well-established longitudinal FreeSurfer stream for T1-weighted brain images. We found excellent between-scanner reliability for cortical and subcortical measures of grey matter structure (intra-class correlation coefficient > 0.8). Yet, paired t-tests revealed statistically significant differences in at least 75% of the regions, with percent differences up to 5%, depending on the outcome measure. Offline correction for gradient distortions only slightly reduced these biases. Further, T1-imaging based quality measures systematically differed between scanners. We conclude that scanner upgrades during a longitudinal study introduce bias in measures of cortical and subcortical grey matter structure. Therefore, before upgrading a MRI scanner during an ongoing study, researchers should prepare to implement an appropriate correction method for these effects.

neuroscience↗

bioRxiv2020

Genetic variants for head size share genes and pathways with cancer

The size of the human head is determined by growth in the first years of life, while the rest of the body typically grows until early adulthood1. Such complex developmental processes are regulated by various genes and growth pathways2. Rare genetic syndromes have revealed genes that affect head size3, but the genetic drivers of variation in head size within the general population remain largely unknown. To elucidate biological pathways underlying the growth of the human head, we performed the largest genome-wide association study on human head size to date (N = 79,107). We identified 67 genetic loci, 50 of which are novel, and found that these loci are preferentially associated with head size and mostly independent from height. In subsequent neuroimaging analyses, the majority of genetic variants demonstrated widespread effects on the brain, whereas the effects of 17 variants could be localized to one or two specific brain regions. Through hypothesis-free approaches, we find a strong overlap of head size variants with both cancer pathways and cancer genes. Gene set analyses showed enrichment for different types of cancer and the p53, Wnt and ErbB signalling pathway. Genes overlapping or close to lead variants - such as TP53, PTEN and APC - were enriched for genes involved in macrocephaly syndromes (up to 37-fold) and high-fidelity cancer genes (up to 9-fold), whereas this enrichment was not seen for human height variants. This indicates that genes regulating early brain and cranial growth are associated with a propensity to neoplasia later in life, irrespective of height. Our results warrant further investigations of the link between head size and cancer, as well as its clinical implications in the general population.

genomics↗

bioRxiv2019

Weight loss reduces head motion: re-visiting a major confound in neuroimaging

Head motion during magnetic resonance imaging (MRI) induces image artifacts that affect virtually every brain measure. In parallel, cross-sectional observations indicate a correlation of head motion with age, psychiatric disease status and obesity, raising the possibility of a systematic artifact-induced bias in neuroimaging outcomes in these conditions, due to the differences in head motion. Yet, a causal link between obesity and head motion has not been tested in an experimental design. Here, we show that a change in body mass index (i.e., weight loss after bariatric surgery) systematically decreases head motion during MRI. In this setting, reduced imaging artifacts due to lower head motion might result in biased estimates of neural differences induced by changes in BMI. Overall, our finding urges the need to rigorously control for within-scanner head motion to enable valid results of neuroimaging outcomes in populations that differ in head motion due to obesity or other conditions.

neuroscience↗

bioRxiv2019

Hemispheric asymmetries in resting-state EEG and fMRI are related to approach and avoidance behaviour, but not to eating behaviour or BMI

Much of our behaviour is driven by two motivational dimensions - approach and avoidance. These have been related to frontal hemispheric asymmetries in clinical and resting-state EEG studies: approach was linked to higher activity of the left relative to the right hemisphere, while avoidance was related to the opposite pattern. Increased approach behaviour, specifically towards unhealthy foods, is also observed in obesity and has been linked to asymmetry in the framework of the right-brain hypothesis of obesity. Here, we aimed to replicate previous EEG findings of hemispheric asymmetries for self-reported approach/avoidance behaviour and to relate them to eating behaviour. Further, we assessed whether resting fMRI hemispheric asymmetries can be detected and whether they are related to approach/avoidance, eating behaviour, and BMI. We analysed 3 samples: Sample 1 (n=117) containing EEG and fMRI data from lean participants, and Samples 2 (n=89) and 3 (n=152) containing fMRI data from lean, overweight, and obese participants. While in Sample 1 approach in women was related to EEG and fMRI hemispheric asymmetries, in Samples 2 and 3 this effect was not significant. Here, hemispheric asymmetries were neither related to BMI nor eating behaviour. Our study partly replicates previous EEG findings regarding hemispheric asymmetries and indicates that this relationship could also be captured using fMRI. Our findings suggest that eating behaviour and obesity are likely to be mediated by mechanisms not directly relating to frontal asymmetries in neuronal activation quantified with EEG and fMRI.

neuroscience↗

bioRxiv2019

Altered hypothalamic microstructure in human obesity

Obesity is a multifactorial disorder driven by sustained energy imbalance. The hypothalamus is an important regulator of energy homeostasis and therefore likely involved in obesity pathophysiology. Animal studies suggest that obesity-related diets induce structural changes in the hypothalamus through inflammation-like processes. Whether this translates to humans is however largely unknown. Therefore, we aimed to assess obesity-related differences in hypothalamic macro- and microstructure based on a multimodal approach using T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) acquired at 3 Tesla in a large well-characterized sample of the Leipzig Research Center for Civilization Diseases (LIFE) cohort (n1 = 338, 48% females, age 21-78 years, BMI 18-43 kg/m2). We found that higher body mass index (BMI) selectively predicted higher mean proton diffusivity (MD) within the hypothalamus, indicative of compromised microstructure in the underlying tissue. Results were independent from confounders and confirmed in another independent sample (n2 = 236). In addition, while hypothalamic volume was not associated with obesity, we identified a sexual dimorphism and larger hypothalamic volumes in the left compared to the right hemisphere. Using two large samples of the general population, we showed that a higher BMI specifically relates to altered microstructure in the hypothalamus, independent from confounders such as age, sex and obesity-associated co-morbidities. This points to persisting microstructural changes in a key regulatory area of energy homeostasis occurring with excessive weight. These findings may help to better understand the pathomechanisms of obesity and other eating-related disorders.

neuroscience↗

bioRxiv2019

10Kin1day: A bottom-up neuroimaging initiative

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain.

neuroscience↗