Biology subjects

Witte, A. V.

Publications and source records attributed to Witte, A. V..

13 recordsLinked to original sources

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↗

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

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.

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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.

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bioRxiv2020

Lower intake of animal-based products links to improved weight status, independent of depressive symptoms and personality in the general population

BackgroundRestricting animal-based products from diet may exert beneficial effects on weight status, however whether this is also true for emotional health is unclear. Moreover, differential personality traits may underlie restrictive eating habits and therefore potentially confound diet-health associations. To systematically assess whether restrictive dietary intake of animal-based products relates to lower weight and higher depressive symptoms, and how this is linked to personality traits in the general population. MethodsCross-sectional data was taken from the baseline LIFE-Adult study collected from 2011-2014 in Leipzig, Germany (n = 8943). Main outcomes of interest were 12-month dietary frequency of animal-derived products measured using a Food Frequency Questionnaire (FFQ), body mass index (BMI) (kg/m2), and the Center of Epidemiological Studies Depression Scale (CES-D). Personality traits were assessed in a subsample of n = 7906 using the Five Factor Inventory (NEO-FFI). FindingsHigher restriction of animal-based product intake was associated with a lower BMI (age-, sex- and education-adjusted, n = 8943; {beta} = -.07, p < .001), but not depression score. Personality, i.e. lower extraversion (F (1,7897) = 9.8, p = .002), was related to frequency of animal product intake. Further, not diet but personality was significantly associated with depression, i.e. higher neuroticism ({beta} = .024), lower extraversion ({beta} = -.006), lower agreeableness ({beta} = -.001), lower conscientiousness ({beta} = -.007) and higher BMI ({beta} = .004) (all p < .001, overall model, R2 = .21). The beneficial association with lower weight seemed to be driven by the frequency of meat product intake and not secondary animal products. Likewise, the overall number of excluded food items from the individual diet was associated with a lower BMI (age-, sex- and education-adjusted, n = 8938, {beta} = -.15, p < .001) and additionally with lower depression scores ({beta} = -.004, t = -4.1, p < .001, R2 = .05, corrected for age, sex and education), also when additionally correcting for differences in personality traits ({beta} = -.003, t = -2.7, p = .007, R2 = .21). InterpretationHigher restriction of animal-based products in the diet was significantly associated with a lower BMI, but not with depressive symptoms scores in a large well-characterized population-based sample of adults. In addition, we found that certain personality traits related to restricting animal-based products - and that those traits, but not dietary habits, explained a considerable amount of variance in depressive symptoms. Upcoming longitudinal studies need to confirm these findings and to test the hypothesis if restricting animal-based products, esp. primary animal products ((processed) meat, wurst), conveys benefits on weights status, hinting to a beneficial relationship of animal-based restricted diets in regard to prevention and treatment of overweight and obesity. FundingWe thank all study participants. We very much appreciate the help of the physicians who performed the clinical examinations and data collection, in particular Ulrike Scharrer, Annett Wiedemann, Kerstin Wirkner and her team. This work was supported by LIFE--Leipzig Research Centre for Civilisation Diseases, University of Leipzig. LIFE is funded by means of the European Union, by means of the European Social Fund (ESF), by the European Regional Development Fund (ERDF), and by means of the Free State of Saxony within the framework of the excellence initiative. This work was supported by a scholarship (EM) by the German Federal Environmental Foundation and by the grants of the German Research Foundation contract grant number CRC 1052 "Obesity mechanisms" Project A1 (AV) and WI 3342/3-1 (AVW). The corresponding author had full access to all the data in the included in the analysis and had final responsibility for the decision to submit for publication. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=146 HEIGHT=200 SRC="FIGDIR/small/940460v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@1f81d8org.highwire.dtl.DTLVardef@85bd35org.highwire.dtl.DTLVardef@48fd83org.highwire.dtl.DTLVardef@1fbc053_HPS_FORMAT_FIGEXP M_FIG C_FIG

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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.

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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↗

bioRxiv2018

Neuroanatomical correlates of food addiction and obesity in the general population

The food addiction model suggests neurobiological similarities between substance-related and addictive disorders and obesity. While structural brain differences have been consistently reported in these conditions, little is known about the neuroanatomical correlates of food addiction. We therefore assessed whether food addiction, assessed with the Yale Food Addiction Scale (YFAS), related to obesity, personality and brain structure in a large population-based sample (n=625; 20-59 years old, 45% women). A higher YFAS symptom score correlated with obesity and disinhibited eating. In a whole-brain analysis, YFAS symptom score was not associated with cortical thickness nor subcortical gray matter volumes. Higher body mass index (BMI) correlated with reduced thickness of (pre)frontal, temporal and occipital cortex. Bayes factor analysis suggested that BMI and - to a smaller extent - YFAS symptom score contributed independently to right lateral orbitofrontal cortex thickness. Our study shows that food addiction is not associated with neuroanatomical differences in a large population-based sample, and does not account for the major part of obesity-associated gray matter alterations. Yet, food addiction might explain additional variance in orbitofrontal cortex, a hub area of the reward network. Longitudinal studies implementing both anatomical and functional MRI could further disentangle the neural mechanisms of addictive eating behaviors.

neuroscience↗

bioRxiv2018

The age-dependent relationship between resting heart rate variability and functional brain connectivity

Resting heart rate variability (HRV), an index of parasympathetic cardioregulation and an individual trait marker related to mental and physical health, decreases with age. Previous studies have associated resting HRV with structural and functional properties of the brain - mainly in cortical midline and limbic structures. We hypothesized that HRV may alter its relationship with brain structure and function across the adult lifespan. In 388 healthy subjects of three age groups (140 younger: 26.0{+/-}4.2 years, 119 middle-aged: 46.3{+/-}6.2 years, 129 older: 66.9{+/-}4.7 years), gray matter structure (voxel-based morphometry) and resting-state functional connectivity (eigenvector centrality mapping and exploratory seed-based functional connectivity) were related to resting HRV, measured as the root mean square of successive differences (RMSSD). Confirming previous findings, resting HRV decreased with age. For HRV-related gray matter volume, there were no statistically significant differences between the age groups, nor similarities across all age groups. In whole-brain functional connectivity analyses, we found an age-dependent association between resting HRV and eigenvector centrality in the bilateral ventromedial prefrontal cortex (vmPFC), driven by the younger adults. Across all age groups, HRV was positively correlated with network centrality in bilateral posterior cingulate cortex. Seed-based functional connectivity analysis using the vmPFC cluster revealed an HRV-related cortico-cerebellar network in younger but not in middle-aged or older adults. Our results indicate that the decrease of HRV with age is accompanied by changes in functional connectivity along the cortical midline. This extends our knowledge of brain-body interactions and their changes over the lifespan.

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bioRxiv2017

Association of Peripheral Blood Pressure with Grey Matter Volume in 19- to 40-Year-Old Adults

ObjectiveTo test whether elevated blood pressure (BP) relates to grey matter volume (GMV) changes in young adults who had not previously been diagnosed as hypertensive (systolic BP (SBP)/diastolic BP (DBP)[≥]140/90 mmHg).\n\nMethodsWe associated BP with GMV from structural 3 Tesla T1-weighted MRI of 423 healthy adults between 19-40 years (mean age=27.7{+/-}5.3 years, 177 women, SBP/DBP=123.2/73.4{+/-}12.2/8.5 mmHg). Data originated from four previously unpublished cross-sectional studies conducted in Leipzig, Germany. We performed voxel-based morphometry on each study separately and combined results in image-based meta-analyses (IBMA) to assess cumulative effects across studies. Resting BP was assigned to one of four categories: (1) SBP<120 and DBP<80 mmHg, (2) SBP 120-129 or DBP 80-84 mmHg, (3) SBP 130-139 or DBP 85-89 mmHg, (4) SBP[≥]140 or DBP[≥]90 mmHg.\n\nResultsIBMA yielded: (a) lower regional GMV was correlated with higher peripheral BP; (b) lower GMV with higher BP when comparing individuals in sub-hypertensive categories 3 and 2, respectively, to those in category 1; (c) lower BP-related GMV was found in regions including hippocampus, amygdala, thalamus, frontal and parietal structures (e.g. precuneus).\n\nConclusionsBP[≥]120/80 mmHg was associated with lower GMV in regions that have previously been related to GM decline in older individuals with manifest hypertension. Our study shows that BP-associated GM alterations emerge continuously across the range of BP and earlier in adulthood than previously assumed. This suggests that treating hypertension or maintaining lower BP in early adulthood might be essential for preventing the pathophysiological cascade of asymptomatic cerebrovascular disease to symptomatic end-organ damage, such as stroke or dementia.

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bioRxiv2016

Predicting brain-age from multimodal imaging data captures cognitive impairment

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically-relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brainimaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N = 2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N = 475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.\n\nHighlightsO_LIBrain-based age prediction is improved with multimodal neuroimaging data.\nC_LIO_LIParticipants with cognitive impairment show increased brain aging.\nC_LIO_LIAge prediction models are robust to motion and generalize to independent datasets from other sites.\nC_LI

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