Biology subjects

Gruber, O.

Publications and source records attributed to Gruber, O..

11 recordsLinked to original sources

bioRxiv2025

Lifestyle, Early-life, and Genetic Health Risk Factors Underlying the Brain Age Gap: A Mega-Analysis Across 3,934 Individuals from the ENIGMA MDD Consortium

BackgroundLarge-scale studies show that adults with major depressive disorder (MDD) generally have a higher imaging-predicted age relative to their chronological age (i.e., positive brain age gap) compared to controls, though considerable within-group variation exists. This study examines lifestyle, early-life, and genetic health risk factors contributing to the brain age gap. Identifying risk and resilience factors could help protect brain and mental health. MethodsUsing an established model trained on FreeSurfer-derived brain regions (www.photon-ai.com/enigma_brainage), we generated brain age predictions for 1,846 controls and 2,088 individuals with MDD (aged 18-75) from 12 international cohorts. Polygenic risk scores (PRS) were calculated for major depression, C-reactive protein, and body mass index (BMI) using large-scale GWAS results. Linear mixed models were applied to assess lifestyle (BMI, smoking, education), early-life childhood trauma, and genetic (PRS) health risk associations with the brain age gap. Additionally, we evaluated the link between the brain age gap and peripheral biological age indicators (epigenetic clocks). ResultsHigher brain age gaps were significantly associated with BMI ({beta}=0.01, PFDR=0.02) and smoking ({beta}=0.11, PFDR=0.02), while lower brain age gaps were linked to higher education ({beta}=-0.02, PFDR=0.02). Higher childhood trauma scores predicted a higher brain age gap ({beta}=0.04, P=0.01). Higher brain age gaps were positively associated with all PRS ({beta}s=0.04-0.16, PsFDR=0.02-0.03). There were no significant interactions between diagnosis and assessed factors on the brain age gap. In a multivariable model, only modifiable health factors--BMI, smoking, and education--remained uniquely associated with brain age gaps. ConclusionsGenetic liability for depression and related traits is linked to poorer brain health, but health behaviors potentially offer a key opportunity for intervention. This study underscores the importance of targeting modifiable lifestyle factors to mitigate poor brain health in depressed individuals, an approach perhaps under-recognized in clinical practice.

neuroscience↗

bioRxiv2025

Investigation of the Role of the LC3 Conjugation System in Autophagy for Human Reward System Reactivity

ObjectivesThe dopaminergic reward system is involved in the etiology of psychiatric disorders, and autophagy has been suggested to interfere with dopamine release. LC3 conjugation plays a key role in autophagy and comprises modification of autophagy protein LC3 with phosphatidylethanolamine. We investigated whether LC3 conjugation may impact on the strength of activation in key regions of the mesolimbic reward system. MethodsTo test our hypothesis, responses of the reward system to conditioned stimuli were assessed using the desire-reason dilemma(DRD) paradigm, that allows for investigation of reward processing during fMRI. Association of a set of missense variants with reward system responses was analyzed in a sample of 214 participants. ResultsAs a main finding, the gene set was associated with both ventral tegmental area (VTA) and nucleus accumbens (NAc) responses to conditioned reward stimuli (empirical P-value R-VTA: 0.008, R-NAc: 0.009). Strongest missense variants were MAP1LC3B_rs113610787 (P=3.219e-05) for association with response in the L-NAc, and ATG4B_rs143448469 (P=4.366e-05) for the R-NAc. ConclusionsFindings provide evidence that variation of the LC3 conjugation system influences responses of the VTA and NAc to conditioned reward stimuli. Further studies are required to replicate the findings, and to investigate the possible role of LC3 conjugation in psychiatric disorders.

genetics↗

bioRxiv2025

Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study

ImportanceMajor depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients. ObjectiveTo use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics. Design, setting, and participantsA normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices). Main outcome measuresZ-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC. ResultsZ-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms. Conclusions and relevanceOur study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture. Key points QuestionCan z-scores derived from normative modelling shed light on the heterogeneous group-level findings of cortical thickness abnormalities in major depression and what characterises individuals at the extreme ends of cortical thickness abnormalities? FindingWe confirmed a large overlap in z-score distributions between depressed individuals and healthy controls and a heterogeneous spatial distribution of extreme z-deviations across brain regions across individual patients. Lower z-scores for cortical thickness were related to more severe clinical characteristics. MeaningOur findings confirm the heterogeneity in individual variation in the location and extent of CT abnormalities across patients with MDD and stress the importance of individualised predictions when examining cortical thickness abnormalities.

neuroscience↗

bioRxiv2024

Impact of Unc-51 Like Kinase 4 (ULK4) on the Reactivity of the Extended Reward System in Response to Conditioned Stimuli

ObjectivesULK4 is an established candidate gene for mental disorders and antipsychotic treatment response and codes for a serine/threonine kinase that regulates the neural stem cell pool and controls cortex development. We investigated the effects of functional genetic variation at the ULK4 locus on the human extended dopaminergic reward system using functional magnetic resonance imaging (fMRI) during performance of a well-established reward paradigm. Methods234 study participants with functional neuroimaging (fMRI) data of the extended reward system and with ULK4 genotype data were included in this study. Effects of genetic variation in the ULK4 gene on reward system functioning were determined using the Desire-Reason-Dilemma (DRD) paradigm which allows to assess brain activation in response to conditioned reward stimuli (Diekhof et al. 2010). ResultsAmong common missense variants of the ULK4 gene, variant prioritization revealed strongest functional signatures for variant rs17215589, coding for amino acid exchange Ala715Thr. For rs17215589 minor allele carriers, we detected increased activation responses to conditioned reward stimuli in the ventral tegmental area, the nucleus accumbens and several cortical brain regions of the extended reward system. ConclusionsOur findings provide further evidence in humans that genetic variation in ULK4 may increase the vulnerability to mental disorders by modulating the function of the extended reward system. Future studies are needed to confirm the functional modulation of the extended reward system by ULK4 and to specify the role of this mechanism in the pathogenesis of psychiatric disorders.

genetics↗

bioRxiv2023

Gene Set-Based Analysis of the Endosomal Sorting Processes Cargo Selection and Membrane Tubulation with Human Reward System Reactivity

Dysfunction of the dopaminergic reward system has been implicated in the pathophysiology of several neuropsychiatric disorders. The endosomal network encompasses important processes related to neurotransmission in dopamine neurons, e.g, endocytosis, sorting, recycling and degradation of receptors. In the present study, we investigated whether genetic variation influencing the endosomal sorting machinery, in particular cargo selection and membrane tubulation, may impact on the activation strength in key regions of the mesolimbic reward system, i.e. the ventral tegmental area (VTA) and the nucleus accumbens (NAc). To test our hypothesis, the VTA and NAc responses to conditioned reward stimuli were investigated using the desire-reason dilemma (DRD) paradigm during functional magnetic resonance imaging (fMRI). Association of these neural responses with a set of genetic variants related to endosomal sorting processes were tested in two independent samples (N = 182; N = 214). In the first sample, the gene set was associated with both VTA and NAc responses to conditioned reward stimuli [empirical P -values: R-VTA 0.0036; R-NAc 0.0016; L-NAc 0.0094], while the effect in the R-VTA could be replicated in the second sample [empirical P -value: R-VTA 0.0443] at the level of the gene set. For the NAc, an additional exploratory analysis of a patient-only subsample of the first sample (N = 64) suggested that the gene set may express its effect in this brain region predominantly in patients. These findings provide first evidence that the endosomal sorting processes cargo selection and membrane tubulation influence neural responses of the reward system to conditioned stimuli. Further studies are required to clarify the role of endosomal sorting processes in the pathophysiology of neuropsychiatric disorders.

genetics↗

bioRxiv2023

Normative Modeling of Brain Morphometry Across the Lifespan using CentileBrain: Algorithm Benchmarking and Model Optimization

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

bioinformatics↗

bioRxiv2022

DNAJC13 influences responses of the extended reward system to conditioned stimuli: a genome-wide association study

Reward system dysfunction is implicated in the pathogenesis of major psychiatric disorders. We conducted a genome-wide association study (GWAS) to identify genes that influence activation strength of brain regions within the extended reward system in humans. A large homogeneous sample of 214 participants was genotyped and underwent functional magnetic resonance imaging (fMRI). All subjects performed the desire-reason dilemma (DRD) paradigm allowing systematic investigation of systems-level mechanisms of reward processing in humans. As a main finding, we identified the single nucleotide variant rs113408797 in the DnaJ Heat Shock Protein Family Member C13 gene (DNAJC13, alias RME-8), that strongly influenced the activation of the ventral tegmental area (VTA; p = 2.50E-07) and the nucleus accumbens (NAcc; p = 5.31E-05) in response to conditioned reward stimuli. Moreover, haplotype analysis assessing the information across the entire DNAJC13 locus demonstrated an impact of a five-marker haplotype on VTA activation (p = 3.21E-07), which further corroborates a link between this gene and reward processing. The present findings provide first direct empirical evidence that genetic variation of DNAJC13 influences neural responses within the extended reward system to conditioned stimuli. Further studies are required to investigate the role of this gene in the pathogenesis and pathophysiology of neuropsychiatric disorders.

genomics↗

bioRxiv2022

Dissociable Cellular and Genetic Mechanisms of Cortical Thinning at Different Life Stages

Mechanisms underpinning age-related variations in cortical thickness in the human brain remain poorly understood. We investigated whether inter-regional age-related variations in cortical thinning (in a multicohort neuroimaging dataset from the ENIGMA Lifespan Working Group totalling 14,248 individuals, aged 4-89 years) depended on cell-specific marker gene expression levels. We found differences amidst early-life (<20 years), mid-life (20-60 years), and late-life (>60 years) in the patterns of association between inter-regional profiles of cortical thickness and expression profiles of marker genes for CA1 and S1 pyramidal cells, astrocytes, and microglia. Gene ontology and enrichment analyses indicated that each of the three life-stages was associated with different biological processes and cellular components: synaptic modeling in early life, neurotransmission in mid-life, and neurodegeneration in late-life. These findings provide mechanistic insights into age-related cortical thinning during typical development and aging.

neuroscience↗

bioRxiv2020

Connectivity patterns of task-specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to molecular architecture

BackgroundDespite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. MethodsWe investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior molecular imaging in healthy populations. ResultsTen-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. ConclusionsWe revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.

neuroscience↗

bioRxiv2019

Brain Aging in Major Depressive Disorder: Results from the ENIGMA Major Depressive Disorder working group

BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset. MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain predicted age difference (brain-PAD). FindingsOn average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohens d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004). InterpretationThis highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates. FundingThis work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.

neuroscience↗

bioRxiv2018

Brain-based ranking of cognitive domains to predict schizophrenia

Schizophrenia is a devastating brain disorder that disturbs sensory perception, motor action, and abstract thought. Its clinical phenotype implies dysfunction of various mental domains, which has motivated a series of theories regarding the underlying pathophysiology. Aiming at a predictive benchmark of a catalogue of cognitive functions, we developed a bottom-up machine-learning strategy and provide a proof of principle in a multi-site clinical dataset (n=324). Existing neuroscientific knowledge on diverse cognitive domains was first condensed into neuro-topographical maps. We then examined how the ensuing meta-analytic cognitive priors can distinguish patients and controls using brain morphology and intrinsic functional connectivity. Some affected cognitive domains supported well-studied directions of research on auditory evaluation and social cognition. However, rarely suspected cognitive domains also emerged as disease-relevant, including self-oriented processing of bodily sensations in gustation and pain. Such algorithmic charting of the cognitive landscape can be used to make targeted recommendations for future mental health research.

neuroscience↗