Biology subjects

Opel, N.

Publications and source records attributed to Opel, N..

6 recordsLinked to original sources

bioRxiv2024

Spermidine alleviates depression via control of the stress response

Depression is a stress-associated disorder, and it represents a major global health issue. Its pathophysiology is complex and remains insufficiently understood, with current medications often showing limited efficacy and undesirable side effects. Here, we identify imbalanced polyamine levels and dysregulated autophagy as key components of the acute stress response in humans, and as hallmarks of chronic stress and depressive disorders. Moreover, conventional antidepressant pharmacotherapy increases endogenous plasma concentrations of the polyamine spermidine exclusively in patients who respond to the treatment, suggesting a link between spermidine and successful outcomes. In a clinical trial, involving drug-naive depressed individuals, three weeks of spermidine supplementation increased autophagy and alleviated symptoms of depression. Behavioral and mechanistic findings of spermidine supplementation were validated in various mouse stress and depression models. In summary, spermidine supplementation mitigates polyamine dysregulation and stimulates autophagy under pathological stress conditions, offering a novel and well-tolerated treatment approach for stress-related depressive disorders.

neuroscience↗

bioRxiv2024

Gray matter correlates of childhood maltreatment in the context of major depression: searching for replicability in a multi-cohort brain-wide association study of 3225 adults

Childhood maltreatment effects on cerebral gray matter have been frequently discussed as a neurobiological pathway for depression. However, localizations are highly heterogeneous, and recent reports have questioned the replicability of mental health neuroimaging findings. Here, we investigate the replicability of gray matter correlates of maltreatment (measured retrospectively via the Childhood Trauma Questionnaire) across three large adult cohorts (total N=3225). Pooling cohorts revealed maltreatment-related gray matter reductions, with most extensive effects when not controlling for depression diagnosis (maximum partial R2=.022). However, none of these effects significantly replicated across cohorts. Non-replicability was consistent across a variety of maltreatment subtypes and operationalizations, as well as subgroup analyses with and without depression, and stratified by sex. In this work we show that there is little evidence for the replicability of gray matter correlates of childhood maltreatment, when adequately controlling for psychopathology. This underscores the need to focus on replicability research in mental health neuroimaging.

neuroscience↗

bioRxiv2023

Cross-validation for the estimation of effect size generalizability in mass-univariate brain-wide association studies

IntroductionStatistical effect sizes are systematically overestimated in small samples, leading to poor generalizability and replicability of findings in all areas of research. Due to the large number of variables, this is particularly problematic in neuroimaging research. While cross-validation is frequently used in multivariate machine learning approaches to assess model generalizability and replicability, the benefits for mass-univariate brain analysis are yet unclear. We investigated the impact of cross-validation on effect size estimation in univariate voxel-based brain-wide associations, using body mass index (BMI) as an exemplary predictor. MethodsA total of n=3401 adults were pooled from three independent cohorts. Brain-wide associations between BMI and gray matter structure were tested using a standard linear mass-univariate voxel-based approach. First, a traditional non-cross-validated analysis was conducted to identify brain-wide effect sizes in the total sample (as an estimate of a realistic reference effect size). The impact of sample size (bootstrapped samples ranging from n=25 to n=3401) and cross-validation on effect size estimates was investigated across selected voxels with differing underlying effect sizes (including the brain-wide lowest effect size). Linear effects were estimated within training sets and then applied to unseen test set data, using 5-fold cross-validation. Resulting effect sizes (explained variance) were investigated. ResultsAnalysis in the total sample (n=3401) without cross-validation yielded mainly negative correlations between BMI and gray matter density with a maximum effect size of R2p=.036 (peak voxel in the cerebellum). Effects were overestimated exponentially with decreasing sample size, with effect sizes up to R2p=.535 in samples of n=25 for the voxel with the brain-wide largest effect and up to R2p=.429 for the voxel with the brain-wide smallest effect. When applying cross-validation, linear effects estimated in small samples did not generalize to an independent test set. For the largest brain-wide effect a minimum sample size of n=100 was required to start generalizing (explained variance >0 in unseen data), while n=400 were needed for smaller effects of R2p =.005 to generalize. For a voxel with an underlying null effect, linear effects found in non-cross-validated samples did not generalize to test sets even with the maximum sample size of n=3401. Effect size estimates obtained with and without cross-validation approached convergence in large samples. DiscussionCross-validation is a useful method to counteract the overestimation of effect size particularly in small samples and to assess the generalizability of effects. Train and test set effect sizes converge in large samples which likely reflects a good generalizability for models in such samples. While linear effects start generalizing to unseen data in samples of n>100 for large effect sizes, the generalization of smaller effects requires larger samples (n>400). Cross-validation should be applied in voxel-based mass-univariate analysis to foster accurate effect size estimation and improve replicability of neuroimaging findings. We provide open-source python code for this purpose (https://osf.io/cy7fp/?view_only=a10fd0ee7b914f50820b5265f65f0cdb).

neuroscience↗

bioRxiv2022

Molecular and connectomic vulnerability shape cross-disorder cortical abnormalities

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21 000 patients and N = 26 000 controls, collected using a harmonized processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find that regional molecular vulnerability and macroscale brain network architecture interact to drive the spatial patterning of cortical abnormalities in multiple disorders. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, medial temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local biological attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

neuroscience↗

bioRxiv2019

Subcortical Shape Alterations in Major Depressive Disorder: Findings from the ENIGMA Major Depressive Disorder Working Group

Alterations in regional subcortical brain volumes have been widely investigated as part of the efforts of an international consortium, ENIGMA, to determine reliable structural brain signatures for Major Depressive Disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work to precisely map localized MDD-related differences in subcortical regions using shape analysis. In this meta-analysis of subcortical shape from the ENIGMA-MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta-analysis. Relative to CTL, patients with MDD had lower surface area in the subiculum of the hippocampus, the basolateral amygdala, and the nucleus accumbens shell. Relative to CTL, patients with adolescent-onset MDD ([≤] 21 years) had lower thickness and surface area of the subiculum of the hippocampus and the basolateral amygdala. Relative to first-episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala. Our results suggest that previously reported MDD-associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.

neuroscience↗

bioRxiv2018

Variation of HbA1c affects cognition and white matter microstructure in healthy, young adults

The metabolic serum marker HbA1c has been associated with both impaired cognitive performance and altered white matter integrity in patients suffering from diabetes mellitus. However, it remains unclear if higher levels of HbA1c might also affect brain structure and function in healthy subjects. With the present study we therefore aimed to investigate the relationship between HbA1c levels and cognitive performance as well as white matter microstructure in healthy, young adults.\n\nTo address this question, associations between HbA1c and cognitive measures (NIH Cognition Toolbox) as well as DTI-derived imaging measures of white matter microstructure were investigated in a publicly available sample of healthy, young adults as part of the Humane Connectome Project (n= 1206, mean age= 28.8 years, 45.5 % male).\n\nWe found that HbA1c levels (range 4.1-6.3%) were significantly inversely correlated with measures of cognitive performance. Higher HbA1c levels were associated with significant and widespread reductions in fractional anisotropy (FA) controlling for age, sex, body-mass index and education. FA reductions were furthermore found to covary with measures of cognitive performance. The same pattern of results could be observed in analyses restricted to participants with HBAlc levels below 5.7%.\n\nThe present study demonstrates that low-grade HbA1c variation below diagnostic threshold for diabetes is related to both cognitive performance and white matter integrity in healthy, young adults. These findings highlight the detrimental impact of metabolic risk factors on brain physiology and underscore the importance of intensified preventive measures independent of the currently applied diagnostic HbA1c cut-off scores.\n\nHighlightsO_LIHbAlc levels are negatively associated with cognitive performance in healthy, young adults in a sample of n=1206 (Human Connectome Project).\nC_LIO_LIHbA1c levels even below strict prediabetes thresholds are negatively associated with white matter integrity throughout the brain.\nC_LIO_LIWhite matter integrity in affected regions is positively associated with cognitive performance.\nC_LI

neuroscience↗