Biology subjects

Lanzenberger, R.

Publications and source records attributed to Lanzenberger, R..

31 records · Page 2Linked to original sources

bioRxiv2021

Correlation of receptor density and mRNA expression patterns in the human cerebral cortex

Changes in distribution of associated molecular targets have been reported across several neuropsychiatric disorders. However, the high-resolution topology of most proteins is unknown and simultaneous in vivo measurement in multi-receptor systems is complicated. To account for the missing proteomic information, mRNA transcripts are typically used as a surrogate. Nonetheless, post-transcriptional and post-translational processes might cause the discrepancy between the final distribution of proteins and gene expression patterns. Therefore, this study aims to investigate ex vivo links between mRNA expression and corresponding receptor density in the human cerebral cortex. To this end, autoradiography data on the density of 15 different receptors in 38 brain regions were correlated with the expression patterns of 50 associated genes derived from microarray data (mA), RNA sequencing data (RNA-Seq) provided by the Allen Human Brain Atlas and predicted mRNA expression patterns (pred-mRNA). Spearmans rank correlation was used to evaluate the possible links between proteomic data and mRNA expression patterns. Correlations between mRNA and protein density varied greatly between targets: Positive associations were found for e.g. the serotonin 1A (pred-mRNA: rs = 0.708; mA: rs = 0.601) or kainate receptor (pred-mRNA: rs = 0.655; mA: rs = 0.601; RNA-Seq: rs = 0.575), while most of the investigated target receptors showed low or negative correlations. The high variability in the correspondence of mRNA expression and receptor warrants caution when inferring the topology of molecular targets in the brain from transcriptome data. This highlights the longstanding value of molecular imaging data and the need for comprehensive proteomic data.

neuroscience↗

bioRxiv2021

Learning induces coordinated neuronal plasticity of metabolic demands and functional brain networks

The neurobiological basis of learning is reflected in adaptations of brain structure, network organization and energy metabolism. However, it is still unknown how different neuroplastic mechanisms act together and if cognitive advancements relate to general or task-specific changes. To address these questions, we tested how hierarchical network interactions contribute to improvements in the performance of a visuo-spatial processing task by employing simultaneous PET/MR neuroimaging before and after a 4-week learning period. We combined functional PET with metabolic connectivity mapping (MCM) to infer directional interactions across brain regions and subsequently performed simulations to disentangle the role of functional network dynamics and glucose metabolism. As a result, learning altered the top-down regulation of the salience network onto the occipital cortex, with increases in MCM at resting-state and decreases during task execution. Accordingly, a higher divergence between resting-state and task-specific effects was associated with better cognitive performance, indicating that these adaptations are complementary and both required for successful skill learning. Simulations further showed that changes at resting-state were dependent on glucose metabolism, whereas those during task performance were driven by functional connectivity between salience and visual networks. Referring to previous work, we suggest that learning establishes a metabolically expensive skill engram at rest, whose retrieval serves for efficient task execution by minimizing prediction errors between neuronal representations of brain regions on different hierarchical levels.

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bioRxiv2021

Effects of Escitalopram and Relearning on Cortical and Subcortical Grey Matter in Healthy Humans

The antidepressant effect of selective serotonin reuptake inhibitors (SSRI) is related to increased neuroplasticity during relearning. Stress-induced dendritic atrophy in key brain areas for learning and memory such as the hippocampus and prefrontal cortex is reversed by SSRI treatment. This finding is accompanied by behavioral stabilization. The aim of this study was to investigated serotonergic modulation effects on structural neuroplasticity (cortical thickness, subcortical volumes) during relearning in healthy subjects. Participants performed daily associative learning tasks over 3 weeks followed by a 3-week relearning phase combined with intake of the SSRI escitalopram or placebo. Evidence suggests that SSRIs promote the brains susceptibility to change on the basis of environment factors. We found no effect of SSRI on grey matter measures during relearning. Here, non-findings might be a consequence of the implemented intensity and duration of study interventions. With sparse literature on healthy participants in this field, future studies will have to further elucidate SSRIs properties on relearning and structural neuroplasticity.

neuroscience↗

bioRxiv2021

Escitalopram Administration, Neuroplastic Effects and Relearning: A Diffusion Tensor Imaging Study in Healthy Individuals

BackgroundNeuroplastic processes are influenced by selective serotonergic reuptake inhibitors, while learning in conjunction with the administration of serotonergic agents alters white matter microstructure in humans. The goal of this double-blind, placebo-controlled imaging study was to investigate the influence of escitalopram on white matter plasticity during (re)learning. MethodsSeventy-one healthy individuals (age = 25.6{+/-}5.0, 43 females) underwent 3 diffusion magnetic resonance imaging sessions: at baseline, after 3-weeks of associative learning (emotional/non-emotional content) and after relearning shuffled associations for an additional 3 weeks. During the relearning phase, subjects received daily escitalopram 10 mg or placebo orally. Statistical analysis was performed with statistical parametric mapping (SPM) and using sandwich estimator. ResultsA three-way and two-way rmANOVA was conducted to analyze the effects of escitalopram on AD, FA, MD and RD during the learning and relearning period. We found no significant three-way or two-way interactions for each DTI metrics (pFDR > 0.05), thus neither after 3 nor after 6 weeks we found significant changes in white matter microstructure. ConclusionWe examined neither an effect of escitalopram nor learning (or relearning) interventions on different DTI metrics. The duration and intensity of study interventions (i.e. administration of escitalopram and learning as the relearning task) might have been chosen insufficiently to induce detectable alterations. Previous studies examining the effects of SSRIs on white matter tracts in humans are underrepresented, but do mainly yield towards non-significant findings. The results implicate that escitalopram does not impact white matter microstructures in healthy subjects.

neuroscience↗

bioRxiv2021

Effects of learning and escitalopram administration on serum BDNF levels, a randomised placebo-controlled trial

BackgroundThe brain-derived neurotrophic factor (BDNF) has been implicated in processes essential for neuroplasticity. Learning and serotonin reuptake inhibitors (SSRI) foster neuronal reorganization, a mechanism potentially related to BDNF. This study aims to assess the effects of associative learning and escitalopram on serum BDNF (sBDNF) levels, to gain further knowledge on their dynamic interplay. MethodsFor three weeks, 37 participants performed one of two associative learning paradigms with either emotional or semantic content daily. During a subsequent three-week period of reversal learning, subjects either received escitalopram (10mg per day) or placebo. Before and after each learning period sBDNF values were assessed. Citalopram plasma levels were measured at the last time point. Linear mixed effects models (LME) and partial Spearmans rank and Pearson correlations were used for statistical analyses. ResultsOne-way LME resulted in a significant effect of time during the first learning period over both groups (p<0.01). Two-way LME revealed a significant interaction effect of the emotional content learning group and time (p=0.02). Three-way LME (time x reversal learning group x substance) showed no significant effects (all p> 0.05). Furthermore, correlation between citalopram and sBDNF level after three weeks of escitalopram administration exhibit a negative trend (partial Pearson correlation: r=-0.30, p=0.05; partial Spearmans rank: r=-0.22, p=0.15). ConclusionThe results suggest that three weeks of associative emotional content learning affect sBDNF levels, while subsequently assessed citalopram plasma and sBDNF levels tend to correlate negatively. Key PointsO_LIEmotional learning may affect serum BDNF levels in healthy human subjects C_LIO_LIBlood levels of citalopram and serum BDNF exhibit a negative correlation C_LI

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bioRxiv2021

Serotonin modulates learning content-specific neuroplasticity of functional brain networks

Learning-induced neuroplastic changes, further modulated by content and setting, are mirrored in brain functional connectivity (FC). In animal models, serotonergic agents were shown to facilitate neuroplasticity. This is especially prominent during emotional relearning, such as fear extinction, which may translate to clinical improvements in human patients. To investigate this assumption, 99 healthy subjects underwent six weeks of emotional or non-emotional learning and subsequent relearning. Resting-state functional magnetic resonance imaging was performed before and after the learning phases to investigate changes in FC. During relearning, subjects received either a daily dose of the selective serotonin reuptake inhibitor (SSRI) escitalopram or placebo. Escitalopram intake modulated FC changes in a network comprising Brocas area, the medial prefrontal cortex, the right inferior temporal and left lingual gyrus. More specifically, escitalopram increased the bidirectional connectivity between medial prefrontal cortex and lingual gyrus for non-emotional and additionally the connectivity from medial prefrontal cortex to Brocas area for emotional relearning. The context-dependence of these effects supports the assumption that SSRIs in clinical practice might improve neuroplasticity rather than psychiatric symptoms per se. Correlations with learning behavior further point towards a relationship with extinction processes in relearning. These results demonstrate that escitalopram intake during relearning results in content-dependent network adaptations and support the conclusion that enhanced neuroplasticity might be the major underlying mechanism also in humans. Beyond expanding the complexities of learning, these findings emphasize the influence of external factors on serotonin-facilitated neuroplasticity of the human brain.

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bioRxiv2020

No metabolic "default mode" of human brain function

The finding of reduced activity in the default mode network (DMN) during externally focused cognitive control has been highly influential to our understanding of human brain function, but deactivations have also prompted major questions of interpretation. Using hybrid functional PET-MR imaging, this study shows that fMRI task activations and deactivations do not reflect antagonistic patterns of synaptic metabolism. FMRI activations were accompanied by concomitant increases in metabolism during cognitive control, but, unlike the BOLD response, metabolism in the core DMN did not change between rest and task. Metabolic increases along the borders of the DMN during task performance further revealed a set of regions that guide engagement and suppression of neighboring networks during cognitive control. Collectively, dissociations between metabolism and BOLD signal specific to the DMN reveal functional heterogeneity in this network and demonstrate that BOLD deactivations during cognitive control should not be interpreted to reflect reduced synaptic activity.

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bioRxiv2020

Differential brain atrophy and neurogenetic profiles in cognitively-defined Alzheimer's disease subgroups

Elucidating mechanisms underlying the clinical heterogeneity observed among individuals with Alzheimers disease (AD) is key to facilitate personalized treatments. We categorized 679 individuals with AD into subgroups based on a relative impairment in one cognitive domain (i.e. AD-Memory, AD-Executive-Functioning, AD-Language and AD-Visuospatial-Functioning). We compared atrophy patterns derived from MRI and identified patterns that closely matched the respective cognitive profiles, i.e. medial temporal lobe atrophy in AD-Memory, fronto-parietal in AD-Executive-Functioning, asymmetric left-temporal in AD-Language, and posterior in AD-Visuospatial-Functioning. We then determined spatial correlations between subgroup-specific atrophy and a transcriptomic atlas of gene expression, which revealed both shared (e.g. mitochondrial respiration and synaptic function/plasticity) and subgroup-specific (e.g. cell-cycle for AD-Memory, protein metabolism in AD-Language, and modification of gene expression in AD-Visuospatial-Functioning) biological pathways associated with each subgroups atrophy patterns. We conclude that cognitive heterogeneity in AD is related to neuroanatomical differences, and specific biological pathways may be involved in their emergence.

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bioRxiv2020

Regional expression profiles of risk genes for depression are associated with brain activation patterns in emotion and reward tasks

The spatial relationship between gene expression profiles and neural response patterns known to be altered in neuropsychiatric disorders, e.g. depression, can guide the development of more targeted therapies. Here, we estimated the correlation between human transcriptome data and two different brain activation maps measured with functional magnetic resonance imaging (fMRI) in healthy subjects. Whole-brain activation patterns evoked during an emotional face recognition task were associated with topological mRNA expression of genes involved in cellular transport. In contrast, fMRI activation patterns related to the acceptance of monetary rewards were associated with genes implicated in neuronal development, synapse regulation, and gene transcription. An overlap of these genes with risk genes from major depressive disorder genome-wide associations studies revealed the involvement of the master regulators TCF4 and MEF2C. These results were further validated by means of meta-analytic fMRI data. Overall, the identification of stable relationships between spatial gene expression profiles and fMRI data may reshape the prospects for imaging transcriptomics studies.

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bioRxiv2020

JuSpace: A tool for spatial correlation analyses of magnetic resonance imaging data with positron emission tomography derived neurotransmitter maps

Recent studies have shown that drug-induced spatial alteration patterns in resting state functional activity as measured using magnetic resonance imaging (rsfMRI) are associated with the distribution of specific receptor systems targeted by respective compounds. Based on this approach, we introduce a toolbox (JuSpace) allowing for cross-modal correlation of MRI- based measures with nuclear imaging derived estimates covering various neurotransmitter systems including dopaminergic, serotonergic, noradrenergic, and GABAergic (gamma- aminobutric acid) neurotransmission. We apply JuSpace to two datasets covering Parkinsons disease patients (PD) and risperidone-induced changes in rsfMRI and cerebral blood flow (CBF). Consistently with the predominant neurodegeneration of dopaminergic and serotonergic system in PD, we find significant spatial associations between rsfMRI activity alterations in PD and dopaminergic (D2) and serotonergic systems (5-HT1b). Risperidone induced CBF alterations were correlated with its main targets in serotonergic and dopaminergic systems. JuSpace provides a biologically meaningful framework for linking neuroimaging to underlying neurotransmitter information.

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bioRxiv2020

Hierarchical clustering of the human cerebral cortex based on comprehensive transcriptome data

The parcellation of the cerebral cortex serves the investigation of the emergence of uniquely human brain functions and disorders. We employed hierarchical clustering based on comprehensive transcriptomic data of the human cortex in order to delineate areas with distinct gene expression profiles. These profiles were analyzed for the enrichment of gene sets associated with brain disorders by genome-wide studies (GWAS) and expert curation. This suggested new roles of specific cortical areas in psychiatric, neurodegenerative, congenital and other neurological disorders while reproducing some well-established links for movement disorders and dementias. GWAS-derived gene sets for psychiatric disorders exhibited similar enrichment patterns in the posterior fusiform gyrus and inferior parietal lobule driven by pleiotropic genes. This implies that the effects of risk variants shared between neuropsychiatric disorders might converge in these areas. For several diseases, specific genes were highlighted, which may aid the discovery of novel disease mechanisms and urgently needed treatments.

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bioRxiv2020

A causal role for estradiol in human reinforcement learning

The sex hormone estrogen is hypothesized to play a key role in human cognition via its interactions with the dopaminergic system. Work in rodents has shown that estrogens most potent form, estradiol, impacts striatal dopamine functioning predominately via increased D1-receptor signalling, while human work has suggested that high estradiol levels are associated with altered reward sensitivity. Here, we addressed two fundamental questions: 1) whether estradiol causally alters reward sensitivity in men, and 2) whether this effect of estradiol is moderated by individual variation in polymorphisms of dopaminergic genes. To test this, we performed a double-blind placebo-controlled administration study in which hundred men received either a single dose of estradiol (2 mg) or placebo. We found that estradiol administration increased reward sensitivity, which was moderated by baseline dopamine. This was observed in choice behaviour and increased learning rates. These results confirm a causal role of estradiol in reinforcement learning in men that is moderated by the striatal dopaminergic pathway.

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bioRxiv2019

Sex differences in monetary reward and punishment processing are associated with fast dynamics of dopamine synthesis

PurposeIn the human brain endogenous dopamine release is commonly assessed by the PET competition model. Although thoroughly validated, cognitive processing yields low signal changes and the assessment of several task conditions requires repeated scanning. Using the framework of functional PET imaging we introduce a novel approach which leverages the incorporation of the radioligand 6-[18F]FDOPA into the dynamic fast-acting regulation of the corresponding enzyme activities by neuronal firing and neurotransmitter release. We demonstrate the feasibility of the approach by the assessment of widely described sex differences in dopamine neurotransmission. MethodsReward and punishment processing was behaviorally investigated in 36 healthy participants, where 16 underwent fPET and fMRI while performing the monetary incentive delay task. 6-[18F]FDOPA was applied as bolus+infusion during a single 50 min PET acquisition. Task-specific changes in dopamine synthesis were identified with the general linear model and quantified with the Gjedde-Patlak plot. ResultsMonetary gain induced 78% increase in nucleus accumbens dopamine synthesis vs. 49% for loss in men. Interestingly, the opposite was discovered in women (gain: 51%, loss: 78%). Behavioral modeling revealed direct associations of task-specific dopamine synthesis with reward sensitivity in men (rho = -0.7) and with punishment sensitivity in women (rho = 0.89). As expected, fMRI showed robust task-specific neuronal activation but no sex difference. ConclusionsOur findings provide a dopaminergic basis for well-known behavioral differences in reward and punishment processing between women and men. This has important implications in psychiatric conditions showing sex-specific prevalence rates, altered reward processing and dopamine signaling. The high temporal resolution and pronounced magnitude of task-specific changes make fPET a promising tool to investigate functional neurotransmitter dynamics during cognitive or emotional processing in various brain disorders.

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