Biology subjects

Deserno, L.

Publications and source records attributed to Deserno, L..

7 recordsLinked to original sources

bioRxiv2023

Impaired updating of working memory representations in individuals with high BMI: evidence for dopaminergic mechanisms

Everyday life requires an adaptive balance between distraction-resistant maintenance of information and the flexibility to update this information when needed. These opposing mechanisms are proposed to be balanced through a working memory gating mechanism. Prior research indicates that obesity may elevate the risk of working memory deficits, yet the underlying mechanisms remain elusive. Dopaminergic alterations have emerged as a potential mediator. However, current models suggest these alterations should only shift the balance in working memory tasks, not produce overall deficits. The empirical support for this notion is currently lacking, however. To address this gap, we pooled data from three studies (N = 320) where participants performed a working memory gating task. Higher BMI was associated with overall poorer working memory, irrespective of whether there was a need to maintain or update information. However, when participants, in addition to BMI level, were categorized based on certain putative dopamine-signaling characteristics (Single Nucleotide Polymorphisms; specifically, Taq1A and DARPP-32), distinct working memory gating effects emerged. These SNPs, primarily associated with striatal dopamine transmission, appear to be linked with differences in updating, specifically, among high-BMI individuals. Moreover, blood amino acid ratio, which indicates central dopamine synthesis capacity, combined with BMI, shifted the balance between distractor-resistant maintenance and updating. These findings suggest that both dopamine-dependent and dopamine-independent cognitive effects exist in obesity. Understanding these effects is crucial if we aim to modify maladaptive cognitive profiles in individuals with obesity.

neuroscience↗

bioRxiv2022

Cortical grey matter mediates increases in model-based control and learning from positive feedback from adolescence to adulthood

Adolescents undergo maturation in cognition and brain structure. Model-based (MB) control is known to increase from childhood to young adulthood, which is mediated by cognitive abilities. Here, we asked two questions unaddressed in previous developmental studies: Firstly, what are the brain structural correlates of age-related increases in MB control? Secondly, how are age-related increases in MB control from adolescence to adulthood influenced by motivational context? A developmental sample (n=103, age: 12-42) completed structural MRI and an established task to capture MB control. The task was modified with respect to outcome valence by including (1) reward and punishment blocks to manipulate the motivational context and (2) an additional choice test to assess learning from positive vs. negative feedback. After replicating that an age-dependent increase in MB control is mediated by cognitive abilities, we demonstrate first-time evidence that grey matter density (GMD) in the parietal cortex mediates the increase of MB control with age. While motivational context did not relate to age-related changes in MB control, learning from positive feedback improved with age. Meanwhile, negative feedback learning showed no age effects. We present a first report that an age-related increase in learning from positive feedback was mediated by reduced GMD in the parietal, medial and dorsolateral prefrontal cortex. Our findings indicate that efficient brain maturation, as putatively reflected in lower GMD, in distinct and partially overlapping brain regions is a key developmental step towards age-related increases in planning and value-based choice. Significance StatementAdolescents undergo extensive maturation in cognition and brain structure. Interestingly, model-based decision-making is also known to increase from childhood to adulthood. Here, we demonstrate for the first time that grey matter density in the parietal cortex mediates an age-dependent increase in model-based control. An age-related increase in positive feedback learning was mediated by reduced grey matter density in the parietal, medial and dorsolateral prefrontal cortex. Interestingly, a manipulation of motivational context (gain reward vs. avoid punishment) did not impact age-related changes in model-based control. These findings highlight that efficient brain maturation in distinct and overlapping cortical brain regions constitutes a key developmental step towards increases in model-based planning and value-based choice.

neuroscience↗

bioRxiv2021

Distributed networks for auditory memory contribute differentially to recall precision

Re-directing attention to objects in working memory can enhance their representational fidelity. However, how this attentional enhancement of memory representations is implemented across distinct, sensory and cognitive-control brain network is unspecified. The present fMRI experiment leverages psychophysical modelling and multivariate auditory- pattern decoding as behavioral and neural proxies of mnemonic fidelity. Listeners performed an auditory syllable pitch-discrimination task and received retro-active cues to selectively attend to a to-be-probed syllable in memory. Accompanied by increased neural activation in fronto-parietal and cingulo-opercular networks, valid retro-cues yielded faster and more perceptually sensitive responses in recalling acoustic detail of memorized syllables. Information about the cued auditory object was decodable from hemodynamic response patterns in superior temporal sulcus (STS), fronto-parietal, and sensorimotor regions. However, among these regions retaining auditory memory objects, neural fidelity in the left STS and its enhancement through attention-to-memory best predicted individuals gain in auditory memory recall precision. Our results demonstrate how functionally discrete brain regions differentially contribute to the attentional enhancement of memory representations.

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bioRxiv2021

Dopamine enhances model-free credit assignment through boosting of retrospective model-based inference

Dopamine is implicated in signalling model-free (MF) reward prediction errors and various aspects of model-based (MB) credit assignment and choice. Recently, we showed that cooperative interactions between MB and MF systems include guidance of MF credit assignment by MB inference. Here, we used a double-blind, placebo-controlled, within-subjects design to test the hypothesis that enhancing dopamine levels, using levodopa, boosts the guidance of MF credit assignment by MB inference. We found that levodopa enhanced retrospective guidance of MF credit assignment by MB inference, without impacting on MF and MB influences per se. This drug effect positively correlated with working memory, but only in a context where reward needed to be recalled for MF credit assignment. The dopaminergic enhancement in MB-MF interactions correlated negatively with a dopamine-dependent change in MB credit assignment, possibly reflecting a potential trade-off between these two components of behavioural control. Thus, our findings demonstrate that dopamine boosts MB inference during guidance of MF learning, supported in part by working memory, but trading-off with a dopaminergic enhancement of MB credit assignment. The findings highlight a novel role for a DA influence on MB-MF interactions.

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bioRxiv2019

Individual differences in dopamine function underlying the balance between model-based and model-free control

Reinforcement learning involves a balance between model-free (MF) and model-based (MB) systems. Recent studies suggest that individuals with either pharmacologically enhanced levels of dopamine (DA) or higher baseline levels of DA exhibit more MB control. However, it remains unknown whether such pharmacological effects depend on baseline DA. Here, we investigated whether effects of L-DOPA on the balance of MB/MF control depend on ventral striatal baseline DA. Sixty participants had two functional magnetic resonance imaging (fMRI) scans while performing a two-stage sequential decision-making task under 150 mg L-DOPA or placebo (counterbalanced), followed by a 4-hour 18F-DOPA positron emission tomography (PET) scan (on a separate occasion). We found an interaction between baseline DA levels and L-DOPA induced changes in MB control. Individuals with higher baseline DA levels showed a greater L-DOPA induced enhancement in MB control. Surprisingly, we found a corresponding drug-by-baseline DA interaction on MF, but not MB learning signals in the ventromedial prefrontal cortex. We did not find a significant interaction between baseline DA levels and L-DOPA effects on MF control or MB/MF balance. In sum, our findings point to a baseline dependency of L-DOPA effects on differential aspects of MB and MF control. Individual differences in DA washout may be an important moderator of L-DOPA effects. Overall, our findings complement the general notion where higher DA levels is related to a greater reliance on MB control. Although the relationship between phasic DA firing and MF learning is conventionally assumed in the animal literature, the relationship between DA and MF control is not as straightforward and requires further clarification.

neuroscience↗

bioRxiv2017

Overestimating environmental volatility increases switching behavior and is linked to activation of dorsolateral prefrontal cortex in schizophrenia

BackgroundReward-based decision-making is impaired in patients with schizophrenia (PSZ) as reflected by increased choice switching. The underlying cognitive and motivational processes as well as associated neural signatures remain unknown. Reinforcement Learning (RL) and hierarchical Bayesian learning account for choice switching in different ways. We hypothesized that enhanced choice switching, as seen in PSZ during reward-based decision-making, relates to higher-order beliefs about environmental volatility and examined the associated neural activity.\n\nMethods46 medicated PSZ and 43 healthy controls (HC) performed a reward-based decision-making task requiring flexible responses to changing action-outcome contingencies during functional Magnetic Resonance Imaging (fMRI). Detailed computational modeling of choice data was performed, including RL and the hierarchical Gaussian filter (HGF). Trajectories of learning from computational modeling informed the analysis of fMRI data.\n\nResultsA three-level HGF accounted best for the observed choice data. This model revealed a heightened initial belief about environmental volatility and a stronger influence of volatility on lower-level learning of action-outcome contingencies in PSZ as compared to HC. This was replicated in an independent sample of non-medicated PSZ. Beliefs about environmental volatility were reflected by higher activity in dorsolateral prefrontal cortex of PSZ as compared to HC.\n\nConclusionsOur study suggests that PSZ inferred the environment as overly volatile, which may explain increased choice switching. In PSZ, activity in dorsolateral prefrontal cortex was more strongly related to beliefs about environmental volatility. Our computational phenotyping approach may provide useful information to dissect clinical heterogeneity and could improve prediction of outcome.

neuroscience↗

bioRxiv2017

Dopaminergic Modulation Of Brain Signal Variability And The Functional Connectome During Cognitive Performance

Dopamine underlies important aspects of cognition, and has been suggested to boost cognitive performance. However, how dopamine modulates the large-scale cortical dynamics during cognitive performance has remained elusive. Using functional MRI during a working memory task in healthy young human listeners (N=22), we investigated the effect of levodopa (L-dopa) on two aspects of cortical dynamics, blood oxygen-level-dependent (BOLD) signal variability and the functional connectome of large-scale cortical networks. We here show that enhanced dopaminergic signaling modulates the two potentially interrelated aspects of large-scale cortical dynamics during cognitive performance, and the degree of these modulations is able to explain inter-individual differences in L-dopa-induced behavioral benefits. Relative to placebo, L-dopa increased BOLD signal variability in task-relevant temporal, inferior frontal, parietal and cingulate regions. On the connectome level, however, L-dopa diminished functional integration across temporal and cingulo-opercular regions. This hypo-integration was expressed as a reduction in network efficiency and modularity in more than two thirds of the participants and to different degrees. Hypo-integration co-occurred with relative hyper-connectivity in paracentral lobule and precuneus, as well as posterior putamen. Both, L-dopa-induced BOLD signal variability modulation and functional connectome modulations proved predictive of an individuals L-dopa-induced gain in behavioral performance, namely response speed and perceptual sensitivity. Lastly, L-dopa-induced modulations of BOLD signal variability were correlated with L-dopa-induced modulation of nodal connectivity and network efficiency. Our findings underline the role of dopamine in maintaining the dynamic range of, and communication between, cortical systems, and their explanatory power for inter-individual differences in benefits from dopamine during cognitive performance.

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