Liraglutide restores impaired associative learning in individuals with obesity.

Survival under selective pressure is driven by the ability of our brain to use sensory information to our advantage to control physiological needs. To that end, neural circuits receive and integrate external environmental cues and internal metabolic signals to form learned sensory associations, consequently motivating and adapting our behaviour. The dopaminergic midbrain plays a crucial role in learning adaptive behaviour and is particularly sensitive to peripheral metabolic signals, including intestinal peptides, such as glucagon-like peptide 1 (GLP-1).

Habitual daily intake of a sweet and fatty snack modulates reward processing in humans.

Western diets rich in fat and sugar promote excess calorie intake and weight gain; however, the underlying mechanisms are unclear. Despite a well-documented association between obesity and altered brain dopamine function, it remains elusive whether these alterations are (1) pre-existing, increasing the individual susceptibility to weight gain, (2) secondary to obesity, or (3) directly attributable to repeated exposure to western diet. To close this gap, we performed a randomized, controlled study (NCT05574660) with normal-weight participants exposed to a high-fat/high-sugar snack or a low-fat/low-sugar snack for 8 weeks in addition to their regular diet.

Monitoring PHB production in Synechocystis sp. with hyperspectral images.

Microalgae wastewater treatment systems have the potential for producing added-value products. More specifically, cyanobacteria are able to accumulate polyhydroxybutyrates (PHBs), which can be extracted and used for bioplastics production. Nonetheless, PHB production requires proper culture conditions and continue monitoring, challenging the state-of-the-art technologies.

Auditory mismatch responses are differentially sensitive to changes in muscarinic acetylcholine versus dopamine receptor function.

The auditory mismatch negativity (MMN) has been proposed as a biomarker of NMDA receptor (NMDAR) dysfunction in schizophrenia. Such dysfunction may be caused by aberrant interactions of different neuromodulators with NMDARs, which could explain clinical heterogeneity among patients. In two studies (N = 81 each), we used a double-blind placebo-controlled between-subject design to systematically test whether auditory mismatch responses under varying levels of environmental stability are sensitive to diminishing and enhancing cholinergic vs.

Interoception of breathing and its relationship with anxiety.

Interoception, the perception of internal bodily states, is thought to be inextricably linked to affective qualities such as anxiety. Although interoception spans sensory to metacognitive processing, it is not clear whether anxiety is differentially related to these processing levels. Here we investigated this question in the domain of breathing, using computational modeling and high-field (7 T) fMRI to assess brain activity relating to dynamic changes in inspiratory resistance of varying predictability.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry.

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output).

A Hilbert-based method for processing respiratory timeseries.

In this technical note, we introduce a new method for estimating changes in respiratory volume per unit time (RVT) from respiratory bellows recordings. By using techniques from the electrophysiological literature, in particular the Hilbert transform, we show how we can better characterise breathing rhythms, with the goal of improving physiological noise correction in functional magnetic resonance imaging (fMRI). Specifically, our approach leads to a representation with higher time resolution and better captures atypical breathing events than current peak-based RVT estimators.

Cholinergic and dopaminergic effects on prediction error and uncertainty responses during sensory associative learning.

Navigating the physical world requires learning probabilistic associations between sensory events and their change in time (volatility). Bayesian accounts of this learning process rest on hierarchical prediction errors (PEs) that are weighted by estimates of uncertainty (or its inverse, precision). In a previous fMRI study we found that low-level precision-weighted PEs about visual outcomes (that update beliefs about associations) activated the putative dopaminergic midbrain; by contrast, precision-weighted PEs about cue-outcome associations (that update beliefs about volatility) activated the cholinergic basal forebrain.

A Computational Theory of Mindfulness Based Cognitive Therapy from the "Bayesian Brain" Perspective.

Mindfulness Based Cognitive Therapy (MBCT) was developed to combine methods from cognitive behavioral therapy and meditative techniques, with the specific goal of preventing relapse in recurrent depression. While supported by empirical evidence from multiple clinical trials, the cognitive mechanisms behind the effectiveness of MBCT are not well understood in computational (information processing) or biological terms. This article introduces a testable theory about the computational mechanisms behind MBCT that is grounded in "Bayesian brain" concepts of perception from cognitive neuroscience, such as predictive coding.

Effects of hunger, satiety and oral glucose on effective connectivity between hypothalamus and insular cortex.

The hypothalamus and insular cortex play an essential role in the integration of endocrine and homeostatic signals and their impact on food intake. Resting-state functional connectivity alterations of the hypothalamus, posterior insula (PINS) and anterior insula (AINS) are modulated by metabolic states and caloric intake. Nevertheless, a deeper understanding of how these factors affect the strength of connectivity between hypothalamus, PINS and AINS is missing.

Modulation of midbrain neurocircuitry by intranasal insulin.

Insulin modulates dopamine neuron activity in midbrain and affects processes underlying food intake behaviour, including impulsivity and reward processing. Here, we used intranasal administration and task-free functional MRI in humans to assess time- and dose-dependent effects of insulin on functional connectivity of the dopaminergic midbrain - and how these effects varied depending on systemic insulin sensitivity as measured by HOMA-IR. Specifically, we used a repeated-measures design with factors dose (placebo, 40 IU, 100 IU, 160 IU), time (7 time points during a 90 min post-intervention interval), and group (low vs.

The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data.

Background: Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies.

Models of neuromodulation for computational psychiatry.

Psychiatry faces fundamental challenges: based on a syndrome-based nosology, it presently lacks clinical tests to infer on disease processes that cause symptoms of individual patients and must resort to trial-and-error treatment strategies. These challenges have fueled the recent emergence of a novel field-computational psychiatry-that strives for mathematical models of disease processes at physiological and computational (information processing) levels. This review is motivated by one particular goal of computational psychiatry: the development of 'computational assays' that can be applied to behavioral or neuroimaging data from individual patients and support differential diagnosis and guiding patient-specific treatment.

Translational Perspectives for Computational Neuroimaging.

Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. Promising new avenues for translation are provided by computational modeling of neuroimaging data.

Uncertainty in perception and the Hierarchical Gaussian Filter.

In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes.

Hierarchical prediction errors in midbrain and basal forebrain during sensory learning.

In Bayesian brain theories, hierarchically related prediction errors (PEs) play a central role for predicting sensory inputs and inferring their underlying causes, e.g., the probabilistic structure of the environment and its volatility.