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Stage 1 IPA at PCI RR

1. Background Obesity has become a major global health challenge. According to the World Health Organization in 2016 over 13% of the world’s adult population were obese. The worldwide prevalence of obesity nearly tripled between 1975 and 2016 (World Health Organization, 2022). Obesity is defined by a body-mass-index higher or equal to 30. The BMI is calculated by dividing weight in kilograms by height in meters squared. A high body-mass index is a risk factor for several diseases for example neurodegenerative diseases, cardiovascular diseases, diabetes mellitus, chronic kidney disease and cancer (GBD 2015 Obesity Collaborators et al., 2017). Additionally, obesity has a significant impact on mental health as well and was associated with an approximately 25% increase in odds of mood disorders (Simon et al., 2006). A large scale meta-analysis and systematic review that included almost 2 million participants from 29 cross-sectional studies, 6 cohort studies and 8 randomized-controlled-trials (RCTs) came to the conclusion that obesity increases the risk of developing depression (Jung et al., 2017). Depression and obesity are linked in a bidirectional and multifactorial manner. Weight related stigma and increased inflammation are commonly described to contribute to this relationship. However, the increased susceptibility of obese individuals to depressive symptoms could be due to profound changes in their endocrine system. For instance, studies found that circulating serum ghrelin levels are decreased in human obesity (Rosická et al., 2003; Wang et al., 2022; Zigman et al., 2016), delivering a possible metabolic link between obesity and depressive symptoms. Ghrelin is a short orexigenic peptide produced by enteroendocrine cells of the stomach (Kojima et al., 1999). Ghrelin stimulates neurons in the arcuate nucleus of the hypothalamus leading to a feeling of hunger. It reaches the hypothalamus by crossing the blood-brain-barrier at the circumventricular organs (Howick et al., 2017). Another possible way for ghrelin to reach the brain is via active transport across the blood-brain-barrier (Banks et al., 2002). Nevertheless, ghrelin does not only target the hypothalamus but other brain regions that are important for the development of depressive symptoms like hippocampus, amygdala, pituitary gland and ventral tegmental area (Mortazaei et al., 2019; Spencer et al., 2015). Researchers found evidence that ghrelin might exert antidepressant effects in mice. (Lutter et al., 2008). In the study of Lutter et al. mice with four-fold elevated ghrelin levels did show antidepressant-like behavior in the elevated plus maze and forced swim test. In the forced swim test, mice with elevated ghrelin levels had a longer latency to immobility and a shorter time of total immobility, indicating ghrelin’s antidepressant effects. Mice with a genetic blockade of ghrelin signaling exhibited more signs of social avoidance compared to their wildtype littermates with elevated ghrelin levels after chronic social defeat stress procedure (CSDS) (Lutter et al., 2008). A diminished interest in social activities is a common symptom of depression in humans as well. Moreover, ghrelin administration led to antidepressant effects and increased memory retention in rats (Carlini et al., 2002). In humans however, evidence for ghrelin’s antidepressant effects is scarce as only one study showed an antidepressant effect at trend level in men (Kluge et al., 2011). Yet, there was no statistically significant change in self rated depressive symptoms after administration of 4x50 μg ghrelin compared to placebo. One limitation of the study is the small sample size (n = 7 males). Thereby it could be possible that an existing antidepressant effect was not detected because of the low statistical power. There are some possible mechanisms that could explain antidepressant effects of ghrelin. Firstly, studies suggest that ghrelin increases serotonergic transmission in the brain (Hansson et al., 2014; Ogaya et al., 2011; Skibicka et al., 2012). A higher concentration of serotonin in the synaptic cleft has antidepressant effects in humans as it ameliorates sleep and mood. Furthermore, ghrelin is shown to have proneurogenic properties in the hippocampus of mice (Walker et al., 2015). In this study, ghrelin and another neuroprotective compound enhanced hippocampal neurogenesis and led to antidepressant effects. Another study pointed out that ghrelin has neuroprotective function as well (Frago et al., 2011). However, there is still some discussion about ghrelin’s role in mood and depression. Some studies even indicate a depressogenic effect of ghrelin in rodents (Kanehisa et al., 2006; Hansson et al., 2011). Yet, according to the systematic review performed by Wittekind & Kluge, 2015 the data mainly suggests an antidepressant effect of ghrelin. Indeed, studies on the association between ghrelin serum levels and major depressive disorder had various results. One study showed lowered ghrelin levels in patients with MDD compared to healthy participants (Barim et al., 2009; Ünler et al., 2022) whereas other studies found higher levels of ghrelin in patients with MDD (Gecici et al., 2005; Kurt et al., 2007; Ozsoy et al., 2014). Some studies found no significant difference in ghrelin levels between MDD patients and healthy participants at all (Huang et al., 2021; Kluge et al., 2009; Pinar et al., 2008; Schanze et al., 2008). Furthermore, ghrelin serum levels did not show a significant association with self-rating depression scores in an population-based sample (Wittekind et al., 2022). However, it is important to keep in mind that in some of these studies patients were under the influence of medication or electro-convulsive therapy. However none of the studies included a study population with a mean BMI over or equal to 30. Since ghrelin levels do depend on BMI (le Roux et al., 2005; Tschöp et al., 2001), it is important to analyze the association between ghrelin serum levels and depressive symptoms in obese individuals. To our knowledge, we are the first to study the association of ghrelin levels and symptoms of depression in an obese subpopulation. In conclusion, remembering that ghrelin presumably mediates antidepressant effects in humans, lowered ghrelin levels could partially explain an increased susceptibility of obese individuals to symptoms of depression. It is yet unknown to which extend the association between obesity and depressive symptoms can be explained through fasting ghrelin levels. In this current study we thus aim to systematically address this issue by analyzing the relationship between obesity, fasting ghrelin serum levels and depressive symptoms. Additionally, a mediation analysis will be performed to clarify this relationship. We use data from the LIFE study with a population based approach and a large sample size.

Effects of plant-based meals on host physiology and psychology remain largely understudied. Real-world large-scale studies looking at differential effects of plant-based vs. animal-based meals are missing. Yet, investigating the effects of meal category on post-meal markers will help to understand underlying decision-making strategies and inform future menu design and individual meal choices. In three large-scale online studies set in a real-world environment with over 16,000 datapoints, we showed that meal category had minor impact only on post-meal satiety and mood ratings compared to timepoint and demographic factors. We propose that our findings should be considered when designing cafeteria menus, by increasing taste and protein content, in particular of plant-based meals, in (university) cafeterias. Data in this repository include raw and curated datasheets for analysis for: study A - free choice (n = 173) study B - randomized choice (n = 71) Both include omnivorous dieters only, optionally also include - pre- and post-meal photos incl. meal description - dietary intake info (1-week FFQ) - eating traits (TFEQ) - personality traits (NEOFFI).

The AgeWell.de-MRI study aims to assess the effects of a multicomponent intervention on brain structure and function in older adults at risk for Alzheimer’s disease (AD). The study includes Leipzig-based participants from the AgeWell.de study, a cluster-randomized trial performed in 4 study centers over Germany. The AgeWell.de study investigates the effect of a multicomponent intervention targeting modifiable risk factors for dementia similar to the finnish FINGER study. In the AgeWell.de-MRI study, participants undergo a MRI session before and after the two-year intervention period of the AgeWell.de study. They also perform an additional cognitive task targeting pattern separation. We will assess changes in brain structure and function between the intervention and the control group and correlate those changes to cognitive performance, assessed in the AgeWell.de study and by the pattern separation task.

This project aims to investigate the neural correlates of long COVID using magnetic resonance imaging (MRI). Anatomical, diffusion-weighted, fluid-attenuated and quantitative susceptibility imaging will be performed. We will test the association of SarsCov2 infection and long COVID symptoms with gray matter volume of olfactory, gustatory and memory-related brain regions as well as white matter microstructure of relevant limbic tracts. Further, we will explore other neuroimaging measures in relation to the disease status as well as cognitive, smell and taste function.

Two studies investigating the effect of radical weight loss on brains structure and function.

Aim: This project aims to convert raw self-reported dietary intake data for DEGS-1 FFQ into mean daily portion of food items and then into nutrient intake per day.

Obesity and its comorbidities are linked to impaired cognitive performance, accelerated cognitive decline and neurodegenerative pathologies (Dye et al., 2017). Besides frequently reported cortical thinning and alterations in subcortical gray matter volume (Tanaka et al., 2020), obesity seems to have serious repercussions on global white matter coherence and the microstructure of the brain region crucially involved in feeding behavior and metabolism, the hypothalamus (Lampe, et al., 2019; Schwartz et al., 2000; Thomas, et al. 2019). A dominant explanation for obesity-related structural and functional alterations and associated cognitive impairments is that a high-fat fat diet and accumulation of visceral fat set off a metabolic and immunological cascade which may result in systemic chronic low-grade inflammation as well as neuroinflammation attacking the central nervous system (Leigh & Morris, 2020). Bariatric surgery encompasses procedures that surgically modify the stomach and the intestines to induce weight loss by food restriction and nutrients malabsorption. To date, it is the most effective intervention to reliably induce considerable reduction of excess body fat and improve metabolic risk factors. It has been shown to reduce the characteristic low-grade inflammation and, importantly, to exert positive effects on the central nervous system. Widespread effects on brain structure and function are documented by a growing body of literature (Nota et al., 2020). This project aims to determine whether surgical interventions for morbidly obese individuals lead to a recovery in microstructure of major white matter tracts and the hypothalamus, comparing a group of bariatric patients with a waiting-list control group. The overarching goal will be to characterize the mechanisms by which the proposed recovery is driven and the time course in which positive effects unfold.

art.pics database: an open access database for art stimuli for experimental research We provide a large database of novel artificial art stimuli (n = 2332) for use in experimental research on the perception and valuation of art as a secondary reward. The database comprises eight different art styles (based on known and unknown art paintings) and three image categories (objects, plants, animals). Pictures were taken from two large food-related image databases, namely the food-pics_extended database (Blechert et al. 2019) and the FoodCast research image database (FRiDa) (Foroni et al. 2013). Original images were provided on the basis of a license agreement with the authors and are available at their respective online resources for food-pics and FRiDa. For use of the database, please fill out the license agreement PDF and send it to Veronica Witte (mailto:witte@cbs.mpg.de)

To understand the effect of adherence to a plant-based diet on markers of brain aging

Metabolic disorders related to higher cardiovascular risk including metabolic syndrome have become more prevalent in countries following Western lifestyle. These health problems have been associated with increased systemic inflammation parameters in the blood serum (Libby et al., 2009; Pradhan et al., 2001). This study aims to investigate if a high-fiber diet for a period of two weeks shows beneficial effects with respect to reducing inflammatory processes in the systemic circulation as well as in the mediobasal hypothalamus as opposed to a placebo intervention.

This project investigates the relationship between BMI, head motion and physiological/psychological parameters.

Add-on to: https://osf.io/f6qz5 pre-registration of the task-based fMRI analysis Does a two-week high-fiber diet modulate food wanting, specifically brain activity in the reward network and homeostatic control regions, and contemporaneous decision-making (reward valuation), measured in a within-subject cross-over design, seen in a post-intervention change in the high-fiber condition? Are these differential effects mediated by changes in gut signals such as microbial diversity, gut hormones and other metabolic markers?

Dietary fiber extension for the Food-pics_extended database The Food-pics database extended (Blechert et al. 2019) contains a multitude of standardized food stimuli, with extensive information including macronutrient information, yet fiber content is not available. Food category extension for the Food-pics_extended database To provide this information, we first added new standardized food categories to the Food-pics_extended database according to the WHO and Food and Agriculture Organization (FAO) “codex alimentarius” (https://www.fao.org/gsfaonline/foods/index.html) (table: food_pics_database_extended2016_food_types+fibre_content_curated.csv; column: type). Here, we excluded images no. 11 (cheese and cold meat platter), no. 150 and no. 294 (popcorn as it could be sweet or salty) due to ambiguity. We also excluded images no. 134 and no. 137 as they depicted the same chocolate muffin as in image no. 80, only from a different perspective, as well as no. 306 showing roast potatoes, which we found not clearly identifiable. Additionally, we unified the German naming of the stimuli (table: food_pics_database_extended2016_food_types+fibre_content.csv; column: Item_description_german_unified) as through original description, we could not identify all images depicting similar or identical food items. Dietary fiber extension for the Food-pics_extended database Fiber content of the remaining Food-pics stimuli no. 1 to 568 (n=562) was evaluated by four non-expert raters. These 562 stimuli were selected for further evaluation because of available macronutrient information. Dietary fiber reference nutritional information stemmed from various nutrition databases (e.g. FoodData Central App by the USDA, Swiss Food Composition Database or Food Database GmbH), online producer declarations and nutrition science books (Elmadfa et al. 2015). The respective sources of each rater and the derived fiber content per 100 grams are provided (table: food_pics_fibre_ratings_single_raters.csv; columns: “*_source”). Mean fiber per 100 g and per item across four raters was computed. Inter-rater reliability across four raters was good overall (using psych() intraclass correlations and two-way random effects models for single rater: ICC2 kappa = 0.76, 95% CI: 0.74, 0.79). Yet, to ensure plausibility of the fiber ratings, we checked those ratings with larger standard deviation than the mean across the four raters (n=44 items). The inter-rater variability in fiber ratings among these identified items was curated for some (n=9 items due to unreliable sources) and deemed valid nonetheless for all others (n=35 items) because of the following reasons: i) visual assessment only of the depicted item and its ingredients leading to an approximation of the exact ingredients (e.g. whole-grain or white flour in bakery items), ii) imprecision of matching the depicted food item to food database entry due to multi-component items (e.g. piece of layered caked or sushi rolls), or iii) variation in the databases for similar or identical items (e.g. Magnum Classic). After curation, inter-rater reliability across four raters improved slightly, but remained good overall (ICC2 kappa = 0.77, 95% CI: 0.75, 0.80). In order to add the fiber value to the original Food-pics_extended table, we also calculated the total fiber amount for the depicted food portion based on the provided portion size (grams_total). The calculation and exclusion process can be followed up in the table: food_pics_mean_fibre_rating.xlsx. On average, fiber content was high in ready-to-eat savories (such as nuts or pretzels), fruits and vegetables, and low in dairy & eggs, fish, meat and beverages (Table 1, also note the differences in SD range). How to cite: If you use the provided fiber ratings for the Food-pics_extended database, cite Medawar, Evelyn, Thieleking, Ronja, Witte, A. Veronica. “Dietary Fiber Extension for the Food-Pics_extended Database.” OSF. 2021. Original database: Blechert, J., Lender, A., Polk, S., Busch, N. A., & Ohla, K. (2019). Food-pics_extended—an image database for experimental research on eating and appetite: additional images, normative ratings and an updated review. Frontiers in psychology, 10, 307.

Higher adherence to a Mediterranean diet predicts healthier sleep (St-Onge & Zuraikat, 2019). Yet, there is not much known about potential mechanisms, mediating this bi-directional relation. This study aims to investigate gut-brain-communication, especially the impact of high fiber and tryptophan intake on the composition of the gut microbiome as a potential mediator and how it relates to human sleep quality.

Tyrosine is the precursor of the neurotransmitter dopamine (DA), which is known to modulate various cognitive functions such as executive attention (Posner et al., 2006). Research findings suggest that dopamine-modulated cognitive performance can be influenced by tyrosine supplementation via direct single-dose administration or indirect habitual food intake (Aquili, 2020; Hase et al., 2015; Jongkees et al., 2015; Van De Rest et al., 2013; Young, 1996). However, results are still mixed, not only regarding the presence or strength, but also the direction of the association between tyrosine supplementation and cognitive functioning. Therefore, the aim of the study is to elucidate the relationship between diet-associated availability of tyrosine in blood and the efficiency of the dopaminergic executive attention network. To this end, we conduct a cross-sectional analysis with 61 healthy, overweight subjects. We measured serum tyrosine levels and performance in the attention network test (ANT) to investigate the relationship between serum tyrosine and executive attention performance (serum precursor-function relationship). Because the strength and direction of dopaminergic drugs effects depend on differences in white matter microstructure of pathways associated with the targeted cognitive function (van der Schaaf et al., 2013; Van Schouwenburg et al., 2013), we acquired diffusion tensor images (DTI). For data analysis of executive attention performance, a two-way analysis of variance (ANOVA) is performed. In addition, individual differences in the efficiency of the three attention networks are quantified by calculating alerting, orienting, and conflict effects (Fan et al., 2005). Using these conflict effects as a measure of executive attention performance, a linear regression analysis will be performed to examine the serum precursor-function relationship. Additionally, because all large neutral amino acids (LNAAs) enter the brain via a competitive transport system (Wurtman et al., 2003), the proportional amount of serum tyrosine relative to the other six LNAAs will be computed as an index of brain dopamine levels and added to the model. Furthermore, a whole-brain analysis correlating Fractional Anisotropy (FA) values with conflict scores is performed to reveal possible structure-function relationships. Subsequently, mean FA values of significant clusters will be computed to perform a moderation analysis aiming to investigate if the serum precursor-function relationship is moderated by interindividual differences in white matter microstructure of the executive attention network. Finally, to investigate whether serum tyrosine levels can be predicted by interindividual differences in dietary behavior, we compute dietary tyrosine intake based on self-reported food consumption.

Statistical Analysis > Behavioral analysis was updated in the main preregistration to i) adapted naming of models according to fMRI models (model 0, model A, model B, model C) and to ii) added model C.

This study investigates the impact of bariatric surgery on brain structure and function.

This pre-registration describes the hypotheses and data-analysis plans of our Menstrual Cycle Plasticity Project, which will assess how endogenous ovarian hormone fluctuations influence hippocampal subfields and medial temporal lobe regions across the menstrual cycle.

It is widely established that there is a worldwide increase in the prevalence of obesity. Obesity is problematic as it can cause metabolic disorders such as cardiovascular disease[1]⁠ and type II-diabetes[2]⁠. Often, unhealthy food decisions are one factor leading to obesity. Food decision-making is a very complex topic including not only homeostatic factors but also cognitive processes. One of these processes that plays a role in food decision-making is the food memory. Therefore, it is necessary to get a better understanding of the interplay of food decisions and food memory and the underlying neural systems. We here aim to identify how factors such as food wanting and hunger shape food memory and how the underlying neural anatomy contributes to these memory processes.