Electro/magnetoencephalographic signatures of human brain insulin resistance

Human insulin action influences eating behavior, peripheral metabolism and cognition. Detailed insights into the neuronal processes related to human brain insulin action can be obtained by direct measures of neuronal activity with electroencephalography and magnetoencephalography. Results of recent studies show that spontaneous, task and stimulus related neuronal activity is modulated by insulin and that several factors like increased body weight and body composition can result in brain insulin resistance. Recent technological advances even allow the investigation of human brain functions in utero in relation to the metabolic status of the mother and indicate an effect of the mother’s insulin sensitivity on the brain function of the fetus. In conclusion, studies based on direct neuronal measurements may help to determine the developmental trajectory related to insulin action and resistance

An Obesity Risk SNP (rs17782313) near the MC4R Gene Is Associated with Cerebrocortical Insulin Resistance in Humans

Activation of melanocortin-4 receptor (MC4R) by insulin sensitive neurons is a central mechanism in body weight regulation, and genetic variants in the MC4R gene (e.g., rs17782313) are associated with obesity. By using magnetoencephalography, we addressed whether rs17782313 affects the cerebrocortical insulin response. We measured the cerebrocortical insulin response by using magnetoencephalography in a hyperinsulinemic euglycemic clamp (versus placebo) in 51 nondiabetic humans (26 f/25 m, age 35 ± 3 years, BMI 28 ± 1 kg/m2). The C-allele of rs17782313 was minor allele (frequency 23%), and the genotype distribution (TT 30, TC 19, CC 2) was in Hardy-Weinberg-Equilibrium. Insulin-stimulated cerebrocortical theta activity was decreased in the presence of the C-allele (TT 33 ± 16 fT; TC/CC −27 ± 20 fT; P = .023), and this effect remained significant after adjusting for BMI and peripheral insulin sensitivity (P = .047). Cerebrocortical theta activity was impaired in carriers of the obesity risk allele. Therefore, cerebral insulin resistance may contribute to the obesity effect of rs17782313

The effect of hunger state on hypothalamic functional connectivity in response to food cues

ACKNOWLEDGMENT The authors thank Lisette Charbonnier for her relentless efforts in setting up the study at all three sites and collecting the Dutch data. Open Access funding enabled and organized by Projekt DEAL. FUNDING INFORMATION This work was financially supported by the European Union Seventh Framework Programme (FP7/2007–2013) for research, technological development, and demonstration under grant agreement 266408 (Full4Health, www.full4health.eu). Furthermore, the study was supported in parts by a grant (01GI0925) from the Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD e.V.).Peer reviewedPublisher PD

Interaction between the obesity-risk gene FTO and the dopamine D2 receptor gene ANKK1/TaqIA on insulin sensitivity

Variations in FTO are the strongest common genetic determinants of adiposity, and may partly act by influencing dopaminergic signalling in the brain leading to altered reward processing that promotes increased food intake. Therefore, we investigated the impact of such an interaction on body composition, and peripheral and brain insulin sensitivity. Participants from the Tubingen Family study (n = 2245) and the Malmo Diet and Cancer study (n = 2921) were genotyped for FTO SNP rs8050136 and ANKK1 SNP rs1800497. Insulin sensitivity in the caudate nucleus, an important reward area in the brain, was assessed by fMRI in 45 participants combined with intranasal insulin administration. We found evidence of an interaction between variations in FTO and an ANKK1 polymorphism that associates with dopamine (D2) receptor density. In cases of reduced D2 receptor availability, as indicated by the ANKK1 polymorphism, FTO variation was associated with increased body fat and waist circumference and reduced peripheral insulin sensitivity. Similarly, altered central insulin sensitivity was observed in the caudate nucleus in individuals with the FTO obesity-risk allele and diminished D2 receptors. The effects of variations in FTO are dependent on dopamine D2 receptor density (determined by the ANKK1 polymorphism). Carriers of both risk alleles might, therefore, be at increased risk of obesity and diabetes.Peer reviewe

Synchronization analysis of the uterine magnetic activity during contractions

BACKGROUND: Our objective was to quantify and compare the extent of synchronization of the spatial-temporal myometrial activity over the human uterus before and during a contraction using transabdominal magnetomyographic (MMG) recordings. Synchronization can be an important indicator for the quantification of uterine contractions. METHODS: The spatialtermporal myometrial activity recordings were performed using a 151-channel noninvasive magnetic sensor system called SARA. This device covers the entire pregnant abdomen and records the magnetic field corresponding to the electrical activity generated in the uterine myometrium. The data was collected at 250 samples/sec and was resampled with 25 samples/sec and then filtered in the band of 0.1–0.2 Hz to study the primary magnetic activity of the uterus related to contractions. The synchronization between a channel pair was computed. It was inferred from a statistical tendency to maintain a nearly constant phase difference over a given period of time even though the analytic phase of each channel may change markedly during that time frame. The analytic phase was computed after taking Hilbert transform of the magnetic field data. The process was applied on the pairs of magnetic field traces (240 sec length) with a stepping window of 20 sec duration which is long enough to cover two cycle of the lowest frequency of interest (0.1 Hz). The analysis was repeated by stepping the window at 10 sec intervals. The spatial patterns of the synchronization indices covering the anterior transabdominal area were computed. For this, regional coil-pairs were used. For a given coil, the coil pairs were constructed with the surrounding six coils. The synchronization indices were computed for each coil pair, averaged over the 21 coil-pairs and then assigned as the synchronization index to that particular coil. This procedure was tested on six pregnant subjects at the gestational age between 29 and 40 weeks admitted to the hospital for contractions. The RMS magnetic field for each coil was also computed. RESULTS: The results show that the spatial patterns of the synchronization indices change and follow the periodic pattern of the uterine contraction cycle. Spatial patterns of synchronization indices and the RMS magnetic fields show similarities in few window frames and also show large differences in few other windows. For six subjects, the average synchronization indices were: 0.346 ± 0.068 for the quiescent baseline period and 0.545 ± 0.022 at the peak of the contraction. DISCUSSION: These results show that synchronization indices and their spatial distributions depict uterine contractions and relaxations

Good practice in food-related neuroimaging

The use of neuroimaging tools, especially functional magnetic resonance imaging, in nutritional research has increased substantially over the past 2 decades. Neuroimaging is a research tool with great potential impact on the field of nutrition, but to achieve that potential, appropriate use of techniques and interpretation of neuroimaging results is necessary. In this article, we present guidelines for good methodological practice in functional magnetic resonance imaging studies and flag specific limitations in the hope of helping researchers to make the most of neuroimaging tools and avoid potential pitfalls. We highlight specific considerations for food-related studies, such as how to adjust statistically for common confounders, like, for example, hunger state, menstrual phase, and BMI, as well as how to optimally match different types of food stimuli. Finally, we summarize current research needs and future directions, such as the use of prospective designs and more realistic paradigms for studying eating behavior

Eating less or more – Mindset induced changes in neural correlates of pre-meal planning

Obesity develops due to an imbalance between energy intake and expenditure. Besides the decision about what to eat, daily energy intake might be even more dependent on the decision about the portion size to be consumed. For decisions between different foods, attentional focus is considered to play a key role in the choice selection. In the current study, we investigated the attentional modulation of portion size selection during pre-meal planning. We designed a functional magnetic resonance task in which healthy participants were directed to adopt different mindsets while selecting their portion size for lunch. Compared with a free choice condition, participants reduced their portion sizes when considering eating for health or pleasure, which was accompanied by increased activity in left prefrontal cortex and left orbitofrontal cortex, respectively. When planning to be full until dinner, participants selected larger portion sizes and showed a trend for increased activity in left insula. These results provide first evidence that also the cognitive process of pre-meal planning is influenced by the attentional focus at the time of choice, which could provide an opportunity for influencing the control of meal size selection by mindset manipulation

Review of the BCI Competition IV

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.BMBF, 01IB001A, LOKI - Lernen zur Organisation komplexer Systeme der Informationsverarbeitung - Lernen im Kontext der SzenenanalyseBMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine InteraktionEC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBIEC/FP7/216886/EU/Pattern Analysis, Statistical Modelling and Computational Learning 2/PASCAL2BMBF, 01GQ0420, Verbundprojekt: Bernstein-Zentrum für Neural Dynamics, Freiburg - CNDFBMBF, 01GQ0761, Bewegungsassoziierte Aktivierung - Dekodierung bewegungsassoziierter GehirnsignaleBMBF, 01GQ0762, Bewegungsassoziierte Aktivierung - Gehirn- und Maschinenlerne

The determinants of food choice

Health nudge interventions to steer people into healthier lifestyles are increasingly applied by governments worldwide, and it is natural to look to such approaches to improve health by altering what people choose to eat. However, to produce policy recommendations that are likely to be effective, we need to be able to make valid predictions about the consequences of proposed interventions, and for this, we need a better understanding of the determinants of food choice. These determinants include dietary components (e.g. highly palatable foods and alcohol), but also diverse cultural and social pressures, cognitive-affective factors (perceived stress, health attitude, anxiety and depression), and familial, genetic and epigenetic influences on personality characteristics. In addition, our choices are influenced by an array of physiological mechanisms, including signals to the brain from the gastrointestinal tract and adipose tissue, which affect not only our hunger and satiety but also our motivation to eat particular nutrients, and the reward we experience from eating. Thus, to develop the evidence base necessary for effective policies, we need to build bridges across different levels of knowledge and understanding. This requires experimental models that can fill in the gaps in our understanding that are needed to inform policy, translational models that connect mechanistic understanding from laboratory studies to the real life human condition, and formal models that encapsulate scientific knowledge from diverse disciplines, and which embed understanding in a way that enables policy-relevant predictions to be made. Here we review recent developments in these areas.</p