Fanny Cazettes

Obesity-mediated inflammation may damage the brain circuit that regulates food intake

Adiposity is associated with chronic low-grade systemic inflammation and increased inflammation in the hypothalamus, a key structure in feeding behavior. It remains unknown whether inflammation impacts other brain structures that regulate feeding behavior. We studied 44 overweight/obese and 19 lean individuals with MRI and plasma fibrinogen levels (marker of inflammation). We performed MRI-based segmentations of the medial and lateral orbitofrontal cortex (OFC) and hippocampal volumes. Gray matter (GM) volumes were adjusted for head size variability. We conducted logistic and hierarchical regressions to assess the association between fibrinogen levels and brain volumetric data. Using diffusion tensor imaging (DTI), we created apparent diffusion coefficient (ADC) maps and conducted voxelwise correlational analyses. Fibrinogen concentrations were higher among the overweight/obese (t[61] = -2.33, P = 0.023). Lateral OFC associated together with fibrinogen correctly classified those with excess of weight (accuracy = 76.2%, sensitivity = 95.5%, and specificity=31.6%). The lateral OFC volumes of overweight/obese were negatively associated with fibrinogen (r = -0.37, P = 0.016) and after accounting for age, hypertension, waist/hip ratio and lipid and sugar levels, fibrinogen significantly explained an additional 9% of the variance in the lateral OFC volume (β = -0.348, ΔR(2) = 0.093, ΔF P = 0.046). Among overweight/obese the associations between GM ADC and fibrinogen were significantly positive (P < 0.001) in the left and right amygdala and the right parietal region. Among lean individuals these associations were negative and located in the left prefrontal, the right parietal and the left occipital lobes. This is the first study to report that adiposity-related inflammation may reduce the integrity of some of the brain structures involved in reward and feeding behaviors.

Automatic removal of high-amplitude stimulus artefact from neuronal signal recorded in the subthalamic nucleus

Over the last few years, deep brain stimulation (DBS) with targets such as the subthalamic nucleus or the pallidum were found to be beneficial in the treatment of Parkinsons disease and dystonia. The investigation of the mechanisms of action of DBS by recording concomitant neural activities in basal ganglia is hampered by the large stimulus artefacts (SA). Approaches to remove the SA with conventional filters, or other conventional digital methods, are not always effective due to the significant overlap between the spectral contents of the neuronal signal and the SA. Thus, such approaches may produce a significant residual SA or alter the neuronal signal dynamics by removing its frequency contents. In this work, we propose a method based on an on-line SA template extraction and on the Ensemble empirical mode decomposition (EEMD) to automatically detect and remove the dynamics of the SA without altering the embedded dynamics of the neuronal signal during stimulation. The results, based on real signals recorded in the subthalamic nucleus during Motor cortex stimulation (MCS) experiments, show that this technique, which may be applied on-line, effectively identifies, separates and removes the SA, and uncovers neuronal potentials superimposed on the artefact.

Abnormal cholesterol is associated with prefrontal white matter abnormalities among obese adults, a diffusion tensor imaging study

The brain is the most cholesterol-rich organ in the body. Although most of the cholesterol in the brain is produced endogenously, some studies suggest that systemic cholesterol may be able to enter the brain. We investigated whether abnormal cholesterol profiles correlated with diffusion-tensor-imaging-based estimates of white matter microstructural integrity of lean and overweight/obese (o/o) adults. Twenty-two lean and 39 obese adults underwent magnetic resonance imaging, kept a three-day food diary, and had a standardized assessment of fasting blood lipids. The lean group ate less cholesterol-rich food than o/o although both groups ate equivalent servings of food per day. Voxelwise correlational analyses controlling for age, diabetes, and white matter hyperintensities, resulted in two significant clusters of negative associations between abnormal cholesterol profile and fractional anisotropy, located in the left and right prefrontal lobes. When the groups were split, the lean subjects showed no associations, whereas the o/o group expanded the association to three significant clusters, still in the frontal lobes. These findings suggest that cholesterol profile abnormalities may explain some of the reductions in white matter microstructural integrity that are reported in obesity.

Spatial cue reliability drives frequency tuning in the barn Owl's midbrain

The robust representation of the environment from unreliable sensory cues is vital for the efficient function of the brain. However, how the neural processing captures the most reliable cues is unknown. The interaural time difference (ITD) is the primary cue to localize sound in horizontal space. ITD is encoded in the firing rate of neurons that detect interaural phase difference (IPD). Due to the filtering effect of the head, IPD for a given location varies depending on the environmental context. We found that, in barn owls, at each location there is a frequency range where the head filtering yields the most reliable IPDs across contexts. Remarkably, the frequency tuning of space-specific neurons in the owls midbrain varies with their preferred sound location, matching the range that carries the most reliable IPD. Thus, frequency tuning in the owls space-specific neurons reflects a higher-order feature of the code that captures cue reliability. DOI: http://dx.doi.org/10.7554/eLife.04854.001

Neural representation of probabilities for Bayesian inference

Bayesian models are often successful in describing perception and behavior, but the neural representation of probabilities remains in question. There are several distinct proposals for the neural representation of probabilities, but they have not been directly compared in an example system. Here we consider three models: a non-uniform population code where the stimulus-driven activity and distribution of preferred stimuli in the population represent a likelihood function and a prior, respectively; the sampling hypothesis which proposes that the stimulus-driven activity over time represents a posterior probability and that the spontaneous activity represents a prior; and the class of models which propose that a population of neurons represents a posterior probability in a distributed code. It has been shown that the non-uniform population code model matches the representation of auditory space generated in the owl’s external nucleus of the inferior colliculus (ICx). However, the alternative models have not been tested, nor have the three models been directly compared in any system. Here we tested the three models in the owl’s ICx. We found that spontaneous firing rate and the average stimulus-driven response of these neurons were not consistent with predictions of the sampling hypothesis. We also found that neural activity in ICx under varying levels of sensory noise did not reflect a posterior probability. On the other hand, the responses of ICx neurons were consistent with the non-uniform population code model. We further show that Bayesian inference can be implemented in the non-uniform population code model using one spike per neuron when the population is large and is thus able to support the rapid inference that is necessary for sound localization.

Likelihood representation in the owl's sound localization system

We investigate how frequency convergence governs ITD integration across frequency to understand the physiological representation of likelihood. We examine the tuning properties of midbrain neurons using in vivo extracellular recordings. The preferred ITDs and ITD tuning curve widths were measured at fine resolution in ICx. Consistent with our hypothesis, we found that the non-uniform ITD tuning predicted by the likelihood model is not present at stages in the ITD pathway where neurons do not integrate across frequency. We have also examined the relationship between frequency tuning and ITD tuning. Importantly, we found that the distribution of preferred frequency in ICx also depends on ITD tuning. We are currently testing if the represen