Obesity is associated with high serotonin 4 receptor availability in the brain reward circuitry.

The neurobiology underlying obesity is not fully understood. The neurotransmitter serotonin (5-HT) is established as a satiety-generating signal, but its rewarding role in feeding is less well elucidated. From animal experiments there is now evidence that the 5-HT(4) receptor (5-HT(4)R) is involved in food intake, and that pharmacological or genetic manipulation of the receptor in reward-related brain areas alters food intake. Here, we used positron emission tomography in humans to examine the association between cerebral 5-HT(4)Rs and common obesity. We found in humans a strong positive association between body mass index and the 5-HT(4)R density bilaterally in the two reward 'hot spots' nucleus accumbens and ventral pallidum, and additionally in the left hippocampal region and orbitofrontal cortex. These findings suggest that the 5-HT(4)R is critically involved in reward circuits that regulate people's food intake. They also suggest that pharmacological stimulation of the cerebral 5-HT(4)R may reduce reward-related overeating in humans.

Functional connectivity metrics during stroke recovery.

We explore functional connectivity in nine subjects measured with 1.5T fMRI-BOLD in a longitudinal study of recovery from unilateral stroke affecting the motor area (Small et al., 2002). We found that several measures of complexity of covariance matrices show strong correlations with behavioral measures of recovery. In Schmah et al. (2010), we applied Linear and Quadratic Discriminants (LD and QD) computed on a principal components (PC) subspace to classify the fMRI volumes into "early" and "late" sessions. We demonstrated excellent classification accuracy with QD but not LD, indicating that potentially important differences in functional connectivity exist between the early and late sessions. Motivated by Mclntosh et al. (2008), who showed that EEG brain-signal variability and behavioral performance both increased with age during development, we investigated complexity of the covariance matrix for this longitudinal stroke recovery data set. We used three complexity measures: the sphericity index described by Abdi (2010); "unsupervised dimensionality", which is the number of PCs that minimizes unsupervised generalization error of a covariance matrix (Hansen et al., 1999); and "QD dimensionality", which is the number of PCs that minimizes the classification accuracy of QD. Although these approaches measure different kinds of complexity, all showed strong correlations with one or more behavioral tests: nine-hole peg test, hand grip test and pinch test. We could not demonstrate that either sphericity or unsupervised dimensionality were significantly different for the "early" and "late" sessions using a paired Wilcoxon test. However, the amount of relative behavioral improvement was correlated with sphericity of the overall covariance matrix (pooled across all sessions), as well as with the divergence of the eigenspectra between the "early" and "late" covariance matrices. Complexity measures that use the number of PCs (which optimize QD classification or unsupervised generalization) were correlated with the behavioral performance of the final session, but not with the relative improvement. These are suggestive, but limited, results given the sample size, restricted behavioral measurements and older 1.5T BOLD data sets. Nevertheless, they indicate one potentially fruitful direction for future data-driven fMRI studies of stroke recovery in larger, better-characterized longitudinal stroke data sets recorded at higher field strength. Finally, we produced sensitivity maps (Kjems et al., 2002) corresponding to both linear and quadratic discriminants for the "early" vs. "late" classification. These maps measure the influence of each voxel on the class assignments for a given classifier. Differences between the scaled sensitivity maps for the linear and quadratic discriminants indicate brain regions involved in changes in functional connectivity. These regions are highly variable across subjects, but include the cerebellum and the motor area contralateral to the lesion.

Brain serotonin 2A receptor binding: relations to body mass index, tobacco and alcohol use.

Manipulations of the serotonin levels in the brain can affect impulsive behavior and influence our reactivity to conditioned reinforcers. Eating, tobacco smoking, and alcohol consumption are reinforcers that are influenced by serotonergic neurotransmission; serotonergic hypofunction leads to increased food and alcohol intake, and conversely, stimulation of the serotonergic system induces weight reduction and decreased food/alcohol intake as well as tobacco smoking. To investigate whether body weight, alcohol intake and tobacco smoking were related to the regulation of the cerebral serotonin 2A receptor (5-HT(2A)) in humans, we tested in 136 healthy human subjects if body mass index (BMI), degree of alcohol consumption and tobacco smoking was associated to the cerebral in vivo 5-HT(2A) receptor binding as measured with (18)F-altanserin PET. The subjects' BMI's ranged from 18.4 to 42.8 (25.2+/-4.3) kg/m(2). Cerebral cortex 5-HT(2A) binding was significantly positively correlated to BMI, whereas no association between cortical 5-HT(2A) receptor binding and alcohol or tobacco use was detected. We suggest that our observation is driven by a lower central 5-HT level in overweight people, leading both to increased food intake and to a compensatory upregulation of cerebral 5-HT(2A) receptor density.