Gray Matter Volumes in Obsessive-Compulsive Disorder Before and After Fluoxetine or Cognitive-Behavior Therapy: A Randomized Clinical Trial

Serotonin reuptake inhibitors and cognitive-behavior therapy (CBT) are considered first-line treatments for obsessive-compulsive disorder (OCD). However, little is known about their modulatory effects on regional brain morphology in OCD patients. We sought to document structural brain abnormalities in treatment-naive OCD patients and to determine the effects of pharmacological and cognitive-behavioral treatments on regional brain volumes. Treatment-naive patients with OCD (n = 38) underwent structural magnetic resonance imaging scan before and after a 12-week randomized clinical trial with either fluoxetine or group CBT. Matched-healthy controls (n = 36) were also scanned at baseline. Voxel-based morphometry was used to compare regional gray matter (GM) volumes of regions of interest (ROIs) placed in the orbitofrontal, anterior cingulate and temporolimbic cortices, striatum, and thalamus. Treatment-naive OCD patients presented smaller GM volume in the left putamen, bilateral medial orbitofrontal, and left anterior cingulate cortices than did controls (p<0.05, corrected for multiple comparisons). After treatment with either fluoxetine or CBT (n = 26), GM volume abnormalities in the left putamen were no longer detectable relative to controls. ROI-based within-group comparisons revealed that GM volume in the left putamen significantly increased (p<0.012) in fluoxetine-treated patients (n = 13), whereas no significant GM volume changes were observed in CBT-treated patients (n = 13). This study supports the involvement of orbitofronto/cingulo-striatal loops in the pathophysiology of OCD and suggests that fluoxetine and CBT may have distinct neurobiological mechanisms of action. Neuropsychopharmacology (2012) 37, 734-745; doi: 10.1038/npp.2011.250; published online 26 October 2011

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.