Humans learn to trust each other by evaluating the outcomes of repeated interpersonal interactions. However, available prior information on the reputation of traders may alter the way outcomes affect learning. Our functional magnetic resonance imaging study is the first to allow the direct comparison of interaction-based and prior-based learning. Twenty participants played repeated trust games with anonymous counterparts. We manipulated two experimental conditions: whether or not reputational priors were provided, and whether counterparts were generally trustworthy or untrustworthy. When no prior information is available our results are consistent with previous studies in showing that striatal activation patterns correlate with behaviorally estimated reinforcement learning measures. However, our study additionally shows that this correlation is disrupted when reputational priors on counterparts are provided. Indeed participants continue to rely on priors even when experience sheds doubt on their accuracy. Notably, violations of trust from a cooperative counterpart elicited stronger caudate deactivations when priors were available than when they were not. However, tolerance to such violations appeared to be mediated by prior-enhanced connectivity between the caudate nucleus and ventrolateral prefrontal cortex, which anticorrelated with retaliation rates. Moreover, on top of affecting learning mechanisms, priors also clearly oriented initial decisions to trust, reflected in medial prefrontal cortex activity.

The Attention Deficit Hyperactivity Disorder (ADHD) affects the school-age population and has large social costs. The scientific community is still lacking a pathophysiological model of the disorder and there are no objective biomarkers to support the diagnosis. In 2011 the ADHD-200 Consortium provided a rich, heterogeneous neuroimaging dataset aimed at studying neural correlates of ADHD and to promote the development of systems for automated diagnosis. Concurrently a competition was set up with the goal of addressing the wide range of different types of data for the accurate prediction of the presence of ADHD. Phenotypic information, structural magnetic resonance imaging (MRI) scans and resting state fMRI recordings were provided for nearly 1000 typical and non-typical young individuals. Data were collected by eight different research centers in the consortium. This work is not concerned with the main task of the contest, i.e., achieving a high prediction accuracy on the competition dataset, but we rather address the proper handling of such a heterogeneous dataset when performing classification-based analysis. Our interest lies in the clustered structure of the data causing the so-called batch effects which have strong impact when assessing the performance of classifiers built on the ADHD-200 dataset. We propose a method to eliminate the biases introduced by such batch effects. Its application on the ADHD-200 dataset generates such a significant drop in prediction accuracy that most of the conclusions from a standard analysis had to be revised. In addition we propose to adopt the dissimilarity representation to set up effective representation spaces for the heterogeneous ADHD-200 dataset. Moreover we propose to evaluate the quality of predictions through a recently proposed test of independence in order to cope with the unbalancedness of the dataset.

No abstract

Machine learning techniques are increasingly adopted in computer-aided diagnosis. Evaluation methods for classification results that are based on the study of one or more metrics can be unable to distinguish between cases in which the classifier is discriminating the classes from cases in which it is not. In the binary setting, such circumstances can be encountered when data are unbalanced with respect to the diagnostic groups. Having more healthy controls than pathological subjects, datasets meant for diagnosis frequently show a certain degree of unbalancedness. In this work, we propose to recast the evaluation of classification results as a test of statistical independence between the predicted and the actual diagnostic groups. We address the problem within the Bayesian hypothesis testing framework. Different from the standard metrics, the proposed method is able to handle unbalanced data and takes into account the size of the available data. We show experimental evidence of the efficacy of the approach both on simulated data and on real data about the diagnosis of the Attention Deficit Hyperactivity Disorder (ADHD).