Human Cortical Activity Evoked by the Assignment of Authenticity when Viewing Works of Art

There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.

Adaptive behavior is optimized in organisms that maintain flexible representations of the value of sensory-predictive cues. To identify central representations of predictive reward value in humans, we used reinforcer devaluation while measuring neural activity with functional magnetic resonance imaging. We presented two arbitrary visual stimuli, both before and after olfactory devaluation, in a paradigm of appetitive conditioning. In amygdala and orbitofrontal cortex, responses evoked by a predictive target stimulus were decreased after devaluation, whereas responses to the nondevalued stimulus were maintained. Thus, differential activity in amygdala and orbitofrontal cortex encodes the current value of reward representations accessible to predictive cues.

The invariant properties of human cortical neurons cannot be studied directly by fMRI due to its limited spatial resolution. Here, we circumvented this limitation by using fMR adaptation, namely, reduction of the fMR signal due to repeated presentation of identical images. Object-selective regions (lateral occipital complex [LOC]) showed a monotonic signal decrease as repetition frequency increased. The invariant properties of fMR adaptation were studied by presenting the same object in different viewing conditions. LOC exhibited stronger fMR adaptation to changes in size and position (more invariance) compared to illumination and viewpoint. The effect revealed two putative subdivisions within LOC: caudal-dorsal (LO), which exhibited substantial recovery from adaptation under all transformations, and posterior fusiform (PF/LOa), which displayed stronger adaptation. This study demonstrates the utility of fMR adaptation for revealing functional characteristics of neurons in fMRI studies.