The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These "brain-reading" analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively sound and qualitatively satisfying answer to most questions addressed in functional neuroimaging studies. Here, we examine the theoretical, statistical, and practical underpinnings of pattern-based classification approaches to functional neuroimaging analyses. Pattern-based classification analyses are well positioned to become the standard approach to analyzing functional neuroimaging data.

The goal of this study was to evaluate human accuracy at identifying people from static and dynamic presentations of faces and bodies. Participants matched identity in pairs of videos depicting people in motion (walking or conversing) and in "best" static images extracted from the videos. The type of information presented to observers was varied to include the face and body, the face-only, and the body-only. Identification performance was best when people viewed the face and body in motion. There was an advantage for dynamic over static stimuli, but only for conditions that included the body. Control experiments with multiple-static images indicated that some of the motion advantages we obtained were due to seeing multiple images of the person, rather than to the motion, per se. To computationally assess the contribution of different types of information for identification, we fused the identity judgments from observers in different conditions using a statistical learning algorithm trained to optimize identification accuracy. This fusion achieved perfect performance. The condition weights that resulted suggest that static displays encourage reliance on the face for recognition, whereas dynamic displays seem to direct attention more equitably across the body and face.

No abstract

Although there is emergent evidence illustrating neural sensitivity to cannabis cues in cannabis users, the specificity of this effect to cannabis cues as opposed to a generalized hyper‐sensitivity to hedonic stimuli has not yet been directly tested. Using fMRI, we presented 53 daily, long‐term cannabis users and 68 non‐using controls visual and tactile cues for cannabis, a natural reward, and, a sensory‐perceptual control object to evaluate brain response to hedonic stimuli in cannabis users. The results showed an interaction between group and reward type such that the users had greater response during cannabis cues relative to natural reward cues (i.e., fruit) in the orbitofrontal cortex, striatum, anterior cingulate gyrus, and ventral tegmental area compared to non‐users (cluster‐threshold z = 2.3, P < 0.05). In the users, there were positive brain‐behavior correlations between neural response to cannabis cues in fronto‐striatal‐temporal regions and subjective craving, marijuana‐related problems, withdrawal symptoms, and levels of THC metabolites (cluster‐threshold z = 2.3, P < 0.05). These findings demonstrate hyper‐responsivity, and, specificity of brain response to cannabis cues in long‐term cannabis users that are above that of response to natural reward cues. These observations are concordant with incentive sensitization models suggesting sensitization of mesocorticolimbic regions and disruption of natural reward processes following drug use. Although the cross‐sectional nature of this study does not provide information on causality, the positive correlations between neural response and indicators of cannabis use (i.e., THC levels) suggest that alterations in the reward system are, in part, related to cannabis use. Hum Brain Mapp 37:3431–3443, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Background Emergent studies show that similar to other substances of abuse, cue-reactivity to cannabis is also associated with neural response in the brain’s reward pathway (Filbey et al., 2009). However, the inter-relatedness of brain regions during cue-reactivity in cannabis users remains unknown. Methods In this study, we conducted a series of investigations to determine functional connectivity during cue-reactivity in 71 cannabis users. First, we used psychophysiological interaction (PPI) analysis to examine coherent neural response to cannabis cues. Second, we evaluated whether these patterns of network functional connectivity differentiated dependent and non-dependent users. Finally, as an exploratory analysis, we determined the directionality of these connections via Granger connectivity analyses. Results PPI analyses showed reward network functional connectivity with the nucleus accumbens (NAc) seed region during cue exposure. Between-group contrasts found differential effects of dependence status. Dependent users (N = 31) had greater functional connectivity with amygdala and anterior cingulate gyrus (ACG) seeds while the non-dependent users (N = 24) had greater functional connectivity with the NAc, orbitofrontal cortex (OFC) and hippocampus seeds. Granger analyses showed that hippocampal and ACG activation preceded neural response in reward areas. Conclusions Both PPI and Granger analyses demonstrated strong functional coherence in reward regions during exposure to cannabis cues in current cannabis users. Functional connectivity (but not regional activation) in the reward network differentiated dependent from non-dependent cannabis users. Our findings suggest that repeated cannabis exposure causes observable changes in functional connectivity in the reward network and should be considered in intervention strategies.