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Although many neuroimaging studies have investigated adolescent risk taking, few studies have dissociated between decision-making under risk (known probabilities) and ambiguity (unknown probabilities). Furthermore, which brain regions are sensitive to individual differences in task-related and self-reported risk taking remains elusive. We presented 198 adolescents (11–24 years, an age-range in which individual differences in risk taking are prominent) with an fMRI paradigm that separated decision-making (choosing to gamble or not) and reward outcome processing (gains, no gains) under risky and ambiguous conditions, and related this to task-related and self-reported risk taking. We observed distinct neural mechanisms underlying risky and ambiguous gambling, with risk more prominently associated with activation in parietal cortex, and ambiguity more prominently with dorsolateral prefrontal cortex (PFC), as well as medial PFC during outcome processing. Individual differences in task-related risk taking were positively associated with ventral striatum activation in the decision phase, specifically for risk, and negatively associated with insula and dorsomedial PFC activation, specifically for ambiguity. Moreover, dorsolateral PFC activation in the outcome phase seemed a prominent marker for individual differences in task-related risk taking under ambiguity as well as self-reported daily-life risk taking, in which greater risk taking was associated with reduced activation in dorsolateral PFC. Together, this study demonstrates the importance of considering multiple risk-taking measures, and contextual moderators, in understanding the neural mechanisms underlying adolescent risk taking. [ABSTRACT FROM AUTHOR]

The brain is capable of producing coordinated fast changing neural dynamics across multiple brain regions in order to adapt to rapidly changing environments. However, it is non-trivial to identify multiregion dynamics at fast sub-second time-scales in electrophysiological data. We propose a method that, with no knowledge of any task timings, can simultaneously identify and describe fast transient multiregion dynamics in terms of their temporal, spectral and spatial properties. The approach models brain activity using a discrete set of sequential states, with each state distinguished by its own multiregion spectral properties. This can identify potentially very short-lived visits to a brain state, at the same time as inferring the state's properties, by pooling over many repeated visits to that state. We show how this can be used to compute state-specific measures such as power spectra and coherence. We demonstrate that this can be used to identify short-lived transient brain states with distinct power and functional connectivity (e.g., coherence) properties in an MEG data set collected during a volitional motor task. [ABSTRACT FROM AUTHOR]

Abstract: We report experiments designed to learn whether different kinds of perceptually unstable visual images engage different neural mechanisms. 21 subjects viewed two types of bi-stable images while we scanned the activity in their brains with functional magnetic resonance imaging (fMRI); in one (intra-categorical type) the two percepts remained within the same category (e.g. face–face) while in the other (cross-categorical type) they crossed categorical boundaries (e.g. face–body). The results showed that cross- and intra-categorical reversals share a common reversal-related neural circuitry, which includes fronto-parietal cortex and primary visual cortex (area V1). Cross-categorical reversals alone engaged additional areas, notably anterior cingulate cortex and superior temporal gyrus, which have been posited to be involved in conflict resolution. [Copyright &y& Elsevier]