Separate Neural Networks for Gains and Losses in Intertemporal Choice / 神经科学通报·英文版

An important and unresolved question is how human brain regions process information and interact with each other in intertemporal choice related to gains and losses. Using psychophysiological interaction and dynamic causal modeling analyses, we investigated the functional interactions between regions involved in the decision-making process while participants performed temporal discounting tasks in both the gains and losses domains. We found two distinct intrinsic valuation systems underlying temporal discounting in the gains and losses domains: gains were specifically evaluated in the medial regions, including the medial prefrontal and orbitofrontal cortices, and losses were evaluated in the lateral dorsolateral prefrontal cortex. In addition, immediate reward or punishment was found to modulate the functional interactions between the dorsolateral prefrontal cortex and distinct regions in both the gains and losses domains: in the gains domain, the mesolimbic regions; in the losses domain, the medial prefrontal cortex, anterior cingulate cortex, and insula. These findings suggest that intertemporal choice of gains and losses might involve distinct valuation systems, and more importantly, separate neural interactions may implement the intertemporal choices of gains and losses. These findings may provide a new biological perspective for understanding the neural mechanisms underlying intertemporal choice of gains and losses.

Messages from the Brain Connectivity Regarding Neural Correlates of Consciousness

Consciousness has become a legitimate theme of neuroscientific discourse over the last two decades. Neuroscientific investigation seeking neural correlates of consciousness (NCC) has ranged from the neuronal level to the system level. Regarding system level studies, there is a large body of evidence supporting the idea that functional connectivity studies can help in examining NCC. Functional connectivity studies have suggested the involvement of the thalamo-cortical, frontoparietal, and other cortico-cortical connectivity under anesthetic-induced unconsciousness and in disorders of consciousness. Likewise, effective connectivity has been used to investigate the causal interactions among elements of functional connectivity in various consciousness states, and provided a deeper understanding of NCC. Moreover, as an extended version of connectivity studies, complex network methods have also been used for studies on NCC. In this review, we focused on the aspect of the brain system level of NCC including functional and effective connectivity networks from methodological perspectives. In addition, as for states of consciousness, anesthetic-induced unconsciousness and disorders of consciousness are the main subjects. This review discusses what we have learned from recent studies about the exploration of human brain connectivity on consciousness and its neural correlates.

Progress in Functional Connectivity Analysis / 生物化学与生物物理进展

Conventional neuroimaging methods primarily focus on functional localization, through which specific cognitive functions are localized to specific brain regions. However, fully understanding the human brain function requires characterization of functional integration within and among the functionally specialized regions in addition to functional localization. Functional connectivity and effective connectivity analyses have been developed to investigate functional integration in human brain. Several approaches for modeling functional connectivity and effective connectivity, including the time-series correlation, psychophysiological interaction (PPI), structural equation modeling (SEM), dynamic casual modeling (DCM), and diffusion tensor imaging (DTI) are reviewed. The applications of functional connectivity analysis to the studies of object representation, motor coordinate, language, and autism are demonstrated. Functional connectivity study will highly enrich our knowledge about the dynamic integration in the human brain.