Intracranial stereoelectroencephalography (SEEG) has the unprecedented sensitivity and specificity in recording human neurophysiology. However, analyzing SEEG data across patients is challenging when pooling results into a common atlas due to disparate electrode implantation across individuals. To mitigate this challenge, we propose the distributed source modeling of SEEG based on the individual’s brain anatomy. We demonstrate how this method is used to estimate the spatial distribution of intracranial event-related potential sources and high broadband gamma activity, a putative correlate of local neural firing. We then extend SEEG distributed source modeling to estimate neural activity across cortical depths, thereby disentangling feedforward and feedback information processing in the sensory cortices. Using auditory stimuli, we found that neural current estimates were stronger in the deep and superficial depths before and after 500 ms since stimulus onset, respectively. Neural current estimates in the auditory cortex under cross-modal audiovisual stimulation were less variable across cortical depths at early (150 ms) and intermediate (250 ms) peaks than under the unimodal audio stimulation, while variability across cortical depths was larger after 500 ms.  Together, these results demonstrate the potential of SEEG for achieving high spatiotemporal-resolution imaging of neural activity, complementing other imaging modalities. 

     The brain must often make decisions in tasks -- like mazes, social situations, or investment -- where candidate actions are separated from their consequences by many steps of space and time. A central computational problem in decision making is spanning these gaps to work out the long-term consequences of candidate actions. I review recent experimental and theoretical work aimed at understanding the mechanisms by which the brain solves this problem. Our understanding of this parallels the development of approaches to this problem in artificial intelligence: following early enthusiasm about planning by exhaustive search, both computer scientists and neuroscientists have come to understand the importance of judiciously pretraining and adapting one's computations to future needs. This offers a nerw perspective on a range of issues such as habits and automaticity in the healthy brain, but also suggests candidate mechanisms that may underlie dysfunctions such as compulsion, rumination, and avoidance.