Predicting Decisions in Human Social Interactions Using Real-Time fMRI and Pattern Classification

Negotiation and trade typically require a mutual interaction while simultaneously resting in uncertainty which decision the partner ultimately will make at the end of the process. Assessing already during the negotiation in which direction one's counterpart tends would provide a tremendous advantage. Recently, neuroimaging techniques combined with multivariate pattern classification of the acquired data have made it possible to discriminate subjective states of mind on the basis of their neuronal activation signature. However, to enable an online-assessment of the participant's mind state both approaches need to be extended to a real-time technique. By combining real-time functional magnetic resonance imaging (fMRI) and online pattern classification techniques, we show that it is possible to predict human behavior during social interaction before the interacting partner communicates a specific decision. Average accuracy reached approximately 70% when we predicted online the decisions of volunteers playing the ultimatum game, a well-known paradigm in economic game theory. Our results demonstrate the successful online analysis of complex emotional and cognitive states using real-time fMRI, which will enable a major breakthrough for social fMRI by providing information about mental states of partners already during the mutual interaction. Interestingly, an additional whole brain classification across subjects confirmed the online results: anterior insula, ventral striatum, and lateral orbitofrontal cortex, known to act in emotional self-regulation and reward processing for adjustment of behavior, appeared to be strong determinants of later overt behavior in the ultimatum game. Using whole brain classification we were also able to discriminate between brain processes related to subjective emotional and motivational states and brain processes related to the evaluation of objective financial incentives

The environmental impacts of non-food biomass production through land-use changes: Scope, foci and methodology of current research

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Natively glycosylated HIV-1 Env structure reveals new mode for antibody recognition of the CD4-binding site

HIV-1 vaccine design is informed by structural studies elucidating mechanisms by which broadly neutralizing antibodies (bNAbs) recognize and/or accommodate N-glycans on the trimeric envelope glycoprotein (Env). Variability in high-mannose and complex-type Env glycoforms leads to heterogeneity that usually precludes visualization of the native glycan shield. We present 3.5-angstrom- and 3.9-angstrom-resolution crystal structures of the HIV-1 Env trimer with fully processed and native glycosylation, revealing a glycan shield of high-mannose and complex-type N-glycans, which we used to define complete epitopes of two bNAbs. Env trimer was complexed with 10-1074 (against the V3-loop) and IOMA, a new CD4-binding site (CD4bs) antibody. Although IOMA derives from VH1-2*02, the germline gene of CD4bs-targeting VRC01-class bNAbs, its light chain lacks the short CDRL3 that defines VRC01-class bNAbs. Thus IOMA resembles 8ANC131-class/VH1-46 derived CD4bs bNAbs, which have normal-length CDRL3s. The existence of bNAbs that combine features of VRC01-class and 8ANC131-class antibodies has implications for immunization strategies targeting VRC01-like bNAbs