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

Self-Locking Optoelectronic Tweezers for Single-Cell and Microparticle Manipulation across a Large Area in High Conductivity Media

Optoelectronic tweezers (OET) has advanced within the past decade to become a promising tool for cell and microparticle manipulation. Its incompatibility with high conductivity media and limited throughput remain two major technical challenges. Here a novel manipulation concept and corresponding platform called Self-Locking Optoelectronic Tweezers (SLOT) are proposed and demonstrated to tackle these challenges concurrently. The SLOT platform comprises a periodic array of optically tunable phototransistor traps above which randomly dispersed single cells and microparticles are self-aligned to and retained without light illumination. Light beam illumination on a phototransistor turns off the trap and releases the trapped cell, which is then transported downstream via a background flow. The cell trapping and releasing functions in SLOT are decoupled, which is a unique feature that enables SLOT’s stepper-mode function to overcome the small field-of-view issue that all prior OET technologies encountered in manipulation with single-cell resolution across a large area. Massively parallel trapping of more than 100,000 microparticles has been demonstrated in high conductivity media. Even larger scale trapping and manipulation can be achieved by linearly scaling up the number of phototransistors and device area. Cells after manipulation on the SLOT platform maintain high cell viability and normal multi-day divisibility

Light-activated cell identification and sorting (LACIS) for selection of edited clones on a nanofluidic device

Annamaria Mocciaro et al. present LACIS, a method for identifying and selecting gene-edited clones using a microfluidic device. The authors apply LACIS to primary T cells after CRISPR-Cas9 editing of CXCR4 and show that selection of edited clones was possible within 10 days from initiation of gene editing

Manipulating and assembling metallic beads with Optoelectronic Tweezers

Optoelectronic tweezers (OET) or light-patterned dielectrophoresis (DEP) has been developed as a micromanipulation technology for controlling micro- and nano-particles with applications such as cell sorting and studying cell communications. Additionally, the capability of moving small objects accurately and assembling them into arbitrary 2D patterns also makes OET an attractive technology for microfabrication applications. In this work, we demonstrated the use of OET to manipulate conductive silver-coated Poly(methyl methacrylate) (PMMA) microspheres (50 μm diameter) into tailored patterns. It was found that the microspheres could be moved at a max velocity of 3200 μm/s, corresponding to 4.2 nano-newton (10−9 N) DEP force, and also could be positioned with high accuracy via this DEP force. The underlying mechanism for this strong DEP force is shown by our simulations to be caused by a significant increase of the electric field close to the particles, due to the interaction between the field and the silver shells coating the microspheres. The associated increase in electrical gradient causes DEP forces that are much stronger than any previously reported for an OET device, which facilitates manipulation of the metallic microspheres efficiently without compromise in positioning accuracy and is important for applications on electronic component assembling and circuit construction