Maurice Hollmann

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 ones 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 participants 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.

In vivo magnetic resonance elastography of human brain at 7 T and 1.5 T

To investigate the feasibility of quantitative in vivo ultrahigh field magnetic resonance elastography (MRE) of the human brain in a broad range of low‐frequency mechanical vibrations.

Brain regulation of food craving: Relationships with weight status and eating behavior

Objectives:Food craving is a driving force for overeating and obesity. However, the relationship between brain mechanisms involved in its regulation and weight status is still an open issue. Gaps in the studied body mass index (BMI) distributions and focusing on linear analyses might have contributed to this lack of knowledge. Here, we investigated brain mechanisms of craving regulation using functional magnetic resonance imaging in a balanced sample including normal-weight, overweight and obese participants. We investigated associations between characteristics of obesity, eating behavior and regulatory brain function focusing on nonlinear relationships.Subjects/Methods:Forty-three hungry female volunteers (BMI: 19.4–38.8 kg m−2, mean: 27.5±5.3 s.d.) were presented with visual food stimuli individually pre-rated according to tastiness and healthiness. The participants were instructed to either admit to the upcoming craving or regulate it. We analyzed the relationships between regulatory brain activity as well as functional connectivity and BMI or eating behavior (Three-Factor Eating Questionnaire, scales: Cognitive Restraint, Disinhibition).Results:During regulation, BMI correlated with brain activity in the left putamen, amygdala and insula in an inverted U-shaped manner. Functional connectivity between the putamen and the dorsolateral prefrontal cortex (dlPFC) correlated positively with BMI, whereas that of amygdala with pallidum and lingual gyrus was nonlinearly (U-shaped) associated with BMI. Disinhibition correlated negatively with the strength of functional connectivity between amygdala and dorsomedial prefrontal (dmPFC) cortex as well as caudate.Conclusions:This study is the first to reveal quadratic relationships of food-related brain processes and BMI. Reported nonlinear associations indicate inverse relationships between regulation-related motivational processing in the range of normal weight/overweight compared with the obese range. Connectivity analyses suggest that the need for top-down (dlPFC) adjustment of striatal value representations increases with BMI, whereas the interplay of self-monitoring (dmPFC) or eating-related strategic action planning (caudate) and salience processing (amygdala) might be hampered with high Disinhibition.

A new concept of a unified parameter management, experiment control, and data analysis in fMRI: Application to real-time fMRI at 3 T and 7 T

In functional MRI (fMRI) complex experiments and applications require increasingly complex parameter handling as the experimental setup usually consists of separated soft- and hardware systems. Advanced real-time applications such as neurofeedback-based training or brain computer interfaces (BCIs) may even require adaptive changes of the paradigms and experimental setup during the measurement. This would be facilitated by an automated management of the overall workflow and a control of the communication between all experimental components. We realized a concept based on an XML software framework called Experiment Description Language (EDL). All parameters relevant for real-time data acquisition, real-time fMRI (rtfMRI) statistical data analysis, stimulus presentation, and activation processing are stored in one central EDL file, and processed during the experiment. A usability study comparing the central EDL parameter management with traditional approaches showed an improvement of the complete experimental handling. Based on this concept, a feasibility study realizing a dynamic rtfMRI-based brain computer interface showed that the developed system in combination with EDL was able to reliably detect and evaluate activation patterns in real-time. The implementation of a centrally controlled communication between the subsystems involved in the rtfMRI experiments reduced potential inconsistencies, and will open new applications for adaptive BCIs.

Brain imaging in the context of food perception and eating.

Purpose of review Eating behavior depends heavily on brain function. In recent years, brain imaging has proved to be a powerful tool to elucidate brain function and brain structure in the context of eating. In this review, we summarize recent findings in the fast growing body of literature in the field and provide an overview of technical aspects as well as the basic brain mechanisms identified with imaging. Furthermore, we highlight findings linking neural processing of eating-related stimuli with obesity. Recent findings The consumption of food is based on a complex interplay between homeostatic and hedonic mechanisms. Several hormones influence brain activity to regulate food intake and interact with the brains reward circuitry, which is partly mediated by dopamine signaling. Additionally, it was shown that food stimuli trigger cognitive control mechanisms that incorporate internal goals into food choice. The brain mechanisms observed in this context are strongly influenced by genetic factors, sex and personality traits. Summary Overall, a complex picture arises from brain-imaging findings, because a multitude of factors influence human food choice. Although several key mechanisms have been identified, there is no comprehensive model that is able to explain the behavioral observations to date. Especially a careful characterization of patients according to genotypes and phenotypes could help to better understand the current and future findings in neuroimaging studies.

Flexible Adaptive Paradigms for fMRI Using a Novel Software Package ‘Brain Analysis in Real-Time’ (BART)

In this work we present a new open source software package offering a unified framework for the real-time adaptation of fMRI stimulation procedures. The software provides a straightforward setup and highly flexible approach to adapt fMRI paradigms while the experiment is running. The general framework comprises the inclusion of parameters from subject’s compliance, such as directing gaze to visually presented stimuli and physiological fluctuations, like blood pressure or pulse. Additionally, this approach yields possibilities to investigate complex scientific questions, for example the influence of EEG rhythms or fMRI signals results themselves. To prove the concept of this approach, we used our software in a usability example for an fMRI experiment where the presentation of emotional pictures was dependent on the subject’s gaze position. This can have a significant impact on the results. So far, if this is taken into account during fMRI data analysis, it is commonly done by the post-hoc removal of erroneous trials. Here, we propose an a priori adaptation of the paradigm during the experiment’s runtime. Our fMRI findings clearly show the benefits of an adapted paradigm in terms of statistical power and higher effect sizes in emotion-related brain regions. This can be of special interest for all experiments with low statistical power due to a limited number of subjects, a limited amount of time, costs or available data to analyze, as is the case with real-time fMRI.

Predicting Human Decisions in socioeconomic Interaction using Real-Time Functional Magnetic Resonance Imaging (rtfMRI)

A major field in cognitive neuroscience investigates neuronal correlates of human decision-making processes [1, 2]. Is it possible to predict a decision before it is actually revealed by the volunteer? In the presented manuscript we use a standard paradigm from economic behavioral research that proved emotional influences on human decision making: the Ultimatum Game (UG). In the UG, two players have the opportunity to split a sum of money. One player is deemed the proposer and the other, the responder. The proposer makes an offer as to how this money should be split between the two. The second player can either accept or reject this offer. If it is accepted, the money is split as proposed. If rejected, then neither player receives anything. In the presented study a real-time fMRI system was used to derive the brain activation of the responder. Using a Relevance-Vector-Machine classifier it was possible to predict if the responder will accept or reject an offer. The classification result was presented to the operator 1-2 seconds before the volunteer pressed a button to convey his decision. The classification accuracy reached about 70% averaged over six subjects.

Real-time classification of activated brain areas for fMRI-based human-brain-interfaces

Functional MR imaging (fMRI) enables to detect different activated brain areas according to the performed tasks. However, data are usually evaluated after the experiment, which prohibits intra-experiment optimization or more sophisticated applications such as biofeedback experiments. Using a human-brain-interface (HBI), subjects are able to communicate with external programs, e.g. to navigate through virtual scenes, or to experience and modify their own brain activation. These applications require the real-time analysis and classification of activated brain areas. Our paper presents first results of different strategies for real-time pattern analysis and classification realized within a flexible experiment control system that enables the volunteers to move through a 3D virtual scene in real-time using finger tapping tasks, and alternatively only thought-based tasks.

Intermittent compared to continuous real-time fMRI neurofeedback boosts control over amygdala activation

&NA; Real‐time fMRI neurofeedback is a feasible tool to learn the volitional regulation of brain activity. So far, most studies provide continuous feedback information that is presented upon every volume acquisition. Although this maximizes the temporal resolution of feedback information, it may be accompanied by some disadvantages. Participants can be distracted from the regulation task due to (1) the intrinsic delay of the hemodynamic response and associated feedback and (2) limited cognitive resources available to simultaneously evaluate feedback information and stay engaged with the task. Here, we systematically investigate differences between groups presented with different variants of feedback (continuous vs. intermittent) and a control group receiving no feedback on their ability to regulate amygdala activity using positive memories and feelings. In contrast to the feedback groups, no learning effect was observed in the group without any feedback presentation. The group receiving intermittent feedback exhibited better amygdala regulation performance when compared with the group receiving continuous feedback. Behavioural measurements show that these effects were reflected in differences in task engagement. Overall, we not only demonstrate that the presentation of feedback is a prerequisite to learn volitional control of amygdala activity but also that intermittent feedback is superior to continuous feedback presentation. HighlightsComparison of continuous vs. intermittent real‐time fMRI neurofeedback in amygdala.Feedback is necessary to learn volitional regulation of amygdala activity.Intermittent feedback presentation outperforms continuous feedback during training runs.Behavioral data show analogous differences in task engagement between conditions.Results are promising for connectivity‐based rt‐fMRI neurofeedback approaches.

Real-time fMRI-based activation analysis and stimulus control

The real-time analysis of brain activation using functional MRI data offers a wide range of new experiments such as investigating self-regulation or learning strategies. However, besides special data acquisition and real-time data analysing techniques such examination requires dynamic and adaptive stimulus paradigms and self-optimising MRI-sequences. This paper presents an approach that enables the unified handling of parameters influencing the different software systems involved in the acquisition and analysing process. By developing a custom-made Experiment Description Language (EDL) this concept is used for a fast and flexible software environment which treats aspects like extraction and analysis of activation as well as the modification of the stimulus presentation. We describe how extracted real-time activation is subsequently evaluated by comparing activation patterns to previous acquired templates representing activated regions of interest for different predefined conditions. According to those results the stimulus presentation is adapted. The results showed that the developed system in combination with EDL is able to reliably detect and evaluate activation patterns in real-time. With a processing time for data analysis of about one second the approach is only limited by the natural time course of the hemodynamic response function of the brain activation.