# Brain Dynamics on Multiple Scales - Paradigms, their Relations, and Integrated Approaches

For each poster contribution there will be one poster wall (width: 97 cm, height: 250 cm) available. Please do not feel obliged to fill the whole space. Posters can be put up for the full duration of the event.

### Seeing faces in noises: Predicting perceptual decision by prestimulus brain oscillations

Bhattacharya, Joydeep

The perception of an external stimulus is not just stimulus-dependent but is also influenced by the ongoing brain activity prior to the presentation of stimulus. In this work, we directly tested whether spontaneous electrical brain activities in prestimulus period could predict perceptual outcome in face pareidolia, seeing faces in white noise images, on a trial-by-trial basis. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images while their EEG signals were recorded. Participants reported their perceptual decision, face or no-face, on each trial. Using features based on large scale neural oscillations in a machine learning classifier, we demonstrated that prestimulus brain oscillations could achieve a 74% classification accuracy. The time-frequency features representing hemispheric asymmetry yielded the best classification performance and prestimulus alpha oscillations were found to be most crucially involved in predicting perceptual decision. These findings suggest a mechanism of how prior expectation in the prestimulus period may shape post-stimulus decision making.

### An integrate-and-fire network model for grid cell dynamics

Bonilla Quintana, Mayte

In 2005, Hafting et al. at the Moser Lab, discovered grid cells in the Medial Entorhinal Cortex (MEC). These cells fire at multiple locations while an animal is wondering through an environment defining a periodic triangular array that covers the entire surface, hence the name. Furthermore, grid cells fire at the same position regardless of changes in the animal's speed and direction, and firing persists in the absence of visual input. It is therefore believed to correspond to the animal's own sense of location. For the discovery of these type of cells May-Britt and Edvard Moser won the 2014 Nobel Prize in Physiology or Medicine. Since the discovery of grid cells many models have been developed in order to address the mechanism of grid cell firing. However only few models link the firing of grid cells to data on intracellular resonance and rebound spiking in layer II stellate cells of the MEC, that represent the 70\% of the total MEC II neural population and therefore a large fraction of the grid cell population. We propose a neural field integrate and fire model with a hyperpolarisation activated cation current (h-current). The model is motivated by previous ones in which wave generation in spiking neural networks is hypothesised to underly the formation of grid cell firing fields but within a framework that allows for analytical tractability. Furthermore, inspired by relevant MEC data we consider only inhibitory neural connectivity. Simulations of our model show sustained rebound spiking that is propagated across the network after injecting a initial hyperpolarising current to a small fraction of the neurons. Our aim is to show that a difference in the h-currrent time constant seen experimentally along the dorsal to ventral axis of the MEC can produce a difference in the size and spacing between the grid cell firing fields. In order to achieve this, we first perform a piece-wise linear reduction of our model that preserves its dynamics. Such a reduction allows us to obtain a self-consistent solution for a periodic travelling wave. We developed a wave stability analysis using theory of nonsmooth systems and observed a strong dependence of the period on the h-current time constant.

### Simulating large-scale human brain networks with a mean-field model of EIF neurons: exploring resting state FC and stimulation with electric fields

Cakan, Caglar

The use of whole-brain networks for understanding the dynamics of the interaction between brain regions has experienced a rise in popularity in the last few years. Here, we calibrate a whole-brain network model to human resting state data, and use it to explore the effects of weak electric fields on the network dynamics. The structural connectivity of the brain network is extracted from parcellated brain scans using an atlas with 68 regions (Desikan et al., 2006) and DTI tractography of long-range axons to estimate coupling strengths and delays between regions averaged over 48 individuals (Ritter et al. 2013, Schirner et al. 2015). The mean activity of each brain region is described by a mean-field population model of EIF neurons (Ladenbauer, 2015). After fitting local parameters such as recurrent coupling strengths and delays and the global parameters coupling strength, axonal signal transmission speed and external noise intensity, our model can produce simulated BOLD functional connectivity (FC) with high Pearson correlation (mean .55, max/min .78/.25) to the empirical 20 minute resting state BOLD FC of these individuals. A local model with a limit cycle at gamma frequencies and a bistability with a low and a high-activity fixed point was found to produce good fits. A range of global parameters can produce good grand average FC fits. However, the FC in the resting state is not stationary. To capture the brain's dynamical properties in the resting state, the FC fit is complemented by a fit of the FCD matrix (Hansen et al, 2015). We show that the simulated FCD matrix is well comparable to empirical data (Kolmogorov distance around 0.1) on several timescales. Clustering of the power spectra of the local nodes shows that nodes of the brain graph can be divided into two sets with dominant alpha and gamma frequencies respectively and that contralateral regions end up in the same cluster. Lastly, we present results of modeling the effect of external tACS-like brain stimulation on the global network activity. By modifying the dynamics of a subset of nodes, the global dynamics of the brain network can be shaped. We stimulate the bilateral entorhinal cortices, the main interface of the cortex to Hippocampus. We show that on the global network level, transitions from a DOWN state to an UP state tend to lock on the onset of oscillatory stimulation and relate these results to experimental findings in the rat brain conducted in Ref. (Battaglia et al, 2004). --- References Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., … Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968–80. http://doi.org/10.1016/j.neuroimage.2006.01.021 Ritter, P., Schirner, M., McIntosh, A. R., & Jirsa, V. K. (2013). The Virtual Brain Integrates Computational Modeling and Multimodal Neuroimaging. Brain Connectivity, 3(2), 121–145. http://doi.org/10.1089/brain.2012.0120 Schirner, M., Rothmeier, S., Jirsa, V. K., McIntosh, A. R., & Ritter, P. (2015). An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data. NeuroImage, 117, 343–57. http://doi.org/10.1016/j.neuroimage.2015.03.055 Ladenbauer, J. (2015). The Collective Dynamics of Adaptive Neurons: Insights from Single Cell and Network Models. PhD Thesis. http://dx.doi.org/10.14279/depositonce-4791 Hansen, E. C. A., Battaglia, D., Spiegler, A., Deco, G., & Jirsa, V. K. (2015). Functional connectivity dynamics: Modeling the switching behavior of the resting state. Neuroimage, 105, 525–535. http://www.sciencedirect.com/science/article/pii/S1053811914009033 Battaglia, F. P., Sutherland, G. R., & McNaughton, B. L. (2004). Hippocampal sharp wave bursts coincide with neocortical “up-state” transitions. Learning & Memory (Cold Spring Harbor, N.Y.), 11(6), 697–704. http://doi.org/10.1101/lm.73504

### Reconstructing networks of pulse-coupled oscillators from non-invasive observations

Cestnik, Rok

We present a method for reconstructing a network of pulse-coupled oscillators from non-invasive observations of the system's output. Assuming that the pulse trains of all nodes are known and that the coupling between the elements is sufficiently weak to justify the phase dynamics description, we recover the connectivity of the network and properties of the nodes. Our basic model for the network nodes are phase oscillators which issue a spike when their phase $\varphi$ reaches $2\pi$. (We consider the phases in the $[0, 2\pi)$ interval, i.e., after the spike generation the phase of the unit is reset to zero). This spike affects all other units of the network according to the strength of the corresponding connections. Let the size of the network be $N$ and let the connectivity be described by an $N \times N$ coupling matrix ${\cal E}$, whose elements $\epsilon_{km}$ quantify the strength of the coupling from unit $m$ to unit $k$. Between the spiking events, phases of all units obey $\dot{\varphi_k} = \omega_k$, where $\omega_k$ are natural frequencies. If unit $k$ receives a spike from oscillator $m$, then it reacts to the stimulus according to it’s phase response curve (PRC), $Z_k(\varphi)$. This means that the phase of the stimulated unit is instantaneously reset, $\varphi_k \rightarrow \varphi_k + \epsilon_{km} Z_k(\varphi_k)$. Our approach is based on making a preliminary estimation of the network connectivity by evaluating the impact an oscillator has on another oscillator's inter-spike intervals. This estimation, although crude, gives us some insight into the network, such that together with linearly approximating the phases and representing the PRC as a finite size Fourier series, we can get an approximation for the PRC. Once both approximations (connectivity and PRC) are obtained, they can be used to better approximate phases, which in turn yields better approximations of connectivity and PRC in the next iteration of the process. The more iterations one does, the better the recovery.

### Influence of Inherent Prior Values in Decision-Making

Chien, Samson

Reinforcement learning (RL) has become the predominant model for predicting a subject’s decision choice based on the expected reward value (EV) of each cue, which is continuously adjusted during learning in proportion to a reward prediction error (PE). The common experimental setup utilizes value-neutral cues (e.g., fractal images) to purely study the emergence of EVs. However, most environmental cues are not value-neutral but exhibit certain inherent values. Here we investigate how these inherent values affect the learning of new (reward-based) EVs. One possible mechanism is that inherent values differentially affect learning rates such that congruent cue-outcome associations, in which the inherent values and the EVs are similar, are learned more quickly (i.e., with a higher learning rate) than incongruent pairings. We tested the hypothesis in a 2x2 factorial design, using facial attractiveness (high/low) of a visual cue as a proxy for inherent value and reward probability (0.7/0.3) as a target for newly learned EVs. Subjects were shown both attractive and unattractive face pictures of the opposite gender. Each picture was paired with a positive or negative monetary reward either congruently or incongruently. Subjects were instructed to select the pictures with the goal of maximizing the overall monetary reward. Computational RL models were fitted to the behavioral data to derive cue-specific learning rates. Concurrent fMRI data were correlated with these learning rates, EVs, and PEs. The behavioral results indicated both a faster response time and a faster learning rate for the congruent cue-outcome pairings. The model-based fMRI data analysis revealed well-established brain regions involved in decision making, such as the ventromedial prefrontal cortex for EVs and the ventral striatum for PEs. In addition, we identified a formerly unreported correlation between the cue-specific learning rates and the BOLD activity in a sub-region of the ventral striatum distinct from those representing the PEs and rewards. Our result complemented earlier findings and further established the roles of the ventral striatum in decision-making.

### Chimera states in hierarchical networks of FitzHugh-Nagumo oscillators and their role in epileptic seizures

Chouzouris, Teresa

Authors: Teresa Chouzouris, Iryna Omelchenko, Anna Zakharova, Eckehard Schöll, Institut für Theoretische Physik, Technische Universität Berlin, Germany The collective behavior in networks of oscillators is of great current interest. Besides various zero-lag, cluster, or group synchronization patterns and oscillation death, special attention has recently been paid to chimera states where incoherent and coherent oscillations occur in spatially coexisting domains. Surprisingly, this symmetry-breaking behavior exists for identical elements and symmetric coupling configurations. One important application of chimera states in nature is the study of neural networks. Synchronization and desynchronization of neural activity is essential for explaining brain disorders, such as epileptic seizures and Parkinson disease. During an epileptic seizure the electrical activity in the brain is excessive or synchronous, and studying chimera states can give further insight in the underlying mechanisms of the generation and death of epileptic seizures. Recent studies on the architecture of the neuron interconnectivity of the human and mammalian brain have shown that the connectivity of the neuron axons network represents a hierarchical structure. Furthermore, the existence of chimera states in hierarchically coupled systems has been recently discovered [1,2]. A systematical analysis and comparison of the transition from asynchronous behaviour to synchrony via chimera states in structural neural networks derived from diffusion magnetic resonance imaging and in hierarchical networks is made. For this, the paradigmatic model of a FitzHugh Nagumo oscillator, describing the activation and inhibition dynamics of a spiking neuron is used. The parameter values shifting the network closer to the synchronous epileptic state are investigated and simulations of epileptic seizures are presented. References: [1] Omelchenko I., Provata, A., Hizanidis, J., Schöll, E. and Hövel, P., Robustness of chimera states for coupled FitzHugh-Nagumo oscillators Phys. Rev. E 91, 022917 (2015) [2] Krishnagopal, S., Lehnert, J., Poel, W., Zakharova, A. and Schöll, E. , Synchronization Patterns: From Network Motifs to Hierarchical Networks, Phil. Trans. R. Soc. A 375, 20160216 (2017)

### Collective oscillations and neuronal avalanches in a network of noisy excitatory and inhibitory neurons

Dalla Porta Dornelles, Leonardo

### Thermodynamics, signatures of criticalityin a network of neurons

Tkacik, Gasper

The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. Based on this observation, we define a new class of probabilistic models that can be tractably inferred from data, and are designed to properly capture code ensembles near the critical point. We show that our model is a learnable generalization of a recently proposed toy mechanism involving a fluctuating latent field, which produces zipfian distributions of responses without fine tuning.

### Semantic grounding in a neurocomputational-constrained model including spiking neurons and realistic connectivity

Tomasello, Rosario

Previous neurocomputational work has addressed the question why and how many cortical areas contribute to semantic processing and, specifically, why semantic hubs involved in all types of semantics contrast with category-specific areas preferentially processing certain meaning subtypes. However, much of the pre-existing work used either basic neuron models or much simplified connectivity so that a more sophisticated and biologically-realistic model would be desirable. Here, we applied a neural-network model replicating anatomical and physiological features of a range of cortical areas in the temporal-occipital and frontal lobes to simulate the learning of semantic relationships between word-forms and specific object perceptions and motor movements of the own body. The two neuronal architectures differed in the level of detail with which cortico-cortical connectivity was implemented. Furthermore, model (A) adopted a mean-field approach by using graded-response neurons, whereas model (B) implemented leaky integrate-and-fire neurons. Equipped with correlation-based learning rules and under the impact of repeated sensorimotor pattern presentations, both models showed spontaneous emergence of specific tightly interlinked cell assemblies within the larger networks, interlinking the processing of word-form information to that of sensorimotor semantic information. Both models also showed category-specificity in the cortical distribution of word-related circuits, with high-degree connection hub areas central to the network architecture exhibiting involvement in all types of semantic processing and only moderate category-specificity (see Figure 1). The present simulations account for the emergence of both category-specific and general-semantic hub areas in the human brain and show that realistic neurocomputational models at different levels of detail consistently provide such explanation.

### Cold to the core: altered energy metabolism is associated with inattention, anxiety and aggression

van Heukelum, Sabrina

Aims: Antisocial behavior and aggression in childhood and adolescence, particularly seen in conduct disorder (CD) and attention-deficit hyperactivity disorder (ADHD), represents an increasing socioeconomic burden due to the persistent and repeated nature of offences. Animal models may provide additional insights into the neurogenesis of these traits. Methods: In this study the BALB/cJ mouse was extensively phenotyped versus the BALB/cByJ mouse (control) and physical factors were measured by telemetry. In addition, the neurometabolic status was assessed using single-voxel 1H-magnetic resonance spectroscopy. Results: Using the resident-intruder task, increased pathological aggression translational to aggression observed in CD, was found in BALB/cJ mice. This was positively correlated with their level of anxiety as measured in the open field test. A global attention deficit was found in the BALB/cJ mice as measured by an increased number of omissions in the 5-choice serial reaction time task. Furthermore, telemetric measurements demonstrate that BALB/cJ mice are hyperactive in the dark phase of the light/dark cycle with a lower basal body temperature, indicating a difference in energy metabolism. This finding was confirmed by an increase in cytochrome c oxidase, the terminal oxidase of the mitochondrial respiratory pathway. This may be related to decreased taurine and GABA concentrations that were found in the anterior cingulate cortex (ACC). In addition, follow-up studies indicated anatomical differences in the volume of the ACC between BALB/cJ and BALB/cByJ mice. Conclusions: These data indicate that pathological aggression observed in the BALB/cJ mice may be related to metabolic changes that decrease attention and increase anxiety such that BALB/cJ mice express heightened stress reactivity resulting in inappropriate behavior such as aggression.

### The Long-Term Effects of an Induced ´Experiential Awareness´ Versus a ´Cognitive Reappraisal´ in the Processing of Bottom Up Generated Emotions: Revealed by Heart-Brain Coupling

Wang, Yulin

Over recent years, the interest in emotion regulation research has grown, with a special interest in cognitive reappraisal. Cognitive reappraisal is assumed to be one of the most adaptive emotion regulation strategies. However, the effectiveness of cognitive reappraisal may not be always guaranteed, considering that emotionally arousing stimuli are bottom-up driven while the reappraisal works as a top-down emotion regulation strategy. In contrast, experiential awareness, an emotion regulation strategy originated from experiential psychotherapy, has recently captured some research attention as an effective bottom-up strategy. Although abundant research has focused on top-down cognitive reappraisal, research is needed to validate the effectiveness of experiential awareness in a long run on a behavioral and neuroimaging level. Therefore, our research seeks out to compare the long-term effects of experiential awareness in the processing of bottom-up generated emotions compared to cognitive reappraisal by exploring the neural correspondence of these heart rate alterations. 30 participants will undergo 3T-fMRI scanning. Simultaneously, high frequency heart rate variability will be acquired during the whole experiment. We are going to adopt a within-subject design for the fMRI experiment. All the subjects will have the instruction” Kijk” (watch) or “Doorleef” (experiential awareness),” Herinterpreteer” (reappraise). In total, we have two sessions with each session consists of 4 blocks: ´watch negative´, ´watch neutral´, ´reappraise negative´, ´experiential awareness negative´. We are now on the phase of collecting our data. Heart-Brain coupling methods will be applied in our data analysis to reveal the long-term effects of emotion regulation. Keywords: Experiential Awareness; Reappraisal; Emotion Regulation; HRV; Long Term