Cologne Theoretical Neuroscience Forum

This session is a double feature of the Cologne Theoretical Neuroscience Forum and the Institute of Neuroscience and Medicine (INM-6) Computational and Systems Neuroscience of the Jülich Research Center.

It is widely known that motor learning leads to reorganization changes in the motor cortex. Recently, we have shown that using navigated transcranial magnetic stimulation (TMS) allows us to reliably trace interactions among motor cortical representations (MCRs) of different upper limb muscles. Using this approach, we investigate changes in the MCRs after fine finger movement training. Our preliminary results demonstrated that areas of the APB and ADM and their overlaps tended to increase after finger independence training. Considering the behavioral data, hand dexterity increased for both hands, but the amplitudes of voluntary contraction of the muscles for the APB and ADM did not change significantly. The behavioral results correspond with a previously described suggestion that hand strength and hand dexterity are not directly related as well as an increase in overlaps between MCRs of the trained muscles supports the idea that voluntary muscle relaxation is an active physiological process.

While non-invasive Brain-to-Computer interface can accurately classify the lateralization of hand moments, the distinction of fingers activation in the same hand is limited by their local and overlapping representation in the motor cortex. In particular, the low signal-to-noise ratio restrains the opportunity to identify meaningful patterns in a supervised fashion. Here we combined Magnetoencephalography (MEG) recordings with advanced decoding strategy to classify finger movements at single trial level. We recorded eight subjects performing a serial reaction time task, where they pressed four buttons with left and right index and middle fingers. We evaluated the classification performance of hand and finger movements with increasingly complex approaches: supervised common spatial patterns and logistic regression (CSP + LR) and unsupervised linear finite convolutional neural network (LF-CNN). The right vs left fingers classification performance was accurate above 90% for all methods. However, the classification of the single finger provided the following accuracy: CSP+SVM : – 68 ± 7%, LF-CNN : 71 ± 10%. CNN methods allowed the inspection of spatial and spectral patterns, which reflected activity in the motor cortex in the theta and alpha ranges. Thus, we have shown that the use of CNN in decoding MEG single trials with low signal to noise ratio is a promising approach that, in turn, could be extended to a manifold of problems in clinical and cognitive neuroscience.

Empathy is supposed to play a functional role in prosocial behavior. However, there has been behavioral evidence that people do not empathize everyone equally. I’ll present studies that show brain imaging evidence for racial ingroup bias in empathy for pain. These studies reveal multiple-level neural mechanisms underlying racial ingroup bias in empathy. I’ll also discuss potential intervention of racial ingroup bias in empathy and its social implications.

We consider new measures of centrality in networks which take into account parameters of nodes and group influence of nodes to nodes. Several examples are discussed.

How the brain stores a sequence in memory remains largely unknown. We investigated the neural code underlying sequence working memory using two-photon calcium imaging to record thousands of neurons in the prefrontal cortex of macaque monkeys memorizing and then reproducing a sequence of locations after a delay. We discovered a regular geometrical organization: The high-dimensional neural state space during the delay could be decomposed into a sum of low-dimensional subspaces, each storing the spatial location at a given ordinal rank, which could be generalized to novel sequences and explain monkey behavior. The rank subspaces were distributed across large overlapping neural groups, and the integration of ordinal and spatial information occurred at the collective level rather than within single neurons. Thus, a simple representational geometry underlies sequence working memory.

It has long been established that the hippocampus plays a crucial role for episodic memory. As opposed to the modular approach, now it is generally assumed that being a complex structure, the HC performs multiplex interconnected functions, whose hierarchical organization provides basis for the higher cognitive functions such as semantics-based encoding and retrieval. However, the «where, when and how» properties of distinct memory aspects within and outside the HC are still under debate. Here we used a visual associative memory task as a probe to test the hypothesis about the differential involvement of the rostral and caudal portions of the human hippocampus in memory encoding, recognition and associative recall. In epilepsy patients implanted with stereo-EEG, we show that at retrieval the rostral HC is selectively active for recognition memory, whereas the caudal HC is selectively active for the associative memory. Low frequency desynchronization and high frequency synchronization characterize the temporal dynamic in encoding and retrieval. Therefore, we describe here anatomical segregation in the hippocampal contributions to associative and recognition memory.

The study of mirror neurons (MN) has a long way since its discovery on monkeys and later on humans. However, in literature there are inconsistencies on the ways stimuli are presented and on the time of presentation. Which is the best way to present motor movement stimuli? Is it possible to estimate when the mirror neurons effect take place by using Transcranial Magnetic Stimulation at specific time windows? In the current study we test different ways of stimuli presentation (photo and video of hand movements) and brain stimulation (e.g. TMS) delivered on the dominant primary motor cortex (M1) at different time windows. Our aim is to solve this void still present on the field and create a standardized protocol that will generate the strongest mirror neurons response in order to have the way for future studies on the field.

Existing studies show that there is a positive association between pro-migrant integration policies and the subjective well-being of immigrants. However, there is a lack of research elucidating the relations between migrant integration policies and the subjective well-being of the host (i.e., non-migrant) population. This study is based on European data and uses multilevel analysis to clarify the relations between migrant integration policy (both as a whole and its 8 separate components such as: Labour market mobility and Family reunion) and the subjective well-being of the non-immigrant population in European countries. We examined relations between the Migrant Integration Policy Index (MIPEX) for 22 countries in Europe and subjective well-being, as assessed by the European Social Survey (ESS) data. The results demonstrated that there is a positive relation between the MIPEX and subjective well-being for non-immigrants. Considering different components of the MIPEX separately, we found most of them being positively related to the subjective well-being of non-immigrants. As no negative relationship was identified between any of the eight MIPEX components and subjective well-being, policies in favour of immigrant integration also seem to benefit the non-immigrant population.

The application of neuroimaging methods to marketing has recently gained lots of attention. In analyzing consumer behaviors, the inclusion of neuroimaging tools and methods is improving our understanding of consumer’s preferences. Human emotions play a significant role in decision making and critical thinking. Emotion classification using EEG data and machine learning techniques has been on the rise in the recent past. We evaluate different feature extraction techniques, feature selection techniques and propose the optimal set of features and electrodes for emotion recognition.Affective neuroscience research can help in detecting emotions when a consumer responds to an advertisement. Successful emotional elicitation is a verification of the effectiveness of an advertisement. EEG provides a cost effective alternative to measure advertisement effectiveness while eliminating several drawbacks of the existing market research tools which depend on self-reporting. We used Graph theoretical principles to differentiate brain connectivity graphs when a consumer likes a logo versus a consumer disliking a logo. The fusion of EEG and sentiment analysis can be a real game changer and this combination has the power and potential to provide innovative tools for market research.

Humans and other animals make decisions in order to satisfy their goals. However, it remains unknown how neural circuits compute which of multiple possible goals should be pursued (e.g., when balancing hunger and thirst) and how to combine these signals with estimates of available reward alternatives. Here, humans undergoing fMRI accumulated two distinct assets over a sequence of trials. Financial outcomes depended on the minimum cumulate of either asset, creating a need to maintain “value equilibrium” by redressing any imbalance among the assets. Blood-oxygen-level-dependent (BOLD) signals in the rostral anterior cingulate cortex (rACC) tracked the level of imbalance among goals, whereas the ventromedial prefrontal cortex (vmPFC) signaled the level of redress incurred by a choice rather than the overall amount received. These results suggest that a network of medial frontal brain regions compute a value signal that maintains value equilibrium among internal goals.

International organizations, governments and companies are increasingly committed to developing measures that encourage adoption of sustainable consumption patterns among the population. However, their success requires a deep understanding of the everyday purchasing decision process and the elements that shape it. Price is an element that stands out. Prior research concluded that the influence of price on purchase decisions varies across consumer profiles. Yet no consumer behavior study to date has assessed the differences of price processing among consumers adopting sustainable habits (prosocial) as opposed to those who have not (non-prosocial). This is the first study to resort to neuroimaging tools to explore the underlying neural mechanisms that reveal the effect of price on prosocial and non-prosocial consumers. Self-reported findings indicate that prosocial consumers place greater value on collective costs and benefits while non-prosocial consumers place a greater weight on price. The neural data gleaned from this analysis offers certain explanations as to the origin of the differences. Non-prosocial (vs. prosocial) consumers, in fact, exhibit a greater activation in brain areas involved with reward, valuation and choice when evaluating price information. These findings could steer managers to improve market segmentation and assist institutions in their design of campaigns fostering environmentally sustainable behaviors

Recent economics literature has seen a revival of interest to psychologically-grounded theories of decision under risk. We review the recent proposals in this direction, compare it to classical estimations based on utility functions, and discuss their appropriateness using some original experimental data.

Menstruation is a normal physiological process in women occurring as a result of changes in two ovarian produced hormones – estrogen and progesterone. As a result of these fluctuations, women experience different symptoms in their bodies – their immune system changes (Sekigawa et al, 2004), there are changes in their cardiovascular and digestive system (Millikan, 2006), as well as skin (Hall and Phillips, 2005). But these hormone fluctuations produce major changes in their behavioral pattern as well causing: anxiety, sadness, heightened irritability and anger (Severino and Moline, 1995) which is usually classified as premenstrual syndrome (PMS). In some cases these symptoms severely impair women’s lives and professional help is required. The official diagnosis according to DSM-5 (2013) is premenstrual dysphoric disorder (PMDD). Despite its ubiquitous presence the origins of PMS and PMDD are poorly understood. Some efforts to understand the underlying brain state during the menstruation cycle were performed by using TMS (Smith et al, 1999; 2002; 2003; Inghilleri et al, 2004; Hausmann et al, 2006). But all of these experiments suffer from major shortcomings - no control groups and small number of subjects. Our plan is to address all of these shortcomings and make this the biggest (to our knowledge) experiment of its kind which will, hopefully, provide us with some much needed answers.

The question of how syntactic structure can be built at the neural level has come to the forefront of cognitive neuroscience in the last decade. Neural oscillations have been widely recognised as playing an important role in building syntactic representations. In this talk I will review existing oscillatory approaches to syntactic structure building and assess their functionality in light of basic properties of a hierarchical syntactic structure, such as varied length of syntactic phrases, nesting of constituents, overlap in length between different levels of the syntactic hierarchy and others. I will also briefly discuss key requirements on neural structure building mechanisms from the perspective of a real-time parser.