Die Rolle der Zielnähe und der investierten Anstrengung für den erwarteten Wert einer Handlung

In human neuroscientific research, there has been an increasing interest in how the brain computes the value of an anticipated outcome. However, evidence is still missing about which valuation related brain regions are modulated by the proximity to an expected goal and the previously invested effort to reach a goal. The aim of this dissertation is to investigate the effects of goal proximity and invested effort on valuation related regions in the human brain. We addressed this question in two fMRI studies by integrating a commonly used reward anticipation task in differential versions of a Multitrial Reward Schedule Paradigm. In both experiments, subjects had to perform consecutive reward anticipation tasks under two different reward contingencies: in the delayed condition, participants received a monetary reward only after successful completion of multiple consecutive trials. In the immediate condition, money was earned after every successful trial. In the first study, we could demonstrate that the rostral cingulate zone of the posterior medial frontal cortex signals action value contingent to goal proximity, thereby replicating neurophysiological findings about goal proximity signals in a homologous region in non-human primates. The findings of the second study imply that brain regions associated with general cognitive control processes are modulated by previous effort investment. Furthermore, we found the posterior lateral prefrontal cortex and the orbitofrontal cortex to be involved in coding for the effort-based context of a situation. In sum, these results extend the role of the human rostral cingulate zone in outcome evaluation to the continuous updating of action values over a course of action steps based on the proximity to the expected reward. Furthermore, we tentatively suggest that previous effort investment invokes processes under the control of the executive system, and that posterior lateral prefrontal cortex and the orbitofrontal cortex are involved in an effort-based context representation that can be used for outcome evaluation that is dependent on the characteristics of the current situation.Derzeit besteht im Bereich der Neurowissenschaften ein großes Interesse daran aufzuklären, auf welche Weise verschiedene Variablen die Wertigkeit eines erwarteten Handlungsziels beeinflussen bzw. welche Hirnregionen an der Repräsentation der Wertigkeit eines Handlungsziels beteiligt sind. Die meisten Untersuchungen beziehen sich dabei auf Einflussgrößen wie die erwartete Belohnungshöhe, die Wahrscheinlichkeit, mit der ein bestimmtes Ereignis eintritt, oder die Dauer bis zum Erhalt einer Belohnung. Bisher liegen jedoch kaum Untersuchungen vor bezüglich zweier anderer Variablen, die ebenfalls den erwarteten Wert eines Handlungsergebnisses beeinflussen. Das sind (a) die Nähe zu dem erwarteten Ziel und (b) die bisher investierte Anstrengung, um ein Ziel zu erreichen. Das Ziel der vorliegenden Dissertation ist zu untersuchen, wie die Nähe zum Ziel und die bisher investierte Anstrengung Gehirnregionen beeinflussen, die mit der Repräsentation von Wertigkeit im Zusammenhang stehen. Dazu führten wir zwei fMRT-Studien durch, in denen wir eine klassische Belohnungs-Antizipationsaufgabe in unterschiedliche Versionen eines „Multitrial Reward Schedule“ Paradigmas integriert haben. Das bedeutet, dass die Probanden Belohnungs-Antizipationsaufgaben unter zwei unterschiedlichen Belohnungskontingenzen bearbeiteten: In der verzögerten Bedingung erhielten die Probanden einen Geldbetrag nach der erfolgreichen Bearbeitung von mehreren aufeinanderfolgenden Aufgaben, in der direkten Bedingung dagegen nach jeder korrekt ausgeführten Aufgabe. In der ersten Studie konnte eine sukzessiv ansteigende Aktivität in Abhängigkeit zur Zielnähe in der rostralen cingulären Zone identifiziert werden. Das deutet darauf hin, dass dieses Areal den Wert einer Handlung in Abhängigkeit zur Nähe zum Ziel kodiert. Die Ergebnisse der zweiten Studie zeigten, dass die bisher investierte Anstrengung kortikale Regionen moduliert, die klassischerweise mit kognitiven Kontrollfunktionen in Zusammenhang gebracht werden. Außerdem repräsentierten der posteriore laterale präfrontale Cortex und der orbitofrontale Cortex den motivationalen Kontext eines Trials anhand des Risikos des Verlustes von bisher investierter Anstrengung. Insgesamt weisen diese Befunde darauf hin, dass die rostrale cinguläre Zone eine entscheidende Rolle spielt für die Kontrolle sequenzieller Handlungsstufen, die auf eine verzögerte Belohnung ausgerichtet sind. Diese Kontrollfunktion scheint auf der kontinuierlichen Aktualisierung des Wertes einer Handlungsstufe zu basieren, der von der aktuellen Zielnähe bestimmt wird. Die Befunde der zweiten Studie lassen darauf schließen, dass sich die bisher investierte Anstrengung zur Erreichung eines Handlungsziels auf die Bereitstellung von allgemeinen kognitiven Ressourcen auswirkt. Das Risiko des Verlustes von bisher investierter Anstrengung kann außerdem ein kontextuelles Merkmal der Situation darstellen, das als Bezugsrahmen für die Evaluation des erwarteten Wertes dienen kann

Decoding face categories in diagnostic subregions of primary visual cortex

Higher visual areas in the occipitotemporal cortex contain discrete regions for face processing, but it remains unclear if V1 is modulated by top-down influences during face discrimination, and if this is widespread throughout V1 or localized to retinotopic regions processing task-relevant facial features. Employing functional magnetic resonance imaging (fMRI), we mapped the cortical representation of two feature locations that modulate higher visual areas during categorical judgements – the eyes and mouth. Subjects were presented with happy and fearful faces, and we measured the fMRI signal of V1 regions processing the eyes and mouth whilst subjects engaged in gender and expression categorization tasks. In a univariate analysis, we used a region-of-interest-based general linear model approach to reveal changes in activation within these regions as a function of task. We then trained a linear pattern classifier to classify facial expression or gender on the basis of V1 data from ‘eye’ and ‘mouth’ regions, and from the remaining non-diagnostic V1 region. Using multivariate techniques, we show that V1 activity discriminates face categories both in local ‘diagnostic’ and widespread ‘non-diagnostic’ cortical subregions. This indicates that V1 might receive the processed outcome of complex facial feature analysis from other cortical (i.e. fusiform face area, occipital face area) or subcortical areas (amygdala)

A supervised clustering approach for fMRI-based inference of brain states

We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal. Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches. Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task

Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti

Diffusion weighted imaging has become and will certainly continue to be an important tool in medical research and diagnostics. Data obtained with diffusion weighted imaging are characterized by a high noise level. Thus, estimation of quantities like anisotropy indices or the main diffusion direction may be significantly compromised by noise in clinical or neuroscience applications. Here, we present a new package dti for R, which provides functions for the analysis of diffusion weighted data within the diffusion tensor model. This includes smoothing by a recently proposed structural adaptive smoothing procedure based on the propagation-separation approach in the context of the widely used diffusion tensor model. We extend the procedure and show, how a correction for Rician bias can be incorporated. We use a heteroscedastic nonlinear regression model to estimate the diffusion tensor. The smoothing procedure naturally adapts to different structures of different size and thus avoids oversmoothing edges and fine structures. We illustrate the usage and capabilities of the package through some examples.

Frequency-splitting Dynamic MRI Reconstruction using Multi-scale 3D Convolutional Sparse Coding and Automatic Parameter Selection

Department of Computer Science and EngineeringIn this thesis, we propose a novel image reconstruction algorithm using multi-scale 3D con- volutional sparse coding and a spectral decomposition technique for highly undersampled dy- namic Magnetic Resonance Imaging (MRI) data. The proposed method recovers high-frequency information using a shared 3D convolution-based dictionary built progressively during the re- construction process in an unsupervised manner, while low-frequency information is recovered using a total variation-based energy minimization method that leverages temporal coherence in dynamic MRI. Additionally, the proposed 3D dictionary is built across three different scales to more efficiently adapt to various feature sizes, and elastic net regularization is employed to promote a better approximation to the sparse input data. Furthermore, the computational com- plexity of each component in our iterative method is analyzed. We also propose an automatic parameter selection technique based on a genetic algorithm to find optimal parameters for our numerical solver which is a variant of the alternating direction method of multipliers (ADMM). We demonstrate the performance of our method by comparing it with state-of-the-art methods on 15 single-coil cardiac, 7 single-coil DCE, and a multi-coil brain MRI datasets at different sampling rates (12.5%, 25% and 50%). The results show that our method significantly outper- forms the other state-of-the-art methods in reconstruction quality with a comparable running time and is resilient to noise.ope

Common and Distinct Functional Brain Networks for Intuitive and Deliberate Decision Making

Reinforcement learning studies in rodents and primates demonstrate that goal-directed and habitual choice behaviors are mediated through different fronto-striatal systems, but the evidence is less clear in humans. In this study, functional magnetic resonance imaging (fMRI) data were collected whilst participants ( n = 20) performed a conditional associative learning task in which blocks of novel conditional stimuli (CS) required a deliberate choice, and blocks of familiar CS required an intuitive choice. Using standard subtraction analysis for fMRI event-related designs, activation shifted from the dorso-fronto-parietal network, which involves dorsolateral prefrontal cortex (DLPFC) for deliberate choice of novel CS, to ventro-medial frontal (VMPFC) and anterior cingulate cortex for intuitive choice of familiar CS. Supporting this finding, psycho-physiological interaction (PPI) analysis, using the peak active areas within the PFC for novel and familiar CS as seed regions, showed functional coupling between caudate and DLPFC when processing novel CS and VMPFC when processing familiar CS. These findings demonstrate separable systems for deliberate and intuitive processing, which is in keeping with rodent and primate reinforcement learning studies, although in humans they operate in a dynamic, possibly synergistic, manner particularly at the level of the striatum.Peer reviewedFinal Published versio