Decision-making in the (stressed) brain

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Decision-making in the (stressed) brain

Dissertation

zur Erlangung des akademischen Grades des Doktors der Naturwissenschaften (Dr. rer. nat.)

eingereicht beim Fachbereich Psychologie der Mathematisch-Naturwissenschaftlichen Sektion der Universität Konstanz

vorgelegt von

Astrid Christine Steffen

Konstanz, im September 2010

Konstanzer Online-Publikations-System (KOPS) URN: http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-126600

URL: http://kops.ub.uni-konstanz.de/volltexte/2010/12660/

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Decision-making in the (stressed) brain

Dissertation

zur Erlangung des akademischen Grades des Doktors der Naturwissenschaften

eingereicht beim Fachbereich Psychologie der Mathematisch-Naturwissenschaftlichen Sektion der Universität Konstanz

vorgelegt von

Astrid Christine Steffen

Konstanz, im September 2010

Tag der mündlichen Prüfung: 06. Dezember 2010

1. Referentin: Prof. Dr. Brigitte Rockstroh 2. Referent: PD Dr. Christian Wienbruch

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DANKSAGUNG & ACKNOWLEDGEMENT

Ich habe die sparsamere, kurz-knappe Variante dessen, was nun kommt, verworfen.

Denn wer über 120 Seiten eine Dissertation schreiben kann, der sollte sich auch ausreichend Zeit nehmen können, um den vielen Unterstützern und Helfern Dank zu sagen – das möchte ich hiermit aufrichtig tun: DANKE...

... Brigitte Rockstroh für ihre nachhaltige, kritische und fürsorgliche Unterstützung, die von der Erstellung des Stipendiumsantrags bis zum heutigen Tage reicht: Erst dadurch konnte diese Arbeit so zu Stande kommen – und noch dazu in dieser angenehmen Atmosphäre ihrer Arbeitsgruppe. All das empfinde ich als großes Glück.

... Christian Wienbruch für seine Rettungsaktionen aus MEG-, Trigger- & Presentation- Katastrophen – und für die vielen, über den Tellerrand hinausweisenden Denkanstöße.

... Greg Miller. He enriched my work with his neat thinking, his critical questions and his innumerable advice, thereby astonishingly never losing his humor.

... Gerald Schneider für die politikwissenschaftliche Ideen, durch die er wesentlich am Entstehen der Arbeit über Entscheiden im sozialen Kontext beteiligt war.

... Bobo Nick für den klaren, kritischen „gestaltline“-Blick auf meine Daten, und für das angenehm schnelle, produktive Zusammenarbeiten.

... Dem Studienförderwerk Klaus Murmann / der Stiftung der Deutschen Wirtschaft für die finanzielle Unterstützung – und für die ideelle, die mit Einblicken in Themen fernab von „Decision-making in the (stressed) brain“ einher ging.

... Katharina Matz dafür, dass sie Dissertations-Freud & -Leid mit mir geteilt und (nicht nur) durch unzählige Vorarbeiten zur Patientenstudie, aufmerksames Lesen dieser Arbeit, ihr fröhliches Lachen & die Picknick-Bastmatte erleichtert hat. Ihr und

... Ursel Lommen, Christian Pietrek, Bärbel Awiszus, Christiane Wolf, Laura Steidle, Hanna Fiess, Katrin Helmbold, Sabine Scheermesser, Amra Covic und Daniel Muller, die durch ihre tatkräftige Hilfe bei den Experimenten maßgeblich zum Gelingen dieser Arbeit beigetragen haben. Aber auch

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Danksagung & Acknowledgement

... den übrigen „Kellerkindern“ Patrick Berg, Christine Naegele und Veronika Müller, die immer wieder mit eingesprungen sind und mit ihrer humorvollen Art unseren Keller lebendig werden lassen. Ihnen allen und

... Thomas Elbert, Katja Weber, Anne Hauswald, Johanna Kissler, Michael Odenwald, Tzvetan Popov, Todor Iordanov, und Nathan Weisz für den bereichernden Diskurs – dafür und auch für die schönen, sonnigen, amüsanten Pausen: Julian Keil, Thomas Hartmann, Hannah Schulz, Judith Stöckel, Gilava Hamuni, Katalin Dohrmann, Julia Morath, Winny Schlee, Anna Mädl, Susanne Kößler, Veronika Müller, Vera Leirer, Daniela Briem.

... Nadia Müller! Das Thema Hirnforschung verbindet uns spätestens seit unserer gemeinsamen ‚Maastricht-Zeit’... Mindestens genauso wichtig waren uns erfreulicherweise aber immer DER See, die Pausen innerhalb und außerhalb des ZPRs :-) – Danke dafür & für die ausdauernde Korrektur dieser Arbeit.

... Hanna Goepel: Sie hat meine Zeit im ZPR bunter gestaltet, mit Abwechslung versehen und ist darüber hinaus in dieser Zeit zu einer zuverlässigen Freundin geworden... nicht nur in puncto Korrekturlesen.

... Kerstin Huber! Sie war die erste, die Zeilen aus dieser Arbeit zu lesen bekommen hat, weil ich seit dem Studium sehr auf ihr Urteil und glücklicherweise auch auf ihre Freundschaft zählen kann, ihr und

... den großartigen Menschen, die mir in den letzten Jahren, egal ob nah oder fern (jedenfalls fernab dieser Dissertation) so gute Freunde waren, besonders: Tine Tersek, Sonja Veelen, Nikola Verhalen, Florian Popp, Andreas Kolloch, Vanessa Dinter, Matthias Götz, Ulrike Magner und Maike Fliegner.

... Gregor Klatt! Er versteht und unterstützt mich liebevoll, begleitet mich kritisch und hellt meinen Alltag deutlich auf... Auch in Zeiten der Doktorarbeit(en) :-) – Dafür bin ich Dir zutiefst dankbar.

... MEINEN ELTERN, meiner Familie! Eure wunderbare Fürsorge, Eure Geduld und Euer schier grenzenloses Vertrauen haben mich immer gestärkt und sind Grund für meine umfassende Dankbarkeit an Euch, deshalb auch an dieser Stelle ein herzliches und aufrichtiges: DANKE an Euch.

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LIST OF FIGURES

Figure Description of content Page

Introduction

1 Schematic illustration of a deterrence game with one-sided incomplete information (depicted from Schneider, Steffen, Nick, & Rockstroh (work in progress)) with SA = strong aggressor, D = defender, WA = weak aggressor, N = nature, P = the probability to succeed, assigned by nature (and s = status quo, f = result from fighting, a = result from accepting, l = cost or back down, and c = weak aggressor’s additional fighting costs).

Depicted to the right are the preferences resulting from this deterrence game, with different preference orders for the aggressor (namely: fA- c<sA-l<fA<sA< aS) and the defender (fD<aD<sD). Characteristically for

such a game, all players prefer integration to a negotiation breakdown. 11 2 Schematic illustration of the mesolimbic-frontocortical dopaminergic

reward system (adapted from Hecht, 2010). 14

Gambling Studies

3 Illustration of the experimental design with an example of the sequence and timing of stimuli in a typical trial. Words were presented in German, translated for illustration here. For half of the participants, the presentation order of the value and probability information in each trial

was reversed from that illustrated here. 21

4 (a) Time course of response in MEG sensor space averaged across all sensors and subjects, separately for effects of the two reward-value levels (10 c, 50 c), the three reward-probability levels (10%, 50%, 90%), the decision prompt (the second stimulus, whether value or probability), and the three types of feedback (gain, loss, no change in balance due to

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List of Figures

choice not to gamble).

(b) The dipole projections forming the regions of interest (ROIs) are marked by (inflated) dots on left, right, and bottom rendering of spherical

configurations. 28

5 (a) Time course of response in dipole source space averaged across dipoles with an ROI and subjects, separately for effects of reward value, reward probability, decision prompt, and feedback. Time intervals considered for topographical analyses are marked by grey-shaded bars, with arrows pointing to the topographical maps illustrated in (b) related to these time intervals.

(b) Dipolar activity projected on standard cortical sheet with yellow and red colors indicating more power or higher MNE values. Activity is averaged across participants, trials, and time points during the respective time intervals. Top row: 150-230 ms after onset of reward value information. Second row: 215-255 ms, 235-275 ms and 250-350 ms after onset of probability information. Third row: 215-255 ms and 235-275 ms after onset of decision prompts. Bottom row: 120-190 ms and 300-350 ms after onset of feedback, separately for gain (upper

graphs) and loss (lower graphs) on low- (left) and high-risk (right) trials. 30 6 Mean reaction times (ms ±1 SD) indicating decision speed as a function

of reward value and reward prediction:

(a) Reaction times averaged across ‘yes’ and ‘no’ responses as a function of value and probability.

(b) Mean reaction times as a function of decision risk and decision choice, separately for decisions to gamble (black bars: ‘yes’ responses) and decisions not to gamble (dark grey bars: ‘no’ responses. Low-risk decisions (left bars) include ‘yes’ responses to 90% probability stimuli and ‘no’ responses to 10% probability stimuli. High-risk decisions (right bars) include ‘yes’ and ‘no’ responses to 50% probability stimuli. Stars

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List of Figures

indicate significant pairwise differences: ***: p< .001. 33 7 Top row: Schematic illustrations of regions of interest (ROIs) as

described in Figure 4.

Remainder: Bar charts showing significant effects in the respective time windows. The error bars are 1 SD. Post hoc confirmation of significant

pairwise differences indicated by * p< .05, ** p< .01, *** p< .001. 34 8 (a) Time course of response in dipole source space to the onset of

probability information (averaged across levels) separately for decisions to gamble (‘yes’ responses) and decisions not to gamble (‘no’

responses) for perceived low- and high-risk decisions. The 250-350 ms interval defined for ROI analysis is marked by a grey-shaded bar.

(b) Topographical map (front view) of dipolar activity projected onto a standard cortical sheet, averaged across participants for the 250-350 ms after onset of probability information separately for low- and high-risk decisions and separately for trials in which the participant decided to gamble (‘yes’-responses, top) and trials in which the participant decided

not to gamble (‘no’-responses, bottom). 35

9 The dipole projections forming the regions of interest (ROIs) are marked by (inflated) dots on left, right, and bottom rendering of spherical

configurations. 48

10 Mean reactions time (in ms with SE-bars) as a function of

(a) reward value and reward prediction averaged across all responses (‘yes’ and ‘no’), and

(b) decision risk and decision choice with low-risk decisions including

‘yes’-responses to 90%-chance and ‘no’-responses to 10%-chance; and high-risk decisions including ‘yes’ and ‘no’-responses to 50% chance cues. ELS did not yield any significant effect for RTs. **: p<.01,***:

p<.001. 50

11 Estimated source activity projected on a standard cortical sheet

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List of Figures

averaged across subjects, trials and time intervals

(a) 215-255 ms after onset of value stimulus and for ‘control‘, ‘low‘ and

‘high‘ stress group, depicted for the right hemisphere,

(b) 215 - 255 ms after onset of probability stimulus and per ELS-group, top view depicted (with left = left), and

(c) 250-350 ms after onset of decision prompts, averaged on low-risk

and high-risk decisions and per ELS-group, front view. 51 12 Estimated source activity after feedback-cue onset between 120 and

190 ms, averaged for trials with ‘gain’, ‘loss’ and ‘same‘ feedback and for

‘control‘, ‘low‘ and ‘high‘ stress group. 52

Bargaining Studies

13 Experimental Design including timing and variations. 58 14 Schematic positions of the dipole projections that reflect the (a) temporo-

parietal and (b) superior fronto-temporal ROIs. 59

15 Grand means of estimated source activity for following the stimulus onset of (a) the offer stimulus in the superior fronto-temporal ROI 170- 250 ms, and the feedback stimulus in:

(b) the superior fronto-temporal ROI 150.200 ms, (c) the superior fronto-temporal ROI 120-190 ms and

(d) the temporo-parietal ROI 120-190 ms. 61

16 Illustration of the experimental design with the sequence and timing of stimuli within a typical trial. The feedback stimulus including words was

presented in German; feedback has been translated for illustration here. 66 17 Time course of response in MEG sensor space averaged across all

sensors and participants, separately for

(a) the demand stimuli per demand (top graph: 90-c demand, 50-c demand) and the decision choices (accept, reject) per decision strategy (rational, irrational) and

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List of Figures

(b) the feedback stimuli indicating decision outcome. Hereby the top graph showing the outcome evaluation upon accept decisions (50-c retained, 10-c retained); the middle graph showing the outcome evaluation following reject decisions (dashed lines: 1€ was lost, solid

lines1€ was retained) per demand 70

18 The dipole projections forming the regions of interest (ROIs) are marked by dots on left, right, and bottom rendering of spherical configurations separately for (from left to right) temporo-parietal, fronto-temporal,

superior frontal and frontal activity. 71

19 (a) Topographical maps (top and right hemisphere view) of dipolar activity projected onto a standard cortical sheet, averaged across participants 180-360 ms after onset of demand stimuli. Maps represent differences in activity between ‘rational’ minus ‘irrational’ (see text for definition) decisions

(b) Top row: Schematic illustrations of regions of interest (ROIs) as described in Figure 18. Remainder: Bar charts showing significant effects in the time windows emphasized in Figure 17. Post hoc confirmation of significant pairwise differences indicated by * p< .05, **

p< .01. 73

20 (a) Topographical maps (top and right hemisphere view) of dipolar activity projected onto a standard cortical sheet, averaged across participants after onset of feedback stimuli. The upper two maps represent differences in activity subsequent on accept decisions, with yellow-to-red color indicating more activity, when 50 c compared to 10 c were retained. The lower two maps represent differences in activity subsequent upon reject decisions, with yellow-to-red color indicating more activity, when 1 € was retained, compared to its loss.

(b) Top row: Schematic illustrations of regions of interest (ROIs) as described in Figure 18. Remainder: Bar charts showing significant

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List of Figures

effects in the respective time windows as described in Figure 17. Post hoc confirmation of significant pairwise differences indicated by * p< .05,

** p< .01, *** p< .001. 76

General Discussion

21 Gestaltline. Depicted are the data of 27 participants from Steffen et al., submitted, Psychophysiology. Gestaltlines are grouped by the participants’ stress level and afterwards sorted according to their final payoff, which is indicated next to the participant’s code. Each bar represents one decision in a single trial. Straight bars represent low-risk decisions (“yes” decisions on trials with 90% probability to gain and “no”

decisions on 10%). Bars tilted to the right represent decisions to play, with those slightly tilted representing such decisions on 50%, and those tilted more representing yes-decisions on 10% probability. Bars tilted to slightly to the left representing no-decisions on 50%- and bars tilted more to the left representing no-decisions to 90%-probability. The width of the stripe represented the value (thin = 10 c, bold = 50 c). In a different version, feedback information was conveyed as differently

colored dot in the middle of a bar. 86

22 Gestaltline. Depicted is the dataset described in Steffen et al., submitted, Social Neuroscience. Each experimental block is represented by one line of bars and dots. One bar represents a decision in a single trial, with forward tilted bars representing rational decisions and backward tilted bars representing irrational decisions. Dots represent

timeout-trials without decisions. 87

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LIST OF TABLES

Table Description of content Page

1 Early Life Stress (ELS) determined by Early Trauma Inventory Bremner

et al. (2000) for stress groups and for diagnoses. 44 2 (a) Number of trials included in analysis. Mean number (+/- standard

deviation) of average artifact-free trials per condition that were included into further dipole analyses on demand stimuli after noise and cardiac removal.

(b) Number of decisions optimizing outcome. Number of optimal

decisions with expected outcome across decisions in brackets. 68 3 Statistical effects for averaged responses to feedback stimuli. Statistical

effects (F-ratios and p-values) from the ANOVAs with the factors Decision Strategy, Demand, and Decision Choice. Listed per Region of Interest and time window are the effects for activity following the outcome / feedback stimulus. Listed are the results for outcome evaluation following both types of decisions; for outcome evaluation upon accept decisions (10 or 50 c), and for outcome evaluation after reject

decisions (1 € retained, 1 € lost) and per demand. 75

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ABBREVIATIONS

ACC Anterior cingulated cortex

ADHD Attention-deficit / hyperactivity disorder ANOVA Analysis of variance

approx. Approximately

BOLD Blood oxygen level dependency BPD Borderline personality disorder

C Eurocent

cf. Confer

ed. Edition

eds. Editors

EEG Electroencephalography

e.g. For example (Latin: exempli gratia) ELS Early life stress

EOG Electro-Oculogram

ERF Event-related field

ERN Error-related negativity (same as Ne) ERP Event-related potential

Et al. And others (Latin: et alii) ETI Early Trauma Inventory

€ Euro

Df Degrees of freedom

FKK Fragebogen zu Kontroll- und Kompetenzüberzeugungen fMRI Functional magnetic resonance imaging

FRN Feedback-related negativity

fT femtoTesla

i.a. Among other things (Latin: inter alia) IAPS International Affective Picture System i.e. That is (Latin: id est)

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Abbreviations

MEG Magnetoencephalograhy

MPFC Medial Prefrontal Cortex MNE Minimum norm estimates

ms Milliseconds

NAcc Nucleus Accumbens

nAm/cm Nanoampermeter per centimeter

(nAm/cm)² Squared nanoampermeter per square centimeter N2 or N200 Negative ERP-component at approximately 200 ms OFC Orbitofrontal Cortex

PFC Prefrontal Cortex

PTSD Posttraumatic stress disorder

P3 or P300 Positive ERP-component at approximately 300 ms ROI Region of interest

RT Reaction time

SAM Synthetic aperture magnetometry

SD Standard deviation

SPM Statistical parametric map

TMS Transcranial magnetic stimulation TSST Trier Social Stress Test

VEOG Vertical electro-oculogram VMPFC Ventromedial prefrontal cortex

vs. Versus

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CONDUCTED STUDIES &

OWN RESEARCH CONTRIBUTIONS

The studies presented in this thesis were supported by a number of colleagues, listed below are the four different studies and my own research contributions.

Study One: Distinct Cognitive Mechanisms in a Gambling Task Share Neural Mechanisms

Authors: Astrid Steffen,¹ Brigitte Rockstroh¹, Christian Wienbruch¹, Gregory A. Miller²

Submitted to Psychophysiology

Own Contributions: Research on the theoretical background, planning of the study, development of the basic design, running MEG- experiments, data analyses, and drafting the manuscript.

Study Two: Childhood Stress and Psychiatric Disorder Modify Reward Processing Authors: Astrid Steffen¹, Katharina Matz¹, Christine Naegele¹, Brigitte Rockstroh¹

Abstract and poster presentation at the 49th Annual Meeting of the Society for Psychophysiological Research, SPR, in Berlin, 2009.

Own Contributions: Research on the theoretical background, planning of the study, running MEG-experiments, data analyses, and drafting the manuscript.

1 Department of Psychology, University of Konstanz, Konstanz, Germany

2 Departments of Psychology and Psychiatry and Beckman Institute Biomedical Imaging Center, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, and Zukunftskolleg, University of Konstanz, Konstanz, Germany

3 Department Political Sciences, University of Konstanz, Konstanz, Germany

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Conducted Studies & Own Research Contributions

Study Three: Fair Play in the Brain – Cortical Activity in Response to Fair and Unfair Offers of a Fictitious Partner in a Gambling Design

Authors: Astrid Steffen¹, Gerald Schneider³, Brigitte Rockstroh¹

Proccedings Paper in IFMBE Proceedings, 17th International Conference on Biogmagnetism Advances in Biomagnetism – Biomag 2010, 28(11):358-361, Heidelberg: Springer.

Own Contributions: Research on the theoretical background, planning of the study, development of the design, running MEG-experiments, data analyses, and drafting the manuscript.

Study Four: Dealing with Unfair Demands: Electromagnetic Correlates of Decisions in a Hold-up Situation

Authors: Astrid Steffen, Gerald Schneider, Brigitte Rockstroh¹ Submitted to Social Neuroscience

Own Contributions: Research on the theoretical background, planning of the study, development of the design, running MEG-experiments, data analyses, and drafting the manuscript.

1 Department of Psychology, University of Konstanz, Konstanz, Germany

2 Departments of Psychology and Psychiatry and Beckman Institute Biomedical Imaging Center, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, and Zukunftskolleg, University of Konstanz, Konstanz, Germany

3 Department Political Sciences, University of Konstanz, Konstanz, Germany

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ZUSAMMENFASSUNG

Zahlreiche, unterschiedliche Faktoren bestimmen und verändern die Verarbeitung von Belohnungs- und Entscheidungsprozessen: Neben der Bewertung und der Vorhersage von (zu erwartender) Belohnung gehören dazu zum Beispiel die Abwägung einhergehender Risiken und die Beurteilung des Resultats, das sich aus dem Entscheidungsprozess ergibt. Die Bedeutung dieser unterschiedlichen Einflussfaktoren ist durch verschiedene hämodynamische Studien, die gleichzeitig auch dabei geholfen haben, ihre Verarbeitung in Strukturen des Gehirns zu lokalisieren, belegt.

Unzureichend bleibt bislang unser Wissen darüber, wie diese Entscheidungskomponenten ihren Einfluss geltend machen, das heißt, wie die zeitliche Entwicklung des Entscheidungsprozesses im Detail aussieht und wie die unterschiedlichen Faktoren interagieren.

An dieser Stelle setzt die vorliegende Dissertation an: In einer Serie von vier verschiedenen, magnetenzephalographischen Studien wurden kortikale Korrelate von Entscheidungsprozessen untersucht. Zunächst wurde ein experimentelles Design, das auf der Simulation eines (Glücks-)Spiels beruht, entwickelt und getestet. In weiteren Studien wurden zwei Varianten dieses Spiels eingesetzt, innerhalb derer ein kooperativer Mitspieler oder ein unkooperativer Gegenspieler mit den Versuchsteilnehmern verhandelte. Die Stichproben der unterschiedlichen Studien bestanden aus gesunden Studenten und aus psychiatrischen Patienten, die unterschiedlich viel Stress in ihrer Kindheit erfahren haben.

Unter der Verwendung des Ausgangs-Designs zeigte sich in Studie eins, dass verschiedene kognitive Mechanismen der Entscheidungsfindung die gleichen oder ähnliche neuronale Mechanismen nutzen: Unter dem Einfluss unterschiedlicher Belohnungsbewertungs-, Belohnungsvorhersage- und Risikoabwägungsprozesse veränderten sich die Muster der Hirnaktivierung vor, während und nach der Entscheidungsfindung. Dies geschah jeweils in Strukturen von temporo-parietal bis frontal, und in Zeitfenstern von 150 bis 350 ms nach Präsentation des visuellen Stimulus, der den jeweiligen Prozess auslöste.

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Zusammenfassung

Man weiß, Psychopathologie und/oder Stresserfahrungen in der Kindheit und der frühen Jugend generell zu einer Reduzierung der Gehirnaktivität in kortikalen Regionen führen, die mit Belohnungsbewertung und -vorhersage und der anschließenden Feedbackbewertung in Verbindung gebracht werden. Unter Verwendung des gleichen Experimentalaufbaus wie in Studie eins, wurde in Studie zwei an psychiatrischen Patienten untersucht, welchen Einfluss Stresserfahrungen aus der Kindheit auf unterschiedliche Komponenten der Entscheidungsfindung und das Verarbeiten verschiedener Belohnungsaspekte haben. Die netzwerkähnlichen Aktivierungsmuster, die schon in der ersten Studie, gefunden worden waren, wurden hierbei repliziert.

Darüber hinaus zeigte sich, dass vermehrte Stresserfahrungen zu erhöhter Risikosensitivität führen. Vermutlich aufgrund der kleinen und eher heterogenen Stichprobe ließen sich in Richtung reduzierter Belohnungsbewertungs- und Belohnungsvorhersagefähigkeiten nur statistische Trends ermitteln.

Forschungsergebnisse aus unterschiedlichen Disziplinen wie etwa den Wirtschafts- wissenschaften, der Politikwissenschaft, der Psychologie und den Neurowissenschaften deuten darauf hin, dass sozialer Kontext im Sinne von kooperativer Fairness oder auch unkooperativer Abschreckung die Entscheidungsfindung beeinflussen. In Studie drei wurde eine Version des ursprünglichen Spiel-Designs verwendet, die um einen Verhandlungskontext erweitert wurde. Hierin bot pro Spieldurchgang ein Mitspieler dem Versuchsteilnehmer einen bestimmten Anteil eines Euros an: Dieser Mitspieler machte entweder faire (50:50) oder unfaire (10:90) Angebote. Die korrelierte Hirnaktivität variierte in diesem Experimentaldesign zwischen 120 und 250 ms und ging einher mit Aktivierung, die räumlich fokussierter war, als in den beiden vorangegangenen Studien.

Zusätzlich zum temporo-parietalen Areal wurde dabei eine superior frontal gelegene Region aktiviert, von der man weiß, dass sie speziell für die Verarbeitung sozialen Kontexts relevant ist.

Für Studie vier wurden die zuvor verwendeten Designs in ein Abschreckungsspiel umgewandelt. Darin bedrohte ein aggressiver Gegenspieler den Versuchsteilnehmer damit, dass er ihm mehr (90 c) oder weniger (50 c) des Euros, der pro Trial zur Disposition stand, stehlen würde. In dieser feindlichen Umgebung wurden sowohl alle

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Zusammenfassung

Gehirnregionen aktiviert, die bei den beiden Glücksspielstudien ohne sozialen Kontext beteiligt waren, als auch das superior-frontale Areal. Die beobachteten Aktivierungen in dieser überfallsartigen Situation variierten in verschiedenen Zeitfenstern zwischen 80 und 370 ms nach Präsentation des jeweiligen Stimulus mit der Art der Entscheidung und verschiedenen Feedbackaspekten.

Zusammengenommen liefert diese Serie aus vier Studien erste Belege über den räumlichen und zeitlichen Verlauf von Entscheidungsfindung – und zwar für Prozesse vor, während und nach dem Treffen einer Entscheidung. Darüber hinaus wurden Erkenntnisse über die (wichtige) Rolle von Risikoverarbeitung und die Abwägung verschiedener Entscheidungsstrategien gesammelt. Es wurden die Einflüsse von Stresserfahrungen in der Kindheit und auch die (stressreichen) sozialen Kontextes untersucht.

Nachfolgende Studien sollten auf der Basis der gewonnen Erkenntnisse (sozialen) Kontext, (Verlust-)Framing und (andere) inter-individuelle Unterschiede variieren, um ihre Einflüsse auf Entscheidungsstrategien und räumliche und zeitliche Aktivierungsmuster im Gehirn genauer zu untersuchen. Außerdem ist eine Erweiterung des Fokus auf die mit Entscheidungsprozessen korrelierten (De-) Synchronisationen von Oszilationen für zukünftige Analysen vielversprechend.

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SUMMARY

Numerous components determine reward processing and decision-making, and numerous influences modify this processing: Reward valuation, reward prediction, risk calculation and feedback evaluation are some of them. Hemodynamic studies demonstrate which brain regions are active in decision-making. Knowledge about how these factors develop their impact over time or about how they are inter-related is still insufficient.

In a series of magnetoencephalographic studies, cortical correlates of decision-making were examined using one non-social gambling design and two bargaining variants with either a cooperative teammate or an aggressive opponent. Samples included healthy students and psychiatric patients who had experienced different amounts of early life stress.

Study one revealed that distinct cognitive decision-making mechanisms in a gambling task share neural mechanisms: Brain activity patterns extending from temporo-parietal to frontal regions varied with reward valuation, reward prediction, and risk calculation before, during, and after decision-making – each in time windows from 150 to 350 ms after the visual stimulus indicating and triggering the particular processing.

These spatio-temporal decision-making patterns can be distorted: Psychopathology and/or early life stress reduce correlated brain activity in cortical regions associated with reward valuation, reward prediction and/or feedback evaluation. Study two exploited the identical gambling setup and examined the impact of childhood stress on reward processing and decision-making in psychiatric patients. The previously identified network-like decision-making patterns were replicated in this sample. Moreover, early life stress experience correlated with increased sensitivity towards risk. Probably due to the rather small and heterogeneous sample, only trends towards reduced reward valuation and outcome were obtained.

Results from different research disciplines such as economics, politics, psychology, and neuroscience indicate that social context, i.e. cooperative fairness or non-cooperative deterrence influence decision-making. Study three used a bargaining version of the

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Summary

initial design in which a teammate offered a proportion of 1€ per trial to the participants, making fair (50:50) or unfair (10:90) offers. Correlated activity varied between 120- 250 ms only in temporo-parietal and superior frontal regions: An additional region was active when social context was introduced.

Study four modified the previous designs into a deterrence game in which an aggressive opponent threatened to steal more (90 c) or less (50 c) of 1€ per trial from the participant. Within this adverse setting, all brain regions that had been active in the two gambling studies and the superior frontal region varied with different decision- making components in this hold-up situation in time windows between 70 and 370 ms after the respective stimuli.

In sum, this series of studies gives first evidence about the sequence of temporo-spatial activity before, during, and after decision-making processes. Moreover, insights on the role of risk processing and the weighting of different decision strategies have been accumulated; and early life stress influences and those of (stressful) social context have been considered.

On this basis future studies should determine the role of varying (social) context, (loss) framing and other inter-individual differences on decision strategy and spatio-temporal activity patterns in the brain more closely. Also, the benefits of additional analyses focusing on the correlated (de-)synchronization of oscillations could be promising.

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Summary

1 INTRODUCTION

Decision-making literature has proposed several distinct cognitive components, including appraisal of the target of choice (i.e. reward valuation), estimation of the chance of receiving a reward (i.e. reward prediction), and feedback-contingent evaluation of the outcome relative to expectation (for overviews see Knutson, Fong, Bennett, Adams, & Hommer, 2003; Knutson, Rick, Wimmer, Prelec, & Loewenstein, 2007; Ernst & Paulus, 2005; Smith, Mitchell, Hardin, Jazbec, Fridberg, Blaird & Ernst, 2009; Bechara, Damasio. Tranel, & Damasio, 2005; Cohen, Elger, & Ranganath, 2007).

Different psychiatric disorders and early life stress modify reward processing and decision-making (e.g. Gold, Waltz, Prentice, Morris, & Heerey, 2008; Hikosaka, 2010).

Moreover, individual trends for ‘rational’ or ‘irrational’, and for ‘risky’ or ‘risk-aversive’

choices influence decision-making – but variables such as rationality of choice have been shown to be vulnerable to social context and to be modified via upcoming heuristics and framing effects (e.g. Kahneman & Tversky, 1979; Tversky & Kahneman, 1981).

Step-by-step, this thesis tries to address this multifarious range of decision-making issues with four different studies that used simple gambling designs without and bargaining designs with social context. Different samples with a participant-range from low-stress healthy controls to high-stress patients with mixed psychiatric diagnoses helped hereby to examine the impact of individuals’ early life stress experiences.

Magnetoencephalography (MEG) with its reasonable spatial and excellent temporal resolution was applied, as the main interest of this thesis did not lie in the spatial location of individual sources of brain activities related to decision-making and reward processing, but in expanding our view on the dynamic temporo-spatial process of human decision-making in the brain.

After describing the theoretical basis of this work in more detail, the four empirical studies will be presented and then finally discussed.

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1 Introduction

1.1 Theoretical models of decision-making in cognitive neuroscience Reward, that reinforces an individual’s behavior, and punishment, that reduces it, are central concepts to classical and instrumental conditioning (e.g. Pavlov, 1927; Skinner, 1951). Most neurocognitive models of decision-making evolved from this tradition, thus focusing on the importance of reward and punishment, too. For example Damasio’s somatic marker hypothesis (Damasio, 1994; Bechara et al., 2005), or Rangel, Camerer

& Montague’s (2008) recent model of value-based decision-making both discuss reward and punishment as constitutive elements of human behavior. In cognitive sciences, for example Fishbein & Ajzen (1975) explained reward as a product of value and expectancy. Similarly, Ernst & Paulus (e.g. 2005) distinguished “formation of preferences among options“, “selection and execution of an action“ and “experience or evaluation of an outcome“ as major elements of decision-making processes.

Recent models on decision-making try to integrate ideas from multiple disciplines such as neuroscience, economics, experimental psychology, sociology or politics. Glimcher (2003) termed this interdisciplinary approach ‘neuroeconomics’, leading for example to the neuroscientific testing of predictions from formal economic models. Classical decision-making theories (e.g. from Savage, 1954; Neumann & Morgenstern, 1947; or Markowitz, 1959) featured logically coherent and mathematically self-contained models without empirical verification that describe humans as rational decision makers. Mercer (2005) proclaimed that correspondence models challenge these coherence models by using rather statistical, but empirically examined statements about decision-making behavior: For example, Simon (1978) and the so-called Carnegie school gave first empirical evidence as well as theoretical explanations of human “bounded rationality”;

and Kahneman and Tversky (1979) demonstrated that people rely on different heuristics when deciding and that their decisions change with the framing of the situation.

An example of an integrating decision-making model with reference to preparatory work from psychology, neuroscience and economics is Rangel et al.’s (2008): They tried to combine several empirical (Pavlovian, habitual and goal-directed) approaches to form a model of “value-based” decision-making that focuses on different (valuation) processes such as learning, representation, valuation, action selection and outcome evaluation.

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1 Introduction

Besides reward valuation, outcome prediction, and feedback evaluation (e.g. Ernst &

Paulus, 2005; Rangel et al., 2008; Smith et al. 2009), factors such as risk, ambiguity and/or uncertainty have become more important to the description of decision-making processes (e.g. Krain, Wilson, Arbuckle, Castellanos, & Milham, 2006; Smith et al., 2009; DeMartino, Kumaran, Seymour, & Dolan, 2006).

Moreover, social context and the resulting social cognitions have been addressed as modifying strictly gain-maximizing and loss-minimizing (thus: rational) strategies in favor of rather fairness-determined decision-making (e.g. Fehr & Gächter, 2002; Fehr &

Fischbacher, 2003; DeQuervain, Fischbacher, Treyer, Schellhammer, Schnyder, Buck,

& Fehr, 2004). Zak (2005) integrated several of these results when speaking about the

“neuroeconomics of trust”, thereby stressing the role of altruism and fairness in decision- making.

1.2 Empirical approaches to decision-making

Capturing decision-making processes under experimental conditions is challenging: The assortment of thinkable decision-making situations is immense, and a multitude of processes takes place during decision-making, including attention, memory retrieval (e.g., outcomes of previous gambles), working memory updating, (compare e.g. Banich, Mackiewicz, Depue, Whitmer, Miller, & Heller, 2008), or calculation (Dehaene, Piazza, Pinel, & Cohen, 2003) – hardly all of such possibly confounding influences can be experimentally controlled. Nevertheless, gambling allows for variations of many factors (reward, punishment, prediction – to name only a few) and can be constructed as a standardized setting for decision-making.

Experimental gambling has been frequently used to examine decision-making processes in neurological patients e.g. via card sorting tests that evolved from the Wisconsin Card Sorting Test (Milner, 1964): Bechara and colleagues (Bechara, Tranel, Damasio, & Damasio, 1996; Bechara, Tranel, & Damasio, 2000) introduced loss- and gain-possibilities in such tasks and studied decision-behavior in neurological patients to show the importance of orbital frontal cortex for risky choices.

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1 Introduction

At the same time, in economics behavioral game theory (Camerer, 2003) was developed from the initial theory of games (Neumann & Morgenstern, 1947) to model decision-making processes. Also in this field of research, games, gambling and bargaining situations were modeled to examine decision-making experimentally: For example, Knutson et al. (e.g. 2007) developed a playful task in which participants had to choose products. Daw, O’Doherty, Dayan, Seymour & Dolan (2006) used a gambling task with slot-machines to predict subjects’ decision-making behavior and correlated brain responses: In an explore/exploit dilemma, subjects had to choose one out of several slot-machines that were associated with different degrees of familiarity and gain- probabilities (and degrees of uncertainty).

1.3 Empirical approaches to decision-making in social context

Decision-making processes change with context information. Influences of social context have been examined by means of bargaining designs: For example in ultimatum and dictator games, participants have to decide how to share certain amounts of money with other players – contrary to the ultimatum game, in the dictator game, the second player has no choice whether to accept the first player’s offer, thus, herein the first player’s offers are lower (e.g. Lee, 2008; Frith & Singer, 2008; Henrich, Boyed, Bowles, Camerer, Fehr, & Gintis, 2004).

Cooperative interaction

In economics, humans were traditionally expected to optimize their own outcomes when making decisions: Predictions from rational choice models expected logically coherent choices that are consistent and (on the basis of the information available) loss- minimizing and gain-maximizing (Neumann & Morgenstern, 1947; Savage, 1954; March, 1978). Kahneman and Tversky (1979) falsified these predictions. They showed that human decision strategies vary with the way the situation is “framed”: whether losses or gains are stressed modifies decision-making, and also moods have an effect (e.g. via the ‘affect heuristic’, Slovic et al., 2002).

Moreover, fairness is a factor prevalent in decision-making settings that involve social context: In ultimatum and even in dictator games, subjects do not optimize their own

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1 Introduction

outcomes, but stick to principles of fair sharing (e.g. Lee, 2008; Frith & Singer, 2008;

Henrich et al., 2004). Altruistic punishment studies indicate that fairness is so pervasive that participants even tend to make costly decisions that punish a teammate in order to shape his/her fairness irrespective of the own loss (e.g. Fehr & Gächter, 2002; Fehr &

Fischbacher, 2003).

Non-cooperative interaction

Aversive, risky conditions are known to shape decision-making (e.g. Krain et al., 2006;

Smith et al., 2009; DeMartino et al. 2006) – nevertheless, still little is known about how behavior and brain correlates change if an aversive or risky atmosphere is created by other individuals, e.g. by their threats, while negotiating or bargaining.

In political sciences, threat is assumed to provoke counter-threats or retreats of the opponent that is threatened: Deterrence theory makes detailed predictions about when attempts of counter-threats and when attempts of backing-down are to be expected.

Within this model the importance of the opponents’ credibility (Schelling, 1960) and that of information asymmetries between two opponents, namely the defender and the aggressor, is emphasized (Schneider & Cederman, 1994). In a so-called one-sided information game (Schneider & Cederman, 1994) it is assumed that the defender knows that the aggressor is likely to push his/her demand: The aggressive side has already issued a demand, and since the defender cannot make a counter-threat he can only either accept the demand (and thereby back-down) or reject it (defend the demanded good by fighting). In deterrence theory, this disproportion is resulting from a cascade of events as depicted in Figure 1: This one-sided hold-up situation implicates precise, model-based hypotheses of the defender’s behavior. Unlike described in the approaches in section 1.3, in the assumptions made by deterrence theory fairness is not discussed as a motif relevant for behavioral outcome.

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Figure 1 Schematic illustration of a deterrence game with one-sided incomplete information (depicted from Schneider, Steffen, Nick, & Rockstroh (in preparation)) with SA = strong aggressor, D = defender, WA = weak aggressor, N = nature, P = the probability to succeed, assigned by nature (and s = status quo, f = result from fighting, a = result from accepting, l = cost or back down, and c = weak aggressor’s additional fighting costs). Depicted to the right are the preferences resulting from this deterrence game, with different preference orders for the aggressor (namely: fA-c<sA-l<fA<sA< aS) and the defender (fD<aD<sD). Characteristically for such a game, all players prefer integration to a negotiation breakdown.

1.4 Decision-making and the inter-individual variables of early life stress experience and psychopathology

Interindividual differences and experiences distort the image that is constructed in general models of decision-making – especially psychopathology takes such deviations from normal behavior to extremes. Psychiatric disorders such as schizophrenia have been shown to change reward prediction capabilities (e.g. Juckel, Schlagenhauf, Koslowski, Wüstenberg, Villringer, Knutson, Wrase, & Heinz, 2006; Murray, Corlett, Clark, Pessiglione, Blackwell, Honey, Jones, Bullmore, Robbins, & Fletcher, 2008).

Reward valuation processes are modified in the consequence of many different mental

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1 Introduction

disorders, too (e.g. Hare, O’Doherty, Camerer, Schultz, & Rangel, 2008; Gold et al., 2008). Changes are not only allocated to specific psychiatric disorders, but are found in various disorders such as attention-deficit/ hyperactivity disorder (ADHD; e.g. Scheres, Milham, Knutson, & Castellanos, 2007), major depression (MDD; e.g. Nestler &

Carlezon, 2006; Schlaepfer, Cohen, Frick, Kosel, Brodesser, Axmacher, Joe, Kreft, Lennartz, & Sturm, 2007), posttraumatic stress disorder (PTSD; e.g. Sailer, Robinson, Fischmeister, König, Oppenauer, Lueger-Schuster, Moser, Kryspin-Exner, & Bauer, 2008), borderline personality disorder (BPD; Crowell, Beauchaine, & Linehan, 2009) eating disorders (e.g. Fladung, Grön, Grammer, Herrnberger, Schilly, Grastreit, Wolf, Walter, & von Wietersheim, 2010; Davis, Levitan, Kaplan, Cater, Reid, & Curtis, Patte, 2008) or schizophrenia (Paulus, Hozack, Zauscher, Frank, Brown, McDowell, & Braff, 2002; Gold et al., 2008). Maladaptation of reward processing has been interpreted as a main reason for the comorbidity of drug abuse in psychiatric disorders like PTSD, depression and schizophrenia (Bruijnzeel, Repetto, & Gold, 2004): Hereby, a misperception of reward is assumed to leading to self-medication via drugs in order to overcome reward-insensitivity. Moreover, Koob & LeMoal (1997) showed that stress alters neuroadaptive processes in the reward system, increasing the rewarding effects of drugs. In the brain, especially prefrontal functions that are otherwise related to reward processing and decision-making become modulated by stress (Bremner, Vythilingam, Vermetten, Nazeer, Adill, Khan, Staib, & Charney, 2002; McEwen, 2004).

Stress is defined as a real or perceived threat, resulting in specific physiological (e.g. concerning hormone release) and psychological responses (e.g. ‘fight or flight’;

Selye, 1956; McEwen, 2004). Early life stress (ELS) concerns stressful experiences in childhood, before the onset of sexual maturation (e.g. Heim, Plotsky, & Nemeroff, 2004;

Weber, Miller, Schupp, Borgelt, Awiszus, Borgelt, Popov, Elbert, & Rockstroh, 2009;

Matz, Pietrek, & Rockstroh, 2010a; Matz, Junghöfer, Elbert, Weber, Wienbruch, &

Rockstroh, 2010b). Examples for ELS are sexual, physical and emotional abuse, e.g. in instable families, or following poor parental care due to physical or mental illness, or dysfunctional relationships – but also poverty, parental loss, accidents, illness, natural disasters or war. Avital & Richter-Levin (2005) found that (in rats) exposure to stress in

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1 Introduction

youth and adulthood had a greater effect on anxiety than exposure to the same amount and quality of stress twice in adulthood. In humans, ELS has been identified as risk factor for psychopathological diagnoses across diagnostic categories (Weber, Rockstroh, Borgelt, Awiszus, Popov, Hoffmann, Schonauer, Watzl, & Pröpster, 2008) such as depression (e.g. Mullen, Martin, Anderson, Romans, & Herbison, 1996), substance related disorders, BPD (e.g. Teicher, Andersen, Polcari, Anderson, &

Navalta, 2002; Crowell et al., 2009), and schizophrenia (e.g. Lysaker, Beattie, Strasburger, & Davis, 2005; Read, van Os, Morrison, & Ross, 2005). On the level of brain activity, amongst other things, ELS permanently decreases feedback sensitivity of the hypothalamus-pituitary-adrenal axis (HPA; e.g. Seckl, 2004). Effects of early life stress on anterior cingulate cortex and the prefrontal cortex have been reported (e.g.

Gunnar & Quevedo, 2007; Gunnar & Davis, 2003). Generally, evidence converges that early life stress correlates (a) with the development of mental disorders and (b) with alterations in (decision-making and) reward processing, as it reduces especially the ability to process reward value (cf. Gold et al., 2008; Hikosaka, 2010).

ELS can be assessed retrospectively by structured interviews or self-report questionnaires with sufficient inter-rater reliability, test-retest reliability and internal consistency (Bremner, Vermetten, & Mazure, 2000; Bremner, Bolus, & Mayer, 2007;

Durrett, Trull, & Silk, 2004). For this thesis, ELS-effects on decision-making and reward processing data could be assessed from a well pre-examined sample that had been studied on long-term effects of stressful experiences before (Weber et al., 2008).

Moreover, also long-term interactions of ELS-effects and disorder on psychological and brain responses had been identified in previous studies with the same sample (Weber et al., 2009; Matz et al., 2010a, b).

1.5 Brain activity related to decision-making

The mesolimbic-frontocortical dopaminergic reward system as depicted in Figure 2 is central to processing reward expectations and allocated rewards (Walter, Abler, Ciaramidaro, & Erk, 2005; Volz, Schubortz, & von Cramon, 2006; Jensen, Smith, Willeit, Crawley, Mikulius, Victu, & Kapur, 2007), but also to decision-making. Reward

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1 Introduction

processing is frequently studied using decision-making as a model, which allows distinction of major components of reward processing (see also above, sections 1.1 – 1.4). The reward system ascends from subcortical to frontocortical structures.

Subcortically, striatum, insula and the nucleus accumbens are involved in decision- making: Insula and striatal regions are active in salience prediction error and in decision- making processes that involve uncertainty and risk (Naqvi & Bechara, 2009); the dorsal striatum activity reflects anticipated satisfaction (reward) from punishing (DeQuervain et al., 2004); activity in the nucleus accumbens is correlated with reward prediction (Abler, Walter, Erk, Kammerer, & Spitzer, 2006; Knutson & Cooper, 2005).

Figure 2 Schematic illustration of the mesolimbic-frontocortical dopaminergic reward system (adapted from Hecht, 2010).

Anterior cingulate cortex (ACC) activation varies with the decision, whether a value allocated to an expected reward is indeed worth initializing the according action (Rushworth, Walton, Kennerly, & Bannerman, 2004; Ernst & Paulus, 2005).

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Frontocortcally, orbitofrontal and prefrontal structures are involved in decision-making and in risk processing. Especially superior frontal and ventromedial prefrontal cortex (VMPFC) come into play when other individuals and social cognition are involved:

Orbitofrontal areas are active whenever there is a trade-off between options (Kringelbach, 2005; Padoa-Schioppa & Assad, 2006; Jensen et al., 2007; Ernst &

Paulus, 2005) or whenever response outcome is uncertain (Fecteau, Pascual-Leone, Zaid, Liguori, Theoret, Boggio, & Fregni, 2007). Framing effects describe alterations in decision-making behavior due to changes in the context description (e.g. stressing possible gains vs. stressing possible losses, cf. Tversky & Kahneman, 1979). Such framing effects were reflected in variation of orbitofrontal activity, too (DeMartino et al., 2006).

Medial prefrontal cortex activation has been related to outcome evaluation (O’Doherty, 2004) including ‘money illusion’ (Weber, Rangel, Wibral, & Falk, 2009). Also, modulation of frontal activity by uncertainty in the estimation of reward expectation and risk aversion has been reported (e.g. Fecteau et al., 2007; Smith et al., 2009; Krain et al., 2006;

Rushworth & Behrens, 2008). Electrophysiological and transcranial magnetic stimulation (TMS) studies on response certainty found more pronounced activity in medial, dorsolateral and / or orbital frontal cortex, whenever response outcome was uncertain (e.g. Schultz, 2004; Hsu, Bhatt, Adolphs, Tranel, & Camerer, 2005; Volz et al., 2006;

Fecteau et al., 2007).

Evidence from hemodynamic studies suggests superior frontal activity to be associated with social cognition (Saxe, 2006; Rilling, Sanfey, Aronson, Nystrom, & Cohen, 2004;

Lee, 2008; Frith & Frith, 2003; Sanfey, Hastie, Colvin, & Grafman, 2003a). In the ultimatum game, ventromedial prefrontal cortex (VMPFC) activity was more pronounced when subjects engaged in ‘fair’ rather than in gain-maximizing behavior (Koenigs &

Tranel, 2007). In the altruistic punishment design, altruistic performance was found to be related to more pronounced activity in VMPFC, but also orbitofrontal cortex, and the dorsal striatum compared to gain-maximizing behavior (DeQuervain et al., 2004).

Besides these structures not explicitly being described as part of the dopaminergic reward system, also temporocortical structures have been shown to be involved in

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processes relevant for decision-making: Estimation of reward probabilities and social- emotional evaluation processes have been related to activity in temporo-parietal regions (e.g. Singer & Fehr, 2005; Frith & Frith, 2003).

Despite the hierarchical distribution of these decision-making-related processes across subcortical via temporal to prefrontal structures, mechanisms and time courses remain a matter of investigation, and also their neural implementation remains to be fully described (Rangel et al., 2008).

EEG studies provide some guidance about rapid phenomena during decision-making tasks, with activation related to reward valuation at 150-200 ms after stimulus onset. A negative deflection of the event-related brain potential (ERP) in Go/NoGo-paradigms around 200 ms (N200) has been related to information-based decisions (Sasaki &

Gemba, 1986; Schmitt, Schlitz, Zaake, Kutas, & Münte, 2001; Steffen, Rockstroh, &

Jansma, 2008).

Studies of feedback-related negativity (FRN, 250-350 ms) suggest that feedback-evoked activity varies with the certainty and confirmation (or violation) of prediction (Hajcak, Moser, Holroyd, & Simons, 2006; Moser & Simons, 2009; Yu & Zhou, 2009). Activity at 300 ms (P300) has also been reported in response to feedback stimuli (Carlson, Zayas,

& Guthormsen, 2009; Leng & Zhou, 2010).

1.6 Trading-off spatial versus temporal resolution: MEG for measuring brain activity

Imaging brain activity with functional magnetic resonance imaging (fMRI) is one of the most prevalent psychophysiological measurement technique in neuroscience, and its advantages are evident: Due to a very high spatial resolution, correlates of ongoing brain activity can be located precisely. Results can be presented in a colorful, clear and concrete way. Limitations arise from the fact that only the change in blood-/oxygen- supply (= blood oxygen level dependency, BOLD) is measured, but how this BOLD- response is correlated to electrocortical activity is still a matter of discussion (e.g. Sirotin

& Das, 2009). Hypotheses about the location of brain activity have to be preclusive (i.a.

based on model assumptions to reduce the huge amount of accumulated data), and the

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temporal resolution of fMRI-data is still poor (e.g. Logothetis, 2008). Despite ongoing discussions about these limitations (e.g. Gusnard & Raichle, 2001; Logothetis, 2008;

Sirotin & Das, 2009), fMRI is the leading source for neuroscientific data in research of decision-making processes, too, and only much fewer electrophysiological studies are available (e.g. Hewig, Trippe, Hecht, Coles, Holroyd, & Miltner, 2007).

Electroencephalography (EEG) and magnetoencephalography (MEG) both have excellent temporal resolution, and deliver precise statements about the exact time course of actual brain activities (not only correlates of nutrient supply) within milliseconds. The main difference between the data quality of EEG and MEG lies in spatial resolution: While the electromagnetic fields recorded with a MEG have a good spatial resolution without significant distortions, signal quality of the electrocortical currents measured in EEG depend on the individual resistances of e.g. meninges and skull. Thus MEG-measurements should be favored if besides temporal also spatial statements shall be generated.

MEG signals mainly originate from the net effect of ionic currents flowing in the dendrites of neurons during synaptic transmission. About one million synchronously active synapses (that is 50 000 neurons in approx. 1mm² cortex) generate extracranially recordable signals, involving intra- and extracellular as well as transmembrane currents.

If having similar orientations tangentially to the scalp surface, the according magnetic fields can reinforce each other to be measurable with a MEG outside of the head. Most of such measurable signals originate from the layer of pyramidal cells, typically from neurons located in the sulci. Current dipoles can be derived from these signals, but even though MEG has the better spatial resolution than EEG, both techniques have to fight with the inverse problem: Only estimates for current dipole sources, but no unique source solutions can be identified for a given activity pattern. The assumptions underlying the applied theoretical model thereby determine the accuracy of source localization. Data from intracranial ablations and other neuroscientific data have helped improving and validating source models. Some researchers even proclaim that now spatial resolution with MEG approximates that of routine fMRI (e.g. Miller, Elbert, Sutton,

& Heller, 2007; Hanlon, Miller, Thoma, Irwin, Jones, Moses, et al., 2005).

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Different methods to localize activity measured with MEG are available: e.g. discrete source models, minimum norm estimates (MNE), or synthetic aperture magnetometry (SAM) – to name only a few. Source models can be under- or over-determined. An over- determined model may consist of a few point-like sources, whose locations and time courses are then estimated from the data. For example discrete source models (as established in BESA, Megis Software GmbH, Munich, Germany) use hypotheses from previously identified research results to determine sources.

Under-determined models may be used in cases where several possible current distributions could explain the measurement results and e.g. so many different distributed areas are activated, that it starts becoming difficult to derive all involved sources from specific literature-based hypotheses (such as in decision-making). Out of several possible solutions here only the most likely is chosen. An example for under- determined models is the L2-minimum-norm-pseudoinverse model that provides minimum norm estimates (MNEs) for the source-current distribution with minimal a priori assumptions, calculating the shortest vector in the source-current space that can explain the measurements (Hamaelaeinen & Ilmoniemi, 1994; Hauk, 2004; Hauk, Keil, Elbert, &

Muller, 2002). The resulting source-current space is modeled as a spherical configuration of evenly distributed dipoles, the MNEs.

A third approach estimates the current at a fixed location ignoring assumptions such as independence of different sources. Central to this approach is beamforming, a signal processing technique, which uses adaptive or fixed beampatterns in sensor arrays for directional signal transmission or reception in order to achieve spatial selectivity. SAM would be an example for such an approach, using a non-linear fitting of the dipole in the temporal domain (e.g. Hillebrand & Barnes, 2003), other approaches than SAM use the Fourier transform of the signals and a linear dipole fit. An advantage of dipole source solutions generated like this is that they can be used to estimate the synchronization of large brain networks, a disadvantage being the dependency on several assumptions which (especially for symmetrically located sources) can lead to mis-locating sources, moreover a relatively huge amount of calculating capacity is needed.

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Whether fMRI, EEG or MEG - each of the described neuroscientific methods has its constraints, and no better alternatives are available. As a consequence, choosing one method always goes along with a trade-off between temporal and spatial resolution.

If not attempting to assess activity in the full range of structures that the literature has proposed as relevant e.g. for decision-making, but rather focusing on the time course of readily scalp-visible activity co-varying rapidly with manipulations of decision-making relevant components, MEG should be the method of choice. And as decision-making recruits sources distributed across the whole frontocortico-mesolimbic network, here, the well-established method of MNE was used to model the time course of such distributed, cortical activity.

1.7 Research question, objectives, and hypotheses

What makes us decide the way we do? Reward valuation, reward prediction, risk calculation and feedback evaluation significantly influence decision-making (see 1.1- 1.5). Nevertheless, knowledge about relations and interrelations between these modifying factors is scarce. Particularly the temporal course of processes related to decision-making is understudied. Thus, a basic design with sequential presentation of stimuli representing reward value, reward prediction and feedback moreover allowing the assessment of ongoing risk evaluation was developed. Afterwards, MEG-studies were conducted to examine the spatio-temporal activity patterns that correlate with decision-making processes in the brain.

Further it was examined how the identified spatio-temporal patterns varied with inter- individual differences (that is: early life stress experience and psychopathology) and with different social context variables such as fairness or threat.

Cortical activity was expected to be located in structures extending from the temporal pole to pre-frontal regions, roughly corresponding to those cortical structures being involved in the mesolimbic-frontocortical, dopaminergic reward system. As MNE- estimates were used due to the complexity of decision-making processes, no hypotheses for subcortical structures were included. Activity was expected to vary around 200 ms for the decision-relevant stimuli, as studies examining the N2/N200-ERP

Decision-making in the (stressed) brain

Decision-making in the (stressed) brain

Decision-making in the (stressed) brain

DANKSAGUNG & ACKNOWLEDGEMENT

CONTENTS

LIST OF FIGURES

LIST OF TABLES

ABBREVIATIONS

CONDUCTED STUDIES &

OWN RESEARCH CONTRIBUTIONS

ZUSAMMENFASSUNG

SUMMARY

1 INTRODUCTION