Cardiac artefact correction for human brainstem fMRI at 7T

Kasper, Lars; Marti, Stefanie; Vannesjö, SJ; Hutton, Chloe; Dolan, Robert; Weiskopf, Nikolaus; Prüssmann, KP; Stephan, KE

doi:10.1016/s1053-8119(09)70854-7

Cited by 15 publications

(11 citation statements)

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“…To control for any movement-related artifacts, we also modeled the force-execution period (block duration, 5,000 ms) with executed effort as parametric modulator. Moreover, we regressed out movements using the realignment parameters, as well as pulsatile and breathing artifacts (85)(86)(87)(88). Each run was modeled as a separate session to account for offset differences in signal intensity.…”

Section: Methodsmentioning

confidence: 99%

Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

Hauser

Eldar

Dolan

2017

Proc. Natl. Acad. Sci. U.S.A.

122

127

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Optimal decision making mandates organisms learn the relevant features of choice options. Likewise, knowing how much effort we should expend can assume paramount importance. A mesolimbic network supports reward learning, but it is unclear whether other choice features, such as effort learning, rely on this same network. Using computational fMRI, we show parallel encoding of effort and reward prediction errors (PEs) within distinct brain regions, with effort PEs expressed in dorsomedial prefrontal cortex and reward PEs in ventral striatum. We show a common mesencephalic origin for these signals evident in overlapping, but spatially dissociable, dopaminergic midbrain regions expressing both types of PE. During action anticipation, reward and effort expectations were integrated in ventral striatum, consistent with a computation of an overall net benefit of a stimulus. Thus, we show that motivationally relevant stimulus features are learned in parallel dopaminergic pathways, with formation of an integrated utility signal at choice. effort prediction errors | reward prediction errors | apathy | substantia nigra/ventral tegmental area | dorsomedial prefrontal cortex O rganisms need to make energy-efficient decisions to maximize benefits and minimize costs, a tradeoff exemplified in effort expenditure (1-3). A key example occurs during foraging, where an overestimation of effort can lead to inaction and starvation (4), whereas underestimation of effort can result in persistent failure, as exemplified in the myth of Sisyphus (5).In a naturalistic environment, we often simultaneously learn about success in expending sufficient effort into an action as well as the reward we obtain from this same action. The reward outcomes that signal success and failure of an action are usually clear, although the effort necessary to attain success is often less transparent. Only by repeatedly experiencing success and failure is it possible to acquire an estimate of an optimal level of effort needed to succeed, without unnecessary waste of energy. This type of learning is important in contexts as diverse as foraging, hunting, and harvesting (6-8). Hull in his "law of less work" proposed that organisms "gradually learn" how to minimize effort expenditure (9). Surprisingly, we know little regarding the neurocognitive mechanisms that guide this form of simultaneous learning about reward and effort.A mesolimbic dopamine system encodes a teaching signal tethered to prediction of reward outcomes (10, 11). These reward prediction errors (PEs) arise from dopaminergic neurons in substantia nigra and ventral tegmental area (SN/VTA) and are broadcast to ventral striatum (VS) to mediate reward-related adaptation and learning (12, 13). Dopamine is also thought to provide a motivational signal (14-18), while dopaminergic deficits in rodents impair how effort and reward are arbitrated (1, 4, 19). The dorsomedial prefrontal cortex (dmPFC; spanning presupplementary motor area [pre-SMA] and dorsal anterior cingulate cortex [dACC]) is a candidate substrate...

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Section: Methodsmentioning

confidence: 99%

Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

Hauser

Eldar

Dolan

2017

Proc. Natl. Acad. Sci. U.S.A.

122

127

View full text Add to dashboard Cite

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“…For the main effect analysis of RPEs in cognitive flexibility, we entered the model-derived RPEs as parametric modulator at the time of feedback into the first-level analysis. We additionally entered several regressors-of-no-interest into the GLM to improve model validity: choice values (value of chosen object) as parametric modulator at cue presentation, realignment-derived movement parameters, scan-to-scan movements greater than 1 mm, and cardiac pulsations ( http://www.translationalneuromodeling.org/tapas/ ; Glover et al, 2000; Kasper et al, 2009 ). Furthermore, we regressed out missing answers and the temporal and spatial derivatives of all task-related regressors.…”

Section: Methodsmentioning

confidence: 99%

Cognitive flexibility in adolescence: Neural and behavioral mechanisms of reward prediction error processing in adaptive decision making during development

et al. 2015

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Adolescence is associated with quickly changing environmental demands which require excellent adaptive skills and high cognitive flexibility. Feedback-guided adaptive learning and cognitive flexibility are driven by reward prediction error (RPE) signals, which indicate the accuracy of expectations and can be estimated using computational models. Despite the importance of cognitive flexibility during adolescence, only little is known about how RPE processing in cognitive flexibility deviates between adolescence and adulthood.In this study, we investigated the developmental aspects of cognitive flexibility by means of computational models and functional magnetic resonance imaging (fMRI). We compared the neural and behavioral correlates of cognitive flexibility in healthy adolescents (12–16 years) to adults performing a probabilistic reversal learning task. Using a modified risk-sensitive reinforcement learning model, we found that adolescents learned faster from negative RPEs than adults. The fMRI analysis revealed that within the RPE network, the adolescents had a significantly altered RPE-response in the anterior insula. This effect seemed to be mainly driven by increased responses to negative prediction errors.In summary, our findings indicate that decision making in adolescence goes beyond merely increased reward-seeking behavior and provides a developmental perspective to the behavioral and neural mechanisms underlying cognitive flexibility in the context of reinforcement learning.

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“…Many studies have employed online motion correction and some studies measured heart rate and breathing rates as covariates for systematic errors. Tools exist to account for physiological artifacts in post-hoc analysis such as RETROICOR (Glover et al, 2000; Kasper et al, 2009), but real-time versions have yet to be published. Recent developments in signal processing in rtfMRI can further improve the robustness against such unspecific effects and noise (Hinds et al, 2011; Koush et al, 2012).…”

Section: Considerations In Study Designmentioning

confidence: 99%

Real-time fMRI neurofeedback: Progress and challenges

et al. 2013

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In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook

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Cardiac artefact correction for human brainstem fMRI at 7T

Cited by 15 publications

References 0 publications

Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

Cognitive flexibility in adolescence: Neural and behavioral mechanisms of reward prediction error processing in adaptive decision making during development

Real-time fMRI neurofeedback: Progress and challenges

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