Striatal BOLD and midfrontal theta power express motivation for action

Algermissen, Johannes; Swart, Jennifer C.; Scheeringa, René; Cools, Roshan; Ouden, H.E.M. den

doi:10.1093/cercor/bhab391

Cited by 17 publications

(47 citation statements)

References 99 publications

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“…Such a relationship has been reported in previous simultaneous EEG-fMRI studies. Specifically, MFT amplitude showed negative correlations to BOLD signal in the default mode network, including the midline prefrontal cortex, during the resting state (Prestel et al, 2018; Scheeringa et al, 2008), working memory (Scheeringa et al, 2009), and decision (Algermissen et al, 2021) tasks. Scheeringa and colleagues (2009) suggested that theta serves a role in task-orientedness by inhibiting irrelevant information and enabling optimal performance on a task.…”

Section: Discussionmentioning

confidence: 99%

A thin line between conflict and reaction time effects on EEG and fMRI brain signals

Beldzik

Ullsperger

2023

Preprint

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The last two decades of electrophysiological and neuroimaging research converged that the activity in the medial frontal cortex plays a pivotal role in cognitive control processes. Notably, the midfrontal theta (MFT) oscillatory EEG power, as well as activity in the anterior midcingulate cortex (aMCC) or pre-supplementary motor area (preSMA), were consistently proclaimed as markers of conflict processing. However, these brain signals are strongly correlated with response time (RT) variability in various non-conflict tasks, which overshadows the true nature of their involvement. Our previous study (Beldzik et al., 2022) successfully identified these brain signals during a simultaneous EEG-fMRI experiment implementing Stroop and Simon tasks. Based on the assumption that overcoming the habitual prepotent response during high interference trials require additional neural resources beyond simple decision variable represented in RTs, here we aim to verify if these markers exhibit a congruency effect beyond RT variations. Furthermore, we explored if these brain signals represent either proactive or reactive cognitive control mechanisms by investigating two widely known behavioral phenomena observed in conflict tasks: proportion congruency and congruency sequence effects. The results revealed partially null findings for MFT activity, yet a distinct cognitive control specialization between aMCC and preSMA. Our study provides novel evidence that the former is involved in proactive control mechanisms, possibly contingency learning, whereas the latter reflects reactive control mechanisms by exhibiting a strong congruency effect regardless of RT variation and responding to adaptive behavior.

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Section: Discussionmentioning

confidence: 99%

A thin line between conflict and reaction time effects on EEG and fMRI brain signals

Beldzik

Ullsperger

2023

Preprint

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“…These brain regions belong to a wider network, often referred to as the salience network, which is sensitive to homeostatically relevant stimuli independent of whether their valence is negative (penalising) or positive (reinforcing) 18 . It is becoming increasingly clear that neural responses in the absolute PE network rise quickly after an outcome is revealed [24][25][26] . Here we observe that the CRS is parametrically modulated by the outcome's absolute PE and that this is mainly due to the first heartbeat recorded immediately after the outcome onset.…”

Section: Discussionmentioning

confidence: 99%

“…The topographical distribution of the neural phenomena comprising the HEP was defined by computing mean voltages of the HEP time-locked to R-wave onset for all trials at the group level using the cluster-based permutation test including all electrodes sites and across the entire time window where the HEP typically takes place, this is, 0.1-0.5s 22 2, 24 25 . In this analysis, no a-priori electrode clusters were formed (all active electrodes were treated as a distinct variable).…”

Section: Methodsmentioning

confidence: 99%

“…Signed PE-related activity has been reported in a number of brain areas but absolute PE-related activity has been most often linked to ACC and AI [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21] --in or adjacent to brain areas associated with cardiac-related activity 23 . In addition, recent studies have shown that absolute PE-related activity appears shortly after the outcome while signed PE-related activity has a longer latency after the outcome [24][25][26] . Because absolute PE information is neurally encoded early after outcome onset and given the adjacency of brain areas encoding both saliency and cardiac activity, we hypothesised that cardiac signals might interact with the impact that the absolute PE, as opposed to the signed PE, has on learning.…”

Section: Introductionmentioning

confidence: 99%

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Timing along the cardiac cycle modulates neural signals of reward-based learning

Fouragnan

Hosking

Cheung

et al. 2022

Preprint

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Natural fluctuations in cardiac activity influence brain activity associated with sensory stimuli and affect perceptual decisions about low magnitude, near-threshold stimuli. However, little is known about the impact of fluctuations in heart activity on other internal representations. Here we investigate cardiac influences on learning-related internal representations: the absolute and signed prediction errors. By combining machine learning techniques with electroencephalography (EEG) and both simple, direct indices of task performance and computational model-derived indices of learning, we demonstrate that just as people are more sensitive to low magnitude, near threshold sensory stimuli in certain cardiac phases, so are they more sensitive to low magnitude absolute prediction errors in the same cycles. Importantly, however, this occurs even when the low magnitude prediction errors are associated with clearly suprathreshold sensory events. In addition, participants exhibiting stronger difference in their prediction errors representations between cardiac cycles exhibited higher learning rates and greater task accuracy.

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“…Like outcome anticipation, intertemporal decisionmaking usually includes an evaluation process and instrumental approach behavior (see also Scheres et al (2013)). Therefore, differences in neural activation between loss and reward decisionmaking could be confounded with differences in the motivational value of the reward or loss as reflected in the activation during reward and loss anticipation (Algermissen et al, 2021). To this end, we developed a sequence of decision-making and outcome anticipation by combining intertemporal choice tasks with a MID task (Kirsch et al, 2003).This further allowed us to conduct a MID task with highly salient outcomes which have been chosen by the participants themselves.…”

Section: Introductionmentioning

confidence: 99%

Comparing Discounting of Potentially Real Rewards and Losses by Means of Functional Magnetic Resonance Imaging

Pinger

Thome

Halli

et al. 2022

Front. Syst. Neurosci.

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AimDelay discounting (DD) has often been investigated in the context of decision making whereby individuals attribute decreasing value to rewards in the distant future. Less is known about DD in the context of negative consequences. The aim of this pilot study was to identify commonalities and differences between reward and loss discounting on the behavioral as well as the neural level by means of computational modeling and functional Magnetic Resonance Imaging (fMRI). We furthermore compared the neural activation between anticipation of rewards and losses.MethodWe conducted a study combining an intertemporal choice task for potentially real rewards and losses (decision-making) with a monetary incentive/loss delay task (reward/loss anticipation). Thirty healthy participants (age 18-35, 14 female) completed the study. In each trial, participants had to choose between a smaller immediate loss/win and a larger loss/win at a fixed delay of two weeks. Task-related brain activation was measured with fMRI.ResultsHyperbolic discounting parameters of loss and reward conditions were correlated (r = 0.56). During decision-making, BOLD activation was observed in the parietal and prefrontal cortex, with no differences between reward and loss conditions. During reward and loss anticipation, dissociable activation was observed in the striatum, the anterior insula and the anterior cingulate cortex.ConclusionWe observed behavior concurrent with DD in both the reward and loss condition, with evidence for similar behavioral and neural patterns in the two conditions. Intertemporal decision-making recruited the fronto-parietal network, whilst reward and loss anticipation were related to activation in the salience network. The interpretation of these findings may be limited to short delays and small monetary outcomes.

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Striatal BOLD and midfrontal theta power express motivation for action

Cited by 17 publications

References 99 publications

A thin line between conflict and reaction time effects on EEG and fMRI brain signals

A thin line between conflict and reaction time effects on EEG and fMRI brain signals

Timing along the cardiac cycle modulates neural signals of reward-based learning

Comparing Discounting of Potentially Real Rewards and Losses by Means of Functional Magnetic Resonance Imaging

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