Effects of motor preparation and spatial attention on corticospinal excitability in a delayed-response paradigm

The preparation of motor responses during the delay period of an instructed delay task is associated with sustained neural firing in the primate premotor cortex. It remains unclear how and when such preparation-related premotor activity influences the motor output system. In this study, we tested modulation of corticospinal excitability using single-pulse transcranial magnetic stimulation (TMS) during a delayed-response task. At the beginning of the delay interval participants were either provided with no information, spatial attentional information concerning location but not identity of an upcoming imperative stimulus, or information regarding the upcoming response. Behavioral data indicate that participants used all information available to them. Only when information concerning the upcoming response was provided did corticospinal excitability show differential modulation for the effector muscle compared to other task-unrelated muscles. We conclude that modulation of corticospinal excitability reflects specific response preparation, rather than non-specific event preparation

Time-Dependent Changes in Human Corticospinal Excitability Reveal Value-Based Competition for Action during Decision Processing

Our choices often require appropriate actions to obtain a preferred outcome, but the neural underpinnings that link decision making and action selection remain largely undetermined. Recent theories propose that action selection occurs simultaneously, i.e., parallel in time, with the decision process. Specifically, it is thought that action selection in motor regions originates from a competitive process that is gradually biased by evidence signals originating in other regions, such as those specialized in value computations. Biases reflecting the evaluation of choice options should thus emerge in the motor system before the decision process is complete. Using transcranial magnetic stimulation, we sought direct physiological evidence for this prediction by measuring changes in corticospinal excitability in human motor cortex during value-based decisions. We found that excitability for chosen versus unchosen actions distinguishes the forthcoming choice before completion of the decision process. Both excitability and reaction times varied as a function of the subjective value-difference between chosen and unchosen actions, consistent with this effect being value-driven. This relationship was not observed in the absence of a decision. Our data provide novel evidence in humans that internally generated value-based decisions influence the competition between action representations in motor cortex before the decision process is complete. This is incompatible with models of serial processing of stimulus, decision, and action

Response repetition biases in human perceptual decisions are explained by activity decay in competitive attractor models

Animals and humans have a tendency to repeat recent choices, a phenomenon known as choice hysteresis. The mechanism for this choice bias remains unclear. Using an established, biophysically informed model of a competitive attractor network for decision making, we found that decaying tail activity from the previous trial caused choice hysteresis, especially during difficult trials, and accurately predicted human perceptual choices. In the model, choice variability could be directionally altered through amplification or dampening of post-trial activity decay through simulated depolarizing or hyperpolarizing network stimulation. An analogous intervention using transcranial direct current stimulation (tDCS) over left dorsolateral prefrontal cortex (dlPFC) yielded a close match between model predictions and experimental results: net soma depolarizing currents increased choice hysteresis, while hyperpolarizing currents suppressed it. Residual activity in competitive attractor networks within dlPFC may thus give rise to biases in perceptual choices, which can be directionally controlled through non-invasive brain stimulation

Neural Signatures of Value Comparison in Human Cingulate Cortex during Decisions Requiring an Effort-Reward Trade-off

UNLABELLED: Integrating costs and benefits is crucial for optimal decision-making. Although much is known about decisions that involve outcome-related costs (e.g., delay, risk), many of our choices are attached to actions and require an evaluation of the associated motor costs. Yet how the brain incorporates motor costs into choices remains largely unclear. We used human fMRI during choices involving monetary reward and physical effort to identify brain regions that serve as a choice comparator for effort-reward trade-offs. By independently varying both options' effort and reward levels, we were able to identify the neural signature of a comparator mechanism. A network involving supplementary motor area and the caudal portion of dorsal anterior cingulate cortex encoded the difference in reward (positively) and effort levels (negatively) between chosen and unchosen choice options. We next modeled effort-discounted subjective values using a novel behavioral model. This revealed that the same network of regions involving dorsal anterior cingulate cortex and supplementary motor area encoded the difference between the chosen and unchosen options' subjective values, and that activity was best described using a concave model of effort-discounting. In addition, this signal reflected how precisely value determined participants' choices. By contrast, separate signals in supplementary motor area and ventromedial prefrontal cortex correlated with participants' tendency to avoid effort and seek reward, respectively. This suggests that the critical neural signature of decision-making for choices involving motor costs is found in human cingulate cortex and not ventromedial prefrontal cortex as typically reported for outcome-based choice. Furthermore, distinct frontal circuits seem to drive behavior toward reward maximization and effort minimization. SIGNIFICANCE STATEMENT: The neural processes that govern the trade-off between expected benefits and motor costs remain largely unknown. This is striking because energetic requirements play an integral role in our day-to-day choices and instrumental behavior, and a diminished willingness to exert effort is a characteristic feature of a range of neurological disorders. We use a new behavioral characterization of how humans trade off reward maximization with effort minimization to examine the neural signatures that underpin such choices, using BOLD MRI neuroimaging data. We find the critical neural signature of decision-making, a signal that reflects the comparison of value between choice options, in human cingulate cortex, whereas two distinct brain circuits drive behavior toward reward maximization or effort minimization

Neural Signatures of Value Comparison in Human Cingulate Cortex during Decisions Requiring an Effort-Reward Trade-off

UNLABELLED: Integrating costs and benefits is crucial for optimal decision-making. Although much is known about decisions that involve outcome-related costs (e.g., delay, risk), many of our choices are attached to actions and require an evaluation of the associated motor costs. Yet how the brain incorporates motor costs into choices remains largely unclear. We used human fMRI during choices involving monetary reward and physical effort to identify brain regions that serve as a choice comparator for effort-reward trade-offs. By independently varying both options' effort and reward levels, we were able to identify the neural signature of a comparator mechanism. A network involving supplementary motor area and the caudal portion of dorsal anterior cingulate cortex encoded the difference in reward (positively) and effort levels (negatively) between chosen and unchosen choice options. We next modeled effort-discounted subjective values using a novel behavioral model. This revealed that the same network of regions involving dorsal anterior cingulate cortex and supplementary motor area encoded the difference between the chosen and unchosen options' subjective values, and that activity was best described using a concave model of effort-discounting. In addition, this signal reflected how precisely value determined participants' choices. By contrast, separate signals in supplementary motor area and ventromedial prefrontal cortex correlated with participants' tendency to avoid effort and seek reward, respectively. This suggests that the critical neural signature of decision-making for choices involving motor costs is found in human cingulate cortex and not ventromedial prefrontal cortex as typically reported for outcome-based choice. Furthermore, distinct frontal circuits seem to drive behavior toward reward maximization and effort minimization. SIGNIFICANCE STATEMENT: The neural processes that govern the trade-off between expected benefits and motor costs remain largely unknown. This is striking because energetic requirements play an integral role in our day-to-day choices and instrumental behavior, and a diminished willingness to exert effort is a characteristic feature of a range of neurological disorders. We use a new behavioral characterization of how humans trade off reward maximization with effort minimization to examine the neural signatures that underpin such choices, using BOLD MRI neuroimaging data. We find the critical neural signature of decision-making, a signal that reflects the comparison of value between choice options, in human cingulate cortex, whereas two distinct brain circuits drive behavior toward reward maximization or effort minimization

Neurodynamic Evidence Supports a Forced-Excursion Model of Decision-Making under Speed/Accuracy Instructions

Evolutionary pressures suggest that choices should be optimised to maximise rewards, by appropriately trading speed for accuracy. This speed-accuracy tradeoff (SAT) is commonly explained by variation in just the baseline-to-boundary distance, i.e. excursion, of accumulation-to-bound models of perceptual decision making. However, neural evidence is not consistent with this explanation. A compelling account of speeded choice should explain both overt behaviour and the full range of associated brain signatures. Here, we reconcile seemingly contradictory behavioural and neural findings. In two variants of the same experiment, we triangulated upon the neural underpinnings of the SAT in the human brain using both EEG and TMS. We found that distinct neural signals, namely the ERP centroparietal positivity (CPP) and a smoothed motor-evoked potential (MEP) signal, which have both previously been shown to relate to decision-related accumulation, revealed qualitatively similar average neurodynamic profiles with only subtle differences between SAT conditions. These signals were then modelled from behaviour by either incorporating traditional boundary variation or utilising a forced excursion. These model variants are mathematically equivalent, in terms of their behavioural predictions, hence providing identical fits to correct and erroneous reaction time distributions. However, the forced-excursion version instantiates SAT via a more global change in parameters and implied neural activity, a process conceptually akin to, but mathematically distinct from, urgency. This variant better captured both ERP and MEP neural profiles, suggesting that the SAT may be implemented via neural gain modulation, and reconciling standard modelling approaches with human neural data

Using generative models to make probabilistic statements about hippocampal engagement in MEG

Magnetoencephalography (MEG) enables non-invasive real time characterization of brain activity. However, convincing demonstrations of signal contributions from deeper sources such as the hippocampus remain controversial and are made difficult by its depth, structural complexity and proximity to neocortex. Here, we demonstrate a method for quantifying hippocampal engagement probabilistically using simulated hippocampal activity and realistic anatomical and electromagnetic source modelling. We construct two generative models, one which supports neuronal current flow on the cortical surface, and one which supports it on both the cortical and hippocampal surfaces. Using Bayesian model comparison, we then infer which of the two models provides a more likely explanation of the dataset at hand. We also carry out a set of control experiments to rule out bias, including simulating medial temporal lobe sources to assess the risk of falsely positive results, and adding different types of displacements to the hippocampal portion of the mesh to test for anatomical specificity of the results. In addition, we test the robustness of this inference by adding co-registration and sensor level noise. We find that the model comparison framework is sensitive to hippocampal activity when co-registration error is -20 dB. These levels of co-registration error and SNR can now be achieved empirically using recently developed subject-specific head-casts

Hemispheric differences in frontal and parietal influences on human occipital cortex: direct confirmation with concurrent TMS-fMRI

We used concurrent TMS-fMRI to test directly for hemispheric differences in causal influences of the right or left fronto-parietal cortex on activity (BOLD signal) in the human occipital cortex. Clinical data and some behavioral TMS studies have been taken to suggest right-hemisphere specialization for top-down modulation of vision in humans, based on deficits such as spatial neglect or extinction in lesioned patients, or findings that TMS to right (vs. left) fronto-parietal structures can elicit stronger effects on visual performance. But prior to the recent advent of concurrent TMS and neuroimaging, it was not possible to directly examine the causal impact of one (stimulated) brain region upon others in humans. Here we stimulated the frontal or intraparietal cortex in the left or right hemisphere with TMS, inside an MR scanner, while measuring with fMRI any resulting BOLD signal changes in visual areas V1-V4 and V5/MT+. For both frontal and parietal stimulation, we found clear differences between effects of right- versus left-hemisphere TMS on activity in the visual cortex, with all differences significant in direct statistical comparisons. Frontal TMS over either hemisphere elicited similar BOLD decreases for central visual field representations in V1-V4, but only right frontal TMS led to BOLD increases for peripheral field representations in these regions. Hemispheric differences for effects of parietal TMS were even more marked: Right parietal TMS led to strong BOLD changes in V1-V4 and V5/MT+, but left parietal TMS did not. These data directly confirm that the human frontal and parietal cortex show right-hemisphere specialization for causal influences on the visual cortex

Acute stress selectively impairs learning to act

Stress interferes with instrumental learning. However, choice is also influenced by non-instrumental factors, most strikingly by biases arising from Pavlovian associations that facilitate action in pursuit of rewards and inaction in the face of punishment. Whether stress impacts on instrumental learning via these Pavlovian associations is unknown. Here, in a task where valence (reward or punishment) and action (go or no-go) were orthogonalised, we asked whether the impact of stress on learning was action or valence specific. We exposed 60 human participants either to stress (socially-evaluated cold pressor test) or a control condition (room temperature water). We contrasted two hypotheses: that stress would lead to a non-selective increase in the expression of Pavlovian biases; or that stress, as an aversive state, might specifically impact action production due to the Pavlovian linkage between inaction and aversive states. We found support for the second of these hypotheses. Stress specifically impaired learning to produce an action, irrespective of the valence of the outcome, an effect consistent with a Pavlovian linkage between punishment and inaction. This deficit in action-learning was also reflected in pupillary responses; stressed individuals showed attenuated pupillary responses to action, hinting at a noradrenergic contribution to impaired action-learning under stress

Non-invasive laminar inference with MEG: comparison of methods and source inversion algorithms

Magnetoencephalography (MEG) is a direct measure of neuronal current flow; its anatomical resolution is therefore not constrained by physiology but rather by data quality and the models used to explain these data. Recent simulation work has shown that it is possible to distinguish between signals arising in the deep and superficial cortical laminae given accurate knowledge of these surfaces with respect to the MEG sensors. This previous work has focused around a single inversion scheme (multiple sparse priors) and a single global parametric fit metric (free energy). In this paper we use several different source inversion algorithms and both local and global, as well as parametric and non-parametric fit metrics in order to demonstrate the robustness of the discrimination between layers. We find that only algorithms with some sparsity constraint can successfully be used to make laminar discrimination. Importantly, local t-statistics, global cross-validation and free energy all provide robust and mutually corroborating metrics of fit. We show that discrimination accuracy is affected by patch size estimates, cortical surface features, and lead field strength, which suggests several possible future improvements to this technique. This study demonstrates the possibility of determining the laminar origin of MEG sensor activity, and thus directly testing theories of human cognition that involve laminar- and frequency-specific mechanisms. This possibility can now be achieved using recent developments in high precision MEG, most notably the use of subject-specific head-casts, which allow for significant increases in data quality and therefore anatomically precise MEG recordings