Optimal level of human intracranial theta activity for behavioral switching in the subthalamo-medio-prefrontal circuit.

The ability to switch between rules associating stimuli and responses depend on a circuit including the dorsomedial prefrontal cortex (dmPFC) and the subthalamic nucleus (STN). However, the precise neural implementations of switching remain unclear. To address this issue, we recorded local field potentials from the STN and from the dmPFC of neuropsychiatric patients during behavioral switching. Drift-diffusion modeling revealed that switching is associated with a shift in the starting point of evidence accumulation. Theta activity increases in dmPFC and STN during successful switch trials, while temporally delayed and excessive levels of theta lead to premature switch errors. This seemingly opposing impact of increased theta in successful and unsuccessful switching is explained by a negative correlation between theta activity and the starting point. Together, these results shed a new light on the neural mechanisms underlying the rapid reconfiguration of stimulus-response associations, revealing a Goldilocks' effect of theta activity on switching behavior.

Direct stimulation of anterior insula and ventromedial prefrontal cortex disrupts economic choices.

Neural activity within the ventromedial prefrontal cortex (vmPFC) and anterior insula (aIns) is often associated with economic choices and confidence. However, it remains unclear whether these brain regions are causally related to these processes. To address this issue, we leveraged intracranial electrical stimulation (iES) data obtained from patients with epilepsy performing an economic choice task. Our results reveal opposite effects of stimulation on decision-making depending on its location along a dorso-ventral axis within each region. Specifically, stimulation of the ventral subregion within aIns reduces risk-taking by increasing participants' sensitivity to potential losses, whereas stimulation of the dorsal subregion of aIns and the ventral portion of the vmPFC increases risk-taking by reducing participants' sensitivity to losses. Moreover, stimulation of the aIns consistently decreases participants' confidence, regardless of its location within the aIns. These findings suggest the existence of functionally dissociated neural subregions and circuits causally involved in accepting or avoiding challenges.

Neural interactions in the human frontal cortex dissociate reward and punishment learning.

How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.

Contribution of the subthalamic nucleus to motor, cognitive and limbic processes: an electrophysiological and stimulation study in monkeys.

Deep brain stimulation of the subthalamic nucleus (STN) has become the gold standard surgical treatment for Parkinson's disease and is being investigated for obsessive compulsive disorders. Even if the role of the STN in the behavior is well documented, its organization and especially its division into several functional territories is still debated. A better characterization of these territories and a better knowledge of the impact of stimulation would address this issue. We aimed to find specific electrophysiological markers of motor, cognitive and limbic functions within the STN and to specifically modulate these components. Two healthy non-human primates (Macaca fascicularis) performed a behavioral task allowing the assessment of motor, cognitive and limbic reward-related behavioral components. During the task, four contacts in the STN allowed recordings and stimulations, using low frequency stimulation (LFS) and high frequency stimulation (HFS). Specific electrophysiological functional markers were found in the STN with beta band activity for the motor component of behavior, theta band activity for the cognitive component, and, gamma and theta activity bands for the limbic component. For both monkeys, dorsolateral HFS and LFS of the STN significantly modulated motor performances, whereas only ventromedial HFS modulated cognitive performances. Our results validated the functional overlap of dorsal motor and ventral cognitive subthalamic territories, and, provide information that tends toward a diffuse limbic territory sensitive to the reward within the STN.

The parietal cortex has a causal role in ambiguity computations in humans.

Humans often face the challenge of making decisions between ambiguous options. The level of ambiguity in decision-making has been linked to activity in the parietal cortex, but its exact computational role remains elusive. To test the hypothesis that the parietal cortex plays a causal role in computing ambiguous probabilities, we conducted consecutive fMRI and TMS-EEG studies. We found that participants assigned unknown probabilities to objective probabilities, elevating the uncertainty of their decisions. Parietal cortex activity correlated with the objective degree of ambiguity and with a process that underestimates the uncertainty during decision-making. Conversely, the midcingulate cortex (MCC) encodes prediction errors and increases its connectivity with the parietal cortex during outcome processing. Disruption of the parietal activity increased the uncertainty evaluation of the options, decreasing cingulate cortex oscillations during outcome evaluation and lateral frontal oscillations related to value ambiguous probability. These results provide evidence for a causal role of the parietal cortex in computing uncertainty during ambiguous decisions made by humans.

Human thalamic low-frequency oscillations correlate with expected value and outcomes during reinforcement learning.

Reinforcement-based adaptive decision-making is believed to recruit fronto-striatal circuits. A critical node of the fronto-striatal circuit is the thalamus. However, direct evidence of its involvement in human reinforcement learning is lacking. We address this gap by analyzing intra-thalamic electrophysiological recordings from eight participants while they performed a reinforcement learning task. We found that in both the anterior thalamus (ATN) and dorsomedial thalamus (DMTN), low frequency oscillations (LFO, 4-12 Hz) correlated positively with expected value estimated from computational modeling during reward-based learning (after outcome delivery) or punishment-based learning (during the choice process). Furthermore, LFO recorded from ATN/DMTN were also negatively correlated with outcomes so that both components of reward prediction errors were signaled in the human thalamus. The observed differences in the prediction signals between rewarding and punishing conditions shed light on the neural mechanisms underlying action inhibition in punishment avoidance learning. Our results provide insight into the role of thalamus in reinforcement-based decision-making in humans.

Reward and punishment learning deficits among bipolar disorder subtypes.

BACKGROUND: Reward sensitivity is an essential dimension related to mood fluctuations in bipolar disorder (BD), but there is currently a debate around hypersensitivity or hyposensitivity hypotheses to reward in BD during remission, probably related to a heterogeneous population within the BD spectrum and a lack of reward bias evaluation. Here, we examine reward maximization vs. punishment avoidance learning within the BD spectrum during remission. METHODS: Patients with BD-I (n = 45), BD-II (n = 34) and matched (n = 30) healthy controls (HC) were included. They performed an instrumental learning task designed to dissociate reward-based from punishment-based reinforcement learning. Computational modeling was used to identify the mechanisms underlying reinforcement learning performances. RESULTS: Behavioral results showed a significant reward learning deficit across BD subtypes compared to HC, captured at the computational level by a lower sensitivity to rewards compared to punishments in both BD subtypes. Computational modeling also revealed a higher choice randomness in BD-II compared to BD-I that reflected a tendency of BD-I to perform better during punishment avoidance learning than BD-II. LIMITATIONS: Our patients were not naive to antipsychotic treatment and were not euthymic (but in syndromic remission) according to the International Society for Bipolar Disorder definition. CONCLUSIONS: Our results are consistent with the reward hyposensitivity theory in BD. Computational modeling suggests distinct underlying mechanisms that produce similar observable behaviors, making it a useful tool for distinguishing how symptoms interact in BD versus other disorders. In the long run, a better understanding of these processes could contribute to better prevention and management of BD.

Effort-Cost Decision-making Among Individuals With Schizophrenia: A Systematic Review and Meta-analysis.

Importance: Motivational impairments in schizophrenia are by definition associated with poor outcome. It is postulated that the reduction of goal-directed behavior arises from abnormal trade-offs between rewards and efforts. Objective: To examine whether schizophrenia is associated with impairments in effort-cost decision-making. Data Sources: For this systematic review and meta-analysis, the PubMed, ScienceDirect, PsycINFO, Embase, and ClinicalTrials.gov databases were searched from inception to July 2022 for studies that investigated effort-cost decision-making in schizophrenia. Search terms included effort, cost, and schizophrenia. Study Selection: Consensual criteria for inclusion were peer-reviewed studies published in English that used a computerized effort-cost decision-making behavioral paradigm and compared individuals with schizophrenia with control individuals. Data Extraction and Synthesis: The Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline was used for abstracting data. Data were extracted independently by 2 authors and then pooled using random-effects sizes and bayesian approaches. Main Outcomes and Measures: The main outcomes were performance on effort-cost decision-making tasks requiring an effort-reward trade-off, measured by Hedges g effect size. Effects of moderators were tested with meta-regressions and subgroup analyses. Results: Twenty studies involving 1503 participants were included: 837 individuals with schizophrenia (541 [64.6%] male; mean [SD] age, 35.89 [6.70] years) and 666 control individuals without schizophrenia (360 [54.1%] male; mean [SD] age, 34.16 [5.92] years). Participants with schizophrenia had significantly reduced willingness to expend effort for rewards compared with controls (k = 20; effect size, 0.43; 95% CI, 0.30-0.56; P < .001; I2 = 33.1%; Q test P = .08). The magnitude of the deficit was significantly greater for high-reward trials. The severity of negative symptoms was negatively associated with effort-cost decision-making (k = 8; effect size, -0.33; 95% CI, -0.50 to -0.15; P < .001), while participants with a high number of negative symptoms had a significantly larger impairment in effort-cost decision-making (k = 5; effect size, 0.47; 95% CI, 0.10-0.84; P = .01). Conclusions and Relevance: In this systematic review and meta-analysis, schizophrenia was associated with deficits in effort allocation as indexed by effort-cost decision-making tasks. Understanding the cognitive and neurobiological mechanisms driving effort allocation impairments may assist in developing novel interventions.

Efficacy and auditory biomarker analysis of fronto-temporal transcranial direct current stimulation (tDCS) in targeting cognitive impairment associated with recent-onset schizophrenia: study protocol for a multicenter randomized double-blind sham-controlled trial.

BACKGROUND: In parallel to the traditional symptomatology, deficits in cognition (memory, attention, reasoning, social functioning) contribute significantly to disability and suffering in individuals with schizophrenia. Cognitive deficits have been closely linked to alterations in early auditory processes (EAP) that occur in auditory cortical areas. Preliminary evidence indicates that cognitive deficits in schizophrenia can be improved with a reliable and safe non-invasive brain stimulation technique called tDCS (transcranial direct current stimulation). However, a significant proportion of patients derive no cognitive benefits after tDCS treatment. Furthermore, the neurobiological mechanisms of cognitive changes after tDCS have been poorly explored in trials and are thus still unclear. METHOD: The study is designed as a randomized, double-blind, 2-arm parallel-group, sham-controlled, multicenter trial. Sixty participants with recent-onset schizophrenia and cognitive impairment will be randomly allocated to receive either active (n=30) or sham (n=30) tDCS (20-min, 2-mA, 10 sessions during 5 consecutive weekdays). The anode will be placed over the left dorsolateral prefrontal cortex and the cathode over the left auditory cortex. Cognition, tolerance, symptoms, general outcome and EAP (measured with EEG and multimodal MRI) will be assessed prior to tDCS (baseline), after the 10 sessions, and at 1- and 3-month follow-up. The primary outcome will be the number of responders, defined as participants demonstrating a cognitive improvement ≥Z=0.5 from baseline on the MATRICS Consensus Cognitive Battery total score at 1-month follow-up. Additionally, we will measure how differences in EAP modulate individual cognitive benefits from active tDCS and whether there are changes in EAP measures in responders after active tDCS. DISCUSSION: Besides proposing a new fronto-temporal tDCS protocol by targeting the auditory cortical areas, we aim to conduct a randomized controlled trial (RCT) with follow-up assessments up to 3 months. In addition, this study will allow identifying and assessing the value of a wide range of neurobiological EAP measures for predicting and explaining cognitive deficit improvement after tDCS. The results of this trial will constitute a step toward the use of tDCS as a therapeutic tool for the treatment of cognitive impairment in recent-onset schizophrenia. TRIAL REGISTRATION: ClinicalTrials.gov NCT05440955. Prospectively registered on July 1st, 2022.

Intracerebral mechanisms explaining the impact of incidental feedback on mood state and risky choice.

Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. Here, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of subjects (n=30) while they were performing interleaved quiz and choice tasks that were designed to examine how a series of unrelated feedbacks affect decisions between safe and risky options. Neural baseline activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedbacks received in the quiz task, and then to the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that (1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) was respectively signaling periods of high and low mood, (2) increased vmPFC and daIns BGA respectively promoted and tempered risk taking by overweighting gain vs. loss prospects. Thus, incidental feedbacks induce brain states that correspond to different moods and bias the evaluation of risky options. More generally, these findings might explain why people experiencing positive (or negative) outcome in some part of their life tend to expect success (or failure) in any other.

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data.

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuracy in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present an open-source Python toolbox called Frites1 that includes the proposed statistical pipeline using information-theoretic metrics such as single-trial functional connectivity estimations for the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

The Insula: A Stimulating Island of the Brain.

Direct cortical stimulation (DCS) in epilepsy surgery patients has a long history of functional brain mapping and seizure triggering. Here, we review its findings when applied to the insula in order to map the insular functions, evaluate its local and distant connections, and trigger seizures. Clinical responses to insular DCS are frequent and diverse, showing a partial segregation with spatial overlap, including a posterior somatosensory, auditory, and vestibular part, a central olfactory-gustatory region, and an anterior visceral and cognitive-emotional portion. The study of cortico-cortical evoked potentials (CCEPs) has shown that the anterior (resp. posterior) insula has a higher connectivity rate with itself than with the posterior (resp. anterior) insula, and that both the anterior and posterior insula are closely connected, notably between the homologous insular subdivisions. All insular gyri show extensive and complex ipsilateral and contralateral extra-insular connections, more anteriorly for the anterior insula and more posteriorly for the posterior insula. As a rule, CCEPs propagate first and with a higher probability around the insular DCS site, then to the homologous region, and later to more distal regions with fast cortico-cortical axonal conduction delays. Seizures elicited by insular DCS have rarely been specifically studied, but their rate does not seem to differ from those of other DCS studies. They are mainly provoked from the insular seizure onset zone but can also be triggered by stimulating intra- and extra-insular early propagation zones. Overall, in line with the neuroimaging studies, insular DCS studies converge on the view that the insula is a multimodal functional hub with a fast propagation of information, whose organization helps understand where insular seizures start and how they propagate.

Anatomical dissociation of intracerebral signals for reward and punishment prediction errors in humans.

Whether maximizing rewards and minimizing punishments rely on distinct brain systems remains debated, given inconsistent results coming from human neuroimaging and animal electrophysiology studies. Bridging the gap across techniques, we recorded intracerebral activity from twenty participants while they performed an instrumental learning task. We found that both reward and punishment prediction errors (PE), estimated from computational modeling of choice behavior, correlate positively with broadband gamma activity (BGA) in several brain regions. In all cases, BGA scaled positively with the outcome (reward or punishment versus nothing) and negatively with the expectation (predictability of reward or punishment). However, reward PE were better signaled in some regions (such as the ventromedial prefrontal and lateral orbitofrontal cortex), and punishment PE in other regions (such as the anterior insula and dorsolateral prefrontal cortex). These regions might therefore belong to brain systems that differentially contribute to the repetition of rewarded choices and the avoidance of punished choices.

Decoding the neural dynamics of free choice in humans.

How do we choose a particular action among equally valid alternatives? Nonhuman primate findings have shown that decision-making implicates modulations in unit firing rates and local field potentials (LFPs) across frontal and parietal cortices. Yet the electrophysiological brain mechanisms that underlie free choice in humans remain ill defined. Here, we address this question using rare intracerebral electroencephalography (EEG) recordings in surgical epilepsy patients performing a delayed oculomotor decision task. We find that the temporal dynamics of high-gamma (HG, 60-140 Hz) neural activity in distinct frontal and parietal brain areas robustly discriminate free choice from instructed saccade planning at the level of single trials. Classification analysis was applied to the LFP signals to isolate decision-related activity from sensory and motor planning processes. Compared with instructed saccades, free-choice trials exhibited delayed and longer-lasting HG activity during the delay period. The temporal dynamics of the decision-specific sustained HG activity indexed the unfolding of a deliberation process, rather than memory maintenance. Taken together, these findings provide the first direct electrophysiological evidence in humans for the role of sustained high-frequency neural activation in frontoparietal cortex in mediating the intrinsically driven process of freely choosing among competing behavioral alternatives.

Four core properties of the human brain valuation system demonstrated in intracranial signals.

Estimating the value of alternative options is a key process in decision-making. Human functional magnetic resonance imaging and monkey electrophysiology studies have identified brain regions, such as the ventromedial prefrontal cortex (vmPFC) and lateral orbitofrontal cortex (lOFC), composing a value system. In the present study, in an effort to bridge across species and techniques, we investigated the neural representation of value ratings in 36 people with epilepsy, using intracranial electroencephalography. We found that subjective value was positively reflected in both vmPFC and lOFC high-frequency activity, plus several other brain regions, including the hippocampus. We then demonstrated that subjective value could be decoded (1) in pre-stimulus activity, (2) for various categories of items, (3) even during a distractive task and (4) as both linear and quadratic signals (encoding both value and confidence). Thus, our findings specify key functional properties of neural value signals (anticipation, generality, automaticity, quadraticity), which might provide insights into human irrational choice behaviors.

Human Anterior Insula Encodes Performance Feedback and Relays Prediction Error to the Medial Prefrontal Cortex.

Adaptive behavior requires the comparison of outcome predictions with actual outcomes (e.g., performance feedback). This process of performance monitoring is computed by a distributed brain network comprising the medial prefrontal cortex (mPFC) and the anterior insular cortex (AIC). Despite being consistently co-activated during different tasks, the precise neuronal computations of each region and their interactions remain elusive. In order to assess the neural mechanism by which the AIC processes performance feedback, we recorded AIC electrophysiological activity in humans. We found that the AIC beta oscillations amplitude is modulated by the probability of performance feedback valence (positive or negative) given the context (task and condition difficulty). Furthermore, the valence of feedback was encoded by delta waves phase-modulating the power of beta oscillations. Finally, connectivity and causal analysis showed that beta oscillations relay feedback information signals to the mPFC. These results reveal that structured oscillatory activity in the anterior insula encodes performance feedback information, thus coordinating brain circuits related to reward-based learning.

Direct Recordings from Human Anterior Insula Reveal its Leading Role within the Error-Monitoring Network.

The ability to monitor our own errors is mediated by a network that includes dorsomedial prefrontal cortex (dmPFC) and anterior insula (AI). However, the dynamics of the underlying neurophysiological processes remain unclear. In particular, whether AI is on the receiving or driving end of the error-monitoring network is unresolved. Here, we recorded intracerebral electroencephalography signals simultaneously from AI and dmPFC in epileptic patients while they performed a stop-signal task. We found that errors selectively modulated broadband neural activity in human AI. Granger causality estimates revealed that errors were immediately followed by a feedforward influence from AI onto anterior cingulate cortex and, subsequently, onto presupplementary motor area. The reverse pattern of information flow was observed on correct responses. Our findings provide the first direct electrophysiological evidence indicating that the anterior insula rapidly detects and conveys error signals to dmPFC, while the latter might use this input to adapt behavior following inappropriate actions.

Self-induced intracerebral gamma oscillations in the human cortex.

Gamma oscillations play a pivotal role in multiple cognitive functions. They enable coordinated activity and communication of local assemblies, while abnormalities in gamma oscillations exist in different neurological and psychiatric diseases. Thus, a specific rectification of gamma synchronization could potentially compensate the deficits in pathological conditions. Previous experiments have shown that animals can voluntarily modulate their gamma power through operant conditioning. Using a closed-loop experimental setup, we show in six intracerebrally recorded epileptic patients undergoing presurgical evaluation that intracerebral power spectrum can be increased in the gamma frequency range (30-80 Hz) at different fronto-temporal cortical sites in human subjects. Successful gamma training was accompanied by increased gamma power at other cortical locations and progressively enhanced cross-frequency coupling between gamma and slow oscillations (3-12 Hz). Finally, using microelectrode targets in two subjects, we report that upregulation of gamma activities is possible also in spatial micro-domains, without the spread to macroelectrodes. Overall, our findings indicate that intracerebral gamma modulation can be achieved rapidly, beyond the motor system and with high spatial specificity, when using micro targets. These results are especially significant because they pave the way for use of high-resolution therapeutic approaches for future clinical applications.

Response inhibition rapidly increases single-neuron responses in the subthalamic nucleus of patients with Parkinson's disease.

The subthalamic nucleus (STN) plays a critical role during action inhibition, perhaps by acting like a fast brake on the motor system when inappropriate responses have to be rapidly suppressed. However, the mechanisms involving the STN during motor inhibition are still unclear, particularly because of a relative lack of single-cell responses reported in this structure in humans. In this study, we used extracellular microelectrode recordings during deep brain stimulation surgery in patients with Parkinson's disease (PD) to study STN neurophysiological correlates of inhibitory control during a stop signal task. We found two neuronal subpopulations responding either during motor execution (GO units) or during motor inhibition (STOP units). GO units fired selectively before patients' motor responses whereas STOP units fired selectively when patients successfully withheld their move at a latency preceding the duration of the inhibition process. These results provide electrophysiological evidence for the hypothesized role of the STN in current models of response inhibition.