An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood.

Human brain networks that encode variation in mood on naturalistic timescales remain largely unexplored. Here we combine multi-site, semi-chronic, intracranial electroencephalography recordings from the human limbic system with machine learning methods to discover a brain subnetwork that correlates with variation in individual subjects' self-reported mood over days. First we defined the subnetworks that influence intrinsic brain dynamics by identifying regions that showed coordinated changes in spectral coherence. The most common subnetwork, found in 13 of 21 subjects, was characterized by ß-frequency coherence (13-30 Hz) between the amygdala and hippocampus. Increased variability of this subnetwork correlated with worsening mood across these 13 subjects. Moreover, these subjects had significantly higher trait anxiety than the 8 of 21 for whom this amygdala-hippocampus subnetwork was absent. These results demonstrate an approach for extracting network-behavior relationships from complex datasets, and they reveal a conserved subnetwork associated with a psychological trait that significantly influences intrinsic brain dynamics and encodes fluctuations in mood.

Assessing Spontaneous Categorical Processing of Visual Shapes via Frequency-Tagging EEG.

Categorization is an essential cognitive and perceptual process, which happens spontaneously. However, earlier research often neglected the spontaneous nature of this process by mainly adopting explicit tasks in behavioral or neuroimaging paradigms. Here, we use frequency-tagging (FT) during electroencephalography (EEG) in 22 healthy human participants (both male and female) as a direct approach to pinpoint spontaneous visual categorical processing. Starting from schematic natural visual stimuli, we created morph sequences comprising 11 equal steps. Mirroring a behavioral categorical perception discrimination paradigm, we administered a FT-EEG oddball paradigm, assessing neural sensitivity for equally sized differences within and between stimulus categories. Likewise, mirroring a behavioral category classification paradigm, we administered a sweep FT-EEG oddball paradigm, sweeping from one end of the morph sequence to the other, thereby allowing us to objectively pinpoint the neural category boundary. We found that FT-EEG can implicitly measure categorical processing and discrimination. More specifically, we could derive an objective neural index of the required level to differentiate between the two categories, and this neural index showed the typical marker of categorical perception (i.e., stronger discrimination across as compared with within categories). The neural findings of the implicit paradigms were also validated using an explicit behavioral task. These results provide evidence that FT-EEG can be used as an objective tool to measure discrimination and categorization and that the human brain inherently and spontaneously (without any conscious or decisional processes) uses higher-level meaningful categorization information to interpret ambiguous (morph) shapes.

Distinct Cortical Correlates of Perception and Motor Function in Balance Control.

Fluctuations in brain activity alter how we perceive our body and generate movements but have not been investigated in functional whole-body behaviors. During reactive balance, we recently showed that evoked brain activity is associated with the balance ability in young individuals. Furthermore, in PD, impaired whole-body motion perception in reactive balance is associated with impaired balance. Here, we investigated the brain activity during the whole-body motion perception in reactive balance in young adults (9 female, 10 male). We hypothesized that both ongoing and evoked cortical activity influences the efficiency of information processing for successful perception and movement during whole-body behaviors. We characterized two cortical signals using electroencephalography localized to the SMA: (1) the "N1," a perturbation-evoked potential that decreases in amplitude with expectancy and is larger in individuals with lower balance function, and (2) preperturbation ß power, a transient rhythm that favors maintenance of the current sensorimotor state and is inversely associated with tactile perception. In a two-alternative forced choice task, participants judged whether pairs of backward support surface perturbations during standing were in the "same" or "different" direction. As expected, lower whole-body perception was associated with lower balance ability. Within a perturbation pair, N1 attenuation was larger on correctly perceived trials and associated with better balance, but not perception. In contrast, preperturbation ß power was higher on incorrectly perceived trials and associated with poorer perception, but not balance. Together, ongoing and evoked cortical activity have unique roles in information processing that give rise to distinct associations with perceptual and balance ability.

Theta-Alpha Connectivity in the Hippocampal-Entorhinal Circuit Predicts Working Memory Load.

Working memory (WM) maintenance relies on multiple brain regions and inter-regional communications. The hippocampus and entorhinal cortex (EC) are thought to support this operation. Besides, EC is the main gateway for information between the hippocampus and neocortex. However, the circuit-level mechanism of this interaction during WM maintenance remains unclear in humans. To address these questions, we recorded the intracranial electroencephalography from the hippocampus and EC while patients (N = 13, six females) performed WM tasks. We found that WM maintenance was accompanied by enhanced theta/alpha band (2-12 Hz) phase synchronization between the hippocampus to the EC. The Granger causality and phase slope index analyses consistently showed that WM maintenance was associated with theta/alpha band-coordinated unidirectional influence from the hippocampus to the EC. Besides, this unidirectional inter-regional communication increased with WM load and predicted WM load during memory maintenance. These findings demonstrate that WM maintenance in humans engages the hippocampal-entorhinal circuit, with the hippocampus influencing the EC in a load-dependent manner.

Neural Representations of Non-native Speech Reflect Proficiency and Interference from Native Language Knowledge.

Learning to process speech in a foreign language involves learning new representations for mapping the auditory signal to linguistic structure. Behavioral experiments suggest that even listeners that are highly proficient in a non-native language experience interference from representations of their native language. However, much of the evidence for such interference comes from tasks that may inadvertently increase the salience of native language competitors. Here we tested for neural evidence of proficiency and native language interference in a naturalistic story listening task. We studied electroencephalography responses of 39 native speakers of Dutch (14 male) to an English short story, spoken by a native speaker of either American English or Dutch. We modeled brain responses with multivariate temporal response functions, using acoustic and language models. We found evidence for activation of Dutch language statistics when listening to English, but only when it was spoken with a Dutch accent. This suggests that a naturalistic, monolingual setting decreases the interference from native language representations, whereas an accent in the listener's own native language may increase native language interference, by increasing the salience of the native language and activating native language phonetic and lexical representations. Brain responses suggest that such interference stems from words from the native language competing with the foreign language in a single word recognition system, rather than being activated in a parallel lexicon. We further found that secondary acoustic representations of speech (after 200 ms latency) decreased with increasing proficiency. This may reflect improved acoustic-phonetic models in more proficient listeners.Significance Statement Behavioral experiments suggest that native language knowledge interferes with foreign language listening, but such effects may be sensitive to task manipulations, as tasks that increase metalinguistic awareness may also increase native language interference. This highlights the need for studying non-native speech processing using naturalistic tasks. We measured neural responses unobtrusively while participants listened for comprehension and characterized the influence of proficiency at multiple levels of representation. We found that salience of the native language, as manipulated through speaker accent, affected activation of native language representations: significant evidence for activation of native language (Dutch) categories was only obtained when the speaker had a Dutch accent, whereas no significant interference was found to a speaker with a native (American) accent.

A Cortical Mechanism Linking Saliency Detection and Motor Reactivity in Rhesus Monkeys.

Sudden and surprising sensory events trigger neural processes that swiftly adjust behavior. To study the phylogenesis and the mechanism of this phenomenon, we trained two male rhesus monkeys to keep a cursor inside a visual target by exerting force on an isometric joystick. We examined the effect of surprising auditory stimuli on exerted force, scalp electroencephalographic (EEG) activity, and local field potentials (LFPs) recorded from the dorsolateral prefrontal cortex. Auditory stimuli elicited (1) a biphasic modulation of isometric force, a transient decrease followed by a corrective tonic increase, and (2) EEG and LFP deflections dominated by two large negative-positive waves (N70 and P130). The EEG potential was symmetrical and maximal at the scalp vertex, highly reminiscent of the human "vertex potential." Electrocortical potentials and force were tightly coupled: the P130 amplitude predicted the magnitude of the corrective force increase, particularly in the LFPs recorded from deep rather than superficial cortical layers. These results disclose a phylogenetically preserved corticomotor mechanism supporting adaptive behavior in response to salient sensory events.Significance Statement Survival in the natural world depends on an animal's capacity to adapt ongoing behavior to abrupt unexpected events. To study the neural mechanisms underlying this capacity, we trained monkeys to apply constant force on a joystick while we recorded their brain activity from the scalp and the prefrontal cortex contralateral to the hand holding the joystick. Unexpected auditory stimuli elicited a biphasic force modulation: a transient reduction followed by a corrective adjustment. The same stimuli also elicited EEG and LFP responses, dominated by a biphasic wave that predicted the magnitude of the behavioral adjustment. These results disclose a phylogenetically preserved corticomotor mechanism supporting adaptive behavior in response to unexpected events.

Transient Attention Gates Access Consciousness: Coupling N2pc and P3 Latencies Using Dynamic Time Warping.

The N2pc and P3 event-related potentials (ERPs), used to index selective attention and access to working memory and conscious awareness, respectively, have been important tools in cognitive sciences. Although it is likely that these two components and the underlying cognitive processes are temporally and functionally linked, such links have not yet been convincingly demonstrated. Adopting a novel methodological approach based on dynamic time warping (DTW), we provide evidence that the N2pc and P3 ERP components are temporally linked. We analyzed data from an experiment where 23 participants (16 women) monitored bilateral rapid serial streams of letters and digits in order to report a target digit indicated by a shape cue, separately for trials with correct responses and trials where a temporally proximal distractor was reported instead (distractor intrusion). DTW analyses revealed that N2pc and P3 latencies were correlated in time, both when the target or a distractor was reported. Notably, this link was weaker on distractor intrusion trials. This N2pc-P3 association is discussed with respect to the relationship between attention and access consciousness. Our results demonstrate that our novel method provides a valuable approach for assessing temporal links between two cognitive processes and their underlying modulating factors. This method allows to establish links and their modulator for any two time-series across all domains of the field (general-purpose MATLAB functions and a Python module are provided alongside this paper).

Risk-Taking Is Associated with Decreased Subjective Value Signals and Increased Prediction Error Signals in the Hot Columbia Card Task.

It remains a pressing concern to understand how neural computations relate to risky decisions. However, most observations of brain-behavior relationships in the risk-taking domain lack a rigorous computational basis or fail to emulate of the dynamic, sequential nature of real-life risky decision-making. Recent advances emphasize the role of neural prediction error (PE) signals. We modeled, according to prospect theory, the choices of n = 43 human participants (33 females, 10 males) performing an EEG version of the hot Columbia Card Task, featuring rounds of sequential decisions between stopping (safe option) and continuing with increasing odds of a high loss (risky option). Single-trial regression EEG analyses yielded a subjective value signal at centroparietal (300-700 ms) and frontocentral (>800 ms) electrodes and in the delta band, as well as PE signals tied to the feedback-related negativity, P3a, and P3b, and in the theta band. Higher risk preference (total number of risky choices) was linked to attenuated subjective value signals but increased PE signals. Higher P3-like activity associated with the most positive PE in each round predicted stopping in the present round but not risk-taking in the subsequent round. Our findings indicate that decreased representation of decision values and increased sensitivity to winning despite low odds (positive PE) facilitate risky choices at the subject level. Strong neural responses when gains are least expected (the most positive PE on each round) adaptively contribute to safer choices at the trial-by-trial level but do not affect risky choice at the round-by-round level.

Cerebral Gray Matter May Not Explain Sleep Slow-Wave Characteristics after Severe Brain Injury.

Sleep slow waves are the hallmark of deeper non-rapid eye movement sleep. It is generally assumed that gray matter properties predict slow-wave density, morphology, and spectral power in healthy adults. Here, we tested the association between gray matter volume (GMV) and slow-wave characteristics in 27 patients with moderate-to-severe traumatic brain injury (TBI, 32.0 ± 12.2 years old, eight women) and compared that with 32 healthy controls (29.2 ± 11.5 years old, nine women). Participants underwent overnight polysomnography and cerebral MRI with a 3 Tesla scanner. A whole-brain voxel-wise analysis was performed to compare GMV between groups. Slow-wave density, morphology, and spectral power (0.4-6 Hz) were computed, and GMV was extracted from the thalamus, cingulate, insula, precuneus, and orbitofrontal cortex to test the relationship between slow waves and gray matter in regions implicated in the generation and/or propagation of slow waves. Compared with controls, TBI patients had significantly lower frontal and temporal GMV and exhibited a subtle decrease in slow-wave frequency. Moreover, higher GMV in the orbitofrontal cortex, insula, cingulate cortex, and precuneus was associated with higher slow-wave frequency and slope, but only in healthy controls. Higher orbitofrontal GMV was also associated with higher slow-wave density in healthy participants. While we observed the expected associations between GMV and slow-wave characteristics in healthy controls, no such associations were observed in the TBI group despite lower GMV. This finding challenges the presumed role of GMV in slow-wave generation and morphology.

Influence of Large-Scale Brain State Dynamics on the Evoked Response to Brain Stimulation.

Understanding how spontaneous brain activity influences the response to neurostimulation is crucial for the development of neurotherapeutics and brain-computer interfaces. Localized brain activity is suggested to influence the response to neurostimulation, but whether fast-fluctuating (i.e., tens of milliseconds) large-scale brain dynamics also have any such influence is unknown. By stimulating the prefrontal cortex using combined transcranial magnetic stimulation (TMS) and electroencephalography, we examined how dynamic global brain state patterns, as defined by microstates, influence the magnitude of the evoked brain response. TMS applied during what resembled the canonical Microstate C was found to induce a greater evoked response for up to 80 ms compared with other microstates. This effect was found in a repeated experimental session, was absent during sham stimulation, and was replicated in an independent dataset. Ultimately, ongoing and fast-fluctuating global brain states, as probed by microstates, may be associated with intrinsic fluctuations in connectivity and excitation-inhibition balance and influence the neurostimulation outcome. We suggest that the fast-fluctuating global brain states be considered when developing any related paradigms.

Belief Updating during Social Interactions: Neural Dynamics and Causal Role of Dorsomedial Prefrontal Cortex.

In competitive interactions, humans have to flexibly update their beliefs about another person's intentions in order to adjust their own choice strategy, such as when believing that the other may exploit their cooperativeness. Here we investigate both the neural dynamics and the causal neural substrate of belief updating processes in humans. We used an adapted prisoner's dilemma game in which participants explicitly predicted the coplayer's actions, which allowed us to quantify the prediction error between expected and actual behavior. First, in an EEG experiment, we found a stronger medial frontal negativity (MFN) for negative than positive prediction errors, suggesting that this medial frontal ERP component may encode unexpected defection of the coplayer. The MFN also predicted subsequent belief updating after negative prediction errors. In a second experiment, we used transcranial magnetic stimulation (TMS) to investigate whether the dorsomedial prefrontal cortex (dmPFC) causally implements belief updating after unexpected outcomes. Our results show that dmPFC TMS impaired belief updating and strategic behavioral adjustments after negative prediction errors. Taken together, our findings reveal the time course of the use of prediction errors in social decisions and suggest that the dmPFC plays a crucial role in updating mental representations of others' intentions.

CDKL5-mediated developmental tuning of neuronal excitability and concomitant regulation of transcriptome.

Cyclin-dependent kinase-like 5 (CDKL5) is a serine-threonine kinase enriched in the forebrain to regulate neuronal development and function. Patients with CDKL5 deficiency disorder (CDD), a severe neurodevelopmental condition caused by mutations of CDKL5 gene, present early-onset epilepsy as the most prominent feature. However, spontaneous seizures have not been reported in mouse models of CDD, raising vital questions on the human-mouse differences and the roles of CDKL5 in early postnatal brains. Here, we firstly measured electroencephalographic (EEG) activities via a wireless telemetry system coupled with video-recording in neonatal mice. We found that mice lacking CDKL5 exhibited spontaneous epileptic EEG discharges, accompanied with increased burst activities and ictal behaviors, specifically at postnatal day 12 (P12). Intriguingly, those epileptic spikes disappeared after P14. We next performed an unbiased transcriptome profiling in the dorsal hippocampus and motor cortex of Cdkl5 null mice at different developmental timepoints, uncovering a set of age-dependent and brain region-specific alterations of gene expression in parallel with the transient display of epileptic activities. Finally, we validated multiple differentially expressed genes, such as glycine receptor alpha 2 and cholecystokinin, at the transcript or protein levels, supporting the relevance of these genes to CDKL5-regulated excitability. Our findings reveal early-onset neuronal hyperexcitability in mouse model of CDD, providing new insights into CDD etiology and potential molecular targets to ameliorate intractable neonatal epilepsy.

Mapping interictal discharges using intracranial EEG-fMRI to predict postsurgical outcomes.

Various subjective and objective methods have been proposed to identify which interictal epileptiform discharge (IED)-related EEG-fMRI results are more likely to delineate seizure generating tissue in patients with drug-resistant focal epilepsy for the purposes of surgical planning. In this intracranial EEG-fMRI study, we evaluated the utility of these methods to localize clinically relevant regions pre-operatively and compared the extent of resection of these areas to post-operative outcome. Seventy patients admitted for intracranial video-EEG monitoring were recruited for a simultaneous intracranial EEG-fMRI study. For all analyses of blood oxygen level-dependent responses associated with IEDs, an experienced epileptologist identified the most Clinically Relevant brain activation cluster using available clinical information. The Maximum cluster (the cluster with the highest z-score) was also identified for all analyses and assigned to one of three confidence levels (low, medium, or high) based on the difference of the peak z-scores between the Maximum and Second Maximum cluster (the cluster with the second highest peak z-value). The distance was measured and compared between the peak voxel of the aforementioned clusters and the electrode contacts where the interictal discharge and seizure onset were recorded. In patients who subsequently underwent epilepsy surgery, the spatial concordance between the aforementioned clusters and the area of resection was determined and compared to post-operative outcome. We evaluated 106 different IEDs in 70 patients. Both subjective (identification of the Clinically Relevant cluster) and objective (Maximum cluster much more significant than the second maximum cluster) methods of culling non-localizing EEG-fMRI activation maps increased the spatial concordance between these clusters and the corresponding IED or seizure onset zone contacts. However, only the objective methods of identifying medium and high confidence maps resulted in a significant association between resection of the peak voxel of the Maximum cluster and post-operative outcome. Resection of this area was associated with good post-operative outcomes but was not sufficient for seizure freedom. On the other hand, we found that failure to resect the medium and high confidence Maximum clusters was associated with a poor post-surgical outcome (negative predictive value = 1.0, sensitivity = 1.0). Objective methods to identify higher confidence EEG-fMRI results are needed to localize areas necessary for good post-operative outcomes. However, resection of the peak voxel within higher confidence Maximum clusters is not sufficient for good outcomes. Conversely, failure to resect the peak voxel in these clusters is associated with a poor post-surgical outcome.

Anatomo-functional basis of emotional and motor resonance elicited by facial expressions.

Simulation theories predict that the observation of other's expressions modulates neural activity in the same centres controlling their production. This hypothesis has been developed by two models, postulating that the visual input is directly projected either to the motor system for action recognition (motor resonance) or to emotional/interoceptive regions for emotional contagion and social synchronization (emotional resonance). Here we investigated the role of frontal/insular regions in the processing of observed emotional expressions by combining intracranial recording, electrical stimulation and effective connectivity. First, we intracranially recorded from prefrontal, premotor or anterior insular regions of 44 patients during the passive observation of emotional expressions, finding widespread modulations in prefrontal/insular regions (anterior cingulate cortex, anterior insula, orbitofrontal cortex and inferior frontal gyrus) and motor territories (Rolandic operculum and inferior frontal junction). Subsequently, we electrically stimulated the activated sites, finding that (i) in the anterior cingulate cortex and anterior insula, the stimulation elicited emotional/interoceptive responses, as predicted by the 'emotional resonance model'; (ii) in the Rolandic operculum it evoked face/mouth sensorimotor responses, in line with the 'motor resonance' model; and (iii) all other regions were unresponsive or revealed functions unrelated to the processing of facial expressions. Finally, we traced the effective connectivity to sketch a network-level description of these regions, finding that the anterior cingulate cortex and the anterior insula are reciprocally interconnected while the Rolandic operculum is part of the parieto-frontal circuits and poorly connected with the former. These results support the hypothesis that the pathways hypothesized by the 'emotional resonance' and the 'motor resonance' models work in parallel, differing in terms of spatio-temporal fingerprints, reactivity to electrical stimulation and connectivity patterns.

The interictal suppression hypothesis is the dominant differentiator of seizure onset zones in focal epilepsy.

Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8-13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment.

Distinct neural markers of evidence accumulation index metacognitive processing before and after simple visual decisions.

Perceptual decision-making is affected by uncertainty arising from the reliability of incoming sensory evidence (perceptual uncertainty) and the categorization of that evidence relative to a choice boundary (categorical uncertainty). Here, we investigated how these factors impact the temporal dynamics of evidence processing during decision-making and subsequent metacognitive judgments. Participants performed a motion discrimination task while electroencephalography was recorded. We manipulated perceptual uncertainty by varying motion coherence, and categorical uncertainty by varying the angular offset of motion signals relative to a criterion. After each trial, participants rated their desire to change their mind. High uncertainty impaired perceptual and metacognitive judgments and reduced the amplitude of the centro-parietal positivity, a neural marker of evidence accumulation. Coherence and offset affected the centro-parietal positivity at different time points, suggesting that perceptual and categorical uncertainty affect decision-making in sequential stages. Moreover, the centro-parietal positivity predicted participants' metacognitive judgments: larger predecisional centro-parietal positivity amplitude was associated with less desire to change one's mind, whereas larger postdecisional centro-parietal positivity amplitude was associated with greater desire to change one's mind, but only following errors. These findings reveal a dissociation between predecisional and postdecisional evidence processing, suggesting that the CPP tracks potentially distinct cognitive processes before and after a decision.

Cortical activations associated with spatial remapping of finger touch using EEG.

The spatial coding of tactile information is functionally essential for touch-based shape perception and motor control. However, the spatiotemporal dynamics of how tactile information is remapped from the somatotopic reference frame in the primary somatosensory cortex to the spatiotopic reference frame remains unclear. This study investigated how hand position in space or posture influences cortical somatosensory processing. Twenty-two healthy subjects received electrical stimulation to the right thumb (D1) or little finger (D5) in three position conditions: palm down on right side of the body (baseline), hand crossing the body midline (effect of position), and palm up (effect of posture). Somatosensory-evoked potentials (SEPs) were recorded using electroencephalography. One early-, two mid-, and two late-latency neurophysiological components were identified for both fingers: P50, P1, N125, P200, and N250. D1 and D5 showed different cortical activation patterns: compared with baseline, the crossing condition showed significant clustering at P1 for D1, and at P50 and N125 for D5; the change in posture showed a significant cluster at N125 for D5. Clusters predominated at centro-parietal electrodes. These results suggest that tactile remapping of fingers after electrical stimulation occurs around 100-125 ms in the parietal cortex.

Top-down and bottom-up oscillatory dynamics regulate implicit visuomotor sequence learning.

Implicit visuomotor sequence learning is crucial for acquiring skills that result in automated behaviors. The oscillatory dynamics underpinning this learning process are not well understood. To address this gap, the current study employed electroencephalography with a medium-density array (64 electrodes) to investigate oscillatory activity associated with implicit visuomotor sequence learning in the Serial Reaction Time task. In the task, participants unknowingly learn a series of finger movements. Eighty-five healthy adults participated in the study. Analyses revealed that theta activity at the vertex and alpha/beta activity over the motor areas decreased over the course of learning. No associations between alpha/beta and theta power were observed. These findings are interpreted within a dual-process framework: midline theta activity is posited to regulate top-down attentional processes, whereas beta activity from motor areas underlies the bottom-up encoding of sensory information from movement. From this model, we suggest that during implicit visuomotor sequence learning, top-down processes become disengaged (indicated by a reduction in theta activity), and modality specific bottom-up processes encode the motor sequence (indicated by a reduction in alpha/beta activity).

My choice, my actions: self-determination, not instrumental value of outcomes enhances outcome monitoring during learning.

Freedom of choice enhances our sense of agency. During goal-directed behavior, the freedom to choose between different response options increases the neural processing of positive and negative feedback, indicating enhanced outcome monitoring under conditions of high agency experience. However, it is unclear whether this enhancement is predominantly driven by an increased salience of self- compared to externally determined action outcomes or whether differences in the perceived instrumental value of outcomes contribute to outcome monitoring in goal-directed tasks. To test this, we recorded electroencephalography while participants performed a reinforcement learning task involving free choices, action-relevant forced choices, and action-irrelevant forced choices. We observed larger midfrontal theta power and N100 amplitudes for feedback following free choices compared with action-relevant and action-irrelevant forced choices. In addition, a Reward Positivity was only present for free but not forced choice outcomes. Crucially, our results indicate that enhanced outcome processing is not driven by the relevance of outcomes for future actions but rather stems from the association of outcomes with recent self-determined choice. Our findings highlight the pivotal role of self-determination in tracking the consequences of our actions and contribute to an understanding of the cognitive processes underlying the choice-induced facilitation in outcome monitoring.

Exploring functional connectivity in large-scale brain networks in obsessive-compulsive disorder: a systematic review of EEG and fMRI studies.

Obsessive-compulsive disorder (OCD) is a debilitating psychiatric condition that is difficult to treat due to our limited understanding of its pathophysiology. Functional connectivity in brain networks, as evaluated through neuroimaging studies, plays a pivotal role in understanding OCD. While both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been extensively employed in OCD research, few have fully synthesized their findings. To bridge this gap, we reviewed 166 studies (10 EEG, 156 fMRI) published up to December 2023. In EEG studies, OCD exhibited lower connectivity in delta and alpha bands, with inconsistent findings in other frequency bands. Resting-state fMRI studies reported conflicting connectivity patterns within the default mode network (DMN) and sensorimotor cortico-striato-thalamo-cortical (CSTC) circuitry. Many studies observed decreased resting-state connectivity between the DMN and salience network (SN), implicating the 'triple network model' in OCD. Task-related hyperconnectivity within the DMN-SN and hypoconnectivity between the SN and frontoparietal network suggest OCD-related cognitive inflexibility, potentially due to triple network dysfunction. In conclusion, our review highlights diverse connectivity differences in OCD, revealing complex brain network interplay that contributes to symptom manifestation. However, the presence of conflicting findings underscores the necessity for targeted research to achieve a comprehensive understanding of the pathophysiology of OCD.