Thresholds and mechanisms of human magnetophosphene perception induced by low frequency sinusoidal magnetic fields.

BACKGROUND: Virtually everyone is exposed to power-frequency MF (50/60 Hz), inducing in our body electric fields and currents, potentially modulating brain function. MF-induced electric fields within the central nervous system can generate flickering visual perceptions (magnetophosphenes), which form the basis of international MF exposure guidelines and recommendations protecting workers and the general public. However, magnetophosphene perception thresholds were estimated 40 years ago in a small, unreplicated study with significant uncertainties and leaving open the question of the involved interaction site. METHODS: We used a stimulation modality termed transcranial alternating magnetic stimulation (tAMS), delivering in situ sinusoidal electric fields comparable to transcranial alternating current stimulation (tACS). Magnetophosphene perception was quantified in 81 volunteers exposed to MF (eye or occipital exposure) between 0 and 50 mT at frequencies of 20, 50, 60 and 100 Hz. RESULTS: Reliable magnetophosphene perception was induced with tAMS without any scalp sensation, a major advantage as compared to tACS. Frequency-dependent thresholds were quantified using binary logistic regressions hence allowing to establish condition dependent probabilities of perception. Results support an interaction between induced current density and retinal rod cells. CONCLUSION: Beyond fundamental and immediate implications for international safety guidelines, and for identifying the interaction site underlying phosphene perception (ubiquitous in tACS experiments), our results support exploring the potential of tAMS for the differential diagnosis of retinal disorders and neuromodulation therapy.

Identification of Parkinson's Disease Subtypes from Resting-State Electroencephalography.

BACKGROUND: Parkinson's disease (PD) patients present with a heterogeneous clinical phenotype, including motor, cognitive, sleep, and affective disruptions. However, this heterogeneity is often either ignored or assessed using only clinical assessments. OBJECTIVES: We aimed to identify different PD sub-phenotypes in a longitudinal follow-up analysis and their electrophysiological profile based on resting-state electroencephalography (RS-EEG) and to assess their clinical significance over the course of the disease. METHODS: Using electrophysiological features obtained from RS-EEG recordings and data-driven methods (similarity network fusion and source-space spectral analysis), we have performed a clustering analysis to identify disease sub-phenotypes and we examined whether their different patterns of disruption are predictive of disease outcome. RESULTS: We showed that PD patients (n = 44) can be sub-grouped into three phenotypes with distinct electrophysiological profiles. These clusters are characterized by different levels of disruptions in the somatomotor network (Δ and ß band), the frontotemporal network (α2 band) and the default mode network (α1 band), which consistently correlate with clinical profiles and disease courses. These clusters are classified into either moderate (only-motor) or mild-to-severe (diffuse) disease. We showed that EEG features can predict cognitive evolution of PD patients from baseline, when the cognitive clinical scores were overlapped. CONCLUSIONS: The identification of novel PD subtypes based on electrical brain activity signatures may provide a more accurate prognosis in individual patients in clinical practice and help to stratify subgroups in clinical trials. Innovative profiling in PD can also support new therapeutic strategies that are brain-based and designed to modulate brain activity disruption. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study.

Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of connectivity patterns in this so-called "resting-state" has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different analytical choices induce significant discrepancies in results and drawn conclusions, thereby hindering the reproducibility of neuroimaging research. Hence, our objective in this study was to shed light on the effect of analytical variability on outcome consistency by evaluating the implications of parameters involved in the EEG source connectivity analysis on the accuracy of resting-state networks (RSNs) reconstruction. We simulated, using neural mass models, EEG data corresponding to two RSNs, namely the default mode network (DMN) and dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low-resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure, high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed significant variability in the performance of the tested inverse solutions and connectivity measures. Such methodological variability and absence of analysis standardization represent a critical issue for neuroimaging studies that should be prioritized. We believe that this work could be useful for the field of electrophysiology connectomics, by increasing awareness regarding the challenge of variability in methodological approaches and its implications on reported results.

Methods Used to Estimate EEG Source-Space Networks: A Comparative Simulation-Based Study.

Along with the study of the brain activity evoked by external stimuli, an important advance in current neuroscience involves understanding the spontaneous brain activity that occurs during resting conditions. Interestingly, the identification of the connectivity patterns in "resting-state" has been the subject of a great number of electrophysiology-based studies. In this context, the Electroencephalography (EEG) source connectivity method enables estimating resting-state cortical networks from scalp-EEG recordings. However, there is still no consensus over a unified pipeline adapted in all cases (e.g., type of task, a priori on studied networks) and numerous methodological questions remain unanswered. In order to address this problem, we simulated, using neural mass models, EEG data corresponding to the default mode network (DMN), the most widely studied resting-state network, and tested the effect of different channel densities, two inverse solutions and two functional connectivity measures on the correspondence between the reconstructed networks and the reference networks. Results showed that increasing the number of electrodes enhances the accuracy of the network reconstruction, and that eLORETA/PLV led to better accuracy than other inverse solution/connectivity measure combinations in terms of the correlation between reconstructed and reference connectivity matrices. This work has a wide range of implications in the field of electrophysiology connectomics, and is a step towards a convergence and standardization of approaches in this emerging field.

Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients.

Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.

Mean-Field Modeling of Brain-Scale Dynamics for the Evaluation of EEG Source-Space Networks.

Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called "electroencephalography (EEG) source connectivity" has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted to avoid pitfalls. Thus, a validation toolbox that provides flexible "ground truth" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the "EEG source connectivity" pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.

Dynamics of cognitive action control in late-life depression during action selection.

INTRODUCTION: Late-Life Depression (LLD) is characterized by deficits in cognitive control. We investigated the effect of LLD on a subset of cognitive control functions, the Cognitive Action Control (CAC), distinguishing on-line and adaptive control. METHODS: We compared LLD subjects (n = 31) and Healthy Controls (HC, n = 31) on their performance in a Simon task. The online congruency effect and adaptive effect were compared for reaction times (RT) and accuracy rates between the groups using mixed models. We applied distributional analyses of RT to differentiate the strength of impulsive action selection and the proficiency of selective action suppression. Finally, we measured correlations between the performances on the task and clinical scores of the LLD group. RESULTS: LLD had increased error rates in congruent trials compared to HC. Conversely, the adaptive CAC was equivalent between the groups. Distributional analyses showed that the fastest actions were less led by pertinent information in LLD. This phenomenon was found exclusively for congruent trials preceded by non-congruent trials. On the other hand, LLD patients, when they take time, were better than HC to suppress selectively non-relevant information. No difference was observed for adaptation to the preceding condition. No association between behavioral measurements and clinical scores were found. CONCLUSION: Our results suggest that LLD participants have a specific cognitive disturbance of CAC, showing less facilitation than HC in congruent situations. We propose that this originates in a difficulty in LLD patients in disengaging their attention from conflict situations, which is consistent with a biased CAC to aversive stimuli in depression.

Synchronization between Keyboard Typing and Neural Oscillations.

Rhythmic neural activity synchronizes with certain rhythmic behaviors, such as breathing, sniffing, saccades, and speech. The extent to which neural oscillations synchronize with higher-level and more complex behaviors is largely unknown. Here, we investigated electrophysiological synchronization with keyboard typing, which is an omnipresent behavior daily engaged by an uncountably large number of people. Keyboard typing is rhythmic, with frequency characteristics roughly the same as neural oscillatory dynamics associated with cognitive control, notably through midfrontal theta (4-7 Hz) oscillations. We tested the hypothesis that synchronization occurs between typing and midfrontal theta and breaks down when errors are committed. Thirty healthy participants typed words and sentences on a keyboard without visual feedback, while EEG was recorded. Typing rhythmicity was investigated by interkeystroke interval analyses and by a kernel density estimation method. We used a multivariate spatial filtering technique to investigate frequency-specific synchronization between typing and neuronal oscillations. Our results demonstrate theta rhythmicity in typing (around 6.5 Hz) through the two different behavioral analyses. Synchronization between typing and neuronal oscillations occurred at frequencies ranging from 4 to 15 Hz, but to a larger extent for lower frequencies. However, peak synchronization frequency was idiosyncratic across participants, therefore not specific to theta nor to midfrontal regions, and correlated somewhat with peak typing frequency. Errors and trials associated with stronger cognitive control were not associated with changes in synchronization at any frequency. As a whole, this study shows that brain-behavior synchronization does occur during keyboard typing but is not specific to midfrontal theta.

Functional Role of the Cerebellum in Parkinson Disease: A PET Study.

OBJECTIVES: To test for cerebellar involvement in motor and nonmotor impairments in Parkinson disease (PD) and to determine patterns of metabolic correlations with supratentorial brain structures, we correlated clinical motor, cognitive, and psychiatric scales with cerebellar metabolism. METHODS: We included 90 patients with PD. Motor, cognitive, and psychiatric domains were assessed, and resting-state 18FDG-PET metabolic imaging was performed. The motor, cognitive, and psychiatric scores were entered separately into a principal component analysis. We looked for correlations between these 3 principal components and cerebellar metabolism. Furthermore, we extracted the mean glucose metabolism value for each significant cerebellar cluster and looked for patterns of cerebrum-cerebellum metabolic correlations. RESULTS: Severity of impairment was correlated with increased metabolism in the anterior lobes and vermis (motor domain); the right crus I, crus II, and declive (cognitive domain); and the right crus I and crus II (psychiatric domain). No results survived multiple testing corrections regarding the psychiatric domain. Moreover, we found distributed and overlapping, but not identical, patterns of metabolic correlations for motor and cognitive domains. Specific supratentorial structures (cortical structures, basal ganglia, and thalamus) were strongly correlated with each of the cerebellar clusters. CONCLUSIONS: These results confirm the role of the cerebellum in nonmotor domains of PD, with differential but overlapping patterns of metabolic correlations suggesting the involvement of cerebello-thalamo-striatal-cortical loops.

Midfrontal theta phase coordinates behaviorally relevant brain computations during cognitive control.

Neural oscillations are thought to provide a cyclic time frame for orchestrating brain computations. Following this assumption, midfrontal theta oscillations have recently been proposed to temporally organize brain computations during conflict processing. Using a multivariate analysis approach, we show that brain-behavior relationships during conflict tasks are modulated according to the phase of ongoing endogenous midfrontal theta oscillations recorded by scalp EEG. We found reproducible results in two independent datasets, using two different conflict tasks: brain-behavior relationships (correlation between reaction time and theta power) were theta phase-dependent in a subject-specific manner, and these "behaviorally optimal" theta phases were also associated with fronto-parietal cross-frequency dynamics emerging as theta phase-locked beta power bursts. These effects were present regardless of the strength of conflict. Thus, these results provide empirical evidence that midfrontal theta oscillations are involved in cyclically orchestrating brain computations likely related to response execution during the tasks rather than purely related to conflict processing. More generally, this study supports the hypothesis that phase-based computation is an important mechanism giving rise to cognitive processing.

Subthalamic nucleus local field potentials recordings reveal subtle effects of promised reward during conflict resolution in Parkinson's disease.

Cognitive action control depends on cortical-subcortical circuits, involving notably the subthalamic nucleus (STN), as evidenced by local field potentials recordings (LFPs) studies. The STN consistently shows an increase in theta oscillations power during conflict resolution. Some studies have shown that cognitive action control in Parkinson's disease (PD) could be influenced by the occurrence of monetary reward. In this study, we investigated whether incentive motivation could modulate STN activity, and notably STN theta activity, during response conflict resolution. To achieve this objective, we recorded STN LFPs during a motivated Simon task in PD patients who had undergone deep brain stimulation surgery. Behavioral results revealed that promised rewards increased the difficulty in resolving conflict situations, thus replicating previous findings. Signal analyses locked on the imperative stimulus onset revealed the typical pattern of increased theta power in a conflict situation. However, this conflict-related modulation of theta power was not influenced by the size of the reward cued. We nonetheless identified a significant effect of the reward size on local functional organization (indexed by inter-trial phase clustering) of theta oscillations, with higher organization associated with high rewards while resolving conflict. When focusing on the period following the onset of the reward cue, we unveiled a stronger beta power decrease in higher reward conditions. However, these LFPs results were not correlated to behavioral results. Our study suggests that the STN is involved in how reward information can influence computations during conflict resolution. However, considering recent studies as well as the present results, we suspect that these effects are subtle.

Effects of continuous subcutaneous apomorphine infusion in Parkinson's disease without cognitive impairment on motor, cognitive, psychiatric symptoms and quality of life.

INTRODUCTION: Treatment optimization using continuous subcutaneous apomorphine infusion (CSAI) improves the control of motor fluctuations of patients with Parkinson's disease (PD). Although CSAI seems to be cognitively and behaviorally safe and to improve the quality of life, very few studies have investigated its influence in these domains, especially in patients without cognitive impairment. METHODS: We estimated the impact of CSAI on motor symptoms, cognition, psychiatric domains and quality of life in parkinsonian patients without cognitive impairment by comparing the scores of 22 patients assessed before and 6 months after the start of add-on CSAI. RESULTS: Optimized treatment with CSAI was associated with i) reduced motor fluctuations, ii) unchanged cognition, iii) unchanged psychiatric domains, and iv) improved quality of life in physical and psychological aspects. CONCLUSION: In PD patients without cognitive impairment, CSAI improves motor symptoms and quality of life and, as suggested by previous studies, alters neither cognition nor mental health.

Continuous subcutaneous apomorphine infusion does not impair the dynamics of cognitive action control in mild to moderate Parkinson's disease.

INTRODUCTION: Continuous subcutaneous apomorphine infusion (CSAI) is increasingly used in Parkinson's disease (PD), notably in patients contraindicated for subthalamic deep brain stimulation. Although it has been suggested that CSAI is safe regarding cognition, few studies have actually investigated its effect, especially on cognitive control which is a crucial process for goal-directed behavior. More specifically, its impact on the dynamics of cognitive action control, as reflected by the activation and suppression of impulsive responses, has yet to be investigated, which is the objective of the present study. METHODS: We compared cognitive action control between baseline (M0) and 6 months (M6) after the start of add-on CSAI by administering an oculomotor Simon task to 20 patients with mild to moderate PD. We used the activation-suppression model to determine whether CSAI had an effect on either the impulsive errors made in conflict situations or the suppression of these responses. RESULTS: We found no difference between M0 and M6 in the congruence effect regarding either reaction time or accuracy, indicating that overall conflict resolution was not influenced by CSAI. Furthermore, the rate of fast errors in the conflict situation and the last slope of the delta plots (reflecting the strength of impulsive response suppression) were unaffected by the treatment. The 95% confidence intervals calculated for the treatment effect on both of these measures fell below the range of usual meaningful effects. CONCLUSION: We found no difference between M0 and M6, which strongly suggests that CSAI does not impair the dynamics of cognitive action control.

Impulsive oculomotor action selection in Parkinson's disease.

The effects of Parkinson's disease (PD) on the dynamics of impulsive action selection and suppression have recently been studied using distributional analyses, but with mixed results, especially for selection. Furthermore, some authors have suggested that impulsivity, regarded as a personality trait, shares common features with behavioral tasks' measures. The current study was designed to clarify the impact of PD on impulsive action selection and suppression, and investigate the link between cognitive action control and self-reported impulsivity. We administered an oculomotor version of the Simon task to 32 patients with PD and 32 matched healthy controls (HC), and conducted distributional analyses in accordance with the activation-suppression model. Patients and HC also filled out the Barratt Impulsiveness Scale (BIS) questionnaire. Results showed that patients with PD were faster overall and exhibited a greater congruence effect than HC. They also displayed enhanced impulsive action selection. By contrast, the suppression of impulsive responses was similar across both groups. Furthermore, patients had higher impulsivity scores, which were correlated with higher impulsive action selection and higher suppression. Our study yielded two interesting findings. First, PD resulted in a higher number of fast errors. The activation-suppression model suggests that patients with PD are more susceptible to the impulsive action selection induced by the irrelevant stimulus dimension. Second, impulsive action selection and suppression were both associated with trait impulsivity, as measured by the BIS, indicating that these two aspects of impulsivity share common features.

Enhanced Impulsive Action Selection in Middle-Aged Adults-Insights From an Oculomotor Simon Task.

Several studies have investigated the age-related impact in cognitive action control. However, to our knowledge, none of the studies have focused on the effect of moderate age on the strength of automatic activation according to the activation-suppression model. We therefore investigated the effect of moderate age on cognitive action control using an oculomotor version of the Simon task and distributional analyses. A group of middle-aged (n = 39; 57 ± 9 years) healthy adults were compared to a group of young healthy participants (n = 43; 24 ± 3 years). We first analyzed the overall impact of age on the congruence effect and then used conditional accuracy functions (CAFs) and delta plots to assess the strength of automatic activation and selective inhibition, respectively. Compared to young participants, middle-aged participants showed a greater congruence effect as well as higher rates of fast errors in conflict situations indicating an enhanced impulsive action selection. Furthermore, the overall downward slope of the congruence effect's evolution was significantly steeper in older participants and the last slope tended to be significantly steeper. This may indicate that the middle-aged participants exerted a stronger selective inhibition. Our results suggest that middle-aged adults are more prone to impulsive action selection than young adults. Recent theories postulate that older adults might implement compensatory mechanisms to supply cognitive difficulties. This is in line with our results suggesting a potential greater selective inhibition. Overall, this study proposes that moderate aging impacts both processes of impulsive response selection and suppression underlying cognitive action control.

Influence of subthalamic deep-brain stimulation on cognitive action control in incentive context.

Subthalamic nucleus deep-brain stimulation (STN-DBS) is an effective treatment in Parkinson's disease (PD), but can have cognitive side effects, such as increasing the difficulty of producing appropriate responses when a habitual but inappropriate responses represent strong alternatives. STN-DBS also appears to modulate representations of incentives such as monetary rewards. Furthermore, conflict resolution can be modulated by incentive context. We therefore used a rewarded Simon Task to assess the influence of promised rewards on cognitive action control in 50 patients with PD, half of whom were being treated with STN-DBS. Results were analyzed according to the activation-suppression model. We showed that STN-DBS (i) favored the expression of motor impulsivity, as measured with the Barratt Impulsiveness Scale, (ii) facilitated the expression of incentive actions as observed with a greater increase in speed according to promised reward in patients with versus without DBS and (iii) may increase impulsive action selection in an incentive context. In addition, analysis of subgroups of implanted patients suggested that those who exhibited the most impulsive action selection had the least severe disease. This may indicate that patients with less marked disease are more at risk of developing impulsivity postoperatively. Finally, in these patients, incentive context increased the difficulty of resolving conflict situations. As a whole, the current study revealed that in patients with PD, STN-DBS affects the cognitive processes involved in conflict resolution, reward processing and the influence of promised rewards on conflict resolution.

Influence of promised rewards on conflict resolution in healthy participants and patients with Parkinson's disease.

The influence of promised rewards on conflict resolution processes is not clearly defined in the literature, and the underlying mechanisms are poorly understood. Some studies have shown no effect of reward, while others have demonstrated a beneficial influence. In addition, although the basal ganglia are known to play a critical role in the association between motivation and cognition, the influence of promised rewards on conflict resolution processes in Parkinson's disease (PD) has received little attention. In this context, we assessed the influence of promised rewards on both impulse activation and suppression in 36 healthy participants and 36 patients with PD, using a rewarded Simon task. Analysis of performances revealed that promised rewards worsened the overall congruence effect, but only in healthy participants. Although the incentive context did not modulate the congruence effect in patients, by using the activation-suppression model, we were able to show that promised rewards did influence impulse suppression in patients-but not in healthy participants. Suppressing inappropriate response activation in an incentive context appears to be harder in medically treated Parkinson's disease. This indicates that incentive motivation can modulate at least one cognitive process involved in cognitive action control in patients with medically treated PD. The activation-suppression model provides essential additional information concerning the influence of promised rewards on conflict resolution processes in a pathological population.

Does Facial Amimia Impact the Recognition of Facial Emotions? An EMG Study in Parkinson's Disease.

According to embodied simulation theory, understanding other people's emotions is fostered by facial mimicry. However, studies assessing the effect of facial mimicry on the recognition of emotion are still controversial. In Parkinson's disease (PD), one of the most distinctive clinical features is facial amimia, a reduction in facial expressiveness, but patients also show emotional disturbances. The present study used the pathological model of PD to examine the role of facial mimicry on emotion recognition by investigating EMG responses in PD patients during a facial emotion recognition task (anger, joy, neutral). Our results evidenced a significant decrease in facial mimicry for joy in PD, essentially linked to the absence of reaction of the zygomaticus major and the orbicularis oculi muscles in response to happy avatars, whereas facial mimicry for expressions of anger was relatively preserved. We also confirmed that PD patients were less accurate in recognizing positive and neutral facial expressions and highlighted a beneficial effect of facial mimicry on the recognition of emotion. We thus provide additional arguments for embodied simulation theory suggesting that facial mimicry is a potential lever for therapeutic actions in PD even if it seems not to be necessarily required in recognizing emotion as such.

Weight Gain following Pallidal Deep Brain Stimulation: A PET Study.

The mechanisms behind weight gain following deep brain stimulation (DBS) surgery seem to be multifactorial and suspected depending on the target, either the subthalamic nucleus (STN) or the globus pallidus internus (GPi). Decreased energy expenditure following motor improvement and behavioral and/or metabolic changes are possible explanations. Focusing on GPi target, our objective was to analyze correlations between changes in brain metabolism (measured with PET) and weight gain following GPi-DBS in patients with Parkinson's disease (PD). Body mass index was calculated and brain activity prospectively measured using 2-deoxy-2[18F]fluoro-D-glucose PET four months before and four months after the start of GPi-DBS in 19 PD patients. Dopaminergic medication was included in the analysis to control for its possible influence on brain metabolism. Body mass index increased significantly by 0.66 ± 1.3 kg/m2 (p = 0.040). There were correlations between weight gain and changes in brain metabolism in premotor areas, including the left and right superior gyri (Brodmann area, BA 6), left superior gyrus (BA 8), the dorsolateral prefrontal cortex (right middle gyrus, BAs 9 and 46), and the left and right somatosensory association cortices (BA 7). However, we found no correlation between weight gain and metabolic changes in limbic and associative areas. Additionally, there was a trend toward a correlation between reduced dyskinesia and weight gain (r = 0.428, p = 0.067). These findings suggest that, unlike STN-DBS, motor improvement is the major contributing factor for weight gain following GPi-DBS PD, confirming the motor selectivity of this target.

Evaluating Cognitive Action Control Using Eye-Movement Analysis: An Oculomotor Adaptation of the Simon Task.

Cognitive action control has been extensively studied using conflict tasks such as the Simon task. In most recent studies, this process has been investigated in the light of the dual route hypothesis and more specifically of the activation-suppression model using distributional analyses. Some authors have suggested that cognitive action control assessment is not specific to response modes. In this study we adapted the Simon task, using oculomotor responses instead of manual responses, in order to evaluate whether the resolution of conflict induced by a two-dimensional stimulus yielded similar results to what is usually reported in tasks with manual responses. Results obtained from 43 young healthy participants revealed the typical congruence effect, with longer reaction times (RT) and lesser accuracy in the incongruent condition. Conditional accuracy functions (CAF) also revealed a higher proportion of fast errors in the incongruent condition and delta plots confirmed that conflict resolution was easier, as the time taken to respond increased. These results are very similar to what has been reported in the literature. Furthermore, our observations are in line with the assumptions of the activation-suppression model, in which automatic activation in conflict situations is captured in the fastest responses and selective inhibition of cognitive action control needs time to build up. Altogether, our results suggest that conflict resolution has core mechanisms whatever the response mode, manual or oculomotor. Using oculomotor responses in such tasks could be of interest when investigating cognitive action control in patients with severe motor disorders.