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.

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.

35Neural oscillations are thought to provide a cyclic time frame for orchestrating brain 36 computations. Following this assumption, midfrontal theta oscillations have recently been 37 proposed to temporally organize brain computations during conflict processing. Using a 38 multivariate analysis approach, we show that brain-behavior relationships during conflict 39 tasks are modulated according to the phase of ongoing endogenous midfrontal theta 40 oscillations recorded by scalp EEG. We found reproducible results in two independent 41 datasets, using two different conflict tasks: brain-behavior relationships (correlation between 42 reaction time and theta power) were theta phase-dependent in a subject-specific manner, and 43 these "behaviorally optimal" theta phases were also associated with fronto-parietal cross-44 frequency dynamics emerging as theta phase-locked beta power bursts. These effects were 45 present regardless of the strength of conflict. Thus, these results provide empirical evidence 46 that midfrontal theta oscillations are involved in cyclically orchestrating brain computations 47 likely related to response execution during the tasks rather than purely related to conflict 48 processing. More generally, this study supports the hypothesis that phase-based computation 49 is an important mechanism giving rise to cognitive processing. 50 51

ObjectiveThe decrease in verbal fluency in patients with Parkinson’s disease (PD) undergoing subthalamic nucleus deep brain stimulation (STN-DBS) is usually assumed to reflect a frontal lobe-related cognitive dysfunction, although evidence for this is lacking.MethodsTo explore its underlying mechanisms, we combined neuropsychological, psychiatric and motor assessments with an examination of brain metabolism using F-18 fluorodeoxyglucose positron emission tomography, in 26 patients with PD, 3 months before and after surgery. We divided these patients into two groups, depending on whether or not they exhibited a postoperative deterioration in either phonemic (10 patients) or semantic (8 patients) fluency. We then compared the STN-DBS groups with and without verbal deterioration on changes in clinical measures and brain metabolism.ResultsWe did not find any neuropsychological change supporting the presence of an executive dysfunction in patients with a deficit in either phonemic or semantic fluency. Similarly, a comparison of patients with or without impaired fluency on brain metabolism failed to highlight any frontal areas involved in cognitive functions. However, greater changes in cognitive slowdown and apathy were observed in patients with a postoperative decrease in verbal fluency.ConclusionsThese results suggest that frontal lobe-related cognitive dysfunction could play only a minor role in the postoperative impairment of phonemic or semantic fluency, and that cognitive slowdown and apathy could have a more decisive influence. Furthermore, the phonemic and semantic impairments appeared to result from the disturbance of distinct mechanisms.

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.

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.

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.

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 good spatio-temporal resolution, while reducing mixing and volume conduction effects. However, the method is still immature and several methodological issues should be carefully accounted for to avoid pitfalls. Therefore, optimizing the EEG source connectivity pipelines is required, which involves the evaluation of several parameters. One key issue to address those evaluation aspects is the availability of a ‘ground truth’. 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 (epileptiform activity) 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 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 show that a high electrode density (at least 64 channels) is needed 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). The COALIA model and the simulations used in this paper are freely available and made accessible for the community. We believe that this model-based approach will help researchers to address some current and future cognitive and clinical neuroscience questions, and ultimately transform EEG brain network imaging into a mature technology.