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.

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.

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.

Cognitive symptoms in Parkinson’s disease are common and can significantly affect patients’ quality of life. Therefore, there is an urgent clinical need to identify a signature derived from behavioural and/or neuroimaging indicators that could predict which patients are at increased risk for early and rapid cognitive decline. Recently, converging evidence identified that aperiodic activity of the EEG reflects meaningful physiological information associated with age, development, cognitive and perceptual states or pathologies. In this study, we aimed to investigate aperiodic activity in Parkinson’s disease during cognitive control and characterize its possible association with behaviour. Here, we recorded high-density EEG in 30 healthy controls and 30 Parkinson’s disease patients during a Simon task. We analysed task-related behavioural data in the context of the activation–suppression model and extracted aperiodic parameters (offset, exponent) at both scalp and source levels. Our results showed lower behavioural performances in cognitive control as well as higher offsets in patients in the parieto-occipital areas, suggesting increased excitability in Parkinson’s disease. A small congruence effect on aperiodic parameters in pre- and post-central brain areas was also found, possibly associated with task execution. Significant differences in aperiodic parameters between the resting-state, pre- and post-stimulus phases were seen across the whole brain, which confirmed that the observed changes in aperiodic activity are linked to task execution. No correlation was found between aperiodic activity and behaviour or clinical features. Our findings provide evidence that EEG aperiodic activity in Parkinson’s disease is characterized by greater offsets, and that aperiodic parameters differ depending on arousal state. However, our results do not support the hypothesis that the behaviour-related differences observed in Parkinson’s disease are related to aperiodic changes. Overall, this study highlights the importance of considering aperiodic activity contributions in brain disorders and further investigating the relationship between aperiodic activity and behaviour.

The 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. To 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. We 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. These 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.