Evidence for a general performance-monitoring system in the human brain

Abnormal stability of spontaneous neuronal activity as a predictor of diagnosis conversion from major depressive disorder to bipolar disorder

OBJECTIVE

Bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD) in the early stage, which may lead to inappropriate treatment. This study aimed to characterize the alterations of spontaneous neuronal activity in patients with depressive episodes whose diagnosis transferred from MDD to BD.

METHODS

532 patients with MDD and 132 healthy controls (HCs) were recruited over 10 years. During the follow-up period, 75 participants with MDD transferred to BD (tBD), and 157 participants remained with the diagnosis of unipolar depression (UD). After excluding participants with poor image quality and excessive head movement, 68 participants with the diagnosis of tBD, 150 participants with the diagnosis of UD, and 130 HCs were finally included in the analysis. The dynamic amplitude of low-frequency fluctuations (dALFF) of spontaneous neuronal activity was evaluated in tBD, UD and HC using functional magnetic resonance imaging at study inclusion. Receiver operating characteristic (ROC) analysis was performed to evaluate sensitivity and specificity of the conversion prediction from MDD to BD based on dALFF.

RESULTS

Compared to HC, tBD exhibited elevated dALFF at left premotor cortex (PMC_L), right lateral temporal cortex (LTC_R) and right early auditory cortex (EAC_R), and UD showed reduced dALFF at PMC_L, left paracentral lobule (PCL_L), bilateral medial prefrontal cortex (mPFC), right orbital frontal cortex (OFC_R), right dorsolateral prefrontal cortex (DLPFC_R), right posterior cingulate cortex (PCC_R) and elevated dALFF at LTC_R. Furthermore, tBD exhibited elevated dALFF at PMC_L, PCL_L, bilateral mPFC, bilateral OFC, DLPFC_R, PCC_R and LTC_R than UD. In addition, ROC analysis based on dALFF in differential areas obtained an area under the curve (AUC) of 72.7%.

CONCLUSIONS

The study demonstrated the temporal dynamic abnormalities of tBD and UD in the critical regions of the somatomotor network (SMN), default mode network (DMN), and central executive network (CEN). The differential abnormal patterns of temporal dynamics between the two diseases have the potential to predict the diagnosis transition from MDD to BD.

Testing the generalization of neural representations

Multivariate analysis methods are widely used in neuroscience to investigate the presence and structure of neural representations. Representational similarities across time or contexts are often investigated using pattern generalization, e.g. by training and testing multivariate decoders in different contexts, or by comparable pattern-based encoding methods. It is however unclear what conclusions can be validly drawn on the underlying neural representations when significant pattern generalization is found in mass signals such as LFP, EEG, MEG, or fMRI. Using simulations, we show how signal mixing and dependencies between measurements can drive significant pattern generalization even though the true underlying representations are orthogonal. We suggest that, using an accurate estimate of the expected pattern generalization given identical representations, it is nonetheless possible to test meaningful hypotheses about the generalization of neural representations. We offer such an estimate of the expected magnitude of pattern generalization and demonstrate how this measure can be used to assess the similarity and differences of neural representations across time and contexts.

Unsupervised adaptation of an ECoG based brain-computer interface using neural correlates of task performance

Brain-computer interfaces (BCIs) translate brain signals into commands to external effectors, and mainly target severely disabled users. The usability of BCIs may be improved by reducing their major constraints, such as the necessity for special training sessions to initially calibrate and later keep up to date the neural signal decoders. In this study, we show that it is possible to train and update BCI decoders during free use of motor BCIs. In addition to the neural signal decoder controlling effectors (control decoder), one more classifier is proposed to detect neural correlates of BCI motor task performances (MTP). MTP decoders reveal whether the actions performed by BCI effectors matched the user's intentions. The combined outputs of MTP and control decoders allow forming training datasets to update the control decoder online and in real time during free use of BCIs. The usability of the proposed auto-adaptive BCI (aaBCI) is demonstrated for two principle BCIs paradigms: with discrete outputs (4 classes BCI, virtual 4-limb exoskeleton), and with continuous outputs (cursor 2D control). The proof of concept was performed in an online simulation study using an ECoG dataset collected from a tetraplegic during a BCI clinical trial. The control decoder reached a multiclass area under the ROC curve of 0.7404 using aaBCI, compared to a chance level of 0.5173 and to 0.8187 for supervised training for the multiclass BCI, and a cosine similarity of 0.1211 using aaBCI, compared to a chance level of 0.0036 and to 0.2002 for supervised training for the continuous BCI.

The process of emotion identification: Considerations for psychiatric disorders

Emotional regulation is important for mental health and behavioral regulation. A relevant precursor to emotional regulation may involve identification of one's emotions. Here, we propose a model of seven components that may provide a foundation for emotion identification. These factors include baseline mood, monitoring, physiological responses, interoception, past personal experiences regarding emotions/metacognition, context, and labeling. We additionally examine how deficits in different components may contribute to the concept of alexithymia, which is defined by difficulty identifying and describing one's own emotions. Ultimately, we explore how the model may support a relationship between specific psychiatric disorders and alexithymia. The proposed model may help explain emotional identification impairment in multiple psychiatric disorders and guide future research and treatment development efforts.

Large-scale network dysfunction in youths with Internet gaming disorder: a meta-analysis of resting-state functional connectivity studies

Internet gaming disorder (IGD) has been defined as a specific behavioral disorder, associated with abnormal interactions among large-scale brain networks. Researchers have sought to identify the network dysfunction in IGD using resting-state functional connectivity (rsFC). However, results across studies have not reached an agreement yet and the mechanism remains unclear. The present research aimed to investigate network dysfunction in IGD through a meta-analysis of rsFC studies. Twenty-two seed-based voxel-wise rsFC studies from 25 publications (594 individuals with IGD and 496 healthy controls) were included. By categorizing seeds into seed-networks based on their location within a prior functional network parcellations, we performed a Multilevel kernel density analysis (MKDA) within each seed-network to identify which brain systems showed abnormal interaction with particular seed-network in individuals with IGD. Compared to healthy control groups, individuals with IGD exhibited significant hypoconnectivity within the default mode network, and enhanced connectivity between the default mode network and insula within the ventral attention network. IGD was also associated with increased connectivity between the ventral attention network and somatomotor regions. Furthermore, the IGD groups showed hyperconnectivity between the limbic network and regions of the frontoparietal network. The results suggest that individuals with IGD show large-scale functional network alteration which underpins their core symptoms including poor emotional competence, cue-reactivity and craving, habitual addictive behaviors and impaired executive control. Whether the compensation mechanism exists in IGD is discussed, and further research is needed. The findings provide a neurocognitive network model of IGD, which may serve as functional biomarkers for IGD and have potentials for development of effective diagnosis and therapeutic interventions.

Disentangling effects of expectancy, accuracy, and empathy on the processing of observed actions

A number of studies suggest that event-related potential (ERP) components previously associated with error processing might represent expectation violation instead of valence. When observing others, these processes might further be modulated by trait empathy. We suggest that trait empathy modulates expectancy formation and that these expectancies then influence observed response processing as reflected in a frontocentral negative ERP component resembling the previously described observer error-related negativity. We acquired single trial ERPs of participants who observed another person in a true- or false-belief condition answering correctly or erroneously. Additionally, we prompted participants' expectancy in some trials. Using linear mixed model analyses, we found that for low empathy participants, expectations for the false-belief condition decreased throughout the experiment, so that expectations were more pronounced in participants with higher empathy toward the end of the experiment. We also found that single trial expectancy measures derived from regression models of the measured expectancies predicted the amplitude of the frontocentral negative ERP component, and that neither the addition of empathy nor accuracy or trial type (true- or false-belief) led to the explanation of significantly more variance compared with the model just containing expectancy as predictor. These results suggest that empathy modulates the processing of observed responses indirectly via its effect on expectancy of the response.

Midline frontal and occipito-temporal activity during error monitoring in dyadic motor interactions

Discrepancies between sensory predictions and action outcome are at the base of error coding. However, these phenomena have mainly been studied focussing on individual performance. Here, we explored EEG responses to motor prediction errors during a human-avatar interaction and show that Theta/Alpha activity of the frontal error-monitoring system works in phase with activity of the occipito-temporal node of the action observation network. Our motor interaction paradigm required healthy individuals to synchronize their reach-to-grasp movements with those of a virtual partner in conditions that did (Interactive) or did not require (Cued) movement prediction and adaptation to the partner's actions. Crucially, in 30% of the trials the virtual partner suddenly and unpredictably changed its movement trajectory thereby violating the human participant's expectation. These changes elicited error-related neuromarkers (ERN/Pe - Theta/Alpha modulations) over fronto-central electrodes during the Interactive condition. Source localization and connectivity analyses showed that the frontal Theta/Alpha activity induced by violations of the expected interactive movements was in phase with occipito-temporal Theta/Alpha activity. These results expand current knowledge about the neural correlates of on-line interpersonal motor interactions linking the frontal error-monitoring system to visual, body motion-related, responses.

Meta-analyses of the functional neural alterations in subjects with Internet gaming disorder: Similarities and differences across different paradigms

Internet gaming disorder (IGD) has become a global public health concern due to its increasing prevalence and potential negative consequences. Researchers have sought to identify which brain regions are associated with this disorder. However, inconsistent results have been reported among studies due to the heterogeneity of paradigms and subjects. The present research aimed to combine the results of individual studies to provide a more coherent and powerful explanation. By selecting 40 studies utilizing a qualified whole-brain analysis, we performed a comprehensive series of meta-analyses that employed seed-based d mapping. We divided the existing experimental paradigms into 3 categories: game-related cue-reactivity, executive control, and risk-reward-related decision-making tasks. We divided all studies into three subgroups according to their paradigms. In cue-reactivity tasks, patients with IGD exhibited significant hyperactivation in the bilateral precuneus and bilateral cingulate and significant hypoactivation in the insula, but there were no differences in the striatum. In executive control tasks, patients with IGD displayed significant hyperactivation in the right superior temporal gyrus, bilateral precuneus, bilateral cingulate, and insula and hypoactivation in the left inferior frontal gyrus. In risky decision-making paradigms, IGD patients exhibited significant hyperactivation in the left striatum, right inferior frontal gyrus, and insula and hypoactivation in the left superior frontal gyrus, left inferior frontal gyrus, and right precentral gyrus. Our study aimed to discover the similarities among all studies and to explore the uniqueness of the different paradigms. This study further confirmed the critical role of reward circuitry and executive control circuitry in IGD but not under all conditions.

Evidence for a general performance-monitoring system in the human brain

Adaptive behavior relies on the ability of the brain to form predictions and monitor action outcomes. In the human brain, the same system is thought to monitor action outcomes regardless of whether the information originates from internal (e.g., proprioceptive) and external (e.g., visual) sensory channels. Neural signatures of processing motor errors and action outcomes communicated by external feedback have been studied extensively; however, the existence of such a general action‐monitoring system has not been tested directly. Here, we use concurrent EEG‐MEG measurements and a probabilistic learning task to demonstrate that event‐related responses measured by electroencephalography and magnetoencephalography display spatiotemporal patterns that allow an effective transfer of a multivariate statistical model discriminating the outcomes across the following conditions: (a) erroneous versus correct motor output, (b) negative versus positive feedback, (c) high‐ versus low‐surprise negative feedback, and (d) erroneous versus correct brain–computer‐interface output. We further show that these patterns originate from highly‐overlapping neural sources in the medial frontal and the medial parietal cortices. We conclude that information about action outcomes arriving from internal or external sensory channels converges to the same neural system in the human brain, that matches this information to the internal predictions.