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Learning from errors as the main mechanism for motor adaptation has two fundamental prerequisites: a mismatch between the intended and performed movement and the ability to adapt motor actions. Many neurological patients are limited in their ability to transfer an altered motor representation into motor action due to a compromised motor pathway. Studies that have investigated the effects of a sustained and unresolvable mismatch over multiple days found changes in brain processing that seem to optimize the potential for motor learning (increased drive for motor adaptation and a weakening of the current implementation of motor programs). However, it remains unclear whether the observed effects can be induced experimentally and more important after shorter periods. Here, we used task-based and resting-state fMRI to investigate whether the known pattern of cortical adaptations due to a sustained mismatch can be induced experimentally by a short (20 min), but unresolvable, sensory–motor mismatch by impaired facial movements in healthy participants by transient facial tapping. Similar to long-term mismatch, we found plastic changes in a network that includes the striatal, cerebellar and somatosensory brain areas. However, in contrast to long-term mismatch, we did not find the involvement of the cerebral motor cortex. The lack of the involvement of the motor cortex can be interpreted both as an effect of time and also as an effect of the lack of a reduction in the motor error. The similar effects of long-term and short-term mismatch on other parts of the sensory–motor network suggest that the brain-state caused by long-term mismatch can be (at least partly) induced by short-term mismatch. Further studies should investigate whether short-term mismatch interventions can be used as therapeutic strategy to induce an altered brain-state that increase the potential for motor learning.

Background: Temporal fractals are characterized by prominent scale-invariance and self-similarity across time scales. Monofractal analysis quantifies this scaling behavior in a single parameter, the Hurst exponent (H). Higher H reflects greater correlation in the signal structure, which is taken as being more fractal. Previous fMRI studies have observed lower H during conventional tasks relative to resting state conditions, and shown that H is negatively correlated with task difficulty and novelty. To date, no study has investigated the fractal dynamics of BOLD signal during naturalistic conditions.Methods: We performed fractal analysis on Human Connectome Project 7T fMRI data (n = 72, 41 females, mean age 29.46 ± 3.76 years) to compare H across movie-watching and rest.Results: In contrast to previous work using conventional tasks, we found higher H values for movie relative to rest (mean difference = 0.014; p = 5.279 × 10−7; 95% CI [0.009, 0.019]). H was significantly higher in movie than rest in the visual, somatomotor and dorsal attention networks, but was significantly lower during movie in the frontoparietal and default networks. We found no cross-condition differences in test-retest reliability of H. Finally, we found that H of movie-derived stimulus properties (e.g., luminance changes) were fractal whereas H of head motion estimates were non-fractal.Conclusions: Overall, our findings suggest that movie-watching induces fractal signal dynamics. In line with recent work characterizing connectivity-based brain state dynamics during movie-watching, we speculate that these fractal dynamics reflect the configuring and reconfiguring of brain states that occurs during naturalistic processing, and are markedly different than dynamics observed during conventional tasks.

Sleep is the brain state when cortical activity decreases and memory consolidates. However, in human epileptic patients, including genetic epileptic seizures such as Dravet syndrome, sleep is the preferential period when epileptic spike-wave discharges (SWDs) appear, with more severe epileptic symptoms in female patients than male patients, which influencing patient sleep quality and memory. Currently, seizure onset mechanisms during sleep period still remain unknown. Our previous work has shown that the sleep-like state-dependent synaptic potentiation mechanism can trigger epileptic SWDs (Zhang et al., 2021). In this study, using one heterozygous (het) knock-in (KI) transgenic mice (GABAA receptor γ2 subunit Gabrg2Q390X mutation) and an optogenetic method, we hypothesized that slow-wave oscillations (SWOs) themselves in vivo could trigger epileptic seizures. We found that epileptic SWDs in het Gabrg2+/Q390X KI mice exhibited preferential incidence during NREM sleep period, accompanied by motor immobility/ facial myoclonus/vibrissal twitching, with more frequent incidence in female het KI mice than male het KI mice. Optogenetic induced SWOs in vivo significantly increased epileptic seizure incidence in het Gabrg2+/Q390X KI mice with increased duration of NREM sleep or quiet-wakeful states. Furthermore, suppression of SWO-related homeostatic synaptic potentiation by 4-(diethylamino)-benzaldehyde (DEAB) injection (i.p.) greatly decreased seizure incidence in het KI mice, suggesting that SWOs did trigger seizure activity in het KI mice. In addition, EEG delta-frequency (0.1-4 Hz) power spectral density during NREM sleep was significantly larger in female het Gabrg2+/Q390X KI mice than male het Gabrg2+/Q390X KI mice, which likely contributes to the gender difference in seizure incidence during NREM sleep/quiet-wake as that in human patients.

Seizure is one of the most common neurodegenerative illnesses in humans, and it can result in serious brain damage, strokes, and tumors. Seizures can be detected early, which can assist prevent harm and aid in the treatment of epilepsy sufferers. A seizure prediction system's goal is to correctly detect the pre-ictal brain state, which occurs before a seizure occurs. Patient-independent seizure prediction models have been recognized as a real-world solution to the seizure prediction problem, since they are designed to provide accurate performance across different patients by using the recorded dataset. Furthermore, building such models to adjust to the significant inter-subject variability in EEG data has received little attention. We present a patient-independent deep learning architectures that can train a global function using data from numerous people with its own learning strategy. On the CHB- MIT-EEG dataset, the proposed models reach state-of-the-art accuracy for seizure prediction, with 95.54 percent accuracy. While predicting seizures, the Siamese model trained on the suggested learning technique is able to understand patterns associated to patient differences in data. Our models outperform the competition in terms of patient-independent seizure prediction, and following model adaption, the same architecture may be employed as a patient-specific classifier. We show that the MFCC feature map used by our models contains predictive biomarkers associated to inter-ictal and pre-ictal brain states, and we are the first study to use model interpretation to explain classifier behaviour for the task of seizure prediction.

Online end-to-end electroencephalogram (EEG) classification with high performance can assess the brain status of patients with Major Depression Disabled (MDD) and track their development status in time with minimizing the risk of falling into danger and suicide. However, it remains a grand research challenge due to (1) the embedded intensive noises and the intrinsic non-stationarity determined by the evolution of brain states, (2) the lack of effective decoupling of the complex relationship between neural network and brain state during the attack of brain diseases. This study designs a Frequency Channel-based convolutional neural network (CNN), namely FCCNN, to accurately and quickly identify depression, which fuses the brain rhythm to the attention mechanism of the classifier with aiming at focusing the most important parts of data and improving the classification performance. Furthermore, to understand the complexity of the classifier, this study proposes a calculation method of information entropy based on the affinity propagation (AP) clustering partition to measure the complexity of the classifier acting on each channel or brain region. We perform experiments on depression evaluation to identify healthy and MDD. Results report that the proposed solution can identify MDD with an accuracy of 99±0.08%, the sensitivity of 99.07±0.05%, and specificity of 98.90±0.14%. Furthermore, the experiments on the quantitative interpretation of FCCNN illustrate significant differences between the frontal, left, and right temporal lobes of depression patients and the healthy control group.

EEG-based brain–computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

AbstractThe subjective presence experience in virtual reality (VR) is associated with distinct brain activation patterns. Particularly, the dorsolateral prefrontal cortex (DLPFC) seems to play a central role. We investigated the effects of electric brain stimulation (transcranial direct current, tDCS) on the presence experience as well as on brain activity and connectivity. Thirty-eight participants received either anodal (N = 18) or cathodal (N = 20) stimulation of the DLPFC before interacting in an immersive VR as well as sham stimulation. During VR interaction, EEG and heart rate were recorded. After VR interaction, participants rated their subjective presence experience using standardized questionnaires. Cathodal stimulation led to stronger brain connectivity than sham stimulation. Increased brain connectivity was associated with numerically lower levels of subjective presence. Anodal stimulation did not lead to changes in brain connectivity, and no differences in subjective presence ratings were found between the anodal and sham stimulation. These results indicate that cathodal tDCS over the DLPFC leads to a more synchronized brain state, which might hamper the activity in networks, which are generally associated with the evolvement of the subjective presence experience. Our results underline the importance of the DLPFC for the presence experience in VR.

Background: Psilocin, the neuroactive metabolite of psilocybin, is a serotonergic psychedelic that induces an acute altered state of consciousness, evokes lasting changes in mood and personality in healthy individuals, and has potential as an antidepressant treatment. Examining the acute effects of psilocin on resting-state dynamic functional connectivity implicates network-level connectivity motifs that may underlie acute and lasting behavioral and clinical effects. Aim: Evaluate the association between resting-state dynamic functional connectivity (dFC) characteristics and plasma psilocin level (PPL) and subjective drug intensity (SDI) before and right after intake of a psychedelic dose of psilocybin in healthy humans. Methods: Fifteen healthy individuals completed the study. Before and at multiple time points after psilocybin intake, we acquired 10-minute resting-state blood-oxygen-level-dependent functional magnetic resonance imaging scans. Leading Eigenvector Dynamics Analysis (LEiDA) and diametrical clustering were applied to estimate discrete, sequentially active brain states. We evaluated associations between the fractional occurrence of brain states during a scan session and PPL and SDI using linear mixed-effects models. We examined associations between brain state dwell time and PPL and SDI using frailty Cox proportional hazards survival analysis. Results: Fractional occurrences for two brain states characterized by lateral frontoparietal and medial fronto-parietal-cingulate coherence were statistically significantly negatively associated with PPL and SDI. Dwell time for these brain states was negatively associated with SDI and, to a lesser extent, PPL. Conversely, fractional occurrence and dwell time of a fully connected brain state was positively associated with PPL and SDI. Conclusion: Our findings suggest that the acute perceptual psychedelic effects induced by psilocybin may stem from drug-level associated decreases in the occurrence and duration of lateral and medial frontoparietal connectivity motifs in exchange for increases in a uniform connectivity structure. We apply and argue for a modified approach to modeling eigenvectors produced by LEiDA that more fully acknowledges their underlying structure. Together these findings contribute to a more comprehensive neurobiological framework underlying acute effects of serotonergic psychedelics.