Randomized trial of rTMS in traumatic brain injury: improved subjective neurobehavioral symptoms and increases in EEG delta activity.

PRIMARY OBJECTIVE: While repetitive transcranial magnetic stimulation (rTMS) has shown efficacy for cognitive difficulties accompanying depression, it is unknown if it can improve cognition in persons with traumatic brain injury. RESEARCH DESIGN: Using a sham-controlled crossover design, we tested the capacity of high frequency rTMS of the prefrontal cortex to improve neuropsychological performance in attention, learning and memory, and executive function. METHODS: Twenty-six participants with cognitive complaints and a history of mild-to-moderate traumatic brain injury were randomly assigned to receive first either active or sham 10 Hz stimulation for 20 minutes (1200 pulses) per session for five consecutive days. After a one-week washout, the other condition (active or sham) was applied. Pre- and post-treatment measures included neuropsychological tests, cognitive and emotional symptoms, and EEG. MAIN OUTCOMES AND RESULTS: Results indicated no effect of treatment on cognitive function. Subjective measures of depression, sleep dysfunction, post-concussive symptoms (PCS), and executive function showed significant improvement with stimulation, retaining improved levels at two-week follow-up. EEG delta power exhibited elevation one week after stimulation cessation. CONCLUSIONS: While there is no indication that rTMS is beneficial for neuropsychological performance, it may improve PCS and subjective cognitive dysfunction. Long-term alterations in cortical oscillations may underlie the therapeutic effects of rTMS.

Frequency of helping friends and helping strangers is explained by different neural signatures.

Acts of helping friends and strangers are part of everyday life. However, people vary significantly with respect to how often they help others and with respect to whom they actually help on a day-to-day basis. Despite everyday helping being so pervasive, these individual differences are poorly understood. Here, we used source-localized resting electroencephalography to measure objective and stable individual differences in neural baseline activation in combination with an ecologically valid method that allows assessment of helping behavior in the field. Results revealed that neural baseline activation in the right dorsolateral prefrontal cortex (DLPFC) - a brain region associated with self-control and strategic social behavior - predicts the daily frequency of helping friends, whereas the daily frequency of helping strangers was predicted by neural baseline activation in the dorsomedial prefrontal cortex (DMPFC) - a brain region associated with social cognition processes. These findings offer evidence that distinct neural signatures and associated psychological and cognitive processes may underlie the propensity to help friends and strangers in daily life.

Resting EEG in alpha and beta bands predicts individual differences in attentional breadth.

Whether individuals focus their attention on the global level (the forest), or local elements that make up the stimulus (the trees) remains relatively stable over a period of at least 10 days in multiple global/local measures. Greater attentional approach tendencies and vigilance, which are likely reflected by lower alpha and higher beta power, are associated with narrowed attentional breadth. The current study investigated whether individual differences in the propensity for individuals to focus on the global or local levels (attentional breadth) can be predicted based on EEG power in alpha and its neighbouring frequency bands during a preceding rest period. Greater levels of posterior alpha and preponderance of alpha-to-beta power at rest were associated with greater attentional breadth during the subsequent Navon letters task. These results suggest that neural indices of attentional approach when not engaged in a goal-orientated task are associated with individual differences in attentional breadth.

Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data.

Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed and implemented within the past few decades, providing an improved isolation of these distinct processes. Within the present study, we demonstrate the feasibility of multi-subject BSS for deriving distinct EEG spatiospectral maps. Multi-subject spatiospectral EEG decompositions were implemented using the EEGIFT toolbox ( http://mialab.mrn.org/software/eegift/ ) with real and realistic simulated datasets (the simulation code is available at http://mialab.mrn.org/software/simeeg ). Twelve different decomposition algorithms were evaluated. Within the simulated data, WASOBI and COMBI appeared to be the best performing algorithms, as they decomposed the four sources across a range of component numbers and noise levels. RADICAL ICA, ERBM, INFOMAX ICA, ICA EBM, FAST ICA, and JADE OPAC decomposed a subset of sources within a smaller range of component numbers and noise levels. INFOMAX ICA, FAST ICA, WASOBI, and COMBI generated the largest number of stable sources within the real dataset and provided partially distinct views of underlying spatiospectral maps. We recommend the multi-subject BSS approach and the selected algorithms for further studies examining distinct spatiospectral networks within healthy and clinical populations.

A Preliminary Study of Central Nervous System Arousal and Sleep Quality in Bipolar Disorder.

Sleep disturbances are commonly reported in patients with bipolar I disorder (BPI) and are risk factors for mood episodes. In other populations, central nervous system (CNS) hyperarousal is associated with sleep initiation and maintenance problems, and CNS hypoarousal is associated with increased sleep drive. However, it is unclear whether CNS arousal levels are a useful index of sleep disruption in BPI. This study aimed to investigate daytime CNS arousal levels in relation to perceived sleep quality in BPI. Resting EEG, mood state, and self-reported sleep quality data were collected from 34 individuals with BPI. CNS hyperarousal was associated with pervasive poor subjective sleep quality including increased sleep disturbances, increased sleep latency, and reduced global sleep quality. CNS hypoarousal was associated with greater daytime sleepiness, indicating reduced arousal. These preliminary findings suggest that CNS arousal may be a useful index for identifying individuals at high risk for relapse into a mood episode. A limitation of this study is the use of self-report instruments for sleep quality assessment. Future research should investigate the temporal relationship of CNS arousal to sleep disturbances using objective measurements of sleep quality such as polysomnography. If these findings are replicated, measures of CNS arousals may allow for identification of high-risk patients with BPI.

Correlates of electroencephalographic resting states and erythrocyte membrane docosahexaenoic and eicosapentaenoic acid levels in individuals at ultra-high risk of psychosis.

OBJECTIVE: Abnormal levels of polyunsaturated fatty acids (PUFAs) have been reported in individuals suffering from schizophrenia. The main aim of the present study was to investigate the relationship between erythrocyte membrane fatty acid levels and resting-state brain activity occurring in individuals at ultra-high risk (UHR) of psychosis. METHOD: The association between erythrocyte membrane fatty acids levels and resting-state brain activity and its value in predicting psychosis was examined in 72 UHR individuals. RESULTS: In the frontal area, the activity in the fast frequency band Beta2 was positively associated with docosahexaenoic acid (DHA) levels (R = 0.321, P = 0.017), and in the fronto-central area, Beta2 activity showed a positive correlation with eicosapentaenoic acid (EPA) levels (R = 0.305, P = 0.009), regardless of psychosis transition status. Conversely, the slow frequency band Theta was significantly negatively associated with EPA levels in the parieto-occipital region (R = -0.251, P = 0.033. Results also showed that Alpha power was negatively correlated with DHA levels in UHR individuals who did not transition to psychosis, while this correlation was not present in individuals who later transitioned. CONCLUSION: Our results suggest that individuals at UHR for psychosis who have higher basal omega-3 fatty acids levels present with resting EEG features associated with better states of alertness and vigilance. Furthermore, the improvement in the Alpha synchrony observed along with increased DHA levels in participants who did not transition to psychosis is disturbed in those who did transition. However, these interesting results are limited by the small sample size and low statistical power of the study.

Individual differences in inhibitory control--relationship between baseline activation in lateral PFC and an electrophysiological index of response inhibition.

The capacity to inhibit inappropriate responses is crucial for goal-directed behavior. Inhibiting such responses seems to come more easily to some of us than others, however. From where do these individual differences originate? Here, we measured 263 participants' neural baseline activation using resting electroencephalogram. Then, we used this stable neural marker to predict a reliable electrophysiological index of response inhibition capacity in the cued Continuous Performance Test, the NoGo-Anteriorization (NGA). Using a source-localization technique, we found that resting delta, theta, and alpha1 activity in the left middle frontal gyrus and resting alpha1 activity in the right inferior frontal gyrus were negatively correlated with the NGA. As a larger NGA is thought to represent better response inhibition capacity, our findings demonstrate that lower levels of resting slow-wave oscillations in the lateral prefrontal cortex, bilaterally, are associated with a better response inhibition capacity.

Early brain cognitive development in late preterm infants: an event-related potential and resting EEG study.

BACKGROUND: Late preterm infants (LPIs) are at risk of neurodevelopmental delay. Research on their cognitive development is helpful for early intervention and follow-up. METHODS: Event-related potential (ERP) and resting electroencephalography (RS-EEG) were used to study the brain cognitive function of LPIs in the early stage of life. The Gesell Developmental Scale (GDS) was used to track the neurodevelopmental status at the age of 1 year after correction, and to explore the neurophysiological indicators that could predict the outcome of cognitive development in the early stage. RESULTS: The results showed that mismatch response (MMR) amplitude, RS-EEG power spectrum and functional connectivity all suggested that LPIs were lagging behind. At the age of 1 year after correction, high-risk LPIs showed no significant delay in gross motor function, but lagged behind in fine motor function, language, personal social interaction and adaptability. The ROC curve was used to evaluate the predictive role of MMR amplitude in the brain cognitive development prognosis at 1 year, showing a sensitivity of 80.00% and a specificity of 90.57%. The area under the curve (AUC) was 0.788, with a P-value of 0.007. CONCLUSIONS: Based on our findings we supposed that the cognitive function of LPI lags behind that of full-term infants in early life. Preterm birth and perinatal diseases or high risk factors affected brain cognitive function in LPIs. MMR amplitude can be used as an early predictor of brain cognitive development in LPIs. TRIAL REGISTRATION: This clinical trial is registered with the Chinese Clinical Trial Registry (ChiCTR). TRIAL REGISTRATION NUMBER: ChiCTR2100041929. Date of registration: 2021-01-10. URL of the trial registry record: https://www.chictr.org.cn/ .

A biomarker of brain arousal mediates the intergenerational link between maternal and child post-traumatic stress disorder.

This study examined whether there is a biological basis in the child's resting brain activity for the intergenerational link between maternal interpersonal violence-related posttraumatic stress disorder (IPV-PTSD) and child subclinical symptoms. We used high-density EEG recordings to investigate the resting brain activity in a sample of 57 children, 34 from mothers with IPV-PTSD, and 23 from mothers without PTSD. These children were part of a prospective, longitudinal study focusing on the offspring of mothers with and without IPV-PTSD, reporting how the severity of a mother's IPV-PTSD can impact her child's emotional regulation and risk for developing mental illness. However, we had not yet looked into potential EEG biomarkers during resting state that might mediate and/or moderate effects of maternal IPV-PTSD severity on child mental health, and in particular the risk for PTSD. The alpha band spectral power as well as the aperiodic exponent of the power spectrum (PLE; power-law exponent) were examined as mediators of maternal IPV-PTSD and child PTSD. While there was no difference in alpha spectral power between the two groups, PLE was significantly reduced in children of mothers with IPV-PTSD compared to control children, indicating cortical hyper-arousal. Interestingly, child PLE was negatively correlated with the severity of maternal IPV-PTSD, suggesting an intergenerational interaction. This interpretation was reinforced by a negative correlation between child PLE and child PTSD symptoms. Finally, causal analyses using structural equation modelling indicated that child PLE mediated the relationship between maternal PTSD severity and child PTSD. Our observations suggest that maternal IPV-PTSD has an intergenerational impact on the child neurobehavioral development through a correlated abnormal marker of brain arousal (i.e. child PLE). These findings are potentially relevant to psychotherapy research and to the development of more effective psycho-neurobehavioral therapies (i.e. neurofeedback) among affected individuals.

Resting brain activity in early childhood predicts IQ at 18 years.

Resting brain activity has been widely used as an index of brain development in neuroscience and clinical research. However, it remains unclear whether early differences in resting brain activity have meaningful implications for predicting long-term cognitive outcomes. Using data from the Bucharest Early Intervention Project (Zeanah et al., 2003), we examined the impact of institutional rearing and the consequences of early foster care intervention on 18-year IQ. We found that higher resting theta electroencephalogram (EEG) power, reflecting atypical neurodevelopment, across three assessments from 22 to 42 months predicted lower full-scale IQ at 18 years, providing the first evidence that brain activity in early childhood predicts cognitive outcomes into adulthood. In addition, both institutional rearing and later (vs. earlier) foster care intervention predicted higher resting theta power in early childhood, which in turn predicted lower IQ at 18 years. These findings demonstrate that experientially-induced changes in brain activity early in life have profound impact on long-term cognitive development, highlighting the importance of early intervention for promoting healthy development among children living in disadvantaged environments.

Early development of electrophysiological activity: Contribution of periodic and aperiodic components of the EEG signal.

Brain function rapidly changes in the first 2 years of life. In the last decades, resting-state EEG has been widely used to explore those changes. Previous studies have focused on the relative power of the signal in established frequency bands (i.e., theta, alpha, and beta). However, EEG power is a mixture of a 1/f-like background power (aperiodic) in combination with narrow peaks that appear over that curve (periodic activity, e.g., alpha peak). Therefore, it is possible that relative power captures both, aperiodic and periodic brain activity, contributing to changes in electrophysiological activity observed in infancy. For this reason, we explored the early developmental trajectory of the relative power in theta, alpha, and beta frequency bands from infancy to toddlerhood and compared it with changes in periodic activity in a longitudinal study with three waves at age 6, 9, and 16 to 18 months. Finally, we tested the contribution of periodic activity and aperiodic components of the EEG to age changes in relative power. We found that relative power and periodic activity trajectories differed in this period in all the frequency bands but alpha. Furthermore, aperiodic EEG activity flattened between 6 and 18 months. More importantly, only alpha relative power was exclusively related to periodic activity, whereas aperiodic components of the signal significantly contributed to the relative power of activity in theta and beta bands. Thus, relative power in these frequencies is influenced by developmental changes of the aperiodic activity, which should be considered for future studies.

Resting State Dynamics in People with Varying Degrees of Anxiety and Mindfulness: A Nonlinear and Nonstationary Perspective.

Anxiety and mindfulness are two inversely linked traits shown to be involved in various physiological domains. The current study used resting state electroencephalography (EEG) to explore differences between people with low mindfulness-high anxiety (LMHA) (n = 29) and high mindfulness-low anxiety (HMLA) (n = 27). The resting EEG was collected for a total of 6 min, with a randomized sequence of eyes closed and eyes opened conditions. Two advanced EEG analysis methods, Holo-Hilbert Spectral Analysis and Holo-Hilbert cross-frequency phase clustering (HHCFPC) were employed to estimate the power-based amplitude modulation of carrier frequencies, and cross-frequency coupling between low and high frequencies, respectively. The presence of higher oscillation power across the delta and theta frequencies in the LMHA group than the HMLA group might have been due to the similarity between the resting state and situations of uncertainty, which reportedly triggers motivational and emotional arousal. Although these two groups were formed based on their trait anxiety and trait mindfulness scores, it was anxiety that was found to be significant predictor of the EEG power, not mindfulness. It led us to conclude that it might be anxiety, not mindfulness, which might have contributed to higher electrophysiological arousal. Additionally, a higher δ-ß and δ-γ CFC in LMHA suggested greater local-global neural integration, consequently a greater functional association between cortex and limbic system than in the HMLA group. The present cross-sectional study may guide future longitudinal studies on anxiety aiming with interventions such as mindfulness to characterize the individuals based on their resting state physiology.

Cues to body-related distortions and hallucinations? Spontaneous sensations correlate with EEG oscillatory activity recorded at rest in the somatosensory cortices.

Body awareness may arise in the total absence of sensory input, as suggested by the spontaneous occurrence of normal and pathological (i.e., hallucinatory) bodily sensations. These phenomena may arise due to back-projections from higher-order cortical areas to the primary (SI) and secondary (SII) somatosensory cortices, and would appear to be reflected in cortical oscillatory activity in both SI and SII. Here, we set to investigate the relationship of SI and SII in SPS. Healthy participants underwent an EEG recording session at rest, and then completed an experiment on the perception of spontaneous sensations occurring on the hands. Cortical oscillatory activity was extracted from specified ROIs in the somatosensory cortices. The findings showed that (i) SPS perceived in the fingers correlated positively with alpha-band oscillations recorded in SI, and that (ii) SPS perceived in the palm correlated positively with gamma-band oscillations and negatively with beta-band oscillations recorded in SII. Apart from supporting the idea that the somatosensory cortices are involved in bodily awareness even in the absence of sensory input, these findings also suggest that default oscillatory activity in the somatosensory cortices reflects individual differences in bodily awareness. The results are interpreted in terms of neural and cognitive processes that may give rise to bodily awareness and modulate it, and their importance in understanding body perception distortions and bodily delusions and hallucinations is discussed.

A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA.

Although conventional averaging across predefined frequency bands reduces the complexity of EEG functional connectivity (FC), it obscures the identification of resting-state brain networks (RSN) and impedes accurate estimation of FC reliability. Extending prior work, we combined scalp current source density (CSD; spherical spline surface Laplacian) and spectral-spatial PCA to identify FC components. Phase-based FC was estimated via debiased-weighted phase-locking index from CSD-transformed resting EEGs (71 sensors, 8 min, eyes open/closed, 35 healthy adults, 1-week retest). Spectral PCA extracted six robust alpha and theta components (86.6% variance). Subsequent spatial PCA for each spectral component revealed seven robust regionally focused (posterior, central, and frontal) and long-range (posterior-anterior) alpha components (peaks at 8, 10, and 13 Hz) and a midfrontal theta (6 Hz) component, accounting for 37.0% of FC variance. These spatial FC components were consistent with well-known networks (e.g., default mode, visual, and sensorimotor), and four were sensitive to eyes open/closed conditions. Most FC components had good-to-excellent internal consistency (odd/even epochs, eyes open/closed) and test-retest reliability (ICCs ≥ .8). Moreover, the FC component structure was generally present in subsamples (session × odd/even epoch, or smaller subgroups [n = 7-10]), as indicated by high similarity of component loadings across PCA solutions. Apart from systematically reducing FC dimensionality, our approach avoids arbitrary thresholds and allows quantification of meaningful and reliable network components that may prove to be of high relevance for basic and clinical research applications.

Prediction of dispositional dialectical thinking from resting-state electroencephalography.

This study aims to explore the possibility of predicting the dispositional level of dialectical thinking using resting-state electroencephalography signals. Thirty-four participants completed a self-reported measure of dialectical thinking, and their resting-state electroencephalography was recorded. After wave filtration and eye movement removal, time-frequency electroencephalography signals were converted into four frequency domains: delta (1-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), and beta (13-30 Hz). Functional principal component analysis with B-spline approximation was then applied for feature reduction. Five machine learning methods (support vector regression, least absolute shrinkage and selection operator, K-nearest neighbors, random forest, and gradient boosting decision tree) were applied to the reduced features for prediction. The model ensemble technique was used to create the best performing final model. The results showed that the alpha wave of the electroencephalography signal in the early period (12-15 s) contributed most to the prediction of dialectical thinking. With data-driven electrode selection (FC1, FCz, Fz, FC3, Cz, AFz), the prediction model achieved an average coefficient of determination of 0.45 on 200 random test sets. Furthermore, a significant positive correlation was found between the alpha value of standardized low-resolution electromagnetic tomography activity in the right dorsal anterior cingulate cortex and dialectical self-scale score. The prefrontal and midline alpha oscillations of resting electroencephalography are good predictors of the dispositional level of dialectical thinking, possibly reflecting these brain structures' involvement in dialectical thinking.

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals.

The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (-3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.

Differential Effects of Up- and Down-Regulation of SMR Coherence on EEG Activity and Memory Performance: A Neurofeedback Training Study.

Modulating connectivity measures in EEG-based neurofeedback studies is assumed to be a promising therapeutic and training tool. However, little is known so far about its effects and trainability. In the present study, we investigated the effects of up- and down-regulating SMR (12-15 Hz) coherence by means of neurofeedback training on EEG activity and memory functions. Twenty adults performed 10 neurofeedback training sessions in which half of them tried to increase EEG coherence between Cz and CPz in the SMR frequency range, while the other half tried to down-regulate coherence. Up-regulation of SMR coherence led to between- and within-session changes in EEG coherence. SMR power increased across neurofeedback training sessions but not within training sessions. Cross-over training effects on baseline EEG measures were also observed in this group. Up-regulation of SMR coherence was also associated with improvements in memory functions when comparing pre- and post-test results. Participants were not able to down-regulate SMR coherence. This group did not show any changes in baseline EEG measures or memory functions comparing pre- and post-test. Our results provide insights in the trainability and effects of connectivity-based neurofeedback training and indications for its practical application.

Enhanced Vigilance Stability during Daytime in Insomnia Disorder.

Central nervous hyperarousal is as a key component of current pathophysiological concepts of chronic insomnia disorder. However, there are still open questions regarding its exact nature and the mechanisms linking hyperarousal to sleep disturbance. Here, we aimed at studying waking state hyperarousal in insomnia by the perspective of resting-state vigilance dynamics. The VIGALL (Vigilance Algorithm Leipzig) algorithm has been developed to investigate resting-state vigilance dynamics, and it revealed, for example, enhanced vigilance stability in depressive patients. We hypothesized that patients with insomnia also show a more stable vigilance regulation. Thirty-four unmedicated patients with chronic insomnia and 25 healthy controls participated in a twenty-minute resting-state electroencephalography (EEG) measurement following a night of polysomnography. Insomnia patients showed enhanced EEG vigilance stability as compared to controls. The pattern of vigilance hyperstability differed from that reported previously in depressive patients. Vigilance hyperstability was also present in insomnia patients showing only mildly reduced sleep efficiency. In this subgroup, vigilance hyperstability correlated with measures of disturbed sleep continuity and arousal. Our data indicate that insomnia disorder is characterized by hyperarousal at night as well as during daytime.

Emotional working memory training reduces rumination and alters the EEG microstate in anxious individuals.

Rumination is an important etiological factor of anxiety pathology, with its mechanism related to the deficit of working memory. The current study examined whether working memory training (WM-T) and emotional working memory training (EWM-T) could reduce rumination in anxious individuals. The participants with high trait anxiety underwent 21 days of mobile applications-based WM-T (n = 34), EWM-T (n = 36) or placebo control (n = 36), with questionnaires, cognitive tasks, and resting electroencephalogram (EEG) as the pre-post-test indicators. The results revealed that two training groups obtained comparable operation span increases (WM-T: d = 0.53; EWM-T: d = 0.65), updating improvement (WM-T: d = 0.43; EWM-T: d = 0.60) and shifting improvement (WM-T: d = 0.49; EWM-T: d = 0.72). Furthermore, compared to the control group, the EWM-T showed significant self-reported rumination reduction (d = 0.69), increased inhibition ability (d = 0.72), as well as modification of resting EEG microstate C parameters (Duration C: d = 0.42, Coverage C: d = 0.39), which were closely related to rumination level (r ~ 0.4). The WM-T group also showed the potential to reduced self-reported rumination (d = 0.45), but with the absence of the observable inhibition improvement and resting EEG changes. The correlation analysis suggested that the emotional benefits of WM-T depending more on improved updating and shifting, and that of EWM-T depending more on improved inhibition ability. The advantage to add emotional distractions into general working memory training for targeting rumination related anxiety has been discussed.

Efficacy, Trainability, and Neuroplasticity of SMR vs. Alpha Rhythm Shooting Performance Neurofeedback Training.

Previous literature on shooting performance neurofeedback training (SP-NFT) to enhance performance usually focused on changes in behavioral indicators, but research on the physiological features of SP-NFT is lacking. To explore the effects of SP-NFT on trainability and neuroplasticity, we conducted a study in which 45 healthy participants were randomly divided into three groups: based on sensory-motor rhythm of C3, Cz and C4 (SMR group), based on alpha rhythm of T3 and T4 (Alpha group), and no NFT (control group). The training was performed for six sessions for 3 weeks. Before and after the SP-NFT, we evaluated changes in shooting performance and resting electroencephalography (EEG) frequency power, participant's subjective task appraisal, neurofeedback trainability score, and EEG feature. Statistical analysis showed that the shooting performance of the participants in the SMR group improved significantly, the participants in the Alpha group decreased, and that of participants in the control group have no change. Meanwhile, the resting EEG power features of the two NFT groups changed specifically after training. The training process data showed that the training difficulty was significantly lower in the SMR group than in the Alpha group. Both NFT groups could improve the neurofeedback trainability scores and change the feedback features by means of their mind strategy. These results may provide evidence of trainability and neuroplasticity for SP-NFT, suggesting that the SP-NFT is effective in brain regulation and thus provide a potential method to improve shooting performance.