Search

SMR neurofeedback shows potential as a therapeutic tool for reducing sleep problems. It is hypothesized that SMR neurofeedback trains the reticulo-thalamocortical-cortical circuit involved in sleep-spindle generation. As such, strengthening this circuit is hypothesized to reduce sleep problems. The current study aims to investigate the effectiveness of a home-based device that uses SMR neurofeedback to help reduce sleep problems. Thirty-seven participants reporting sleep problems received the SMR neurofeedback-based program for 40 (n = 21) or 60 (n = 16) sessions. The Pittsburgh Sleep Quality Index (PSQI) and Holland Sleep Disorders Questionnaire (HSDQ) were assessed at baseline, session 20, outtake, and follow-up (FU). Actigraphy measurements were taken at baseline, session 20, and outtake. Significant improvements were observed in PSQI Total (d = 0.78), PSQI Sleep Duration (d = 0.52), HSDQ Total (d = 0.80), and HSDQ Insomnia (d = 0.79). Sleep duration (based on PSQI) increased from 5.3 h at baseline to 5.8 after treatment and 6.0 h. at FU. No effects of number of sessions were found. Participants qualified as successful SMR-learners demonstrated a significantly larger gain in sleep duration (d = 0.86 pre-post; average gain = 1.0 h.) compared to non-learners. The home-based SMR tele-neurofeedback device shows the potential to effectively reduce sleep problems, with SMR-learners demonstrating significantly better improvement. Although randomized controlled trials (RCTs) are needed to further elucidate the specific effect of this device on sleep problems, this is the first home-based SMR neurofeedback device using dry electrodes demonstrating effectiveness and feasibility.

Previous studies reported that children with autism spectrum disorder (ASD) show a certain interest in social robots. This makes social robots potential to be a model to teach social skills. This exploratory study aims to investigate whether three types of joint attention skills (i.e., eye-contact, pointing, gaze-following) could be improved for five preschoolers with ASD using an evidence-based robot-modeling intervention with a humanoid social robot NAO. Our observation shows that these children were motivated when interacting with NAO by following and responding correctly to NAO’s joint attention behaviors. Although some improvements were found, no pattern or systematic effect could be revealed. In the future, more evidence-based studies are needed to investigate the benefits of robot-assisted therapy more deeply.

Contains fulltext : 231309.pdf (Publisher’s version ) (Open Access) Objective: The aim of the current study was to attempt to replicate the finding that the individual alpha frequency (IAF) as well as the absolute difference between IAF and 10 Hz stimulation frequency (IAF-prox) is related to treatment outcome. Methods: Correlations were performed to investigate the relationship between IAF-prox and percentage symptom improvement in a sample of 153 patients with major depressive disorder treated with 10 Hz (N = 59) to the left dorsolateral prefrontal cortex (DLPFC) or 1 Hz (N = 94) to the right DLPFC repetitive Transcranial Magnetic Stimulation (rTMS). Results: There was a significant negative correlation between IAF-prox and the percentage of symptom improvement only for the 10 Hz group. Curve fitting models revealed that there was a quadratic association between IAF and treatment response in the 10 Hz group, with a peak at 10 Hz IAF. Conclusion: The main result of Corlier and colleagues was replicated, and the findings suggest that the distance between 10 Hz stimulation frequency and the IAF may influence clinical outcome in a non-linear manner. Significance: rTMS is often administered at a frequency of 10 Hz, which is the center of the EEG alpha frequency band. The results can make a significant contribution to optimizing the clinical application of rTMS. 7 p.

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

Background Frontocentral Spindling Excessive Beta (SEB), a spindle-like beta-activity observed in the electroencephalogram (EEG), has been transdiagnostically associated with more problems with impulse control and sleep maintenance. The current study aims to replicate and elaborate on these findings. Methods Participants reporting sleep problems (n = 31) or Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms (n = 48) were included. Baseline ADHD-Rating Scale (ADHD-RS), Pittsburgh Sleep Quality Index (PSQI), Holland Sleep Disorder Questionnaire (HSDQ), and EEG were assessed. Analyses were confined to adults with frontocentral SEB. Results Main effects of SEB showed more impulse control problems (d = 0.87) and false positive errors (d = 0.55) in participants with SEB. No significant associations with sleep or interactions with Sample were observed. Discussion This study partially replicates an earlier study and demonstrates that participants exhibiting SEB report more impulse control problems, independent of diagnosis. Future studies should focus on automating SEB classification and further investigate the transdiagnostic nature of SEB.

OBJECTIVE: Our previous research showed high predictive accuracy at differentiating responders from non-responders to repetitive transcranial magnetic stimulation (rTMS) for depression using resting electroencephalography (EEG) and clinical data from baseline and one-week following treatment onset using a machine learning algorithm. In particular, theta (4-8 Hz) connectivity and alpha power (8-13 Hz) significantly differed between responders and non-responders. Independent replication is a necessary step before the application of potential predictors in clinical practice. This study attempted to replicate the results in an independent dataset.METHODS: We submitted baseline resting EEG data from an independent sample of participants who underwent rTMS treatment for depression (N = 193, 128 responders) (Krepel et al., 2018) to the same between group comparisons as our previous research (Bailey et al., 2019).RESULTS: Our previous results were not replicated, with no difference between responders and non-responders in theta connectivity (p = 0.250, Cohen's d = 0.1786) nor alpha power (p = 0.357, ηp2 = 0.005).CONCLUSIONS: These results suggest that baseline resting EEG theta connectivity or alpha power are unlikely to be generalisable predictors of response to rTMS treatment for depression.SIGNIFICANCE: These results highlight the importance of independent replication, data sharing and using large datasets in the prediction of response research.

Highlights • QEEG-informed neurofeedback resulted in remission rates of 55%. • In the total sample, non-remitters had higher hyperactivity scores at baseline. • In women, non-remitters had longer P300 latencies. • In boys, a low individual alpha peak frequency (iAPF) was associated with remission., Introduction Quantitative Electroencephalogram-(QEEG-)informed neurofeedback is a method in which standard neurofeedback protocols are assigned, based on individual EEG characteristics in order to enhance effectiveness. Thus far clinical effectiveness data have only been published in a small sample of 21 ADHD patients. Therefore, this manuscript aims to replicate this effectiveness in a new sample of 114 patients treated with QEEG-informed neurofeedback, from a large multicentric dataset and to investigate potential predictors of neurofeedback response. Methods A sample of 114 patients were included as a replication sample. Patients were treated with standard neurofeedback protocols (Sensori-Motor-Rhythm (SMR), Theta-Beta (TBR), or Slow Cortical Potential (SCP) neurofeedback), in combination with coaching and sleep hygiene advice. The ADHD Rating Scale (ADHD-RS) and Pittsburgh Sleep Quality Index (PSQI) were assessed at baseline, every 10th session, and at outtake. Holland Sleep Disorder Questionnaire (HSDQ) was assessed at baseline and outtake. Response was defined as ≥25% reduction (R25), ≥50% reduction (R50), and remission. Predictive analyses were focused on predicting remission status. Results In the current sample, response rates were 85% (R25), 70% (R50), and remission was 55% and clinical effectiveness was not significantly different from the original 2012 sample. Non-remitters exhibited significantly higher baseline hyperactivity ratings. Women who remitted had significantly shorter P300 latencies and boys who remitted had significantly lower iAPF’s. Discussion In the current sample, clinical effectiveness was replicated, suggesting it is possible to assign patients to a protocol based on their individual baseline QEEG to enhance signal-to-noise ratio. Furthermore, remitters had lower baseline hyperactivity scores. Likewise, female remitters had shorter P300 latencies, whereas boys who remitted have a lower iAPF. Our data suggests initial specificity in treatment allocation, yet further studies are needed to replicate the predictors of neurofeedback remission.

Low heart rate variability (HRV) has strongly been associated with an increased risk for cardiovascular disease. With cardiovascular disease being the number one cause of global deaths, factors that influence its development are relevant to understand. Season of birth has been suggested as one of the factors influencing the development of HRV. The current study was set up to replicate the finding that men born in winter have higher HRV later in life compared to those born in other seasons. To this end, we studied a sample of 1,871 healthy participants from the Brain Resource International Database during rest and during task. Furthermore, sex and age differences and associations with personality traits and psychiatric symptoms were explored. We replicated the earlier finding that men born in winter have a lower ratio of low frequency (LF) power to high frequency (HF) power during rest compared to summer and fall, and, although less pronounced, higher HF compared to summer. A difference between summer and winter for LF/HF in men was internally replicated using data recorded during task. Additionally, for both sexes, LF/HF ratio increased with age, and LF and HF both decreased. In general, LF/HF was lower in women, but heart rate was higher. In men, low HRV was associated with depression and the personality trait openness. In conclusion, results from a large multicenter data set covering the entire lifespan demonstrate that HRV changes with age in both sexes and confirm that season of birth influences HRV later in life in men., This is a replication study, showing heart rate variability (HRV) to vary with season of birth (SoB). Since both HRV and SoB have been related to cardiovascular disease, the relationship is relevant to understand. We used an almost five times larger sample than the original sample, covering wider geographic areas and the full lifespan. Additionally, age and sex differences in HRV were found. Replicating earlier findings provides solid evidence for a relationship between SoB and HRV later in life, thereby providing insights on how HRV develops and ultimately how increased risk for cardiovascular disease can be reduced.

BackgroundFew studies focused on the relationship between psychological measures, major depressive disorder (MDD) and repetitive transcranial magnetic stimulation (rTMS) response. This study investigated several psychological measures as potential predictors for rTMS treatment response. Additionally, this study employed two approaches to evaluate the robustness of our findings by implementing immediate replication and full-sample exploration with strict p-thresholding.MethodsThis study is an open-label, multi-site study with a total of 196 MDD patients. The sample was subdivided in a Discovery (60% of total sample, n = 119) and Replication sample (40% of total sample, n = 77). Patients were treated with right low frequency (1 Hz) or left high frequency (10 Hz) rTMS at the dorsolateral prefrontal cortex. Clinical variables [Beck Depression Inventory (BDI), Neuroticism, Extraversion, Openness Five-Factor Inventory, and Depression, Anxiety, and Stress Scale, and BDI subscales] were obtained at baseline, post-treatment, and at follow-up. Predictors were analyzed in terms of statistical association, robustness (independent replication), as well as for their clinical relevance [positive predictive value (PPV) and negative predictive value (NPV)].ResultsUnivariate analyses revealed that non-responders had higher baseline anhedonia scores. Anhedonia scores at baseline correlated negatively with total BDI percentage change over time. This finding was replicated. However, anhedonia scores showed to be marginally predictive of rTMS response, and neither PPV nor NPV reached the levels of clinical relevance.ConclusionsThis study suggests that non-responders to rTMS treatment have higher baseline anhedonia scores. However, anhedonia was only marginally predictive of rTMS response. Since all other psychological measures did not show predictive value, it is concluded that psychological measures cannot be used as clinically relevant predictors to rTMS response in MDD.

Introduction A recent study showed hypoactivity in the beta/gamma band in female suicide ideators and suicide attempters diagnosed with depression, relative to a low-risk group. The current study aimed to conceptually replicate these results. Methods In the iSPOT-D sub-sample (n = 402), suicide ideators and low-risk individuals were identified. Confining analyses to females only, differences between low-risk individuals and suicide ideators were tested for using the electroencephalogram (EEG) frequency bands SMR (Sensori-Motor-Rhythm; 12−15 Hz), beta (14.5−30 Hz), beta I (14.5−20 Hz), beta II (20−25 Hz), beta III (25−30 Hz), gamma I (31−49 Hz) using LORETA-software. Results None of the tested frequency bands showed to be significantly different between suicide ideators and low-risk individuals. Conclusions The current study could not conceptually replicate the earlier published results. Several reasons could explain this non-replication, among which possible electromyographic (EMG) contamination in the beta/gamma band in the original study. Trial Registration ClinicalTrials.gov identifier: NCT00693849. URL: http://clinicaltrials.gov/ct2/show/NCT00693849 .

In depression treatment there is a clear need for novel treatments, biomarkers and personalized treatment approaches. This symposium addresses rTMS in the treatment of depression and specifically focusses on target engagement principles of these treatments and the ability to individualise treatments. Topics covered include: – a new method to quantify the DLPFC-sgACC-vagal nerve pathway as a measure of target engagement of rTMS in the treatment of depression that can also be used to individualise rTMS stimulation locations by specifically looking at heart rate decelaration (Neuro-Cardiac-Guided TMS); – baseline clinical and EEG measures that dissociate rTMS responders and non-responders, using a strict discovery-replication approach; – Accelerated Theta Burst Stimulation and effects on depressive symptoms and suicide ideation with insights from brain perfusion and functional connectivity.