Characterizing Population EEG Dynamics throughout Adulthood.

For decades, electroencephalography (EEG) has been a useful tool for investigating the neural mechanisms underlying human psychological processes. However, the amount of time needed to gather EEG data means that most laboratory studies use relatively small sample sizes. Using the Muse, a portable and wireless four-channel EEG headband, we obtained EEG recordings from 6029 subjects 18-88 years in age while they completed a category exemplar task followed by a meditation exercise. Here, we report age-related changes in EEG power at a fine chronological scale for δ, θ, α, and ß bands, as well as peak α frequency and α asymmetry measures for both frontal and temporoparietal sites. We found that EEG power changed as a function of age, and that the age-related changes depended on sex and frequency band. We found an overall age-related shift in band power from lower to higher frequencies, especially for females. We also found a gradual, year-by-year slowing of the peak α frequency with increasing age. Finally, our analysis of α asymmetry revealed greater relative right frontal activity. Our results replicate several previous age- and sex-related findings and show how some previously observed changes during childhood extend throughout the lifespan. Unlike previous age-related EEG studies that were limited by sample size and restricted age ranges, our work highlights the advantage of using large, representative samples to address questions about developmental brain changes. We discuss our findings in terms of their relevance to attentional processes and brain-based models of emotional well-being and aging.

Attentional and affective consequences of technology supported mindfulness training: a randomised, active control, efficacy trial.

BACKGROUND: Mindfulness training (MT) programs represent an approach to attention training with well-validated mental health benefits. However, research supporting MT efficacy is based predominantly on weekly-meeting, facilitator-led, group-intervention formats. It is unknown whether participants might benefit from neurofeedback-assisted, technology-supported MT (N-tsMT), in which meditation is delivered individually, without the need for a facilitator, travel to a training site, or the presence of a supportive group environment. Mirroring the validation of group MT interventions, the first step in addressing this question requires identifying whether N-tsMT promotes measurable benefits. Here, we report on an initial investigation of a commercial N-tsMT system. METHODS: In a randomized, active control trial, community-dwelling healthy adult participants carried out 6 weeks of daily practice, receiving either N-tsMT (n = 13), or a control condition of daily online math training (n = 13). Training effects were assessed on target measures of attention and well-being. Participants also completed daily post-training surveys assessing effects on mood, body awareness, calm, effort, and stress. RESULTS: Analysis revealed training effects specific to N-tsMT, with attentional improvements in overall reaction time on a Stroop task, and well-being improvements via reduced somatic symptoms on the Brief Symptom Inventory. Attention and well-being improvements were correlated, and effects were greatest for the most neurotic participants. However, secondary, exploratory measures of attention and well-being did not show training-specific effects. N-tsMT was associated with greater body awareness and calm, and initially greater effort that later converged with effort in the control condition. CONCLUSIONS: Preliminary findings indicate that N-tsMT promotes modest benefits for attention and subjective well-being in a healthy community sample relative to an active control condition. However, the findings would benefit from replication in a larger sample, and more intensive practice or more comprehensive MT instruction might be required to promote the broader benefits typically reported in group format, facilitated MT. TRIAL REGISTRATION: Current Controlled Trials ISRCTN43629398 . Retrospectively registered on June 16, 2016.

Comparison of an Electronic and Paper-based Montreal Cognitive Assessment Tool.

This pilot study compared a novel electronic Montreal Cognitive Assessment (eMoCA) tool to the original paper-based MoCA. Potential participants were approached at primary care practices, a geriatric day hospital, and a university campus. Each of the 401 participants were randomly assigned to either the eMoCA (N=182) or MoCA (N=219). Scores were adjusted by self-reported demographic and health information using regression analysis. The difference in average scores (26.21±3.11 for the MoCA group and 24.84±4.21 for the eMoCA group) was found to be statistically significant. Controlling for the effect of potential covariate factors with regression analyses, the adjusted difference is -0.90 (95% confidence interval, -1.45 to -0.35). This difference may be due to factors related to use of the electronic device or software usability. However, the standardized, self-administered eMoCA may offer an opportunity for health systems to screen for early changes in cognitive function in primary care settings and offer greater access to assessment for rural or remote communities. Population-level research may be required to identify whether the score difference between test versions requires a downward adjustment to the eMoCA score taken as indicative of cognitive impairment.

Probabilistic muscle characterization using quantitative electromyography: application to facioscapulohumeral muscular dystrophy.

Based on quantitative electromyography, a muscle can be categorized as normal or affected by a neuromuscular disorder. The objective of this work was to compare the utility of probabilistic to conventional means and outlier methods of categorization of myopathic and normal muscles. Various sets of motor unit potential (MUP) features detected in biceps brachii muscles of control subjects and patients with facioscapulohumeral muscular dystrophy were used to categorize them as normal or myopathic based on conventional means and outlier categorization (CMC) as well as a new probabilistic muscle categorization (PMC). The sensitivity, specificity, and accuracy provided by each categorization method were compared. The categorizations made using PMC were significantly more accurate (by at least 10%) compared with CMC (P < 10(-10)) for muscles evaluated in this study. Area, duration, and thickness were highly discriminative MUP features.

Using motor unit potential characterizations to estimate neuromuscular disorder level of involvement.

Based on the analysis of electromyographic (EMG) data muscles are often characterized as normal or affected by a neuromuscular disorder. Motor unit potential (MUP) characterizations comprised of the conditional probabilities of a MUP being detected from a muscle of each of the following categories: myopathic, normal, and neuropathic, were estimated. The sets of MUP characterizations of a set of MUPs detected in a muscle were averaged to produce a set of muscle characterization measures related to the probability of the muscle belonging to each category conditioned on the set of MUPs detected. Using simulated EMG signals, the objective of this work was to evaluate the correlation between the muscle characterization measures produced by different MUP characterization methods and the level of involvement of a disorder. The results showed a correlation of 0.9 between myopathic and neuropathic muscle characterization measures and the actual level of involvement when using a Pattern Discovery (PD) method to estimate MUP characterizations. This work suggests that MUP characterizations can be used to assist clinicians in tracking the progress of a disease process.