The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and “interrelatedness” at posterior alpha (8‐12 Hz) and widespread delta (< 4 Hz) and theta (4‐8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact‐free rsEEG periods are needed; (2) power density and “interrelatedness” rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

Concerns over effects of 'textisms' on literacy have been reinforced by research identifying processing costs associated with reading textisms. But to what extent do such studies reflect actual textism use? This study examined the textual characteristics of 936 text messages in English (13391 words). Message length, nonstandard spelling, sender and message characteristics and word frequency were analyzed. The data showed that 25% of word content used nonstandard spelling, the most frequently occurring category involving omission of capital letters. Types of nonstandard spelling varied only slightly depending on the purpose of the text message, while the overall proportion of nonstandard spelling did not differ significantly. Less than 0.2% of content was 'semantically unrecoverable.' Implications for experimental studies of textisms are discussed.

No abstract

No abstract

Adults with attention‐deficit/hyperactivity disorder (ADHD) have been described as having altered resting‐state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR). However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behaviour (mediational endophenotypes). Resting‐state eyes‐open and eyes‐closed EEG was measured from 38 adults with ADHD, 45 first‐degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1–45 Hz) and TBR could classify participants into ADHD, first‐degree relatives and/or control groups. Random‐label permutation was used to quantify any bias in the analysis. Eyes‐open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic = 0.71–0.77). The best predictors of ADHD status were increased power in delta, theta and low‐alpha over centro‐parietal regions, and in frontal low‐beta and parietal mid‐beta. TBR did not successfully classify ADHD status. Elevated eyes‐open power in delta, theta, low‐alpha and low‐beta distinguished first‐degree relatives from controls (area under receiver operating characteristic = 0.68–0.72), suggesting that these features may be a mediational endophenotype for adult ADHD. Resting‐state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD.