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According to the Centers for Disease Control and Prevention, about 1 in 4 Americans have a disability.1 As the population ages, there is an increased need for technology and accessibility (equal access), including Web accessibility. Accessibility is often defined in terms of 2 distinct measures: compliance with standards and usability for people with disabilities. The most common accessibility guidelines are the Web Content Accessibility Guidelines (WCAG) 2.0/2.1 and the Section 508 of the Rehabilitation Act. They are written to support the accessibility of HTML content. According to the W3C Web Accessibility Initiative, “Web accessibility means that websites, tools, and technologies are designed and developed so that people with disabilities can use them.”² Business owners, medical writers, technical editors, social media managers, Web designers, and information technology administrators play a critical role in ensuring a better Web experience for all of us. In this article, we share practical ideas for you to show your commitment to diversity, equity, and inclusion with changes you can make to your website and content today. Accessibility solutions, such as using image alternative text, camel case in hashtags, white space around headings, margins, indentations, and columns, and strategically using fonts and color contrasting, are a few of many effective ways to provide accessible content to a larger audience. We also share other ways to address accessibility with creative strategies, such as podcasting. For medical communicators, strategies in communication, such as descriptive language and the use of storytelling techniques, are vital to ensuring your audience has a clear understanding of the message.

Event-related potentials (ERPs) were used to assess the neural mechanisms underlying visual-spatial attention abnormalities associated with psychopathic personality traits. Sixty-nine undergraduates (56 women, 13 men) completed the Psychopathic Personality Inventory-Revised (PPI-R; Lilienfeld & Widows, 2005) and performed two cognitive tasks in which search displays containing a lateralized singleton encircled a fixation point that changed luminance from trial-to-trial. When searching for the singleton as a target, PPI-R scores were uncorrelated with ERP measures of its salience (Ppc), goal-directed selection (N2pc), and working memory evaluation (negative amplitude CDA). In contrast, when responding to the changes in luminance at fixation and ignoring the lateral singleton as a salient distractor, PPI-R Self-Centered Impulsivity factor scores were positively correlated with a potential indicator of distractor suppression (a sustained positive amplitude CDA). These findings provide support for a neurophysiological interpretation of the changes in visual-spatial attention associated with psychopathic personality traits: normal selection of target information accompanied by greater elimination of distractor information at a later visual working memory stage.

This prognostic study of patients with major depressive disorder estimates how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic data., Key Points Question Is it possible to predict whether the condition of a patient with depression will improve after escitalopram treatment by analyzing their resting-state electroencephalographic signals? Findings In this prognostic study of data from 122 patients diagnosed with major depressive disorder, support vector machine classifiers demonstrated an accuracy of 82.4% for predicting escitalopram treatment outcome. Meaning When complemented by appropriate analysis methods, resting-state electroencephalographic recordings may be instrumental in improving treatment of patients with depression., Importance Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient’s response to treatment could significantly reduce the burden of depression. Objective To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression. Design, Setting, and Participants This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment. Interventions All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment. Main Outcomes and Measures The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%. Results Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%). Conclusions and Relevance These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.

While reward associative learning has been studied extensively across different species, punishment avoidance learning has received far less attention. Of particular interest is how the two types of learning change perceptual processing of the learned stimuli. We designed a task that required participants to learn the association of emotionally neutral images with reward, punishment, and no incentive value outcomes through trial-and-error. During learning, participants received monetary reward, neutral outcomes or avoided punishment by correctly identifying corresponding images. Results showed an early bias in favor of learning reward associations, in the form of higher accuracy and fewer trials needed to reach learning criterion. We subsequently assessed electrophysiological learning effects with a task in which participants viewed the stimuli with no feedback or reinforcement. Critically, we found modulation of two early event-related potential components for reward images: the frontocentral P2 (170 – 230 ms) and the anterior N2/Early Anterior Positivity (N2/EAP; 210 – 310 ms). We suggest that reward associations may change stimuli detection and incentive salience as indexed by P2 and N2/EAP. We also reported, on an exploratory basis, a late negativity with frontopolar distribution enhanced by punishment images.

Language is often thought to play a pivotal role in organizing complex thought and action (Vygotsky, 1986). However, there is surprisingly little known about how exactly we might use language for this purpose. One way in which language may be helpful is in that it allows us to cue upcoming demands in a proactive manner. Verbal labels may become associated with non-verbal, action-relevant codes that represent aspects such as where to direct attention or which motor actions to select (Hommel, Pratt, Colzato, & Godijn, 2001). Articulating verbal labels activates the associated codes and thus prepares their fluent application. In fact, work with the task-switching paradigm has demonstrated that verbalizing task labels reduces task-switch costs (Goschke, 2000; Kray, Eber, & Karbach, 2008). Moreover, in situations in which people need to select tasks according to an internal sequential rule, articulatory suppression produces substantial interference with performance (Baddeley, Chincotta, & Adlam, 2001) and it has been specifically argued that articulatory suppression interferes with preparatory self-cueing (Miyake, Emerson, Padilla, & Ahn, 2004). The notion of preparatory control during complex sequential action is also consistent with standard models of serial-order control. Such models assume that complex sequences are organized hierarchically in terms of chunks of 2–4 sequential elements and these chunks themselves can become recursively embedded within higher-order chunks (Klapp, 1976; Rosenbaum, Kenny, & Derr, 1983). Executing such a sequence requires retrieving the currently relevant chunk into working memory, which explains why inter-response times between successive chunks are usually much larger than within-chunk inter-response times. Mayr (2009), using a paradigm that required executing complex sequences of tasks (Schneider & Logan, 2006) recently reported evidence for another type of sequence-structure effect, namely positional interference. For example, when three different tasks A, B, and C are combined in the two-chunk sequence ABC-ACB, retrieval of the task C in the middle of the second chunk is impaired because of interference from that task's association with the final position of the first chunk (see also Koch, Philipp, & Gade, 2006; Schneider, 2007).1 Undoubtedly, most proponents of the standard model of serial-order control would assume that verbal self-cuing should help with exactly these types of retrieval-related costs. Thus, if speaking during sequencing is in fact used in a proactive manner (i.e., before the relevant action needs to be executed) then it should lead to a marked reduction or even elimination of such sequence-structure effects. Traditionally, research on serial-order control has used paradigms in which people executed complete sequences (e.g., of key-presses) from memory, which allows no control over inter-response timing and thus does not allow explicit tests of the effects of preparation and/or verbal self-cuing. This has changed with the so-called task-span procedure (Koch, 2003; Mayr, 2009; Schneider & Logan, 2006), where subjects work through memorized sequences of tasks. Given that here a response can be executed only after the stimulus has been presented, the time available for preparation—the interval between the previous response and the next stimulus (RSI)—is under full experimentor control. For example, Schneider and Logan (2006) reported a study in which subjects had to work through multi-chunk sequences. Chunk-initiation times were slightly reduced for long RSIs. However, this preparation-related reduction was much smaller than would be expected if proactive, verbal self-cuing actually allows loading upcoming sequential elements into working memory. Similarly, Koch (2003) had asked subjects to alternate between two tasks in runs of two trials (AA–BB) and found no evidence for reduced task-switch costs (which here are confounded with chunk-initiation times) as a result of increased RSIs. Finally, in the above-mentioned study (Mayr, 2009) large RT and error effects of both positional interference and chunk-initiation were observed despite a 1000 ms RSI, a duration that should have easily sufficed to retrieve the next sequential element into working memory. Given that there can be little doubt that language is critical during complex sequencing demands (e.g., Baddeley et al., 2001) and given the generally assumed role of language in allowing to plan and precue upcoming behavior, the results summarized here clearly present a puzzle. What is missing is research that directly assesses and experimentally manipulates the role of speech during serial-order control.

Dense-array EEG recordings are now commonplace in research and gaining acceptance in clinical settings. Application of many sensors with traditional electrolytes is time consuming. Saline electrolytes can be used to minimize application time but recording duration is limited due to evaporation. In the present study, we evaluate a NIPAm (N-isopropyl acrylamide:acrylic acid) base electrolyte extender for use with saline electrolytes.Sensor-scalp impedances and EEG data quality acquired with the electrolyte extender are compared with those obtained for saline and an EEG electrolyte commonly used in clinical exams (Elefix).The results show that when used in conjunction with saline, electrode-scalp impedances and data across the EEG spectrum are comparable with those obtained using Elefix EEG paste.When used in conjunction with saline, the electrolyte extender permits rapid application of dense-sensor arrays and stable, high-quality EEG data to be obtained for at least 4.5 h.This is an enabling technology that will make benefits of dense-array EEG recordings practical for clinical applications.