Functional connectivity in slow-wave sleep: identification of synchronous cortical activity during wakefulness and sleep using time series analysis of electroencephalographic data.

Sleep is a behavioral state ideal for studying functional connectivity because it minimizes many sources of between-subject variability that confound waking analyses. This is particularly important for potential connectivity studies in mental illness where cognitive ability, internal milieu and active psychotic symptoms can vary widely across subjects. We, therefore, sought to adapt techniques applied to magnetoencephalography for use in high-density electroencephalography (EEG), the gold-standard in brain-recording methods during sleep. Autoregressive integrative moving average modeling was used to reduce spurious correlations between recording sites (electrodes) in order to identify functional networks. We hypothesized that identified network characteristics would be similar to those found with magnetoencephalography, and would demonstrate sleep stage-related differences in a control population. We analysed 60-s segments of low-artifact data from seven healthy human subjects during wakefulness and sleep. EEG analysis of eyes-closed wakefulness revealed widespread nearest-neighbor positive synchronous interactions, similar to magnetoencephalography, though less consistent across subjects. Rapid eye movement sleep demonstrated positive synchronous interactions akin to wakefulness but weaker. Slow-wave sleep (SWS), instead, showed strong positive interactions in a large left fronto-temporal-parietal cluster markedly more consistent across subjects. Comparison of connectivity from early SWS to SWS from a later sleep cycle indicated sleep-related reduction in connectivity in this region. The consistency of functional connectivity during SWS within and across subjects suggests this may be a promising technique for comparing functional connectivity between mental illness and health.

A Quality Improvement Curriculum for Psychiatry Residents.

Introduction: Quality improvement (QI) is an increasingly important aspect of health care and residency education. There is relatively little research describing QI curricula for residents in psychiatry. Although QI curricula have been published in MedEdPORTAL, the current resource represents the first such curriculum specific to psychiatry residents. This resource aims to present a QI curriculum for psychiatry residents. Methods: The University of Wisconsin psychiatry residency program implemented a QI curriculum for our PGY 3 psychiatry residents in 2010. The initial version of the curriculum has undergone marked changes over the ensuing years, reflecting feedback received from learners and faculty instructors, as well as ongoing review of the literature, to ascertain best practices in this area of medical education. Steps taken have included faculty training, development of evaluation forms, and implementation of elements to increase accountability for successful, sustainable project development. Results: During the 8 completed years of this curriculum, 77 PGY 3 psychiatry residents have completed it. The Quality Improvement Knowledge Application Tool adapted for psychiatry was completed by PGY 3 residents in advance of and upon completion of the curriculum for the first 2 years of the curriculum; results demonstrated a significant improvement in scores as a measurement of QI knowledge and skills. Thirty-one of 32 resident teams (97%) have implemented a QI project. Discussion: Our QI curriculum for PGY 3 psychiatry residents has been successful in equipping residents with QI knowledge and having them implement QI projects.

Synchronous neural interactions assessed by magnetoencephalography: a functional biomarker for brain disorders.

We report on a test to assess the dynamic brain function at high temporal resolution using magnetoencephalography (MEG). The essence of the test is the measurement of the dynamic synchronous neural interactions, an essential aspect of the brain function. MEG signals were recorded from 248 axial gradiometers while 142 human subjects fixated a spot of light for 45-60 s. After fitting an autoregressive integrative moving average (ARIMA) model and taking the stationary residuals, all pairwise, zero-lag, partial cross-correlations (PCC(ij)(0)) and their z-transforms (z(ij)(0)) between i and j sensors were calculated, providing estimates of the strength and sign (positive, negative) of direct synchronous coupling at 1 ms temporal resolution. We found that subsets of z(ij)(0) successfully classified individual subjects to their respective groups (multiple sclerosis, Alzheimer's disease, schizophrenia, Sjögren's syndrome, chronic alcoholism, facial pain, healthy controls) and gave excellent external cross-validation results.

Psychiatrists' use of electronic communication and social media and a proposed framework for future guidelines.

BACKGROUND: Recent and ongoing advances in information technology present opportunities and challenges in the practice of medicine. Among all medical subspecialties, psychiatry is uniquely suited to help guide the medical profession's response to the ethical, legal, and therapeutic challenges--especially with respect to boundaries--posed by the rapid proliferation of social media in medicine. Ironically, while limited guidelines exist for other branches of medicine, guidelines for the responsible use of social media and information technology in psychiatry are lacking. OBJECTIVE: To collect data about patterns of use of electronic communications and social media among practicing psychiatrists and to establish a conceptual framework for developing professional guidelines. METHODS: A structured survey was developed to assess the use of email, texting, and social media among the active membership of the Group for the Advancement of Psychiatry (GAP) to gain insight into current practices across a spectrum of the field and to identify areas of concern not addressed in existing guidelines. This survey was distributed by mail and at an annual meeting of the GAP and a descriptive statistical analysis was conducted with SPSS. RESULTS: Of the 212 members, 178 responded (84% response rate). The majority of respondents (58%) reported that they rarely or never evaluated their online presence, while 35% reported that they had at some time searched for information online about patients. Only 20% posted content about themselves online and few of these restricted that information. Approximately 25% used email to communicate with patients, and very few obtained written consent to do so. CONCLUSION: Discipline-specific guidelines for psychiatrists' interactions with social media and electronic communications are needed. Informed by the survey described here, a review of the literature, and consensus opinion, a framework for developing such a set of guidelines is proposed. The model integrates four key areas: treatment frame, patient privacy, medico-legal concerns, and professionalism. This conceptual model, applicable to many psychiatric settings, including clinical practice, residency training, and continuing medical education, will be helpful in developing discipline-wide guidelines for psychiatry and can be applied to a decision-making process by individual psychiatrists in day-to-day practice.

Health care reform and integrated care: a golden opportunity for preventive psychiatry.

The Affordable Care Act (ACA) includes provisions to shift the U.S. health care system to address achieving wellness rather than just treating illness. In this Open Forum, the Prevention Committee of the Group for the Advancement of Psychiatry describes opportunities created by the ACA for improving prevention of mental illnesses and promotion of mental health. These include improved coverage of preventive services, models to integrate primary and behavioral health care, and establishment of the National Prevention, Health Promotion, and Public Health Council, which has developed a National Prevention Strategy. The authors describe the important role that psychiatrists can play in advancing prevention of mental illnesses, in particular by working to incorporate prevention strategies in integrated care initiatives and by collaborating with primary care providers to screen for risk factors and promote mental and emotional well-being.

Synchronous dynamic brain networks revealed by magnetoencephalography.

We visualized synchronous dynamic brain networks by using prewhitened (stationary) magnetoencephalography signals. Data were acquired from 248 axial gradiometers while 10 subjects fixated on a spot of light for 45 s. After fitting an autoregressive integrative moving average model and taking the residuals, all pairwise, zero-lag, partial cross-correlations (PCC(ij)(0)) between the i and j sensors were calculated, providing estimates of the strength and sign (positive and negative) of direct synchronous coupling between neuronal populations at a 1-ms temporal resolution. Overall, 51.4% of PCC(ij)(0) were positive, and 48.6% were negative. Positive PCC(ij)(0) occurred more frequently at shorter intersensor distances and were 72% stronger than negative ones, on the average. On the basis of the estimated PCC(ij)(0), dynamic neural networks were constructed (one per subject) that showed distinct features, including several local interactions. These features were robust across subjects and could serve as a blueprint for evaluating dynamic brain function.

Dipole analysis of magnetoencephalographic data during continuous shape copying.

High density, whole head magnetoencephalography (MEG) was used to study ten healthy human subjects (five females and five males) participating in a continuous shape-copying task. The task was performed with eyes open and fixated. The three-part task began with 45 s of fixation on a blue dot, after which the dot turned red, and a pentagon was presented around it. Subjects continued to fixate on the red dot for 45 s, after which it turned green. The green dot instructed subjects to begin copying the shape continuously for 45 s, without visual feedback, using a joystick mounted at arm's length. Data were collected at 1,017.25 Hz with a 248 sensor axial-gradiometer system. After cardiac artifact subtraction (Leuthold 2003), each corner was identified, and 1 s epochs (centered on each corner) were averaged and filtered from 1 to 44 Hz. Grand average flux maps demonstrated dipolar distributions identifying the most relevant sensors. With these sensors, which were located over flux extrema (Valaki et al. 2004), dipole models were used for source localization within subjects. Consistent dipole locations included the left motor cortex, bilateral parietal, frontal and temporal regions, and the occipital cortex. These results indicate that MEG source-localization may be derived from a limited number of trials of continuous data, and that visual cortex activity may be consistently present during continuous motor activity despite the absence of novel visual stimulation and eye-movements.

Magnetoencephalographic signals predict movement trajectory in space.

Brain-machine interface (BMI) efforts have been focused on using either invasive implanted electrodes or training-extensive conscious manipulation of brain rhythms to control prosthetic devices. Here we demonstrate an excellent prediction of movement trajectory by real-time magnetoencephalography (MEG). Ten human subjects copied a pentagon for 45 s using an X-Y joystick while MEG signals were being recorded from 248 sensors. A linear summation of weighted contributions of the MEG signals yielded a predicted movement trajectory of high congruence to the actual trajectory (median correlation coefficient: r=0.91 and 0.97 for unsmoothed and smoothed predictions, respectively). This congruence was robust since it remained high in cross-validation analyses (based on the first half of data to predict the second half; median correlation coefficient: r=0.76 and 0.85 for unsmoothed and smoothed predictions, respectively).

Time series analysis of magnetoencephalographic data during copying.

We used standard time series modeling to analyze magnetoencephalographic (MEG) data acquired during three tasks. Each task lasted 45 s, for a total data acquisition period of 135 s. Ten healthy human subjects fixated their eyes on a central blue point for 45 s (fixation only, "F" task). Then a pentagon (visual template) appeared surrounding the fixation point which simultaneously became red (fixation + template, "FT" task). After 45 s, the fixation point changed to green, which was the "go" signal for the subjects to begin continuously copying the pentagon for 45 s using a joystick and without visual feedback of their movement trajectory (fixation + template + copying, "FTC" task). MEG data were acquired continuously from 248 axial gradiometers at a sampling rate of 1017.25 Hz. After removal of cardiac artifacts and rejection of records with eyeblink artifacts, a Box-Jenkins autoregressive integrative moving average (ARIMA) analysis was applied to the unsmoothed, unaveraged MEG time series for model identification and estimation within 25 time lags (approximately 25 ms). We found that an ARIMA model of 25th order autoregressive, first order differencing, and first order moving average (p=25, d=1, q=1) adequately modeled the series and yielded residuals practically stationary with respect to their mean, variance, and autocorrelation structure. These "prewhitened" residuals were then used for assessing pairwise associations between series using crosscorrelation analysis with +/-25 time lags (approximately +/-25 ms). The cross-correlograms thus obtained revealed rich and consistent patterns of interactions between series with respect to positive and/or negative correlations. The overall prevalence of these patterns was very similar in the three tasks used, and, for particular sensor pairs, they tended to be preserved across tasks.

Cortical systems associated with covert music rehearsal.

Musical representation and overt music production are necessarily complex cognitive phenomena. While overt musical performance may be observed and studied, the act of performance itself necessarily skews results toward the importance of primary sensorimotor and auditory cortices. However, imagined musical performance (IMP) represents a complex behavioral task involving components suited to exploring the physiological underpinnings of musical cognition in music performance without the sensorimotor and auditory confounds of overt performance. We mapped the blood oxygenation level-dependent fMRI activation response associated with IMP in experienced musicians independent of the piece imagined. IMP consistently activated supplementary motor and premotor areas, right superior parietal lobule, right inferior frontal gyrus, bilateral mid-frontal gyri, and bilateral lateral cerebellum in contrast with rest, in a manner distinct from fingertapping versus rest and passive listening to the same piece versus rest. These data implicate an associative network independent of primary sensorimotor and auditory activity, likely representing the cortical elements most intimately linked to music production.