Hallucinations are common and distressing symptoms in Parkinson’s disease (PD). Treatment response in clinical trials is measured using validated questionnaires, including the Scale for Assessment of Positive Symptoms-Hallucinations (SAPS-H) and University of Miami PD Hallucinations Questionnaire (UM-PDHQ). The minimum clinically important difference (MCID) has not been determined for either scale. This study aimed to estimate a range of MCIDs for SAPS-H and UM-PDHQ using both consensus-based and statistical approaches.

A Delphi survey was used to seek opinions of researchers, clinicians, and people with lived experience. We defined consensus as agreement ≥75%. Statistical approaches used blinded data from the first 100 PD participants in the Trial for Ondansetron as Parkinson’s Hallucinations Treatment (TOP HAT, NCT04167813). The distribution-based approach defined the MCID as 0.5 of the standard deviation of change in scores from baseline at 12 weeks. The anchor-based approach defined the MCID as the average change in scores corresponding to a 1-point improvement in clinical global impression-severity scale (CGI-S).

Fifty-one researchers and clinicians contributed to three rounds of the Delphi survey and reached consensus that the MCID was 2 points on both scales. Sixteen experts with lived experience reached the same consensus. Distribution-defined MCIDs were 2.6 points for SAPS-H and 1.3 points for UM-PDHQ, whereas anchor-based MCIDs were 2.1 and 1.3 points, respectively.

We used triangulation from multiple methodologies to derive the range of MCID estimates for the two rating scales, which was between 2 and 2.7 points for SAPS-H and 1.3 and 2 points for UM-PDHQ.

Sodium-glucose cotransporter-2 inhibitors reduce cardiovascular outcomes in patients with congestive heart failure and a biventricular circulation. Congestive heart failure in Fontan univentricular circulation is distinctly different. Experience with sodium-glucose cotransporter-2 inhibitors in this group has not yet been well described.

This work describes safety and tolerability of sodium-glucose cotransporter-2 inhibitors in patients with Fontan circulation.

Single-centre review of patients with Fontan circulation prescribed a sodium-glucose cotransporter-2 inhibitors for congestive heart failure. Primary outcome was tolerability or need for discontinuation. Secondary outcomes were changes in New York Heart Association class, congestive heart failure hospitalisation, ventricular function, exercise performance, and laboratory values.

We identified 25 patients with Fontan circulation prescribed an sodium-glucose cotransporter-2 inhibitors, most with a systemic right ventricle. Over a third of subjects had at least moderately reduced baseline ventricular function. Baseline catheterisation showed a mean Fontan pressure of 17.1 ± 3.7 mmHg and pulmonary capillary wedge pressure 11.7 ± 3.2 mmHg at rest; 59% had occult diastolic dysfunction with abnormal pulmonary capillary wedge pressure elevation following volume expansion. Most were on congestive heart failure medications and/or a pulmonary vasodilator prior to sodium-glucose cotransporter-2 inhibitors addition, and three had a congestive heart failure hospitalisation within the previous year. All reported good medication tolerance except one patient was nonadherent to medications and two discontinued sodium-glucose cotransporter-2 inhibitors for perceived side effects. There were no significant differences in secondary outcomes. There was, however, a downward trend of serum brain natriuretic peptide (n = 13) and improved peak VO2 (n = 6), though neither statistically significant (p > 0.05).

This series, the largest published to date, suggests that sodium-glucose cotransporter-2 inhibitors are safe and tolerable congestive heart failure therapy in Fontan circulation. Further research is warranted to explore therapy in this unique population.

Chronic insomnia is a highly prevalent disorder affecting approximately one-in-three Americans. Insomnia is associated with increased cognitive and brain arousal. Compared to healthy individuals, those with insomnia tend to show greater activation/connectivity within the default mode network (DMN) of the brain, consistent with the hyperarousal theory. We investigated whether it would be possible to suppress activation of the DMN to improve sleep using a type of repetitive transcranial magnetic stimulation (rTMS) known as continuous theta burst stimulation (cTBS).

Participants (n=9, 6 female; age=25.4, SD=5.9 years) meeting criteria for insomnia/sleep disorder on standardized scales completed a counterbalanced sham-controlled crossover design in which they served as their own controls on two separate nights of laboratory monitored sleep on separate weeks. Each session included two resting state functional magnetic resonance imaging (fMRI) sessions separated by a brief rTMS session. Stimulation involved a 40 second cTBS stimulation train applied over an easily accessible cortical surface node of the DMN located at the left inferior parietal lobe. After scanning/stimulation, the participant was escorted to an isolated sleep laboratory bedroom, fitted with polysomnography (PSG) electrodes, and allowed an 8-hour sleep opportunity from 2300 to 0700. PSG was monitored continuously and scored for standard outcomes, including total sleep time (TST), percentage of time various sleep stages, and number of arousals.

Consistent with our hypothesis, a single session of active cTBS produced a significant reduction of functional connectivity (p < .05, FDR corrected) within the DMN. In contrast, the sham condition produced no changes in functional connectivity from pre- to post-treatment. Furthermore, after controlling for age, we also found that the active treatment was associated with meaningful trends toward greater overnight improvements in sleep compared to the sham condition. First, the active cTBS condition was associated with significantly greater TST compared to sham (F(1,7)=14.19, p=.007, partial eta-squared=.67). Overall, individuals obtained 26.5 minutes more sleep on the nights that they received the active cTBS compared to the sham condition. Moreover, the active cTBS condition was associated with a significant increase in the percentage of time in rapid eye movement (REM%) sleep compared to the sham condition (F(1,7)=7.05, p=.033, partial eta-squared=.50), which was significant after controlling for age. Overall, active treatment was associated with an increase of 6.76% more of total sleep time in REM compared to sham treatment. Finally, active cTBS was associated with fewer arousals from sleep (t(8) = -1.84, p = .051, d = .61), with an average of 15.1 fewer arousals throughout the night than sham.

Overall, these findings suggest that this simple and brief cTBS approach can alter DMN brain functioning in the expected direction and was associated with trends toward improved objectively measured sleep, including increased TST and REM% and fewer arousals during the night following stimulation. These findings emerged after only a single 40-second treatment, and it remains to be seen whether multiple treatments over several days or weeks can sustain or even improve upon these outcomes.

While there exist numerous validated neuropsychological tests and batteries to measure cognitive and behavioral capacities, the vast majority of these are time intensive and difficult to administer and score outside of the clinic. Moreover, many existing assessments may have limited ecological validity in some contexts (e.g., military operations). Therefore, we have been developing a novel approach to administering neuropsychological assessment using a virtual reality (VR) “game” that will collect simultaneously acquired multidimensional data that is synthesized by machine learning algorithms to identify neurocognitive strengths and weaknesses in a fraction of the time of typical assessment approaches. For our initial pilot project, we developed a preliminary VR task that involved a brief game-like military “shoot/no-shoot” task that collected data on hits, false alarms, discriminability, and response times under a context-dependent rule set. This prototype task will eventually be expanded to include a significantly more complex set of tasks with greater cognitive demands, sensor feeds, and response variables that could be modified to fit many other contexts. The objective of this project was to construct a rudimentary pilot version and demonstrate whether it could predict outcomes on standard neuropsychological assessments.

To demonstrate proof-of-concept, we collected data from 20 healthy participants from the general population (11 male; age=24.8, SD=7.8) with high average intelligence (IQ = 112, SD=10.7). All participants completed the Wechsler Abbreviated Scale of Intelligence-II (WASI-II), and several neuropsychological tests including the ImPACT, the Attention and Executive Function modules of the Neuropsychological Assessment Battery (NAB), and the VR task. Initially, we used a prior dataset from 359 participants (n=191 mild traumatic brain injury; n=120healthy control; n=48 sleep deprived) to serve as a training sample for machine learning models. Based on these outcomes, we applied machine learning, as well as standard multiple regression approaches to predict neuropsychological outcomes in the 20 test participants.

In this limited study, the machine learning approach did not converge on a meaningful prediction due to the instability of the small sample. However, standard multiple linear regression using stepwise entry/deletion of the VR task variables significantly predicted neuropsychological performance. The VR task predicted WASI-II vocabulary (R=.457, p=.043), NAB Attention Index (R=.787, p=.001), and NAB Executive Function Index (R=.715, p=.002). Interestingly, these performances were generally as good or better than the predictions resulting from the ImPACT, a commercially available neuropsychological test battery, which correlated with WASI-II vocabulary (R=.557, p=.011), NAB Attention Index (R=.574, p=.008), and NAB Executive Function Index (R=.619, p=.004).

Our pilot VR task was able to predict performances on standard neuropsychological assessment measures at a level comparable to that of a commercially available computerized assessment battery, providing preliminary evidence of concurrent validity. Ongoing work is expanding this rudimentary task into one involving greater complexity and nuance. As multivariate data integration models are incorporated into the tasks and extraction features, future work will collect data on much larger samples of individuals to develop and refine the machine learning models. With additional work this approach may provide an important advance in neuropsychological assessment methods.