Neural predictors underlying variability in depression outcomes are poorly understood. Functional MRI measures of subgenual cortex connectivity, self-blaming and negative perceptual biases have shown prognostic potential in treatment-naïve, medication-free and fully remitting forms of major depressive disorder (MDD). However, their role in more chronic, difficult-to-treat forms of MDD is unknown.

Forty-five participants (n = 38 meeting minimum data quality thresholds) fulfilled criteria for difficult-to-treat MDD. Clinical outcome was determined by computing percentage change at follow-up from baseline (four months) on the self-reported Quick Inventory of Depressive Symptomatology (16-item). Baseline measures included self-blame-selective connectivity of the right superior anterior temporal lobe with an a priori Brodmann Area 25 region-of-interest, blood-oxygen-level-dependent a priori bilateral amygdala activation for subliminal sad vs happy faces, and resting-state connectivity of the subgenual cortex with an a priori defined ventrolateral prefrontal cortex/insula region-of-interest.

A linear regression model showed that baseline severity of depressive symptoms explained 3% of the variance in outcomes at follow-up (F[3,34] = .33, p = .81). In contrast, our three pre-registered neural measures combined, explained 32% of the variance in clinical outcomes (F[4,33] = 3.86, p = .01).

These findings corroborate the pathophysiological relevance of neural signatures of emotional biases and their potential as predictors of outcomes in difficult-to-treat depression.

The field of healthcare epidemiology is increasingly focused on identifying, characterizing, and addressing social determinants of health (SDOH) to address inequities in healthcare quality. To identify evidence gaps, we examined recent systematic reviews examining the association of race, ethnicity, and SDOH with inpatient quality measures.

We searched Medline via OVID for English language systematic reviews from 2010 to 2022 addressing race, ethnicity, or SDOH domains and inpatient quality measures in adults using specific topic questions. We imported all citations to Covidence (www.covidence.org, Veritas Health Innovation) and removed duplicates. Two blinded reviewers assessed all articles for inclusion in 2 phases: title/abstract, then full-text review. Discrepancies were resolved by a third reviewer.

Of 472 systematic reviews identified, 39 were included. Of these, 23 examined all-cause mortality; 6 examined 30-day readmission rates; 4 examined length of stay, 4 examined falls, 2 examined surgical site infections (SSIs) and one review examined risk of venous thromboembolism. The most evaluated SDOH measures were sex (n = 9), income and/or employment status (n = 9), age (n = 6), race and ethnicity (n = 6), and education (n = 5). No systematic reviews assessed medication use errors or healthcare-associated infections. We found very limited assessment of other SDOH measures such as economic stability, neighborhood, and health system access.

A limited number of systematic reviews have examined the association of race, ethnicity and SDOH measures with inpatient quality measures, and existing reviews highlight wide variability in reporting. Future systematic evaluations of SDOH measures are needed to better understand the relationships with inpatient quality measures.

In ESCAPE-TRD esketamine nasal spray (ESK-NS) significantly increased the probability of achieving remission at Week (Wk) 8 and being relapse‑free through Wk32 after remission at Wk8 versus (vs) quetiapine extended release (Q-XR) in patients (pts) with treatment resistant depression (TRD) (Reif et al. DGPPN 2022; P-01-04). We report ESK-NS vs Q-XR effects on pt-reported health-related quality of life (HRQoL) over 32 wks.

Evaluate pt-reported HRQoL using the generic 36-item Short-Form Health Survey version 2 (SF-36v2, 4-wk recall, 2009 US population norms) in ESCAPE-TRD.

ESCAPE‑TRD (NCT04338321) was a randomised phase IIIb trial comparing the efficacy of ESK-NS vs Q-XR, both alongside an ongoing selective serotonin/serotonin-norepinephrine reuptake inhibitor, in pts with TRD. SF-36v2 was assessed every 4 wks (on-treatment and retrieved dropout visits). Domain scores and change from baseline (CfB) were analysed using a mixed model for repeated measures (MMRM; observed cases) adjusted for age, prior treatment failures, baseline score. Higher scores indicate better HRQoL. P values were not adjusted for multiple testing.

336 and 340 pts were randomised to ESK-NS and Q-XR. Baseline domain scores were below general population norms and lowest in Role Emotional, Mental Health and Social Functioning (Figure 1A). All scores improved to Wk32 in both arms (Figure 1B). At Wk4, CfB was significantly higher (better HRQoL) with ESK-NS vs Q-XR across domains (all p<0.01). At Wk8, CfB was significantly higher with ESK-NS vs Q-XR across all domains (p<0.05) except Bodily Pain and Role Physical. At Wk32, CfB was significantly higher with ESK-NS vs Q-XR for Mental Health (p=0.014), Role Emotional (p=0.001), Role Physical (p=0.046) and Social Functioning (p=0.006); a trend of numerical advantage was seen for all other domains (Figure 2).

Image:

Image 2:

In addition to the superior clinical benefits provided by ESK-NS vs Q-XR in ESCAPE-TRD, pts receiving ESK-NS experienced significantly greater improvements in HRQoL vs Q-XR over 32 wks.

We thank the patients who participated. Study funding: Janssen, medical writing: Costello Medical, UK.

A. Young Grant / Research support from: Received grants from Janssen; independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London; the views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health, Consultant of: Received consulting fees from Allegan, AstraZeneca, Bionomics, Eli Lilly, Janssen, Johnson & Johnson, LivaNova, Lundbeck, Servier, and Sumitomo Dainippon Pharma and Sunovion, Speakers bureau of: Received speaker’s honoraria from Allegan, AstraZeneca, Bionomics, Eli Lilly, Janssen, Johnson & Johnson, LivaNova, Lundbeck, Servier, and Sumitomo Dainippon Pharma and Sunovion, B. Baune Grant / Research support from: Received research grants from private industries or non-profit funds from AstraZeneca, Lundbeck, and Sanofi-Synthélabo; received research grants from the BMBF and BMG Germany, the DFG, Germany, the National Health and Medical Research Council, Australia, and Horizon Europe 2021; received research grants from the Fay Fuller Foundation, and James & Diana Ramsay Foundation, Adelaide, Consultant of: Received consulting fees for roles with the National Health and Medical Research Council, Australia; received honoraria from Angelini, AstraZeneca, Biogen, BMS, Boehringer Ingelheim, Johnson & Johnson, LivaNova, Lundbeck, Otsuka, Pfizer, Roche, Servier, Sumitomo Dainippon Pharma and Sunovion, and Wyeth; served on advisory boards for Biogen, Boehringer-Ingelheim, Janssen-Cilag, LivaNova, Lundbeck, Novartis, and Otsuka, N. Cardoner Grant / Research support from: Received research grants from the Ministry of Health, Ministry of Science and Innovation (CIBERSAM), and the Strategic Plan for Research and Innovation in Health (PERIS) for the period 2016–2020, as well as from Marato TV3 and Recercaixa, Consultant of: Served on advisory boards for Angelini, Esteve, Janssen, Lundbeck, Novartis, Pfizer and Viatris, Speakers bureau of: Received speaker’s honoraria from Angelini, Esteve, Janssen, Lundbeck, Novartis, Pfizer and Viatris, R. Frey Grant / Research support from: Received travel fees from Janssen and LivaNova; received grants or contracts from Alkermes (Principal Investigator), Janssen (Principal Investigator), LivaNova (Principal Investigator) and Medizinisch‑Wissenschaftlicher Fonds des Bürgermeisters von Wien (academic study), Consultant of: Received consulting fees from Boehringer Ingelheim and Janssen, Speakers bureau of: Received speaker’s honoraria from Janssen and Lundbeck, T. Ito Shareolder of: Johnson & Johnson, Employee of: Janssen, Y. Kambarov Employee of: Janssen, A. Lacerda Grant / Research support from: Received grants from Azidus, Biophytis, Boehringer-Ingelheim, Cellavita, Celltrion, CNPq, Eli Lilly, EOM, FAPESP, Genova, IQVIA, Janssen, Nordisk, Novartis, Novo, Parexel and PPD, Consultant of: Received consulting fees from Aché, Apsen, Biogen, Boehringer-Ingelheim, Cristalia, Daiichi, Eurofarma, Sankyo, EMS, Janssen, Libbs, LivaNova, Lundbeck, Sanofi and Torrent, Speakers bureau of: Received speaker’s honoraria from Aché, Apsen, Biogen, Boehringer-Ingelheim, Cristalia, Daiichi, Eurofarma, Sankyo, EMS, Janssen, Libbs, LivaNova, Lundbeck, Sanofi and Torrent, B. Rive Employee of: Janssen, C. von Holt Shareolder of: Johnson & Johnson, Employee of: Janssen, A. Oliveira-Maia Grant / Research support from: Received grants from Compass Pathways, Ltd., Janssen, and Schuhfried GmBH; investigator‑driven research funded by Fundação para Ciência e Tecnologia (PTDC/SAU-NUT/3507/2021; PTDC/MED-NEU/1552/2021; PTDC/MED‑NEU/31331/2017), Fundação para Ciência e Tecnologia and FEDER (PTDC/MED-NEU/30845/2017_LISBOA-01-0145-FEDER-030845; PTDC/MEC-PSQ/30302/2017_LISBOA-01-0145-FEDER-30302), the European Research Council (ERC-2020-STG-Grant 950357), the European Commission Horizon 2020 Research and Innovation program (H2020‑SC1‑2017‑CNECT‑2‑777167‑ΒΟUNCE; H2020‑SC1‑DTH‑2019‑875358‑FAITH), and the European Joint Programme in Rare Diseases (Joint Translational Call 2019) through Fundação para Ciência e Tecnologia (EJPRD/0001/2020), Consultant of: Received payment or honoraria from MSD (Portugal), Neurolite AG, and the European Monitoring Centre for Drugs and Drug Addiction; received support for attending meetings from Janssen (Portugal); participated in advisory boards for Angelini (Portugal) and Janssen (Portugal), Employee of: Vice-President of the Portuguese Society for Psychiatry and Mental Health; Head of the Psychiatry Working Group for the National Board of Medical Examination (GPNA) at the Portuguese Medical Association and Portuguese Ministry of Health

The ability to remotely monitor cognitive skills is increasing with the ubiquity of smartphones. The Mobile Toolbox (MTB) is a new measurement system that includes measures assessing Executive Functioning (EF) and Processing Speed (PS): Arrow Matching, Shape-Color Sorting, and Number-Symbol Match. The purpose of this study was to assess their psychometric properties.

MTB measures were developed for smartphone administration based on constructs measured in the NIH Toolbox® (NIHTB). Psychometric properties of the resulting measures were evaluated in three studies with participants ages 18 to 90. In Study 1 (N = 92), participants completed MTB measures in the lab and were administered both equivalent NIH TB measures and other external measures of similar cognitive constructs. In Study 2 (N = 1,021), participants completed the equivalent NIHTB measures in the lab and then took the MTB measures on their own, remotely. In Study 3 (N = 168), participants completed MTB measures twice remotely, two weeks apart.

All three measures exhibited very high internal consistency and strong test-retest reliability, as well as moderately high correlations with comparable NIHTB tests and moderate correlations with external measures of similar constructs. Phone operating system (iOS vs. Android) had a significant impact on performance for Arrow Matching and Shape-Color Sorting, but no impact on either validity or reliability.

Results support the reliability and convergent validity of MTB EF and PS measures for use across the adult lifespan in remote, self-administered designs.

Amygdala and dorsal anterior cingulate cortex responses to facial emotions have shown promise in predicting treatment response in medication-free major depressive disorder (MDD). Here, we examined their role in the pathophysiology of clinical outcomes in more chronic, difficult-to-treat forms of MDD.

Forty-five people with current MDD who had not responded to ⩾2 serotonergic antidepressants (n = 42, meeting pre-defined fMRI minimum quality thresholds) were enrolled and followed up over four months of standard primary care. Prior to medication review, subliminal facial emotion fMRI was used to extract blood-oxygen level-dependent effects for sad v. happy faces from two pre-registered a priori defined regions: bilateral amygdala and dorsal/pregenual anterior cingulate cortex. Clinical outcome was the percentage change on the self-reported Quick Inventory of Depressive Symptomatology (16-item).

We corroborated our pre-registered hypothesis (NCT04342299) that lower bilateral amygdala activation for sad v. happy faces predicted favorable clinical outcomes (rs[38] = 0.40, p = 0.01). In contrast, there was no effect for dorsal/pregenual anterior cingulate cortex activation (rs[38] = 0.18, p = 0.29), nor when using voxel-based whole-brain analyses (voxel-based Family-Wise Error-corrected p < 0.05). Predictive effects were mainly driven by the right amygdala whose response to happy faces was reduced in patients with higher anxiety levels.

We confirmed the prediction that a lower amygdala response to negative v. positive facial expressions might be an adaptive neural signature, which predicts subsequent symptom improvement also in difficult-to-treat MDD. Anxiety reduced adaptive amygdala responses.

To present validation evidence for the first eight cognitive measures available through Mobile Toolbox (MTB). These measures use a remote self-administered platform to assess language, working memory, episodic memory, executive function, and processing speed.

We used two separate samples, recruited as part of a larger study, to validate MTB measures. Sample I, comprised of 92 English-speaking adults ages 18-85, was used to assess internal consistency and construct validity. Participants were first administered “gold standard” cognitive measures (Wechsler Memory Scale-IV Verbal Paired Associates I and II; Wechsler Adult Intelligence Scale-IV Symbol Search, Digit Span, Coding, and Letter-Number Sequencing; Delis-Kaplan Executive Function System Color-Word Interference Test, Peabody Picture Vocabulary Test, Wechsler Individual Achievement Test-4 Spelling, and the Wisconsin Card Sorting Test), after which they completed MTB (pre-loaded on a study-provided smartphone) on their own. Internal consistency was evaluated using measure-appropriate indices (split-half reliability, Cronbach’s alpha or IRT-based indices). Pearson correlation coefficients between MTB tests and measures of similar constructs were used to evaluate concurrent validity. For two tests with timing-dependent scores, Arrow Matching and Shape-Color Sorting, separate analyses were performed for iOS and Android devices. Sample II, with 1,120 English-speaking participants ages 18-90, was used to evaluate age-related change. Participants completed MTB measures remotely on their own smartphones, in a preset order, within a 14-day period. Spearman correlation coefficients, corrected for education, were calculated to evaluate relationships between age and test scores.

Sample I participants were 67% female, 52% white, 99% non-Hispanic; average age=48 (SD= 17). Education was: < high school (1%); high school (55%); some college (21%); college (15%); graduate degree (8%). Internal consistency estimates ranged from 0.81 to 0.99. Pearson correlations between MTB and external measures ranged from 0.41 to 0.86 (all p < .01). Of the timed tests, only Shape-Color sorting showed significant score differences between Android and iOS devices. Sample II was 57% female, 13% Hispanic, 72% white, mean age = 45 (SD = 21). Education distribution was: < high school (2%); high school (34%); some college (34%), college (20%); graduate degree (11%). Measures of executive function (r = -0.50; r=-0.57) and processing speed (r= -0.61) showed the expected negative correlation with age (all p <0.001). Negative correlations, although weaker, were also seen on measures of working memory (r=-0.2) and episodic memory (r=-0.2, r=-0.37; p.<.001). Vocabulary performance improved with age (r=0.4; p<.001), while spelling scores remained stable (r=0.09).

Initial studies support the validity and reliability of the first eight MTB cognitive measures in two diverse samples. MTB tests showed satisfactory construct validity, as demonstrated by the associations between MTB and well-established tests. Furthermore, most MTB measures correlated with age in the expected directions. Executive function, processing speed and memory typically decrease with age and this decrease was reflected in MTB test performance. In contrast, spelling and vocabulary, typically preserved as we age, did not decrease in our sample. Our results support the use of MTB in cognitive aging research.

During the COVID-19 pandemic, research organizations accelerated adoption of technologies that enable remote participation. Now, there’s a pressing need to evaluate current decentralization practices and develop appropriate research, education, and operations infrastructure. The purpose of this study was to examine current adoption of decentralization technologies in a sample of clinical research studies conducted by academic research organizations (AROs).

The setting was three data coordinating centers in the U.S. These centers initiated coordination of 44 clinical research studies during or after 2020, with national recruitment and enrollment, and entailing coordination between one and one hundred sites. We determined the decentralization technologies used in these studies.

We obtained data for 44/44 (100%) trials coordinated by the three centers. Three technologies have been adopted across nearly all studies (98–100%): eIRB, eSource, and Clinical Trial Management Systems. Commonly used technologies included e-Signature (32/44, 73%), Online Payments Portals (26/44, 59%), ePROs (23/44, 53%), Interactive Response Technology (22/44, 50%), Telemedicine (19/44, 43%), and eConsent (18/44, 41%). Wearables (7/44,16%) and Online Recruitment Portals (5/44,11%) were less common. Rarely utilized technologies included Direct-to-Patient Portals (1/44, 2%) and Home Health Nurse Portals (1/44, 2%).

All studies incorporated some type of decentralization technology, with more extensive adoption than found in previous research. However, adoption may be strongly influenced by institution-specific IT and informatics infrastructure and support. There are inherent needs, responsibilities, and challenges when incorporating decentralization technology into a research study, and AROs must ensure that infrastructure and informatics staff are adequate.

Older brain age – as estimated from structural MRI data – is known to be associated with detrimental mental and physical health outcomes in older adults. Social isolation, which has similar detrimental effects on health, may be associated with accelerated brain aging though little is known about how different trajectories of social isolation across the life course moderate this association. We examined the associations between social isolation trajectories from age 5 to age 38 and brain age assessed at age 45.

We previously created a typology of social isolation based on onset during the life course and persistence into adulthood, using group-based trajectory analysis of longitudinal data from a New Zealand birth cohort. The typology comprises four groups: ‘never-isolated’, ‘adult-only’, ‘child-only’, and persistent ‘child-adult’ isolation. A brain age gap estimate (brainAGE) – the difference between predicted age from structural MRI date and chronological age – was derived at age 45. We undertook analyses of brainAGE with trajectory group as the predictor, adjusting for sex, family socio-economic status, and a range of familial and child-behavioral factors.

Older brain age in mid-adulthood was associated with trajectories of social isolation after adjustment for family and child confounders, particularly for the ‘adult-only’ group compared to the ‘never-isolated’ group.

Although our findings are associational, they indicate that preventing social isolation, particularly in mid-adulthood, may help to avert accelerated brain aging associated with negative health outcomes later in life.