3q29 deletion syndrome (3q29del) is a rare (~1:30 000) genomic disorder associated with a wide array of neurodevelopmental and psychiatric phenotypes. Prior work by our team identified clinically significant executive function (EF) deficits in 47% of individuals with 3q29del; however, the nuances of EF in this population have not been described.

We used the Behavior Rating Inventory of Executive Function (BRIEF) to perform the first in-depth assessment of real-world EF in a cohort of 32 individuals with 3q29del (62.5% male, mean age = 14.5 ± 8.3 years). All participants were also evaluated with gold-standard neuropsychiatric and cognitive assessments. High-resolution structural magnetic resonance imaging was performed on a subset of participants (n = 24).

We found global deficits in EF; individuals with 3q29del scored higher than the population mean on the BRIEF global executive composite (GEC) and all subscales. In total, 81.3% of study subjects (n = 26) scored in the clinical range on at least one BRIEF subscale. BRIEF GEC T scores were higher among 3q29del participants with a diagnosis of attention deficit/hyperactivity disorder (ADHD), and BRIEF GEC T scores were associated with schizophrenia spectrum symptoms as measured by the Structured Interview for Psychosis-Risk Syndromes. BRIEF GEC T scores were not associated with cognitive ability. The BRIEF-2 ADHD form accurately (sensitivity = 86.7%) classified individuals with 3q29del based on ADHD diagnosis status. BRIEF GEC T scores were correlated with cerebellar white matter and subregional cerebellar cortex volumes.

Together, these data expand our understanding of the phenotypic spectrum of 3q29del and identify EF as a core feature linked to both psychiatric and neuroanatomical features of the syndrome.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

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.

There is an urgent need to address pervasive inequities in health and healthcare in the USA. Many areas of health inequity are well known, but there remain important unexplored areas, and for many populations in the USA, accessing data to visualize and monitor health equity is difficult.

We describe the development and evaluation of an open-source, R-Shiny application, the “Health Equity Explorer (H2E),” designed to enable users to explore health equity data in a way that can be easily shared within and across common data models (CDMs).

We have developed a novel, scalable informatics tool to explore a wide variety of drivers of health, including patient-reported Social Determinants of Health (SDoH), using data in an OMOP CDM research data repository in a way that can be easily shared. We describe our development process, data schema, potential use cases, and pilot data for 705,686 people who attended our health system at least once since 2016. For this group, 996,382 unique observations for questions related to food and housing security were available for 324,630 patients (at least one answer for all 46% of patients) with 65,152 (20.1% of patients with at least one visit and answer) reporting food or housing insecurity at least once.

H2E can be used to support dynamic and interactive explorations that include rich social and environmental data. The tool can support multiple CDMs and has the potential to support distributed health equity research and intervention on a national scale.

To compare the agreement and cost of two recall methods for estimating children’s minimum dietary diversity (MDD).

We assessed child’s dietary intake on two consecutive days: an observation on day one, followed by two recall methods (list-based recall and multiple-pass recall) administered in random order by different enumerators at two different times on day two. We compared the estimated MDD prevalence using survey-weighted linear probability models following a two one-sided test equivalence testing approach. We also estimated the cost-effectiveness of the two methods.

Cambodia (Kampong Thom, Siem Reap, Battambang, and Pursat provinces) and Zambia (Chipata, Katete, Lundazi, Nyimba, and Petauke districts).

Children aged 6–23 months: 636 in Cambodia and 608 in Zambia.

MDD estimations from both recall methods were equivalent to the observation in Cambodia but not in Zambia. Both methods were equivalent to the observation in capturing most food groups. Both methods were highly sensitive although the multiple-pass method accurately classified a higher proportion of children meeting MDD than the list-based method in both countries. Both methods were highly specific in Cambodia but moderately so in Zambia. Cost-effectiveness was better for the list-based recall method in both countries.

The two recall methods estimated MDD and most other infant and young child feeding indicators equivalently in Cambodia but not in Zambia, compared to the observation. The list-based method produced slightly more accurate estimates of MDD at the population level, took less time to administer and was less costly to implement.

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.

Prior literature has documented how motives for cannabis use predict frequency of use and cannabis use problems among adolescents. However, few studies have examined possible moderating variables that may influence the association between cannabis use motives and frequency of use. The current study examines how risky decision-making moderates this association to help better understand which individuals are at greater risk for cannabis use escalation. The current study will be the first to examine the interactive effects of motives for cannabis use (i.e., health or recreational reasons) and risky decision-making on cannabis use trajectories among a sample of adolescent cannabis users.

Data from 194 adolescent cannabis users aged 14–17 at baseline were analyzed as part of a larger longitudinal study. Participants included those who self-reported use of cannabis within six months prior to the baseline assessment. The Marijuana Reasons for Use Questionnaire (MJRUQ) was used to assess motives for cannabis use from a list of 13 items. A confirmatory factor analysis identified “health” and “recreational” factors for motives for cannabis use. Lifetime frequency of cannabis use (number of days used) was assessed through the Drug Use History Questionnaire, while risky decision-making was assessed using the Game of Dice Task. We used latent growth curve modeling and linear regression analyses to examine the interactive effects of motives for cannabis use and risky decision-making on initial levels of lifetime cannabis use at baseline, and rate of cannabis use escalation over time.

No significant interactive effects were found for health motives for cannabis use; however, we found significant main effects of health motives on initial levels of lifetime cannabis use at baseline (b = 100.82, p < .01) and rate of cannabis use escalation (b = 24.79, p < .01). Those with a greater proclivity to use cannabis for health purposes showed higher initial levels of lifetime use at baseline and steeper increases in the rate of cannabis use escalation relative to those less likely to use for health purposes. Furthermore, we found a significant interactive effect of recreational motives for use and risky decision-making on the rate of cannabis use escalation (b = -2.53, p < .01). Follow-up analyses revealed that among those less likely to use cannabis for recreational purposes, higher risky decision-making was associated with a steeper increase in the rate of cannabis use escalation relative to those who exhibited lower risky decision-making.

The current study replicated findings suggesting that cannabis use motives influence cannabis use trajectories. We found that using cannabis primarily for health reasons was associated with higher initial levels and steeper increases in use regardless of decision-making. Furthermore, we found that both motives for use and risky decision-making interacted to influence associations with cannabis use trajectories. Specifically, among individuals reporting less cannabis use for recreational reasons, those with relatively riskier decision-making showed steeper increases in the rate of cannabis use escalation. These findings inform prevention and intervention practices that focus on decision-making by tailoring approaches based on an individual’s primary motives for cannabis use.

A recent review called for a more robust assessment of cannabis use (CU), including amount and timing of recent use to assess neurocognitive effects of CU among people living with HIV (PWH) (Ellis et al., 2021). The current study addresses some issues raised by investigating between group neurocognitive differences among healthy controls and PWH who differ on their cannabis use histories, using strict inclusion criteria, robust classification of CU, and administration of an established neurocognitive test battery.

Among this community sample of adults (N=309), 58 were classified as CU+/HIV+ group (84.5% Male), 76 as CU-/HIV+ (57.9% M), 86 as CU+/HIV- (58.1% M), and 89 as CU-/HIV- (53.9% M). Exclusion criteria included history of past 12-month dependence and extensive lifetime dependence or significant use of illicit substances other than cannabis, severe or current mood or thought disorder, and other medical conditions that adversely impact neurocognitive functioning. Inclusion criteria for CU+ groups included <30-days since last CU, >10 times of CU in last month, 3 times of CU per month in last 12 months, > 1 year of CU, and > 500 times used in lifetime. CU parameters did not statistically differ between HIV+/CU+ and HIV-/CU+. CU- groups’ inclusion criteria required no CU in last 6 months, 196 lifetime number of times used, and no history of CU dependance. Lifetime CU did not statistically differ between CU-/HIV+ and CU-/HIV- groups. HIV+ groups did not differ significantly on HIV viral load in plasma or nadir CD4+ counts. Significant between group differences included age, sex, years of education, and amount of alcohol and nicotine use within 12 months. The aforementioned sociodemographic and substance use variables that differed between groups were covariates in analyses. A battery of 10 neurocognitive measures, two measures per each domain of learning, memory, motor, executive functioning, and processing speed. Global composite summary scores for overall neurocognitive performance were calculated by averaging M T-scores for each neurocognitive domain. Data transformations were used to address any violations of statistical assumptions.

To facilitate data reduction, neurocognitive task scores were standardized to T-scores using the M and SD of the CU-/HIV-group. An omnibus model of between-group comparisons on global neurocognitive task performance revealed no significant differences, F(3) = .16, p = .923. Subsequent Tukey’s post hoc test revealed no significant differences among the four groups. Results also revealed nonsignificant differences between groups in neurocognitive performance within each domain. However, the CU-/HIV- group performed significantly worse than the CU-/HIV+ group on the Executive Functioning domain, based on Tukey’s post hoc test.

We found no significant global neurocognitive differences among groups; however, there was some evidence for domain-specific neurocognitive differences in executive functioning. This contrasts somewhat with existing literature on HIV and cannabis-associated neurocognitive deficits. Several factors may have contributed to this, including our relatively healthy PWH sample. Future analyses will examine interactive effects of HIV severity and severity of CU on neurocognition. This analysis will better determine who, among PWH, are most at-risk for cannabis-associated neurocognitive effects and what factors may exacerbate them.

Obesity is associated with adverse effects on brain health, including increased risk for neurodegenerative diseases. Changes in cerebral metabolism may underlie or precede structural and functional brain changes. While bariatric surgery is known to be effective in inducing weight loss and improving obesity-related medical comorbidities, few studies have examined whether it may be able to improve brain metabolism. In the present study, we examined change in cerebral metabolite concentrations in participants with obesity who underwent bariatric surgery.

35 patients with obesity (BMI > 35 kg/m2) were recruited from a bariatric surgery candidate nutrition class. They completed single voxel 1H-proton magnetic resonance spectroscopy at baseline (pre-surgery) and within one year post-surgery. Spectra were obtained from a large medial frontal brain region. Tissue-corrected absolute concentrations for metabolites including choline-containing compounds (Cho), myo-inositol (mI), N-acetylaspartate (NAA), creatine (Cr), and glutamate and glutamine (Glx) were determined using Osprey. Paired t-tests were used to examine within-subject change in metabolite concentrations, and correlations were used to relate these changes to other health-related outcomes, including weight loss and glycemic control.

Bariatric surgery was associated with a reduction in cerebral Cho (f[34j = -3.79, p < 0.001, d = -0.64) and mI (f[34] = -2.81, p < 0.01, d = -0.47) concentrations. There were no significant changes in NAA, Glx, or Cr concentrations. Reductions in Cho were associated with greater weight loss (r = 0.40, p < 0.05), and reductions in mI were associated with greater reductions in HbA1c (r = 0.44, p < 0.05).

Participants who underwent bariatric surgery exhibited reductions in cerebral Cho and mI concentrations, which were associated with improvements in weight loss and glycemic control. Given that elevated levels of Cho and mI have been implicated in neuroinflammation, reduction in these metabolites after bariatric surgery may reflect amelioration of obesity-related neuroinflammatory processes. As such, our results provide evidence that bariatric surgery may improve brain health and metabolism in individuals with obesity.

Subthreshold depressive symptoms are both prevalent and associated with negative outcomes in older adults, including conversion to major depressive disorder and other medical conditions. Antidepressants are not recommended as first-line or sole intervention for subthreshold depression; thus, finding other efficacious interventions is important. In depressed adults, transcranial direct current stimulation (tDCS) applied to the frontal lobe has antidepressant properties and pairing tDCS with cognitive training results in additional benefit due to enhancement of frontal cortical activity. However, these studies have primarily targeted depressed adults under age 65 years and less is known about whether this intervention combination is beneficial or affects subthreshold depressive symptoms in older adults.

We are reporting secondary data analyses from Nissim et al. (2019), who recruited 30 non-demented healthy older adults and randomized them to receive active or sham tDCS in combination with cognitive training for 2 weeks. Active tDCS was delivered bifrontally over F3 (cathode) and F4 (anode) for 20-min at 2 mA intensity through two 5x7 cm2 saline saturated sponge electrodes using the Soterix Medical 1x1 tDCS clinical trials device. Sham tDCS had identical set-up with 2 mA stimulation for 30-sec with 30-sec ramp up and down. Cognitive training was administered for 40-min daily using attention/processing speed and working memory modules from BrainHQ. The first 20-min of cognitive training was paired with active or sham tDCS. To allow room for symptom improvement, we only included participants with Beck Depression Inventory, 2nd edition (BDI-II) scores of 5 or greater ("minimal" depression severity). We identified 15 participants who met this cut-off (70.93 ± 5.41 years old, 10 females, 16.4 years ± 2.32 years education, MoCA = 27.27 ± 2.34; 7 active, 8 sham).

tDCS conditions did not significantly differ in age, sex, years of education, MoCA scores, number of completed intervention days, or baseline BDI-II (active: 7.71 ± 2.93, sham: 11.38 ± 6.44). There were no differences in sensation ratings between groups or in confidence ratings for condition received (suggesting successful blinding). Results indicated the combination of active (and not sham) tDCS with cognitive training was associated with reduced depressive symptoms (2.7 vs. 1.4 points, active vs. sham). Including covariates (age, sex, education, MoCA scores, and number of completed intervention days) in the model further strengthened this discrepancy (3.7 vs. 0.51 points, active vs. sham).

While preliminary, these results suggest this intervention combination may be a potential method for improving subthreshold depressive symptoms in older adults via targeting prefrontal neural circuitry and promoting neuroplasticity of the underlying neural network. While baseline BDI-II scores did not significantly differ, the active tDCS group had a lower score than sham, but saw greater improvement in BDI-II scores post-intervention despite having less room for change. Adequate treatment of subthreshold depressive symptoms may prevent or reduce negative outcomes associated with depressive symptoms in at-risk older adults. Larger randomized clinical trials are needed to better understand tDCS plus cognitive training antidepressant effects in this age group.

Identifying youths most at risk to COVID-19-related mental illness is essential for the development of effective targeted interventions.

To compare trajectories of mental health throughout the pandemic in youth with and without prior mental illness and identify those most at risk of COVID-19-related mental illness.

Data were collected from individuals aged 18–26 years (N = 669) from two existing cohorts: IMAGEN, a population-based cohort; and ESTRA/STRATIFY, clinical cohorts of individuals with pre-existing diagnoses of mental disorders. Repeated COVID-19 surveys and standardised mental health assessments were used to compare trajectories of mental health symptoms from before the pandemic through to the second lockdown.

Mental health trajectories differed significantly between cohorts. In the population cohort, depression and eating disorder symptoms increased by 33.9% (95% CI 31.78–36.57) and 15.6% (95% CI 15.39–15.68) during the pandemic, respectively. By contrast, these remained high over time in the clinical cohort. Conversely, trajectories of alcohol misuse were similar in both cohorts, decreasing continuously (a 15.2% decrease) during the pandemic. Pre-pandemic symptom severity predicted the observed mental health trajectories in the population cohort. Surprisingly, being relatively healthy predicted increases in depression and eating disorder symptoms and in body mass index. By contrast, those initially at higher risk for depression or eating disorders reported a lasting decrease.

Healthier young people may be at greater risk of developing depressive or eating disorder symptoms during the COVID-19 pandemic. Targeted mental health interventions considering prior diagnostic risk may be warranted to help young people cope with the challenges of psychosocial stress and reduce the associated healthcare burden.

Everyday affective fluctuations are more extreme and more frequent in adolescence compared to any other time in development. Successful regulation of these affective experiences is important for good mental health and has been proposed to depend on affective control. The present study examined whether improving affective control through a computerised affective control training app (AffeCT) would benefit adolescent mental health.

One-hundred and ninety-nine participants (11–19 years) were assigned to complete 2 weeks of AffeCT or placebo training on an app. Affective control (i.e. affective inhibition, affective updating and affective shifting), mental health and emotion regulation were assessed at pre- and post-training. Mental health and emotion regulation were assessed again one month and one year later.

Compared with the placebo group, the AffeCT group showed significantly greater improvements in affective control on the trained measure. AffeCT did not, relative to placebo, lead to better performance on untrained measures of affective control. Pre- to post-training change in affective control covaried with pre- to post-training change in mental health problems in the AffeCT but not the placebo group. These mental health benefits of AffeCT were only observed immediately following training and did not extend to 1 month or year post-training.

In conclusion, the study provides preliminary evidence that AffeCT may confer short-term preventative benefits for adolescent mental health.