Persistent brain fog is common in adults with Post-Acute Sequelae of SARS-CoV-2 infection (PASC), in whom it causes distress and in many cases interferes with performance of instrumental activities of daily living (IADL) and return-to-work. There are no interventions with rigorous evidence of efficacy for this new, often disabling condition. The purpose of this pilot is to evaluate the efficacy, on a preliminary basis, of a new intervention for this condition termed Constraint-Induced Cognitive therapy (CICT). CICT combines features of two established therapeutic approaches: cognitive speed of processing training (SOPT) developed by the laboratory of K. Ball and the Transfer Package and task-oriented training components of Constraint-Induced Movement therapy developed by the laboratory of E. Taub and G. Uswatte.

Participants were > 3 months after recovery from acute COVID symptoms and had substantial brain fog and impairment in IADL. Participants were randomized to CICT immediately or after a 3-month delay. CICT involved 36 hours of outpatient therapy distributed over 4-6 weeks. Sessions had three components: (a) videogamelike training designed to improve how quickly participants process sensory input (SOPT), (b) training on IADLs following shaping principles, and (c) a set of behavioral techniques designed to transfer gains from the treatment setting to daily life, i.e., the Transfer Package. The Transfer Package included (a) negotiating a behavioral contract with participants and one or more family members about the responsibilities of the participants, family members, and treatment team; (b) assigning homework during and after the treatment period; (c) monitoring participants’ out-of-session behavior; (d) supporting problem-solving by participants and family members about barriers to performance of IADL; and (e) making follow-up phone calls. IADL performance, brain fog severity, and cognitive impairment were assessed using validated, trans-diagnostic measures before and after treatment and three months afterwards in the immediate-CICT group and on parallel occasions in the delayed-CICT group (aka waitlist controls).

To date, five were enrolled in the immediate-CICT group; four were enrolled in the wait-list group. All had mild cognitive impairment, except for one with moderate impairment in the immediate-CICT group. Immediate-CICT participants, on average, had large reductions in brain fog severity on the Mental Clutter Scale (MCS, range = 0 to 10 points, mean change = -3.7, SD = 2.0); wait-list participants had small increases (mean change = 1.0, SD = 1.4). Notably, all five in the immediate-CICT group had clinically meaningful improvements (i.e., changes > 2 points) in performance of IADL outside the treatment setting as measured by the Canadian Occupational Performance Measure (COPM) Performance scale; only one did in the wait-list group. The advantage for the immediate-CICT group was very large on both the MCS and COPM (d’s = 1.7, p’s < .05). In follow-up, immediate-CICT group gains were retained or built-upon.

These preliminary findings warrant confirmation by a large-scale randomized controlled trial. To date, CICT shows high promise as an efficacious therapy for brain fog due to PASC. CICT participants had large, meaningful improvements in IADL performance outside the treatment setting, in addition to large reductions in brain fog severity.

Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

Neurocognitive testing may advance the goal of predicting near-term suicide risk. The current study examined whether performance on a Go/No-go (GNG) task, and computational modeling to extract latent cognitive variables, could enhance prediction of suicide attempts within next 90 days, among individuals at high-risk for suicide.

136 Veterans at high-risk for suicide previously completed a computer-based GNG task requiring rapid responding (Go) to target stimuli, while withholding responses (No-go) to infrequent foil stimuli; behavioral variables included false alarms to foils (failure to inhibit) and missed responses to targets. We conducted a secondary analysis of these data, with outcomes defined as actual suicide attempt (ASA), other suicide-related event (OtherSE) such as interrupted/aborted attempt or preparatory behavior, or neither (noSE), within 90-days after GNG testing, to examine whether GNG variables could improve ASA prediction over standard clinical variables. A computational model (linear ballistic accumulator, LBA) was also applied, to elucidate cognitive mechanisms underlying group differences.

On GNG, increased miss rate selectively predicted ASA, while increased false alarm rate predicted OtherSE (without ASA) within the 90-day follow-up window. In LBA modeling, ASA (but not OtherSE) was associated with decreases in decisional efficiency to targets, suggesting differences in the evidence accumulation process were specifically associated with upcoming ASA.

These findings suggest that GNG may improve prediction of near-term suicide risk, with distinct behavioral patterns in those who will attempt suicide within the next 90 days. Computational modeling suggests qualitative differences in cognition in individuals at near-term risk of suicide attempt.

There is a wealth of literature on the observed association between childhood trauma and psychotic illness. However, the relationship between childhood trauma and psychosis is complex and could be explained, in part, by gene–environment correlation.

The association between schizophrenia polygenic scores (PGS) and experiencing childhood trauma was investigated using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Norwegian Mother, Father and Child Cohort Study (MoBa). Schizophrenia PGS were derived in each cohort for children, mothers, and fathers where genetic data were available. Measures of trauma exposure were derived based on data collected throughout childhood and adolescence (0–17 years; ALSPAC) and at age 8 years (MoBa).

Within ALSPAC, we found a positive association between schizophrenia PGS and exposure to trauma across childhood and adolescence; effect sizes were consistent for both child or maternal PGS. We found evidence of an association between the schizophrenia PGS and the majority of trauma subtypes investigated, with the exception of bullying. These results were comparable with those of MoBa. Within ALSPAC, genetic liability to a range of additional psychiatric traits was also associated with a greater trauma exposure.

Results from two international birth cohorts indicate that genetic liability for a range of psychiatric traits is associated with experiencing childhood trauma. Genome-wide association study of psychiatric phenotypes may also reflect risk factors for these phenotypes. Our findings also suggest that youth at higher genetic risk might require greater resources/support to ensure they grow-up in a healthy environment.