Doris Miller Department of Veterans Affairs Medical Center

The amygdala is central to the pathophysiology of many psychiatric illnesses. An imprecise understanding of how the amygdala fits into the larger network organization of the human brain, however, limits our ability to create models of dysfunction in individual patients to guide personalized treatment. Therefore, we investigated the position of the amygdala and its functional subdivisions within the network organization of the brain in 10 highly sampled individuals (5 h of fMRI data per person). We characterized three functional subdivisions within the amygdala of each individual. We discovered that one subdivision is preferentially correlated with the default mode network; a second is preferentially correlated with the dorsal attention and fronto-parietal networks; and third subdivision does not have any networks to which it is preferentially correlated relative to the other two subdivisions. All three subdivisions are positively correlated with ventral attention and somatomotor networks and negatively correlated with salience and cingulo-opercular networks. These observations were replicated in an independent group dataset of 120 individuals. We also found substantial across-subject variation in the distribution and magnitude of amygdala functional connectivity with the cerebral cortex that related to individual differences in the stereotactic locations both of amygdala subdivisions and of cortical functional brain networks. Finally, using lag analyses, we found consistent temporal ordering of fMRI signals in the cortex relative to amygdala subdivisions. Altogether, this work provides a detailed framework of amygdala-cortical interactions that can be used as a foundation for models relating aberrations in amygdala connectivity to psychiatric symptoms in individual patients.

Previous studies have reported genetic linkage evidence for a schizophrenia gene on chromosome 15q. Here, chromosome 15 was examined by genetic linkage analysis using 166 schizophrenia families, each with two or more affected subjects. The families, assembled from multiple centers by the Department of Veterans Affairs Cooperative Study Program, consisted of 392 sampled affected subjects and 216 affected sibling pairs. By DSM-III-R criteria, 360 subjects (91.8%) had a diagnosis of schizophrenia and 32 (8.2%) were classified as schizo-affective disorder, depressed. Participating families had diverse ethnic backgrounds. The largest single group were northern European American families (n = 62, 37%), but a substantial proportion was African American kindreds (n = 60, 36%). The chromosome 15 markers tested were spaced at intervals of approximately 10 cM over the entire chromosome and 2-5 cM for the region surrounding the alpha-7 nicotinic cholinergic receptor subunit gene (CHRNA7). These markers were genotyped and the data analyzed using semiparametric affecteds-only linkage analysis. In the European American families, there was a maximum Z-score of 1.65 between markers D15S165 and D15S1010. These markers are within 1 cM from CHRNA-7, the site previously implicated in schizophrenia. However, there was no evidence for linkage to this region in the African America kindreds.

OBJECTIVE: Intraindividual variability (IIV) in cognitive performance has been associated with cognitive decline and reductions in white matter integrity, but the predictive utility of IIV-between versus IIV-within domains is unknown. The present study aimed to determine if IIV-within a "frontal-subcortical" domain may be a more robust predictor of changes in general cognitive status and functional independence over time than IIV-between cognitive domains. METHOD: Mixed linear modeling was used to analyze the relationship between cognitive IIV and cognitive and functional status in 651 controls, 211 people with mild cognitive impairment, and 218 people with Alzheimer's disease over a 5-year period. RESULTS: Both IIV-between and IIV-within a frontal-subcortical domain improved prediction of cognitive and functional declines beyond demographic characteristics, genetic risk, and vascular integrity. IIV-between showed the greatest effect over time and was driven primarily by increases in IIV-within. CONCLUSIONS: Cognitive IIV, especially between cognitive domains, may be useful for identifying individuals at risk for cognitive and functional decline. Findings may facilitate investigations into mechanisms underlying declines in global cerebral integrity and aid clinical trials aimed at early detection and treatment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Wei and Hemmings [2000: Nat Genet 25:376-377], using 80 British parent-offspring trios, identified a number of NOTCH4 variants and haplotypes that showed statistically significant evidence of association to schizophrenia. Specifically, the 10 repeat allele of a (CTG)(n) marker and the 8 repeat allele of a (TAA)(n) marker demonstrated excess transmission to affected individuals; SNP21 and haplotypes SNP2-(CTG)(n) and SNP12-SNP2-(CTG)(n) also showed significant associations. In an attempt to replicate these findings, we tested for linkage and association between the same five markers used by Wei and Hemmings in 166 families collected from a multi-center study conducted by the Department of Veterans Affairs (DVA) Cooperative Study Program (CSP). The families include 392 affected subjects (schizophrenia or schizoaffective disorder, depressed) and 216 affected sibling pairs. The families represent a mix of European Americans (n = 62, 37%), African Americans (n = 60, 36%), and racially mixed or other races (n = 44, 27%). We identified moderate evidence for linkage in the pooled race sample (LOD = 1.25) and found excess transmission of the 8 (P = 0.06) and 13 (P = 0.04) repeat alleles of the (TAA)(n) marker to African American schizophrenic subjects. The 8 and 13 repeat alleles were previously identified to be positively associated with schizophrenia by Wei and Hemmings [2000: Nat Genet 25:376-377] and Sklar et al. [2001: Nat Genet 28:126-128], respectively.

Several prior reports have suggested that chromosomal region 13q32 may harbor a schizophrenia susceptibility gene. In an attempt to replicate this finding, we assessed linkage between chromosome 13 markers and schizophrenia in 166 families, each with two or more affected members. The families, assembled from multiple centers by the Department of Veterans Affairs Cooperative Studies Program, included 392 sampled affected subjects and 216 affected sib pairs. By DSM-III-R criteria, 360 subjects (91.8%) had a diagnosis of schizophrenia and 32 (8.2%) were classified as schizoaffective disorder, depressed. The families had mixed ethnic backgrounds. The majority were northern European-American families (n = 62, 37%), but a substantial proportion were African-American kindreds (n = 60, 36%). Chromosome 13 markers, spaced at intervals of approximately 10 cM over the entire chromosome and 2-5 cM for the 13q32 region were genotyped and the data analyzed using semi-parametric affected only linkage analysis. For the combined sample (with race broadly defined and schizophrenia narrowly defined) the maximum LOD score was 1.43 (Z-score of 2.57; P = 0.01) at 79.0 cM between markers D13S1241 (76.3 cM) and D13S159 (79.5 cM). Both ethnic groups showed a peak in this region. The peak is within 3 cM of the peak reported by Brzustowicz et al. [1999: Am J Hum Genet 65:1096-1103].

Genome-wide linkage analyses of schizophrenia have identified several regions that may harbor schizophrenia susceptibility genes but, given the complex etiology of the disorder, it is unlikely that all susceptibility regions have been detected. We report results from a genome scan of 166 schizophrenia families collected through the Department of Veterans Affairs Cooperative Studies Program. Our definition of affection status included schizophrenia and schizoaffective disorder, depressed type and we defined families as European American (EA) and African American (AA) based on the probands' and parents' races based on data collected by interviewing the probands. We also assessed evidence for racial heterogeneity in the regions most suggestive of linkage. The maximum LOD score across the genome was 2.96 for chromosome 18, at 0.5 cM in the combined race sample. Both racial groups showed LOD scores greater than 1.0 for chromosome 18. The empirical P-value associated with that LOD score is 0.04 assuming a single genome scan for the combined sample with race narrowly defined, and 0.06 for the combined sample allowing for broad and narrow definitions of race. The empirical P-value of observing a LOD score as large as 2.96 in the combined sample, and of at least 1.0 in each racial group, allowing for narrow and broad racial definitions, is 0.04. Evidence for the second and third largest linkage signals come solely from the AA sample on chromosomes 6 (LOD = 2.11 at 33.2 cM) and 14 (LOD = 2.13 at 51.0). The linkage evidence differed between the AA and EA samples (chromosome 6 P-value = 0.007 and chromosome 14 P-value = 0.004).

= 200) to evaluate two community-based interventions. The first, Team Red, White, and Blue (RWB), connects TSMVs to their community through physical/social activities. The second, Expiration Term of Service Sponsorship Program (ETS-SP) provides one-on-one certified sponsors to TSMVs who provide support during the reintegration process. TSMVs were assessed at baseline, 3, 6, and 12 months. The primary hypothesis was not supported as reintegration difficulties and social support were not significantly different for participants randomly assigned to the two community-based interventions (Arm-2/RWB and Arm-3/RWB + ETS-SP), when the data from the separate arms were collapsed and combined, compared to the waitlist. The results did support the secondary hypothesis as Arm-3/RWB + ETS-SP had less reintegration difficulties over 12 months and initially had more social support compared to Arm-2/RWB, which suggest that augmenting interventions with sponsors outperforms participation in community-based interventions alone. Overall, the results show some limitations of the studied community-based interventions, as implemented and researched within this study. The authors identified factors that may have contributed to the null findings for the primary hypothesis, which can be addressed in future studies, such as addressing the unique needs of TSMVs, enrolling TSMVs into interventions prior to military discharge, measuring and improving participation levels, and providing stepped-care interventions based on risk levels. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

BACKGROUND: 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. METHODS: = 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. RESULTS: 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. CONCLUSIONS: 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.

BACKGROUND: The USA is undergoing a suicide epidemic for its youngest Veterans (18-to-34-years-old) as their suicide rate has almost doubled since 2001. Veterans are at the highest risk during their first-year post-discharge, thus creating a "deadly gap." In response, the nation has developed strategies that emphasize a preventive, universal, and public health approach and embrace the value of community interventions. The three-step theory of suicide suggests that community interventions that reduce reintegration difficulties and promote connectedness for Veterans as they transition to civilian life have the greatest likelihood of reducing suicide. Recent research shows that the effectiveness of community interventions can be enhanced when augmented by volunteer and certified sponsors (1-on-1) who actively engage with Veterans, as part of the Veteran Sponsorship Initiative (VSI). METHOD/DESIGN: The purpose of this randomized hybrid type 2 effectiveness-implementation trial is to evaluate the implementation of the VSI in six cities in Texas in collaboration with the US Departments of Defense, Labor and Veterans Affairs, Texas government, and local stakeholders. Texas is an optimal location for this large-scale implementation as it has the second largest population of these young Veterans and is home to the largest US military installation, Fort Hood. The first aim is to determine the effectiveness of the VSI, as evidenced by measures of reintegration difficulties, health/psychological distress, VA healthcare utilization, connectedness, and suicidal risk. The second aim is to determine the feasibility and potential utility of a stakeholder-engaged plan for implementing the VSI in Texas with the intent of future expansion in more states. The evaluators will use a stepped wedge design with a sequential roll-out to participating cities over time. Participants (n=630) will be enrolled on military installations six months prior to discharge. Implementation efforts will draw upon a bundled implementation strategy that includes strategies such as ongoing training, implementation facilitation, and audit and feedback. Formative and summative evaluations will be guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework and will include interviews with participants and periodic reflections with key stakeholders to longitudinally identify barriers and facilitators to implementation. DISCUSSION: This evaluation will have important implications for the national implementation of community interventions that address the epidemic of Veteran suicide. Aligned with the Evidence Act, it is the first large-scale implementation of an evidence-based practice that conducts a thorough assessment of TSMVs during the "deadly gap." TRIAL REGISTRATION: ClinicalTrials.gov ID number: NCT05224440 . Registered on 04 February 2022.

The effect of the amount of biofeedback training received upon abstinence from alcohol was studied at 3, 6, and 12 months postdischarge for 233 male veterans in an inpatient alcoholic rehabilitation unit (ARU). The frequency of sobriety for those patients with at least 6 training sessions was significantly better than for those with less or no training at all three time periods. The effect was most prominent for those receiving the highest level of biofeedback training (8+ sessions), and at the earlier time frame (3 months). The discussion focused upon the implications for overall rehabilitation programming for the alcoholic and on factors involved in the efficacy of biofeedback therapy.

Importance: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions. Objective: To develop a model based on administrative data for regular US Army soldiers that can predict suicides 1 to 120 months after leaving active service. Design, Setting, and Participants: In this prognostic study, a consolidated administrative database was created for all regular US Army soldiers who left service from 2010 through 2019. Machine learning models were trained to predict suicides over the next 1 to 120 months in a random 70% training sample. Validation was implemented in the remaining 30%. Data were analyzed from March 2023 through March 2024. Main outcome and measures: The outcome was suicide in the National Death Index. Predictors came from administrative records available before leaving service on sociodemographics, Army career characteristics, psychopathologic risk factors, indicators of physical health, social networks and supports, and stressors. Results: Of the 800 579 soldiers in the cohort (84.9% male; median [IQR] age at discharge, 26 [23-33] years), 2084 suicides had occurred as of December 31, 2019 (51.6 per 100 000 person-years). A lasso model assuming consistent slopes over time discriminated as well over all but the shortest risk horizons as more complex stacked generalization ensemble machine learning models. Test sample area under the receiver operating characteristic curve ranged from 0.87 (SE = 0.06) for suicides in the first month after leaving service to 0.72 (SE = 0.003) for suicides over 120 months. The 10% of soldiers with highest predicted risk accounted for between 30.7% (SE = 1.8) and 46.6% (SE = 6.6) of all suicides across horizons. Calibration was for the most part better for the lasso model than the super learner model (both estimated over 120-month horizons.) Net benefit of a model-informed prevention strategy was positive compared with intervene-with-all or intervene-with-none strategies over a range of plausible intervention thresholds. Sociodemographics, Army career characteristics, and psychopathologic risk factors were the most important classes of predictors. Conclusions and relevance: These results demonstrated that a model based on administrative variables available at the time of leaving active Army service can predict suicides with meaningful accuracy over the subsequent decade. However, final determination of cost-effectiveness would require information beyond the scope of this report about intervention content, costs, and effects over relevant horizons in relation to the monetary value placed on preventing suicides.

Abstract Introduction Numerous strategies exist following antipsychotic monotherapy failure including transition to another antipsychotic, dosing above FDA recommendations, or dual antipsychotic therapy. This study described antipsychotic prescribing practices on an acute psychiatry unit following antipsychotic monotherapy failure and compared outcomes to determine if any strategy resulted in superior short-term outcomes. Methods This retrospective chart review assessed postintervention time to discharge for patients with schizophrenia or schizoaffective disorder requiring therapy change following treatment failure. Secondary outcomes included 30-day readmission rate, length of stay, and discharge chlorpromazine equivalents. Results There were no differences in number of past antipsychotic trials between groups (4.8 vs 4.5; P = .73). Of all the patients, 73% (n = 30) discharged on alternative antipsychotic monotherapy while 27% (n = 11) discharged on dual antipsychotic therapy. No patients had doses increased above FDA recommendations. The alternative antipsychotic group had shorter mean postintervention time to discharge (8.8 vs 20.6 days; P = .003) and shorter mean length of stay (16.7 vs 32.1 days; P = .03). Median time to discharge was not statistically significant (6.4 vs 14.0 days; P = .17). The dual antipsychotic group had higher mean chlorpromazine equivalents (723 mg vs 356 mg; P = .002). There was no difference in 30-day readmission rates (16.7% vs 27.3%; χ2 = 0.5765; P = .45). Discussion This study found that following failure of antipsychotic monotherapy, transition to an alternative antipsychotic was associated with decreased mean time to discharge as compared to dual antipsychotic therapy. Further studies are needed to assess long-term clinical implications of these findings.

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Abstract Background Discharge delays are difficult to quantify without standardized indicators for when patients are medically ready for discharge. We aimed to estimate the proportion of increased hospital length of stay attributable to discharge delays, as proxied by increases in ‘avoidable’ days. Methods We conducted a retrospective cohort study of Veterans Health Administration hospitals in the continental United States with emergency departments between 1 March 2019 and 28 February 2023. We included Veterans who were discharged from an acute medicine service without a COVID-19 diagnosis. We used standardized utilization management criteria to count ‘avoidable’ days, defined as hospital days when acute care was no longer required. Our primary outcome was geometric mean length of the discharging stay (the final acute medicine segment prior to discharge), which reflects the time most susceptible to discharge delays. Results During the study period there were 868,031 eligible hospitalizations. Adjusted geometric mean length of discharging stay increased 9.3% (95% CI, 8.7% to 9.9%) from the pre-pandemic year to the third pandemic year, with the largest increase among discharges to facility-based post-acute care (23.3% [95% CI, 21.6 to 24.9%]). However, among all hospitalizations only 16% (95% CI, 15 to 17%) of the increase in discharging stay was attributable to an increase in avoidable days. Conclusions Most of the increase in length of hospital discharging stay was not explained by discharge delays and may instead reflect longer periods of acute care delivery. Improving acute care processes may more effectively reduce hospital capacity strain than bolstering post-acute care availability.

Abstract Background Discharge delays are difficult to quantify without standardized indicators for when patients are medically ready for discharge. We aimed to estimate the proportion of increased hospital length of stay attributable to discharge delays, as proxied by increases in ‘avoidable’ days. Methods We conducted a retrospective cohort study of Veterans Health Administration hospitals in the continental United States with emergency departments between 1 March 2019 and 28 February 2023. We included Veterans who were discharged from an acute medicine service without a COVID-19 diagnosis. We used standardized utilization management criteria to count ‘avoidable’ days, defined as hospital days when acute care was no longer required. Our primary outcome was geometric mean length of the discharging stay (the final acute medicine segment prior to discharge), which reflects the time most susceptible to discharge delays. Results During the study period there were 868,031 eligible hospitalizations. Adjusted geometric mean length of discharging stay increased 9.3% (95% CI, 8.7% to 9.9%) from the pre-pandemic year to the third pandemic year, with the largest increase among discharges to facility-based post-acute care (23.3% [95% CI, 21.6 to 24.9%]). However, among all hospitalizations only 16% (95% CI, 15 to 17%) of the increase in discharging stay was attributable to an increase in avoidable days. Conclusions Most of the increase in length of hospital discharging stay was not explained by discharge delays and may instead reflect longer periods of acute care delivery. Improving acute care processes may more effectively reduce hospital capacity strain than bolstering post-acute care availability.

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Supportive parenting can protect children who have been exposed to intimate partner violence (IPV) from developing adjustment problems, and there is evidence suggesting that supportive parenting increases under certain circumstances after IPV. However, research on IPV and supportive parenting focuses almost exclusively on parenting by mothers, and there is very little research examining the circumstances in which experiencing IPV (i.e., victimization) bolsters supportive parenting. The present research addresses these gaps. Participants included 397 families (mothers, fathers, and children). Mothers and fathers reported on experiences of IPV, whether they experienced adverse consequences from IPV (physical injury and fear), and supportive parenting. Children reported on their mothers' and fathers' supportive parenting. Results indicated that adverse consequences from IPV (i.e., physical injury and fear) were more common among mothers than fathers. Additionally, adverse consequences moderated the association between the frequency of experiencing IPV and both parents' self-reports of supportive parenting; for both mothers and fathers, when adverse consequences were reported, IPV experiences were associated with more supportive parenting. In the absence of adverse consequences, IPV experiences were associated with less supportive parenting. Adverse consequences also moderated the association between IPV experiences and child reports of fathers' supportive parenting. Again, when fathers reported experiencing adverse consequences of IPV, fathers' IPV experiences were associated with more supportive parenting. The findings underscore the complex relations between experiencing IPV and supportive parenting and suggest that both mothers' and fathers' adverse experiences from IPV may lead to increases in supportive parenting. These results highlight the importance of recognizing the nuanced ways that adversity may activate protective parenting responses and underscore the potential utility of parenting programs to consider both mothers' and fathers' parenting.