The best predictor of a suicide attempt is a previous attempt, apart from psychiatric diagnoses also associated. Some studies found other indicators of great risk for suicide reattempts. Machine Learning algorithms offer the potential for systematic detection of features that carry greater risk for an event. This study sought to develop a classification algorithm distinguishing between Single Suicide Attempters (SSA) and Multiple Suicide Attempters (MSA) in a Spanish multicentre national cohort to explore prediction of subsequent attempts in suicidal patients. Two models including the same sociodemographic and clinical variables grouped in more specific (Model I) or broad (Model II) categories were developed to explore risk factors for suicide reattempts. A Least Absolute Shrinkage and Regression Operator logistic regression with a 10-fold cross-validation was adopted. 1443 adult patients from the SURVIVE cohort were included (582 SSA and 861 MSA). Both Model I (AUC = 0.696; BAC = 0.644) and Model II (AUC = 0.678; BAC = 0.621) outperformed naïve majority-class classification for SSA and MSA. Bipolar disorder type II, binge-eating disorder, and schizophrenia variables weighted heavier on Model I for suicide reattempt-related; while eating disorder diagnosis, Africa as birthplace, affective disorder diagnosis, being employed, schizophrenia-spectrum disorder and substance use disorder diagnoses were the most important suicide reattempt-related of Model II. Affective disorders, eating disorders and schizophrenia-spectrum disorders emerged as the most important variables in predicting reattempts. Both models showed similar sensitivity and specificity when discriminating between SSA and MSA. Identifying specific risk factors for reattempts could have a significant impact on tailoring prevention strategies and interventions.

OBJECTIVE:Suicidal behaviors and non-suicidal self-injury (NSSI) are highly prevalent and often co-occur among individuals with borderline personality disorder (BPD). Individuals with both a BPD diagnosis and NSSI are at heightened risk for suicide. However, few studies have examined which factors differentiate those with BPD and NSSI who have attempted suicide from those who have not. This study aimed to identify such clinical factors to inform suicide risk assessment within this vulnerable population. METHODS:Participants were 78 individuals with BPD and a history of NSSI. Fifty-four had a lifetime history of suicide attempt (SA). Participants completed structured interviews and self-report measures assessing childhood trauma, dissociation, and anxiety, as well as current suicidal ideation, depressive symptoms, global functioning, and demographics. Logistic regression was used to evaluate whether these factors are associated with an increased likelihood of having a lifetime SA history in this unique population. RESULTS:Dissociative amnesia was associated with decreased likelihood of having a lifetime SA, while dissociative absorption was associated with increased likelihood of having such a history, while controlling for education level, current suicidal ideation, global functioning, and depression severity. CONCLUSIONS:In individuals with BPD who engage in NSSI, those who have made a SA present with distinct dissociative symptoms. Future studies should further investigate other risk factors for suicidal behavior among individuals with BPD who engage in NSSI to improve suicide risk assessment in this population.

Borderline personality disorder (BPD) is characterized by interpersonal dysfunction, emotional lability, impulsivity, and risk of self-injurious behaviors, and is frequently comorbid with mood disorders. Several BPD symptoms have been associated with altered serotonin (5-HT) system function. We sought to determine whether 5-HT 1A (5-HT1A) receptor binding measured with [11C]WAY-100635 positron emission tomography (PET) differed in individuals with mood disorders (Major Depressive Disorder or Bipolar Disorder I or II) and comorbid BPD (n=21), compared to those with mood disorders without comorbid BPD (n=63) and healthy volunteers (HV; n=27). In thirteen a priori determined brain regions, the groups differed significantly in binding potential (BPF), with higher relative BPF in the mood disorders group compared to HV (F=3.64; p=0.03). Planned post-hoc analyses found that these differences in binding compared to HV were driven by higher binding associated with mood disorder, and not with BPD. There were no effects of impulsivity, aggression, depression severity or suicidal ideation on 5-HT1A receptor binding. The finding that higher 5-HT1A binding appears to be related primarily to the presence of a comorbid mood disorder may explain why treatments that target the 5-HT1A autoreceptor, like selective serotonin uptake inhibitors (SSRIs), are effective for mood disorders but have limited efficacy for BPD.

Hippocampal neuroplasticity regulates memory and emotional responses, which are altered in major depression, a leading cause of disability worldwide 1 . Molecular underpinnings of reduced hippocampal neuroplasticity 2 , fewer neurons, and smaller volume 3,4 in MDD are unknown and may involve blunted adult hippocampal neurogenesis (AHN), a debated phenomenon in human brain 5–8 . Dynamic changes in gene expression (GEX) are mediated, in part, by cis-regulatory elements (CREs), in response to developmental cues, environmental signals, and cellular contexts. However, the accessibility of CREs and their relation to GEX in specific hippocampus cell types in MDD is unknown. To this end, we simultaneously profiled chromatin accessibility and GEX in 349,847 human hippocampal nuclei, used machine learning 9,10 , RNA Velocity 11 and pseudotime trajectory inference 12 to investigate the presence of progenitor cells and their progeny, and spatial transcriptomics in intact hippocampus sections to sequence cytoplasm and neuronal processes providing anatomical transcriptome mapping. We identified Type I, II and III neural progenitors and immature granule neurons. We find hyperactivated inflammation and apoptosis in immature and mature granule neurons, possibly affecting AHN and cell survival in MDD. Proteomics and transcriptomics convergent dysregulations in MDD, point to lipid metabolism, neurogenesis, synaptogenesis, cell adhesion, and plasticity. Identified molecular markers may drive biomarker and drug discovery for MDD and hippocampus resilience.

Importance:Suicidal behavior is heritable and influenced by various clinical traits and external stressors. Clarifying these genetic and environmental influences may guide targeted intervention. Objective:To examine the interplay among polygenic scores for suicide attempt (suicide-PGSs), clinical characteristics, and external stressors in suicidal behavior. Design, Setting, and Participants:This case-control study included genome-wide genotyping of individuals with nonfatal suicide behavior, individuals who died by suicide, and alive and deceased control individuals with no suicidal behavior or death by suicide, respectively. Data were collected in New York, New York; Montreal, Canada; and Munich, Germany, between 1991 and 2011, and analyses were performed from July 1 to October 30, 2024. Exposure:Suicide-PGSs, internal traits (hostility, impulsivity, aggression), clinical variables (depression severity, suicidal ideation, number and lethality of suicide attempts, number of depressive episodes), and external stressors (childhood abuse, recent life events). Main Outcomes and Measures:Associations between suicide-PGS and suicidal behavior, traits, clinical features, and environmental stressors were assessed via logistic regression, linear regression, and Poisson models. Results:The sample included 1699 individuals across 2 cohorts. The live cohort included 1275 participants, of whom 239 had attempted suicide (mean [SD] age, 41.8 years; 147 female [61.5%]) and 1036 were control participants who had not (mean [SD] age, 37.3 [17.3] years; 574 female [55.4%]), and the postmortem cohort included 424 individuals, of whom 294 died by suicide (mean [SD] age, 45.1 [17.0] years; 219 male [74.5%]) and 130 were control individuals who died of other causes (mean [SD] age, 49.6 [18.3] years; 102 male [78.5%]). Suicide-PGS was associated with suicide attempts in the live (odds ratio [OR], 1.35; 95% CI, 1.17-1.56) and postmortem (OR, 1.34; 95% CI, 1.07-1.70) cohorts. Among live participants who attempted suicide, higher suicide-PGS was associated with lifetime aggression severity (b = 0.67; 95% CI, 0.41-0.94), depression severity (b = 0.20; 95% CI, 0.12-0.28), and less hostility (b = -0.51; 95% CI, -0.82 to -0.19) but not with impulsivity or lethality. Suicide-PGS was also associated with more depressive episodes (b [SE], 0.11 [0.04]) but not the number of lifetime suicide attempts in the live cohort. Suicide-PGS was associated with reported childhood abuse (OR, 1.16; 95% CI, 1.02-1.33) and recent life stress (b [SE], 0.17 [0.05]) in the live cohort, though these stressors did not moderate genetic associations with suicide. Conclusions and Relevance:This case-control study found that genetic liability for suicide attempt was associated with clinical characteristics, internal traits, and external stressors, highlighting the complexity of genetic and environmental interactions. Larger studies with detailed phenotyping are needed to clarify genetic-environment contributions to suicide risk.

Suicide is a significant public health problem that usually co-occurs with major psychiatric disorders. Suicidal behaviors have heritability of 30-50%, and the largest genome-wide association studies identified 12 loci linked to suicide attempts (SA). These findings indicate shared genetic architecture among SA and psychiatric disorders. We analyzed public GWAS summary statistics of SA, major depressive disorder (MDD), bipolar disorder (BIP), schizophrenia (SCZ), and attention deficit hyperactivity disorder (ADHD) to quantify genetic overlap using statistical genetics methods: MiXeR for polygenic overlap, LAVA for locus-specific genetic correlations, and HyPrColoc for multi-trait colocalization. MTAG and conditional false discovery rate (condFDR) identified SA- associated loci, while conjunctional false discovery rate (conjFDR) identified shared loci with psychiatric disorders. Additionally, we used polygenic risk scores (PRS) calculated in the UK Biobank to validate associations between genetic liabilities of psychiatric disorders and SA. SA involve approximately 6.9k (SD = 1.5k) risk variants with substantial overlap between psychiatric disorders, ranging from 53.2% with SCZ to 82.1% with BIP. Using MTAG, condFDR, and conjFDR, we identified 14 and 48 novel risk loci for SA, respectively. Through conjFDR, we identified 78 loci associated with SA and major psychiatric disorders, including one locus shared across all five traits. Genes linked to SA were enriched in synaptic components and signaling pathways. Despite significant genetic overlap, the SA PRS was the single strongest predictor of SA, followed by the MDD and ADHD PRS. The genetic overlap reflects potential common comorbidities complicating the identification of biological processes unique to SA, instead reflecting a complex genetic framework shared with psychiatric disorders.

Microtubules are cytoskeletal components that play essential roles in the brain, including supporting neuronal structure, protein transport, and cognitive function. In vivo quantification of microtubule dynamics using PET is possible with 11C-MPC6827, a brain-penetrant radiotracer that has been evaluated in mice and nonhuman primates. Here we present the first assessment of 11C-MPC6827 test-retest properties in a healthy human brain and examine the influence of scan duration on repeatability. Methods: Five healthy volunteers underwent two 90-min 11C-MPC6827 PET scans with concurrent arterial blood sampling (4 imaged twice in 1 d, 1 rescanned after 1 y). Radiotracer time-activity curves were extracted from 46 individual brain regions, in addition to the whole brain. Total distribution volume (V T) was estimated using kinetic models and graphical analysis. SUVs and the SUV ratio to whole brain (SUVRWB) were computed in 2 postinjection time windows. Repeatability was assessed using absolute test-retest percent difference (TRPD) and intraclass correlation coefficient (ICC). Time stability was examined by truncating imaging data from 90-min to 60-min acquisition and comparing computed outcomes to the 90-min value as gold standard. Results: V T estimates were stable with truncated scan time (average shorter V T/90-min V T, 1.003-1.030). For the same-day test-retest pairs, V T estimates obtained with kinetic models showed average TRPD values, across regions and participants, between 10.18% and 14.26%, and ICCs between 0.483 and 0.966. Graphical approaches demonstrated lower repeatability than kinetic models. Across methods, TRPDs were lower and ICCs were higher with shorter scans. SUV showed higher interscan variability in TRPD. SUVRWB was more stable (TRPD, 2.22%-5.47%) and less influenced by scan duration. However, ICCs were overall much lower for SUV and SUVRWB than for V T Conclusion: 11C-MPC6827 shows good repeatability for quantifying microtubule dynamics in humans using arterial blood even at a 60-min scan duration. SUVRWB could be a convenient blood-free alternative to V T.

We present the first spatial tri-omic (proteomic, transcriptomic, and epigenomic) taxonomy of human hippocampus, addressing two key challenges in single-cell and spatial transcriptomics: visualizing cell morphology and epigenetic gene regulation in situ. By designing and multiplex imaging a novel panel of 25 protein targets, we generated the first large scale spatial proteomics map of the hippocampus at single cell resolution (330 nanometers). Through integrated spatial proteomic and transcriptomic analyses, we identified canonical hippocampal subfields and distinct cell types within the dentate gyrus and cornu Ammonis (CA). RNA synthesis rates were the highest in the CA pyramidal neurons than any other hippocampal subfields. To map gene regulation, we applied state-of-art spatial ATAC-seq to identify open chromatin regions and cis-regulatory elements, revealing how chromatin landscapes differ across hippocampal subfields. These regional differences help explain why certain neurons are more excitable and recruited for circuit formation, as their chromatin is open and responsive to transcription factors. Together, these approaches created the most comprehensive spatial atlas of the human hippocampus to date, consisting of over 1.6 million single cells from 16 individuals. This study demonstrates the use of spatial proteomics and epigenomics to investigate the molecular neuropathology of human brain in both healthy and diseased states.

This Viewpoint argues that suicide risk prediction models are advancing faster than the shared infrastructure health systems need to evaluate, select, and sustain them and proposes a federated framework for closing that gap.

Depression and its severe outcomes, suicidal ideation and behavior, are exacerbated by stress. A broad literature links dysregulation of the hypothalamic-pituitary-adrenocortical (HPA) stress response system to depression, suicidal ideation and behavior. Our midazolam-controlled clinical trial of subanesthetic, intravenous ketamine infusion for suicidal depressed patients found rapid reduction in suicidal ideation within 24 h. Here we report on an exploratory aim of the trial to investigate relationships of treatment effect and clinical response to HPA axis function via saliva cortisol analysis. Saliva cortisol awakening response was measured at baseline/pre-infusion and 24 h after with samples obtained upon morning awakening and 30 min later. A significant increase in waking cortisol 24 h after ketamine treatment was observed (t(61) = -3.15, p = 0.0025). Baseline to post-infusion increase in waking cortisol had a small to medium, nonsignificant, correlation with decrease in SI (r = -0.25, p = 0.052). These preliminary results need to be replicated but are consistent with studies suggesting increases in cortisol, if not excessive or prolonged, may enhance stress-resilience.

BACKGROUND:Suicide rates have risen disproportionately in Black youth, yet little is known about risk factors specific to this population. AIMS:We sought to identify proximal risk factors for suicide-related behavior (SRB) in a sample of Black youth at high risk for mood disorders. METHODS:The sample, drawn from a longitudinal study, included 156 Black youth aged 17 years or younger at baseline. Participants underwent baseline diagnostic and clinical assessment, including suicidal ideation and SRB, and yearly assessments for new SRB. Observations were censored at the time point closest to the first episode of SRB or the last observation for those without SRB. Chi-square, Fisher's exact test, t-tests, and Firth logistic regression were used. RESULTS:Twelve participants experienced SRB during follow-up. The average age of participants at baseline was 12.6 years (SD = 2.7). Participants were 44% female and 10% identified as Hispanic. Self-reported irritability, aggression, suicidal ideation, and a depression diagnosis were significant at the univariate level. Medium to large effect sizes were found for self-reported impulsive aggression, parental substance use disorder, and childhood abuse, all consistent with prior reports to be associated with SRB in youth. Using Firth logistic regression, younger age (β = -.60; p = .001) and depression diagnosis (β = 2.32, p = .006) were proximal risk indicators of SRB. CONCLUSIONS:Risk factors identified in this study are consistent with previous reports in majority White samples. A depression diagnosis was the strongest indicator, highlighting the importance of early identification of depression in Black youth, particularly in younger youth with a family history of mood disorder.

Suicidal ideation (SI) is a clinical risk factor for suicidal behavior. Elucidating the neural mechanism underlying SI may lay the groundwork for future methods for its assessment. Our group previously showed that SI is associated with altered resting hippocampal activity, as measured using functional magnetic resonance imaging Amplitude of Low Frequency Fluctuations (ALFF). However, the role of hippocampal functional connectivity (FC) in SI has not been fully characterized. Here, we compared resting-state FC data in unmedicated adult participants experiencing a major depressive episode and SI compared with those without SI. The previously identified hippocampal region of higher ALFF in individuals with SI was used as a seed region of interest (ROI), and we examined its whole-brain functional connectivity pattern. Across the entire sample, the hippocampal ROI connectivity pattern had a spatial distribution consistent with the Default Network (DN). FC from the hippocampal ROI to DN was lower in participants with SI (n = 24) compared with those without SI (n = 10), suggesting reduced coupling with DN. Intra-network FC between all the DN regions was not significantly associated with SI, suggesting a more specific association with hippocampus connectivity. The FC result was examined in a larger, publicly available dataset, REST-MDD. FC of the right hippocampus to DN was lower in participants with SI (n = 158) compared with those without SI (n = 60), demonstrating a cross-dataset convergence. These results indicate that hippocampal resting connectivity is associated with the presence of SI. Future studies using longitudinal designs should examine how changes in clinical status influence this FC pattern.

BACKGROUND:Suicidal ideation and behaviors in preteens have increased in recent years. This study sought to describe clinical issues related to suicide prevention in children younger than 10 years old by clinicians who serve children. METHODS:Data were collected as part of the evaluation of the Zero Suicide initiative in New York State outpatient mental health clinics. Participants (N = 177 mental health clinicians) responded to the question of how working with suicidal children under the age of 10 yrs. differed from working with suicidal adolescents or adults. Clinician's open ended text responses to this question underwent thematic analysis using an inductive approach. RESULTS:The identified themes included: (1) child's characteristics, e.g., impulsivity, limited insight, limited verbal skills, immature understanding of the death and suicide concepts, and general developmental differences; and (2) environmental factors, e.g., perception of a child's suicidality by others and complicated environment; both in the context of their effect on assessment and prevention methods. Additionally, (3) suicide risk assessment-barriers, such as ambiguity in the child's suicidal statements, difficulty evaluating suicide risk level, and problems with current assessment instruments; and facilitators, including use of age-appropriate language or other communication modalities, and use of collateral sources of information; as well as (4) suicide prevention interventions-barriers, including limited coping skills and child and parent engagement in treatment; and facilitators, such as psychoeducation, parent involvement, and modifying existing interventions. CONCLUSIONS:Clinician responses indicate the need for evidence-based practice guidelines tailored to this population's characteristics. Recommendations for clinicians are discussed.

Rationale:"Gold standard" blood-based quantification of dynamic 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) data has limited practical clinical applications due to cost and complexity of data collection and analysis. We previously presented a blood-free quantification alternative, STARE (Source-to-Target Automatic Rotating Estimation), that was validated on 18F-FDG data acquired on a ECAT EXACT HR+ scanner. Here, we extend that initial work by externally validating STARE using within-subject data acquired with both a Siemens Biograph mCT scanner and a portable Brain Biosciences CerePET scanner. Methods:Performance was assessed by comparing regional net influx rates (Ki) estimated using STARE and the standard blood-based Patlak approach. Twenty participants underwent 60-minute 18F-FDG scans, on two different days, once in each scanner. The time-stability of both STARE- and Patlak-based Ki estimates was evaluated by applying each method to the first 20 (STARE only), 30, 40, and 50 minutes of data. Results:STARE demonstrated high correlation with Patlak Ki estimates across both scanner types, particularly in the Biograph mCT (r = 0.93), with lower correlation in the CerePET (r = 0.71). In the Biograph dataset, STARE provided reliable Ki estimates at all evaluated scan durations (20 minutes and above), while in the CerePET dataset, only the 50-minute duration yielded STARE Ki estimates that were not significantly different from the full 60 minutes. The Patlak approach provided Ki estimates at 40 minutes scan duration and above that did not differ from the 60-min scan results in both datasets. Conclusion:STARE is a viable, noninvasive alternative to traditional blood-based quantification of dynamic 18F-FDG PET data, facilitating shorter, blood-free acquisition. This advancement could make dynamic 18F-FDG PET imaging more accessible and comfortable for patients, promoting broader clinical adoption.