Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci.

Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index.. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder.

Trans-diagnostic determinants of psychotherapeutic treatment response: The pressing need and new opportunities for a more systematic way of selecting psychotherapeutic treatment in the age of virtual service delivery.

Numerous forms of psychotherapy have demonstrated effectiveness for individuals with specific mental disorders. It is, therefore, the task of the clinician to choose the most appropriate therapeutic approach for any given client to maximize effectiveness. This can prove to be a difficult task due to at least three considerations: (1) there is no treatment approach, method or model that works well on all patients, even within a particular diagnostic class; (2) several treatments are equally efficacious (i.e., more likely to be effective than no treatment at all) when considered only in terms of the patient's diagnosis; and (3) effectiveness in the real-world therapeutic setting is determined by a host of non-diagnostic factors. Typically, consideration of these latter, trans-diagnostic factors is unmethodical or altogether excluded from treatment planning - often resulting in suboptimal patient care, inappropriate clinic resource utilization, patient dissatisfaction with care, patient demoralization/hopelessness, and treatment failure. In this perspective article, we argue that a more systematic research on and clinical consideration of trans-diagnostic factors determining psychotherapeutic treatment outcome (i.e., treatment moderators) would be beneficial and - with the seismic shift toward online service delivery - is more feasible than it used to be. Such a transition toward more client-centered care - systematically considering variables such as sociodemographic characteristics, patient motivation for change, self-efficacy, illness acuity, character pathology, trauma history when making treatment choices - would result in not only decreased symptom burden and improved quality of life but also better resource utilization in mental health care and improved staff morale reducing staff burnout and turnover.

The Translational Machine: A novel machine-learning approach to illuminate complex genetic architectures.

The Translational Machine (TM) is a machine learning (ML)-based analytic pipeline that translates genotypic/variant call data into biologically contextualized features that richly characterize complex variant architectures and permit greater interpretability and biological replication. It also reduces potentially confounding effects of population substructure on outcome prediction. The TM consists of three main components. First, replicable but flexible feature engineering procedures translate genome-scale data into biologically informative features that appropriately contextualize simple variant calls/genotypes within biological and functional contexts. Second, model-free, nonparametric ML-based feature filtering procedures empirically reduce dimensionality and noise of both original genotype calls and engineered features. Third, a powerful ML algorithm for feature selection is used to differentiate risk variant contributions across variant frequency and functional prediction spectra. The TM simultaneously evaluates potential contributions of variants operative under polygenic and heterogeneous models of genetic architecture. Our TM enables integration of biological information (e.g., genomic annotations) within conceptual frameworks akin to geneset-/pathways-based and collapsing methods, but overcomes some of these methods' limitations. The full TM pipeline is executed in R. Our approach and initial findings from its application to a whole-exome schizophrenia case-control data set are presented. These TM procedures extend the findings of the primary investigation and yield novel results.

Educational, supportive and behavioural interventions to improve usage of continuous positive airway pressure machines in adults with obstructive sleep apnoea.

BACKGROUND: Although highly effective in the treatment of obstructive sleep apnoea (OSA), continuous positive airway pressure (CPAP) is not universally accepted by users. Educational, supportive and behavioural interventions may help people with OSA initiate and maintain regular and continued use of CPAP. OBJECTIVES: To assess the effectiveness of educational, supportive, behavioural, or mixed (combination of two or more intervention types) strategies that aim to encourage adults who have been prescribed CPAP to use their devices. SEARCH METHODS: Searches were conducted on the Cochrane Airways Group Specialised Register of trials. Searches are current to 29 April 2019. SELECTION CRITERIA: We included randomised controlled trials (RCTs) that assessed intervention(s) designed to inform participants about CPAP/OSA, to support them in using CPAP, or to modify their behaviour to increase use of CPAP devices. DATA COLLECTION AND ANALYSIS: We assessed studies to determine their suitability for inclusion in the review. Data were extracted independently and were entered into RevMan for analysis. 'Risk of bias' assessments were performed, using the updated 'Risk of bias 2' tool, for the primary outcome, CPAP usage. Study-level 'Risk of bias' assessments were performed using the original 'Risk of bias' tool. GRADE assessment was performed using GRADEpro. MAIN RESULTS: Forty-one studies (9005 participants) are included in this review; 16 of these studies are newly identified with updated searches. Baseline Epworth Sleepiness Scale (ESS) scores indicate that most participants suffered from excessive daytime sleepiness. The majority of recruited participants had not used CPAP previously. When examining risk of bias for the primary outcome of hourly machine usage/night, 58.3% studies have high overall risk (24/41 studies), 39.0% have some concerns (16/41 studies), and 2.4% have low overall risk (1/41 studies). We are uncertain whether educational interventions improve device usage, as the certainty of evidence was assessed as very low. We were unable to perform meta-analyses for number of withdrawals and symptom scores due to high study heterogeneity. Supportive interventions probably increase device usage by 0.70 hours/night (95% confidence interval (CI) 0.36 to 1.05, N = 1426, 13 studies, moderate-certainty evidence), and low-certainty evidence indicates that the number of participants who used their devices ≥ 4 hours/night may increase from 601 to 717 per 1000 (odds ratio (OR), 1.68, 95% CI 1.08 to 2.60, N = 376, 2 studies). However, the number of withdrawals may also increase from 136 to 167 per 1000 (OR 1.27, 95% CI 0.97 to 1.66, N = 1702, 11 studies, low-certainty evidence). Participants may experience small improvements in symptoms (ESS score -0.32 points, 95% CI -1.19 to 0.56, N = 470, 5 studies, low-certainty evidence), and we are uncertain whether quality of life improves with supportive interventions, as the certainty of evidence was assessed as very low. When compared with usual care, behavioural interventions produce a clinically-meaningful increase in device usage by 1.31 hours/night (95% CI 0.95 to 1.66, N = 578, 8 studies, high-certainty evidence), probably increase the number of participants who used their machines ≥ 4 hours/night from 371 to 501 per 1000 (OR 1.70, 95% CI 1.20 to 2.41, N = 549, 6 studies, high-certainty evidence), and reduce the number of study withdrawals from 146 to 101 per 1000 (OR 0.66, 95% CI 0.44 to 0.98, N = 939, 10 studies, high-certainty evidence). Behavioural interventions may reduce symptoms (ESS score -2.42 points, 95% CI -4.27 to -0.57, N = 272, 5 studies, low-certainty evidence), but probably have no effect on quality of life (Functional Outcomes of Sleep Questionnaire (FOSQ), standardised mean difference (SMD) 0.00, 0.95% CI -0.26 to 0.26, N = 228, 3 studies, moderate-certainty evidence). We are uncertain whether behavioural interventions improve apnoea hypopnoea index (AHI), as the certainty of evidence was assessed as very low. We are uncertain if mixed interventions improve device usage, increase the number of participants using their machines ≥ 4 hours/night, reduce study withdrawals, improve quality of life, or reduce anxiety symptoms, as the certainty of evidence for these outcomes was assessed to be very low. Symptom scores via the ESS could not be measured due to considerable heterogeneity between studies. AUTHORS' CONCLUSIONS: In CPAP-naïve people with OSA, high-certainty evidence indicates that behavioural interventions yield a clinically-significant increase in hourly device usage when compared with usual care. Moderate certainty evidence shows that supportive interventions increase usage modestly. Very low-certainty evidence shows that educational and mixed interventions may modestly increase CPAP usage. The impact of improved CPAP usage on daytime sleepiness, quality of life, and mood and anxiety scores remains unclear since these outcomes were not assessed in the majority of included studies. Studies addressing the choice of interventions that best match individual patient needs and therefore result in the most successful and cost-effective therapy are needed.

Genome Wide Association Study (GWAS) between Attention Deficit Hyperactivity Disorder (ADHD) and Obsessive Compulsive Disorder (OCD).

Objective: The aim of this study was to identify any potential genetic overlap between attention deficit hyperactivity disorder (ADHD) and obsessive compulsive disorder (OCD). We hypothesized that since these disorders share a sub-phenotype, they may share common risk alleles. In this manuscript, we report the overlap found between these two disorders. Methods: A meta-analysis was conducted between ADHD and OCD, and polygenic risk scores (PRS) were calculated for both disorders. In addition, a protein-protein analysis was completed in order to examine the interactions between proteins; p-values for the protein-protein interaction analysis was calculated using permutation. Conclusion: None of the single nucleotide polymorphisms (SNPs) reached genome wide significance and there was little evidence of genetic overlap between ADHD and OCD.

Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy.

The study objective was to apply machine learning methodologies to identify predictors of remission in a longitudinal sample of 296 adults with a primary diagnosis of obsessive compulsive disorder (OCD). Random Forests is an ensemble machine learning algorithm that has been successfully applied to large-scale data analysis across vast biomedical disciplines, though rarely in psychiatric research or for application to longitudinal data. When provided with 795 raw and composite scores primarily from baseline measures, Random Forest regression prediction explained 50.8% (5000-run average, 95% bootstrap confidence interval [CI]: 50.3-51.3%) of the variance in proportion of time spent remitted. Machine performance improved when only the most predictive 24 items were used in a reduced analysis. Consistently high-ranked predictors of longitudinal remission included Yale-Brown Obsessive Compulsive Scale (Y-BOCS) items, NEO items and subscale scores, Y-BOCS symptom checklist cleaning/washing compulsion score, and several self-report items from social adjustment scales. Random Forest classification was able to distinguish participants according to binary remission outcomes with an error rate of 24.6% (95% bootstrap CI: 22.9-26.2%). Our results suggest that clinically-useful prediction of remission may not require an extensive battery of measures. Rather, a small set of assessment items may efficiently distinguish high- and lower-risk patients and inform clinical decision-making.

Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS.

BACKGROUND: It is increasingly clear that common human diseases have a complex genetic architecture characterized by both additive and nonadditive genetic effects. The goal of the present study was to determine whether patterns of both additive and nonadditive genetic associations aggregate in specific functional groups as defined by the Gene Ontology (GO). RESULTS: We first estimated all pairwise additive and nonadditive genetic effects using the multifactor dimensionality reduction (MDR) method that makes few assumptions about the underlying genetic model. Statistical significance was evaluated using permutation testing in two genome-wide association studies of ALS. The detection data consisted of 276 subjects with ALS and 271 healthy controls while the replication data consisted of 221 subjects with ALS and 211 healthy controls. Both studies included genotypes from approximately 550,000 single-nucleotide polymorphisms (SNPs). Each SNP was mapped to a gene if it was within 500 kb of the start or end. Each SNP was assigned a p-value based on its strongest joint effect with the other SNPs. We then used the Exploratory Visual Analysis (EVA) method and software to assign a p-value to each gene based on the overabundance of significant SNPs at the α = 0.05 level in the gene. We also used EVA to assign p-values to each GO group based on the overabundance of significant genes at the α = 0.05 level. A GO category was determined to replicate if that category was significant at the α = 0.05 level in both studies. We found two GO categories that replicated in both studies. The first, 'Regulation of Cellular Component Organization and Biogenesis', a GO Biological Process, had p-values of 0.010 and 0.014 in the detection and replication studies, respectively. The second, 'Actin Cytoskeleton', a GO Cellular Component, had p-values of 0.040 and 0.046 in the detection and replication studies, respectively. CONCLUSIONS: Pathway analysis of pairwise genetic associations in two GWAS of sporadic ALS revealed a set of genes involved in cellular component organization and actin cytoskeleton, more specifically, that were not reported by prior GWAS. However, prior biological studies have implicated actin cytoskeleton in ALS and other motor neuron diseases. This study supports the idea that pathway-level analysis of GWAS data may discover important associations not revealed using conventional one-SNP-at-a-time approaches.

Ion channels and schizophrenia: a gene set-based analytic approach to GWAS data for biological hypothesis testing.

Schizophrenia is a complex genetic disorder. Gene set-based analytic (GSA) methods have been widely applied for exploratory analyses of large, high-throughput datasets, but less commonly employed for biological hypothesis testing. Our primary hypothesis is that variation in ion channel genes contribute to the genetic susceptibility to schizophrenia. We applied Exploratory Visual Analysis (EVA), one GSA application, to analyze European-American (EA) and African-American (AA) schizophrenia genome-wide association study datasets for statistical enrichment of ion channel gene sets, comparing GSA results derived under three SNP-to-gene mapping strategies: (1) GENIC; (2) 500-Kb; (3) 2.5-Mb and three complimentary SNP-to-gene statistical reduction methods: (1) minimum p value (pMIN); (2) a novel method, proportion of SNPs per Gene with p values below a pre-defined α-threshold (PROP); and (3) the truncated product method (TPM). In the EA analyses, ion channel gene set(s) were enriched under all mapping and statistical approaches. In the AA analysis, ion channel gene set(s) were significantly enriched under pMIN for all mapping strategies and under PROP for broader mapping strategies. Less extensive enrichment in the AA sample may reflect true ethnic differences in susceptibility, sampling or case ascertainment differences, or higher dimensionality relative to sample size of the AA data. More consistent findings under broader mapping strategies may reflect enhanced power due to increased SNP inclusion, enhanced capture of effects over extended haplotypes or significant contributions from regulatory regions. While extensive pMIN findings may reflect gene size bias, the extent and significance of PROP and TPM findings suggest that common variation at ion channel genes may capture some of the heritability of schizophrenia.

Comparative linkage meta-analysis reveals regionally-distinct, disparate genetic architectures: application to bipolar disorder and schizophrenia.

New high-throughput, population-based methods and next-generation sequencing capabilities hold great promise in the quest for common and rare variant discovery and in the search for "missing heritability." However, the optimal analytic strategies for approaching such data are still actively debated, representing the latest rate-limiting step in genetic progress. Since it is likely a majority of common variants of modest effect have been identified through the application of tagSNP-based microarray platforms (i.e., GWAS), alternative approaches robust to detection of low-frequency (1-5% MAF) and rare (<1%) variants are of great importance. Of direct relevance, we have available an accumulated wealth of linkage data collected through traditional genetic methods over several decades, the full value of which has not been exhausted. To that end, we compare results from two different linkage meta-analysis methods--GSMA and MSP--applied to the same set of 13 bipolar disorder and 16 schizophrenia GWLS datasets. Interestingly, we find that the two methods implicate distinct, largely non-overlapping, genomic regions. Furthermore, based on the statistical methods themselves and our contextualization of these results within the larger genetic literatures, our findings suggest, for each disorder, distinct genetic architectures may reside within disparate genomic regions. Thus, comparative linkage meta-analysis (CLMA) may be used to optimize low-frequency and rare variant discovery in the modern genomic era.

Pathways-based analyses of whole-genome association study data in bipolar disorder reveal genes mediating ion channel activity and synaptic neurotransmission.

Despite known heritability, the complex genetic architecture of bipolar disorder (likely including trait, locus and allelic heterogeneity, as well as genetic interactions) has confounded genetic discovery for many years. Even modern day whole genome association studies (WGAS) using over half a million common SNPs have implicated only a handful of genes at the genomewide level. Temporally coincident with this series of WGAS, a host of pathways-based analyses (PBAs) have emerged as novel computational approaches in the examination of large-scale datasets, but thus far rarely have been applied to WGAS data in psychiatric disorders. Here, we report a series of PBAs conducted using exploratory visual analysis, an analytic and visualization software tool for examining genomic data, to examine results from the National Institutes of Mental Health and Wellcome-Trust Case Control Consortium WGAS in bipolar disorder. Consistent with a host of prior linkage findings, some candidate gene association studies, and recent WGAS, our strongest findings suggest involvement of ion channel structural and regulatory genes, including voltage-gated ion channels and the broader ion channel group that comprises both voltage- and ligand-gated channels. Moreover, we found only modest overlap in the particular genes driving the significance of these gene sets across the analyses. This observation strongly suggests that variation in ion channel genes, as a class of genes, may contribute to the susceptibility of bipolar disorder and that heterogeneity may figure prominently in the genetic architecture of this susceptibility.

The evolution of deep brain stimulation for neuropsychiatric disorders.

Deep brain stimulation (DBS) is the most focal method for stimulating the human brain. In contrast to lesions, DBS is nonablative, with the advantages of reversibility and adjustability. Thus, therapeutic effectiveness can be enhanced and stimulation-related side effects minimized during long-term patient management. While DBS is an approved adjunct therapy for severe, medication-refractory movement disorders, it remains investigational in neuropsychiatry. However, experience to date, though limited, suggests that DBS may offer a degree of hope to patients with severe and treatment-resistant neuropsychiatric illness. Thus far, work in obsessive-compulsive disorder (OCD), the first psychiatric condition studied using modern DBS devices, has shown consistently positive results across multiple small-scale studies. Work in treatment-resistant Major Depressive Disorder (MDD) also suggests therapeutic potential in preliminary studies, generating cautious optimism for this indication. With the increase in potential applications, a number of clinical and preclinical research efforts have now focused on understanding the mechanisms of action of DBS. Further development of DBS for these and other illnesses with primarily behavioral symptoms will require thoughtful collaboration among multiple disciplines.

Determinants of prostate cancer stage in northern New England: USA Franco-American contextual effects.

Despite screening for prostate cancer, mortality in the United States remains substantial. In northern New England, we know little about either determinants of stage at diagnosis--an important predictor of survival--or health outcomes for Franco-Americans, the region's largest ethnic minority. The objective of this investigation was to identify predictors of late prostate cancer stage in a rural, predominantly white state with a large Franco-American population. The Maine Cancer Registry provided incident cases from 1995 to 1998. We modeled individual-level variables (age, sex, race, French ethnicity by surname, and payer) and contextual/town-level variables (socioeconomic measures, population density, Franco ancestry proportion, distance to health care, and weather severity) with multiple logistic regression for late stage. We found that age categories 50-64, 65-74, and 75-84 years--but not 40-49 years--(versus 85+) were protective for late stage, as was residence in higher snowfall areas. Diagnosis in the earlier years of the study, particularly for French-surnamed men, and residence in a high-Franco area conferred greater risk for late disease. However, in a two-way interaction, residence in towns with high Franco ancestry proportion protected French-surnamed men (OR=0.09, type 3 p<0.0593). Using an established framework for social network theory we explore the potential reasons for this interaction, including: high social cohesion, a wide range of strong ties of long duration, and frequent contact, which might have facilitated access to resources as well as social support and normative influences toward health care seeking. The absence of an association of cancer stage with socioeconomic variables may stem from the mixed sociodemographic profiles in rural and urban regions of Maine. We feel that further research should therefore refine these and other contextual measures to elucidate effects on preventable morbidity and mortality; expand our knowledge of Franco-American health outcomes and social networks; and evaluate the utility of assigning French ethnicity by surname.

Cancer of the colorectum in Maine, 1995-1998: determinants of stage at diagnosis in a rural state.

CONTEXT: Despite screening for colorectal cancer, mortality in the United States remains substantial. In northern New England, little is known about predictors of stage at diagnosis, an important determinant of survival and mortality. PURPOSE: The objective of this study was to identify predictors of late stage at diagnosis for colorectal cancer in a rural state with a predominantly white population and a large Franco-American minority. METHODS: Incident cases from 1995-1998 were obtained from the Maine Cancer Registry. Individual-level variables (age, sex, race, French ethnicity by surname, and payer) and contextual/town-level variables (socioeconomic status, population density, Franco ancestry proportion, distance to health care, and weather) were modeled with multiple logistic regression for late stage. FINDINGS: Increasing distance to primary care provider was associated with late stage for colorectal cancer. Compared to patients aged > or =85 years, those aged 65-84 years were less likely to be diagnosed late, while those aged 35-49 years were more likely--although not significantly--to have late stage at diagnosis. Associations were not found with socioeconomic variables. CONCLUSIONS: The finding regarding distance to primary care may be consistent with studies showing that rurality and distance to care predict reduced utilization of health care services and worse health outcomes. The finding regarding age has implications for the education of younger high-risk patients and their physicians. The absence of positive findings with regard to socioeconomic variables may stem from the uniquely mixed sociodemographic profiles in rural and urban regions of Maine. Further research should refine these and other contextual measures to elucidate effects on rural health and should further evaluate the utility of assigning French ethnicity by surname in order to identify health disparities.

Toward a biaxial model of "bipolar" affective disorders: further exploration of genetic, molecular and cellular substrates.

Current epidemiologic and genetic evidence strongly supports the heritability of bipolar disease. Inconsistencies across linkage and association analyses have been primarily interpreted as suggesting polygenic, nonMendelian and variably-penetrant inheritance (i.e., in terms of interacting disease models). An equally-likely explanation for this genetic complexity is that trait, locus and allelic heterogeneities (i.e., a heterogeneous disease model) are primarily responsible for observed variability at the population level. The two models of genetic complexity are not mutually-exclusive, and are in fact likely to co-exist both in trait determination and disease expression. However, the current model proposes that, while both types of complex genetics are likely central to observable affective trait spectra, inheritance patterns, gross phenotypic categories and treatment-responsiveness in affective disease (as well as the widespread inconsistencies across such studies) may be primarily explained in terms of a heterogeneous disease model. Gene-gene, gene-protein and protein-protein interactions, then, are most likely to serve as trait determinants and 'phenotypic modifiers' rather than as primary pathogenic determinants. Moreover, while locus heterogeneity indicates the presence of multiple susceptibility genes at the population level, it does not necessitate polygenic inheritance at the individual or pedigree level. Rather, it is compatible with the possibility of mono- or bigenic determination of disease susceptibility within individuals/pedigrees. More specifically, the biaxial model proposes that integration of specific findings from genetic linkage and association studies, ion channels research as well as pharmacologic mechanism, phenotypic specificity and effectiveness studies suggests that each gene of potential etiologic significance in primary affective illness might be categorized into one of two classes, according to their primary role in neuronal functioning--neuroelectrical and neurochemical. The class(es) of primary genetic alteration (i.e., neuroelectrical, neurochemical or both) determines the type, while the locus and specific allelic variant determines the direction, of pathologic trait alteration(s). In addition to the class, locus and allelic variant of the primary genetic alteration, the cellular--and system-level expressions-including functional trait interaction therein--determine the nature and degree of clinical expression of each trait. Finally, the type, direction and presence of functional interaction between pathologic alterations would indicate the most appropriate pharmacology.

Toward a biaxial model of "bipolar" affective disorders: spectrum phenotypes as the products of neuroelectrical and neurochemical alterations.

Based upon a synthesis of findings related to affective illness, a pathophysiologic hypothesis and possible nosology are proposed. The authors reviewed recent (2001-2004) phenomenologic, diagnostic and treatment literature for pathophysiologic implications and selectively reviewed genetic, neuroimaging, neurochemical and neuropathological studies (1966-2004) that might inform the hypothesized model. Modern DSM nosology of affective illness is based upon the accumulation of observations in the purely descriptive tradition and has little pathophysiologic basis. A new perspective, that of the bipolar spectrum, deriving from clinical, theoretical and epidemiologic considerations, appears more promising for building bridges with the emerging neurobiology of bipolar disorder. Research seeking associations between structural alterations (molecular, cellular and system-level) and behavioral-level pathophysiologic processes has provided many clues, but no population-wide, major genetic loci have been identified. Three general hypotheses have emerged in the literature implicating: 1) presynaptic electrical signaling, an early suggestion which has received less recent consideration, 2) neurotransmitter-receptor systems (first messenger) and 3) post-receptor neurochemical signaling systems (second-- and third messenger). The current 'biaxial' hypothesis proposes: 1) affective regulation may be understood in terms of two primary functional spectra: mood range and mood tonicity, 2) these spectra, in turn, are determined by neurochemical capacity and neuroelectrical modulation-and their functional interaction, and 3) proposed cellular pathophysiology suggests primary molecular loci, that may predict phenomenologic patterns and treatment responsiveness.

A public health response to a cluster of suicidal behaviors: clinical psychiatry, prevention, and community health.

OBJECTIVE: To develop and implement a community public health response to a suicidal behavior cluster, including collection of risk factor data in order to prevent further behaviors. METHODS: A three-phase response, including school-wide educational debriefings, individual screening for referrals, and on-site crisis management, was implemented. Incidence of suicidal behaviors and their association with hypothesized risk factors were measured. RESULTS: Thirty-three percent of students were screened. Depression and poor social functioning were associated with an increased risk of suicidal ideation. Poor social functioning and school adjustment were associated with an increased risk of suicide attempts. CONCLUSIONS: Development and implementation of a timely public health response, including elucidation of critical risk factors, might prevent further suicidal behaviors.