Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis.

INTRODUCTION: High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders. METHODS: Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R. RESULTS: Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen's d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition. CONCLUSION: These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain's ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.

Effects of weather and season on human brain volume.

We present an exploratory cross-sectional analysis of the effect of season and weather on Freesurfer-derived brain volumes from a sample of 3,279 healthy individuals collected on two MRI scanners in Hartford, CT, USA over a 15 year period. Weather and seasonal effects were analyzed using a single linear regression model with age, sex, motion, scan sequence, time-of-day, month of the year, and the deviation from average barometric pressure, air temperature, and humidity, as covariates. FDR correction for multiple comparisons was applied to groups of non-overlapping ROIs. Significant negative relationships were found between the left- and right- cerebellum cortex and pressure (t = -2.25, p = 0.049; t = -2.771, p = 0.017). Significant positive relationships were found between left- and right- cerebellum cortex and white matter between the comparisons of January/June and January/September. Significant negative relationships were found between several subcortical ROIs for the summer months compared to January. An opposing effect was observed between the supra- and infra-tentorium, with opposite effect directions in winter and summer. Cohen's d effect sizes from monthly comparisons were similar to those reported in recent psychiatric big-data publications, raising the possibility that seasonal changes and weather may be confounds in large cohort studies. Additionally, changes in brain volume due to natural environmental variation have not been reported before and may have implications for weather-related and seasonal ailments.

Biotyping in psychosis: using multiple computational approaches with one data set.

Focusing on biomarker identification and using biomarkers individually or in clusters to define biological subgroups in psychiatry requires a re-orientation from behavioral phenomenology to quantifying brain features, requiring big data approaches for data integration. Much still needs to be accomplished, not only to refine but also to build support for the application and customization of such an analytical phenotypic approach. In this review, we present some of what Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has learned so far to guide future applications of multivariate phenotyping and their analyses to understanding psychosis. This paper describes several B-SNIP projects that use phenotype data and big data computations to generate novel outcomes and glimpse what phenotypes contribute to disease understanding and, with aspiration, to treatment. The source of the phenotypes varies from genetic data, structural neuroanatomic localization, immune markers, brain physiology, and cognition. We aim to see guiding principles emerge and areas of commonality revealed. And, we will need to demonstrate not only data stability but also the usefulness of biomarker information for subgroup identification enhancing target identification and treatment development.

Characterizing functional regional homogeneity (ReHo) as a B-SNIP psychosis biomarker using traditional and machine learning approaches.

BACKGROUND: Recently, a biologically-driven psychosis classification (B-SNIP Biotypes) was derived using brain-based cognitive and electrophysiological markers. Here, we characterized a local functional-connectivity measure, regional homogeneity (ReHo), as a biomarker across Biotypes and conventional DSM diagnoses. METHODS: Whole-brain ReHo measures of resting-state functional MRI were examined in psychosis patients and healthy controls organized by Biotype and by DSM-IV-TR diagnosis (n = 737). Group-level ANOVA and individual-level prediction models using support vector machines (SVM) were employed to evaluate the discriminative characteristics in comparisons of 1) DSM diagnostic groups, 2) Biotypes, to controls, and 3) within-proband subgroups with each other. RESULTS: Probands grouped by Biotype versus controls showed a unique abnormality pattern: Biotype-1 displayed bidirectional ReHo differences in more widespread areas, with higher ReHo in para-hippocampus, fusiform, inferior temporal, cerebellum, thalamus and caudate, plus lower ReHo in the postcentral gyrus, middle temporal, cuneus, and middle occipital cortex; Biotype-2 and Biotype-3 showed lesser and unidirectional ReHo changes. Among diagnostic groups, only schizophrenia showed higher ReHo versus control values in the inferior/middle temporal area and fusiform gyrus. For within-patient comparisons, Biotype-1 showed characteristic ReHo when compared to Biotype-2 and Biotype-3. SVM results more accurately identified Biotypes than DSM diagnoses. CONCLUSION: We characterized patterns of ReHo abnormalities across both Biotypes and DSM sub-groups. Both group-level statistical and machine-learning methods were more sensitive in capturing ReHo deficits in Biotypes than DSM. Overall ReHo is a robust psychosis biomarker.

Subcortical surface morphometry in substance dependence: An ENIGMA addiction working group study.

While imaging studies have demonstrated volumetric differences in subcortical structures associated with dependence on various abused substances, findings to date have not been wholly consistent. Moreover, most studies have not compared brain morphology across those dependent on different substances of abuse to identify substance-specific and substance-general dependence effects. By pooling large multinational datasets from 33 imaging sites, this study examined subcortical surface morphology in 1628 nondependent controls and 2277 individuals with dependence on alcohol, nicotine, cocaine, methamphetamine, and/or cannabis. Subcortical structures were defined by FreeSurfer segmentation and converted to a mesh surface to extract two vertex-level metrics-the radial distance (RD) of the structure surface from a medial curve and the log of the Jacobian determinant (JD)-that, respectively, describe local thickness and surface area dilation/contraction. Mega-analyses were performed on measures of RD and JD to test for the main effect of substance dependence, controlling for age, sex, intracranial volume, and imaging site. Widespread differences between dependent users and nondependent controls were found across subcortical structures, driven primarily by users dependent on alcohol. Alcohol dependence was associated with localized lower RD and JD across most structures, with the strongest effects in the hippocampus, thalamus, putamen, and amygdala. Meanwhile, nicotine use was associated with greater RD and JD relative to nonsmokers in multiple regions, with the strongest effects in the bilateral hippocampus and right nucleus accumbens. By demonstrating subcortical morphological differences unique to alcohol and nicotine use, rather than dependence across all substances, results suggest substance-specific relationships with subcortical brain structures.

NRXN1 is associated with enlargement of the temporal horns of the lateral ventricles in psychosis.

Schizophrenia, Schizoaffective, and Bipolar disorders share behavioral and phenomenological traits, intermediate phenotypes, and some associated genetic loci with pleiotropic effects. Volumetric abnormalities in brain structures are among the intermediate phenotypes consistently reported associated with these disorders. In order to examine the genetic underpinnings of these structural brain modifications, we performed genome-wide association analyses (GWAS) on 60 quantitative structural brain MRI phenotypes in a sample of 777 subjects (483 cases and 294 controls pooled together). Genotyping was performed with the Illumina PsychChip microarray, followed by imputation to the 1000 genomes multiethnic reference panel. Enlargement of the Temporal Horns of Lateral Ventricles (THLV) is associated with an intronic SNP of the gene NRXN1 (rs12467877, P = 6.76E-10), which accounts for 4.5% of the variance in size. Enlarged THLV is associated with psychosis in this sample, and with reduction of the hippocampus and enlargement of the choroid plexus and caudate. Eight other suggestively significant associations (P < 5.5E-8) were identified with THLV and 5 other brain structures. Although rare deletions of NRXN1 have been previously associated with psychosis, this is the first report of a common SNP variant of NRXN1 associated with enlargement of the THLV in psychosis.

Multivariate Analyses Reveal Biological Components Related to Neuronal Signaling and Immunity Mediating Electroencephalograms Abnormalities in Alcohol-Dependent Individuals from the Collaborative Study on the Genetics of Alcoholism Cohort.

BACKGROUND: The underlying molecular mechanisms associated with alcohol use disorder (AUD) risk have only been partially revealed using traditional approaches such as univariate genomewide association and linkage-based analyses. We therefore aimed to identify gene clusters related to Electroencephalograms (EEG) neurobiological phenotypes distinctive to individuals with AUD using a multivariate approach. METHODS: The current project adopted a bimultivariate data-driven approach, parallel independent component analysis (para-ICA), to derive and explore significant genotype-phenotype associations in a case-control subset of the Collaborative Study on the Genetics of Alcoholism (COGA) dataset. Para-ICA subjects comprised N = 799 self-reported European Americans (367 controls and 432 AUD cases), recruited from COGA, who had undergone resting EEG and genotyping. Both EEG and genomewide single nucleotide polymorphism (SNP) data were preprocessed prior to being subjected to para-ICA in order to derive genotype-phenotype relationships. RESULTS: From the data, 4 EEG frequency and 4 SNP components were estimated, with 2 significantly correlated EEG-genetic relationship pairs. The first such pair primarily represented theta activity, negatively correlated with a genetic cluster enriched for (but not limited to) ontologies/disease processes representing cell signaling, neurogenesis, transmembrane drug transportation, alcoholism, and lipid/cholesterol metabolism. The second component pair represented mainly alpha activity, positively correlated with a genetic cluster with ontologies similarly enriched as the first component. Disease-related enrichments for this component revealed heart and autoimmune disorders as top hits. Loading coefficients for both the alpha and theta components were significantly reduced in cases compared to controls. CONCLUSIONS: Our data suggest plausible multifactorial genetic components, primarily enriched for neuronal/synaptic signaling/transmission, immunity, and neurogenesis, mediating low-frequency alpha and theta abnormalities in alcohol addiction.

Longitudinal Effects of Alcohol Consumption on the Hippocampus and Parahippocampus in College Students.

BACKGROUND: The hazardous effects of alcohol consumption on both the hippocampus and memory have been well established. However, the longitudinal effects of ethanol on the developing brain and related consequences on memory are not well explored. Given the above, we investigated the longitudinal effects of college drinking on hippocampal volume in emerging college adults. METHODS: Data were derived from the longitudinal Brain and Alcohol Research with College Students study. A subset of 146 freshmen (mean age at baseline = 18.5 years) underwent brain magnetic resonance imaging scans at baseline and 24 months later. Four drinking-related measures derived from monthly surveys were reduced to a single alcohol use index using principal component analysis. Gray matter volumetric change (GMV-c) data were derived using a longitudinal pipeline. Voxelwise hippocampal/para-hippocampal GMV-c associations with the drinking index were derived using a multiple regression framework within SPM12. Supplementary associations were assessed between GMV-c and memory scores computed from the California Verbal Learning Test-II (assessed at the end of the study), and between GMV-c and total alcohol-induced memory blackouts. RESULTS: Larger alcohol use index was associated with an accelerated GMV decline in the hippocampus/para-hippocampus. Also, larger hippocampal volume decline was associated with poorer memory performance and more memory blackouts. CONCLUSIONS: Our study extends prior cross-sectional literature by showing that a heavier drinking burden while in college is associated with greater hippocampal GMV decline that is in turn associated with poorer memory scores, all of which could ultimately have a significant impact on student success.

Genetic analysis of deep phenotyping projects in common disorders.

Several studies of complex psychotic disorders with large numbers of neurobiological phenotypes are currently under way, in living patients and controls, and on assemblies of brain specimens. Genetic analyses of such data typically present challenges, because of the choice of underlying hypotheses on genetic architecture of the studied disorders and phenotypes, large numbers of phenotypes, the appropriate multiple testing corrections, limited numbers of subjects, imputations required on missing phenotypes and genotypes, and the cross-disciplinary nature of the phenotype measures. Advances in genotype and phenotype imputation, and in genome-wide association (GWAS) methods, are useful in dealing with these challenges. As compared with the more traditional single-trait analyses, deep phenotyping with simultaneous genome-wide analyses serves as a discovery tool for previously unsuspected relationships of phenotypic traits with each other, and with specific molecular involvements.

Heavy Drinking in College Students Is Associated with Accelerated Gray Matter Volumetric Decline over a 2 Year Period.

Background: Heavy and/or harmful alcohol use while in college is a perennial and significant public health issue. Despite the plethora of cross-sectional research suggesting deleterious effects of alcohol on the brain, there is a lack of literature investigating the longitudinal effects of alcohol consumption on the adolescent brain. We aim to probe the longitudinal effects of college drinking on gray matter change in students during this crucial neurodevelopmental period. Methods: Data were derived from the longitudinal Brain and Alcohol Research in College Students (BARCS) study of whom a subset underwent brain MRI scans at two time points 24 months apart. Students were young adults with a mean age at baseline of about 18.5 years. Based on drinking metrics assessed at both baseline and followup, subjects were classified as sustained abstainers/light drinkers (N = 45) or sustained heavy drinkers (N = 84) based on criteria established in prior literature. Gray matter volumetric change (GMV-c) maps were derived using the longitudinal DARTEL pipeline as implemented in SPM12. GMV-c maps were then subjected to a 1-sample and 2-sample t-test in SPM12 to determine within- and between-group GMV-c differences in drinking groups. Supplementary between-group differences were also computed at baseline only. Results: Within-group analysis revealed significant decline in GMV in both groups across the 2 year followup period. However, tissue loss in the sustained heavy drinking group was more significant, larger per region, and more widespread across regions compared to abstainers/light drinkers. Between-group analysis confirmed the above and showed a greater rate of GMV-c in the heavy drinking group in several brain regions encompassing inferior/medial frontal gyrus, parahippocampus, and anterior cingulate. Supplementary analyses suggest that some of the frontal differences existed at baseline and progressively worsened. Conclusion: Sustained heavy drinking while in college was associated with accelerated GMV decline in brain regions involved with executive functioning, emotional regulation, and memory, which are critical to everyday life functioning. Areas of significant GMV decreases also overlapped largely with brain reward and stress systems implicated in addictive behavior.

S100A10 identified in a genome-wide gene × cannabis dependence interaction analysis of risky sexual behaviours.

BACKGROUND: We conducted a genome-wide gene × environment interaction analysis to identify genetic variants that interact with cannabis dependence (CaD) in influencing risky sexual behaviours (RSB). METHODS: Our sample included cannabis-exposed and sexually experienced African-American and European-American participants. A DSM-IV CaD diagnosis and RSB were evaluated using the Semi-Structured Assessment for Drug Dependence and Alcoholism. We analyzed RSBs as a score that takes into account experiences of unprotected sex and multiple sexual partners. RESULTS: A total of 3350 people participated in our study; 43% had a CaD diagnosis, 56% were African-American and 33% were women. We identified a genome-wide significant locus in African-American participants (S100A10 rs72993629, p = 2.73 × 10-8) and a potential transpopulation signal in women (CLTC rs12944716, p = 5.27 × 10-8). A resting-state fMRI follow-up analysis of S100A10 rs72993629 conducted in an independent cohort showed 2 significant associations: reduced power of the left paracentral lobule in amplitude of low frequency fluctuations (ALFF) analysis (p = 7.8 × 10-3) and reduced power of the right pallidum in fractional ALFF analysis (p = 4.6 × 10-3). The activity of these brain regions is known to be involved in sexual functions and behaviours. The S100A10 result functionally recapitulated our S100B finding observed in our previous genome-wide association study of CaD. The probability of identifying 2 S100 genes in 2 independent genome-wide investigations by chance is approximately 1 in 1.1 million. LIMITATIONS: We were not able to identify any African-American cohort with appropriate sample size, and phenotypic assessment is available to replicate our findings. CONCLUSION: The S100A10 and S100B genes, which are located on different chromosomes, encode specialized calcium-binding proteins. These data support a role for calcium homeostasis in individuals with CaD and its induced behaviours.

Longitudinal influence of alcohol and marijuana use on academic performance in college students.

BACKGROUND: Alcohol and marijuana are the two most abused substances in US colleges. However, research on the combined influence (cross sectional or longitudinal) of these substances on academic performance is currently scant. METHODS: Data were derived from the longitudinal 2-year Brain and Alcohol Research in College Students (BARCS) study including 1142 freshman students who completed monthly marijuana use and alcohol consumption surveys. Subjects were classified into data-driven groups based on their alcohol and marijuana consumption. A linear mixed-model (LMM) was employed using this grouping factor to predict grade point average (GPA), adjusted for a variety of socio-demographic and clinical factors. RESULTS: Three data-driven clusters emerged: 1) No/low users of both, 2) medium-high alcohol/no-low marijuana, and 3) medium-high users of both substances. Individual cluster derivations between consecutive semesters remained stable. No significant interaction between clusters and semester (time) was noted. Post-hoc analysis suggest that at the outset, compared to sober peers, students using moderate to high levels of alcohol and low marijuana demonstrate lower GPAs, but this difference becomes non-significant over time. In contrast, students consuming both substances at moderate-to-high levels score significantly lower at both the outset and across the 2-year investigation period. Our follow-up analysis also indicate that when students curtailed their substance use over time they had significantly higher academic GPA compared to those who remained stable in their substance use patterns over the two year period. CONCLUSIONS: Overall, our study validates and extends the current literature by providing important implications of concurrent alcohol and marijuana use on academic achievement in college.

The Interplay Between Risky Sexual Behaviors and Alcohol Dependence: Genome-Wide Association and Neuroimaging Support for LHPP as a Risk Gene.

To identify genetic mechanisms involved in the interplay of risky sexual behaviors (RSBs) and alcohol dependence (AD), we conducted genome-wide gene-by-AD (GW-GxAD) analyses of RSB in 3924 alcohol-exposed and sexually experienced subjects. RSBs were defined as a score based on lifetime experiences of unprotected sex and multiple sexual partners. Diagnosis of lifetime AD was defined by DSM-IV criteria. To follow-up the genetic findings, functional magnetic resonance imaging analyses were conducted in an independent sample. A trans-population genome-wide significant signal was identified in LHPP (rs34997829; z=-5.573, p=2.51 × 10-8) in the GxAD analysis that also showed associations in the AD-stratified association analysis (AD z=-2.032 and non-AD z=4.903). The clinical relevance of the result was confirmed by the significant interaction between LHPP rs34997829 and AD with respect to self-reported sexually transmitted disease (STD; z=-2.809, p=4.97 × 10-3). The neuroimaging follow-up analysis of LHPP rs34997829 showed reduced power of the left superior frontal gyrus (t=-3.386, p=9.56 × 10-4) and increased power at the right amygdala (t=3.287, p=1.33 × 10-3) in the resting amplitude of low frequency fluctuations analysis; and reduced activation of the anterior cingulate region (t=-2.961, p=3.69 × 10-3) in the monetary incentive delay task. In conclusion, LHPP locus is associated to AD-RSB interaction; and with brain circuitries previously implicated in the inhibition of risky behavior and impulsiveness, emotional regulation, and impulse control/error monitoring. Thus, LHPP is a strong candidate to influence RSB and STD risk in the context of AD.

Brain Structure Biomarkers in the Psychosis Biotypes: Findings From the Bipolar-Schizophrenia Network for Intermediate Phenotypes.

BACKGROUND: The current definitions of psychotic illness lack biological validity, motivating alternative biomarker-driven disease entities. Building on experimental constructs-Biotypes-that were previously developed from cognitive and neurophysiologic measures, we contrast brain anatomy characteristics across Biotypes alongside conventional diagnoses, examining gray matter density (GMD) as an independent validator for the Biotypes. METHODS: Whole brain GMD measures were examined in probands, their relatives, and healthy subjects organized by Biotype and then by DSM-IV-TR diagnosis (n = 1409) using voxel-based morphometry with subsequent subject-level regional characterization and distribution analyses. RESULTS: Probands grouped by Biotype versus healthy controls showed a stepwise pattern of GMD reductions as follows: Biotype1, extensive and diffusely distributed GMD loss, with the largest effects in frontal, anterior/middle cingulate cortex, and temporal regions; Biotype2, intermediate and more localized reductions, with the largest effects in insula and frontotemporal regions; and Biotype3, small reductions localized to anterior limbic regions. Relatives showed regionally distinct GMD reductions versus healthy controls, with primarily anterior (frontotemporal) effects in Biotype1; posterior (temporo-parieto-cerebellar) in Biotype2; and normal GMD in Biotype3. Schizophrenia and schizoaffective probands versus healthy controls showed overlapping GMD reductions, with the largest effects in frontotemporal and parietal regions; psychotic bipolar probands had small reductions, primarily in frontal regions. GMD changes in relatives followed regional patterns observed in probands, albeit less extensive. Biotypes showed stronger between-group separation based on GMD than the conventional diagnoses and were the strongest predictor of GMD change. CONCLUSIONS: GMD biomarkers depicted unique brain structure characteristics within Biotypes, consistent with their cognitive and sensorimotor profiles, and provided stronger discrimination for biologically driven biotypes than symptom-based diagnoses.

Novel gene-brain structure relationships in psychotic disorder revealed using parallel independent component analyses.

BACKGROUND: Schizophrenia, schizoaffective disorder, and psychotic bipolar disorder overlap with regard to symptoms, structural and functional brain abnormalities, and genetic risk factors. Neurobiological pathways connecting genes to clinical phenotypes across the spectrum from schizophrenia to psychotic bipolar disorder remain largely unknown. METHODS: We examined the relationship between structural brain changes and risk alleles across the psychosis spectrum in the multi-site Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) cohort. Regional MRI brain volumes were examined in 389 subjects with a psychotic disorder (139 schizophrenia, 90 schizoaffective disorder, and 160 psychotic bipolar disorder) and 123 healthy controls. 451,701 single-nucleotide polymorphisms were screened and processed using parallel independent component analysis (para-ICA) to assess associations between genes and structural brain abnormalities in probands. RESULTS: 482 subjects were included after quality control (364 individuals with psychotic disorder and 118 healthy controls). Para-ICA identified four genetic components including several risk genes already known to contribute to schizophrenia and bipolar disorder and revealed three structural components that showed overlapping relationships with the disease risk genes across the three psychotic disorders. Functional ontologies representing these gene clusters included physiological pathways involved in brain development, synaptic transmission, and ion channel activity. CONCLUSIONS: Heritable brain structural findings such as reduced cortical thickness and surface area in probands across the psychosis spectrum were associated with somewhat distinct genes related to putative disease pathways implicated in psychotic disorders. This suggests that brain structural alterations might represent discrete psychosis intermediate phenotypes along common neurobiological pathways underlying disease expression across the psychosis spectrum.

Examining Functional Resting-State Connectivity in Psychosis and Its Subgroups in the Bipolar-Schizophrenia Network on Intermediate Phenotypes Cohort.

BACKGROUND: We sought to examine resting-state functional magnetic resonance imaging connectivity measures in psychotic patients to both identify cumulative differences across psychosis and subsequently probe deficits across conventional DSM-IV diagnoses and a newly identified classification using cognitive/neurophysiological data (Biotypes). METHODS: We assessed 1125 subjects, including healthy control subjects, probands (schizophrenia, schizoaffective disorder, psychotic bipolar disorder), and relatives of probands. Probands and relatives were also segregated into Biotype groups (B1-B3, B1R-B3R using a method reported previously). Empirical resting-state functional magnetic resonance imaging networks were derived using independent component analysis. Global psychosis-related abnormalities were first identified. Subsequent post hoc t tests were performed across various diagnostic categories. Follow-up linear mixed model compared significance of within-proband differences across categories. Secondary analyses assessed correlations with biological profile scores. RESULTS: Voxelwise tests between proband and control subjects revealed nine abnormal networks. Post hoc analysis revealed lower connectivity in most networks for all proband subgroups (DSM and Biotypes). Within-proband effect sizes of discrimination were marginally better for Biotypes over DSM. Reduced connectivity was noted in relatives of patients with schizophrenia in two networks and relatives of patients with psychotic bipolar disorder in one network. Biotype relatives showed similar deficits in one network. Connectivity deficits across four networks were significantly associated with cognitive control profile scores. CONCLUSIONS: Overall, we found psychosis-related connectivity deficits in nine large-scale networks. Deficits in these networks tracked more closely with cognitive control factors, suggesting potential implications for disease profiling and therapeutic intervention. Biotypes performed marginally better in terms of separating out psychosis subgroups compared with conventional DSM or psychiatric diagnoses.

A Preliminary Prospective Study of an Escalation in 'Maximum Daily Drinks', Fronto-Parietal Circuitry and Impulsivity-Related Domains in Young Adult Drinkers.

Excessive alcohol use in young adults is associated with greater impulsivity and neurobiological alterations in executive control systems. The maximum number of drinks consumed during drinking occasions ('MaxDrinks') represents a phenotype linked to vulnerability of alcohol use disorders, and an increase, or 'escalation', in MaxDrinks may be indicative of greater risk for problematic drinking. Thirty-six young adult drinkers performed a Go/No-Go task during fMRI, completed impulsivity-related assessments, and provided monthly reports of alcohol use during a 12-month follow-up period. Participants were characterized by MaxDrinks at baseline and after follow-up, identifying 18 escalating drinkers and 18 constant drinkers. Independent component analysis was used to investigate functional brain networks associated with response inhibition, and relationships with principal component analysis derived impulsivity-related domains were examined. Greater baseline MaxDrinks was associated with an average reduction in the engagement of a right-lateralized fronto-parietal functional network, while an escalation in MaxDrinks was associated with a greater difference in fronto-parietal engagement between successful inhibitions and error trials. Escalating drinkers displayed greater impulsivity/compulsivity-related domain scores that were positively associated with fronto-parietal network engagement and change in MaxDrinks during follow-up. In young adults, an escalating MaxDrinks trajectory was prospectively associated with altered fronto-parietal control mechanisms and greater impulsivity/compulsivity scores. Continued longitudinal studies of MaxDrinks trajectories, functional network activity, and impulsivity/compulsivity-related features may lend further insight into an intermediate phenotype vulnerable for alcohol use and addictive disorders.

Frequency-Specific Neural Signatures of Spontaneous Low-Frequency Resting State Fluctuations in Psychosis: Evidence From Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) Consortium.

BACKGROUND: We quantified frequency-specific, absolute, and fractional amplitude of low-frequency fluctuations (ALFF/fALFF) across the schizophrenia (SZ)-psychotic bipolar disorder (PBP) psychosis spectrum using resting functional magnetic resonance imaging data from the large BSNIP family study. METHODS: We assessed 242 healthy controls (HC), 547 probands (180 PBP, 220 SZ, and 147 schizoaffective disorder-SAD), and 410 of their first-degree relatives (134 PBPR, 150SZR, and 126 SADR). Following standard preprocessing in statistical parametric mapping (SPM8), we computed absolute and fractional power (ALFF/fALFF) in 2 low-frequency bands: slow-5 (0.01-0.027 Hz) and slow-4 (0.027-0.073 Hz). We evaluated voxelwise post hoc differences across traditional Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition diagnostic categories. RESULTS: Across ALFF/fALFF, in contrast to HC, BP/SAD showed hypoactivation in frontal/anterior brain regions in the slow-5 band and hypoactivation in posterior brain regions in the slow-4 band. SZ showed consistent hypoactivation in precuneus/cuneus and posterior cingulate across both bands and indices. Increased ALFF/fALFF was noted predominantly in deep subcortical and temporal structures across probands in both bands and indices. Across probands, spatial ALFF/fALFF differences in SAD resembled PBP more than SZ. None of these ALFF/fALFF differences were detected in relatives. CONCLUSIONS: Results suggest ALFF/fALFF is a putative biomarker rather than a familial endophenotype. Overall sensitivity to discriminate proband brain alteration was stronger for fALFF than ALFF. Patterns of differences noted in SAD were more similar to those observed in PBP. Differential effects were noted across the 2 frequency bands, more prominently for BP/SAD compared with SZ, suggesting frequency-sensitive physiologic mechanisms for the former.

Multivariate analysis of subjective responses to d-amphetamine in healthy volunteers finds novel genetic pathway associations.

RATIONALE: Researchers studying behavioral and physiologic effects of d-amphetamine have explored individual response differences to the drug. Concurrently, genome-wide analyses have identified several single-nucleotide polymorphisms (SNPs) associated with these traits. Univariate methods can identify SNPs associated with behavioral and physiological traits, but multivariate analyses allow identification of clusters of related biologically relevant SNPs and behavioral components. OBJECTIVES: The aim of the study was to identify clusters of related biologically relevant SNPs and behavioral components in the responses of healthy individuals to d-amphetamine using multivariate analysis. METHODS: Individuals (N = 375) without substance abuse histories completed surveys and detailed cardiovascular monitoring during randomized, blinded sessions: d-amphetamine (10 and 20 mg) and placebo. We applied parallel independent component analysis (Para-ICA) to data previously analyzed with univariate approaches, revealing new associations between genes and behavioral responses to d-amphetamine. RESULTS: Three significantly associated (p < .001) phenotype-genotype pairs emerged. The first component included physiologic measures of systolic and diastolic blood pressure (BP) and mean arterial pressure (MAP) along with SNPs in calcium and glutamatergic signaling pathways. The second associated components included the "Anger" items from the Profile of Mood States (POMS) questionnaire and the marijuana effects from the Addiction Research Center Inventory (Cuyas, Verdejo-Garcia et al.), with enriched genetic pathways involved in cardiomyopathy and MAPK signaling. The final pair included "Anxious," "Fatigue," and "Confusion" items from the POMS questionnaire, plus functional pathways related to cardiac muscle contraction and cardiomyopathy. CONCLUSIONS: Multifactorial genetic networks related to calcium signaling, glutamatergic and dopaminergic synapse function, and amphetamine addiction appear to mediate common behavioral and cardiovascular responses to d-amphetamine.

Large-Scale Fusion of Gray Matter and Resting-State Functional MRI Reveals Common and Distinct Biological Markers across the Psychosis Spectrum in the B-SNIP Cohort.

To investigate whether aberrant interactions between brain structure and function present similarly or differently across probands with psychotic illnesses [schizophrenia (SZ), schizoaffective disorder (SAD), and bipolar I disorder with psychosis (BP)] and whether these deficits are shared with their first-degree non-psychotic relatives. A total of 1199 subjects were assessed, including 220 SZ, 147 SAD, 180 psychotic BP, 150 first-degree relatives of SZ, 126 SAD relatives, 134 BP relatives, and 242 healthy controls (1). All subjects underwent structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) scanning. Joint-independent component analysis (jICA) was used to fuse sMRI gray matter and rs-fMRI amplitude of low-frequency fluctuations data to identify the relationship between the two modalities. jICA revealed two significantly fused components. The association between functional brain alteration in a prefrontal-striatal-thalamic-cerebellar network and structural abnormalities in the default mode network was found to be common across psychotic diagnoses and correlated with cognitive function, social function, and schizo-bipolar scale scores. The fused alteration in the temporal lobe was unique to SZ and SAD. The above effects were not seen in any relative group (including those with cluster-A personality). Using a multivariate-fused approach involving two widely used imaging markers, we demonstrate both shared and distinct biological traits across the psychosis spectrum. Furthermore, our results suggest that the above traits are psychosis biomarkers rather than endophenotypes.