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.

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.

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.

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.

Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia.

The brain's default mode network (DMN) is highly heritable and is compromised in a variety of psychiatric disorders. However, genetic control over the DMN in schizophrenia (SZ) and psychotic bipolar disorder (PBP) is largely unknown. Study subjects (n = 1,305) underwent a resting-state functional MRI scan and were analyzed by a two-stage approach. The initial analysis used independent component analysis (ICA) in 324 healthy controls, 296 SZ probands, 300 PBP probands, 179 unaffected first-degree relatives of SZ probands (SZREL), and 206 unaffected first-degree relatives of PBP probands to identify DMNs and to test their biomarker and/or endophenotype status. A subset of controls and probands (n = 549) then was subjected to a parallel ICA (para-ICA) to identify imaging-genetic relationships. ICA identified three DMNs. Hypo-connectivity was observed in both patient groups in all DMNs. Similar patterns observed in SZREL were restricted to only one network. DMN connectivity also correlated with several symptom measures. Para-ICA identified five sub-DMNs that were significantly associated with five different genetic networks. Several top-ranking SNPs across these networks belonged to previously identified, well-known psychosis/mood disorder genes. Global enrichment analyses revealed processes including NMDA-related long-term potentiation, PKA, immune response signaling, axon guidance, and synaptogenesis that significantly influenced DMN modulation in psychoses. In summary, we observed both unique and shared impairments in functional connectivity across the SZ and PBP cohorts; these impairments were selectively familial only for SZREL. Genes regulating specific neurodevelopment/transmission processes primarily mediated DMN disconnectivity. The study thus identifies biological pathways related to a widely researched quantitative trait that might suggest novel, targeted drug treatments for these diseases.

A large scale multivariate parallel ICA method reveals novel imaging-genetic relationships for Alzheimer's disease in the ADNI cohort.

The underlying genetic etiology of late onset Alzheimer's disease (LOAD) remains largely unknown, likely due to its polygenic architecture and a lack of sophisticated analytic methods to evaluate complex genotype-phenotype models. The aim of the current study was to overcome these limitations in a bi-multivariate fashion by linking intermediate magnetic resonance imaging (MRI) phenotypes with a genome-wide sample of common single nucleotide polymorphism (SNP) variants. We compared associations between 94 different brain regions of interest derived from structural MRI scans and 533,872 genome-wide SNPs using a novel multivariate statistical procedure, parallel-independent component analysis, in a large, national multi-center subject cohort. The study included 209 elderly healthy controls, 367 subjects with amnestic mild cognitive impairment and 181 with mild, early-stage LOAD, all of them Caucasian adults, from the Alzheimer's Disease Neuroimaging Initiative cohort. Imaging was performed on comparable 1.5 T scanners at over 50 sites in the USA/Canada. Four primary "genetic components" were associated significantly with a single structural network including all regions involved neuropathologically in LOAD. Pathway analysis suggested that each component included several genes already known to contribute to LOAD risk (e.g. APOE4) or involved in pathologic processes contributing to the disorder, including inflammation, diabetes, obesity and cardiovascular disease. In addition significant novel genes identified included ZNF673, VPS13, SLC9A7, ATP5G2 and SHROOM2. Unlike conventional analyses, this multivariate approach identified distinct groups of genes that are plausibly linked in physiologic pathways, perhaps epistatically. Further, the study exemplifies the value of this novel approach to explore large-scale data sets involving high-dimensional gene and endophenotype data.

Prefrontal cortical response to emotional faces in individuals with major depressive disorder in remission.

Abnormalities in the response of the orbitofrontal cortex (OFC) and dorsolateral prefrontal cortex (DLPFC) to negative emotional stimuli have been reported in acutely depressed patients. However, there is a paucity of studies conducted in unmedicated individuals with major depressive disorder in remission (rMDD) to assess whether these are trait abnormalities. To address this issue, 19 medication-free rMDD individuals and 20 healthy comparison (HC) participants were scanned using functional magnetic resonance imaging while performing an implicit emotion processing task in which they labeled the gender of faces depicting negative (fearful), positive (happy) and neutral facial expressions. The rMDD and HC groups were compared using a region-of-interest approach for two contrasts: fear vs. neutral and happy vs. neutral. Relative to HC, rMDD showed reduced activation in left OFC and DLPFC to fearful (vs. neutral) faces. Right DLPFC activation to fearful (vs. neutral) faces in the rMDD group showed a significant positive correlation with duration of euthymia. The findings support deficits in left OFC and DLPFC responses to negative emotional stimuli during euthymic periods of MDD, which may reflect trait markers of the illness or a 'scar' due to previous depression. Recovery may also be associated with compensatory increases in right DLPFC functioning.

Functional imaging of cognitive control during acute alcohol intoxication.

BACKGROUND: The anterior cingulate and several other prefrontal and parietal brain regions are implicated in error processing and cognitive control. The effects of different doses of alcohol on activity within these brain regions during a functional magnetic resonance imaging (fMRI) task where errors are frequently committed have not been fully explored. METHODS: This study examined the impact of a placebo [breath alcohol concentration (BrAC) = 0.00%], moderate (BrAC = 0.05%), and high (BrAC = 0.10%) doses of alcohol on brain hemodynamic activity during a functional MRI (fMRI) Go/No-Go task in 38 healthy volunteers. RESULTS: Alcohol increased reaction time and false alarm errors in a dose-dependent manner. fMRI analyses showed alcohol decreased activity in anterior cingulate, lateral prefrontal cortex, insula, and parietal lobe regions during false alarm responses to No-Go stimuli. CONCLUSIONS: These findings indicate that brain regions implicated in error processing are affected by alcohol and might provide a neural basis for alcohol's effects on behavioral performance.

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.

A pilot multivariate parallel ICA study to investigate differential linkage between neural networks and genetic profiles in schizophrenia.

Understanding genetic influences on both healthy and disordered brain function is a major focus in psychiatric neuroimaging. We utilized task-related imaging findings from an fMRI auditory oddball task known to be robustly associated with abnormal activation in schizophrenia, to investigate genomic factors derived from multiple single nucleotide polymorphisms (SNPs) from genes previously shown to be associated with schizophrenia. Our major aim was to investigate the relationship of these genomic factors to normal/abnormal brain functionality between controls and schizophrenia patients. We studied a Caucasian-only sample of 35 healthy controls and 31 schizophrenia patients. All subjects performed an auditory oddball task, which consists of detecting an infrequent sound within a series of frequent sounds. Each subject was characterized on 24 different SNP markers spanning multiple risk genes previously associated with schizophrenia. We used a recently developed technique named parallel independent component analysis (para-ICA) to analyze this multimodal data set (Liu et al., 2008). The method aims to identify simultaneously independent components of each modality (functional imaging, genetics) and the relationships between them. We detected three fMRI components significantly correlated with two distinct gene components. The fMRI components, along with their significant genetic profile (dominant SNP) correlations were as follows: (1) Inferior frontal-anterior/posterior cingulate-thalamus-caudate with SNPs from Brain derived neurotropic factor (BDNF) and dopamine transporter (DAT) [r=-0.51; p<0.0001], (2) superior/middle temporal gyrus-cingulate-premotor with SLC6A4_PR and SLC6A4_PR_AG (serotonin transporter promoter; 5HTTLPR) [r=0.27; p=0.03], and (3) default mode-fronto-temporal gyrus with Brain derived neurotropic factor and dopamine transporter (BDNF, DAT) [r=-0.25; p=0.04]. Functional components comprised task-relevant regions (including PFC, ACC, STG and MTG) frequently identified as abnormal in schizophrenia. Further, gene-fMRI combinations 1 (Z=1.75; p=0.03), 2 (Z=1.84; p=0.03) and 3 (Z=1.67; p=0.04) listed above showed significant differences between controls and patients, based on their correlated loading coefficients. We demonstrate a framework to identify interactions between "clusters" of brain function and of genetic information. Our results reveal the effect/influence of specific interactions, (perhaps epistastatic in nature), between schizophrenia risk genes on imaging endophenotypes representing attention/working memory and goal directed related brain function, thus establishing a useful methodology to probe multivariate genotype-phenotype relationships.

Disruptions in functional network connectivity during alcohol intoxicated driving.

BACKGROUND: Driving while under the influence of alcohol is a major public health problem whose neural basis is not well understood. In a recently published functional magnetic resonance imaging (fMRI) study (Meda et al., 2009), our group identified 5, independent critical driving-associated brain circuits whose inter-regional connectivity was disrupted by alcohol intoxication. However, the functional connectivity between these circuits has not yet been explored in order to determine how these networks communicate with each other during sober and alcohol-intoxicated states. METHODS: In the current study, we explored such differences in connections between the above brain circuits and driving behavior, under the influence of alcohol versus placebo. Forty social drinkers who drove regularly underwent fMRI scans during virtual reality driving simulations following 2 alcohol doses, placebo and an individualized dose producing blood alcohol concentrations (BACs) of 0.10%. RESULTS: At the active dose, we found specific disruptions of functional network connectivity between the frontal-temporal-basal ganglia and the cerebellar circuits. The temporal connectivity between these 2 circuits was found to be less correlated (p < 0.05) when driving under the influence of alcohol. This disconnection was also associated with an abnormal driving behavior (unstable motor vehicle steering). CONCLUSIONS: Connections between frontal-temporal-basal ganglia and cerebellum have recently been explored; these may be responsible in part for maintaining normal motor behavior by integrating their overlapping motor control functions. These connections appear to be disrupted by alcohol intoxication, in turn associated with an explicit type of impaired driving behavior.

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.

Alcohol dose effects on brain circuits during simulated driving: an fMRI study.

Driving while intoxicated remains a major public health hazard. Driving is a complex task involving simultaneous recruitment of multiple cognitive functions. The investigators studied the neural substrates of driving and their response to different blood alcohol concentrations (BACs), using functional magnetic resonance imaging (fMRI) and a virtual reality driving simulator. We used independent component analysis (ICA) to isolate spatially independent and temporally correlated driving-related brain circuits in 40 healthy, adult moderate social drinkers. Each subject received three individualized, separate single-blind doses of beverage alcohol to produce BACs of 0.05% (moderate), 0.10% (high), or 0% (placebo). 3 T fMRI scanning and continuous behavioral measurement occurred during simulated driving. Brain function was assessed and compared using both ICA and a conventional general linear model (GLM) analysis. ICA results replicated and significantly extended our previous 1.5T study (Calhoun et al. [2004a]: Neuropsychopharmacology 29:2097-2017). GLM analysis revealed significant dose-related functional differences, complementing ICA data. Driving behaviors including opposite white line crossings and mean speed independently demonstrated significant dose-dependent changes. Behavior-based factors also predicted a frontal-basal-temporal circuit to be functionally impaired with alcohol dosage across baseline scaled, good versus poorly performing drivers. We report neural correlates of driving behavior and found dose-related spatio-temporal disruptions in critical driving-associated regions including the superior, middle and orbito frontal gyri, anterior cingulate, primary/supplementary motor areas, basal ganglia, and cerebellum. Overall, results suggest that alcohol (especially at high doses) causes significant impairment of both driving behavior and brain functionality related to motor planning and control, goal directedness, error monitoring, and memory.

Effects of alcohol on performance on a distraction task during simulated driving.

BACKGROUND: Prior studies report that accidents involving intoxicated drivers are more likely to occur during performance of secondary tasks. We studied this phenomenon, using a dual-task paradigm, involving performance of a visual oddball (VO) task while driving in an alcohol challenge paradigm. Previous functional MRI (fMRI) studies of the VO task have shown activation in the anterior cingulate, hippocampus, and prefrontal cortex. Thus, we predicted dose-dependent decreases in activation of these areas during VO performance. METHODS: Forty healthy social drinkers were administered 3 different doses of alcohol, individually tailored to their gender and weight. Participants performed a VO task while operating a virtual reality driving simulator in a 3T fMRI scanner. RESULTS: Analysis showed a dose-dependent linear decrease in Blood Oxygen Level Dependent activation during task performance, primarily in hippocampus, anterior cingulate, and dorsolateral prefrontal areas, with the least activation occurring during the high dose. Behavioral analysis showed a dose-dependent linear increase in reaction time, with no effects associated with either correct hits or false alarms. In all dose conditions, driving speed decreased significantly after a VO stimulus. However, at the high dose this decrease was significantly less. Passenger-side line crossings significantly increased at the high dose. CONCLUSIONS: These results suggest that driving impairment during secondary task performance may be associated with alcohol-related effects on the above brain regions, which are involved with attentional processing/decision-making. Drivers with high blood alcohol concentrations may be less able to orient or detect novel or sudden stimuli during driving.

Investigating the behavioral and self-report constructs of impulsivity domains using principal component analysis.

Impulsivity, often defined as a human behavior characterized by the inclination of an individual to act on urge rather than thought, with diminished regard to consequences, encompasses a range of maladaptive behaviors, which are in turn affected by distinct neural systems. Congruent with the above definition, behavioral studies have consistently shown that the underlying construct of impulsivity is multidimensional in nature. However, research to date has been inconclusive regarding the different domains or constructs that constitute this behavior. In addition there is also no clear consensus as to whether self-report and laboratory based measures of impulsivity measure the same or different domains. This study aimed to: (i) characterize the underlying multidimensional construct of impulsivity using a sample with varying degrees of putative impulsivity related to substance misuse, including subjects who were at-risk of substance use or addicted (ARA), and (ii) assess relationships between self-report and laboratory measures of impulsivity, using a principal component-based factor analysis. In addition, our supplementary goal was to evaluate the structural constructs of impulsivity within each group separately (healthy and ARA). We used five self-report measures (Behavioral Inhibition System/Behavioral Activation System, Barratt Impulsivity Scale-11, Padua Inventory, Zuckerman Sensation Seeking Scale, and Sensitivity to Punishment and Sensitivity to Reward Questionnaire) and two computer-based laboratory tasks (Balloon Analog Risk Task and the Experiential Discounting Task) to measure the aspects of impulsivity in a total of 176 adult subjects. Subjects included healthy controls (n = 89), nonalcoholic subjects with family histories of alcoholism (family history positive; n = 36) and both former (n = 20) and current (n = 31) cocaine users. Subjects with a family history of alcoholism and cocaine abusers were grouped together as 'at-risk/addicted' (ARA) to evaluate our supplementary goal. Our overall results revealed the multidimensional nature of the impulsivity construct as captured optimally through a five-factor solution that accounted for nearly 70% of the total variance. The five factors/components were imputed as follows 'Self-Reported Behavioral Activation', 'Self-Reported Compulsivity and Reward/Punishment', 'Self-Reported Impulsivity', 'Behavioral Temporal Discounting', and 'Behavioral Risk-Taking'. We also found that contrary to previously published reports, there was significant overlap between certain laboratory and self-report measures, indicating that they might be measuring the same impulsivity domain. In addition, our supplemental analysis also suggested that the impulsivity constructs were largely, but not entirely the same within the healthy and ARA groups.

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.

Diffusion tensor imaging in schizophrenia: relationship to symptoms.

In this diffusion tensor imaging (DTI) study, the authors investigated white matter integrity in schizophrenia and the relationships between white matter alterations and specific symptoms of the disorder. We compared DTI images of 25 schizophrenia patients and 25 matched healthy controls and performed voxel-wise correlational analyses using the patient's DTI data and their severity scores of positive and negative symptoms. We found diffuse deficits in multiple types of white matter tracts in schizophrenia, and an inverse relationship of DTI fractional anisotropy (FA) values with positive symptom scores in association fibers, supporting a "disconnection" hypothesis of positive symptoms in schizophrenia.

A large scale (N=400) investigation of gray matter differences in schizophrenia using optimized voxel-based morphometry.

BACKGROUND: Many studies have employed voxel-based morphometry (VBM) of MRI images as an automated method of investigating cortical gray matter differences in schizophrenia. However, results from these studies vary widely, likely due to different methodological or statistical approaches. OBJECTIVE: To use VBM to investigate gray matter differences in schizophrenia in a sample significantly larger than any published to date, and to increase statistical power sufficiently to reveal differences missed in smaller analyses. METHODS: Magnetic resonance whole brain images were acquired from four geographic sites, all using the same model 1.5T scanner and software version, and combined to form a sample of 200 patients with both first episode and chronic schizophrenia and 200 healthy controls, matched for age, gender and scanner location. Gray matter concentration was assessed and compared using optimized VBM. RESULTS: Compared to healthy controls, schizophrenia patients showed significantly less gray matter concentration in multiple cortical and subcortical regions, some previously unreported. Overall, we found lower concentrations of gray matter in regions identified in prior studies, most of which reported only subsets of the affected areas. CONCLUSIONS: Gray matter differences in schizophrenia are most comprehensively elucidated using a large, diverse and representative sample.

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.