Differential expression of haptoglobin in individuals at clinical high risk of psychosis and its association with global functioning and clinical symptoms

BACKGROUND

Immune dysregulation has been observed in patients with schizophrenia or first-episode psychosis, but few have examined dysregulation in those at clinical high-risk (CHR) for psychosis. The aim of this study was to examine whether the peripheral blood-based proteome was dysregulated in those with CHR. Secondly, we examined whether baseline dysregulation was related to current and future functioning and clinical symptoms.

METHODS

We used data from participants of the North American Prodromal Longitudinal Studies (NAPLS) 2 and 3 (n = 715) who provided blood samples (Unaffected Comparison subjects (UC) n = 223 and CHR n = 483). Baseline proteomic data was quantified from plasma samples using mass spectrometry. Differential expression was examined between CHR and UC using logistic regression. Psychosocial functioning was measured using the Global Assessment of Functioning scale (GAF). Symptoms were measured using the subscale scores from the Scale of Psychosis-risk Symptoms; positive, negative, general, and disorganised. Three measures of each outcome were included: baseline, longest available follow-up (last follow-up) and most severe follow-up (MSF). Associations between the proteomic data, GAF and symptoms were assessed using ordinal regression.

RESULTS

Of the 99 proteins quantified, six were differentially expressed between UC and CHR. However, only haptoglobin (HP) survived FDR-correction (OR:1.45, 95 %CI:1.23-1.69, padj = <0.001). HP was cross-sectionally and longitudinally associated with functioning and symptoms such that higher HP values were associated with poorer functioning and more severe symptoms. Results were evident after stringent adjustment and poorer functioning was observed in both NAPLS cohort separately.

CONCLUSION

We demonstrate that elevated HP is robustly observed in those at CHR for psychosis, irrespective of transition to psychosis. HP is longitudinally associated with poorer functioning and greater symptom severity. These results agree with previous reports of increased HP gene expression in individuals at-risk for psychosis and with the dysfunction of the acute phase inflammatory response seen in psychotic disorders.

Validation of a suite of ERP and QEEG biomarkers in a pre-competitive, industry-led study in subjects with schizophrenia and healthy volunteers

OBJECTIVE

Complexity and lack of standardization have mostly limited the use of event-related potentials (ERPs) and quantitative EEG (QEEG) biomarkers in drug development to small early phase trials. We present results from a clinical study on healthy volunteers (HV) and patients with schizophrenia (SZ) that assessed test-retest, group differences, variance, and correlation with functional assessments for ERP and QEEG measures collected at clinical and commercial trial sites with standardized instrumentation and methods, and analyzed through an automated data analysis pipeline.

METHODS

81 HV and 80 SZ were tested at one of four study sites. Subjects were administered two ERP/EEG testing sessions on separate visits. Sessions included a mismatch negativity paradigm, a 40 Hz auditory steady-state response paradigm, an eyes-closed resting state EEG, and an active auditory oddball paradigm. SZ subjects were also tested on the Brief Assessment of Cognition (BAC), Positive and Negative Syndrome Scale (PANSS), and Virtual Reality Functional Capacity Assessment Tool (VRFCAT).

RESULTS

Standardized ERP/EEG instrumentation and methods ensured few test failures. The automated data analysis pipeline allowed for near real-time analysis with no human intervention. Test-retest reliability was fair-to-excellent for most of the outcome measures. SZ subjects showed significant deficits in ERP and QEEG measures consistent with published academic literature. A subset of ERP and QEEG measures correlated with functional assessments administered to the SZ subjects.

CONCLUSIONS

With standardized instrumentation and methods, complex ERP/EEG testing sessions can be reliably performed at clinical and commercial trial sites to produce high-quality data in near real-time.

The association between area-level residential instability and gray matter volume changes

Introduction

Area-level residential instability (ARI), an index of social fragmentation, has been shown to explain the association between urbanicity and psychosis. Urban upbringing has been shown to be associated with decreased gray matter volumes (GMV)s of brain regions corresponding to the right caudal middle frontal gyrus (CMFG) and rostral anterior cingulate cortex (rACC).

Objectives

We hypothesize that greater ARI will be associated with reduced right posterior CMFG and rACC GMVs.

Methods

Data were collected at baseline as part of the North American Prodrome Longitudinal Study. Counties where participants resided during childhood were geographically coded using the US Censuses to area-level factors. ARI was defined as the percentage of residents living in a different house five years ago. Generalized linear mixed models tested associations between ARI and GMVs.

Results

This study included 29 HC and 64 CHR-P individuals who were aged 12 to 24 years, had remained in their baseline residential area, and had magnetic resonance imaging scans. ARI was associated with reduced right CMFG (adjusted β = -0.258; 95% CI = -0.502 – -0.015) and right rACC volumes (adjusted β = -0.318; 95% CI = -0.612 – -0.023). The interaction terms (ARI X diagnostic group) in the prediction of both brain regions were not significant, indicating that the relationships between ARI and regional brain volumes held for both CHR-P and HCs.

Conclusions

Like urban upbringing, ARI may be an important social environmental characteristic that adversely impacts brain regions related to schizophrenia.

Disclosure

No significant relationships.

Tri-Clustering Dynamic Functional Network Connectivity Identifies Significant Schizophrenia Effects Across Multiple States in Distinct Subgroups of Individuals

Background: Brain imaging data collected from individuals are highly complex with unique variation; however, such variation is typically ignored in approaches that focus on group averages or even supervised prediction. State-of-the-art methods for analyzing dynamic functional network connectivity (dFNC) subdivide the entire time course into several (possibly overlapping) connectivity states (i.e., sliding window clusters). However, such an approach does not factor in the homogeneity of underlying data and may result in a less meaningful subgrouping of the data set. Methods: Dynamic-N-way tri-clustering (dNTiC) incorporates a homogeneity benchmark to approximate clusters that provide a more "apples-to-apples" comparison between groups within analogous subsets of time-space and subjects. dNTiC sorts the dFNC states by maximizing similarity across individuals and minimizing variance among the pairs of components within a state. Results: Resulting tri-clusters show significant differences between schizophrenia (SZ) and healthy control (HC) in distinct brain regions. Compared with HC subjects, SZ show hypoconnectivity (low positive) among subcortical, default mode, cognitive control, but hyperconnectivity (high positive) between sensory networks in most tri-clusters. In tri-cluster 3, HC subjects show significantly stronger connectivity among sensory networks and anticorrelation between subcortical and sensory networks than SZ. Results also provide a statistically significant difference in SZ and HC subject's reoccurrence time for two distinct dFNC states. Conclusions: Outcomes emphasize the utility of the proposed method for characterizing and leveraging variance within high-dimensional data to enhance the interpretability and sensitivity of measurements in studying a heterogeneous disorder such as SZ and unconstrained experimental conditions as resting functional magnetic resonance imaging. Impact statement The current methods for analyzing dynamic functional network connectivity (dFNC) run k-means on a collection of dFNC windows, and each window includes all the pairs of independent component analysis networks. As such, it depicts a short-time connectivity pattern of the entire brain, and the k-means clusters fixed-length signatures that have an extent throughout the neural system. Consequently, there is a chance of missing connectivity signatures that span across a smaller subset of pairs. Dynamic-N-way tri-clustering further sorts the dFNC states by maximizing similarity across individuals, minimizing variance among the pairs of components within a state, and reporting more complex and transient patterns.

048 Increased Cognitive Load Under Stress Modulates Sleep Spindles and Slow Oscillations in a Sleep-Stage Dependent Manner

Introduction

The effect of increased cognitive load especially under duress has been known to affect brain rhythms in humans. However, this effect has been shown primarily in the awake brain; the effect of stressful cognitive load on sleep rhythms is yet unclear. We leveraged a unique opportunity to understand the effect of cognitive load under laboratory stress on sleep spindles and slow oscillations that are hallmark rhythms of NREM sleep.

Methods

Cortical 6-channel EEG nap data were collected from 45 subjects over two separate days: after a control session without laboratory stressors and after an experimental session in which they underwent fear condiitoning and negative-emotional-image viewing sessions. We detected sleep spindles (11-13Hz over frontal regions and 13-16Hz over centroposterior regions) and slow oscillations (0.16–1.25Hz oscillations) as discrete events at each of the six electrodes, and staged them by the sleep hypnogram. We evaluated the spindle rate in N2 sleep and the proportion of slow oscillations nested with a spindle in N3 sleep.

Results

Over all 6 EEG electrodes, N2 spindle rates increased on average by 14% in the experimental session compared to the control session (mixed-effect models p&lt;0.001). In addition, over all 6 electrodes, the proportion of slow oscillations in N3 nested with a spindle increased by 2.3% in the experimental session compared to the control session (mixed effect model, p=0.005).

Conclusion

We show for the first time how increased cognitive load under stressful laboratory conditions affects sleep rhythms. Such an increased response in sleep might correspond to a continued emotional response due to the cognitive load under duress. Ongoing work seeks to tie these findings to possible emotional memory consolidation.

Support (if any)

VA Career Development Award to Dr. Richards (5IK2CX000871-05)

Brain Density Clustering Analysis: A New Approach to Brain Functional Dynamics

BACKGROUND

A number of studies in recent years have explored whole-brain dynamic connectivity using pairwise approaches. There has been less focus on trying to analyze brain dynamics in higher dimensions over time.

METHODS

We introduce a new approach that analyzes time series trajectories to identify high traffic nodes in a high dimensional space. First, functional magnetic resonance imaging (fMRI) data are decomposed using spatial ICA to a set of maps and their associated time series. Next, density is calculated for each time point and high-density points are clustered to identify a small set of high traffic nodes. We validated our method using simulations and then implemented it on a real data set.

RESULTS

We present a novel approach that captures dynamics within a high dimensional space and also does not use any windowing in contrast to many existing approaches. The approach enables one to characterize and study the time series in a potentially high dimensional space, rather than looking at each component pair separately. Our results show that schizophrenia patients have a lower dynamism compared to healthy controls. In addition, we find patients spend more time in nodes associated with the default mode network and less time in components strongly correlated with auditory and sensorimotor regions. Interestingly, we also found that subjects oscillate between state pairs that show opposite spatial maps, suggesting an oscillatory pattern.

CONCLUSION

Our proposed method provides a novel approach to analyze the data in its native high dimensional space and can possibly provide new information that is undetectable using other methods.

Increased global cognition correlates with increased thalamo-temporal connectivity in response to targeted cognitive training for recent onset schizophrenia

Patients with schizophrenia exhibit disrupted thalamocortical connections that relate to aspects of symptoms and deficits in cognition. Targeted cognitive training (TCT) of the auditory system in schizophrenia has been shown to improve cognition, but its impact on thalamocortical connectivity is not known. Here we examined thalamocortical connections that may be neuroplastic in response to TCT using a region of interest (ROI) approach. Participants were randomly assigned to either 40 h of TCT (N = 24) or an active control condition (CG; N = 20). Participants underwent resting state fMRI and cognitive testing both before and after training. Changes in thalamocortical connectivity were measured in 15 ROIs derived from a previous study comparing a large sample of schizophrenia subjects with healthy controls. A significant group by time interaction was observed in a left superior temporal ROI which was previously found to exhibit thalamocortical hyper-connectivity in patients with schizophrenia. Changes in this ROI reflected thalamic connectivity increases in the TCT group, while the CG group showed decreases. Additionally, the relationship between connectivity change and change in global cognition showed a slope difference between groups, with increases in thalamo-temporal connectivity correlating with improvements in global cognition in TCT. No significant relationships were observed with changes in clinical symptoms or functioning. These findings demonstrate that TCT may influence intrinsic functional connections in young individuals with schizophrenia, such that improvements in cognition correspond to compensatory increases in connectivity in a temporal region previously shown to exhibit thalamic hyper-connectivity.

Weighted average of shared trajectory: A new estimator for dynamic functional connectivity efficiently estimates both rapid and slow changes over time

BACKGROUND

Dynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC.

NEW METHOD

Here we first calculate the gradients of each time series pair and use the magnitude of these gradients to calculate weighted average of shared trajectory (WAST) as a new estimator for dFNC.

RESULTS

Using WAST to compare healthy control and schizophrenia patients using a large dataset, we show disconnectivity between different regions associated with schizophrenia. In addition, WAST results reveals patients with schizophrenia stay longer in a connectivity state with negative connectivity between motor and sensory regions than do healthy controls.

COMPARISON WITH EXISTING METHODS

We compare WAST with SWPC and multiplication of temporal derivatives (MTD) using different simulation scenarios. We show that WAST enables us to detect very rapid changes in dFNC (undetected by SWPC) while MTD performance is generally lower.

CONCLUSIONS

As large window sizes are unable to detect short states, using shorter window size is desirable if the estimator is robust enough. We provide evidence that WAST requires fewer samples (compared to SWPC) to reach a robust estimation. As a result, we were able to identify rapidly varying dFNC patterns undetected by SWPC while still being able to robustly estimate slower dFNC patterns.

Autoconnectivity: A new perspective on human brain function

BACKGROUND

Autocorrelation (AC) in fMRI time-series is a well-known phenomenon, typically attributed to colored noise and therefore removed from the data. We hypothesize that AC reflects systematic and meaningful signal fluctuations that may be tied to neural activity and provide evidence to support this hypothesis.

NEW METHOD

Each fMRI time-series is modeled as an autoregressive process from which the autocorrelation is quantified. Then, autocorrelation during resting-state fMRI and auditory oddball (AOD) task in schizophrenia and healthy volunteers is examined.

RESULTS

During resting-state, AC was higher in the visual cortex while during AOD task, frontal part of the brain exhibited higher AC in both groups. AC values were significantly lower in specific brain regions in schizophrenia patients (such as thalamus during resting-state) compared to healthy controls in two independent datasets. Moreover, AC values had significant negative correlation with patients' symptoms. AC differences discriminated patients from healthy controls with high accuracy (resting-state).

COMPARISON WITH EXISTING METHODS

Contrary to most prior works, the results suggest AC shows meaningful patterns that are discriminative between patients and controls. Our results are in line with recent works attributing autocorrelation to feedback loop of brain's regulatory circuit.

CONCLUSIONS

Autoconnectivity is cognitive state dependent (resting-state vs. task) and mental state dependent (healthy vs. schizophrenia). The concept of autoconnectivity resembles a recurrent neural network and provides a new perspective of functional integration in the brain. These findings may have important implications for understanding of brain function in health and disease as well as for analysis of fMRI time-series.

N-BiC: A Method for Multi-Component and Symptom Biclustering of Structural MRI Data: Application to Schizophrenia

OBJECTIVE

We propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia.

METHODS

It uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa.

RESULTS

Findings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively.

CONCLUSION

N-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications.

SIGNIFICANCE

The use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.

Changes in symptom content from a clinical high-risk state to conversion to psychosis

AIM

There is an interest in the transition to psychosis for those at clinical high risk of developing psychosis. This transition is typically determined by a change in severity of the attenuated symptoms as they reach a psychotic level. However, any concomitant change in the content of such symptoms has not been examined. The current study aimed to examine potential qualitative changes in the symptom content from a clinical high-risk state to a first episode of psychosis.

METHODS

Sixty-seven individuals, who had been identified as meeting the attenuated psychotic syndrome based on the Structured Interview of Psychosis-Risk Syndromes and who later developed a full-blown psychosis were included in the study. Comprehensive clinical vignettes were written and raters were trained using the Content of Attenuated Psychotic Symptoms codebook to code for the presence of specific symptom content found within the attenuated psychotic symptoms of unusual thought content, suspicious ideas, grandiose ideas and perceptual abnormalities.

RESULTS

Two main changes in symptom content from baseline to conversion were observed. First, content that was vague and lacked intensity progressed to being more specific, concrete and severe. Second, new symptoms appeared whose onset occurred for the first time at conversion.

CONCLUSION

A change in symptom content should be monitored by clinicians, as changes in content may be indications of a possible transition to psychosis.

Perceptual abnormalities in clinical high risk youth and the role of trauma, cannabis use and anxiety

Recent research suggests that perceptual abnormalities are a group of diverse experiences, which have been associated with trauma, cannabis use, and anxiety. Of the attenuated psychotic symptoms that are present in youth at clinical high risk (CHR) of psychosis, perceptual abnormalities tend to be one of the most frequently endorsed symptoms. However, very few studies have explored perceptual abnormalities and their relationships with the above environmental and affective factors in a CHR sample. Four hundred and forty-one CHR individuals who met criteria for attenuated psychotic symptom syndrome (APSS) determined by the Structured Interview for Psychosis-risk Syndromes (SIPS) were assessed on the content of their perceptual abnormalities, early traumatic experience, cannabis use and self-reported anxiety. Logistic regression analyses suggested that both simple auditory and simple visual perceptual abnormalities were more likely to be reported by CHR who had early traumatic experiences, who are current cannabis users, and who have higher levels of anxiety. Multiple regression analysis revealed that only trauma and anxiety were independent predictors of both simple auditory and simple visual perceptual abnormalities. It is possible that examining subtypes of perceptual abnormalities in CHR leads to an improved understanding of the prevalence of such symptoms.

Go/No Go task performance predicts cortical thickness in the caudal inferior frontal gyrus in young adults with and without ADHD

Response inhibition deficits are widely believed to be at the core of Attention-Deficit Hyperactivity Disorder (ADHD). Several studies have examined neural architectural correlates of ADHD, but research directly examining structural correlates of response inhibition is lacking. Here we examine the relationship between response inhibition as measured by a Go/No Go task, and cortical surface area and thickness of the caudal inferior frontal gyrus (cIFG), a region implicated in functional imaging studies of response inhibition, in a sample of 114 young adults with and without ADHD diagnosed initially during childhood. We used multiple linear regression models to test the hypothesis that Go/No Go performance would be associated with cIFG surface area or thickness. Results showed that poorer Go/No Go performance was associated with thicker cIFG cortex, and this effect was not mediated by ADHD status or history of substance use. However, independent of Go/No Go performance, persistence of ADHD symptoms and more frequent cannabis use were associated with thinner cIFG. Go/No Go performance was not associated with cortical surface area. The association between poor inhibitory functioning and thicker cIFG suggests that maturation of this region may differ in low performing participants. An independent association of persistent ADHD symptoms and frequent cannabis use with thinner cIFG cortex suggests that distinct neural mechanisms within this region may play a role in inhibitory function, broader ADHD symptomatology, and cannabis use. These results contribute to Research Domain Criteria (RDoC) by revealing novel associations between neural architectural phenotypes and basic neurobehavioral processes measured dimensionally.

Neural mechanisms of mood-induced modulation of reality monitoring in schizophrenia

Reality monitoring is the ability to accurately distinguish the source of self-generated information from externally-presented information. Although people with schizophrenia (SZ) show impaired reality monitoring, nothing is known about how mood state influences this higher-order cognitive process. Accordingly, we induced positive, neutral and negative mood states to test how different mood states modulate subsequent reality monitoring performance. Our findings indicate that mood affected reality monitoring performance in HC and SZ participants in both similar and dissociable ways. Only a positive mood facilitated task performance in Healthy Control (HC) subjects, whereas a negative mood facilitated task performance in SZ subjects. Yet, when both HC and SZ participants were in a positive mood, they recruited medial prefrontal cortex (mPFC) to bias better subsequent self-generated item identification, despite the fact that mPFC signal was reduced in SZ participants. Additionally, in SZ subjects, negative mood states also modulated left and right dorsal mPFC signal to bias better externally-presented item identification. Together our findings reveal that although the mPFC is hypoactive in SZ participants, mPFC signal plays a functional role in mood-cognition interactions during both positive and negative mood states to facilitate subsequent reality monitoring decision-making.

Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia

Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development.

The Violent Content in Attenuated Psychotic Symptoms

The relationship between psychosis and violence has typically focused on factors likely to predict who will commit violent acts. One unexplored area is violence in the content of subthreshold positive symptoms. The current aim was to conduct an exploratory analysis of violent content in the attenuated psychotic symptoms (APS) of those at clinical high risk of psychosis (CHR) who met criteria for attenuated psychotic symptom syndrome (APSS). The APS of 442 CHR individuals, determined by the Structured Interview for Prodromal Syndromes, were described in comprehensive vignettes. The content of these symptoms were coded using the Content of Attenuated Positive Symptoms Codebook. Other measures included clinical symptoms, functioning, beliefs and trauma. Individuals with violent content had significantly higher APS, greater negative beliefs about the self and others, and increased bullying. The same findings and higher ratings on anxiety symptoms were present when participants with self-directed violence were compared to participants with no violent content. Individuals reporting violent content differ in their clinical presentation compared to those who do not experience violent content. Adverse life events, like bullying, may impact the presence of violent content in APS symptoms. Future studies should explore violent content in relation to actual behavior.

Traumatic brain injury in individuals at clinical high risk for psychosis

BACKGROUND

Recent research suggests that a traumatic brain injury (TBI) can significantly increase the risk of later development of psychosis. However, it is unknown whether people at clinical high risk (CHR) of psychosis have experienced TBI at higher rates, compared to otherwise healthy individuals. This study evaluated the prevalence of mild TBI, whether it was related to past trauma and the relationship of mild TBI to later transition to psychosis.

METHODS

Seven-hundred forty-seven CHR and 278 healthy controls (HC) were assessed on past history of mild TBI, age at first and last injury, severity of worst injury and number of injuries using the Traumatic Brain Injury Interview. Attenuated psychotic symptoms were assessed with the Scale of Psychosis-risk Symptoms. IQ was estimated using the Wechsler Abbreviated Scale of Intelligence and past trauma and bullying were recorded using the Childhood Trauma and Abuse Scale.

RESULTS

CHR participants experienced a mild TBI more often than the HC group. CHR participants who had experienced a mild TBI reported greater total trauma and bullying scores than those who had not, and those who experienced a mild TBI and later made the transition to psychosis were significantly younger at the age at first and most recent injury than those who did not.

CONCLUSION

A history of mild TBI is more frequently observed in CHR individuals than in HC. Inclusion or study of CHR youth with more severe TBI may provide additional insights on the relationship between TBI and later transition to psychosis in CHR individuals.

Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients

Resting-state functional brain imaging studies of network connectivity have long assumed that functional connections are stationary on the timescale of a typical scan. Interest in moving beyond this simplifying assumption has emerged only recently. The great hope is that training the right lens on time-varying properties of whole-brain network connectivity will shed additional light on previously concealed brain activation patterns characteristic of serious neurological or psychiatric disorders. We present evidence that multiple explicitly dynamical properties of time-varying whole-brain network connectivity are strongly associated with schizophrenia, a complex mental illness whose symptomatic presentation can vary enormously across subjects. As with so much brain-imaging research, a central challenge for dynamic network connectivity lies in determining transformations of the data that both reduce its dimensionality and expose features that are strongly predictive of important population characteristics. Our paper introduces an elegant, simple method of reducing and organizing data around which a large constellation of mutually informative and intuitive dynamical analyses can be performed. This framework combines a discrete multidimensional data-driven representation of connectivity space with four core dynamism measures computed from large-scale properties of each subject’s trajectory, ie., properties not identifiable with any specific moment in time and therefore reasonable to employ in settings lacking inter-subject time-alignment, such as resting-state functional imaging studies. Our analysis exposes pronounced differences between schizophrenia patients (N_sz = 151) and healthy controls (N_hc = 163). Time-varying whole-brain network connectivity patterns are found to be markedly less dynamically active in schizophrenia patients, an effect that is even more pronounced in patients with high levels of hallucinatory behavior. To the best of our knowledge this is the first demonstration that high-level dynamic properties of whole-brain connectivity, generic enough to be commensurable under many decompositions of time-varying connectivity data, exhibit robust and systematic differences between schizophrenia patients and healthy controls.

Patterns of Gray Matter Abnormalities in Schizophrenia Based on an International Mega-analysis

Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both source-based morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects.

Δ9-THC Disrupts Gamma (γ)-Band Neural Oscillations in Humans

Gamma (γ)-band oscillations play a key role in perception, associative learning, and conscious awareness and have been shown to be disrupted by cannabinoids in animal studies. The goal of this study was to determine whether cannabinoids disrupt γ-oscillations in humans and whether these effects relate to their psychosis-relevant behavioral effects. The acute, dose-related effects of Δ-9-tetrahydrocannabinol (Δ(9)-THC) on the auditory steady-state response (ASSR) were studied in humans (n=20) who completed 3 test days during which they received intravenous Δ(9)-THC (placebo, 0.015, and 0.03 mg/kg) in a double-blind, randomized, crossover, and counterbalanced design. Electroencephalography (EEG) was recorded while subjects listened to auditory click trains presented at 20, 30, and 40 Hz. Psychosis-relevant effects were measured with the Positive and Negative Syndrome scale (PANSS). Δ(9)-THC (0.03 mg/kg) reduced intertrial coherence (ITC) in the 40 Hz condition compared with 0.015 mg/kg and placebo. No significant effects were detected for 30 and 20 Hz stimulation. Furthermore, there was a negative correlation between 40 Hz ITC and PANSS subscales and total scores under the influence of Δ(9)-THC. Δ(9)-THC (0.03 mg/kg) reduced evoked power during 40 Hz stimulation at a trend level. Recent users of cannabis showed blunted Δ(9)-THC effects on ITC and evoked power. We show for the first time in humans that cannabinoids disrupt γ-band neural oscillations. Furthermore, there is a relationship between disruption of γ-band neural oscillations and psychosis-relevant phenomena induced by cannabinoids. These findings add to a growing literature suggesting some overlap between the acute effects of cannabinoids and the behavioral and psychophysiological alterations observed in psychotic disorders.

Substance use in individuals at clinical high risk of psychosis

BACKGROUND

A series of research reports has indicated that the use of substances such as cannabis, alcohol and tobacco are higher in youth at clinical high risk (CHR) of developing psychosis than in controls. Little is known about the longitudinal trajectory of substance use, and findings on the relationship between substance use and later transition to psychosis in CHR individuals are mixed.

METHOD

At baseline and 6- and 12-month follow-ups, 735 CHR and 278 control participants completed the Alcohol and Drug Use Scale and a cannabis use questionnaire. The longitudinal trajectory of substance use was evaluated with linear mixed models.

RESULTS

CHR participants endorsed significantly higher cannabis and tobacco use severity, and lower alcohol use severity, at baseline and over a 1-year period compared with controls. CHR youth had higher lifetime prevalence and frequency of cannabis, and were significantly younger upon first use, and were more likely to use alone and during the day. Baseline substance use did not differentiate participants who later transitioned to psychosis (n = 90) from those who did not transition (n = 272). Controls had lower tobacco use than CHR participants with a prodromal progression clinical outcome and lower cannabis use than those with a psychotic clinical outcome at the 2-year assessment.

CONCLUSIONS

In CHR individuals cannabis and tobacco use is higher than in controls and this pattern persists across 1 year. Evaluation of clinical outcome may provide additional information on the longitudinal impact of substance use that cannot be detected through evaluation of transition/non-transition to psychosis alone.

A genome-wide association study of schizophrenia using brain activation as a quantitative phenotype

BACKGROUND

Genome-wide association studies (GWASs) are increasingly used to identify risk genes for complex illnesses including schizophrenia. These studies may require thousands of subjects to obtain sufficient power. We present an alternative strategy with increased statistical power over a case-control study that uses brain imaging as a quantitative trait (QT) in the context of a GWAS in schizophrenia.

METHODS

Sixty-four subjects with chronic schizophrenia and 74 matched controls were recruited from the Functional Biomedical Informatics Research Network (FBIRN) consortium. Subjects were genotyped using the Illumina HumanHap300 BeadArray and were scanned while performing a Sternberg Item Recognition Paradigm in which they learned and then recognized target sets of digits in an functional magnetic resonance imaging protocol. The QT was the mean blood oxygen level-dependent signal in the dorsolateral prefrontal cortex during the probe condition for a memory load of 3 items.

RESULTS

Three genes or chromosomal regions were identified by having 2 single-nucleotide polymorphisms (SNPs) each significant at P < 10(-6) for the interaction between the imaging QT and the diagnosis (ROBO1-ROBO2, TNIK, and CTXN3-SLC12A2). Three other genes had a significant SNP at <10(-6) (POU3F2, TRAF, and GPC1). Together, these 6 genes/regions identified pathways involved in neurodevelopment and response to stress.

CONCLUSION

Combining imaging and genetic data from a GWAS identified genes related to forebrain development and stress response, already implicated in schizophrenic dysfunction, as affecting prefrontal efficiency. Although the identified genes require confirmation in an independent sample, our approach is a screening method over the whole genome to identify novel SNPs related to risk for schizophrenia.

Brain-performance correlates of working memory retrieval in schizophrenia: a cognitive modeling approach

Correlations of cognitive functioning with brain activation during a sternberg item recognition paradigm (SIRP) were investigated in patients with schizophrenia and in healthy controls studied at 8 sites. To measure memory scanning times, 4 response time models were fit to SIRP data. The best fitting model assumed exhaustive serial memory scanning followed by self-terminating memory search and involved one intercept parameter to represent SIRP processes not contributing directly to memory scanning. Patients displayed significantly longer response times with increasing memory load and differed on the memory scanning, memory search, and intercept parameters of the best fitting probability model. Groups differed in the correlation between the memory scanning parameter and linear brain response to increasing memory load within left inferior and left middle frontal gyrus, bilateral caudate, and right precuneus. The pattern of findings in these regions indicated that high scanning capacity was associated with high neural capacity among healthy subjects but that scanning speed was uncoupled from brain response to increasing memory load among schizophrenia patients. Group differences in correlation of the best fitting model's scanning parameter with a quadratic trend in brain response to increasing memory load suggested inefficient or disordered patterns of neural inhibition among individuals with schizophrenia, especially in the left perirhinal and entorhinal cortices. The results show at both cognitive and neural levels that disordered memory scanning contributes to deficient SIRP performance among schizophrenia patients.

NMDA receptor antagonist effects, cortical glutamatergic function, and schizophrenia: toward a paradigm shift in medication development

There is an urgent need to improve the pharmacotherapy of schizophrenia despite the introduction of important new medications. New treatment insights may come from appreciating the therapeutic implications of model psychoses. In particular, basic and clinical studies have employed the N-methyl-D-aspartate (NMDA) glutamate receptor antagonist, ketamine, as a probe of NMDA receptor contributions to cognition and behavior. These studies illustrate a translational neuroscience approach for probing mechanistic hypotheses related to the neurobiology and treatment of schizophrenia and other disorders. Two particular pathophysiologic themes associated with schizophrenia, the disturbance of cortical connectivity and the disinhibition of glutamatergic activity may be modeled by the administration of NMDA receptor antagonists. The purpose of this review is to consider the possibility that agents that attenuate these two components of NMDA receptor antagonist response may play complementary roles in the treatment of schizophrenia.

A profile of cortical gray matter volume deficits characteristic of schizophrenia

Quantitative magnetic resonance imaging (MRI) studies from our laboratory have reported that patients with schizophrenia show a widespread cortical gray matter volume deficit, which is especially pronounced in the prefrontal and anterior superior temporal cortices. The present study compared two separate samples of schizophrenic patients -- 71 men from a Veterans Administration (VA) hospital and a sample of 57 severely ill men from a state hospital (SH) -- in an effort to test whether the pattern of brain volume abnormalities previously observed in VA schizophrenic patients can be generalized to other groups of schizophrenic patients. MRI-derived brain volumes of gray matter, white matter and sulcal cerebrospinal fluid (CSF) in six cortical regions, and CSF in the lateral and third ventricles were computed. All MRI volumes were adjusted for normal variation in head size and age and were expressed as standardized Z-scores, which also permitted structures of different sizes to be compared directly. The two schizophrenic groups displayed similar patterns of volume abnormalities: cortical gray matter but not white matter volume deficits that were widespread but especially notable in the prefrontal and temporal regions. The regional gray matter deficits in the SH group were generally greater than those in the VA group, particularly in the prefrontal and posterior superior temporal regions. Both schizophrenic groups had abnormally large volumes of the cortical sulci and lateral and third ventricles; however, the SH group showed greater enlargements, the most prominent occurring in the ventricles and temporal sulci. The overlapping patterns of cortical gray matter deficits in the two groups provide evidence for generality of this pattern of regional brain volume abnormalities in schizophrenia.

Event-related potentials in alcoholic men: P3 amplitude reflects family history but not alcohol consumption

Multilead event-related potentials (ERPs), elicited by auditory and visual stimuli requiring a button press response and by a startling noise requiring no response, were recorded from male alcoholics and age-matched male controls (26-60 years old). Single-trial analyses of blink responses to the startling stimuli indicated that alcoholics startle less frequently but with equivalent amplitude as the controls. In contrast, single-trial analyses of P3 indicated that alcoholics generate a P3 as often as controls, but that their individual P3s are smaller. Alcoholics who reported a positive family history of problem drinking had larger startle blink amplitudes and smaller auditory and visual P3s than did alcoholics who reported a negative family history. Hierarchical regression analysis was used to demonstrate that smaller P3s in family history positive alcoholics were independent of lifetime alcohol consumption.