Pituitary volume in first-episode schizophrenia

Pituitary volumes were measured in 55 first-episode schizophrenia patients at a baseline timepoint with 38 receiving a followup scan after antipsychotic treatment. Fifty-nine healthy volunteers had baseline scans with 34 receiving a followup scan. There were no baseline group differences in pituitary volumes or changes in volume following antipsychotic treatment.

Magnetic resonance imaging predictors of treatment response in first-episode schizophrenia

Identifying neurobiological predictors of response to antipsychotics in patients with schizophrenia is a critical goal of translational psychiatry. Few studies, however, have investigated the relationship between indices of brain structure and treatment response in the context of a controlled clinical trial. In this study, we sought to identify magnetic resonance (MR) imaging measures of the brain that predict treatment response in patients experiencing a first-episode of schizophrenia. Structural MR imaging scans were acquired in 39 patients experiencing a first-episode of schizophrenia with minimal or no prior exposure to antipsychotics participating in a double-blind 16-week clinical trial comparing the efficacy of risperidone vs olanzapine. Twenty-five patients were classified as responders by meeting operationally defined treatment response criteria on 2 consecutive study visits. Fourteen patients never responded to antipsychotic medication at any point during the clinical trial. MR imaging scans were also acquired in 45 age- and sex-matched healthy volunteers. Cortical pattern matching methods were used to compare cortical thickness and asymmetry measures among groups. Statistical mapping results, confirmed by permutation testing, indicated that responders had greater cortical thickness in occipital regions and greater frontal cortical asymmetry compared with nonresponders. Moreover, among responders, greater thickness in temporal regions was associated with less time to respond. Our findings are consistent with the hypothesis that plasticity and cortical thickness may be more preserved in responders and that MR imaging may assist in the prediction of antipsychotic drug response in patients experiencing a first-episode of schizophrenia.

Effects of age on prefrontal subregions and hippocampal volumes in young and middle-aged healthy humans

There are limited data available regarding the effects of age and sex on discrete prefrontal gray and white matter volumes or posterior and anterior hippocampal volumes in healthy humans. Volumes of the superior frontal gyrus, anterior cingulate gyrus, and orbital frontal lobe were computed manually from contiguous magnetic resonance (MR) images in 83 (39M/44F) healthy humans (age range = 16-40) and segmented into gray and white matter. Volumes of the posterior and anterior hippocampal formation were also computed with reliable separation of the anterior hippocampal formation from the amygdala. There were significant age-by-tissue type interactions for the superior frontal gyrus and orbital frontal lobe such that gray matter within these regions correlated significantly and inversely with age. In contrast, no significant age effects were evident within regional white matter volumes. Analysis of hippocampal volumes indicated that men had larger volumes of the anterior, but not posterior hippocampal formation compared to women even following correction for total brain size. These data highlight age effects within discrete prefrontal cortical gray matter regions in young and middle aged healthy humans and suggest that the white matter comprising these regions may be more resistant to age effects. Furthermore, understanding the potential role of sex and age in mediating prefrontal cortical and hippocampal volumes may have strong relevance for psychiatric disorders such as schizophrenia that have implicated neurodevelopmental abnormalities within frontotemporal circuits in their pathogenesis.

Executive control: balancing stability and flexibility via the duality of evolutionary neuroanatomical trends

The concept of executive functions has a rich history and remains current despite increased use of other terms, including working memory and cognitive control. Executive functions have sometimes been equated with functions subserved by the frontal cortex, but this adds little clarity, given that we so far lack a comprehensive theory of frontal function. Pending a more complete mechanistic understanding, clinically useful generalizations can help characterize both healthy cognition and multiple varieties of cognitive impairment. This article surveys several hierarchical and autoregulatory control theories, and suggests that the evolutionary cytoarchitectonic trends theory provides a valuable neuroanatomical framework to help organize research on frontal structure-function relations. The theory suggests that paleocortical/ventrolateral and archicortical/dorsomedial trends are associated with neural network flexibility and stability respectively, which comports well with multiple other conceptual distinctions that have been proposed to characterize ventral and dorsal frontal functions, including the "initiation/inhibition," "what/where," and "classification/expectation" hypotheses.

The cognitive atlas: toward a knowledge foundation for cognitive neuroscience

Cognitive neuroscience aims to map mental processes onto brain function, which begs the question of what “mental processes” exist and how they relate to the tasks that are used to manipulate and measure them. This topic has been addressed informally in prior work, but we propose that cumulative progress in cognitive neuroscience requires a more systematic approach to representing the mental entities that are being mapped to brain function and the tasks used to manipulate and measure mental processes. We describe a new open collaborative project that aims to provide a knowledge base for cognitive neuroscience, called the Cognitive Atlas (accessible online at http://www.cognitiveatlas.org), and outline how this project has the potential to drive novel discoveries about both mind and brain.

Decoding continuous variables from neuroimaging data: basic and clinical applications

The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.

A brief cognitive assessment tool for schizophrenia: construction of a tool for clinicians

Cognitive impairment in schizophrenia is often severe, enduring, and contributes significantly to chronic disability. But clinicians have difficulty in assessing cognition due to a lack of brief instruments. We evaluated whether a brief battery of cognitive tests derived from larger batteries could generate a summary score representing global cognitive function. Using data from 3 previously published trials, we calculated the corrected item-total correlations (CITCs) or the correlation of each test with the battery total score. We computed the proportion of variance that each test shares with the global score excluding that test (R(t)(2)=CITC(2)) and the variance explained per minute of administration time for each test (R(t)(2)/min). The 3 tests with the highest R(t)(2)/min were selected for the brief battery. The composite score from the trail making test B, category fluency, and digit symbol correlated .86 with the global score of the larger battery in 2 of the studies and correlated between .73 and .82 with the total battery scores excluding these 3 tests. A Brief Cognitive Assessment Tool for Schizophrenia (B-CATS) using the above 3 tests can be administered in 10-11 min. The full batteries of the larger studies have administration times ranging from 90 to 210 min. Given prior research suggesting that a single factor of global cognition best explains the pattern of cognitive deficit in schizophrenia, an instrument like B-CATS can provide clinicians with meaningful data regarding their patients' cognitive function. It can also serve researchers who want an estimate of global cognitive function without requiring a full neuropsychological battery.

Diffusion tensor imaging reliably differentiates patients with schizophrenia from healthy volunteers

The objective of this research was to determine whether fractional anisotropy (FA) and mean diffusivity (MD) maps derived from diffusion tensor imaging (DTI) of the brain are able to reliably differentiate patients with schizophrenia from healthy volunteers. DTI and high resolution structural magnetic resonance scans were acquired in 50 patients with schizophrenia and 50 age- and sex-matched healthy volunteers. FA and MD maps were estimated from the DTI data and spatially normalized to the Montreal Neurologic Institute standard stereotactic space. Individuals were divided randomly into two groups of 50, a training set, and a test set, each comprising 25 patients and 25 healthy volunteers. A pattern classifier was designed using Fisher's linear discriminant analysis (LDA) based on the training set of images to categorize individuals in the test set as either patients or healthy volunteers. Using the FA maps, the classifier correctly identified 94% of the cases in the test set (96% sensitivity and 92% specificity). The classifier achieved 98% accuracy (96% sensitivity and 100% specificity) when using the MD maps as inputs to distinguish schizophrenia patients from healthy volunteers in the test dataset. Utilizing FA and MD data in combination did not significantly alter the accuracy (96% sensitivity and specificity). Patterns of water self-diffusion in the brain as estimated by DTI can be used in conjunction with automated pattern recognition algorithms to reliably distinguish between patients with schizophrenia and normal control subjects.

A schizophrenia risk gene, ZNF804A, influences neuroanatomical and neurocognitive phenotypes

ZNF804A is one of the strongest candidate genes for schizophrenia (SZ), yet its function and role in disease pathophysiology are largely unknown. The only in vivo endophenotype study of the SZ-associated SNP (rs1344706) pointed towards effects on brain functional connectivity. We examined the relationship of this SNP to neuroanatomical and neurocognitive phenotypes that were assessed in healthy individuals. Volunteers with no history of psychiatric illness were assessed with structural magnetic resonance imaging (1.5T GE scanner, standard gradient-echo acquisition). Carriers of the minor allele were compared with homozygotes for the T (SZ-associated) allele on measures of total volume of the white matter (WM), gray matter (GM), and cerebrospinal fluid compartments, as well as on voxel-wise measurements of regional brain volumes. After examining the correlation between genotype-associated regions of interest and neurocognitive performance measures, the effects of rs1344706 genotype on a measure of visuomotor performance speed (trails A) were examined in an independent cohort of volunteers. Among healthy subjects, risk allele homozygotes showed larger total WM volumes than carriers of the other allele. Controlling for WM volumes, these same subjects showed reduced GM volumes in several regions comprising the 'default mode network,' including angular gyrus, parahippocampal gyrus, posterior cingulate, and medial orbitofrontal gyrus/gyrus rectus (FDR-corrected p<0.05). The risk allele dosage also predicted impairments on a timed visuomotor performance task (trails A). Results support a role of ZNF804A in phenotypes reflecting altered neural connectivity.

The Cognitive Assessment Interview (CAI): development and validation of an empirically derived, brief interview-based measure of cognition

BACKGROUND

Practical, reliable "real world" measures of cognition are needed to supplement neurocognitive performance data to evaluate possible efficacy of new drugs targeting cognitive deficits associated with schizophrenia. Because interview-based measures of cognition offer one possible approach, data from the MATRICS initiative (n=176) were used to examine the psychometric properties of the Schizophrenia Cognition Rating Scale (SCoRS) and the Clinical Global Impression of Cognition in Schizophrenia (CGI-CogS).

METHOD

We used classical test theory methods and item response theory to derive the 10-item Cognitive Assessment Interview (CAI) from the SCoRS and CGI-CogS ("parent instruments"). Sources of information for CAI ratings included the patient and an informant. Validity analyses examined the relationship between the CAI and objective measures of cognitive functioning, intermediate measures of cognition, and functional outcome.

RESULTS

The rater's score from the newly derived CAI (10 items) correlate highly (r=.87) with those from the combined set of the SCoRS and CGI-CogS (41 items). Both the patient (r=.82) and the informant (r=.95) data were highly correlated with the rater's score. The CAI was modestly correlated with objectively measured neurocognition (r=-.32), functional capacity (r=-.44), and functional outcome (r=-.32), which was comparable to the parent instruments.

CONCLUSIONS

The CAI allows for expert judgment in evaluating a patient's cognitive functioning and was modestly correlated with neurocognitive functioning, functional capacity, and functional outcome. The CAI is a brief, repeatable, and potentially valuable tool for rating cognition in schizophrenia patients who are participating in clinical trials.

Decoding developmental differences and individual variability in response inhibition through predictive analyses across individuals

Response inhibition is thought to improve throughout childhood and into adulthood. Despite the relationship between age and the ability to stop ongoing behavior, questions remain regarding whether these age-related changes reflect improvements in response inhibition or in other factors that contribute to response performance variability. Functional neuroimaging data shows age-related changes in neural activity during response inhibition. While traditional methods of exploring neuroimaging data are limited to determining correlational relationships, newer methods can determine predictability and can begin to answer these questions. Therefore, the goal of the current study was to determine which aspects of neural function predict individual differences in age, inhibitory function, response speed, and response time variability. We administered a stop-signal task requiring rapid inhibition of ongoing motor responses to healthy participants aged 9-30. We conducted a standard analysis using GLM and a predictive analysis using high-dimensional regression methods. During successful response inhibition we found regions typically involved in motor control, such as the ACC and striatum, that were correlated with either age, response inhibition (as indexed by stop-signal reaction time; SSRT), response speed, or response time variability. However, when examining which variables neural data could predict, we found that age and SSRT, but not speed or variability of response execution, were predicted by neural activity during successful response inhibition. This predictive relationship provides novel evidence that developmental differences and individual differences in response inhibition are related specifically to inhibitory processes. More generally, this study demonstrates a new approach to identifying the neurocognitive bases of individual differences.

A unique adolescent response to reward prediction errors

Previous work has demonstrated that human adolescents may be hypersensitive to rewards; it is unknown which aspect of reward processing this reflects. We separated decision value and prediction error signals and found that neural prediction error signals in the striatum peaked in adolescence, whereas neural decision value signals varied depending upon how value was modeled. This suggests that one contributor to adolescent reward-seeking may be heightened dopaminergic prediction error responsivity.

Cognitive effects of topiramate in migraine patients aged 12 through 17 years

Neuropsychologic data are presented from a randomized, double-blind, placebo-controlled, multicenter study with placebo, topiramate 50 mg/day, and topiramate 100 mg/day. The Cambridge Neuropsychological Test Automated Battery (CANTAB) and cognitive adverse events were used to evaluate neurocognitive effects of topiramate. Topiramate 100 mg/day vs placebo was associated with slight statistically significant score increases, indicating slowing, from baseline vs placebo in three CANTAB measures: five-choice reaction time (P = 0.028), pattern recognition memory mean correct latency (P = 0.027), and rapid visual information processing mean latency (P = 0.040). No other patterns related to topiramate treatment were observed in measurements of learning, memory, and visual information processing, except for potential improvement with topiramate 100 mg/day vs placebo in spatial span total errors (accuracy test) (P = 0.040). The most common cognitive and neuropsychiatric adverse events with a higher incidence in the topiramate 50 and 100 mg/day groups vs placebo were anorexia (9% and 11% vs 3%), insomnia (9% and 3% vs 3%), fatigue (6% and 9% vs 6%), and dizziness (6% and 9% vs 0%). Thus, topiramate 100 mg/day was associated with modest increases in psychomotor reaction times. Learning, memory, and executive function were unchanged. The tolerability profile, including cognitive adverse events, appeared to be acceptable.

DTNBP1 is associated with imaging phenotypes in schizophrenia

Dystrobrevin binding protein 1 (DTNBP1) has been identified as putative schizophrenia susceptibility gene, but it remains unknown whether polymorphisms relate to altered cerebral structure. We examined relationships between a previously implicated DTNBP1 risk variant [P1578] and global and segmented brain tissue volumes and regional cortical thickness in schizophrenia (n = 62; 24 risk carriers) and healthy subjects (n = 42; 11 risk carriers), across ethnic groups and within Caucasians. Schizophrenia patients showed similar brain volumes, but significantly reduced brain-size adjusted gray matter and CSF volumes and cortical thinning in a widespread neocortical distribution compared to controls. DTNBP1 risk was found associated with reduced brain volume, but not with tissue sub-compartments. Cortical thickness, which was weakly associated with brain size, showed regional variations in association with genetic risk, although effects were dominated by highly significant genotype by diagnosis interactions over broad areas of cortex. Risk status was found associated with regional cortical thinning in patients, particularly in temporal networks, but with thickness increases in controls. DTNBP1 effects for brain volume and cortical thickness appear driven by different neurobiological processes. Smaller brain volumes observed in risk carriers may relate to previously reported DTNBP1/cognitive function relationships irrespective of diagnosis. Regional cortical thinning in patient, but not in control risk carriers, may suggest that DTNBP1 interacts with other schizophrenia-related risk factors to affect laminar thickness. Alternatively, DTNBP1 may influence neural processes for which individuals with thicker cortex are less vulnerable. Although DTNBP1 relates to cortical thinning in schizophrenia, morphological changes in the disorder are influenced by additional genetic and/or environmental factors.

Neural components underlying behavioral flexibility in human reversal learning

The ability to flexibly respond to changes in the environment is critical for adaptive behavior. Reversal learning (RL) procedures test adaptive response updating when contingencies are altered. We used functional magnetic resonance imaging to examine brain areas that support specific RL components. We compared neural responses to RL and initial learning (acquisition) to isolate reversal-related brain activation independent of cognitive control processes invoked during initial feedback-based learning. Lateral orbitofrontal cortex (OFC) was more activated during reversal than acquisition, suggesting its relevance for reformation of established stimulus-response associations. In addition, the dorsal anterior cingulate (dACC) and right inferior frontal gyrus (rIFG) correlated with change in postreversal accuracy. Because optimal RL likely requires suppression of a prior learned response, we hypothesized that similar regions serve both response inhibition (RI) and inhibition of learned associations during reversal. However, reversal-specific responding and stopping (requiring RI and assessed via the stop-signal task) revealed distinct frontal regions. Although RI-related regions do not appear to support inhibition of prepotent learned associations, a subset of these regions, dACC and rIFG, guide actions consistent with current reward contingencies. These regions and lateral OFC represent distinct neural components that support behavioral flexibility important for adaptive learning.

Cognitive ontologies for neuropsychiatric phenomics research

Now that genome-wide association studies (GWAS) are dominating the landscape of genetic research on neuropsychiatric syndromes, investigators are being faced with complexity on an unprecedented scale. It is now clear that phenomics, the systematic study of phenotypes on a genome-wide scale, comprises a rate-limiting step on the road to genomic discovery. To gain traction on the myriad paths leading from genomic variation to syndromal manifestations, informatics strategies must be deployed to navigate increasingly broad domains of knowledge and help researchers find the most important signals. The success of the Gene Ontology project suggests the potential benefits of developing schemata to represent higher levels of phenotypic expression. Challenges in cognitive ontology development include the lack of formal definitions of key concepts and relations among entities, the inconsistent use of terminology across investigators and time, and the fact that relations among cognitive concepts are not likely to be well represented by simple hierarchical "tree" structures. Because cognitive concept labels are labile, there is a need to represent empirical findings at the cognitive test indicator level. This level of description has greater consistency, and benefits from operational definitions of its concepts and relations to quantitative data. Considering cognitive test indicators as the foundation of cognitive ontologies carries several implications, including the likely utility of cognitive task taxonomies. The concept of cognitive "test speciation" is introduced to mark the evolution of paradigms sufficiently unique that their results cannot be "mated" productively with others in meta-analysis. Several projects have been initiated to develop cognitive ontologies at the Consortium for Neuropsychiatric Phenomics (www.phenomics.ucla.edu), in the hope that these ultimately will enable more effective collaboration, and facilitate connections of information about cognitive phenotypes to other levels of biological knowledge. Several free web applications are available already to support examination and visualisation of cognitive concepts in the literature (PubGraph, PubAtlas, PubBrain) and to aid collaborative development of cognitive ontologies (Phenowiki and the Cognitive Atlas). It is hoped that these tools will help formalise inference about cognitive concepts in behavioural and neuroimaging studies, and facilitate discovery of the genetic bases of both healthy cognition and cognitive disorders.

Challenges in phenotype definition in the whole-genome era: multivariate models of memory and intelligence

Refining phenotypes for the study of neuropsychiatric disorders is of paramount importance in neuroscience. Poor phenotype definition provides the greatest obstacle for making progress in disorders like schizophrenia, bipolar disorder, Attention Deficit/Hyperactivity Disorder (ADHD), and autism. Using freely available informatics tools developed by the Consortium for Neuropsychiatric Phenomics (CNP), we provide a framework for defining and refining latent constructs used in neuroscience research and then apply this strategy to review known genetic contributions to memory and intelligence in healthy individuals. This approach can help us begin to build multi-level phenotype models that express the interactions between constructs necessary to understand complex neuropsychiatric diseases. These results are available online through the http://www.phenowiki.org database. Further work needs to be done in order to provide consensus-building applications for the broadly defined constructs used in neuroscience research.

Volumetric and shape analysis of the thalamus in first-episode schizophrenia

Thalamic abnormalities have been implicated in the pathogenesis of schizophrenia, although the majority of studies used chronic samples treated extensively with antipsychotics. Moreover, the clinical and neuropsychological correlates of these abnormalities remain largely unknown. Using high-resolution MR imaging and novel methods for shape analysis, we investigated thalamic subregions in 35 (25 M/10 F) first-episode schizophrenia patients compared with 33 (23 M/10 F) healthy volunteers. The right and left thalami were traced bilaterally on coronal brain slices and volumes were compared between groups. In addition, regional abnormalities were identified by comparing distances, measured from homologous thalamic surface points to the central core of each individual's surface model, between groups in 3D space. Patients had significantly less total thalamic volume compared with healthy volunteers. Statistical mapping demonstrated most pronounced shape abnormalities in the pulvinar; however, estimated false discovery rates in these regions were sizable. Smaller thalamus volume was significantly correlated with worse overall neuropsychological functioning and specific deficits were observed in the language, motor, and executive domains. There were no significant associations between thalamus volume and positive or negative symptoms. Our findings suggest that thalamic abnormalities are evident at the onset of a first episode of schizophrenia prior to extensive pharmacologic intervention and that these abnormalities have neuropsychological correlates.

Phenomics: the systematic study of phenotypes on a genome-wide scale

Phenomics is an emerging transdiscipline dedicated to the systematic study of phenotypes on a genome-wide scale. New methods for high-throughput genotyping have changed the priority for biomedical research to phenotyping, but the human phenome is vast and its dimensionality remains unknown. Phenomics research strategies capable of linking genetic variation to public health concerns need to prioritize development of mechanistic frameworks that relate neural systems functioning to human behavior. New approaches to phenotype definition will benefit from crossing neuropsychiatric syndromal boundaries, and defining phenotypic features across multiple levels of expression from proteome to syndrome. The demand for high throughput phenotyping may stimulate a migration from conventional laboratory to web-based assessment of behavior, and this offers the promise of dynamic phenotyping-the iterative refinement of phenotype assays based on prior genotype-phenotype associations. Phenotypes that can be studied across species may provide greatest traction, particularly given rapid development in transgenic modeling. Phenomics research demands vertically integrated research teams, novel analytic strategies and informatics infrastructure to help manage complexity. The Consortium for Neuropsychiatric Phenomics at UCLA has been supported by the National Institutes of Health Roadmap Initiative to illustrate these principles, and is developing applications that may help investigators assemble, visualize, and ultimately test multi-level phenomics hypotheses. As the transdiscipline of phenomics matures, and work is extended to large-scale international collaborations, there is promise that systematic new knowledge bases will help fulfill the promise of personalized medicine and the rational diagnosis and treatment of neuropsychiatric syndromes.

A randomized double-blind comparison of ziprasidone vs. clozapine for cognition in patients with schizophrenia selected for resistance or intolerance to previous treatment

BACKGROUND

Recent data have suggested few differences in the cognitive effects of antipsychotic medications. However, assessment of such effects can be complex, due to a number of factors. Clozapine has previously shown greater clinical and lesser cognitive benefits than other atypicals. This study compared the cognitive benefits of clozapine and ziprasidone in schizophrenia patients (n=130) with a history of either failure to respond to or intolerance of previous adequate antipsychotic treatments.

METHODS

Patients were randomized (double-blind) to either clozapine or ziprasidone in a single country (Italy), multi-site trial. The cognitive assessments examined episodic memory (RAVLT), executive functioning (Stroop test), and processing speed (Trail-making test (TMT) Parts A and B).

RESULTS

Analyses found statistically significant within-group improvements for ziprasidone in learning and delayed recall on the RAVLT and on TMT Parts A and B. Clozapine-treated patients improved on the RAVLT, but not on the TMT. A composite cognitive score improved from baseline in both groups, but the improvements were significantly larger in the ziprasidone group (p=.029).

IMPLICATIONS

These results indicated that cognitive functioning improved following treatment with ziprasidone in patients with a history of either treatment resistance or intolerance, and that the effects are comparable or greater than those observed with clozapine. One interpretation of these findings is that clozapine treatment interferes with the performance benefits associated with practice.

Patterns of stress in schizophrenia

Although it is widely recognized that stress plays a key role in the pathophysiology of schizophrenia, little is known regarding the particular types of stress patients experience. Less is known about the interplay among stressful events, personality mediators, and emotional responses. In this study, we investigated 10 stress dimensions in 29 patients with schizophrenia and 36 healthy volunteers using the Derogatis Stress Profile (DSP), and the relationship between these dimensions and symptoms in patients. Overall, patients had an approximate 0.75 standard deviation increase in stress compared with healthy volunteers. Significant increases in stress among patients compared with healthy volunteers were observed specifically in areas related to domestic environment, driven behavior, and depression, but not in health, attitude posture, time pressure, relaxation potential, role definition, hostility, or anxiety. More DSP-rated depression among patients correlated significantly with greater negative symptom severity. Patients with a shorter duration of antipsychotic drug exposure had significantly greater hostility than did patients with a longer duration of exposure, but did not differ in any other dimension. Continued investigation of domestic environmental stressors, driven behavior, and depression may be useful in identifying high-risk groups, and understanding symptom exacerbation and precipitants of relapse in patients already diagnosed with schizophrenia.

Clinical and neuropsychological correlates of white matter abnormalities in recent onset schizophrenia

The objective of this study was to investigate the clinical and neuropsychological correlates of white matter abnormalities in patients with schizophrenia studied early in the course of illness. A total of 33 (21 male/12 female) patients with recent onset schizophrenia and 30 (18 male/12 female) healthy volunteers completed structural and diffusion tensor imaging exams. Patients also received clinical and neuropsychological assessments. Fractional anisotropy (FA) maps were compared between groups in the white matter using a voxelwise analysis following intersubject registration to Talairach space and correlated with functional indices. Compared to healthy volunteers, patients demonstrated significantly (p<0.001, cluster size >or=100) lower FA within temporal lobe white matter regions corresponding approximately to the right and left uncinate fasciculus, left inferior fronto-occipital fasciculus, and left superior longitudinal fasciculus. There were no areas of significantly higher FA in patients compared to healthy volunteers. Lower FA in the bilateral uncinate fasciculus correlated significantly with greater severity of negative symptoms (alogia and affective flattening), and worse verbal learning/memory functioning. In addition, higher FA in the inferior fronto-occipital fasciculus correlated significantly with greater severity of delusions and hallucinations. White matter abnormalities are evident in patients with schizophrenia early in the course of illness, appearing most robust in left temporal regions. These abnormalities have clinical and neuropsychological correlates, which may be useful in further characterizing structure-function relations in schizophrenia and constraining neurobiological models of the disorder.

Construction of a 3D probabilistic atlas of human cortical structures

We describe the construction of a digital brain atlas composed of data from manually delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal volunteers. This labeling was performed according to a set of protocols developed for this project. Pairs of raters were assigned to each structure and trained on the protocol for that structure. Each rater pair was tested for concordance on 6 of the 40 brains; once they had achieved reliability standards, they divided the task of delineating the remaining 34 brains. The data were then spatially normalized to well-known templates using 3 popular algorithms: AIR5.2.5's nonlinear warp (Woods et al., 1998) paired with the ICBM452 Warp 5 atlas (Rex et al., 2003), FSL's FLIRT (Smith et al., 2004) was paired with its own template, a skull-stripped version of the ICBM152 T1 average; and SPM5's unified segmentation method (Ashburner and Friston, 2005) was paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced 3 variants of our atlas, where each was constructed from 40 representative samples of a data processing stream that one might use for analysis. For each normalization algorithm, the individual structure delineations were then resampled according to the computed transformations. We next computed averages at each voxel location to estimate the probability of that voxel belonging to each of the 56 structures. Each version of the atlas contains, for every voxel, probability densities for each region, thus providing a resource for automated probabilistic labeling of external data types registered into standard spaces; we also computed average intensity images and tissue density maps based on the three methods and target spaces. These atlases will serve as a resource for diverse applications including meta-analysis of functional and structural imaging data and other bioinformatics applications where display of arbitrary labels in probabilistically defined anatomic space will facilitate both knowledge-based development and visualization of findings from multiple disciplines.