Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium.

The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen's d=-0.46), amygdala (d=-0.31), thalamus (d=-0.31), accumbens (d=-0.25) and intracranial volumes (d=-0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.

Quantitative spectroscopic imaging with in situ measurements of tissue water T1, T2, and density.

The use of tissue water as a concentration standard in proton magnetic resonance spectroscopy ((1)H-MRS) of the brain requires that the water proton signal be adjusted for relaxation and partial volume effects. While single voxel (1)H-MRS studies have often included measurements of water proton T(1), T(2), and density based on additional (1)H-MRS acquisitions (e.g., at multiple echo or repetition times), this approach is not practical for (1)H-MRS imaging ((1)H-MRSI). In this report we demonstrate a method for using in situ measurements of water T(1), T(2), and density to calculate metabolite concentrations from (1)H-MRSI data. The relaxation and density data are coregistered with the (1)H-MRSI data and provide detailed information on the water signal appropriate to the individual subject and tissue region. We present data from both healthy subjects and a subject with brain lesions, underscoring the importance of water parameter measurements on a subject-by-subject and voxel-by-voxel basis.

A national human neuroimaging collaboratory enabled by the Biomedical Informatics Research Network (BIRN).

The aggregation of imaging, clinical, and behavioral data from multiple independent institutions and researchers presents both a great opportunity for biomedical research as well as a formidable challenge. Many research groups have well-established data collection and analysis procedures, as well as data and metadata format requirements that are particular to that group. Moreover, the types of data and metadata collected are quite diverse, including image, physiological, and behavioral data, as well as descriptions of experimental design, and preprocessing and analysis methods. Each of these types of data utilizes a variety of software tools for collection, storage, and processing. Furthermore sites are reluctant to release control over the distribution and access to the data and the tools. To address these needs, the Biomedical Informatics Research Network (BIRN) has developed a federated and distributed infrastructure for the storage, retrieval, analysis, and documentation of biomedical imaging data. The infrastructure consists of distributed data collections hosted on dedicated storage and computational resources located at each participating site, a federated data management system and data integration environment, an Extensible Markup Language (XML) schema for data exchange, and analysis pipelines, designed to leverage both the distributed data management environment and the available grid computing resources.

Multimodal Neuroimaging in Schizophrenia: Description and Dissemination.

In this paper we describe an open-access collection of multimodal neuroimaging data in schizophrenia for release to the community. Data were acquired from approximately 100 patients with schizophrenia and 100 age-matched controls during rest as well as several task activation paradigms targeting a hierarchy of cognitive constructs. Neuroimaging data include structural MRI, functional MRI, diffusion MRI, MR spectroscopic imaging, and magnetoencephalography. For three of the hypothesis-driven projects, task activation paradigms were acquired on subsets of ~200 volunteers which examined a range of sensory and cognitive processes (e.g., auditory sensory gating, auditory/visual multisensory integration, visual transverse patterning). Neuropsychological data were also acquired and genetic material via saliva samples were collected from most of the participants and have been typed for both genome-wide polymorphism data as well as genome-wide methylation data. Some results are also presented from the individual studies as well as from our data-driven multimodal analyses (e.g., multimodal examinations of network structure and network dynamics and multitask fMRI data analysis across projects). All data will be released through the Mind Research Network's collaborative informatics and neuroimaging suite (COINS).

Insular cortex abnormalities in schizophrenia: a structural magnetic resonance imaging study of first-episode patients.

The insular cortex is a limbic integration region that is engaged in emotional and cognitive functions. To investigate possible insular cortex abnormalities in schizophrenia, we measured insular gray matter volume and cortical surface size in drug-naive first-episode patients. Magnetic resonance images were used to explore the morphology of the insular cortex of 25 healthy male volunteers, and 25 male schizophrenic patients. Groups were matched for age, sex, height, and parental socio-economic status. Clinical dimension scores were correlated with insular gray matter volume and cortical surface area. Patients had a significant reduction in cortical surface area [patients=2020 (206); controls=2142 (204); F=5.83, df=1, 47; P=0.01] and gray matter volume [patients=8.12 (0.77); controls=8.57 (0.94); F=3.93, df=1,47; P=0.05] in the left insular cortex. Insular gray matter volume and cortical surface size correlated negatively and significantly with the psychotic symptom dimension. Schizophrenic patients show morphological abnormalities in the insular cortex at early stages of the illness. These abnormalities are related to the severity of psychotic symptoms. Further investigations are needed to evaluate the role of the insula in the pathophysiology of schizophrenia.

In vitro thermosensitivity of the midline thalamus.

This study compared the thermosensitivity and spontaneous activity of thalamic midline neurons with those of neurons in areas widely regarded to be involved in thermoregulation (preoptic/anterior hypothalamus and posterior hypothalamus). In vitro single unit recordings were made from neurons within the thalamic midline nuclei, the preoptic/anterior hypothalamus and posterior hypothalamus prior to and during a temperature change 3-7 degrees C above and below 37 degrees C. There were no significant differences in the degree of thermosensitivity or the proportion of thermosensitive neurons in the three areas. In each area examined, the thermosensitive neurons had a spontaneous activity which was significantly greater than that of the temperature-insensitive neurons. The results suggest that structures of the midline thalamus may play a role similar to that of the preoptic/anterior hypothalamus and posterior hypothalamus in the processing of temperature related information.

Cerebral cortex: a topographic segmentation method using magnetic resonance imaging.

Remarkable developments in magnetic resonance imaging (MRI) technology provide a broad range of potential applications to explore in vivo morphological characteristics of the human cerebral cortex. MR-based parcellation methods of the cerebral cortex may clarify the structural anomalies in specific brain subregions that reflect underlying neuropathological processes in brain illnesses. The present study describes detailed guidelines for the parcellation of the cerebral cortex into 41 subregions. Our method conserves the topographic uniqueness of individual brains and is based on our ability to visualize the three orthogonal planes, the triangulated gray matter isosurface and the three-dimensional (3D) rendered brain simultaneously. Based upon topographic landmarks of individual sulci, every subregion was manually segmented on a set of serial coronal or transaxial slices consecutively. The reliability study indicated that the cerebral cortex could be parcelled reliably; intraclass correlation coefficients for each subregion ranged from 0.60 to 0.99. The validity of the method is supported by the fact that gyral subdivisions are similar to regions delineated in functional imaging studies conducted in our center. Ultimately, this method will permit us to detect subtle morphometric impairments or to find abnormal patterns of functional activation in circumscribed cortical subregions. The description of a thorough map of regional structural and functional cortical abnormalities will provide further insight into the role that different subregions play in the pathophysiology of brain illnesses.

Antipsychotic dose and diminished neural modulation: a multi-site fMRI study.

BACKGROUND: The effect of antipsychotics on the blood oxygen level dependent signal in schizophrenia is poorly understood. The purpose of the present investigation is to examine the effect of antipsychotic medication on independent neural networks during a motor task in a large, multi-site functional magnetic resonance imaging investigation. METHODS: Seventy-nine medicated patients with schizophrenia and 114 comparison subjects from the Mind Clinical Imaging Consortium database completed a paced, auditory motor task during functional magnetic resonance imaging (fMRI). Independent component analysis identified temporally cohesive but spatially distributed neural networks. The independent component analysis time course was regressed with a model time course of the experimental design. The resulting beta weights were evaluated for group comparisons and correlations with chlorpromazine equivalents. RESULTS: Group differences between patients and comparison subjects were evident in the cortical and subcortical motor networks, default mode networks, and attentional networks. The chlorpromazine equivalents correlated with the unimotor/bitemporal (rho=-0.32, P=0.0039), motor/caudate (rho=-0.22, P=0.046), posterior default mode (rho=0.26, P=0.020), and anterior default mode networks (rho=0.24, P=0.03). Patients on typical antipsychotics also had less positive modulation of the motor/caudate network relative to patients on atypical antipsychotics (t(77)=2.01, P=0.048). CONCLUSION: The results suggest that antipsychotic dose diminishes neural activation in motor (cortical and subcortical) and default mode networks in patients with schizophrenia. The higher potency, typical antipsychotics also diminish positive modulation in subcortical motor networks. Antipsychotics may be a potential confound limiting interpretation of fMRI studies on the disease process in medicated patients with schizophrenia.

Improving tissue classification in MRI: a three-dimensional multispectral discriminant analysis method with automated training class selection.

PURPOSE: To improve the reliability, accuracy, and computational efficiency of tissue classification with multispectral sequences [T1, T2, and proton density (PD)], we developed an automated method for identifying training classes to be used in a discriminant function analysis. We compared it with a supervised operator-dependent method, evaluating its reliability and validity. We also developed a fuzzy (continuous) classification to correct for partial voluming. METHOD: Images were obtained on a 1.5 T GE Signa MR scanner using three pulse sequences that were co-registered. Training classes for the discriminant analysis were obtained in two ways. The operator-dependent method involved defining circular ROIs containing 5-15 voxels that represented "pure" samples of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), using a total of 150-300 voxels for each tissue type. The automated method involved selecting a large number of samples of brain tissue with sufficiently low variance and randomly placed throughout the brain ("plugs"), partitioning these samples into GM, WM, and CSF, and minimizing the amount of variance within each partition of samples to optimize its "purity." The purity of the plug was estimated by calculating the variance of 8 voxels in all modalities (T1, T2, and PD). We also compared "sharp" (discrete) measurements (which classified tissue only as GM, WM, or CSF) and "fuzzy" (continuous) measurements (which corrected for partial voluming by weighting the classification based on the mixture of tissue types in each voxel). RESULTS: Reliability was compared for the operator-dependent and automated methods as well as for the fuzzy versus sharp classification. The automated sharp classifications consistently had the highest interrater and intrarater reliability. Validity was assessed in three ways: reproducibility of measurements when the same individuals were scanned on multiple occasions, sensitivity of the method to detecting changes associated with aging, and agreement between the automated segmentation values and those produced through expert manual segmentation. The sharp automated classification emerged as slightly superior to the other three methods according to each of these validators. Its reproducibility index (intraclass r) was 0.97, 0.98, and 0.98 for total CSF, total GM, and total WM, respectively. Its correlations with age were 0.54, -0.61, and -0.53, respectively. Its percent agreement with the expert manually segmented tissue for the three tissue types was 93, 90, and 94%, respectively. CONCLUSION: Automated identification of training classes for discriminant analysis was clearly superior to a method that required operator intervention. A sharp (discrete) classification into three tissue types was also slightly superior to one that used "fuzzy" classification to produce continuous measurements to correct for partial voluming. This multispectral automated discriminant analysis method produces a computationally efficient, reliable, and valid method for classifying brain tissue into GM, WM, and CSF. It corrects some of the problems with reliability and computational inefficiency previously observed for operator-dependent approaches to segmentation.

A new method for the in vivo volumetric measurement of the human hippocampus with high neuroanatomical accuracy.

Accurate and reproducible in vivo measurement of hippocampal volumes using magnetic resonance (MR) imaging is complicated by the morphological complexity of the structure. Additionally, separation of certain parts of the hippocampus from the adjacent brain structures on MR images is sometimes very difficult. These difficulties have led most investigators to either use arbitrary landmarks or to exclude certain parts of the structure from their measurements. Based on three-dimensional MR data, we have developed a reliable in vivo volumetric measurement of the human hippocampus. In contrast to most of the previously described volumetric MR-based methods, we aimed to sample the entire hippocampal formation using its true anatomical definition. This was accomplished by relying on the capacity of the BRAINS software to simultaneously visualize in multiple planes, to "telegraph" tracings or cursor position from one plane to another, and to simultaneously rely on multispectral data from three different image sets (T1, T2, and tissue classified). The methods for identifying boundaries and measuring the hippocampal volume are described. The method has excellent reliability, sensitivity, and specificity. The method may be of use in studies of structure-function relationships in neuropsychiatric disorders such as schizophrenia, temporal lobe epilepsy, and Alzheimer's disease. Future work will use these measurements as training data for a neural net-based technique to identify the anatomical boundaries automatically.