SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration

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The MATRICS Consensus Cognitive Battery, part 1: test selection, reliability, and validity.

Keith Nuechterlein, Michael Green, Robert Kern … (2008)

The lack of an accepted standard for measuring cognitive change in schizophrenia has been a major obstacle to regulatory approval of cognition-enhancing treatments. A primary mandate of the National Institute of Mental Health's Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative was to develop a consensus cognitive battery for clinical trials of cognition-enhancing treatments for schizophrenia through a broadly based scientific evaluation of measures. The MATRICS Neurocognition Committee evaluated more than 90 tests in seven cognitive domains to identify the 36 most promising measures. A separate expert panel evaluated the degree to which each test met specific selection criteria. Twenty tests were selected as a beta battery. The beta battery was administered to 176 individuals with schizophrenia and readministered to 167 of them 4 weeks later so that the 20 tests could be compared directly. The expert panel ratings are presented for the initially selected 36 tests. For the beta battery tests, data on test-retest reliability, practice effects, relationships to functional status, practicality, and tolerability are presented. Based on these data, 10 tests were selected to represent seven cognitive domains in the MATRICS Consensus Cognitive Battery. The structured consensus method was a feasible and fair mechanism for choosing candidate tests, and direct comparison of beta battery tests in a common sample allowed selection of a final consensus battery. The MATRICS Consensus Cognitive Battery is expected to be the standard tool for assessing cognitive change in clinical trials of cognition-enhancing drugs for schizophrenia. It may also aid evaluation of cognitive remediation strategies.

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Mediators in the architecture of future information systems

Jan Wiederhold (1992)

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Working memory and DLPFC inefficiency in schizophrenia: the FBIRN study.

Brandt Wible, Doug Greve, G Glover … (2008)

The Functional Imaging Biomedical Informatics Network is a consortium developing methods for multisite functional imaging studies. Both prefrontal hyper- or hypoactivity in chronic schizophrenia have been found in previous studies of working memory. In this functional magnetic resonance imaging (fMRI) study of working memory, 128 subjects with chronic schizophrenia and 128 age- and gender-matched controls were recruited from 10 universities around the United States. Subjects performed the Sternberg Item Recognition Paradigm1,2 with memory loads of 1, 3, or 5 items. A region of interest analysis examined the mean BOLD signal change in an atlas-based demarcation of the dorsolateral prefrontal cortex (DLPFC), in both groups, during both the encoding and retrieval phases of the experiment over the various memory loads. Subjects with schizophrenia performed slightly but significantly worse than the healthy volunteers and showed a greater decrease in accuracy and increase in reaction time with increasing memory load. The mean BOLD signal in the DLPFC was significantly greater in the schizophrenic group than the healthy group, particularly in the intermediate load condition. A secondary analysis matched subjects for mean accuracy and found the same BOLD signal hyperresponse in schizophrenics. The increase in BOLD signal change from minimal to moderate memory loads was greater in the schizophrenic subjects than in controls. This effect remained when age, gender, run, hemisphere, and performance were considered, consistent with inefficient DLPFC function during working memory. These findings from a large multisite sample support the concept not of hyper- or hypofrontality in schizophrenia, but rather DLPFC inefficiency that may be manifested in either direction depending on task demands. This redirects the focus of research from direction of difference to neural mechanisms of inefficiency.