Objective: Cognitive deficits are among the most reliable predictors of functional impairment in schizophrenia and a particular concern for older individuals with schizophrenia. Previous reviews have focused on the nature and course of cognitive impairments in younger cohorts, but a quantitative meta-analysis in older patients is pending. Method: A previously used search strategy identified studies assessing performance on tests of global cognition and specific neuropsychological domains in older patients with schizophrenia and age-matched comparison groups. Both crosssectional and longitudinal studies were included. Potential methodological, demographic, and clinical moderators were analyzed. Results: Twenty-nine cross-sectional (2110 patients, 1738 comparison subjects) and 14 longitudinal (954 patients) studies met inclusion criteria. Patients were approximately 65 years old, with 11 years of education, 53% male and 79% Caucasian. Longitudinal analysis (range 1-6 years) revealed homogeneity with small effect sizes (d 5 20.097) being observed. Cross-sectional analyses revealed large and heterogeneous deficits in global cognition (d 5 21.19) and on specific neuropsychological tests (d 5 20.7 to 21.14). Moderator analysis revealed a significant role for demographic (age, sex, education, race) and clinical factors (diagnosis, inpatient status, age of onset, duration of illness, positive and negative symptomology). Medication status (medicated vs nonmedicated) and chlorpromazine equivalents were inconsequential, albeit underrepresented. Conclusions: Large and generalized cognitive deficits in older individuals with schizophrenia represent a robust finding paralleling impairments across the life span, but these deficits do not decline over a 1-6 year period. The importance of considering demographic and clinical moderators in cross-sectional analyses is highlighted.

We describe and evaluate an algorithm that uses a distance map to automatically calculate the biovolume of a planktonic organism from its two‐dimensional boundary. Compared with existing approaches, this algorithm dramatically increases the speed and accuracy of biomass estimates from plankton images, and is thus especially suited for use with automated cell imaging technologies that produce large quantities of data. The algorithm operates on a two‐dimensional image processed to identify organism boundaries. First, the distance of each interior pixel to the nearest boundary is calculated; next these same distances are assumed to apply for projection in the third dimension; and finally the resulting volume is adjusted by a multiplicative factor assuming locally circular cross‐sections in the third dimension. Other cross‐sectional shape factors can be applied as needed. In this way, the simple, computationally efficient, volume calculation can be refined to include taxon‐specific shape information if available. We show that compared to traditional manual microscopic analysis, the distance map algorithm is unbiased and accurate (mean difference = −0.25%, standard deviation = 17%) for a range of cell morphologies, including those with concave boundaries that deviate from simple geometric shapes and whose volumes are not well represented by a solid of revolution around a single axis. Automated calculation of cell volumes can now be implemented with a combination of this new distance map algorithm for complex shapes and the solid of revolution approach for simple shapes, with an automated decision criterion to choose the appropriate approach for each image.

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