Regional structural differences in patients with schizophrenia include bilaterally reduced volume of medial temporal lobe structures. There is a need for greater integration of results from structural MRI studies to avoid redundant research activity.

We describe almost entirely automated procedures for estimation of global, voxel, and cluster-level statistics to test the null hypothesis of zero neuroanatomical difference between two groups of structural magnetic resonance imaging (MRI) data. Theoretical distributions under the null hypothesis are available for 1) global tissue class volumes; 2) standardized linear model [analysis of variance (ANOVA and ANCOVA)] coefficients estimated at each voxel; and 3) an area of spatially connected clusters generated by applying an arbitrary threshold to a two-dimensional (2-D) map of normal statistics at voxel level. We describe novel methods for economically ascertaining probability distributions under the null hypothesis, with fewer assumptions, by permutation of the observed data. Nominal Type I error control by permutation testing is generally excellent; whereas theoretical distributions may be over conservative. Permutation has the additional advantage that it can be used to test any statistic of interest, such as the sum of suprathreshold voxel statistics in a cluster (or cluster mass), regardless of its theoretical tractability under the null hypothesis. These issues are illustrated by application to MRI data acquired from 18 adolescents with hyperkinetic disorder and 16 control subjects matched for age and gender.

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lower-level units in the higher-level units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unit-level latent variables (factors or random coefficients) can be regressed on cluster-level latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.

Two questions arising in the analysis of functional magnetic resonance imaging (fMRI) data acquired during periodic sensory stimulation are: i) how to measure the experimentally determined effect in fMRI time series; and ii) how to decide whether an apparent effect is significant. Our approach is first to fit a time series regression model, including sine and cosine terms at the (fundamental) frequency of experimental stimulation, by pseudogeneralized least squares (PGLS) at each pixel of an image. Sinusoidal modeling takes account of locally variable hemodynamic delay and dispersion, and PGLS fitting corrects for residual or endogenous autocorrelation in fMRI time series, to yield best unbiased estimates of the amplitudes of the sine and cosine terms at fundamental frequency; from these parameters the authors derive estimates of experimentally determined power and its standard error. Randomization testing is then used to create inferential brain activation maps (BAMs) of pixels significantly activated by the experimental stimulus. The methods are illustrated by application to data acquired from normal human subjects during periodic visual and auditory stimulation.

Objective: To determine, using a systematic review of case-control studies, whether head injury is a significant risk factor for Alzheimer's disease. We sought to replicate the findings of the meta-analysis of Mortimer et al (1991). Methods: A predefined inclusion criterion specified case-control studies eligible for inclusion. A comprehensive and systematic search of various electronic databases, up to August 2001, was undertaken. Two independent reviewers screened studies for eligibility. Fifteen case-control studies were identified that met the inclusion criteria, of which seven postdated the study of Mortimer et al. The most convincing evidence to date in support of an association between head injury and Alzheimer's disease is the meta-analysis by Mortimer et al of seven case-control studies conducted before 1991. 5 In this study, the raw data for each case-control study were collected directly from the original authors. Mortimer et al reported a relative risk of 1.82 (95% confidence interval (CI) 1.26 to 2.67) for head injury with a loss of consciousness. 5 The relative risk, when adjusted for a family history of dementing illness, education, and alcohol consumption, remained significant but was only true for males (2.67, 95% CI 1.64 to 4.41) and not females (0.85, 95% CI 0.43 to 1.70).In view of the equivocal findings from Mortimer et al's study, 5 and knowing that further case-control studies have been reported, we aimed to replicate the findings of Mortimer et al using a systematic review of case-control studies conducted in the past 10 years. We also sought to review the evidence for a relation between APOE status and head injury as risk factors for Alzheimer's disease. We have primarily relied on the data presented in published papers and, consequently, were unable to analyse covariate risk factors such as alcohol consumption, family history of dementing illness, and education. METHODS Inclusion criteriaThis study identified case-control studies that reported on head injury, or the interaction between head injury and APOE status, as risk factors for Alzheimer's disease. The inclusion criteria were developed on the basis of a comprehensive review of the literature, and consideration of the criteria used by Mortimer et al, 5 in order to identify the major sources of potential bias and the measures taken in an attempt to minimise such bias. Seven factors were identified as essential requirements for entry into this study:(1) Head injury with loss of consciousness: We were interested in head injury of a severity that occurs infrequently and that is likely to produce neurological effect. Therefore, we required that studies defined head trauma in terms of the presence of loss of consciousness. By excluding lesser head injuries, studies should be less exposed to recall bias and less likely to find an association that merely reflects a consequence of the prodrome of the dementia. No time restriction was placed on the period of unconsciousness.(2) Matching of case and control subjects: Two different types o...