Objectives

To determine the spatial distribution of cortical and subcortical volume loss in patients with diffuse traumatic axonal injury and to assess the relationship between regional atrophy and functional outcome.

Design

Prospective imaging study. Longitudinal changes in global and regional brain volumes were assessed using high-resolution magnetic resonance imaging (MRI)-based morphometric analysis.

Setting

Inpatient traumatic brain injury unit

Patients or Other Participants

Twenty-five patients with diffuse traumatic axonal injury and 22 age- and sex-matched controls.

Main Outcome Measure

Changes in global and regional brain volumes between initial and follow-up MRI were used to assess the spatial distribution of post-traumatic volume loss. The Glasgow Outcome Scale – Extended was the primary measure of functional outcome.

Results

Patients underwent substantial global atrophy with mean brain parenchymal volume loss of 4.5% (95% Confidence Interval: 2.7 – 6.3%). Decreases in volume (at a false discovery rate of 0.05) were seen in several brain regions including the amygdala, hippocampus, thalamus, corpus callosum, putamen, precuneus, postcentral gyrus, paracentral lobule, and parietal and frontal cortices, while other regions such as the caudate and inferior temporal cortex were relatively resistant to atrophy. Loss of whole brain parenchymal volume was predictive of long-term disability, as was atrophy of particular brain regions including the inferior parietal cortex, pars orbitalis, pericalcarine cortex, and supramarginal gyrus.

Conclusion

Traumatic axonal injury leads to substantial post-traumatic atrophy that is regionally selective rather than diffuse, and volume loss in certain regions may have prognostic value for functional recovery.

Spatial variation in regional flows within the heart, skeletal muscle, and in other organs, and temporal variations in local arteriolar velocities and flows is measurable even with low resolution techniques. A problem in the assessment of the importance of such variations has been that the observed variance increases with increasing spatial or temporal resolution in the measurements. This resolution-dependent variance is now shown to be described by the fractal dimension, D. For example, the relative dispersion (RD=SD/mean) of the spatial distribution of flows for a given spatial resolution, is given by: RD(m)=RD(mref)·(mmref)1−Dg where m is the mass of the pieces of tissue in grams, and the reference level of dispersion, RD(mref), is taken arbitrarily to be the RD found using pieces of mass mref, which is chosen to be 1 g. Thus, the variation in regional flow within an organ can be described with two parameters, RD(mref) and the slope of the logarithmic relationship defined by the spatial fractal dimension Ds. In the heart, this relation has been found to hold over a wide range of piece sizes, the fractal Ds being about 1.2 and the correlation coefficient 0.99. A Ds of 1.2 suggests moderately strong correlation between local flows; a Ds=1.0 indicates uniform flow and a Ds= 1.5 indicates complete randomness.

Quantifying the connectivity between arbitrary surface patches in the human brain cortex can be used in studies on brain function and to characterize clinical diseases involving abnormal connectivity. Cortical regions of human brain in their natural forms can be represented in surface formats. In this paper, we present a framework to quantify connectivity using cortical surface segmentation and labeling from structural magnetic resonance images, tractography from diffusion tensor images, and nonlinear inter-subject registration. For a single subject, the connectivity intensity of any point on the cortical surface is set to unity if the point is connected and zero if it is not connected. The connectivity proportion is defined as the ratio of the total connected surface area to the total area of the surface patch. By nonlinearly registering the connectivity data of a group of normal controls into a template space, a population connectivity metric can be defined as either the average connectivity intensity of a cortical point or the average connectivity proportion of a cortical region. In the template space, a connectivity profile and a connectivity histogram of an arbitrary cortical region of interest can then be derived from these connectivity quantification values. Results from the application of these quantification metrics to a population of schizophrenia patients and normal controls are presented, revealing connectivity signatures of specified cortical regions and detecting connectivity abnormalities.

It has been known for some time that regional blood flows within an organ are not uniform. Useful measures of heterogeneity of regional blood flows are the standard deviation and coefficient of variation or relative dispersion of the probability density function (PDF) of regional flows obtained from the regional concentrations of tracers that are deposited in proportion to blood flow. When a mathematical model is used to analyze dilution curves after tracer solute administration, for many solutes it is important to account for flow heterogeneity and the wide range of transit times through multiple pathways in parallel. Failure to do so leads to bias in the estimates of volumes of distribution and membrane conductances. Since in practice the number of paths used should be relatively small, the analysis is sensitive to the choice of the individual elements used to approximate the distribution of flows or transit times. Presented here is a method for modeling heterogeneous flow through an organ using a scheme that covers both the high flow and long transit time extremes of the flow distribution. With this method, numerical experiments are performed to determine the errors made in estimating parameters when flow heterogeneity is ignored, in both the absence and presence of noise. The magnitude of the errors in the estimates depends upon the system parameters, the amount of flow heterogeneity present, and whether the shape of the input function is known. In some cases, some parameters may be estimated to within 10% when heterogeneity is ignored (homogeneous model), but errors of 15–20% may result, even when the level of heterogeneity is modest. In repeated trials in the presence of 5% noise, the mean of the estimates was always closer to the true value with the heterogeneous model than when heterogeneity was ignored, but the distributions of the estimates from the homogeneous and heterogeneous models overlapped for some parameters when outflow dilution curves were analyzed. The separation between the distributions was further reduced when tissue content curves were analyzed. It is concluded that multipath models accounting for flow heterogeneity are a vehicle for assessing the effects of flow heterogeneity under the conditions applicable to specific laboratory protocols, that efforts should be made to assess the actual level of flow heterogeneity in the organ being studied, and that the errors in parameter estimates are generally smaller when the input function is known rather than estimated by deconvolution.

Fractal analysis methods are used to quantify the complexity of the human cerebral cortex. Many recent studies have focused on high resolution three-dimensional reconstructions of either the outer (pial) surface of the brain or the junction between the grey and white matter, but ignore the structure between these surfaces. This study uses a new method to incorporate the entire cortical thickness. Data were obtained from the Alzheimer’s Disease (AD) Neuroimaging Initiative database (Control N=35, Mild AD N=35). Image segmentation was performed using a semi-automated analysis program. The fractal dimensions of three cortical models (the pial surface, grey/white surface and entire cortical ribbon) were calculated using a custom cube-counting triangle-intersection algorithm. The fractal dimension of the cortical ribbon showed highly significant differences between control and AD subjects (p<0.001). The inner surface analysis also found smaller but significant differences (p< 0.05). The pial surface dimensionality was not significantly different between the two groups. All three models had a significant positive correlation with the cortical gyrification index (r > 0.55, p<0.001). Only the cortical ribbon had a significant correlation with cortical thickness (r = 0.832, p< 0.001) and the Alzheimer’s Disease Assessment Scale cognitive battery (r = −0.513, p = 0.002). The cortical ribbon dimensionality showed a larger effect size (d=1.12) in separating control and mild AD subjects than cortical thickness (d=1.01) or gyrification index (d=0.84). The methodological change shown in this paper may allow for further clinical application of cortical fractal dimension as a biomarker for structural changes that accrue with neurodegenerative diseases.

Background

Identifying persons at risk for sudden cardiac death (SCD) is challenging. A comprehensive evaluation may reveal clues about the clinical, anatomic, genetic and metabolic risk factors for SCD.

Methods

Seventy-one SCD victims (25–60 years-old) without an initially apparent cause of death were evaluated at the Hennepin County Medical Examiner’s office from August, 2001 to July, 2004. We reviewed their clinic records conducted next-of-kin interviews and performed autopsy, laboratory testing and genetic analysis for mutations in genes associated with the long-QT syndrome.

Results

Mean age was 49.5±7 years, 86% were male and 2 subjects had history of coronary heart disease (CHD). Coronary risk factors were highly prevalent in comparison to individuals of the same age group in this community (e.g. smoking 61%; hypertension 27%; hyperlipidemia 25%) but inadequately treated. On autopsy, 80% of the subjects had high-grade coronary stenoses. Acute coronary lesions and previous silent myocardial infarction (MI) were found in 27% and 34%, respectively. Further, 32% of the subjects had recently smoked cigarettes and 50% had ingested analgesics. Possible deleterious mutations of the ion channel genes were detected in 5 (7%) subjects. Of these, 4 were in the sodium channel gene SCN5A.

Conclusions

Overwhelming majority of the SCD victims in the community had severe subclinical CHD, including undetected previous MI. Traditional coronary risk factors were prevalent and under-treated. Mutations in the long-QT syndrome genes were detected in a few subjects. These findings imply that improvements in the detection and treatment of subclinical CHD in the community are needed to prevent SCD.

The purpose of this project is to apply a modified fractal analysis technique to high-resolution T1 weighted magnetic resonance images in order to quantify the alterations in the shape of the cerebral cortex that occur in patients with Alzheimer’s disease. Images were selected from the Alzheimer’s Disease Neuroimaging Initiative database (Control N=15, Mild-Moderate AD N=15). The images were segmented using a semi-automated analysis program. Four coronal and three axial profiles of the cerebral cortical ribbon were created. The fractal dimensions (Df) of the cortical ribbons were then computed using a box-counting algorithm. The mean Df of the cortical ribbons from AD patients were lower than age-matched controls on six of seven profiles. The fractal measure has regional variability which reflects local differences in brain structure. Fractal dimension is complementary to volumetric measures and may assist in identifying disease state or disease progression.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modeling haplotype variation, Hardy–Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data, and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct, or analysis.

Distal third tibial fractures are prone to non-union following tibial nail insertion. The purpose of this study was to assess the union of distal third tibial fractures in patients who have undergone intra-medullary (IM) tibial nailing with one versus two distal locking screws. Sixty-five patients who had intramedullary tibial nail fixation were retrospectively analysed. Our results showed that 80% of non-unions in distal third fractures had only one distal locking screw compared to 20% who had two distal locking screws. This is statistically significant (p<0.01). We therefore conclude that two distal locking screws are essential for distal third fractures.

Julian Little and colleagues present the STREGA recommendations, which are aimed at improving the reporting of genetic association studies.

Objective

To identify genes involved in phenotypes that increase one's risk for developing asthma, a complex disease that is likely genetically heterogeneous. Unlike other approaches to locus discovery in the presence of heterogeneity, this method seeks loci that segregate in all or most ascertained families while recognizing that other genes and environmental factors that modify the action of the common gene may vary across families.

Methods

The method is based on seeking groups of families that differ, between groups, in the way affected idndividuals express the genotype. Then we use the distance of each individual to the cluster center for his family to define a quantitative trait. This quantitative trait is then subjected to a genome scan using variance components methods.

Results

The method is applied to a data set of 27 multigenerational families with asthma, and a novel locus at 2q33 (at 210 cM) is identified.

Conclusions

The proposed method has the potential to identify loci near genes that increase risk for asthma related phenotypes. The method could be used for other complex disorders that exhibit locus heterogeneity.

Gene expression microarrays can estimate the prevalence of mRNA for thousands of genes in a small sample of cells or tissue. Organ transplant researchers are increasingly using microarrays to identify specific patterns of gene expression that predict and characterize acute and chronic rejection, and to improve our understanding of the mechanisms underlying organ allograft dysfunction. We used microarrays to assess gene expression in bronchoalveolar lavage cell samples from lung transplant recipients with and without acute rejection on simultaneous lung biopsies. These studies showed increased expression during acute rejection of genes involved in inflammation, apoptosis, and T-cell activation and proliferation. We also studied gene expression during the evolution of airway obliteration in a murine heterotopic tracheal transplant model of chronic rejection. These studies demonstrated specific patterns of gene expression at defined time points after transplantation in allografts, whereas gene expression in isografts reverted back to that of native tracheas within 2 wk after transplantation. These studies demonstrate the potential power of microarrays to identify biomarkers of acute and chronic lung rejection. The application of new genetic, genomic, and proteomic technologies is in its infancy, and the microarray-based studies described here are clearly only the beginning of their application to lung transplantation. The massive amount of data generated per tissue or cell sample has spawned an outpouring of invention in the bioinformatics field, which is developing methodologies to turn data into meaningful and reproducible clinical and mechanistic inferences.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by the production of autoantibodies to a wide range of self-antigens. Recent genome screens have implicated numerous chromosomal regions as potential SLE susceptibility loci. Among these, the 1q41 locus is of particular interest, because evidence for linkage has been found in several independent SLE family collections. Additionally, the 1q41 locus appears to be syntenic with a susceptibility interval identified in the NZM2410 mouse model for SLE. Here, we report the results of genotyping of 11 microsatellite markers within the 1q41 region in 210 SLE sibpair and 122 SLE trio families. These data confirm the modest evidence for linkage at 1q41 in our family collection (LOD = 1.21 at marker D1S2616). Evidence for significant linkage disequilibrium in this interval was also found. Multiple markers in the region exhibit transmission disequilibrium, with the peak single marker multiallelic linkage disequilibrium noted at D1S490 (pedigree disequilibrium test [PDT] global P value = 0.0091). Two- and three-marker haplotypes from the 1q41 region similarly showed strong transmission distortion in the collection of 332 SLE families. The finding of linkage together with significant transmission disequilibrium provides strong evidence for a susceptibility locus at 1q41 in human SLE.

We have histocompatibility (HLA) genotyped 28 families with insulin-dependent diabetics in two or more consecutive generations, usually parent and child. This strategy of ascertainment was used to maximize the likelihood of obtaining a homogeneous type of disease within a family, and an autosomal dominant mode of inheritance. 76 diabetics and 169 nondiabetics were studied in these families.

The frequencies of the antigens Dw3 and Dw4, and the genotype Dw3/Dw4 among the diabetics are 59, 68, and 30%, respectively, as compared with 15, 12, and 2% in normal controls, and 43, 41, and 10% in the nondiabetic relatives of the diabetics. Dw2 is present in only one diabetic (4%), as compared with 18% in normal controls and 17% in nondiabetic relatives.

HLA haplotype concordance was analyzed for sib pairs in relation to the haplotype shared by the affected parent/child pair, and for the diabetic sib pairs within each sibship. The results failed to reveal deviations in the expected HLA haplotype assortment. Assuming an autosomal dominant mode and several penetrance levels, linkage analysis between the HLA and diabetes was performed. The total lod score is 0.37 for a recombination fraction of 0.29 at 50% penetrance. Although the linkage and concordance analysis results are inconclusive, they seem to be different from those reported by us for families with normal parents and two or more diabetic sibs. Because ascertainment biases may have influenced these results in an unquantifiable manner, it is not certain whether the two types of families are genetically different. However, the marked difference in the lod scores for the 50% penetrant autosomal recessive model between the two types of families is compatible with a genetic dissimilarity between them. The high frequency of the Dw3 and Dw4 antigens, the Dw3/Dw4 genotype, and the decreased frequency of Dw2, however, indicate the existence of two or more important diabetic genetic factors associated with the D region of the HLA in these families.

We have histocompatibility (HLA) genotyped 24 families with two or more juvenile, insulin-dependent, ketosis-prone diabetic siblings. This criterion for family selection was used to obtain a homogeneous form of diabetes within a sibship, because diabetes appears to be a genetically heterogeneous disease. 58 diabetic and 53 nondiabetic sibs and 40 parents were studied. 55% of the diabetic pairs were concordant for both HLA haplotypes (expected 25%), 40% were concordant for one haplotype (expected 50%), and 5% were discordant for both haplotypes (expected 25%). These values are significantly different from the expected values (P < 0.001). On the other hand, the inheritance of haplotypes among the nondiabetic sibs in these families was not significantly different from the expected mendelian segregation.

When comparing 20 pairs of HLA identical (sharing two haplotypes) with 15 pairs of haploidential (sharing one haplotype) diabetic sibs for the intrapair difference in age of onset of disease, we found that the HLA identical sibs were significantly more concordant for age of onset (3.9 yr difference) than the haploidential (7.3 yr difference) (P < 0.05). The same type of analysis for the difference in seasonal incidence in months revealed that the HLA indentical sibs were more concordant (1.8 mo difference) than the haploidentical sibs (3.2 mo difference) (P < 0.025). Furthermore, the HLA identical diabetic sibs were more likely to develop diabetes in the winter months (78%) than the haploidentical diabetic sibs (21%).

No particular HLA haplotype or antigen seemed to be associated with any particular clinical feature.

These data are compatible with the theory of genetic heterogeneity of juvenile, insulin-dependent diabetes. It is suggested that there are one or more diabetes response genes in the HLA region playing an important role in the pathogenesis of juvenile, insulin-dependent diabetes in the families studied here. It is, however, possible that other genes, not associated with the HLA complex, may play an etiologic role in some cases of juvenile, insulin-dependent diabetes, resulting in lack of association between HLA and some forms of diabetes.

Making sense of rapidly evolving evidence on genetic associations is crucial to making genuine advances in human genomics and the eventual integration of this information in the practice of medicine and public health. Assessment of the strengths and weaknesses of this evidence, and hence the ability to synthesize it, has been limited by inadequate reporting of results. The STrengthening the REporting of Genetic Association studies (STREGA) initiative builds on the STrengthening the Reporting of OBservational Studies in Epidemiology (STROBE) Statement and provides additions to 12 of the 22 items on the STROBE checklist. The additions concern population stratification, genotyping errors, modelling haplotype variation, Hardy–Weinberg equilibrium, replication, selection of participants, rationale for choice of genes and variants, treatment effects in studying quantitative traits, statistical methods, relatedness, reporting of descriptive and outcome data and the volume of data issues that are important to consider in genetic association studies. The STREGA recommendations do not prescribe or dictate how a genetic association study should be designed, but seek to enhance the transparency of its reporting, regardless of choices made during design, conduct or analysis.