Allometric scaling of brain regions to intra-cranial volume: An epidemiological MRI study.

There is growing evidence that sub-structures of the brain scale allometrically to total brain size, that is, in a non-proportional and non-linear way. Here, scaling of different volumes of interest (VOI) to intra-cranial volume (ICV) was examined. It was assessed whether scaling was allometric or isometric and whether scaling coefficients significantly differed from each other. We also tested to what extent allometric scaling of VOI was introduced by the automated segmentation technique. Furthermore, reproducibility of allometric scaling was studied different age groups and study populations. Study samples included samples of cognitively healthy adults from the community-based Age Gene/Environment Susceptibility-Reykjavik Study (AGES-Reykjavik Study) (N = 3,883), the Coronary Artery Risk Development in Young Adults Study (CARDIA) (N =709), and the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 180). Data encompassed participants with different age, ethnicity, risk factor profile, and ICV and VOI obtained with different automated MRI segmentation techniques. Our analysis showed that (1) allometric scaling is a trait of all parts of the brain, (2) scaling of neo-cortical white matter, neo-cortical gray matter, and deep gray matter structures including the cerebellum are significantly different from each other, and (3) allometric scaling of brain structures cannot solely be explained by age-associated atrophy, sex, ethnicity, or a systematic bias from study-specific segmentation algorithm, but appears to be a true feature of brain geometry. Hum Brain Mapp 38:151-164, 2017. © 2016 Wiley Periodicals, Inc.

Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry.

A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.

White Matter Lesion Progression: Genome-Wide Search for Genetic Influences.

BACKGROUND AND PURPOSE: White matter lesion (WML) progression on magnetic resonance imaging is related to cognitive decline and stroke, but its determinants besides baseline WML burden are largely unknown. Here, we estimated heritability of WML progression, and sought common genetic variants associated with WML progression in elderly participants from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. METHODS: Heritability of WML progression was calculated in the Framingham Heart Study. The genome-wide association study included 7773 elderly participants from 10 cohorts. To assess the relative contribution of genetic factors to progression of WML, we compared in 7 cohorts risk models including demographics, vascular risk factors plus single-nucleotide polymorphisms that have been shown to be associated cross-sectionally with WML in the current and previous association studies. RESULTS: A total of 1085 subjects showed WML progression. The heritability estimate for WML progression was low at 6.5%, and no single-nucleotide polymorphisms achieved genome-wide significance (P<5×10(-8)). Four loci were suggestive (P<1×10(-5)) of an association with WML progression: 10q24.32 (rs10883817, P=1.46×10(-6)); 12q13.13 (rs4761974, P=8.71×10(-7)); 20p12.1 (rs6135309, P=3.69×10(-6)); and 4p15.31 (rs7664442, P=2.26×10(-6)). Variants that have been previously related to WML explained only 0.8% to 11.7% more of the variance in WML progression than age, vascular risk factors, and baseline WML burden. CONCLUSIONS: Common genetic factors contribute little to the progression of age-related WML in middle-aged and older adults. Future research on determinants of WML progression should focus more on environmental, lifestyle, or host-related biological factors.

Early cortical thickness changes predict ß-amyloid deposition in a mouse model of Alzheimer's disease.

Magnetic resonance imaging (MRI) studies have identified aberrant cortical structure in Alzheimer's disease (AD). The association between MRI-derived cortical morphometry measures and ß-amyloid, however, remains poorly understood. In this study, we explored the potential relationship between early alterations in cortical thickness and later stage ß-amyloid deposition, using a novel approach, in a transgenic AD mouse model. We acquired longitudinal anatomical MRI scans from mutant amyloid precursor protein (APP) transgenic mice and age-matched wild-type mice at 1 and 3.5months-of-age, and employed fully-automated image processing methods to derive objective, quantitative measures of cortical thickness on a region-of-interest basis. We also generated 3D quantitative immunohistochemistry (qIHC) volumes of deposited ß-amyloid burden from 18month-old transgenic mice using an automated, production-level process. These studies revealed thinner cortex in most regions in the 1month-old transgenic mice relative to age-matched wild-types, with the exception of the frontal, perirhinal/entorhinal, posterior cingulate, and retrosplenial cortical regions. Between 1 and 3.5months-of-age, the transgenic mice demonstrated stable or increasing cortical thickness, while the wild-type mice showed cortical thinning. Based on data from co-registered 3D MRI and qIHC volumes, we identified an association between abnormal, early, regional cortical thickness change over 2.5months and later ß-amyloid deposition. These observations suggest that the spatio-temporal pattern of early (pre-plaque) alterations in cerebral cortical structure is indicative of regional predisposition to later ß-amyloid pathology in a transgenic AD mouse model.

Spatial association between distributed ß-amyloid and tau varies with cognition.

Several PET studies have explored the relationship between ß-amyloid load and tau uptake at the early stages of Alzheimer's disease (AD) progression. Most of these studies have focused on the linear relationship between ß-amyloid and tau at the local level and their synergistic effect on different AD biomarkers. We hypothesize that patterns of spatial association between ß-amyloid and tau might be uncovered using alternative association metrics that account for linear as well as more complex, possible nonlinear dependencies. In the present study, we propose a new Canonical Distance Correlation Analysis (CDCA) to generate distinctive spatial patterns of the cross-correlation structure between tau, as measured by [18F]flortaucipir PET, and ß-amyloid, as measured by [18F]florbetapir PET, from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We found that the CDCA-based ß-amyloid scores were not only maximally distance-correlated to tau in cognitively normal (CN) controls and mild cognitive impairment (MCI), but also differentiated between low and high levels of ß-amyloid uptake. The most distinctive spatial association pattern was characterized by a spread of ß-amyloid covering large areas of the cortex and localized tau in the entorhinal cortex. More importantly, this spatial dependency varies according to cognition, which cannot be explained by the uptake differences in ß-amyloid or tau between CN and MCI subjects. Hence, the CDCA-based scores might be more accurate than the amyloid or tau SUVR for the enrollment in clinical trials of those individuals on the path of cognitive deterioration.

Brain tissue volumes in the general population of the elderly: the AGES-Reykjavik study.

Imaging studies have reported conflicting findings on how brain structure differs with age and sex. This may be explained by discrepancies and limitations in study population and study design. We report a study on brain tissue volumes in one of the largest cohorts of individuals studied to date of subjects with high mean age (mean ± standard deviation (SD) 76 ± 6 years). These analyses are based on magnetic resonance imaging (MRI) scans acquired at baseline on 4303 non-demented elderly, and 367 who had a second MRI, on average 2.5 ± 0.2 years later. Tissue segmentation was performed with an automatic image analysis pipeline. Total brain parenchymal (TBP) volume decreased with increasing age while there was an increase in white matter hyperintensities (WMH) in both sexes. A reduction in both normal white matter (NWM)- and gray matter (GM) volume contributed to the brain shrinkage. After adjusting for intra-cranial volume, women had larger brain volumes compared to men (3.32%, p < 0.001) for TBP volume in the cross-sectional analysis. The longitudinal analysis showed a significant age-sex interaction in TBP volume with a greater rate of annual change in men (-0.70%, 95%CI: -0.78% to -0.63%) than women (-0.55%, 95%CI: -0.61% to -0.49%). The annual change in the cross-sectional data was approximately 40% less than the annual change in the longitudinal data and did not show significant age-sex interaction. The findings indicate that the cross-sectional data underestimate the rate of change in tissue volumes with age as the longitudinal data show greater rate of change in tissue volumes with age for all tissues.

Genome-wide association studies of cerebral white matter lesion burden: the CHARGE consortium.

OBJECTIVE: White matter hyperintensities (WMHs) detectable by magnetic resonance imaging are part of the spectrum of vascular injury associated with aging of the brain and are thought to reflect ischemic damage to the small deep cerebral vessels. WMHs are associated with an increased risk of cognitive and motor dysfunction, dementia, depression, and stroke. Despite a significant heritability, few genetic loci influencing WMH burden have been identified. METHODS: We performed a meta-analysis of genome-wide association studies (GWASs) for WMH burden in 9,361 stroke-free individuals of European descent from 7 community-based cohorts. Significant findings were tested for replication in 3,024 individuals from 2 additional cohorts. RESULTS: We identified 6 novel risk-associated single nucleotide polymorphisms (SNPs) in 1 locus on chromosome 17q25 encompassing 6 known genes including WBP2, TRIM65, TRIM47, MRPL38, FBF1, and ACOX1. The most significant association was for rs3744028 (p(discovery) = 4.0 × 10(-9) ; p(replication) = 1.3 × 10(-7) ; p(combined) = 4.0 × 10(-15) ). Other SNPs in this region also reaching genome-wide significance were rs9894383 (p = 5.3 × 10(-9) ), rs11869977 (p = 5.7 × 10(-9) ), rs936393 (p = 6.8 × 10(-9) ), rs3744017 (p = 7.3 × 10(-9) ), and rs1055129 (p = 4.1 × 10(-8) ). Variant alleles at these loci conferred a small increase in WMH burden (4-8% of the overall mean WMH burden in the sample). INTERPRETATION: This large GWAS of WMH burden in community-based cohorts of individuals of European descent identifies a novel locus on chromosome 17. Further characterization of this locus may provide novel insights into the pathogenesis of cerebral WMH.

A Simulation Toolkit for Testing the Sensitivity and Accuracy of Corticometry Pipelines.

In recent years, the replicability of neuroimaging findings has become an important concern to the research community. Neuroimaging pipelines consist of myriad numerical procedures, which can have a cumulative effect on the accuracy of findings. To address this problem, we propose a method for simulating artificial lesions in the brain in order to estimate the sensitivity and specificity of lesion detection, using different automated corticometry pipelines. We have applied this method to different versions of two widely used neuroimaging pipelines (CIVET and FreeSurfer), in terms of coefficients of variation; sensitivity and specificity of detecting lesions in 4 different regions of interest in the cortex, while introducing variations to the lesion size, the blurring kernel used prior to statistical analyses, and different thickness metrics (in CIVET). These variations are tested in a between-subject design (in two random groups, with and without lesions, using T1-weigted MRIs of 152 individuals from the International Consortium of Brain Mapping (ICBM) dataset) and in a within-subject pre-/post-lesion design [using 21 T1-Weighted MRIs of a single adult individual, scanned in the Infant Brain Imaging Study (IBIS)]. The simulation method is sensitive to partial volume effect and lesion size. Comparisons between pipelines illustrate the ability of this method to uncover differences in sensitivity and specificity of lesion detection. We propose that this method be adopted in the workflow of software development and release.

Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load.

White matter tracts, which play a crucial role in the coordination of information flow between different regions of grey matter, are particularly vulnerable to multiple sclerosis. Many studies have shown that the white matter lesions in multiple sclerosis are associated with focal abnormalities of grey matter, but little is known about the alterations in the coordinated patterns of cortical morphology among regions in the disease. Here, we used cortical thickness measurements from structural magnetic resonance imaging to investigate the relationship between the white matter lesion load and the topological efficiency of structural cortical networks in multiple sclerosis. Network efficiency was defined using a 'small-world' network model that quantifies the effectiveness of information transfer within brain networks. In this study, we first classified patients (n = 330) into six subgroups according to their total white matter lesion loads, and identified structural brain networks for each multiple sclerosis group by thresholding the corresponding inter-regional cortical thickness correlation matrix, followed by a network efficiency analysis with graph theoretical approaches. The structural cortical networks in multiple sclerosis demonstrated efficient small-world architecture regardless of the lesion load, an organization that maximizes the information processing at a relatively low wiring cost. However, we found that the overall small-world network efficiency in multiple sclerosis was significantly disrupted in a manner proportional to the extent of total white matter lesions. Moreover, regional efficiency was also significantly decreased in specific brain regions, including the insula and precentral gyrus as well as regions of prefrontal and temporal association cortices. Finally, we showed that the lesions also altered many cortical thickness correlations in the frontal, temporal and parietal lobes. Our results suggest that the white matter lesions in multiple sclerosis might be associated with aberrant neuronal connectivity among widely distributed brain regions, and provide structural (morphological) evidence for the notion of multiple sclerosis as a disconnection syndrome.

Spatially Distributed Amyloid-ß Reduces Glucose Metabolism in Mild Cognitive Impairment.

BACKGROUND: Several positron emission tomography (PET) studies have explored the relationship between amyloid-ß (Aß), glucose metabolism, and the APOEɛ4 genotype. It has been reported that APOEɛ4, and not aggregated Aß, contributes to glucose hypometabolism in pre-clinical stages of Alzheimer's disease (AD) pathology. OBJECTIVE: We hypothesize that typical measurements of Aß taken either from composite regions-of-interest with relatively high burden actually cover significant patterns of the relationship with glucose metabolism. In contrast, spatially weighted measures of Aß are more related to glucose metabolism in cognitively normal (CN) aging and mild cognitive impairment (MCI). METHODS: We have generated a score of amyloid burden based on a joint singular value decomposition (SVD) of the cross-correlation structure between glucose metabolism, as measured by [18F]2-fluoro-2-deoxyglucose (FDG) PET, and Aß, as measured by [18F]florbetapir PET, from the Alzheimer's Disease Neuroimaging Initiative study. This SVD-based score reveals cortical regions where a reduced glucose metabolism is maximally correlated with distributed patterns of Aß. RESULTS: From an older population of CN and MCI subjects, we found that the SVD-based Aß score was significantly correlated with glucose metabolism in several cortical regions. Additionally, the corresponding Aß network has hubs that contribute to distributed glucose hypometabolism, which, in turn, are not necessarily foci of Aß deposition. CONCLUSIONS: Our approach uncovered hidden patterns of the glucose metabolism-Aß relationship. We showed that the SVD-based Aß score produces a stronger relationship with decreasing glucose metabolism than either APOEɛ4 genotype or global measures of Aß burden.

Impact of Reference and Target Region Selection on Amyloid PET SUV Ratios in the Phase 1b PRIME Study of Aducanumab.

SUV ratios (SUVRs) are commonly used to quantify tracer uptake in amyloid-ß PET. Here, we explore the impact of target and reference region-of-interest (ROI) selection on SUVR effect sizes using interventional data from the ongoing phase 1b PRIME study (NCT01677572) of aducanumab (BIIB037) in patients with prodromal or mild Alzheimer disease. Methods: The florbetapir PET SUVR was calculated at baseline (screening) and at weeks 26 and 54 for patients randomized to receive placebo and each of 4 aducanumab doses (1, 3, 6, and 10 mg/kg) using the whole cerebellum, cerebellar gray matter, cerebellar white matter, pons, and subcortical white matter as reference regions. In addition to the prespecified composite cortex target ROI, individual cerebral cortical ROIs were assessed as targets. Results: Of the reference regions used, subcortical white matter, cerebellar white matter, and the pons, alone or in combination, generated the largest effect sizes. The use of the anterior cingulate cortex as a target ROI resulted in larger effect sizes than the use of the composite cortex. SUVR calculations were not affected by correction for brain volume changes over time. Conclusion: Dose- and time-dependent reductions in the amyloid PET SUVR were consistently observed with aducanumab only in cortical regions prone to amyloid plaque deposition, regardless of the reference region used. These data support the hypothesis that florbetapir SUVR responses associated with aducanumab treatment are a result of specific dose- and time-dependent reductions in the amyloid burden in patients with Alzheimer disease.

Gray matter differences correlate with spontaneous strategies in a human virtual navigation task.

Young healthy participants spontaneously use different strategies in a virtual radial maze, an adaptation of a task typically used with rodents. Functional magnetic resonance imaging confirmed previously that people who used spatial memory strategies showed increased activity in the hippocampus, whereas response strategies were associated with activity in the caudate nucleus. Here, voxel based morphometry was used to identify brain regions covarying with the navigational strategies used by individuals. Results showed that spatial learners had significantly more gray matter in the hippocampus and less gray matter in the caudate nucleus compared with response learners. Furthermore, the gray matter in the hippocampus was negatively correlated to the gray matter in the caudate nucleus, suggesting a competitive interaction between these two brain areas. In a second analysis, the gray matter of regions known to be anatomically connected to the hippocampus, such as the amygdala, parahippocampal, perirhinal, entorhinal and orbitofrontal cortices were shown to covary with gray matter in the hippocampus. Because low gray matter in the hippocampus is a risk factor for Alzheimer's disease, these results have important implications for intervention programs that aim at functional recovery in these brain areas. In addition, these data suggest that spatial strategies may provide protective effects against degeneration of the hippocampus that occurs with normal aging.

Voxel-based modeling and quantification of the proximal femur using inter-subject registration of quantitative CT images.

We have developed a general framework which employs quantitative computed tomography (QCT) imaging and inter-subject image registration to model the three-dimensional structure of the hip, with the goal of quantifying changes in the spatial distribution of bone as it is affected by aging, drug treatment or mechanical unloading. We have adapted rigid and non-rigid inter-subject registration techniques to transform groups of hip QCT scans into a common reference space and to construct composite proximal femoral models. We have applied this technique to a longitudinal study of 16 astronauts who on average, incurred high losses of hip bone density during spaceflights of 4-6 months on the International Space Station (ISS). We compared the pre-flight and post-flight composite hip models, and observed the gradients of the bone loss distribution. We performed paired t-tests, on a voxel by voxel basis, corrected for multiple comparisons using false discovery rate (FDR), and observed regions inside the proximal femur that showed the most significant bone loss. To validate our registration algorithm, we selected the 16 pre-flight scans and manually marked 4 landmarks for each scan. After registration, the average distance between the mapped landmarks and the corresponding landmarks in the target scan was 2.56 mm. The average error due to manual landmark identification was 1.70 mm.

The AGES-Reykjavik study atlases: Non-linear multi-spectral template and atlases for studies of the ageing brain.

Quantitative analyses of brain structures from Magnetic Resonance (MR) image data are often performed using automatic segmentation algorithms. Many of these algorithms rely on templates and atlases in a common coordinate space. Most freely available brain atlases are generated from relatively young individuals and not always derived from well-defined cohort studies. In this paper, we introduce a publicly available multi-spectral template with corresponding tissue probability atlases and regional atlases, optimised to use in studies of ageing cohorts (mean age 75 ± 5 years). Furthermore, we provide validation data from a regional segmentation pipeline to assure the integrity of the dataset.

Modulation of glucose metabolism and metabolic connectivity by ß-amyloid.

Glucose hypometabolism in the pre-clinical stage of Alzheimer's disease (AD) has been primarily associated with the APOE ɛ4 genotype, rather than fibrillar ß-amyloid. In contrast, aberrant patterns of metabolic connectivity are more strongly related to ß-amyloid burden than APOE ɛ4 status. A major limitation of previous studies has been the dichotomous classification of subjects as amyloid-positive or amyloid-negative. Dichotomous treatment of a continuous variable, such as ß-amyloid, potentially obscures the true relationship with metabolism and reduces the power to detect significant changes in connectivity. In the present work, we assessed alterations of glucose metabolism and metabolic connectivity as continuous function of ß-amyloid burden using positron emission tomography scans from the Alzheimer's Disease Neuroimaging Initiative study. Modeling ß-amyloid as a continuous variable resulted in better model fits and improved power compared to the dichotomous model. Using this continuous model, we found that both APOE ɛ4 genotype and ß-amyloid burden are strongly associated with glucose hypometabolism at early stages of Alzheimer's disease. We also determined that the cumulative effects of ß-amyloid deposition result in a particular pattern of altered metabolic connectivity, which is characterized by global, synchronized hypometabolism at early stages of the disease process, followed by regionally heterogeneous, progressive hypometabolism.

MINC 2.0: A Flexible Format for Multi-Modal Images.

It is often useful that an imaging data format can afford rich metadata, be flexible, scale to very large file sizes, support multi-modal data, and have strong inbuilt mechanisms for data provenance. Beginning in 1992, MINC was developed as a system for flexible, self-documenting representation of neuroscientific imaging data with arbitrary orientation and dimensionality. The MINC system incorporates three broad components: a file format specification, a programming library, and a growing set of tools. In the early 2000's the MINC developers created MINC 2.0, which added support for 64-bit file sizes, internal compression, and a number of other modern features. Because of its extensible design, it has been easy to incorporate details of provenance in the header metadata, including an explicit processing history, unique identifiers, and vendor-specific scanner settings. This makes MINC ideal for use in large scale imaging studies and databases. It also makes it easy to adapt to new scanning sequences and modalities.

Optimal Target Region for Subject Classification on the Basis of Amyloid PET Images.

UNLABELLED: Classification of subjects on the basis of amyloid PET scans is increasingly being used in research studies and clinical practice. Although qualitative, visual assessment is currently the gold standard approach, automated classification techniques are inherently more reproducible and efficient. The objective of this work was to develop a statistical approach for the automated classification of subjects with different levels of cognitive impairment into a group with low amyloid levels (AßL) and a group with high amyloid levels (AßH) through the use of amyloid PET data from the Alzheimer Disease Neuroimaging Initiative study. METHODS: In our framework, an iterative, voxelwise, regularized discriminant analysis is combined with a receiver operating characteristic approach that optimizes the selection of a region of interest (ROI) and a cutoff value for the automated classification of subjects into the AßL and AßH groups. The robustness, spatial stability, and generalization of the resulting target ROIs were evaluated by use of the standardized uptake value ratio for (18)F-florbetapir PET images from subjects who served as healthy controls, subjects who had mild cognitive impairment, and subjects who had Alzheimer disease and were participating in the Alzheimer Disease Neuroimaging Initiative study. RESULTS: We determined that several iterations of the discriminant analysis improved the classification of subjects into the AßL and AßH groups. We found that an ROI consisting of the posterior cingulate cortex/precuneus and the medial frontal cortex yielded optimal group separation and showed good stability across different reference regions and cognitive cohorts. A key step in this process was the automated determination of the cutoff value for group separation, which was dependent on the reference region used for the standardized uptake value ratio calculation and which was shown to have a relatively narrow range across subject groups. CONCLUSION: We developed a data-driven approach for the determination of an optimal target ROI and an associated cutoff value for the separation of subjects into the AßL and AßH groups. Future work should include the application of this process to other datasets to facilitate the determination of the translatability of the optimal ROI obtained in this study to other populations. Ideally, the accuracy of our target ROI and cutoff value could be further validated with PET-autopsy data from large-scale studies. It is anticipated that this approach will be extremely useful for the enrichment of study populations in clinical trials involving putative disease-modifying therapeutic agents for Alzheimer disease.

Voxel-based morphometry of human brain with age and cerebrovascular risk factors.

The objectives of this study were to evaluate the correlations of the volumes of the gray matter and white matter with age, and the correlations of the tissue probabilities of the gray matter and white matter with age and several cerebrovascular risk factors. We obtained magnetic resonance (MR) images of the brain and clinical information from 769 normal Japanese subjects. We processed the MR images automatically by correcting for inter-individual differences in brain size and shape, and by segmenting the MR images into the gray matter and white matter. Volumetry of the brain revealed a significant negative correlation between the gray matter volume and age, which was not observed between white matter volume and age. Voxel-based morphometry showed that age, systolic blood pressure, and alcohol drinking correlated with the regional tissue probabilities of the gray matter and white matter.

Structural brain MRI abnormalities in healthy siblings of patients with childhood-onset schizophrenia.

OBJECTIVE: Childhood-onset schizophrenia shows progressive brain magnetic resonance imaging (MRI) changes during adolescence, which follow a back-to-front "wave." The authors' goal was to examine whether healthy siblings of patients with childhood-onset schizophrenia show structural brain abnormalities and the age-related pattern of abnormalities seen in patients with childhood-onset schizophrenia. METHOD: Anatomic brain MRI scans were obtained from 15 psychiatrically healthy full siblings of 15 patients with childhood-onset schizophrenia and from 32 matched community volunteers. Automated measures were used to compare total and regional brain volumes of the siblings and volunteers. RESULTS: Siblings of patients with childhood-onset schizophrenia had smaller total cerebral volume and total, frontal, and parietal gray matter volumes than volunteers. When divided into younger and older groups, younger siblings had smaller parietal gray matter volumes and older siblings showed trends for smaller total and frontal gray matter volumes. CONCLUSIONS: Healthy siblings of patients with childhood-onset schizophrenia share brain MRI abnormalities with the patients that may follow a similar pattern of progression. Developmental brain abnormalities in childhood-onset schizophrenia may thus be genetic trait markers.

Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis.

The quantitative analysis of magnetic resonance imaging (MRI) data has become increasingly important in both research and clinical studies aiming at human brain development, function, and pathology. Inevitably, the role of quantitative image analysis in the evaluation of drug therapy will increase, driven in part by requirements imposed by regulatory agencies. However, the prohibitive length of time involved and the significant intraand inter-rater variability of the measurements obtained from manual analysis of large MRI databases represent major obstacles to the wider application of quantitative MRI analysis. We have developed a fully automatic "pipeline" image analysis framework and have successfully applied it to a number of large-scale, multicenter studies (more than 1,000 MRI scans). This pipeline system is based on robust image processing algorithms, executed in a parallel, distributed fashion. This paper describes the application of this system to the automatic quantification of multiple sclerosis lesion load in MRI, in the context of a phase III clinical trial. The pipeline results were evaluated through an extensive validation study, revealing that the obtained lesion measurements are statistically indistinguishable from those obtained by trained human observers. Given that intra- and inter-rater measurement variability is eliminated by automatic analysis, this system enhances the ability to detect small treatment effects not readily detectable through conventional analysis techniques. While useful for clinical trial analysis in multiple sclerosis, this system holds widespread potential for applications in other neurological disorders, as well as for the study of neurobiology in general.