Sensitivity of voxel-based morphometry analysis to choice of imaging protocol at 3 T

Voxel-based morphometry studies of personality: issue of statistical model specification--effect of nuisance covariates

There are an increasing number of studies on the localization of personality using voxel-based morphometry. Due to the complex analytic challenge in volumetric studies, the specification and treatment of the nuisance covariate (such as age, gender, and global measures) is currently not consistent. Here, we present a study in which we conducted voxel-based morphometry with Five-Factor Model (FFM) of personality traits (extraversion, neuroticism, openness to experience, agreeableness, and conscientiousness) that aimed to test the influence of NC specification in the determination of the results. In this study, 62 healthy subjects underwent MRI investigation and completed a German version of the FFM personality questionnaire. Voxel-based morphometry was used to investigate the correlation between the FFM personality traits and subtle brain structure. Different NC combinations were used during the model specification. Significant clusters were found only under the condition of some of the NC combinations but not under the others. In addition, we use the structure equation modeling (automated specification search from AMOS) to narrow down the possible choices of NC combinations according to a set of goodness-of-fit indices to identify well-fitted statistic models. As a final step, theoretical implications of the results are discussed, before accepting the selected model.

Regional impact of field strength on voxel-based morphometry results

The objective of this study was to characterize the sensitivity of voxel-based morphometry (VBM) results to choice field strength. We chose to investigate the two most widespread acquisition sequences for VBM, FLASH and MP-RAGE, at 1.5 and 3 T. We first evaluated image quality of the four acquisition protocols in terms of SNR and image uniformity. We then performed a VBM study on eight subjects scanned twice using the four protocols to evaluate differences in grey matter (GM) density and corresponding scan-rescan variability, and a power analysis for each protocol in the context a longitudinal and cross-sectional VBM study. As expected, the SNR increased significantly at 3 T for both FLASH and MP-RAGE. Image non-uniformity increased as well, in particular for MP-RAGE. The differences in CNR and contrast non-uniformity cause regional biases between protocols in the VBM results, in particular between sequences at 3 T. The power analysis results show an overall decrease in the number of subjects required in a longitudinal study to detect a difference in GM density at 3 T for MP-RAGE, but an increase for FLASH. The number of subjects required in a cross-sectional VBM study is higher at 3 T for both sequences. Our results show that each protocol has a distinct regional sensitivity pattern to morphometric change, which goes against the classical view of VBM as an unbiased whole brain analysis technique, complicates the combination of data within a VBM study and the direct comparison of VBM studies based on different protocols.

Grey matter volume changes over the whole brain in amyotrophic lateral sclerosis: A voxel-wise meta-analysis of voxel based morphometry studies

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with selected both upper and lower motor neuron involvement. Although some inconsistent results exist, both pathological studies and many structural neuroimaging studies have revealed brain volume changes in ALS. To provide an objective overview of structural changes in ALS, a voxel-wise meta-analysis was performed in published voxel based morphometry (VBM) studies. A systematic search of VBM studies was applied in ALS. Five studies met the inclusion criteria, comprising 84 ALS patients and 81 normal controls. A voxel-wise meta-analysis was performed on the retrieved VBM studies using signed differential mapping. Descriptive analysis showed that 25% of ALS patients had right precentral gyrus atrophy (2373 voxels). Group analysis demonstrated regional grey matter loss over the whole brain in the right precentral gyrus (p = 7.96 × 10(-4)). Sensitivity analysis showed good sensitivity (157 voxels). In conclusion, right precentral grey matter atrophy was a common finding and prominent feature of brain structural changes in ALS.

Mapping reliability in multicenter MRI: voxel-based morphometry and cortical thickness

Multicenter structural MRI studies can have greater statistical power than single-center studies. However, across-center differences in contrast sensitivity, spatial uniformity, etc., may lead to tissue classification or image registration differences that could reduce or wholly offset the enhanced statistical power of multicenter data. Prior work has validated volumetric multicenter MRI, but robust methods for assessing reliability and power of multisite analyses with voxel-based morphometry (VBM) and cortical thickness measurement (CORT) are not yet available. We developed quantitative methods to investigate the reproducibility of VBM and CORT to detect group differences and estimate heritability when MRI scans from different scanners running different acquisition protocols in a multicenter setup are included. The method produces brain maps displaying information such as lowest detectable effect size (or heritability) and effective number of subjects in the multicenter study. We applied the method to a five-site multicenter calibration study using scanners from four different manufacturers, running different acquisition protocols. The reliability maps showed an overall good comparability between the sites, providing a reasonable gain in sensitivity in most parts of the brain. In large parts of the cerebrum and cortex scan pooling improved heritability estimates, with "effective-N" values upto the theoretical maximum. For some areas, "optimal-pool" maps indicated that leaving out a site would give better results. The reliability maps also reveal which brain regions are in any case difficult to measure reliably (e.g., around the thalamus). These tools will facilitate the design and analysis of multisite VBM and CORT studies for detecting group differences and estimating heritability.

Intra- and interscanner variability of automated voxel-based volumetry based on a 3D probabilistic atlas of human cerebral structures

The intra- and inter-scanner variability of an automated method for MRI-based volumetry was investigated. Using SPM5 algorithms and predefined masks derived from a probabilistic whole-brain atlas, this method allows to determine the volumes of various brain structures (e.g., hemispheres, lobes, cerebellum, basal ganglia, grey and white matter etc.) in single subjects in an observer-independent fashion. A healthy volunteer was scanned three times at six different MRI scanners (including different vendors and field strengths) to calculate intra- and inter-scanner volumetric coefficients of variation (CV). The mean intra-scanner CV values per brain structure ranged from 0.50% to 4.4% (median, 0.89%), while the inter-scanner CV results varied between 0.66% and 14.7% (median, 4.74%). The overall (=combined intra- and inter-scanner) variability of measurements was only marginally higher, with CV results of 0.87-15.1% (median, 4.80%). Furthermore, the minimum percentage volume difference for detecting a significant volume change between two volume measurements in the same subject was calculated for each substructure. For example, for the total brain volume, mean intra-scanner, inter-scanner, and overall CV results were 0.50%, 3.78%, and 3.80%, respectively, and the cut-offs for significant volume changes between two measurements in the same subject amounted to 1.4% for measurements on the same scanner and 10.5% on different scanners. These findings may be useful for planning and assessing volumetric studies in neurological diseases, for the differentiation of certain patterns of atrophy, or for longitudinal studies monitoring the course of a disease and potential therapeutic effects.

Computational morphometry for detecting changes in brain structure due to development, aging, learning, disease and evolution

The brain, like any living tissue, is constantly changing in response to genetic and environmental cues and their interaction, leading to changes in brain function and structure, many of which are now in reach of neuroimaging techniques. Computational morphometry on the basis of Magnetic Resonance (MR) images has become the method of choice for studying macroscopic changes of brain structure across time scales. Thanks to computational advances and sophisticated study designs, both the minimal extent of change necessary for detection and, consequently, the minimal periods over which such changes can be detected have been reduced considerably during the last few years. On the other hand, the growing availability of MR images of more and more diverse brain populations also allows more detailed inferences about brain changes that occur over larger time scales, way beyond the duration of an average research project. On this basis, a whole range of issues concerning the structures and functions of the brain are now becoming addressable, thereby providing ample challenges and opportunities for further contributions from neuroinformatics to our understanding of the brain and how it changes over a lifetime and in the course of evolution.