Measures of brain morphometry derived from T1-weighted (T1W) magnetic resonance imaging (MRI) are widely used to elucidate the relation between brain structure and function. However, the computation of T1W morphometric measures can be confounded by subject-related factors such as head motion and level of hydration. A recent study reported subtle yet significant changes in brain volume from morning to evening in a large group of patient populations as well as in healthy elderly individuals. In addition, there is a growing recognition that factors such as circadian rhythm can impact MRI measures of brain function and structure. Here, we provide a comprehensive assessment of the impact of time-of-day (TOD) on widely used measures of brain morphometry in a group of 19 healthy young adults. Our results show that (a) even in a small group of healthy adult volunteers, a highly significant reduction in apparent brain volume, from morning to evening, could be detected; (b) the apparent volume of all three major tissue compartments – gray matter, white matter, and cerebrospinal fluid –were influenced by TOD, and the magnitude of the TOD effect varied across the tissue compartments; (c) measures of cortical thickness, cortical surface area, and gray matter density computed with widely used neuroimaging software suites (i.e., FreeSurfer, FSL-VBM) were all affected by TOD, while other measures, such as curvature indices and sulcal depth, were not; and (d) the effect of TOD appeared to have a greater impact on morphometric measures of the frontal and temporal lobe than on other major lobes of the brain. Our results suggest that the TOD effect is a physiological phenomenon and that controlling for the effect of TOD is crucial for proper interpretation of apparent structural differences measured with T1W morphometry.

The human brain undergoes rapid and dynamic development early in life. Assessment of brain growth patterns relevant to neurological disorders and disease requires a normative population model of growth and variability in order to evaluate deviation from typical development. In this paper, we focus on maturation of brain white matter as shown in diffusion tensor MRI (DT-MRI), measured by fractional anisotropy (FA), mean diffusivity (MD), as well as axial and radial diffusivities (AD, RD). We present a novel methodology to model temporal changes of white matter diffusion from longitudinal DT-MRI data taken at discrete time points. Our proposed framework combines nonlinear modeling of trajectories of individual subjects, population analysis, and testing for regional differences in growth pattern. We first perform deformable mapping of longitudinal DT-MRI of healthy infants imaged at birth, 1 year, and 2 years of age, into a common unbiased atlas. An existing template of labeled white matter regions is registered to this atlas to define anatomical regions of interest. Diffusivity properties of these regions, presented over time, serve as input to the longitudinal characterization of changes. We use non-linear mixed effect (NLME) modeling where temporal change is described by the Gompertz function. The Gompertz growth function uses intuitive parameters related to delay, rate of change, and expected asymptotic value; all descriptive measures which can answer clinical questions related to quantitative analysis of growth patterns. Results suggest that our proposed framework provides descriptive and quantitative information on growth trajectories that can be interpreted by clinicians using natural language terms that describe growth. Statistical analysis of regional differences between anatomical regions which are known to mature differently demonstrates the potential of the proposed method for quantitative assessment of brain growth and differences thereof. This will eventually lead to a prediction of white matter diffusion properties and associated cognitive development at later stages given imaging data at early stages.

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In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM.

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PurposeThe analysis of exosome/microvesicle (extracellular vesicles (EVs)) and the RNA packaged within them (exoRNA) has the potential to provide a non-invasive platform to detect and monitor disease related gene expression potentially in lieu of more invasive procedures such as biopsy. However, few studies have tested the diagnostic potential of EV analysis in humans.Experimental DesignThe ability of EV analysis to accurately reflect prostate tissue mRNA expression was examined by comparing urinary EV TMPRSS2:ERG exoRNA from pre-radical prostatectomy (RP) patients versus corresponding RP tissue in 21 patients. To examine the differential expression of TMPRSS2:ERG across patient groups a random urine sample was taken without prostate massage from a cohort of 207 men including prostate biopsy negative (Bx Neg, n = 39), prostate biopsy positive (Bx Pos, n = 47), post-radical prostatectomy (post-RP, n = 37), un-biopsied healthy age-matched men (No Bx, n = 44), and young male controls (Cont, n = 40). The use of EVs was also examined as a potential platform to non-invasively differentiate Bx Pos versus Bx Neg patients via the detection of known prostate cancer genes TMPRSS2:ERG, BIRC5, ERG, PCA3 and TMPRSS2.ResultsIn this technical pilot study urinary EVs had a sensitivity: 81% (13/16), specificity: 80% (4/5) and an overall accuracy: 81% (17/21) for non-invasive detection of TMPRSS2:ERG versus RP tissue. The rate of TMPRSS2:ERG exoRNA detection was found to increase with age and the expression level correlated with Bx Pos status. Receiver operator characteristic analyses demonstrated that various cancer-related genes could differentiate Bx Pos from Bx Neg patients using exoRNA isolated from urinary EVs: BIRC5 (AUC 0.674 (CI:0.560–0.788), ERG (AUC 0.785 (CI:0.680–0.890), PCA3 (AUC 0.681 (CI:0.567–0.795), TMPRSS2:ERG (AUC 0.744 (CI:0.600–0.888), and TMPRSS2 (AUC 0.637 (CI:0.519–0.754).ConclusionThis pilot study suggests that urinary EVs have the potential to be used as a platform to non-invasively differentiate patients with prostate cancer with very good accuracy. Larger studies are needed to confirm the potential for clinical utility.