Amritha Nayak

A framework for the analysis of phantom data in multicenter diffusion tensor imaging studies

Diffusion tensor imaging (DTI) is commonly used for studies of the human brain due to its inherent sensitivity to the microstructural architecture of white matter. To increase sampling diversity, it is often desirable to perform multicenter studies. However, it is likely that the variability of acquired data will be greater in multicenter studies than in single‐center studies due to the added confound of differences between sites. Therefore, careful characterization of the contributions to variance in a multicenter study is extremely important for meaningful pooling of data from multiple sites. We propose a two‐step analysis framework for first identifying outlier datasets, followed by a parametric variance analysis for identification of intersite and intrasite contributions to total variance. This framework is then applied to phantom data from the NIH MRI study of normal brain development (PedsMRI). Our results suggest that initial outlier identification is extremely important for accurate assessment of intersite and intrasite variability, as well as for early identification of problems with data acquisition. We recommend the use of the presented framework at frequent intervals during the data acquisition phase of multicenter DTI studies, which will allow investigators to identify and solve problems as they occur. Hum Brain Mapp 34:2439–2454, 2013.

The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI)

The NIH MRI Study of normal brain development sought to characterize typical brain development in a population of infants, toddlers, children and adolescents/young adults, covering the socio-economic and ethnic diversity of the population of the United States. The study began in 1999 with data collection commencing in 2001 and concluding in 2007. The study was designed with the final goal of providing a controlled-access database; open to qualified researchers and clinicians, which could serve as a powerful tool for elucidating typical brain development and identifying deviations associated with brain-based disorders and diseases, and as a resource for developing computational methods and image processing tools. This paper focuses on the DTI component of the NIH MRI study of normal brain development. In this work, we describe the DTI data acquisition protocols, data processing steps, quality assessment procedures, and data included in the database, along with database access requirements. For more details, visit http://www.pediatricmri.nih.gov. This longitudinal DTI dataset includes raw and processed diffusion data from 498 low resolution (3 mm) DTI datasets from 274 unique subjects, and 193 high resolution (2.5 mm) DTI datasets from 152 unique subjects. Subjects range in age from 10 days (from date of birth) through 22 years. Additionally, a set of age-specific DTI templates are included. This forms one component of the larger NIH MRI study of normal brain development which also includes T1-, T2-, proton density-weighted, and proton magnetic resonance spectroscopy (MRS) imaging data, and demographic, clinical and behavioral data.

DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures

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.

Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brain.

Metrics derived from the diffusion tensor, such as fractional anisotropy (FA) and mean diffusivity (MD) have been used in many studies of postnatal brain development. A common finding of previous studies is that these tensor-derived measures vary widely even in healthy populations. This variability can be due to inherent inter-individual biological differences as well as experimental noise. Moreover, when comparing different studies, additional variability can be introduced by different acquisition protocols. In this study we examined scans of 61 individuals (aged 4-22 years) from the NIH MRI study of normal brain development. Two scans were collected with different protocols (low and high resolution). Our goal was to separate the contributions of biological variability and experimental noise to the overall measured variance, as well as to assess potential systematic effects related to the use of different protocols. We analyzed FA and MD in seventeen regions of interest. We found that biological variability for both FA and MD varies widely across brain regions; biological variability is highest for FA in the lateral part of the splenium and body of the corpus callosum along with the cingulum and the superior longitudinal fasciculus, and for MD in the optic radiations and the lateral part of the splenium. These regions with high inter-individual biological variability are the most likely candidates for assessing genetic and environmental effects in the developing brain. With respect to protocol-related effects, the lower resolution acquisition resulted in higher MD and lower FA values for the majority of regions compared with the higher resolution protocol. However, the majority of the regions did not show any age-protocol interaction, indicating similar trajectories were obtained irrespective of the protocol used.

Investigation of vibration-induced artifact in clinical diffusion-weighted imaging of pediatric subjects.

It has been reported that mechanical vibrations of the magnetic resonance imaging scanner could produce spurious signal dropouts in diffusion‐weighted images resulting in artifactual anisotropy in certain regions of the brain with red appearance in the Directionally Encoded Color maps. We performed a review of the frequency of this artifact across pediatric studies, noting differences by scanner manufacturer, acquisition protocol, as well as weight and position of the subject. We also evaluated the ability of automated and quantitative methods to detect this artifact. We found that the artifact may be present in over 50% of data in certain protocols and is not limited to one scanner manufacturer. While a specific scanner had the highest incidence, low body weight and positioning were also associated with appearance of the artifact for both scanner types evaluated, making children potentially more susceptible than adults. Visual inspection remains the best method for artifact identification. Software for automated detection showed very low sensitivity (10%). The artifact may present inconsistently in longitudinal studies. We discuss a published case report that has been widely cited and used as evidence to set policy about diagnostic criteria for determining vegetative state. That report attributed longitudinal changes in anisotropy to white matter plasticity without considering the possibility that the changes were caused by this artifact. Our study underscores the need to check for the presence of this artifact in clinical studies, analyzes circumstances for when it may be more likely to occur, and suggests simple strategies to identify and potentially avoid its effects. Hum Brain Mapp 36:4745–4757, 2015.

Impact of time-of-day on diffusivity measures of brain tissue derived from diffusion tensor imaging

&NA; Diurnal fluctuations in MRI measures of structural and functional properties of the brain have been reported recently. These fluctuations may have a physiological origin, since they have been detected using different MRI modalities, and cannot be explained by factors that are typically known to confound MRI measures. While preliminary evidence suggests that measures of structural properties of the brain based on diffusion tensor imaging (DTI) fluctuate as a function of time‐of‐day (TOD), the underlying mechanism has not been investigated. Here, we used a longitudinal within‐subjects design to investigate the impact of time‐of‐day on DTI measures. In addition to using the conventional monoexponential tensor model to assess TOD‐related fluctuations, we used a dual compartment tensor model that allowed us to directly assess if any change in DTI measures is due to an increase in CSF/free‐water volume fraction or due to an increase in water diffusivity within the parenchyma. Our results show that Trace or mean diffusivity, as measured using the conventional monoexponential tensor model tends to increase systematically from morning to afternoon scans at the interface of grey matter/CSF, most prominently in the major fissures and the sulci of the brain. Interestingly, in a recent study of the glymphatic system, these same regions were found to show late enhancement after intrathecal injection of a CSF contrast agent. The increase in Trace also impacts DTI measures of diffusivity such as radial and axial diffusivity, but does not affect fractional anisotropy. The dual compartment analysis revealed that the increase in diffusivity measures from PM to AM was driven by an increase in the volume fraction of CSF‐like free‐water. Taken together, our findings provide important insight into the likely physiological origins of diurnal fluctuations in MRI measurements of structural properties of the brain. HighlightsAlthough diurnal fluctuations in MRI measures of structural and functional properties of the brain have been reported recently, the underlying physiological mechanisms are unclear.Here, we first used diffusion tensor MRI to measures diurnal changes in diffusivity measures and found that Trace or mean diffusivity as measured using the conventional monoexponential tensor model, tends to increase systematically from morning to afternoon scans.We then used a dual compartment tensor model that allowed us to directly assess if any change in DTI measures is due to an increase in CSF/free‐water volume fraction and demonstrate that the increase in Trace from morning to afternoon can be explained by an increase in CSF‐like free‐water.The time‐of‐day related changes are localized along the interface of grey matter and CSF and is most prominent along the major fissures and sulci of the brain, that have been associated with the glymphatic system.

Tensor-based morphometry using scalar and directional information of diffusion tensor MRI data (DTBM): Application to hereditary spastic paraplegia

Tensor‐based morphometry (TBM) performed using T1‐weighted images (T1WIs) is a well‐established method for analyzing local morphological changes occurring in the brain due to normal aging and disease. However, in white matter regions that appear homogeneous on T1WIs, T1W‐TBM may be inadequate for detecting changes that affect specific pathways. In these regions, diffusion tensor MRI (DTI) can identify white matter pathways on the basis of their different anisotropy and orientation. In this study, we propose performing TBM using deformation fields constructed using all scalar and directional information provided by the diffusion tensor (DTBM) with the goal of increasing sensitivity in detecting morphological abnormalities of specific white matter pathways. Previously, mostly fractional anisotropy (FA) has been used to drive registration in diffusion MRI‐based TBM (FA‐TBM). However, FA does not have the directional information that the tensors contain, therefore, the registration based on tensors provides better alignment of brain structures and better localization of volume change. We compare our DTBM method to both T1W‐TBM and FA‐TBM in investigating differences in brain morphology between patients with complicated hereditary spastic paraplegia of type 11 (SPG11) and a group of healthy controls. Effect size maps of T1W‐TBM of SPG11 patients showed diffuse atrophy of white matter. However, DTBM indicated that atrophy was more localized, predominantly affecting several long‐range pathways. The results of our study suggest that DTBM could be a powerful tool for detecting morphological changes of specific white matter pathways in normal brain development and aging, as well as in degenerative disorders.