Relating brain tissue properties to diffusion tensor imaging (DTI) is limited when an image voxel contains partial volume of brain tissue with free water, such as cerebrospinal fluid or edema, rendering the DTI indices no longer useful for describing the underlying tissue properties. We propose here a method for separating diffusion properties of brain tissue from surrounding free water while mapping the free water volume. This is achieved by fitting a bi-tensor model for which a mathematical framework is introduced to stabilize the fitting. Applying the method on datasets from a healthy subject and a patient with edema yielded corrected DTI indices and a more complete tract reconstruction that passed next to the ventricles and through the edema. We were able to segment the edema into areas according to the condition of the underlying tissue. In addition, the volume of free water is suggested as a new quantitative contrast of diffusion MRI. The findings suggest that free water is not limited to the borders of the brain parenchyma; it therefore contributes to the architecture surrounding neuronal bundles and may indicate specific anatomical processes. The analysis requires a conventional DTI acquisition and can be easily merged with existing DTI pipelines. Key words: diffusion; brain; edema; partial volume; DTI; MRIDiffusion imaging is an MRI technique sensitive to the mean displacement of water molecules along a specified direction (1). Within typical experimental diffusion times (a few tens of milliseconds) water molecules in brain tissue are expected to have a mean displacement on the order of 5-10 m, reflecting the degree of hindrance by surrounding cellular structures. The most common method for inferring tissue macroscopic geometry from water displacement is diffusion tensor imaging (DTI) (2), which models displacements in multiple directions using a diffusion tensor. The geometry is inferred from DTI indices (3) such as: fractional anisotropy (FA), which provides good segmentation of white matter and an indication of white matter coherence; the principal eigenvector of the tensor, which provides the orientation of white matter bundles; and the mean diffusivity (MD) or apparent diffusion coefficient (ADC), which provides a contrast mechanism for identifying areas with increased bulk diffusivity that may represent an increase in tissue water content. DTI indices have proven to have significant value both in clinical evaluation and brain research (4), including the unique ability to delineate neuronal fibers via tractography (5).Free water is defined as water molecules that do not experience flow and are not restricted by their surroundings. In the human brain, free water is found as cerebrospinal fluid (CSF) confined to the ventricles and around the brain parenchyma. Free water may also accumulate in the form of vasogenic edema within the brain parenchyma in the extracellular space due to processes such as tumors, brain trauma, or hemorrhage that cause ruptures in the blood-brain barrier (6 -8). Free water ...

Diffusion tensor imaging (DTI) has become one of the most popular MRI techniques in brain research, as well as in clinical practice. The number of brain studies with DTI is growing steadily and, over the last decade, has produced more than 700 publications. Diffusion tensor imaging enables visualization and characterization of white matter fascicli in two and three dimensions. Since the introduction of this methodology in 1994, it has been used to study the white matter architecture and integrity of the normal and diseased brains (multiple sclerosis, stroke, aging, dementia, schizophrenia, etc.). Although it provided image contrast that was not available with routine MR techniques, unique information on white matter and 3D visualization of neuronal pathways, many questions were raised regarding the origin of the DTI signal. Diffusion tensor imaging is constantly validated, challenged, and developed in terms of acquisition scheme, image processing, analysis, and interpretation. While DTI offers a powerful tool to study and visualize white matter, it suffers from inherent artifacts and limitations. The partial volume effect and the inability of the model to cope with non-Gaussian diffusion are its two main drawbacks. Nevertheless, when combined with functional brain mapping, DTI provides an efficient tool for comprehensive, noninvasive, functional anatomy mapping of the human brain. This review summarizes the development of DTI in the last decade with respect to the specificity and utility of the technique in radiology and anatomy studies.

Mild traumatic brain injury (mTBI), also referred to as concussion, remains a controversial diagnosis because the brain often appears quite normal on conventional computed tomography (CT) and magnetic resonance imaging (MRI) scans. Such conventional tools, however, do not adequately depict brain injury in mTBI because they are not sensitive to detecting diffuse axonal injuries (DAI), also described as traumatic axonal injuries (TAI), the major brain injuries in mTBI. Furthermore, for the 15 to 30% of those diagnosed with mTBI on the basis of cognitive and clinical symptoms, i.e., the “miserable minority,” the cognitive and physical symptoms do not resolve following the first three months post-injury. Instead, they persist, and in some cases lead to long-term disability. The explanation given for these chronic symptoms, i.e., postconcussive syndrome, particularly in cases where there is no discernible radiological evidence for brain injury, has led some to posit a psychogenic origin. Such attributions are made all the easier since both post-traumatic stress disorder (PTSD) and depression are frequently co-morbid with mTBI. The challenge is thus to use neuroimaging tools that are sensitive to DAI/TAI, such as diffusion tensor imaging (DTI), in order to detect brain injuries in mTBI. Of note here, recent advances in neuroimaging techniques, such as DTI, make it possible to characterize better extant brain abnormalities in mTBI. These advances may lead to the development of biomarkers of injury, as well as to staging of reorganization and reversal of white matter changes following injury, and to the ability to track and to characterize changes in brain injury over time. Such tools will likely be used in future research to evaluate treatment efficacy, given their enhanced sensitivity to alterations in the brain. In this article we review the incidence of mTBI and the importance of characterizing this patient population using objective radiological measures. Evidence is presented for detecting brain abnormalities in mTBI based on studies that use advanced neuroimaging techniques. Taken together, these findings suggest that more sensitive neuroimaging tools improve the detection of brain abnormalities (i.e., diagnosis) in mTBI. These tools will likely also provide important information relevant to outcome (prognosis), as well as play an important role in longitudinal studies that are needed to understand the dynamic nature of brain injury in mTBI. Additionally, summary tables of MRI and DTI findings are included. We believe that the enhanced sensitivity of newer and more advanced neuroimaging techniques for identifying areas of brain damage in mTBI will be important for documenting the biological basis of postconcussive symptoms, which are likely associated with subtle brain alterations, alterations that have heretofore gone undetected due to the lack of sensitivity of earlier neuroimaging techniques. Nonetheless, it is noteworthy to point out that detecting brain abnormalities in mTBI does not mean that other disorder...

The regional distribution of white matter (WM) abnormalities in schizophrenia remains poorly understood, and reported disease effects on the brain vary widely between studies. In an effort to identify commonalities across studies, we perform what we believe is the first ever large-scale coordinated study of WM microstructural differences in schizophrenia. Our analysis consisted of 2359 healthy controls and 1963 schizophrenia patients from 29 independent international studies; we harmonized the processing and statistical analyses of diffusion tensor imaging (DTI) data across sites and meta-analyzed effects across studies. Significant reductions in fractional anisotropy (FA) in schizophrenia patients were widespread, and detected in 20 of 25 regions of interest within a WM skeleton representing all major WM fasciculi. Effect sizes varied by region, peaking at (d=0.42) for the entire WM skeleton, driven more by peripheral areas as opposed to the core WM where regions of interest were defined. The anterior corona radiata (d=0.40) and corpus callosum (d=0.39), specifically its body (d=0.39) and genu (d=0.37), showed greatest effects. Significant decreases, to lesser degrees, were observed in almost all regions analyzed. Larger effect sizes were observed for FA than diffusivity measures; significantly higher mean and radial diffusivity was observed for schizophrenia patients compared with controls. No significant effects of age at onset of schizophrenia or medication dosage were detected. As the largest coordinated analysis of WM differences in a psychiatric disorder to date, the present study provides a robust profile of widespread WM abnormalities in schizophrenia patients worldwide. Interactive three-dimensional visualization of the results is available at www.enigma-viewer.org.

This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture.

Diffusion MRI has been successful in identifying the existence of white matter abnormalities in schizophrenia, in-vivo. However, the role of these abnormalities in the etiology of schizophrenia is not well understood. Accumulating evidence from imaging, histological, genetic and immunochemical studies support the involvement of axonal degeneration and neuroinflammation - ubiquitous components of neurodegenerative disorders - as the underlying pathologies of these abnormalities. Nevertheless, the current imaging modalities cannot distinguish neuroinflammation from axonal degeneration, and therefore provide little specificity with respect to the pathophysiology progression and whether it is related to a neurodegenerative process. Free-water imaging is a new methodology that is sensitive to water molecules diffusing in the extracellular space. Excessive extracellular volume is a surrogate biomarker for neuroinflammation, and can be separated out to reveal abnormalities such as axonal degeneration that affect diffusion characteristics in the tissue. We applied free-water imaging on diffusion MRI data acquired from schizophrenia-diagnosed human subjects with a first psychotic episode. We found a significant increase in the extracellular volume in both white and gray matter. In contrast, significant signs of axonal degeneration were limited to focal areas in the frontal lobe white matter. Our findings demonstrate that neuroinflammation is more prominent than axonal degeneration in the early stage of schizophrenia, revealing a pattern shared by many neurodegenerative disorders, in which prolonged inflammation leads to axonal degeneration. These findings promote anti-inflammatory treatment for early-diagnosed schizophrenia patients.

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BackgroundActivity-induced structural remodeling of dendritic spines and glial cells was recently proposed as an important factor in neuroplasticity and suggested to accompany the induction of long-term potentiation (LTP). Although T1 and diffusion MRI have been used to study structural changes resulting from long-term training, the cellular basis of the findings obtained and their relationship to neuroplasticity are poorly understood.Methodology/Principal FindingHere we used diffusion tensor imaging (DTI) to examine the microstructural manifestations of neuroplasticity in rats that performed a spatial navigation task. We found that DTI can be used to define the selective localization of neuroplasticity induced by different tasks and that this process is age-dependent in cingulate cortex and corpus callosum and age-independent in the dentate gyrus.Conclusion/SignificanceWe relate the observed DTI changes to the structural plasticity that occurs in astrocytes and discuss the potential of MRI for probing structural neuroplasticity and hence indirectly localizing LTP.