Microstructure Imaging Core

Carl-Fredrik Westin
Carl-Fredrik Westin, PhD
Core PI

Our Publications

The goal of the Microstructure Imaging Core is to pursue technological developments that improve current understanding of white matter anatomy and pathology in the brain. Given the small dimension of the neural pathways in relation to current imaging resolution, these technologies are vital to analyzing and visualizing the neural anatomy in normal and diseased states. Our current clinical objective is to analyze abnormalities in white matter architecture that underlie schizophrenia by using techniques in diffusion magnetic resonance imaging (dMRI) and post-process imaging. The expectation is that new knowledge acquired from these studies not only will benefit the understanding and treatment of schizophrenia, but also a broad range of other white matter diseases. The work of this core is organized around the following themes:

  1. Development of Microstructural Imaging Biomarkers
  2. Development of Personalized Anatomical Analysis Methods
  3. Population Statistics and Patient-Specific Diagnosis

The objective of this research is to develop and implement quantitative measures from diffusion MRI (dMRI). Methods to be explored include multi-shell dMRI to determine free vs. multi-compartmental or restricted diffusion; compressed sensing (CS) in conjunction with MRS to allow detection of microstructural metabolic change within the tissue; and double-pulsed field gradient dMRI (double-PFG) to provide previously unavailable diffusion properties that can be mapped into families of new types of geometric and tissue-specific parameters. Software for measuring the proposed biomarkers will be developed and tested.

Featured Technologies

Standard dMRI (left) allows measurement of the angular distribution of the diffusion function; whereas the radial part of this function measured with multi-shell dMRI (right) reflects the type of diffusion observed (free vs. multicompartmental, or restricted).

 

 

 

 

Traditional diffusion MRI (single-PFG) would only provide the first pair of gradients, G1,G1. Increased microstructural detail (G2, G2) can be measured with the double-PFG diffusion sequence.

 

Diffusion MRI in 3D Slicer

Image legend: Diffusion MRI data for neurosurgical planning. The tractography region of interest (ROI) is depicted by the box placed around the tumor (in green) in the frontal lobe. The ROI is also visualized with rectangles in the slice views below. Tracts are then created based on the principal diffusion directions, which are color coded (bottom). Diffusion ellipsoids are shown along the tract to visualize the shape of the local diffusion.

The 3D Slicer (or simply Slicer) software was initially developed as a joint effort by the Surgical Planning Laboratory of Brigham and Women's Hospital and the Medical Vision Group of the MIT-based Computer Science and Artificial Intelligence Laboratory (CSAIL). The goal was to create an interactive, open source software platform for biomedical research. The program has evolved into a national platform supported by a variety of federal funding sources. This versatile research environment has resulted in a wide array of functionality and supports a variety of medical imaging projects.

Slicer is a "point and click" end-user application. It is used as a vehicle for delivering algorithms to computer scientists, biomedical researchers, and clinical investigators. Slicer is distributed under an open source license without a reciprocity requirement and without restrictions on use. For a sampling of the portfolio of applications, please see the Slicer Community page. 3D Slicer consists of over a million lines of code, mostly C++. This massive software development effort was enabled by the participation of several large-scale NIH-funded efforts, including the National Alliance for Medical Image Computing (NA-MIC), Neuroimage Analysis Center (NAC), Biomedical Informatics Research Network (BIRN), Center for Integration of Medicine and Innovative Technology (CIMIT), and National Center for Image Guided Therapy (NCIGT) communities. Federal funding sources include the National Center for Research Resources (NCRR), National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH Roadmap, National Cancer Institute (NCI), National Science Foundation (NSF), and the Department of Defense (DOD) among others.

DTMRI is a module in the 3D Slicer collection used for interactive visualization of diffusion tensor MRI (DT-MRI, or DTI). A cross-platform end user application used for analyzing and visualizing medical images, Slicer consists of a collection of Open Source libraries for developing and deploying new image computing technologies. It is an algorithm development platform with a powerful new Execution Model to facilitate creation of new modules. The White Matter Architecture Core has made major contributions in the diffusion MRI analysis and visualization packages in Slicer2, Slicer3, and Slicer4 (www.slicer.org).

 

Estimation Procedures for High Angular Resolution Diffusion MRI

The White Matter Architure Core has developed novel estimation procedures for High Angular Resolution Diffusion MRI (HARDI). Our novel method “Orientation Probability Density Transform” (OPDT) of estimating the diffusion function has received widespread attention [Tristan-Vega, 2009], [Tristan-Vega, 2010]. Compared to the popular approaches of Q-Ball imaging and the diffusion orientation transform (DOT), our formulation introduces considerably less blurring of the angular function as shown in the figure.

In our work, instead of radial projections, true angular marginalizations were computed. We showed that the Jacobian of the spherical coordinates (in this case r2) needs to be included, a crucial contribution as the transform has been calculated without the correct term since 2003. Our formulation, the OPDT, is advantageous as it has a strict probabilistic interpretation, and the correct expression introduces considerably less blurring of the angular function in comparison to other methods such as Q-Ball and DOT.

The figure shows diffusion profiles for crossing fibers with different angles reconstructed using the Q-Ball imaging (top), the diffusion orientation transform (middle), and our formulation (OPDT). Note that the OPTD method introduces considerably less blurring of the angular function, making it easier to detect the peaks of the function corresponding to the two underlying diffusion directions.
Image legend: Diffusion profiles for crossing fibers with different angles reconstructed using the Q-Ball imaging (top), the diffusion orientation transform (middle), and our formulation (OPDT).

Multiple Fiber Model Tractography



Image legend: Multiple fiber model tractography using unscented Kalman filtering. Note the additional tracts (blue) the new method finds.

The White Matter Architecture Core has developed tractography methods that can trace complex white matter regions, such as regions where there is heavy crossing and branching of the fiber tracts [Malcolm, 2009]. In this work we propose a technique to simultaneously estimate the local fiber orientations and perform multi-fiber tractography. Existing techniques estimate the local fiber orientation at each voxel independently. Consequently, there is no running knowledge of confidence in the measured signal or estimated fiber orientation. Further, to overcome noise, many algorithms use a filter as a post-processing step to obtain a smooth trajectory. Fiber tracking is formulated as a causal estimation: at each step of tracing the fiber, the current estimate of the signal is guided by the previous. The signal is modeled as a discrete mixture of Watson directional functions, and the tractography is performed within a filtering framework. Starting from a seed point, each fiber is traced to its termination using an unscented Kalman filter to simultaneously fit the signal and propagate in the most consistent direction. Despite the presence of noise and uncertainty, this provides an accurate estimate of the local structure at each point along the fiber. The Watson function was chosen since it provides a compact representation of the signal parameterized by the principal diffusion direction and a scaling parameter describing anisotropy. It also allows analytic reconstruction of the oriented diffusion function from those parameters. Using a mixture of two and three components (corresponding to two-fiber and three-fiber models) we demonstrate in synthetic experiments that this approach reduces signal reconstruction error and significantly improves the angular resolution at crossings and branchings. In vivo experiments shown in the figure examine the corpus callosum and internal capsule and confirm the ability to trace through regions known to contain such crossing and branching while providing inherent path regularization.

Tract-Based Diffusion Feature Analysis

Image legend: SLICER interface for the EM fiber clustering module.

The White Matter Architecture Core has also worked on developing an automatic method that we call tract-based morphometry, or TBM, for measurement and analysis of diffusion MRI data along white matter fiber tracts [O'Donnell, 2009]. Using subject-specific tractography bundle segmentations, the method generates an arc length parameterization of the bundle with point correspondences across all fibers and all subjects, allowing tract-based measurement and analysis. In this work the team presents a quantitative comparison of fiber coordinate systems from the literature and we introduce an improved optimal match method that reduces spatial distortion and improves intra- and inter-subject variability of FA measurements.

The team proposed a method for generating arc length correspondences across hemispheres, enabling a TBM study of interhemispheric diffusion asymmetries in the arcuate fasciculus (AF) and cingulum bundle (CB). The results of this study demonstrate that TBM can detect differences that may not be found by measuring means of scalar invariants in entire tracts, such as the mean diffusivity (MD) differences found in AF. TBM results are reported of higher fractional anisotropy (FA) in the left hemisphere in AF (caused primarily by lower λ3, the smallest eigenvalue of the diffusion tensor, in the left AF), and higher left hemisphere FA in CB (related to higher λ1, the largest eigenvalue of the diffusion tensor, in the left CB). By mapping the significance levels onto the tractography trajectories for each structure, the anatomical locations of the interhemispheric differences are shown. The TBM approach brings analysis of DTI data into the clinically and neuroanatomically relevant framework of the tract anatomy. The team has also developed tools for tract-based analysis in 3D Slicer. As shown in the figure, the new command line module for EM clustering developed in collaboration with GE Research (Jim Miller) makes anatomical grouping and quantitative analysis of diffusion measures available to the research community. The team has also created an on-line tutorial for this module.


Asymmetry of White Matter Tract

Image legend: Our white matter clustering method defines homologous anatomy across subjects (left). It has been validated by comparison to interactive tract selection (middle). The clustering method has been used in several studies of schizophrenia (SZ), e.g., to define callosal subdivisions, detecting reduced FA in the anterior corpus callosum in SZ (right).

The team has proposed a new method for studying the asymmetry of white matter tracts in the entire brain. This approach has been applied to a preliminary study of normal subjects across the handedness spectrum [O’Donnell, 2010]. The quantification of brain asymmetries may provide biomarkers for presurgical localization of language function and can improve our understanding of neural structure-function relationships in health and disease. Methods for quantifying white matter asymmetry using diffusion MRI tractography have thus far been based on comparing numbers of fibers or volumes of a single fiber tract across hemispheres. The team further proposes a generalization of such methods, where the “number of fibers” laterality measurement is extended to the entire brain using a soft fiber comparison metric. The distribution of fiber laterality indices over the whole brain is summarized in a histogram, and defines properties of the distribution using its skewness, median, and inter-quartile range. The whole-brain fiber laterality histogram can be measured in an exploratory fashion without hypothesizing asymmetries only in particular structures. The team has demonstrated an overall difference in white matter asymmetry in consistent- and inconsistent-handers: the skewness of the fiber laterality histogram is significantly different across handedness groups. The figure shows fiber laterality indices and fiber laterality histograms in example subjects from each handedness group.

Research Highlights

CF1
Figure 1: Q-space trajectory imaging for multidimensional diffusion MRI of the human brain. This visualization of four proposed QTI measures demonstrates how the measures would change in eight illustrative synthetic macrodomains (voxels). Note that these measures intuitively separate size, shape, and orientation coherence, as well as provide the traditional measures of macroscopic anisotropy. The microstructure measures proposed from QTI can disambiguate complex microenvironments that are indistinguishable using today’s standard single diffusion encoding (SDE) methods. For example, the macroscopic anisotropy (FA) of all diffusion tensor distributions (DTDs) found in the bottom row is very low, while the microscopic anisotropy (μFA) reflects the anisotropy of the individual micro-environments [Westin, 2016].

CF2

Figure 2: Constrained optimization of gradient waveforms for generalized diffusion encoding. The team provided a solution to the problem of constrained optimization of gradient waveforms imposed by clinical hardware, while maximizing the diffusion encoding strength afforded by QTI, by solving a constrained optimization problem that accommodates constraints on maximum gradient amplitude, slew rate, coil heating and positioning of radio frequency pulses. The method's efficacy and flexibility is demonstrated both experimentally and by comparison with previous work on optimization of isotropic diffusion sequences. In this figure, raw diffusion weighted images are shown on an axial slice through the corpus callosum in a healthy volunteer. The encoding strength is b=2000 s/mm2 in both images. The measured signal is markedly higher in the images encoded with the optimized waveform (A, echo time 116 ms) compared to the qMAS waveform (B, echo time 170 ms). The histogram shows the distribution of voxelwise SNR from brain tissue located within the imaging slab. There is a clear tendency towards higher SNR for the optimized waveform, due to the shorter echo time [Sjölund, 2015].
 

CF3

Figure 3A: Sampling strategies and compressed sensing for high spatial resolution dMRI. T1 weighted MRI image with a yellow inset showing the corresponding inset of the colored-by-orientation DTI images. The low resolution images have 1.83 mm3 isotropic voxels, while the interpolated DTI and the super resolution data set have 0.93 mm3 isotropic voxels. The yellow arrows point out the fine details in the super resolution results that are missing in the low resolution and the interpolated results.

CF4
Figure 3B: Sampling strategies and compressed sensing for high spatial resolution dMRI. B. Left: T1 weighted MRI images with a yellow inset indicating the anatomical area of the DTI images with color-coded tensor glyphs visualizing the local white matter fiber orientation. Middle: the low resolution data set (with 1.83 mm3 isotropic voxels). Right: The result of the proposed compressed sensing super resolution method (with 0.93 mm3 isotropic voxels). The rectangle shows the glyphs in a gray-matter region that were missing in the low resolution results [Radmanesh, 2015].

CF5Figure 4: White matter segmentation using clustering for traumatic brain injury. Results from clustering the result of whole brain white matter tractography. Left: 650 color-coded clusters with a distinct color for each segmented tract segment. Right: Example of one selected cluster with the corresponding tract from the control group and the player group. By O'Donnell et al. (preliminary unpublished findings).]
 

CF6
Figure 5: Using abnormal white matter connections between medial frontal regions to predict symptoms in patients with first episode schizophrenia. Scatter plots of mean fractional anisotropy for first episode schizophrenia and the healthy control group. Abbreviations: OFC = orbitofrontal cortex; ACC = anterior cingulate cortex; SZ = schizophrenia group; HC = healthy control group. *p < .001. ]
 

CF7

Figure 6: New microscopic measures derived from QTI applied to schizophrenia population. Comparison of normalized measures in schizophrenia patients (SZ) and healthy controls (CTR). The CMD, CM and Cμ averaged across the white matter were all significantly reduced in the schizophrenia group. Changes in Cc were not found to be significant between the groups. Significance was tested using the Wilcoxon rank-sum U-test. ]


PROGRESS UNDER AIM 1: DEVELOPMENT OF MICROSTRUCTURAL IMAGING BIOMARKERS

Dr. Westin’s team reported progress in four projects designed to further the goals of aim 1.

Q-space trajectory imaging for multidimensional diffusion MRI of the human brain (FIGURE 1). The team has developed a framework called q-space trajectory imaging (QTI), which uses gradient waveforms to probe trajectories in q-space. In contrast to single diffusion encoding (SDE) sequences that probe a single point in q-space, a single QTI measurement encodes multiple diffusion directions (Figure 1). The team has shown that measuring a family of trajectories introduces higher-order (multi-dimensional) correlations, which permit separate quantification of microstructural properties that are intrinsically entangled in traditional SDE. Within this framework the microstructural properties, mathematically expressed in terms of variability in size, shape, and orientation, are extracted from a diffusion tensor distribution model representing a mixture of distinct neuronal tissue micro-environments, such as neurites, cellular domains, and extracellular spaces [Westin, 2016].

Characterizing magnetic resonance signal decay using the path integral approach. The influence of Gaussian diffusion on the magnetic resonance signal is determined by the apparent diffusion coefficient (ADC) and tensor of the diffusing fluid as well as the gradient waveform applied to sensitize the signal to diffusion. Estimations of the ADC and the diffusion tensor from diffusion-weighted acquisitions necessitate computations of, respectively, the b-value and b-matrix associated with the employed pulse sequence. The team has established the relationship between these quantities and the gradient waveform by expressing the problem as a path integral, which is then explicitly evaluated. Further, the team has shown that these important quantities can be conveniently computed for any gradient waveform using a simple algorithm that requires a few lines of code. With this representation, the new technique complements the multiple correlation function method commonly used to compute the effects of restricted diffusion. It also provides a consistent and convenient framework for studies that aim to infer the microstructural features. To understand the meaning of MR signals, it is important, theoretically, to relate the applied waveform to an expected MR signal for a given structure or geometry. This has been studied extensively for single diffusion encoding (SDE). During the past year the team began this analysis for QTI by presenting the initial steps towards a general analytical framework with which one can derive explicit relationships for MR signal decay for a general time-dependent gradient waveform. Unlike earlier derivations, this one is based on path integrals and is more intuitive as it is based solely on probabilistic notions rather than the Bloch-Torrey equation. The team plans to extend this framework to develop efficient numerical methods for Gaussian and non-Gaussian diffusion models.

Constrained optimization of gradient waveforms for generalized diffusion encoding (FIGURE 2). The conventional diffusion encoding sequence, i.e., the single pulsed field gradient (PFG), has recently been challenged as more general gradient waveforms have been introduced, such as q-space trajectory imaging (QTI), which generalizes the scalar b-value to a tensor valued entity. To design gradient waveforms with specific features while taking full advantage of clinical scanner capabilities, it is imperative to respect the constraints imposed by the hardware while maximizing the diffusion encoding strength. The team has provided a tool that achieves this goal by solving a constrained optimization problem to accommodate constraints on maximum gradient amplitude, slew rate, coil heating, and positioning of radiofrequency pulses. The efficacy and flexibility of this method is demonstrated both experimentally and by comparison with previous work on optimization of isotropic diffusion sequences. Q-space trajectory imaging enables diffusion encoding with a general measurement tensor B. Although the “b-matrix” concept is well established and can be found in standard textbooks on diffusion NMR and MRI, the characterization of the b-matrix using double-PFG and more general gradient waveform diffusion MRI is novel and different. The team showed that it is possible to perform diffusion encoding imaging of the human brain (FIGURE 2) with arbitrary q-space trajectories while maintaining good SNR to generalize the concept that b-values can enable new types of measurements not available with single-PFG.

Sampling strategies and compressed sensing for high spatial resolution dMRI (FIGURES 3A, 3B). Compressed sensing (CS) is used in signal processing to reconstruct a signal from very few measurements or samples. CS has received significant attention in the MRI community for its ability to reconstruct an image with fewer data points, thereby reducing scan time. During the past year the team developed a novel sampling and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution images that effectively reduces acquisition time while improving signal-to-noise (SNR) ratio. The proposed method, called compressed-sensing super-resolution reconstruction, uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signals with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) using spherical ridgelets with total-variation regularization to account for signal correlation in neighboring voxels. The performance of the proposed method is quantitatively evaluated on several in vivo human data sets. The experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super-resolution dMRI data with very good data fidelity in a clinically feasible acquisition time. 
 

PROGRESS UNDER AIM 2: DEVELOPMENT OF PERSONALIZED ANATOMICAL ANALYSIS METHODS

Dr. Westin’s team further refined their white matter tractography clustering method to permit automated measurement of white matter regions in subjects with traumatic brain injury.

White matter segmentation using clustering for traumatic brain injury (FIGURE 4). The team continued to develop a white matter atlas creation method for the purpose of studying subjects with traumatic brain injury (TBI). The method learns a model of the common white matter structures present in a group of subjects. Based on group spectral clustering of tractography, the method discovers structures corresponding to expected white matter anatomy such as the corpus callosum, uncinate fasciculus, cingulum bundles, arcuate fasciculus, and corona radiata. The white matter clusters are augmented with expert anatomical labels and stored in a new type of atlas called a high-dimensional white matter atlas. The automatic tractography segmentation identifies corresponding white matter regions across hemispheres and across subjects, thus enabling group comparison of white matter anatomy. This preliminary study analyzed the white matter structures of 63 retired National Football League (NFL) players vs 26 healthy age-matched (ages 45-69) controls. The clustering method enabled automated measurement of 650 white matter regions in each subject, with 7% of the clusters showing lower fractional anisotropy (FA) in the player group. 
 

PROGRESS UNDER AIM 3: APPLICATION TO TBI: POPULATION STATISTICS AND PATIENT-SPECIFIC DIAGNOSIS

Dr. Westin’s team developed new QTI analysis methods and applied them to population studies in schizophrenia.

Application of analysis methods to population studies in schizophrenia (FIGURE 5). The team studied abnormal white matter connections between medial frontal regions to determine their value in predicting symptoms in patients with first episode schizophrenia. Clinically, a majority of patients with early stages of schizophrenia, a population frequently referred to as first episode schizophrenia (FESZ), demonstrate positive symptoms such as hallucinations and delusions. Negative symptoms also exist in the early stage of the illness, although they are observed less frequently in FESZ than in chronic schizophrenia populations. It has been suggested that the medial orbitofrontal cortex (mOFC) and rostral part of anterior cingulate cortex (rACC) are involved in the neural network of salience and emotional processing and associated with specific clinical symptoms of schizophrenia. Considering the schizophrenia dysconnectivity hypothesis, the connectivity abnormalities between mOFC and rACC might be associated with clinical characteristics in first episode schizophrenia patients (Figure 5). Diffusion tensor imaging (DTI) is a promising method for characterizing microstructural changes or differences with neuropathology and treatment. Diffusion MRI (dMRI) encodes information on translational displacements of water on the micrometer scale. Thus, the dMRI signal is an excellent probe for microstructural geometries in tissue such as the human brain. In dMRI, each millimeter-scale measurement contains an aggregate of information from a multitude of microscopic environments (microenvironments). The diffusion tensor characterizes the magnitude, anisotropy, and orientation of the diffusion tensor to reveal the relationships between DTI measures and white matter pathologic features (ischemia, myelination, axonal damage, inflammation, and edema). FA is highly sensitive to microstrutural changes, but not very specific as to the orientation of the change. The main findings of this study include a reduction of FA and significant increases in radial diffusivity (RD) and Trace (magnitude) without significant changes in axial diffusivity (AD) in white matter fibers connecting the mOFC and the rACC, suggesting that dismyelination might affect normal connectivity in this region in patients with FESZ. In addition, FA reduction in the fibers connecting the posterior mOFC and rACC was related to the severity of delusions and emotion-related negative symptoms in FESZ, implying an association between posterior mOFC- rACC disconnectivity and the psychopathology of early stage of schizophrenia.

The team also derived new microscopic measures based on q-trajectory imaging (QTI) and applied them to a schizophrenia population (FIGURE 6). Parameter maps derived from QTI were compared between patients with chronic schizophrenia and healthy controls. Nine 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 studying complex tissue architecture. Following a new approach, the team separated the bulk and shear variances into measures that are more intuitively meaningful, with the goal of separating size, shape, and orientation coherence. The measures were normalized and ranged between 0 and 1. Normalizing the bulk variance (VMD) yields a natural size variation parameter, CMD. Normalizing the shear variance is more challenging because it is affected by both the shape of the microenvironment and its orientation coherence (or dispersion). Using this approach, the team derived a measure of shape, Cμ, related to μFA and a separate measure of microscopic orientation coherence, Cc, related to previously presented order parameters (Figure 6). As expected, the schizophrenia patients showed elevated mean diffusivity (MD) in the white matter. Interestingly, this increase was matched by an increase in VMD. The increase cannot be explained by a homogeneous increase in the local mean diffusivity, rather it can be explained by an increase in the fraction of free water. This finding suggests that increased extracellular water, e.g., due to chronic neuroinflammatory processes or atrophy, is the primary mechanism underlying white matter diffusion changes in this cohort of patients with chronic schizophrenia. However, while reduced Cμ was also observed, Cc in the schizophrenia patients was unaltered, which could indicate axonal degradation at a microscopic level. Further studies with larger sample sizes are needed to confirm these findings. The current results, obtained by averaging throughout the entire white matter, do not lend themselves to the precise interpretation of the newly proposed measures in comparison with more traditional SDE measures. It may be more meaningful to study localized changes in both the white matter and the gray matter, and then to assess these results with analogy to simple prototype distributions.

Publications

Filip Szczepankiewicz, Scott Hoge, and Carl-Fredrik Westin. 7/2019. “Linear, Planar and Spherical Tensor-valued Diffusion MRI Data by Free Waveform Encoding in Healthy Brain, Water, Oil and Liquid Crystals.” Data Brief, 25, Pp. 104208.Abstract
Recently, several biophysical models and signal representations have been proposed for microstructure imaging based on tensor-valued, or multidimensional, diffusion MRI. The acquisition of the necessary data requires non-conventional pulse sequences, and data is therefore not available to the wider diffusion MRI community. To facilitate exploration and development of analysis techniques based on tensor-valued diffusion encoding, we share a comprehensive data set acquired in a healthy human brain. The data encompasses diffusion weighted images using linear, planar and spherical diffusion tensor encoding at multiple b-values and diffusion encoding directions. We also supply data acquired in several phantoms that may support validation. The data is hosted by GitHub: https://github.com/filip-szczepankiewicz/Szczepankiewicz_DIB_2019.
Fan Zhang, Lipeng Ning, Lauren J O'Donnell, and Ofer Pasternak. 4/2019. “MK-curve - Characterizing the Relation between Mean Kurtosis and Alterations in the Diffusion MRI Signal .” Neuroimage, 196, Pp. 68-80.Abstract
Diffusion kurtosis imaging (DKI) is a diffusion MRI (dMRI) technique to quantify brain microstructural properties. While DKI measures are sensitive to tissue alterations, they are also affected by signal alterations caused by imaging artifacts such as noise, motion and Gibbs ringing. Consequently, DKI often yields output parameter values (e.g. mean kurtosis; MK) that are implausible. These include implausible values that are outside of the range dictated by physics/biology, and visually apparent implausible values that form unexpected discontinuities, being too high or too low comparing with their neighborhood. These implausible values will introduce bias into any following data analyses (e.g. between-population statistical computation). Existing studies have attempted to correct implausible DKI parameter values in multiple ways; however, these approaches are not always effective. In this study, we propose a novel method for detecting and correcting voxels with implausible values to enable improved DKI parameter estimation. In particular, we focus on MK parameter estimation. We first characterize the relation between MK and alterations in the dMRI signal including diffusion weighted images (DWIs) and the baseline (b0) images. This is done by calculating MK for a range of synthetic DWI or b0 for each voxel, and generating curves (MK-curve) representing how alterations to the input dMRI signals affect the resulting output MK. We find that voxels with implausible MK values are more likely caused by artifacts in the b0 images than artifacts in DWIs with higher b-values. Accordingly, two characteristic b0 values, which define a range of synthetic b0 values that generate implausible MK values, are identified on the MK-curve. Based on this characterization, we propose an automatic approach for detection of voxels with implausible MK values by comparing a voxel's original b0 signal to the identified two characteristic b0 values, along with a correction strategy to replace the original b0 in each detected implausible voxel with a synthetic b0 value computed from the MK-curve. We evaluate the method on a DKI phantom dataset and dMRI datasets from the Human Connectome Project (HCP), and we compare the proposed correction method with other previously proposed correction methods. Results show that our proposed method is able to identify and correct most voxels with implausible DKI parameter values as well as voxels with implausible diffusion tensor parameter values.
Magnus Herberthson, Cem Yolcu, Hans Knutsson, Carl-Fredrik Westin, and Evren Özarslan. 3/2019. “Orientationally-averaged Diffusion-attenuated Magnetic Resonance Signal for Locally-anisotropic Diffusion.” Sci Rep, 9, 1, Pp. 4899.Abstract
Diffusion-attenuated MR signal for heterogeneous media has been represented as a sum of signals from anisotropic Gaussian sub-domains to the extent that this approximation is permissible. Any effect of macroscopic (global or ensemble) anisotropy in the signal can be removed by averaging the signal values obtained by differently oriented experimental schemes. The resulting average signal is identical to what one would get if the micro-domains are isotropically (e.g., randomly) distributed with respect to orientation, which is the case for "powdered" specimens. We provide exact expressions for the orientationally-averaged signal obtained via general gradient waveforms when the microdomains are characterized by a general diffusion tensor possibly featuring three distinct eigenvalues. This extends earlier results which covered only axisymmetric diffusion as well as measurement tensors. Our results are expected to be useful in not only multidimensional diffusion MR but also solid-state NMR spectroscopy due to the mathematical similarities in the two fields.
Lauren J O'Donnell, Alessandro Daducci, Demian Wassermann, and Christophe Lenglet. 4/2019. “Advances in Computational and Statistical Diffusion MRI.” NMR Biomed., 32, 4, Pp. e3805.Abstract
Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole-brain connectivity information that describes the brain's wiring diagram and population-based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high-level overview of interest to diffusion MRI researchers, with a more in-depth treatment to illustrate selected computational advances.
Ye Wu, Fan Zhang, Nikos Makris, Yuping Ning, Isaiah Norton, Shenglin She, Hongjun Peng, Yogesh Rathi, Yuanjing Feng, Huawang Wu, and Lauren J O'Donnell. 11/2018. “Investigation into Local White Matter Abnormality in Emotional Processing and Sensorimotor Areas using an Automatically Annotated Fiber Clustering in Major Depressive Disorder.” Neuroimage, 181, Pp. 16-29.Abstract
This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.
Fan Zhang, Ye Wu, Isaiah Norton, Laura Rigolo, Yogesh Rathi, Nikos Makris, and Lauren J O'Donnell. 11/2018. “An Anatomically Curated Fiber Clustering White Matter Atlas for Consistent White Matter Tract Parcellation across the Lifespan .” Neuroimage, 179, Pp. 429-47.Abstract
This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.
Jordan A Chad, Ofer Pasternak, David H Salat, and Jean J Chen. 11/2018. “Re-examining Age-related Differences in White Matter Microstructure with Free-water Corrected Diffusion Tensor Imaging.” Neurobiol Aging, 71, Pp. 161-70.Abstract
Diffusion tensor imaging (DTI) has been used extensively to investigate white matter (WM) microstructural changes during healthy adult aging. However, WM fibers are known to shrink throughout the lifespan, leading to larger interstitial spaces with age. This could allow more extracellular free water molecules to bias DTI metrics, which are relied upon to provide WM microstructural information. Using a cohort of 212 participants, we demonstrate that WM microstructural changes in aging are potentially less pronounced than previously reported once the free water compartment is eliminated. After free water elimination, DTI parameters show age-related differences that match histological evidence of myelin degradation and debris accumulation. The fraction of free water is further shown to associate better with age than any of the conventional DTI parameters. Our findings suggest that DTI analyses involving free water are likely to yield novel insight into retrospective re-analysis of data and to answer new questions in ongoing DTI studies of brain aging.
Yi Hong, Lauren J O'Donnell, Peter Savadjiev, Fan Zhang, Demian Wassermann, Ofer Pasternak, Hans Johnson, Jane Paulsen, Jean-Paul Vonsattel, Nikos Makris, Carl F Westin, and Yogesh Rathi. 10/2018. “Genetic Load Determines Atrophy in Hand Cortico-striatal Pathways in Presymptomatic Huntington's Disease.” Hum Brain Mapp, 39, 10, Pp. 3871-83.Abstract
Huntington's disease (HD) is an inherited neurodegenerative disorder that causes progressive breakdown of striatal neurons. Standard white matter integrity measures like fractional anisotropy and mean diffusivity derived from diffusion tensor imaging were analyzed in prodromal-HD subjects; however, they studied either a whole brain or specific subcortical white matter structures with connections to cortical motor areas. In this work, we propose a novel analysis of a longitudinal cohort of 243 prodromal-HD individuals and 88 healthy controls who underwent two or more diffusion MRI scans as part of the PREDICT-HD study. We separately trace specific white matter fiber tracts connecting the striatum (caudate and putamen) with four cortical regions corresponding to the hand, face, trunk, and leg motor areas. A multi-tensor tractography algorithm with an isotropic volume fraction compartment allows estimating diffusion of fast-moving extra-cellular water in regions containing crossing fibers and provides quantification of a microstructural property related to tissue atrophy. The tissue atrophy rate is separately analyzed in eight cortico-striatal pathways as a function of CAG-repeats (genetic load) by statistically regressing out age effect from our cohort. The results demonstrate a statistically significant increase in isotropic volume fraction (atrophy) bilaterally in hand fiber connections to the putamen with increasing CAG-repeats, which connects the genetic abnormality (CAG-repeats) to an imaging-based microstructural marker of tissue integrity in specific white matter pathways in HD. Isotropic volume fraction measures in eight cortico-striatal pathways are also correlated significantly with total motor scores and diagnostic confidence levels, providing evidence of their relevance to HD clinical presentation.
Shun Gong, Fan Zhang, Isaiah Norton, Walid I Essayed, Prashin Unadkat, Laura Rigolo, Ofer Pasternak, Yogesh Rathi, Lijun Hou, Alexandra J Golby, and Lauren J O'Donnell. 5/2018. “Free Water Modeling of Peritumoral Edema using Multi-fiber Tractography: Application to Tracking the Arcuate Fasciculus for Neurosurgical Planning.” PLoS One, 13, 5, Pp. e0197056.Abstract
PURPOSE: Peritumoral edema impedes the full delineation of fiber tracts due to partial volume effects in image voxels that contain a mixture of cerebral parenchyma and extracellular water. The purpose of this study is to investigate the effect of incorporating a free water (FW) model of edema for white matter tractography in the presence of edema. MATERIALS AND METHODS: We retrospectively evaluated 26 consecutive brain tumor patients with diffusion MRI and T2-weighted images acquired presurgically. Tractography of the arcuate fasciculus (AF) was performed using the two-tensor unscented Kalman filter tractography (UKFt) method, the UKFt method with a reduced fiber tracking stopping fractional anisotropy (FA) threshold (UKFt+rFA), and the UKFt method with the addition of a FW compartment (UKFt+FW). An automated white matter fiber tract identification approach was applied to delineate the AF. Quantitative measurements included tract volume, edema volume, and mean FW fraction. Visual comparisons were performed by three experts to evaluate the quality of the detected AF tracts. RESULTS: The AF volume in edematous brain hemispheres was significantly larger using the UKFt+FW method (p<0.0001) compared to UKFt, but not significantly larger (p = 0.0996) in hemispheres without edema. The AF size increase depended on the volume of edema: a significant correlation was found between AF volume affected by (intersecting) edema and AF volume change with the FW model (Pearson r = 0.806, p<0.0001). The mean FW fraction was significantly larger in tracts intersecting edema (p = 0.0271). Compared to the UKFt+rFA method, there was a significant increase of the volume of the AF tract that intersected the edema using the UKFt+FW method, while the whole AF volumes were similar. Expert judgment results, based on the five patients with the smallest AF volumes, indicated that the expert readers generally preferred the AF tract obtained by using the FW model, according to their anatomical knowledge and considering the potential influence of the final results on the surgical route. CONCLUSION: Our results indicate that incorporating biophysical models of edema can increase the sensitivity of tractography in regions of peritumoral edema, allowing better tract visualization in patients with high grade gliomas and metastases.
Evren Özarslan, Cem Yolcu, Magnus Herberthson, Hans Knutsson, and Carl-Fredrik Westin. 1/2018. “Influence of the Size and Curvedness of Neural Projections on the Orientationally Averaged Diffusion MR Signal .” Front Phys, 6.Abstract
Neuronal and glial projections can be envisioned to be tubes of infinitesimal diameter as far as diffusion magnetic resonance (MR) measurements via clinical scanners are concerned. Recent experimental studies indicate that the decay of the orientationally-averaged signal in white-matter may be characterized by the power-law, () ∝ , where is the wavenumber determined by the parameters of the pulsed field gradient measurements. One particular study by McKinnon . [1] reports a distinctively faster decay in gray-matter. Here, we assess the role of the size and curvature of the neurites and glial arborizations in these experimental findings. To this end, we studied the signal decay for diffusion along general curves at all three temporal regimes of the traditional pulsed field gradient measurements. We show that for curvy projections, employment of longer pulse durations leads to a disappearance of the decay, while such decay is robust when narrow gradient pulses are used. Thus, in clinical acquisitions, the lack of such a decay for a fibrous specimen can be seen as indicative of fibers that are curved. We note that the above discussion is valid for an intermediate range of -values as the true asymptotic behavior of the signal decay is () ∝ for narrow pulses (through Debye-Porod law) or steeper for longer pulses. This study is expected to provide insights for interpreting the diffusion-weighted images of the central nervous system and aid in the design of acquisition strategies.
Lipeng Ning, Markus Nilsson, Samo Lasič, Carl-Fredrik Westin, and Yogesh Rathi. 2/2018. “Cumulant Expansions for Measuring Water Exchange using Diffusion MRI.” J Chem Phys, 148, 7, Pp. 074109.Abstract
The rate of water exchange across cell membranes is a parameter of biological interest and can be measured by diffusion magnetic resonance imaging (dMRI). In this work, we investigate a stochastic model for the diffusion-and-exchange of water molecules. This model provides a general solution for the temporal evolution of dMRI signal using any type of gradient waveform, thereby generalizing the signal expressions for the Kärger model. Moreover, we also derive a general nth order cumulant expansion of the dMRI signal accounting for water exchange, which has not been explored in earlier studies. Based on this analytical expression, we compute the cumulant expansion for dMRI signals for the special case of single diffusion encoding (SDE) and double diffusion encoding (DDE) sequences. Our results provide a theoretical guideline on optimizing experimental parameters for SDE and DDE sequences, respectively. Moreover, we show that DDE signals are more sensitive to water exchange at short-time scale but provide less attenuation at long-time scale than SDE signals. Our theoretical analysis is also validated using Monte Carlo simulations on synthetic structures.
Angela Albi, Antonio Meola, Fan Zhang, Pegah Kahali, Laura Rigolo, Chantal MW Tax, Pelin Aksit Ciris, Walid I Essayed, Prashin Unadkat, Isaiah Norton, Yogesh Rathi, Olutayo Olubiyi, Alexandra J Golby, and Lauren J O'Donnell. 3/2018. “Image Registration to Compensate for EPI Distortion in Patients with Brain Tumors: An Evaluation of Tract-Specific Effects.” J Neuroimaging, 28, 2, Pp. 173-82.Abstract
BACKGROUND AND PURPOSE: Diffusion magnetic resonance imaging (dMRI) provides preoperative maps of neurosurgical patients' white matter tracts, but these maps suffer from echo-planar imaging (EPI) distortions caused by magnetic field inhomogeneities. In clinical neurosurgical planning, these distortions are generally not corrected and thus contribute to the uncertainty of fiber tracking. Multiple image processing pipelines have been proposed for image-registration-based EPI distortion correction in healthy subjects. In this article, we perform the first comparison of such pipelines in neurosurgical patient data. METHODS: Five pipelines were tested in a retrospective clinical dMRI dataset of 9 patients with brain tumors. Pipelines differed in the choice of fixed and moving images and the similarity metric for image registration. Distortions were measured in two important tracts for neurosurgery, the arcuate fasciculus and corticospinal tracts. RESULTS: Significant differences in distortion estimates were found across processing pipelines. The most successful pipeline used dMRI baseline and T2-weighted images as inputs for distortion correction. This pipeline gave the most consistent distortion estimates across image resolutions and brain hemispheres. CONCLUSIONS: Quantitative results of mean tract distortions on the order of 1-2 mm are in line with other recent studies, supporting the potential need for distortion correction in neurosurgical planning. Novel results include significantly higher distortion estimates in the tumor hemisphere and greater effect of image resolution choice on results in the tumor hemisphere. Overall, this study demonstrates possible pitfalls and indicates that care should be taken when implementing EPI distortion correction in clinical settings.
Fan Zhang, Weining Wu, Lipeng Ning, Gloria McAnulty, Deborah Waber, Borjan Gagoski, Kiera Sarill, Hesham M Hamoda, Yang Song, Weidong Cai, Yogesh Rathi, and Lauren J O'Donnell. 5/2018. “Suprathreshold Fiber Cluster Statistics: Leveraging White Matter Geometry to Enhance Tractography Statistical Analysis.” Neuroimage, 171, Pp. 341-54.Abstract
This work presents a suprathreshold fiber cluster (STFC) method that leverages the whole brain fiber geometry to enhance statistical group difference analyses. The proposed method consists of 1) a well-established study-specific data-driven tractography parcellation to obtain white matter tract parcels and 2) a newly proposed nonparametric, permutation-test-based STFC method to identify significant differences between study populations. The basic idea of our method is that a white matter parcel's neighborhood (nearby parcels with similar white matter anatomy) can support the parcel's statistical significance when correcting for multiple comparisons. We propose an adaptive parcel neighborhood strategy to allow suprathreshold fiber cluster formation that is robust to anatomically varying inter-parcel distances. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder patients and 29 healthy controls. Evaluations are conducted using both synthetic and in-vivo data. The results indicate that the STFC method gives greater sensitivity in finding group differences in white matter tract parcels compared to several traditional multiple comparison correction methods.
Laura Stefanik, Lauren Erdman, Stephanie H Ameis, George Foussias, Benoit H Mulsant, Tina Behdinan, Anna Goldenberg, Lauren J O'Donnell, and Aristotle N Voineskos. 4/2018. “Brain-Behavior Participant Similarity Networks Among Youth and Emerging Adults with Schizophrenia Spectrum, Autism Spectrum, or Bipolar Disorder and Matched Controls.” Neuropsychopharmacology, 43, 5, Pp. 1180-8.Abstract
There is considerable heterogeneity in social cognitive and neurocognitive performance among people with schizophrenia spectrum disorders (SSD), autism spectrum disorders (ASD), bipolar disorder (BD), and healthy individuals. This study used Similarity Network Fusion (SNF), a novel data-driven approach, to identify participant similarity networks based on relationships among demographic, brain imaging, and behavioral data. T1-weighted and diffusion-weighted magnetic resonance images were obtained for 174 adolescents and young adults (aged 16-35 years) with an SSD (n=51), an ASD without intellectual disability (n=38), euthymic BD (n=34), and healthy controls (n=51). A battery of social cognitive and neurocognitive tasks were administered. Data integration, cluster determination, and biological group formation were then obtained using SNF. We identified four new groups of individuals, each with distinct neural circuit-cognitive profiles. The most influential variables driving the formation of the new groups were robustly reliable across embedded resampling techniques. The data-driven groups showed considerably greater differentiation on key social and neurocognitive circuit nodes than groups generated by diagnostic analyses or dimensional social cognitive analyses. The data-driven groups were validated through functional outcome and brain network property measures not included in the SNF model. Cutting across diagnostic boundaries, our approach can effectively identify new groups of people based on a profile of neuroimaging and behavioral data. Our findings bring us closer to disease subtyping that can be leveraged toward the targeting of specific neural circuitry among participant subgroups to ameliorate social cognitive and neurocognitive deficits.
Fan Zhang, Peter Savadjiev, Weidong Cai, Yang Song, Yogesh Rathi, Birkan Tunç, Drew Parker, Tina Kapur, Robert T Schultz, Nikos Makris, Ragini Verma, and Lauren J O'Donnell. 5/2018. “Whole Brain White Matter Connectivity Analysis using Machine Learning: An Application to Autism.” Neuroimage, 172, Pp. 826-37.Abstract
In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.
Johanna Seitz, Yogesh Rathi, Amanda Lyall, Ofer Pasternak, Elisabetta C Del Re, Margaret Niznikiewicz, Paul Nestor, Larry J Seidman, Tracey L Petryshen, Raquelle I Mesholam-Gately, Joanne Wojcik, Robert W McCarley, Martha E Shenton, Inga K Koerte, and Marek Kubicki. 2/2018. “Alteration of Gray Matter Microstructure in Schizophrenia.” Brain Imaging Behav, 12, 1, Pp. 54-63.Abstract
Neuroimaging studies demonstrate gray matter (GM) macrostructural abnormalities in patients with schizophrenia (SCZ). While ex-vivo and genetic studies suggest cellular pathology associated with abnormal neurodevelopmental processes in SCZ, few in-vivo measures have been proposed to target microstructural GM organization. Here, we use diffusion heterogeneity- to study GM microstructure in SCZ. Structural and diffusion magnetic resonance imaging (MRI) were acquired on a 3 Tesla scanner in 46 patients with SCZ and 37 matched healthy controls (HC). After correction for free water, diffusion heterogeneity as well as commonly used diffusion measures FA and MD and volume were calculated for the four cortical lobes on each hemisphere, and compared between groups. Patients with early course SCZ exhibited higher diffusion heterogeneity in the GM of the frontal lobes compared to controls. Diffusion heterogeneity of the frontal lobe showed excellent discrimination between patients and HC, while none of the commonly used diffusion measures such as FA or MD did. Higher diffusion heterogeneity in the frontal lobes in early SCZ may be due to abnormal brain maturation (migration, pruning) before and during adolescence and early adulthood. Further studies are needed to investigate the role of heterogeneity as potential biomarker for SCZ risk.
Markus Nilsson, Johan Larsson, Dan Lundberg, Filip Szczepankiewicz, Thomas Witzel, Carl-Fredrik Westin, Karin Bryskhe, and Daniel Topgaard. 3/2018. “Liquid Crystal Phantom for Validation of Microscopic Diffusion Anisotropy Measurements on Clinical MRI Systems.” Magn Reson Med, 79, 3, Pp. 1817-28.Abstract
PURPOSE: To develop a phantom for validating MRI pulse sequences and data processing methods to quantify microscopic diffusion anisotropy in the human brain. METHODS: Using a liquid crystal consisting of water, detergent, and hydrocarbon, we designed a 0.5-L spherical phantom showing the theoretically highest possible degree of microscopic anisotropy. Data were acquired on the Connectome scanner using echo-planar imaging signal readout and diffusion encoding with axisymmetric b-tensors of varying magnitude, anisotropy, and orientation. The mean diffusivity, fractional anisotropy (FA), and microscopic FA (µFA) parameters were estimated. RESULTS: The phantom was observed to have values of mean diffusivity similar to brain tissue, and relaxation times compatible with echo-planar imaging echo times on the order of 100 ms. The estimated values of µFA were at the theoretical maximum of 1.0, whereas the values of FA spanned the interval from 0.0 to 0.8 as a result of varying orientational order of the anisotropic domains within each voxel. CONCLUSIONS: The proposed phantom can be manufactured by mixing three widely available chemicals in volumes comparable to a human head. The acquired data are in excellent agreement with theoretical predictions, showing that the phantom is ideal for validating methods for measuring microscopic diffusion anisotropy on clinical MRI systems. 
Toshiyuki Ohtani, Paul G Nestor, Sylvain Bouix, Dominick Newell, Eric D Melonakos, Robert W McCarley, Martha E Shenton, and Marek Kubicki. 1/2017. “Exploring the Neural Substrates of Attentional Control and Human Intelligence: Diffusion Tensor Imaging of Prefrontal White Matter Tractography in Healthy Cognition.” Neuroscience, 341, Pp. 52-60.Abstract
We combined diffusion tension imaging (DTI) of prefrontal white matter integrity and neuropsychological measures to examine the functional neuroanatomy of human intelligence. Healthy participants completed the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) along with neuropsychological tests of attention and executive control, as measured by Trail Making Test (TMT) and Wisconsin Card Sorting Test (WCST). Stochastic tractography, considered the most effective DTI method, quantified white matter integrity of the medial orbital frontal cortex (mOFC) and rostral anterior cingulate cortex (rACC) circuitry. Based on prior studies, we hypothesized that posterior mOFC-rACC connections may play a key structural role linking attentional control processes and intelligence. Behavioral results provided strong support for this hypothesis, specifically linking attentional control processes, measured by Trails B and WCST perseverative errors, to intelligent quotient (IQ). Hierarchical regression results indicated left posterior mOFC-rACC fractional anisotropy (FA) and Trails B performance time, but not WCST perseverative errors, each contributed significantly to IQ, accounting for approximately 33.95-51.60% of the variance in IQ scores. These findings suggested that left posterior mOFC-rACC white matter connections may play a key role in supporting the relationship of executive functions of attentional control and general intelligence in healthy cognition.
Klaus H Maier-Hein, Peter F Neher, Jean-Christophe Houde, Marc-Alexandre Côté, Eleftherios Garyfallidis, Jidan Zhong, Maxime Chamberland, Fang-Cheng Yeh, Ying-Chia Lin, Qing Ji, Wilburn E Reddick, John O Glass, David Qixiang Chen, Yuanjing Feng, Chengfeng Gao, Ye Wu, Jieyan Ma, H Renjie, Qiang Li, Carl-Fredrik Westin, Samuel Deslauriers-Gauthier, Omar Ocegueda J González, Michael Paquette, Samuel St-Jean, Gabriel Girard, François Rheault, Jasmeen Sidhu, Chantal MW Tax, Fenghua Guo, Hamed Y Mesri, Szabolcs Dávid, Martijn Froeling, Anneriet M Heemskerk, Alexander Leemans, Arnaud Boré, Basile Pinsard, Christophe Bedetti, Matthieu Desrosiers, Simona Brambati, Julien Doyon, Alessia Sarica, Roberta Vasta, Antonio Cerasa, Aldo Quattrone, Jason Yeatman, Ali R Khan, Wes Hodges, Simon Alexander, David Romascano, Muhamed Barakovic, Anna Auría, Oscar Esteban, Alia Lemkaddem, Jean-Philippe Thiran, Ertan H Cetingul, Benjamin L Odry, Boris Mailhe, Mariappan S Nadar, Fabrizio Pizzagalli, Gautam Prasad, Julio E Villalon-Reina, Justin Galvis, Paul M Thompson, Francisco De Santiago Requejo, Pedro Luque Laguna, Luis Miguel Lacerda, Rachel Barrett, Flavio Dell'Acqua, Marco Catani, Laurent Petit, Emmanuel Caruyer, Alessandro Daducci, Tim B Dyrby, Tim Holland-Letz, Claus C Hilgetag, Bram Stieltjes, and Maxime Descoteaux. 11/2017. “The Challenge of Mapping the Human Connectome Based on Diffusion Tractography.” Nat Commun, 8, 1, Pp. 1349.Abstract
Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.
Isaiah Norton, Walid Ibn Essayed, Fan Zhang, Sonia Pujol, Alex Yarmarkovich, Alexandra J Golby, Gordon Kindlmann, Demian Wasserman, Raul San Jose Estepar, Yogesh Rathi, Steve Pieper, Ron Kikinis, Hans J Johnson, Carl-Fredrik Westin, and Lauren J O'Donnell. 11/2017. “SlicerDMRI: Open Source Diffusion MRI Software for Brain Cancer Research.” Cancer Res, 77, 21, Pp. e101-e103.Abstract
Diffusion MRI (dMRI) is the only noninvasive method for mapping white matter connections in the brain. We describe SlicerDMRI, a software suite that enables visualization and analysis of dMRI for neuroscientific studies and patient-specific anatomic assessment. SlicerDMRI has been successfully applied in multiple studies of the human brain in health and disease, and here, we especially focus on its cancer research applications. As an extension module of the 3D Slicer medical image computing platform, the SlicerDMRI suite enables dMRI analysis in a clinically relevant multimodal imaging workflow. Core SlicerDMRI functionality includes diffusion tensor estimation, white matter tractography with single and multi-fiber models, and dMRI quantification. SlicerDMRI supports clinical DICOM and research file formats, is open-source and cross-platform, and can be installed as an extension to 3D Slicer (www.slicer.org). More information, videos, tutorials, and sample data are available at dmri.slicer.org Cancer Res; 77(21); e101-3. ©2017 AACR.