Coherence and causality measures are often used to analyze the influence of one region on another during analysis of functional brain networks. The analysis methods usually involve a regression problem, where the signal of interest is decomposed into a mixture of regressor and a residual signal. In this paper, we revisit this basic problem and present solutions that provide the minimal-entropy residuals for different types of regression filters, such as causal, instantaneously causal, and noncausal filters. Using optimal prediction theory, we derive several novel frequency-domain expressions for partial coherence, causality, and conditional causality analysis. In particular, our solution provides a more accurate estimation of the frequency-domain causality compared with the classical Geweke causality measure. Using synthetic examples and in vivo resting-state functional magnetic resonance imaging data from the human connectome project, we show that the proposed solution is more accurate at revealing frequency-domain linear dependence among high-dimensional signals.
This paper presents a novel approach to modeling the pos terior distribution in image registration that is computationally efficient for large deformation diffeomorphic metric mapping (LDDMM). We develop a Laplace approximation of Bayesian registration models entirely in a bandlimited space that fully describes the properties of diffeomorphic transformations. In contrast to current methods, we compute the inverse Hessian at the mode of the posterior distribution of diffeomorphisms directly in the low dimensional frequency domain. This dramatically reduces the computational complexity of approximating posterior marginals in the high dimensional imaging space. Experimental results show that our method is significantly faster than the state-of-the-art diffeomorphic image registration uncertainty quantification algorithms, while producing comparable results. The efficiency of our method strengthens the feasibility in prospective clinical applications, e.g., real- time image-guided navigation for brain surgery.
We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the intermediate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incomplete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Compared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.
INTRODUCTION: Diffusion tensor imaging detects early tissue alterations in Alzheimer's disease and cerebral small vessel disease (SVD). However, the origin of diffusion alterations in SVD is largely unknown. METHODS: To gain further insight, we applied free water (FW) imaging to patients with genetically defined SVD (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy [CADASIL], n = 57), sporadic SVD (n = 444), and healthy controls (n = 28). We modeled freely diffusing water in the extracellular space (FW) and measures reflecting fiber structure (tissue compartment). We tested associations between these measures and clinical status (processing speed and disability). RESULTS: Diffusion alterations in SVD were mostly driven by increased FW and less by tissue compartment alterations. Among imaging markers, FW showed the strongest association with clinical status (Rup to 34%, P < .0001). Findings were consistent across patients with CADASIL and sporadic SVD. DISCUSSION: Diffusion alterations and clinical status in SVD are largely determined by extracellular fluid increase rather than alterations of white matter fiber organization.
Mild traumatic brain injuries (mTBIs) are often associated with posttraumatic stress disorder (PTSD). In cases of chronic mTBI, accurate diagnosis can be challenging due to the overlapping symptoms this condition shares with PTSD. Furthermore, mTBIs are heterogeneous and not easily observed using conventional neuroimaging tools, despite the fact that diffuse axonal injuries are the most common injury. Diffusion tensor imaging (DTI) is sensitive to diffuse axonal injuries and is thus more likely to detect mTBIs, especially when analyses account for the inter-individual variability of these injuries. Using a subject-specific approach, we compared fractional anisotropy (FA) abnormalities between groups with a history of mTBI (n = 35), comorbid mTBI and PTSD (mTBI + PTSD; n = 22), and healthy controls (n = 37). We compared all three groups on the number of abnormal FA clusters derived from subject-specific injury profiles (i.e., individual z-score maps) along a common white matter skeleton. The mTBI + PTSD group evinced a greater number of abnormally low FA clusters relative to both the healthy controls and the mTBI group without PTSD (p < .05). Across the groups with a history of mTBI, increased numbers of abnormally low FA clusters were significantly associated with PTSD symptom severity, depression, post-concussion symptoms, and reduced information processing speed (p < .05). These findings highlight the utility of subject-specific microstructural analyses when searching for mTBI-related brain abnormalities, particularly in patients with PTSD. This study also suggests that patients with a history of mTBI and comorbid PTSD, relative to those without PTSD, are at increased risk of FA abnormalities.
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.
OBJECTIVE: To address the feasibility and predictive value of multimodal image-based virtual reality in detecting and assessing features of neurovascular confliction (NVC), particularly regarding the detection of offending vessels, degree of compression exerted on the nerve root, in patients who underwent microvascular decompression for nonlesional trigeminal neuralgia and hemifacial spasm (HFS). METHODS: This prospective study includes 42 consecutive patients who underwent microvascular decompression for classic primary trigeminal neuralgia or HFS. All patients underwent preoperative 1.5-T magnetic resonance imaging (MRI) with T2-weighted three-dimensional (3D) sampling perfection with application-optimized contrasts by using different flip angle evolutions, 3D time-of-flight magnetic resonance angiography, and 3D T1-weighted gadolinium-enhanced sequences in combination, whereas 2 patients underwent extra experimental preoperative 7.0-T MRI scans with the same imaging protocol. Multimodal MRIs were then coregistered with open-source software 3D Slicer, followed by 3D image reconstruction to generate virtual reality (VR) images for detection of possible NVC in the cerebellopontine angle. Evaluations were performed by 2 reviewers and compared with the intraoperative findings. RESULTS: For detection of NVC, multimodal image-based VR sensitivity was 97.6% (40/41) and specificity was 100% (1/1). Compared with the intraoperative findings, the κ coefficients for predicting the offending vessel and the degree of compression were >0.75 (P < 0.001). The 7.0-T scans have a clearer view of vessels in the cerebellopontine angle, which may have significant impact on detection of small-caliber offending vessels with relatively slow flow speed in cases of HFS. CONCLUSIONS: Multimodal image-based VR using 3D sampling perfection with application-optimized contrasts by using different flip angle evolutions in combination with 3D time-of-flight magnetic resonance angiography sequences proved to be reliable in detecting NVC and in predicting the degree of root compression. The VR image-based simulation correlated well with the real surgical view.
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.
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.
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.
Diffusion tensor imaging studies report childhood adversity (CA) is associated with reduced fractional anisotropy (FA) in multiple white matter tracts in adults. Reduced FA may result from changes in tissue, suggesting myelin/axonal damage, and/or from increased levels of extracellular free-water, suggesting atrophy or neuroinflammation. Free-water imaging can separately identify FA in tissue (FA) and the fractional volume of free-water (FW). We tested whether CA was associated with altered FA, FA, and FW in seven white matter regions of interest (ROI), in which FA changes had been previously linked to CA (corona radiata, corpus callosum, fornix, cingulum bundle: hippocampal projection, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, uncinate fasciculus). Tract-based spatial statistics were performed in 147 psychiatrically healthy adults who had completed a self-report questionnaire on CA primarily stemming from parental maltreatment. ROI were extracted according to the protocol provided by the ENIGMA-DTI working group. Analyses were performed both treating CA as a continuous and a categorical variable. CA was associated with reduced FA in all ROI (although categorical analyses failed to find an association in the fornix). In contrast, CA was only associated with reduced FAin the corona radiata, corpus callosum, and uncinate fasciculus (with the continuous measure of CA finding evidence of a negative relation between CA and FAin the fornix). There was no association between CA on FW in any ROI. These results provide preliminary evidence that childhood adversity is associated with changes to the microstructure of white matter itself in adulthood. However, these results should be treated with caution until they can be replicated by future studies which address the limitations of the present study.
Current theories hold that brain function is highly related to long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcellation based on extrinsic connectivity remains challenging. Current parcellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity of the cortex. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parceling technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with structural and functional parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with the motor strip mapping included in the Human Connectome Project data.
OBJECTIVE Endoscopic endonasal approaches are increasingly performed for the surgical treatment of multiple skull base pathologies. Preventing postoperative CSF leaks remains a major challenge, particularly in extended approaches. In this study, the authors assessed the potential use of modern multimaterial 3D printing and neuronavigation to help model these extended defects and develop specifically tailored prostheses for reconstructive purposes. METHODS Extended endoscopic endonasal skull base approaches were performed on 3 human cadaveric heads. Preprocedure and intraprocedure CT scans were completed and were used to segment and design extended and tailored skull base models. Multimaterial models with different core/edge interfaces were 3D printed for implantation trials. A novel application of the intraoperative landmark acquisition method was used to transfer the navigation, helping to tailor the extended models. RESULTS Prostheses were created based on preoperative and intraoperative CT scans. The navigation transfer offered sufficiently accurate data to tailor the preprinted extended skull base defect prostheses. Successful implantation of the skull base prostheses was achieved in all specimens. The progressive flexibility gradient of the models' edges offered the best compromise for easy intranasal maneuverability, anchoring, and structural stability. Prostheses printed based on intraprocedure CT scans were accurate in shape but slightly undersized. CONCLUSIONS Preoperative 3D printing of patient-specific skull base models is achievable for extended endoscopic endonasal surgery. The careful spatial modeling and the use of a flexibility gradient in the design helped achieve the most stable reconstruction. Neuronavigation can help tailor preprinted prostheses.
Objectives: To determine whether or not automated FreeSurfer segmentation of brain regions considered important in repetitive head trauma can be analyzed accurately without manual correction. Materials and methods: 3 T MR neuroimaging was performed with automated FreeSurfer segmentation and manual correction of 11 brain regions in former National Football League (NFL) players with neurobehavioral symptoms and in control subjects. Automated segmentation and manually-corrected volumes were compared using an intraclass correlation coefficient (ICC). Linear mixed effects regression models were also used to estimate between-group mean volume comparisons and to correlate former NFL player brain volumes with neurobehavioral factors. Results: Eighty-six former NFL players (55.2 ± 8.0 years) and 22 control subjects (57.0 ± 6.6 years) were evaluated. ICC was highly correlated between automated and manually-corrected corpus callosum volumes (0.911), lateral ventricular volumes (right 0.980, left 0.967), and amygdala-hippocampal complex volumes (right 0.713, left 0.731), but less correlated when amygdalae (right -0.170, left -0.090) and hippocampi (right 0.539, left 0.637) volumes were separately delineated and also less correlated for cingulate gyri volumes (right 0.639, left 0.351). Statistically significant differences between former NFL player and controls were identified in 8 of 11 regions with manual correction but in only 4 of 11 regions without such correction. Within NFL players, manually corrected brain volumes were significantly associated with 3 neurobehavioral factors, but a different set of 3 brain regions and neurobehavioral factor correlations was observed for brain region volumes segmented without manual correction. Conclusions: Automated FreeSurfer segmentation of the corpus callosum, lateral ventricles, and amygdala-hippocampus complex may be appropriate for analysis without manual correction. However, FreeSurfer segmentation of the amygdala, hippocampus, and cingulate gyrus need further manual correction prior to performing group comparisons and correlations with neurobehavioral measures.
Mastering the technical skills required to perform pediatric cardiac valve surgery is challenging in part due to limited opportunity for practice. Transformation of 3D echocardiographic (echo) images of congenitally abnormal heart valves to realistic physical models could allow patient-specific simulation of surgical valve repair. We compared materials, processes, and costs for 3D printing and molding of patient-specific models for visualization and surgical simulation of congenitally abnormal heart valves. Pediatric atrioventricular valves (mitral, tricuspid, and common atrioventricular valve) were modeled from transthoracic 3D echo images using semi-automated methods implemented as custom modules in 3D Slicer. Valve models were then both 3D printed in soft materials and molded in silicone using 3D printed "negative" molds. Using pre-defined assessment criteria, valve models were evaluated by congenital cardiac surgeons to determine suitability for simulation. Surgeon assessment indicated that the molded valves had superior material properties for the purposes of simulation compared to directly printed valves (p < 0.01). Patient-specific, 3D echo-derived molded valves are a step toward realistic simulation of complex valve repairs but require more time and labor to create than directly printed models. Patient-specific simulation of valve repair in children using such models may be useful for surgical training and simulation of complex congenital cases.
Free Water Imaging is a novel diffusion magnetic resonance (MR) imaging method that is able to separate changes affecting the extracellular space from those that reflect changes in neuronal cells and processes. A previous Free Water Imaging study in schizophrenia identified significantly greater extracellular water volume in the early stages of the disorder; however, its clinical and functional sequelae have not yet been investigated. Here, we applied Free Water Imaging to a larger cohort of 63 first-episode patients with psychosis and 70 healthy matched controls to better understand the functional significance of greater extracellular water. We used diffusion MR imaging data and the Tract-Based Spatial Statistics analytic pipeline to first analyze fractional anisotropy (FA), the most commonly employed metric for assessing white matter. This comparison was then followed by Free Water Imaging analysis, where two parameters, the fractional volume of extracellular free-water (FW) and cellular tissue FA (FA-t), were estimated and compared across the entire white matter skeleton between groups, and correlated with cognitive measures at baseline and following 12 weeks of antipsychotic treatment. Our results indicated lower FA across the whole brain in patients compared with healthy controls that overlap with significant increases in FW, with only limited decreases in FA-t. In addition, higher FW correlated with better neurocognitive functioning following 12 weeks of antipsychotic treatment. We believe this is the first study to suggest that an extracellular water increase during the first-episode of psychosis, which may be indicative of an acute neuroinflammatory process, and/or cerebral edema may predict better functional outcome.
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.
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.
BACKGROUND AND PURPOSE: Free water in the posterior substantia nigra obtained from a bi-tensor diffusion MR imaging model has been shown to significantly increase over 1- and 4-year periods in patients with early-stage idiopathic Parkinson disease compared with healthy controls, which suggests that posterior substantia nigra free water may be an idiopathic Parkinson disease progression biomarker. Due to the known temporal posterior-to-anterior substantia nigra degeneration in idiopathic Parkinson disease, we assessed longitudinal changes in free water in both the posterior and anterior substantia nigra in patients with later-stage idiopathic Parkinson disease and age-matched healthy controls for comparison. MATERIALS AND METHODS: Nineteen subjects with idiopathic Parkinson disease and 19 age-matched healthy control subjects were assessed on the same 3T MR imaging scanner at baseline and after approximately 3 years. RESULTS: Baseline mean idiopathic Parkinson disease duration was 7.1 years. Both anterior and posterior substantia nigra free water showed significant intergroup differences at baseline (< .001 and= .014, respectively, idiopathic Parkinson disease versus healthy controls); however, only anterior substantia nigra free water showed significant longitudinal group × time interaction increases (= .021, idiopathic Parkinson disease versus healthy controls). There were no significant longitudinal group × time interaction differences found for conventional diffusion tensor imaging or free water-corrected DTI assessments in either the anterior or posterior substantia nigra. CONCLUSIONS: Results from this study provide further evidence supporting substantia nigra free water as a promising disease-progression biomarker in idiopathic Parkinson disease that may help to identify disease-modifying therapies if used in future clinical trials. Our novel finding of longitudinal increases in anterior but not posterior substantia nigra free water is potentially a result of the much longer disease duration of our cohort compared with previously studied cohorts and the known posterior-to-anterior substantia nigra degeneration that occurs over time in idiopathic Parkinson disease.
We investigated brain wiring in chronic schizophrenia and healthy controls in frontostriatal circuits using diffusion magnetic resonance imaging tractography in a novel way. We extracted diffusion streamlines in 27 chronic schizophrenia and 26 healthy controls connecting 4 frontal subregions to the striatum. We labeled the projection zone striatal surface voxels into 2 subtypes: dominant-input from a single cortical subregion, and, functionally integrative, with mixed-input from diverse cortical subregions. We showed: 1) a group difference for total striatal surface voxel number (P = .045) driven by fewer mixed-input voxels in the left (P = .007), but not right, hemisphere; 2) a group by hemisphere interaction for the ratio quotient between voxel subtypes (P = .04) with a left (P = .006), but not right, hemisphere increase in schizophrenia, also reflecting fewer mixed-input voxels; and 3) fewer mixed-input voxel counts in schizophrenia (P = .045) driven by differences in left hemisphere limbic (P = .007) and associative (P = .01), but not sensorimotor, striatum. These results demonstrate a less integrative pattern of frontostriatal structural connectivity in chronic schizophrenia. A diminished integrative pattern yields a less complex input pattern to the striatum from the cortex with less circuit integration at the level of the striatum. Further, as brain wiring occurs during early development, aberrant brain wiring could serve as a developmental biomarker for schizophrenia.
PURPOSE: We present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health-supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors. PATIENTS AND METHODS: In this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI. RESULTS: We illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each. CONCLUSION: SlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.
Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting the Laplace transform to recover the joint distribution of relaxation-diffusion. However, as is well-known, this problem is notoriously ill-posed and numerically unstable. In this work, we address this issue by directly computing the joint moments of transverse relaxation rate and diffusivity, which can be robustly estimated. To zoom into different parts of the joint distribution, we further enhance our method by applying multiplicative filters to the joint probability density function of relaxation and diffusion and compute the corresponding moments. We propose an approach to use these moments to compute several novel scalar indices to characterize specific properties of the underlying tissue microstructure. Furthermore, for the first time, we propose an algorithm to estimate diffusion signals that are independent of echo time based on the moments of the marginal probability density function of diffusion. We demonstrate its utility in extracting tissue information not contaminated with multiple intra-voxel relaxation rates. We compare the performance of four types of filters that zoom into tissue components with different relaxation and diffusion properties and demonstrate it on an in-vivo human dataset. Experimental results show that these filters are able to characterize heterogeneous tissue microstructure. Moreover, the filtered diffusion signals are also able to distinguish fiber bundles with similar orientations but different relaxation rates. The proposed method thus allows to characterize the neural microstructure information in a robust and unique manner not possible using existing techniques.
PURPOSE: To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T values within a two-compartment model of white matter, and to explore the approach in vivo. METHODS: Sampling protocols featuring different b-values (b), b-tensor shapes (b ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB). Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin." RESULTS: Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/b /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm /ms) but higher T values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm /ms) and T values (150 ms). CONCLUSIONS: Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities.
OBJECTIVE: Accurate biopsy sampling of the suspected lesions is critical for the diagnosis and clinical management of prostate cancer. Transperineal in-bore MRI-guided prostate biopsy (tpMRgBx) is a targeted biopsy technique that was shown to be safe, efficient, and accurate. Our goal was to develop an open source software platform to support evaluation, refinement, and translation of this biopsy approach. METHODS: We developed SliceTracker, a 3D Slicer extension to support tpMRgBx. We followed modular design of the implementation to enable customization of the interface and interchange of image segmentation and registration components to assess their effect on the processing time, precision, and accuracy of the biopsy needle placement. The platform and supporting documentation were developed to enable the use of software by an operator with minimal technical training to facilitate translation. Retrospective evaluation studied registration accuracy, effect of the prostate segmentation approach, and re-identification time of biopsy targets. Prospective evaluation focused on the total procedure time and biopsy targeting error (BTE). RESULTS: Evaluation utilized data from 73 retrospective and ten prospective tpMRgBx cases. Mean landmark registration error for retrospective evaluation was 1.88 ± 2.63 mm, and was not sensitive to the approach used for prostate gland segmentation. Prospectively, we observed target re-identification time of 4.60 ± 2.40 min and BTE of 2.40 ± 0.98 mm. CONCLUSION: SliceTracker is modular and extensible open source platform for supporting image processing aspects of the tpMRgBx procedure. It has been successfully utilized to support clinical research procedures at our site.