Sydnor VJ, Bouix S, Pasternak O, Hartl E, Levin-Gleba L, Reid B, Tripodis Y, Guenette JP, Kaufmann D, Makris N, Fortier C, Salat DH, Rathi Y, Milberg WP, McGlinchey RE, Shenton ME, Koerte IK. Mild Traumatic Brain Injury Impacts Associations Between Limbic System Microstructure and Post-Traumatic Stress Disorder Symptomatology. Neuroimage Clin. 2020;26:102190.

BACKGROUND: Post-traumatic stress disorder (PTSD) is a psychiatric disorder that afflicts many individuals, yet the neuropathological mechanisms that contribute to this disorder remain to be fully determined. Moreover, it is unclear how exposure to mild traumatic brain injury (mTBI), a condition that is often comorbid with PTSD, particularly among military personnel, affects the clinical and neurological presentation of PTSD. To address these issues, the present study explores relationships between PTSD symptom severity and the microstructure of limbic and paralimbic gray matter brain regions, as well as the impact of mTBI comorbidity on these relationships. METHODS: Structural and diffusion MRI data were acquired from 102 male veterans who were diagnosed with current PTSD. Diffusion data were analyzed with free-water imaging to quantify average CSF-corrected fractional anisotropy (FA) and mean diffusivity (MD) in 18 limbic and paralimbic gray matter regions. Associations between PTSD symptom severity and regional average dMRI measures were examined with repeated measures linear mixed models. Associations were studied separately in veterans with PTSD only, and in veterans with PTSD and a history of military mTBI. RESULTS: Analyses revealed that in the PTSD only cohort, more severe symptoms were associated with higher FA in the right amygdala-hippocampus complex, lower FA in the right cingulate cortex, and lower MD in the left medial orbitofrontal cortex. In the PTSD and mTBI cohort, more severe PTSD symptoms were associated with higher FA bilaterally in the amygdala-hippocampus complex, with higher FA bilaterally in the nucleus accumbens, with lower FA bilaterally in the cingulate cortex, and with higher MD in the right amygdala-hippocampus complex. CONCLUSIONS: These findings suggest that the microstructure of limbic and paralimbic brain regions may influence PTSD symptomatology. Further, given the additional associations observed between microstructure and symptom severity in veterans with head trauma, we speculate that mTBI may exacerbate the impact of brain microstructure on PTSD symptoms, especially within regions of the brain known to be vulnerable to chronic stress. A heightened sensitivity to the microstructural environment of the brain could partially explain why individuals with PTSD and mTBI comorbidity experience more severe symptoms and poorer illness prognoses than those without a history of brain injury. The relevance of these microstructural findings to the conceptualization of PTSD as being a disorder of stress-induced neuronal connectivity loss is discussed.

Zhang F, Xie G, Leung L, Mooney MA, Epprecht L, Norton I, Rathi Y, Kikinis R, Al-Mefty O, Makris N, Golby AJ, Donnell LJO. Creation of a Novel Trigeminal Tractography Atlas for Automated Trigeminal Nerve Identification. Neuroimage. 2020;220:117063.

Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.

Haehn D, Franke L, Zhang F, Cetin-Karayumak S, Pieper S, Donnell LJO, Rathi Y. TRAKO: Efficient Transmission of Tractography Data for Visualization. Med Image Comput Comput Assist Interv. 2020;12267:322–32.

Fiber tracking produces large tractography datasets that are tens of gigabytes in size consisting of millions of streamlines. Such vast amounts of data require formats that allow for efficient storage, transfer, and visualization. We present TRAKO, a new data format based on the Graphics Layer Transmission Format (glTF) that enables immediate graphical and hardware-accelerated processing. We integrate a state-of-the-art compression technique for vertices, streamlines, and attached scalar and property data. We then compare TRAKO to existing tractography storage methods and provide a detailed evaluation on eight datasets. TRAKO can achieve data reductions of over 28x without loss of statistical significance when used to replicate analysis from previously published studies.

Wang CJ, Rost NS, Golland P. Spatial-Intensity Transform GANs for High Fidelity Medical Image-to-Image Translation. Med Image Comput Comput Assist Interv. 2020;12262:749–59.

Despite recent progress in image-to-image translation, it remains challenging to apply such techniques to clinical quality medical images. We develop a novel parameterization of conditional generative adversarial networks that achieves high image fidelity when trained to transform MRIs conditioned on a patient’s age and disease severity. The spatial-intensity transform generative adversarial network (SIT-GAN) constrains the generator to a smooth spatial transform composed with sparse intensity changes. This technique improves image quality and robustness to artifacts, and generalizes to different scanners. We demonstrate SIT-GAN on a large clinical image dataset of stroke patients, where it captures associations between ventricle expansion and aging, as well as between white matter hyperintensities and stroke severity. Additionally, SIT-GAN provides a disentangled view of the variation in shape and appearance across subjects.

Chauhan G, Liao R, Wells W, Andreas J, Wang X, Berkowitz S, Horng S, Szolovits P, Golland P. Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment. Med Image Comput Comput Assist Interv. 2020;12262:529–39.

We propose and demonstrate a novel machine learning algorithm that assesses pulmonary edema severity from chest radiographs. While large publicly available datasets of chest radiographs and free-text radiology reports exist, only limited numerical edema severity labels can be extracted from radiology reports. This is a significant challenge in learning such models for image classification. To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time. Our experimental results suggest that the joint image-text representation learning improves the performance of pulmonary edema assessment compared to a supervised model trained on images only. We also show the use of the text for explaining the image classification by the joint model. To the best of our knowledge, our approach is the first to leverage free-text radiology reports for improving the image model performance in this application. Our code is available at:

Lampinen B, Szczepankiewicz F, Mårtensson J, van Westen D, Hansson O, Westin CF, Nilsson M. Towards Unconstrained Compartment Modeling in White Matter Using Diffusion-Relaxation MRI with Tensor-Valued Diffusion Encoding. Magn Reson Med. 2020;84(3):1605–23.

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 Cram\ er-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.

Dalamagkas K, Tsintou M, Rathi Y, O’Donnell LJ, Pasternak O, Gong X, Zhu A, Savadjiev P, Papadimitriou GM, Kubicki M, Yeterian EH, Makris N. Individual Variations of the Human Corticospinal Tract and Its Hand-Related Motor Fibers Using Diffusion MRI Tractography. Brain Imaging Behav. 2020;14(3):696–714.

The corticospinal tract (CST) is one of the most well studied tracts in human neuroanatomy. Its clinical significance can be demonstrated in many notable traumatic conditions and diseases such as stroke, spinal cord injury (SCI) or amyotrophic lateral sclerosis (ALS). With the advent of diffusion MRI and tractography the computational representation of the human CST in a 3D model became available. However, the representation of the entire CST and, specifically, the hand motor area has remained elusive. In this paper we propose a novel method, using manually drawn ROIs based on robustly identifiable neuroanatomic structures to delineate the entire CST and isolate its hand motor representation as well as to estimate their variability and generate a database of their volume, length and biophysical parameters. Using 37 healthy human subjects we performed a qualitative and quantitative analysis of the CST and the hand-related motor fiber tracts (HMFTs). Finally, we have created variability heat maps from 37 subjects for both the aforementioned tracts, which could be utilized as a reference for future studies with clinical focus to explore neuropathology in both trauma and disease states.

Gullett JM, Shea AO, Lamb DG, Porges EC, Shea DMO, Pasternak O, Cohen RA, Woods AJ. The Association of White Matter Free Water With Cognition in Older Adults. Neuroimage. 2020;219:117040.

BACKGROUND: Extracellular free water within cerebral white matter tissue has been shown to increase with age and pathology, yet the cognitive consequences of free water in typical aging prior to the development of neurodegenerative disease remains unclear. Understanding the contribution of free water to cognitive function in older adults may provide important insight into the neural mechanisms of the cognitive aging process. METHODS: A diffusion-weighted MRI measure of extracellular free water as well as a commonly used diffusion MRI metric (fractional anisotropy) along nine bilateral white matter pathways were examined for their relationship with cognitive function assessed by the NIH Toolbox Cognitive Battery in 47 older adults (mean age ​= ​74.4 years, SD ​= ​5.4 years, range ​= ​65-85 years). Probabilistic tractography at the 99th percentile level of probability (Tracts Constrained by Underlying Anatomy; TRACULA) was utilized to produce the pathways on which microstructural characteristics were overlaid and examined for their contribution to cognitive function independent of age, education, and gender. RESULTS: When examining the 99th percentile probability core white matter pathway derived from TRACULA, poorer fluid cognitive ability was related to higher mean free water values across the angular and cingulum bundles of the cingulate gyrus, as well as the corticospinal tract and the superior longitudinal fasciculus. There was no relationship between cognition and mean FA or free water-adjusted FA across the 99th percentile core white matter pathway. Crystallized cognitive ability was not associated with any of the diffusion measures. When examining cognitive domains comprising the NIH Toolbox Fluid Cognition index relationships with these white matter pathways, mean free water demonstrated strong hemispheric and functional specificity for cognitive performance, whereas mean FA was not related to age or cognition across the 99th percentile pathway. CONCLUSIONS: Extracellular free water within white matter appears to increase with normal aging, and higher values are associated with significantly lower fluid but not crystallized cognitive functions. When using TRACULA to estimate the core of a white matter pathway, a higher degree of free water appears to be highly specific to the pathways associated with memory, working memory, and speeded decision-making performance, whereas no such relationship existed with FA. These data suggest that free water may play an important role in the cognitive aging process, and may serve as a stronger and more specific indicator of early cognitive decline than traditional diffusion MRI measures, such as FA.

Di Biase MA, Zhang F, Lyall A, Kubicki M, Mandl RCW, Sommer IE, Pasternak O. Neuroimaging Auditory Verbal Hallucinations in Schizophrenia Patient and Healthy Populations. Psychol Med. 2020;50(3):403–12.
BACKGROUND: Auditory verbal hallucinations (AVH) are a cardinal feature of schizophrenia, but they can also appear in otherwise healthy individuals. Imaging studies implicate language networks in the generation of AVH; however, it remains unclear if alterations reflect biologic substrates of AVH, irrespective of diagnostic status, age, or illness-related factors. We applied multimodal imaging to identify AVH-specific pathology, evidenced by overlapping gray or white matter deficits between schizophrenia patients and healthy voice-hearers. METHODS: Diffusion-weighted and T1-weighted magnetic resonance images were acquired in 35 schizophrenia patients with AVH (SCZ-AVH), 32 healthy voice-hearers (H-AVH), and 40 age- and sex-matched controls without AVH. White matter fractional anisotropy (FA) and gray matter thickness (GMT) were computed for each region comprising ICBM-DTI and Desikan-Killiany atlases, respectively. Regions were tested for significant alterations affecting both SCZ-AVH and H-AVH groups, relative to controls. RESULTS: Compared with controls, the SCZ-AVH showed widespread FA and GMT reductions; but no significant differences emerged between H-AVH and control groups. While no overlapping pathology appeared in the overall study groups, younger (<40 years) H-AVH and SCZ-AVH subjects displayed overlapping FA deficits across four regions (p < 0.05): the genu and splenium of the corpus callosum, as well as the anterior limbs of the internal capsule. Analyzing these regions with free-water imaging ascribed overlapping FA abnormalities to tissue-specific anisotropy changes. CONCLUSIONS: We identified white matter pathology associated with the presence of AVH, independent of diagnostic status. However, commonalities were constrained to younger and more homogenous groups, after reducing pathologic variance associated with advancing age and chronicity effects.
Herz C, Tuncali K, Fedorov A, MacNeil K, Behringer PA, Tokuda J, Mehrtash A, Mousavi P, Kikinis R, Fennessy FM, Tempany CM. Open Source Platform for Transperineal In-Bore MRI-Guided Targeted Prostate Biopsy. IEEE Trans Biomed Eng. 2020;67(2):565–76.
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.