We perform a review of the literature in the field of white matter tractography for neurosurgical planning, focusing on those works where tractography was correlated with clinical information such as patient outcome, clinical functional testing, or electro-cortical stimulation. We organize the review by anatomical location in the brain and by surgical procedure, including both supratentorial and infratentorial pathologies, and excluding spinal cord applications. Where possible, we discuss implications of tractography for clinical care, as well as clinically relevant technical considerations regarding the tractography methods. We find that tractography is a valuable tool in variable situations in modern neurosurgery. Our survey of recent reports demonstrates multiple potentially successful applications of white matter tractography in neurosurgery, with progress towards overcoming clinical challenges of standardization and interpretation.

The neural correlates of spaceflight-induced sensorimotor impairments are unknown. Head down-tilt bed rest (HDBR) serves as a microgravity analog because it mimics the headward fluid shift and axial body unloading of spaceflight. We investigated focal brain white matter (WM) changes and fluid shifts during 70 days of 6° HDBR in 16 subjects who were assessed pre (2x), during (3x), and post-HDBR (2x). Changes over time were compared to those in control subjects (n = 12) assessed four times over 90 days. Diffusion MRI was used to assess WM microstructure and fluid shifts. Free-Water Imaging was used to quantify distribution of intracranial extracellular free water (FW). Additionally, we tested whether WM and FW changes correlated with changes in functional mobility and balance measures. HDBR resulted in FW increases in fronto-temporal regions and decreases in posterior-parietal regions that largely recovered by two weeks post-HDBR. WM microstructure was unaffected by HDBR. FW decreases in the post-central gyrus and precuneus correlated negatively with balance changes. We previously reported that gray matter increases in these regions were associated with less HDBR-induced balance impairment, suggesting adaptive structural neuroplasticity. Future studies are warranted to determine causality and underlying mechanisms.

We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space.

Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially in patients with brain tumors, is tracing fiber tracts that are affected by vasogenic edema, which increases water content in the tissue and lowers diffusion anisotropy. One strategy for improving fiber tracking is to use a tractography method that is more sensitive than the traditional single-tensor streamline tractography. We performed experiments to assess the performance of two-tensor unscented Kalman filter (UKF) tractography in edema. UKF tractography fits a diffusion model to the data during fiber tracking, taking advantage of prior information from the previous step along the fiber. We studied UKF performance in a synthetic diffusion MRI digital phantom with simulated edema and in retrospective data from two neurosurgical patients with edema affecting the arcuate fasciculus and corticospinal tracts. We compared the performance of several tractography methods including traditional streamline, UKF single-tensor, and UKF two-tensor. To provide practical guidance on how the UKF method could be employed, we evaluated the impact of using various seed regions both inside and outside the edematous regions, as well as the impact of parameter settings on the tractography sensitivity. We quantified the sensitivity of different methods by measuring the percentage of the patient-specific fMRI activation that was reached by the tractography. We expected that diffusion anisotropy threshold parameters, as well as the inclusion of a free water model, would significantly influence the reconstruction of edematous WM fiber tracts, because edema increases water content in the tissue and lowers anisotropy. Contrary to our initial expectations, varying the fractional anisotropy threshold and including a free water model did not affect the UKF two-tensor tractography output appreciably in these two patient datasets. The most effective parameter for increasing tracking sensitivity was the generalized anisotropy (GA) threshold, which increased the length of tracked fibers when reduced to 0.075. In addition, the most effective seeding strategy was seeding in the whole brain or in a large region outside of the edema. Overall, the main contribution of this study is to provide insight into how UKF tractography can work, using a two-tensor model, to begin to address the challenge of fiber tract reconstruction in edematous regions near brain tumors.

PRIMARY OBJECTIVE: There is a need to understand pathologic processes of the brain following mild traumatic brain injury (mTBI). Previous studies report axonal injury and oedema in the first week after injury in a rodent model. This study aims to investigate the processes occurring 1 week after injury at the time of regeneration and degeneration using diffusion tensor imaging (DTI) in the impact acceleration rat mTBI model. RESEARCH DESIGN: Eighteen rats were subjected to impact acceleration injury, and three rats served as sham controls. Seven days post injury, DTI was acquired from fixed rat brains using a 7T scanner. Group comparison of Fractional Anisotropy (FA) values between traumatized and sham animals was performed using Tract-Based Spatial Statistics (TBSS), a method that we adapted for rats. MAIN OUTCOMES AND RESULTS: TBSS revealed white matter regions of the brain with increased FA values in the traumatized versus sham rats, localized mainly to the contrecoup region. Regions of increased FA included the pyramidal tract, the cerebral peduncle, the superior cerebellar peduncle and to a lesser extent the fibre tracts of the corpus callosum, the anterior commissure, the fimbria of the hippocampus, the fornix, the medial forebrain bundle and the optic chiasm. CONCLUSION: Seven days post injury, during the period of tissue reparation in the impact acceleration rat model of mTBI, microstructural changes to white matter can be detected using DTI.

BACKGROUND: Mixed vascular and neurodegenerative dementia, such as Alzheimer’s disease (AD) with concomitant cerebrovascular disease, has emerged as the leading cause of age-related cognitive impairment. The brain white matter (WM) microstructural changes in neurodegeneration well-documented by diffusion tensor imaging (DTI) can originate from brain tissue or extracellular free water changes. The differential microstructural and free water changes in AD with and without cerebrovascular disease, especially in normal-appearing WM, remain largely unknown. To cover these gaps, we aimed to characterize the WM free water and tissue microstructural changes in AD and mixed dementia as well as their associations with cognition using a novel free water imaging method. METHODS: We compared WM free water and free water-corrected DTI measures as well as white matter hyperintensity (WMH) in patients with AD with and without cerebrovascular disease, patients with vascular dementia, and age-matched healthy control subjects. RESULTS: The cerebrovascular disease groups had higher free water than the non-cerebrovascular disease groups. Importantly, besides the cerebrovascular disease groups, patients with AD without cerebrovascular disease also had increased free water in normal-appearing WM compared with healthy control subjects, reflecting mild vascular damage. Such free water increases in WM or normal-appearing WM (but not WMH) contributed to dementia severity. Whole-brain voxel-wise analysis revealed a close association between widespread free water increases and poorer attention, executive functioning, visual construction, and motor performance, whereas only left hemispheric free water increases were related to language deficits. Moreover, compared with the original DTI metrics, the free water-corrected DTI metric revealed tissue damage-specific (frontal and occipital) microstructural differences between the cerebrovascular disease and non-cerebrovascular disease groups. In contrast to both lobar and subcortical/brainstem free water increases, only focal lobar microstructural damage was associated with poorer cognitive performance. CONCLUSIONS: Our findings suggest that free water analysis isolates probable mild vascular damage from WM microstructural alterations and underscore the importance of normal-appearing WM changes underlying cognitive and functional impairment in AD with and without cerebrovascular disease. Further developed, the combined free water and tissue neuroimaging assays could help in differential diagnosis, treatment planning, and disease monitoring of patients with mixed dementia.

OBJECTIVE: To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI-GENetics Interface Exploration (MRI-GENIE) study. METHODS: MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include the manual and automated assessments of established MRI markers. A high-throughput MRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease. CONCLUSIONS: The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.

PURPOSE: Accurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time consuming and affected by inter-observer variability. We evaluated the robustness and accuracy of a publically available semiautomatic segmentation algorithm that is implemented in the 3D Slicer Chest Imaging Platform (CIP) and compared it with the performance of manual segmentation. METHODS: CT images of 354 manually segmented nodules were downloaded from the LIDC database. Four radiologists performed the manual segmentation and assessed various nodule characteristics. The semiautomatic CIP segmentation was initialized using the centroid of the manual segmentations, thereby generating four contours for each nodule. The robustness of both segmentation methods was assessed using the region of uncertainty (δ) and Dice similarity index (DSI). The robustness of the segmentation methods was compared using the Wilcoxon-signed rank test (pWilcoxon<0.05). The Dice similarity index (DSIAgree) between the manual and CIP segmentations was computed to estimate the accuracy of the semiautomatic contours. RESULTS: The median computational time of the CIP segmentation was 10 s. The median CIP and manually segmented volumes were 477 ml and 309 ml, respectively. CIP segmentations were significantly more robust than manual segmentations (median δCIP = 14ml, median dsiCIP = 99% vs. median δmanual = 222ml, median dsimanual = 82%) with pWilcoxon 10-16. The agreement between CIP and manual segmentations had a median DSIAgree of 60%. While 13% (47/354) of the nodules did not require any manual adjustment, minor to substantial manual adjustments were needed for 87% (305/354) of the nodules. CIP segmentations were observed to perform poorly (median DSIAgree≈50%) for non-/sub-solid nodules with subtle appearances and poorly defined boundaries. CONCLUSION: Semi-automatic CIP segmentation can potentially reduce the physician workload for 13% of nodules owing to its computational efficiency and superior stability compared to manual segmentation. Although manual adjustment is needed for many cases, CIP segmentation provides a preliminary contour for physicians as a starting point.

We propose a general dynamic regression framework for partial correlation and causality analysis of functional brain networks. Using the optimal prediction theory, we present the solution of the dynamic regression problem by minimizing the entropy of the associated stochastic process. We also provide the relation between the solutions and the linear dependence models of Geweke and Granger and derive novel expressions for computing partial correlation and causality using an optimal prediction filter with minimum error variance. We use the proposed dynamic framework to study the intrinsic partial correlation and causal- ity between seven different brain networks using resting state functional MRI (rsfMRI) data from the Human Connectome Project (HCP) and compare our results with those obtained from standard correlation and causality measures. The results show that our optimal prediction filter explains a significant portion of the variance in the rsfMRI data at low frequencies, unlike standard partial correlation analysis.

This work presents a supra-threshold fiber cluster (STFC) analysis that leverages the whole brain fiber geometry to enhance sta- tistical group difference analysis. The proposed method consists of (1) a study-specific data-driven tractography parcellation to obtain white matter (WM) tract parcels according to the WM anatomy and (2) a nonparametric permutation-based STFC test to identify significant dif- ferences between study populations (e.g. disease and healthy). The basic idea of our method is that a WM parcel’s neighborhood (parcels with similar WM anatomy) can support the parcel’s statistical significance when correcting for multiple comparisons. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder (ADHD) patients and 29 healthy controls (HCs). Evaluations are conducted using both synthetic and real data. The results indicate that our STFC method gives greater sensitivity in finding group differences in WM tract parcels compared to several traditional multiple comparison correction methods.