Structure-specific statistical mapping of white matter tracts

Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy

We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding. This framework enables us to conduct a coherent statistical inference within the embedded space. Our results indicate that brain network topology in TLE patients exhibits increased rigidity in 0D topology but more rapid flections compared to that of normal controls in 1D topology.

Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis

Purpose

Deformable medial modeling is an inverse skeletonization approach to representing anatomy in medical images, which can be used for statistical shape analysis and assessment of patient-specific anatomical features such as locally varying thickness. It involves deforming a pre-defined synthetic skeleton, or template, to anatomical structures of the same class. The lack of software for creating such skeletons has been a limitation to more widespread use of deformable medial modeling. Therefore, the objective of this work is to present an open-source user interface (UI) for the creation of synthetic skeletons for a range of medial modeling applications in medical imaging.

Approach

A UI for interactive design of synthetic skeletons was implemented in 3D Slicer, an open-source medical image analysis application. The steps in synthetic skeleton design include importation and skeletonization of a 3D segmentation, followed by interactive 3D point placement and triangulation of the medial surface such that the desired branching configuration of the anatomical structure's medial axis is achieved. Synthetic skeleton design was evaluated in five clinical applications. Compatibility of the synthetic skeletons with open-source software for deformable medial modeling was tested, and representational accuracy of the deformed medial models was evaluated.

Results

Three users designed synthetic skeletons of anatomies with various topologies: the placenta, aortic root wall, mitral valve, cardiac ventricles, and the uterus. The skeletons were compatible with skeleton-first and boundary-first software for deformable medial modeling. The fitted medial models achieved good representational accuracy with respect to the 3D segmentations from which the synthetic skeletons were generated.

Conclusions

Synthetic skeleton design has been a practical challenge in leveraging deformable medial modeling for new clinical applications. This work demonstrates an open-source UI for user-friendly design of synthetic skeletons for anatomies with a wide range of topologies.

A systematic review of automated methods to perform white matter tract segmentation

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, "white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation," 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.

Quantification of perineural satellitosis in pretreatment glioblastoma with structural MRI and a diffusion tensor imaging template

Background

Survival outcomes for glioblastoma (GBM) patients remain unfavorable, and tumor recurrence is often observed. Understanding the radiological growth patterns of GBM could aid in improving outcomes. This study aimed to examine the relationship between contrast-enhancing tumor growth direction and white matter, using an image registration and deformation strategy.

Methods

In GBM patients 2 pretreatment scans (diagnostic and neuronavigation) were gathered retrospectively, and coregistered to a template and diffusion tensor imaging (DTI) atlas. The GBM lesions were segmented and coregistered to the same space. Growth vectors were derived and divided into vector populations parallel (Φ = 0-20°) and perpendicular (Φ = 70-90°) to white matter. To test for statistical significance between parallel and perpendicular groups, a paired samples Student's t-test was performed. O6-methylguanine-DNA methyltransferase (MGMT) methylation status and its correlation to growth rate were also tested using a one-way ANOVA test.

Results

For 78 GBM patients (mean age 61 years ± 13 SD, 32 men), the included GBM lesions showed a predominant preference for perineural satellitosis (P < .001), with a mean percentile growth of 30.8% (95% CI: 29.6-32.0%) parallel (0° < |Φ| < 20°) to white matter. Perpendicular tumor growth with respect to white matter microstructure (70° < |Φ| < 90°) showed to be 22.7% (95% CI: 21.3-24.1%) of total tumor growth direction.

Conclusions

The presented strategy showed that tumor growth direction in pretreatment GBM patients correlated with white matter architecture. Future studies with patient-specific DTI data are required to verify the accuracy of this method prospectively to identify its usefulness as a clinical metric in pre and posttreatment settings.

Post mortem mapping of connectional anatomy for the validation of diffusion MRI

Diffusion MRI (dMRI) is a unique tool for the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.

Neonatal White Matter Microstructure and Emotional Development during the Preschool Years in Children Who Were Born Very Preterm

Children born very preterm (<33 weeks of gestation) are at a higher risk of developing socio-emotional difficulties compared with those born at term. In this longitudinal study, we tested the hypothesis that diffusion characteristics of white matter (WM) tracts implicated in socio-emotional processing assessed in the neonatal period are associated with socio-emotional development in 151 very preterm children previously enrolled into the Evaluation of Preterm Imaging study (EudraCT 2009-011602-42). All children underwent diffusion tensor imaging at term-equivalent age and fractional anisotropy (FA) was quantified in the uncinate fasciculus (UF), inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), and superior longitudinal fasciculus (SLF). Children's socio-emotional development was evaluated at preschool age (median = 4.63 years). Exploratory factor analysis conducted on the outcome variables revealed a three-factor structure, with latent constructs summarized as: "emotion moderation," "social function," and "empathy." Results of linear regression analyses, adjusting for full-scale IQ and clinical and socio-demographic variables, showed an association between lower FA in the right UF and higher "emotion moderation" scores (β = -0.280; p < 0.001), which was mainly driven by negative affectivity scores (β = -0.281; p = 0.001). Results further showed an association between higher full-scale IQ and better social functioning (β = -0.334, p < 0.001). Girls had higher empathy scores than boys (β = -0.341, p = 0.006). These findings suggest that early alterations of diffusion characteristics of the UF could represent a biological substrate underlying the link between very preterm birth and emotional dysregulation in childhood and beyond.

White matter tract-specific alterations in male patients with untreated obstructive sleep apnea are associated with worse cognitive function

STUDY OBJECTIVES

Neurocognitive impairment is one of the daytime symptoms of obstructive sleep apnea (OSA). We proposed to use tract-specific statistical analysis (TSSA) to investigate whether there are fiber tract abnormalities in OSA, which may be undiscovered using voxel-based approaches, and whether such tract-specific disruptions in brain connectivity are associated with neuropsychological deficits in patients with untreated OSA.

METHODS

We enrolled 38 patients with OSA diagnosed by overnight polysomnography, and 41 healthy sleepers. Fractional anisotropy (FA) and mean diffusivity (MD) maps were obtained from whole-brain diffusion tensor imaging, and TSSA were used to assess regional deficits of white matter tracts. All participants underwent a battery of neuropsychological tests. To evaluate the association between FA values and clinical, polysomnographic, and neuropsychological parameters in the OSA group, permutation-based tests for correlation were performed preceding cluster-based statistics.

RESULTS

Compared to healthy controls, patients with OSA showed decreased values of FA in the left and right anterior thalamic radiations, and right uncinate fasciculus (UNC) (p < 0.001, p = 0.005, and p = 0.008, respectively). A lower score of digit span backward was associated with lower FA values of right UNC in the OSA group (p = 0.023). The Rey Complex Figure Test copy score revealed a positive correlation with FA values in the right UNC (p = 0.010).

CONCLUSIONS

The TSSA method indeed identified previously unrevealed tract-specific disruptions in OSA. Furthermore, reduced FA values in the frontal lobe portion of the right UNC which has been known to be involved in working memory function were significantly associated with lower cognitive performance in patients with untreated OSA.

Tract-specific analysis and neurocognitive functioning in sickle cell patients without history of overt stroke

INTRODUCTION

Sickle cell disease (SCD) is a hereditary blood disorder in which the oxygen-carrying hemoglobin molecule in red blood cells is abnormal. SCD patients are at increased risks for strokes and neurocognitive deficit, even though neurovascular screening and treatments have lowered the rate of overt strokes. Tract-specific analysis (TSA) is a statistical method to evaluate microstructural WM damage in neurodegenerative disorders, using diffusion tensor imaging (DTI).

METHODS

We utilized TSA and compared 11 major brain WM tracts between SCD patients with no history of overt stroke, anemic controls, and healthy controls. We additionally examined the relationship between the most commonly used DTI metric of WM tracts and neurocognitive performance in the SCD patients and healthy controls.

RESULTS

Disruption of WM microstructure orientation-dependent metrics for the SCD patients was found in the genu of the corpus callosum (CC), cortico-spinal tract, inferior fronto-occipital fasciculus, right inferior longitudinal fasciculus, superior longitudinal fasciculus, and left uncinate fasciculus. Neurocognitive performance indicated slower processing speed and lower response inhibition skills in SCD patients compared to controls. TSA abnormalities in the CC were significantly associated with measures of processing speed, working memory, and executive functions.

CONCLUSION

Decreased DTI-derived metrics were observed on six tracts in chronically anemic patients, regardless of anemia subtype, while two tracks with decreased measures were unique to SCD patients. Patients with WMHs had more significant FA abnormalities. Decreased FA values in the CC significantly correlated with all nine neurocognitive tests, suggesting a critical importance for CC in core neurocognitive processes.

Changes Over Time of Diffusion MRI in the White Matter of Aging Brain, a Good Predictor of Verbal Recall

Objective: Extensive research using water-diffusion MRI reported age-related modifications of cerebral White Matter (WM). Moreover, water-diffusion parameter modifications have been frequently associated with cognitive performances in the elderly sample, reinforcing the idea of aging inducing microstructural disconnection of the brain which in turn impacts cognition. However, only few studies really assessed over-time modifications of these parameters and their relationship with episodic memory outcome of elderly. Materials and Methods: One-hundred and thirty elderly subjects without dementia (74.1 ± 4.1 years; 47% female) were included in this study. Diffusion tensor imaging (DTI) was performed at two-time points (3.49 ± 0.68 years apart), allowing the assessment of changes in water-diffusion parameters over time using a specific longitudinal pipeline. White matter hyperintensity (WMH) burden and gray matter (GM) atrophy were also measured on FLAIR and T1-weighted sequences collected during these two MRI sessions. Free and cued verbal recall scores assessed at the last follow-up of the cohort were used as episodic memory outcome. Changes in water-diffusion parameters over time were included in serial linear regression models to predict retrieval or storage ability of elderly. Results: GM atrophy and an increase in mean diffusivity (MD) and WMH load between the two-time points were observed. The increase in MD was significantly correlated with WMH load and the different memory scores. In models accounting for the baseline cognitive score, GM atrophy, or WMH load, MD changes still significantly predict free verbal recall, and not total verbal recall, suggesting the specific association with the retrieval deficit in healthy aging. Conclusion: In elderly, microstructural WM changes are good predictors of lower free verbal recall performances. Moreover, this contribution is not only driven by WMH load increase. This last observation is in line with studies reporting early water-diffusion modification in WM tissue during aging, resulting lately in the appearance of WMH on conventional MRI.

Structural neuroimaging of the altered brain stemming from pediatric and adolescent hearing loss-Scientific and clinical challenges

There has been a spurt in structural neuroimaging studies of the effect of hearing loss on the brain. Specifically, magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) technologies provide an opportunity to quantify changes in gray and white matter structures at the macroscopic scale. To date, there have been 32 MRI and 23 DTI studies that have analyzed structural differences accruing from pre- or peri-lingual pediatric hearing loss with congenital or early onset etiology and postlingual hearing loss in pre-to-late adolescence. Additionally, there have been 15 prospective clinical structural neuroimaging studies of children and adolescents being evaluated for cochlear implants. The results of the 70 studies are summarized in two figures and three tables. Plastic changes in the brain are seen to be multifocal rather than diffuse, that is, differences are consistent across regions implicated in the hearing, speech and language networks regardless of modes of communication and amplification. Structures in that play an important role in cognition are affected to a lesser extent. A limitation of these studies is the emphasis on volumetric measures and on homogeneous groups of subjects with hearing loss. It is suggested that additional measures of morphometry and connectivity could contribute to a greater understanding of the effect of hearing loss on the brain. Then an interpretation of the observed macroscopic structural differences is given. This is followed by discussion of how structural imaging can be combined with functional imaging to provide biomarkers for longitudinal tracking of amplification. This article is categorized under: Developmental Biology > Developmental Processes in Health and Disease Translational, Genomic, and Systems Medicine > Translational Medicine Laboratory Methods and Technologies > Imaging.

Cognitive and White-Matter Compartment Models Reveal Selective Relations between Corticospinal Tract Microstructure and Simple Reaction Time

The speed of motor reaction to an external stimulus varies substantially between individuals and is slowed in aging. However, the neuroanatomical origins of interindividual variability in reaction time (RT) remain unclear. Here, we combined a cognitive model of RT and a biophysical compartment model of diffusion-weighted MRI (DWI) to characterize the relationship between RT and microstructure of the corticospinal tract (CST) and the optic radiation (OR), the primary motor output and visual input pathways associated with visual-motor responses. We fitted an accumulator model of RT to 46 female human participants' behavioral performance in a simple reaction time task. The non-decision time parameter (T _er) derived from the model was used to account for the latencies of stimulus encoding and action initiation. From multi-shell DWI data, we quantified tissue microstructure of the CST and OR with the neurite orientation dispersion and density imaging (NODDI) model as well as the conventional diffusion tensor imaging model. Using novel skeletonization and segmentation approaches, we showed that DWI-based microstructure metrics varied substantially along CST and OR. The T _er of individual participants was negatively correlated with the NODDI measure of the neurite density in the bilateral superior CST. Further, we found no significant correlation between the microstructural measures and mean RT. Thus, our findings suggest a link between interindividual differences in sensorimotor speed and selective microstructural properties in white-matter tracts.SIGNIFICANCE STATEMENT How does our brain structure contribute to our speed to react? Here, we provided anatomically specific evidence that interindividual differences in response speed is associated with white-matter microstructure. Using a cognitive model of reaction time (RT), we estimated the non-decision time, as an index of the latencies of stimulus encoding and action initiation, during a simple reaction time task. Using an advanced microstructural model for diffusion MRI, we estimated the tissue properties and their variations along the corticospinal tract and optic radiation. We found significant location-specific correlations between the microstructural measures and the model-derived parameter of non-decision time but not mean RT. These results highlight the neuroanatomical signature of interindividual variability in response speed along the sensorimotor pathways.

Combining white matter diffusion and geometry for tract-specific alignment and variability analysis

We present a framework for along-tract analysis of white matter (WM) fiber bundles based on diffusion tensor imaging (DTI) and tractography. We introduce the novel concept of fiber-flux density for modeling fiber tracts' geometry, and combine it with diffusion-based measures to define vector descriptors called Fiber-Flux Diffusion Density (FFDD). The proposed model captures informative features of WM tracts at both the microscopic (diffusion-related) and macroscopic (geometry-related) scales, thus enabling improved sensitivity to subtle structural abnormalities that are not reflected by either diffusion or geometrical properties alone. A key step in this framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tracts enable meaningful inter-subject comparisons and group-wise statistical analysis. Moreover, we show that the FMM alignment can be generalized in a straight forward manner to a single-shot co-alignment of multiple fiber bundles. The proposed alignment technique is shown to outperform a well-established, commonly used DTI registration algorithm. We demonstrate the FFDD framework on the Human Connectome Project (HCP) diffusion MRI dataset, as well as on two different datasets of contact sports players. We test our method using longitudinal scans of a basketball player diagnosed with a traumatic brain injury, showing compatibility with structural MRI findings. We further perform a group study comparing mid- and post-season scans of 13 active football players exposed to repetitive head trauma, to 17 non-player control (NPC) subjects. Results reveal statistically significant FFDD differences (p-values<0.05) between the groups, as well as increased abnormalities over time at spatially-consistent locations within several major fiber tracts of football players.

Detection of occult neoplastic infiltration in the corpus callosum and prediction of overall survival in patients with glioblastoma using diffusion tensor imaging

OBJECTIVE

Corpus callosum (CC) involvement is a poor prognostic factor in patients with glioblastoma (GBM). The purpose of this study was to determine whether diffusion tensor imaging (DTI) can quantify occult tumor infiltration in the CC and predict the overall survival in GBM patients.

METHODS

Forty-eight patients with pathologically proven GBM and 17 normal subjects were included in this retrospective study. Patients were divided into four groups based on CC invasion and overall survival: long survivors without CC invasion; short survivors without CC invasion; long survivors with CC invasion; short survivors with CC invasion. All patients underwent DTI at 3T MRI scanner. Fractional anisotropy (FA) and mean diffusivity (MD) values were measured from genu, mid-body, and splenium of the CC. The mean values of these parameters were compared between different groups and Kaplan Meier curves were used for prediction of overall survival.

RESULTS

Patients with short survival and CC invasion had the lowest FA values (0.64 ± 0.05) from the CC compared with other groups (p < 0.05). Receiver operator characteristic curve (ROC) analysis indicated that a FA cutoff value of 0.70 was the best predictor for overall survival with an area under the curve (AUC) of 0.77, sensitivity 1, specificity 0.59. Kaplan-Meier survival curves demonstrated that the mean survival time was significantly longer for patients with high FA (>0.70) compared with those with low FA (<0.70) (p < 0.001).

CONCLUSIONS

FA values from the CC can quantify occult tumor infiltration and serve as a sensitive prognostic marker for prediction of overall survival in GBM patients.

Macroscale White Matter Alterations Due to Traumatic Cerebral Microhemorrhages Are Revealed by Diffusion Tensor Imaging

With the advent of susceptibility-weighted imaging (SWI), the ability to identify cerebral microbleeds (CMBs) associated with mild traumatic brain injury (mTBI) has become increasingly commonplace. Nevertheless, the clinical significance of post-traumatic CMBs remains controversial partly because it is unclear whether mTBI-related CMBs entail brain circuitry disruptions which, although structurally subtle, are functionally significant. This study combines magnetic resonance and diffusion tensor imaging (MRI and DTI) to map white matter (WM) circuitry differences across 6 months in 26 healthy control volunteers and in 26 older mTBI victims with acute CMBs of traumatic etiology. Six months post-mTBI, significant changes (p < 0.001) in the mean fractional anisotropy of perilesional WM bundles were identified in 21 volunteers, and an average of 47% (σ = 21%) of TBI-related CMBs were associated with such changes. These results suggest that CMBs can be associated with lasting changes in perilesional WM properties, even relatively far from CMB locations. Future strategies for mTBI care will likely rely on the ability to assess how subtle circuitry changes impact neural/cognitive function. Thus, assessing CMB effects upon the structural connectome can play a useful role when studying CMB sequelae and their potential impact upon the clinical outcome of individuals with concussion.

Tract-Specific Group Analysis in Fetal Cohorts Using in utero Diffusion Tensor Imaging

Diffusion tensor imaging (DTI) based group analysis has helped uncover the impact of white matter injuries in a wide range of studies involving subjects from preterm neonates to adults. The application of these methods to fetal cohorts, however, has been hampered by the challenging nature of in utero fetal DTI caused by unconstrained fetal motion, limited scan times, and limited signal-to-noise ratio. We present a framework that addresses these issues to systematically evaluate group differences in fetal cohorts. A motion-robust DTI computation approach with a new unbiased DTI template construction method is unified with kernel-regression in age and tensor-specific registration to normalize DTI volumes in an unbiased space. A robust statistical approach is used to map region-specific group differences to the medial representation of the tracts of interest. The proposed approach was applied and showed, for the first time, differences in local white matter fractional anisotropy based on in utero DTI of fetuses with congenital heart disease and age-matched healthy controls. This paper suggests the need for fetal-specific pipelines to be used for DTI-based group analysis involving fetal cohorts.

Double Diffeomorphism: Combining Morphometry and Structural Connectivity Analysis

The brain is composed of several neural circuits which may be seen as anatomical complexes composed of grey matter structures interconnected by white matter tracts. Grey and white matter components may be modeled as 3-D surfaces and curves, respectively. Neurodevelopmental disorders involve morphological and organizational alterations which cannot be jointly captured by usual shape analysis techniques based on single diffeomorphisms. We propose a new deformation scheme, called double diffeomorphism, which is a combination of two diffeomorphisms. The first one captures changes in structural connectivity, whereas the second one recovers the global morphological variations of both grey and white matter structures. This deformation model is integrated into a Bayesian framework for atlas construction. We evaluate it on a data-set of 3-D structures representing the neural circuits of patients with Gilles de la Tourette syndrome (GTS). We show that this approach makes it possible to localise, quantify, and easily visualise the pathological anomalies altering the morphology and organization of the neural circuits. Furthermore, results also indicate that the proposed deformation model better discriminates between controls and GTS patients than a single diffeomorphism.

Neuroinformatics and Analysis of Connectomic Alterations Due to Cerebral Microhemorrhages in Geriatric Mild Neurotrauma

Connectomics alterations associated with subtle forms of cerebrovascular neuropathology-such as cerebral microbleeds (CMBs)-can result in substantial neurological and/or cognitive deficits in victims of traumatic brain injury (TBI). Quantifying CMB-related connectome changes in mild TBI (mTBI) patients requires ingenious neuroinformatics to integrate structural magnetic resonance imaging (sMRI) with diffusion-weighted imaging (DWI) for patient-tailored profiling while preserving the data scientist's ability to implement population studies. Such solutions, however, can assist the refinement of rehabilitation protocols and streamline large-scale analysis while accommodating the heterogeneity of mTBI. This study describes a pipeline for the multimodal integration of sMRI/DWI/DTI to quantify white matter (WM) neural network circuitry alterations associated with mTBI-related CMBs. The approach incorporates WM streamline matching, topology-compliant streamline prototyping and along-tract analysis within a unified framework. When applied to the analysis of neuroimaging data acquired from both mTBI and healthy control volunteers, the approach facilitates the identification of patient-specific CMB-related connectomic changes while incorporating the ability to perform group analyses. This pipeline for the identification and profiling of connectopathies can assist the adaptation of clinical rehabilitation protocols to patients' individual needs.

White Matter Degradation is Associated with Reduced Financial Capacity in Mild Cognitive Impairment and Alzheimer's Disease

Financial capacity (FC) is a cognitively complex activity of daily living that declines in mild cognitive impairment (MCI) and Alzheimer's disease (AD), limiting an individual's ability to manage one's finances and function independently. The neural underpinnings of this decline in function are poorly understood but likely involve age-related and disease-related degradation across structural networks. The purpose of the current study was to determine if altered white matter integrity is associated with declining FC in persons with MCI and AD compared to older controls. Individuals with MCI due to AD (n = 31), mild dementia (n = 39), and cognitively healthy older adults (n = 60) were administered a neuropsychological battery including the FC Instrument, a performance-based measure of FC. All 130 participants also underwent diffusion tensor imaging (DTI) upon which tract-based spatial statistics were performed. Both FC and white matter integrity decreased in accordance with disease severity with little to no effect in healthy elderly, significant effects in MCI, and greater effects in AD. Regional white matter degradation (increased diffusivities and decreased fractional anisotropy) was associated with reduced FC in both MCI and AD groups even after controlling for age, education, and gender. Specifically, in MCI, decreased fractional anisotropy, but not increased diffusivities, was associated with poorer FC in widespread cingulo-parietal-frontal and temporo-occipital areas. In AD, rather than anisotropy, increased mean and axial diffusivities in anterior cingulate, callosum, and frontal areas associated with poorer FC. These findings suggest a severity gradient of white matter degradation across DTI metrics and AD stages that predict declining financial skill and knowledge.

Suprathreshold fiber cluster statistics: Leveraging white matter geometry to enhance tractography statistical analysis

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.

White matter microstructure in boys with persistent depressive disorder

BACKGROUND

Persistent depressive symptoms in children and adolescents are considered a risk factor for the development of major depressive disorder (MDD) later in life. Previous research has shown alterations in white matter microstructure in pediatric MDD but discrepancies exist as to the specific tracts affected. The current study aimed to improve upon previous methodology and address the question whether previous findings of lower fractional anisotropy (FA) replicate in a sample of children with persistent depressive disorder characterized by mild but more chronic symptoms of depression.

METHODS

White matter microstructure was examined in 25 boys with persistent depressive disorder and 25 typically developing children. Tract specific analysis implemented with the Diffusion Tensor Imaging - ToolKit (DTI-TK) was used to probe fractional anisotropy (FA) in eleven major white matter tracts.

RESULTS

Clusters within the left uncinate, inferior fronto-occipital and cerebrospinal tracts showed lower FA in the clinical group. FA in the left uncinate showed a negative association with self-reported symptoms of depression.

CONCLUSIONS

The results demonstrate lower FA in several white matter tracts in children with persistent depressive disorder. These findings support the contention that early onset depression is associated with altered white matter microstructure, which may contribute to the maintenance and recurrence of symptoms.

FGWAS: Functional genome wide association analysis

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs.

HFPRM: Hierarchical Functional Principal Regression Model for Diffusion Tensor Image Bundle Statistics

Diffusion-weighted magnetic resonance imaging (MRI) provides a unique approach to understand the geometric structure of brain fiber bundles and to delineate the diffusion properties across subjects and time. It can be used to identify structural connectivity abnormalities and helps to diagnose brain-related disorders. The aim of this paper is to develop a novel, robust, and efficient dimensional reduction and regression framework, called hierarchical functional principal regression model (HFPRM), to effectively correlate high-dimensional fiber bundle statistics with a set of predictors of interest, such as age, diagnosis status, and genetic markers. The three key novelties of HFPRM include the simultaneous analysis of a large number of fiber bundles, the disentanglement of global and individual latent factors that characterizes between-tract correlation patterns, and a bi-level analysis on the predictor effects. Simulations are conducted to evaluate the finite sample performance of HFPRM. We have also applied HFPRM to a genome-wide association study to explore important genetic variants in neonatal white matter development.

Test-Retest and Interreader Reproducibility of Semiautomated Atlas-Based Analysis of Diffusion Tensor Imaging Data in Acute Cervical Spine Trauma in Adult Patients

BACKGROUND AND PURPOSE

DTI is a tool for microstructural spinal cord injury evaluation. This study evaluated the reproducibility of a semiautomated segmentation algorithm of spinal cord DTI.

MATERIALS AND METHODS

Forty-two consecutive patients undergoing acute trauma cervical spine MR imaging underwent 2 axial DTI scans in addition to their clinical scan. The datasets were put through a semiautomated probabilistic segmentation algorithm that selected white matter, gray matter, and 24 individual white matter tracts. Regional and white matter tract volume, fractional anisotropy, and mean diffusivity values were calculated. Two readers performed the nonautomated steps to evaluate interreader reproducibility. The coefficient of variation and intraclass correlation coefficient were used to assess test-retest and interreader reproducibility.

RESULTS

Of 42 patients, 30 had useable data. Test-retest reproducibility of fractional anisotropy was high for white matter as a whole (coefficient of variation, 3.8%; intraclass correlation coefficient, 0.93). Test-retest coefficient-of-variation ranged from 8.0%-18.2% and intraclass correlation coefficients from 0.47-0.80 across individual white matter tracts. Mean diffusivity metrics also had high test-retest reproducibility (white matter: coefficient-of-variation, 5.6%; intraclass correlation coefficient, 0.86) with coefficients of variation from 11.6%-18.3% and intraclass correlation coefficients from 0.57-0.74 across individual tracts, with better agreement for larger tracts. The coefficients of variation of fractional anisotropy and mean diffusivity both had significant negative relationships with white matter volume (26%-27% decrease for each doubling of white matter volume, P < .01).

CONCLUSIONS

DTI spinal cord segmentation is reproducible in the setting of acute spine trauma, specifically for larger white matter tracts and total white or gray matter.

Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging and disease. Developments in MRI acquisition, image processing and data modeling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and for inferring microstructural properties; we also describe key artifacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, although methods need to improve and caution is required in interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works.

A tract-specific approach to assessing white matter in preterm infants

Diffusion-weighted imaging (DWI) is becoming an increasingly important tool for studying brain development. DWI analyses relying on manually-drawn regions of interest and tractography using manually-placed waypoints are considered to provide the most accurate characterisation of the underlying brain structure. However, these methods are labour-intensive and become impractical for studies with large cohorts and numerous white matter (WM) tracts. Tract-specific analysis (TSA) is an alternative WM analysis method applicable to large-scale studies that offers potential benefits. TSA produces a skeleton representation of WM tracts and projects the group's diffusion data onto the skeleton for statistical analysis. In this work we evaluate the performance of TSA in analysing preterm infant data against results obtained from native space tractography and tract-based spatial statistics. We evaluate TSA's registration accuracy of WM tracts and assess the agreement between native space data and template space data projected onto WM skeletons, in 12 tracts across 48 preterm neonates. We show that TSA registration provides better WM tract alignment than a previous protocol optimised for neonatal spatial normalisation, and that TSA projects FA values that match well with values derived from native space tractography. We apply TSA for the first time to a preterm neonatal population to study the effects of age at scan on WM tracts around term equivalent age. We demonstrate the effects of age at scan on DTI metrics in commissural, projection and association fibres. We demonstrate the potential of TSA for WM analysis and its suitability for infant studies involving multiple tracts.

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Evaluation of tract-specific analysis (TSA) for white matter studies in infants.

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TSA improves white matter tract alignment over scalar-based registration.

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TSA closely approximates native space tractography DTI values.

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The first application of TSA to a neonatal population.

Quantifying the brain's sheet structure with normalized convolution

The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as "pages of a book" has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of "path neighborhoods" reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure.

Voxel-based analysis of diffusion tensor imaging in patients with mesial temporal lobe epilepsy

PURPOSE

Quantitative techniques of diffusion analysis allow for an in-vivo investigation of the physiopathology of epilepsies. The objective of this study was to evaluate the variation of the main diffusion parameters and explore differences between two methodologies of voxel-wise analysis comparing a group of patients with mesial temporal lobe epilepsy (MTLE) with controls.

METHODS

24 patients with a diagnosis of MTLE were selected. All patients and a control group of 36 individuals were submitted to 3T magnetic resonance imaging. Diffusion parameters were obtained from the raw images. Based on the tensors, a customized template was created, and images were registered into standard space. Voxel-based comparisons between patients and controls was performed by whole brain voxel-wise analysis and tract-based spatial statistics (TBSS). Tract-specific analysis (TSA) was performed in the mostly damaged fasciculi.

RESULTS

10 patients presented with right hippocampal sclerosis (HS), 11 with left HS and 3 with bilateral HS with left predominance. Whole brain voxel-wise analysis showed abnormalities mainly localized in the temporal lobes (total volume of 3859mm³). TBSS showed more widespread abnormalities (21931mm³). TSA pointed to abnormalities situated essentially in the temporal stem topography. Fractional anisotropy (FA) and radial diffusivity (RD) were the parameters that showed more abnormalities.

CONCLUSION

Whole brain voxel-wise analysis was more restricted than TBSS. The methods were complementary stressing the significance of the findings. The abnormalities were more frequently observed in FA and RD indicating the need for using several diffusion parameters for the investigation of patients with MTLE.

Surface-based vertexwise analysis of morphometry and microstructural integrity for white matter tracts in diffusion tensor imaging: With application to the corpus callosum in Alzheimer's disease

In this article, we present a unified statistical pipeline for analyzing the white matter (WM) tracts morphometry and microstructural integrity, both globally and locally within the same WM tract, from diffusion tensor imaging. Morphometry is quantified globally by the volumetric measurement and locally by the vertexwise surface areas. Meanwhile, microstructural integrity is quantified globally by the mean fractional anisotropy (FA) and trace values within the specific WM tract and locally by the FA and trace values defined at each vertex of its bounding surface. The proposed pipeline consists of four steps: (1) fully automated segmentation of WM tracts in a multi-contrast multi-atlas framework; (2) generation of the smooth surface representations for the WM tracts of interest; (3) common template surface generation on which the localized morphometric and microstructural statistics are defined and a variety of statistical analyses can be conducted; (4) multiple comparison correction to determine the significance of the statistical analysis results. Detailed herein, this pipeline has been applied to the corpus callosum in Alzheimer's disease (AD) with significantly decreased FA values and increased trace values, both globally and locally, being detected in patients with AD when compared to normal aging populations. A subdivision of the corpus callosum in both hemispheres revealed that the AD pathology primarily affects the body and splenium of the corpus callosum. Validation analyses and two multiple comparison correction strategies are provided. Hum Brain Mapp 38:1875-1893, 2017. © 2017 Wiley Periodicals, Inc.

Compromised Neurocircuitry in Chronic Blast-Related Mild Traumatic Brain Injury

The aim of this study was to apply recently developed automated fiber segmentation and quantification methods using diffusion tensor imaging (DTI) and DTI-based deterministic and probabilistic tractography to access local and global diffusion changes in blast-induced mild traumatic brain injury (bmTBI). Two hundred and two (202) male active US service members who reported persistent post-concussion symptoms for more than 6 months after injury were recruited. An additional forty (40) male military controls were included for comparison. DTI results were examined in relation to post-concussion and post-traumatic stress disorder (PTSD) symptoms. No significant group difference in DTI metrics was found using voxel-wise analysis. However, group comparison using tract profile analysis and tract specific analysis, as well as single subject analysis using tract profile analysis revealed the most prominent white matter microstructural injury in chronic bmTBI patients over the frontal fiber tracts, that is, the front-limbic projection fibers (cingulum bundle, uncinate fasciculus), the fronto-parieto-temporal association fibers (superior longitudinal fasciculus), and the fronto-striatal pathways (anterior thalamic radiation). Effects were noted to be sensitive to the number of previous blast exposures, with a negative association between fractional anisotropy (FA) and time since most severe blast exposure in a subset of the multiple blast-exposed group. However, these patterns were not observed in the subgroups classified using macrostructural changes (T2 white matter hyperintensities). Moreover, post-concussion symptoms and PTSD symptoms, as well as neuropsychological function were associated with low FA in the major nodes of compromised neurocircuitry. Hum Brain Mapp 38:352-369, 2017. © 2016 Wiley Periodicals, Inc.

Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles

Fiber bundles stemming from tractography algorithms contain many streamlines. They require therefore a great amount of computer memory and computational resources to be stored, visualised and processed. We propose an approximation scheme for fiber bundles which results in a parsimonious representation of weighted prototypes. Prototypes are chosen among the streamlines and they represent groups of similar streamlines. Their weight is related to the number of approximated streamlines. Both streamlines and prototypes are modelled as weighted currents. This computational model does not need point-to-point correspondences and two streamlines are considered similar if their endpoints are close to each other and if their pathways follow similar trajectories. Moreover, the space of weighted currents is a vector space with a closed-form metric. This permits easy computation of the approximation error and the selection of the prototypes is based on the minimisation of this error. We propose an iterative algorithm which approximates independently and simultaneously all the fascicles of the bundle in a fast and accurate way. We show that the resulting representation preserves the shape of the bundle and it can be used to accurately reconstruct the original structural connectivity. We evaluate our algorithm on bundles obtained from both deterministic and probabilistic tractography algorithms. The resulting approximations use on average only 2% of the original streamlines as prototypes. This drastically reduces the computational burden of the processes where the geometry of the streamlines is considered. We demonstrate its effectiveness using as example the registration between two fiber bundles.

Parametrization of white matter manifold-like structures using principal surfaces

In this manuscript, we are concerned with data generated from a diffusion tensor imaging (DTI) experiment. The goal is to parameterize manifold-like white matter tracts, such as the corpus callosum, using principal surfaces. The problem is approached by finding a geometrically motivated surface-based representation of the corpus callosum and visualized fractional anisotropy (FA) values projected onto the surface. The method also applies to any other diffusion summary. An algorithm is proposed that 1) constructs the principal surface of a corpus callosum; 2) flattens the surface into a parametric 2D map; 3) projects associated FA values on the map. The algorithm is applied to a longitudinal study containing 466 diffusion tensor images of 176 multiple sclerosis (MS) patients observed at multiple visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 20,000 voxels. Extensive simulation studies demonstrate fast convergence and robust performance of the algorithm under a variety of challenging scenarios.

A study of brain white matter plasticity in early blinds using tract-based spatial statistics and tract statistical analysis

Early blind individuals are known to exhibit structural brain reorganization. Particularly, early-onset blindness may trigger profound brain alterations that affect not only the visual system but also the remaining sensory systems. Diffusion tensor imaging (DTI) allows in-vivo visualization of brain white matter connectivity, and has been extensively used to study brain white matter structure. Among statistical approaches based on DTI, tract-based spatial statistics (TBSS) is widely used because of its ability to automatically perform whole brain white matter studies. Tract specific analysis (TSA) is a more recent method that localizes changes in specific white matter bundles. In the present study, we compare TBSS and TSA results of DTI scans from 12 early blind individuals and 13 age-matched sighted controls, with two aims: (a) to investigate white matter alterations associated with early visual deprivation; (b) to examine the relative sensitivity of TSA when compared with TBSS, for both deficit and hypertrophy of white matter microstructures. Both methods give consistent results for broad white matter regions of deficits. However, TBSS does not detect hypertrophy of white matter, whereas TSA shows a higher sensitivity in detecting subtle differences in white matter colocalized to the posterior parietal lobe.

Concurrent decrease of brain white matter tracts' thicknesses and fractional anisotropy after antenatal hypoxia-ischemia detected with tract-based spatial statistics analysis

PURPOSE

To examine the extent of gray and white matter (WM) injury following global antenatal hypoxia-ischemia (H-I) and resulting in muscle hypertonia in newborns in a rabbit cerebral palsy model.

MATERIALS AND METHODS

Rabbit dams (n = 15) underwent uterine ischemia procedure resulting in a global fetal H-I at embryonic day 22 (embryonic 22 days gestation). Newborn's brains underwent high resolution diffusion tensor imaging on a 14 Tesla magnet ex vivo. Fractional anisotropy (FA) in brains of hypertonic (n = 9), nonhypertonic (n = 6), and sham control (n = 5) kits were compared voxel-wise using Tract-Based Spatial Statistics (TBSS) approach. Herein, we used a novel method to assess local WM tracts' thicknesses in TBSS analysis and compare between the groups.

RESULTS

Significant (corrected P < 0.05) reduction of WM FA was found in corpus callosum splenium (91.2%), periventricular WM (83.5%), fimbria hippocampi (78.8%), cingulum (81.4%), anterior commissure (95%), internal capsule (83.2%), and optic tract (82.9%) in the hypertonic group. Significant (corrected P < 0.05) reduction in WM tracts' thicknesses was found in corpus callosum (73.3%), periventricular WM (82.5%), cingulum (73.4%), bilaterally in the hypertonic group.

CONCLUSION

WM injury in newborn hypertonic kits 10 days after global fetal H-I is widespread and involves not only motor but also limbic and commissural fibers in multiple regions. WM injury in newborn hypertonic kits is manifested by changes in microstructural properties and decreased FA, as well as reduction of WM volumes, relative to nonhypertonic kits. J. Magn. Reson. Imaging 2017;45:700-709.

LEVEL OF EVIDENCE

1 J. Magn. Reson. Imaging 2017;45:829-838.

Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI

The question whether our brain pathways adhere to a geometric grid structure has been a popular topic of debate in the diffusion imaging and neuroscience societies. Wedeen et al. (2012a, b) proposed that the brain's white matter is organized like parallel sheets of interwoven pathways. Catani et al. (2012) concluded that this grid pattern is most likely an artifact, resulting from methodological biases that cause the tractography pathways to cross in orthogonal angles. To date, ambiguities in the mathematical conditions for a sheet structure to exist (e.g. its relation to orthogonal angles) combined with the lack of extensive quantitative evidence have prevented wide acceptance of the hypothesis. In this work, we formalize the relevant terminology and recapitulate the condition for a sheet structure to exist. Note that this condition is not related to the presence or absence of orthogonal crossing fibers, and that sheet structure is defined formally as a surface formed by two sets of interwoven pathways intersecting at arbitrary angles within the surface. To quantify the existence of sheet structure, we present a novel framework to compute the sheet probability index (SPI), which reflects the presence of sheet structure in discrete orientation data (e.g. fiber peaks derived from diffusion MRI). With simulation experiments we investigate the effect of spatial resolution, curvature of the fiber pathways, and measurement noise on the ability to detect sheet structure. In real diffusion MRI data experiments we can identify various regions where the data supports sheet structure (high SPI values), but also areas where the data does not support sheet structure (low SPI values) or where no reliable conclusion can be drawn. Several areas with high SPI values were found to be consistent across subjects, across multiple data sets obtained with different scanners, resolutions, and degrees of diffusion weighting, and across various modeling techniques. Under the strong assumption that the diffusion MRI peaks reflect true axons, our results would therefore indicate that pathways do not form sheet structures at every crossing fiber region but instead at well-defined locations in the brain. With this framework, sheet structure location, extent, and orientation could potentially serve as new structural features of brain tissue. The proposed method can be extended to quantify sheet structure in directional data obtained with techniques other than diffusion MRI, which is essential for further validation.

Abnormal white matter integrity in rapists as indicated by diffusion tensor imaging

BACKGROUND

Recent research has documented structural brain abnormalities in various criminal offenders. However, there have been few brain imaging studies of sex offenders, and none on white matter integrity. The current study tested the hypothesis that rapists, when compared to matched controls, would show abnormal cortical and subcortical white matter integrity.

RESULTS

Rapists showed significantly increased fractional anisotropy in the internal capsul e in the thalamus, caudate, and globus pallidus, and also in white matter tracts near the angular gyrus, posterior cingulate, frontal pole, lateral occipital cortex, and genu compared to controls matched for age, gender, and educational status. Reduced fractional anisotropy was observed in rapists in the posterior cingulum and in the inferior fronto-occipital fasciculus.

CONCLUSIONS

To our knowledge, this is the first study indicating white matter abnormalities in rapists. Findings indicate abnormalities in white matter connectivity in brain regions involved in reward/motivation and moral judgment, which may predispose rapists to be both over-responsive to sexual reward stimuli and also to make inappropriate moral decisions.

Maturation Along White Matter Tracts in Human Brain Using a Diffusion Tensor Surface Model Tract-Specific Analysis

Previous diffusion tensor imaging tractography studies have demonstrated exponential patterns of developmental changes for diffusion parameters such as fractional anisotropy (FA) and mean diffusivity (MD) averaged over all voxels in major white matter (WM) tracts of the human brain. However, this assumes that the entire tract is changing in unison, which may not be the case. In this study, a surface model based tract-specific analysis was applied to a cross-sectional cohort of 178 healthy subjects (83 males/95 females) aged from 6 to 30 years to spatially characterize the age-related changes of FA and MD along the trajectory of seven major WM tracts - corpus callosum (CC) and six bilateral tracts. There were unique patterns of regions that showed different exponential and linear rates of increasing FA or decreasing MD and age at which FA or MD levels off along each tract. Faster change rate of FA was observed in genu of CC and frontal-parietal part of superior longitudinal fasciculus (SLF). Inferior corticospinal tract (CST), posterior regions of association tracts such as inferior longitudinal fasciculus, inferior frontal occipital fasciculus and uncinate fasciculus also displayed earlier changing patterns for FA. MD decreases with age also exhibited this posterior-to-anterior WM maturation pattern for most tracts in females. Both males and females displayed similar FA/MD patterns of change with age along most large tracts; however, males had overall reached the FA maxima or MD minima later compared with females in most tracts with the greater differences occurring in the CST and frontal-parietal part of SLF for MD. Therefore, brain WM development has spatially varying trajectories along tracts that depend on sex and the tract.

Tract specific analysis in patients with sickle cell disease

Sickle cell disease (SCD) is a hereditary blood disorder in which the oxygen-carrying hemoglobin molecule in red blood cells is abnormal. It affects numerous people in the world and leads to a shorter life span, pain, anemia, serious infections and neurocognitive decline. Tract-Specific Analysis (TSA) is a statistical method to evaluate white matter alterations due to neurocognitive diseases, using diffusion tensor magnetic resonance images. Here, for the first time, TSA is used to compare 11 major brain white matter (WM) tracts between SCD patients and age-matched healthy subjects. Alterations are found in the corpus callosum (CC), the cortico-spinal tract (CST), inferior fronto-occipital fasciculus (IFO), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF), and uncinated fasciculus (UNC). Based on previous studies on the neurocognitive functions of these tracts, the significant areas found in this paper might be related to several cognitive impairments and depression, both of which are observed in SCD patients.

Medially constrained deformable modeling for segmentation of branching medial structures: Application to aortic valve segmentation and morphometry

Deformable modeling with medial axis representation is a useful means of segmenting and parametrically describing the shape of anatomical structures in medical images. Continuous medial representation (cm-rep) is a "skeleton-first" approach to deformable medial modeling that explicitly parameterizes an object's medial axis and derives the object's boundary algorithmically. Although cm-rep has effectively been used to segment and model a number of anatomical structures with non-branching medial topologies, the framework is challenging to apply to objects with branching medial geometries since branch curves in the medial axis are difficult to parameterize. In this work, we demonstrate the first clinical application of a new "boundary-first" deformable medial modeling paradigm, wherein an object's boundary is explicitly described and constraints are imposed on boundary geometry to preserve the branching configuration of the medial axis during model deformation. This "boundary-first" framework is leveraged to segment and morphologically analyze the aortic valve apparatus in 3D echocardiographic images. Relative to manual tracing, segmentation with deformable medial modeling achieves a mean boundary error of 0.41 ± 0.10 mm (approximately one voxel) in 22 3DE images of normal aortic valves at systole. Deformable medial modeling is additionally demonstrated on pathological cases, including aortic stenosis, Marfan syndrome, and bicuspid aortic valve disease. This study demonstrates a promising approach for quantitative 3DE analysis of aortic valve morphology.

Segmentation of the Cerebellar Peduncles Using a Random Forest Classifier and a Multi-object Geometric Deformable Model: Application to Spinocerebellar Ataxia Type 6

The cerebellar peduncles, comprising the superior cerebellar peduncles (SCPs), the middle cerebellar peduncle (MCP), and the inferior cerebellar peduncles (ICPs), are white matter tracts that connect the cerebellum to other parts of the central nervous system. Methods for automatic segmentation and quantification of the cerebellar peduncles are needed for objectively and efficiently studying their structure and function. Diffusion tensor imaging (DTI) provides key information to support this goal, but it remains challenging because the tensors change dramatically in the decussation of the SCPs (dSCP), the region where the SCPs cross. This paper presents an automatic method for segmenting the cerebellar peduncles, including the dSCP. The method uses volumetric segmentation concepts based on extracted DTI features. The dSCP and noncrossing portions of the peduncles are modeled as separate objects, and are initially classified using a random forest classifier together with the DTI features. To obtain geometrically correct results, a multi-object geometric deformable model is used to refine the random forest classification. The method was evaluated using a leave-one-out cross-validation on five control subjects and four patients with spinocerebellar ataxia type 6 (SCA6). It was then used to evaluate group differences in the peduncles in a population of 32 controls and 11 SCA6 patients. In the SCA6 group, we have observed significant decreases in the volumes of the dSCP and the ICPs and significant increases in the mean diffusivity in the noncrossing SCPs, the MCP, and the ICPs. These results are consistent with a degeneration of the cerebellar peduncles in SCA6 patients.

Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres

In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres.

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We introduce the fixel—a specific fibre population within a voxel.

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A novel method for whole-brain fixel-based analysis of diffusion MRI is presented.

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Structural connectivity between fixels is derived from template-based tractography.

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Connectivity information is used for tract-specific smoothing and enhancement.

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Quantitative assessment and an in vivo demonstration is performed.

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

The neuroscientific community today is very much interested in analyzing specific white matter bundles like the arcuate fasciculus, the corticospinal tract, or the recently discovered Aslant tract to study sex differences, lateralization and many other connectivity applications. For this reason, experts spend time manually segmenting these fascicles and bundles using streamlines obtained from diffusion MRI tractography. However, to date, there are very few computational tools available to register these fascicles directly so that they can be analyzed and their differences quantified across populations. In this paper, we introduce a novel, robust and efficient framework to align bundles of streamlines directly in the space of streamlines. We call this framework Streamline-based Linear Registration. We first show that this method can be used successfully to align individual bundles as well as whole brain streamlines. Additionally, if used as a piecewise linear registration across many bundles, we show that our novel method systematically provides higher overlap (Jaccard indices) than state-of-the-art nonlinear image-based registration in the white matter. We also show how our novel method can be used to create bundle-specific atlases in a straightforward manner and we give an example of a probabilistic atlas construction of the optic radiation. In summary, Streamline-based Linear Registration provides a solid registration framework for creating new methods to study the white matter and perform group-level tractometry analysis.

Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images

We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template.

White matter disease contributes to apathy and disinhibition in behavioral variant frontotemporal dementia

OBJECTIVE

To relate changes in fractional anisotropy associated with behavioral variant frontotemporal dementia to measures of apathy and disinhibition.

BACKGROUND

Apathy and disinhibition are the 2 most common behavioral features of behavioral variant frontotemporal dementia, and these symptoms are associated with accelerated patient decline and caregiver stress. However, little is known about how white matter disease contributes to these symptoms.

METHODS

We collected neuropsychiatric data, volumetric magnetic resonance imaging, and diffusion-weighted imaging in 11 patients who met published criteria for behavioral variant frontotemporal dementia and had an autopsy-validated cerebrospinal fluid profile consistent with frontotemporal lobar degeneration. We also collected imaging data on 34 healthy seniors for analyses defining regions of disease in the patients. We calculated and analyzed fractional anisotropy with a white matter tract-specific method. This approach uses anatomically guided data reduction to increase sensitivity, and localizes results within canonically defined tracts. We used nonparametric, cluster-based statistical analysis to relate fractional anisotropy to neuropsychiatric measures of apathy and disinhibition.

RESULTS

The patients with behavioral variant frontotemporal dementia had widespread reductions in fractional anisotropy in anterior portions of frontal and temporal white matter, compared to the controls. Fractional anisotropy correlated with apathy in the left uncinate fasciculus and with disinhibition in the right corona radiata.

CONCLUSIONS

In patients with behavioral variant frontotemporal dementia, apathy and disinhibition are associated with distinct regions of white matter disease. The implicated fiber tracts likely support frontotemporal networks that are involved in goal-directed behavior.

Atlasing the frontal lobe connections and their variability due to age and education: a spherical deconvolution tractography study

In neuroscience, there is a growing consensus that higher cognitive functions may be supported by distributed networks involving different cerebral regions, rather than by single brain areas. Communication within these networks is mediated by white matter tracts and is particularly prominent in the frontal lobes for the control and integration of information. However, the detailed mapping of frontal connections remains incomplete, albeit crucial to an increased understanding of these cognitive functions. Based on 47 high-resolution diffusion-weighted imaging datasets (age range 22-71 years), we built a statistical normative atlas of the frontal lobe connections in stereotaxic space, using state-of-the-art spherical deconvolution tractography. We dissected 55 tracts including U-shaped fibers. We further characterized these tracts by measuring their correlation with age and education level. We reported age-related differences in the microstructural organization of several, specific frontal fiber tracts, but found no correlation with education level. Future voxel-based analyses, such as voxel-based morphometry or tract-based spatial statistics studies, may benefit from our atlas by identifying the tracts and networks involved in frontal functions. Our atlas will also build the capacity of clinicians to further understand the mechanisms involved in brain recovery and plasticity, as well as assist clinicians in the diagnosis of disconnection or abnormality within specific tracts of individual patients with various brain diseases.

Statistical Instability of TBSS Analysis Based on DTI Fitting Algorithm

Voxel-based DTI analysis is an important approach in the comparison of subject groups by detecting and localizing gray and white matter changes in the brain. One of the principal problems for intersubject comparison is the absence of a "gold standard" processing pipeline. As a result, contradictory results may be obtained from identical data using different data processing pipelines, for example, in the data normalization or smoothing procedures. Tract-based spatial statistics (TBSS) shows potential to overcome this problem by automatic detection of white matter changes and decreasing variation in the performed analysis. However, skeleton projection approaches, such as TBSS, critically depend on the accuracy of the diffusion scalar metric estimations. In this work, we demonstrate that the agreement and reliability of TBSS results depend on the applied DTI data processing algorithm. Statistical tests have been performed using two in vivo measured datasets and compared with different implementations of the least squares algorithm. As a result, we recommend repeating TBSS analysis using different fitting algorithms, in particular, using on iteratively-assessed robust estimators, as accurate and more reliable approach in voxel-based analysis, particularly, for TBSS. Repeating TBSS analysis allows one to detect and localize suspicious regions in white matter which were estimated as the regions with significant difference. Finally, we did not find a favorite fitting algorithm (or class of them) which can be marked as more reliable for group comparison.

Toward tract-specific fractional anisotropy (TSFA) at crossing-fiber regions with clinical diffusion MRI

PURPOSE

White matter fractional anisotropy (FA), a measure suggesting microstructure, is significantly underestimated with single diffusion tensor model at crossing-fiber regions (CFR). We propose a tract-specific FA (TSFA), corrected for the effects of crossing-fiber geometry and free water at CFR, and adapted for tract analysis with diffusion MRI (dMRI) in clinical research.

METHODS

At CFR voxels, the proposed technique estimates free water fraction (fiso ) as a linear function of mean apparent diffusion coefficient (mADC), fits the dual tensors and estimates TSFA. Digital phantoms were designed for testing the accuracy of fiso and fitted dual-anisotropies at CFR. The technique was applied to clinical dMRI of normal subjects and hereditary spastic paraplegia (HSP) patients to test the effectiveness of TSFA.

RESULTS

Phantom simulation showed unbiased estimates of dual-tensor anisotropies at CFR and high accuracy of fiso as a linear function of mADC. TSFA at CFR was highly consistent to the single tensor FA at non-CFR within the same tract with normal human dMRI. Additional HSP imaging biomarkers with significant correlation to clinical motor function scores could be identified with TSFA.

CONCLUSION

Results suggest the potential of the proposed technique in estimating unbiased TSFA at CFR and conducting tract analysis in clinical research.

White matter segmentation based on a skeletonized atlas: effects on diffusion tensor imaging studies of regions of interest

PURPOSE

To compare the influence of conventional and skeletonized atlas-based white matter (WM) segmentation on diffusion tensor imaging (DTI) region-of-interest (ROI) investigations.

MATERIALS AND METHODS

A conventional WM atlas was skeletonized by thinning the corresponding fractional anisotropy (FA) map and labels. The conventional and skeletonized versions of the atlas were used for WM segmentation. The percentage of non-WM voxels assigned to WM labels, as well as statistical summaries of tensor-derived quantities, were compared between segmentation approaches. The ability to detect small differences in diffusion properties across groups of subjects was also compared between segmentation approaches.

RESULTS

Skeletonized segmentation resulted in significantly lower non-WM percentage (P < 0.05), higher mean FA and lower trace (P < 0.05) in most WM labels, and mainly lower standard deviation of FA and trace in labels neighboring the ventricles. In terms of maximizing the ability to detect intergroup DTI differences, skeletonized segmentation was superior in the corpus callosum, but the optimal approach varied for other WM labels.

CONCLUSION

Conventional and skeletonized atlas-based segmentation probe different portions of brain tissue and lead to different statistical summaries of diffusion characteristics in WM labels. Careful selection of segmentation approach is required for DTI investigations of WM ROIs.

Methodological considerations on tract-based spatial statistics (TBSS)

Having gained a tremendous amount of popularity since its introduction in 2006, tract-based spatial statistics (TBSS) can now be considered as the standard approach for voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data. Aiming to improve the sensitivity, objectivity, and interpretability of multi-subject DTI studies, TBSS includes a skeletonization step that alleviates residual image misalignment and obviates the need for data smoothing. Although TBSS represents an elegant and user-friendly framework that tackles numerous concerns existing in conventional VBA methods, it has limitations of its own, some of which have already been detailed in recent literature. In this work, we present general methodological considerations on TBSS and report on pitfalls that have not been described previously. In particular, we have identified specific assumptions of TBSS that may not be satisfied under typical conditions. Moreover, we demonstrate that the existence of such violations can severely affect the reliability of TBSS results. With TBSS being used increasingly, it is of paramount importance to acquaint TBSS users with these concerns, such that a well-informed decision can be made as to whether and how to pursue a TBSS analysis. Finally, in addition to raising awareness by providing our new insights, we provide constructive suggestions that could improve the validity and increase the impact of TBSS drastically.

Fusion of white and gray matter geometry: a framework for investigating brain development

Current neuroimaging investigation of the white matter typically focuses on measurements derived from diffusion tensor imaging, such as fractional anisotropy (FA). In contrast, imaging studies of the gray matter oftentimes focus on morphological features such as cortical thickness, folding and surface curvature. As a result, it is not clear how to combine findings from these two types of approaches in order to obtain a consistent picture of morphological changes in both gray and white matter. In this paper, we propose a joint investigation of gray and white matter morphology by combining geometrical information from white and the gray matter. To achieve this, we first introduce a novel method for computing multi-scale white matter tract geometry. Its formulation is based on the differential geometry of curve sets and is easily incorporated into a continuous scale-space framework. We then incorporate this method into a novel framework for "fusing" white and gray matter geometrical information. Given a set of fiber tracts originating in a particular cortical region, the key idea is to compute two scalar fields that represent geometrical characteristics of the white matter and of the surface of the cortical region. A quantitative marker is created by combining the distributions of these scalar values using Mutual Information. This marker can be then used in the study of normal and pathological brain structure and development. We apply this framework to a study on autism spectrum disorder in children. Our preliminary results support the view that autism may be characterized by early brain overgrowth, followed by reduced or arrested growth (Courchesne, 2004).