A hybrid deep learning framework for integrated segmentation and registration: evaluation on longitudinal white matter tract changes

To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space. In this work, we propose a novel hybrid convolutional neural network (CNN) that integrates segmentation and registration into a single procedure. We hypothesize that the joint optimization leads to increased performance on both tasks. The hybrid CNN is trained by minimizing an integrated loss function composed of four different terms, measuring segmentation accuracy, similarity between registered images, deformation field smoothness, and segmentation consistency. We applied this method to the segmentation of white matter tracts, describing functionally grouped axonal fibers, using N=8045 longitudinal brain MRI data of 3249 individuals. The proposed method was compared with two multistage pipelines using two existing segmentation methods combined with a conventional deformable registration algorithm. In addition, we assessed the added value of the joint optimization for segmentation and registration separately. The hybrid CNN yielded significantly higher accuracy, consistency and reproducibility of segmentation than the multistage pipelines, and was orders of magnitude faster. Therefore, we expect it can serve as a novel tool to support clinical and epidemiological analyses on understanding microstructural brain changes over time.Comment: MICCAI 2019 (oral presentation

Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset

Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract segmentation. However, because of the limited consistency and long processing time, such methods may not be suitable for clinical practice. We therefore developed a novel convolutional neural network based method to directly segment white matter tract trained on a low-resolution dataset of 9149 DTI images. The method is optimized on input, loss function and network architecture selections. We evaluated both segmentation accuracy and reproducibility, and reproducibility of determining tract-specific diffusion measures. The reproducibility of the method is higher than that of the reference standard and the determined diffusion measures are consistent. Therefore, we expect our method to be applicable in clinical practice and in longitudinal analysis of white matter microstructure.Comment: Machine Learning in Medical Imaging (MLMI), 201

Evidence of wide-spread white matter compromise in children and adults with autism: a large-scale diffusion imaging study using repository data

Autism Spectrum Disorder (ASD) pathology involves multiple distributed neural networks. Abnormal white matter (WM) connectivity has been implicated as being responsible for perturbed neural functioning across distributed networks. Much of the existing research surrounding WM connectivity in ASD has primarily focused on children, while little is known about the anatomical and behavioral profile across the lifespan into adulthood. The present study used diffusion tensor imaging (DTI) to explore the degree to which WM connectivity is abnormal in ASD in a large sample of children and adults. Diffusion MRI data was acquired from the ABIDE II repository, Carnegie Mellon University, and the University of Pittsburgh. In total, we analyzed 336 subjects (199 ASD, 137 IQ-matched neurotypical (NT) controls). All data were collected on a 3‑Tesla scanner. Tract Based Spatial Statistics (TBSS) was performed using FMRIB’s FSL software. Due to the nested nature of the data, linear mixed-effects modeling was used to examine if there were group differences in diffusion measures between individuals with ASD and NT controls while controlling for several covariates (age, IQ, motion, brain volume). Individuals with ASD exhibited poorer overall diffusion, even after controlling for age, motion and IQ. Compared to NT controls, individuals with ASD showed decreased fractional anisotropy (FA), increased mean diffusivity (MD) and increased radial diffusivity (RD). Together, these results suggest that WM compromise begins early in ASD and persists throughout adulthood. Due to the nonlinear and dynamic nature of brain development in ASD, it is difficult to isolate aberrant changes in WM connectivity that might be a direct result of ASD. Focusing more efforts on adults with ASD may give researchers a better understanding of which of these changes in diffusion are sustained beyond development and maturation. Our findings highlight the need for longitudinal studies to better understand how age-related changes in WM diffusion properties may relate to the behavioral profile often seen in ASD across the lifespan

A Simplified Crossing Fiber Model in Diffusion Weighted Imaging

Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models

The relationship between adolescents' externalizing and internalizing symptoms and brain development over a period of three years

Background: Adolescence is a crucial period for both brain maturation and the emergence of mental health disorders. Associations between brain morphology and internalizing/externalizing symptomatology have been identified in clinical or at-risk samples, but age-related developmental differences were rarely considered. The current study investigated the longitudinal relationship between internalizing/externalizing symptoms and brain development in the absence of psychiatric disorders during early and late adolescence. Methods: 98 healthy adolescents within two cohorts (younger: 9 years, older: 12 years) participated in annual assessments for three years; a clinical assessment measuring their externalizing and internalizing symptoms (SDQ) and an MRI assessment measuring their brain volume and white matter icrostructure, including fractional anisotropy (FA), mean diffusivity (MD) and average path length. Results: Linear mixed effect models and cross-lagged panel models showed that larger subcortical gray matter volume predicted more externalizing symptoms in older adolescents whereas decreases of subcortical gray matter volume predicted more externalizing symptoms for younger adolescents. Additionally, longer average white matter path length predicted more externalizing symptoms for older adolescents, while decreases in cerebral white matter volume were predictive of more externalizing symptoms for younger adolescents. There were no predictive effects for internalizing symptoms, FA or MD. Conclusions: Delays in subcortical brain maturation, in both early and late adolescence, are associated with increases in externalizing behavior which indicates a higher risk for psychopathology and warrants further investigations

A Simplified Crossing Fiber Model in Diffusion Weighted Imaging

Diffusion MRI (dMRI) is a vital source of imaging data for identifying anatomical connections in the living human brain that form the substrate for information transfer between brain regions. dMRI can thus play a central role toward our understanding of brain function. The quantitative modeling and analysis of dMRI data deduces the features of neural fibers at the voxel level, such as direction and density. The modeling methods that have been developed range from deterministic to probabilistic approaches. Currently, the Ball-and-Stick model serves as a widely implemented probabilistic approach in the tractography toolbox of the popular FSL software package and FreeSurfer/TRACULA software package. However, estimation of the features of neural fibers is complex under the scenario of two crossing neural fibers, which occurs in a sizeable proportion of voxels within the brain. A Bayesian non-linear regression is adopted, comprised of a mixture of multiple non-linear components. Such models can pose a difficult statistical estimation problem computationally. To make the approach of Ball-and-Stick model more feasible and accurate, we propose a simplified version of Ball-and-Stick model that reduces parameter space dimensionality. This simplified model is vastly more efficient in the terms of computation time required in estimating parameters pertaining to two crossing neural fibers through Bayesian simulation approaches. Moreover, the performance of this new model is comparable or better in terms of bias and estimation variance as compared to existing models

Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress

Diffusion magnetic resonance imaging (dMRI) continues to grow in popularity as a useful neuroimaging method to study brain development, and longitudinal studies that track the same individuals over time are emerging. Over the last decade, seminal work using dMRI has provided new insights into the development of brain white matter (WM) microstructure, connections and networks throughout childhood and adolescence. This review provides an introduction to dMRI, both diffusion tensor imaging (DTI) and other dMRI models, as well as common acquisition and analysis approaches. We highlight the difficulties associated with ascribing these imaging measurements and their changes over time to specific underlying cellular and molecular events. We also discuss selected methodological challenges that are of particular relevance for studies of development, including critical choices related to image acquisition, image analysis, quality control assessment, and the within-subject and longitudinal reliability of dMRI measurements. Next, we review the exciting progress in the characterization and understanding of brain development that has resulted from dMRI studies in childhood and adolescence, including brief overviews and discussions of studies focusing on sex and individual differences. Finally, we outline future directions that will be beneficial to the field

Investigating structural network disruption in multiple sclerosis

Multiple sclerosis (MS) is an inflammatory, demyelinating and neurodegenerative disease of the central nervous system (CNS). Conventional whole brain magnetic resonance imaging (MRI) measures do not sufficiently explain disability in MS. Network science provides a powerful approach to study brain organizational principles and in combination with graph theory has revealed fundamental connectivity patterns in neurological conditions including MS. The overarching aim of this thesis is to investigate structural network disruption in MS evaluating the potential of brain networks analyses as novel biomarkers in MS pathology. The results of this thesis add to the current scientific knowledge. In particular, by applying an optimised structural network reconstruction pipeline we demonstrated that network metrics explain disability better in MS over and above conventional non- network metrics. In addition, in the absence of any longitudinal network studies, we developed a longitudinal network pipeline which we then applied to our longitudinal data. These findings demonstrated for the first time that baseline structural network metrics are predictors of future deep grey matter atrophy and increased lesion load. Finally, we applied a data-driven network decomposition approach detecting progressive weakening of connections that is linked to the severity of MS subtypes suggesting that these techniques are sensitive to pathology. The results presented here highlight the potential of network-based approaches as complementary methods for disease biomarkers to better predict disease course and monitor treatment effects. We believe that these findings may provide a framework for future studies with the aim to bridge the gap between imaging and symptomatology