Pierre-Louis Bazin

Cortico-subthalamic white matter tract strength predicts interindividual efficacy in stopping a motor response

The subthalamic nucleus (STN) is a small but vitally important structure in the basal ganglia. Because of its small volume, and its localization in the basal ganglia, the STN can best be visualized using ultra-high resolution 7 Tesla (T) magnetic resonance imaging (MRI). In the present study, first we individually segmented 7 T MRI STN masks to generate atlas probability maps. Secondly, the individually segmented STN masks and the probability maps were used to derive cortico-subthalamic white matter tract strength. Tract strength measures were then taken to test two functional STN hypotheses which account for the efficiency in stopping a motor response: the right inferior fronto-subthalamic (rIFC-STN) hypothesis and the posterior medial frontal cortex-subthalamic (pMFC-STN) hypothesis. Results of two independent experiments show that increased white matter tract strength between the pMFC and STN results in better stopping behaviour.

Anatomically motivated modeling of cortical laminae.

Improvements in the spatial resolution of structural and functional MRI are beginning to enable analysis of intracortical structures such as heavily myelinated layers in 3D, a prerequisite for in-vivo parcellation of individual human brains. This parcellation can only be performed precisely if the profiles used in cortical analysis are anatomically meaningful. Profiles are often constructed as traverses that are perpendicular to computed laminae. In this case they are fully determined by these laminae. The aim of this study is to evaluate models for cortical laminae used so far and to establish a new model. Methods to model the laminae used so far include constructing laminae that keep a constant distance to the cortical boundaries, so-called equidistant laminae. Another way is to compute equipotentials between the cortical boundary surfaces with the Laplace equation. The Laplace profiles resulting from the gradients to the equipotentials were often-used because of their nice mathematical properties. However, the equipotentials these Laplacian profiles are constructed from and the equidistant laminae do not follow the anatomical layers observed using high resolution MRI of cadaver brain. To remedy this problem, we introduce a novel equi-volume model that derives from work by Bok (1929). He argued that cortical segments preserve their volume, while layer thickness changes to compensate cortical folding. We incorporate this preservation of volume in our new equi-volume model to generate a three-dimensional well-adapted undistorted coordinate system of the cortex. When defined by this well-adapted coordinate system, cortical depth is anatomically meaningful. We compare isocontours from these cortical depth values to locations of myelinated bands on high-resolution ex-vivo and in-vivo three-dimensional MR images. A similar comparison was performed with equipotentials computed with the Laplace equation and with equidistant isocontours. A quantitative evaluation of the equi-volume model using measured image intensities confirms that it provides a much better fit to observed cortical layering.

SIMPLE PARADIGM FOR EXTRA-CEREBRAL TISSUE REMOVAL: ALGORITHM AND ANALYSIS

Extraction of the brain-i.e. cerebrum, cerebellum, and brain stem-from T1-weighted structural magnetic resonance images is an important initial step in neuroimage analysis. Although automatic algorithms are available, their inconsistent handling of the cortical mantle often requires manual interaction, thereby reducing their effectiveness. This paper presents a fully automated brain extraction algorithm that incorporates elastic registration, tissue segmentation, and morphological techniques which are combined by a watershed principle, while paying special attention to the preservation of the boundary between the gray matter and the cerebrospinal fluid. The approach was evaluated by comparison to a manual rater, and compared to several other leading algorithms on a publically available data set of brain images using the Dice coefficient and containment index as performance metrics. The qualitative and quantitative impact of this initial step on subsequent cortical surface generation is also presented. Our experiments demonstrate that our approach is quantitatively better than six other leading algorithms (with statistical significance on modern T1-weighted MR data). We also validated the robustness of the algorithm on a very large data set of over one thousand subjects, and showed that it can replace an experienced manual rater as preprocessing for a cortical surface extraction algorithm with statistically insignificant differences in cortical surface position.

Quantifying inter-individual anatomical variability in the subcortex using 7 T structural MRI.

Functional magnetic resonance imaging (MRI) data are usually registered into standard anatomical space. However, standard atlases, such as LPBA40, the Harvard-Oxford atlas, FreeSurfer, and the Jülich cytoarchitectonic maps all lack important detailed information about small subcortical structures like the substantia nigra and subthalamic nucleus. Here we introduce a new subcortical probabilistic atlas based on ultra-high resolution in-vivo anatomical imaging from 7 T MRI. The atlas includes six important but elusive subcortical nuclei: the striatum, the globus pallidus internal and external segment (GPi/e), the subthalamic nucleus, the substantia nigra, and the red nucleus. With a sample of 30 young subjects and carefully cross-validated delineation protocols, our atlas is able to capture the anatomical variability within healthy populations for each of the included structures at an unprecedented level of detail. All the generated probabilistic atlases are registered to MNI standard space and are publicly available.

A computational framework for ultra-high resolution cortical segmentation at 7 Tesla

This paper presents a computational framework for whole brain segmentation of 7Tesla magnetic resonance images able to handle ultra-high resolution data. The approach combines multi-object topology-preserving deformable models with shape and intensity atlases to encode prior anatomical knowledge in a computationally efficient algorithm. Experimental validation on simulated and real brain images shows accuracy and robustness of the method and demonstrates the benefits of an increased processing resolution.

Ultra-high 7T MRI of structural age-related changes of the subthalamic nucleus

The subthalamic nucleus (STh) is a small subcortical structure which is involved in regulating motor as well as cognitive functions. Due to its small size and close proximity to other small subcortical structures, it has been a challenge to localize and visualize it using magnetic resonance imaging (MRI). Currently there are several standard atlases available that are used to localize the STh in functional MRI studies and clinical procedures such as deep brain stimulation (DBS). DBS is an increasingly common neurosurgical procedure that has been successfully used to alleviate motor symptoms present in Parkinsons disease. However, current atlases are based on low sample sizes and restricted age ranges (Schaltenbrand and Wahren, 1977), and hence the use of these atlases effectively ignores the substantial structural brain changes that are associated with aging. In the present study, ultra-high field 7 tesla (T) magnetic resonance imaging (MRI) in humans was used to visualize and segment the STh in young, middle-aged, and elderly participants. The resulting probabilistic atlas maps for all age groups show that the STh shifts in the lateral direction with increasing age. In sum, the results of the present study suggest that age has to be taken into account in atlases for the optimal localization of the STh in healthy and diseased brains.

Longitudinal changes in diffusion tensor–based quantitative MRI in multiple sclerosis

Objective: To estimate longitudinal changes in a quantitative whole-brain and tract-specific MRI study of multiple sclerosis (MS), with the intent of assessing the feasibility of this approach in clinical trials. Methods: A total of 78 individuals with MS underwent a median of 3 scans over 2 years. Diffusion tensor imaging indices, magnetization transfer ratio, and T2 relaxation time were analyzed in supratentorial brain, corpus callosum, optic radiations, and corticospinal tracts by atlas-based tractography. Linear mixed-effect models estimated annualized rates of change for each index, and sample size estimates for potential clinical trials were determined. Results: There were significant changes over time in fractional anisotropy and perpendicular diffusivity in the supratentorial brain and corpus callosum, mean diffusivity in the supratentorial brain, and magnetization transfer ratio in all areas studied. Changes were most rapid in the corpus callosum, where fractional anisotropy decreased 1.7% per year, perpendicular diffusivity increased 1.2% per year, and magnetization transfer ratio decreased 0.9% per year. The T2 relaxation time changed more rapidly than diffusion tensor imaging indices and magnetization transfer ratio but had higher within-participant variability. Magnetization transfer ratio in the corpus callosum and supratentorial brain declined at an accelerated rate in progressive MS relative to relapsing-remitting MS. Power analysis yielded reasonable sample sizes (on the order of 40 participants per arm or fewer) for 1- or 2-year trials. Conclusions: Longitudinal changes in whole-brain and tract-specific diffusion tensor imaging indices and magnetization transfer ratio can be reliably quantified, suggesting that small clinical trials using these outcome measures are feasible.

A cytoarchitecture-driven myelin model reveals area-specific signatures in human primary and secondary areas using ultra-high resolution in-vivo brain MRI

This work presents a novel approach for modelling laminar myelin patterns in the human cortex in brain MR images on the basis of known cytoarchitecture. For the first time, it is possible to estimate intracortical contrast visible in quantitative ultra-high resolution MR images in specific primary and secondary cytoarchitectonic areas. The presented technique reveals different area-specific signatures which may help to study the spatial distribution of cortical T1 values and the distribution of cortical myelin in general. It may lead to a new discussion on the concordance of cyto- and myeloarchitectonic boundaries, given the absence of such concordance atlases. The modelled myelin patterns are quantitatively compared with data from human ultra-high resolution in-vivo 7T brain MR images (9 subjects). In the validation, the results are compared to one post-mortem brain sample and its ex-vivo MRI and histological data. Details of the analysis pipeline are provided. In the context of the increasing interest in advanced methods in brain segmentation and cortical architectural studies, the presented model helps to bridge the gap between the microanatomy revealed by classical histology and the macroanatomy visible in MRI.

A JOINT REGISTRATION AND SEGMENTATION APPROACH TO SKULL STRIPPING

Extraction of the cerebrum, cerebellum, and brain stem from structural magnetic resonances images (MRIs) is an important initial step in neuroimaging. We present an automated algorithm that solves this difficult problem, often referred to as skull stripping, which is novel for its use of registration, segmentation, and morphological operations. Our algorithm is also concerned with an accurate representation of the grey matter boundary, which is a unique feature. We also present results demonstrating the accuracy of this approach

A subject-specific framework for in vivo myeloarchitectonic analysis using high resolution quantitative MRI

Structural magnetic resonance imaging can now resolve laminar features within the cerebral cortex in vivo. A variety of intracortical contrasts have been used to study the cortical myeloarchitecture with the purpose of mapping cortical areas in individual subjects. In this article, we first briefly review recent advances in MRI analysis of cortical microstructure to portray the potential and limitations of the current state-of-the-art. We then present an integrated framework for the analysis of intracortical structure, composed of novel image processing tools designed for high resolution cortical images. The main features of our framework are the segmentation of quantitative T1 maps to delineate the cortical boundaries (Bazin et al., 2014), and the use of an equivolume layering model to define an intracortical coordinate system that follows the anatomical layers of the cortex (Waehnert et al., 2014). We evaluate the framework with 150μm isotropic post mortem T2(∗)-weighted images and 0.5mm isotropic in vivo T1 maps, a quantitative index of myelin content. We study the laminar structure of the primary visual cortex (Brodmann area 17) in the post mortem and in vivo data, as well as the central sulcus region in vivo, in particular Brodmann areas 1, 3b and 4. We also investigate the impact of the layering models on the relationship between T1 and cortical curvature. Our experiments demonstrate that the equivolume intracortical surfaces and transcortical profiles best reflect the laminar structure of the cortex in areas of curvature in comparison to the state-of-the-art equidistant and Laplace implementations. This framework generates a subject specific intracortical coordinate system, the basis for subsequent architectonic analyses of the cortex. Any structural or functional contrast co-registered to the T1 maps, used to segment the cortex, can be sampled on the curved grid for analysis. This work represents an important step towards in vivo structural brain mapping of individual subjects.