An MRI based average macaque monkey stereotaxic atlas and space (MNI monkey space)

In studies of the human brain, a standard stereotaxic space such as the Montreal Neurological Institute (MNI space) is widely used to provide a common reference for the three-dimensional localization of functional activation foci and anatomical structures, enabling the comparison of results obtained across different studies. Here we present a standard macaque monkey brain MRI template that offers a common stereotaxic reference frame to localize anatomical and functional information in an organized and reliable way for comparison across individual monkeys and studies. We have used MRI volumes from a group of 25 normal adult macaque monkeys (18 cynomolgus and 7 rhesus) to create a common standard macaque monkey brain as well as atlases for each of these species separately. In addition, the digital macaque monkey volume was subjected to 3D volumetric analysis and comparison of brain structures between the individual brains and the average atlas. Furthermore, we provide a means of transforming any macaque MRI volume into MNI monkey space coordinates in 3D using simple web based tools. Coordinates in MNI monkey space can also be transformed into the coordinate system of a detailed neuroanatomical paper atlas (Paxinos et al., 2008), enabling researchers to identify and delineate cortical and subcortical structures in their individual macaque monkey brains.     

Brain templates and atlases

The core concept within the field of brain mapping is the use of a standardized, or "stereotaxic", 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or "atlases" that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from "native" space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target "template" or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease-specificity. Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution.     

Unbiased diffeomorphic atlas construction for computational anatomy

Construction of population atlases is a key issue in medical image analysis, and particularly in brain mapping. Large sets of images are mapped into a common coordinate system to study intra-population variability and inter-population differences, to provide voxel-wise mapping of functional sites, and help tissue and object segmentation via registration of anatomical labels. Common techniques often include the choice of a template image, which inherently introduces a bias. This paper describes a new method for unbiased construction of atlases in the large deformation diffeomorphic setting. A child neuroimaging autism study serves as a driving application. There is lack of normative data that explains average brain shape and variability at this early stage of development. We present work in progress toward constructing an unbiased MRI atlas of 2 years of children and the building of a probabilistic atlas of anatomical structures, here the caudate nucleus. Further, we demonstrate the segmentation of new subjects via atlas mapping. Validation of the methodology is performed by comparing the deformed probabilistic atlas with existing manual segmentations.     

A three-dimensional MRI atlas of the zebra finch brain in stereotaxic coordinates

The neurobiology of birdsong, as a model for human speech, is a fast growing area of research in the neurosciences and involves electrophysiological, histological and more recently magnetic resonance imaging (MRI) approaches. Many of these studies require the identification and localization of different brain areas (nuclei) involved in the sensory and motor control of song. Until now, the only published atlases of songbird brains consisted in drawings based on histological slices of the canary and of the zebra finch brain. Taking advantage of high-magnetic field (7 Tesla) MRI technique, we present the first high-resolution (80 x 160 x 160 microm) 3-D digital atlas in stereotaxic coordinates of a male zebra finch brain, the most widely used species in the study of birdsong neurobiology. Image quality allowed us to discern most of the song control, auditory and visual nuclei. The atlas can be freely downloaded from our Web site and can be interactively explored with MRIcro. This zebra finch MRI atlas should become a very useful tool for neuroscientists working on birdsong, especially for co-registrating MRI data but also for determining accurately the optimal coordinates and angular approach for injections or electrophysiological recordings.     

Construction and assessment of a 3-T MRI brain template

New MR imaging protocols enable visualization of brain structures. However, for dedicated clinical applications such as targeting deep brain stimulation (DBS), a more accurate localization requires the use of atlases. We developed a three-dimensional digitized mono-subject anatomical template of the human brain based on 3-T magnetic resonance images (MRI). By averaging 15 registered T1 image acquisitions, we have shown that the final image corresponds to an optimal image, limited by the performance of the 3-T MR machine. We compared different preprocessing workflows for template construction. With the optimal strategy, along with validated existing processing methods, one T1 template and one T1–T2 mixing template were created in order to improve visualization of spatially complex deep structures. Reduction of voxel size to 0.25 mm³ was also advantageous to observe fine structures and white matter/gray matter intensity crossings. Results demonstrated that such a template also improved inter-patient registration for population comparison in DBS. These MR templates are made freely available to our community (http://www.vmip.org/mritemplate) to serve as a reference for neuroimage processing methods.     

The creation of a brain atlas for image guided neurosurgery using serial histological data

Digital and print brain atlases have been used with success to help in the planning of neurosurgical interventions. In this paper, a technique presented for the creation of a brain atlas of the basal ganglia and the thalamus derived from serial histological data. Photographs of coronal histological sections were digitized and anatomical structures were manually segmented. A slice-to-slice nonlinear registration technique was used to correct for spatial distortions introduced into the histological data set at the time of acquisition. Since the histological data were acquired without any anatomical reference (e.g., block-face imaging, post-mortem MRI), this registration technique was optimized to use an error metric which calculates a nonlinear transformation minimizing the mean distance between the segmented contours between adjacent pairs of slices in the data set. A voxel-by-voxel intensity correction field was also estimated for each slice to correct for lighting and staining inhomogeneity. The reconstructed three-dimensional (3D) histological volume can be viewed in transverse and sagittal directions in addition to the original coronal. Nonlinear transformations used to correct for spatial distortions of the histological data were applied to the segmented structure contours. These contours were then tessellated to create three-dimensional geometric objects representing the different anatomic regions in register with the histological volumes. This yields two alternate representations (one histological and one geometric) of the atlas. To register the atlas to a standard reference MR volume created from the average of 27 T1-weighted MR volumes, a pseudo-MRI was created by setting the intensity of each anatomical region defined in the geometric atlas to match the intensity of the corresponding region of the reference MR volume. This allowed the estimation of a 3D nonlinear transformation using a correlation based registration scheme to fit the atlas to the reference MRI. The result of this procedure is a contiguous 3D histological volume, a set of 3D objects defining the basal ganglia and thalamus, both of which are registered to a standard MRI data set, for use for neurosurgical planning.     

A spatially unbiased atlas template of the human cerebellum

This article presents a new high-resolution atlas template of the human, cerebellum and brainstem, based on the anatomy of 20 young healthy individuals. The atlas is spatially unbiased, i.e., the location of each structure is equal to the expected location of that structure across individuals in MNI space, a result that is cross-validated with an independent sample of 16 individuals. At the same time, the new template preserves the anatomical detail of cerebellar structures through a nonlinear atlas generation algorithm. In comparison to current whole-brain templates, it allows for an improved voxel-by-voxel normalization for functional MRI and lesion analysis. Alignment to the template requires that the cerebellum and brainstem are isolated from the surrounding tissue, a process for which an automated algorithm has been developed. Compared to normalization to the MNI whole-brain template, the new method strongly improves the alignment of individual fissures, reducing their spatial spread by 60%, and improves the overlap of the deep cerebellar nuclei. Applied to functional MRI data, the new normalization technique leads to a 5-15% increase in peak t values and in the activated volume in the cerebellar cortex for movement vs. rest contrasts. This indicates that the new template significantly improves the overlap of functionally equivalent cerebellar regions across individuals. The template and software are freely available as an SPM-toolbox, which also allows users to relate the new template to the annotated volumetric (Schmahmann, J.D., Doyon, J., Toga, A., Petrides, M., Evans, A. (2000). MRI atlas of the human cerebellum. San Diego: Academic Press) and surface-based (Van Essen, D.C. (2002a) Surface-based atlases of cerebellar cortex in the human, macaque, and mouse. Ann. N. Y. Acad. Sci. 978:468-479.) atlas of one individual, the "colin27"-brain.     

In vivo tracing of major rat brain pathways using manganese-enhanced magnetic resonance imaging and three-dimensional digital atlasing

The magnetic resonance imaging (MRI)-detectable T1 contrast agent manganese (Mn2+) has recently been introduced as a neural tracer in rodents, birds, and monkeys. We have tested to what extent this in vivo method is useful for three-dimensional (3-D) survey of connectivity patterns in the rat somatosensory system. A commonly available 3 T human clinical MRI scanner was used to trace neural pathways following focal injection of manganese chloride (MnCl2) in the somatosensory cortex. Six to 10 h after MnCl2 injection, we found significant signal enhancement in major projection systems, including corticocortical, corticostriatal, corticothalamic, corticotectal, corticopontine, and corticospinal pathways. To facilitate the assignment of anatomic localization to the observed Mn2+ signal enhancement, we registered the MRI data with a 3-D digital reconstruction of a stereotaxic rat brain atlas. Across-animal comparison using the digital model allowed demonstration of a corticothalamic 3-D topographic organization in agreement with previously published two-dimensional topographic schemes based on classical neural tracing data. We conclude that anterograde MnCl2/MRI tracing allows rapid analysis of topographic organization across multiple brain regions. The method allows a higher data throughput for 3-D studies of large-scale brain connectivity than conventional methods based on tissue sectioning.     

Detection, visualization and animation of abnormal anatomic structure with a deformable probabilistic brain atlas based on random vector field transformations

This paper describes the design, implementation and preliminary results of a technique for creating a comprehensive probabilistic atlas of the human brain based on high-dimensional vector field transformations. The goal of the atlas is to detect and quantify distributed patterns of deviation from normal anatomy, in a 3-D brain image from any given subject. The algorithm analyzes a reference population of normal scans and automatically generates color-coded probability maps of the anatomy of new subjects. Given a 3-D brain image of a new subject, the algorithm calculates a set of high-dimensional volumetric maps (with typically 384² × 256 × 3 ≈ 10⁸ degrees of freedom) elastically deforming this scan into structural correspondence with other scans, selected one by one from an anatomic image database. The family of volumetric warps thus constructed encodes statistical properties and directional biases of local anatomical variation throughout the architecture of the brain. A probability space of random transformations, based on the theory of anisotropic Gaussian random fields, is then developed to reflect the observed variability in stereotaxic space of the points whose correspondences are found by the warping algorithm. A complete system of 384² × 256 probability density functions is computed, yielding confidence limits in stereotaxic space for the location of every point represented in the 3-D image lattice of the new subject's brain. Color-coded probability maps are generated, densely defined throughout the anatomy of the new subject. These indicate locally the probability of each anatomic point being unusually situated, given the distributions of corresponding points in the scans of normal subjects. 3-D MRI and high-resolution cryosection volumes are analyzed from subjects with metastatic tumors and Alzheimer's disease. Gradual variations and continuous deformations of the underlying anatomy are simulated and their dynamic effects on regional probability maps are animated in video format (on the accompanying CD-ROM). Applications of the deformable probabilistic atlas include the transfer of multi-subject 3-D functional, vascular and histologic maps onto a single anatomic template, the mapping of 3-D atlases onto the scans of new subjects, and the rapid detection, quantification and mapping of local shape changes in 3-D medical images in disease and during normal or abnormal growth and development.     

More accurate Talairach coordinates for neuroimaging using non-linear registration

While the Talairach atlas remains the most commonly used system for reporting coordinates in neuroimaging studies, the absence of an actual 3-D image of the original brain used in its construction has severely limited the ability of researchers to automatically map locations from 3-D anatomical MRI images to the atlas. Previous work in this area attempted to circumvent this problem by constructing approximate linear and piecewise-linear mappings between standard brain templates (e.g. the MNI template) and Talairach space. These methods are limited in that they can only account for differences in overall brain size and orientation but cannot correct for the actual shape differences between the MNI template and the Talairach brain. In this paper we describe our work to digitize the Talairach atlas and generate a non-linear mapping between the Talairach atlas and the MNI template that attempts to compensate for the actual differences in shape between the two, resulting in more accurate coordinate transformations. We present examples in this paper and note that the method is available freely online as a Java applet.     

Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template

Brain registration to a stereotaxic atlas is an effective way to report anatomic locations of interest and to perform anatomic quantification. However, existing stereotaxic atlases lack comprehensive coordinate information about white matter structures. In this paper, white matter-specific atlases in stereotaxic coordinates are introduced. As a reference template, the widely used ICBM-152 was used. The atlas contains fiber orientation maps and hand-segmented white matter parcellation maps based on diffusion tensor imaging (DTI). Registration accuracy by linear and non-linear transformation was measured, and automated template-based white matter parcellation was tested. The results showed a high correlation between the manual ROI-based and the automated approaches for normal adult populations. The atlases are freely available and believed to be a useful resource as a target template and for automated parcellation methods.     

Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection

The technique of diffusion tensor tractography is gaining increasing prominence as a non-invasive method for studying the architecture of the white matter pathways in the human brain. Numerous studies have been published that attempt to identify or reconstruct particular pathways of interest. An atlas or map of all the pathways in the white matter would be particularly useful for providing detailed anatomical data that is not available in studies based on conventional MRI data. In this paper we present a method for constructing a white matter atlas to define structures from diffusion tensor tractography by making use of the locations of the anatomical terminations of individual streamlines that pass through white matter. We show how a map of unique seed regions can be used to generate tracts of interest. This approach provides anatomical information that can be rapidly applied to MRI datasets for the clear identification of white matter tracts. We show close correspondence of the tracts generated from the atlas with tracts isolated with classical dissection of post-mortem brain tissue.     

Construction of a 3D probabilistic atlas of human cortical structures

We describe the construction of a digital brain atlas composed of data from manually delineated MRI data. A total of 56 structures were labeled in MRI of 40 healthy, normal volunteers. This labeling was performed according to a set of protocols developed for this project. Pairs of raters were assigned to each structure and trained on the protocol for that structure. Each rater pair was tested for concordance on 6 of the 40 brains; once they had achieved reliability standards, they divided the task of delineating the remaining 34 brains. The data were then spatially normalized to well-known templates using 3 popular algorithms: AIR5.2.5's nonlinear warp (Woods et al., 1998) paired with the ICBM452 Warp 5 atlas (Rex et al., 2003), FSL's FLIRT (Smith et al., 2004) was paired with its own template, a skull-stripped version of the ICBM152 T1 average; and SPM5's unified segmentation method (Ashburner and Friston, 2005) was paired with its canonical brain, the whole head ICBM152 T1 average. We thus produced 3 variants of our atlas, where each was constructed from 40 representative samples of a data processing stream that one might use for analysis. For each normalization algorithm, the individual structure delineations were then resampled according to the computed transformations. We next computed averages at each voxel location to estimate the probability of that voxel belonging to each of the 56 structures. Each version of the atlas contains, for every voxel, probability densities for each region, thus providing a resource for automated probabilistic labeling of external data types registered into standard spaces; we also computed average intensity images and tissue density maps based on the three methods and target spaces. These atlases will serve as a resource for diverse applications including meta-analysis of functional and structural imaging data and other bioinformatics applications where display of arbitrary labels in probabilistically defined anatomic space will facilitate both knowledge-based development and visualization of findings from multiple disciplines.     

MRI atlas of the human cerebellar nuclei

The differential role of the cerebellar cortex and nuclei has rarely been addressed in human lesion and functional brain imaging studies. One important reason is the difficulty of defining the localization of the cerebellar nuclei and extent of possible lesions based on CT or MR scans. The present MRI investigation was specifically designed to study the anatomy of the deep cerebellar nuclei. In both basal ganglia and cerebellar nuclei of healthy human subjects the amount of iron is high compared to the rest of the brain. Clusters of iron are paramagnetic and, therefore, tend to cause local inhomogenities in a magnetic field. The iron-induced susceptibility artefacts were used to visualize the cerebellar nuclei as hypointensities on MR images. A three-dimensional atlas of the dentate (D), interposed (I), and fastigial (F) nuclei is presented in standard proportional stereotaxic space coordinates based on findings in a healthy 26-year-old female. A three-dimensional axial volume of the cerebellum was acquired using a T1-weighted fast low-angle shot (FLASH) sequence on a Siemens Sonata 1.5 Tesla MR. To increase the signal to noise ratio the sequence was acquired 5 times and averaged. Each volume was registered, resampled to 1.00 x 1.00 x 1.00-mm3 voxel size and spatially normalized into a standard proportional stereotaxic space (the MNI-space) using SPM99. Localization of cerebellar nuclei were confirmed by comparison with postmortem MRI and histological microsections of another brain.     

Construction of a neuroanatomical shape complex atlas from 3D MRI brain structures

Brain atlas construction has attracted significant attention lately in the neuroimaging community due to its application to the characterization of neuroanatomical shape abnormalities associated with various neurodegenerative diseases or neuropsychiatric disorders. Existing shape atlas construction techniques usually focus on the analysis of a single anatomical structure in which the important inter-structural information is lost. This paper proposes a novel technique for constructing a neuroanatomical shape complex atlas based on an information geometry framework. A shape complex is a collection of neighboring shapes - for example, the thalamus, amygdala and the hippocampus circuit - which may exhibit changes in shape across multiple structures during the progression of a disease. In this paper, we represent the boundaries of the entire shape complex using the zero level set of a distance transform function S(x). We then re-derive the relationship between the stationary state wave function ψ(x) of the Schr?dinger equation [formula in text] and the eikonal equation [formula in text] satisfied by any distance function. This leads to a one-to-one map (up to scale) between ψ(x) and S(x) via an explicit relationship. We further exploit this relationship by mapping ψ(x) to a unit hypersphere whose Riemannian structure is fully known, thus effectively turn ψ(x) into the square-root of a probability density function. This allows us to make comparisons - using elegant, closed-form analytic expressions - between shape complexes represented as square-root densities. A shape complex atlas is constructed by computing the Karcher mean ψˉ(x) in the space of square-root densities and then inversely mapping it back to the space of distance transforms in order to realize the atlas shape. We demonstrate the shape complex atlas computation technique via a set of experiments on a population of brain MRI scans including controls and epilepsy patients with either right anterior medial temporal or left anterior medial temporal lobectomies.     

Detection and statistical analysis of human cortical sulci

Many studies dealing with the human brain use the spatial coordinate system of brain anatomy to localize functional regions. Unfortunately, brain anatomy, and especially cortical sulci, is characterized by a high interindividual variability. Specific tools called anatomical atlases must then be considered to make the interpretation of anatomical examinations easier. The work described here first aims at building a numerical atlas of the main cortical sulci. Our system is based on a database containing a collection of anatomical MRI of healthy volunteer brains. Their sulci have been manually drawn and labeled for both hemispheres. Sulci are represented as 3D superficial curves. After a nonlinear registration process, a statistical atlas of the cortical topography of a particular MRI is built from the database. It is an a priori model of cortical sulci, including three major components: an average curve represents the average shape and position of each sulcus; a search area accounts for its spatial variation domain; a set of quantitative parameters describes the variability of sulci geometry and topology. This atlas is completely individualized and adapted to the features of the brain under examination. The atlas is represented by a graph, the nodes of which represent sulci and the edges the relations between sulci. It can also be considered a statistical model that describes the cortical topography as well as its variability.     

Learning a cortical parcellation of the brain robust to the MRI segmentation with convolutional neural networks

The parcellation of the human cortex into meaningful anatomical units is a common step of various neuroimaging studies. There have been multiple successful efforts to process magnetic resonance (MR) brain images automatically and identify specific anatomical regions, following atlases defined from cortical landmarks. Those definitions usually rely first on a high-quality brain surface reconstruction. On the other hand, when high accuracy is not a requirement, simpler methods based on warping a probabilistic atlas have been widely adopted. Here, we develop a cortical parcellation method for MR brain images based on Convolutional Neural Networks (ConvNets), a machine-learning method, with the goal of automatically transferring the knowledge obtained from surface analyses onto something directly applicable on simpler volume data. We train a ConvNet on a large (thousand) set of cortical ribbons of multiple MRI cohorts, to reproduce parcellations obtained from a surface method, in this case FreeSurfer. Further, to make the model applicable in a broader context, we force the model to generalize to unseen segmentations. The model is evaluated on unseen data of unseen cohorts. We characterize the behavior of the model during learning, and quantify its reliance on the dataset itself, which tends to give support for the necessity of large training sets, augmentation, and multiple contrasts. Overall, ConvNets can provide an efficient way to parcel MRI images, following the guidance established within more complex methods, quickly and accurately. The trained model is embedded within a open-source parcellation tool available at https://github.com/bthyreau/parcelcortex.     

Characterizing brain anatomical connections using diffusion weighted MRI and graph theory

A new methodology based on Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) and Graph Theory is presented for characterizing the anatomical connections between brain gray matter areas. In a first step, brain voxels are modeled as nodes of a non-directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by nervous fibers. This probability is estimated by means of probabilistic tissue segmentation and intravoxel white matter orientational distribution function, obtained from anatomical MRI and DW-MRI, respectively. A new tractography algorithm for finding white matter routes is also introduced. This algorithm solves the most probable path problem between any two nodes, leading to the assessment of probabilistic brain anatomical connection maps. In a second step, for assessing anatomical connectivity between K gray matter structures, the previous graph is redefined as a K+1 partite graph by partitioning the initial nodes set in K non-overlapped gray matter subsets and one subset clustering the remaining nodes. Three different measures are proposed for quantifying anatomical connections between any pair of gray matter subsets: Anatomical Connection Strength (ACS), Anatomical Connection Density (ACD) and Anatomical Connection Probability (ACP). This methodology was applied to both artificial and actual human data. Results show that nervous fiber pathways between some regions of interest were reconstructed correctly. Additionally, mean connectivity maps of ACS, ACD and ACP between 71 gray matter structures for five healthy subjects are presented.     

Developmental change in regional brain structure over 7 months in early adolescence: comparison of approaches for longitudinal atlas-based parcellation

Early adolescence is a time of rapid change in neuroanatomy and sexual development. Precision in tracking changes in brain morphology with structural MRI requires image segmentation with minimal error. Here, we compared two approaches to achieve segmentation by image registration with an atlas to quantify regional brain structural development over a 7-month interval in normal, early adolescent boys and girls. Adolescents were scanned twice (average interval=7.3 months), yielding adequate data for analysis in 16 boys (baseline age 10.9 to 13.9 years; Tanner Stage=1 to 4) and 12 girls (baseline age=11.2 to 13.7 years; Tanner Stage=3 to 4). Brain volumes were derived from T1-weighted (SPGR) images and dual-echo Fast Spin-Echo (FSE) images collected on a GE 3T scanner with an 8-channel phased-array head coil and analyzed by registration-based parcellation using the SRI24 atlas. The "independent" method required two inter-subject registrations: both baseline (MRI 1) to atlas and follow-up (MRI 2) to the atlas. The "sequential" method required one inter-subject registration, which was MRI 1 to the atlas, and one intra-subject registration, which was MRI 2 to MRI 1. Gray matter/white matter/CSF were segmented in both MRI-1 and MRI-2 using FSL FAST with tissue priors also based on the SRI24 atlas. Gray matter volumes were derived for 10 cortical regions, gray+white matter volumes for 5 subcortical structures, and CSF volumes for 4 ventricular regions and the cortical sulci. Across the 15 tissue regions, the coefficient of variation (CV) of change scores across individuals was significantly lower for the sequential method (CV=3.02), requiring only one inter-subject registration, than for the independent method (CV=9.43), requiring two inter-subject registrations. Volume change based on the sequential method revealed that total supratentorial and CSF volumes increased, while cortical gray matter volumes declined significantly (p<0.01) in anterior (lateral and medial frontal, anterior cingulate, precuneus, and parietal) but not posterior (occipital, calcarine) cortical regions. These volume changes occurred in all boys and girls who advanced a step in Tanner staging. Subcortical structures did not show consistent changes. Thus, longitudinal MRI assessment using robust registration methods is sufficiently sensitive to identify significant regional brain changes over a 7-month interval in boys and girls in early adolescence. Increasing the temporal resolution of the retest interval in longitudinal developmental studies could increase accuracy in timing of peak growth of regional brain tissue and refine our understanding of the neural mechanisms underlying the dynamic changes in brain structure throughout adolescence.     

Restoration of MRI data for intensity non-uniformities using local high order intensity statistics

MRI at high magnetic fields (>3.0 T) is complicated by strong inhomogeneous radio-frequency fields, sometimes termed the "bias field". These lead to non-biological intensity non-uniformities across the image. They can complicate further image analysis such as registration and tissue segmentation. Existing methods for intensity uniformity restoration have been optimized for 1.5 T, but they are less effective for 3.0 T MRI, and not at all satisfactory for higher fields. Also, many of the existing restoration algorithms require a brain template or use a prior atlas, which can restrict their practicalities. In this study an effective intensity uniformity restoration algorithm has been developed based on non-parametric statistics of high order local intensity co-occurrences. These statistics are restored with a non-stationary Wiener filter. The algorithm also assumes a smooth non-uniformity and is stable. It does not require a prior atlas and is robust to variations in anatomy. In geriatric brain imaging it is robust to variations such as enlarged ventricles and low contrast to noise ratio. The co-occurrence statistics improve robustness to whole head images with pronounced non-uniformities present in high field acquisitions. Its significantly improved performance and lower time requirements have been demonstrated by comparing it to the very commonly used N3 algorithm on BrainWeb MR simulator images as well as on real 4 T human head images.