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.     

Volumes, spatial extents and a probabilistic atlas of the human basal ganglia and thalamus

The basal ganglia and thalamus are involved in processing all physiological behaviors and affected by many diseases. Accurate localization is a crucial issue in neuroimaging, particularly when working with groups of normalized images in a standard stereotaxic space. Here, manual delineation of the central structures (thalamus; nucleus caudatus and accumbens; putamen, pallidum, substantia nigra) was performed on 30 high resolution MRIs of healthy young adults (15 female, median age 31 years) in native space. Protocol inter-rater reliabilities were quantified as structure overlap (similarity indices, SIs). Structural volumes were calculated in native space, and after spatial normalization to stereotaxic space (MNI/ICBM152) and in relation to hemispheric volumes. Spatial extents relative to the anterior commissure (AC) were extracted. The 30 resulting atlases were then used to create probabilistic maps in stereotaxic space. Inter-rater SIs were high at 0.85-0.92 except for the nucleus accumbens. In native space, caudate, nucleus accumbens and putamen were significantly larger on the left, and the globus pallidus larger in males. After normalizing for brain volume, the nucleus accumbens, putamen and thalamus were larger on the left, with the gender difference in the globus pallidus still detectable. Some of these volume differences translated into significantly different distances from the AC. The probabilistic maps showed that overall the central structures' boundaries are relatively unchanged after spatial normalization. We present a comprehensive assessment of thalamic and basal ganglia volumetric and geometric data in both native and stereotaxic spaces. Probabilistic maps in MNI/ICBM152 space will allow accurate localization in group analyses.     

Virtual spatial registration of stand-alone fNIRS data to MNI space

The registration of functional brain data to common stereotaxic brain space facilitates data sharing and integration across different subjects, studies, and even imaging modalities. Thus, we previously described a method for the probabilistic registration of functional near-infrared spectroscopy (fNIRS) data onto Montreal Neurological Institute (MNI) coordinate space that can be used even when magnetic resonance images of the subjects are not available. This method, however, requires the careful measurement of scalp landmarks and fNIRS optode positions using a 3D-digitizer. Here we present a novel registration method, based on simulations in place of physical measurements for optode positioning. First, we constructed a holder deformation algorithm and examined its validity by comparing virtual and actual deformation of holders on spherical phantoms and real head surfaces. The discrepancies were negligible. Next, we registered virtual holders on synthetic heads and brains that represent size and shape variations among the population. The registered positions were normalized to MNI space. By repeating this process across synthetic heads and brains, we statistically estimated the most probable MNI coordinate values, and clarified errors, which were in the order of several millimeters across the scalp, associated with this estimation. In essence, the current method allowed the spatial registration of completely stand-alone fNIRS data onto MNI space without the use of supplementary measurements. This method will not only provide a practical solution to the spatial registration issues in fNIRS studies, but will also enhance cross-modal communications within the neuroimaging community.     

Atlas-based hippocampus segmentation in Alzheimer's disease and mild cognitive impairment

This study assesses the performance of public-domain automated methodologies for MRI-based segmentation of the hippocampus in elderly subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Structural MR images of 54 age- and gender-matched healthy elderly individuals, subjects with probable AD, and subjects with MCI were collected at the University of Pittsburgh Alzheimer's Disease Research Center. Hippocampi in subject images were automatically segmented by using AIR, SPM, FLIRT, and the fully deformable method of Chen to align the images to the Harvard atlas, MNI atlas, and randomly selected, manually labeled subject images ("cohort atlases"). Mixed-effects statistical models analyzed the effects of side of the brain, disease state, registration method, choice of atlas, and manual tracing protocol on the spatial overlap between automated segmentations and expert manual segmentations. Registration methods that produced higher degrees of geometric deformation produced automated segmentations with higher agreement with manual segmentations. Side of the brain, presence of AD, choice of reference image, and manual tracing protocol were also significant factors contributing to automated segmentation performance. Fully automated techniques can be competitive with human raters on this difficult segmentation task, but a rigorous statistical analysis shows that a variety of methodological factors must be carefully considered to insure that automated methods perform well in practice. The use of fully deformable registration methods, cohort atlases, and user-defined manual tracings are recommended for highest performance in fully automated hippocampus segmentation.     

MR-based statistical atlas of the G?ttingen minipig brain.

Thedomestic pig is increasingly being used as an experimental model for brain imaging studies with positron emission tomography (PET). The recording of radiotracer uptake by PET gives functional and physiological information, but with poor spatial resolution. To date, anatomical regions of interest in pig brain have been defined in MR images obtained for each individual animal, because of the lack of a standard stereotaxic coordinate system for the pig brain. In order to define a stereotaxic coordinate system, we coregistered T1-weighted MR images from 22 male G?ttingen minipigs and obtained a statistically defined surface rendering of the average minipig brain in which stereotaxic zero is defined by the position of the pineal gland. The average brain is now used as a target for registration of dynamic PET data, so that time-activity curves can be extracted from standard volumes of interest. In order to define these volumes, MR images from each individual pig were manually segmented into a total of 34 brain structures, including cortical regions, white matter, caudate and putamen, ventricular system, and cerebellum. The mean volumes of these structures had variances in the range of 10-20%. The 34 brain volumes were transformed into the common coordinate system and then used to generate surface renderings with probabilistic threshold greater than 50%. This probabilistic threshold gave nearly quantitative recovery of the mean volumes in native space. The probabilistic volumes in stereotaxic space are now being used to extract time-radioactivity curves from dynamic PET recordings.     

Linear normalization of MR brain images in pediatric patients with periventricular leukomalacia

The feasibility of linear normalization of child brain images with structural abnormalities due to periventricular leukomalacia (PVL) was assessed in terms of success rate and accuracy of the normalization algorithm. Ten T1-weighted brain images from healthy adult subject and 51 from children (4-11 years of age) were linearly transformed to achieve spatial registration with the standard MNI brain template. Twelve of the child brain images were radiologically normal, 22 showed PVL and 17 showed PVL with additional enlargement of the lateral ventricles. The effects of simple modifications to the normalization process were evaluated: changing the initial orientation and zoom parameters, masking non-brain areas, smoothing the images and using a pediatric template instead of the MNI template. Normalization failure was reduced by changing the initial zoom parameters and by removing background noise. The overall performance of the normalization algorithm was only improved when background noise was removed from the images. The results show that linear normalization of PVL affected brain images is feasible.     

Deformation-based brain morphometry in rats

Magnetic resonance imaging (MRI)-based morphometry provides in vivo evidence for macro-structural plasticity of the brain. Experiments on small animals using automated morphometric methods usually require expensive measurements with ultra-high field dedicated animal MRI systems. Here, we developed a novel deformation-based morphometry (DBM) tool for automated analyses of rat brain images measured on a 3-Tesla clinical whole body scanner with appropriate coils. A landmark-based transformation of our customized reference brain into the coordinates of the widely used rat brain atlas from Paxinos and Watson (Paxinos Atlas) guarantees the comparability of results to other studies. For cross-sectional data, we warped images onto the reference brain using the low-dimensional nonlinear registration implemented in the MATLAB software package SPM8. For the analysis of longitudinal data sets, we chose high-dimensional registrations of all images of one data set to the first baseline image which facilitate the identification of more subtle structural changes. Because all deformations were finally used to transform the data into the space of the Paxinos Atlas, Jacobian determinants could be used to estimate absolute local volumes of predefined regions-of-interest. Pilot experiments were performed to analyze brain structural changes due to aging or photothrombotically-induced cortical stroke. The results support the utility of DBM based on commonly available clinical whole-body scanners for highly sensitive morphometric studies on rats.     

Automated brain tissue assessment in the elderly and demented population: construction and validation of a sub-volume probabilistic brain atlas

OBJECTIVES: To develop an automated imaging assessment tool that accommodates the anatomic variability of the elderly and demented population as well as the registration errors occurring during spatial normalization. METHODS: 20 subjects with Alzheimer's disease (AD), mild cognitive impairment, or normal cognition underwent MRI brain imaging and had their 3D volumetric datasets manually partitioned into 68 regions of interest (ROI) termed sub-volumes. Gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) voxel counts were then made in the subject's native space for comparison against automated volumetric measures within three sub-volume probabilistic atlas (SVPA) models. The three SVPAs were constructed using 12 parameter affine (12 p), 2nd order (2nd), and 6th order (6th) transforms derived from registering the manually partitioned scans into a Talairach compatible AD population-based target. The three SVPA automated measures were compared to the manually derived measures in the 20 subjects' native space with a "jack-knife" procedure in which each subject was assessed by an SVPA they did not contribute toward constructing. RESULTS: The mean left and right GM ratio (GM ratio = [GM + CSF] / CSF) "r values" for the 3 SVPAs compared to the manually derived ratios across the 68 ROIs were 0.85 for the 12p SVPA, 0.88 for the 2nd SVPA, and 0.89 for the 6th SVPA. The mean left and right WM ratio (WM ratio = [WM + CSF] / CSF) "r values" for the 3 SVPAs being 0.84 for the 12p SVPA, 0.86 for the 2nd SVPA, and 0.88 for the 6th SVPA. CONCLUSION: We have constructed, from an elderly and demented cohort, an automated brain volumetric tool that has excellent accuracy compared to a manual gold standard and is capable of regional hypothesis testing and individual patient assessment compared to a population.     

Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images

This paper introduces a novel large deformation diffeomorphic metric mapping algorithm for whole brain registration where sulcal and gyral curves, cortical surfaces, and intensity images are simultaneously carried from one subject to another through a flow of diffeomorphisms. To the best of our knowledge, this is the first time that the diffeomorphic metric from one brain to another is derived in a shape space of intensity images and point sets (such as curves and surfaces) in a unified manner. We describe the Euler-Lagrange equation associated with this algorithm with respect to momentum, a linear transformation of the velocity vector field of the diffeomorphic flow. The numerical implementation for solving this variational problem, which involves large-scale kernel convolution in an irregular grid, is made feasible by introducing a class of computationally friendly kernels. We apply this algorithm to align magnetic resonance brain data. Our whole brain mapping results show that our algorithm outperforms the image-based LDDMM algorithm in terms of the mapping accuracy of gyral/sulcal curves, sulcal regions, and cortical and subcortical segmentation. Moreover, our algorithm provides better whole brain alignment than combined volumetric and surface registration (Postelnicu et al., 2009) and hierarchical attribute matching mechanism for elastic registration (HAMMER) (Shen and Davatzikos, 2002) in terms of cortical and subcortical volume segmentation.     

Automated morphometric study of brain variation in XXY males

This paper studies brain morphometry variation associated with XXY males (Klinefelter's syndrome) by using an automated whole-brain volumetric analysis method. The application to 34 XXY males and 62 normal male controls reveals pronounced volume reduction in the brains of XXY males, relative to the brains of normal controls, localized at the insula, temporal gyri, amygdala, hippocampus, cingulate, and occipital gyri. Most of these statistically significant regions are in the gray matter structures, with the exception of one cluster of atrophy involved in white matter structure, i.e., right parietal lobe white matter. Compared to previous findings documented in the literature, our findings provide a better spatial localization of the affected regions. In addition to the reduction of local volume, overall enlargement of ventricles and overall volume reduction of both white matter and gray matter are also found in XXY males.     

Stereotaxic probabilistic maps of the magnocellular cell groups in human basal forebrain

The basal forebrain contains several interdigitating anatomical structures, including the diagonal band of Broca, the basal nucleus of Meynert, the ventral striatum, and also cell groups underneath the globus pallidus that bridge the centromedial amygdala to the bed nucleus of the stria terminalis. Among the cell populations, the magnocellular, cholinergic corticopetal projection neurons have received particular attention due to their loss in Alzheimer's disease. In MRI images, the precise delineation of these structures is difficult due to limited spatial resolution and contrast. Here, using microscopic delineations in ten human postmortem brains, we present stereotaxic probabilistic maps of the basal forebrain areas containing the magnocellular cell groups. Cytoarchitectonic mapping was performed in silver stained histological serial sections. The positions and the extent of the magnocellular cell groups within the septum (Ch1–2), the horizontal limb of the diagonal band (Ch3), and in the sublenticular part of the basal forebrain (Ch4) were traced in high-resolution digitized histological sections, 3D reconstructed, and warped to the reference space of the MNI single subject brain. The superposition of the cytoarchitectonic maps in the MNI brain shows the intersubject variability of the various Ch compartments and their stereotaxic position relative to other brain structures. Both the right and left Ch4 regions showed significantly smaller volumes when age was considered as a covariate. Probabilistic maps of compartments of the basal forebrain magnocellular system are now available as an open source reference for correlation with fMRI, PET, and structural MRI data of the living human brain.     

Optimum template selection for atlas-based segmentation

Atlas-based segmentation of MR brain images typically uses a single atlas (e.g., MNI Colin27) for region identification. Normal individual variations in human brain structures present a significant challenge for atlas selection. Previous researches mainly focused on how to create a specific template for different requirements (e.g., for a certain population). We address atlas selection with a different approach: instead of choosing a fixed brain atlas, we use a family of brain templates for atlas-based segmentation. For each subject and each region, the template selection method automatically chooses the 'best' template with the highest local registration accuracy, based on normalized mutual information. The region classification performances of the template selection method and the single template method were quantified by the overlap ratios (ORs) and intraclass correlation coefficients (ICCs) between the manual tracings and the respective automated labeled results. Two groups of brain images and multiple regions of interest (ROIs), including the right anterior cingulate cortex (ACC) and several subcortical structures, were tested for both methods. We found that the template selection method produced significantly higher ORs than did the single template method across all of the 13 analyzed ROIs (two-tailed paired t-test, right ACC at t(8)=4.353, p=0.0024; right amygdala, matched paired t test t(8)>3.175, p<0.013; for the remaining ROIs, t(8)=4.36, p<0.002). The template selection method also provided more reliable volume estimates than the single template method with increased ICCs. Moreover, the improved accuracy of atlas-based segmentation using optimum templates approaches the accuracy of manual tracing, and thus is valid for automated brain imaging analyses.     

Longitudinal neuroanatomical changes determined by deformation-based morphometry in a mouse model of Alzheimer's disease

Magnetic resonance imaging (MRI) of transgenic mice has the potential to provide valuable insight into the complex mechanisms underlying Alzheimer's disease (AD). Quantification of pathological changes is typically performed using manual segmentation methods, and requires a priori hypotheses about anatomical structures for volumetric measurement. Alternatively, deformation-based morphometry (DBM) has been shown to be a powerful, automated technique for detecting anatomical differences between populations by examining the deformation fields used to nonlinearly warp MR images. In this multiple timepoint, in vivo study, we have applied an automated, unbiased technique for the creation of a nonlinear, population-specific reference space from which robust DBM analysis can be performed. A general, linear mixed-effects model framework was developed to follow the evolution of structural changes in mouse brain from 2.5 to 9 months of age, and to examine neuroanatomical differences between a transgenic (TG) APP/PS1 murine model of AD and wild-type (WT) littermates. Morphometric abnormalities in the TG group were localized to regions of the hippocampus, cortex, olfactory bulbs, stria terminalis, brain stem, cerebellum, and ventricles. Although volumetric reductions were detected in TG mice, no general brain atrophy was found, suggesting a developmental, rather than a degenerative, pathological process. Finally, we established a strong correlation between a DBM summary measure and manually segmented volumes for each image in the dataset. These results support the utility of DBM to study longitudinal morphological changes in mouse models of central nervous system diseases in an automated and exploratory fashion.     

Longitudinal characterization of brain atrophy of a Huntington's disease mouse model by automated morphological analyses of magnetic resonance images

Mouse models of human diseases play crucial roles in understanding disease mechanisms and developing therapeutic measures. Huntington's disease (HD) is characterized by striatal atrophy that begins long before the onset of motor symptoms. In symptomatic HD, striatal volumes decline predictably with disease course. Thus, imaging based volumetric measures have been proposed as outcomes for presymptomatic as well as symptomatic clinical trials of HD. Magnetic resonance imaging of the mouse brain structures is becoming widely available and has been proposed as one of the biomarkers of disease progression and drug efficacy testing. However, three-dimensional and quantitative morphological analyses of the brains are not straightforward. In this paper, we describe a tool for automated segmentation and voxel-based morphological analyses of the mouse brains. This tool was applied to a well-established mouse model of Huntington's disease, the R6/2 transgenic mouse strain. Comparison between the automated and manual segmentation results showed excellent agreement in most brain regions. The automated method was able to sensitively detect atrophy as early as 4 weeks of age and accurately follow disease progression. Comparison between ex vivo and in vivo MRI suggests that the ex vivo end-point measurement of brain morphology is also a valid approach except for the morphology of the ventricles. This is the first report of longitudinal characterization of brain atrophy in a mouse model of Huntington's disease by using automatic morphological analysis.     

Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.     

Enantiomorphic normalization of focally lesioned brains

In order to make spatial inferences about the brain across a group of patients, it is usually necessary to employ some means of bringing each brain image into register with either a group mean image or a standard template. In the presence of focal brain lesions, automated methods for performing such so-called normalization are liable to distortion from the abnormal signal within the lesion, especially when the non-linear warping necessary for maximum registration fidelity is used. The most frequently used method for minimizing this distortion--cost function masking--simply eliminates the lesioned area when deriving the normalization parameters. As lesion size increases, however, the normalization error may be expected to rise steeply since the volume of brain from which the parameters are derived falls with it. Here we propose an alternative non-linear registration method that exploits a natural redundancy in the brain--the enantiomorphic relation between the two hemispheres--to correct the signal within the lesion using information from the undamaged homologous region within the contralesional hemisphere. As lesion size increases, the normalization error should theoretically asymptote to inter-hemispheric differences, which are both quantifiable and much lower than the inter-subject difference. Using SPM's non-linear normalization routines, we evaluate this technique with images of normal brains to which lesions selected from a large dataset have been artificially applied. Our results show the enantiomorphic method to be vastly superior to cost function masking across subjects, lesion characteristics, and brain voxels. We therefore propose that it should be the method of choice for normalizing images of focally lesioned brains.     

Automated hippocampal segmentation by regional fluid registration of serial MRI: validation and application in Alzheimer's disease

The application of voxel-level three-dimensional registration to serial magnetic resonance imaging (MRI) is described. This fluid registration determines deformation fields modeling brain change, which are consistent with a model describing a viscous fluid. The objective was to validate the measurement of hippocampal volumetric change by fluid registration in Alzheimer's disease (AD) against current methodologies. The hippocampus was chosen for this study because it is difficult to measure reproducibly by manual segmentation and is widely studied; however, the technique is applicable to any structure which can be delineated on a scan. First, suitable values for the viscosity-body-force-ratio, alpha (0.01), and the number of iterations (300), were established and the convergence, repeatability, linearity, and accuracy investigated and compared with expert manual segmentation. A simple model of hippocampal atrophy was used to compare simulated volumetric change against that obtained by fluid registration. Finally the serial segmentation was compared with the current gold standard technique-expert human labeling with a volume repeatability of approximately 4%-in 27 subjects (15 normal controls, 12 clinically diagnosed with Alzheimer's disease). The scan-rescan volumetric consistency of serial segmentation by fluid-registration was shown to be superior to human serial segmentors ( approximately 2%). The mean absolute volume difference between fluid and manual segmentation was 0.7%. Fluid registration has potential importance for tracking longitudinal structural changes in brain particularly in the context of the clinical trial where large numbers of subjects may have multiple MR scans.     

3D pattern of brain abnormalities in Fragile X syndrome visualized using tensor-based morphometry

Fragile X syndrome (FraX), a genetic neurodevelopmental disorder, results in impaired cognition with particular deficits in executive function and visuo-spatial skills. Here we report the first detailed 3D maps of the effects of the Fragile X mutation on brain structure, using tensor-based morphometry. TBM visualizes structural brain deficits automatically, without time-consuming specification of regions-of-interest. We compared 36 subjects with FraX (age: 14.66+/-1.58 S.D., 18 females/18 males), and 33 age-matched healthy controls (age: 14.67+/-2.2 S.D., 17 females/16 males), using high-dimensional elastic image registration. All 69 subjects' 3D T1-weighted brain MRIs were spatially deformed to match a high-resolution single-subject average MRI scan in ICBM space, whose geometry was optimized to produce a minimal deformation target. Maps of the local Jacobian determinant (expansion factor) were computed from the deformation fields. Statistical maps showed increased caudate (10% higher; p = 0.001) and lateral ventricle volumes (19% higher; p = 0.003), and trend-level parietal and temporal white matter excesses (10% higher locally; p = 0.04). In affected females, volume abnormalities correlated with reduction in systemically measured levels of the Fragile X mental retardation protein (FMRP; Spearman's r < -0.5 locally). Decreased FMRP correlated with ventricular expansion (p = 0.042; permutation test), and anterior cingulate tissue reductions (p = 0.0026; permutation test) supporting theories that FMRP is required for normal dendritic pruning in fronto-striatal-limbic pathways. No sex differences were found; findings were confirmed using traditional volumetric measures in regions of interest. Deficit patterns were replicated by performing statistics after logarithmic transformation, which may be more appropriate for tensor-valued data. Investigation of how these anomalies emerge over time will accelerate our understanding of FraX and its treatment.