Brain atlases of normal and diseased populations

Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability

Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.

Quantization and analysis of hippocampal morphometric changes due to dementia of Alzheimer type using metric distances based on large deformation diffeomorphic metric mapping

The metric distance obtained from the large deformation diffeomorphic metric mapping (LDDMM) algorithm is used to quantize changes in morphometry of brain structures due to neuropsychiatric diseases. For illustrative purposes we consider changes in hippocampal morphometry (shape and size) due to very mild dementia of the Alzheimer type (DAT). LDDMM, which was previously used to calculate dense one-to-one correspondence vector fields between hippocampal shapes, measures the morphometric differences with respect to a template hippocampus by assigning metric distances on the space of anatomical images thereby allowing for direct comparison of morphometric differences. We characterize what information the metric distances provide in terms of size and shape given the hippocampal, brain and intracranial volumes. We demonstrate that metric distance is a measure of morphometry (i.e., shape and size) but mostly a measure of shape, while volume is mostly a measure of size. Moreover, we show how metric distances can be used in cross-sectional, longitudinal analysis, as well as left-right asymmetry comparisons, and provide how the metric distances can serve as a discriminative tool using logistic regression. Thus, we show that metric distances with respect to a template computed via LDDMM can be a powerful tool in detecting differences in shape.

Large Deformation Diffeomorphic Metric Mapping Registration of Reconstructed 3D Histological Section Images and in vivo MR Images

Our current understanding of neuroanatomical abnormalities in neuropsychiatric diseases is based largely on magnetic resonance imaging (MRI) and post mortem histological analyses of the brain. Further advances in elucidating altered brain structure in these human conditions might emerge from combining MRI and histological methods. We propose a multistage method for registering 3D volumes reconstructed from histological sections to corresponding in vivo MRI volumes from the same subjects: (1) manual segmentation of white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) compartments in histological sections, (2) alignment of consecutive histological sections using 2D rigid transformation to construct a 3D histological image volume from the aligned sections, (3) registration of reconstructed 3D histological volumes to the corresponding 3D MRI volumes using 3D affine transformation, (4) intensity normalization of images via histogram matching, and (5) registration of the volumes via intensity based large deformation diffeomorphic metric (LDDMM) image matching algorithm. Here we demonstrate the utility of our method in the transfer of cytoarchitectonic information from histological sections to identify regions of interest in MRI scans of nine adult macaque brains for morphometric analyses. LDDMM improved the accuracy of the registration via decreased distances between GM/CSF surfaces after LDDMM (0.39 ± 0.13 mm) compared to distances after affine registration (0.76 ± 0.41 mm). Similarly, WM/GM distances decreased to 0.28 ± 0.16 mm after LDDMM compared to 0.54 ± 0.39 mm after affine registration. The multistage registration method may find broad application for mapping histologically based information, for example, receptor distributions, gene expression, onto MRI volumes.

Design and implementation of an automated partial volume correction in PET: application to dopamine receptor quantification in the normal human striatum

The considerable effort and potential lack of reproducibility of human-driven PET quantification and partial volume correction (PVC) can be alleviated by use of atlas-based automatic analysis. The present study examined the application of a new algorithm designed to automatically define 3-dimensional regions of interest (ROIs) and their effect on dopamine receptor quantification in the normal human brain striatum, both without and with PVC.

METHODS

A total of 90 healthy volunteers (age range, 18-46 y) received a single injection of (11)C-raclopride, and automatic segmentation of concomitant structural MR images was performed using a maximum-probability atlas in combination with a trained neural network. For each identified tissue segment considered homogeneous for the tracer (or volumes of interest [VOIs]), an a priori criterion based on minimum axial recovery coefficient (RC(zmin) = 50%, 75%, and 90%) was used to constrain the extent of each ROI.

RESULTS

With ROIs essentially overlapping the entire VOI volume (obtained with RC(zmin) = 50%), the binding potential (BP(ND)) of (11)C-raclopride was found to be around 2.2 for caudate and 2.9 for putamen, an underestimation by 35% and 28%, respectively, according to PVC values. At increased RC(zmin), BP(ND) estimates of (11)C-raclopride were increased by 12% and 21% for caudate and 8% and 15% for putamen when the associated ROIs decreased to around 65% and 43% of total tissue volume (VOI) for caudate and 67% and 31% for putamen. After PVC, we observed relative increases in BP(ND) variance of 12% for caudate and 20% for putamen, whereas estimated BP(ND) values all increased to 3.4 for caudate and 4.0 for putamen, regardless of ROI size. Dopamine receptor concentrations appeared less heterogeneous in the normal human striatum after PVC than they did without PVC: the 25%-30% difference in BP(ND) estimates observed between caudate and putamen remained significant after PVC but was reduced to slightly less than 20%. Furthermore, the results were comparable with those obtained with a manual method currently in use in our laboratory.

CONCLUSION

The new algorithm allows for traditional PET data extraction and PVC in an entirely automatic fashion, thus avoiding labor-intensive analyses and potential intra- or interobserver variability. This study also offers the first, to our knowledge, large-scale application of PVC to dopamine D(2)/D(3) receptor imaging with (11)C-raclopride in humans.

Decreased brain volume in adults with childhood lead exposure

BACKGROUND

Although environmental lead exposure is associated with significant deficits in cognition, executive functions, social behaviors, and motor abilities, the neuroanatomical basis for these impairments remains poorly understood. In this study, we examined the relationship between childhood lead exposure and adult brain volume using magnetic resonance imaging (MRI). We also explored how volume changes correlate with historic neuropsychological assessments.

METHODS AND FINDINGS

Volumetric analyses of whole brain MRI data revealed significant decreases in brain volume associated with childhood blood lead concentrations. Using conservative, minimum contiguous cluster size and statistical criteria (700 voxels, unadjusted p < 0.001), approximately 1.2% of the total gray matter was significantly and inversely associated with mean childhood blood lead concentration. The most affected regions included frontal gray matter, specifically the anterior cingulate cortex (ACC). Areas of lead-associated gray matter volume loss were much larger and more significant in men than women. We found that fine motor factor scores positively correlated with gray matter volume in the cerebellar hemispheres; adding blood lead concentrations as a variable to the model attenuated this correlation.

CONCLUSIONS

Childhood lead exposure is associated with region-specific reductions in adult gray matter volume. Affected regions include the portions of the prefrontal cortex and ACC responsible for executive functions, mood regulation, and decision-making. These neuroanatomical findings were more pronounced for males, suggesting that lead-related atrophic changes have a disparate impact across sexes. This analysis suggests that adverse cognitive and behavioral outcomes may be related to lead's effect on brain development producing persistent alterations in structure. Using a simple model, we found that blood lead concentration mediates brain volume and fine motor function.

Towards construction of an ideal stereotactic brain atlas

BACKGROUND

The role of the brain atlas is changing in many aspects with the advancements in stereotactic and functional neurosurgery. Therefore, there is a critical need to construct a new atlas. This paper addresses the definition and construction of an atlas, ideal (in our opinion) for stereotactic and functional neurosurgery. The essence of the new atlas is not only its population-based structural and functional content, but also its continuous "self-updatability" with the new clinical results obtained.

METHOD

The ideal atlas defined here contains four major components: brain models, knowledge database, tools, and clinical results. Towards its creation, a multi-atlas is proposed. The construction of the initial version of the multi-atlas is detailed with the probabilistic functional atlas (PFA), interpolated Talairach-Tournoux atlas, and enhanced Schaltenbrand-Wahren atlas. These atlases are put in a spatial register by matching their AC-PC distances and heights of the thalamus; the Schaltenbrand coronal and sagittal microseries are scaled laterally to match the target structure centroids with the locations of the best targets of the PFA.

FINDINGS

Construction of an initial version of the ideal stereotactic atlas is feasible at present from the available resources. To achieve that, our three atlases (PFA, Talairach and Schaltenbrand) are enhanced and combined together. A single lateral scaling factor per target structure is feasible to co-register the Schaltenbrand atlas with PFA in four situations (compensated against the third ventricle, non-compensated, bilateral, and non-bilateral). The STN has to be stretched by 18% more than the VIM on the Schaltenbrand coronal microseries, and the VIM has to be compressed by 13% less than the STN on the Schaltenbrand sagittal microseries.

CONCLUSION

The new multi-atlas can potentially be more useful than the currently employed atlases and will facilitate further development of the ideal atlas for stereotactic and functional neurosurgery.

Surface-based and probabilistic atlases of primate cerebral cortex

Brain atlases play an increasingly important role in neuroimaging, as they are invaluable for analysis, visualization, and comparison of results across studies. For both humans and macaque monkeys, digital brain atlases of many varieties are in widespread use, each having its own strengths and limitations. For studies of cerebral cortex there is particular utility in hybrid atlases that capitalize on the complementary nature of surface and volume representations, are based on a population average rather than an individual brain, and include measures of variation as well as averages. Linking different brain atlases to one another and to online databases containing a growing body of neuroimaging data will enable powerful forms of data mining that accelerate discovery and improve research efficiency.

Learning-based deformable registration of MR brain images

This paper presents a learning-based method for deformable registration of magnetic resonance (MR) brain images. There are two novelties in the proposed registration method. First, a set of best-scale geometric features are selected for each point in the brain, in order to facilitate correspondence detection during the registration procedure. This is achieved by optimizing an energy function that requires each point to have its best-scale geometric features consistent over the corresponding points in the training samples, and at the same time distinctive from those of nearby points in the neighborhood. Second, the active points used to drive the brain registration are hierarchically selected during the registration procedure, based on their saliency and consistency measures. That is, the image points with salient and consistent features (across different individuals) are considered for the initial registration of two images, while other less salient and consistent points join the registration procedure later. By incorporating these two novel strategies into the framework of the HAMMER registration algorithm, the registration accuracy has been improved according to the results on simulated brain data, and also visible improvement is observed particularly in the cortical regions of real brain data.