A computerized three-dimensional atlas of the human skull and brain

The VALiDATe29 MRI Based Multi-Channel Atlas of the Squirrel Monkey Brain

We describe the development of the first digital atlas of the normal squirrel monkey brain and present the resulting product, VALiDATe29. The VALiDATe29 atlas is based on multiple types of magnetic resonance imaging (MRI) contrast acquired on 29 squirrel monkeys, and is created using unbiased, nonlinear registration techniques, resulting in a population-averaged stereotaxic coordinate system. The atlas consists of multiple anatomical templates (proton density, T1, and T2* weighted), diffusion MRI templates (fractional anisotropy and mean diffusivity), and ex vivo templates (fractional anisotropy and a structural MRI). In addition, the templates are combined with histologically defined cortical labels, and diffusion tractography defined white matter labels. The combination of intensity templates and image segmentations make this atlas suitable for the fundamental atlas applications of spatial normalization and label propagation. Together, this atlas facilitates 3D anatomical localization and region of interest delineation, and enables comparisons of experimental data across different subjects or across different experimental conditions. This article describes the atlas creation and its contents, and demonstrates the use of the VALiDATe29 atlas in typical applications. The atlas is freely available to the scientific community.

Simulation and resident education in spinal neurosurgery

BACKGROUND

A host of factors have contributed to the increasing use of simulation in neurosurgical resident education. Although the number of simulation-related publications has increased exponentially over the past two decades, no studies have specifically examined the role of simulation in resident education in spinal neurosurgery.

METHODS

We performed a structured search of several databases to identify articles detailing the use of simulation in spinal neurosurgery education in an attempt to catalogue potential applications for its use.

RESULTS

A brief history of simulation in medicine is given, followed by current trends of spinal simulation utilization in residency programs. General themes from the literature are identified that are integral for implementing simulation into neurosurgical residency curriculum. Finally, various applications are reported.

CONCLUSION

The use of simulation in spinal neurosurgery education is not as ubiquitous in comparison to other neurosurgical subspecialties, but many promising methods of simulation are available for augmenting resident education.

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.

A review of MRI findings in schizophrenia

After more than 100 years of research, the neuropathology of schizophrenia remains unknown and this is despite the fact that both Kraepelin (1919/1971: Kraepelin, E., 1919/1971. Dementia praecox. Churchill Livingston Inc., New York) and Bleuler (1911/1950: Bleuler, E., 1911/1950. Dementia praecox or the group of schizophrenias. International Universities Press, New York), who first described 'dementia praecox' and the 'schizophrenias', were convinced that schizophrenia would ultimately be linked to an organic brain disorder. Alzheimer (1897: Alzheimer, A., 1897. Beitrage zur pathologischen anatomie der hirnrinde und zur anatomischen grundlage einiger psychosen. Monatsschrift fur Psychiarie und Neurologie. 2, 82-120) was the first to investigate the neuropathology of schizophrenia, though he went on to study more tractable brain diseases. The results of subsequent neuropathological studies were disappointing because of conflicting findings. Research interest thus waned and did not flourish again until 1976, following the pivotal computer assisted tomography (CT) finding of lateral ventricular enlargement in schizophrenia by Johnstone and colleagues. Since that time significant progress has been made in brain imaging, particularly with the advent of magnetic resonance imaging (MRI), beginning with the first MRI study of schizophrenia by Smith and coworkers in 1984 (Smith, R.C., Calderon, M., Ravichandran, G.K., et al. (1984). Nuclear magnetic resonance in schizophrenia: A preliminary study. Psychiatry Res. 12, 137-147). MR in vivo imaging of the brain now confirms brain abnormalities in schizophrenia. The 193 peer reviewed MRI studies reported in the current review span the period from 1988 to August, 2000. This 12 year period has witnessed a burgeoning of MRI studies and has led to more definitive findings of brain abnormalities in schizophrenia than any other time period in the history of schizophrenia research. Such progress in defining the neuropathology of schizophrenia is largely due to advances in in vivo MRI techniques. These advances have now led to the identification of a number of brain abnormalities in schizophrenia. Some of these abnormalities confirm earlier post-mortem findings, and most are small and subtle, rather than large, thus necessitating more advanced and accurate measurement tools. These findings include ventricular enlargement (80% of studies reviewed) and third ventricle enlargement (73% of studies reviewed). There is also preferential involvement of medial temporal lobe structures (74% of studies reviewed), which include the amygdala, hippocampus, and parahippocampal gyrus, and neocortical temporal lobe regions (superior temporal gyrus) (100% of studies reviewed). When gray and white matter of superior temporal gyrus was combined, 67% of studies reported abnormalities. There was also moderate evidence for frontal lobe abnormalities (59% of studies reviewed), particularly prefrontal gray matter and orbitofrontal regions. Similarly, there was moderate evidence for parietal lobe abnormalities (60% of studies reviewed), particularly of the inferior parietal lobule which includes both supramarginal and angular gyri. Additionally, there was strong to moderate evidence for subcortical abnormalities (i.e. cavum septi pellucidi-92% of studies reviewed, basal ganglia-68% of studies reviewed, corpus callosum-63% of studies reviewed, and thalamus-42% of studies reviewed), but more equivocal evidence for cerebellar abnormalities (31% of studies reviewed). The timing of such abnormalities has not yet been determined, although many are evident when a patient first becomes symptomatic. There is, however, also evidence that a subset of brain abnormalities may change over the course of the illness. The most parsimonious explanation is that some brain abnormalities are neurodevelopmental in origin but unfold later in development, thus setting the stage for the development of the symptoms of schizophrenia. Or there may be additional factors, such as stress or neurotoxicity, that occur during adolescence or early adulthood and are necessary for the development of schizophrenia, and may be associated with neurodegenerative changes. Importantly, as several different brain regions are involved in the neuropathology of schizophrenia, new models need to be developed and tested that explain neural circuitry abnormalities effecting brain regions not necessarily structurally proximal to each other but nonetheless functionally interrelated. (ABSTRACT TRUNCATED)

The role of image registration in brain mapping

Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain.