Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe

Mindboggle: automated brain labeling with multiple atlases

Background

To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural correspondences between labeled atlases and unlabeled image data, where an atlas consists of a set of labels manually assigned to a single brain image. In the present work, we study the influence of using variable numbers of individual atlases to nonlinearly label human brain image data.

Methods

Each brain image voxel of each of 20 human subjects is assigned a label by each of the remaining 19 atlases using Mindboggle. The most common label is selected and is given a confidence rating based on the number of atlases that assigned that label. The automatically assigned labels for each subject brain are compared with the manual labels for that subject (its atlas). Unlike recent approaches that transform subject data to a labeled, probabilistic atlas space (constructed from a database of atlases), Mindboggle labels a subject by each atlas in a database independently.

Results

When Mindboggle labels a human subject's brain image with at least four atlases, the resulting label agreement with coregistered manual labels is significantly higher than when only a single atlas is used. Different numbers of atlases provide significantly higher label agreements for individual brain regions.

Conclusion

Increasing the number of reference brains used to automatically label a human subject brain improves labeling accuracy with respect to manually assigned labels. Mindboggle software can provide confidence measures for labels based on probabilistic assignment of labels and could be applied to large databases of brain images.

Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI

The registration of functional brain data to the common brain space offers great advantages for inter-modal data integration and sharing. However, this is difficult to achieve in functional near-infrared spectroscopy (fNIRS) because fNIRS data are primary obtained from the head surface and lack structural information of the measured brain. Therefore, in our previous articles, we presented a method for probabilistic registration of fNIRS data to the standard Montreal Neurological Institute (MNI) template through international 10-20 system without using the subject's magnetic resonance image (MRI). In the current study, we demonstrate our method with a new statistical model to facilitate group studies and provide information on different components of variability. We adopt an analysis similar to the single-factor one-way classification analysis of variance based on random effects model to examine the variability involved in our improvised method of probabilistic registration of fNIRS data. We tested this method by registering head surface data of twelve subjects to seventeen reference MRI data sets and found that the standard deviation in probabilistic registration thus performed for given head surface points is approximately within the range of 4.7 to 7.0 mm. This means that, if the spatial registration error is within an acceptable tolerance limit, it is possible to perform multi-subject fNIRS analysis to make inference at the population level and to provide information on positional variability in the population, even when subjects' MRIs are not available. In essence, the current method enables the multi-subject fNIRS data to be presented in the MNI space with clear description of associated positional variability. Such data presentation on a common platform, will not only strengthen the validity of the population analysis of fNIRS studies, but will also facilitate both intra- and inter-modal data sharing among the neuroimaging community.

In vivo imaging of microglial activation with [11C](R)-PK11195 PET in idiopathic Parkinson's disease

Idiopathic Parkinson's disease (PD) is a neurodegenerative disorder associated with akinesia, tremor and rigidity. While the characteristic Lewy body pathology targets pigmented and other brainstem nuclei at post-mortem, activated microglia are found in both subcortical and cortical areas. [11C](R)-PK11195 is a positron emission tomography (PET) marker of peripheral benzodiazepine sites (PBBS), which are selectively expressed by activated microglia. We examined 18 PD patients clinically and with [11C](R)-PK11195 and [18F]-dopa PET. Compared to 11 normal controls, the PD patients showed significantly increased mean levels of [11C](R)-PK11195 binding in the pons, basal ganglia and frontal and temporal cortical regions. Eight PD patients were examined longitudinally, and their [11C](R)-PK11195 signal remained stable over 2 years. Levels of microglial activation did not correlate with clinical severity or putamen [18F]-dopa uptake. Our in vivo findings confirm that widespread microglial activation is associated with the pathological process in PD. The absence of significant longitudinal changes suggests that microglia are activated early in the disease process, and levels then remain relatively static, possibly driving the disease via cytokine release.

A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy

A comprehensive three-dimensional digital atlas database of the C57BL/6J mouse brain was developed based on magnetic resonance microscopy images acquired on a 17.6-T superconducting magnet. By using both manual tracing and an atlas-based semi-automatic segmentation approach, T2-weighted magnetic resonance microscopy images of 10 adult male formalin-fixed, excised C57BL/6J mouse brains were segmented into 20 anatomical structures. These structures included the neocortex, hippocampus, amygdala, olfactory bulbs, basal forebrain and septum, caudate-putamen, globus pallidus, thalamus, hypothalamus, central gray, superior colliculi, inferior colliculi, the rest of midbrain, cerebellum, brainstem, corpus callosum/external capsule, internal capsule, anterior commissure, fimbria, and ventricles. The segmentation data were formatted and stored into a database containing three different atlas types: 10 single-specimen brain atlases, an average brain atlas and a probabilistic atlas. Additionally, quantitative group information, such as variations in structural volume, surface area, magnetic resonance microscopy image intensity and local geometry, were computed and stored as an integral part of the database. The database augments ongoing efforts with other high priority strains as defined by the Mouse Phenome Database focused on providing a quantitative framework for accurate mapping of functional, genetic and protein expression patterns acquired by a myriad of technologies and imaging modalities.

Lateralisation of striatal function: evidence from 18F-dopa PET in Parkinson's disease

OBJECTIVES

The aetiology of the cognitive changes seen in Parkinson's disease (PD) is multifactorial but it is likely that a significant contribution arises from the disruption of dopaminergic pathways. This study aimed to investigate the contribution of the dopaminergic system to performance on two executive tasks using (18)F-6-fluorodopa positron emission tomography ((18)F-dopa PET) in PD subjects with early cognitive changes.

METHODS

16 non-demented, non-depressed PD subjects were evaluated with the Tower of London (TOL) spatial planning task, a verbal working memory task (VWMT) and (18)F-dopa PET, all known to be affected in early PD. Statistical parametric mapping (SPM) localised brain regions in which (18)F-dopa uptake covaried with performance scores. Frontal cortical resting glucose metabolism was assessed with (18)F-fluoro-2-deoxy-D-glucose ((18)F-FDG) PET.

RESULTS

SPM localised significant covariation between right caudate (18)F-dopa uptake (Ki) and TOL scores and between left anterior putamen Ki and VWMT performance. No significant covariation was found between task scores and (18)F-dopa Ki values in either limbic or cortical regions. Frontal cortical glucose metabolism was preserved in all cases.

CONCLUSIONS

These findings support a causative role of striatal dopaminergic depletion in the early impairment of executive functions seen in PD. They suggest that spatial and verbal executive tasks require integrity of the right and left striatum, respectively, and imply that the pattern of cognitive changes manifest by a patient with PD may reflect differential dopamine loss in the two striatal complexes.

Positron emission tomography and single-photon emission computed tomography in central nervous system drug development

In this review, the value of functional imaging [positron emission tomography (PET)/single-photon emission computed tomography (SPECT)] in drug development is considered. Radionuclide imaging can help establish the diagnosis of neurodegenerative disorders where this is in doubt and provides a potential biomarker for following drug effects on disease progression. PET and SPECT can help understand mechanisms of disease and determine the functional effects of therapeutic approaches on neurotransmission and metabolism. Synthesizing radiotracer analogs of novel drugs can provide proof of principle that these agents reach their enzyme or receptor targets and delineate their regional brain distribution. If such radiotracers do not prove to have ideal properties for imaging, the concept of microdosing potentially allows multiple other drug analogs to be tested with less stringent regulatory requirements than for novel medicinals. Finally, PET tracers can provide receptor and enzyme active site dose occupancy profiles, thereby guiding dosage selection for phase 1 and phase 2 trials. The eventual hope is that radiotracer imaging will provide a surrogate marker for drug efficacy, although this has yet to be realized, and progress the concept of personalized medicine where receptor/enzyme binding profiles help predict therapeutic outcome.

Automated cortical projection of head-surface locations for transcranial functional brain mapping

Recent advancements in two noninvasive transcranial neuroimaging techniques, near-infrared spectroscopy (NIRS) and transcranial magnetic stimulation (TMS), signify the increasing importance of establishing structural compatibility between transcranial methods and conventional tomographic methods, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). The transcranial data obtained from the head surface should be projected onto the cortical surface to present the transcranial brain-mapping data on the same platform as tomographic methods. Thus, we developed two transcranial projection algorithms that project given head-surface points onto the cortical surface in structural images, and computer programs based on them. The convex-hull algorithm features geometric handling of the cortical surface, while the balloon-inflation algorithm is faster, and better reflects the local cortical structure. The automatic cortical projection methods proved to be as effective as the manual projection method described in our previous study. These methods achieved perfect correspondence between any given point on the head surface or a related nearby point in space, and its cortical projection point. Moreover, we developed a neighbor-reference method that enables transcranial cortical projection of a given head-surface point in reference to three neighboring points and one additional standard point, even when no structural image of the subject is available. We also calculated an error factor associated with these probabilistic estimations. The current study presents a close topological link between transcranial and tomographic brain-mapping modalities, which could contribute to inter-modal data standardization.

Reliable manual segmentation of the frontal, parietal, temporal, and occipital lobes on magnetic resonance images of healthy subjects

BACKGROUND

It is a challenge to reliably measure the lobar volumes from magnetic resonance imaging (MRI) data.

OBJECTIVE

Description of a landmark-based method for volumetric segmentation of the brain into the four cerebral lobes from MR images.

METHOD

The segmentation method relies on a combination of anatomical landmarks and geometrical definitions. The first step, described previously, is a segmentation of the four lobes on the surface of the brain. The internal borders between the lobes are defined on the axial slices of the brain. The intra- and inter- rater reliability was determined from the MRI scans of a group of 10 healthy control subjects measured by 2 independent raters.

RESULTS

The intra-rater relative error (and intra-class correlation coefficient) of the lobar volume measures ranged from 0.81% to 3.85% (from 0.97 to 0.99). The inter-rater relative error (and intra-class correlation coefficient) ranged from 0.55% to 3.09% (from 0.94 to 0.99).

CONCLUSION

This technique has been shown to have high intra- and inter-rater reliability. The current method provides a method to obtain volumetric estimates of the 4 cerebral lobes.

In vivo imaging of microglial activation with [11C](R)-PK11195 PET in corticobasal degeneration

Corticobasal degeneration (CBD) is a neurodegenerative parkinsonian disorder of unknown cause that shows considerable clinical heterogeneity. In CBD, activated microglia have been shown to be associated closely with the extensive tau pathology found in the affected basal ganglia, brainstem nuclei, and cortical regions. We report on the use of [(11)C](R)-(1-[2-chlorophenyl]-N-methyl-N-[1-methylpropyl]-3-isoquinoline carboxamide) (PK11195) positron emission tomography (PET), a marker of peripheral benzodiazepine binding sites (PBBS) that are expressed by activated microglia, to demonstrate in vivo the degree and distribution of glial response to the degenerative process in 4 patients with CBD. Compared with normal age-matched controls, the CBD patient group showed significantly increased mean [(11)C](R)-PK11195 binding in the caudate nucleus, putamen, substantia nigra, pons, pre- and postcentral gyrus, and the frontal lobe. [11C](R)-PK11195 PET reveals a pattern of increased microglial activation in CBD patients involving cortical regions and the basal ganglia that corresponds well with the known distribution of neuropathological changes, which may therefore help to characterize in vivo the underlying disease activity in CBD.

Construction and Validation of Mean Shape Atlas Templates for Atlas-Based Brain Image Segmentation

In this paper, we evaluate different schemes for constructing a mean shape anatomical atlas for atlas-based segmentation of MR brain images. Each atlas is constructed and validated using a database of 20 images for which detailed manual delineations of 49 different subcortical structures are available. Atlas construction and atlas based segmentation are performed by non-rigid intensity-based registration using a viscous fluid deformation model with parameters that were optimally tuned for this particular task. The segmentation performance of each atlas scheme is evaluated on the same database using a leave-one-out approach and measured by the volume overlap of corresponding regions in the ground-truth manual segmentation and the warped atlas label image.

MR-based automatic delineation of volumes of interest in human brain PET images using probability maps

The purpose of this study was to develop and validate an observer-independent approach for automatic generation of volume-of-interest (VOI) brain templates to be used in emission tomography studies of the brain. The method utilizes a VOI probability map created on the basis of a database of several subjects' MR-images, where VOI sets have been defined manually. High-resolution structural MR-images and 5-HT(2A) receptor binding PET-images (in terms of (18)F-altanserin binding) from 10 healthy volunteers and 10 patients with mild cognitive impairment were included for the analysis. A template including 35 VOIs was manually delineated on the subjects' MR images. Through a warping algorithm template VOI sets defined from each individual were transferred to the other subjects MR-images and the voxel overlap was compared to the VOI set specifically drawn for that particular individual. Comparisons were also made for the VOI templates 5-HT(2A) receptor binding values. It was shown that when the generated VOI set is based on more than one template VOI set, delineation of VOIs is better reproduced and shows less variation as compared both to transfer of a single set of template VOIs as well as manual delineation of the VOI set. The approach was also shown to work equally well in individuals with pronounced cerebral atrophy. Probability-map-based automatic delineation of VOIs is a fast, objective, reproducible, and safe way to assess regional brain values from PET or SPECT scans. In addition, the method applies well in elderly subjects, even in the presence of pronounced cerebral atrophy.

Improved agreement between Talairach and MNI coordinate spaces in deep brain regions

Disagreement between the Talairach atlas and the stereotaxic space commonly used in software like SPM is a widely recognized problem. Others have proposed affine transformations to improve agreement in surface areas such as Brodmann's areas. This article proposes a similar transformation with the goal of improving agreement specifically in the deep brain region. The task is accomplished by finding an affine transformation that minimizes the mean distance between the surface coordinates of the lateral ventricles in the Talairach atlas and the MNI templates. The result is a transformation that improves deep brain agreement over both the untransformed Talairach coordinates and the surface-oriented transformation. While the transformation improves deep brain agreement, surface agreement is generally made worse. For areas near the lateral ventricle, the transformation presented herein is valuable for applications such as region of interest (ROI) modeling.

Retrieving meaning after temporal lobe infarction: The role of the basal language area

During speech comprehension the auditory association cortex in the superior temporal cortex is involved in perceptual analysis of the speech signal, whereas the basal language area in the inferior temporal cortex mediates access to word meaning. Disruption of the interaction between the superior and inferior temporal cortices is one factor that may determine recovery from aphasic stroke. We used positron emission tomography to investigate semantic processing within inferior temporal cortex in control subjects and after infarction involving the superior temporal cortex. In the control group, semantic decision making on clear speech activated both anterior fusiform gyri. Chronic aphasic patients were impaired at the task and demonstrated reduced activation within the left anterior fusiform gyrus. A similar pattern of impaired performance and reduced left anterior fusiform gyrus activation was observed when control subjects heard perceptually degraded speech. Performance in both groups predicted activity in the right anterior fusiform gyrus and the temporal poles, where accuracy linearly correlated with activity. These results demonstrate that the function of the basal language area is sensitive to changes in the quality of perceptual input. In addition, different profiles of response observed in each hemisphere suggest distinct contributions of both left and right inferior temporal cortices to the semantic processing of speech.