A region-of-interest template for three-dimensional stereotactic surface projection images: Initial application to the analysis of Alzheimer??s disease and mild cognitive impairment

Structural brain atlases: design, rationale, and applications in normal and pathological cohorts

Structural magnetic resonance imaging (MRI) provides anatomical information about the brain in healthy as well as in diseased conditions. On the other hand, functional MRI (fMRI) provides information on the brain activity during performance of a specific task. Analysis of fMRI data requires the registration of the data to a reference brain template in order to identify the activated brain regions. Brain templates also find application in other neuroimaging modalities, such as diffusion tensor imaging and multi-voxel spectroscopy. Further, there are certain differences (e.g., brain shape and size) in the brains of populations of different origin and during diseased conditions like in Alzheimer's disease (AD), population and disease-specific brain templates may be considered crucial for accurate registration and subsequent analysis of fMRI as well as other neuroimaging data. This manuscript provides a comprehensive review of the history, construction and application of brain atlases. A chronological outline of the development of brain template design, starting from the Talairach and Tournoux atlas to the Chinese brain template (to date), along with their respective detailed construction protocols provides the backdrop to this manuscript. The manuscript also provides the automated workflow-based protocol for designing a population-specific brain atlas from structural MRI data using LONI Pipeline graphical workflow environment. We conclude by discussing the scope of brain templates as a research tool and their application in various neuroimaging modalities.

ICA-fNORM: Spatial Normalization of fMRI Data Using Intrinsic Group-ICA Networks

A common pre-processing challenge associated with group level fMRI analysis is spatial registration of multiple subjects to a standard space. Spatial normalization, using a reference image such as the Montreal Neurological Institute brain template, is the most common technique currently in use to achieve spatial congruence across multiple subjects. This method corrects for global shape differences preserving regional asymmetries, but does not account for functional differences. We propose a novel approach to co-register task-based fMRI data using resting state group-ICA networks. We posit that these intrinsic networks (INs) can provide to the spatial normalization process with important information about how each individual’s brain is organized functionally. The algorithm is initiated by the extraction of single subject representations of INs using group level independent component analysis (ICA) on resting state fMRI data. In this proof-of-concept work two of the robust, commonly identified, networks are chosen as functional templates. As an estimation step, the relevant INs are utilized to derive a set of normalization parameters for each subject. Finally, the normalization parameters are applied individually to a different set of fMRI data acquired while the subjects performed an auditory oddball task. These normalization parameters, although derived using rest data, generalize successfully to data obtained with a cognitive paradigm for each subject. The improvement in results is verified using two widely applied fMRI analysis methods: the general linear model and ICA. Resulting activation patterns from each analysis method show significant improvements in terms of detection sensitivity and statistical significance at the group level. The results presented in this article provide initial evidence to show that common functional domains from the resting state brain may be used to improve the group statistics of task-fMRI data.

Joint analysis of structural and perfusion MRI for cognitive assessment and classification of Alzheimer's disease and normal aging

Structural magnetic resonance imaging (MRI) of brain tissue loss and physiological imaging of regional cerebral blood flow (rCBF) can provide complimentary information for the characterization of brain disorders, such as Alzheimer's disease (AD) but studies into gains in classification power for AD using these image modalities jointly have been limited. Our aim in this study was to determine the joint contribution of structural and perfusion-weighted imaging for the classification of AD in a cross-sectional study using an integrated multimodality MRI processing framework and a cortical surface-based analysis approach. We used logistic regression analysis to determine sequentially the value of cortical thickness, rCBF, and cortical thickness and rCBF jointly for classification for diagnosis of AD compared to controls. We further tested the extent to which cortical thinning and reduced rCBF explain individually or together variability in dementia severity. Separate analysis of structural MRI and perfusion-weighted MRI data yielded the well-established pattern of cortical thinning and rCBF reduction in AD, affecting predominantly temporo-parietal brain regions. Using structural MRI and perfusion-weighted MRI jointly indicated that cortical thinning dominated the classification of AD and controls without significant contributions from rCBF. However there was also a positive interaction between reduced rCBF and cortical thinning in the right superior temporal sulcus, implying that structural and physiological brain alterations in AD can be complementary. Compared to reduced rCBF, regional cortical thinning better explained the variability in dementia severity. In conclusion, structural brain alterations compared to physiological variations are the dominant features of MRI in AD.

Study-specific EPI template improves group analysis in functional MRI of young and older adults

Spatial normalization to a common coordinate space, e.g. via the Montreal Neurological Institute (MNI) brain template, is an essential step of analyzing multi-subject functional MRI (fMRI) datasets. The imperfect compensation for individual regional discrepancies during spatial transformation, which could potentially introduce localization errors of the activation foci and/or reduce the detection sensitivity, may be minimized if a template specifically designed for the subjects of a study is applied. In this fMRI study, we proposed and evaluated the use of a study-specific template (SST) based on the mean of individually normalized echo-planar images for group data analysis. A hand flexion and a word generation tasks were performed on young volunteers in experiment 1. Comparing with the MNI template approach, greater t-values of local maxima and activated voxels were detected within volume-of-interests (VOIs) with the SST approach in both tasks. Moreover, the SST approach reduced Euclidean distances between activation foci of individuals and group by 1.52 mm in motor fMRI and 5.84 mm in language fMRI. Similar results were obtained with or without spatial smoothing of the echo-planar images. Experiment 2 further examined these two approaches in older adults, in which volumetric differences between subjects are of great concerns. With a working memory task, the SST approach showed greater t-values of local maxima and activated voxels within the VOI of prefrontal gyrus. This study demonstrated that the SST resulted in more focused activation patterns and effectively improved the fMRI sensitivity, which suggested potentials of reducing number of subjects required for group analysis.

Diagnostic performance of Tc-99m HMPAO SPECT for early and late onset Alzheimer's disease: a clinical evaluation of linearization correction

OBJECTIVE

This study examined the influence of linearization correction (LC) on brain perfusion single-photon emission computed tomography (SPECT) for the diagnosis of Alzheimer's disease (AD).

METHODS

The early onset group (<65 years old) consisted of 10 patients with AD, and the late onset group (>/=65 years old) of 13 patients with AD. Age-matched controls included seven younger and seven older normal volunteers. Tc-99m hexamethyl propyleneamine oxine (HMPAO) SPECT images were reconstructed with or without LC [LC (+) or LC (-)] and a statistical analysis was performed using a three-dimensional stereotactic surface projection (3D-SSP). In addition, a fully automatic diagnostic system was developed, which calculated the proportion of the number of abnormal pixels in the superior and inferior parietal lobule, as well as in the precuneus and posterior cingulate gyrus.

RESULTS

The areas under the receiver-operating characteristic curve (AUCs) of the early onset group for conventional axial SPECT images, SPECT + 3D-SSP images and the fully automatic diagnostic system were 0.71, 0.88, and 0.92 in LC (-) and 0.67, 0.85, and 0.91 in LC (+), respectively. The AUCs of the late onset group were 0.50, 0.61, and 0.79 in LC (-) and 0.49, 0.67, and 0.85 in LC (+), respectively.

CONCLUSION

LC on Tc-99m HMPAO SPECT did not significantly influence the diagnostic performance for differentiating between AD and normal controls in either early or late onset AD. Further examination with individuals suffering from very mild dementia is, therefore, expected to elucidate the effect of LC on minimally hypoperfused areas.

Evaluation of a new expert system for fully automated detection of the Alzheimer's dementia pattern in FDG PET

OBJECTIVE

Fluorodeoxyglucose (FDG) positron emission tomography (PET) is increasingly used to support a diagnosis of Alzheimer's disease. The aim of the present study was to evaluate a new expert system (PALZ) for the fully automated analysis of FDG PET images for diagnosis of the disease.

METHODS

The PALZ tool is based on the detection of the typical disease pattern in FDG PET images. Its potential for this task was evaluated in 22 consecutive patients with suspected Alzheimer's disease who had been graded as positive for the pattern by an experienced reader (visual analysis supported by statistical parametric mapping (SPM)), and in 18 controls. Dependence on scanner performance was assessed by variation of the spatial resolution of the PET images.

RESULTS

All the Alzheimer's disease subjects were classified as pattern-positive by the PALZ tool. Fifteen controls were classified as normal. Sensitivity and specificity for differentiation of the patients with suspected Alzheimer's disease from the controls were 100% and 83%, respectively. The false positive finding in three controls most likely was caused by differences in attenuation correction between the normal data base of the PALZ tool (cold transmission scan) and the local data sets (hot transmission scan). There was only mild dependence on spatial resolution.

CONCLUSIONS

The results of the present study suggest that the PALZ tool provides similar performance for the detection of the typical Alzheimer's disease pattern in FDG PET images as an experienced reader supported by SPM. The PALZ tool is fully automated, easy to use, and insensitive to the spatial resolution of the PET scanner used. Therefore, it has the potential for widespread clinical use.