A brain image database for structure/function analysis

Seminars in Nuclear Medicine, 1994

We describe a reference device that provides accurate correlation between anatomic and functional brain images. The reference device, which generates fiduciary reference points on sequential scan planes, is positioned adjacent to the canthomeatal line of the subject and held in place by a glasses-like framework anchored to the external auditory meatus. The reference system was tested on 17 subjects undergoing ~mTc hexamethylpropylene amine oxime ([=~Tc]HMPAO) brain single-photon emission computed tomography (SPECT} and cranial computed tomography (CT) scans. The centers of the caudate nuclei, thalami, brain stem, and cerebellar vermis were identified independently on CT and SPECT. The average difference-+1 SD between structure locations (x, y, and z) on SPECT and CT were calculated as 1.86-+ 1.5, 2.16 +-1.4, and 1.83-+ 1.9 mm, respectively. The clinical application of the method is showed by coregistration of images from SPECT to MRI. An example of sequential [~mTc]HM-PAO brain SPECT scan sections precisely coregistered with MRI scan sections oriented parallel to and sequentially above the r line illustrates the correlation between regional cerebral blood flow (rCBF) tracer activity on SPECT and normal anatomic structures. Test-retest activation paradigms in brain SPECT requires precise SPECT-to-SPECT image coregistration to evaluate changes in rCBF during activation. Precisely coregistered rest, 48-hour repeat rest ["mTc]HMPAO SPECT studies are shown to illustrate the normal intrasubject variability of tracer uptake. An example of the usefulness of in~ge coregistration for evaluation of viable residual brain tumor and its application to tumor biopsy is presented. An example of developmental abnormalities identified by ["mTc]HMPAO brain SPECT is illustrated by a case of autistic disorder. An example of image coregistration in stroke and evaluation of cerebrovascular disease with Diamox (Lederle Laboratory Division, Pearl River, NY) cerebrovasculature stress testing is presented. The usefulness in epilepsy using a protocol whereby the tracer is injected during the ictal phase of seizure is presented. We conclude that the reference system provides an accurate, rapid, and noninvesive patientspecific method for correlating brain structure with brain function.

2012

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.

Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant’s structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant’s non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design.

PubMed, 2003

We present a MR brain atlas for structure and function (diffusion weighted images). The atlas is customizable for contrast and orientation to match the current patient images. In addition, the atlas also provides normative values of MR parameters (T1, T2 and ADC values). The atlas is designed on informatics principles to provide context sensitive decision support at the time of primary image interpretation. Additional support for diagnostic interpretation is provided by a list of expert created most relevant 'Image Finding Descriptors' that will serve as cues to the user. The architecture of the atlas module is integrated into the image workflow of a radiology department to provide support at the time of primary diagnosis.

Computerized Medical Imaging and Graphics, 1994

We describe a reference device that provides accurate correlations between anatomic and functional brain images. The reference device, which generates fiduciary reference points on sequential scan planes, is positioned adjacent to the orbitomeatal line of the subject, and held in place by a framework anchored to the external auditory meatus. The reference system was tested on 17 subjects undergoing Tc-99m-hexamethylpropyteneamine oxime (Tc-99m-HM-PAO) brain single photon emission computed tomography (SPECT) and cranial computed tomography (CT) sams. The centers of the caudate nuclei, thalami, brain stem, and cerebelhu vermis were identified independently on m and SPECT. The average difference + 1 sd between structure locations (x, y, and z) on SPECT and CI were calculated as 1.86 + 1.5,2.16 + 1.4, and 1.83 + 1.9 mm, respectively. The relevance of the method to clinical applications is illustrated by the localization of a recurrent viable gtioma and an epiteptogenic focus. This reference system provides an accurate, rapid, and noninvasive patient-specific method for the correlation of brain structure with brain function.

The relationships between structural and functional measures of the human brain remain largely unknown. A majority of our limited knowledge regarding structure-function associations has been obtained through comparisons between specific groups of patients and healthy controls. Unfortunately, a direct and complete view of the associations across multiple structural and functional metrics in normal population is missing. We filled this gap by learning cross-individual co-variance among structural and functional measures using large-scale neuroimaging datasets. A discover-confirm scheme was applied to two independent samples (N = 184 and N = 340) of multimodal neuroimaging datasets. A data mining tool, gRAI-CAR, was employed in the discover stage to generate quantitative and unbiased hypotheses of the co-variance among six functional and six structural imaging metrics. These hypotheses were validated using an independent dataset in the confirm stage. Fifteen multi-metric co-variance units, representing different co-variance relationships among the 12 metrics, were reliable across the two sets of neuroimaging datasets. The reliable co-variance units were summarized into a database, where users can select any location on the cortical map of any metric to examine the co-varying maps with the other 11 metrics. This database characterized the six functional metrics based on their covariance with structural metrics, and provided a detailed reference to connect previous findings using different metrics and to predict maps of unexamined metrics. Gender, age, and handedness were associated to the co-variance units, and a sub-study of schizophrenia demonstrated the usefulness of the co-variance database.

2006 International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

A number of neurological diseases are associated with structural and functional alterations in the brain. This paper presents a method of using both structural and functional MR images for brain disease diagnosis, by machine learning and high-dimensional template warping. First, a highdimensional template warping technique is used to compute morphological and functional representations for each individual brain in a template space, within a mass preserving framework. Then, statistical regional features are extracted to reduce the dimensionality of morphological and functional representations, as well as to achieve the robustness to registration errors and inter-subject variations. Finally, the most discriminative regional features are selected by a hybrid feature selection method for brain classification, using a nonlinear support vector machine. The proposed method has been applied to classifying the brain images of prenatally cocaine-exposed young adults from those of socioeconomically matched controls, resulting in 91.8% correct classification rate using a leave-one-out cross-validation. Comparison results show the effectiveness of our method and also the importance of simultaneously using both structural and functional images for brain classification.

Journal of Magnetic Resonance Imaging, 2011

Purpose: To compare the robustness of region of interest (ROI) analysis of magnetic resonance imaging (MRI) brain data in real space with analysis in standard space and to test the hypothesis that standard space image analysis introduces more partial volume effect errors compared to analysis of the same dataset in real space. Materials and Methods: Twenty healthy adults with no history or evidence of neurological diseases were recruited; high-resolution T 1-weighted, quantitative T 1 , and B 0 field-map measurements were collected. Algorithms were implemented to perform analysis in real and standard space and used to apply a simple standard ROI template to quantitative T 1 datasets. Regional relaxation values and histograms for both gray and white matter tissues classes were then extracted and compared. Results: Regional mean T 1 values for both gray and white matter were significantly lower using real space compared to standard space analysis. Additionally, regional T 1 histograms were more compact in real space, with smaller right-sided tails indicating lower partial volume errors compared to standard space analysis. Conclusion: Standard space analysis of quantitative MRI brain data introduces more partial volume effect errors biasing the analysis of quantitative data compared to analysis of the same dataset in real space.

NeuroImage, 2011

Whole brain extraction is an important pre-processing step in neuro-image analysis. Manual or semi-automated brain delineations are labour-intensive and thus not desirable in large studies, meaning that automated techniques are preferable. The accuracy and robustness of automated methods are crucial because human expertise may be required to correct any sub-optimal results, which can be very time consuming. We compared the accuracy of four automated brain extraction methods: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), Hybrid Watershed Algorithm (HWA) and a Multi-Atlas Propagation and Segmentation (MAPS) technique we have previously developed for hippocampal segmentation. The four methods were applied to extract whole brains from 682 1.5T and 157 3T T 1-weighted MR baseline images from the Alzheimer's Disease Neuroimaging Initiative database. Semi-automated brain segmentations with manual editing and checking were used as the gold-standard to compare with the results. The median Jaccard index of MAPS was higher than HWA, BET and BSE in 1.5T and 3T scans (p < 0.05, all tests), and the 1st-99th centile range of the Jaccard index of MAPS was smaller than HWA, BET and BSE in 1.5T and 3T scans (p < 0.05, all tests). HWA and MAPS were found to be best at including all brain tissues (median false negative rate ≤ 0.010% for 1.5T scans and ≤ 0.019% for 3T scans, both methods). The median Jaccard index of MAPS were similar in both 1.5T and 3T scans, whereas those of BET, BSE and HWA were higher in 1.5T scans than 3T scans (p < 0.05, all tests). We found that the diagnostic group had a small effect on the median Jaccard index of all four methods. In conclusion, MAPS had relatively high accuracy and low variability compared to HWA, BET and BSE in MR scans with and without atrophy.

NeuroImage, 2007

A multivariate classification approach has been presented to examine the brain abnormalities, i.e., due to prenatal cocaine exposure, using both structural and functional brain images. First, a regional statistical feature extraction scheme was adopted to capture discriminative features from voxel-wise morphometric and functional representations of brain images, in order to reduce the dimensionality of the features used for classification, as well as to achieve the robustness to registration error and inter-subject variations. Then, this feature extraction method was used in conjunction with a hybrid feature selection method and a nonlinear support vector machine for the classification of brain abnormalities. This brain classification approach has been applied to detecting the brain abnormality associated with prenatal cocaine exposure in adolescents. A promising classification performance was achieved on a data set of 49 subjects (24 normal and 25 prenatally cocaine-exposed teenagers), with a leave-one-out crossvalidation. Experimental results demonstrated the efficacy of our method, as well as the importance of incorporating both structural and functional images for brain classification. Moreover, spatial patterns of group difference derived from the constructed classifier were mostly consistent with the results of the conventional statistical analysis method. Therefore, the proposed approach provided not only a multivariate classification method for detecting brain abnormalities, but also an alternative way for group analysis of multimodality images.