Atlas-assisted segmentation of hippocampus

Master'sMASTER OF SCIENC

Comparison of manual and semi-automated delineation of regions of interest for radioligand PET imaging analysis

BACKGROUND As imaging centers produce higher resolution research scans, the number of man-hours required to process regional data has become a major concern. Comparison of automated vs. manual methodology has not been reported for functional imaging. We explored validation of using automation to delineate regions of interest on positron emission tomography (PET) scans. The purpose of this study was to ascertain improvements in image processing time and reproducibility of a semi-automated brain region extraction (SABRE) method over manual delineation of regions of interest (ROIs). METHODS We compared 2 sets of partial volume corrected serotonin 1a receptor binding potentials (BPs) resulting from manual vs. semi-automated methods. BPs were obtained from subjects meeting consensus criteria for frontotemporal degeneration and from age- and gender-matched healthy controls. Two trained raters provided each set of data to conduct comparisons of inter-rater mean image processing time, rank order of BPs for 9 PET scans, intra- and inter-rater intraclass correlation coefficients (ICC), repeatability coefficients (RC), percentages of the average parameter value (RM%), and effect sizes of either method. RESULTS SABRE saved approximately 3 hours of processing time per PET subject over manual delineation (p 0.8) for both methods. RC and RM% were lower for the manual method across all ROIs, indicating less intra-rater variance across PET subjects' BPs. CONCLUSION SABRE demonstrated significant time savings and no significant difference in reproducibility over manual methods, justifying the use of SABRE in serotonin 1a receptor radioligand PET imaging analysis. This implies that semi-automated ROI delineation is a valid methodology for future PET imaging analysis

Identification and segmentation of the central sulcus from human brain MR image

Master'sMASTER OF SCIENC

Neuroinformatics in Functional Neuroimaging

This Ph.D. thesis proposes methods for information retrieval in functional neuroimaging through automatic computerized authority identification, and searching and cleaning in a neuroscience database. Authorities are found through cocitation analysis of the citation pattern among scientific articles. Based on data from a single scientific journal it is shown that multivariate analyses are able to determine group structure that is interpretable as particular “known ” subgroups in functional neuroimaging. Methods for text analysis are suggested that use a combination of content and links, in the form of the terms in scientific documents and scientific citations, respectively. These included context sensitive author ranking and automatic labeling of axes and groups in connection with multivariate analyses of link data. Talairach foci from the BrainMap ™ database are modeled with conditional probability density models useful for exploratory functional volumes modeling. A further application is shown with conditional outlier detection where abnormal entries in the BrainMap ™ database are spotted using kernel density modeling and the redundancy between anatomical labels and spatial Talairach coordinates. This represents a combination of simple term and spatial modeling. The specific outliers that were found in the BrainMap ™ database constituted among others: Entry errors, errors in the article and unusual terminology

Functional Neuroanatomy of Dynamic Visuo-Spatial Imagery

The aim of this thesis was the examination of the neural bases of dynamic visuo-spatial imagery. In addition to the assessment of brain activity during dy-namic visuo-spatial imagery using single-trial functional magnetic resonance im-aging (fMRI) and slow cortical potentials (SCPs), several methodological issues have been investigated. The theoretical part of this thesis consists of a selective overview of fMRI and SCPs, and of the advantages of their combination for functional neuroimaging (chapter 2). The methodological and empirical chapters include: Ø the presentation of a new, highly accurate and practicable method for the co-registration of MRI- and EEG-data (chapter 3), Ø the description of the increase in the accuracy of SCP mapping resulting from the use of individual electrode coordinates and realistic head models (chapter 4), Ø the description of regional differences in the consistency of brain activity across several executions of the same task type, as assessed by a new analysis con-cept based on single-trial fMRI data (chapter 5), Ø the demonstration of the involvement of premotor regions in dynamic visuo-spatial imagery, as assessed via a combination of single-trial fMRI and SCPs (chapter 6), Ø the description of a combined fMRI-SCP investigation in which earlier findings concerning individual differences in neural efficiency during dynamic imagery could not be replicated (chapter 7)

MRI measures of brain integrity and their relation to processing speed in the elderly

A significant percentage of the elderly population experiences at least one geriatric disability. Previous studies have shown that geriatric disabilities are preceded by sub-clinical cognitive changes of aging and brain changes seen on magnetic resonance imaging (MRI). Decreased information processing speed has been identified as a common factor associated with age-related disabilities in gait, cognition, and mood. However, the current neurocognitive model of aging is incomplete; there remains uncertainty about the relationships between the different components of brain integrity and cognitive function. The goals of this dissertation are to characterize the relationships between different functional and structural MRI markers (for example: macro-structural, micro-structural, physiologic) with respect to cognitive aging and to improve the neuroimaging toolset for oldest old.We studied the relationship between functional MRI markers, structural MRI markers, and information processing speed in a sample of twenty-five healthy elderly subjects. We found that recruitment of fronto-parietal brain areas was associated with higher performance. Also, greater structural damage (white matter integrity) was associated with lower activation in prefrontal and anterior cingulate regions. In the presence of underlying brain connectivity structural abnormalities, additional posterior parietal activation was found to be important for maintaining higher task performance.MRI MEASURES OF BRAIN INTEGRITY AND THEIR RELATION TO PROCESSING SPEED IN THE ELDERLYVijay Krishna Venkatraman, Ph.D.University of Pittsburgh, 2010vWe also studied MRI measures of brain structure in a sample of 277 community-dwelling older adults free from neurological diseases. This study used a set of neuroimage analysis pathways optimized for the MRI images and examined the macro- and micro-structural indices. The results indicate that both the macro- and micro-structural MRI indices may provide complementary information on neuroanatomical correlates of information processing speed. The micro-structural MRI indices of white matter integrity were found to be the strongest correlate of information processing speed in this sample.While developing the image analysis pipelines for this dataset, we noticed that the diffusion tensor-imaging pathway was particularly sensitive to the approach of localizing the white matter tracts. We used both empirical and simulated datasets to confirm our hypothesis that the mean fractional anisotropy of the white matter tract is more sensitive to individual differences in the elderly when compared to a skeleton based approach

Functional Magnetic Resonance Imaging

"Functional Magnetic Resonance Imaging - Advanced Neuroimaging Applications" is a concise book on applied methods of fMRI used in assessment of cognitive functions in brain and neuropsychological evaluation using motor-sensory activities, language, orthographic disabilities in children. The book will serve the purpose of applied neuropsychological evaluation methods in neuropsychological research projects, as well as relatively experienced psychologists and neuroscientists. Chapters are arranged in the order of basic concepts of fMRI and physiological basis of fMRI after event-related stimulus in first two chapters followed by new concepts of fMRI applied in constraint-induced movement therapy; reliability analysis; refractory SMA epilepsy; consciousness states; rule-guided behavioral analysis; orthographic frequency neighbor analysis for phonological activation; and quantitative multimodal spectroscopic fMRI to evaluate different neuropsychological states

Methods to assess changes in human brain structure across the lifecourse

Human brain structure can be measured across the lifecourse (“in vivo”) with magnetic resonance imaging (MRI). MRI data are often used to create “atlases” and statistical models of brain structure across the lifecourse. These methods may define how brain structure changes through life and support diagnoses of increasingly common, yet still fatal, age-related neurodegenerative diseases. As diseases such as Alzheimer’s (AD) cast an ever growing shadow over our ageing population, it is vitally important to robustly define changes which are normal for age and those which are pathological. This work therefore assessed existing MR brain image data, atlases, and statistical models. These assessments led me to propose novel methods for accurately defining the distributions and boundaries of normal ageing and pathological brain structure. A systematic review found that there were fewer than 100 appropriately tested normal subjects aged ≥60 years openly available worldwide. These subjects did not have the range of MRI sequences required to effectively characterise the features of brain ageing. The majority of brain image atlases identified in this review were found to contain data from few or no subjects aged ≥60 years and were in a limited range of MRI sequences. All of these atlases were created with parametric (mean-based) statistics that require the assumptions of equal variance and Gaussian distributions. When these assumptions are not met, mean-based atlases and models may not well represent the distributions and boundaries of brain structure. I tested these assumptions and found that they were not met in whole brain, subregional, and voxel-based models of ~580 subjects from across the lifecourse (0- 90 years). I then implemented novel whole brain, subregional, and voxel-based statistics, e.g. percentile rank atlases and nonparametric effect size estimates. The equivalent parametric statistics led to errors in classification and inflated effects by up to 45% in normal ageing-AD comparisons. I conclude that more MR brain image data, age appropriate atlases, and nonparametric statistical models are needed to define the true limits of normal brain structure. Accurate definition of these limits will ultimately improve diagnoses, treatment, and outcome of neurodegenerative disease