Grant review: American Idol or big brother?
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National Institutes of Health (U.S.)↗
Biomedical subjects
Ivo D Dinov
Publications and source records attributed to Ivo D Dinov.
3D comparison of hippocampal atrophy in amnestic mild cognitive impairment and Alzheimer's disease.
Alzheimer's disease is the most common neurodegenerative disorder in the elderly. Amnestic mild cognitive impairment (MCI) is a relatively newly defined clinical entity that requires memory decline while activities of daily living remain intact. Most amnestic MCI patients develop Alzheimer's disease. Using an innovative surface-based hippocampal analytic technique we analysed the structural magnetic resonance hippocampal data of 31 amnestic MCI and 34 Alzheimer's disease subjects. We tested the hypothesis that Alzheimer's disease subjects have greater atrophy of the CA1, CA2 and CA3 hippocampal subfields relative to amnestic MCI subjects. 3D hippocampal maps localized the main group differences to the CA1 region bilaterally and the CA2 and CA3 region on the left [corrected] (right [corrected] P = 0.0024, left [corrected] P = 0.0002, both corrected for multiple comparisons). Age, race, gender, education and Mini-Mental State Examination were significant predictors of hippocampal volume. Hippocampal volume was a significant predictor of clinical diagnosis. Our study suggests that as Alzheimer's disease progresses, subregional hippocampal atrophy spreads in a pattern that follows the known trajectory of neurofibrillary tangle dissemination. Novel hippocampal analytic techniques that can track the spread of hippocampal pathology in 3D with such precision are a promising research tool.
Aged↗
Conversion of mild cognitive impairment to Alzheimer disease predicted by hippocampal atrophy maps.
BACKGROUND: While most patients with mild cognitive impairment (MCI) transition to Alzheimer disease (AD), others develop non-AD dementia, remain in the MCI state, or improve. OBJECTIVE: To test the following hypotheses: smaller hippocampal volumes predict conversion of MCI to AD, whereas larger hippocampal volumes predict cognitive stability and/or improvement; and patients with MCI who convert to AD have greater atrophy in the CA1 hippocampal subfield and subiculum. DESIGN: Prospective longitudinal cohort study. SETTING: University of California-Los Angeles Alzheimer's Disease Research Center. PATIENTS: We followed up 20 MCI subjects clinically and neuropsychologically for 3 years. MAIN OUTCOME MEASURE: Baseline regional hippocampal atrophy was analyzed with region-of-interest and 3-dimensional hippocampal mapping techniques. RESULTS: During the 3-year study, 6 patients developed AD (MCI-c), 7 remained stable (MCI-nc), and 7 improved (MCI-i). Patients with MCI-c had 9% smaller left and 13% smaller right mean hippocampal volumes compared with MCI-nc patients. Radial atrophy maps showed greater atrophy of the CA1 subregion in MCI-c. Patients with MCI-c had significantly smaller hippocampi than MCI-i patients (left, 24%; right, 27%). Volumetric analyses showed a trend for greater hippocampal atrophy in MCI-nc relative to MCI-i patients (eg, 16% volume loss). After permutation tests corrected for multiple comparison, the atrophy maps showed a significant difference on the right. Subicular differences were seen between MCI-c and MCI-i patients, and MCI-nc and MCI-i patients. Multiple linear regression analysis confirmed the group effect to be highly significant and independent of age, hemisphere, and Mini-Mental State Examination scores at baseline. CONCLUSIONS: Smaller hippocampi and specifically CA1 and subicular involvement are associated with increased risk for conversion from MCI to AD. Patients with MCI-i tend to have larger hippocampal volumes and relative preservation of both the subiculum and CA1.
Aged↗
LONI visualization environment.
Over the past decade, the use of informatics to solve complex neuroscientific problems has increased dramatically. Many of these research endeavors involve examining large amounts of imaging, behavioral, genetic, neurobiological, and neuropsychiatric data. Superimposing, processing, visualizing, or interpreting such a complex cohort of datasets frequently becomes a challenge. We developed a new software environment that allows investigators to integrate multimodal imaging data, hierarchical brain ontology systems, on-line genetic and phylogenic databases, and 3D virtual data reconstruction models. The Laboratory of Neuro Imaging visualization environment (LONI Viz) consists of the following components: a sectional viewer for imaging data, an interactive 3D display for surface and volume rendering of imaging data, a brain ontology viewer, and an external database query system. The synchronization of all components according to stereotaxic coordinates, region name, hierarchical ontology, and genetic labels is achieved via a comprehensive BrainMapper functionality, which directly maps between position, structure name, database, and functional connectivity information. This environment is freely available, portable, and extensible, and may prove very useful for neurobiologists, neurogenetisists, brain mappers, and for other clinical, pedagogical, and research endeavors.
Brain Mapping↗
Automated brain tissue assessment in the elderly and demented population: construction and validation of a sub-volume probabilistic brain atlas.
OBJECTIVES: To develop an automated imaging assessment tool that accommodates the anatomic variability of the elderly and demented population as well as the registration errors occurring during spatial normalization. METHODS: 20 subjects with Alzheimer's disease (AD), mild cognitive impairment, or normal cognition underwent MRI brain imaging and had their 3D volumetric datasets manually partitioned into 68 regions of interest (ROI) termed sub-volumes. Gray matter (GM), white matter (WM), and cerebral spinal fluid (CSF) voxel counts were then made in the subject's native space for comparison against automated volumetric measures within three sub-volume probabilistic atlas (SVPA) models. The three SVPAs were constructed using 12 parameter affine (12 p), 2nd order (2nd), and 6th order (6th) transforms derived from registering the manually partitioned scans into a Talairach compatible AD population-based target. The three SVPA automated measures were compared to the manually derived measures in the 20 subjects' native space with a "jack-knife" procedure in which each subject was assessed by an SVPA they did not contribute toward constructing. RESULTS: The mean left and right GM ratio (GM ratio = [GM + CSF] / CSF) "r values" for the 3 SVPAs compared to the manually derived ratios across the 68 ROIs were 0.85 for the 12p SVPA, 0.88 for the 2nd SVPA, and 0.89 for the 6th SVPA. The mean left and right WM ratio (WM ratio = [WM + CSF] / CSF) "r values" for the 3 SVPAs being 0.84 for the 12p SVPA, 0.86 for the 2nd SVPA, and 0.88 for the 6th SVPA. CONCLUSION: We have constructed, from an elderly and demented cohort, an automated brain volumetric tool that has excellent accuracy compared to a manual gold standard and is capable of regional hypothesis testing and individual patient assessment compared to a population.
Aged↗
Metabolic patterns associated with the clinical response to galantamine therapy: a fludeoxyglucose f 18 positron emission tomographic study.
BACKGROUND: Regional brain correlates of treatment with cholinesterase inhibitors in those with Alzheimer disease are unknown. OBJECTIVE: To map brain metabolism associated with the treatment response to galantamine with fludeoxyglucose F 18 positron emission tomography in patients with Alzheimer disease. DESIGN: This is a hypothesis-driven, prospective, open-label study of 19 patients with mild to moderate Alzheimer disease examined before and after treatment with the cholinesterase inhibitor galantamine. Clinical examinations included the cognitive portion of the Alzheimer Disease Assessment Scale, the Mini-Mental State Examination, and the Neuropsychiatric Inventory. Imaging was performed using fludeoxyglucose F 18 positron emission tomography. The positron emission tomographic data, registered to a probabilistic anatomical atlas, were subjected to a voxel-based analysis of 3 subgroups: total patient analysis, cognitive analysis, and behavioral analysis. Subvolume thresholding corrected random lobar noise to produce 3-dimensional significance maps. RESULTS: The total group analysis showed an increase in left caudate metabolism with no significant change in clinical outcomes for the total group with treatment. Subgroup analysis of cognitive and behavioral responders demonstrated a significant activation of a striatal-thalamofrontal network with galantamine treatment that was not present in patients whose condition worsened or was unchanged by therapy. In cognitive subgroups, change in left anterior cingulate metabolism significantly correlated with change in the cognitive portion of the Alzheimer Disease Assessment Scale (r = 0.70, P = .02); in behavioral subgroups, right cingulate metabolic change significantly correlated with improvement in depression and right ventral putamen metabolic change with improvement in apathy (r = 0.63, P<.05 for both). CONCLUSION: Cognitive and behavioral responders to galantamine therapy show clinically related improvements in prefrontal network metabolism along with thalamic activation.
Aged↗
A wavelet-based statistical analysis of FMRI data: I. motivation and data distribution modeling.
We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We use structural magnetic resonance imaging (MRI) and fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and spatial locations. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of which is processed separately. A frequency-adaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. The empirical distributions of the signals on all the regions are computed in a compressed wavelet space. These are modeled by heavy-tail distributions because their histograms exhibit slower tail decay than the Gaussian. We discovered that the Cauchy, Bessel K Forms, and Pareto distributions provide the most accurate asymptotic models for the distribution of the wavelet coefficients of the data. Finally, we propose a new model for statistical analysis of functional MRI data using this atlas-based wavelet space representation. In the second part of our investigation, we will apply this technique to analyze a large fMRI dataset involving repeated presentation of sensory-motor response stimuli in young, elderly, and demented subjects.
Adult↗
A multimodal, multidimensional atlas of the C57BL/6J mouse brain.
Strains of mice, through breeding or the disruption of normal genetic pathways, are widely used to model human diseases. Atlases are an invaluable aid in understanding the impact of such manipulations by providing a standard for comparison. We have developed a digital atlas of the adult C57BL/6J mouse brain as a comprehensive framework for storing and accessing the myriad types of information about the mouse brain. Our implementation was constructed using several different imaging techniques: magnetic resonance microscopy, blockface imaging, classical histology and immunohistochemistry. Along with raw and annotated images, it contains database management systems and a set of tools for comparing information from different techniques. The framework allows facile correlation of results from different animals, investigators or laboratories by establishing a canonical representation of the mouse brain and providing the tools for the insertion of independent data into the same space as the atlas. This tool will aid in managing the increasingly complex and voluminous amounts of information about the mammalian brain. It provides a framework that encompasses genetic information in the context of anatomical imaging and holds tremendous promise for producing new insights into the relationship between genotype and phenotype. We describe a suite of tools that enables the independent entry of other types of data, facile retrieval of information and straightforward display of images. Thus, the atlas becomes a framework for managing complex genetic and epigenetic information about the mouse brain. The atlas and associated tools may be accessed at http://www.loni.ucla.edu/MAP.
Anatomy, Artistic↗
The informatics of a C57BL/6J mouse brain atlas.
The Mouse Atlas Project (MAP) aims to produce a framework for organizing and analyzing the large volumes of neuroscientific data produced by the proliferation of genetically modified animals. Atlases provide an invaluable aid in understanding the impact of genetic manipulations by providing a standard for comparison. We use a digital atlas as the hub of an informatics network, correlating imaging data, such as structural imaging and histology, with text-based data, such as nomenclature, connections, and references. We generated brain volumes using magnetic resonance microscopy (MRM), classical histology, and immunohistochemistry, and registered them into a common and defined coordinate system. Specially designed viewers were developed in order to visualize multiple datasets simultaneously and to coordinate between textual and image data. Researchers can navigate through the brain interchangeably, in either a text-based or image-based representation that automatically updates information as they move. The atlas also allows the independent entry of other types of data, the facile retrieval of information, and the straight-forward display of images. In conjunction with centralized servers, image and text data can be kept current and can decrease the burden on individual researchers' computers. A comprehensive framework that encompasses many forms of information in the context of anatomic imaging holds tremendous promise for producing new insights. The atlas and associated tools can be found at http://www.loni.ucla.edu/MAP.
Anatomy, Artistic↗
Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage.
In the field of template-based medical image analysis, image registration and normalization are frequently used to evaluate and interpret data in a standard template or reference atlas space. Despite the large number of image-registration (warping) techniques developed recently in the literature, only a few studies have been undertaken to numerically characterize and compare various alignment methods. In this paper, we introduce a new approach for analyzing image registration based on a selective-wavelet reconstruction technique using a frequency-adaptive wavelet shrinkage. We study four polynomial-based and two higher complexity nonaffine warping methods applied to groups of stereotaxic human brain structural (magnetic resonance imaging) and functional (positron emission tomography) data. Depending upon the aim of the image registration, we present several warp classification schemes. Our method uses a concise representation of the native and resliced (pre- and post-warp) data in compressed wavelet space to assess quality of registration. This technique is computationally inexpensive and utilizes the image compression, image enhancement, and denoising characteristics of the wavelet-based function representation, as well as the optimality properties of frequency-dependent wavelet shrinkage.
Brain↗