OBJECTIVES:A cross-sectional study to establish whether a subject's cognitive state can be predicted based on regional values obtained from brain cortical maps of FDDNP Distribution Volume Ratio (DVR), which shows the pattern of beta amyloid and neurofibrillary binding, along with those of early summed FDDNP PET images (reflecting the pattern of perfusion) was performed. METHODS:Dynamic FDDNP PET studies were performed in a group of 23 subjects (8 control (NL), 8 Mild Cognitive Impairment (MCI) and 7 Alzheimer's Disease (AD) subjects). FDDNP DVR images were mapped to the MR derived hemispheric cortical surface map warped into a common space. A set of Regions of Interest (ROI) values of FDDNP DVR and early summed FDDNP PET (0-6 min post tracer injection), were thus calculated for each subject which along with the MMSE score were used to construct a linear mathematical model relating ROI values to MMSE. After the MMSE prediction models were developed, the models' predictive ability was tested in a non-overlapping set of 8 additional individuals, whose cognitive status was unknown to the investigators who constructed the predictive models. RESULTS:Among all possible subsets of ROIs, we found that the standard deviation of the predicted MMSE was 1.8 by using only DVR values from medial and lateral temporal and prefrontal regions plus the early summed FDDNP value in the posterior cingulate gyrus. The root mean square prediction error for the eight new subjects was 1.6. CONCLUSION:FDDNP scans reflect progressive neuropathology accumulation and can potentially be used to predict the cognitive state of an individual.

Objectives: To assess quantitatively the cortical pattern profile of regional FDDNP binding to beta-amyloid and neurofibrillary tangles on MR derived cortical maps, FDDNP PET images were corrected for movement and partial volume (PV), and optimized for kernel size.Methods: FDDNP DVR PET images from 23 subjects (7 with Alzheimer's disease (AD), 6 with mild cognitive impairment and 10 controls) were obtained from Logan analysis using cerebellum as reference. A hemispheric cortical surface model for each subject was extracted from the MRI. The same transformations were applied to the FDDNP DVR PET images to map them into the same space. The cortical map with PV correction was calculated as the ratio of the DVR cortical surface and that of the simulated map, created from the mask derived from MRI and smoothed to the PET resolution. Discriminant analysis was used to order the FDDNP DVR cortical surfaces based on subjects' disease state. Linear regression was used to assess the rate of change of DVR vs. MMSE for each hemispheric cortical surface point.Results: The FDDNP DVR Cortical surface corrected for movement and PV had less hemispheric asymmetry. Optimal kernel size was determined to be 9 mm. The corrected cortical surface map of FDDNP DVR showed clear spatial pattern that was consistent with the known pathological progression of AD.Conclusion: Correcting for movement, PV as well as optimizing kernel size provide sensitive statistical analysis of FDDNP distribution which confirms in the living brain known pathology patterns earlier observed with cognitive decline with brain specimens. (C) 2009 Elsevier Inc. All rights reserved.

Despite converging evidence that major depressive illness is associated with both memory impairment and hippocampal pathology, findings vary widely across studies and it is not known whether these changes are regionally specific. In the present study we acquired brain MRIs (magnetic resonance images) from 31 unmedicated patients with MDD (major depressive disorder; mean age 39.2±11.9 years; 77% female) and 31 demographically comparable controls. Three-dimensional parametric mesh models were created to examine localized alterations of hippocampal morphology. Although global volumes did not differ between groups, statistical mapping results revealed that in MDD patients, more severe depressive symptoms were associated with greater left hippocampal atrophy, particularly in CA1 (cornu ammonis 1) subfields and the subiculum. However, previous treatment with atypical antipsychotics was associated with a trend towards larger left hippocampal volume. Our findings suggest effects of illness severity on hippocampal size, as well as a possible effect of past history of atypical antipsychotic treatment, which may reflect prolonged neuroprotective effects. This possibility awaits confirmation in longitudinal studies.

Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimer's disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1 years+/-7.7 SD). We warped each individual brain image (N=120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.

Progressive brain atrophy in HIV/AIDS is associated with impaired psychomotor performance, perhaps partly reflecting cerebellar degeneration; yet little is known about how HIV/AIDS affects the cerebellum. We visualized the three-dimensional profile of atrophy in 19 HIV-positive patients (age: 42.9±8.3 years) versus 15 healthy controls (age: 38.5±12.0 years). We localized consistent patterns of subregional atrophy with an image analysis method that automatically deforms each patient's scan, in three dimensions, to match a reference image. Atrophy was greatest in the posterior cerebellar vermis (14.9% deficit) and correlated with depression severity (P=0.009, corrected), but not with dementia, alcohol/substance abuse, CD4+T-cell counts, or viral load. Profound cerebellar deficits in HIV/AIDS (P=0.007, corrected) were associated with depression, suggesting a surrogate disease marker for antiretroviral trials.

Amyloid plaques and tau neurofibrillary tangles, the pathological hallmarks of Alzheimer's disease (AD), begin accumulating in the healthy human brain decades before clinical dementia symptoms can be detected. There is great interest in how this pathology spreads in the living brain and its association with cognitive deterioration. Using MRI-derived cortical surface models and four-dimensional animation techniques, we related cognitive ability to positron emission tomography (PET) signal from 2-(1-{6-[(2-[F-18]fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)malononitrile ([(18)F]FDDNP), a molecular imaging probe for plaques and tangles. We examined this relationship at each cortical surface point in 23 older adults (10 cognitively intact, 6 with amnestic mild cognitive impairment, 7 with AD). [(18)F]FDDNP-PET signal was highly correlated with cognitive performance, even in cognitively intact subjects. Animations of [(18)F]FDDNP signal growth with decreased cognition across all subjects (http://www.loni.ucla.edu/ approximately thompson/FDDNP/video.html) mirrored the classic Braak and Braak trajectory in lateral temporal, parietal, and frontal cortices. Regions in which cognitive performance was significantly correlated with [(18)F]FDDNP signal include those that deteriorate earliest in AD, suggesting the potential utility of [(18)F]FDDNP for early diagnosis.

Declarative memory impairments are common in patients with bipolar illness, suggesting underlying hippocampal pathology. However, hippocampal volume deficits are rarely observed in bipolar disorder. Here we used surface-based anatomic mapping to examine hippocampal anatomy in bipolar patients treated with lithium relative to matched control subjects and unmedicated patients with bipolar disorder. High-resolution brain magnetic resonance images were acquired from 33 patients with bipolar disorder (21 treated with lithium and 12 unmedicated), and 62 demographically matched healthy control subjects. Three-dimensional parametric mesh models were created from manual tracings of the hippocampal formation. Total hippocampal volume was significantly larger in lithium-treated bipolar patients compared with healthy controls (by 10.3%; p=0.001) and unmedicated bipolar patients (by 13.9%; p=0.003). Statistical mapping results, confirmed by permutation testing, revealed localized deficits in the right hippocampus, in regions corresponding primarily to cornu ammonis 1 subfields, in unmedicated bipolar patients, as compared to both normal controls (p=0.01), and in lithium-treated bipolar patients (p=0.03). These findings demonstrate the sensitivity of these anatomic mapping methods for detecting subtle alterations in hippocampal structure in bipolar disorder. The observed reduction in subregions of the hippocampus in unmedicated bipolar patients suggests a possible neural correlate for memory deficits frequently reported in this illness. Moreover, increased hippocampal volume in lithium-treated bipolar patients may reflect postulated neurotrophic effects of this agent, a possibility warranting further study in longitudinal investigations.

We developed an analysis pipeline enabling population studies of HARDI data, and applied it to map genetic influences on fiber architecture in 90 twin subjects. We applied tensor-driven 3D fluid registration to HARDI, resampling the spherical fiber orientation distribution functions (ODFs) in appropriate Riemannian manifolds, after ODF regularization and sharpening. Fitting structural equation models (SEM) from quantitative genetics, we evaluated genetic influences on the Jensen-Shannon divergence (JSD), a novel measure of fiber spatial coherence, and on the generalized fiber anisotropy (GFA; [1]) a measure of fiber integrity. With random-effects regression, we mapped regions where diffusion profiles were highly correlated with subjects' intelligence quotient (IQ). Fiber complexity was predominantly under genetic control, and higher in more highly anisotropic regions; the proportion of genetic versus environmental control varied spatially. Our methods show promise for discovering genes affecting fiber connectivity in the brain.

Using time-lapse maps, we visualized the dynamics of schizophrenia progression, revealing spreading cortical changes that depend on the type of antipsychotic treatment. Dynamic, 4-dimensional models of disease progression were created from 4 repeated high-resolution brain magnetic resonance imaging scans of 36 first-episode schizophrenia patients (30 men/6 women; mean age: 24.2 +/- 5.1 SD years) randomized to haloperidol (HAL) (n = 15) or olanzapine (OLZ) treatment (n = 21), imaged at baseline, 3, 6, and 12 months (144 scans). Based on surface-based cortical models and point-by-point measures of gray matter volume, we generated time-lapse maps for each treatment. Disease trajectories differed for atypical versus typical neuroleptic drugs. A rapidly advancing parietal-to-frontal deficit trajectory, in HAL-treated patients, mirrored normal cortical maturation but greatly intensified. The disease trajectory advanced even after symptom normalization, involving the frontal cortex within 12 months with typical drug treatment. Areas with fastest tissue loss shifted anteriorly in the first year of psychosis. This trajectory was not seen with OLZ. Whether this association reflects either reduced neurotoxicity or neuroprotection cannot be addressed with neuroimaging; changes may relate to glial rather than neural components. These maps revise current models of schizophrenia progression; due to power limitations, the findings require confirmation in a sample large enough to model group x time interactions.

We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpolation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color- coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.

We apply an information-theoretic cost metric, the symmetrized Kullback-Leibler (sKL) divergence, or J-divergence, to fluid registration of diffusion tensor images. The difference between diffusion tensors is quantified based on the sKL-divergence of their associated probability density functions (PDFs). Three-dimensional DTI data from 34 subjects were fluidly registered to an optimized target image. To allow large image deformations but preserve image topology, we regularized the flow with a large-deformation diffeomorphic mapping based on the kinematics of a Navier-Stokes fluid. A driving force was developed to minimize the J-divergence between the deforming source and target diffusion functions, while reorienting the flowing tensors to preserve fiber topography. In initial experiments, we showed that the sKL-divergence based on full diffusion PDFs is adaptable to higher-order diffusion models, such as high angular resolution diffusion imaging (HARDI). The sKL-divergence was sensitive to subtle differences between two diffusivity profiles, showing promise for nonlinear registration applications and multisubject statistical analysis of HARDI data.

Earlier studies revealed progressive cortical gray matter (GM) loss in childhood-onset schizophrenia (COS) across both lateral and medial surfaces of the developing brain. Here, we use tensor-based morphometry to visualize white matter (WM) growth abnormalities in COS throughout the brain. Using high-dimensional elastic image registration, we compared 3D maps of local WM growth rates in COS patients and healthy children over a 5-year period, based on analyzing longitudinal brain MRIs from 12 COS patients and 12 healthy controls matched for age, gender, and scan interval. COS patients showed up to 2.2% slower growth rates per year than healthy controls in WM ( P = 0.02, all P values corrected). The greatest differences were in the right hemisphere ( P = 0.006). This asymmetry was attributable to a right slower than left hemisphere growth rate mapped in COS patients ( P = 0.037) but not in healthy controls. WM growth rates reached 2.6% per year in healthy controls ( P = 0.0002). COS patients showed only a 1.3% per year trend for growth in the left hemisphere ( P = 0.066). In COS, WM growth rates were associated with improvement in the Children's Global Assessment Scale ( R = 0.64, P = 0.029). Growth rates were reduced throughout the brain in COS, but this process appeared to progress in a front-to-back (frontal-parietal) fashion, and this effect was not attributable to lower IQ. Growth rates were correlated with functional prognosis and were visualized as detailed 3D maps. Finally, these findings also confirm that the progressive GM deficits seen in schizophrenia are not the result of WM overgrowth.

We propose a new information-theoretic metric, the symmetric Kullback-Leibler divergence (sKL-divergence), to measure the difference between two water diffusivity profiles in high angular resolution diffusion imaging (HARDI). Water diffusivity profiles are modeled as probability density functions on the unit sphere, and the sKL-divergence is computed from a spherical harmonic series, which greatly reduces computational complexity. Adjustment of the orientation of diffusivity functions is essential when the image is being warped, so we propose a fast algorithm to determine the principal direction of diffusivity functions using principal component analysis (PCA). We compare sKL-divergence with other inner-product based cost functions using synthetic samples and real HARDI data, and show that the sKL-divergence is highly sensitive in detecting small differences between two diffusivity profiles and therefore shows promise for applications in the nonlinear registration and multisubject statistical analysis of HARDI data.

PET scans after intravenous injection of 2-(1-{6-{(2-[F-18]fluoroethyl)(methyl) amino}-2-naphthyl} ethylidene)malononitrile (FDDNP), a molecule that binds to amyloid senile plaques and neurofibrillary tangles, differentiate patients with AD, those with MCI, and healthy controls. Cortical surface maps of PET images provide a standardized space that allows easy inter-subject comparisons and three-dimensional visualization while retaining the quantitative aspect of PET and may be useful tools for monitoring AD prevention treatments. We used brain cortical surface maps to determine FDDNP binding values and their relationship to the mini-mental state examination (MMSE), a common measure of cognitive function. PET scans of 23 subjects (7 ADs, 6 MCIs, 10 controls) were used to generate FDDNP distribution volume (DVR) images, using Logan graphical plots with the cerebellum as reference region. Affine transformation was applied to the MRI to place the data into International Consortium of Brain Mapping (ICBM) space. A hemispheric cortical surface model for each subject was extracted from the MRI in ICBM space. The same transformations were applied to the DVR images to put them into the same cortical space. Regional FDDNP values were obtained directly on the cortical surface. For partial volume correction (PVC), a simulated PET was created from a mask based on the MR derived cortical surface. A cortical map with PVC also was calculated as the ratio of the DVR cortical surface and that of the simulated PET. Linear regression and Spearman correlations were used to determine relationships between MMSE scores and regional and global DVR measures. MMSE scores ranged from 19 to 30 (mean±SD=26.4±3.06) and correlated with FDDNP DVRs in cortical regions controlling cognitive function, including temporal, frontal, parietal, and posterior cingulate (F statistic, 4.32 with alpha of 0.05; figure shows significant regions of correlation in green and yellow), and these relationships increased after PVC. Global DVR correlated significantly with MMSE scores before and after PVC (rs=-0.5, p=0.01). Hemispheric cortical surface mapping of FDDNP binding values demonstrate correlations with cognitive scores in people at risk for dementia and those with AD, suggesting their utility as diagnostic and treatment monitoring tools in future AD prevention trials.

Measures of brain change can be computed from sequential MRI scans, providing valuable information on disease progression. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy. In this paper, we examine the reproducibility and stability of different techniques in TBM. In particular, we compare matching functionals (sum of squared differences and mutual information), and registration schemes (unbiased large-deformation registration and viscous fluid registration) using serial MRI scans of nine normal elderly subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results show that the unbiased large-deformation method has higher reproducibility. When coupled with unbiased registration, sum of squared differences outperforms mutual information. In contrast, when coupled with fluid registration, mutual information outperforms sum of squared difference. Moreover, the regions with least stability, due to both spatial distortion and intensity inhomogeneity, are the brain stem, thalamus, and ventricles.

Maps of local tissue compression or expansion are often computed by comparing magnetic resonance imaging (MRI) scans using nonlinear image registration. The resulting changes are commonly analyzed using tensor-based morphometry to make inferences about anatomical differences, often based on the Jacobian map, which estimates local tissue gain or loss. Here, we provide rigorous mathematical analyses of the Jacobian maps, and use themto motivate a new numerical method to construct unbiased nonlinear image registration. First, we argue that logarithmic transformation is crucial for analyzing Jacobian values representing morphometric differences. We then examine the statistical distributions of log-Jacobian maps by defining the Kullback-Leibler (KL) distance on material density functions arising in continuum-mechanical models. With this framework, unbiased image registration can be constructed by quantifying the symmetric KL-distance between the identity map and the resulting deformation. Implementation details, addressing the proposed unbiased registration as well as the minimization of symmetric image matching functionals, are then discussed and shown to be applicable to other registration methods, such as inverse consistent registration. In the results section, we test the proposed framework, as well as present an illustrative application mapping detailed 3-D brain changes in sequential magnetic resonance imaging scans of a patient diagnosed with semantic dementia. Using permutation tests, we show that the symmetrization of image registration statistically reduces skewness in the log-Jacobian map.

1749 Objectives: FDDNP PET has been shown to be able to distinguish patients with Alzheimer’s disease (AD) from normal subjects. In this project we use brain cortical surface map to examine the rate of change in FDDNP distribution volume (DVR) in the progression of AD. Cortical surface map of PET images provides a standardized space that allows easy inter-subject comparison and for 3D visualization while retaining the quantitative aspect of PET. In this study, we examined the FDDNP DVR changes versus a common behavior score, MMSE. Effect of partial volume on cortical maps of FDDNP was also examined. Methods: Images from 23 subjects (with 7 AD) were studied. Logan graphical plots were used with the cerebellum as reference region to generate the DVR image of FDDNP. Affine transformation was applied to the MRI to put it into ICBM space. A hemispheric cortical surface model for each subject was extracted from the MRI in ICBM space. The same transformations were applied to the DVR images to put them into the same cortical space. For partial volume correction (PVC), a simulated PET was created from a mask based on the MR derived cortical surface. This image was smoothed to the PET resolution and mapped to the cortical surface to generate a surface map of the simulated PET. The cortical map with PVC was calculated as the ratio of the DVR cortical surface and that of the simulated PET. Linear regression was used to assess the rate of change of DVR vs MMSE for each point on the hemispheric cortical surface. Results: Without PVC, DVR values on the lateral surface were significantly lower than 1.0 and were lower than on the medical surface. After PVC, DVR on the lateral surface was raised to values expected from original 3D DVR image; values on medial side were no longer higher than on the lateral surface. Rate of change vs MMSE was found to be about 0.02 in the frontal and temporal regions especially in the left brain hemisphere. Conclusions: Hemispheric cortical surface mapping is useful for assessing and visualizing the rate of change of DVR vs behavior scores. PVC is important to preserve the quantitative nature of the DVR values on the cortical surface map. Research Support (if any): DOE contract DE-FC03-87-ER60615

In the past decade, information theory has been studied extensively in medical imaging. In particular, maximization of mutual information has been shown to yield good results in multi-modal image registration. In this paper, we apply information theory to quantifying the magnitude of deformations. We examine the statistical distributions of Jacobian maps in the logarithmic space, and develop a new framework for constructing image registration methods. The proposed framework yields both theoretically and intuitively correct deformation maps, and is compatible with large-deformation models. In the results section, we tested the proposed method using a pair of serial MRI images. We compared our results to those computed using the viscous fluid registration method, and demonstrated that the proposed method is advantageous when recovering voxel-wise local tissue change