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Featured researches published by A.W. Toga.


IEEE Transactions on Medical Imaging | 2008

Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models

Zhuowen Tu; Katherine L. Narr; Piotr Dollár; Ivo D. Dinov; Paul M. Thompson; A.W. Toga

In this paper, a hybrid discriminative/generative model for brain anatomical structure segmentation is proposed. The learning aspect of the approach is emphasized. In the discriminative appearance models, various cues such as intensity and curvatures are combined to locally capture the complex appearances of different anatomical structures. A probabilistic boosting tree (PBT) framework is adopted to learn multiclass discriminative models that combine hundreds of features across different scales. On the generative model side, both global and local shape models are used to capture the shape information about each anatomical structure. The parameters to combine the discriminative appearance and generative shape models are also automatically learned. Thus, low-level and high-level information is learned and integrated in a hybrid model. Segmentations are obtained by minimizing an energy function associated with the proposed hybrid model. Finally, a grid-face structure is designed to explicitly represent the 3-D region topology. This representation handles an arbitrary number of regions and facilitates fast surface evolution. Our system was trained and tested on a set of 3-D magnetic resonance imaging (MRI) volumes and the results obtained are encouraging.


Molecular Psychiatry | 2009

Brain surface contraction mapped in first-episode schizophrenia: a longitudinal magnetic resonance imaging study

Daqiang Sun; Geoffrey W. Stuart; Mark Jenkinson; Stephen J. Wood; Patrick D. McGorry; Dennis Velakoulis; T G M van Erp; Paul M. Thompson; A.W. Toga; Deidre J. Smith; Tyrone D. Cannon; Christos Pantelis

Schizophrenia is associated with structural brain abnormalities, but the timing of onset and course of these changes remains unclear. Longitudinal magnetic resonance imaging (MRI) studies have demonstrated progressive brain volume decreases in patients around and after the onset of illness, although considerable discrepancies exist regarding which brain regions are affected. The anatomical pattern of these progressive changes in schizophrenia is largely unknown. In this study, MRI scans were acquired repeatedly from 16 schizophrenia patients approximately 2 years apart following their first episode of illness, and also from 14 age-matched healthy subjects. Cortical Pattern Matching, in combination with Structural Image Evaluation, using Normalisation, of Atrophy, was applied to compare the rates of cortical surface contraction between patients and controls. Surface contraction in the dorsal surfaces of the frontal lobe was significantly greater in patients with first-episode schizophrenia (FESZ) compared with healthy controls. Overall, brain surface contraction in patients and healthy controls showed similar anatomical patterns, with that of the former group exaggerated in magnitude across the entire brain surface. That the pattern of structural change in the early course of schizophrenia corresponds so closely to that associated with normal development is consistent with the hypothesis that a schizophrenia-related factor interacts with normal adolescent brain developmental processes in the pathophysiology of schizophrenia. The exaggerated progressive changes seen in patients with schizophrenia may reflect an increased rate of synaptic pruning, resulting in excessive loss of neuronal connectivity, as predicted by the late neurodevelopmental hypothesis of the illness.


Molecular Psychiatry | 2009

Abnormal temporal and prefrontal cortical gray matter thinning in psychopaths

Yaling Yang; Adrian Raine; Patrick M. Colletti; A.W. Toga; Katherine L. Narr

Pavlidis P, Smyrniotopoulos P et al. Biol Psychiatry 2005; 57: 549–558. 4 Galfalvy HC, Erraji-Benchekroun L, Smyrniotopoulos P, Pavlidis P, Ellis SP, Mann JJ et al. BMC Bioinformatics 2003; 4: 37. 5 Vawter MP, Evans S, Choudary P, Tomita H, Meador-Woodruff J, Molnar M et al. Neuropsychopharmacology 2004; 29: 373–384. 6 Hinoi E, Balcar VJ, Kuramoto N, Nakamichi N, Yoneda Y. Prog Neurobiol 2002; 68: 145–165.


Brain Mapping: The Disorders | 2000

6 – Disease-Specific Brain Atlases

Paul M. Thompson; Michael S. Mega; A.W. Toga

This chapter focuses on the disease specific brain atlas. Brain atlases have traditionally relied on static representations of anatomy. Many of the diseases that affect the human brain are progressive such as dementia and neoplastic tumors. Other disorders may be relapsing–remitting (e.g., multiple sclerosis) or may display a normal phenotype with an aberrant time course. The progression of a disease may also be modulated by therapy, ranging from drug treatment to surgery. This type of atlas is designed to reflect the unique anatomy and physiology of a particular clinical subpopulation. Disease-specific atlases are a type of probabilistic atlas specialized to represent a particular clinical group. The resulting atlases can identify patterns of altered structure or function and can guide algorithms for knowledge-based image analysis, automated image labeling, tissue classification, and functional image analysis. Atlas data on anatomic variability can also act as Bayesian prior information to guide algorithms for automated image registration and labeling. Rapid progress has been made in recent years by research groups developing standardized three-dimensional brain atlases. While few of these atlases aim to represent anatomy and function in disease, several commercially available atlases of pathology combine histologic data with illustrative metabolic or structural images.


Brain Mapping: The Disorders | 2000

9 – Brain Mapping in Dementia

Michael S. Mega; Paul M. Thompson; A.W. Toga; Jeffrey L. Cummings

This chapter discusses the application of brain mapping techniques in elderly and demented populations. The application of brain mapping techniques in dementia is challenged by the disease-related anatomic changes superimposed over the normal morphological variability of the human brain. Utilizing brain mapping techniques in combination with a profile of cognitive performance and risk factors is currently the most powerful predictor of incipient Alzheimers disease (AD) in elderly individuals with mild cognitive impairment (MCI). In the absence of a biochemical marker, functional and structural neuroimaging is now the best biological marker for AD. The current challenge in applying brain mapping techniques to hippocampal volumetry is to detect subtle early changes in persons with preclinical AD using a standardized technique that controls for normal anatomic variability and allows easy application across centers. The use of a common brain space for hippocampal analysis fulfills the brain mapping objective of standardization in methodology and ease of application across centers. A further objective in brain mapping is the development of automated techniques. The future automation and dissemination of current brain mapping techniques promise preclinical detection of AD-specific structural and functional abnormalities on an individual basis.


international symposium on biomedical imaging | 2004

Genetic influences on human brain morphology

T G M van Erp; Tyrone D. Cannon; Tran Hl; Wobbekind A; Matti O. Huttunen; Jan-Erik Lönnqvist; Jaakko Kaprio; Oili Salonen; Leena Valanne; V.P. Poutanen; Carl-Gustav Standertskjöld-Nordenstam; A.W. Toga; Paul M. Thompson

Structural magnetic resonance imaging (MRI) studies have started Io address the contributions of genetic and environmental factors to brain morphology in healthy individuals. These studies have largely been limited to assessing heritability for single and mostly gross anatomical structures. To avoid this limitation, we developed a method using high-dimensional 3D nonlinear registration to apply an elastic deformation vector field in the cortical parameter space and assess regional cortical gray matter density on the entire cortical surface in healthy monozygotic (MZ) and dizygotic (DZ) twins. Once aligned across subjects, surface maps of gray matter density were subjected to variance component analyses. By fitting structural equation models, we generated cortical surface maps to reflect the regional percent variance explained by genetic, shared and unique environmental factors. Initial analyses suggest that cortical structure in primary motor and sensory areas is highly heritable with variance components for additive genetic influences reaching as high as 70%.


NeuroImage | 2009

Brain Surface Conformal Parameterization with Holomorphic Flow Method and Its Application to HIV/AIDS

Yalin Wang; Jingwen Zhang; Tony F. Chan; A.W. Toga; Paul M. Thompson

Methods: The introduced conformal parameterization method can work on the high genus and branching surfaces. By finding the canonical holomorphic 1-form, ventricle surfaces are parameterized by their intrinsic geometry structure thus they can seperated into the consisent parts. We can then canonically partition surface into patches and compute its conformal grids. The parameterization results are consistent and the subdivided surfaces can be matched to each other. This algorithm can match complex (possibly branching) topology surfaces.


NeuroImage | 2009

Can tissue segmentation improve registration? A study of 92 twins

Yi-Yu Chou; Natasha Lepore; Caroline C. Brun; Marina Barysheva; Katie L. McMahon; G. I. de Zubicaray; Margie Wright; A.W. Toga; Paul M. Thompson

Introduction: Robust and automatic non-rigid registration depends on many parameters that have not yet been systematically explored. Here we determined how tissue classification influences non-linear fluid registration of brain MRI. Twin data is ideal for studying this question, as volumetric correlations between corresponding brain regions that are under genetic control should be higher in monozygotic twins (MZ) who share 100% of their genes when compared to dizygotic twins (DZ) who share half their genes on average. When these substructure volumes are quantified using tensor-based morphometry, improved registration can be defined based on which method gives higher MZ twin correlations when compared to DZs, as registration errors deplete these correlations.


NeuroImage | 2009

Genetic Influences on White Matter Architecture in Twins: A Diffusion Tensor Tractography Study

Nathan S. Hageman; Paul M. Thompson; David W. Shattuck; Christina Avedissian; Marina Barysheva; Katie L. McMahon; Gi deZubicaray; Margie Wright; A.W. Toga

Introduction: Advances in brain imaging have provided investigators with new ways to probe the white matter architecture of the brain through diffusion tensor imaging (DTI). Prior studies have shown that white matter is under tight genetic control during embryonic development [1]. Neuroimaging studies have shown that white matter brain volume and fiber architecture are strongly heritable and are linked to IQ through common genes [2‐ 5]. Using imaging measures of fiber characteristics and genetic data from identical and fraternal twins, we fitted quantitative genetic models to fiber tracts to determine where common genes underlie fiber integrity and the expression of IQ. Methods: 23 monozygotic and dizygotic young adult twin pairs (92 subjects) received DTI scans, genotyping, and neurocognitive evaluations. DTI data were acquired using an optimized diffusion sequence with 94 non‐collinear gradient directions. After diffusion tensor reconstruction, mean diffusivity, lattice index, and fractional anisotropy were computed for each voxel. Segmentation of major white matter tracts, including the corpus callosum, cingulum, and corticospinal tracts, was performed using a fluid mechanics based tractography method [6]. We assessed fiber integrity by calculating the average DTI scalar metric along the segmented fiber tracts. Using the ACE model for genetic analysis, we fitted a structural equation model to the twin covariances for each tracts scalar measures with the subjects IQ data to estimate the relative contributions of genetic and environmental factors to variance in fiber integrity measures and intelligence scores. Results: The ACE model showed a significant additive genetic component (p < .05; Figure 1) for mean diffusivity (MD), which suggests that fiber integrity of the corpus callosum is genetically influenced. By contrast, we did not detect genetic influences on the lattice index (LI), a measure of spatial coherence of a fiber tract. We used a bivariate form of the ACE genetic model (cross‐trait design) to reveal that common genes mediate CC mean diffusivity and IQ, extending prior findings that corpus callosum fiber integrity (MD) is heritable. Mean diffusivity was correlated (p < .05; Figure 2) with all forms of IQ tested ‐ full‐scale, performance and verbal IQ (FIQ/PIQ/VIQ) ‐, which suggests that the structural heritability of the corpus callosum has a genetic correlation to the expression of intelligence. Initial results for the other fiber tracts show similar genetic trends. Conclusions: Using diffusion tensor tractography, we identified genetic factors that affect brain fiber architecture and its link to intelligence. Based on diffusion imaging scans of …


Journal of Mathematical Imaging and Vision | 2016

A Unified Variational Volume Registration Method Based on Automatically Learned Brain Structures

Carl Lederman; Anand A. Joshi; Ivo D. Dinov; John Darrell Van Horn; Luminita A. Vese; A.W. Toga

We introduce a new volumetric registration technique that effectively combines active surfaces with the finite element method. The method simultaneously aligns multi-label automatic structural segmentation results, which can be obtained by the application of existing segmentation software, to produce an anatomically accurate 3D registration. This registration is obtained by the minimization of a single energy functional. Just like registering raw images, obtaining a 3D registration this way still requires solving a fundamentally ill-posed problem. We explain through academic examples as well as an MRI dataset with manual anatomical labels, which are hidden from the registration method, how the quality of a registration method can be measured and the advantages our approach offers.

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Paul M. Thompson

University of Southern California

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Yi-Yu Chou

University of California

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Natasha Lepore

Children's Hospital Los Angeles

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Agatha D. Lee

University of California

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Paul Thompson

University of California

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