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Landmark constrained genus-one surface Teichmüller map applied to surface registration in medical imaging. Med Image Anal 2015; 25:45-55. [PMID: 25977154 DOI: 10.1016/j.media.2015.04.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2014] [Revised: 04/07/2015] [Accepted: 04/09/2015] [Indexed: 11/22/2022]
Abstract
We address the registration problem of genus-one surfaces (such as vertebrae bones) with prescribed landmark constraints. The high-genus topology of the surfaces makes it challenging to obtain a unique and bijective surface mapping that matches landmarks consistently. This work proposes to tackle this registration problem using a special class of quasi-conformal maps called Teichmüller maps (T-Maps). A landmark constrained T-Map is the unique mapping between genus-1 surfaces that minimizes the maximal conformality distortion while matching the prescribed feature landmarks. Existence and uniqueness of the landmark constrained T-Map are theoretically guaranteed. This work presents an iterative algorithm to compute the T-Map. The main idea is to represent the set of diffeomorphism using the Beltrami coefficients (BC). The BC is iteratively adjusted to an optimal one, which corresponds to our desired T-Map that matches the prescribed landmarks and satisfies the periodic boundary condition on the universal covering space. Numerical experiments demonstrate the effectiveness of our proposed algorithm. The method has also been applied to register vertebrae bones with prescribed landmark points and curves, which gives accurate surface registrations.
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El Mendili MM, Chen R, Tiret B, Villard N, Trunet S, Pélégrini-Issac M, Lehéricy S, Pradat PF, Benali H. Fast and accurate semi-automated segmentation method of spinal cord MR images at 3T applied to the construction of a cervical spinal cord template. PLoS One 2015; 10:e0122224. [PMID: 25816143 PMCID: PMC4376938 DOI: 10.1371/journal.pone.0122224] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2014] [Accepted: 02/19/2015] [Indexed: 12/11/2022] Open
Abstract
Objective To design a fast and accurate semi-automated segmentation method for spinal cord 3T MR images and to construct a template of the cervical spinal cord. Materials and Methods A semi-automated double threshold-based method (DTbM) was proposed enabling both cross-sectional and volumetric measures from 3D T2-weighted turbo spin echo MR scans of the spinal cord at 3T. Eighty-two healthy subjects, 10 patients with amyotrophic lateral sclerosis, 10 with spinal muscular atrophy and 10 with spinal cord injuries were studied. DTbM was compared with active surface method (ASM), threshold-based method (TbM) and manual outlining (ground truth). Accuracy of segmentations was scored visually by a radiologist in cervical and thoracic cord regions. Accuracy was also quantified at the cervical and thoracic levels as well as at C2 vertebral level. To construct a cervical template from healthy subjects’ images (n=59), a standardization pipeline was designed leading to well-centered straight spinal cord images and accurate probability tissue map. Results Visual scoring showed better performance for DTbM than for ASM. Mean Dice similarity coefficient (DSC) was 95.71% for DTbM and 90.78% for ASM at the cervical level and 94.27% for DTbM and 89.93% for ASM at the thoracic level. Finally, at C2 vertebral level, mean DSC was 97.98% for DTbM compared with 98.02% for TbM and 96.76% for ASM. DTbM showed similar accuracy compared with TbM, but with the advantage of limited manual interaction. Conclusion A semi-automated segmentation method with limited manual intervention was introduced and validated on 3T images, enabling the construction of a cervical spinal cord template.
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Affiliation(s)
- Mohamed-Mounir El Mendili
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, F-75013, Paris, Île de France, France
- * E-mail:
| | - Raphaël Chen
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, F-75013, Paris, Île de France, France
| | - Brice Tiret
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, F-75013, Paris, Île de France, France
| | - Noémie Villard
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, F-75013, Paris, Île de France, France
| | - Stéphanie Trunet
- APHP, Groupe Hospitalier Pitié-Salpêtrière, Service de Neuroradiologie, F-75013, Paris, Île de France, France
| | - Mélanie Pélégrini-Issac
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, F-75013, Paris, Île de France, France
| | - Stéphane Lehéricy
- APHP, Groupe Hospitalier Pitié-Salpêtrière, Service de Neuroradiologie, F-75013, Paris, Île de France, France
- Sorbonne Universités, UPMC Univ Paris 06, UMR-S975, Inserm U975, CNRS UMR7225, Centre de recherche de l’Institut du Cerveau et de la Moelle épinière—CRICM, Centre de Neuroimagerie de Recherche—CENIR, F-75013, Paris, Île de France, France
| | - Pierre-François Pradat
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, F-75013, Paris, Île de France, France
- APHP, Groupe Hospitalier Pitié-Salpêtrière, Département des Maladies du Système Nerveux, F-75013, Paris, Île de France, France
| | - Habib Benali
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale, F-75013, Paris, Île de France, France
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Shi J, Stonnington CM, Thompson PM, Chen K, Gutman B, Reschke C, Baxter LC, Reiman EM, Caselli RJ, Wang Y. Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry. Neuroimage 2015; 104:1-20. [PMID: 25285374 PMCID: PMC4252650 DOI: 10.1016/j.neuroimage.2014.09.062] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2014] [Revised: 09/20/2014] [Accepted: 09/29/2014] [Indexed: 11/29/2022] Open
Abstract
Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | | | - Paul M Thompson
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - Boris Gutman
- Imaging Genetics Center, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA
| | - Cole Reschke
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - Leslie C Baxter
- Human Brain Imaging Laboratory, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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Lao Y, Wang Y, Shi J, Ceschin R, Nelson MD, Panigrahy A, Leporé N. Thalamic alterations in preterm neonates and their relation to ventral striatum disturbances revealed by a combined shape and pose analysis. Brain Struct Funct 2014; 221:487-506. [PMID: 25366970 DOI: 10.1007/s00429-014-0921-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2014] [Accepted: 10/15/2014] [Indexed: 10/24/2022]
Abstract
Finding the neuroanatomical correlates of prematurity is vital to understanding which structures are affected, and to designing efficient prevention and treatment strategies. Converging results reveal that thalamic abnormalities are important indicators of prematurity. However, little is known about the localization of the abnormalities within the subnuclei of the thalamus, or on the association of altered thalamic development with other deep gray matter disturbances. Here, we aim to investigate the effect of prematurity on the thalamus and the putamen in the neonatal brain, and further investigate the associated abnormalities between these two structures. Using brain structural magnetic resonance imaging, we perform a novel combined shape and pose analysis of the thalamus and putamen between 17 preterm (41.12 ± 5.08 weeks) and 19 term-born (45.51 ± 5.40 weeks) neonates at term equivalent age. We also perform a set of correlation analyses between the thalamus and the putamen, based on the surface and pose results. We locate significant alterations on specific surface regions such as the anterior and ventral anterior (VA) thalamic nuclei, and significant relative pose changes of the left thalamus and the right putamen. In addition, we detect significant association between the thalamus and the putamen for both surface and pose parameters. The regions that are significantly associated include the VA, and the anterior and inferior putamen. We detect statistically significant surface deformations and pose changes on the thalamus and putamen, and for the first time, demonstrate the feasibility of using relative pose parameters as indicators for prematurity in neonates. Our methods show that regional abnormalities of the thalamus are associated with alterations of the putamen, possibly due to disturbed development of shared pre-frontal connectivity. More specifically, the significantly correlated regions in these two structures point to frontal-subcortical pathways including the dorsolateral prefrontal-subcortical circuit, the lateral orbitofrontal-subcortical circuit, the motor circuit, and the oculomotor circuit. These findings reveal new insight into potential subcortical structural covariates for poor neurodevelopmental outcomes in the preterm population.
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Affiliation(s)
- Yi Lao
- Department of Radiology, University of Southern California and Children's Hospital, 4650 Sunset Blvd, MS#81, Los Angeles, CA, 90027, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA
| | - Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, 85281, USA
| | - Rafael Ceschin
- Department of Radiology, Children's Hospital of Pittsburgh UPMC, Pittsburgh, PA, USA
| | - Marvin D Nelson
- Department of Radiology, University of Southern California and Children's Hospital, 4650 Sunset Blvd, MS#81, Los Angeles, CA, 90027, USA
| | - Ashok Panigrahy
- Department of Radiology, University of Southern California and Children's Hospital, 4650 Sunset Blvd, MS#81, Los Angeles, CA, 90027, USA.,Department of Radiology, Children's Hospital of Pittsburgh UPMC, Pittsburgh, PA, USA
| | - Natasha Leporé
- Department of Radiology, University of Southern California and Children's Hospital, 4650 Sunset Blvd, MS#81, Los Angeles, CA, 90027, USA.
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Shi J, Leporé N, Gutman BA, Thompson PM, Baxter LC, Caselli RJ, Wang Y. Genetic influence of apolipoprotein E4 genotype on hippocampal morphometry: An N = 725 surface-based Alzheimer's disease neuroimaging initiative study. Hum Brain Mapp 2014; 35:3903-18. [PMID: 24453132 PMCID: PMC4269525 DOI: 10.1002/hbm.22447] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2013] [Revised: 11/23/2013] [Accepted: 11/26/2013] [Indexed: 01/12/2023] Open
Abstract
The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database-the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High-order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T(2) test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State UniversityTempeArizona
| | - Natasha Leporé
- Department of RadiologyChildren's Hospital Los AngelesLos AngelesCalifornia
| | - Boris A. Gutman
- Imaging Genetics CenterInstitute for Neuroimaging and InformaticsUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Paul M. Thompson
- Department of NeurologyImaging Genetics CenterLaboratory of Neuro ImagingUCLA School of MedicineLos AngelesCalifornia
- Department of Psychiatry and Biobehavioral SciencesSemel Institute, UCLA School of MedicineLos AngelesCalifornia
| | - Leslie C. Baxter
- Human Brain Imaging Laboratory, Barrow Neurological InstitutePhoenixArizona
| | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State UniversityTempeArizona
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Nir TM, Jahanshad N, Busovaca E, Wendelken L, Nicolas K, Thompson PM, Valcour VG. Mapping white matter integrity in elderly people with HIV. Hum Brain Mapp 2014; 35:975-92. [PMID: 23362139 PMCID: PMC3775847 DOI: 10.1002/hbm.22228] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2012] [Revised: 11/02/2012] [Accepted: 11/05/2012] [Indexed: 01/23/2023] Open
Abstract
People with HIV are living longer as combination antiretroviral therapy (cART) becomes more widely available. However, even when plasma viral load is reduced to untraceable levels, chronic HIV infection is associated with neurological deficits and brain atrophy beyond that of normal aging. HIV is often marked by cortical and subcortical atrophy, but the integrity of the brain's white matter (WM) pathways also progressively declines. Few studies focus on older cohorts where normal aging may be compounded with HIV infection to influence deficit patterns. In this relatively large diffusion tensor imaging (DTI) study, we investigated abnormalities in WM fiber integrity in 56 HIV+ adults with access to cART (mean age: 63.9 ± 3.7 years), compared to 31 matched healthy controls (65.4 ± 2.2 years). Statistical 3D maps revealed the independent effects of HIV diagnosis and age on fractional anisotropy (FA) and diffusivity, but we did not find any evidence for an age by diagnosis interaction in our current sample. Compared to healthy controls, HIV patients showed pervasive FA decreases and diffusivity increases throughout WM. We also assessed neuropsychological (NP) summary z-score associations. In both patients and controls, fiber integrity measures were associated with NP summary scores. The greatest differences were detected in the corpus callosum and in the projection fibers of the corona radiata. These deficits are consistent with published NP deficits and cortical atrophy patterns in elderly people with HIV.
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Affiliation(s)
- Talia M Nir
- Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, California
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57
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Shi J, Thompson PM, Gutman B, Wang Y. Surface fluid registration of conformal representation: application to detect disease burden and genetic influence on hippocampus. Neuroimage 2013; 78:111-34. [PMID: 23587689 PMCID: PMC3683848 DOI: 10.1016/j.neuroimage.2013.04.018] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2012] [Revised: 03/06/2013] [Accepted: 04/05/2013] [Indexed: 11/23/2022] Open
Abstract
In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometry (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E[element of]4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Paul M. Thompson
- Laboratory of Neuro Imaging, UCLA Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Boris Gutman
- Laboratory of Neuro Imaging, UCLA Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
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Hua X, Boyle CP, Harezlak J, Tate DF, Yiannoutsos CT, Cohen R, Schifitto G, Gongvatana A, Zhong J, Zhu T, Taylor MJ, Campbell TB, Daar ES, Alger JR, Singer E, Buchthal S, Toga AW, Navia B, Thompson PM. Disrupted cerebral metabolite levels and lower nadir CD4 + counts are linked to brain volume deficits in 210 HIV-infected patients on stable treatment. NEUROIMAGE-CLINICAL 2013; 3:132-42. [PMID: 24179857 PMCID: PMC3791291 DOI: 10.1016/j.nicl.2013.07.009] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2013] [Revised: 07/03/2013] [Accepted: 07/25/2013] [Indexed: 12/18/2022]
Abstract
Cognitive impairment and brain injury are common in people with HIV/AIDS, even when viral replication is effectively suppressed with combined antiretroviral therapies (cART). Metabolic and structural abnormalities may promote cognitive decline, but we know little about how these measures relate in people on stable cART. Here we used tensor-based morphometry (TBM) to reveal the 3D profile of regional brain volume variations in 210 HIV + patients scanned with whole-brain MRI at 1.5 T (mean age: 48.6 ± 8.4 years; all receiving cART). We identified brain regions where the degree of atrophy was related to HIV clinical measures and cerebral metabolite levels assessed with magnetic resonance spectroscopy (MRS). Regional brain volume reduction was linked to lower nadir CD4 + count, with a 1–2% white matter volume reduction for each 25-point reduction in nadir CD4 +. Even so, brain volume measured by TBM showed no detectable association with current CD4 + count, AIDS Dementia Complex (ADC) stage, HIV RNA load in plasma or cerebrospinal fluid (CSF), duration of HIV infection, antiretroviral CNS penetration-effectiveness (CPE) scores, or years on cART, after controlling for demographic factors, and for multiple comparisons. Elevated glutamate and glutamine (Glx) and lower N-acetylaspartate (NAA) in the frontal white matter, basal ganglia, and mid frontal cortex — were associated with lower white matter, putamen and thalamus volumes, and ventricular and CSF space expansion. Reductions in brain volumes in the setting of chronic and stable disease are strongly linked to a history of immunosuppression, suggesting that delays in initiating cART may result in imminent and irreversible brain damage. We mapped the 3D pattern of brain abnormalities in 210 HIV patients on stable cART. Brain atrophy was linked to MRS metabolite disturbances reflecting neuronal injury. Lower nadir CD4 + count was associated with greater white matter atrophy.
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Affiliation(s)
- Xue Hua
- Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA
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Shi J, Wang Y, Ceschin R, An X, Lao Y, Vanderbilt D, Nelson MD, Thompson PM, Panigrahy A, Leporé N. A multivariate surface-based analysis of the putamen in premature newborns: regional differences within the ventral striatum. PLoS One 2013; 8:e66736. [PMID: 23843961 PMCID: PMC3700976 DOI: 10.1371/journal.pone.0066736] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2013] [Accepted: 05/09/2013] [Indexed: 11/20/2022] Open
Abstract
Many children born preterm exhibit frontal executive dysfunction, behavioral problems including attentional deficit/hyperactivity disorder and attention related learning disabilities. Anomalies in regional specificity of cortico-striato-thalamo-cortical circuits may underlie deficits in these disorders. Nonspecific volumetric deficits of striatal structures have been documented in these subjects, but little is known about surface deformation in these structures. For the first time, here we found regional surface morphological differences in the preterm neonatal ventral striatum. We performed regional group comparisons of the surface anatomy of the striatum (putamen and globus pallidus) between 17 preterm and 19 term-born neonates at term-equivalent age. We reconstructed striatal surfaces from manually segmented brain magnetic resonance images and analyzed them using our in-house conformal mapping program. All surfaces were registered to a template with a new surface fluid registration method. Vertex-based statistical comparisons between the two groups were performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We found statistically significant differences in regional morphology between the two groups that are consistent across statistics, but more extensive for multivariate measures. Differences were localized to the ventral aspect of the striatum. In particular, we found abnormalities in the preterm anterior/inferior putamen, which is interconnected with the medial orbital/prefrontal cortex and the midline thalamic nuclei including the medial dorsal nucleus and pulvinar. These findings support the hypothesis that the ventral striatum is vulnerable, within the cortico-stiato-thalamo-cortical neural circuitry, which may underlie the risk for long-term development of frontal executive dysfunction, attention deficit hyperactivity disorder and attention-related learning disabilities in preterm neonates.
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Affiliation(s)
- Jie Shi
- School of Computing, Informatics, Decision Systems and Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Yalin Wang
- School of Computing, Informatics, Decision Systems and Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Rafael Ceschin
- Department of Radiology, Children’s Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America
| | - Xing An
- School of Computing, Informatics, Decision Systems and Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Yi Lao
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
| | - Douglas Vanderbilt
- Department of Pediatrics, University of Southern California, Los Angeles, California, United States of America
- Developmental-Behavioral Pediatrics Fellowship Program, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
| | - Marvin D. Nelson
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
- Department of Radiology, University of Southern California, Los Angeles, California, United States of America
| | - Paul M. Thompson
- Imaging Genetics Center, Laboratory of Neuro Imaging, University of California Los Angeles School of Medicine, Los Angeles, California, United States of America
| | - Ashok Panigrahy
- Department of Radiology, Children’s Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, United States of America
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
| | - Natasha Leporé
- Department of Radiology, Children’s Hospital Los Angeles, Los Angeles, California, United States of America
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
- Department of Radiology, University of Southern California, Los Angeles, California, United States of America
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Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis. Neuroimage 2013; 74:209-30. [PMID: 23435208 DOI: 10.1016/j.neuroimage.2013.02.011] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2012] [Revised: 01/18/2013] [Accepted: 02/09/2013] [Indexed: 11/23/2022] Open
Abstract
Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification.
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Liu M, Vemuri BC, Deriche R. A robust variational approach for simultaneous smoothing and estimation of DTI. Neuroimage 2013; 67:33-41. [PMID: 23165324 DOI: 10.1016/j.neuroimage.2012.11.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Revised: 09/11/2012] [Accepted: 11/07/2012] [Indexed: 10/27/2022] Open
Abstract
Estimating diffusion tensors is an essential step in many applications - such as diffusion tensor image (DTI) registration, segmentation and fiber tractography. Most of the methods proposed in the literature for this task are not simultaneously statistically robust and feature preserving techniques. In this paper, we propose a novel and robust variational framework for simultaneous smoothing and estimation of diffusion tensors from diffusion MRI. Our variational principle makes use of a recently introduced total Kullback-Leibler (tKL) divergence for DTI regularization. tKL is a statistically robust dissimilarity measure for diffusion tensors, and regularization by using tKL ensures the symmetric positive definiteness of tensors automatically. Further, the regularization is weighted by a non-local factor adapted from the conventional non-local means filters. Finally, for the data fidelity, we use the nonlinear least-squares term derived from the Stejskal-Tanner model. We present experimental results depicting the positive performance of our method in comparison to competing methods on synthetic and real data examples.
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Affiliation(s)
- Meizhu Liu
- Siemens Corporate Research & Technology, Princeton, NJ, 08540, USA.
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62
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Shi J, Wang Y, Ceschin R, An X, Nelson MD, Panigrahy A, Leporé N. Surface fluid registration and multivariate tensor-based morphometry in newborns - the effects of prematurity on the putamen. SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE (APSIPA), ... ASIA-PACIFIC. ASIA-PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE 2012; 2012:https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6411997. [PMID: 29938710 PMCID: PMC6014739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Many disorders that affect the brain can cause shape changes in subcortical structures, and these may provide biomarkers for disease detection and progression. Automatic tools are needed to accurately identify and characterize these alterations. In recent work, we developed a surface multivariate tensor-based morphometry analysis (mTBM) to detect morphological group differences in subcortical structures, and we applied this method to study HIV/AIDS, William's syndrome, Alzheimer's disease and prematurity. Here we will focus more specifically on mTBM in neonates, which, in its current form, starts with manually segmented subcortical structures from MRI images of a two subject groups, places a conformal grid on each of their surfaces, registers them to a template through a constrained harmonic map and provides statistical comparisons between the two groups, at each vertex of the template grid. We improve this pipeline in two ways: first by replacing the constrained harmonic map with a new fluid registration algorithm that we recently developed. Secondly, by optimizing the pipeline to study the putamen in newborns. Our analysis is applied to the comparison of the putamen in premature and term born neonates. Recent whole-brain volumetric studies have detected differences in this structure in babies born preterm. Here we add to the literature on this topic by zooming in on this structure, and by generating the first surface-based maps of these changes. To do so, we use a dataset of manually segmented putamens from T1-weighted brain MR images from 17 preterm and 18 term-born neonates. Statistical comparisons between the two groups are performed via four methods: univariate and multivariate tensor-based morphometry, the commonly used medial axis distance, and a combination of the last two statistics. We detect widespread statistically significant differences in morphology between the two groups that are consistent across statistics, but more extensive for multivariate measures.
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Affiliation(s)
- Jie Shi
- Computer Science and Engineering, Arizona State University Tempe, AZ, USA
| | - Yalin Wang
- Computer Science and Engineering, Arizona State University Tempe, AZ, USA
| | - Rafael Ceschin
- Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
| | - Xing An
- Computer Science and Engineering, Arizona State University Tempe, AZ, USA
| | - Marvin D Nelson
- Radiology, University of Southern California and Childrens Hospital, Los Angeles, CA, USA
| | - Ashok Panigrahy
- Radiology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
| | - Natasha Leporé
- Radiology, University of Southern California and Childrens Hospital, Los Angeles, CA, USA
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63
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Spudich S, González-Scarano F. HIV-1-related central nervous system disease: current issues in pathogenesis, diagnosis, and treatment. Cold Spring Harb Perspect Med 2012; 2:a007120. [PMID: 22675662 PMCID: PMC3367536 DOI: 10.1101/cshperspect.a007120] [Citation(s) in RCA: 144] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
HIV-associated central nervous system (CNS) injury continues to be clinically significant in the modern era of HIV infection and therapy. A substantial proportion of patients with suppressed HIV infection on optimal antiretroviral therapy have impaired performance on neuropsychological testing, suggesting persistence of neurological abnormalities despite treatment and projected long-term survival. In the underresourced setting, limited accessibility to antiretroviral medications means that CNS complications of later-stage HIV infection continue to be a major concern. This article reviews key recent advances in our understanding of the neuropathogenesis of HIV, focusing on basic and clinical studies that reveal viral and host features associated with viral neuroinvasion, persistence, and immunopathogenesis in the CNS, as well as issues related to monitoring and treatment of HIV-associated CNS injury in the current era.
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Affiliation(s)
- Serena Spudich
- Department of Neurology, Yale University School of Medicine, New Haven, Connecticut, USA
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64
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Wang Y, Gu X, Chan TF, Thompson PM, Yau ST. Brain surface conformal parameterization with the Ricci flow. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:251-64. [PMID: 21926017 PMCID: PMC3571860 DOI: 10.1109/tmi.2011.2168233] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
In brain mapping research, parameterized 3-D surface models are of great interest for statistical comparisons of anatomy, surface-based registration, and signal processing. Here, we introduce the theories of continuous and discrete surface Ricci flow, which can create Riemannian metrics on surfaces with arbitrary topologies with user-defined Gaussian curvatures. The resulting conformal parameterizations have no singularities and they are intrinsic and stable. First, we convert a cortical surface model into a multiple boundary surface by cutting along selected anatomical landmark curves. Secondly, we conformally parameterize each cortical surface to a parameter domain with a user-designed Gaussian curvature arrangement. In the parameter domain, a shape index based on conformal invariants is computed, and inter-subject cortical surface matching is performed by solving a constrained harmonic map. We illustrate various target curvature arrangements and demonstrate the stability of the method using longitudinal data. To map statistical differences in cortical morphometry, we studied brain asymmetry in 14 healthy control subjects. We used a manifold version of Hotelling's T(2) test, applied to the Jacobian matrices of the surface parameterizations. A permutation test, along with the cumulative distribution of p-values, were used to estimate the overall statistical significance of differences. The results show our algorithm's power to detect subtle group differences in cortical surfaces.
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Affiliation(s)
- Yalin Wang
- Mathematics Department, UCLA
- Lab. of Neuro Imaging and Brain Research Institute, UCLA School of Medicine
| | - Xianfeng Gu
- Computer Science Department, Stony Brook University
| | | | - Paul M. Thompson
- Lab. of Neuro Imaging and Brain Research Institute, UCLA School of Medicine
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65
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Abstract
Neurological imaging represents a powerful paradigm for investigation of brain structure, physiology and function across different scales. The diverse phenotypes and significant normal and pathological brain variability demand reliable and efficient statistical methodologies to model, analyze and interpret raw neurological images and derived geometric information from these images. The validity, reproducibility and power of any statistical brain map require appropriate inference on large cohorts, significant community validation, and multidisciplinary collaborations between physicians, engineers and statisticians.
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Affiliation(s)
- Ivo D Dinov
- SOCR Resource and Laboratory of Neuro Imaging, UCLA Statistics, 8125 Mathematical Science Bldg, Los Angeles, CA 90095, USA, Tel.: +1 310 825 8430
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Yotter RA, Dahnke R, Thompson PM, Gaser C. Topological correction of brain surface meshes using spherical harmonics. Hum Brain Mapp 2011; 32:1109-24. [PMID: 20665722 PMCID: PMC6869946 DOI: 10.1002/hbm.21095] [Citation(s) in RCA: 133] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2009] [Revised: 03/23/2010] [Accepted: 04/19/2010] [Indexed: 11/06/2022] Open
Abstract
Surface reconstruction methods allow advanced analysis of structural and functional brain data beyond what can be achieved using volumetric images alone. Automated generation of cortical surface meshes from 3D brain MRI often leads to topological defects and geometrical artifacts that must be corrected to permit subsequent analysis. Here, we propose a novel method to repair topological defects using a surface reconstruction that relies on spherical harmonics. First, during reparameterization of the surface using a tiled platonic solid, the original MRI intensity values are used as a basis to select either a "fill" or "cut" operation for each topological defect. We modify the spherical map of the uncorrected brain surface mesh, such that certain triangles are favored while searching for the bounding triangle during reparameterization. Then, a low-pass filtered alternative reconstruction based on spherical harmonics is patched into the reconstructed surface in areas that previously contained defects. Self-intersections are repaired using a local smoothing algorithm that limits the number of affected points to less than 0.1% of the total, and as a last step, all modified points are adjusted based on the T1 intensity. We found that the corrected reconstructions have reduced distance error metrics compared with a "gold standard" surface created by averaging 12 scans of the same brain. Ninety-three percent of the topological defects in a set of 10 scans of control subjects were accurately corrected. The entire process takes 6-8 min of computation time. Further improvements are discussed, especially regarding the use of the T1-weighted image to make corrections.
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Yotter RA, Nenadic I, Ziegler G, Thompson PM, Gaser C. Local cortical surface complexity maps from spherical harmonic reconstructions. Neuroimage 2011; 56:961-73. [PMID: 21315159 DOI: 10.1016/j.neuroimage.2011.02.007] [Citation(s) in RCA: 154] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2010] [Revised: 02/01/2011] [Accepted: 02/01/2011] [Indexed: 01/29/2023] Open
Affiliation(s)
- Rachel A Yotter
- Department of Psychiatry, Friedrich-Schiller University, Jena, Germany.
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68
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Abstract
HIV can infect the brain and impair central nervous system (CNS) function. Combination antiretroviral therapy (cART) has not eradicated CNS complications. HIV-associated neurocognitive disorders (HAND) remain common despite cART, although attenuated in severity. This may result from a combination of factors including inadequate treatment of HIV reservoirs such as circulating monocytes and glia, decreased effectiveness of cART in CNS, concurrent illnesses, stimulant use, and factors associated with prescribed drugs, including antiretrovirals. This review highlights recent investigations of HIV-related CNS injury with emphasis on cART-era neuropathological mechanisms in the context of both US and international settings.
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69
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Ventricular maps in 804 ADNI subjects: correlations with CSF biomarkers and clinical decline. Neurobiol Aging 2011; 31:1386-400. [PMID: 20620663 DOI: 10.1016/j.neurobiolaging.2010.05.001] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2010] [Revised: 04/28/2010] [Accepted: 05/01/2010] [Indexed: 01/25/2023]
Abstract
Ideal biomarkers of Alzheimer's disease (AD) should correlate with accepted measures of pathology in the cerebrospinal fluid (CSF); they should also correlate with, or predict, future clinical decline, and should be readily measured in hundreds to thousands of subjects. Here we explored the utility of automated 3D maps of the lateral ventricles as a possible biomarker of AD. We used our multi-atlas fluid image alignment (MAFIA) method, to compute ventricular models automatically, without user intervention, from 804 brain MRI scans with 184 AD, 391 mild cognitive impairment (MCI), and 229 healthy elderly controls (446 men, 338 women; age: 75.50 +/- 6.81 [SD] years). Radial expansion of the ventricles, computed pointwise, was strongly correlated with current cognition, depression ratings, Hachinski Ischemic scores, language scores, and with future clinical decline after controlling for any effects of age, gender, and educational level. In statistical maps ranked by effect sizes, ventricular differences were highly correlated with CSF measures of Abeta(1-42), and correlated with ApoE4 genotype. These statistical maps are highly automated, and offer a promising biomarker of AD for large-scale studies.
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Wang Y, Song Y, Rajagopalan P, An T, Liu K, Chou YY, Gutman B, Toga AW, Thompson PM. Surface-based TBM boosts power to detect disease effects on the brain: an N=804 ADNI study. Neuroimage 2011; 56:1993-2010. [PMID: 21440071 DOI: 10.1016/j.neuroimage.2011.03.040] [Citation(s) in RCA: 93] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2010] [Revised: 02/20/2011] [Accepted: 03/16/2011] [Indexed: 11/19/2022] Open
Abstract
Computational anatomy methods are now widely used in clinical neuroimaging to map the profile of disease effects on the brain and its clinical correlates. In Alzheimer's disease (AD), many research groups have modeled localized changes in hippocampal and lateral ventricular surfaces, to provide candidate biomarkers of disease progression for drug trials. We combined the power of parametric surface modeling and tensor-based morphometry to study hippocampal differences associated with AD and mild cognitive impairment (MCI) in 490 subjects (97 AD, 245 MCI, 148 controls) and ventricular differences in 804 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI; 184 AD, 391 MCI, 229 controls). We aimed to show that a new multivariate surface statistic based on multivariate tensor-based morphometry (mTBM) and radial distance provides a more powerful way to detect localized anatomical differences than conventional surface-based analysis. In our experiments, we studied correlations between hippocampal atrophy and ventricular enlargement and clinical measures and cerebrospinal fluid biomarkers. The new multivariate statistics gave better effect sizes for detecting morphometric differences, relative to other statistics including radial distance, analysis of the surface tensor and the Jacobian determinant. In empirical tests using false discovery rate curves, smaller sample sizes were needed to detect associations with diagnosis. The analysis pipeline is generic and automated. It may be applied to analyze other brain subcortical structures including the caudate nucleus and putamen. This publically available software may boost power for morphometric studies of subcortical structures in the brain.
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Affiliation(s)
- Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA.
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71
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Becker JT, Maruca V, Kingsley LA, Sanders JM, Alger JR, Barker PB, Goodkin K, Martin E, Miller EN, Ragin A, Sacktor N, Selnes O. Factors affecting brain structure in men with HIV disease in the post-HAART era. Neuroradiology 2011; 54:113-21. [PMID: 21424708 DOI: 10.1007/s00234-011-0854-2] [Citation(s) in RCA: 90] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2011] [Accepted: 02/22/2011] [Indexed: 10/18/2022]
Abstract
INTRODUCTION The purpose of this study was to characterize brain volumetric differences in HIV seropositive and seronegative men and to determine effects of age, cardiovascular risk, and HIV infection on structural integrity. METHODS Magnetic resonance imaging was used to acquire high-resolution neuroanatomic data in 160 men aged 50 years and over, including 84 HIV seropositive and 76 seronegative controls. Voxel-based morphometry was used to derive volumetric measurements at the level of the individual voxel. Data from a detailed neuropsychological test battery were recombined into four summary scores representing psychomotor speed, visual memory, verbal memory, and verbal fluency. RESULTS Both age and HIV status had a significant effect on both gray matter (GM) and white matter (WM) volume. The age-related GM atrophy was primarily in the superior temporal and inferior frontal regions; the HIV-related GM loss included the posterior and inferior temporal lobes, the parietal lobes, and the cerebellum. Among all subjects, the performance on neuropsychological tests, as indexed by a summary variable, was related to the volume of both the GM and WM. Contrary to our predictions, the CVD variables were not linked to brain volume in statistically adjusted models. CONCLUSION In the post-HAART era, having HIV infection is still linked to atrophy in both GM and WM. Secondly, advancing age, even in this relatively young cohort, is also linked to changes in GM and WM volume. Thirdly, CNS structural integrity is associated with overall cognitive functions, regardless of the HIV infection status of the study volunteers.
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Affiliation(s)
- James T Becker
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
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Tate DF, Khedraki R, McCaffrey D, Branson D, Dewey J. The role of medical imaging in defining CNS abnormalities associated with HIV-infection and opportunistic infections. Neurotherapeutics 2011; 8:103-16. [PMID: 21274690 PMCID: PMC3075743 DOI: 10.1007/s13311-010-0010-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
In this review of the current literature, we examine the role of medical imaging in providing new and relevant information on central nervous system (CNS) injury associated with human immunodeficiency virus (HIV) infection and various clinical manifestations of this injury. Common imaging modalities used to examine CNS injury in HIV infection include structural magnetic resonance imaging, magnetic resonance spectroscopy, diffusion tensor imaging, functional MRI, and positron emissions tomography. Clinical implications for the findings are discussed for each of these modalities individually and collectively. In addition, the direction for future studies is suggested in an attempt to provide possible methods that might answer the many questions that remain to be answered on the evolution and progression of CNS injury in the context of HIV infection.
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Affiliation(s)
- David F Tate
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.
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