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Automated ventricular mapping alignment reveals genetic effects with multi-atlas fluid image in Alzheimer's disease
Abstract
We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles in brain MRI scans, providing an efficient approach to monitor degenerative disease in clinical studies and drug trials. First, we used a set of parameterized surfaces to represent the ventricles in four subjects' manually labeled brain MRI scans (atlases). We fluidly registered each atlas and mesh model to MRIs from 17 Alzheimer's disease (AD) patients and 13 age- and gender-matched healthy elderly control subjects, and 18 asymptomatic ApoE4-carriers and 18 age- and gender-matched non-carriers. We examined genotyped healthy subjects with the goal of detecting subtle effects of a gene that confers heightened risk for Alzheimer's disease. We averaged the meshes extracted for each 3D MR data set, and combined the automated segmentations with a radial mapping approach to localize ventricular shape differences in patients. Validation experiments comparing automated and expert manual segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease- and gene-related alterations improved, as the number of atlases, N, was increased from 1 to 9. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases. We formulated a statistical stopping criterion to determine the optimal number of atlases to use. Healthy ApoE4-carriers and those with AD showed local ventricular abnormalities. This high-throughput method for morphometric studies further motivates the combination of genetic and neuroimaging strategies in predicting AD progression and treatment response.
... To comprehensively capture deformations along the surface normal directions and within surfaces, we developed multivariate morphometry statistics (MMS) combining mTBM and RD to detect brain abnormalities associated with neurodegenerative diseases (Wang et al. 2011;Shi et al. 2014Shi et al. , 2015Li et al. 2016;Dong et al. 2019 Few studies have revealed ventricular morphometry abnormalities of CU progressors who imminently progressed to clinically significant memory decline. Previous studies of ventricular morphometric modeling (Thompson et al. 2004a;Ferrarini et al. 2008;Chou et al. 2008;Wang et al. 2011;Apostolova et al. 2012;Roussotte et al. 2014b) mapped only part of anatomical ventricular surfaces, with coverage of inferior or posterior horns being incomplete. In this work, we propose a complete ventricular morphometry analysis system (VMAS), which is based on MMS proposed by our previous methods (Wang et al. 2010(Wang et al. , 2011, but includes an automated ventricular segmentation method (Zhang et al. 2016), together with an efficient morphometric expansion/atrophy visualization analysis module (Yao et al. 2018;Dong et al. 2019). ...
... Our study is among the first to describe a completely automated VMAS capable of generating a whole connected 3D ventricular shape model. Lateral ventricular boundaries (CSF/brain) have high contrast from adjacent tissue, which facilitates ventricular segmentation in MRI scans, so that ventricular measures may be the most reliable and robust for studying AD pathophysiologic progression (Ferrarini et al. 2008;Chou et al. 2008;Madsen et al. 2013Madsen et al. , 2015. Previous studies (Weiner et al. 2015;Madsen et al. 2015;Coupé et al. 2019) demonstrated VV measures can detect ventricular enlargements associated with AD prior to clinically significant memory decline. ...
Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular frontal horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of the pharmacological intervention at a preclinical stage.
... An expert rater (D.Z., intra-rater reliability Cronbach's alpha = 0.995) traced the lateral ventricles of 4 subjects in three partitions -frontal horn, temporal horn, and body/occipital horn, as previously described [62]. Traces were converted to one of four atlases, or 3D parametric ventricular mesh models. ...
... Traces were converted to one of four atlases, or 3D parametric ventricular mesh models. Atlases were then fluidly registered to each unsegmented study image [62]. Four separate ventricular segmentations for each participant were created and then averaged to reduce segmentation bias that occurs when a single atlas is used; reducing these errors allows true ventricle anatomy to be captured more accurately at the individual level. ...
We analyzed structural magnetic resonance imaging data from 58 cognitively normal and 101 mild cognitive impairment subjects. We used a general linear regression model to study the association between cognitive performance with hippocampal atrophy and ventricular enlargement using the radial distance method.Bilateral hippocampal atrophy was associated with baseline and longitudinal memory performance. Left hippocampal atrophy predicted longitudinal decline in visuospatial function. The multidomain ventricular analysis did not reveal any significant predictors.
... There is no doubt that this will hinder the evaluation of the ventricles of large-scale samples [13]. Because of this, manual segmentation is often impractical in large-scale clinical practice, and more automated methods are urgently needed to complete it [32,33]. So, the automated brain image segmentation method is a research hotspot in recent years [16]. ...
Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multimodal and high-performance automatic ventricle segmentation method to achieve an efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use the machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2 ± 2.6, respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0 ± 3.8, respectively. The whole process takes 3.4 ± 0.3 s. In MRI images, the DSC, ICC, Pearson correlation, and Bland–Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0 ± 0.6, respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9 ± 3.8, respectively. The whole process took 1.9 ± 0.1 s. We have established a multimodal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.
... There is no doubt that this will hinder the evaluation of the ventricles of large-scale samples [13]. Because of this, manual segmentation is often impractical in large-scale clinical practice, and more automated methods are urgently needed to complete it [32,33]. So, the automated brain image segmentation method is a research hotspot in recent years [16]. ...
Based on CT and MRI images acquired from normal pressure hydrocephalus (NPH) patients, using machine learning methods, we aim to establish a multi-modal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume. First, we extract the brain CT and MRI images of 143 definite NPH patients. Second, we manually label the ventricular volume (VV) and intracranial volume (ICV). Then, we use machine learning method to extract features and establish automatic ventricle segmentation model. Finally, we verify the reliability of the model and achieved automatic measurement of VV and ICV. In CT images, the Dice similarity coefficient (DSC), Intraclass Correlation Coefficient (ICC), Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.95, 0.99, 0.99, and 4.2$\pm$2.6 respectively. The results of ICV were 0.96, 0.99, 0.99, and 6.0$\pm$3.8 respectively. The whole process takes 3.4$\pm$0.3 seconds. In MRI images, the DSC, ICC, Pearson correlation, and Bland-Altman analysis of the automatic and manual segmentation result of the VV were 0.94, 0.99, 0.99, and 2.0$\pm$0.6 respectively. The results of ICV were 0.93, 0.99, 0.99, and 7.9$\pm$3.8 respectively. The whole process took 1.9$\pm$0.1 seconds. We have established a multi-modal and high-performance automatic ventricle segmentation method to achieve efficient and accurate automatic measurement of the ventricular volume of NPH patients. This can help clinicians quickly and accurately understand the situation of NPH patient's ventricles.
... The ApoE ε4 polymorphism has often been associated with atrophy of various temporal lobe regions 6 and other gray matter areas 6,47 , and globally (ventricular volume) 48 . Here, an association with VBR following stroke was not observed for this polymorphism, possibly because ApoE ε4 effects may be strongest in subjects with dementia, or in specific brain areas such as hippocampus. ...
Objective
Patients show substantial differences in response to rehabilitation therapy after stroke. We hypothesized that specific genetic profiles might explain some of this variance and, secondarily, that genetic factors are related to cerebral atrophy post-stroke.
Methods
The phase 3 ICARE study examined response to motor rehabilitation therapies. In 216 ICARE enrollees, DNA was analyzed for presence of the BDNF val ⁶⁶ met and the ApoE ε4 polymorphism. The relationship of polymorphism status to 12-month change in motor status (Wolf Motor Function Test, WMFT) was examined. Neuroimaging data were also evaluated (n=127).
Results
Subjects were 61±13 years old (mean±SD) and enrolled 43±22 days post-stroke; 19.7% were BDNF val ⁶⁶ met carriers and 29.8% ApoE ε4 carriers. Carrier status for each polymorphism was not associated with WMFT, either at baseline or over 12 months of follow-up. Neuroimaging, acquired 5±11 days post-stroke, showed that BDNF val ⁶⁶ met polymorphism carriers had a 1.34-greater degree of cerebral atrophy compared to non-carriers (P=.01). Post hoc analysis found that age of stroke onset was 4.6 years younger in subjects with the ApoE ε4 polymorphism (P=.02).
Conclusion
Neither the val ⁶⁶ met BDNF nor ApoE ε4 polymorphism explained inter-subject differences in response to rehabilitation therapy. The BDNF val ⁶⁶ met polymorphism was associated with cerebral atrophy at baseline, echoing findings in healthy subjects, and suggesting an endophenotype. The ApoE ε4 polymorphism was associated with younger age at stroke onset, echoing findings in Alzheimer’s disease and suggesting a common biology. Genetic associations provide insights useful to understanding the biology of outcomes after stroke.
... It is realized by segmentation, which can be roughly categorized into automated segmentation and manual segmentation (Huff et al., 2019). The manual segmentation technique is the gold standard for volumetric quantification of regional brain structures (Kocaman et al., 2019), but when dealing with more data, manual segmentation of the ventricles is time-consuming, subjective, and less reproducible (Chou et al., 2008;Liu et al., 2009;Poh et al., 2012). Therefore, it is highly in demand for an automated ventricle segmentation method to be developed and machine and deep learning based methods have emerged as the new era. ...
Background and Objective: Ventricle volume is closely related to hydrocephalus, brain atrophy, Alzheimer's, Parkinson's syndrome, and other diseases. To accurately measure the volume of the ventricles for elderly patients, we use deep learning to establish a systematic and comprehensive automated ventricle segmentation framework.
Methods: The study participation included 20 normal elderly people, 20 patients with cerebral atrophy, 64 patients with normal pressure hydrocephalus, and 51 patients with acquired hydrocephalus. Second, get their imaging data through the picture archiving and communication systems (PACS) system. Then use ITK software to manually label participants' ventricular structures. Finally, extract imaging features through machine learning.
Results: This automated ventricle segmentation method can be applied not only to CT and MRI images but also to images with different scan slice thicknesses. More importantly, it produces excellent segmentation results (Dice > 0.9).
Conclusion: This automated ventricle segmentation method has wide applicability and clinical practicability. It can help clinicians find early disease, diagnose disease, understand the patient's disease progression, and evaluate the patient's treatment effect.
... As such, its direct application is the development of tools helping the daily medical practice. In fact, organ shapes often represent a statistically significant predictors for various clinical parameters: the brain shape is corelated to neurodegenerative diseases like Alzheimer [Gerber 2010, Chou 2008, Baron 2001, Lorenzi 2011, Apostolova 2007] but also to other neurological and neuropsychiatric illnesses, including epilepsy [Eriksson 2001] and schizophrenia [Kubicki 2007, Brignell 2010 for example. Therefore, a computational representation of the anatomy can serve the diagnosis of diseases from medical images. ...
This thesis develops Geometric Statistics to analyze the normal andpathological variability of organ shapes in Computational Anatomy. Geometricstatistics consider data that belong to manifolds with additional geometricstructures. In Computational Anatomy, organ shapes may be modeled asdeformations of a template - i.e. as elements of a Lie group, a manifold with agroup structure - or as the equivalence classes of their 3D configurations underthe action of transformations - i.e. as elements of a quotient space, a manifoldwith a stratification. Medical images can be modeled as manifolds with ahorizontal distribution. The contribution of this thesis is to extend GeometricStatistics beyond the now classical Riemannian and metric geometries in orderto account for these additional structures. First, we tackle the definition ofGeometric Statistics on Lie groups. We provide an algorithm that constructs a(pseudo-)Riemannian metric compatible with the group structure when itexists. We find that some groups do not admit such a (pseudo-)metric andadvocate for non-metric statistics on Lie groups. Second, we use GeometricStatistics to analyze the algorithm of organ template computation. We show itsasymptotic bias by considering the geometry of quotient spaces. We illustratethe bias on brain templates and suggest an improved algorithm. We then showthat registering organ shapes induces a bias in their statistical analysis, whichwe offer to correct. Third, we apply Geometric Statistics to medical imageprocessing, providing the mathematics to extend sub-Riemannian structures,already used in 2D, to our 3D images.
... Quelques unes de ces approches ont été décrites dans l'état de l'art présenté dans la section 5.4. D'autres approches ont abordé la segmentation des ventricules comme un problème à part entière et ont utilisés des caractéristiques des niveaux de gris et de forme propres aux ventricules à l'instar de [Schnack et al., 2001;Wu et al., 2003;Chou et al., 2008;Liu et al., 2009]. Nous proposons dans ce qui suit une nouvelle méthode de segmentation des ventricules. ...
The recent advances in magnetic resonance imaging helped understanding brain anatomy and function. Today, MR imaging is a key tool for inferring imaging-based biomarkers for most neuropathologies. In this work, we focused on the anatomical connectivity of the basal ganglia which are involved in several cortico-subcortical loops and which dysfunction is the origin of motor disorders like Huntington and Parkinson diseases and Gilles de la Tourette syndrome. We developed several tools allowing the segmentation of the basal ganglia and inferring their anatomical connectivity. First, we developed a method for deep nuclei segmentation using several contrasts and that was adapted for pathological cases presenting high modifications in the morphology of these structures. Second, we developed robust methods for the analysis and the selection of the fiber tracts linking different brain structures and obtained using dMRI and tractography methods. These novel tools have the advantage of taking into account anatomical prior knowledge. Therefore the obtained results are closer to the real anatomy than those obtained using the tools available in the literature. We also developed surface connectivity maps that project the cortical connections of the deep nuclei directly on the cortical surface and that allow the comparison of the connectivity profile of the deep nuclei between different subjects and different groups. Finally, we used these tools to study the putative modifications of the anatomical connectivity of the deep nuclei in the Huntington disease and Gilles de la Tourette syndrome.
... Raw MRI scans were pre-processed to reduce signal inhomogeneity and linearly registered to a template (using 9 parameter registration). Prior methods for ventricular segmentation have used semi-automated, automated [17], and single-atlas or multi-atlas methods [18]. Here we segmented the ventricles with our modified multi-atlas approach described previously [19]. ...
Genetic variants in DAT1, the gene encoding the dopamine transporter (DAT) protein, have been implicated in many brain disorders. In a recent case-control study of Alzheimer's disease (AD), a regulatory polymorphism in DAT1 showed a significant association with the clinical stages of dementia.
We tested whether this variant was associated with increased AD risk, and with measures of cognitive decline and longitudinal ventricular expansion, in a large sample of elderly participants with genetic, neurocognitive, and neuroimaging data from the Alzheimer's Disease Neuroimaging Initiative.
The minor allele-previously linked with increased DAT expression in vitro-was more common in AD patients than in both individuals with mild cognitive impairment and healthy elderly controls. The same allele was also associated with poorer cognitive performance and faster ventricular expansion, independently of diagnosis.
These results may be due to reduced dopaminergic transmission in carriers of the DAT1 mutation.
Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.
Artificial Intelligence (AI) is an interdisciplinary science with multiple approaches to solve a problem. Advancements in machine learning (ML) and deep learning are creating a paradigm shift in virtually every tech industry sector.
This handbook provides a quick introduction to concepts in AI and ML. The sequence of the book contents has been set in a way to make it easy for students and teachers to understand relevant concepts with a practical orientation. This book starts with an introduction to AI/ML and its applications. Subsequent chapters cover predictions using ML, and focused information about AI/ML algorithms for different industries (health care, agriculture, autonomous driving, image classification and segmentation, SEO, smart gadgets and security). Each industry use-case demonstrates a specific aspect of AI/ML techniques that can be used to create pipelines for technical solutions such as data processing, object detection, classification and more.
Additional features of the book include a summary and references in every chapter, and several full-color images to visualize concepts for easy understanding. It is an ideal handbook for both students and instructors in undergraduate level courses in artificial intelligence, data science, engineering and computer science who are required to understand AI/ML in a practical context.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
An informative and comprehensive review from the leading researchers in the field, this book provides a complete one-stop guide to neuroimaging techniques and their application to a wide range of neuropsychiatric disorders. For each disorder or group of disorders, separate chapters review the most up-to-date findings from structural imaging, functional imaging and/or molecular imaging. Each section ends with an overview from a internationally-renowned luminary in the field, addressing the question of 'What do we know and where are we going?' Richly illustrated throughout, each chapter includes a 'summary box', providing readers with explicit take-home messages. This is an essential resource for clinicians, researchers and trainees who want to learn how neuroimaging tools lead to new discoveries about brain and behaviour associations in neuropsychiatric disorders.
In this chapter, the authors review a variety of algorithms developed by different groups for automatically segmenting structures in medical images, such as brain MRI scans. Some of the simpler methods, based on active contours, deformable image registration, and anisotropic Markov random fields, have known weaknesses, which can be largely overcome by learning methods that better encode knowledge on anatomical variability. The authors show how the anatomical segmentation problem may be re-cast in a Bayesian framework. They then present several different learning techniques increasing in complexity until they derive two algorithms recently proposed by the authors. The authors show how these automated algorithms are validated empirically, by comparison with segmentations by experts, which serve as independent ground truth, and in terms of their power to detect disease effects in Alzheimer’s disease. They show how these methods can be used to investigate factors that influence disease progression in databases of thousands of images. Finally the authors indicate some promising directions for future work.
In this chapter, the authors review a variety of algorithms developed by different groups for automatically segmenting structures in medical images, such as brain MRI scans. Some of the simpler methods, based on active contours, deformable image registration, and anisotropic Markov random fields, have known weaknesses, which can be largely overcome by learning methods that better encode knowledge on anatomical variability. The authors show how the anatomical segmentation problem may be re-cast in a Bayesian framework. They then present several different learning techniques increasing in complexity until they derive two algorithms recently proposed by the authors. The authors show how these automated algorithms are validated empirically, by comparison with segmentations by experts, which serve as independent ground truth, and in terms of their power to detect disease effects in Alzheimer’s disease. They show how these methods can be used to investigate factors that influence disease progression in databases of thousands of images. Finally the authors indicate some promising directions for future work.
Neuropathological changes associated with Alzheimer's disease (AD) precede symptom onset by more than a decade. Possession of an apolipoprotein E (APOE) ɛ4 allele is the strongest genetic risk factor for late onset AD. Cross-sectional studies of cognitively intact elders have noted smaller hippocampal/medial temporal volumes in ɛ4 carriers (ɛ4+) compared to ɛ4 non-carriers (ɛ4-). Few studies, however, have examined long-term, longitudinal, anatomical brain changes comparing healthy ɛ4+ and ɛ4- individuals. The current five-year study examined global and regional volumes of cortical and subcortical grey and white matter and ventricular size in 42 ɛ4+ and 30 ɛ4- individuals. Cognitively intact participants, ages 65-85 at study entry, underwent repeat anatomical MRI scans on three occasions: baseline, 1.5, and 4.75 years. Results indicated no between group volumetric differences at baseline. Over the follow-up interval, the ɛ4+ group experienced a greater rate of volume loss in total grey matter, bilateral hippocampi, right hippocampal subfields, bilateral lingual gyri, parahippocampal gyrus, and right lateral orbitofrontal cortex compared to the ɛ4- group. Greater loss in grey matter volumes in ɛ4+ participants were accompanied by greater increases in lateral, third, and fourth ventricular volumes. Rate of change in white matter volumes did not differentiate the groups. The current results indicate that longitudinal measurements of brain atrophy can serve as a sensitive biomarker for identifying neuropathological changes in persons at genetic risk for AD and potentially, for assessing the efficacy of treatments designed to slow or prevent disease progression during the preclinical stage of AD.
In medical field, image processing plays a vital role in research and diagnosing disease. Segmentation of images is widely used for medical purpose such as pre-surgery and postsurgery decisions, which is required for planning treatment. The abnormal growth of tissues can be detected using computer aided detection and it is used for achieving maximum classification accuracy. Magnetic resonance imaging is widely used in computer aided design for the detection of abnormalities. Even though MRI is an efficient method, it is time consuming and needs reasonable amount of human resources. Many studies are going on in the medical field using Markov random fields in segmentation. In this paper, the MRI images are used as a dataset to the proposed algorithm, MRF-artificial bee colony optimization algorithm, with fuzzy possibility cmeans is used to obtain the optimal solution. The main aim of the proposed algorithm is to reduce the computational complexity and achieving the higher accuracy. The performance of the proposed algorithm is calculated using region non uniformity, correlation and computation time. The experimental results were compared with the existing approaches such as simulated annealing and MRF with improved genetic algorithm (GA).
Rare variants of phospholipase D3 (PLD3) have been identified as Alzheimer's disease (AD) susceptibility loci, whereas little is known about the potential role of common variants in the progression of AD. To examine the impact of genetic variations in PLD3 on neuroimaging phenotypes in a large non-demented population. A total of 261 normal cognition (NC) and 456 mild cognitive impairment (MCI) individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database are included in our analysis. Multiple linear regression models were applied to examine the association between four single-nucleotide polymorphisms (SNPs; rs7249146, rs4490097, rs12151243, and rs10407447) with the florbetapir retention on florbetapir 18F amyloid positron emission tomography (AV45-PET), the cerebral metabolic rate for glucose (CMRgl) on 18F-fluorodeoxyglucose PET (FDG-PET), and regional volume on magnetic resonance imaging (MRI) at baseline and in the cohort study. We did not detect any significant associations of PLD3 SNPs with florbetapir retention on AV45-PET. In the analysis of FDG-PET, rs10407447 was associated with the CMRgl in the left angular gyrus and bilateral posterior cingulate cortex in the MCI group. Regarding the MRI analysis, rs10407447 was also associated with bilateral inferior lateral ventricle and lateral ventricle volume in MCI group. The main findings of our study provide evidence that support the possible role of PLD3 common variants in influencing AD-related neuroimaging phenotypes. Nevertheless, further work is necessary to explain the functional mechanisms of differences and confirm the causal variants.
Tensor-based morphometry (TBM) is an analysis approach that can be applied to structural brain MRI scans to detect group dif-ferences or changes in brain structure. TBM uses nonlinear image reg-istration to align a set of images to a common template or atlas. De-tection sensitivity is crucial for clinical applications such as drug trials, but few studies have examined how the choice of deformation model (regularizer or Bayesian prior) affects sensitivity. Here we tested a new registration algorithm based on a fluid extension of Riemannian Elastic-ity [17], which penalizes deviations from zero strain in a log-Euclidean tensor framework, but has the desirable property of enforcing one-to-one mappings. We compared it to a standard large-deformation continuum-mechanical registration approach based on hyperelasticity. To compare the sensitivity of the two models, we studied corpus callosum morphol-ogy in 26 HIV/AIDS patients and 12 matched healthy controls. We an-alyzed the spatial gradients of the deformation fields in a multivariate Log-Euclidean framework [1] [12] to map the profile of systematic group differences. In cumulative p-value plots, the Riemannian prior detected disease-related atrophy with greater signal-to-noise than the standard hyperelastic approach. Riemannian priors regularize the full multivari-ate deformation tensor, yielding statistics on deformations that are unbi-ased in the associated Log-Euclidean metrics. Compared with standard continuum-mechanical registration, these Riemannian fluid models may more sensitively detect disease effects on the brain.
The expansion of the ventricular CSF-white matter boundary over time is of interest in short term serial MRI because of its high contrast and high rate of change. We examined magnitudes and patterns of ventricular expansion rate in cognitively normal elderly without (N=34) and with (N=18) lacunes, and in 18 Alzheimer's patients without lacunes, using high-resolution tensor morphometry. Normal fusion visualization of the tensor field with respect to a reference ventricular surface revealed significant increases in expansion rate associated with both lacunes and Alzheimer's disease. These findings highlight the importance of considering cerebrovascular and Alzheimer' s disease as independent pathological processes. Objectives: To examine differences in the common patterns of ventricular expansion over time in three groups: cognitively normal subjects without lacunes, cognitively normal with lacunes and subjects diagnosed with Alzheimer's disease. Methods: 34 Cognitively normal subjects without lacunes (CN: Age=73.5±6.5), 18 subjects with 1 or more lacunes (CN+L:Age=74±7.8) and 18 AD patients without apparent vascular disease on MRI (AD:Age=74.8±6.4) were studied with structural MRI obtained at two time points using a T1-weighted MP-RAGE (TR/TE/TI=10/4/300ms, 1x1x1.5mm resolution) at intervals of between 1 and 3 years. Lacunes were identified as discrete signal hyperintensities >3mm in diameter in proton density MRI. The location of lacunes was thalamic (n=6), basal ganglia (n=6) and white matter (n=6). The paired images for each subject were spatially registered using an entropy driven (1) fine lattice (1.5mm knot spacing Cubic B-Spline) free form deformation (2) to capture local changes in tissue volume over time. Maps of local point-wise volume changes in each subject were then estimated by analytically evaluating the Jacobian of this transformation from the B-Spline transformation. A further transformation between each subject's time point 1 MRI and a common reference control subject MRI (72yo CN female) was estimated using a multi-resolution B-Spline registration with lattice spacings of 14.4, 7.2, 3.6 and 1.8mm. This transformation was then used to spatially normalize each subject's volume change map to the common anatomical coordinate system of the reference MRI. The ventricular region of the single reference MRI anatomy was manually segmented and the statistics of the tensor fields were then visualized with respect to this anatomical boundary using a normal fusion (3) volume visualization algorithm. Results: Figure 1 shows the spatial patterns CSF expansion rate effect size between groups with respect to the reference ventricular surface. This differential expansion rate of the CSF- white matter boundary indicates increased rates of white matter tissue loss in regions normal to the ventricular surface. The presence of lacunes alone in cognitive normal subjects appears associated with an increase in the overall rate of CSF expansion and the rate of adjacent white matter loss. Although the increase in mean rate of expansion is large, the effect size reaches only 0.75, due, possibly to the inherent variability of lacune location. The strongest effects due to the presence of lacunes occur in the rate of expansion of the frontal lateral ventricles and in the ventricular space adjacent to the hippocampus. In contrast, AD increases expansion rate with a relatively large effect over most of the ventricular surface, concentrated most strongly in the posterior temporal horns, which is consistent with the loss of neurons and white matter in the adjacent temporal and parietal lobes. The effect of AD with respect to CN+L indicates that expansion of the frontal lateral ventricle is not specific to AD. Rather, the location of effects that most strongly differentiates between CN+L and AD groups is CSF expansion in the posterior temporal horn. Discussion: Brain shape analysis from serial MRI can only examine tissue loss indirectly by inferring its location from the shift in boundaries between tissues with MRI contrast. The expansion of ventricular CSF reflects the loss of deep white and gray matter structures adjacent to the ventricle. The use of high- resolution non-rigid registration between subjects MRI allows us to find corresponding regions on the otherwise anatomically featureless surfaces of the ventricles, permitting us to study common local patterns of change not easily identified by human observation across multiple subjects. By studying ventricular expansion separately in AD and cognitively normal subjects with and without lacunes we have identified separate and confounding patterns of shape change associated with these clinical conditions. A surprising finding is that the presence of lacunes, even in cognitively normal subjects, is associated with an increased rate of ventricular expansion. It is not clear whether this reflects the local or distal effects of lacunes or whether lacunes represent surrogate markers for more
Statistical Shape Analysis (SSA) is a powerful tool for noninvasive studies of pathophysiology and diagnosis of brain diseases. It also provides a shape constraint for the segmentation of brain structures. There are two key problems in SSA: the representation of shapes and their alignments. The widely used parameterized representations are obtained by preserving angles or areas and the alignments of shapes are achieved by rotating parameter net. However, representations preserving angles or areas do not really guarantee the anatomical correspondence of brain structures. In this paper, we incorporate shape-based landmarks into parameterization of banana-like 3D brain structures to address this problem. Firstly, we get the triangulated surface of the object and extract two landmarks from the mesh, i.e. the ends of the banana-like object. Then the surface is parameterized by creating a continuous and bijective mapping from the surface to a spherical surface based on a heat conduction model. The correspondence of shapes is achieved by mapping the two landmarks to the north and south poles of the sphere and using an extracted origin orientation to select the dateline during parameterization. We apply our approach to the parameterization of lateral ventricle and a multi-resolution shape representation is obtained by using the Discrete Fourier Transform.
The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses – the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferroni-type procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
We present a population registration framework that acts on large collections or populations of data volumes. The data alignment procedure runs in a simul-taneous fashion, with every member of the population approaching the central tendency of the collection at the same time. Such a mechanism eliminates the need for selecting a particular reference frame a priori, resulting in a non-biased estimate of a digital atlas. Our algorithm adopts an a±ne congealing framework with an information theoretic objective function and is optimized via a gradient-based stochastic approximation process embedded in a multi-resolution setting. We present experimental results on both synthetic and real images.
We describe an algorithm for obtaining correspondences across a group of images of deformable objects. The approach is to construct a statistical model of appearance which can encode the training images as compactly as possible (a Minimum Description Length framework). Correspondences are defined by piece-wise linear interpolation between a set of control points defined on each image. Given such points a model can be constructed, which can ap- proximate every image in the set. The description length encodes the cost of the model, the parameters and most importantly, the residuals not explained by the model. By modifying the positions of the control points we can opti- mise the description length, leading to good correspondence. We describe the algorithm in detail and give examples of its application to MR brain images and to faces. We also describe experiments which use a recently-introduced specificity measure to evaluate the performance of different components of the algorithm.
We compared four automated methods for hippocampal segmentation using different machine learning algorithms: 1) hierarchical AdaBoost, 2) support vector machines (SVM) with manual feature selection, 3) hierarchical SVM with automated feature selection (Ada-SVM), and 4) a publicly available brain segmentation package (FreeSurfer). We trained our approaches using T1-weighted brain MRIs from 30 subjects [10 normal elderly, 10 mild cognitive impairment (MCI), and 10 Alzheimer's disease (AD)], and tested on an independent set of 40 subjects (20 normal, 20 AD). Manually segmented gold standard hippocampal tracings were available for all subjects (training and testing). We assessed each approach's accuracy relative to manual segmentations, and its power to map AD effects. We then converted the segmentations into parametric surfaces to map disease effects on anatomy. After surface reconstruction, we computed significance maps, and overall corrected p-values, for the 3-D profile of shape differences between AD and normal subjects. Our AdaBoost and Ada-SVM segmentations compared favorably with the manual segmentations and detected disease effects as well as FreeSurfer on the data tested. Cumulative p-value plots, in conjunction with the false discovery rate method, were used to examine the power of each method to detect correlations with diagnosis and cognitive scores. We also evaluated how segmentation accuracy depended on the size of the training set, providing practical information for future users of this technique.
Conflicting results have been reported on the association of plasma total homocysteine (tHcy) and cholesterol levels in Alzheimer disease (AD). The objective of this study was to determine the relationship between cognitive performance and plasma levels of tHcy and its biological determinants folate and vitamin B(12), and lipids in clinically diagnosed AD patients.
A cross-sectional database review was performed on two separate groups of patients (n = 191). Mini-Mental State Exam (MMSE) scores, plasma levels of tHcy, vitamin B(12), folate, cholesterol, and triglycerides were analyzed.
The MMSE scores were inversely correlated with age, plasma levels of tHcy and LDL cholesterol. However, only the inverse relationship between MMSE scores and LDL cholesterol levels persisted after adjustment for age, sex, and status of statin treatment. Plasma tHcy levels increased significantly with age and were inversely related to vitamin B(12) and folate levels, which modified the relationship between MMSE scores and plasma tHcy levels.
The plasma tHcy levels appeared to relate more to aging than to cognition. Cognitive performance was inversely associated with plasma LDL cholesterol levels in AD patients. Our findings provide further evidence that high LDL cholesterol levels may play a role in the pathogenesis of AD.
The apolipoprotein E type 4 allele (APOE-epsilon 4) is genetically associated with the common late onset familial and sporadic
forms of Alzheimer's disease (AD). Risk for AD increased from 20% to 90% and mean age at onset decreased from 84 to 68 years
with increasing number of APOE-epsilon 4 alleles in 42 families with late onset AD. Thus APOE-epsilon 4 gene dose is a major
risk factor for late onset AD and, in these families, homozygosity for APOE-epsilon 4 was virtually sufficient to cause AD
by age 80.
Apolipoprotein E is immunochemically localized to the senile plaques, vascular amyloid, and neurofibrillary tangles of Alzheimer disease. In vitro, apolipoprotein E in cerebrospinal fluid binds to synthetic beta A4 peptide (the primary constituent of the senile plaque) with high avidity. Amino acids 12-28 of the beta A4 peptide are required. The gene for apolipoprotein E is located on chromosome 19q13.2, within the region previously associated with linkage of late-onset familial Alzheimer disease. Analysis of apolipoprotein E alleles in Alzheimer disease and controls demonstrated that there was a highly significant association of apolipoprotein E type 4 allele (APOE-epsilon 4) and late-onset familial Alzheimer disease. The allele frequency of the APOE-epsilon 4 in 30 random affected patients, each from a different Alzheimer disease family, was 0.50 +/- 0.06; the allele frequency of APOE-epsilon 4 in 91 age-matched unrelated controls was 0.16 +/- 0.03 (Z = 2.44, P = 0.014). A functional role of the apolipoprotein E-E4 isoform in the pathogenesis of late-onset familial Alzheimer disease is suggested.
Morphometric variance of the human brain is qualitatively observable in surface features of the cortex. Statistical analysis of sulcal geometry will facilitate multisubject atlasing, neurosurgical studies, and multimodality brain mapping applications. This investigation describes the variability in location and geometry of five sulci surveyed in each hemisphere of six postmortem human brains placed within the Talairach stereotaxic grid. The sulci were modeled as complex internal surfaces in the brain. Heterogeneous profiles of three-dimensional (3D) variation were quantified locally within individual sulci.
Whole human heads, sectioned at 50 μm, were digitally photographed and high-resolution 3D data volumes were reconstructed. The parieto-occipital sulcus, the anterior and posterior rami of the calcarine sulcus, the cingulate and marginal sulci, and the supracallosal sulcus were delineated manually on sagittally resampled sections. Sulcal outlines were reparameterized for surface comparisons. Statistics of 3D variation for arbitrary points on each surface were calculated locally from the standardized individual data. Additional measures of surface area, extent in three dimensions, surface curvature, and fractal dimension were used to characterize variations in sulcal geometry.
Paralimbic sulci exhibited a greater degree of anterior– posterior variability than vertical variability. Occipital sulci displayed the reverse trend. Both trends were consistent with developmental growth patterns. Points on the occipital sulci displayed a profile of variability highly correlated with their 3D distance from the posterior commissure. Surface curvature was greater for the arched paralimbic sulci than for those bounding occipital gyri in each hemisphere. On the other hand, fractal dimension measures were remarkably similar for all sulci examined, and no significant hemispheric asymmetries were found for any of the selected spatial and geometric parameters. Implications of cortical morphometric variability for multisubject comparisons and brain mapping applications are discussed.
The present study investigated the effect of age on total and regional brain volumes and compared age-associated changes in 20 healthy controls with those observed in 12 patients with Alzheimer's disease (AD). Weights and volumes of the whole brain and cerebrum, as well as the fractional volumes of the frontal, temporal, and parieto-occipital cortices, medial temporal structures, deep brain structures, and white matter were measured. Males had larger and heavier brains than females of comparable age. A small decline in brain volume with age was found (approximately 2 ml per year), but only within the white matter. In comparison, no further loss of white matter occurred in AD; however, the cerebral cortex was significantly reduced in volume, with the greatest loss from the medial temporal structures. This loss was related to disease progression; greater proportional loss was associated with more rapid decline in older patients. This study suggests that significant brain atrophy is not a consequence of advancing age. In addition, it suggests a regional specificity of damage in AD.
Recent evidence indicates that the apolipoprotein E (ApoE) epsilon4 allele is a risk factor for developing Alzheimer's disease. It has also been proposed that it is associated with increased counts of amyloid plaques and neurofibrillary tangles that in turn are neuropathological hallmarks initially appearing in the medial temporal lobe structures in Alzheimer's disease. In this study, the effect of the ApoE epsilon4 allele on the volume of the entorhinal cortex was evaluated in vivo.
The volume of the entorhinal cortex was measured on MR images using a recently designed histology based protocol in 16 patients with Alzheimer's disease with ApoE epsilon4 (mean age 70.4 (SD 9.9)), 11 patients with Alzheimer's disease without ApoE epsilon4 (mean age 69.1 (SD7.1)), and in 31 healthy age and sex matched normal controls (72.2 (SD 3.9)). The patients met the NINCDS-ADRDA criteria for probable Alzheimer's disease and were in mild to moderate stages of the disease. MRI was performed with a 1.5 Tesla Magnetom and a 3D technique permitting the reconstruction of 2.0 mm thick contiguous slices perpendicular to the axis of the anterior-posterior commissure.
The patients with Alzheimer's disease without the ApoE epsilon4 allele had atrophy in the entorhinal cortex, the volume was reduced by 27% compared with control subjects. However, the most prominent shrinkage (45%) in the entorhinal cortex was seen in patients with Alzheimer's disease with the ApoE epsilon4 allele (p=0.0001). The effect of epsilon4 on the entorhinal cortex volume was especially prominent in female patients with Alzheimer's disease compared to male patients with Alzheimer's disease (p=0.014). Additionally, patients with the ApoE epsilon4 allele had inferior performance in verbal and visual memory functions than those without the allele
Volumetric MRI measurements disclose that ApoE epsilon4 is associated with the degree of atrophy in the entorhinal cortex in early Alzheimer's disease, this effect being especially prominent in female patients with Alzheimer's disease.
To test the hypothesis that the e4 allele of APOE is associated with a region-specific pattern of brain atrophy in AD.
Volumes of the hippocampi, entorhinal cortices, and anterior temporal and frontal lobes were measured in 28 mild to moderate AD patients and 30 controls using MRI. Within the AD group, 14 patients were noncarriers (-/-), 9 were heterozygous (e4/-), and 5 were homozygous (e4/4) for the e4 allele. Dementia severity was similar across the three AD groups.
Smaller volumes were found with increasing dose of the e4 allele in the hippocampus, entorhinal cortex, and anterior temporal lobes in AD patients. When compared with controls, the volume loss in the right and left temporal regions ranged from -15.3 to -22.7% in the -/- AD group, from -26.2 to -36.0% in the e4/- group, and from -24.0 to -48.0% in the e4/4 group (p < 0.0005). In contrast, larger volumes were found in the frontal lobes with increasing e4 gene dose. When compared with controls, volume differences of the right frontal lobe were -11.8% in the -/- AD group, -8.5 in the e4/- group, and -1.4% in the e4/4 group (p = 0.03).
We found smaller volumes in the temporal lobe regions but larger volumes in the frontal lobes with increasing APOE-e4 gene dose in AD patients. These data suggest a region-specific biological effect of the e4 allele in the brains of AD patients.
Asymmetry of brain structures is common to many species and is even present in utero. Some developmental, pathological, and dementing diseases are associated with alterations in normal anatomical asymmetries. Anatomical asymmetries, however, have been only superficially studied in Alzheimer's disease. Recent evidence indicates that the allele epsilon4 of the apolipoprotein E (ApoE), a well known risk factor for Alzheimer's disease, might play a part in determining some brain morphological changes both in normal carriers and in patients with Alzheimer's disease. This study evaluated the effect of the ApoE genotype on hippocampal asymmetry in patients with Alzheimer's disease carrying 0, 1, and 2 copies of the allele. Volumetric right-left differences of the hippocampi were computed in 28 right handed patients with Alzheimer's disease (14 -/-, 9 epsilon4/-, and 5 epsilon4/4) and 30 controls without detectable cognitive deficit. In controls, the right hippocampus was larger than the left, whereas in patients with Alzheimer's disease this asymmetry was progressively reduced with increasing gene dose of the epsilon4 allele, and the asymmetry was reversed in the epsilon4/4 Alzheimer's disease group. The mean right-left volume differences were: 1.2, 0.7, 0.2, and -1.0 in controls, -/-, epsilon4/-, and epsilon4/4 patients, respectively (sex adjusted p for trend=0.017). The data indicate a dose dependent effect of the ApoE epsilon4 allele on hippocampal volume asymmetry in Alzheimer's disease.
In five patients with mesial temporal sclerosis, the authors verified the precision and reproducibility of hippocampal segmentations with deformation-based magnetic resonance (MR) imaging. The overall percentage overlap between automated segmentations was 92.8% (SD, 3.5%), between manual segmentations was 73.1% (SD, 9.5%), and between automated and manual segmentations was 74.8% (SD, 10.3%). Deformation-based hippocampal segmentations provided a precise method of hippocampal volume measurement in this patient population.
The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses — the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
The apolipoprotein E type 4 allele (APOE-epsilon 4) is genetically associated with the common late onset familial and sporadic forms of Alzheimer's disease (AD). Risk for AD increased from 20% to 90% and mean age at onset decreased from 84 to 68 years with increasing number of APOE-epsilon 4 alleles in 42 families with late onset AD. Thus APOE-epsilon 4 gene dose is a major risk factor for late onset AD and, in these families, homozygosity for APOE-epsilon 4 was virtually sufficient to cause AD by age 80.
This chapter addresses the problem of spatial normalization, namely, how to map a single subject's brain image into a standard space. The solution of this problem, assuming that the standard space adopted has a known relationship to other standard spaces, allows for a wide range of voxel-based analyses and facilitates the comparison of different subjects and databases. There are a variety of approaches to these types of spatial normalization. These include the use of spatial basis functions, viscous fluid models, elastic models, and multiresolution approaches. The mathematical foundations and ideas that are applied in a number of contexts include Gauss–Newton-like optimization algorithms and a Bayesian framework used to find maximum a posteriori (MAP) estimates of the deformation fields. The chapter describes methods for performing rapid and automatic nonlabel based nonlinear spatial normalizations. The method which is considered in detail in this chapter minimizes the residual squared difference between the image and a template image of the same modality. The first step of the registration is to correct for differences in position, orientation, and size by optimizing the parameters of an affine transformation. Knowledge of the variability in head size is incorporated into this optimization to obtain a more stable solution and rapid convergence.
40 million people worldwide are now infected with HIV/AIDS, an illness that often leads to rapidly progressing dementia and death. Even so, little is known about how AIDS affects the brain. Using computational anatomy techniques, we mapped how AIDS impacts the corpus callosum (CC) and ventricular system, two systems that show prominent changes on MRI. We (1) identified regions with greatest differences between AIDS patients and healthy controls and (2) correlated specific 3D patterns of structural differences with measures of immune system deterioration and cognitive decline.
51 3D brain MRI scans from 30 non-demented AIDS patients (age: 43.4 years +/- 7.6 SD) and 21 HIV-seronegative controls (age: 39.5 years +/- 12.2) were aligned to ICBM standard space. 3D surface mesh reconstructions of the lateral ventricles and CC were spatially averaged and compared across diagnostic groups. Structural alterations were correlated with viral load, T cell counts, and cognitive impairment.
Statistical maps revealed the 3D profile of ventricular expansion and callosal thinning in AIDS. Specific 3D ventricular changes were linked with immune system decline (CD4+ T cell counts; P < 0.001) and cognitive impairment (P < 0.009), but not viral load. Frontal horn maps distinguished AIDS patients from controls better than occipital and temporal horn measures. T cell decline linked with callosal thinning in anterior regions connecting frontal areas with greatest cortical atrophy.
These maps (1) reveal how brain changes in HIV/AIDS relate to immune decline and impaired cognition, and, after further validation and testing, (2) may offer possible neuroimaging markers for anti-viral drug trials, which gauge how well treatments oppose disease progression in the brain.
Elevated cerebral ventricular volume may be associated with dementia risk and progression. A fully-automated technique that agreed highly with radiological readings was used to estimate lateral ventricle volume on MR scans done at baseline in 1997-99 of 377 subjects in the Cardiovascular Health Study (CHS) from the Pittsburgh Center. 327 subjects were normal or diagnosed with mild cognitive impairment (MCI) at baseline and were evaluated 4 years later. Baseline ventricular volume was analyzed in multivariate models with age, gender, education level, presence and incidence of cerebral infarcts, and dementia category (normal, MCI, or dementia) at baseline and follow-up as fixed effects. Ventricular volume at baseline was significantly higher among subjects normal at baseline and demented 4 years later. Age, gender, education level, and dementia progression were significant factors affecting ventricular volume. Ventricular volume was higher in dementia compared to MCI, higher in MCI compared to controls, and higher in Possible-Alzheimer's-disease (AD) dementia compared to Probable-AD. Larger ventricles in healthy subjects may indicate susceptibility to, or progression of, dementia-related pathology.
The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate our approach, the entire brain, the cerebellum and the head of the caudate have been segmented manually on one of the volumes and mapped back onto all the other volumes using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain. Manual delineation was repeated to test intra-rater variability. Contours were quantitatively compared using a similarity index defined as 2 times the area encircled by both contours divided by the sum of the areas encircled by each contour. This index ranges from 0 to 1 with zero indicating zero overlap and one indicating a perfect agreement between two contours. It is sensitive to both displacement and differences in shape and it is thus preferable to a simple area comparison. Results indicate that the method we proposed can be used to segment accurately and fully automatically large and small structures in high resolution 3D images of the brain. The average similarity indices between the manual and automatic segmentations are 0.96, 0.97, and 0.845 for the whole head, the cerebellum, and the head of the caudate respectively. These numbers are 0.97, 0.97, and 0.88 when two manual delineations are compared. Statistical analysis reveals that the differences in mean similarity indices between the two manual delineations and between the manual delineations and the automatic segmentation method are statistically significant for the whole head and the caudate but not for the cerebellum. It is shown, however, that similarity indices in the range of 0.85 correspond to contours that are virtually undistinguishable.
We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.
We compare high-resolution cardiac gated brain magnetic resonance imaging of 19 young healthy subjects with a small group of demented subjects - mainly probable senile dementia of the Alzheimer type (SDAT) - with varying degrees of ventricular enlargement. Computer-measured ventricular volume demonstrated cyclic ventricular size variations in all subjects. During cardiac systole, the lateral ventricles vary in size, an average of 10% in the young cohort (0.109 ± 0.061); the lateral ventricles enlarge, often after an initial decrease in size. A later volume expansion may occur, but the amount varies between subjects. One healthy 60-year-old subject, whose ventricles were slightly larger than those of the younger individuals, and all of the demented subjects, had less enlargement when compared to the average young subject. Two demented subjects with ventriculomegaly had small fluctuations: one had only 6.9% variation, the other subject with larger ventricles had no enlargement. One individual with SDAT had more than a 24% volume change while another subject's increase was delayed. Our results suggest a complex interplay between blood flow, brain compliance and CSF outflow resistance. This small study further suggests that the choroid plexus plays an important role in systole-related lateral ventricular changes, that changes in choroid plexus function may occur in hydrocephalus and that CSF dynamics may be impaired in SDAT disease.Copyright © 1991 S. Karger AG, Basel
Although apolipoprotein E ε4 is an established risk factor for Alzheimer's disease, its effect on the rate of progression of Alzheimer's disease remains unknown. The purpose of this longitudinal study was to elucidate whether the rate of hippocampal atrophy is a function of the apolipoprotein E genotypes and severity of disease. Fifty-five patients with probable Alzheimer's disease were the subjects. The annual rate of hippocampal atrophy was determined by using magnetic resonance imaging repeated at a 1-year interval. On a two-way analysis of variance, the effect of the apolipoprotein E ε4 allele on hippocampal atrophy was significant, but neither the effect of severity nor the interaction term was significant. In further analysis with one-way analysis of variance, the mean annual rate of hippocampal atrophy was significantly different between the groups of patients with (9.76 ± 4.27%) and without the apolipoprotein E ε4 allele (6.99 ± 4.24%). Apolipoprotein E ε4 dose was significantly correlated with the rate of hippocampal atrophy (rs = 0.277, Spearman rank correlation coefficient), suggesting a gene dose effect. The involvement of the apolipoprotein E ε4 allele in the progression of hippocampal atrophy has implications for therapeutic approaches in Alzheimer's disease and should be taken into consideration in longitudinal studies including clinical drug trials.
This paper offers a new fast algorithm for non-rigid Viscous Fluid Registration of medical images that is at least an order of magnitude faster than the previous method by Christensen et al. [4]. The core algorithm in the fluid registration method is based on a linear elastic deformation of the velocity field of the fluid. Using the linearity of this deformation we derive a convolution filter which we use in a scalespace framework. We also demonstrate that the ’demon’-based registration method of Thirion [13] can be seen as an approximation to the fluid registration method and point to possible problems.
We consider the problem of computing a translation that minimizes the Hausdorff distance between two sets of points. For points in @@@@1 in the worst case there are ⊖(mn) translations at which the Hausdorff distance is a local minimum, where m is the number of points in one set and n is the number in the other. For points in @@@@2 there are ⊖(mn(m + n)) such local minima. We show how to compute the minimal Hausdorff distance in time &Ogr;(mn log mn) for points in @@@@1 and in time &Ogr;(m2n2α(mn)) for points in @@@@2. The results for the one-dimensional case are applied to the problem of comparing polygons under general affine transformations, where we extend the recent results of Arkin et al on polygon resemblance under rigid body motion. The two-dimensional case is closely related to the problem of finding an approximate congruence between two points sets under translation in the plane, as considered by Alt et al.
Lateral ventricular volume asymmetries in schizophrenia were studied using high resolution 3D magnetic resonance imaging in conjunction with segmentation and quantitation techniques. Comparisons were made between two clinical syndromes that have been associated with opposite patterns of functional hemispheric activation, namely an active and a withdrawn syndrome. Ratios of both left to right ventricular volume and left to right ventricle-to-brain ratios differed significantly between the two groups. These results primarily reflected differences in the left ventricular volume, in keeping with previous reports which have usually implicated left hemispheric structural abnormalities in schizophrenia. It is suggested that a syndromal approach might help to resolve some of the inconsistencies in the existing literature on lateralised neuroanatomical differences in schizophrenia.
Patients with advanced cancer, previously untreated, were given 60 mg/m2 cisplatin plus 60 mg/m2 adriamycin by monthly intravenous injections. Signs and symptoms of a predominantly sensory peripheral neuropathy developed in 92% of the patients. Patients complained of dysesthesias and paresthesias in hands and feet. Clinically, there was progressive decrease or loss of tendon reflexes, decreased vibratory sense, and mild decrease in light touch and pin sensation. Distal sensory latencies became prolonged or dropped out completely, but there was little change in motor nerve conduction velocities or motor unit action potentials. Sural nerve biopsies showed loss of large-diameter nerve fibers, with axonal and myelin degeneration.
Clinical criteria for the diagnosis of Alzheimer's disease include insidious onset and progressive impairment of memory and other cognitive functions. There are no motor, sensory, or coordination deficits early in the disease. The diagnosis cannot be determined by laboratory tests. These tests are important primarily in identifying other possible causes of dementia that must be excluded before the diagnosis of Alzheimer's disease may be made with confidence. Neuropsychological tests provide confirmatory evidence of the diagnosis of dementia and help to assess the course and response to therapy. The criteria proposed are intended to serve as a guide for the diagnosis of probable, possible, and definite Alzheimer's disease; these criteria will be revised as more definitive information become available.
An increased frequency of apolipoprotein E E4 allele has been reported in patients with late onset Alzheimer's disease. Apolipoprotein E participates in the transport of cholesterol and other lipids and interferes with the growth and regeneration of both peripheral and central nervous system tissues during development and after injury. Apolipoprotein E is also implicated in synaptogenesis. Apolipoprotein E isoforms differ in binding to amyloid-beta-protein and tau protein in vitro. Here, we wanted to study the effect of apolipoprotein E genotype on the magnitude of damage in the hippocampus, where a marked synapse loss exists in Alzheimer's disease. We measured by magnetic resonance imaging the volumes of the hippocampus, amygdala, and frontal lobes in the three Alzheimer subgroups: patients with 2, 1 or 0 E4 alleles. We also investigated the profile of deficits on tests assessing memory, language, visuospatial, executive, and praxic functions of these Alzheimer subgroups. All Alzheimer patients were at early stage of the disease. We found that Alzheimer patients with E4/4 genotype (N = 5) had smaller volumes of the hippocampus and the amygdala than those with E3/4 (N = 9) and those with E3/3 or E2/3 (N = 12). The difference was significant for the right hippocampus (-54% of control) and the right amygdala (-37% of control). The volumes of the frontal lobes were similar across the Alzheimer subgroups. The patients with E4/4 also showed lowest scores on delayed memory tests and differed from E3/3, 3/2 patients in the list learning test (< 0.05).(ABSTRACT TRUNCATED AT 250 WORDS)
In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system.
The registration method is based on multi-scale, three-dimensional (3D) cross-correlation with an average (n > 300) MR brain image volume aligned with the Talariach stereotaxic space. Once the data set is re-sampled by the transformation recovered by the algorithm, atlas slices can be directly superimposed on the corresponding slices of the re-sampled volume. the use of such a standardized space also allows the direct comparison, voxel to voxel, of two or more data sets brought into stereotaxic space.
With use of a two-tailed Student t test for paired samples, there was no significant difference in the transformation parameters recovered by the automatic algorithm when compared with two manual landmark-based methods (p > 0.1 for all parameters except y-scale, where p > 0.05). Using root-mean-square difference between normalized voxel intensities as an unbiased measure of registration, we show that when estimated and averaged over 60 volumetric MR images in standard space, this measure was 30% lower for the automatic technique than the manual method, indicating better registrations. Likewise, the automatic method showed a 57% reduction in standard deviation, implying a more stable technique. The algorithm is able to recover the transformation even when data are missing from the top or bottom of the volume.
We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method requires no manual identification of points or contours and therefore does not suffer the drawbacks involved in user intervention such as reproducibility and interobserver variability.
To test automated three-dimensional magnetic resonance (MR) imaging morphometry of the human hippocampus, to determine the potential gain in precision compared with conventional manual morphometry.
A canonical three-dimensional MR image atlas was used as a deformable template and automatically matched to three-dimensional MR images of 10 individuals (five healthy and five schizophrenic subjects). A subvolume containing the hippocampus was defined by using 16 landmarks that constrained the automated search for hippocampal boundaries. Transformation of the hippocampus template was automatically performed by using global pattern matching through a sequence of low-then high-dimensional translations, rotations, and scalings.
The average test-retest volume difference measured with the automatic method was 3.1%, compared with the manual test-retest difference of 7.1%. Correlation between automated and manually determined volumes demonstrated the validity of the automated technique (intraclass correlation coefficient = .86).
The automated method estimates hippocampal volumes with less variability (ie, lower variance) than that of manual out-lining.
This paper describes the design, implementation, and results of a technique for creating a three-dimensional (3D) probabilistic surface atlas of the human brain. We have developed, implemented, and tested a new 3D statistical method for assessing structural variations in a database of anatomic images. The algorithm enables the internal surface anatomy of new subjects to be analyzed at an extremely local level. The goal was to quantify subtle and distributed patterns of deviation from normal anatomy by automatically generating detailed probability maps of the anatomy of new subjects. Connected systems of parametric meshes were used to model the internal course of the following structures in both hemispheres: the parieto-occipital sulcus, the anterior and posterior rami of the calcarine sulcus, the cingulate and marginal sulci, and the supracallosal sulcus. These sulci penetrate sufficiently deeply into the brain to introduce an obvious topological decomposition of its volume architecture. A family of surface maps was constructed, encoding statistical properties of local anatomical variation within individual sulci. A probability space of random transformations, based on the theory of Gaussian random fields, was developed to reflect the observed variability in stereotaxic space of the connected system of anatomic surfaces. A complete system of probability density functions was computed, yielding confidence limits on surface variation. The ultimate goal of brain mapping is to provide a framework for integrating functional and anatomical data across many subjects and modalities. This task requires precise quantitative knowledge of the variations in geometry and location of intracerebral structures and critical functional interfaces. The surface mapping and probabilistic tec...
The present study analyzes the relationship between cortical and subcortical brain volumes in patients with Huntington's disease. The brains of seven patients with a clinical diagnosis and positive family history of Huntington's disease and 12 controls were collected at autopsy with consent from relatives. Detailed clinical assessments were available for all study subjects with genotype confirmation for patients with Huntington's disease. Volume analysis of the brain on serial 3-mm coronal slices was performed as previously described. All patients with Huntington's disease exhibited significant brain atrophy resulting from volume reductions in both cortical and subcortical grey matter. Atrophy of the cortex was relatively uniform, although the medial temporal lobe structures were spared. The caudate nucleus and putamen were strikingly reduced in all cases and this atrophy correlated with the severity of cortical atrophy, suggesting an associated disease process. The rate of cortical but not subcortical atrophy correlated with CAG repeat numbers. Loss of frontal white matter correlated with both cortical and striatal atrophy. Age of onset of chorea correlated with the amount of subcortical atrophy, while duration of chorea correlated negatively with atrophy of the white matter. These results suggest a more widespread and global disease process in patients with Huntington's disease.
Schizophrenia is characterized by subcortical and cortical brain abnormalities. Evidence indicates that some nonpsychotic relatives of schizophrenic patients manifest biobehavioral abnormalities, including brain abnormalities. The goal of this study was to determine whether amygdala-hippocampal and thalamic abnormalities are present in relatives of schizophrenic patients.
Subjects were 28 nonpsychotic, and nonschizotypal, first-degree adult relatives of schizophrenics and 26 normal control subjects. Sixty contiguous 3 mm coronal, T1-weighted 3D magnetic resonance images of the brain were acquired on a 1.5 Tesla magnet. Cortical and subcortical gray and white matter and cerebrospinal fluid (CSF) were segmented using a semi-automated intensity contour mapping algorithm. Analyses of covariance of the volumes of brain regions, controlling for expected intellectual (i.e., reading) ability and diagnosis, were used to compare groups.
The main findings were that relatives had significant volume reductions bilaterally in the amygdala-hippocampal region and thalamus compared to control subjects. Marginal differences were noted in the pallidum, putamen, cerebellum, and third and fourth ventricles.
Results support the hypothesis that core components of the vulnerability to schizophrenia include structural abnormalities in the thalamus and amygdala-hippocampus. These findings require further work to determine if the abnormalities are an expression of the genetic liability to schizophrenia.
The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free-form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate our approach, the entire brain, the cerebellum, and the head of the caudate have been segmented manually by two raters on one of the volumes (the reference volume) and mapped back onto all the other volumes, using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain. Manual delineation was performed twice by the same two raters to test inter- and intrarater variability. For the brain and the cerebellum, results indicate that for each rater, contours obtained manually and contours obtained automatically by deforming his own atlas are virtually indistinguishable. Furthermore, contours obtained manually by one rater and contours obtained automatically by deforming this rater's own atlas are more similar than contours obtained manually by two raters. For the caudate, manual intra- and interrater similarity indexes remain slightly better than manual versus automatic indexes, mainly because of the spatial resolution of the images used in this study. Qualitative results also suggest that this method can be used for the segmentation of more complex structures, such as the hippocampus.
The cognitive continuum in the elderly population can be conceptually divided into those who are functioning normally (control subjects), those with a mild cognitive impairment (MCI), and those with probable AD.
To test the hypothesis that the annualized rates of hippocampal atrophy differ as a function of both baseline and change in clinical group membership (control, MCI, or AD).
The authors identified 129 subjects from the Mayo Clinic AD Research Center/AD Patient Registry who met established criteria for normal control subjects, MCI, or probable AD, both at entry and at the time of a subsequent clinical follow-up evaluation 3 +/- 1 years later. Each subject underwent an MRI examination of the head at the time of the initial assessment and at follow-up clinical assessment; the annualized percentage change in hippocampal volume was computed. Subjects who were classified as controls or patients with MCI at baseline could either remain cognitively stable or could decline to a lower functioning group over the period of observation.
The annualized rates of hippocampal volume loss for each of the three initial clinical groups decreased progressively in the following order: AD > MC > control. Within the control and MCI groups, those who declined had a significantly greater rate of volume loss than those who remained clinically stable. The mean annualized rates of hippocampal atrophy by follow-up clinical group were: control-stable 1.73%, control-decliner 2.81%, MCI-stable 2.55%, MCI-decliner 3.69%, AD 3. 5%.
Rates of hippocampal atrophy match both baseline cognitive status and the change in cognitive status over time in elderly persons who lie along the cognitive continuum from normal to MCI to AD.
The sensitivity of MRI volumetric measures to detect cognitive dysfunction is examined in 39 participants of an epidemiological field study (age 75-85, MMSE 19-30). According to Clinical dementia rating (CDR), 17 subjects had normal cognition (CDR 0), 12 had questionable (CDR 0.5) and 10 mild dementia (CDR 1). Discriminant analysis based on four hippocampal measures resulted in a correct classification of 76.9% of all subjects. Left-sided and posterior hippocampal measures were more responsible for group discrimination than right-sided and anterior measures. In CDR 0.5, a significant hippocampal volume reduction of 14.3% vs.11.3% (left vs. right) relative to normal was found. The right hippocampus was significantly greater than the left in CDR 0 and CDR 0.5, but not in CDR 1. The magnitude of non-directional hippocampal asymmetry increased with decreasing cognitive state. We conclude that hippocampal atrophy is sensitive to detect cognitive dysfunction and subjects at risk for Alzheimer's disease in the elderly population.
Local alterations in morphological parameters are poorly characterized in several brain regions widely implicated in schizophrenia neuropathology.
Surface-based anatomical modeling was applied to magnetic resonance data to obtain three-dimensional (3D) average anatomical maps and measures of location, shape, asymmetry, and volume for the lateral ventricles, hippocampus, amygdala, and superior temporal gyrus in schizophrenic (n = 25; 15 male) and normal subjects (n = 28; 15 male) matched for demographic variables. For all regions, intra-group variability was visualized and group differences assessed statistically to discriminate local alterations in anatomy across sex and diagnosis.
Posterior hippocampal volumes, lengths, and widths were reduced in patients. The right amygdala showed volume increases in schizophrenia patients versus controls. Ventricular enlargements, pronounced in the left hemisphere, occurred in the superior and lateral dimensions in patients, and these effects interacted with gender. Superior horn anterior extremes, inferior horn volumes, and hippocampal asymmetries exhibited gender effects. Significant group differences were absent in superior temporal gyrus parameters. Finally, regional variability profiles differed across groups.
Clear morphometric differences of the lateral ventricles, hippocampus, and amygdala indicate regional displacements and shape distortions in several functional systems in schizophrenia. Alterations in these structures as mapped in 3D may provide the foundation for establishing brain abnormalities not previously defined at such a local level.