Article

3D PIB and CSF biomarker associations with hippocampal atrophy in ADNI subjects.

Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States.
Neurobiology of aging (Impact Factor: 4.85). 08/2010; 31(8):1284-303. DOI: 10.1016/j.neurobiolaging.2010.05.003
Source: PubMed

ABSTRACT Cerebrospinal fluid (CSF) measures of Ab and tau, Pittsburgh Compound B (PIB) imaging and hippocampal atrophy are promising Alzheimer's disease biomarkers yet the associations between them are not known. We applied a validated, automated hippocampal labeling method and 3D radial distance mapping to the 1.5T structural magnetic resonance imaging (MRI) data of 388 ADNI subjects with baseline CSF Ab(42), total tau (t-tau) and phosphorylated tau (p-tau(181)) and 98 subjects with positron emission tomography (PET) imaging using PIB. We used linear regression to investigate associations between hippocampal atrophy and average cortical, parietal and precuneal PIB standardized uptake value ratio (SUVR) and CSF Ab(42), t-tau, p-tau(181), t-tau/Ab(42) and p-tau(181)/Ab(42). All CSF measures showed significant associations with hippocampal volume and radial distance in the pooled sample. Strongest correlations were seen for p-tau(181), followed by p-tau(181)/Ab(42) ratio, t-tau/Ab(42) ratio, t-tau and Ab(42). p-tau(181) showed stronger correlation in ApoE4 carriers, while t-tau showed stronger correlation in ApoE4 noncarriers. Of the 3 PIB measures the precuneal SUVR showed strongest associations with hippocampal atrophy.

0 Followers
 · 
98 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Since the launch in 2003 of the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the USA, ever growing, similarly oriented consortia have been organized and assembled around the world. The various accomplishments of ADNI have contributed substantially to a better understanding of the underlying physiopathology of aging and Alzheimer's disease (AD). These accomplishments are basically predicated in the trinity of multimodality, standardization and sharing. This multimodality approach can now better identify those subjects with AD-specific traits that are more likely to present cognitive decline in the near future and that might represent the best candidates for smaller but more efficient therapeutic trials - trials that, through gained and shared knowledge, can be more focused on a specific target or a specific stage of the disease process. In summary, data generated from ADNI have helped elucidate some of the pathophysiological mechanisms underpinning aging and AD pathology, while contributing to the international effort in setting the groundwork for biomarker discovery and establishing standards for early diagnosis of AD.
    Alzheimer's Research and Therapy 10/2014; 6(5):62. DOI:10.1186/s13195-014-0062-5 · 3.50 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold-based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease-related brain regions useful for disease diagnosis. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    Human Brain Mapping 10/2014; 36(2). DOI:10.1002/hbm.22642 · 6.92 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The blood-brain barrier (BBB) limits entry of blood-derived products, pathogens, and cells into the brain that is essential for normal neuronal functioning and information processing. Post-mortem tissue analysis indicates BBB damage in Alzheimer's disease (AD). The timing of BBB breakdown remains, however, elusive. Using an advanced dynamic contrast-enhanced MRI protocol with high spatial and temporal resolutions to quantify regional BBB permeability in the living human brain, we show an age-dependent BBB breakdown in the hippocampus, a region critical for learning and memory that is affected early in AD. The BBB breakdown in the hippocampus and its CA1 and dentate gyrus subdivisions worsened with mild cognitive impairment that correlated with injury to BBB-associated pericytes, as shown by the cerebrospinal fluid analysis. Our data suggest that BBB breakdown is an early event in the aging human brain that begins in the hippocampus and may contribute to cognitive impairment. Copyright © 2015 Elsevier Inc. All rights reserved.
    Neuron 01/2015; 85(2):296-302. DOI:10.1016/j.neuron.2014.12.032 · 15.98 Impact Factor

Full-text (2 Sources)

Download
55 Downloads
Available from
May 21, 2014