Discriminant Analysis of Brain Imaging Data Identifies Subjects With Early Alzheimer's Disease

1997 ◽  
Vol 9 (S1) ◽  
pp. 229-235 ◽  
Author(s):  
Stanley I. Rapoport
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Discriminant Analysis ◽  
Brain Imaging ◽  
Imaging Modality ◽  
Functional Brain Imaging ◽  
Cognitive Profiles ◽  
Imaging Data ◽  
Patients At Risk

In vivo functional brain imaging provides an opportunity to quantify and localize functional deficits associated with Alzheimer's disease (AD), in relation to dementia severity and heterogeneous cognitive profiles. Such imaging also provides a basis for distinguishing AD from other causes of dementia and for making an early diagnosis of disease. One imaging modality that can elucidate AD is positron emission tomography (PET), which is used to measure regional cerebral metabolic rates for glucose (rCMRglc) and regional cerebral blood flow (rCBF). Resting-state measurements with PET, when related to cognitive profiles in longitudinal studies, indicate that specific cognitive defects are preceded and predicted by reductions in rCMRglc in regions subserving the cognitive functions tested. Metabolic reductions and right/left metabolic asymmetries can be used to convert a “possible” to a “probable” diagnosis of AD by the National Institute of Neurological and Communicative Disorders and Stroke–Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria. Furthermore, discriminant analyses of PET metabolic patterns can identify patients at risk for AD with mild memory deficits as having probable AD. In the future, stimulation PET studies should augment the power of this discriminant analysis.

[18F]-florbetaben PET/CT Imaging in the Alzheimer’s Disease Mouse Model APPswe/PS1dE9

2018 ◽  
Vol 16 (1) ◽  
pp. 49-55 ◽  
Author(s):  
J. Stenzel ◽  
C. Rühlmann ◽  
T. Lindner ◽  
S. Polei ◽  
S. Teipel ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Mouse Model ◽  
New Drugs ◽  
Imaging Data ◽  
Wild Type ◽  
Preclinical Research ◽  
Standardized Uptake Values ◽  
Pet Ct

Background: Positron-emission-tomography (PET) using 18F labeled florbetaben allows noninvasive in vivo-assessment of amyloid-beta (Aβ), a pathological hallmark of Alzheimer’s disease (AD). In preclinical research, [<sup>18</sup>F]-florbetaben-PET has already been used to test the amyloid-lowering potential of new drugs, both in humans and in transgenic models of cerebral amyloidosis. The aim of this study was to characterize the spatial pattern of cerebral uptake of [<sup>18</sup>F]-florbetaben in the APPswe/ PS1dE9 mouse model of AD in comparison to histologically determined number and size of cerebral Aβ plaques. Methods: Both, APPswe/PS1dE9 and wild type mice at an age of 12 months were investigated by smallanimal PET/CT after intravenous injection of [<sup>18</sup>F]-florbetaben. High-resolution magnetic resonance imaging data were used for quantification of the PET data by volume of interest analysis. The standardized uptake values (SUVs) of [<sup>18</sup>F]-florbetaben in vivo as well as post mortem cerebral Aβ plaque load in cortex, hippocampus and cerebellum were analyzed. Results: Visual inspection and SUVs revealed an increased cerebral uptake of [<sup>18</sup>F]-florbetaben in APPswe/ PS1dE9 mice compared with wild type mice especially in the cortex, the hippocampus and the cerebellum. However, SUV ratios (SUVRs) relative to cerebellum revealed only significant differences in the hippocampus between the APPswe/PS1dE9 and wild type mice but not in cortex; this differential effect may reflect the lower plaque area in the cortex than in the hippocampus as found in the histological analysis. Conclusion: The findings suggest that histopathological characteristics of Aβ plaque size and spatial distribution can be depicted in vivo using [<sup>18</sup>F]-florbetaben in the APPswe/PS1dE9 mouse model.

scholarly journals Integrating Transcriptomics, Genomics, and Imaging in Alzheimer's Disease: A Federated Model

2021 ◽  
Author(s):  
Jianfeng Wu ◽  
Yanxi Chen ◽  
Panwen Wang ◽  
Richard J Caselli ◽  
Paul M Thompson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Imaging Modality ◽  
Association Studies ◽  
Imaging Genetics ◽  
Health Concern ◽  
Genome Wide Association Studies ◽  
Imaging Data ◽  
Stable Performance

Alzheimer's disease (AD) affects more than 1 in 9 people age 65 and older and becomes an urgent public health concern as the global population ages. In clinical practice, structural magnetic resonance imaging (sMRI) is the most accessible and widely used diagnostic imaging modality. Additionally, genome-wide association studies (GWAS) and transcriptomic, the study of gene expression, also play an important role in understanding AD etiology and progression. Sophisticated imaging genetics systems have been developed to discover genetic factors that consistently affect brain function and structure. However, most studies to date focused on the relationships between brain sMRI and GWAS or brain sMRI and transcriptomics. To our knowledge, few methods have been developed to discover and infer multimodal relationships among sMRI, GWAS, and transcriptomics. To address this, we propose a novel federated model, Genotype-Expression-Imaging Data Integration (GEIDI), to identify genetic and transcriptomic influences on brain sMRI measures. The relationships between brain imaging measures and gene expression are allowed to depend on a person's genotype at the single-nucleotide polymorphism (SNP) level, making the inferences adaptive and personalized. We performed extensive experiments on publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Experimental results demonstrated our proposed method outperformed state-of-the-art expression quantitative trait loci (eQTL) methods for detecting genetic and transcriptomic factors related to AD and has stable performance when data are integrated from multiple sites. Our GEIDI approach may offer novel insights into the relationship among image biomarkers, genotypes, and gene expression and help discover novel genetic targets for potential AD drug treatments.

scholarly journals A non-parametric approach for co-analysis of multi-modal brain imaging data: Application to Alzheimer's disease

NeuroImage ◽  
2006 ◽  
Vol 30 (3) ◽  
pp. 768-779 ◽  
Author(s):  
Satoru Hayasaka ◽  
An-Tao Du ◽  
Audrey Duarte ◽  
John Kornak ◽  
Geon-Ho Jahng ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Imaging ◽  
Parametric Approach ◽  
Imaging Data ◽  
Data Application ◽  
Brain Imaging Data ◽  
Non Parametric

Functional brain imaging in subjects at genetic risk for Alzheimer's disease

2000 ◽  
Vol 21 ◽  
pp. 141 ◽  
Author(s):  
Eric M. Reiman ◽  
Richard J. Caselli ◽  
Kewei Chen ◽  
Gene E. Alexander ◽  
Daniel Bandy ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Imaging ◽  
Genetic Risk ◽  
Functional Brain Imaging ◽  
Functional Brain

scholarly journals Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer’s disease

2018 ◽  
Author(s):  
Jennifer D. Whitesell ◽  
Alex R. Buckley ◽  
Joseph E. Knox ◽  
Leonard Kuan ◽  
Nile Graddis ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Imaging ◽  
Mouse Models ◽  
Amyloid Β ◽  
Retrosplenial Cortex ◽  
Whole Brain ◽  
Mutant Forms ◽  
Tg2576 Mice

AbstractA variety of Alzheimer’s disease (AD) mouse models overexpress mutant forms of human amyloid precursor protein (APP), producing high levels of amyloid β (Aβ) and forming plaques However, the degree to which these models mimic spatiotemporal patterns of Aβ deposition in brains of AD patients is unknown. Here, we mapped the spatial distribution of Aβ plaques across ages in three APP-overexpression mouse lines (APP/PS1, Tg2576, hAPP-J20) using in vivo labeling with methoxy-X04, high throughput whole brain imaging, and an automated informatics pipeline. Images were acquired with high resolution serial 2-photon tomography and labeled plaques were detected using custom-built segmentation algorithms. Image series were registered to the Allen Mouse Brain Common Coordinate Framework, a 3D reference atlas, enabling automated brain-wide quantification of plaque density, number, and location. In both APP/PS1 and Tg2576 mice, plaques were identified first in isocortex, followed by olfactory, hippocampal, and cortical subplate areas. In hAPP-J20 mice, plaque density was highest in hippocampal areas, followed by isocortex, with little to no involvement of olfactory or cortical subplate areas. Within the major brain divisions, distinct regions were identified with high (or low) plaque accumulation; e.g., the lateral visual area within the isocortex of APP/PS1 mice had relatively higher plaque density compared with other cortical areas, while in hAPP-J20 mice, plaques were densest in the ventral retrosplenial cortex. In summary, we show how whole brain imaging of amyloid pathology in mice reveals the extent to which a given model recapitulates the regional Aβ deposition patterns described in AD.

scholarly journals Brain imaging data quality and denoising: Impact on the study of sex differences in Alzheimer’s disease

2020 ◽  
Vol 16 (S5) ◽  
Author(s):  
Jessica ZK Caldwell ◽  
Zhengshi Yang ◽  
Nikki Kaplan ◽  
MacKenzie Leavitt ◽  
Justin Miller ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Sex Differences ◽  
Data Quality ◽  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data
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