Showing papers by "Shannon L. Risacher published in 2017"

Metabolic network failures in Alzheimer's disease: A biochemical road map

Abstract: Introduction The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. Methods Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ 1–42 , tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. Discussion Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

Novel genetic loci associated with hippocampal volume

Abstract: The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer’s disease (rg=−0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.

Alzheimer disease brain atrophy subtypes are associated with cognition and rate of decline.

Abstract: Objective: To test the hypothesis that cortical and hippocampal volumes, measured in vivo from volumetric MRI (vMRI) scans, could be used to identify variant subtypes of Alzheimer disease (AD) and to prospectively predict the rate of clinical decline. Methods: Amyloid-positive participants with AD from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 1 and ADNI2 with baseline MRI scans (n = 229) and 2-year clinical follow-up (n = 100) were included. AD subtypes (hippocampal sparing [HpSp MRI ], limbic predominant [LP MRI ], typical AD [tAD MRI ]) were defined according to an algorithm analogous to one recently proposed for tau neuropathology. Relationships between baseline hippocampal volume to cortical volume ratio (HV:CTV) and clinical variables were examined by both continuous regression and categorical models. Results: When participants were divided categorically, the HpSp MRI group showed significantly more AD-like hypometabolism on 18 F-fluorodeoxyglucose-PET ( p p MRI also showed faster subsequent clinical decline than participants with LP MRI on the Alzheimer9s Disease Assessment Scale, 13-Item Subscale (ADAS-Cog 13 ), Mini-Mental State Examination (MMSE), and Functional Assessment Questionnaire (all p MRI on the MMSE and Clinical Dementia Rating Sum of Boxes (CDR-SB) (both p 13 score ( p Conclusions: AD subtypes with phenotypes consistent with those observed with tau neuropathology can be identified in vivo with vMRI. An increased HV:CTV ratio was predictive of faster clinical decline in participants with AD who were clinically indistinguishable at baseline except for a greater dysexecutive presentation.

Plasma Tau Association with Brain Atrophy in Mild Cognitive Impairment and Alzheimer's Disease.

Abstract: BACKGROUND Peripheral (plasma) and central (cerebrospinal fluid, CSF) measures of tau are higher in Alzheimer's disease (AD) relative to prodromal stages and controls. While elevated CSF tau concentrations have been shown to be associated with lower grey matter density (GMD) in AD-specific regions, this correlation has yet to be examined for plasma in a large study. OBJECTIVE Determine the neuroanatomical correlates of plasma tau using voxel-based analysis. METHODS Cross-sectional data for 508 ADNI participants were collected for clinical, plasma total-tau (t-tau), CSF amyloid (Aβ42) and tau, and MRI variables. The relationship between plasma tau and GMD and between CSF t-tau and GMD were assessed on a voxel-by-voxel basis using regression models. Age, sex, APOEɛ4 status, diagnosis, and total intracranial volume were used as covariates where appropriate. Participants were defined as amyloid positive (Aβ+) if CSF Aβ42 was <192 pg/mL. RESULTS Plasma tau was negatively correlated with GMD in the medial temporal lobe (MTL), precuneus, thalamus, and striatum. The associations with thalamus and striatum were independent of diagnosis. A negative correlation also existed between plasma tau and GMD in Aβ+ participants in the MTL, precuneus, and frontal lobe. When compared to CSF t-tau, plasma tau showed a notably different associated brain atrophy pattern, with only small overlapping regions in the fusiform gyrus. CONCLUSION Plasma tau may serve as a non-specific marker for neurodegeneration but is still relevant to AD considering low GMD was associated with plasma tau in Aβ+ participants and not Aβ-participants.

Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer's Disease.

Abstract: Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding.

Targeted metabolomics and medication classification data from participants in the ADNI1 cohort.

Abstract: Alzheimer's disease (AD) is the most common neurodegenerative disease presenting major health and economic challenges that continue to grow. Mechanisms of disease are poorly understood but significant data point to metabolic defects that might contribute to disease pathogenesis. The Alzheimer Disease Metabolomics Consortium (ADMC) in partnership with Alzheimer Disease Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for AD. Using targeted and non- targeted metabolomics and lipidomics platforms we are mapping metabolic pathway and network failures across the trajectory of disease. In this report we present quantitative metabolomics data generated on serum from 199 control, 356 mild cognitive impairment and 175 AD subjects enrolled in ADNI1 using AbsoluteIDQ-p180 platform, along with the pipeline for data preprocessing and medication classification for confound correction. The dataset presented here is the first of eight metabolomics datasets being generated for broad biochemical investigation of the AD metabolome. We expect that these collective metabolomics datasets will provide valuable resources for researchers to identify novel molecular mechanisms contributing to AD pathogenesis and disease phenotypes.

Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

Abstract: Motivation Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity Results We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype Availability and implementation The R code and sample data are freely available at http://wwwiuedu/shenlab/tools/gwasmodule/ Contact shenli@iuedu Supplementary information Supplementary data are available at Bioinformatics online

Genetic Architecture of Subcortical Brain Structures in Over 40,000 Individuals Worldwide

Abstract: Subcortical brain structures are integral to motion, consciousness, emotions, and learning. We identified common genetic variation related to the volumes of nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen, and thalamus, using genome-wide association analyses in over 40,000 individuals from CHARGE, ENIGMA and the UK-Biobank. We show that variability in subcortical volumes is heritable, and identify 25 significantly associated loci (20 novel). Annotation of these loci utilizing gene expression, methylation, and neuropathological data identified 62 candidate genes implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.

Two-dimensional enrichment analysis for mining high-level imaging genetic associations

Abstract: Enrichment analysis has been widely applied in the genome-wide association studies, where gene sets corresponding to biological pathways are examined for significant associations with a phenotype to help increase statistical power and improve biological interpretation. In this work, we expand the scope of enrichment analysis into brain imaging genetics, an emerging field that studies how genetic variation influences brain structure and function measured by neuroimaging quantitative traits (QT). Given the high dimensionality of both imaging and genetic data, we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly considers meaningful gene sets (GS) and brain circuits (BC) and examines whether any given GS–BC pair is enriched in a list of gene–QT findings. Using gene expression data from Allen Human Brain Atlas and imaging genetics data from Alzheimer’s Disease Neuroimaging Initiative as test beds, we present an IGEA framework and conduct a proof-of-concept study. This empirical study identifies 25 significant high-level two-dimensional imaging genetics modules. Many of these modules are relevant to a variety of neurobiological pathways or neurodegenerative diseases, showing the promise of the proposal framework for providing insight into the mechanism of complex diseases.

Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via A Novel Structured SCCA Approach.

Abstract: Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations.

Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model.

Abstract: With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer’s Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations of neuroimaging phenotypes. Although some machine learning models were newly presented to capture the longitudinal patterns in genotype-phenotype association study, most of them required fixed longitudinal structures of prediction tasks and could not automatically learn the interrelations among longitudinal prediction tasks. To address this challenge, we proposed a novel temporal structure auto-learning model to automatically uncover longitudinal genotype-phenotype interrelations and utilized such interrelated structures to enhance phenotype prediction in the meantime. We conducted longitudinal phenotype prediction experiments on the ADNI cohort including 3,123 SNPs and two types of imaging markers, VBM and FreeSurfer. Empirical results demonstrated advantages of our proposed model over the counterparts. Moreover, available literature was identified for our top selected SNPs, which demonstrated the rationality of our prediction results. An executable program is available online at https://github.com/littleq1991/sparse_lowRank_regression.

Pattern Discovery in Brain Imaging Genetics via SCCA Modeling with a Generic Non-convex Penalty

Abstract: Brain imaging genetics intends to uncover associations between genetic markers and neuroimaging quantitative traits. Sparse canonical correlation analysis (SCCA) can discover bi-multivariate associations and select relevant features, and is becoming popular in imaging genetic studies. The L1-norm function is not only convex, but also singular at the origin, which is a necessary condition for sparsity. Thus most SCCA methods impose [Formula: see text]-norm onto the individual feature or the structure level of features to pursuit corresponding sparsity. However, the [Formula: see text]-norm penalty over-penalizes large coefficients and may incurs estimation bias. A number of non-convex penalties are proposed to reduce the estimation bias in regression tasks. But using them in SCCA remains largely unexplored. In this paper, we design a unified non-convex SCCA model, based on seven non-convex functions, for unbiased estimation and stable feature selection simultaneously. We also propose an efficient optimization algorithm. The proposed method obtains both higher correlation coefficients and better canonical loading patterns. Specifically, these SCCA methods with non-convex penalties discover a strong association between the APOE e4 rs429358 SNP and the hippocampus region of the brain. They both are Alzheimer's disease related biomarkers, indicating the potential and power of the non-convex methods in brain imaging genetics.

Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules

Abstract: Motivation Network‐based genome‐wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue‐free networks without reflecting phenotypic specificity. Results We propose a novel module identification framework for imaging genetic studies using the tissue‐specific functional interaction network. Our method includes three steps: (i) re‐prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re‐prioritized genes; and (iii) identify phenotype‐relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG‐PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala‐specific functional interaction network. The proposed network‐based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue‐specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype. Availability and implementation The R code and sample data are freely available at http://www.iu.edu/shenlab/tools/gwasmodule/ Contact shenli@iu.edu Supplementary information Supplementary data are available at Bioinformatics online.

Plasma Tau Association with Brain Atrophy in Mild Cognitive Impairment and Alzheimer’s Disease

Abstract: BACKGROUND Peripheral (plasma) and central (cerebrospinal fluid, CSF) measures of tau are higher in Alzheimer's disease (AD) relative to prodromal stages and controls. While elevated CSF tau concentrations have been shown to be associated with lower grey matter density (GMD) in AD-specific regions, this correlation has yet to be examined for plasma in a large study. OBJECTIVE Determine the neuroanatomical correlates of plasma tau using voxel-based analysis. METHODS Cross-sectional data for 508 ADNI participants were collected for clinical, plasma total-tau (t-tau), CSF amyloid (Aβ42) and tau, and MRI variables. The relationship between plasma tau and GMD and between CSF t-tau and GMD were assessed on a voxel-by-voxel basis using regression models. Age, sex, APOEɛ4 status, diagnosis, and total intracranial volume were used as covariates where appropriate. Participants were defined as amyloid positive (Aβ+) if CSF Aβ42 was <192 pg/mL. RESULTS Plasma tau was negatively correlated with GMD in the medial temporal lobe (MTL), precuneus, thalamus, and striatum. The associations with thalamus and striatum were independent of diagnosis. A negative correlation also existed between plasma tau and GMD in Aβ+ participants in the MTL, precuneus, and frontal lobe. When compared to CSF t-tau, plasma tau showed a notably different associated brain atrophy pattern, with only small overlapping regions in the fusiform gyrus. CONCLUSION Plasma tau may serve as a non-specific marker for neurodegeneration but is still relevant to AD considering low GMD was associated with plasma tau in Aβ+ participants and not Aβ-participants.

Predicting Interrelated Alzheimer's Disease Outcomes via New Self-Learned Structured Low-Rank Model.

Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder. As the prodromal stage of AD, Mild Cognitive Impairment (MCI) maintains a good chance of converting to AD. How to efficaciously detect this conversion from MCI to AD is significant in AD diagnosis. Different from standard classification problems where the distributions of classes are independent, the AD outcomes are usually interrelated (their distributions have certain overlaps). Most of existing methods failed to examine the interrelations among different classes, such as AD, MCI conversion and MCI non-conversion. In this paper, we proposed a novel self-learned low-rank structured learning model to automatically uncover the interrelations among different classes and utilized such interrelated structures to enhance classification. We conducted experiments on the ADNI cohort data. Empirical results demonstrated advantages of our model.

Transcriptome-Guided Imaging Genetic Analysis via a Novel Sparse CCA Algorithm.

Abstract: Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. In imaging genetics, genes associated with a phenotype should be at least expressed in the phenotypical region. We study the association between the genotype and amyloid imaging data and propose a transcriptome-guided SCCA framework that incorporates the gene expression information into the SCCA criterion. An alternating optimization method is used to solve the formulated problem. Although the problem is not biconcave, a closed-form solution has been found for each subproblem. The results on real data show that using the gene expression data to guide the feature selection facilities the detection of genetic markers that are not only associated with the identified QTs, but also highly expressed there.

Klk8 as a modulator of alzheimer’s disease pathology: neuroimaging genetics

Abstract: Background:Even though APP, PSEN1 and PSEN2 are major causative genes for Alzheimer’s disease (AD), they are relatively rare. We are perusing to find other novel genes in the disease progression. Since SORL1 gene was suggested in playing a protective role against amyloid beta secretion, mutation in SORL1 could be involved in the disease progression.Methods:We performed a complex genetic screening by NGS for 50 genes, including SORL1 gene. NGS data was confirmed by standard sequencing. We performed PolyPhen2, SIFT and 3D protein structure prediction for the rare mutations. Results:We found several rare probable novel SORL1variants, such as V277I, G511R, D862E, R1159Q, T1483A, G1524R and V2097I. We also detected the common A528T mutation among our patients. Most of them were predicted as possible damaging from the predictions. Conclusions: It is too early to say that these mutations in SORL1 could be involved in disease progression. Mutations in SORL1 may be involved in loss-offunction mechanisms. Previous segregation analyses revealed that rare SORL1 mutations could be also associated with early onset familial AD.

A Fast SCCA Algorithm for Big Data Analysis in Brain Imaging Genetics

Abstract: Mining big data in brain imaging genetics is an emerging topic in brain science. It can uncover meaningful associations between genetic variations and brain structures and functions. Sparse canonical correlation analysis (SCCA) is introduced to discover bi-multivariate correlations with feature selection. However, these SCCA methods cannot be directly applied to big brain imaging genetics data due to two limitations. First, they have cubic complexity in the size of the matrix involved and are computational and memory intensive when the matrix becomes large. Second, the parameters in an SCCA method need to be fine-tuned in advance. This further dramatically increases the computational time, and gets severe in high-dimensional scenarios. In this paper, we propose two fast and efficient algorithms to speed up the structure-aware SCCA (S2CCA) implementations without modification to the original SCCA models. The fast algorithms employ a divide-and-conquer strategy and are easy to implement. The experimental results, compared with conventional algorithms, show that our algorithms reduce the time usage significantly. Specifically, the fast algorithms improve the computational efficiency by tens to hundreds of times compared to conventional algorithms. Besides, our algorithms yield similar correlation coefficients and canonical loading profiles to the conventional implementations. Our fast algorithms can be easily parallelized to further reduce the computational time. This indicates that the proposed fast scalable SCCA algorithms can be a powerful tool for big data analysis in brain imaging genetics.

A robust, simplified braak-type classification scheme for flortaucipir f-18 tau pet images

Abstract: correlation between global FMM retention and brain atrophy in any groups. There were positive correlations between THK retention in the frontal, parietal, occipital cortices or precuneus and FMM retention in the frontal, parietal cortices or precuneus. EOAD had weak positive correlation between THK and FMM retention only in the occipital cortex. Conclusions: LOAD showed gradual increase in both tau and amyloid and those two pathologies have association to each other. Whereas, in EOAD, tau and amyloid may develop more abruptly and independently. Brain atrophy was associated with tau burden inEOAD, however,was not correlatedwith amyloid burden in EOAD or LOAD. These findings suggest LOAD and EOAD may have different courses of pathomechanism.

Axonal density is associated with subjective cognitive decline (scd) in older adults assessed using the cognitive change index

Abstract: (FA). A. The ROI in red denotes the right stria terminalis. B. FA of diffusion tensor imaging (DTI) negatively correlated with CCI-12 (episodic memory) with p1⁄40.0007. Age, sex and education were adjusted in the linear regression analysis. Green dots denote cognitively normal (CN); blue dots denote subjective cognitive decline (SCD); red dots denote mild cognitive impairment (MCI); and the black solid line denotes the regression line.

Associations between social network characteristics and cortical thickness and hippocampal volume in cognitively normal subjects

Abstract: Note: QNPs *p<.05. **p<.01; tw NETWORK CHARACTERISTICS AND CORTICALTHICKNESS AND HIPPOCAMPALVOLUME IN COGNITIVELY NORMAL SUBJECTS Brea L. Perry, Evan Finley, Shannon L. Risacher, Eileen F. Tallman, Liana G. Apostolova, Andrew J. Saykin, Indiana University, Bloomington, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA; Indiana Alzheimer Disease Center, Indianapolis, IN, USA; Indiana University School of Medicine, Indianapolis, IN, USA. Contact e-mail: evfinley@iu.edu

Linking measures of subjective cognition across international aging studies using item response theory

Abstract: Department of Psychiatry, Charit e – Universit€atsmedizin Berlin, Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry, Charit e Universit€atsmedizin Berlin, Berlin, Germany; German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Department of Psychiatry and Psychotherapy, University Medical Center G€ottingen, G€ottingen, Germany; Department of Psychiatry and Psychotherapy, Eberhard Karls University, T€ubingen, Germany; German Center for Neurodegenerative Diseases (DZNE), T€ubingen, Germany; German Center for Neurodegenerative Diseases (DZNE), Bonn-Cologne, Germany; Department of Psychiatry, University of Cologne, Cologne, Germany; University College London, London, United Kingdom. Contact e-mail: david.berron@med.ovgu.de

Brain Atrophy in Early and Late Onset Alzheimer’s Disease and Suspected Non-Alzheimer’s Pathophysiology (P4.098)

Abstract: Objective: To detect the atrophy patterns in amnestic EOAD, LOAD and SNAP. Background: Approximately 5% of Alzheimer’s disease (AD) patients develop symptoms before the age of 65 (EO). EOAD is believed to have a more aggressive course than late onset AD (LOAD). Design/Methods: We analyzed the MRI data of 141 amnestic EO, 255 amnestic LO and 202 amyloid-negative normal control ADNI subjects. 127 EO and all LO cases had amyloid PET or CSF Abeta molecular biomarkers of amyloid pathology. Comparisons of each diagnostic group to NC were done using linear regression in SPM8 controlling for age, gender, education, intracranial volume and scan type. Results were displayed using p Results: EO were more likely to be APOE4+ (67% vs. 54%, p Conclusions: Consistent with previous reports amnestic EO patients from ADNI show more severe neurodegeneration compared to LO. The pattern of atrophy in LO SNAP is suggestive of primary age related tauopathy. Study Supported by: NIA R01 AG040770, NIA K02 AG048240, NIA P30 AG010133 and the Easton Consortium for Alzheimer Drug Discovery and Biomarker Development. Disclosure: Dr. Stage has nothing to disclose. Dr. Phillips has nothing to disclose. Dr. Duran has nothing to disclose. Dr. Canela has nothing to disclose. Dr. Rabinovici has received personal compensation for activities with Eisai, Roche, Lundbeck, and Putnam as a speaker or consultant. Dr. Rabinovici has received research support from NIH, Alzheimer9s Association, Tau Consortium, American College of Radiology, Michael J Fox Foundation, Association for Frontotemporal Degneration, Avid Radiopharmaceuticals/Eli Lilly, GE Healthcare, and Piramal Imaging. Dr. Dickerson has received personal compensation for activities with Merck as a consultant. Dr. Dickerson has received personal compensation in an editorial capacity for Neuroimage: clinical. Dr. Carrillo has nothing to disclose. Dr. De Santi has received personal compensation for activities with Piramal Pharma as an employee. Dr. Goukasian has nothing to disclose. Dr. Risacher has nothing to disclose. Dr. Apostolova received personal compensation for activities with Eli Lilly and Piramal as a speaker and/or advisor. Dr. Apostolova has received research support from General Electric Healthcare.

Predicting Interrelated Alzheimer's Disease Outcomes via New Self-Learned Structured Low-Rank Model

Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder. As the prodromal stage of AD, Mild Cognitive Impairment (MCI) maintains a good chance of converting to AD. How to efficaciously detect this conversion from MCI to AD is significant in AD diagnosis. Different from standard classification problems where the distributions of classes are independent, the AD outcomes are usually interrelated (their distributions have certain overlaps). Most of existing methods failed to examine the interrelations among different classes, such as AD, MCI conversion and MCI non-conversion. In this paper, we proposed a novel self-learned low-rank structured learning model to automatically uncover the interrelations among different classes and utilized such interrelated structures to enhance classification. We conducted experiments on the ADNI cohort data. Empirical results demonstrated advantages of our model.