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https://www.readbyqxmd.com/read/29135998/multivariate-regression-analysis-of-structural-mri-connectivity-matrices-in-alzheimer-s-disease
#1
Javier Rasero, Nicola Amoroso, Marianna La Rocca, Sabina Tangaro, Roberto Bellotti, Sebastiano Stramaglia
Alzheimer's disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects...
2017: PloS One
https://www.readbyqxmd.com/read/29130521/a-variant-in-ppp4r3a-protects-against-alzheimer-related-metabolic-decline
#2
Leigh Christopher, Valerio Napolioni, Raiyan R Khan, Summer S Han, Michael D Greicius
OBJECTIVES: A reduction in glucose metabolism in the posterior cingulate cortex (PCC) predicts conversion to Alzheimer's disease (AD) and tracks disease progression, signifying its importance in AD. We aimed to use decline in PCC glucose metabolism as a proxy for the development and progression of AD to discover common genetic variants associated with disease vulnerability. METHODS: We performed a genome-wide association study (GWAS) of decline in PCC [(18) F] FDG PET measured in Alzheimer's Disease Neuroimaging Initiative (ADNI) participants (n=606)...
November 11, 2017: Annals of Neurology
https://www.readbyqxmd.com/read/29125485/cognitive-variability-predicts-incident-alzheimer-s-disease-and-mild-cognitive-impairment-comparable-to-a-cerebrospinal-fluid-biomarker
#3
Carey E Gleason, Derek Norton, Eric D Anderson, Michelle Wahoske, Danielle T Washington, Emre Umucu, Rebecca L Koscik, N Maritza Dowling, Sterling C Johnson, Cynthia M Carlsson, Sanjay Asthana
BACKGROUND: Alzheimer's disease (AD) biomarkers are emerging as critically important for disease detection and monitoring. Most biomarkers are obtained through invasive, resource-intense procedures. A cognitive marker, intra-individual cognitive variability (IICV) may provide an alternative or adjunct marker of disease risk for individuals unable or disinclined to undergo lumbar puncture. OBJECTIVE: To contrast risk of incident AD and mild cognitive impairment (MCI) associated with IICV to risk associated with well-established biomarkers: cerebrospinal fluid (CSF) phosphorylated tau protein (p-tau181) and amyloid-β 42 (Aβ42) peptide...
November 8, 2017: Journal of Alzheimer's Disease: JAD
https://www.readbyqxmd.com/read/29107865/landmark-based-deep-multi-instance-learning-for-brain-disease-diagnosis
#4
Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen
In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features...
October 27, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/29104963/multi-stage-diagnosis-of-alzheimer-s-disease-with-incomplete-multimodal-data-via-multi-task-deep-learning
#5
Kim-Han Thung, Pew-Thian Yap, Dinggang Shen
Utilization of biomedical data from multiple modalities improves the diagnostic accuracy of neurodegenerative diseases. However, multi-modality data are often incomplete because not all data can be collected for every individual. When using such incomplete data for diagnosis, current approaches for addressing the problem of missing data, such as imputation, matrix completion and multi-task learning, implicitly assume linear data-to-label relationship, therefore limiting their performances. We thus propose multi-task deep learning for incomplete data, where prediction tasks that are associated with different modality combinations are learnt jointly to improve the performance of each task...
September 2017: Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2017)
https://www.readbyqxmd.com/read/29103867/impact-of-analgesics-on-executive-function-and-memory-in-the-alzheimer-s-disease-neuroimaging-initiative-database
#6
Lisa Doan, Daniel Choi, Richard Kline
BACKGROUND AND AIMS: Pain is common in older adults but may be undertreated in part due to concerns about medication toxicity. Analgesics may affect cognition. In this retrospective cohort study, we used the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to examine the interaction of cognitive status and medications, especially non-steroidal anti-inflammatory drugs (NSAIDs). We hypothesized NSAID use would be associated with cognition and that this could be mediated through changes in brain structure...
November 2, 2017: Scandinavian Journal of Pain
https://www.readbyqxmd.com/read/29095836/mapping-longitudinal-scientific-progress-collaboration-and-impact-of-the-alzheimer-s-disease-neuroimaging-initiative
#7
Xiaohui Yao, Jingwen Yan, Michael Ginda, Katy Börner, Andrew J Saykin, Li Shen
BACKGROUND: Alzheimer's disease neuroimaging initiative (ADNI) is a landmark imaging and omics study in AD. ADNI research literature has increased substantially over the past decade, which poses challenges for effectively communicating information about the results and impact of ADNI-related studies. In this work, we employed advanced information visualization techniques to perform a comprehensive and systematic mapping of the ADNI scientific growth and impact over a period of 12 years...
2017: PloS One
https://www.readbyqxmd.com/read/29091778/risk-factors-for-amyloid-positivity-in-older-people-reporting-significant-memory-concern
#8
Jie Zhang, Wenjun Zhou, Ryan M Cassidy, Hang Su, Yindan Su, Xiangyang Zhang
OBJECTIVE: The goal of this study is to identify risk factors for the presence of amyloid accumulation in the brains of patients reporting subjective cognitive decline (SCD). Identifying such risk factors will help better identify patients who ought to receive neuroimaging studies to confirm plaque presence and begin intervention, as well as enhancing the study of the pathogenesis of Alzheimer's disease. METHODS: Ninety-nine SCD participants (72.2±5.6years, 57...
October 6, 2017: Comprehensive Psychiatry
https://www.readbyqxmd.com/read/29089831/improved-diagnostic-accuracy-of-alzheimer-s-disease-by-combining-regional-cortical-thickness-and-default-mode-network-functional-connectivity-validated-in-the-alzheimer-s-disease-neuroimaging-initiative-set
#9
Ji Eun Park, Bumwoo Park, Sang Joon Kim, Ho Sung Kim, Choong Gon Choi, Seung Chai Jung, Joo Young Oh, Jae-Hong Lee, Jee Hoon Roh, Woo Hyun Shim
OBJECTIVE: To identify potential imaging biomarkers of Alzheimer's disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model's diagnostic accuracy in a validation set. MATERIALS AND METHODS: Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer's Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging...
November 2017: Korean Journal of Radiology: Official Journal of the Korean Radiological Society
https://www.readbyqxmd.com/read/29089479/earliest-accumulation-of-%C3%AE-amyloid-occurs-within-the-default-mode-network-and-concurrently-affects-brain-connectivity
#10
Sebastian Palmqvist, Michael Schöll, Olof Strandberg, Niklas Mattsson, Erik Stomrud, Henrik Zetterberg, Kaj Blennow, Susan Landau, William Jagust, Oskar Hansson
It is not known exactly where amyloid-β (Aβ) fibrils begin to accumulate in individuals with Alzheimer's disease (AD). Recently, we showed that abnormal levels of Aβ42 in cerebrospinal fluid (CSF) can be detected before abnormal amyloid can be detected using PET in individuals with preclinical AD. Using these approaches, here we identify the earliest preclinical AD stage in subjects from the ADNI and BioFINDER cohorts. We show that Aβ accumulation preferentially starts in the precuneus, medial orbitofrontal, and posterior cingulate cortices, i...
October 31, 2017: Nature Communications
https://www.readbyqxmd.com/read/29071500/temporal-association-patterns-and-dynamics-of-amyloid-%C3%AE-and-tau-in-alzheimer-s-disease
#11
Alison K Ower, Christoforos Hadjichrysanthou, Luuk Gras, Jaap Goudsmit, Roy M Anderson, Frank de Wolf
The elusive relationship between underlying pathology and clinical disease hampers diagnosis of Alzheimer's disease (AD) and preventative intervention development. We seek to understand the relationship between two classical AD biomarkers, amyloid-β1-42 (Aβ1-42) and total-tau (t-tau), and define their trajectories across disease development, as defined by disease onset at diagnosis of mild cognitive impairment (MCI). Using longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we performed a correlation analysis of biomarkers CSF Aβ1-42 and t-tau, and longitudinal quantile analysis...
October 25, 2017: European Journal of Epidemiology
https://www.readbyqxmd.com/read/29070667/alzheimer-disease-brain-atrophy-subtypes-are-associated-with-cognition-and-rate-of-decline
#12
Shannon L Risacher, Wesley H Anderson, Arnaud Charil, Peter F Castelluccio, Sergey Shcherbinin, Andrew J Saykin, Adam J Schwarz
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 [HpSpMRI], limbic predominant [LPMRI], typical AD [tADMRI]) were defined according to an algorithm analogous to one recently proposed for tau neuropathology...
October 25, 2017: Neurology
https://www.readbyqxmd.com/read/29065660/twin-svm-based-classification-of-alzheimer-s-disease-using-complex-dual-tree-wavelet-principal-coefficients-and-lda
#13
Saruar Alam, Goo-Rak Kwon, Ji-In Kim, Chun-Su Park
Alzheimer's disease (AD) is a leading cause of dementia, which causes serious health and socioeconomic problems. A progressive neurodegenerative disorder, Alzheimer's causes the structural change in the brain, thereby affecting behavior, cognition, emotions, and memory. Numerous multivariate analysis algorithms have been used for classifying AD, distinguishing it from healthy controls (HC). Efficient early classification of AD and mild cognitive impairment (MCI) from HC is imperative as early preventive care could help to mitigate risk factors...
2017: Journal of Healthcare Engineering
https://www.readbyqxmd.com/read/29065619/diagnosis-of-alzheimer-s-disease-based-on-structural-mri-images-using-a-regularized-extreme-learning-machine-and-pca-features
#14
Ramesh Kumar Lama, Jeonghwan Gwak, Jeong-Seon Park, Sang-Woong Lee
Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations...
2017: Journal of Healthcare Engineering
https://www.readbyqxmd.com/read/29060753/11c-pib-pet-image-analysis-for-alzheimer-s-diagnosis-using-weighted-voting-ensembles
#15
Wenjun Wu, Janani Venugopalan, May D Wang
Alzheimer's Disease (AD) is one of the leading causes of death and dementia worldwide. Early diagnosis confers many benefits, including improved care and access to effective treatment. However, it is still a medical challenge due to the lack of an efficient and inexpensive way to assess cognitive function [1]. Although research on data from Neuroimaging and Brain Initiative and the advancement in data analytics has greatly enhanced our understanding of the underlying disease process, there is still a lack of complete knowledge regarding the indicative biomarkers of Alzheimer's Disease...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060285/automated-discrimination-of-dementia-spectrum-disorders-using-extreme-learning-machine-and-structural-t1-mri-features
#16
Jongin Kim, Boreom Lee
The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29059919/atlas-based-sparse-logistic-regression-for-alzheimer-s-disease-classification
#17
Helena Barros, Margarida Silveira
Sparse methods are an effective way to alleviate the curse of dimensionality in neuroimaging applications. By imposing sparsity inducing regularization terms these methods are able to perform feature selection jointly with classification.They have been used for Alzheimer's Disease (AD) and Mild cognitive impairment (MCI) classification using different approaches such as Lasso, Group Lasso and treestructured Group Lasso. The Group Lasso approaches have relied mainly on grouping contiguous voxels, either spatially or temporally...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29039849/targeted-metabolomics-and-medication-classification-data-from-participants-in-the-adni1-cohort
#18
Lisa St John-Williams, Colette Blach, Jon B Toledo, Daniel M Rotroff, Sungeun Kim, Kristaps Klavins, Rebecca Baillie, Xianlin Han, Siamak Mahmoudiandehkordi, John Jack, Tyler J Massaro, Joseph E Lucas, Gregory Louie, Alison A Motsinger-Reif, Shannon L Risacher, Andrew J Saykin, Gabi Kastenmüller, Matthias Arnold, Therese Koal, M Arthur Moseley, Lara M Mangravite, Mette A Peters, Jessica D Tenenbaum, J Will Thompson, Rima Kaddurah-Daouk
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...
October 17, 2017: Scientific Data
https://www.readbyqxmd.com/read/29031664/nonlinearity-aware-based-dimensionality-reduction-and-over-sampling-for-ad-mci-classification-from-mri-measures
#19
Peng Cao, Xiaoli Liu, Jinzhu Yang, Dazhe Zhao, Min Huang, Jian Zhang, Osmar Zaiane
Alzheimer's disease (AD) has been not only a substantial financial burden to the health care system but also an emotional burden to patients and their families. Making accurate diagnosis of AD based on brain magnetic resonance imaging (MRI) is becoming more and more critical and emphasized at the earliest stages. However, the high dimensionality and imbalanced data issues are two major challenges in the study of computer aided AD diagnosis. The greatest limitations of existing dimensionality reduction and over-sampling methods are that they assume a linear relationship between the MRI features (predictor) and the disease status (response)...
October 6, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28987681/the-evans-index-revisited-new-cut-off-levels-for-use-in-radiological-assessment-of-ventricular-enlargement-in-the-elderly
#20
Maiken K Brix, Eric Westman, Andrew Simmons, Geir Andre Ringstad, Per Kristian Eide, Kari Wagner-Larsen, Christian M Page, Valeria Vitelli, Mona K Beyer
BACKGROUND AND PURPOSE: Assessment of ventricular enlargement is subjective and based on the radiologist's experience. Linear indices, such as the Evans Index (EI), have been proposed as markers of ventricular volume with an EI≥0.3 indicating pathologic ventricular enlargement in any subject. However, normal range for EI measured on magnetic resonance imaging (MRI) scans are lacking in healthy elderly according to age and sex. We propose new age and sex specific cut-off values for ventricular enlargement in the elderly population...
October 2017: European Journal of Radiology
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