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https://www.readbyqxmd.com/read/29305751/volumetric-comparison-of-hippocampal-subfields-extracted-from-4-minute-accelerated-vs-8-minute-high-resolution-t2-weighted-3t-mri-scans
#1
Shan Cong, Shannon L Risacher, John D West, Yu-Chien Wu, Liana G Apostolova, Eileen Tallman, Maher Rizkalla, Paul Salama, Andrew J Saykin, Li Shen
The hippocampus has been widely studied using neuroimaging, as it plays an important role in memory and learning. However, hippocampal subfield information is difficult to capture by standard magnetic resonance imaging (MRI) techniques. To facilitate morphometric study of hippocampal subfields, ADNI introduced a high resolution (0.4 mm in plane) T2-weighted turbo spin-echo sequence that requires 8 min. With acceleration, the protocol can be acquired in 4 min. We performed a comparative study of hippocampal subfield volumes using standard and accelerated protocols on a Siemens Prisma 3T MRI in an independent sample of older adults that included 10 cognitively normal controls, 9 individuals with subjective cognitive decline, 10 with mild cognitive impairment, and 6 with a clinical diagnosis of Alzheimer's disease (AD)...
January 5, 2018: Brain Imaging and Behavior
https://www.readbyqxmd.com/read/29276808/efficient-groupwise-registration-for-brain-mri-by-fast-initialization
#2
Pei Dong, Xiaohuan Cao, Jun Zhang, Minjeong Kim, Guorong Wu, Dinggang Shen
Groupwise image registration provides an unbiased registration solution upon a population of images, which can facilitate the subsequent population analysis. However, it is generally computationally expensive for performing groupwise registration on a large set of images. To alleviate this issue, we propose to utilize a fast initialization technique for speeding up the groupwise registration. Our main idea is to generate a set of simulated brain MRI samples with known deformations to their group center. This can be achieved in the training stage by two steps...
2017: Machine Learning in Medical Imaging
https://www.readbyqxmd.com/read/29242123/ensemble-of-random-forests-one-vs-rest-classifiers-for-mci-and-ad-prediction-using-anova-cortical-and-subcortical-feature-selection-and-partial-least-squares
#3
J Ramírez, J M Górriz, A Ortiz, F J Martínez-Murcia, F Segovia, D Salas-Gonzalez, D Castillo-Barnes, I A Illán, C G Puntonet
BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10% to 15% per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments...
December 11, 2017: Journal of Neuroscience Methods
https://www.readbyqxmd.com/read/29225556/an-optimal-transportation-based-univariate-neuroimaging-index
#4
Liang Mi, Wen Zhang, Junwei Zhang, Yonghui Fan, Dhruman Goradia, Kewei Chen, Eric M Reiman, Xianfeng Gu, Yalin Wang
The alterations of brain structures and functions have been considered closely correlated to the change of cognitive performance due to neurodegenerative diseases such as Alzheimer's disease. In this paper, we introduce a variational framework to compute the optimal transformation (OT) in 3D space and propose a univariate neuroimaging index based on OT to measure such alterations. We compute the OT from each image to a template and measure the Wasserstein distance between them. By comparing the distances from all the images to the common template, we obtain a concise and informative index for each image...
2017: Proceedings
https://www.readbyqxmd.com/read/29218897/codon-bias-among-synonymous-rare-variants-is-associated-with-alzheimer-s-disease-imaging-biomarker
#5
Jason E Miller, Manu K Shivakumar, Shannon L Risacher, Andrew J Saykin, Seunggeun Lee, Kwangsik Nho, Dokyoon Kim
Alzheimer's disease (AD) is a neurodegenerative disorder with few biomarkers even though it impacts a relatively large portion of the population and is predicted to affect significantly more individuals in the future. Neuroimaging has been used in concert with genetic information to improve our understanding in relation to how AD arises and how it can be potentially diagnosed. Additionally, evidence suggests synonymous variants can have a functional impact on gene regulatory mechanisms, including those related to AD...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29209199/decreased-complexity-in-alzheimer-s-disease-resting-state-fmri-evidence-of-brain-entropy-mapping
#6
Bin Wang, Yan Niu, Liwen Miao, Rui Cao, Pengfei Yan, Hao Guo, Dandan Li, Yuxiang Guo, Tianyi Yan, Jinglong Wu, Jie Xiang, Hui Zhang
Alzheimer's disease (AD) is a frequently observed, irreversible brain function disorder among elderly individuals. Resting-state functional magnetic resonance imaging (rs-fMRI) has been introduced as an alternative approach to assessing brain functional abnormalities in AD patients. However, alterations in the brain rs-fMRI signal complexities in mild cognitive impairment (MCI) and AD patients remain unclear. Here, we described the novel application of permutation entropy (PE) to investigate the abnormal complexity of rs-fMRI signals in MCI and AD patients...
2017: Frontiers in Aging Neuroscience
https://www.readbyqxmd.com/read/29200540/a-functional-varying-coefficient-single-index-model-for-functional-response-data
#7
Jialiang Li, Chao Huang, Hongtu Zhu
Motivated by the analysis of imaging data, we propose a novel functional varying-coefficient single index model (FVCSIM) to carry out the regression analysis of functional response data on a set of covariates of interest. FVCSIM represents a new extension of varying-coefficient single index models for scalar responses collected from cross-sectional and longitudinal studies. An efficient estimation procedure is developed to iteratively estimate varying coefficient functions, link functions, index parameter vectors, and the covariance function of individual functions...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/29154285/prediction-of-alzheimer-s-dementia-in-patients-with-amnestic-mild-cognitive-impairment-in-clinical-routine-incremental-value-of-biomarkers-of-neurodegeneration-and-brain-amyloidosis-added-stepwise-to-cognitive-status
#8
Catharina Lange, Per Suppa, Uwe Pietrzyk, Marcus R Makowski, Lothar Spies, Oliver Peters, Ralph Buchert
The aim of this study was to evaluate the incremental benefit of biomarkers for prediction of Alzheimer's disease dementia (ADD) in mild cognitive impairment (MCI) when added stepwise in the order of their collection in clinical routine. The model started with cognitive status characterized by the ADAS-13 score. Hippocampus volume (HV), cerebrospinal fluid (CSF) phospho-tau (pTau), and the FDG t-sum score in an AD-meta region-of-interest were compared as neurodegeneration markers. CSF-Aβ1-42 was used as amyloidosis marker...
November 14, 2017: Journal of Alzheimer's Disease: JAD
https://www.readbyqxmd.com/read/29154279/biological-factors-contributing-to-the-response-to-cognitive-training-in-mild-cognitive-impairment
#9
Jessica Peter, Lena V Schumacher, Verena Landerer, Ahmed Abdulkadir, Christoph P Kaller, Jacob Lahr, Stefan Klöppel
In mild cognitive impairment (MCI), small benefits from cognitive training were observed for memory functions but there appears to be great variability in the response to treatment. Our study aimed to improve the characterization and selection of those participants who will benefit from cognitive intervention. We evaluated the predictive value of disease-specific biological factors for the outcome after cognitive training in MCI (n = 25) and also considered motivation of the participants. We compared the results of the cognitive intervention group with two independent control groups of MCI patients (local memory clinic, n = 20; ADNI cohort, n = 302)...
November 16, 2017: Journal of Alzheimer's Disease: JAD
https://www.readbyqxmd.com/read/29151658/generalized-scalar-on-image-regression-models-via-total-variation
#10
Xiao Wang, Hongtu Zhu
The use of imaging markers to predict clinical outcomes can have a great impact in public health. The aim of this paper is to develop a class of generalized scalar-on-image regression models via total variation (GSIRM-TV), in the sense of generalized linear models, for scalar response and imaging predictor with the presence of scalar covariates. A key novelty of GSIRM-TV is that it is assumed that the slope function (or image) of GSIRM-TV belongs to the space of bounded total variation in order to explicitly account for the piecewise smooth nature of most imaging data...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/29151657/mwpcr-multiscale-weighted-principal-component-regression-for-high-dimensional-prediction
#11
Hongtu Zhu, Dan Shen, Xuewei Peng, Leo Yufeng Liu
We propose a multiscale weighted principal component regression (MWPCR) framework for the use of high dimensional features with strong spatial features (e.g., smoothness and correlation) to predict an outcome variable, such as disease status. This development is motivated by identifying imaging biomarkers that could potentially aid detection, diagnosis, assessment of prognosis, prediction of response to treatment, and monitoring of disease status, among many others. The MWPCR can be regarded as a novel integration of principal components analysis (PCA), kernel methods, and regression models...
2017: Journal of the American Statistical Association
https://www.readbyqxmd.com/read/29135998/multivariate-regression-analysis-of-structural-mri-connectivity-matrices-in-alzheimer-s-disease
#12
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
#13
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
#14
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
#15
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...
January 2018: 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
#16
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 Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, Septembe
https://www.readbyqxmd.com/read/29103867/impact-of-analgesics-on-executive-function-and-memory-in-the-alzheimer-s-disease-neuroimaging-initiative-database
#17
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
#18
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
#19
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
#20
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
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