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https://www.readbyqxmd.com/read/28930562/alzheimer-s-disease-diagnostics-by-a-3d-deeply-supervised-adaptable-convolutional-network
#1
Ehsan Hosseini-Asl, Mohammed Ghazal, Ali Mahmoud, Ali Aslantas, Ahmed M Shalaby, Manual F Casanova, Gregory N Barnes, Georgy Gimel'farb, Robert Keynton, Ayman El-Baz
Early diagnosis is playing an important role in preventing progress of the Alzheimer's disease (AD). This paper proposes to improve the prediction of AD with a deep 3D Convolutional Neural Network (3D-CNN), which can show generic features capturing AD biomarkers extracted from brain images, adapt to different domain datasets, and accurately classify subjects with improved fine-tuning method. The 3D-CNN is built upon a convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans for source domain...
January 1, 2018: Frontiers in Bioscience (Landmark Edition)
https://www.readbyqxmd.com/read/28921541/automatic-labeling-of-mr-brain-images-through-extensible-learning-and-atlas-forests
#2
Lijun Xu, Hong Liu, Enmin Song, Meng Yan, Renchao Jin, Chih-Cheng Hung
PURPOSE: Multi-atlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy while time-consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. METHODS: We propose an extensible learning model which allows the multi-atlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling...
September 18, 2017: Medical Physics
https://www.readbyqxmd.com/read/28899422/moderating-effects-of-sex-on-the-impact-of-diagnosis-and-amyloid-positivity-on-verbal-memory-and-hippocampal-volume
#3
Jessica Z K Caldwell, Jody-Lynn Berg, Jeffrey L Cummings, Sarah J Banks
BACKGROUND: Alzheimer's disease (AD) impacts men and women differently, but the effect of sex on predementia stages is unclear. The objective of this study was to examine whether sex moderates the impact of florbetapir positron emission tomography (PET) amyloid positivity (A(+)) on verbal learning and memory performance and hippocampal volume (HV) in normal cognition (NC) and early mild cognitive impairment (eMCI). METHODS: Seven hundred forty-two participants with NC and participants with eMCI from the Alzheimer's Disease Neuroimaging Initiative (second cohort [ADNI2] and Grand Opportunity Cohort [ADNI-GO]) were included...
September 12, 2017: Alzheimer's Research & Therapy
https://www.readbyqxmd.com/read/28888171/discriminative-confidence-estimation-for-probabilistic-multi-atlas-label-fusion
#4
Oualid M Benkarim, Gemma Piella, Miguel Angel González Ballester, Gerard Sanroma
Quantitative neuroimaging analyses often rely on the accurate segmentation of anatomical brain structures. In contrast to manual segmentation, automatic methods offer reproducible outputs and provide scalability to study large databases. Among existing approaches, multi-atlas segmentation has recently shown to yield state-of-the-art performance in automatic segmentation of brain images. It consists in propagating the labelmaps from a set of atlases to the anatomy of a target image using image registration, and then fusing these multiple warped labelmaps into a consensus segmentation on the target image...
September 1, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28870383/advancing-alzheimer-s-research-a-review-of-big-data-promises
#5
REVIEW
Rui Zhang, Gyorgy Simon, Fang Yu
OBJECTIVE: To review the current state of science using big data to advance Alzheimer's disease (AD) research and practice. In particular, we analyzed the types of research foci addressed, corresponding methods employed and study findings reported using big data in AD. METHOD: Systematic review was conducted for articles published in PubMed from January 1, 2010 through December 31, 2015. Keywords with AD and big data analytics were used for literature retrieval...
October 2017: International Journal of Medical Informatics
https://www.readbyqxmd.com/read/28869463/risk-factors-neuroanatomical-correlates-and-outcome-of-neuropsychiatric-symptoms-of-alzheimer-s-disease
#6
Stéphane P Poulin, David Bergeron, Bradford C Dickerson
BACKGROUND: An integrative model of neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD) is lacking. OBJECTIVE: In this study, we aimed to investigate the risk factors, anatomy, biology, and outcomes of NPS in AD. METHODS: 181 subjects were included from the Alzheimer's Disease Neuroimaging Study (ADNI). NPS were assessed with the Neuropsychiatric Inventory Questionnaire at baseline and 6 months. NPI >3 was used as a threshold for NPS positivity...
September 1, 2017: Journal of Alzheimer's Disease: JAD
https://www.readbyqxmd.com/read/28863246/flcrm-functional-linear-cox-regression-model
#7
Dehan Kong, Joseph G Ibrahim, Eunjee Lee, Hongtu Zhu
We consider a functional linear Cox regression model for characterizing the association between time-to-event data and a set of functional and scalar predictors. The functional linear Cox regression model incorporates a functional principal component analysis for modeling the functional predictors and a high-dimensional Cox regression model to characterize the joint effects of both functional and scalar predictors on the time-to-event data. We develop an algorithm to calculate the maximum approximate partial likelihood estimates of unknown finite and infinite dimensional parameters...
September 1, 2017: Biometrics
https://www.readbyqxmd.com/read/28800325/classification-of-alzheimer-s-disease-and-prediction-of-mild-cognitive-impairment-conversion-using-histogram-based-analysis-of-patient-specific-anatomical-brain-connectivity-networks
#8
Iman Beheshti, Norihide Maikusa, Morteza Daneshmand, Hiroshi Matsuda, Hasan Demirel, Gholamreza Anbarjafari
In this study, we investigated the early detection of Alzheimer's disease (AD) and mild cognitive impairment (MCI) conversion to AD through individual structural connectivity networks using structural magnetic resonance imaging (sMRI) data. In the proposed method, the cortical morphometry of individual gray matter images were used to construct structural connectivity networks. A statistical feature generation approach based on histogram-based feature generation procedure was proposed to represent a statistical-pattern of connectivity networks from a high-dimensional space into low-dimensional feature vectors...
2017: Journal of Alzheimer's Disease: JAD
https://www.readbyqxmd.com/read/28798659/enhanced-data-representation-by-kernel-metric-learning-for-dementia-diagnosis
#9
David Cárdenas-Peña, Diego Collazos-Huertas, German Castellanos-Dominguez
Alzheimer's disease (AD) is the kind of dementia that affects the most people around the world. Therefore, an early identification supporting effective treatments is required to increase the life quality of a wide number of patients. Recently, computer-aided diagnosis tools for dementia using Magnetic Resonance Imaging scans have been successfully proposed to discriminate between patients with AD, mild cognitive impairment, and healthy controls. Most of the attention has been given to the clinical data, provided by initiatives as the ADNI, supporting reliable researches on intervention, prevention, and treatments of AD...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28777748/fully-automatic-mri-based-hippocampus-volumetry-using-fsl-first-intra-scanner-test-retest-stability-inter-field-strength-variability-and-performance-as-enrichment-biomarker-for-clinical-trials-using-prodromal-target-populations-at-risk-for-alzheimer-s-disease
#10
Enrica Cavedo, Per Suppa, Catharina Lange, Roland Opfer, Simone Lista, Samantha Galluzzi, Adam J Schwarz, Lothar Spies, Ralph Buchert, Harald Hampel
BACKGROUND: MRI-based hippocampus volume is a core clinical biomarker for identification of Alzheimer's disease (AD). OBJECTIVE: To assess robustness of automatic hippocampus volumetry with the freely available FSL-FIRST software with respect to short-term repeat and across field strength imaging. FSL-FIRST hippocampus volume (FIRST-HV) was also evaluated as enrichment biomarker for mild cognitive impairment (MCI) trials. METHODS: Robustness of FIRST-HV was assessed in 51 healthy controls (HC), 74 MCI subjects, and 28 patients with AD dementia from ADNI1, each with two pairs of back-to-back scans, one at 1...
July 29, 2017: Journal of Alzheimer's Disease: JAD
https://www.readbyqxmd.com/read/28771542/a-multivariate-predictive-modeling-approach-reveals-a-novel-csf-peptide-signature-for-both-alzheimer-s-disease-state-classification-and-for-predicting-future-disease-progression
#11
Daniel A Llano, Saurabh Bundela, Raksha A Mudar, Viswanath Devanarayan
To determine if a multi-analyte cerebrospinal fluid (CSF) peptide signature can be used to differentiate Alzheimer's Disease (AD) and normal aged controls (NL), and to determine if this signature can also predict progression from mild cognitive impairment (MCI) to AD, analysis of CSF samples was done on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The profiles of 320 peptides from baseline CSF samples of 287 subjects over a 3-6 year period were analyzed. As expected, the peptide most able to differentiate between AD vs...
2017: PloS One
https://www.readbyqxmd.com/read/28758145/cross-validation-of-optimized-composites-for-preclinical-alzheimer-s-disease
#12
Michael C Donohue, Chung-Kai Sun, Rema Raman, Philip S Insel, Paul S Aisen
INTRODUCTION: We discuss optimization and validation of composite endpoints for pre-symptomatic Alzheimer's clinical trials. Optimized composites offer hope of substantial gains in statistical power or reduction in sample size. But there is tradeoff between optimization and face validity such that optimization should only be considered if there is a convincing rationale. As with statistically derived regions of interest in neuroimaging, validation on independent datasets is essential...
January 2017: Alzheimer's & Dementia: Translational Research & Clinical Interventions
https://www.readbyqxmd.com/read/28755001/identification-of-clusters-of-rapid-and-slow-decliners-among-subjects-at-risk-for-alzheimer-s-disease
#13
Dragan Gamberger, Nada Lavrač, Shantanu Srivatsa, Rudolph E Tanzi, P Murali Doraiswamy
The heterogeneity of Alzheimer's disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners...
July 28, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28749360/multi-hypergraph-learning-for-incomplete-multi-modality-data
#14
Mingxia Liu, Yue Gao, Pew-Thian Yap, Dinggang Shen
Multi-modality data convey complementary information that can be used to improve the accuracy of prediction models in disease diagnosis. However, effectively integrating multi-modality data remains a challenging problem especially when the data are incomplete. For instance, more than half of the subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database have no fluorodeoxyglucose positron emission tomography (FDG-PET) and cerebrospinal fluid (CSF) data. Currently, there are two commonly-used strategies to handle the problem of incomplete data: 1) discard samples having missing features, and 2) impute those missing values via specific techniques...
July 26, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28746057/solid-lipid-nanoparticles-improve-octyl-gallate-antimetastatic-activity-and-ameliorate-its-renal-and-hepatic-toxic-effects
#15
Clarissa A S Cordova, Claudriana Locatelli, Evelyn Winter, Adny H Silva, Betina G Zanetti-Ramos, Raquel Jasper, Alessandra Mascarello, Rosendo A Yunes, Ricardo J Nunes, Tânia B Creczynski-Pasa
Metastasis is the main cause of cancer-related death and requires the development of effective treatments with reduced toxicity and effective anticancer activity. Gallic acid derivatives have shown significant biological properties including antitumoral activity as shown in a previous study with octyl gallate (G8) in vitro. Thus, the aim of this work was to evaluate the antimetastatic effect of free and solid lipid nanoparticle-loaded G8 in mice in a lung metastasis model. Animals inoculated with melanoma cells presented metastasis in lungs, which was significantly inhibited by treatment with G8 and solid lipid nanoparticle-loaded G8, named G8-NVM...
July 25, 2017: Anti-cancer Drugs
https://www.readbyqxmd.com/read/28736521/group-level-progressive-alterations-in-brain-connectivity-patterns-revealed-by-diffusion-tensor-brain-networks-across-severity-stages-in-alzheimer-s-disease
#16
Javier Rasero, Carmen Alonso-Montes, Ibai Diez, Laiene Olabarrieta-Landa, Lakhdar Remaki, Iñaki Escudero, Beatriz Mateos, Paolo Bonifazi, Manuel Fernandez, Juan Carlos Arango-Lasprilla, Sebastiano Stramaglia, Jesus M Cortes
Alzheimer's disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1 = 36, healthy control subjects, Control), G2 (N2 = 36, early mild cognitive impairment, EMCI), G3 (N3 = 36, late mild cognitive impairment, LMCI) and G4 (N4 = 36, AD)...
2017: Frontiers in Aging Neuroscience
https://www.readbyqxmd.com/read/28736311/imaging-wide-association-study-integrating-imaging-endophenotypes-in-gwas
#17
Zhiyuan Xu, Chong Wu, Wei Pan
A new and powerful approach, called imaging-wide association study (IWAS), is proposed to integrate imaging endophenotypes with GWAS to boost statistical power and enhance biological interpretation for GWAS discoveries. IWAS extends the promising transcriptome-wide association study (TWAS) from using gene expression endophenotypes to using imaging and other endophenotypes with a much wider range of possible applications. As illustration, we use gray-matter volumes of several brain regions of interest (ROIs) drawn from the ADNI-1 structural MRI data as imaging endophenotypes, which are then applied to the individual-level GWAS data of ADNI-GO/2 and a large meta-analyzed GWAS summary statistics dataset (based on about 74,000 individuals), uncovering some novel genes significantly associated with Alzheimer's disease (AD)...
July 20, 2017: NeuroImage
https://www.readbyqxmd.com/read/28732595/probabilistic-modeling-of-anatomical-variability-using-a-low-dimensional-parameterization-of-diffeomorphisms
#18
Miaomiao Zhang, William M Wells, Polina Golland
We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space...
October 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28731444/the-relationship-of-brain-amyloid-load-and-apoe-status-to-regional-cortical-thinning-and-cognition-in-the-adni-cohort
#19
Chunfei Li, David A Loewenstein, Ranjan Duara, Mercedes Cabrerizo, Warren Barker, Malek Adjouadi
BACKGROUND: Both amyloid (Aβ) load and APOE4 allele are associated with neurodegenerative changes in Alzheimer's disease (AD) prone regions and with risk for cognitive impairment. OBJECTIVE: To evaluate the unique and independent contribution of APOE4 allele status (E4+∖E4-), Aβ status (Amy+∖Amy-), and combined APOE4 and Aβ status on regional cortical thickness (CoTh) and cognition among participants diagnosed as cognitively normal (CN, n = 251), early mild cognitive impairment (EMCI, n = 207), late mild cognitive impairment (LMCI, n = 196), and mild AD (n = 162) from the ADNI...
2017: Journal of Alzheimer's Disease: JAD
https://www.readbyqxmd.com/read/28729939/feature-selective-temporal-prediction-of-alzheimer-s-disease-progression-using-hippocampus-surface-morphometry
#20
Sinchai Tsao, Niharika Gajawelli, Jiayu Zhou, Jie Shi, Jieping Ye, Yalin Wang, Natasha Leporé
INTRODUCTION: Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi-task machine learning method (cFSGL) with a novel MR-based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients. METHODS: Previous work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness...
July 2017: Brain and Behavior
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