keyword
MENU ▼
Read by QxMD icon Read
search

Adni

keyword
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
#1
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...
August 8, 2017: Journal of Alzheimer's Disease: JAD
https://www.readbyqxmd.com/read/28798659/enhanced-data-representation-by-kernel-metric-learning-for-dementia-diagnosis
#2
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
#3
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
#4
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
#5
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
#6
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
#7
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
#8
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
#9
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
#10
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
#11
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...
July 8, 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
#12
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...
July 18, 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
#13
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
https://www.readbyqxmd.com/read/28703112/the-usefulness-of-d-dimer-in-diagnosis-and-prediction-of-venous-thromboembolism-in-patients-with-abdominal-malignancy
#14
Marcin Kwietniak, Tariq Al-Amawi, Tomasz Błaszkowski, Violetta Sulżyc-Bielicka, Józef Kładny
THE AIM: of the study was to evaluate the usefulness of D-dimer evaluation in the diagnosis and prediction of venous thromboembolism (VTE) of lower extremities in patients operated on for abdominal tumors depending on the chosen cut-off point for this parameter. MATERIAL AND METHODS: We included 150 patients operated on for abdominal cancer in our department between October 2014 and June 2016. In these patients, concentration of D-dimer was determined, medical histories were taken, and physical examinations were performed...
June 30, 2017: Polski Przeglad Chirurgiczny
https://www.readbyqxmd.com/read/28689050/self-reported-traumatic-brain-injury-and-in-vivo-measure-of-ad-vulnerable-cortical-thickness-and-ad-related-biomarkers-in-the-adni-cohort
#15
Ming-Liang Wang, Xiao-Er Wei, Meng-Meng Yu, Peng-Yang Li, Wen-Bin Li
In this study, we aimed to investigate whether self-reported mild traumatic brain injury (mTBI) was associated with decreased AD-vulnerable cortical thickness, and to assess the relationship between AD-vulnerable cortical thickness and AD-related biomarker in the Alzheimer's Disease Neuroimaging Initiative subjects. We identified 45 self-reported mTBI subjects, who had structural MRI, 18F-AV45 PET, and cerebrospinal fluid (CSF) data. Of them, eight subjects were normal; ten were preclinical AD; seventeen were MCI due to AD; ten were AD...
August 10, 2017: Neuroscience Letters
https://www.readbyqxmd.com/read/28684136/open-innovation-towards-sharing-of-data-models-and-workflows
#16
Daniela J Conrado, Mats O Karlsson, Klaus Romero, Céline Sarr, Justin J Wilkins
Sharing of resources across organisations to support open innovation is an old idea, but which is being taken up by the scientific community at increasing speed, concerning public sharing in particular. The ability to address new questions or provide more precise answers to old questions through merged information is among the attractive features of sharing. Increased efficiency through reuse, and increased reliability of scientific findings through enhanced transparency, are expected outcomes from sharing...
July 3, 2017: European Journal of Pharmaceutical Sciences
https://www.readbyqxmd.com/read/28666881/distinguishing-early-and-late-brain-aging-from-the-alzheimer-s-disease-spectrum-consistent-morphological-patterns-across-independent-samples
#17
Nhat Trung Doan, Andreas Engvig, Krystal Zaske, Karin Persson, Martina Jonette Lund, Tobias Kaufmann, Aldo Cordova-Palomera, Dag Alnæs, Torgeir Moberget, Anne Brækhus, Maria Lage Barca, Jan Egil Nordvik, Knut Engedal, Ingrid Agartz, Geir Selbæk, Ole A Andreassen, Lars T Westlye
Alzheimer's disease (AD) is a debilitating age-related neurodegenerative disorder. Accurate identification of individuals at risk is complicated as AD shares cognitive and brain features with aging. We applied linked independent component analysis (LICA) on three complementary measures of gray matter structure: cortical thickness, area and gray matter density of 137 AD, 78 mild (MCI) and 38 subjective cognitive impairment patients, and 355 healthy adults aged 18-78 years to identify dissociable multivariate morphological patterns sensitive to age and diagnosis...
June 27, 2017: NeuroImage
https://www.readbyqxmd.com/read/28666395/can-a-resting-state-functional-connectivity-index-identify-patients-with-alzheimer-s-disease-and-mild-cognitive-impairment-across-multiple-sites
#18
Keiichi Onoda, Nobuhiro Yada, Kentaro Ozasa, Shinji Hara, Yasushi Yamamoto, Hajime Kitagaki, Shuhei Yamaguchi
Resting-state functional connectivity is one promising biomarker for Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, it is still not known how accurately network analysis identifies AD and MCI across multiple sites. In this study, we examined whether resting-state functional connectivity data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) could identify patients with AD and MCI at our site. We implemented an index based on the functional connectivity frequency distribution, and compared performance for AD and MCI identification with multi-voxel pattern analysis...
June 30, 2017: Brain Connectivity
https://www.readbyqxmd.com/read/28664199/low-dimensional-statistics-of-anatomical-variability-via-compact-representation-of-image-deformations
#19
Miaomiao Zhang, William M Wells, Polina Golland
Using image-based descriptors to investigate clinical hypotheses and therapeutic implications is challenging due to the notorious "curse of dimensionality" coupled with a small sample size. In this paper, we present a low-dimensional analysis of anatomical shape variability in the space of diffeomorphisms and demonstrate its benefits for clinical studies. To combat the high dimensionality of the deformation descriptors, we develop a probabilistic model of principal geodesic analysis in a bandlimited low-dimensional space that still captures the underlying variability of image data...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28662070/machine-learning-and-microsimulation-techniques-on-the-prognosis-of-dementia-a-systematic-literature-review
#20
Ana Luiza Dallora, Shahryar Eivazzadeh, Emilia Mendes, Johan Berglund, Peter Anderberg
BACKGROUND: Dementia is a complex disorder characterized by poor outcomes for the patients and high costs of care. After decades of research little is known about its mechanisms. Having prognostic estimates about dementia can help researchers, patients and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. OBJECTIVE: The goal of this paper is to present evidence on the state of the art of studies investigating and the prognosis of dementia using machine learning and microsimulation techniques...
2017: PloS One
keyword
keyword
37175
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"