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https://www.readbyqxmd.com/read/28703112/the-usefulness-of-d-dimer-in-diagnosis-and-prediction-of-venous-thromboembolism-in-patients-with-abdominal-malignancy
#1
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
#2
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...
July 5, 2017: Neuroscience Letters
https://www.readbyqxmd.com/read/28684136/open-innovation-towards-sharing-of-data-models-and-workflows
#3
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
#4
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
#5
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
#6
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
#7
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
https://www.readbyqxmd.com/read/28642938/correlation-weighted-sparse-group-representation-for-brain-network-construction-in-mci-classification
#8
Renping Yu, Han Zhang, Le An, Xiaobo Chen, Zhihui Wei, Dinggang Shen
Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders, such as Alzheimer's disease and its early stage, mild cognitive impairment (MCI). In all these applications, the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network, sparse learning has been widely used for complex BFCN construction. However, the conventional l1-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network, which ignores the link strength and could remove strong links in the brain network...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/28642699/cerebral-blood-flow-and-amyloid-%C3%AE-interact-to-affect-memory-performance-in-cognitively-normal-older-adults
#9
Katherine J Bangen, Alexandra L Clark, Emily C Edmonds, Nicole D Evangelista, Madeleine L Werhane, Kelsey R Thomas, Lyzette E Locano, My Tran, Zvinka Z Zlatar, Daniel A Nation, Mark W Bondi, Lisa Delano-Wood
Cerebral blood flow (CBF) alterations and amyloid-β (Aβ) accumulation have been independently linked to cognitive deficits in older adults at risk for dementia. Less is known about how CBF and Aβ may interact to affect cognition in cognitively normal older adults. Therefore, we examined potential statistical interactions between CBF and Aβ status in regions typically affected in Alzheimer's disease (AD) within a sample of older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Sixty-two cognitively normal participants (mean age = 72 years) underwent neuroimaging and memory testing...
2017: Frontiers in Aging Neuroscience
https://www.readbyqxmd.com/read/28641921/genome-wide-association-and-interaction-studies-of-csf-t-tau-a%C3%AE-42-ratio-in-adni-cohort
#10
Jin Li, Qiushi Zhang, Feng Chen, Xianglian Meng, Wenjie Liu, Dandan Chen, Jingwen Yan, Sungeun Kim, Lei Wang, Weixing Feng, Andrew J Saykin, Hong Liang, Li Shen
The pathogenic relevance in Alzheimer's disease (AD) presents a decrease of cerebrospinal fluid amyloid-ß42 (Aß42) burden and an increase in cerebrospinal fluid total tau (T-tau) levels. In this work, we performed genome-wide association study (GWAS) and genome-wide interaction study of T-tau/Aß42 ratio as an AD imaging quantitative trait on 843 subjects and 563,980 single-nucleotide polymorphisms (SNPs) in ADNI cohort. We aim to identify not only SNPs with significant main effects but also SNPs with interaction effects to help explain "missing heritability"...
May 15, 2017: Neurobiology of Aging
https://www.readbyqxmd.com/read/28638497/fiber-direction-estimation-smoothing-and-tracking-in-diffusion-mri
#11
Raymond K W Wong, Thomas C M Lee, Debashis Paul, Jie Peng
Diffusion magnetic resonance imaging is an imaging technology designed to probe anatomical architectures of biological samples in an in vivo and noninvasive manner through measuring water diffusion. The contribution of this paper is threefold. First, it proposes a new method to identify and estimate multiple diffusion directions within a voxel through a new and identifiable parametrization of the widely used multi-tensor model. Unlike many existing methods, this method focuses on the estimation of diffusion directions rather than the diffusion tensors...
September 2016: Annals of Applied Statistics
https://www.readbyqxmd.com/read/28626864/detecting-genetic-association-through-shortest-paths-in-a-bidirected-graph
#12
Masao Ueki, Yoshinori Kawasaki, Gen Tamiya
Genome-wide association studies (GWASs) commonly use marginal association tests for each single-nucleotide polymorphism (SNP). Because these tests treat SNPs as independent, their power will be suboptimal for detecting SNPs hidden by linkage disequilibrium (LD). One way to improve power is to use a multiple regression model. However, the large number of SNPs preclude simultaneous fitting with multiple regression, and subset regression is infeasible because of an exorbitant number of candidate subsets. We therefore propose a new method for detecting hidden SNPs having significant yet weak marginal association in a multiple regression model...
June 19, 2017: Genetic Epidemiology
https://www.readbyqxmd.com/read/28624881/discriminative-self-representation-sparse-regression-for-neuroimaging-based-alzheimer-s-disease-diagnosis
#13
Xiaofeng Zhu, Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
In this paper, we propose a novel feature selection method by jointly considering (1) 'task-specific' relations between response variables (e.g., clinical labels in this work) and neuroimaging features and (2) 'self-representation' relations among neuroimaging features in a sparse regression framework. Specifically, the task-specific relation is devised to learn the relative importance of features for representation of response variables by a linear combination of the input features in a supervised manner, while the self-representation relation is used to take into account the inherent information among neuroimaging features such that any feature can be represented by a weighted sum of the other features, regardless of the label information, in an unsupervised manner...
June 17, 2017: Brain Imaging and Behavior
https://www.readbyqxmd.com/read/28622141/analysis-of-structural-brain-mri-and-multi-parameter-classification-for-alzheimer-s-disease
#14
Yingteng Zhang, Shenquan Liu
Incorporating with machine learning technology, neuroimaging markers which extracted from structural Magnetic Resonance Images (sMRI), can help distinguish Alzheimer's Disease (AD) patients from Healthy Controls (HC). In the present study, we aim to investigate differences in atrophic regions between HC and AD and apply machine learning methods to classify these two groups. T1-weighted sMRI scans of 158 patients with AD and 145 age-matched HC were acquired from the ADNI database. Five kinds of parameters (i...
June 15, 2017: Biomedizinische Technik. Biomedical Engineering
https://www.readbyqxmd.com/read/28611848/brain-mr-image-classification-for-alzheimer-s-disease-diagnosis-based-on-multifeature-fusion
#15
Zhe Xiao, Yi Ding, Tian Lan, Cong Zhang, Chuanji Luo, Zhiguang Qin
We propose a novel classification framework to precisely identify individuals with Alzheimer's disease (AD) or mild cognitive impairment (MCI) from normal controls (NC). The proposed method combines three different features from structural MR images: gray-matter volume, gray-level cooccurrence matrix, and Gabor feature. These features can obtain both the 2D and 3D information of brains, and the experimental results show that a better performance can be achieved through the multifeature fusion. We also analyze the multifeatures combination correlation technologies and improve the SVM-RFE algorithm through the covariance method...
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28610695/a-fuzzy-based-system-reveals-alzheimer-s-disease-onset-in-subjects-with-mild-cognitive-impairment
#16
Sabina Tangaro, Annarita Fanizzi, Nicola Amoroso, Roberto Bellotti
Alzheimer's Disease (AD) is the most frequent neurodegenerative form of dementia. Although dementia cannot be cured, it is very important to detect preclinical AD as early as possible. Several studies demonstrated the effectiveness of the joint use of structural Magnetic Resonance Imaging (MRI) and cognitive measures to detect and track the progression of the disease. Since hippocampal atrophy is a well known biomarker for AD progression state, we propose here a novel methodology, exploiting it as a searchlight to detect the best discriminating features for the classification of subjects with Mild Cognitive Impairment (MCI) converting (MCI-c) or not converting (MCI-nc) to AD...
June 2017: Physica Medica: PM
https://www.readbyqxmd.com/read/28609533/association-between-elevated-brain-amyloid-and-subsequent-cognitive-decline-among-cognitively-normal-persons
#17
Michael C Donohue, Reisa A Sperling, Ronald Petersen, Chung-Kai Sun, Michael W Weiner, Paul S Aisen
Importance: Among cognitively normal individuals, elevated brain amyloid (defined by cerebrospinal fluid assays or positron emission tomography regional summaries) can be related to risk for later Alzheimer-related cognitive decline. Objective: To characterize and quantify the risk for Alzheimer-related cognitive decline among cognitively normal individuals with elevated brain amyloid. Design, Setting, and Participants: Exploratory analyses were conducted with longitudinal cognitive and biomarker data from 445 cognitively normal individuals in the United States and Canada...
June 13, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/28602597/performance-comparison-of-10-different-classification-techniques-in-segmenting-white-matter-hyperintensities-in-aging
#18
Mahsa Dadar, Josefina Maranzano, Karen Misquitta, Cassandra J Anor, Vladimir S Fonov, M Carmela Tartaglia, Owen T Carmichael, Charles Decarli, D Louis Collins
INTRODUCTION: White matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimer's disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter-rater and intra-rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general...
June 10, 2017: NeuroImage
https://www.readbyqxmd.com/read/28589856/association-analysis-of-rare-variants-near-the-apoe-region-with-csf-and-neuroimaging-biomarkers-of-alzheimer-s-disease
#19
Kwangsik Nho, Sungeun Kim, Emrin Horgusluoglu, Shannon L Risacher, Li Shen, Dokyoon Kim, Seunggeun Lee, Tatiana Foroud, Leslie M Shaw, John Q Trojanowski, Paul S Aisen, Ronald C Petersen, Clifford R Jack, Michael W Weiner, Robert C Green, Arthur W Toga, Andrew J Saykin
BACKGROUND: The APOE ε4 allele is the most significant common genetic risk factor for late-onset Alzheimer's disease (LOAD). The region surrounding APOE on chromosome 19 has also shown consistent association with LOAD. However, no common variants in the region remain significant after adjusting for APOE genotype. We report a rare variant association analysis of genes in the vicinity of APOE with cerebrospinal fluid (CSF) and neuroimaging biomarkers of LOAD. METHODS: Whole genome sequencing (WGS) was performed on 817 blood DNA samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI)...
May 24, 2017: BMC Medical Genomics
https://www.readbyqxmd.com/read/28580458/structured-sparse-kernel-learning-for-imaging-genetics-based-alzheimer-s-disease-diagnosis
#20
Jailin Peng, Le An, Xiaofeng Zhu, Yan Jin, Dinggang Shen
A kernel-learning based method is proposed to integrate multimodal imaging and genetic data for Alzheimer's disease (AD) diagnosis. To facilitate structured feature learning in kernel space, we represent each feature with a kernel and then group kernels according to modalities. In view of the highly redundant features within each modality and also the complementary information across modalities, we introduce a novel structured sparsity regularizer for feature selection and fusion, which is different from conventional lasso and group lasso based methods...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
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