| scholarly article | Q13442814 |
| P8978 | DBLP publication ID | journals/neuroimage/EskildsenCGFPC13 |
| P356 | DOI | 10.1016/J.NEUROIMAGE.2012.09.058 |
| P932 | PMC publication ID | 4237400 |
| P698 | PubMed publication ID | 23036450 |
| P5875 | ResearchGate publication ID | 232085997 |
| P50 | author | Alzheimer's Disease Neuroimaging Initiative | Q4738819 |
| Vladimir S. Fonov | Q47151307 | ||
| Pierrick Coupé | Q47703137 | ||
| Jens C Pruessner | Q51767545 | ||
| Simon F Eskildsen | Q56994621 | ||
| P2093 | author name string | D Louis Collins | |
| Daniel García-Lorenzo | |||
| P2860 | cites work | The clinical use of structural MRI in Alzheimer disease | Q24609648 |
| Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain | Q25855787 | ||
| Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease | Q27860795 | ||
| Neuropathological stageing of Alzheimer-related changes | Q27860862 | ||
| Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach | Q29614698 | ||
| Circular analysis in systems neuroscience: the dangers of double dipping | Q29614738 | ||
| pROC: an open-source package for R and S+ to analyze and compare ROC curves | Q30050695 | ||
| The Alzheimer's disease neuroimaging initiative | Q31030939 | ||
| A nonparametric method for automatic correction of intensity nonuniformity in MRI data | Q32061718 | ||
| Antemortem MRI based STructural Abnormality iNDex (STAND)-scores correlate with postmortem Braak neurofibrillary tangle stage | Q33345777 | ||
| Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI | Q33444954 | ||
| Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease | Q33727158 | ||
| An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images | Q33896952 | ||
| BEaST: brain extraction based on nonlocal segmentation technique | Q34030054 | ||
| Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data | Q34050836 | ||
| Multi-method analysis of MRI images in early diagnostics of Alzheimer's disease | Q34055598 | ||
| Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's disease | Q34077234 | ||
| Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database | Q34120412 | ||
| The dementias | Q34164452 | ||
| Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification | Q34183016 | ||
| Unbiased average age-appropriate atlases for pediatric studies | Q34233547 | ||
| Staging of Alzheimer-related cortical destruction | Q34349510 | ||
| Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy | Q34445269 | ||
| Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer's disease | Q36093127 | ||
| Multi-template tensor-based morphometry: application to analysis of Alzheimer's disease | Q36561544 | ||
| Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space | Q36744339 | ||
| Posterior cerebral atrophy in the absence of medial temporal lobe atrophy in pathologically-confirmed Alzheimer's disease | Q36852812 | ||
| Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. | Q37111598 | ||
| Mapping progressive brain structural changes in early Alzheimer's disease and mild cognitive impairment | Q37131880 | ||
| Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve | Q37270617 | ||
| The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease | Q39976878 | ||
| Fluorodeoxyglucose-positron-emission tomography, single-photon emission tomography, and structural MR imaging for prediction of rapid conversion to Alzheimer disease in patients with mild cognitive impairment: a meta-analysis | Q43865570 | ||
| COMPARE: classification of morphological patterns using adaptive regional elements. | Q45965571 | ||
| Cortical thinning in obsessive compulsive disorder. | Q48156052 | ||
| Robust Rician noise estimation for MR images | Q48225594 | ||
| Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia. | Q48327773 | ||
| Hippocampal volume is as variable in young as in older adults: implications for the notion of hippocampal atrophy in humans. | Q48359949 | ||
| Automated cortical thickness measurements from MRI can accurately separate Alzheimer's patients from normal elderly controls. | Q48373578 | ||
| Cortical thickness in frontotemporal dementia, mild cognitive impairment, and Alzheimer's disease. | Q48588525 | ||
| AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America. | Q49041459 | ||
| Clinical criteria for the diagnosis of Alzheimer disease: still good after all these years. | Q51891270 | ||
| Mapping Cortical Thickness and Gray Matter Concentration in First Episode Schizophrenia | Q57262523 | ||
| Posterior cortical atrophy | Q57384523 | ||
| Active surface approach for extraction of the human cerebral cortex from MRI | Q62490438 | ||
| AddNeuroMed--the European collaboration for the discovery of novel biomarkers for Alzheimer's disease | Q95800564 | ||
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | Alzheimer's disease | Q11081 |
| mild cognitive impairment | Q1472703 | ||
| cognitive dysfunction | Q57859955 | ||
| P304 | page(s) | 511-521 | |
| P577 | publication date | 2012-10-02 | |
| P1433 | published in | NeuroImage | Q1981225 |
| P1476 | title | Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning | |
| P478 | volume | 65 |
| Q30971403 | 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception |
| Q89821102 | A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis |
| Q60046121 | A Comparative Atlas-Based Recognition of Mild Cognitive Impairment With Voxel-Based Morphometry |
| Q61443162 | A controlled comparison of thickness, volume and surface areas from multiple cortical parcellation packages |
| Q30724134 | A focus on structural brain imaging in the Alzheimer's disease neuroimaging initiative |
| Q64057622 | A highly predictive signature of cognition and brain atrophy for progression to Alzheimer's dementia |
| Q36348208 | A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages |
| Q40283684 | Accurate Prediction of Conversion to Alzheimer's Disease using Imaging, Genetic, and Neuropsychological Biomarkers |
| Q37184305 | Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment |
| Q62747116 | An Overview of Quantitative Magnetic Resonance Imaging Analysis Studies in the Assessment of Alzheimer’s Disease |
| Q97883015 | Assessment of Alzheimer's Disease Based on Texture Analysis of the Entorhinal Cortex |
| Q64071794 | Association of corneal nerve fiber measures with cognitive function in dementia |
| Q34443664 | Baseline shape diffeomorphometry patterns of subcortical and ventricular structures in predicting conversion of mild cognitive impairment to Alzheimer's disease |
| Q33762542 | Brain MR Image Classification for Alzheimer's Disease Diagnosis Based on Multifeature Fusion. |
| Q45944353 | Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers. |
| Q60923478 | Classifying early and late mild cognitive impairment stages of Alzheimer's disease by fusing default mode networks extracted with multiple seeds |
| Q64242210 | Cloud-Based Brain Magnetic Resonance Image Segmentation and Parcellation System for Individualized Prediction of Cognitive Worsening |
| Q37559815 | Combined Plasma and Cerebrospinal Fluid Signature for the Prediction of Midterm Progression From Mild Cognitive Impairment to Alzheimer Disease |
| Q64120749 | Connectome-wide network analysis of white matter connectivity in Alzheimer's disease |
| Q30893529 | Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis |
| Q33701135 | Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images |
| Q93154568 | Cortical graph neural network for AD and MCI diagnosis and transfer learning across populations |
| Q38975597 | Deep ensemble learning of sparse regression models for brain disease diagnosis |
| Q37662557 | Detection of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Longitudinal Brain MRI. |
| Q64983481 | Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification. |
| Q92669923 | Different Cortical Thinning Patterns Depending on Their Prognosis in Individuals with Subjective Cognitive Decline |
| Q37587482 | Differential diagnosis of mild cognitive impairment and Alzheimer's disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry |
| Q35761601 | Domain Transfer Learning for MCI Conversion Prediction |
| Q35712235 | Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool |
| Q30996727 | Evolving Evidence for the Value of Neuroimaging Methods and Biological Markers in Subjects Categorized with Subjective Cognitive Decline |
| Q47225592 | Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease |
| Q37653048 | From mild cognitive impairment to subjective cognitive decline: conceptual and methodological evolution. |
| Q34913389 | Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease |
| Q33881832 | Group-Level Progressive Alterations in Brain Connectivity Patterns Revealed by Diffusion-Tensor Brain Networks across Severity Stages in Alzheimer's Disease |
| Q48185314 | Hippocampal (subfield) volume and shape in relation to cognitive performance across the adult lifespan |
| Q52595017 | Histogram-Based Features Selection and Volume of Interest Ranking for Brain PET Image Classification. |
| Q90206522 | Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI |
| Q92237506 | Identifying Imaging Markers for Predicting Cognitive Assessments Using Wasserstein Distances Based Matrix Regression |
| Q38169551 | Imaging markers of structural and functional brain changes that precede cognitive symptoms in risk for Alzheimer's disease |
| Q37410700 | Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers |
| Q47141267 | Interactive versus additive relationships between regional cortical thinning and amyloid burden in predicting clinical decline in mild AD and MCI individuals. |
| Q64118359 | Lifespan Changes of the Human Brain In Alzheimer's Disease |
| Q37274971 | Locally linear embedding (LLE) for MRI based Alzheimer's disease classification |
| Q45956072 | Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. |
| Q37668090 | Mild cognitive impairment: a concept in evolution |
| Q36288456 | Mindboggling morphometry of human brains. |
| Q33728978 | Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's Disease |
| Q90288676 | Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification |
| Q52597763 | Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. |
| Q38576124 | Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection |
| Q35987702 | Multimodal manifold-regularized transfer learning for MCI conversion prediction |
| Q46656646 | Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer's disease |
| Q92591097 | Neuroimaging Biomarkers for Alzheimer's Disease |
| Q60043911 | Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer's disease: a feature selection ensemble combining stability and predictability |
| Q34431841 | Novel ThickNet features for the discrimination of amnestic MCI subtypes |
| Q91972888 | Predicting Alzheimer's Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data |
| Q64259159 | Predicting progression from mild cognitive impairment to Alzheimer's disease on an individual subject basis by applying the CARE index across different independent cohorts |
| Q34070810 | Predicting progression of Alzheimer's disease using ordinal regression |
| Q38655025 | Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows. |
| Q64079939 | Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models |
| Q36811378 | Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features |
| Q99585524 | Prediction of conversion to Alzheimer's disease using deep survival analysis of MRI images |
| Q40924774 | Reduced cerebral cortical thickness in Non-cirrhotic patients with hepatitis C. |
| Q40132409 | Relationship Induced Multi-Template Learning for Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment |
| Q37584237 | Rey's Auditory Verbal Learning Test scores can be predicted from whole brain MRI in Alzheimer's disease |
| Q37133596 | Scoring by nonlocal image patch estimator for early detection of Alzheimer's disease |
| Q89560267 | Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises |
| Q30878103 | Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach |
| Q59844030 | Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database |
| Q89971040 | Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment |
| Q38219079 | The new DSM-5 diagnosis of mild neurocognitive disorder and its relation to research in mild cognitive impairment |
| Q38501853 | The relationship between atrophy and hypometabolism: is it regionally dependent in dementias? |
| Q50055245 | The utility of a network-based clustering method for dimension reduction of imaging and non-imaging biomarkers predictive of Alzheimer's disease. |
| Q62668456 | Thickness NETwork (ThickNet) Features for the Detection of Prodromal AD |
| Q44985499 | Thickness network features for prognostic applications in dementia |
| Q30543926 | Transcriptomics of cortical gray matter thickness decline during normal aging |
| Q30666527 | Understanding cognitive deficits in Alzheimer's disease based on neuroimaging findings |
| Q96962661 | Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI, AIBL, OASIS, and MIRIAD databases |
| Q38906604 | Your algorithm might think the hippocampus grows in Alzheimer's disease: Caveats of longitudinal automated hippocampal volumetry |
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