| scholarly article | Q13442814 |
| P50 | author | Perminder Sachdev | Q7169326 |
| Dacheng Tao | Q29259267 | ||
| Henry Brodaty | Q57570738 | ||
| Wanlin Zhu | Q62618839 | ||
| Jiyang Jiang | Q86932207 | ||
| Nicole A Kochan | Q87623279 | ||
| Haijun Niu | Q88141696 | ||
| P2093 | author name string | Wei Wen | |
| Tao Liu | |||
| Yilong Wang | |||
| Jian Cheng | |||
| Hao Guan | |||
| Jicong Zhang | |||
| P2860 | cites work | Mild Cognitive Impairment Can Be Distinguished From Alzheimer Disease and Normal Aging for Clinical Trials | Q22253054 |
| Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests | Q22673963 | ||
| “Mini-mental state” | Q25938989 | ||
| Computational anatomy with the SPM software | Q28236652 | ||
| Neuropsychological prediction of conversion to Alzheimer disease in patients with mild cognitive impairment | Q28256381 | ||
| Machine learning classifiers and fMRI: a tutorial overview | Q28310323 | ||
| A review of feature reduction techniques in neuroimaging | Q28658984 | ||
| Circular analysis in systems neuroscience: the dangers of double dipping | Q29614738 | ||
| An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest | Q29615076 | ||
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| Does mild cognitive impairment always lead to dementia? A review. | Q30245171 | ||
| Incidence of mild cognitive impairment in the Pittsburgh Cardiovascular Health Study-Cognition Study | Q30416835 | ||
| Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study | Q30687080 | ||
| Automatic recognition of cortical sulci of the human brain using a congregation of neural networks | Q30695965 | ||
| Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging | Q30797550 | ||
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| Comparison of neuroimaging modalities for the prediction of conversion from mild cognitive impairment to Alzheimer's dementia | Q39365547 | ||
| Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia | Q40068607 | ||
| Effects of MCI subtype and reversion on progression to dementia in a community sample. | Q40206960 | ||
| Avoiding asymmetry-induced bias in longitudinal image processing. | Q40461274 | ||
| Regional brain atrophy rate predicts future cognitive decline: 6-year longitudinal MR imaging study of normal aging | Q40544847 | ||
| Classifying anatomical subtypes of subjective memory impairment | Q40577313 | ||
| Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects. | Q45956072 | ||
| Modeling disease progression via multi-task learning | Q46254532 | ||
| Alzheimer's Disease Diagnosis Using Landmark-Based Features From Longitudinal Structural MR Images | Q46626390 | ||
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| The Sydney Memory and Ageing Study (MAS): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of Australians aged 70-90 years | Q48124401 | ||
| Thalamus structure and function determine severity of cognitive impairment in multiple sclerosis. | Q48362996 | ||
| Gray matter atrophy patterns of mild cognitive impairment subtypes. | Q48691019 | ||
| Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer's disease: Meta-analyses of MRI studies. | Q48691809 | ||
| Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: a combined spatial atrophy and white matter alteration approach. | Q48934466 | ||
| Mild cognitive impairment in a community sample: the Sydney Memory and Ageing Study. | Q50777837 | ||
| Predicting the development of mild cognitive impairment: a new use of pattern recognition. | Q50970519 | ||
| White matter hyperintensities in the forties: their prevalence and topography in an epidemiological sample aged 44-48. | Q51886805 | ||
| A new rating scale for Alzheimer's disease. | Q53197509 | ||
| The Bayer Activities of Daily Living Scale (B-ADL). | Q53223956 | ||
| Gene Selection for Cancer Classification using Support Vector Machines | Q56535529 | ||
| The NumPy Array: A Structure for Efficient Numerical Computation | Q57317251 | ||
| Limited relationships between two-year changes in sulcal morphology and other common neuroimaging indices in the elderly | Q57724914 | ||
| Automatic inference of sulcus patterns using 3D moment invariants | Q62489277 | ||
| Does the source of subjects matter?: absolutely! | Q84258228 | ||
| Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images | Q33701135 | ||
| Update on the biomarker core of the Alzheimer's Disease Neuroimaging Initiative subjects | Q33842486 | ||
| A library of cortical morphology analysis tools to study development, aging and genetics of cerebral cortex | Q33941356 | ||
| Longitudinal MRI atrophy biomarkers: relationship to conversion in the ADNI cohort | Q33996059 | ||
| Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database | Q34120412 | ||
| Mild cognitive impairment: baseline and longitudinal structural MR imaging measures improve predictive prognosis | Q34176310 | ||
| Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification | Q34183016 | ||
| Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers | Q34211760 | ||
| Multi-atlas based representations for Alzheimer's disease diagnosis | Q34214344 | ||
| Staging of Alzheimer-related cortical destruction | Q34349510 | ||
| Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness | Q34432585 | ||
| Enriched white matter connectivity networks for accurate identification of MCI patients | Q34432603 | ||
| Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning | Q34542377 | ||
| Multimodal classification of Alzheimer's disease and mild cognitive impairment | Q34672813 | ||
| Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features | Q34917903 | ||
| Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease | Q35386421 | ||
| The dynamics of cortical and hippocampal atrophy in Alzheimer disease | Q35643088 | ||
| Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism | Q35660011 | ||
| Identification of MCI individuals using structural and functional connectivity networks | Q35663525 | ||
| Mild cognitive impairment--beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment | Q35868499 | ||
| Within-subject template estimation for unbiased longitudinal image analysis | Q36075413 | ||
| A Bayesian model of shape and appearance for subcortical brain segmentation | Q36158330 | ||
| Longitudinal changes in microstructural white matter metrics in Alzheimer's disease | Q36244104 | ||
| Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices. | Q36262487 | ||
| Reversion from mild cognitive impairment to normal or near-normal cognition: risk factors and prognosis | Q36330917 | ||
| Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge | Q37090156 | ||
| Cortical folding in patients with bipolar disorder or unipolar depression | Q37109140 | ||
| 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 | ||
| Prediction of Alzheimer's disease using individual structural connectivity networks | Q37186978 | ||
| Patterns of atrophy differ among specific subtypes of mild cognitive impairment | Q37326509 | ||
| Longitudinal measurement and hierarchical classification framework for the prediction of Alzheimer's disease | Q37581434 | ||
| Higher risk of progression to dementia in mild cognitive impairment cases who revert to normal | Q37593256 | ||
| MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression | Q37620487 | ||
| Prediction of Incipient Alzheimer's Disease Dementia in Patients with Mild Cognitive Impairment | Q38818319 | ||
| P4510 | describes a project that uses | scikit-learn | Q1026367 |
| scikit-image | Q22442795 | ||
| P921 | main subject | biomarker | Q864574 |
| P304 | page(s) | 309 | |
| P577 | publication date | 2017-09-26 | |
| P1433 | published in | Frontiers in Aging Neuroscience | Q21968084 |
| P1476 | title | Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers. | |
| P478 | volume | 9 |
| Q61443162 | A controlled comparison of thickness, volume and surface areas from multiple cortical parcellation packages |
| Q64983481 | Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification. |
| Q62492840 | Machine learning studies on major brain diseases: 5-year trends of 2014-2018 |
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