Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment
about
A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses.Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects3D scattering transforms for disease classification in neuroimaging.Imaging plus X: multimodal models of neurodegenerative disease.A data-driven model of biomarker changes in sporadic Alzheimer's disease.Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data.Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception.Evolving Evidence for the Value of Neuroimaging Methods and Biological Markers in Subjects Categorized with Subjective Cognitive Decline.Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.Predictive modelling using neuroimaging data in the presence of confounds.Multi-Domain Transfer Learning for Early Diagnosis of Alzheimer's DiseaseMachine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review.Predicting progression of Alzheimer's disease using ordinal regression.Genetic algorithm with logistic regression for prediction of progression to Alzheimer's diseasePredicting outcome in clinically isolated syndrome using machine learning.Clinical characteristics, pathophysiology, and management of noncentral nervous system cancer-related cognitive impairment in adultsDomain Transfer Learning for MCI Conversion Prediction.The relative importance of imaging markers for the prediction of Alzheimer's disease dementia in mild cognitive impairment - Beyond classical regressionPredicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification.Multimodal manifold-regularized transfer learning for MCI conversion prediction.Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks.The cortical thickness phenotype of individuals with DISC1 translocation resembles schizophrenia.A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE.Multimodal Image Analysis in Alzheimer's Disease via Statistical Modelling of Non-local Intensity CorrelationsDiscriminative Learning for Alzheimer's Disease Diagnosis via Canonical Correlation Analysis and Multimodal Fusion.Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkersCombined Plasma and Cerebrospinal Fluid Signature for the Prediction of Midterm Progression From Mild Cognitive Impairment to Alzheimer DiseaseBrain Metabolism Changes in Patients Infected with HTLV-1Classification of Alzheimer's Disease and Prediction of Mild Cognitive Impairment Conversion Using Histogram-Based Analysis of Patient-Specific Anatomical Brain Connectivity Networks.The potential of genetic and gene expression analysis in the diagnosis of neuropsychiatric disorders.Accurate Prediction of Conversion to Alzheimer's Disease using Imaging, Genetic, and Neuropsychological Biomarkers.Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM.Ketamine induces a robust whole-brain connectivity pattern that can be differentially modulated by drugs of different mechanism and clinical profileIncremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer's dementia.Data-Driven Sequence of Changes to Anatomical Brain Connectivity in Sporadic Alzheimer's Disease.Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals.FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort.Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease.
P2860
Q27313916-DC0CFC90-C584-4B57-ACEE-142E20822D04Q28656446-806C0005-D979-4CC3-A994-887381BE8025Q30361248-DA5E2F60-49DB-47C0-A50C-ABB490BD26C0Q30491715-760DB078-D5C4-4814-8563-EA57CAC6D3E5Q30835966-F0E3DF1C-B4E1-45EA-8D1B-36CC1E50A2BEQ30887191-704A1839-B5EF-417C-9AA3-9F422C74C84FQ30930965-DD7B2861-D5EB-42FC-B9A8-7B31B59C2746Q30971403-21FBB24D-A4BE-492E-ADA9-AAD228EFEAD4Q30996727-8CDBE450-A52B-41DA-8A7C-EA189EF0DF2DQ31063019-571AA8CC-F98B-405E-A2B6-B7B482BB910AQ31158492-D4905D64-5054-4E93-B04C-870DB1DD4E54Q33728978-E447E3CB-E7C4-4B1E-8230-0A625B8A07EEQ33853042-0F807078-96E9-4895-946D-40E0CB3907B8Q34070810-F25C2131-F1C2-4FE4-84D0-ABDB2F2784D2Q34913389-EE1BC2F9-B571-445F-A6CB-52F1DABEE7CDQ34980811-DBA4B970-C2FF-4765-8490-735E4C39FEC4Q35164117-7A6D0DD1-0862-4072-83B1-9269277B93C7Q35761601-5D8A0F4B-AE71-440C-AB27-BA0849688A06Q35864927-528612C6-5C4F-48D5-B2A2-38810AD398B0Q35930947-28FF1E46-3BE0-438F-860F-C59D71FB5336Q35987702-F8A364A2-323C-4232-82FB-7D83E083EDC8Q36031308-A2965E29-A84C-47BC-98B3-B85F817B4CB2Q36106073-211BCF5B-BE0B-4F4D-998F-0EDF4E2BDA2EQ36348208-806CB68C-CD57-4060-B189-B70896AB4508Q36588774-030F4872-F991-4FF1-A0AC-8AE16D3F4924Q36786448-D583DC51-9C19-47FB-AC5A-E29C108D6BF3Q36906166-B8CBB487-5E4F-44E1-A8EF-38345F201897Q36929004-D97CBA41-41AE-4A3E-856F-5D09006BC62FQ37559815-D8D32026-03B1-4824-925D-A59AC88F5CD8Q37670477-E069DA21-3FAC-43FA-9A20-8BF9683543C2Q38628815-9E66391C-15DB-41B7-8BD1-FE12025E8636Q38790294-C1627884-4132-4542-B689-A8701757175CQ40283684-AC3BE3FB-9A63-4984-AABC-A4A063F2A81FQ41460468-5928A8A8-89F9-4121-B115-0D1566A6CD88Q42009185-5122D6CD-4401-4157-A216-91DBF2DD9F3AQ42374123-F03D9D30-59F6-48C0-AB30-C77411D7F6BDQ44702532-316F50F9-DCA9-4D3B-BCBB-766952A6BDE7Q47184005-7B345FF0-B462-479B-894D-C91C46B47678Q47553874-C51A7187-1584-4547-B278-06213F497D5EQ47722152-5B9361E2-098B-4F41-9CA9-F57AE27D370D
P2860
Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 19 May 2013
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Accurate multimodal probabilis ...... with mild cognitive impairment
@en
Accurate multimodal probabilis ...... ith mild cognitive impairment.
@nl
type
label
Accurate multimodal probabilis ...... with mild cognitive impairment
@en
Accurate multimodal probabilis ...... ith mild cognitive impairment.
@nl
prefLabel
Accurate multimodal probabilis ...... with mild cognitive impairment
@en
Accurate multimodal probabilis ...... ith mild cognitive impairment.
@nl
P2093
P2860
P50
P1433
P1476
Accurate multimodal probabilis ...... with mild cognitive impairment
@en
P2093
Jonathan Young
Manuel J Cardoso
Marc Modat
P2860
P304
P356
10.1016/J.NICL.2013.05.004
P577
2013-05-19T00:00:00Z