Classifying early and late mild cognitive impairment stages of Alzheimer's disease by fusing default mode networks extracted with multiple seeds

scholarly article by Shengbing Pei et al published 31 December 2018 in BMC Bioinformatics

Classifying early and late mild cognitive impairment stages of Alzheimer's disease by fusing default mode networks extracted with multiple seeds is …
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scholarly articleQ13442814

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P8978DBLP publication IDjournals/bmcbi/PeiGZ18
P356DOI10.1186/S12859-018-2528-0
P2888exact matchhttps://scigraph.springernature.com/pub.10.1186/s12859-018-2528-0
P932PMC publication ID6311889
P698PubMed publication ID30598074

P2093author name stringJihong Guan
Shuigeng Zhou
Shengbing Pei
P2860cites workWhat we can do and what we cannot do with fMRIQ22122223
A default mode of brain functionQ24544311
An Information-Maximization Approach to Blind Separation and Blind DeconvolutionQ28291012
Default mode, dorsal attention and auditory resting state networks exhibit differential functional connectivity in tinnitus and hearing lossQ30448507
Generation of the probabilistic template of default mode network derived from resting-state fMRI.Q51084310
Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer's disease.Q51962048
Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA modelQ30502094
Fusion analysis of functional MRI data for classification of individuals based on patterns of activationQ30755485
Joint sparse representation of brain activity patterns in multi-task fMRI data.Q30839376
General nonunitary constrained ICA and its application to complex-valued fMRI dataQ30871384
Data-Driven and Predefined ROI-Based Quantification of Long-Term Resting-State fMRI ReproducibilityQ31004331
A method for multitask fMRI data fusion applied to schizophreniaQ31024846
A Novel Sparse Dictionary Learning Separation (SDLS) Model With Adaptive Dictionary Mutual Incoherence Constraint for fMRI Data AnalysisQ31050675
Estimating the number of independent components for functional magnetic resonance imaging dataQ31097409
How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selectionQ31118772
Task fMRI data analysis based on supervised stochastic coordinate codingQ31167342
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP dataQ33390045
Exploring the brain network: a review on resting-state fMRI functional connectivityQ33578734
The impact of certain methodological choices on multivariate analysis of fMRI data with support vector machinesQ33684392
Structural and functional network connectivity breakdown in Alzheimer’s disease studied with magnetic resonance imaging techniques.Q33812735
Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility StudyQ34002999
Development of deactivation of the default-mode network during episodic memory formationQ34245634
Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinningQ34542377
Joint ICA of ERP and fMRI during error-monitoringQ35592662
Is Model Fitting Necessary for Model-Based fMRI?Q35667401
Default mode network in concussed individuals in response to the YMCA physical stress testQ35828320
Statistical power and prediction accuracy in multisite resting-state fMRI connectivityQ38979014
Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasksQ39850322
Sparse representation of whole-brain fMRI signals for identification of functional networks.Q41726035
Deep learning for neuroimaging: a validation studyQ42068514
Conn: a functional connectivity toolbox for correlated and anticorrelated brain networksQ44033763
Noise contributions to the fMRI signal: An overview.Q44352496
Resting state fMRI feature-based cerebral glioma grading by support vector machineQ47436092
Joint independent component analysis of structural and functional images reveals complex patterns of functional reorganisation in stroke aphasia.Q48572087
P275copyright licenseCreative Commons Attribution 4.0 InternationalQ20007257
P6216copyright statuscopyrightedQ50423863
P433issueSuppl 19
P407language of work or nameEnglishQ1860
P921main subjectAlzheimer's diseaseQ11081
mild cognitive impairmentQ1472703
cognitive dysfunctionQ57859955
P304page(s)523
P577publication date2018-12-31
P1433published inBMC BioinformaticsQ4835939
P1476titleClassifying early and late mild cognitive impairment stages of Alzheimer's disease by fusing default mode networks extracted with multiple seeds
P478volume19

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