scholarly article | Q13442814 |
P8978 | DBLP publication ID | journals/bmcbi/PeiGZ18 |
P356 | DOI | 10.1186/S12859-018-2528-0 |
P2888 | exact match | https://scigraph.springernature.com/pub.10.1186/s12859-018-2528-0 |
P932 | PMC publication ID | 6311889 |
P698 | PubMed publication ID | 30598074 |
P2093 | author name string | Jihong Guan | |
Shuigeng Zhou | |||
Shengbing Pei | |||
P2860 | cites work | What we can do and what we cannot do with fMRI | Q22122223 |
A default mode of brain function | Q24544311 | ||
An Information-Maximization Approach to Blind Separation and Blind Deconvolution | Q28291012 | ||
Default mode, dorsal attention and auditory resting state networks exhibit differential functional connectivity in tinnitus and hearing loss | Q30448507 | ||
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 model | Q30502094 | ||
Fusion analysis of functional MRI data for classification of individuals based on patterns of activation | Q30755485 | ||
Joint sparse representation of brain activity patterns in multi-task fMRI data. | Q30839376 | ||
General nonunitary constrained ICA and its application to complex-valued fMRI data | Q30871384 | ||
Data-Driven and Predefined ROI-Based Quantification of Long-Term Resting-State fMRI Reproducibility | Q31004331 | ||
A method for multitask fMRI data fusion applied to schizophrenia | Q31024846 | ||
A Novel Sparse Dictionary Learning Separation (SDLS) Model With Adaptive Dictionary Mutual Incoherence Constraint for fMRI Data Analysis | Q31050675 | ||
Estimating the number of independent components for functional magnetic resonance imaging data | Q31097409 | ||
How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection | Q31118772 | ||
Task fMRI data analysis based on supervised stochastic coordinate coding | Q31167342 | ||
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data | Q33390045 | ||
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The impact of certain methodological choices on multivariate analysis of fMRI data with support vector machines | Q33684392 | ||
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 Study | Q34002999 | ||
Development of deactivation of the default-mode network during episodic memory formation | Q34245634 | ||
Prediction of Alzheimer's disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning | Q34542377 | ||
Joint ICA of ERP and fMRI during error-monitoring | Q35592662 | ||
Is Model Fitting Necessary for Model-Based fMRI? | Q35667401 | ||
Default mode network in concussed individuals in response to the YMCA physical stress test | Q35828320 | ||
Statistical power and prediction accuracy in multisite resting-state fMRI connectivity | Q38979014 | ||
Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks | Q39850322 | ||
Sparse representation of whole-brain fMRI signals for identification of functional networks. | Q41726035 | ||
Deep learning for neuroimaging: a validation study | Q42068514 | ||
Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks | Q44033763 | ||
Noise contributions to the fMRI signal: An overview. | Q44352496 | ||
Resting state fMRI feature-based cerebral glioma grading by support vector machine | Q47436092 | ||
Joint independent component analysis of structural and functional images reveals complex patterns of functional reorganisation in stroke aphasia. | Q48572087 | ||
P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
P6216 | copyright status | copyrighted | Q50423863 |
P433 | issue | Suppl 19 | |
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) | 523 | |
P577 | publication date | 2018-12-31 | |
P1433 | published in | BMC Bioinformatics | Q4835939 |
P1476 | title | Classifying early and late mild cognitive impairment stages of Alzheimer's disease by fusing default mode networks extracted with multiple seeds | |
P478 | volume | 19 |
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