Sparse network-based models for patient classification using fMRI.
about
Studying depression using imaging and machine learning methodsExploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classificationSingle subject prediction of brain disorders in neuroimaging: Promises and pitfalls.Interpretation of the Precision Matrix and Its Application in Estimating Sparse Brain Connectivity during Sleep Spindles from Human Electrocorticography RecordingsConnectivity strength-weighted sparse group representation-based brain network construction for MCI classification.Multiregional integration in the brain during resting-state fMRI activity.Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders.Supervised, Multivariate, Whole-Brain Reduction Did Not Help to Achieve High Classification Performance in Schizophrenia ResearchResting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping.Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample.Resting-state connectivity biomarkers define neurophysiological subtypes of depression.Extracting patterns of morphometry distinguishing HIV associated neurodegeneration from mild cognitive impairment via group cardinality constrained classification.Prediction of brain maturity in infants using machine-learning algorithms.Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification.Erratum: Resting-state connectivity biomarkers define neurophysiological subtypes of depression.Intrinsic Connectivity Network-Based Classification and Detection of Psychotic Symptoms in Youth With 22q11.2 Deletions.Graph Lasso-Based Test for Evaluating Functional Brain Connectivity in Sickle Cell Disease.Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease.Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.The relation between statistical power and inference in fMRI.Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset.Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.Changes in brain activity following intensive voice treatment in children with cerebral palsy.Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression.Decoding Musical Training from Dynamic Processing of Musical Features in the Brain.Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but ChallengingAnalysis of Progression Toward Alzheimer's Disease Based on Evolutionary Weighted Random Support Vector Machine ClusterData Driven Classification Using fMRI Network Measures: Application to SchizophreniaLearning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification
P2860
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P2860
Sparse network-based models for patient classification using fMRI.
description
2014 nî lūn-bûn
@nan
2014 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Sparse network-based models for patient classification using fMRI.
@ast
Sparse network-based models for patient classification using fMRI.
@en
Sparse network-based models for patient classification using fMRI.
@nl
type
label
Sparse network-based models for patient classification using fMRI.
@ast
Sparse network-based models for patient classification using fMRI.
@en
Sparse network-based models for patient classification using fMRI.
@nl
prefLabel
Sparse network-based models for patient classification using fMRI.
@ast
Sparse network-based models for patient classification using fMRI.
@en
Sparse network-based models for patient classification using fMRI.
@nl
P2093
P2860
P50
P1433
P1476
Sparse network-based models for patient classification using fMRI
@en
P2093
Andreas J Fallgatter
Janaina Mourao-Miranda
Liana Portugal
Maria J Rosa
P2860
P304
P356
10.1016/J.NEUROIMAGE.2014.11.021
P407
P577
2014-11-15T00:00:00Z