Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends

Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends

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http://www.wikidata.org/entity/Q27008516
Support Vector Machine Analysis of Functional Magnetic Resonance Imaging of Interoception Does Not Reliably Predict Individual Outcomes of Cognitive Behavioral Therapy in Panic Disorder with Agoraphobia.Toward literature-based feature selection for diagnostic classification: a meta-analysis of resting-state fMRI in depression.Discriminative Analysis of Migraine without Aura: Using Functional and Structural MRI with a Multi-Feature Classification Approach.Multimodal MRI-Based Classification of Trauma Survivors with and without Post-Traumatic Stress Disorder.Diagnostic classification of unipolar depression based on resting-state functional connectivity MRI: effects of generalization to a diverse sample.Combination of Structural MRI and FDG-PET of the Brain Improves Diagnostic Accuracy in Newly Manifested Cognitive Impairment in Geriatric Inpatients.Connectivity of the anterior insula differentiates participants with first-episode schizophrenia spectrum disorders from controls: a machine-learning study.On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity.Characterization of post-traumatic stress disorder using resting-state fMRI with a multi-level parametric classification approach.Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support.Is Hippocampal Volumetry Really All That Matters?Advance MR imaging in sports-related concussion and mild traumatic brain injury - ready for clinical use? (Commentary on Tremblay et al. 2017).Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy.Recognition of Schizophrenia with Regularized Support Vector Machine and Sequential Region of Interest Selection using Structural Magnetic Resonance Imaging
P2860

Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends

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http://www.wikidata.org/entity/Q27008516
description
2014 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի մայիսին հրատարակված գիտական հոդված
@hy
article scientifique (publié 2014-05)
@fr
articolo scientifico (pubblicato il 2014-05)
@it
artigo científico (publicado na 2014-05)
@pt
artículu científicu espublizáu en 2014
@ast
scientific article (publication date: May 2014)
@en
vedecký článok (publikovaný 2014-05)
@sk
videnskabelig artikel (udgivet 2014-05)
@da
vědecký článek publikovaný v roce 2014
@cs
name
Multivariate pattern recogniti ...... t challenges and future trends
@ast
Multivariate pattern recogniti ...... t challenges and future trends
@en
Multivariate pattern recogniti ...... t challenges and future trends
@nl
type
label
Multivariate pattern recogniti ...... t challenges and future trends
@ast
Multivariate pattern recogniti ...... t challenges and future trends
@en
Multivariate pattern recogniti ...... t challenges and future trends
@nl
prefLabel
Multivariate pattern recogniti ...... t challenges and future trends
@ast
Multivariate pattern recogniti ...... t challenges and future trends
@en
Multivariate pattern recogniti ...... t challenges and future trends
@nl
P1433
P1476
P2860
P2888
P304
P31
P3181
P356
P407
P433
P478
P50
P577
P5875
P6179
P698
P921
P3181
P356
P1433
P1476
Multivariate pattern recogniti ...... t challenges and future trends
@en
P2860
P2888
P304
P31
P3181
P356
P407
P433
P478
P50
P577
P5875
P6179
P698
P921