Literature DB >> 33959789

Artificial intelligence to predict task activation from resting state fMRI.

Burak Kocak1.   

Abstract

Year:  2021        PMID： 33959789     DOI： 10.1007/s00330-021-07975-x

Source DB:  PubMed          Journal:  Eur Radiol        ISSN： 0938-7994            Impact factor:   5.315


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  6 in total

Review 1.  Resting-state functional magnetic resonance imaging: review of neurosurgical applications.

Authors:  Stefan Lang; Niall Duncan; Georg Northoff
Journal:  Neurosurgery       Date:  2014-05       Impact factor: 4.654

2.  Motor homunculus: passive mapping in healthy volunteers by using functional MR imaging--initial results.

Authors:  Mehmet Kocak; John L Ulmer; M Sahin Ugurel; Wolfgang Gaggl; Robert W Prost
Journal:  Radiology       Date:  2009-03-04       Impact factor: 11.105

Review 3.  Functional Magnetic Resonance Imaging Methods.

Authors:  Jingyuan E Chen; Gary H Glover
Journal:  Neuropsychol Rev       Date:  2015-08-07       Impact factor: 7.444

4.  Resting connectivity predicts task activation in pre-surgical populations.

Authors:  O Parker Jones; N L Voets; J E Adcock; R Stacey; S Jbabdi
Journal:  Neuroimage Clin       Date:  2016-12-24       Impact factor: 4.881

5.  Regression-based machine-learning approaches to predict task activation using resting-state fMRI.

Authors:  Alexander D Cohen; Ziyi Chen; Oiwi Parker Jones; Chen Niu; Yang Wang
Journal:  Hum Brain Mapp       Date:  2019-10-22       Impact factor: 5.038

6.  Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data.

Authors:  Javier Rasero; Hannelore Aerts; Marlis Ontivero Ortega; Jesus M Cortes; Sebastiano Stramaglia; Daniele Marinazzo
Journal:  PLoS One       Date:  2018-11-12       Impact factor: 3.240
  6 in total

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