Literature DB >> 34862536

Introduction to Machine Learning in Neuroimaging.

Julius M Kernbach1,2, Jonas Ort3,4, Karlijn Hakvoort3,4, Hans Clusmann4, Georg Neuloh4, Daniel Delev3,4.   

Abstract

Advancements in neuroimaging and the availability of large-scale datasets enable the use of more sophisticated machine learning algorithms. In this chapter, we non-exhaustively discuss relevant analytical steps for the analysis of neuroimaging data using machine learning (ML), while the field of radiomics will be addressed separately (c.f., Chap. 18 -Radiomics). Broadly classified into supervised and unsupervised approaches, we discuss the encoding/decoding framework, which is often applied in cognitive neuroscience, and the use of ML for the analysis of unlabeled data using clustering.
© 2022. The Author(s), under exclusive license to Springer Nature Switzerland AG.

Entities:  

Keywords:  Machine learning; Neuroimaging; Neurosurgery; Resting-state MRI; fMRI

Mesh:

Year:  2022        PMID: 34862536     DOI: 10.1007/978-3-030-85292-4_16

Source DB:  PubMed          Journal:  Acta Neurochir Suppl        ISSN: 0065-1419


  13 in total

1.  The perceptron: a probabilistic model for information storage and organization in the brain.

Authors:  F ROSENBLATT
Journal:  Psychol Rev       Date:  1958-11       Impact factor: 8.934

Review 2.  Advances in functional and structural MR image analysis and implementation as FSL.

Authors:  Stephen M Smith; Mark Jenkinson; Mark W Woolrich; Christian F Beckmann; Timothy E J Behrens; Heidi Johansen-Berg; Peter R Bannister; Marilena De Luca; Ivana Drobnjak; David E Flitney; Rami K Niazy; James Saunders; John Vickers; Yongyue Zhang; Nicola De Stefano; J Michael Brady; Paul M Matthews
Journal:  Neuroimage       Date:  2004       Impact factor: 6.556

Review 3.  A review of feature reduction techniques in neuroimaging.

Authors:  Benson Mwangi; Tian Siva Tian; Jair C Soares
Journal:  Neuroinformatics       Date:  2014-04

4.  Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

Authors:  Benson Mwangi; Klaus P Ebmeier; Keith Matthews; J Douglas Steele
Journal:  Brain       Date:  2012-05       Impact factor: 13.501

Review 5.  Encoding and decoding in fMRI.

Authors:  Thomas Naselaris; Kendrick N Kay; Shinji Nishimoto; Jack L Gallant
Journal:  Neuroimage       Date:  2010-08-04       Impact factor: 6.556

6.  Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python.

Authors:  Krzysztof Gorgolewski; Christopher D Burns; Cindee Madison; Dav Clark; Yaroslav O Halchenko; Michael L Waskom; Satrajit S Ghosh
Journal:  Front Neuroinform       Date:  2011-08-22       Impact factor: 4.081

7.  PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

Authors:  Michael Hanke; Yaroslav O Halchenko; Per B Sederberg; Stephen José Hanson; James V Haxby; Stefan Pollmann
Journal:  Neuroinformatics       Date:  2009-01-28

8.  Statistical Challenges in "Big Data" Human Neuroimaging.

Authors:  Stephen M Smith; Thomas E Nichols
Journal:  Neuron       Date:  2018-01-17       Impact factor: 17.173

Review 9.  Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Authors:  Mohammad R Arbabshirani; Sergey Plis; Jing Sui; Vince D Calhoun
Journal:  Neuroimage       Date:  2016-03-21       Impact factor: 6.556

10.  Shared endo-phenotypes of default mode dsfunction in attention deficit/hyperactivity disorder and autism spectrum disorder.

Authors:  Julius M Kernbach; Theodore D Satterthwaite; Danielle S Bassett; Jonathan Smallwood; Daniel Margulies; Sarah Krall; Philip Shaw; Gaël Varoquaux; Bertrand Thirion; Kerstin Konrad; Danilo Bzdok
Journal:  Transl Psychiatry       Date:  2018-07-17       Impact factor: 6.222

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