| Literature DB >> 34862536 |
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.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