Deep learning in neuroimaging data analysis

applications, challenges, and solutions

Lev Kiar Avberšek (Author), Grega Repovš (Author)

Abstract

Methods for the analysis of neuroimaging data have advanced significantly since the beginning of neuroscience as a scientific discipline. Today, sophisticated statistical procedures allow us to examine complex multivariate patterns, however most of them are still constrained by assuming inherent linearity of neural processes. Here, we discuss a group of machine learning methods, called deep learning, which have drawn much attention in and outside the field of neuroscience in recent years and hold the potential to surpass the mentioned limitations. Firstly, we describe and explain the essential concepts in deep learning: the structure and the computational operations that allow deep models to learn. After that, we move to the most common applications of deep learning in neuroimaging data analysis: prediction of outcome, interpretation of internal representations, generation of synthetic data and segmentation. In the next section we present issues that deep learning poses, which concerns multidimensionality and multimodality of data, overfitting and computational cost, and propose possible solutions. Lastly, we discuss the current reach of DL usage in all the common applications in neuroimaging data analysis, where we consider the promise of multimodality, capability of processing raw data, and advanced visualization strategies. We identify research gaps, such as focusing on a limited number of criterion variables and the lack of a well-defined strategy for choosing architecture and hyperparameters. Furthermore, we talk about the possibility of conducting research with constructs that have been ignored so far or/and moving toward frameworks, such as RDoC, the potential of transfer learning and generation of synthetic data.

Keywords

slikanje možganov;kognitivni procesi;učenje;strojno učenje;nevroznanost;računsko modeliranje;umetna inteligenca;nevronske mreže;neuroimaging;cognitive processes;learning;machine learning;neurosciences;computational modeling;artificial intelligence;neural networks;

Data

Language:	English
Year of publishing:	2022
Typology:	1.01 - Original Scientific Article
Organization:	UL FF - Faculty of Arts
UDC:	159.91:159.955
COBISS:	132600323
ISSN:	2813-1193
Views:	2
Downloads:	1
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	slikanje možganov;kognitivni procesi;učenje;strojno učenje;nevroznanost;računsko modeliranje;umetna inteligenca;nevronske mreže;
Pages:	str. 1-23
Chronology:	26 Oct. 2022
DOI:	10.3389/fnimg.2022.981642
ID:	18027339