@misc{10481/77619,
year = {2022},
month = {9},
url = {https://hdl.handle.net/10481/77619},
abstract = {There remains an open question about the usefulness and the interpretation of machine learning (ML)
approaches for discrimination of spatial patterns of brain images between samples or activation states.
In the last few decades, these approaches have limited their operation to feature extraction and linear
classification tasks for between-group inference. In this context, statistical inference is assessed by randomly
permuting image labels or by the use of random effect models that consider between-subject variability.
These multivariate ML-based statistical pipelines, whilst potentially more effective for detecting
activations than hypotheses-driven methods, have lost their mathematical elegance, ease of interpretation,
and spatial localization of the ubiquitous General linear Model (GLM). Recently, the estimation of
the conventional GLM parameters has been demonstrated to be connected to an univariate classification
task when the design matrix in the GLM is expressed as a binary indicator matrix. In this paper we
explore the complete connection between the univariate GLM and ML-based regressions. To this purpose
we derive a refined statistical test with the GLM based on the parameters obtained by a linear Support
Vector Regression (SVR) in the inverse problem (SVR-iGLM). Subsequently, random field theory (RFT) is
employed for assessing statistical significance following a conventional GLM benchmark. Experimental
results demonstrate how parameter estimations derived from each model (mainly GLM and SVR) result
in different experimental design estimates that are significantly related to the predefined functional task.
Moreover, using real data from a multisite initiative the proposed ML-based inference demonstrates statistical
power and the control of false positives, outperforming the regular GLM.},
organization = {MCIN/AEI},
organization = {FEDER ``Una manera de hacer Europa"	RTI2018-098913-B100},
organization = {Junta de Andalucia},
organization = {European Commission	CV20-45250	
A-TIC-080-UGR18	
B-TIC586-UGR20	
P20-00525},
organization = {research project ACACIA	US-1264994},
organization = {European Commission},
organization = {Junta de Andalucia (Consejeria de Economia, Conocimiento, Empresas y Universidad)},
publisher = {Elsevier},
keywords = {General Linear Model},
keywords = {Linear Regression Model},
keywords = {Support Vector Regression permutation tests},
keywords = {Magnetic resonance imaging},
keywords = {Random Field Theory},
title = {A hypothesis-driven method based on machine learning for neuroimaging data analysis},
doi = {10.1016/j.neucom.2022.09.001},
author = {Gorriz Sáez, Juan Manuel and García Puntonet, Carlos and Ramírez Pérez De Inestrosa, Javier and SIPBA group},
}