Artículo
A hypothesis-driven method based on machine learning for neuroimaging data analysis
Autor/es | Gorriz, Juan M.
Martín Clemente, Rubén Puntonet, C.G. Ortiz García, Andrés Ramírez Pérez, Javier SiPBA Group Suckling, John |
Departamento | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones |
Fecha de publicación | 2022 |
Fecha de depósito | 2023-06-06 |
Publicado en |
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Resumen | 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 ... 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. |
Agencias financiadoras | Agencia Estatal de Investigación. España Fondo Europeo de Desarrollo Regional (FEDER) Consejería de Economía, Innovación, Ciencia y Empleo. Junta de Andalucía Consejería de Economía, Conocimiento, Empresas y Universidad. Junta de Andalucía |
Identificador del proyecto | AEI/10.13039/501100011033
RTI2018-098913-B100 CV20-45250 A-TIC-080-UGR18 B-TIC-586-UGR20 P20-00525 |
Cita | Gorriz, J.M., Martín Clemente, R., Puntonet, C.G., Ortiz García, A., Ramírez Pérez, J., SiPBA Group, y Suckling, J. (2022). A hypothesis-driven method based on machine learning for neuroimaging data analysis. Neurocomputing, 510, 159-171. https://doi.org/10.1016/j.neucom.2022.09.001. |
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