Jumping over baselines with new methods to predict activation maps from resting-state fMRI

Abstract Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsf...

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Autores principales:	Eric Lacosse, Klaus Scheffler, Gabriele Lohmann, Georg Martius
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0538a9a0927a47e3bbe8b7e2ac65f310
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spelling	oai:doaj.org-article:0538a9a0927a47e3bbe8b7e2ac65f3102021-12-02T14:27:02ZJumping over baselines with new methods to predict activation maps from resting-state fMRI10.1038/s41598-021-82681-82045-2322https://doaj.org/article/0538a9a0927a47e3bbe8b7e2ac65f3102021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82681-8https://doaj.org/toc/2045-2322Abstract Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.Eric LacosseKlaus SchefflerGabriele LohmannGeorg MartiusNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Eric Lacosse Klaus Scheffler Gabriele Lohmann Georg Martius Jumping over baselines with new methods to predict activation maps from resting-state fMRI
description	Abstract Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.
format	article
author	Eric Lacosse Klaus Scheffler Gabriele Lohmann Georg Martius
author_facet	Eric Lacosse Klaus Scheffler Gabriele Lohmann Georg Martius
author_sort	Eric Lacosse
title	Jumping over baselines with new methods to predict activation maps from resting-state fMRI
title_short	Jumping over baselines with new methods to predict activation maps from resting-state fMRI
title_full	Jumping over baselines with new methods to predict activation maps from resting-state fMRI
title_fullStr	Jumping over baselines with new methods to predict activation maps from resting-state fMRI
title_full_unstemmed	Jumping over baselines with new methods to predict activation maps from resting-state fMRI
title_sort	jumping over baselines with new methods to predict activation maps from resting-state fmri
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0538a9a0927a47e3bbe8b7e2ac65f310
work_keys_str_mv	AT ericlacosse jumpingoverbaselineswithnewmethodstopredictactivationmapsfromrestingstatefmri AT klausscheffler jumpingoverbaselineswithnewmethodstopredictactivationmapsfromrestingstatefmri AT gabrielelohmann jumpingoverbaselineswithnewmethodstopredictactivationmapsfromrestingstatefmri AT georgmartius jumpingoverbaselineswithnewmethodstopredictactivationmapsfromrestingstatefmri
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Jumping over baselines with new methods to predict activation maps from resting-state fMRI

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