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Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes

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Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes. / Omidvarnia, Amir; Sasse, Leonard; Larabi, Daouia I; Raimondo, Federico; Hoffstaedter, Felix; Kasper, Jan; Dukart, Jürgen; Petersen, Marvin; Cheng, Bastian; Thomalla, Götz; Eickhoff, Simon B; Patil, Kaustubh R.

In: COMMUN BIOL, Vol. 7, No. 1, 26.06.2024, p. 771.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Omidvarnia, A, Sasse, L, Larabi, DI, Raimondo, F, Hoffstaedter, F, Kasper, J, Dukart, J, Petersen, M, Cheng, B, Thomalla, G, Eickhoff, SB & Patil, KR 2024, 'Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes', COMMUN BIOL, vol. 7, no. 1, pp. 771. https://doi.org/10.1038/s42003-024-06438-5

APA

Omidvarnia, A., Sasse, L., Larabi, D. I., Raimondo, F., Hoffstaedter, F., Kasper, J., Dukart, J., Petersen, M., Cheng, B., Thomalla, G., Eickhoff, S. B., & Patil, K. R. (2024). Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes. COMMUN BIOL, 7(1), 771. https://doi.org/10.1038/s42003-024-06438-5

Vancouver

Omidvarnia A, Sasse L, Larabi DI, Raimondo F, Hoffstaedter F, Kasper J et al. Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes. COMMUN BIOL. 2024 Jun 26;7(1):771. https://doi.org/10.1038/s42003-024-06438-5

Bibtex

@article{9d5b42c70d6a4b35a936d8ce97660143,
title = "Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes",
abstract = "In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.",
keywords = "Humans, Magnetic Resonance Imaging/methods, Male, Female, Brain/diagnostic imaging, Phenotype, Middle Aged, Adult, Machine Learning, Aged, Behavior, Rest/physiology, Brain Mapping/methods",
author = "Amir Omidvarnia and Leonard Sasse and Larabi, {Daouia I} and Federico Raimondo and Felix Hoffstaedter and Jan Kasper and J{\"u}rgen Dukart and Marvin Petersen and Bastian Cheng and G{\"o}tz Thomalla and Eickhoff, {Simon B} and Patil, {Kaustubh R}",
note = "{\textcopyright} 2024. The Author(s).",
year = "2024",
month = jun,
day = "26",
doi = "10.1038/s42003-024-06438-5",
language = "English",
volume = "7",
pages = "771",
journal = "COMMUN BIOL",
issn = "2399-3642",
publisher = "NATURE PUBLISHING GROUP",
number = "1",

}

RIS

TY - JOUR

T1 - Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes

AU - Omidvarnia, Amir

AU - Sasse, Leonard

AU - Larabi, Daouia I

AU - Raimondo, Federico

AU - Hoffstaedter, Felix

AU - Kasper, Jan

AU - Dukart, Jürgen

AU - Petersen, Marvin

AU - Cheng, Bastian

AU - Thomalla, Götz

AU - Eickhoff, Simon B

AU - Patil, Kaustubh R

N1 - © 2024. The Author(s).

PY - 2024/6/26

Y1 - 2024/6/26

N2 - In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.

AB - In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.

KW - Humans

KW - Magnetic Resonance Imaging/methods

KW - Male

KW - Female

KW - Brain/diagnostic imaging

KW - Phenotype

KW - Middle Aged

KW - Adult

KW - Machine Learning

KW - Aged

KW - Behavior

KW - Rest/physiology

KW - Brain Mapping/methods

U2 - 10.1038/s42003-024-06438-5

DO - 10.1038/s42003-024-06438-5

M3 - SCORING: Journal article

C2 - 38926486

VL - 7

SP - 771

JO - COMMUN BIOL

JF - COMMUN BIOL

SN - 2399-3642

IS - 1

ER -