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Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

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Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain. / Hofmann, Simon; Beyer, Frauke; Lapuschkin, Sebastian; Goltermann, Ole; Loeffler, Markus; Müller, Klaus-Robert; Villringer, Arno; Samek, Wojciech; Witte, A Veronica.

In: NEUROIMAGE, Vol. 261, 119504, 01.11.2022, p. 119504.

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

Harvard

Hofmann, S, Beyer, F, Lapuschkin, S, Goltermann, O, Loeffler, M, Müller, K-R, Villringer, A, Samek, W & Witte, AV 2022, 'Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain', NEUROIMAGE, vol. 261, 119504, pp. 119504. https://doi.org/10.1016/j.neuroimage.2022.119504

APA

Hofmann, S., Beyer, F., Lapuschkin, S., Goltermann, O., Loeffler, M., Müller, K-R., Villringer, A., Samek, W., & Witte, A. V. (2022). Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain. NEUROIMAGE, 261, 119504. [119504]. https://doi.org/10.1016/j.neuroimage.2022.119504

Vancouver

Hofmann S, Beyer F, Lapuschkin S, Goltermann O, Loeffler M, Müller K-R et al. Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain. NEUROIMAGE. 2022 Nov 1;261:119504. 119504. https://doi.org/10.1016/j.neuroimage.2022.119504

Bibtex

@article{bfd0bc9aec1b4bc79bdfa4ce1fa70211,
title = "Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain",
abstract = "Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n = 2637, 18–82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37–3.86 years). We find that BA estimates capture ageing at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected ageing reflected cardiovascular risk factors and accelerated ageing was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-ageing in healthy and at-risk individuals throughout adulthood.",
author = "Simon Hofmann and Frauke Beyer and Sebastian Lapuschkin and Ole Goltermann and Markus Loeffler and Klaus-Robert M{\"u}ller and Arno Villringer and Wojciech Samek and Witte, {A Veronica}",
year = "2022",
month = nov,
day = "1",
doi = "10.1016/j.neuroimage.2022.119504",
language = "English",
volume = "261",
pages = "119504",
journal = "NEUROIMAGE",
issn = "1053-8119",
publisher = "Academic Press",

}

RIS

TY - JOUR

T1 - Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain

AU - Hofmann, Simon

AU - Beyer, Frauke

AU - Lapuschkin, Sebastian

AU - Goltermann, Ole

AU - Loeffler, Markus

AU - Müller, Klaus-Robert

AU - Villringer, Arno

AU - Samek, Wojciech

AU - Witte, A Veronica

PY - 2022/11/1

Y1 - 2022/11/1

N2 - Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n = 2637, 18–82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37–3.86 years). We find that BA estimates capture ageing at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected ageing reflected cardiovascular risk factors and accelerated ageing was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-ageing in healthy and at-risk individuals throughout adulthood.

AB - Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural features have remained unclear. We combined ensembles of convolutional neural networks with Layer-wise Relevance Propagation (LRP) to detect which brain features contribute to BA. Trained on magnetic resonance imaging (MRI) data of a population-based study (n = 2637, 18–82 years), our models estimated age accurately based on single and multiple modalities, regionally restricted and whole-brain images (mean absolute errors 3.37–3.86 years). We find that BA estimates capture ageing at both small and large-scale changes, revealing gross enlargements of ventricles and subarachnoid spaces, as well as white matter lesions, and atrophies that appear throughout the brain. Divergence from expected ageing reflected cardiovascular risk factors and accelerated ageing was more pronounced in the frontal lobe. Applying LRP, our study demonstrates how superior deep learning models detect brain-ageing in healthy and at-risk individuals throughout adulthood.

U2 - 10.1016/j.neuroimage.2022.119504

DO - 10.1016/j.neuroimage.2022.119504

M3 - SCORING: Journal article

C2 - 35882272

VL - 261

SP - 119504

JO - NEUROIMAGE

JF - NEUROIMAGE

SN - 1053-8119

M1 - 119504

ER -