Beyer, F.

Goltermann, O.

Hofmann, S.

Villringer, A.

Witte, A.

English

MPG.PuRe

Deep neural networks interpret white matter lesions as a signature of higher brain-ageGoltermann O.1,2,3,4*, Hofmann S.M.2,5,6,*, Villringer A.1,2,7,8,9, Witte A.V.2,7, Beyer F.21 Max Planck School of Cognition, Leipzig, Germany, 2 Max Planck Institute of Human Cognitive and Brain Sciences, Department of Neurology Leipzig, Germany, 3 Max Planck Institute for Psycholinguistics, Language and Genetics Department, Nijmegen, Netherlands, 4 Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany, 5 Fraunhofer Institute Heinrich Hertz, Department of Artificial Intelligence, Berlin, Germany, 6 Technical University Berlin, Machine Learning Group, Berlin, Germany, 7 Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany, 8 Charité – Universitätsmedizin Berlin, Germany, 9 Berlin School of Mind and Brain, Berlin, Germany* equal contributionIntroduction• Relatively little is known which specific brain featurescontribute to deep neural network based brain-ageestimations.• To address this issue, Hofmann et al. (2021) used Layer-wise Relevance Propagation (LRP; Lapuschkin et al.,2019) on brain-age predicting multi-level ensembles(MLENS) of 3D-convolutional neural networks (Fig. 1) toidentify which brain features contribute to brain-ageestimations.• Here, we computed binarized white matter lesion (WML)probabilistic maps of 1290 participants from apopulation-based cohort study (LIFE-Adult; Loeffler et al.,2015; age range 18-82 years) using the Lesionsegmentation toolbox (Schmidt et al., 2021).• We hypothesized that the MLENS capture WMLs anduse them as a information source to predict higherbrain-age.ReferencesHofmann, S. M., Beyer, F., Lapuschkin, S., Loeffler, M., Müller, K.-R., Villringer, A. Samek W., & Witte, A. V. (2021). Towards the Interpretability of Deep Learning Models for Human Neuroimaging. bioRxivhttps://doi.org/10.1101/2021.06.25.449906Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019). Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications, 10(1), 1096. https://doi.org/10.1038/s41467-019-08987-4Loeffler, M., Engel, C., Ahnert, P., Alfermann, D., Arelin, K., Baber, R., Beutner, F., Binder, H., Brähler, E., Burkhardt, R., Ceglarek, U., Enzenbach, C., Fuchs, M., Glaesmer, H., Girlich, F., Hagendorff, A., Häntzsch, M., Hegerl, U., Henger, S., … Thiery, J. (2015). The LIFE-Adult-Study: Objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany. BMC Public Health, 15, 691. https://doi.org/10.1186/s12889-015-1983-zMontavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017). Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognition, 65, 211–222. https://doi.org/10.1016/j.patcog.2016.11.008Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., Hoshi, M., Ilg, R., Schmid, V. J., Zimmer, C., Hemmer, B., & Mühlau, M. (2012). An automated tool for detection of FLAIR -hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage, 59(4), 3774–3783. https://doi.org/10.1016/j.neuroimage.2011.11.032LRP heatmaps (relevance maps) werecomputed on FLAIR images of allsubjects and warped to the MNI152space (2mm resolution).For each subject the WML probabilisticmap and the relevance map werealigned and overlaid.In subjects with more than 30 WMLvoxels, relevance values were averagedover WML voxels and compared to theexpected relevance per voxel [=averagerelevance of all brain-voxels withpositive relevance].23Methods1Fig. 3 Top: WML map (red voxels). Bottom: LRPmap of a single subject (age = 68, predicted brain-age = 67.16). Here, red-yellow voxels indicateinformation indicating a higher age, and vice versa.Sum over all relevance represents the age estimateby the model.Probabilistic WML and relevance map 2Conclusion● Deep learning models capture WMLs and associate higher brain age with ahigher lesion load, underlining that these models are capable of learningbiologically relevant age-associated structural brain changes while beingtrained end-to-end, that is on relatively raw MR images.● However, we also found that brain-age estimates do not exclusively rely onWML.● In future studies further known brain features, such as gray matter volume andsulcal widening should be studied with respect to the relevance maps.● This would allow us to test how much deep learning models rely on brainfeatures that are known to be related to aging. Conversely, this approach alsonumerically and visually indicates unexplained variance that could be studiedfurther, given the relevance maps of our study.Fig. 2 Prediction step (top) and relevancepropagation (bottom) highlighting individual brainareas that are relevant for the age estimation.Figure adaptation from Montavon et al. (2017).1 Average relevance of WML voxelsNumber of WML voxels > 30, n = 6543Fig. 4 Average relevance in WML voxels (yellow)and expected relevance per voxel (red). WMLvoxels contributed to higher brain-age estimatessignificantly more than the average brain voxel(Mdiff = 0.001, d = 0.90, t(653) = 22.95, p<.001).ResultsIdentifying input features that drove the decision processTo test whether the FLAIR sub-ensemble of theMLENS (Fig.1) uses WML as an informationsource for higher brain-age we performed thefollowing analyses steps:Fig. 1 Model architecture for brain-age estimations. (Figure from Hofmann et al., 2021)

Deep neural networks interpret white matter lesions as a signature of higher brain-age

http://hdl.handle.net/21.11116/0000-000B-05E7-B

Deep neural networks interpret white matter lesions as a signature of higher brain-age

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