Dr Matthias Treder

2026

• Abdellahi, M. E. et al. 2026. Targeted memory reactivation elicits temporally compressed reactivation linked to spindles. Imaging Neuroscience 4 IMAG.a.1123. (10.1162/IMAG.a.1123)

2024

• Zhang, M. et al. 2024. Explaining the predictions of kernel SVM models for neuroimaging data analysis. Expert Systems with Applications 251 123993. (10.1016/j.eswa.2024.123993)
• Zhang, M. et al. 2024. Fast explanation of RBF-Kernel SVM models using activation patterns. Presented at: International Joint Conference on Neural Networks Yokohama, Japan 30 June – 5 July 2024. Proceedings of IJCNN. IEEE. , pp.1-8. (10.1109/IJCNN60899.2024.10650697)
• Treder, M. , Lee, S. and Tsvetanov, K. 2024. Introduction to Large Language Models (LLMs) for dementia care and research. Frontiers in Dementia 3 1385303. (10.3389/frdem.2024.1385303)

2023

• Wang, H. et al. 2023. A skewed loss function for correcting predictive bias in brain age prediction. IEEE Transactions on Medical Imaging 42 (6), pp.1577-1589. (10.1109/TMI.2022.3231730)
• Abdellahi, M. et al. 2023. Targeted memory reactivation in human REM sleep elicits detectable reactivation. eLife 12 e84324. (10.7554/elife.84324)
• Abdellahi, M. E. A. et al. 2023. Targeting targeted memory reactivation: characteristics of cued reactivation in sleep. NeuroImage 266 119820. (10.1016/j.neuroimage.2022.119820)

2022

• Navarrete, M. et al. 2022. Ongoing neural oscillations predict the post-stimulus outcome of closed loop auditory stimulation during slow-wave sleep. NeuroImage 253 119055. (10.1016/j.neuroimage.2022.119055)
• Plumley, A. et al. 2022. Rigid motion-resolved B1+ prediction using deep learning for real-time parallel-transmission pulse design. Magnetic Resonance in Medicine 87 (5), pp.2254-2270. (10.1002/mrm.29132)

2021

• Chopard, D. et al., 2021. Text mining of adverse events in clinical trials: Deep learning approach. JMIR Medical Informatics 9 (12) e28632. (10.2196/28632)
• Treder, M. S. et al. 2021. The hippocampus as the switchboard between perception and memory. Proceedings of the National Academy of Sciences 118 (50) e2114171118. (10.1073/pnas.2114171118)
• Treder, M. S. et al. 2021. Correlation constraints for regression models: controlling bias in brain age prediction. Frontiers in Psychiatry 12 615754. (10.3389/fpsyt.2021.615754)

2020

• Treder, M. , Mayor-Torres, J. and Teufel, C. 2020. Deriving visual semantics from spatial context: an adaptation of LSA and Word2Vec to generate object and scene embeddings from images. [Online].Cornell University. Available at: https://arxiv.org/abs/2009.09384.
• Treder, M. S. 2020. MVPA-Light: a classification and regression toolbox for multi-dimensional data. Frontiers in Neuroscience 14 289. (10.3389/fnins.2020.00289)

2019

• Krzeminski, D. et al. 2019. Classification of P300 component using a riemannian ensemble approach. Presented at: MEDICON 2019XV Mediterranean Conference on Medical and Biological Engineering and Computing Coimbra, Portugal 26-28 September 2019. Proceedings of MEDICON 2019, September 26-28, 2019, Coimbra, Portugal. Vol. 76.Springer Science. , pp.1885-1889. (10.1007/978-3-030-31635-8_229)
• Linde-Domingo, J. et al., 2019. Evidence that neural information flow is reversed between object perception and object reconstruction from memory. Nature Communications 10 179. (10.1038/s41467-018-08080-2)
• Treder, M. 2019. Direct calculation of out-of-sample predictions in multi-class kernel FDA. Presented at: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning Bruges, Belgium 24-26 April 2019. ESANN 2019 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN. , pp.245-250.

2018

• Treder, M. S. 2018. Improving SNR and reducing training time of classifiers in large datasets via kernel averaging. Presented at: BI 2018 Arlington, TX, USA 7-9 Dec 2018. Published in: Wang, S. et al., Brain Informatics. Vol. 2018.Lecture Notes in Computer Science Cham, Switzerland: Springer Verlag. , pp.239-248. (10.1007/978-3-030-05587-5_23)
• Tsvetanov, K. A. et al., 2018. Activity and connectivity differences underlying inhibitory control across the adult life span. Journal of Neuroscience 38 (36), pp.7887-7900. (10.1523/JNEUROSCI.2919-17.2018)
• Michelmann, S. et al., 2018. Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA). Journal of Neuroscience Methods 307 , pp.125-137. (10.1016/j.jneumeth.2018.06.021)
• Ioannidis, K. et al., 2018. Problematic intemet use as an age-related multifaceted problem: Evidence from a two-site survey. Addictive Behaviors 81 , pp.157-166. (10.1016/j.addbeh.2018.02.017)
• Treder, M. S. 2018. Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA. [Online].arXiv. Available at: http://arxiv.org/abs/1803.10016.

2017

• Price, D. et al., 2017. Age-related delay in visual and auditory evoked responses is mediated by white- and grey-matter differences. Nature Communications 8 15671. (10.1038/ncomms15671)
• Samu, D. et al., 2017. Preserved cognitive functions with age are determined by domain-dependent shifts in network responsivity. Nature Communications 8 14743. (10.1038/ncomms14743)

2016

• Ioannidis, K. et al., 2016. Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. Journal of Psychiatric Research 83 , pp.94-102. (10.1016/j.jpsychires.2016.08.010)
• Wolpe, N. et al., 2016. Ageing increases reliance on sensorimotor prediction through structural and functional differences in frontostriatal circuits. Nature Communications 7 13034. (10.1038/ncomms13034)
• Henson, R. N. et al., 2016. Multiple determinants of lifespan memory differences. Scientific Reports 6 32527. (10.1038/srep32527)
• Treder, M. S. et al. 2016. The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis. NeuroImage 129 , pp.279-291. (10.1016/j.neuroimage.2016.01.019)

2015

• Hwang, H. et al., 2015. A gaze independent brain-computer interface based on visual stimulation through closed eyelids. Scientific Reports 5 15890. (10.1038/srep15890)

2014

• Treder, M. S. et al. 2014. Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification. Journal of Neural Engineering 11 (2) 026009. (10.1088/1741-2560/11/2/026009)
• Sonnleitner, A. et al., 2014. EEG alpha spindles and prolonged brake reaction times during auditory distraction in an on-road driving study. Accident Analysis and Prevention 62 , pp.110-118. (10.1016/j.aap.2013.08.026)