Selected publications

2019

  • M. D. Schirmer, et al., Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke, Stroke.
  • M. D. Schirmer, et al., Rich-Club organization: an important determinant of functional outcome after acute ischemic stroke, Frontiers Neurology.
  • M. D. Schirmer and A. W. Chung, Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism, Connectomics in NeuroImaging 2019.
  • P. Frid, M. Drake, A.-K. Giese, J. Wasselius, M. D. Schirmer, et al., Detailed phenotyping of posterior vs. anterior circulation ischemic stroke: A multi-center MRI study, Journal of Neurology.
  • A. W. Chung and M. D. Schirmer, Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An Application to Autism, Connectomics in NeuroImaging 2019.
  • F. Dubost, M. de Bruijne, M. Nardin, A. V. Dalca, K. L. Donahue, A.-K. Giese, M. R. Etherton, O. Wu, M. de Groot, W. Niessen, M. Vernooij, N. S. Rost, M. D. Schirmer, Automated Image Registration Quality Assessment Utilizing Deep-learning based Ventricle Extraction in Clinical Data, [submitted; ArXiv].
  • B. Egger, M. D. Schirmer, et al., Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators, MICCAI 2019.
  • M. D. Schirmer, et al., White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts – The MRI-GENIE Study, Neuroimage: Clinical.
  • M. D. Schirmer, et al., Network Structural Dependency in the Human Connectome Across the Lifespan, Network Neuroscience.
  • L. Xiong, A. Charidimou, M. Pasi, G. Boulouis, T. Pongpitakmetha, M. D. Schirmer, et al., Predictors for late post-ICH dementia in patients with probable CAA, Journal of Alzheimer’s Disease.
  • M. D. Schirmer, et al., Spatial signature of white matter hyperintensities in stroke patients, Frontiers Neurology.
  • J. Wahsner, P. Desogere, J. Wang, K. A. Graham-O’Regan, E. Abston, M. D. Schirmer, et al., 68Ga-NODAGA-indole, an allysine-reactive PET probe for molecular imaging of actively progressive pulmonary fibrosis, Journal of the American Chemical Society.
  • O. Wu, S. Winzeck, A. Giese, B. L. Hancock, M.J.R.J. Bouts, K. Donahue, M. D. Schirmer, et al., Big data approaches to phenotyping stroke risk and severity: the use of artificial intelligence for acute ischemic lesion segmentation of multi-center clinical diffusion-weighted MRI, Stroke.

2018

  • M. D. Schirmer and Ai Wern Chung, Structural subnetwork evolution across the life-span: rich-club, feeder, seeder, Connectomics in NeuroImaging 2018.
  • M. D. Schirmer et al., Effective reserve: a latent variable to improve outcome prediction in stroke, JSCVD.
  • A Charidimou, A. Giese, M. Parsi, S. J. van Veluw, L. Xiong, P. Fotiadis, S. Marini, M. D. Schirmer, A. Viswanathan, Journal club: Florbetapir imaging in cerebral amyloid angiopathy-related hemorrhages, Neurology.

2017

  • S. Parisot, B. Glocker, S. I. Ktena, S. Arslan, M. D. Schirmer, D. Rueckert, A flexible graphical model for multi-modal parcellation of the cortex, NeuroImage.
  • A. K. Giese, M. D. Schirmer, et al., Design and rationale for examining neuroimaging genetics in ischemic stroke The MRI-GENIE study, Neurology Genetics.

2016

  • A. W. Chung, M. D. Schirmer, et al., Characterising brain network topologies: a dynamic analysis approach using heat kernels, NeuroImage.
  • S. Parisot, B. Glockner, M. D. Schirmer, D. Rueckert, GraMPa: Graph-based Multi-modal Parcellation of the Cortex using Fusion Moves, MICCAI2016.
  • S Parisot, J Passerat-Palmbach, M. D. Schirmer, B Gutman, Proceedings of the Workshop on Brain Analysis using COnnectivity Networks-BACON 2016, arXiv.

2015

  • M. D. Schirmer, Developing Brain Connectivity - Effects of Parcellation Scale on Network Analysis in Neonates, King's College London.
  • M. D. Schirmer, et al., Parcellation-Independent Multi-Scale Framework for Brain Network Analysis, CDMRI 2014. Springer Berlin Heidelberg, 2015.

2013

  • M. Schirmer, et al., Normalisation of Neonatal Brain Network Measures Using Stochastic Approaches, MICCAI2013.
  • T. Yasui, M. Jewariya, T. Yasuda, M. Schirmer, at al., Real-Time Two-Dimensional Spatio-Temporal Terahertz Imaging Based on Non-Collinear Free-Space Electro-Optic Sampling and Application to Functional Terahertz Imaging of Moving Object, IEEE STQE.

2010

  • M. Schirmer, et al., Biomedical applications of a real-time terahertz color scanner, Biomed. Opt. Express.
Publications - Markus-Schirmer.com

Selected publications

2019

  • M. D. Schirmer, et al., Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke, Stroke.
  • M. D. Schirmer, et al., Rich-Club organization: an important determinant of functional outcome after acute ischemic stroke, Frontiers Neurology.
  • M. D. Schirmer and A. W. Chung, Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism, Connectomics in NeuroImaging 2019.
  • P. Frid, M. Drake, A.-K. Giese, J. Wasselius, M. D. Schirmer, et al., Detailed phenotyping of posterior vs. anterior circulation ischemic stroke: A multi-center MRI study, Journal of Neurology.
  • A. W. Chung and M. D. Schirmer, Network Dependency Index Stratified Subnetwork Analysis of Functional Connectomes: An Application to Autism, Connectomics in NeuroImaging 2019.
  • F. Dubost, M. de Bruijne, M. Nardin, A. V. Dalca, K. L. Donahue, A.-K. Giese, M. R. Etherton, O. Wu, M. de Groot, W. Niessen, M. Vernooij, N. S. Rost, M. D. Schirmer, Automated Image Registration Quality Assessment Utilizing Deep-learning based Ventricle Extraction in Clinical Data, [submitted; ArXiv].
  • B. Egger, M. D. Schirmer, et al., Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators, MICCAI 2019.
  • M. D. Schirmer, et al., White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts – The MRI-GENIE Study, Neuroimage: Clinical.
  • M. D. Schirmer, et al., Network Structural Dependency in the Human Connectome Across the Lifespan, Network Neuroscience.
  • L. Xiong, A. Charidimou, M. Pasi, G. Boulouis, T. Pongpitakmetha, M. D. Schirmer, et al., Predictors for late post-ICH dementia in patients with probable CAA, Journal of Alzheimer’s Disease.
  • M. D. Schirmer, et al., Spatial signature of white matter hyperintensities in stroke patients, Frontiers Neurology.
  • J. Wahsner, P. Desogere, J. Wang, K. A. Graham-O’Regan, E. Abston, M. D. Schirmer, et al., 68Ga-NODAGA-indole, an allysine-reactive PET probe for molecular imaging of actively progressive pulmonary fibrosis, Journal of the American Chemical Society.
  • O. Wu, S. Winzeck, A. Giese, B. L. Hancock, M.J.R.J. Bouts, K. Donahue, M. D. Schirmer, et al., Big data approaches to phenotyping stroke risk and severity: the use of artificial intelligence for acute ischemic lesion segmentation of multi-center clinical diffusion-weighted MRI, Stroke.

2018

  • M. D. Schirmer and Ai Wern Chung, Structural subnetwork evolution across the life-span: rich-club, feeder, seeder, Connectomics in NeuroImaging 2018.
  • M. D. Schirmer et al., Effective reserve: a latent variable to improve outcome prediction in stroke, JSCVD.
  • A Charidimou, A. Giese, M. Parsi, S. J. van Veluw, L. Xiong, P. Fotiadis, S. Marini, M. D. Schirmer, A. Viswanathan, Journal club: Florbetapir imaging in cerebral amyloid angiopathy-related hemorrhages, Neurology.

2017

  • S. Parisot, B. Glocker, S. I. Ktena, S. Arslan, M. D. Schirmer, D. Rueckert, A flexible graphical model for multi-modal parcellation of the cortex, NeuroImage.
  • A. K. Giese, M. D. Schirmer, et al., Design and rationale for examining neuroimaging genetics in ischemic stroke The MRI-GENIE study, Neurology Genetics.

2016

  • A. W. Chung, M. D. Schirmer, et al., Characterising brain network topologies: a dynamic analysis approach using heat kernels, NeuroImage.
  • S. Parisot, B. Glockner, M. D. Schirmer, D. Rueckert, GraMPa: Graph-based Multi-modal Parcellation of the Cortex using Fusion Moves, MICCAI2016.
  • S Parisot, J Passerat-Palmbach, M. D. Schirmer, B Gutman, Proceedings of the Workshop on Brain Analysis using COnnectivity Networks-BACON 2016, arXiv.

2015

  • M. D. Schirmer, Developing Brain Connectivity - Effects of Parcellation Scale on Network Analysis in Neonates, King's College London.
  • M. D. Schirmer, et al., Parcellation-Independent Multi-Scale Framework for Brain Network Analysis, CDMRI 2014. Springer Berlin Heidelberg, 2015.

2013

  • M. Schirmer, et al., Normalisation of Neonatal Brain Network Measures Using Stochastic Approaches, MICCAI2013.
  • T. Yasui, M. Jewariya, T. Yasuda, M. Schirmer, at al., Real-Time Two-Dimensional Spatio-Temporal Terahertz Imaging Based on Non-Collinear Free-Space Electro-Optic Sampling and Application to Functional Terahertz Imaging of Moving Object, IEEE STQE.

2010

  • M. Schirmer, et al., Biomedical applications of a real-time terahertz color scanner, Biomed. Opt. Express.