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Niels Reuter

M.Sc. Niels Reuter

Ph.D. student

Research topic:

  • Multi-modal Connectivity-Based Parcellation (CBP) of Human and Macaque Intra-Parietal Sulcus

Methodological topics:

  • fMRI
  • Imaging Modalities (Resting State, Meta-analytic Connectivity Modeling (MACM), Diffusion-Weighted Imaging, Structural Covariance, etc.)
  • Machine Learning (with a primary focus on clustering)

Scientific interests:

  • Multivariate Methods & Machine Learning
  • Brain-Computer Interfaces
  • Neuroimaging
  • Brain Mapping & Connectivity
  • Method Development
  • Memory & Learning

As part of the Human Brain Project (HBP) Sub-Project (SP) 2 on Human Brain Organization, as well as SP 5 on the Neuroinformatics Platform, my doctorate work will focus on multimodal connectivity-based parcellation (CBP) of the human and macaque intraparietal sulcus as well as integration of a CBP pipeline in the Jülich supercomputing centre. The primary focus of my work is the development, evaluation, and use of a Python-based JURECA-ready CBP pipeline tool (pictured below) for processing data from various imaging modalities and datasets.
Multimodal refers to the use of connectivity acquired through multiple imaging modalities which we apply to contrast the limitations of unimodal studies of brain parcellation. For human data this includes resting-state functional connectivity (RSFC) for task-independent functional connectivity, and probabilistic diffusion tractography (PDT) for anatomical fiber-connectivity and structural covariance (SC) as acquired from the Human Connectome Project (HCP) dataset (Van Essen et al., 2013), as well as meta-analytic connectivity modeling for task-dependent functional connectivity and co-activation patterns as acquired from the BrainMap database (Laird et al., 2009). For macaque data we include RSFC under awake and anaesthesia conditions, as well as T1-weighted anatomical scans acquired from the Neurophysiology group at KU Leuven.
For CBP, each of the modalities is used to compute a connectivity matrix between the target region of interest (ROI), defined here as the human IPS through the use of the JuBrain cytoarchitectonical map, and the rest of the brain. The subsequently derived features are then used to perform k-means clustering in order to obtain parcels within the ROI that have similar connectivity patterns. These parcels are then assessed and compared between modalities in order to assign neurobiological meaning and to obtain a parcellation scheme that describes the data best. CBP can both characterize clusters known through histological parcellation, but may also result in more detailed subdivisions not revealed by cytoarchitectonic mapping alone (Eickhoff et al., 2015).

Workflow of the Multi-Modal Connectivity-Based Parcellation PipelineWorkflow of the Multi-Modal Connectivity-Based Parcellation Pipeline


David C. Van Essen, Stephen M. Smith, Deanna M. Barch, Timothy E.J. Behrens, Essa Yacoub, Kamil Ugurbil, for the WU-Minn HCP Consortium. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage 80(2013):62-79.
Eickhoff, S., Thirion, B., Varoquaux, G., & Bzdok, D. (2015). Connectivity-Based Parcellation: Critique and Implications. Human Brain Mapping, 36(12), S. 4771-4792.
Laird AR, Eickhoff SB, Kurth F, Fox PM, Uecker AM, Turner JA, Robinson JL, Lancaster JL, Fox PT. 2009. ALE meta-analysis workflows via the brainmap database: progress towards a probabilistic functional brain atlas. Front Neuroinformat 3:23.


Institute of Neuroscience and Medicine (INM-7)
52425 Jülich


Phone: +49 2461 61-85333
Fax: +49 2461 61-1880