ACKNOWLEDGEMENTS.md

Acknowledgements & Attributions

• The volume reconstruction code is an extension to the work of Marwan Abdellah's PhD (In silico Brain Imaging: Physically-plausible Methods for Visualizing Neocortical Microcircuitry).

• The mesh optimization code in Ultraliser is based on the routiens provided by the GAMer (Geometry-preserving Adaptive MeshER) library. GAMer is developed by Z. Yu, M. Holst, Y. Cheng, and J.A. McCammon, and can be redistributed and modified under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or any later version.

• The watertighness verification code in Ultraliser is based on an extended version of the MeshFix library. MeshFix is developed by Marco Attene, Consiglio Nazionale delle Ricerche, Istituto di Matematica Applicata e Tecnologie Informatiche, Sezione di Genova, IMATI-GE / CNR, and can be redistributed and modified under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or any later version.

• The mesh analysis code is implemented based on the metrics described in the The Verdict Geometric Quality Library.

• The morphology analysis code is implemented based on the metrics described in NeuroMorphoVis.

• The values of the colormaps used to generate the color-coded projections are obtained from the matplotlib library.

• The H5 morphology samples are available with permissions from the Blue Brain Project, Ecole Polytechnique Federale de Lausanne (EPFL).

• The SWC morphology samples of neurons and astrocytes are available from NeuroMorpho.Org. NeuroMorpho.Org is a centrally curated inventory or repository of digitally reconstructed neurons associated with peer-reviewed publications.

• The SWC morphology samples of brain vasculature are available from the Brain Vasculature (BraVa) database. The Brain Vasculature (BraVa) database contains digital reconstructions of the human brain arterial arborizations from 61 healthy adult subjects along with extracted morphological measurements. The arterial arborizations include the six major trees stemming from the circle of Willis, namely: the left and right Anterior Cerebral Arteries (ACAs), Middle Cerebral Arteries (MCAs), and Posterior Cerebral Arteries (PCAs). Citation: Susan N. Wright, Peter Kochunov, Fernando Mut Maurizio Bergamino, Kerry M. Brown, John C. Mazziotta, Arthur W. Toga, Juan R. Cebral, Giorgio A. Ascoli. Digital reconstruction and morphometric analysis of human brain arterial vasculature from magnetic resonance angiography. NeuroImage, 82, 170-181, (2013).

• The VMV vascular morphologies are available with permissions from Pablo Blinder, Department of Neurobiology, Faculty of Life Sciences at Tel Aviv University.

• The H5 vascular morphologies are available with permissions from the Blue Brain Project, Ecole Polytechnique Federale de Lausanne (EPFL). The original dataset courtesy of Bruno Weber, University of Zurich, Switzerland.

• Cortical meshes (in H5 format) are courtesy of the MICrONS Consortium including Seung Lab, Brain-map.org - Allen Institute for Brain Science, Tolias Lab and IARPA Microns.

  • The cortical mm^3 datasets are available from the following pulication:
    • MICrONs Consortium et al. Functional connectomics spanning multiple areas of mouse visual cortex. bioRxiv 2021.07.28.454025; doi: https://doi.org/10.1101/2021.07.28.454025
  • Layer 2/3 datasets are available from the following publications:
    • Dorkenwald, S., Turner, N.L., Macrina, T., Lee, K., Lu, R., Wu, J., Bodor, A.L., Bleckert, A.A., Brittain, D., Kemnitz, N., et al. (2019). Binary and analog variation of synapses between cortical pyramidal neurons. bioRxiv 2019.12.29.890319; doi: https://doi.org/10.1101/2019.12.29.890319
    • Schneider-Mizell, C. Bodor, A.L., Collman, F. Brittain,D. Bleckert, AA, Dorkenwald, S., Turner N.L. Macrina, T. Lee, K. Lu, R. Wu, J. et al. (2020) Chandelier cell anatomy and function suggest a variably distributed but common signal. bioRxiv 2020.03.31.018952v1; doi: https://doi.org/10.1101/2020.03.31.018952