RESEARCH Pattern forming mechanisms of color vision Zily Burstein1, David D. Reid1, Peter J. Thomas2, and Jack D. Cowan3 1Department of Physics, University of Chicago, Chicago, IL, USA 2Department of Mathematics, Applied Mathematics, and Statistics; Department of Biology; Department of Cognitive Science, Case Western Reserve University, Cleveland, OH, USA 3Department of Mathematics, University of Chicago, Chicago, IL, USA Keywords: Color vision, V1, Pattern formation, Turing

RESEARCH Age differences in functional brain networks associated with loneliness and empathy Laetitia Mwilambwe-Tshilobo1, Roni Setton2, Danilo Bzdok1,3,4,5,6, Gary R. Turner7, and R. Nathan Spreng1,4,8,9 1Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada 2Department of Psychology, Harvard University, Boston, MA, USA 3Department of Biomedical Engineering, McGill University, Montreal, QC, Canada 4McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada 5School

RESEARCH Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity Ceren Tozlu1 , Sophie Card2, Keith Jamison1, Susan A. Gauthier1,3,4, and Amy Kuceyeski1,5 1Department of Radiology, Weill Cornell Medicine, New York, NY, USA 2Horace Greeley High School, Chappaqua, NY, USA 3Judith Jaffe Multiple Sclerosis Center, Weill Cornell Medicine, New York, NY,

RESEARCH Trade-offs among cost, integration, and segregation in the human connectome Junji Ma1, Xitian Chen1, Yue Gu1, Liangfang Li1, Cam-CAN2, Ying Lin1, and Zhengjia Dai1,3 1Department of Psychology, Sun Yat-sen University, Guangzhou, China 2Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom 3Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of

METHODS From correlation to communication: Disentangling hidden factors from functional connectivity changes Yuhua Yu1 , Caterina Gratton1,2,3, and Derek M. Smith1,4 1Department of Psychology, Northwestern University, Evanston, IL, USA 2Department of Neurology, Northwestern University, Evanston, IL, USA 3Department of Psychology, Florida State University, Tallahassee, FL, USA 4Department of Neurology, Division of Cognitive Neurology/ Neuropsychology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA a

RESEARCH Dynamic rewiring of electrophysiological brain networks during learning Paolo Ruggeri1 , Jenifer Miehlbradt1, Aya Kabbara2,4, and Mahmoud Hassan3,4 1Brain Electrophysiology Attention Movement Laboratory, Institute of Psychology, University of Lausanne, Switzerland 2Lebanese Association for Scientific Research, Tripoli, Lebanon 3School of Engineering, University of Reykjavik, Reykjavik, Iceland 4MINDig, F-35000 Rennes, France Keywords: Human learning, Brain network dynamics, Electroencephalography a n o p e n a c

METHODS Significant subgraph mining for neural network inference with multiple comparisons correction Aaron J. Gutknecht1,2,3 and Michael Wibral1,2 1Department of Data-driven Analysis of Biological Networks, Göttingen Campus Institute for Dynamics of Biological Networks, Georg August Universtiy, Göttingen, Germany 2Johann-Friedrich-Blumenbach Institute, Georg August University, Göttingen, Germany 3Brain Imaging Center, Goethe University, Frankfurt am Main, Germany Keywords: Graph theory, Statistics, Multiple comparisons, Network inference, Transfer entropy, Autism

RESEARCH Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes Benjamin D. Pedigo1 , Michael Winding2 , Carey E. Priebe2, and Joshua T. Vogelstein1 1Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA 2Zoology, University of Cambridge, Cambridge, UK Keywords: Structural connectome, Graph matching, Network alignment, Network analysis, Homology, Bilateral symmetry a n o p e n a c c e s s

RESEARCH Sex differences in multilayer functional network topology over the course of aging in 37543 UK Biobank participants Mite Mijalkov1, Dániel Veréb1, Oveis Jamialahmadi2, Anna Canal-Garcia1, Emiliano Gómez-Ruiz3, Didac Vidal-Piñeiro4, Stefano Romeo2,5,6, Giovanni Volpe3, and Joana B. Pereira1,7 1Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden 2Department of Molecular and Clinical Medicine, Goteborg University, Goteborg, Sweden 3Department of Physics, Goteborg University, Goteborg,

RESEARCH Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference Raul Gonzalez-Gomez1,2*, Agustín Ibañez1,3,4,5*, and Sebastian Moguilner2,3,4,6 1Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile 2Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibañez, Santiago de Chile, Chile 3Cognitive Neuroscience Center, Universidad de San Andres, Buenos Aires, Argentina 4Global Brain Health Institute, University of

RESEARCH Circuit analysis of the Drosophila brain using connectivity-based neuronal classification reveals organization of key communication pathways Ketan Mehta1 , Rebecca F. Goldin2 , and Giorgio A. Ascoli1 1Department of Bioengineering and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, USA 2Department of Mathematical Sciences and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, USA a n

RESEARCH Multimodal multilayer network centrality relates to executive functioning Lucas C. Breedt1 , Fernando A. N. Santos1,2, Arjan Hillebrand3, Liesbeth Reneman4, Anne-Fleur van Rootselaar5, Menno M. Schoonheim1, Cornelis J. Stam3,6, Anouk Ticheler1, Betty M. Tijms7, Dick J. Veltman8, Chris Vriend1,8, Margot J. Wagenmakers8,9, Guido A. van Wingen10, Jeroen J. G. Geurts1, Anouk Schrantee4, and Linda Douw1 1Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit

RESEARCH Nonoptimal component placement of the human connectome supports variable brain dynamics Christopher James Hayward1,2, Siyu Huo3, Xue Chen4,5,6, and Marcus Kaiser7,8 1Leeds Institute for Data Analytics, University of Leeds, Leeds, United Kingdom 2Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom 3State Key Laboratory of Precision Spectroscopy and School of Physics and Electronic Science, East China Normal University, P. R.

RESEARCH The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia Emmanuel D. Meram, Shahira Baajour, Asadur Chowdury, John Kopchick, Patricia Thomas, Usha Rajan, Dalal Khatib, Caroline Zajac-Benitez, Luay Haddad, Alireza Amirsadri, Jeffrey A. Stanley, and Vaibhav A. Diwadkar Department of Psychiatry and Behavioral Neurosciences, Brain Imaging Research Division, Wayne State University School of Medicine, Detroit, MI, USA a n o p e

RESEARCH H1 persistent features of the resting-state connectome in healthy subjects Darwin Eduardo Martínez-Riaño1 , Fabio González1, and Francisco Gómez2 1Departamento de Ingeniería de Sistemas e Industrial, Universidad Nacional de Colombia, Bogotá, Colombia 2Departamento de Matemáticas, Universidad Nacional de Colombia, Bogotá, Colombia Keywords: Functional connectivity, Topological data analysis, Persistent homology, Resting state ABSTRACT The analysis of the resting-state functional connectome commonly relies on graph representations.

RESEARCH Graph theoretical approach to brain remodeling in multiple sclerosis AmirHussein Abdolalizadeh1,2 , Mohammad Amin Dabbagh Ohadi1,2, Amir Sasan Bayani Ershadi1,2, and Mohammad Hadi Aarabi3 1Students’ Scientific Research Program, Tehran University of Medical Sciences, Tehran, Iran 2Interdisciplinary Neuroscience Research Program, Tehran University of Medical Sciences, Tehran, Iran 3Department of Neuroscience, Padova Neuroscience Center, University of Padova, Padova, Italy Keywords: Multiple sclerosis, Remodeling, Diffusion MRI, Cognition,

RESEARCH Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach Camilla Krämer1,2 , Johanna Stumme1,2, Lucas da Costa Campos1,2, Christian Rubbert3, Julian Caspers3, Svenja Caspers1,2*, and Christiane Jockwitz1,2* 1Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany 2Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf,

METHODS 3M_BANTOR: A regression framework for multitask and multisession brain network distance metrics Chal E. Tomlinson1 , Paul J. Laurienti2,3, Robert G. Lyday2,3, and Sean L. Simpson2,4 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA 2Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, NC, USA 3Department of Radiology, Wake Forest University School of Medicine, Winston-Salem,