Personode: A Toolbox for ICA Map Classification and Individualized ROI Definition

  • Gustavo S. P. PamplonaEmail author
  • Bruno H. Vieira
  • Frank Scharnowski
  • Carlos E. G. Salmon
Original Article


Canonical resting state networks (RSNs) can be obtained through independent component analysis (ICA). RSNs are reproducible across subjects but also present inter-individual differences, which can be used to individualize regions-of-interest (ROI) definition, thus making fMRI analyses more accurate. Unfortunately, no automatic tool for defining subject-specific ROIs exists, making the classification of ICAs as representatives of RSN time-consuming and largely dependent on visual inspection. Here, we present Personode, a user-friendly and open source MATLAB-based toolbox that semi-automatically performs the classification of RSN and allows for defining subject- and group-specific ROIs. To validate the applicability of our new approach and to assess potential improvements compared to previous approaches, we applied Personode to both task-related activation and resting-state data. Our analyses show that for task-related activation analyses, subject-specific spherical ROIs defined with Personode produced higher activity contrasts compared to ROIs derived from single-study and meta-analytic coordinates. We also show that subject-specific irregular ROIs defined with Personode improved ROI-to-ROI functional connectivity analyses.

Hence, Personode might be a useful toolbox for ICA map classification into RSNs and group- as well as subject-specific ROI definitions, leading to improved analyses of task-related activation and functional connectivity.


ROI individualization ICA map classification Resting-state networks Toolbox Independent component analysis Functional connectivity 



This work was supported by the Brazilian National Council for Scientific and Technological Development (CNPq), the Brazilian National Council for the Improvement of Higher Education (CAPES), the Swiss National Science Foundation (BSSG10_155915, 100014_178841, 32003B_166566), the Foundation for Research in Science and the Humanities at the University of Zurich (STWF-17-012), the Baugarten Stiftung, and the Swiss Government. We also thank Dr. Ludovica Griffanti for insightful discussions and comments on the manuscript.

Compliance with Ethical Standards

Information Sharing Statement

NKI/Rockland Sample (RRID:SCR_009435) data used in this work is available at UK Biobank (RRID:SCR_012815) group-averaged RSNs templates where obtained at

SPM12 (RRID:SCR_007037,, MarsBaR (RRID:SCR_009605,, and GIFT (RRID:SCR_001953, are available to the general public.

The source code of Personode is available free of charge for non-commercial use and adaptation, under the condition of proper attribution, at

Supplementary material

12021_2019_9449_MOESM1_ESM.pdf (637 kb)
ESM 1 (PDF 636 kb)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Sensory-Motor Laboratory (SeMoLa), Jules-Gonin Eye Hospital/Fondation Asile des AveuglesDepartment of Ophthalmology/University of LausanneLausanneSwitzerland
  2. 2.Department of Psychiatry, Psychotherapy, and Psychosomatics, Psychiatric HospitalUniversity of ZürichZürichSwitzerland
  3. 3.Inbrain Lab, Department of PhysicsUniversity of São PauloRibeirão PretoBrazil
  4. 4.Neuroscience Center ZürichUniversity of Zürich and Swiss Federal Institute of TechnologyZürichSwitzerland
  5. 5.Zürich Center for Integrative Human Physiology (ZIHP)University of ZürichZürichSwitzerland
  6. 6.Department of Basic Psychological Research and Research Methods, Faculty of PsychologyUniversity of ViennaViennaAustria

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