Principles and open questions in functional brain network reconstruction

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Abstract

Graph theory is now becoming a standard tool in system‐level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.

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An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

Rahul Desikan, Florent Ségonne, Bruce Fischl … (2006)

In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.

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Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

N. Tzourio-Mazoyer, B Landeau, D Papathanassiou … (2003)

An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.

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Complex network measures of brain connectivity: uses and interpretations.

Mikail Rubinov, Olaf Sporns (2010)

Brain connectivity datasets comprise networks of brain regions connected by anatomical tracts or by functional associations. Complex network analysis-a new multidisciplinary approach to the study of complex systems-aims to characterize these brain networks with a small number of neurobiologically meaningful and easily computable measures. In this article, we discuss construction of brain networks from connectivity data and describe the most commonly used network measures of structural and functional connectivity. We describe measures that variously detect functional integration and segregation, quantify centrality of individual brain regions or pathways, characterize patterns of local anatomical circuitry, and test resilience of networks to insult. We discuss the issues surrounding comparison of structural and functional network connectivity, as well as comparison of networks across subjects. Finally, we describe a Matlab toolbox (http://www.brain-connectivity-toolbox.net) accompanying this article and containing a collection of complex network measures and large-scale neuroanatomical connectivity datasets. Copyright (c) 2009 Elsevier Inc. All rights reserved.

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Author and article information

Contributors

Onerva Korhonen:

ORCID: https://orcid.org/0000-0001-5033-4824

onerva.korhonen@gmail.com

David Papo:

ORCID: https://orcid.org/0000-0003-1889-2791

david.papo@iit.it

Journal

Journal ID (nlm-ta): Hum Brain Mapp

Journal ID (iso-abbrev): Hum Brain Mapp

Journal ID (doi): 10.1002/(ISSN)1097-0193

Journal ID (publisher-id): HBM

Title: Human Brain Mapping

Publisher: John Wiley & Sons, Inc. (Hoboken, USA )

ISSN (Print): 1065-9471

ISSN (Electronic): 1097-0193

Publication date (Electronic): 20 May 2021

Publication date Collection: 1 August 2021

Volume: 42

Issue: 11 ( doiID: 10.1002/hbm.v42.11 )

Pages: 3680-3711

Affiliations

[ ¹ ] Department of Computer Science Aalto University, School of Science Helsinki

[ ² ] Centre for Biomedical Technology Universidad Politécnica de Madrid Pozuelo de Alarcón

[ ³ ] Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC‐UIB), Campus UIB Palma de Mallorca Spain

[ ⁴ ] Fondazione Istituto Italiano di Tecnologia Ferrara

[ ⁵ ] Department of Neuroscience and Rehabilitation, Section of Physiology University of Ferrara Ferrara

Author notes

[*] [* ] Correspondence

David Papo, Fondazione Istituto Italiano di Tecnologia, Ferrara, Italy. Email: david.papo@ 123456iit.it

Onerva Korhonen, Department of Computer Science, Aalto University, Helsinki, Finland.

Email: onerva.korhonen@ 123456gmail.com

Author information

Onerva Korhonen https://orcid.org/0000-0001-5033-4824

Massimiliano Zanin https://orcid.org/0000-0002-5839-0393

David Papo https://orcid.org/0000-0003-1889-2791

Article

Publisher ID: HBM25462

DOI: 10.1002/hbm.25462

PMC ID: 8249902

PubMed ID: 34013636

SO-VID: 4f9141d1-8e44-4c71-86ea-64de9a765745

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

History

Date revision received : 11 March 2021

Date received : 26 November 2020

Date accepted : 10 April 2021

Page count

Figures: 5, Tables: 0, Pages: 32, Words: 37669

Funding

Funded by: H2020 European Research Council , open-funder-registry 10.13039/100010663;

Award ID: 851255

Funded by: Emil Aaltonen Foundation , open-funder-registry 10.13039/501100004756;

Award ID: 190095

Funded by: Osk. Huttunen Foundation , open-funder-registry 10.13039/501100013507;

Funded by: Labex (laboratory of excellence) DISTALZ (Development of Innovative Strategies for a Transdisciplinary approach to Alzheimer's disease)

Funded by: Accueil de Talents of the Métropole Européenne de Lille

Funded by: Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D

Funded by: Spanish State Research Agency , open-funder-registry 10.13039/501100011033;

Custom metadata

source-schema-version-number 2.0

cover-date August 1, 2021

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.2 mode:remove_FC converted:02.07.2021

ScienceOpen disciplines: Neurology

Keywords: brain dynamics,brain topology,edges,functional imaging,functional networks,nodes,resting state,structure–function relationship,temporal networks

Data availability:

ScienceOpen disciplines: Neurology

Keywords: brain dynamics, brain topology, edges, functional imaging, functional networks, nodes, resting state, structure–function relationship, temporal networks

Principles and open questions in functional brain network reconstruction

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Abstract

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Most cited references 396

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

Complex network measures of brain connectivity: uses and interpretations.

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