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Cited by 33Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsComplex Network Analysis Techniques

Connectomic consistency: a systematic stability analysis of structural and functional connectivity

Open access
Osmanlıoğlu, Yusuf, Alappatt, Jacob A, Parker, Drew, Verma, Ragini·2020·Journal of Neural Engineering
DOI 10.1088/1741-2552/ab947bPMC PMC7584380

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Abstract

Objective. Connectomics, the study of brain connectivity, has become an indispensable tool in neuroscientific research as it provides insights into brain organization. Connectomes are generated using different modalities such as diffusion MRI to capture structural organization of the brain or functional MRI to elaborate brain’s functional organization. Understanding links between structural and functional organizations is crucial in explaining how observed behavior emerges from the underlying neurobiological mechanisms. Many studies have investigated how these two organizations relate to each other; however, we still lack a comparative understanding on how much variation should be expected in the two modalities, both between people and within a single person across scans. Approach. In this study, we systematically analyzed the consistency of connectomes, that is the similarity between connectomes in terms of individual connections between brain regions and in terms of overall network topology. We present a comprehensive study of consistency in connectomes for a single subject examined longitudinally and across a large cohort of subjects cross-sectionally, in structure and function separately. Within structural connectomes, we compared connectomes generated by different tracking algorithms, parcellations, edge weighting schemes, and edge pruning techniques. In functional connectomes, we compared full, positive, and negative connectivity separately along with thresholding of weak edges. We evaluated consistency using correlation (incorporating information at the level of individual edges) and graph matching accuracy (evaluating connectivity at the level of network topology). We also examined the consistency of connectomes that are generated using different communication schemes. Main results. Our results demonstrate varying degrees of consistency for the two modalities, with structural connectomes showing higher consistency than functional connectomes. Moreover, we observed a wide variation in consistency depending on how connectomes are generated. Significance. Our study sets a reference point for consistency of connectome types, which is especially important for structure-function coupling studies in evaluating mismatches between modalities.

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  • Various parcellation scales have been applied in different studies, ranging from tens to millions of white matter parcels.

    “Various parcellation scales have been applied in different studies, ranging from tens to millions of white matter parcels (Besson et al., 2014; Liu et al., 2017; Osmanlıoğlu et al., 2020; Rodrigues et al., 2013; Zalesky et al., 2010; Zhang et al., 2018a).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Osmanlıoğlu et al., 2020

    The Data and methods section (“Atlases and registrations”; page unavailable) reports structural and functional connectomes at three parcellation scales: 118, 218, and 318 regions. This supports the use of different scales, but the paper does not establish a range from tens to millions of white matter parcels; its reported regions are not described as white matter parcels. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • A coarse-scale parcellation (fewer than 200 parcels) can improve connectome consistency across individuals.

    “studies have suggested a fine scale white matter parcellation (over 2000 parcels) can be beneficial for machine learning and statistical analysis (Liu et al., 2017; Zhang et al., 2018a) while a coarse scale parcellation (fewer than 200 parcels) can improve the connectome consistency across individuals (Osmanlıoğlu et al., 2020; Rodrigues et al., 2013).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Osmanlıoğlu et al., 2020

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Authors
Osmanlıoğlu, Y., Alappatt, J. A., Parker, D., & Verma, R.
Affiliations
University of Pennsylvania (US)
Corresponding author
  • Parker, Drew
  • Verma, Ragini
ORCID
Venue
Journal of Neural Engineering
Publisher
IOP Publishing
Published
2020-07-10
Volume
17
Issue
4
Pages
045004
ISSN
  • 1741-2552 (ISSN-L)
  • 1741-2560
DOI
10.1088/1741-2552/ab947b
PMC
PMC7584380
OpenAlex
W3028531034
Cited by
33
References
77
Citations by year
2026: 4 · 2025: 9 · 2024: 2 · 2023: 6 · 2022: 6 · 2021: 3 · 2020: 3
FWCI
2.42
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Neural Engineering (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institutes of Health (1R01MH117807-01A1)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
structural connectivity, functional connectivity, structural-functional coupling, graph matching, network topology, diffusion MRI, functional MRI, tracking algorithm, parcellation, edge weighting, edge pruning, interindividual variability, within-person variability
MeSH terms
Algorithms, Brain, Humans, Magnetic Resonance Imaging, Neurobiology, Connectome

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