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Predicting human resting-state functional connectivity from structural connectivity
Open accessAbstract
In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networks—including their spatial statistics and their persistence across time—can be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed ( i ) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; ( ii ) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and ( iii ) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.
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“There is an abundance of research demonstrating a correlation between DWI-tractography connection weights and FC in adults (Hagmann et al., 2008; Hagmann et al., 2010; Honey et al., 2009).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Honey et al., 2009
In the citing paper
“Another study in healthy control subjects reported a strong relation between structural connectivity and resting state functional connectivity between regions that show a strong structural connection (23).”
From Relation Between Structural and Functional Connectivity in Major Depressive Disorder (2013) · cites this paper as [23]
In the citing paper
“This analysis, although effective in outlining the anatomical boundaries of the DMN, is unable to distinguish subtle task-related fluctuations in connectivity across various experimental conditions including, for example, lying at rest in a scanner, listening to a list of scrambled words and listening to an intact story several minutes long24–26”
From The default mode network: where the idiosyncratic self meets the shared social world (2021) · cites this paper as [24]
In the citing paper
“there is ample evidence of robust functional connectivity between brain regions that lack direct structural connections28-29, 31.”
From Evolutionarily conserved prefrontal-amygdalar dysfunction in early-life anxiety (2014) · cites this paper as [29]
In the citing paper
“fMRI-derived measures of intrinsic functional connectivity are particularly useful because they are sensitive to functional networks spanning polysynaptic circuits27-29”
From Evolutionarily conserved prefrontal-amygdalar dysfunction in early-life anxiety (2014) · cites this paper as [29]
In the citing paper
“For instance, while there is evidence for a good correlation between functional connectivity and strong short-distance connections, this is not the case for the more numerous long-distance connections (Honey et al. 2009; Adachi et al. 2012).”
From A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex (2014) · cites this paper as Honey et al. 2009
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Metadata
- Authors
- Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P.
- Affiliations
- Indiana University Bloomington (US)
- École Polytechnique Fédérale de Lausanne (CH)
- Centre Hospitalier Universitaire Vaudois (CH)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2009-02-10
- Volume
- 106
- Issue
- 6
- Pages
- 2035-2040
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2634800
- OpenAlex
- W1850231759
- Cited by
- 3,307
- References
- 55
- Citations by year
- 2026: 162 · 2025: 209 · 2024: 197 · 2023: 190 · 2022: 252 · 2021: 223 · 2020: 229 · 2019: 196 · 2018: 230 · 2017: 196 · 2016: 184 · 2015: 203 · 2014: 211 · 2013: 201 · 2012: 146
- FWCI
- 67.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- IRIS (submittedVersion)
- SERVAL (Université de Lausanne) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- resting-state functional connectivity, structural connectivity, cerebral cortex, indirect connections, session-to-session variability
- MeSH terms
- Brain, Brain Mapping, Cerebral Cortex, Humans, Magnetic Resonance Imaging, Models, Neurological, Neural Pathways, Diffusion Magnetic Resonance Imaging
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