Navigating the link between processing speed and network communication in the human brain
Open accessAbstract
Processing speed on cognitive tasks relies upon efficient communication between widespread regions of the brain. Recently, novel methods of quantifying network communication like 'navigation efficiency' have emerged, which aim to be more biologically plausible compared to traditional shortest path length-based measures. However, it is still unclear whether there is a direct link between these communication measures and processing speed. We tested this relationship in forty-five healthy adults (27 females), where processing speed was defined as decision-making time and measured using drift rate from the hierarchical drift diffusion model. Communication measures were calculated from a graph theoretical analysis of the whole-brain structural connectome and of a task-relevant fronto-parietal structural subnetwork, using the large-scale Desikan-Killiany atlas. We found that faster processing speed on trials that require greater cognitive control are correlated with higher navigation efficiency (of both the whole-brain and the task-relevant subnetwork). In contrast, faster processing speed on trials that require more automatic processing are correlated with shorter path length within the task-relevant subnetwork. Our findings reveal that differences in the way communication is modelled between shortest path length and navigation may be sensitive to processing of automatic and controlled responses, respectively. Further, our findings suggest that there is a relationship between the…
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Advanced communication measures can elucidate how the organization of structural connectivity shapes the brain's functional dynamics.
“Nonetheless, recent work has shown that advanced communication measures can elucidate how the organization of structural connectivity shapes the brains functional dynamics (Goñi et al., 2014; Imms et al., 2021; Mišic et al., 2018; Wang et al., 2019).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Imms et al., 2021
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“Nonetheless, recent work has shown that advanced communication measures can elucidate how the organization of structural connectivity shapes neural information processing and functional dynamics [170, 291, 481, 210].”
BackgroundSelf-citation
- Authors
- Imms, P., Domínguez D, J. F., Burmester, A., Seguin, C., Clemente, A., Dhollander, T., Wilson, P. H., Poudel, G., & Caeyenberghs, K.
- Affiliations
- Australian Catholic University (AU)
- Deakin University (AU)
- The University of Melbourne (AU)
- Murdoch Children's Research Institute (AU)
- Corresponding author
- Imms, Phoebe
- ORCID
- Venue
- Brain Structure and Function
- Published
- 2021-03-11
- Volume
- 226
- Issue
- 4
- Pages
- 1281-1302
- ISSN
- 1863-2653 (ISSN-L)
- 1863-2661
- OpenAlex
- W3134586099
- Cited by
- 39
- References
- 10
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 10 · 2023: 12 · 2022: 5 · 2021: 1
- FWCI
- 3.68
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Locations
- Brain Structure and Function (publishedVersion)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Australian Catholic University Research Fund ([#902915)
- National Health and Medical Research Council (APP1143816)
- Melbourne Research, University of Melbourne
- 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
- processing speed, navigation efficiency, shortest path length, structural connectome, cognitive control, automatic processing, drift-diffusion model, decision time, graph theory, human brain networks
- MeSH terms
- Brain, Cognition, Communication, Female, Humans, Male, Connectome
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