Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks
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| Vydáno v: | bioRxiv (Nov 12, 2020), p. n/a |
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| Další autoři: | , , , , , |
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Cold Spring Harbor Laboratory Press
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| On-line přístup: | Citation/Abstract Full text outside of ProQuest |
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| 001 | 2507184297 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2692-8205 | ||
| 024 | 7 | |a 10.1101/2020.11.11.378406 |2 doi | |
| 035 | |a 2507184297 | ||
| 045 | 0 | |b d20201112 | |
| 100 | 1 | |a Küchenhoff, Svenja | |
| 245 | 1 | |a Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks | |
| 260 | |b Cold Spring Harbor Laboratory Press |c Nov 12, 2020 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Abstract Visual information processing requires an efficient visual attention system. The neural theory of visual attention (TVA) proposes that visual processing speed depends on the coordinated activity between frontoparietal and occipital brain areas. Previous research has shown that the coordinated activity between (i.e., functional connectivity, ‘inter-FC’) cingulo-opercular (COn) and right-frontoparietal (RFPn) networks is linked to visual processing speed. However, evidence for how inter-FC of COn and RFPn with visual networks links to visual processing speed is still missing. Forty-eight healthy human adult participants (27 females) underwent resting-state (rs-)fMRI and performed a whole-report psychophysical task. To obtain inter-FC, we analyzed the entire frequency range available in our rs-fMRI data (i.e., 0.01-0.4 Hz) to avoid discarding neural information. Following previous approaches, we analyzed the data across frequency bins (Hz): Slow-5 (0.01-0.027), Slow-4 (0.027-0.073), Slow-3 (0.073-0.198), and Slow-2 (0.198-0.4). We used the mathematical TVA framework to estimate an individual, latent-level visual processing speed parameter. We found that visual processing speed was negatively associated with inter-FC between RFPn and visual networks in Slow-5 and Slow-2, with no corresponding significant association for inter-FC between COn and visual networks. These results provide first empirical evidence that links inter-FC between RFPn and visual networks with the visual processing speed parameter. These findings suggest a direct connectivity between occipital and right frontoparietal, but not frontoinsular, regions, to support visual processing speed. Significance statement An efficient visual processing is at the core of visual cognition. Here, we provide evidence for a brain correlate of how fast individuals process visual stimuli. We used mathematical modeling of performance in a visual report task to estimate visual processing speed. A frequency-based analysis of resting-state fMRI signals revealed that functional connectivity between the right frontoparietal network and primary and dorsal occipital networks is linked to visual processing speed. This link was present in the slowest, typical frequency of the fMRI signal but also in the higher frequencies that are routinely discarded. These findings imply that the coordinated spontaneous activity between right frontoparietal and occipital regions supports the individual potential of the visual system for efficient processing. Competing Interest Statement The authors have declared no competing interest. Footnotes * Conflict of interest statement: The authors declare no competing financial interests. * https://osf.io/nhqg3/ * Abbreviations ACC anterior cingulate cortex BOLD Blood oxygenation level-dependent signal COn cingulo-opercular network IC independent component ICA independent component analysis inter-FC between-network functional connectivity RFPn right frontoparietal network rs-fMRI resting-state functional magnetic resonance imaging TVA theory of visual attention VPS visual processing speed | |
| 653 | |a Cognition | ||
| 653 | |a Visual perception | ||
| 653 | |a Functional magnetic resonance imaging | ||
| 653 | |a Visual system | ||
| 653 | |a Brain mapping | ||
| 653 | |a Information processing | ||
| 653 | |a Psychophysics | ||
| 653 | |a Visual stimuli | ||
| 653 | |a Attention | ||
| 653 | |a Neuroimaging | ||
| 653 | |a Mathematical models | ||
| 653 | |a Cortex (cingulate) | ||
| 653 | |a Processing speed | ||
| 653 | |a Neural networks | ||
| 700 | 1 | |a Sorg, Christian | |
| 700 | 1 | |a Schneider, Sebastian | |
| 700 | 1 | |a Kohl, Oliver | |
| 700 | 1 | |a Müller, Hermann J | |
| 700 | 1 | |a Finke, Kathrin | |
| 700 | 1 | |a Ruiz-Rizzo, Adriana L | |
| 773 | 0 | |t bioRxiv |g (Nov 12, 2020), p. n/a | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2507184297/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://www.biorxiv.org/content/10.1101/2020.11.11.378406v1 |