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| Hauptseite > Publikationsdatenbank > On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI > print |
| Hauptseite > Publikationsdatenbank > On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI > print |
| 001 | 909906 | ||
| 005 | 20230307101830.0 | ||
| 024 | 7 | _ | |a 10.3390/e24081148 |2 doi |
| 024 | 7 | _ | |a 1099-4300 |2 ISSN |
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| 037 | _ | _ | |a FZJ-2022-03509 |
| 082 | _ | _ | |a 510 |
| 100 | 1 | _ | |a Omidvarnia, Amir |0 P:(DE-Juel1)188339 |b 0 |e Corresponding author |
| 245 | _ | _ | |a On the Spatial Distribution of Temporal Complexity in Resting State and Task Functional MRI |
| 260 | _ | _ | |a Basel |c 2022 |b MDPI |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Measuring the temporal complexity of functional MRI (fMRI) time series is one approach to assess how brain activity changes over time. In fact, hemodynamic response of the brain is known to exhibit critical behaviour at the edge between order and disorder. In this study, we aimed to revisit the spatial distribution of temporal complexity in resting state and task fMRI of 100 unrelated subjects from the Human Connectome Project (HCP). First, we compared two common choices of complexity measures, i.e., Hurst exponent and multiscale entropy, and observed a high spatial similarity between them. Second, we considered four tasks in the HCP dataset (Language, Motor, Social, and Working Memory) and found high task-specific complexity, even when the task design was regressed out. For the significance thresholding of brain complexity maps, we used a statistical framework based on graph signal processing that incorporates the structural connectome to develop the null distributions of fMRI complexity. The results suggest that the frontoparietal, dorsal attention, visual, and default mode networks represent stronger complex behaviour than the rest of the brain, irrespective of the task engagement. In sum, the findings support the hypothesis of fMRI temporal complexity as a marker of cognition |
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| 700 | 1 | _ | |a Preti, Maria Giulia |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Zalesky, Andrew |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Van De Ville, Dimitri |0 P:(DE-HGF)0 |b 5 |
| 773 | _ | _ | |a 10.3390/e24081148 |g Vol. 24, no. 8, p. 1148 - |0 PERI:(DE-600)2014734-X |n 8 |p 1148 |t Entropy |v 24 |y 2022 |x 1099-4300 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/909906/files/entropy-24-01148-v2-1.pdf |y OpenAccess |
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