Reconfiguration of functional brain networks and metabolic cost converge during task performance
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Austria
- QIMR Berghofer Medical Research Institute, Australia
- School of Psychology, University of Newcastle, Australia
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Austria
- Center for Biomarker Research in Medicine (CBmed), Austria
Figures
Figure 1
Design and work flow.
(A) The experimental sequence comprised a T1-weighted structural scan (8 min, grey), BOLD (6 min, orange) and ASL (6 min, green) at rest. This was followed by task-specific PET/MR acquisition. …
Figure 2 with 1 supplement
Local metabolism and neural activity.
Task-specific changes of different metabolic demands as obtained with functional PET (fPET), blood oxygen level dependent (BOLD) signal and arterial spin labeling (ASL, all pFWE < 0.05 corrected …
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Figure 2—source data 1
Task-induced changes CMRGlu, CBF and BOLD signal.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig2-data1-v2.txt
Figure 2—figure supplement 1
Task-induced changes in glucose metabolism obtained with an fMRI-independent baseline definition.
To compute task-specific CMRGlu estimates independent of BOLD changes, fPET was also calculated using a method previously developed by our group (Hahn et al., 2016). In this method, the baseline …
Figure 3 with 3 supplements
Metabolic connectivity mapping (MCM).
MCM was calculated as correlation between glucose metabolism and functional connectivity across all subjects (z-transformed Pearson’s r values). The small circles in the plots show z-scores of …
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Figure 3—source data 1
Metabolic connectivity mapping data.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig3-data1-v2.txt
Figure 3—figure supplement 1
Comparison between functional connectivity (FC) and metabolic connectivity mapping (MCM, as shown in Figure 3).
Similar to MCM, also functional connectivity was sensitive to task performance and showed significant increases from rest to task for all three connections. As functional connectivity by definition …
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Figure 3—figure supplement 1—source data 1
Functional connectivity data.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig3-figsupp1-data1-v2.txt
Figure 3—figure supplement 2
Impact of functional connectivity preprocessing on metabolic connectivity mapping (MCM).
It has been suggested that the order of common preprocessing steps may impact the estimates of functional connectivity, especially when performing motion scrubbing (Carp, 2013). To directly test …
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Figure 3—figure supplement 2—source data 1
Metabolic connectivity mapping data with alternative functional connectivity preprocessing.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig3-figsupp2-data1-v2.txt
Figure 3—figure supplement 3
Influence of spatial smoothing on MCM estimates.
Unsmoothed data on functional connectivity and CMRGlu were separately permuted across voxels, yielding MCM values tightly centered on zero (blue bars, z = 0.00005 ± 0.004). Spatial smoothing still …
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Figure 3—figure supplement 3—source data 1
Unsmoothed random permutation MCM data for IPS -> Occ.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig3-figsupp3-data1-v2.txt
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Figure 3—figure supplement 3—source data 2
Smoothed random permutation data for IPS -> Occ.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig3-figsupp3-data2-v2.txt
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Figure 3—figure supplement 3—source data 3
Unsmoothed random permutation MCM data for FEF -> Occ.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig3-figsupp3-data3-v2.txt
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Figure 3—figure supplement 3—source data 4
Smoothed random permutation MCM data for FEF -> Occ.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig3-figsupp3-data4-v2.txt
Figure 4 with 3 supplements
Dynamic causal modeling (DCM).
Inference across the entire model space indicates the highest probability for the first model, which corresponds to the model resulting from the MCM analysis (Figure 3). Testing each task modulation …
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Figure 4—source data 1
Probability of DCM models.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig4-data1-v2.txt
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Figure 4—source data 2
Probability of DCM family inference.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig4-data2-v2.txt
Figure 4—figure supplement 1
Dynamic causal modeling (DCM) regions.
BOLD time series were extracted from spheres of 5 mm radius, each individually shifted to the location with the maximum effect size in the analysis of task changes (F-test) and constrained by the …
Figure 4—figure supplement 2
Dynamic causal modeling (DCM) space.
For the effective connections Occ –> IPS, Occ -> FEF and FEF –> IPS the relevance of task modulation was tested, whereas for the connection IPS -> FEF the relevance of the intrinsic connection was …
Figure 4—figure supplement 3
DCM results with a region of interest size of 8 mm.
Similar to the results from the original analysis (Figure 4), model one showed the highest posterior probability and family inference indicated high probabilities for all four connections.
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Figure 4—figure supplement 3—source data 1
Probability of DCM models with 8 mm smoothing.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig4-figsupp3-data1-v2.txt
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Figure 4—figure supplement 3—source data 2
Probability of DCM family inference with 8 mm smoothing.
- https://cdn.elifesciences.org/articles/52443/elife-52443-fig4-figsupp3-data2-v2.txt
