The Effects of Caffeine on Brain Networks during Rest and Task
Poster No:
1907
Submission Type:
Abstract Submission
Authors:
Cem Karakuzu1, Kübra Eren1, Elif Can1, Belal Tavashi1, Kadir Berat Yıldırım1, Lina Alqam1, Alp Dincer2, Pınar Özbay1
Institutions:
1Bogazici University, Istanbul, Turkey, 2Acibadem University, Istanbul, Turkey
First Author:
Co-Author(s):
Introduction:
Caffeine is one of the most common psychoactive drugs worldwide and is consumed in many cultures. Its positive and adverse effects are investigated by researchers. It increases alertness, heart rate, and respiration. This study explores its effects on brain networks during a cognitive task and resting.
Methods:
The participants took a 200mg caffeine pill without knowing whether it was caffeine or placebo. 10 minutes after the intake, the first session of fMRI scan was conducted. The participants are asked to try to solve the six arithmetic equations, then they rest for six minutes. In the second session, which was 30 minutes after the intake, the same procedure was repeated. Data from 9 healthy subjects were acquired at a 3T MRI scanner with GRE-EPI (FA = 90, TR = 3 s, TE = 36 ms, in-place resolution = 2.5 mm). The data was preprocessed through the 'afni_proc' pipeline and RETROICOR was applied for physiological motion correction (Cox, 1996; Glover et al., 2000). ICA and network analysis were conducted by FSL (Smith et al., 2004).
Group-ICA was performed separately on early and late caffeine groups for resting state and task condition. Afterwards, spatial correlations between the spatial maps were calculated. The most correlated components are investigated.
Also, ICA was used to obtain component maps in the resting state. It was performed by pooling all sessions, 18 scans, with 20 components. Then, dual regression was used to get subject-specific spatial maps according to the results. Later, network modelling was carried out and 20 spatial maps were assigned as nodes. After cleaning, 10 components remained as nodes (Figure 1.B). The maps showing noise instead of an intrinsic network or physiologically related region are discarded manually. Finally, the groups were compared by FSLnets.
Group-ICA was performed separately on early and late caffeine groups for resting state and task condition. Afterwards, spatial correlations between the spatial maps were calculated. The most correlated components are investigated.
Also, ICA was used to obtain component maps in the resting state. It was performed by pooling all sessions, 18 scans, with 20 components. Then, dual regression was used to get subject-specific spatial maps according to the results. Later, network modelling was carried out and 20 spatial maps were assigned as nodes. After cleaning, 10 components remained as nodes (Figure 1.B). The maps showing noise instead of an intrinsic network or physiologically related region are discarded manually. Finally, the groups were compared by FSLnets.
Results:
Separate group-ICA analyses for the task and rest with early and late caffeine intake produced spatial maps for the groups. Spatial correlations between those maps showed that task conditions are more correlated to each other. 8 pairs were higher than r = .40 for task condition and 6 for resting state. The highest correlations were .81, .71, .66, and .66 for task, whereas .63, -.55, .54, and .47 for resting (Figure 1.A). These positively correlated maps primarily contain visual and somatomotor networks, or intraparietal sulcus in the task, while they are generally DMN and ventricular area in the resting. There is more variability in resting and therefore are less strongly correlated maps. Also, spatial maps showing the ventricular area in the resting state were negatively correlated, r = -.55. Indeed, this negative correlation indicates the potential sympathetically mediated effect of caffeine intake. Because the ventricular area fMRI signals are not neuronal, this effect shows us the physiological indirect effect of caffeine (Özbay et al., 2018).
The network analysis showed that caffeine intake affects the networks during resting. Nodes 5 and 6 are regions related to physiological signals. The late caffeine intake group has marginally significantly higher strength between these nodes, p = .06, which is probably the result of the systemic effect of caffeine. In addition, node 6 is associated with nodes 17 and 15 (Figure 2). This implies that physiological activations are related to intrinsic networks such as DMN or attention.
The network analysis showed that caffeine intake affects the networks during resting. Nodes 5 and 6 are regions related to physiological signals. The late caffeine intake group has marginally significantly higher strength between these nodes, p = .06, which is probably the result of the systemic effect of caffeine. In addition, node 6 is associated with nodes 17 and 15 (Figure 2). This implies that physiological activations are related to intrinsic networks such as DMN or attention.
·Correlations between spatial maps and some selected nodes (components) with their power spectra
·Strength between the nodes (components) and their spatial maps
Conclusions:
In summary, the study reveals that, during the resting state, caffeine intake increases variability in spatial correlations and induces a negative correlation in maps pointing to the ventricular region, which is related to physiological activation. Network analysis shows that caffeine influences network dynamics; furthermore, physiological activations that are linked to intrinsic brain networks highlight caffeine's systemic impact.
This study is funded by TUBITAK 2232 grant (121C120).
This study is funded by TUBITAK 2232 grant (121C120).
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI 1
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals
Keywords:
FUNCTIONAL MRI
Other - Caffeine
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Glover, G. H., Li, T.-Q., & Ress, D. (2000). Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magnetic Resonance in Medicine, 44(1), 162–167. https://doi.org/10.1002/1522-2594(200007)44:1<162::AID-MRM23>3.0.CO;2-E
Özbay, P. S., Chang, C., Picchioni, D., Mandelkow, H., Moehlman, T. M., Chappel-Farley, M. G., van Gelderen, P., de Zwart, J. A., & Duyn, J. H. (2018). Contribution of systemic vascular effects to fMRI activity in white matter. NeuroImage, 176, 541–549. https://doi.org/10.1016/j.neuroimage.2018.04.045
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J. M., & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051