Effects of Caffeine Intake as a Sympathetic Stimulant on Brain Dynamics during Cognitive Tasks
Poster No:
1338
Submission Type:
Abstract Submission
Authors:
Cem Karakuzu1, Kübra Eren1, Elif Can1, Belal Tavashi1, Kadir Yildirim1, Lina Alqam1, Alp Dincer2, Pinar S Ozbay1
Institutions:
1Bogazici University, Istanbul, Turkey, 2Acibadem University, Istanbul, Turkey
First Author:
Co-Author(s):
Introduction:
This study explores the impact of caffeine, a sympathetic stimulant, on cognitive brain dynamics, leveraging the power of functional magnetic resonance imaging (fMRI) and independent component analysis (ICA), which is a data-driven clustering method to construct statistically independent spatial maps of fMRI data. This integrative approach promises a more nuanced perspective on the effects of caffeine on cognitive functions.
Methods:
Data were obtained at 3T with GRE-EPI (FA = 90, TR = 3 s, TE = 36 ms, in-place resolution = 2.5 mm, number of TRs = 135). The participant was asked to find the unknown while seeing the equation (see Figure 1A). Preprocessing of fMRI data followed the suggested 'afni_proc' pipeline, including removal of signal drifts, slice-timing correction, realignment of consecutive volumes, registration to MNI template, smoothing (3 mm FWHM), and regression of motion parameters while removing outliers (threshold = 0.2) (Taylor et al., 2018; Cox 1996). PPG and respiratory signals were collected with a pulse oximeter attached to the fingertip and respiratory bellows, respectively. We used RETROICOR to reduce the effects of their cycles in the fMRI.
There were four participants who attended the experiment. Repeated scans were performed following immediate intake of caffeine pills, first scan 10 minutes and second scan 30 minutes following intake. The subjects were not informed if the pill was caffeine or placebo.
Firstly, ICA performed for all subjects by FSL's MELODIC (Jenkinson et al., 2012). Thus, task-related components and RVT related components were determined by examining the association between components' time-courses and experimental design. RVT related components were identified by cross-correlation analysis between the time-course and RVT in MATLAB (MathWorks, 2023). Secondly, those components were investigated to describe how spatial maps changed when RETROICOR was not performed.
Finally, we applied group-ICA on three participants' first sessions and second sessions.
There were four participants who attended the experiment. Repeated scans were performed following immediate intake of caffeine pills, first scan 10 minutes and second scan 30 minutes following intake. The subjects were not informed if the pill was caffeine or placebo.
Firstly, ICA performed for all subjects by FSL's MELODIC (Jenkinson et al., 2012). Thus, task-related components and RVT related components were determined by examining the association between components' time-courses and experimental design. RVT related components were identified by cross-correlation analysis between the time-course and RVT in MATLAB (MathWorks, 2023). Secondly, those components were investigated to describe how spatial maps changed when RETROICOR was not performed.
Finally, we applied group-ICA on three participants' first sessions and second sessions.
Results:
Controlling physiological signals with RETROICOR might make the components take the early places if the physiological signals are controlled. The relationship between RVT and time-course was explored. Cross-correlations between the RVT and the signal in sessions gave contrary results. Moreover, in session 2 of the participant 2, the task-related component 1 has also the highest cross-correlation with RVT.
Group-ICA: component 16 shows distinct patterns of the effect of increased activity of cerebral arteries, unlike the other group (see figure 2A). Also, explained variance of insula activation is higher in the 30-minutes after group and the spatial map is more particular for the insula regions. Besides, task related IPS region is visible earliest in the component 6 in the 10-minutes after group. On the other hand, it can be seen at the component 2 in the 30-minutes after group. Finally, Component 18 in caffeine has a particular map covering thalamus (see figure 2B).
Group-ICA: component 16 shows distinct patterns of the effect of increased activity of cerebral arteries, unlike the other group (see figure 2A). Also, explained variance of insula activation is higher in the 30-minutes after group and the spatial map is more particular for the insula regions. Besides, task related IPS region is visible earliest in the component 6 in the 10-minutes after group. On the other hand, it can be seen at the component 2 in the 30-minutes after group. Finally, Component 18 in caffeine has a particular map covering thalamus (see figure 2B).
Conclusions:
Controlling physiological signals is highly substantial because single subject ICAs shows that provided networks and explained variances could be changed by physiological signals. Also, RVT could be correlated with the task-related networks, which means that cardiac and respiratory activity could interfere with fMRI signals of the brain.
When considering the how cerebral arteries affect the fMRI signals, the importance of this cross-correlation must be considered. Because it is also the most task-related component, the effect of caffeine trough the sympathetic activity increase and its chemical effects on the direct neuronal level requires research to distinguish impact pathways.
When considering the how cerebral arteries affect the fMRI signals, the importance of this cross-correlation must be considered. Because it is also the most task-related component, the effect of caffeine trough the sympathetic activity increase and its chemical effects on the direct neuronal level requires research to distinguish impact pathways.
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Other Methods
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Perception, Attention and Motor Behavior:
Consciousness and Awareness
Keywords:
Data analysis
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
MRI
1|2Indicates the priority used for review
Provide references using author date format
Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). FSL. NeuroImage, 62(2), 782–790.
Taylor PA, Chen G, Glen DR, Rajendra JK, Reynolds RC, Cox RW (2018). FMRI processing with AFNI: Some comments and corrections on ‘Exploring the Impact of Analysis Software on Task fMRI Results’.
The MathWorks Inc. (2023). MATLAB version: 9.14.0 (R2023a), Natick, Massachusetts: The MathWorks Inc. https://www.mathworks.com