Effects of Caffeine during Alert and Rest Conditions: An EEG-fMRI Study
Poster No:
1972
Submission Type:
Abstract Submission
Authors:
Lina Alqam1, Kadir Yildirim1, Kübra Eren1, Belal TAVASHI1, Elif Can1, Cem Karakuzu1, Alp Dincer2, Pınar Senay Özbay1
Institutions:
1Boğaziçi University, İstanbul, Turkey, 2Acıbadem University, İstanbul, Turkey
First Author:
Co-Author(s):
Introduction:
In this study, caffeine intake, brain activity, autonomic signals, and EEG power are examined during resting state and mental arithmetic tasks. To understand caffeine and neural and non-neural dynamics, we investigated the relationships between specific brain regions, autonomic responses, and EEG-fMRI dynamics in caffeine-consuming individuals.
Methods:
Simultaneous EEG-fMRI data were collected at 3T with GRE-EPI (FA = 90°, TR = 3 s, TE = 36 ms, in-place resolution = 2.5 mm, number of TRs = 135). Nine participants performed short eyes-open resting-state scans and a cognitive task involving solving mathematical equations with one unknown. The task included six blocks: 45 s rest (OFF), 9 s task (ON), and 36 s rest (OFF). fMRI preprocessing was conducted using AFNI's 'afni_proc' pipeline (Cox, 1996) for signal drift correction, slice-timing correction, motion correction, MNI registration, 3 mm smoothing, and outlier removal (threshold = 0.2). Cardiac signals were recorded using a fingertip pulse oximeter, and HR was calculated per TR and resampled to match fMRI data (Chang, 2009). EEG preprocessing involved removing MRI gradient artifacts using Brain Products Analyzer, cardioballistic artifacts (Allen, 2000b) via Average Artifact Subtraction (AAS), and eye blink/muscle artifacts via ICA. Data were downsampled to 250 Hz, filtered (0.5-35 Hz), and referenced to Fz. Fatigue predictor frontal-theta split participants into two groups (Trejo, 2015). High theta levels were linked to mental fatigue (Holm, 2009) and workload during demanding tasks (Wascher, 2013). Subjects with a relative theta average absolute difference ≥ 0.5 were categorized as fatigued (Hθ) or otherwise non-fatigued (Lθ). EEG power spectral density (PSD) was used to investigate correlations with with fMRI: i.e., 1) specific brain regions, such as the Prefrontal Cortex (PFC) during the arithmetic task (Takeuchi, 2011) and the Posterior Cingulate Cortex (PCC) during resting state (Pfefferbaum, 2010); 2) physiological metrics, such as heart rate (HR); and 3) inter-regional connectivity using the Phase Lag Index (PLI) to calculate connectivity analyses (Stam, 2007d).
Results:
ARITH: (Hθ) group showed increased delta desynchronization in ARITH30. A negative correlation between EEG bands and fMRI_PFC was observed in the (Hθ) group at TASK10, indicating that caffeine initially disrupted cognitive processing. In contrast, the (Lθ) group showed a positive correlation between EEG bands and fMRI_PFC at TASK10, indicating improved cognitive processing with caffeine. During TASK30, the (Lθ) group showed suppression of delta, theta, and alpha, and increased beta activity in parietal and occipital electrodes. For the Hθ group, high delta activity correlated positively with HR during TASK10 but shifted to a negative delta-HR correlation and positive alpha correlation in TASK30 (Fig.1). (Hθ) group experienced delta desynchronization, while theta and alpha connectivity increased during REST30. Positive correlations between EEG bands and the PCC indicate enhanced connectivity. (Lθ) group showed an increase in alpha and beta connectivity, indicating better cerebral coordination during REST30. However, a negative REST30 fMRI_PCC correlation suggests caffeine reduces non-fatigued connectivity. (Lθ) group had a positive delta-HR correlation in REST10 and a negative theta-HR correlation in REST30. For (Hθ) group, a negative delta-HR correlation emerged in REST30, along with negative alpha correlations in REST10 (Fig.2).
·fig.1: Left: ROI and HR-EEG Correlation, (top left) EEG and fMRI_PFC correlation, (bottom left) HR-EEG correlation, Right: PLI matrices (Delta, Theta, Alpha and Beta) for t = 10 min and t = 30 min
·fig.2: Left: ROI and HR-EEG Correlation, (top left) EEG and fMRI_PCC correlation, (bottom left) HR-EEG correlation, Right: PLI matrices (Delta, Theta, Alpha and Beta) for t = 10 min and t = 30 min
Conclusions:
Our study on caffeine's effects on brain and heart rate using EEG bands showed distinct effects. Caffeine improves arousal and cognition with increased fMRI_PCC delta and theta and fMRI_PFC alpha and beta synchronization during rest and tasks, especially the (Lθ) group.
Novel Imaging Acquisition Methods:
BOLD fMRI
EEG
Multi-Modal Imaging 1
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals 2
Keywords:
Electroencephaolography (EEG)
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
1|2Indicates the priority used for review
Abstract Information
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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.
2. Chang, C. (2009). Effects of model-based physiological noise correction on default mode network anti-correlations and correlations. NeuroImage, 47(4), 1448–1459.
3. Cox, R. W. (1996). AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Computers and Biomedical Research, 29(3), 162–173.
4. Holm, A. (2009). Estimating Brain Load from the EEG. The Scientific World JOURNAL, 9, 639–651.
5. Pfefferbaum, A. (2010). Cerebral Blood Flow in Posterior Cortical Nodes of the Default Mode Network Decreases with Task Engagement but Remains Higher than in Most Brain Regions. Cerebral Cortex, 21(1), 233–244.
6. Stam, C. J. (2007d). Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Human Brain Mapping, 28(11), 1178–1193.
7. Takeuchi, H. (2011). Working Memory Training Using Mental Calculation Impacts Regional Gray Matter of the Frontal and Parietal Regions. PLoS ONE, 6(8), e23175.
8. Trejo, L. J. (2015). EEG-Based Estimation and Classification of Mental Fatigue. Psychology, 06(05), 572–589.
9. Wascher, E. (2013). Frontal theta activity reflects distinct aspects of mental fatigue. Biological Psychology, 96, 57–65.