The acute effects of decaffeinated coffee on cognitive function and brain activity:a single-center experience

The acute effects of decaffeinated coffee on cognitive function and brain activity:a single-center experience | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The acute effects of decaffeinated coffee on cognitive function and brain activity:a single-center experience Shenghui Zhang, Hua Fan, Xiyun Rao, Qingwen Yu, Ziyi Xin, Yongmin Shi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8443818/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Coffee is one of the most widely consumed beverages worldwide and is known for its beneficial effects on various diseases, which is often attributed to caffeine. However, other bioactive compounds in coffee also play an important role. The aim of this study was to investigate the acute effects of decaffeinated coffee. Methods: Thirty-one healthy adults participated in the study. Resting-state functional magnetic resonance imaging (rs-fMRI), cognitive-behavioral tests, self-report scales, and physiological measures were completed at baseline and 30 min post-drink. Based on rs-fMRI, we used static and dynamic amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) to observe changes in spontaneous brain activity following acute decaffeinated coffee intake. Results: After consumption, participants showed significantly faster reaction times in the 1-back ( P = 0.002), 2-back ( P = 0.023), Continuous Performance Test ( P = 0.003), and Finger Tapping Task ( P =0.009). At the neural level, the participants showed significantly higher sfALFF in the fusiform gyrus (FFG) and inferior temporal gyrus (ITG) and significantly lower sReHo in the medial orbital of the superior frontal gyrus (PFCventmed) after consuming decaffeinated coffee compared with before, with no significant differences in sALFF. The sfALFF values in the FFG were significantly negatively correlated with RTs on CPT. Dynamic analysis revealed significantly lower dALFF values in the FFG and cerebellum, and significantly higher dALFF and dReHo values in the supplementary motor area (SMA). No brain regions showed significant differences in dfALFF after consuming decaffeinated coffee compared with before (GRF-corrected, voxel-level P < 0.01, cluster-level P < 0.05). In addition, diastolic blood pressure significantly increased ( P = 0.004), and headache scores were higher ( P = 0.033), while no significant changes in systolic blood pressure or heart rate were observed. Conclusions: These results suggest that decaffeinated coffee may affects cognitive performance and brain activity, providing insights into the effects of non-caffeine components of coffee and their underlying mechanisms. As an initial exploratory study, it provides a foundation for future research. Randomized, placebo-controlled trials with long-term assessments are now needed to validate and extend these findings. Health sciences/Diseases Health sciences/Neurology Biological sciences/Neuroscience Decaffeinated coffee Resting-state functional MRI Cognitive function Amplitude of low-frequency fluctuation Fractional amplitude of low-frequency fluctuation Regional homogeneity Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Coffee is one of the most commonly consumed beverages globally and has beneficial effects on several diseases. It has been shown to reduce the risk of some cancers, decrease cardiovascular disease mortality, and improve outcomes in some metabolism-related diseases such as type 2 diabetes and metabolic syndrome [ 1 ] . Additionally, long-term coffee consumption has been associated with the prevention of cognitive decline and a lower risk of neurological disorders, including Parkinson's disease, Alzheimer's disease, and depression [ 2 ] . Previous studies have focused on the effects of caffeine, which is believed to act as a non-specific, potent inhibitor of A1 and A2 adenosine receptors, leading to central nervous system hyperarousal [ 3 ] , increased alertness and improved attention [ 4 – 6 ] , and reduced burden of neurological disorders [ 7 ] . However, recent studies have found that coffee's effects outweigh caffeine's effects [ 8 ] . A functional magnetic resonance imaging (fMRI) study revealed that caffeine intake duplicated the effects of coffee on only some brain networks [ 9 ] .. On the one hand, this may be related to the expectancy effect. Studies have shown that expectations about caffeine intake affect mood and performance. Moreover, the impact of expectations is compatible with, and may even be preferable to, the impact of caffeine [ 10 ] . On the other hand, evidence suggests that the health and neuroprotective effects of coffee are not solely attributable to caffeine. Instead, other bioactive compounds in coffee play a significant role in enhancing motor and cognitive performance, particularly in the context of aging and depression [ 11 ] . Coffee is a complex chemical mixture containing over 1000 different compounds [ 12 ] , many of which can individually or synergistically influence cognition. In particular, phenolic acids such as chlorogenic acid (CGA) and caffeic acid have been shown to exert neurological benefits similar to caffeine [ 11 ] . These compounds have been found to significantly improve short-term or working memory impairments induced by scopolamine [ 12 , 13 ] . This prompted us to explore decaffeinated coffee as a potential neuroprotective beverage. Some studies have reported that decaffeinated coffee exerts protective effects against liver damage and type 2 diabetes [ 14 ] , prevents age-related and systemic diseases, and reduces cancer risk [ 15 ] . It has also shown efficacy in protecting against neurodegeneration across various neural pathways and conditions [ 11 ] . Nevertheless, the relationship between decaffeinated coffee and cognitive function remains underexplored, with inconsistent findings. Although some studies have reported improvements in working memory, sustained attention, alertness, and mood following decaffeinated coffee consumption [ 8 , 16 , 17 ] , others have found no significant association with cognitive performance [ 18 ] . fMRI enables the investigation of human brain function during various tasks or at rest through blood oxygen level–dependent (BOLD) signaling [ 19 ] . In a task-based fMRI study, no significant differences were observed between the decaffeinated coffee and water groups in terms of response time, mean perfusion, BOLD activation, BOLD mean signal intensity, or BOLD mean baseline during a word stem completion task [ 20 ] . However, research on the effects of decaffeinated coffee on spontaneous brain activity remains limited. Resting-state functional magnetic resonance imaging (RS-fMRI) is a technique that allows the study of spontaneous brain activity when individuals are quiet, relaxed, and awake [ 21 ] . Static metrics, including the amplitude of low-frequency fluctuation (sALFF) and regional homogeneity (sReHo), are frequently employed to characterize the local features of RS-fMRI signals [ 22 – 24 ] . These metrics, however, fail to account for the dynamic changes in brain activity over time. Capturing these temporal fluctuations in brain activity requires the use of dynamic metrics, such as the dynamic amplitude of low-frequency fluctuation (dALFF), the dynamic fractional amplitude of low-frequency fluctuation (dfALFF), and dynamic regional homogeneity (dReHo) [ 25 ] . Combining both static and dynamic metrics can provide a more comprehensive understanding of the acute effects of decaffeinated coffee on brain activity. In this study, we aimed to investigate the acute effects of decaffeinated coffee consumption by combining RS-fMRI with assessments of behavioral performance, emotional state, and physiological indicators. 2 Methods Thirty-one healthy college students [16 male (mean age 23.44 ± 2.34 years) and 15 female (mean age 24.87 ± 2.67 years)] were recruited through advertisements to participate in this study. Participants were recruited from the university campus through online advertisements. Interested individuals completed an initial online screening questionnaire to assess their eligibility based on the following criteria: All participants were right-handed, consumed fewer than four cups of coffee per week, had no history of psychiatric disorders or surgical trauma, and were nonsmokers and nondrinkers. The sample size was determined by adhering to the methodological precedent established in prior rs-fMRI research on acute coffee consumption, which utilized 24 and 32 participants [ 7 ] . All procedures were conducted in accordance with the ethical standards set by the Academic Ethics Committee of the Affiliated Hospital of Hangzhou Normal University (Approval No.[2023 (E2) - KS − 163]). Written informed consent was obtained from all participants before the study commenced. Graphical abstract can be seen in Fig. 1 . 2.1 Participants After a 24-hour caffeine abstinence period, the participants arrived at the laboratory for baseline data collection, which included behavioral tests, physiological measures, State Self-Report Scales, and RS-fMRI scans. Participants then consumed a standard dose of decaffeinated coffee (Starbucks Decaffeinated Espresso with < 5 mg caffeine [ 26 ] ), which was presented to them simply as "coffee" without specifying its decaffeinated nature. This was followed by a second round of data collection 30 min after consumption. The 30-minute post-consumption time point was chosen in accordance with previous neuroimaging research on the acute effects of coffee, where it has been successfully used to detect changes in spontaneous brain activity [ 7 ] . This window is also well within the effective period for observing behavioral and subjective effects in the literature [ 8 ] . Participants were appropriately compensated at the end of the experiment. 2.2 Behavioral Test All procedures were conducted using the E-Prime 2.0 program. Practice tasks were assigned before the first behavioral test. An experimenter provided standardized instructions. The formal test was started after ensuring that the participants understood the task requirements. Full procedural details for all behavioral tasks are provided in the Supplementary Methods. 2.2.1 Working Memory Test Working memory was assessed using a computerized digital N-back task (E-Prime 2.0) with two load conditions: 1-back and 2-back. Participants were presented with a sequence of digits (0–9) at the center of the screen. In the 1-back condition, they pressed the spacebar when the current digit matched the one immediately preceding it. In the 2-back condition, the response was required when the current digit matched the one presented two trials back. Participants were instructed to respond as quickly and accurately as possible. The primary dependent variables were the accuracy (percentage of correct responses) and the mean reaction time (RT) for correct responses, calculated separately for each condition. 2.2.2 Sustained Attention Task The Visual Continuous Performance Test (VCPT) developed by Luo Xuerong et al. was used with appropriate modifications to assess sustained attention [ 27 ] . Participants were required to press the spacebar upon seeing the letter "X" followed by the letter "O". 2.2.3 Psychomotor Task We used the finger tapping task (FTT) to measure psychomotor speed. The participants were instructed to use the index and middle fingers of their right hand to alternately tap the "m" and "n" keys on a computer keyboard as quickly as possible for 300 taps. The time taken to complete the task was recorded. A similar paradigm has been used in previous studies on caffeine [ 28 , 29 ] . 2.3 State Self-Report Scales We used the State Self-Rating Scale proposed in the study on caffeine by Susan et al., with appropriate adaptations [ 29 ] . The participants were asked to mark their current feelings on nine 100-mm line segments. Five segments had endpoints labeled: drowsy/lazy – energetic/sharp, tense – relaxed, sad/frustrated – happy/cheerful, bad mood – good mood, and clear-headed – confused. The other four segments rated dizziness, nervousness/trembling, rapid heartbeat, and headache, with one end labeled "not at all" and the other "extremely.” The scores were based on the position of the markers along each segment. 2.4 Physiological Measures We measured systolic and diastolic blood pressure, as well as heart rate, using the YuWell YE660C electronic blood pressure monitor. During the measurement, the participants were instructed to sit at a table, remain relaxed, and not move or speak. The measurement personnel placed the cuff on the participant's right upper arm, with the lower edge positioned 2–3 cm above the elbow joint. The cuff was loosely adjusted to allow room for one finger, and the measurement was initiated. 2.5 RS-fMRI Data Acquisition and Preprocessing 2.5.1 Data Acquisition MRI scans were conducted at the Affiliated Hospital of Hangzhou Normal University using a 3T GE scanner (MR-750; GE Medical Systems, WI, USA). Before scanning, the participants were instructed to lie on their backs in a resting state, with foam pads placed around their heads to minimize movement. During the scan, the participants were asked to close their eyes and clear their minds. A multi-band gradient-echo echo-planar imaging sequence was used to acquire RS-fMRI images with the following parameters: repetition time/echo time(TR/TE) = 700/30 ms, field of view (FOV) = 216 × 216 mm 2 , matrix = 72×72, thickness/gap = 3/0 mm, fractional isotropy (FA) = 60°, 44 slices. High-resolution T1-weighted anatomical images were acquired using a 3D spoiled gradient echo sequence with the following parameters: 192 sagittal slices, TR/TE = 2678/2.98ms, FOV = 256×256 mm 2 , matrix = 256×256, thickness/gap = 1/0 mm, FA = 7°. 2.5.2 Data Preprocessing MRI image preprocessing was performed using the SPM12 ( http://www.fil.ion.ucl.ac.uk/spm/software/spm12/ ) and DPABI V8.1 toolbox in the MATLAB environment [ 30 ] . The first 10 volumes were removed, followed by slice-timing correction and image realignment to correct head motion. No data were excluded due to head motion artifacts (> 2.5 mm translation or > 2.5° rotation in any direction) [ 7 , 31 ] . Functional images were spatially co-registered with the structural images. The individual structural images were co-registered to the mean functional images and then segmented into gray matter, white matter (WM), and cerebrospinal fluid (CSF). The head motion parameters measured by the Friston-24 model, WM, CSF, and global signals were regressed out as nuisance covariates. Then, functional images were spatially normalized to Montreal Neurological Institute space at a 3-mm isotropic voxel resolution using the unified segmentation parameters for calculating sALFF, sfALFF, dALFF, and dfALFF. The sReHo and dReHo values were calculated after applying band-pass filtering (0.01–0.10 Hz) to reduce the effects of very low-frequency drift and high-frequency noise. 2.5.3 Calculation of Image Data Indicators The sALFF, sfALFF, and sReHo values were calculated using the DPABI software as follows: (1) sALFF and sfALFF: After data preprocessing, the time series for each voxel was transformed into the frequency domain using a fast Fourier transform to obtain the power spectrum. The square root of the power spectrum was then calculated at each frequency. The sALFF was defined as the averaged square root within the 0.01–0.10 Hz frequency range, whereas the sfALFF was defined as the sum of amplitude across 0.01–0.10 Hz divided by that across the entire frequency range [ 22 , 24 ] . (2) sReHo: The sReHo value of each voxel was calculated by computing Kendall's coefficient of concordance for the time series of that voxel and its 26 neighboring voxels [ 23 ] . The dALFF, dfALFF, and dReHo were calculated using temporal dynamic analysis toolkits based on DPABI [ 32 ] . A sliding-time window analysis was employed to generate dynamic indices, with a window size of 100 TRs (70 s) and a step size of 1 TR (0.7 s). The sALFF, sfALFF, and sReHo values were calculated for each time window. We computed each index's mean and standard deviation (SD) map across time windows. The dALFF, dfALFF, and dReHo were defined as each index's coefficient of variation (CV: SD/mean). In addition, to exclude the effect of window width on the results, the window width was set to 60/80 TR to repeat all the calculations in this study. All image indicators were smoothed with full width at half-maximum = 6 mm, and z standardization was applied. 2.6 Statistical Analysis Paired-samples t tests were used to examine the differences in each metric before and after decaffeination. The behavioral data, scale score, and physiological data were analyzed using SPSS (version 27.0; IBM Corp., NY, USA). DPABI was used to analyze the differences in static metrics (sALFF, sfALFF, and sReHo) and dynamic metrics (dALFF, dfALFF, and dReHo). A P value < 0.05 was considered significant. Multiple comparison correction was performed based on Gaussian random-field theory (voxel-level P < 0.01, cluster-level P < 0.05). To investigate the functional relevance of the significant neural changes, Pearson correlation analyses were conducted between the extracted values from the significant brain regions and the other primary outcome measures (behavioral, physiological, and subjective) from the post-consumption session. The significance threshold was set at P < 0.05 (two-tailed). 3 Results 3.1 Cognitive Tests As shown in Table 1 and Fig. 2 , participants showed significantly faster RTs after consuming decaffeinated coffee across all tasks. This included the 1-back (t = -3.309, P = 0.002, d = -0.60), 2-back (t = -2.394, P = 0.023, d = -0.44), and CPT (t = -3.258, P = 0.003, d = -0.59). Similarly, the time to complete the FTT was significantly reduced (t = -2.809, P = 0.009, d = -0.51). Effect sizes for these reaction-time reductions ranged from d = 0.44 to 0.60 (medium effects), indicating practical as well as statistical significance. However, no significant difference was observed in task accuracy. Table 1 Effects of decaf coffee on behavioral performance, blood pressure, heart rate and mood and physical sensations. Pre Post t value p value Cohen's d Mean SD Mean SD Behavioral performance 1-Back ACC (%) 0.998 0.004 0.998 0.004 0.373 0.712 0.07 1-Back RT (ms) 369 149 338 132 -3.309 0.002 -0.6 2-Back ACC (%) 0.988 0.022 0.983 0.019 -1.564 0.128 -0.29 2-Back RT (ms) 404 153 378 129 -2.394 0.023 -0.44 CPT ACC (%) 0.99 0.013 0.99 0.013 0.318 0.752 0.06 CPT RT (ms) 440 104 421 92 -3.258 0.003 -0.59 FTT RT (ms) 73616 11786 70538 12594 -2.809 0.009 -0.51 Blood pressure and heart rate Systolic pressure (mmHg) 109.13 12.179 111.39 11.135 1.833 0.077 0.33 Diastolic pressure (mmHg) 70.61 7.383 74.35 7.419 3.141 0.004 0.57 Heart rate (beats per min) 77 9.91 75.84 8.695 -1.228 0.229 -0.22 Mood and physical sensations Energetic mood 55.533 21.518 59.2 19.926 0.954 0.348 0.17 Tense mood 72.3 14.281 70.967 16.874 -0.384 0.704 -0.07 Hedonic tone 66.533 14.867 66.967 18.678 0.134 0.894 0.02 Overall mood 70.467 15.693 71.667 19.2 0.329 0.745 0.06 Clearheaded-muzzy/dazed 33.9 19.923 28.333 19.86 -1.37 0.181 -0.25 Light-headed/feeling faint 22.133 18.627 25.1 20.292 0.731 0.47 0.13 Jittery/shaky 13.767 13.359 16.633 14.747 1.206 0.238 0.22 Heart pounding 27.3 41.875 22.833 17.481 -0.622 0.539 -0.11 Headache 14.633 14.566 17.167 16.895 2.235 0.033 0.41 Abbreviations: ACC, accuracy; RT, reaction time; CPT, Continuous Attention Test; FTT, finger tapping task. Table 2 Brain regions with significant differences in sfALFF, sReHo, dALFF, and dReHo between pre- and post-decaffeinated coffee. Index Brain Region (AAL) Cluster size (mm 3 ) Peak MNI coordinate Peak t-value X Y Z sfALFF Left fusiform gyrus 6804 -36 -24 -27 6.164 Left inferior temporal gyrus -45 -39 -21 5.167 sReHo Bilateral superior frontal gyrus, medial orbital 4806 9 36 -15 -4.650 dALFF Left fusiform gyrus 2808 -27 -81 -18 -5.593 Bilateral supplementary motor area 1485 0 -12 57 4.205 dReHo Bilateral supplementary motor area 2106 3 -15 51 4.271 Abbreviations: AAL, automated anatomical labelling atlas; MNI: Montreal Neurological Institute; sfALFF, static fractional amplitude of low-frequency fluctuations; sReHo, static regional homogeneity; dALFF, dynamic amplitude of low-frequency fluctuations; dReHo, dynamic regional homogeneity. 3.2 Physiological Measures and State Self-Report Scales As shown in Table 1 , diastolic blood pressure was significantly higher (t = 3.141, P = 0.004), whereas changes in systolic blood pressure and heart rate were not statistically significant after consuming decaffeinated coffee compared with before. In the State Self-Report Scales, the participants reported a significant difference in headache scores (t = 2.235, P = 0.033) but no significant differences in other measures. 3.3 Results of RS-fMRI As shown in Fig. 1 , the participants showed significantly higher sfALFF in the fusiform gyrus (FFG) and inferior temporal gyrus (ITG) and significantly lower sReHo in the medial orbital of the superior frontal gyrus (PFCventmed) after consuming decaffeinated coffee compared with before, with no significant differences in sALFF. Dynamic analysis revealed significantly lower dALFF values in the FFG and cerebellum, and significantly higher dALFF and dReHo values in the supplementary motor area (SMA). No brain regions showed significant differences in dfALFF after consuming decaffeinated coffee compared with before. As shown in the supplementary material, the results of the 2 additional window sizes of 60 TRs and 80 TRs were similar to the main results of this study(Fig. 3 amd Fig. 4 ). No brain regions showed significant differences in dfALFF values after consuming decaff coffee. For more details, please refer to Tables S1 and Tables S2 and Figure S1 and Figure S2. 3.4. Correlation Between Brain Activity and Other Measures To explore the functional relevance of the altered brain activity, we conducted Pearson correlation analyses between the significantly changed fMRI metrics and the primary outcome measures (behavioral, physiological, and subjective) from the post-consumption session. As shown in Table 3 , a significant negative correlation was observed between post-consumption sfALFF in FFG and RTs on CPT (r = -0.250, P = 0.049). This result indicates that higher brain activity in the identified regions after consumption was associated with faster performance on the sustained attention task. No other correlations between the post-consumption fMRI metrics and other behavioral or subjective measures reached statistical significance. Table 3 Correlations between post-consumption fMRI metrics and outcome measures fMRI Metric 1-Back RT 2-Back RT CPT RT FTT RT Diastolic Pressure Headache sfALFF value of FFG/ITG -0.209 -0.093 -0.250* 0.101 0.242 0.07 sReHo value of PFCventmed -0.024 0.071 0.099 -0.075 0.116 -0.023 dALFF value of FFG -0.183 -0.183 -0.228 0.178 -0.076 -0.023 dALFF value of SMA 0.006 0.011 0.013 0.127 -0.072 -0.088 dReHo value of SMA -0.14 -0.084 -0.198 0.135 0.107 0.005 Note: Values represent Pearson correlation coefficients (r). * P < 0.05. Abbreviations: RT, reaction time; CPT, Continuous Attention Test; FTT, finger tapping task; sfALFF, static fractional amplitude of low-frequency fluctuations; sReHo, static regional homogeneity; dALFF, dynamic amplitude of low-frequency fluctuations; dReHo, dynamic regional homogeneity; FFG ,fusiform gyrus; ITG, inferior temporal gyrus; PFCventmed, the medial orbital of the superior frontal gyrus; SMA, the supplementary motor area. Table 4 Brain regions with significant differences in dALFF, dfALFF, and dReHo between pre- and post-decaffeinated coffee (window size = 80 TRs). Index Brain Region(AAL) Cluster size (mm 3 ) Peak MNI coordinate Peak t-value X Y Z dALFF Left fusiform gyrus 2673 -27 -81 -18 -4.821 Bilateral supplementary motor area 1998 -3 0 51 4.390 dReHo Bilateral supplementary motor area 2160 3 -12 51 4.425 Abbreviations: AAL, automated anatomical labelling atlas; MNI: Montreal Neurological Institute. dALFF, dynamic amplitude of low-frequency fluctuations; dReHo, dynamic regional homogeneity. 4 Discussion This study employed a multi-method approach, integrating RS-fMRI with behavioral tests, State Self-Report Scales, and physiological measurements, to investigate the acute effects of decaffeinated coffee. The principal findings were as follows: (1) Following the consumption of decaffeinated coffee, the participants exhibited enhanced performance in the N-back, CPT, and FTT tasks, concomitant with elevated headache scores. (2) Systolic and diastolic blood pressure increased, with no significant alteration in heart rate. (3) Spontaneous brain activity demonstrated significant alterations in multiple regions, including the FFG, ITG, PFCventmed, and SMA. (4) the sfALFF values in the FFG were significantly negatively correlated with RTs on CPT. Through a series of behavioral tests, we found that decaffeinated coffee was associated with faster reaction times in tasks assessing working memory and sustained attention, a result consistent with the improved N-Back performance reported in a previous human trial [ 17 ] . However, accuracy remained unchanged, suggesting increased alertness or motor speed rather than true cognitive enhancement. This profile of improved processing efficiency aligns with the conceptual distinction raised by both animal and human studies: decaffeinated coffee may act more as an optimizer of neural function than as a pure cognitive enhancer. Specifically, our findings resonate with the characterization of decaffeinated coffee as a "cognitive stabilizer" that protects against deficits in animal models [ 12 ] , while in healthy humans it appears to facilitate baseline processing speed [ 17 ] . The observed improvements in RTs might be partly explained by the presence of CGA and other polyphenols in decaffeinated coffee. It is plausible that CGA contributes to these effects, as previous studies have linked CGA intake to enhancements in cognitive domains including psychomotor speed, attention, and executive function [ 33 , 34 ] . Cognitive function is particularly associated with working memory, attention, and executive function in the prefrontal regions [ 33 , 35 , 36 ] . Furthermore, CGA has been demonstrated to markedly improve performance in the FTT, which is used to assess the velocity of voluntary manual movement. Due to its simplicity and reliability, the FTT is one of the most widely used tests in neuropsychology [ 37 – 39 ] . One study found that the FTT activates the frontal lobe [ 39 , 40 ] , and a meta-analysis showed significant activation in the SMA, which is a key region involved in FTT performance [ 41 , 42 ] . A notable finding was the significant increase in self-reported headache scores following the consumption of decaffeinated coffee. As caffeine withdrawal appears an unlikely cause, we speculate that this effect may be related to the vascular influence of other bioactive components, such as chlorogenic acid (CGA). One integrative hypothesis is that CGA may affect cerebral vasculature—for instance, by modulating nitric oxide bioavailability [ 43 – 45 ] —which could in turn alter cerebral blood flow and spontaneous neural activity in regions such as the frontal cortex. Such changes might not only provide a potential neural correlate for the observed improvements in behavioral performance [ 46 ] , but also, in some individuals, contribute to transient headache perception. This proposed pathway remains speculative and was not directly tested here, yet it offers a coherent perspective linking both the potential benefits and adverse effects of decaffeinated coffee. The present study found that decaffeinated coffee reduced sReHo in PFCventmed while increasing dALFF and dReHo in the SMA. The SMA, located in the upper medial frontal gyrus, is associated with simple autonomous movements [ 47 ] . Insufficient activation or reduced dALFF in the SMA may result in motor deficits [ 48 , 49 ] . The SMA is also part of the fronto-parietal network involved in working memory and shows consistent activation regardless of task specificity [ 50 , 51 ] . Cañas et al. found that damage to the SMA impairs the executive function of working memory, which allows people to temporarily store information without mentally manipulating it [ 52 ] . We also found that decaffeinated coffee caused significant changes in sfALFF and dALFF values in the fusiform gyrus. The fusiform gyrus, located on the basal surface of the temporal and occipital lobes, is a key structure in the ventral temporal cortex. It is involved in processing higher-order visual information, particularly faces, bodies, and high spatial frequency stimuli [ 53 ] . Research has shown that the fusiform gyrus plays an important role in the performance of the N-back task [ 54 ] , with its resting-state activity correlating with task-related deactivation during the N-back task [ 46 ] . Moreover, the negative correlation between post-consumption sfALFF in visual regions (FFG/ITG) and CPT reaction time, aligns with the behavioral improvements. It is plausible that the modulation of brain activity is one mechanism through which processing speed was enhanced. The present study found that decaffeinated coffee reduced sReHo in PFCventmed while increasing dALFF and dReHo in the SMA. The SMA, located in the upper medial frontal gyrus, is associated with simple autonomous movements [ 47 ] and forms a core node of the fronto-parietal network (FPN) [ 50 , 51 ] . In this context, the observed increase in its dynamic activity could provide a plausible neural correlate for the improvement in motor speed, as measured by the FTT. Furthermore, evidence suggests that the SMA contributes to working memory processes, as damage to this region has been shown to impair specific executive aspects of working memory [ 52 ] . The enhanced neural dynamics in the SMA may therefore reflect a state of heightened functional flexibility within the FPN, potentially supporting both the faster psychomotor speed and the more efficient cognitive processing reflected in reduced N-back RTs.This interpretation finds some support in research suggesting that increased temporal variability of neural signals in healthy individuals may be associated with more adaptive brain function [ 55 ] . The concomitant improvement in behavioral performance observed in our study appears consistent with this perspective. Additionally, the stability of dfALFF values might indicate that the neuromodulatory effects were relatively specific, primarily influencing the amplitude and local synchronization of spontaneous activity without substantially altering the global spectral profile of brain activity. We also observed that decaffeinated coffee induced significant changes in sfALFF and dALFF values in the FFG. The FFG, a key structure within the ventral visual stream, is involved in processing higher-order visual information [ 53 ] and has been shown to contribute to N-back task performance [ 54 ] . The modulation of FFG activity suggests that, in parallel to its effects on the FPN, decaffeinated coffee may also influence visual processing networks, potentially enhancing the perceptual encoding of task-relevant stimuli. Considered together, the concurrent modulation of the SMA and the FFG implies that bioactive compounds such as CGA may exert coordinated effects on multiple large-scale brain networks. This multi-network perspective is consistent with previous studies reporting that polyphenols can modulate brain activation in frontal, parietal, and visual regions during cognitive tasks [ 56 , 57 ] , possibly through neurovascular mechanisms such as increased regional cerebral blood flow [ 58 ] . Thus, the behavioral improvements observed in this study may not stem from a singular mechanism, but from the co-optimization of at least two systems: the fronto-parietal network, supporting motor planning and cognitive control, and the visual processing network, supporting perceptual efficiency. This study had several limitations: (1) The study included no control group or coffee-flavored hot water group to rule out a placebo effect. (2) The study focused only on non-habitual coffee drinkers without examining the effects on habitual drinkers, who may respond differently to decaffeinated coffee. (3) Only the acute effects of a single cup of decaffeinated coffee were observed. Future studies should investigate the chronic effects of long-term decaffeinated coffee consumption, such as for a week or more, on cognitive function and brain activity. 5 Conclusions This study investigated the acute effects of decaffeinated coffee on behavior, spontaneous brain activity, and emotional and physiological states. The results suggest that decaffeinated coffee may affects cognitive performance and brain activity, shedding light on the effects of the non-caffeine components of coffee on individuals and their underlying mechanisms. As an exploratory pilot study with a limited sample size and no placebo control, these findings should be interpreted as preliminary and hypothesis-generating. Future research employing randomized, placebo-controlled designs and longitudinal assessments is essential to validate these observations and investigate their long-term significance. Declarations Acknowledgments We thank EditorBar (https://www.editorbar.com/) for editing this manuscript. Consent for publication All authors agree to publication Availability of data and materials All study data can be requested from the corresponding author Competing interests The authors declare that they have no competing interests Funding This study was supported by Hangzhou biomedicine and health industry development support science and technology project (No.2022WJCY024); Hangzhou Normal University Dengfeng Project“Clinical Medicine Revitalization Plan”Jiande Hospital Special Project (No. LCYXZXJH001);Hangzhou Natural Science Foundation of China under Grant (No.2024SZRZDH250001). References GROSSO G, GODOS J, GALVANO F, et al. Coffee, Caffeine, and Health Outcomes: An Umbrella Review [J]. Annual Review of Nutrition, 2017, 37(Volume 37, 2017): 131-56. NEHLIG A. Effects of coffee/caffeine on brain health and disease: What should I tell my patients? [J]. 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OSAKA M, OSAKA N, KONDO H, et al. The neural basis of individual differences in working memory capacity: an fMRI study [J]. NeuroImage, 2003, 18(3): 789-97. GUALTIERI C T, JOHNSON L G. Reliability and validity of a computerized neurocognitive test battery, CNS Vital Signs [J]. Arch Clin Neuropsychol, 2006, 21(7): 623-43. LEVIN H S. A Compendium of Neuropsychological Tests: Administration, Norms, and Commentary [J]. Archives of Neurology, 1993, 50(5): 451-. SUZUKAMO C, OCHIAI R, MITSUI Y, et al. Short-Term Intake of Chlorogenic Acids Improves Psychomotor Speed and Motor Speed in Adults: A Randomized Crossover Trial [J]. Brain Sci, 2022, 12(3): 370. PRIGATANO G P, JOHNSON S C, GALE S D. Neuroimaging correlates of the Halstead Finger Tapping Test several years post-traumatic brain injury [J]. Brain Inj, 2004, 18(7): 661-9. HIROSHIMA S, ANEI R, MURAKAMI N, et al. Functional Localization of the Supplementary Motor Area [J]. Neurol Med Chir (Tokyo), 2014, 54(7): 511-20. WITT S T, MEYERAND M E, LAIRD A R. Functional neuroimaging correlates of finger tapping task variations: An ALE meta-analysis [J]. NeuroImage, 2008, 42(1): 343-56. DINGES D F. Cocoa Flavanols, Cerebral Blood Flow, Cognition, and Health: Going Forward [J]. Journal of Cardiovascular Pharmacology, 2006, 47: S223. FISHER N D L, SOROND F A, HOLLENBERG N K. Cocoa Flavanols and Brain Perfusion [J]. Journal of Cardiovascular Pharmacology, 2006, 47: S210. HEISS C, DEJAM A, KLEINBONGARD P, et al. Vascular Effects of Cocoa Rich in Flavan-3-ols [J]. JAMA, 2003, 290(8): 1030-1. ZOU Q, ROSS T J, GU H, et al. Intrinsic resting-state activity predicts working memory brain activation and behavioral performance [J]. Human Brain Mapping, 2013, 34(12): 3204-15. COLEBATCH J G, DEIBER M P, PASSINGHAM R E, et al. Regional cerebral blood flow during voluntary arm and hand movements in human subjects [J]. Journal of Neurophysiology, 1991, 65(6): 1392-401. T W, L W, M H, et al. Neural correlates of bimanual anti-phase and in-phase movements in Parkinson's disease [J]. Brain : a journal of neurology, 2010, 133(Pt 8). ZHANG Y, WANG X, LI Y. Disrupted dynamic pattern of regional neural activity in early-stage cognitively normal Parkinson's disease [J]. Acta Radiol, 2022, 63(12): 1669-77. OWEN A M, MCMILLAN K M, LAIRD A R, et al. N‐back working memory paradigm: A meta‐analysis of normative functional neuroimaging studies [J]. Human Brain Mapping, 2005, 25(1): 46-59. ROTTSCHY C, LANGNER R, DOGAN I, et al. Modelling neural correlates of working memory: A coordinate-based meta-analysis [J]. NeuroImage, 2012, 60(1): 830-46. CAñAS A, JUNCADELLA M, LAU R, et al. Working Memory Deficits After Lesions Involving the Supplementary Motor Area [J]. Front Psychol, 2018, 9: 765. PALEJWALA A H, O’CONNOR K P, MILTON C K, et al. Anatomy and white matter connections of the fusiform gyrus [J]. Sci Rep, 2020, 10: 13489. MENCARELLI L, NERI F, MOMI D, et al. Stimuli, presentation modality, and load‐specific brain activity patterns during n‐back task [J]. Human Brain Mapping, 2019, 40(13): 3810-31. LIAO W, CHEN H, LI J, et al. Endless Fluctuations: Temporal Dynamics of the Amplitude of Low Frequency Fluctuations [J]. IEEE Trans Med Imaging, 2019, 38(11): 2523-32. CAMFIELD D A, SCHOLEY A, PIPINGAS A, et al. Steady state visually evoked potential (SSVEP) topography changes associated with cocoa flavanol consumption [J]. Physiology & Behavior, 2012, 105(4): 948-57. FRANCIS S T, HEAD K, MORRIS P G, et al. The Effect of Flavanol-rich Cocoa on the fMRI Response to a Cognitive Task in Healthy Young People [J]. Journal of Cardiovascular Pharmacology, 2006, 47: S215. LAMPORT D J, PAL D, MOUTSIANA C, et al. The effect of flavanol-rich cocoa on cerebral perfusion in healthy older adults during conscious resting state: a placebo controlled, crossover, acute trial [J]. Psychopharmacology, 2015, 232(17): 3227-34. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 10 May, 2026 Reviews received at journal 28 Apr, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Editor invited by journal 09 Jan, 2026 Submission checks completed at journal 05 Jan, 2026 First submitted to journal 05 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Fu","email":"","orcid":"","institution":"Affiliated Hospital of Hangzhou Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xinyan","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2025-12-24 15:08:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8443818/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8443818/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107255948,"identity":"550afc2c-c53c-4042-bdec-68971f970751","added_by":"auto","created_at":"2026-04-19 12:13:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3922228,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8443818/v1/856a2ca5f6a752e4f4af59af.png"},{"id":107482647,"identity":"b5c0d809-0871-4a09-a40e-2d13703e7174","added_by":"auto","created_at":"2026-04-22 02:24:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57247285,"visible":true,"origin":"","legend":"\u003cp\u003eBrain regions with significant differences in sfALFF, sReHo, dALFF, and dReHo between pre- and post-decaffeinated coffee. Abbreviations: sfALFF, static fractional amplitude of low-frequency fluctuations; sReHo, static regional homogeneity; dALFF, dynamic amplitude of low-frequency fluctuations; dReHo, dynamic regional homogeneity.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8443818/v1/15a984de95928acbfef1cf62.png"},{"id":107482487,"identity":"903432e8-67be-4c9e-b21d-266ffb8c8661","added_by":"auto","created_at":"2026-04-22 02:23:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1733225,"visible":true,"origin":"","legend":"\u003cp\u003eBrain regions with significant differences in dALFF and dReHo between pre- and post-decaffeinated coffee (window size = 60 TRs). Abbreviations: dALFF, dynamic amplitude of low-frequency fluctuations; dReHo, dynamic regional homogeneity.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8443818/v1/52aeb6217b7d947d7cfd78ee.png"},{"id":107255952,"identity":"52aafb09-5198-4ad5-b691-83247c944091","added_by":"auto","created_at":"2026-04-19 12:13:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1758331,"visible":true,"origin":"","legend":"\u003cp\u003eBrain regions with significant differences in dALFF and dReHo between pre- and post-decaffeinated coffee (window size = 80 TRs). Abbreviations: dALFF, dynamic amplitude of low-frequency fluctuations; dReHo, dynamic regional homogeneity.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8443818/v1/b9cea876bbfe1ee249f425c7.png"},{"id":107705075,"identity":"9bcce9de-27a8-42a5-9e19-3a92b6a7aa25","added_by":"auto","created_at":"2026-04-24 09:07:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":61682123,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8443818/v1/30fcb07a-4835-4a21-9afe-24b51da71952.pdf"},{"id":107484684,"identity":"d309dcc3-b808-4b83-9185-768569b5eb10","added_by":"auto","created_at":"2026-04-22 02:32:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":626803,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8443818/v1/f17f8ab253791ceabf61a614.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The acute effects of decaffeinated coffee on cognitive function and brain activity:a single-center experience","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCoffee is one of the most commonly consumed beverages globally and has beneficial effects on several diseases. It has been shown to reduce the risk of some cancers, decrease cardiovascular disease mortality, and improve outcomes in some metabolism-related diseases such as type 2 diabetes and metabolic syndrome\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Additionally, long-term coffee consumption has been associated with the prevention of cognitive decline and a lower risk of neurological disorders, including Parkinson's disease, Alzheimer's disease, and depression\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Previous studies have focused on the effects of caffeine, which is believed to act as a non-specific, potent inhibitor of A1 and A2 adenosine receptors, leading to central nervous system hyperarousal\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, increased alertness and improved attention\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, and reduced burden of neurological disorders\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, recent studies have found that coffee's effects outweigh caffeine's effects\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. A functional magnetic resonance imaging (fMRI) study revealed that caffeine intake duplicated the effects of coffee on only some brain networks \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.. On the one hand, this may be related to the expectancy effect. Studies have shown that expectations about caffeine intake affect mood and performance. Moreover, the impact of expectations is compatible with, and may even be preferable to, the impact of caffeine\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOn the other hand, evidence suggests that the health and neuroprotective effects of coffee are not solely attributable to caffeine. Instead, other bioactive compounds in coffee play a significant role in enhancing motor and cognitive performance, particularly in the context of aging and depression\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Coffee is a complex chemical mixture containing over 1000 different compounds\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, many of which can individually or synergistically influence cognition. In particular, phenolic acids such as chlorogenic acid (CGA) and caffeic acid have been shown to exert neurological benefits similar to caffeine\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. These compounds have been found to significantly improve short-term or working memory impairments induced by scopolamine\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis prompted us to explore decaffeinated coffee as a potential neuroprotective beverage. Some studies have reported that decaffeinated coffee exerts protective effects against liver damage and type 2 diabetes\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, prevents age-related and systemic diseases, and reduces cancer risk \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. It has also shown efficacy in protecting against neurodegeneration across various neural pathways and conditions\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the relationship between decaffeinated coffee and cognitive function remains underexplored, with inconsistent findings. Although some studies have reported improvements in working memory, sustained attention, alertness, and mood following decaffeinated coffee consumption\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, others have found no significant association with cognitive performance\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003efMRI enables the investigation of human brain function during various tasks or at rest through blood oxygen level\u0026ndash;dependent (BOLD) signaling\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. In a task-based fMRI study, no significant differences were observed between the decaffeinated coffee and water groups in terms of response time, mean perfusion, BOLD activation, BOLD mean signal intensity, or BOLD mean baseline during a word stem completion task\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. However, research on the effects of decaffeinated coffee on spontaneous brain activity remains limited. Resting-state functional magnetic resonance imaging (RS-fMRI) is a technique that allows the study of spontaneous brain activity when individuals are quiet, relaxed, and awake\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Static metrics, including the amplitude of low-frequency fluctuation (sALFF) and regional homogeneity (sReHo), are frequently employed to characterize the local features of RS-fMRI signals\u003csup\u003e[\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. These metrics, however, fail to account for the dynamic changes in brain activity over time. Capturing these temporal fluctuations in brain activity requires the use of dynamic metrics, such as the dynamic amplitude of low-frequency fluctuation (dALFF), the dynamic fractional amplitude of low-frequency fluctuation (dfALFF), and dynamic regional homogeneity (dReHo)\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Combining both static and dynamic metrics can provide a more comprehensive understanding of the acute effects of decaffeinated coffee on brain activity.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to investigate the acute effects of decaffeinated coffee consumption by combining RS-fMRI with assessments of behavioral performance, emotional state, and physiological indicators.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003eThirty-one healthy college students [16 male (mean age 23.44\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34 years) and 15 female (mean age 24.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67 years)] were recruited through advertisements to participate in this study. Participants were recruited from the university campus through online advertisements. Interested individuals completed an initial online screening questionnaire to assess their eligibility based on the following criteria: All participants were right-handed, consumed fewer than four cups of coffee per week, had no history of psychiatric disorders or surgical trauma, and were nonsmokers and nondrinkers. The sample size was determined by adhering to the methodological precedent established in prior rs-fMRI research on acute coffee consumption, which utilized 24 and 32 participants\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. All procedures were conducted in accordance with the ethical standards set by the Academic Ethics Committee of the Affiliated Hospital of Hangzhou Normal University (Approval No.[2023 (E2) - KS \u0026minus;\u0026thinsp;163]). Written informed consent was obtained from all participants before the study commenced. Graphical abstract can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eAfter a 24-hour caffeine abstinence period, the participants arrived at the laboratory for baseline data collection, which included behavioral tests, physiological measures, State Self-Report Scales, and RS-fMRI scans. Participants then consumed a standard dose of decaffeinated coffee (Starbucks Decaffeinated Espresso with \u0026lt;\u0026thinsp;5 mg caffeine\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e), which was presented to them simply as \"coffee\" without specifying its decaffeinated nature. This was followed by a second round of data collection 30 min after consumption. The 30-minute post-consumption time point was chosen in accordance with previous neuroimaging research on the acute effects of coffee, where it has been successfully used to detect changes in spontaneous brain activity\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. This window is also well within the effective period for observing behavioral and subjective effects in the literature\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Participants were appropriately compensated at the end of the experiment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Behavioral Test\u003c/h2\u003e \u003cp\u003eAll procedures were conducted using the E-Prime 2.0 program. Practice tasks were assigned before the first behavioral test. An experimenter provided standardized instructions. The formal test was started after ensuring that the participants understood the task requirements. Full procedural details for all behavioral tasks are provided in the Supplementary Methods.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Working Memory Test\u003c/h2\u003e \u003cp\u003eWorking memory was assessed using a computerized digital N-back task (E-Prime 2.0) with two load conditions: 1-back and 2-back. Participants were presented with a sequence of digits (0\u0026ndash;9) at the center of the screen. In the 1-back condition, they pressed the spacebar when the current digit matched the one immediately preceding it. In the 2-back condition, the response was required when the current digit matched the one presented two trials back. Participants were instructed to respond as quickly and accurately as possible. The primary dependent variables were the accuracy (percentage of correct responses) and the mean reaction time (RT) for correct responses, calculated separately for each condition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Sustained Attention Task\u003c/h2\u003e \u003cp\u003eThe Visual Continuous Performance Test (VCPT) developed by Luo Xuerong et al. was used with appropriate modifications to assess sustained attention\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Participants were required to press the spacebar upon seeing the letter \"X\" followed by the letter \"O\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Psychomotor Task\u003c/h2\u003e \u003cp\u003eWe used the finger tapping task (FTT) to measure psychomotor speed. The participants were instructed to use the index and middle fingers of their right hand to alternately tap the \"m\" and \"n\" keys on a computer keyboard as quickly as possible for 300 taps. The time taken to complete the task was recorded. A similar paradigm has been used in previous studies on caffeine\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 State Self-Report Scales\u003c/h2\u003e \u003cp\u003eWe used the State Self-Rating Scale proposed in the study on caffeine by Susan et al., with appropriate adaptations\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The participants were asked to mark their current feelings on nine 100-mm line segments. Five segments had endpoints labeled: drowsy/lazy \u0026ndash; energetic/sharp, tense \u0026ndash; relaxed, sad/frustrated \u0026ndash; happy/cheerful, bad mood \u0026ndash; good mood, and clear-headed \u0026ndash; confused. The other four segments rated dizziness, nervousness/trembling, rapid heartbeat, and headache, with one end labeled \"not at all\" and the other \"extremely.\u0026rdquo; The scores were based on the position of the markers along each segment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Physiological Measures\u003c/h2\u003e \u003cp\u003eWe measured systolic and diastolic blood pressure, as well as heart rate, using the YuWell YE660C electronic blood pressure monitor. During the measurement, the participants were instructed to sit at a table, remain relaxed, and not move or speak. The measurement personnel placed the cuff on the participant's right upper arm, with the lower edge positioned 2\u0026ndash;3 cm above the elbow joint. The cuff was loosely adjusted to allow room for one finger, and the measurement was initiated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5 RS-fMRI Data Acquisition and Preprocessing\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Data Acquisition\u003c/h2\u003e \u003cp\u003eMRI scans were conducted at the Affiliated Hospital of Hangzhou Normal University using a 3T GE scanner (MR-750; GE Medical Systems, WI, USA). Before scanning, the participants were instructed to lie on their backs in a resting state, with foam pads placed around their heads to minimize movement. During the scan, the participants were asked to close their eyes and clear their minds. A multi-band gradient-echo echo-planar imaging sequence was used to acquire RS-fMRI images with the following parameters: repetition time/echo time(TR/TE)\u0026thinsp;=\u0026thinsp;700/30 ms, field of view (FOV)\u0026thinsp;=\u0026thinsp;216 \u0026times; 216 mm\u003csup\u003e2\u003c/sup\u003e, matrix\u0026thinsp;=\u0026thinsp;72\u0026times;72, thickness/gap\u0026thinsp;=\u0026thinsp;3/0 mm, fractional isotropy (FA)\u0026thinsp;=\u0026thinsp;60\u0026deg;, 44 slices. High-resolution T1-weighted anatomical images were acquired using a 3D spoiled gradient echo sequence with the following parameters: 192 sagittal slices, TR/TE\u0026thinsp;=\u0026thinsp;2678/2.98ms, FOV\u0026thinsp;=\u0026thinsp;256\u0026times;256 mm\u003csup\u003e2\u003c/sup\u003e, matrix\u0026thinsp;=\u0026thinsp;256\u0026times;256, thickness/gap\u0026thinsp;=\u0026thinsp;1/0 mm, FA\u0026thinsp;=\u0026thinsp;7\u0026deg;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Data Preprocessing\u003c/h2\u003e \u003cp\u003eMRI image preprocessing was performed using the SPM12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fil.ion.ucl.ac.uk/spm/software/spm12/\u003c/span\u003e\u003cspan address=\"http://www.fil.ion.ucl.ac.uk/spm/software/spm12/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and DPABI V8.1 toolbox in the MATLAB environment\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The first 10 volumes were removed, followed by slice-timing correction and image realignment to correct head motion. No data were excluded due to head motion artifacts (\u0026gt;\u0026thinsp;2.5 mm translation or \u0026gt;\u0026thinsp;2.5\u0026deg; rotation in any direction)\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Functional images were spatially co-registered with the structural images. The individual structural images were co-registered to the mean functional images and then segmented into gray matter, white matter (WM), and cerebrospinal fluid (CSF). The head motion parameters measured by the Friston-24 model, WM, CSF, and global signals were regressed out as nuisance covariates. Then, functional images were spatially normalized to Montreal Neurological Institute space at a 3-mm isotropic voxel resolution using the unified segmentation parameters for calculating sALFF, sfALFF, dALFF, and dfALFF. The sReHo and dReHo values were calculated after applying band-pass filtering (0.01\u0026ndash;0.10 Hz) to reduce the effects of very low-frequency drift and high-frequency noise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Calculation of Image Data Indicators\u003c/h2\u003e \u003cp\u003eThe sALFF, sfALFF, and sReHo values were calculated using the DPABI software as follows: (1) sALFF and sfALFF: After data preprocessing, the time series for each voxel was transformed into the frequency domain using a fast Fourier transform to obtain the power spectrum. The square root of the power spectrum was then calculated at each frequency. The sALFF was defined as the averaged square root within the 0.01\u0026ndash;0.10 Hz frequency range, whereas the sfALFF was defined as the sum of amplitude across 0.01\u0026ndash;0.10 Hz divided by that across the entire frequency range\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. (2) sReHo: The sReHo value of each voxel was calculated by computing Kendall's coefficient of concordance for the time series of that voxel and its 26 neighboring voxels\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe dALFF, dfALFF, and dReHo were calculated using temporal dynamic analysis toolkits based on DPABI\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. A sliding-time window analysis was employed to generate dynamic indices, with a window size of 100 TRs (70 s) and a step size of 1 TR (0.7 s). The sALFF, sfALFF, and sReHo values were calculated for each time window. We computed each index's mean and standard deviation (SD) map across time windows. The dALFF, dfALFF, and dReHo were defined as each index's coefficient of variation (CV: SD/mean). In addition, to exclude the effect of window width on the results, the window width was set to 60/80 TR to repeat all the calculations in this study.\u003c/p\u003e \u003cp\u003eAll image indicators were smoothed with full width at half-maximum\u0026thinsp;=\u0026thinsp;6 mm, and \u003cem\u003ez\u003c/em\u003e standardization was applied.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003ePaired-samples \u003cem\u003et\u003c/em\u003e tests were used to examine the differences in each metric before and after decaffeination. The behavioral data, scale score, and physiological data were analyzed using SPSS (version 27.0; IBM Corp., NY, USA). DPABI was used to analyze the differences in static metrics (sALFF, sfALFF, and sReHo) and dynamic metrics (dALFF, dfALFF, and dReHo). A \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Multiple comparison correction was performed based on Gaussian random-field theory (voxel-level \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, cluster-level \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To investigate the functional relevance of the significant neural changes, Pearson correlation analyses were conducted between the extracted values from the significant brain regions and the other primary outcome measures (behavioral, physiological, and subjective) from the post-consumption session. The significance threshold was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Cognitive Tests\u003c/h2\u003e\n \u003cp\u003eAs shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, participants showed significantly faster RTs after consuming decaffeinated coffee across all tasks. This included the 1-back (t = -3.309, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003ed\u003c/em\u003e = -0.60), 2-back (t = -2.394, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, \u003cem\u003ed\u003c/em\u003e = -0.44), and CPT (t = -3.258, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003ed\u003c/em\u003e = -0.59). Similarly, the time to complete the FTT was significantly reduced (t = -2.809, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, \u003cem\u003ed\u003c/em\u003e = -0.51). Effect sizes for these reaction-time reductions ranged from\u0026nbsp;\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.44 to 0.60 (medium effects), indicating practical as well as statistical significance. However, no significant difference was observed in task accuracy.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffects of decaf coffee on behavioral performance, blood pressure, heart rate and mood and physical sensations.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003ePre\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\n \u003cp\u003ePost\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003et value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eCohen\u0026apos;s \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBehavioral performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1-Back ACC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e1-Back RT (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-3.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e-0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2-Back ACC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-1.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e2-Back RT (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-2.394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCPT ACC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCPT RT (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-3.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFTT RT (ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e73616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e11786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e70538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e12594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-2.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBlood pressure and heart rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSystolic pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e109.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e111.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e11.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiastolic pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e70.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e7.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e74.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e7.419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e3.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHeart rate (beats per min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e9.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e75.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-1.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMood and physical sensations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEnergetic mood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e55.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e21.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e59.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e19.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTense mood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e72.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e70.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHedonic tone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e66.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e66.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e18.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOverall mood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e70.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e15.693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e71.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eClearheaded-muzzy/dazed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e19.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e28.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e19.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLight-headed/feeling faint\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e22.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e18.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e20.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eJittery/shaky\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e13.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e13.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e16.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e14.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e1.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHeart pounding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e41.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e22.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e17.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHeadache\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e14.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e14.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e17.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e2.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eAbbreviations: ACC, accuracy; RT, reaction time; CPT, Continuous Attention Test; FTT, finger tapping task.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBrain regions with significant differences in sfALFF, sReHo, dALFF, and dReHo between pre- and post-decaffeinated coffee.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eBrain Region (AAL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eCluster size (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\n \u003cp\u003ePeak MNI\u0026nbsp;coordinate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003ePeak t-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003esfALFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLeft fusiform gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e6804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e6.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLeft inferior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e5.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003esReHo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eBilateral superior frontal gyrus, medial orbital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e4806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e-4.650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003edALFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLeft fusiform gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e-5.593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eBilateral supplementary motor area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e4.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003edReHo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eBilateral supplementary motor area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e4.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eAbbreviations: AAL, automated anatomical labelling atlas; MNI: Montreal Neurological Institute; sfALFF, static fractional amplitude of low-frequency fluctuations; sReHo, static regional homogeneity; dALFF, dynamic amplitude of low-frequency fluctuations; dReHo, dynamic regional homogeneity.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Physiological Measures and State Self-Report Scales\u003c/h2\u003e\n \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, diastolic blood pressure was significantly higher (t\u0026thinsp;=\u0026thinsp;3.141, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), whereas changes in systolic blood pressure and heart rate were not statistically significant after consuming decaffeinated coffee compared with before. In the State Self-Report Scales, the participants reported a significant difference in headache scores (t\u0026thinsp;=\u0026thinsp;2.235, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033) but no significant differences in other measures.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Results of RS-fMRI\u003c/h2\u003e\n \u003cp\u003eAs shown in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the participants showed significantly higher sfALFF in the fusiform gyrus (FFG) and inferior temporal gyrus (ITG) and significantly lower sReHo in the medial orbital of the superior frontal gyrus (PFCventmed) after consuming decaffeinated coffee compared with before, with no significant differences in sALFF. Dynamic analysis revealed significantly lower dALFF values in the FFG and cerebellum, and significantly higher dALFF and dReHo values in the supplementary motor area (SMA). No brain regions showed significant differences in dfALFF after consuming decaffeinated coffee compared with before.\u003c/p\u003e\n \u003cp\u003eAs shown in the supplementary material, the results of the 2 additional window sizes of 60 TRs and 80 TRs were similar to the main results of this study(Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e amd Fig. \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). No brain regions showed significant differences in dfALFF values after consuming decaff coffee. For more details, please refer to Tables S1 and Tables S2 and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Figure S2.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Correlation Between Brain Activity and Other Measures\u003c/h2\u003e\n \u003cp\u003eTo explore the functional relevance of the altered brain activity, we conducted Pearson correlation analyses between the significantly changed fMRI metrics and the primary outcome measures (behavioral, physiological, and subjective) from the post-consumption session. As shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, a significant negative correlation was observed between post-consumption sfALFF in FFG and RTs on CPT (r = -0.250,\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). This result indicates that higher brain activity in the identified regions after consumption was associated with faster performance on the sustained attention task. No other correlations between the post-consumption fMRI metrics and other behavioral or subjective measures reached statistical significance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelations between post-consumption fMRI metrics and outcome measures\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003efMRI Metric\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1-Back RT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2-Back RT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eCPT RT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eFTT RT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eDiastolic Pressure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eHeadache\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003esfALFF value of FFG/ITG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e-0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.250*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003esReHo value of PFCventmed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003edALFF value of FFG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e-0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003edALFF value of SMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003edReHo value of SMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e-0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: Values represent Pearson correlation coefficients (r).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003eAbbreviations: RT, reaction time; CPT, Continuous Attention Test; FTT, finger tapping task; sfALFF, static fractional amplitude of low-frequency fluctuations; sReHo, static regional homogeneity; dALFF, dynamic amplitude of low-frequency fluctuations; dReHo, dynamic regional homogeneity; FFG ,fusiform gyrus; ITG, \u0026nbsp; inferior temporal gyrus; PFCventmed, the medial orbital of the superior frontal gyrus; SMA, the supplementary motor area.\u003c/div\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBrain regions with significant differences in dALFF, dfALFF, and dReHo between pre- and post-decaffeinated coffee (window size\u0026thinsp;=\u0026thinsp;80 TRs).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eBrain Region(AAL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eCluster size (mm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\n \u003cp\u003ePeak MNI\u0026nbsp;coordinate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003ePeak t-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eX\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003edALFF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLeft fusiform gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e-4.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eBilateral supplementary motor area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e4.390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003edReHo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eBilateral supplementary motor area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e2160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e4.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eAbbreviations: AAL, automated anatomical labelling atlas; MNI: Montreal Neurological Institute. dALFF, dynamic amplitude of low-frequency fluctuations; dReHo, dynamic regional homogeneity.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study employed a multi-method approach, integrating RS-fMRI with behavioral tests, State Self-Report Scales, and physiological measurements, to investigate the acute effects of decaffeinated coffee. The principal findings were as follows: (1) Following the consumption of decaffeinated coffee, the participants exhibited enhanced performance in the N-back, CPT, and FTT tasks, concomitant with elevated headache scores. (2) Systolic and diastolic blood pressure increased, with no significant alteration in heart rate. (3) Spontaneous brain activity demonstrated significant alterations in multiple regions, including the FFG, ITG, PFCventmed, and SMA. (4) the sfALFF values in the FFG were significantly negatively correlated with RTs on CPT.\u003c/p\u003e \u003cp\u003eThrough a series of behavioral tests, we found that decaffeinated coffee was associated with faster reaction times in tasks assessing working memory and sustained attention, a result consistent with the improved N-Back performance reported in a previous human trial\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. However, accuracy remained unchanged, suggesting increased alertness or motor speed rather than true cognitive enhancement. This profile of improved processing efficiency aligns with the conceptual distinction raised by both animal and human studies: decaffeinated coffee may act more as an optimizer of neural function than as a pure cognitive enhancer. Specifically, our findings resonate with the characterization of decaffeinated coffee as a \"cognitive stabilizer\" that protects against deficits in animal models\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, while in healthy humans it appears to facilitate baseline processing speed\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe observed improvements in RTs might be partly explained by the presence of CGA and other polyphenols in decaffeinated coffee. It is plausible that CGA contributes to these effects, as previous studies have linked CGA intake to enhancements in cognitive domains including psychomotor speed, attention, and executive function\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Cognitive function is particularly associated with working memory, attention, and executive function in the prefrontal regions\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, CGA has been demonstrated to markedly improve performance in the FTT, which is used to assess the velocity of voluntary manual movement. Due to its simplicity and reliability, the FTT is one of the most widely used tests in neuropsychology\u003csup\u003e[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. One study found that the FTT activates the frontal lobe\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, and a meta-analysis showed significant activation in the SMA, which is a key region involved in FTT performance\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA notable finding was the significant increase in self-reported headache scores following the consumption of decaffeinated coffee. As caffeine withdrawal appears an unlikely cause, we speculate that this effect may be related to the vascular influence of other bioactive components, such as chlorogenic acid (CGA). One integrative hypothesis is that CGA may affect cerebral vasculature\u0026mdash;for instance, by modulating nitric oxide bioavailability\u003csup\u003e[\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e\u0026mdash;which could in turn alter cerebral blood flow and spontaneous neural activity in regions such as the frontal cortex. Such changes might not only provide a potential neural correlate for the observed improvements in behavioral performance\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, but also, in some individuals, contribute to transient headache perception. This proposed pathway remains speculative and was not directly tested here, yet it offers a coherent perspective linking both the potential benefits and adverse effects of decaffeinated coffee.\u003c/p\u003e \u003cp\u003eThe present study found that decaffeinated coffee reduced sReHo in PFCventmed while increasing dALFF and dReHo in the SMA. The SMA, located in the upper medial frontal gyrus, is associated with simple autonomous movements\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Insufficient activation or reduced dALFF in the SMA may result in motor deficits\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. The SMA is also part of the fronto-parietal network involved in working memory and shows consistent activation regardless of task specificity\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Ca\u0026ntilde;as et al. found that damage to the SMA impairs the executive function of working memory, which allows people to temporarily store information without mentally manipulating it\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe also found that decaffeinated coffee caused significant changes in sfALFF and dALFF values in the fusiform gyrus. The fusiform gyrus, located on the basal surface of the temporal and occipital lobes, is a key structure in the ventral temporal cortex. It is involved in processing higher-order visual information, particularly faces, bodies, and high spatial frequency stimuli\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. Research has shown that the fusiform gyrus plays an important role in the performance of the N-back task\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e, with its resting-state activity correlating with task-related deactivation during the N-back task\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Moreover, the negative correlation between post-consumption sfALFF in visual regions (FFG/ITG) and CPT reaction time, aligns with the behavioral improvements. It is plausible that the modulation of brain activity is one mechanism through which processing speed was enhanced.\u003c/p\u003e \u003cp\u003eThe present study found that decaffeinated coffee reduced sReHo in PFCventmed while increasing dALFF and dReHo in the SMA. The SMA, located in the upper medial frontal gyrus, is associated with simple autonomous movements\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e and forms a core node of the fronto-parietal network (FPN)\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. In this context, the observed increase in its dynamic activity could provide a plausible neural correlate for the improvement in motor speed, as measured by the FTT. Furthermore, evidence suggests that the SMA contributes to working memory processes, as damage to this region has been shown to impair specific executive aspects of working memory\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe enhanced neural dynamics in the SMA may therefore reflect a state of heightened functional flexibility within the FPN, potentially supporting both the faster psychomotor speed and the more efficient cognitive processing reflected in reduced N-back RTs.This interpretation finds some support in research suggesting that increased temporal variability of neural signals in healthy individuals may be associated with more adaptive brain function\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. The concomitant improvement in behavioral performance observed in our study appears consistent with this perspective. Additionally, the stability of dfALFF values might indicate that the neuromodulatory effects were relatively specific, primarily influencing the amplitude and local synchronization of spontaneous activity without substantially altering the global spectral profile of brain activity.\u003c/p\u003e \u003cp\u003eWe also observed that decaffeinated coffee induced significant changes in sfALFF and dALFF values in the FFG. The FFG, a key structure within the ventral visual stream, is involved in processing higher-order visual information\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e and has been shown to contribute to N-back task performance\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. The modulation of FFG activity suggests that, in parallel to its effects on the FPN, decaffeinated coffee may also influence visual processing networks, potentially enhancing the perceptual encoding of task-relevant stimuli.\u003c/p\u003e \u003cp\u003eConsidered together, the concurrent modulation of the SMA and the FFG implies that bioactive compounds such as CGA may exert coordinated effects on multiple large-scale brain networks. This multi-network perspective is consistent with previous studies reporting that polyphenols can modulate brain activation in frontal, parietal, and visual regions during cognitive tasks\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e, possibly through neurovascular mechanisms such as increased regional cerebral blood flow\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Thus, the behavioral improvements observed in this study may not stem from a singular mechanism, but from the co-optimization of at least two systems: the fronto-parietal network, supporting motor planning and cognitive control, and the visual processing network, supporting perceptual efficiency.\u003c/p\u003e \u003cp\u003eThis study had several limitations: (1) The study included no control group or coffee-flavored hot water group to rule out a placebo effect. (2) The study focused only on non-habitual coffee drinkers without examining the effects on habitual drinkers, who may respond differently to decaffeinated coffee. (3) Only the acute effects of a single cup of decaffeinated coffee were observed. Future studies should investigate the chronic effects of long-term decaffeinated coffee consumption, such as for a week or more, on cognitive function and brain activity.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study investigated the acute effects of decaffeinated coffee on behavior, spontaneous brain activity, and emotional and physiological states. The results suggest that decaffeinated coffee may affects cognitive performance and brain activity, shedding light on the effects of the non-caffeine components of coffee on individuals and their underlying mechanisms. As an exploratory pilot study with a limited sample size and no placebo control, these findings should be interpreted as preliminary and hypothesis-generating. Future research employing randomized, placebo-controlled designs and longitudinal assessments is essential to validate these observations and investigate their long-term significance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank EditorBar (https://www.editorbar.com/) for editing this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree to publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll study data can be requested from the corresponding author\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; This study was supported by Hangzhou biomedicine and health industry development support science and technology project (No.2022WJCY024);\u0026nbsp;Hangzhou Normal University Dengfeng Project“Clinical Medicine Revitalization Plan”Jiande Hospital Special Project (No. LCYXZXJH001);Hangzhou Natural Science Foundation of China under Grant (No.2024SZRZDH250001).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGROSSO G, GODOS J, GALVANO F, et al. 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Steady state visually evoked potential (SSVEP) topography changes associated with cocoa flavanol consumption [J]. Physiology \u0026amp; Behavior, 2012, 105(4): 948-57.\u003c/li\u003e\n \u003cli\u003eFRANCIS S T, HEAD K, MORRIS P G, et al. The Effect of Flavanol-rich Cocoa on the fMRI Response to a Cognitive Task in Healthy Young People [J]. Journal of Cardiovascular Pharmacology, 2006, 47: S215.\u003c/li\u003e\n \u003cli\u003eLAMPORT D J, PAL D, MOUTSIANA C, et al. The effect of flavanol-rich cocoa on cerebral perfusion in healthy older adults during conscious resting state: a placebo controlled, crossover, acute trial [J]. Psychopharmacology, 2015, 232(17): 3227-34.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Decaffeinated coffee, Resting-state functional MRI, Cognitive function, Amplitude of low-frequency fluctuation, Fractional amplitude of low-frequency fluctuation, Regional homogeneity","lastPublishedDoi":"10.21203/rs.3.rs-8443818/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8443818/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Coffee is one of the most widely consumed beverages worldwide and is known for its beneficial effects on various diseases, which is often attributed to caffeine. However, other bioactive compounds in coffee also play an important role. The aim of this study was to investigate the acute effects of decaffeinated coffee.\u003c/p\u003e\n\u003cp\u003eMethods: Thirty-one healthy adults participated in the study. Resting-state functional magnetic resonance imaging (rs-fMRI), cognitive-behavioral tests, self-report scales, and physiological measures were completed at baseline and 30 min post-drink. Based on rs-fMRI, we used static and dynamic amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and regional homogeneity (ReHo) to observe changes in spontaneous brain activity following acute decaffeinated coffee intake.\u003c/p\u003e\n\u003cp\u003eResults: After consumption, participants showed significantly faster reaction times in the 1-back (\u003cem\u003eP \u003c/em\u003e= 0.002), 2-back (\u003cem\u003eP\u003c/em\u003e= 0.023), Continuous Performance Test (\u003cem\u003eP\u003c/em\u003e = 0.003), and Finger Tapping Task (\u003cem\u003eP\u003c/em\u003e =0.009). At the neural level, the participants showed significantly higher sfALFF in the fusiform gyrus (FFG) and inferior temporal gyrus (ITG) and significantly lower sReHo in the medial orbital of the superior frontal gyrus (PFCventmed) after consuming decaffeinated coffee compared with before, with no significant differences in sALFF. The sfALFF values in the FFG were significantly negatively correlated with RTs on CPT. Dynamic analysis revealed significantly lower dALFF values in the FFG and cerebellum, and significantly higher dALFF and dReHo values in the supplementary motor area (SMA). No brain regions showed significant differences in dfALFF after consuming decaffeinated coffee compared with before (GRF-corrected, voxel-level \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, cluster-level \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). In addition, diastolic blood pressure significantly increased (\u003cem\u003eP\u003c/em\u003e = 0.004), and headache scores were higher (\u003cem\u003eP\u003c/em\u003e = 0.033), while no significant changes in systolic blood pressure or heart rate were observed.\u003c/p\u003e\n\u003cp\u003eConclusions: These results suggest that decaffeinated coffee may affects cognitive performance and brain activity, providing insights into the effects of non-caffeine components of coffee and their underlying mechanisms. As an initial exploratory study, it provides a foundation for future research. Randomized, placebo-controlled trials with long-term assessments are now needed to validate and extend these findings.\u003c/p\u003e","manuscriptTitle":"The acute effects of decaffeinated coffee on cognitive function and brain activity:a single-center experience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:12:59","doi":"10.21203/rs.3.rs-8443818/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"301689644997376023249546429973102953267","date":"2026-05-10T22:46:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T08:57:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T16:19:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248155193090468886891564734859182106883","date":"2026-04-11T14:50:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314031647557165126268906186246163000431","date":"2026-04-09T15:05:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T14:01:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T18:08:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-09T10:32:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-06T03:37:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-06T03:31:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f58cabab-732a-47ce-83ed-8a5fd1fb2f64","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"301689644997376023249546429973102953267","date":"2026-05-10T22:46:04+00:00","index":141,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66312502,"name":"Health sciences/Diseases"},{"id":66312503,"name":"Health sciences/Neurology"},{"id":66312504,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-19T12:13:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:12:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8443818","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8443818","identity":"rs-8443818","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}