Transcranial Doppler Ultrasonography and EEG Recording: A Novel Approach to Investigating Resting State

Transcranial Doppler Ultrasonography and EEG Recording: A Novel Approach to Investigating Resting State | 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 Research Article Transcranial Doppler Ultrasonography and EEG Recording: A Novel Approach to Investigating Resting State Galina Portnova, Alexandra Maslennikova, Olga Martynova This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9027830/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Transcranial Doppler (TCD) ultrasonography enables non-invasive, real-time assessment of cerebral hemodynamics. This study investigated the relationship between EEG, TCD, and photoplethysmography (PPG) during rest, testing the hypothesis that hemodynamic metrics possess intrinsic dynamics beyond immediate neural demand or autonomic control. Fifty-seven healthy adults underwent simultaneous EEG, bilateral middle cerebral artery TCD, and PPG recordings. We employed time-lagged correlation and quantified the duration of stable "states" for each modality. Specific neurovascular couplings were confirmed, including an association between posterior alpha-rhythm suppression and reduced flow velocity, and between decreased theta power and increased vascular resistance. Crucially, we found a temporal divergence: hemodynamic states identified via TCD were significantly more prolonged (mean ~ 22.6 seconds) than neural states captured by EEG (mean ~ 7.2 seconds). PPG-derived indices showed no significant correlation with steady-state TCD metrics, suggesting the observed hemodynamics are not primarily driven by autonomic control. Furthermore, the duration of stable TCD states correlated with baseline motor performance. We conclude that the resting-state brain is characterized by dissociable neural, vascular, and autonomic activity on distinct timescales. While rapid neurovascular coupling links momentary neural activity to vascular tone, the cerebrovascular system exhibits its own slower dynamics, independent of direct autonomic modulation, likely reflecting the integration of local metabolic and neurogenic processes. Transcranial Doppler (TCD) Electroencephalography (EEG) Neurovascular coupling Cerebral blood flow Resting state Autonomic nervous system Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The investigation of brain resting states represents a highly promising domain for elucidating distinct physiological conditions across diverse patient populations, including those with specific pathologies, disorders of consciousness, and communication impairments such as aphasia. Research into EEG macro- and microstates has demonstrated particular potential in identifying stable resting-state brain activity configurations in clinical groups, such as children with low-functioning autism, as evidenced in studies of atypical neural dynamics in ASD (Das et.al, 2021 ; Portnova & Martynova, 2024 ). However, EEG possesses inherent limitations, most notably its inability to account for concurrent changes in the autonomic nervous and cardiovascular systems—a critical factor for comprehensive patient assessment, particularly in populations with dysregulated autonomic nervous system (ANS) control. Regarding the study of cognitive and mental processes, one of the most promising yet underutilized methods for capturing the physiology dynamics of resting-state brain is the direct, real-time monitoring of cerebral blood flow via transcranial Doppler (TCD) sonography. This technique is often overlooked due to procedural complexity. Specifically, Doppler insonation of the middle cerebral artery (MCA) enables the investigation of cerebral perfusion dynamics with temporal resolution comparable to that of EEG. TCD ultrasonography integrative approach is grounded in the distinct physiology of cerebral vasculature. MCA is regulated by both local brain vascular mechanisms, such as autoregulation and metabolic regulation, and systemic physiology factors, including systemic blood pressure, carbon dioxide, oxygen, and neurogenic stimulation. Local mechanisms allow the artery to adapt independently to changes in cerebral blood flow, while systemic factors influence the overall tone of the brain's vasculature. Thus, a multimodal approach can dissect whether observed changes in cerebral blood flow are driven primarily by local neural demand, systemic physiological shifts, or their interaction—a distinction of paramount importance for a holistic understanding of patient state (Willie et al., 2014 ). MCA flow is primarily autoregulated, which is the intrinsic ability of vessels to maintain constant blood flow despite arterial pressure fluctuations (Harper, 1966 ). This process begins with a rapid myogenic response where pressure-induced stretch activates smooth muscle ion channels, leading to depolarization, calcium influx, and vasoconstriction to protect downstream capillaries; conversely, a pressure drop causes vasodilation. The endothelium also contributes, as shear stress stimulates vasodilator release, while factors like Endothelin-1 promote constriction (Armstead, 2016 ; Fantini et al., 2016 ). Metabolic regulation, driven by local metabolites like CO₂, triggers vasodilation with increased metabolic activity and vasoconstriction with decreased activity, making flow exquisitely sensitive to local demand (Berne et al., 1981 ; Smith & Ainslie, 2017 ). During ischemia, the failure of this system leads to energy depletion and irreversible damage (Hata et al., 2000 ; Siesjö, 1984 ). Metabolic factors like adenosine contribute to vasodilation during hypotension, but cannot fully explain rapid vasoconstriction after hypertension (Strandgaard & Paulson, 1984 ). The blood flow autoregulation is modulated by metabolic demands, neurogenic influences, and flow-dependent vasomotion, and its impairment in conditions like the MCA stenosis is a key indicator of cerebrovascular health (Haubrich et al., 2003 ). Neurogenic control, while limited under normal conditions, can shift the autoregulatory curve upward during acute hypertension. Flow-dependent mechanisms work synergistically with pressure-induced responses (Koller & Toth, 2012 ). ANS is a crucial regulator, working with metabolic and astrocytic mechanisms, and its dysfunction is linked to conditions like migraine and stroke (Goadsby, 2013 ; Sándor, 1999 ). The MCA exhibits rapid tone adjustments (1–3 seconds) via neurovascular coupling, where neuronal activity triggers astrocyte-mediated vasodilation (Attwell et al., 2010 ), a process 30% faster in the MCA than in smaller arterioles (Chen et al., 2021 ). During hypoxia, K⁺ channel activation dilates the MCA within 2 seconds (Taggart & Wray, 1998 ). Sympathetic input causes vasoconstriction, while parasympathetic input promotes dilation (Hamel, 2006 ). The sympathetic part of ANS also modulates the MCA tone and causes vasoconstriction, while parasympathetic input promotes vasodilation (Hamel, 2006 ). The influence of nervous system regulation on the MCA tone has also been observed at the EEG level, where simultaneous recording of TCD ultrasonography signals and EEG was performed. Neural activity precedes hemodynamic changes, as shown by EEG studies where suppression of posterior alpha-rhythm power predicts the MCA vasodilation within 400–600 ms before detection (Katura et al., 2006 ), and gamma oscillations induce a 15–20% flow surge within 1–2 seconds (Scheeringa et al., 2011 ). These findings indicate that neural activity (alpha desynchronization) precedes the hemodynamic response, meaning EEG can predict vascular changes before they manifest physically. In stroke, EEG delta-theta power predicts a reduction of the MCA flow (Rogers et al., 2020 ), while epileptic spikes trigger hyperperfusion (Diehl et al., 2005 ). Hormonal regulation also modulates the MCA flow. Estrogen promotes vasodilation by upregulating endothelial nitric oxide synthase and prostacyclin synthesis (Krause et al., 2002 ), and has broader protective effects (Krause et al., 2006 ). Testosterone generally increases vascular tone, though human studies show complex effects (Edvinsson et al., 2024 ). Melatonin causes direct vasoconstriction (Krause et al., 2002 ). Chronic cortisol exposure disrupts neurovascular coupling and is also linked to cognitive decline (Zhang et al., 2021 ). Vasopressin is a potent vasoconstrictor (Edvinsson et al., 2024 ), oxytocin causes mild constriction but may support stress resilience (Edvinsson et al., 2024 ; Kuchenbecker et al., 2021 ), and thromboxane A2 mediates vasoconstriction in stress responses (Cudd, 1998 ). The aim of this study was to investigate the relationship between changes in EEG and Doppler signals, while also examining their potential independence as distinct components of the resting state. We hypothesized that changes in the MCA wall resistance, despite previously demonstrated correlations with EEG data, may possess their own intrinsic dynamics. This hypothesis is grounded in the complex regulatory mechanisms governing the MCA. Additionally, based on evidence of a close relationship between heart rate (HR) TCD signals of the MCA, this study sought to account for HR's influence on the MCA wall resistance. Earlier research demonstrates that the MCA blood flow velocity is strongly correlated with HR during exercise—a relationship used to assess cerebral autoregulation (Jørgensen et al., 1992 ; Zhang et al., 1998 ). Consequently, we derived an additional parameter, Cycle-Normalized Peak Velocity Change or dVcycle, which describes the rate of change in flow velocity normalized by the fundamental cardiac cycle length (RR interval). In order to further understand the regulation of the MCA blood flow changes, analogous parameters from photoplethysmography (PPG) were also examined. Additionally, we assessed the motor performance of healthy volunteers on the finger tapping test prior to TCD and EEG/PPG acquisition. Tapping speed serves as a metric of psychomotor function, a capacity that is modulated by numerous factors, including age, hormonal function (particularly thyroid hormones), fatigue, and cognitive status (Grigorova & Sherwin, 2012 ; Heimhofer et al., 2024 ; Lismane et al., 2026 ). Methods Participants The study sample included 57 healthy participants (27 women and 20 men) aged 20 to 40 years. All volunteers had no history of mental, neurological, cardiovascular diseases, or substance abuse, and had refrained from taking any medication for 72 hours prior to the study. Female participants were confirmed not to be pregnant, had not used oral contraceptives, and were tested during the follicular phase of their menstrual cycle (days 4–12). All experimental sessions were conducted between 11:00 and 14:00. At the study preparation stage, participants were instructed to avoid night shifts for 72 hours prior to the study, abstain from alcohol for 72 hours, and refrain from smoking and caffeine consumption for 4 hours preceding their session. The study was reviewed and approved by the Ethics Committee of the Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences. The participants provided their written informed consent to participate in this study. Protocol of the study Protocol of the study Immediately prior to the experimental session, participants were asked to complete a set of questionnaires. These included a screening instrument for symptoms of anxiety and depression. Participants were also asked to rate their current emotional state, sleep quality, and physical well-being on a 10-point scale (0–10). Additionally, volunteers performed a tapping test. In this task, they were instructed to click a computer mouse repeatedly for 10-second intervals, first at a comfortable, self-paced rate and then at their maximum possible speed. The results for each task condition (self-paced and maximum speed) were averaged separately to minimize measurement error. Subsequently, the MCAs were identified bilaterally using TCD ultrasonography. The optimal acoustic windows for signal acquisition were marked on the scalp. Following this, an EEG cap was fitted, and the bilateral Doppler probes were secured in their designated positions. Synchronous EEG recordings were acquired during a resting-state protocol. The protocol began with an eyes-open condition (up to 4 minutes), followed by a 5-minute eyes-closed condition, during which participants were instructed to remain still. A standard clinical EEG recording was then performed, which included photic stimulation and a hyperventilation provocation test. This was conducted to screen for any underlying EEG abnormalities. For subsequent analysis, only the eyes-closed resting-state data were used. For each participant, a clean segment of approximately 250–300 seconds was selected from this condition. The questionnaires and testing Before the study participants filled Hospital Anxiety and Depression Scale, HADS. The scale comprises two domains (anxiety and depression) and includes 14 items. Interpretation is based on the total score for each domain: scores of 0–7 are within the normal range, 8–10 indicate subclinical symptoms, and scores of 11 or above are indicative of a clinical disorder. All participants showed results within the normal range. We used a tapping test implemented in Neurobehavioral System Presentation software ( https://www.neurobs.com/ ) and asked participants to click a computer mouse repeatedly for 10-second intervals. The first task was to click at a comfortable, self-paced rate and second – to click at their maximum possible speed. The results for each task condition (self-paced and maximum speed) were averaged separately to minimize measurement error. Doppler signal registration Doppler signal registration Doppler signals were acquired using a Sonomed-300 ultrasound blood flow velocity analyzer (Spectromed, Russia), which is capable of continuous bilateral signal recording. Bilateral recordings were obtained using symmetrical, identical 2-MHz pulsed-wave (PW) Doppler probes. The right and left MCAs were identified by a certified ultrasound specialist using a Logic-9 ultrasound system equipped with a transcranial probe. EEG and PPG registration The EEG was acquired using a 19-channel EEG amplifier with the recording of PPG (Encephalan Poly4, Medicom MTD, Taganrog, Russia) during 10 min. The sampling rate was 250 Hz. The amplifier band-pass filter was nominally set to 0.05–70 Hz. AgCl electrodes (Fp 1 , Fp 2 , F 7 , F 3 , Fz, F 4 , F 8 , T 3 , C 3 , Cz, C 4 , T 4 , T 5 , P 3 , Pz, P 4 , T 6 , O 1 , and O 2 ) were placed according to the International 10–20 system. The electrodes placed on the left and right mastoids served as joint references under unipolar montage. The vertical EOG was recorded with AgCl cup electrodes placed 1 cm above and below the left eye, and the horizontal EOG was acquired by electrodes placed 1 cm lateral from the outer canthi of both eyes. The electrode impedances were kept below 10 kΩ. PPG signal was recorded with a standard surface photoplethysmography sensor. EEG and Doppler synchronization To synchronize the EEG and Doppler equipment, we used a COM port connection to insert event markers. Video monitoring was also implemented to oversee the experimental procedure. The combined data stream from the simultaneous recordings exhibited a maximum timing discrepancy of less than 50 milliseconds. The example of marker synchronization is presented in Fig. 1 . Analysis of Doppler signal The Doppler signal data from the left and right MCA consist of peak systolic velocity (Vmax), end-diastolic velocity (Vmin), and Vmean, pulsatility index (PI), resistivity index (RI), and the time of measurement. The time scale was also exported. These parameters were derived from the Doppler waveform, which were assessed manually by the expert. We excluded data epochs contaminated by artifacts. The final feature matrix used for the analysis included Vmax,Vmin, PI = (v max - v min ) / (v mean ); PI = (peak systolic velocity - minimal diastolic velocity) / (Vmean)), RI=(v max - v min ) / (v max ); RI = (peak systolic velocity - minimal diastolic velocity) / (peak systolic velocity) and RR interval (sec) for the left and right MCA. We also calculated Cycle-Normalized Peak Velocity Change (dVcycle). The dVcycle parameter was normalized by heart rate (HR). A higher value indicates a higher HR in the subject, as it is divided by the RR interval. dVcycle= (Vmax - Vmin) / RR – the parameter describes a rate of change normalized by the fundamental cycle length (often the cardiac cycle RR-interval). Since the differences between the right and left MCA were negligible when extracting these parameters (the discrepancy did not exceed 2 intervals per subject), we averaged these values for further analysis. Intervals segmentation Several approaches were employed in the analysis of the Doppler and EEG data: analysis of the Doppler signal parameters was performed for each individual heartbeat, and for intervals of 1 second (non-overlapping), 2 seconds (non-overlapping), and 5 seconds (non-overlapping), as well as for 1-second intervals with a 500 ms overlap, 2-second intervals with a 1-second overlap, and 5-second intervals with 1-second and 2-second overlaps. Similar intervals were analyzed for the EEG data. The comparison revealed that the 2-second intervals with a 1-second overlap most accurately represented the dynamics of the Doppler signal across the studied parameters (see Fig. 3 for details). This is because 1-second intervals without a 500 ms overlap risked containing no data points when the heart rate was below 60 bpm. Longer intervals (specifically, 5-second windows) demonstrated a lower correlation with the original signal. Furthermore, when comparing the 1-second intervals with a 500 ms overlap to the 2-second intervals with a 1-second overlap, it was determined that the 1-second interval analysis imposes significant limitations on the corresponding EEG analysis. These limitations are particularly evident in the power spectrum for frequencies below 8 Hz and in the analysis of the signal envelope's frequency. Analysis of EEG data EEG Preprocessing Continuous EEG corresponding to the resting state of each subject was cleaned from eye movements by an ICA-based algorithm in the EEGLAB plugin for MATLAB 2022 (Mathworks Inc., Natick, MA, USA). Muscle artifacts were cut out through manual data inspection. The continuous resting-state EEG of each subject was filtered with a band-pass filter at 0.5–30 Hz. We analyzed data over three regions: frontal (F3, Fz, and F4), central (C3, Cz, and C4), parietal (P3, Pz, and P4) and occipital (O1, O2). Power Spectral Density (PSD) Fast Fourier transform was used to analyze power spectral density (PSD). The EEG spectrum was estimated for every 178 ± 22.3 s interval. The resulting normalized spectra were integrated over intervals of unit width in the range of interest (2–3 Hz, 3–4 Hz,…29–30 Hz). We analyzed the PSD in the following bands: 4–8 Hz (theta-rhythm band), 8–10 Hz (alpha1 band), 10–12 Hz (alpha2 band), 12–14 Hz (alpha3 band) and the beta band (14–20 Hz). Fractal Dimension (FD) We performed the calculations of the examined signal band-pass-filtered in the range of interest (1.6–30 Hz); a Butterworth 12th-order filter was used. Further, fractal dimension (FD) was evaluated using the Higuchi algorithm. We calculated all noted EEG parameters for each 2-second EEG intervals with a 1-second overlap - and further call it “raw” data. Photoplethysmography (PPG) analysis To align the photoplethysmography (PPG) data with the Doppler signal, we calculated the PI and RI for each dVsycle using the Doppler indecies—specifically, RI = (systolic peak amplitude – diastolic peak amplitude) / systolic peak amplitude—rather than the conventional PPG-based RI, defined as RI = (systolic peak amplitude / diastolic peak amplitude) × 100. This approach was adopted to enhance the comparability of the inherently variable and diverse PPG metrics with the more consistent Doppler signal, as well as to standardize measurements across volunteers. States detected using Doppler and EEG parameters As noted previously, we selected 2-second intervals with a 1-second overlap to analyze the dynamics of both the EEG, PPG and Doppler signals. We analyzed the raw data (here we mean the analysis of all 2-second EEG intervals with a 1-second overlap) and also transformed each EEG and Doppler parameter into unified metrics. These metrics classified data points as normal (within the 2nd and 3rd quartiles, Q2-Q3), high (> 0.67σ), or low (< -0.67σ), with these thresholds applied uniformly across all participants (as intragroup differences were not significant). Thus, to analyze the temporal changes we used the following thresholds: 0.67σ, and the range between − 0.67σ and 0.67σ. The coverage metric was calculated as the ratio of the sum of intervals in one of the three categories to the total number of intervals for each individual. A prolonged, uninterrupted sequence of values belonging to one of these three categories was defined as a continuous state, and its duration was measured in a number of intervals. Statistical Analysis We analyzed potential associations between EEG metrics and Doppler dynamics using a time-lag approach. For each participant, Spearman’s rank correlation analysis was performed between the EEG data and the Doppler data with time shifts of 0, 1, 2, and 3 seconds within selected time intervals. This means we correlated EEG and Doppler signals from corresponding time periods — each approximately 250–300 seconds long per participant — first with no time shift (zero lag), and then with the Doppler intervals shifted relative to the EEG intervals. We considered an effect significant if at least 75% of participants (43 out of 57) showed a statistically significant correlation. We calculated the coverage and duration of low, normal, and high value fragments for each parameter of Doppler, normalized EEG and PPG signal, defined by the following quartiles: low ( 0.67σ). Duration was calculated as the mean number of consecutive intervals without interruption by other states. Coverage was calculated as the ratio of the sum of intervals for each individual state to the total number of intervals for each specific individual. The analysis of the EEG coverage and duration of low, normal, and high value fragments was performed using normalized EEG data. The repeated measures ANOVAs with the following post hoc comparison (Bonferroni, p < 0.05) were used to determine temporal effects (coverage and duration) of the EEG, PPG and Doppler dynamics. We also used Spearman correlation analysis to assess association between Doppler and EEG signals with testing, questionnaires and Tapping test. The correlations between the EEG metrics with Doppler dynamics were also analyzed according its association with age gender, testing, questionnaires and Tapping test; for that we used Spearman’s rank correlation coefficient and adjusted p -values after correction for multiple correction. Further, the adjusted p -values of the significant correlations are presented in the manuscript. Results Transcranial doppler monitoring On average, the MCAs were located at a depth of 3.8 to 5.2 cm, with a mean flow velocity of 54.0 ± 9 cm/s. The flow characteristics were normal, as were the PI and RI. The Vmax was 77 ± 19 cm/s, and the Vmin was 36 ± 13 cm/s. The main effect of lateral asymmetry was not statistically significant (p > 0.45). However, individual-level asymmetry was observed in 11 out of 57 participants for Vmax (4 with side S > D, 7 with D > S), in 8 participants for Vmin (4 S > D, 4 D > S), and in 6 participants for mean velocity (Vmean) (3 S > D, 3 D > S). No other parameters showed individual asymmetric differences. The descriptive statistics of main Doppler parameters of the right and left MCAs are presented in Table 1 . Table 1 Descriptive statistics of Doppler parameters Doppler data left right Mean Min Max SD Mean Min Max SD depth 4.24 3.84 5.33 0.69 4.40 4.06 5.21 0.98 Vmax 78.47 68.17 99.46 9.29 75.56 70.47 102.42 9.54 Vmin 39.55 20.46 51.45 10.89 40.024 19.55 53.54 13.85 Vmean 53.92 38.06 66.46 9.15 55.09 40.26 62.47 7.55 PI 0.95 0.83 1.21 0.19 0.97 0.82 1.15 0.20 RI 0.59 0.47 0.68 0.08 0.60 0.49 0.63 0.06 Vmax-Vmin 34.46 18.06 43.45 9.08 35.53 20.45 38.46 8.95 dVcycle 0.55 0.19 1.22 0.13 0.59 0.13 1.20 0.15 The correlation between EEG band spectral power and the MCA BloodFlow: We observed that elevations in the beta-rhythm power, occurring at the time of PI measurement and up to one second preceding it, were associated with symmetrical reductions in PI in both cerebral arteries. At the 0-second lag, 77% of participants showed a statistically significant positive correlation (individual r > 0.182, p 0.189, p < 0.039). A reduction in the theta-band power (4–8 Hz) at a 0-second and 1-second lag was associated with a simultaneous, bilateral increase in RI and PI values. A significant negative correlation was observed in 86% of participants at the 0-second lag (mean r = -0.45 ± 0.07; individual r < -0.189, p < 0.039) and in 85% of participants at the 1-second lag (mean r = -0.45 ± 0.07; individual r < -0.194, p < 0.035) for RI parameter. A similar significant negative correlation was found in 84% of participants at the 0-second lag (mean r = -0.40 ± 0.09; individual r < -0.184, p < 0.043) and in 81% of participants at the 1-second lag (mean r = -0.39 ± 0.11; individual r < -0.192, p < 0.037) for PI parameter (see Fig. 3 (a)). The strength of the theta-RI correlation was related to motor performance on the tapping test (see Fgure 3(b)). A more pronounced negative correlation was associated with a lower (i.e., worse) fastest tapping score (mean r = 0.41 ± 0.06, p = 0.004 at the 0-second lag; mean r = 0.34, p = 0.013 at the 1-second lag). A significant correlation was identified between posterior alpha-rhythm power (10–12 Hz) suppression and a reduction in the dVcycle in both hemispheres. This association was observed not only at the point of measurement but also with a temporal shift. At the 0-second lag a significant negative correlation was found in 80% of participants (mean r = -0.32 ± 0.12; individual r < -0.195, p < 0.035). At the 1-second lag the correlation remained significant in 79% of participants (mean r = -0.30 ± 0.15; individual r < -0.193, p < 0.037). At the 2-second lag the correlation persisted in 75% of participants (mean r = -0.27 ± 0.16; individual r < -0.190, p 0.67σ, < -0.67σ, and between − 0.67σ and 0.67σ for both EEG and Doppler parameters, the durations of these states differed significantly. No significant differences were found in the duration of states with low, high, and normal PI, RI, and dVcycle values (p > 0.49), nor among the EEG parameters (p > 0.11). Table 2 Descriptive statistics and inter-modal comparisons for signal duration and coverage. Values represent Mean ± Standard Deviation for EEG, Doppler, and PPG features. Significant differences between modalities were assessed using a linear ANOVA with Bonferroni post-hoc correction. Duration Coverage Minimal Mean Max > 0.67*sigma ± 0.67*sigma <-0.67*sigma mean SD mean SD mean SD mean SD mean SD mean SD Doppler PI 5,8 1,5 11,9 5,6 59,8 21,5 24,6 11,5 50,1 20,4 25,3 12,4 RI 5,5 1,3 12,3 5,9 60,1 23,8 25,8 10,6 50,4 18,5 23,8 11,2 dVcycle 6 1 12,6 4,9 65,7 20,4 24,8 12,5 49,7 18,75 25,5 10,5 Avarge 5,7 1,3 12,3 5,4 60 22,4 25,07 11,53 50,07 19,22 24,87 11,37 EEG 4–8 Hz PSD 2 0,8 3,9 2,2 15,3 5,1 24,5 5,97 51,64 8,9 23,86 5,2 alpha1 PSD 1,9 1 3,8 1,8 15 7,2 23,2 6,746 49,5 10,7 27,3 6,6 alpha2 PSD 1,8 0,6 3,3 2,5 14,6 4,9 24,3 4,546 52,1 9,6 23,6 7,2 beta PSD 1,4 1,1 3,1 1,8 14,2 5,8 26,4 5,7 50,8 12,2 22,8 5,9 FD 1,6 0,7 3,4 1,5 15,2 6,6 24,5 6,3 48,8 11,5 26,7 6 Hjorth 1,5 0,8 3,7 1,7 14,1 6,9 23,9 4,4 49,3 10,3 26,8 5,5 Avarage 1,7 0,9 3,6 1,9 14,7 6,08 24,47 5,61 50,36 10,53 25,18 6,067 PPG PI 4,2 2,1 9,05 5,5 37,2 15,9 25,2 18,9 49,86 22,8 25,1 21,8 RI 3,9 1,9 9,15 5,6 39,6 16,3 25,1 22,4 50,2 22,9 24,7 18,5 dVcycle 3,7 1,9 8,26 5,32 34 12,5 24,6 19,8 51,8 25,9 23,6 18,9 Avarge 3,9 2 8,9 5,5 37 15 24,97 20,37 50,62 23,87 24,47 19,73 Bonferroni test linear ANOVA (p-values adjusted for Minimal, mean and maximal values) Minimal (average) Mean (average) Max (average) Doppler EEG PPG Doppler EEG PPG Doppler EEG PPG Minimal (average) Doppler < 0.001 0.009 EEG < 0.001 PPG 0.009 0.044 Mean (average) Doppler < 0.001 0.193 EEG < 0.001 0.051 PPG 0.193 0.051 Max (average) Doppler 0.003 0.017 EEG 0.003 0.028 PPG 0.017 0.028 For Doppler parameters, the minimum duration of a continuous interval consisted of 5 analyzed intervals, with a mean of 5.7 intervals. In contrast, for EEG, the minimum duration was 1 interval, with a mean of 1.7 intervals. A significant main effect of modality (Doppler/EEG) was found (F(1, 56) = 198.58, p < 0.001, Partial η² = 0.79). The mean duration of a continuous interval was 12.3 intervals for Doppler, compared to 3.6 intervals for EEG. This difference was also statistically significant (main effect of modality: F(1, 56) = 155.58, p < 0.001, Partial η² = 0.73). The highest individual maximum was 107 intervals for doppler (mean 60,2) and 28 for EEG (mean 14.7). The duration of the PPG states was most variable and had minimum 1 interval, with a mean of 3.9 intervals and differed from Doppler minimum states (p < 0.009) and EEG states (p < 0.05). Thus, significant differences were confirmed for both the minimum and mean duration of states between the three signals. The mean values of interval durations of PI (for low, normal variants) and RI (normal variants) significantly correlated with Tapping test results in comfort speed (for PI correspondingly, r=-0.39, r=-0.44, with p < 0.005, for RI correspondingly, r=-0.38 with p < 0.005). The mean values of dVcycle (for low, normal and high variants) significantly correlated with Tapping test results in comfort speed (r=-0.49, r=-0.51, r=-0.42 with p < 0.001). The correlation with maximal (fastest) speed was found only for RI (high variant, r=-0.29, p = 0.03). Discussion The study's primary objective was to investigate the influence of EEG changes on Doppler signal parameters, in order to examine the characteristics of neurogenic regulation of the MCA blood flow and the specific time window of this interaction. We found a moderate negative association between posterior alpha-rhythm power (10–12 Hz) suppression and lower dVcycle in both hemispheres, thereby replicating the previous finding that decreased posterior alpha power predicts vasodilation (Katura et al., 2006 ). The identified relationship between changes in theta power (4–8 Hz) and the dynamics of the RI and PI indices is less straightforward, though the strength of the effect was comparable. Specifically, a reduction in theta-band power (4–8 Hz) at the zero and 1 s lag between EEG and TCD signals was linked to a simultaneous increase in both RI and PI values bilaterally. This inverse relationship between decreased theta rhythm and increased vascular resistance was further associated with performance on the tapping test: a stronger neurovascular correlation was linked to a lower maximum speed of the tapping speed. Previous research has demonstrated that tapping speed is related to age, fatigue, and cognitive status, and is significantly reduced in the context of fatigue, significant motor and cognitive impairments, as well as aging (Grigorova & Sherwin, 2012 ; Heimhofer et al., 2024 ; Lismane et al., 2026 ). These conditions are often associated with increased theta power, particularly in frontal regions (Trejo et al., 2015 ; Wang et al., 2025 ). Furthermore, a relationship between tapping speed and hormonal function, particularly thyroid hormones, has long been established (Grigorova & Sherwin, 2012 ; Stern, 1959 ), and has also frequently been associated with a slowing of brain activity (Atli et al., 2023 ). Our findings also revealed differences in the duration of continuous states between EEG and Doppler parameters, with a minimum duration of 10 seconds for Doppler and a minimum of 2 seconds for EEG resting states. These same timescales correspond to the regulation of cerebral blood flow, which operates over a range of seconds to minutes. Some responses are very rapid (e.g., Aaslid, 1987 ), while others are slower, taking less than 10 seconds for adaptation or up to 3 minutes for neurogenic effects. In contrast to the swift responses of neural mechanisms and metabolic autoregulation (2.3–4.6 seconds), humoral and metabolic factors ensure a sustained, adequate cerebral oxygen supply. This long-term maintenance involves changes in arteriolar diameter and cerebrovascular resistance, with timelines that can also span from 10 seconds to several minutes. Based on our Doppler measurements, we observed changes across two distinct dynamic timescales. Rapid fluctuations, which correlated with EEG dynamics over 2-second intervals, are likely attributable to neurogenic factors. Previous studies have demonstrated that such Doppler shifts occur within 2 to 5 seconds following stimulus onset (Czosnyka et al., 2008 ; Lang & Zimmer, 1974 ). Alternatively, these changes may reflect the metabolic mechanism of cerebral blood flow regulation, which has been shown to onset at approximately 2.3 seconds (Aaslid, 1987 ). The slower dynamics of cerebral blood flow regulation (10 seconds to one minute), associated with the stable states of TCD signal or brain resting-state homeostasis, likely point to autonomic or neurohormonal mechanisms rather than faster metabolic processes (Payne, 2024 ; Sainbhi et al., 2022 ). In particular, several hormones, including melatonin, thyroid hormone (T4), cortisol, and growth hormone, can induce changes in cerebral blood flow within minutes of a fluctuation or administration (Bini et al., 2022 ; Brabant et al., 2011 ; Hodkinson et al., 2014 ; Schroeder & Privalsky, 2014 ). In contrast, the influence of sex hormones like estrogen and testosterone typically develops over days or weeks, as seen during sustained high-hormone states such as the luteal phase (Cote et al., 2021 ; Krause et al., 2002 ). Neurogenic regulation, mediated by peptides like NPY and CGRP, lasting from seconds to minutes, provides a direct and sustained influence on vessel tone (Sándor, 1999 ). This operates in concert with flow-metabolism coupling, where neuronal activity releases vasoactive substances that trigger astrocyte-mediated vasodilation for the duration of the activity (Peterson et al., 2011 ) typically also lasting from seconds to several minutes. Therefore, this study demonstrates that TCD ultrasonography and EEG provide complementary yet independent temporal perspectives for investigating brain states. Crucially, the temporal dynamics of the MCA blood flow cannot be attributed solely to heart rate fluctuations. To test this, we compared TCD metrics with PPG signals, a modality whose indices are known to be primarily regulated by the ANS. The lack of a significant correlation between steady-state TCD and PPG signals across our cohort suggests that the observed TCD signal dynamics are not predominantly under ANS control, pointing instead to other regulatory mechanisms (Allen & Chen, 2022 ; Park et al., 2022 ). Our findings indicate that the duration of the TCD-stable state was negatively correlated with comfortable tapping speed. In contrast, maximal tapping speed exhibited a weaker negative correlation with the duration of RI stable states, but a positive correlation with the association between EEG theta rhythm and Doppler RI. One possible explanation for these observed correlations may involve hormonal regulation. Specifically, the influence of hormonal factors—including T4, cortisol, and other substances—cannot be ruled out as contributing to resting-state conditions associated with Doppler stable states. In support of this hypothesis, previous studies have demonstrated decreased cerebral blood flow in patients with hypothyroidism (Grigorova & Sherwin, 2012 ). Such reductions in flow may be associated with longer durations of Doppler stable states and manifest behaviorally as reduced tapping speed and other forms of decreased motor activity (Constant et al., 2001 ; Krausz et al., 2004 ). However, as hormonal factors were not controlled in the present study, further research is warranted to clarify these relationships. Conclusions This study delineates the temporal architecture linking resting-state neural activity to cerebral hemodynamics and ANS regulation. We confirmed rapid, frequency-specific neurovascular coupling, with changes in EEG theta and alpha band power predicting shifts in vascular resistance indices within 0–3 seconds. Additionally, strong inverse link between decreased theta power and increased vascular resistance, which was tied to individual motor performance in the tapping test and increased beta activity correlating with decreased vascular pulsatility. The expected result is the demonstration of a fundamental temporal and regulatory dissociation between the MCA blood flow and EEG. Hemodynamic states, as measured by TCD, exhibited significantly longer and more stable durations (mean ~ 22.6 seconds) than the fleeting patterns of neural activity captured by EEG (mean ~ 7.2 seconds). Crucially, the lack of significant correlation between these steady-state TCD metrics and analogous PPG indices, which are predominantly modulated by ANS, indicates that the observed cerebrovascular dynamics are not fully under ANScontrol. This points to other regulatory mechanisms, such as local metabolic or neurohormonal factors, governing these slower vascular rhythms. The behavioral correlation, where longer-lasting TCD states were associated with slower comfortable motor tapping, further suggests these vascular patterns may index a broader physiological or metabolic “set-point.” Therefore, EEG, TCD, and PPG provide both complementary and distinct physiological insights: EEG captures rapid neural activity, TCD reveals slower, locally regulated cerebrovascular maintenance states, and PPG reflects ANS cardiovascular influence. This integrated, multi-system perspective is essential for a holistic understanding of the brain resting state homeostasis and provides a novel framework for investigating conditions where the coordination between neural activity, blood flow, and autonomic regulation is impaired. Limitations Due to the absence of mean asymmetry in the PI, RI, and dVcycle parameters, this article does not address individual asymmetry of these measures. However, this parameter may be critically important in the study of pathological changes, including psychiatric and neurological disorders. It will, therefore, be given special attention in subsequent publications Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Fundings No external funding Author Contribution G. P: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – Original draft preparation, Writing – Review & editing, Visualization, Supervision, Project administration. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9027830","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606701875,"identity":"d64557c9-4326-4688-ae30-2836afb73091","order_by":0,"name":"Galina Portnova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYJCCA0Asx8dMkhagHmM2ZgbGBpKsSWxjIFYLf/sZw8MfKu6lt7GzP3/AuOcwYS0SZ3IMDhw4U5zbxsxj2MDwjAgtBgxpCQcOtiWAtAAddoAYLfzPgFr+JaSzMbM/JFKLRPKBAwcbEhKAIWZInBaJG4+BXjmWYAjyy4yEA+mEtfD3JzZ/qKhJkOfnP/7gw4cD1oS1oIIEUjWMglEwCkbBKMAOAH9VO5kJU2EWAAAAAElFTkSuQmCC","orcid":"","institution":"Institute of Higher Nervous Activity and Neurophysiology","correspondingAuthor":true,"prefix":"","firstName":"Galina","middleName":"","lastName":"Portnova","suffix":""},{"id":606701878,"identity":"dcf0ec46-501c-42a8-9c2e-8aca534b86a2","order_by":1,"name":"Alexandra Maslennikova","email":"","orcid":"","institution":"Neuropsychiatry Science Centre, Mental-health hospital No.1 named after N.A. Alexeev","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"Maslennikova","suffix":""},{"id":606701880,"identity":"9a7b2081-3b5f-4823-9e8a-a5aa21ebfb16","order_by":2,"name":"Olga Martynova","email":"","orcid":"","institution":"Institute of Higher Nervous Activity and Neurophysiology","correspondingAuthor":false,"prefix":"","firstName":"Olga","middleName":"","lastName":"Martynova","suffix":""}],"badges":[],"createdAt":"2026-03-04 08:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9027830/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9027830/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104828814,"identity":"e9f8234f-f756-4ee1-be21-8fcf6da70c60","added_by":"auto","created_at":"2026-03-17 16:06:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":279422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSchematic of simultaneous EEG and Doppler signal analysis. The diagram illustrates the synchronization methodology and key Doppler features extracted for the study. PI\u0026nbsp;- pulsatility index, RI\u0026nbsp; - resistivity index, Vmax – peak systolic velocity,\u0026nbsp; Vmin - \u0026nbsp;minimal diastolic velocity) / (mean velocity),\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003edV\u003c/em\u003e\u003csub\u003e\u003cem\u003ecycle\u0026nbsp; \u003c/em\u003e\u003c/sub\u003e\u003cem\u003e\u003cstrong\u003e- \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eresistivity index - Cycle-Normalized Peak Velocity Change\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9027830/v1/c55afd58b0c7f43962a6f491.png"},{"id":104828815,"identity":"5308bdbd-e3df-4e50-a35f-858ce086ea2e","added_by":"auto","created_at":"2026-03-17 16:06:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":477564,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIllustration of the time-series segmentation procedure. Continuous EEG and Doppler signals (with an analogous approach for functional connectivity analysis) were divided into successive 2-second segments using a 1-second overlap (hanning window).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9027830/v1/2e5d2feb750c8ea00fafddf4.png"},{"id":104828816,"identity":"824b6538-20b7-49ce-a636-3432619a816d","added_by":"auto","created_at":"2026-03-17 16:06:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":248391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelations between EEG signals and Middle Cerebral Artery (MCA) blood flow. (a): Significant zero-lag correlations between Doppler parameters and EEG power spectral density (PSD) for each participant. Each dark red dot represents the individual correlation coefficient for one participant. (b): Scatter plot showing the association between individual participant correlation coefficients (between Resistivity Index (RI) and theta-rhythm PSD) and their performance on the Tapping test at the fastest speed. Each blue dot represents the individual correlation coefficient for one participant\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9027830/v1/91fd07ac7d52c61d2624b644.png"},{"id":104828817,"identity":"f5b3b005-3086-4c78-8fde-9d1b28c6e27c","added_by":"auto","created_at":"2026-03-17 16:06:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":193708,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e(a):The intervals corresponding to the normal, high and lower values of PI of Doppler signal, PI of PPG and FD of EEG and in two examples (participant 037 and 039). (b): plot of association between mean duration of Doppler states (PI mean values) and comfort speed of Tapping test. Small blue dots - individual values, green big dots - values of Participant 007 and 039/\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9027830/v1/5fda808f0e28e8b1fb0e9476.png"},{"id":108409072,"identity":"5a47bec9-0813-4e35-9093-d2e313619f20","added_by":"auto","created_at":"2026-05-04 09:56:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1590301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9027830/v1/c5664cc4-4778-434d-8540-718f3fe08773.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcranial Doppler Ultrasonography and EEG Recording: A Novel Approach to Investigating Resting State","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe investigation of brain resting states represents a highly promising domain for elucidating distinct physiological conditions across diverse patient populations, including those with specific pathologies, disorders of consciousness, and communication impairments such as aphasia. Research into EEG macro- and microstates has demonstrated particular potential in identifying stable resting-state brain activity configurations in clinical groups, such as children with low-functioning autism, as evidenced in studies of atypical neural dynamics in ASD (Das et.al, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Portnova \u0026amp; Martynova, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, EEG possesses inherent limitations, most notably its inability to account for concurrent changes in the autonomic nervous and cardiovascular systems\u0026mdash;a critical factor for comprehensive patient assessment, particularly in populations with dysregulated autonomic nervous system (ANS) control. Regarding the study of cognitive and mental processes, one of the most promising yet underutilized methods for capturing the physiology dynamics of resting-state brain is the direct, real-time monitoring of cerebral blood flow via transcranial Doppler (TCD) sonography. This technique is often overlooked due to procedural complexity. Specifically, Doppler insonation of the middle cerebral artery (MCA) enables the investigation of cerebral perfusion dynamics with temporal resolution comparable to that of EEG.\u003c/p\u003e \u003cp\u003eTCD ultrasonography integrative approach is grounded in the distinct physiology of cerebral vasculature. MCA is regulated by both local brain vascular mechanisms, such as autoregulation and metabolic regulation, and systemic physiology factors, including systemic blood pressure, carbon dioxide, oxygen, and neurogenic stimulation. Local mechanisms allow the artery to adapt independently to changes in cerebral blood flow, while systemic factors influence the overall tone of the brain's vasculature. Thus, a multimodal approach can dissect whether observed changes in cerebral blood flow are driven primarily by local neural demand, systemic physiological shifts, or their interaction\u0026mdash;a distinction of paramount importance for a holistic understanding of patient state (Willie et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMCA flow is primarily autoregulated, which is the intrinsic ability of vessels to maintain constant blood flow despite arterial pressure fluctuations (Harper, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1966\u003c/span\u003e). This process begins with a rapid myogenic response where pressure-induced stretch activates smooth muscle ion channels, leading to depolarization, calcium influx, and vasoconstriction to protect downstream capillaries; conversely, a pressure drop causes vasodilation. The endothelium also contributes, as shear stress stimulates vasodilator release, while factors like Endothelin-1 promote constriction (Armstead, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fantini et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Metabolic regulation, driven by local metabolites like CO₂, triggers vasodilation with increased metabolic activity and vasoconstriction with decreased activity, making flow exquisitely sensitive to local demand (Berne et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Smith \u0026amp; Ainslie, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). During ischemia, the failure of this system leads to energy depletion and irreversible damage (Hata et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Siesj\u0026ouml;, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Metabolic factors like adenosine contribute to vasodilation during hypotension, but cannot fully explain rapid vasoconstriction after hypertension (Strandgaard \u0026amp; Paulson, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe blood flow autoregulation is modulated by metabolic demands, neurogenic influences, and flow-dependent vasomotion, and its impairment in conditions like the MCA stenosis is a key indicator of cerebrovascular health (Haubrich et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Neurogenic control, while limited under normal conditions, can shift the autoregulatory curve upward during acute hypertension. Flow-dependent mechanisms work synergistically with pressure-induced responses (Koller \u0026amp; Toth, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). ANS is a crucial regulator, working with metabolic and astrocytic mechanisms, and its dysfunction is linked to conditions like migraine and stroke (Goadsby, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; S\u0026aacute;ndor, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The MCA exhibits rapid tone adjustments (1\u0026ndash;3 seconds) via neurovascular coupling, where neuronal activity triggers astrocyte-mediated vasodilation (Attwell et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), a process 30% faster in the MCA than in smaller arterioles (Chen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). During hypoxia, K⁺ channel activation dilates the MCA within 2 seconds (Taggart \u0026amp; Wray, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Sympathetic input causes vasoconstriction, while parasympathetic input promotes dilation (Hamel, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sympathetic part of ANS also modulates the MCA tone and causes vasoconstriction, while parasympathetic input promotes vasodilation (Hamel, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The influence of nervous system regulation on the MCA tone has also been observed at the EEG level, where simultaneous recording of TCD ultrasonography signals and EEG was performed. Neural activity precedes hemodynamic changes, as shown by EEG studies where suppression of posterior alpha-rhythm power predicts the MCA vasodilation within 400\u0026ndash;600 ms before detection (Katura et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and gamma oscillations induce a 15\u0026ndash;20% flow surge within 1\u0026ndash;2 seconds (Scheeringa et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These findings indicate that neural activity (alpha desynchronization) precedes the hemodynamic response, meaning EEG can predict vascular changes before they manifest physically. In stroke, EEG delta-theta power predicts a reduction of the MCA flow (Rogers et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while epileptic spikes trigger hyperperfusion (Diehl et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHormonal regulation also modulates the MCA flow. Estrogen promotes vasodilation by upregulating endothelial nitric oxide synthase and prostacyclin synthesis (Krause et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and has broader protective effects (Krause et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Testosterone generally increases vascular tone, though human studies show complex effects (Edvinsson et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Melatonin causes direct vasoconstriction (Krause et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Chronic cortisol exposure disrupts neurovascular coupling and is also linked to cognitive decline (Zhang et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Vasopressin is a potent vasoconstrictor (Edvinsson et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), oxytocin causes mild constriction but may support stress resilience (Edvinsson et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kuchenbecker et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and thromboxane A2 mediates vasoconstriction in stress responses (Cudd, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aim of this study was to investigate the relationship between changes in EEG and Doppler signals, while also examining their potential independence as distinct components of the resting state. We hypothesized that changes in the MCA wall resistance, despite previously demonstrated correlations with EEG data, may possess their own intrinsic dynamics. This hypothesis is grounded in the complex regulatory mechanisms governing the MCA. Additionally, based on evidence of a close relationship between heart rate (HR) TCD signals of the MCA, this study sought to account for HR's influence on the MCA wall resistance. Earlier research demonstrates that the MCA blood flow velocity is strongly correlated with HR during exercise\u0026mdash;a relationship used to assess cerebral autoregulation (J\u0026oslash;rgensen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Consequently, we derived an additional parameter, Cycle-Normalized Peak Velocity Change or dVcycle, which describes the rate of change in flow velocity normalized by the fundamental cardiac cycle length (RR interval). In order to further understand the regulation of the MCA blood flow changes, analogous parameters from photoplethysmography (PPG) were also examined. Additionally, we assessed the motor performance of healthy volunteers on the finger tapping test prior to TCD and EEG/PPG acquisition. Tapping speed serves as a metric of psychomotor function, a capacity that is modulated by numerous factors, including age, hormonal function (particularly thyroid hormones), fatigue, and cognitive status (Grigorova \u0026amp; Sherwin, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Heimhofer et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lismane et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe study sample included 57 healthy participants (27 women and 20 men) aged 20 to 40 years. All volunteers had no history of mental, neurological, cardiovascular diseases, or substance abuse, and had refrained from taking any medication for 72 hours prior to the study. Female participants were confirmed not to be pregnant, had not used oral contraceptives, and were tested during the follicular phase of their menstrual cycle (days 4\u0026ndash;12).\u003c/p\u003e \u003cp\u003eAll experimental sessions were conducted between 11:00 and 14:00. At the study preparation stage, participants were instructed to avoid night shifts for 72 hours prior to the study, abstain from alcohol for 72 hours, and refrain from smoking and caffeine consumption for 4 hours preceding their session.\u003c/p\u003e \u003cp\u003e The study was reviewed and approved by the Ethics Committee of the Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences. The participants provided their written informed consent to participate in this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProtocol of the study\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eProtocol of the study\u003c/div\u003e \u003cp\u003eImmediately prior to the experimental session, participants were asked to complete a set of questionnaires. These included a screening instrument for symptoms of anxiety and depression. Participants were also asked to rate their current emotional state, sleep quality, and physical well-being on a 10-point scale (0\u0026ndash;10).\u003c/p\u003e \u003cp\u003eAdditionally, volunteers performed a tapping test. In this task, they were instructed to click a computer mouse repeatedly for 10-second intervals, first at a comfortable, self-paced rate and then at their maximum possible speed. The results for each task condition (self-paced and maximum speed) were averaged separately to minimize measurement error.\u003c/p\u003e \u003cp\u003eSubsequently, the MCAs were identified bilaterally using TCD ultrasonography. The optimal acoustic windows for signal acquisition were marked on the scalp. Following this, an EEG cap was fitted, and the bilateral Doppler probes were secured in their designated positions. Synchronous EEG recordings were acquired during a resting-state protocol. The protocol began with an eyes-open condition (up to 4 minutes), followed by a 5-minute eyes-closed condition, during which participants were instructed to remain still.\u003c/p\u003e \u003cp\u003eA standard clinical EEG recording was then performed, which included photic stimulation and a hyperventilation provocation test. This was conducted to screen for any underlying EEG abnormalities. For subsequent analysis, only the eyes-closed resting-state data were used. For each participant, a clean segment of approximately 250\u0026ndash;300 seconds was selected from this condition.\u003c/p\u003e\n\u003ch3\u003eThe questionnaires and testing\u003c/h3\u003e\n\u003cp\u003eBefore the study participants filled Hospital Anxiety and Depression Scale, HADS. The scale comprises two domains (anxiety and depression) and includes 14 items. Interpretation is based on the total score for each domain: scores of 0\u0026ndash;7 are within the normal range, 8\u0026ndash;10 indicate subclinical symptoms, and scores of 11 or above are indicative of a clinical disorder. All participants showed results within the normal range.\u003c/p\u003e \u003cp\u003eWe used a tapping test implemented in Neurobehavioral System Presentation software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.neurobs.com/\u003c/span\u003e\u003cspan address=\"https://www.neurobs.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and asked participants to click a computer mouse repeatedly for 10-second intervals. The first task was to click at a comfortable, self-paced rate and second \u0026ndash; to click at their maximum possible speed. The results for each task condition (self-paced and maximum speed) were averaged separately to minimize measurement error.\u003c/p\u003e\n\u003ch3\u003eDoppler signal registration\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eDoppler signal registration\u003c/div\u003e \u003cp\u003eDoppler signals were acquired using a Sonomed-300 ultrasound blood flow velocity analyzer (Spectromed, Russia), which is capable of continuous bilateral signal recording. Bilateral recordings were obtained using symmetrical, identical 2-MHz pulsed-wave (PW) Doppler probes. The right and left MCAs were identified by a certified ultrasound specialist using a Logic-9 ultrasound system equipped with a transcranial probe.\u003c/p\u003e\n\u003ch3\u003eEEG and PPG registration\u003c/h3\u003e\n\u003cp\u003eThe EEG was acquired using a 19-channel EEG amplifier with the recording of PPG (Encephalan Poly4, Medicom MTD, Taganrog, Russia) during 10 min. The sampling rate was 250 Hz. The amplifier band-pass filter was nominally set to 0.05\u0026ndash;70 Hz. AgCl electrodes (Fp\u003csub\u003e1\u003c/sub\u003e, Fp\u003csub\u003e2\u003c/sub\u003e, F\u003csub\u003e7\u003c/sub\u003e, F\u003csub\u003e3\u003c/sub\u003e, Fz, F\u003csub\u003e4\u003c/sub\u003e, F\u003csub\u003e8\u003c/sub\u003e, T\u003csub\u003e3\u003c/sub\u003e, C\u003csub\u003e3\u003c/sub\u003e, Cz, C\u003csub\u003e4\u003c/sub\u003e, T\u003csub\u003e4\u003c/sub\u003e, T\u003csub\u003e5\u003c/sub\u003e, P\u003csub\u003e3\u003c/sub\u003e, Pz, P\u003csub\u003e4\u003c/sub\u003e, T\u003csub\u003e6\u003c/sub\u003e, O\u003csub\u003e1\u003c/sub\u003e, and O\u003csub\u003e2\u003c/sub\u003e) were placed according to the International 10\u0026ndash;20 system. The electrodes placed on the left and right mastoids served as joint references under unipolar montage. The vertical EOG was recorded with AgCl cup electrodes placed 1 cm above and below the left eye, and the horizontal EOG was acquired by electrodes placed 1 cm lateral from the outer canthi of both eyes. The electrode impedances were kept below 10 kΩ. PPG signal was recorded with a standard surface photoplethysmography sensor.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEEG and Doppler synchronization\u003c/h2\u003e \u003cp\u003eTo synchronize the EEG and Doppler equipment, we used a COM port connection to insert event markers. Video monitoring was also implemented to oversee the experimental procedure. The combined data stream from the simultaneous recordings exhibited a maximum timing discrepancy of less than 50 milliseconds. The example of marker synchronization is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of Doppler signal\u003c/h3\u003e\n\u003cp\u003eThe Doppler signal data from the left and right MCA consist of peak systolic velocity (Vmax), end-diastolic velocity (Vmin), and Vmean, pulsatility index (PI), resistivity index (RI), and the time of measurement. The time scale was also exported. These parameters were derived from the Doppler waveform, which were assessed manually by the expert. We excluded data epochs contaminated by artifacts.\u003c/p\u003e \u003cp\u003eThe final feature matrix used for the analysis included Vmax,Vmin, PI = (v\u003csub\u003emax\u003c/sub\u003e - v\u003csub\u003emin\u003c/sub\u003e) / (v\u003csub\u003emean\u003c/sub\u003e); PI = (peak systolic velocity - minimal diastolic velocity) / (Vmean)), RI=(v\u003csub\u003emax\u003c/sub\u003e - v\u003csub\u003emin\u003c/sub\u003e) / (v\u003csub\u003emax\u003c/sub\u003e); RI = (peak systolic velocity - minimal diastolic velocity) / (peak systolic velocity) and RR interval (sec) for the left and right MCA. We also calculated Cycle-Normalized Peak Velocity Change (dVcycle). The dVcycle parameter was normalized by heart rate (HR). A higher value indicates a higher HR in the subject, as it is divided by the RR interval. dVcycle= (Vmax - Vmin) / RR \u0026ndash; the parameter describes a rate of change normalized by the fundamental cycle length (often the cardiac cycle RR-interval).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince the differences between the right and left MCA were negligible when extracting these parameters (the discrepancy did not exceed 2 intervals per subject), we averaged these values for further analysis.\u003c/p\u003e\n\u003ch3\u003eIntervals segmentation\u003c/h3\u003e\n\u003cp\u003eSeveral approaches were employed in the analysis of the Doppler and EEG data: analysis of the Doppler signal parameters was performed for each individual heartbeat, and for intervals of 1 second (non-overlapping), 2 seconds (non-overlapping), and 5 seconds (non-overlapping), as well as for 1-second intervals with a 500 ms overlap, 2-second intervals with a 1-second overlap, and 5-second intervals with 1-second and 2-second overlaps. Similar intervals were analyzed for the EEG data.\u003c/p\u003e \u003cp\u003eThe comparison revealed that the 2-second intervals with a 1-second overlap most accurately represented the dynamics of the Doppler signal across the studied parameters (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for details). This is because 1-second intervals without a 500 ms overlap risked containing no data points when the heart rate was below 60 bpm. Longer intervals (specifically, 5-second windows) demonstrated a lower correlation with the original signal. Furthermore, when comparing the 1-second intervals with a 500 ms overlap to the 2-second intervals with a 1-second overlap, it was determined that the 1-second interval analysis imposes significant limitations on the corresponding EEG analysis. These limitations are particularly evident in the power spectrum for frequencies below 8 Hz and in the analysis of the signal envelope's frequency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of EEG data\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eEEG Preprocessing\u003c/h2\u003e \u003cp\u003eContinuous EEG corresponding to the resting state of each subject was cleaned from eye movements by an ICA-based algorithm in the EEGLAB plugin for MATLAB 2022 (Mathworks Inc., Natick, MA, USA). Muscle artifacts were cut out through manual data inspection. The continuous resting-state EEG of each subject was filtered with a band-pass filter at 0.5\u0026ndash;30 Hz. We analyzed data over three regions: frontal (F3, Fz, and F4), central (C3, Cz, and C4), parietal (P3, Pz, and P4) and occipital (O1, O2).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePower Spectral Density (PSD)\u003c/h2\u003e \u003cp\u003eFast Fourier transform was used to analyze power spectral density (PSD). The EEG spectrum was estimated for every 178\u0026thinsp;\u0026plusmn;\u0026thinsp;22.3 s interval. The resulting normalized spectra were integrated over intervals of unit width in the range of interest (2\u0026ndash;3 Hz, 3\u0026ndash;4 Hz,\u0026hellip;29\u0026ndash;30 Hz). We analyzed the PSD in the following bands: 4\u0026ndash;8 Hz (theta-rhythm band), 8\u0026ndash;10 Hz (alpha1 band), 10\u0026ndash;12 Hz (alpha2 band), 12\u0026ndash;14 Hz (alpha3 band) and the beta band (14\u0026ndash;20 Hz).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFractal Dimension (FD)\u003c/h2\u003e \u003cp\u003eWe performed the calculations of the examined signal band-pass-filtered in the range of interest (1.6\u0026ndash;30 Hz); a Butterworth 12th-order filter was used. Further, fractal dimension (FD) was evaluated using the Higuchi algorithm.\u003c/p\u003e \u003cp\u003eWe calculated all noted EEG parameters for each 2-second EEG intervals with a 1-second overlap - and further call it \u0026ldquo;raw\u0026rdquo; data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePhotoplethysmography (PPG) analysis\u003c/h2\u003e \u003cp\u003eTo align the photoplethysmography (PPG) data with the Doppler signal, we calculated the PI and RI for each dVsycle using the Doppler indecies\u0026mdash;specifically, RI = (systolic peak amplitude \u0026ndash; diastolic peak amplitude) / systolic peak amplitude\u0026mdash;rather than the conventional PPG-based RI, defined as RI = (systolic peak amplitude / diastolic peak amplitude) \u0026times; 100. This approach was adopted to enhance the comparability of the inherently variable and diverse PPG metrics with the more consistent Doppler signal, as well as to standardize measurements across volunteers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStates detected using Doppler and EEG parameters\u003c/h2\u003e \u003cp\u003eAs noted previously, we selected 2-second intervals with a 1-second overlap to analyze the dynamics of both the EEG, PPG and Doppler signals. We analyzed the raw data (here we mean the analysis of all 2-second EEG intervals with a 1-second overlap) and also transformed each EEG and Doppler parameter into unified metrics. These metrics classified data points as normal (within the 2nd and 3rd quartiles, Q2-Q3), high (\u0026gt;\u0026thinsp;0.67σ), or low (\u0026lt; -0.67σ), with these thresholds applied uniformly across all participants (as intragroup differences were not significant). Thus, to analyze the temporal changes we used the following thresholds: \u0026lt; -0.67σ, \u0026gt; 0.67σ, and the range between \u0026minus;\u0026thinsp;0.67σ and 0.67σ. The coverage metric was calculated as the ratio of the sum of intervals in one of the three categories to the total number of intervals for each individual. A prolonged, uninterrupted sequence of values belonging to one of these three categories was defined as a continuous state, and its duration was measured in a number of intervals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWe analyzed potential associations between EEG metrics and Doppler dynamics using a time-lag approach. For each participant, Spearman\u0026rsquo;s rank correlation analysis was performed between the EEG data and the Doppler data with time shifts of 0, 1, 2, and 3 seconds within selected time intervals. This means we correlated EEG and Doppler signals from corresponding time periods \u0026mdash; each approximately 250\u0026ndash;300 seconds long per participant \u0026mdash; first with no time shift (zero lag), and then with the Doppler intervals shifted relative to the EEG intervals. We considered an effect significant if at least 75% of participants (43 out of 57) showed a statistically significant correlation.\u003c/p\u003e \u003cp\u003eWe calculated the coverage and duration of low, normal, and high value fragments for each parameter of Doppler, normalized EEG and PPG signal, defined by the following quartiles: low (\u0026lt; -0.67σ), normal (between \u0026plusmn;\u0026thinsp;0.67σ), and high (\u0026gt;\u0026thinsp;0.67σ). Duration was calculated as the mean number of consecutive intervals without interruption by other states. Coverage was calculated as the ratio of the sum of intervals for each individual state to the total number of intervals for each specific individual. The analysis of the EEG coverage and duration of low, normal, and high value fragments was performed using normalized EEG data. The repeated measures ANOVAs with the following post hoc comparison (Bonferroni, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were used to determine temporal effects (coverage and duration) of the EEG, PPG and Doppler dynamics.\u003c/p\u003e \u003cp\u003eWe also used Spearman correlation analysis to assess association between Doppler and EEG signals with testing, questionnaires and Tapping test. The correlations between the EEG metrics with Doppler dynamics were also analyzed according its association with age gender, testing, questionnaires and Tapping test; for that we used Spearman\u0026rsquo;s rank correlation coefficient and adjusted \u003cem\u003ep\u003c/em\u003e-values after correction for multiple correction. Further, the adjusted \u003cem\u003ep\u003c/em\u003e-values of the significant correlations are presented in the manuscript.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTranscranial doppler monitoring\u003c/h2\u003e \u003cp\u003eOn average, the MCAs were located at a depth of 3.8 to 5.2 cm, with a mean flow velocity of 54.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9 cm/s. The flow characteristics were normal, as were the PI and RI. The Vmax was 77\u0026thinsp;\u0026plusmn;\u0026thinsp;19 cm/s, and the Vmin was 36\u0026thinsp;\u0026plusmn;\u0026thinsp;13 cm/s.\u003c/p\u003e \u003cp\u003eThe main effect of lateral asymmetry was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.45). However, individual-level asymmetry was observed in 11 out of 57 participants for Vmax (4 with side S\u0026thinsp;\u0026gt;\u0026thinsp;D, 7 with D\u0026thinsp;\u0026gt;\u0026thinsp;S), in 8 participants for Vmin (4 S\u0026thinsp;\u0026gt;\u0026thinsp;D, 4 D\u0026thinsp;\u0026gt;\u0026thinsp;S), and in 6 participants for mean velocity (Vmean) (3 S\u0026thinsp;\u0026gt;\u0026thinsp;D, 3 D\u0026thinsp;\u0026gt;\u0026thinsp;S). No other parameters showed individual asymmetric differences. The descriptive statistics of main Doppler parameters of the right and left MCAs are presented in Table\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of Doppler parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDoppler data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eleft\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eright\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edepth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e70.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e102.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVmin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVmean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e62.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVmax-Vmin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e38.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edVcycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eThe correlation between EEG band spectral power and the MCA BloodFlow:\u003c/h2\u003e \u003cp\u003eWe observed that elevations in the beta-rhythm power, occurring at the time of PI measurement and up to one second preceding it, were associated with symmetrical reductions in PI in both cerebral arteries. At the 0-second lag, 77% of participants showed a statistically significant positive correlation (individual r\u0026thinsp;\u0026gt;\u0026thinsp;0.182, p\u0026thinsp;\u0026lt;\u0026thinsp;0.045; mean (over all participants) r\u0026thinsp;=\u0026thinsp;0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05). Similarly, at the 1-second lag, 75% of participants exhibited a significant correlation (mean r\u0026thinsp;=\u0026thinsp;0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06; individual r\u0026thinsp;\u0026gt;\u0026thinsp;0.189, p\u0026thinsp;\u0026lt;\u0026thinsp;0.039).\u003c/p\u003e \u003cp\u003eA reduction in the theta-band power (4\u0026ndash;8 Hz) at a 0-second and 1-second lag was associated with a simultaneous, bilateral increase in RI and PI values. A significant negative correlation was observed in 86% of participants at the 0-second lag (mean r = -0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07; individual r \u0026lt; -0.189, p\u0026thinsp;\u0026lt;\u0026thinsp;0.039) and in 85% of participants at the 1-second lag (mean r = -0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07; individual r \u0026lt; -0.194, p\u0026thinsp;\u0026lt;\u0026thinsp;0.035) for RI parameter. A similar significant negative correlation was found in 84% of participants at the 0-second lag (mean r = -0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09; individual r \u0026lt; -0.184, p\u0026thinsp;\u0026lt;\u0026thinsp;0.043) and in 81% of participants at the 1-second lag (mean r = -0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11; individual r \u0026lt; -0.192, p\u0026thinsp;\u0026lt;\u0026thinsp;0.037) for PI parameter (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a)).\u003c/p\u003e \u003cp\u003eThe strength of the theta-RI correlation was related to motor performance on the tapping test (see Fgure 3(b)). A more pronounced negative correlation was associated with a lower (i.e., worse) fastest tapping score (mean r\u0026thinsp;=\u0026thinsp;0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06, p\u0026thinsp;=\u0026thinsp;0.004 at the 0-second lag; mean r\u0026thinsp;=\u0026thinsp;0.34, p\u0026thinsp;=\u0026thinsp;0.013 at the 1-second lag).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA significant correlation was identified between posterior alpha-rhythm power (10\u0026ndash;12 Hz) suppression and a reduction in the dVcycle in both hemispheres. This association was observed not only at the point of measurement but also with a temporal shift. At the 0-second lag a significant negative correlation was found in 80% of participants (mean r = -0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12; individual r \u0026lt; -0.195, p\u0026thinsp;\u0026lt;\u0026thinsp;0.035). At the 1-second lag the correlation remained significant in 79% of participants (mean r = -0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15; individual r \u0026lt; -0.193, p\u0026thinsp;\u0026lt;\u0026thinsp;0.037). At the 2-second lag the correlation persisted in 75% of participants (mean r = -0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16; individual r \u0026lt; -0.190, p\u0026thinsp;\u0026lt;\u0026thinsp;0.042).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDuration of StableStates for Doppler and EEG Signals\u003c/h2\u003e \u003cp\u003eA significant difference was observed in the duration of the identified states. When selecting states based on thresholds of \u0026gt;\u0026thinsp;0.67σ, \u0026lt; -0.67σ, and between \u0026minus;\u0026thinsp;0.67σ and 0.67σ for both EEG and Doppler parameters, the durations of these states differed significantly. No significant differences were found in the duration of states with low, high, and normal PI, RI, and dVcycle values (p\u0026thinsp;\u0026gt;\u0026thinsp;0.49), nor among the EEG parameters (p\u0026thinsp;\u0026gt;\u0026thinsp;0.11).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics and inter-modal comparisons for signal duration and coverage. Values represent Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;Standard Deviation for EEG, Doppler, and PPG features. Significant differences between modalities were assessed using a linear ANOVA with Bonferroni post-hoc correction.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eDuration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMinimal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.67*sigma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026plusmn;\u0026thinsp;0.67*sigma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e\u0026lt;-0.67*sigma\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eDoppler\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e25,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e11,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edVcycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18,75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e25,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11,53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e19,22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e11,37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003eEEG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;8 Hz PSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51,64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealpha1 PSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6,746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e27,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealpha2 PSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e52,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebeta PSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHjorth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e26,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e5,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvarage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5,61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50,36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10,53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e25,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6,067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e \u003cp\u003ePPG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e49,86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e25,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e21,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e18,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edVcycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e18,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24,97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e50,62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23,87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24,47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e19,73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"1\" nameend=\"c3\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eBonferroni test linear ANOVA (p-values adjusted for Minimal, mean and maximal values)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eMinimal (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eMean (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eMax (average)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eDoppler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDoppler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eDoppler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eEEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003ePPG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMinimal (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDoppler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMean (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDoppler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMax (average)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDoppler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEEG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor Doppler parameters, the minimum duration of a continuous interval consisted of 5 analyzed intervals, with a mean of 5.7 intervals. In contrast, for EEG, the minimum duration was 1 interval, with a mean of 1.7 intervals. A significant main effect of modality (Doppler/EEG) was found (F(1, 56)\u0026thinsp;=\u0026thinsp;198.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Partial η\u0026sup2; = 0.79).\u003c/p\u003e \u003cp\u003eThe mean duration of a continuous interval was 12.3 intervals for Doppler, compared to 3.6 intervals for EEG. This difference was also statistically significant (main effect of modality: F(1, 56)\u0026thinsp;=\u0026thinsp;155.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Partial η\u0026sup2; = 0.73). The highest individual maximum was 107 intervals for doppler (mean 60,2) and 28 for EEG (mean 14.7).\u003c/p\u003e \u003cp\u003eThe duration of the PPG states was most variable and had minimum 1 interval, with a mean of 3.9 intervals and differed from Doppler minimum states (p\u0026thinsp;\u0026lt;\u0026thinsp;0.009) and EEG states (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThus, significant differences were confirmed for both the minimum and mean duration of states between the three signals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe mean values of interval durations of PI (for low, normal variants) and RI (normal variants) significantly correlated with Tapping test results in comfort speed (for PI correspondingly, r=-0.39, r=-0.44, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.005, for RI correspondingly, r=-0.38 with p\u0026thinsp;\u0026lt;\u0026thinsp;0.005). The mean values of dVcycle (for low, normal and high variants) significantly correlated with Tapping test results in comfort speed (r=-0.49, r=-0.51, r=-0.42 with p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The correlation with maximal (fastest) speed was found only for RI (high variant, r=-0.29, p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study's primary objective was to investigate the influence of EEG changes on Doppler signal parameters, in order to examine the characteristics of neurogenic regulation of the MCA blood flow and the specific time window of this interaction. We found a moderate negative association between posterior alpha-rhythm power (10\u0026ndash;12 Hz) suppression and lower dVcycle in both hemispheres, thereby replicating the previous finding that decreased posterior alpha power predicts vasodilation (Katura et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The identified relationship between changes in theta power (4\u0026ndash;8 Hz) and the dynamics of the RI and PI indices is less straightforward, though the strength of the effect was comparable. Specifically, a reduction in theta-band power (4\u0026ndash;8 Hz) at the zero and 1 s lag between EEG and TCD signals was linked to a simultaneous increase in both RI and PI values bilaterally. This inverse relationship between decreased theta rhythm and increased vascular resistance was further associated with performance on the tapping test: a stronger neurovascular correlation was linked to a lower maximum speed of the tapping speed. Previous research has demonstrated that tapping speed is related to age, fatigue, and cognitive status, and is significantly reduced in the context of fatigue, significant motor and cognitive impairments, as well as aging (Grigorova \u0026amp; Sherwin, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Heimhofer et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lismane et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). These conditions are often associated with increased theta power, particularly in frontal regions (Trejo et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, a relationship between tapping speed and hormonal function, particularly thyroid hormones, has long been established (Grigorova \u0026amp; Sherwin, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Stern, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1959\u003c/span\u003e), and has also frequently been associated with a slowing of brain activity (Atli et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings also revealed differences in the duration of continuous states between EEG and Doppler parameters, with a minimum duration of 10 seconds for Doppler and a minimum of 2 seconds for EEG resting states. These same timescales correspond to the regulation of cerebral blood flow, which operates over a range of seconds to minutes. Some responses are very rapid (e.g., Aaslid, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), while others are slower, taking less than 10 seconds for adaptation or up to 3 minutes for neurogenic effects. In contrast to the swift responses of neural mechanisms and metabolic autoregulation (2.3\u0026ndash;4.6 seconds), humoral and metabolic factors ensure a sustained, adequate cerebral oxygen supply. This long-term maintenance involves changes in arteriolar diameter and cerebrovascular resistance, with timelines that can also span from 10 seconds to several minutes.\u003c/p\u003e \u003cp\u003eBased on our Doppler measurements, we observed changes across two distinct dynamic timescales. Rapid fluctuations, which correlated with EEG dynamics over 2-second intervals, are likely attributable to neurogenic factors. Previous studies have demonstrated that such Doppler shifts occur within 2 to 5 seconds following stimulus onset (Czosnyka et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lang \u0026amp; Zimmer, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). Alternatively, these changes may reflect the metabolic mechanism of cerebral blood flow regulation, which has been shown to onset at approximately 2.3 seconds (Aaslid, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe slower dynamics of cerebral blood flow regulation (10 seconds to one minute), associated with the stable states of TCD signal or brain resting-state homeostasis, likely point to autonomic or neurohormonal mechanisms rather than faster metabolic processes (Payne, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sainbhi et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In particular, several hormones, including melatonin, thyroid hormone (T4), cortisol, and growth hormone, can induce changes in cerebral blood flow within minutes of a fluctuation or administration (Bini et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Brabant et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hodkinson et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Schroeder \u0026amp; Privalsky, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In contrast, the influence of sex hormones like estrogen and testosterone typically develops over days or weeks, as seen during sustained high-hormone states such as the luteal phase (Cote et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Krause et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Neurogenic regulation, mediated by peptides like NPY and CGRP, lasting from seconds to minutes, provides a direct and sustained influence on vessel tone (S\u0026aacute;ndor, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This operates in concert with flow-metabolism coupling, where neuronal activity releases vasoactive substances that trigger astrocyte-mediated vasodilation for the duration of the activity (Peterson et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) typically also lasting from seconds to several minutes.\u003c/p\u003e \u003cp\u003eTherefore, this study demonstrates that TCD ultrasonography and EEG provide complementary yet independent temporal perspectives for investigating brain states. Crucially, the temporal dynamics of the MCA blood flow cannot be attributed solely to heart rate fluctuations. To test this, we compared TCD metrics with PPG signals, a modality whose indices are known to be primarily regulated by the ANS. The lack of a significant correlation between steady-state TCD and PPG signals across our cohort suggests that the observed TCD signal dynamics are not predominantly under ANS control, pointing instead to other regulatory mechanisms (Allen \u0026amp; Chen, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings indicate that the duration of the TCD-stable state was negatively correlated with comfortable tapping speed. In contrast, maximal tapping speed exhibited a weaker negative correlation with the duration of RI stable states, but a positive correlation with the association between EEG theta rhythm and Doppler RI. One possible explanation for these observed correlations may involve hormonal regulation. Specifically, the influence of hormonal factors\u0026mdash;including T4, cortisol, and other substances\u0026mdash;cannot be ruled out as contributing to resting-state conditions associated with Doppler stable states. In support of this hypothesis, previous studies have demonstrated decreased cerebral blood flow in patients with hypothyroidism (Grigorova \u0026amp; Sherwin, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Such reductions in flow may be associated with longer durations of Doppler stable states and manifest behaviorally as reduced tapping speed and other forms of decreased motor activity (Constant et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Krausz et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, as hormonal factors were not controlled in the present study, further research is warranted to clarify these relationships.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study delineates the temporal architecture linking resting-state neural activity to cerebral hemodynamics and ANS regulation. We confirmed rapid, frequency-specific neurovascular coupling, with changes in EEG theta and alpha band power predicting shifts in vascular resistance indices within 0\u0026ndash;3 seconds. Additionally, strong inverse link between decreased theta power and increased vascular resistance, which was tied to individual motor performance in the tapping test and increased beta activity correlating with decreased vascular pulsatility.\u003c/p\u003e \u003cp\u003eThe expected result is the demonstration of a fundamental temporal and regulatory dissociation between the MCA blood flow and EEG. Hemodynamic states, as measured by TCD, exhibited significantly longer and more stable durations (mean\u0026thinsp;~\u0026thinsp;22.6 seconds) than the fleeting patterns of neural activity captured by EEG (mean\u0026thinsp;~\u0026thinsp;7.2 seconds). Crucially, the lack of significant correlation between these steady-state TCD metrics and analogous PPG indices, which are predominantly modulated by ANS, indicates that the observed cerebrovascular dynamics are not fully under ANScontrol. This points to other regulatory mechanisms, such as local metabolic or neurohormonal factors, governing these slower vascular rhythms. The behavioral correlation, where longer-lasting TCD states were associated with slower comfortable motor tapping, further suggests these vascular patterns may index a broader physiological or metabolic \u0026ldquo;set-point.\u0026rdquo;\u003c/p\u003e \u003cp\u003eTherefore, EEG, TCD, and PPG provide both complementary and distinct physiological insights: EEG captures rapid neural activity, TCD reveals slower, locally regulated cerebrovascular maintenance states, and PPG reflects ANS cardiovascular influence. This integrated, multi-system perspective is essential for a holistic understanding of the brain resting state homeostasis and provides a novel framework for investigating conditions where the coordination between neural activity, blood flow, and autonomic regulation is impaired.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDue to the absence of mean asymmetry in the PI, RI, and dVcycle parameters, this article does not address individual asymmetry of these measures. However, this parameter may be critically important in the study of pathological changes, including psychiatric and neurological disorders. It will, therefore, be given special attention in subsequent publications\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003ch2\u003eFundings\u003c/h2\u003e \u003cp\u003eNo external funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG. P: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing \u0026ndash; Original draft preparation, Writing \u0026ndash; Review \u0026amp; editing, Visualization, Supervision, Project administration. A.M.: Methodology, Investigation, Data curation, Writing \u0026ndash; Review \u0026amp; editingO.M.: Conceptualization, Resources, Supervision, Funding acquisition\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors upon reasonable request after obtaining approval from the Institution.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAaslid R (1987) Visually evoked dynamic blood flow response of the human cerebral circulation. Stroke 18(4):771\u0026ndash;775\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen J, Chen F (2022) Low-frequency variability in photoplethysmography and autonomic function assessment. Photoplethysmography. Academic, pp 277\u0026ndash;318\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmstead WM (2016) Cerebral blood flow autoregulation and dysautoregulation. 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Metabolism 115:154432\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Transcranial Doppler (TCD), Electroencephalography (EEG), Neurovascular coupling, Cerebral blood flow, Resting state, Autonomic nervous system","lastPublishedDoi":"10.21203/rs.3.rs-9027830/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9027830/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTranscranial Doppler (TCD) ultrasonography enables non-invasive, real-time assessment of cerebral hemodynamics. This study investigated the relationship between EEG, TCD, and photoplethysmography (PPG) during rest, testing the hypothesis that hemodynamic metrics possess intrinsic dynamics beyond immediate neural demand or autonomic control. Fifty-seven healthy adults underwent simultaneous EEG, bilateral middle cerebral artery TCD, and PPG recordings. We employed time-lagged correlation and quantified the duration of stable \"states\" for each modality. Specific neurovascular couplings were confirmed, including an association between posterior alpha-rhythm suppression and reduced flow velocity, and between decreased theta power and increased vascular resistance. Crucially, we found a temporal divergence: hemodynamic states identified via TCD were significantly more prolonged (mean\u0026thinsp;~\u0026thinsp;22.6 seconds) than neural states captured by EEG (mean\u0026thinsp;~\u0026thinsp;7.2 seconds). PPG-derived indices showed no significant correlation with steady-state TCD metrics, suggesting the observed hemodynamics are not primarily driven by autonomic control. Furthermore, the duration of stable TCD states correlated with baseline motor performance. We conclude that the resting-state brain is characterized by dissociable neural, vascular, and autonomic activity on distinct timescales. While rapid neurovascular coupling links momentary neural activity to vascular tone, the cerebrovascular system exhibits its own slower dynamics, independent of direct autonomic modulation, likely reflecting the integration of local metabolic and neurogenic processes.\u003c/p\u003e","manuscriptTitle":"Transcranial Doppler Ultrasonography and EEG Recording: A Novel Approach to Investigating Resting State","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 16:06:06","doi":"10.21203/rs.3.rs-9027830/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"871eaa21-286d-428a-b516-970d6c7145a5","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-04T09:48:38+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T09:55:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 16:06:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9027830","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9027830","identity":"rs-9027830","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}