Active, passive, and motor imagery paradigms: component analysis to assess neurovascular coupling

2013 ◽  
Vol 114 (10) ◽  
pp. 1406-1412 ◽  
Author(s):  
Angela S. M. Salinet ◽  
Thompson G. Robinson ◽  
Ronney B. Panerai
Keyword(s):  
Motor Imagery ◽  
Moving Average ◽  
Neurovascular Coupling ◽  
Neural Activation ◽  
Autoregressive Moving Average Model ◽  
Arterial Blood ◽  
Moving Average Model ◽  
End Tidal ◽  
The Right ◽  
Cognitive Paradigms

The association between neural activity and cerebral blood flow (CBF) has been used to assess neurovascular coupling (NVC) in health and diseases states, but little attention has been given to the contribution of simultaneous changes in peripheral covariates. We used an innovative approach to assess the contributions of arterial blood pressure (BP), PaCO2, and the stimulus itself to changes in CBF velocities (CBFv) during active (MA), passive (MP), and motor imagery (MI) paradigms. Continuous recordings of CBFv, beat-to-beat BP, heart rate, and breath-by-breath end-tidal CO2 (EtCO2) were performed in 17 right-handed subjects before, during, and after motor-cognitive paradigms performed with the right arm. A multivariate autoregressive-moving average model was used to calculate the separate contributions of BP, EtCO2, and the neural activation stimulus (represented by a metronome on-off signal) to the CBFv response during paradigms. Differences were found in the bilateral CBFv responses to MI compared with MA and MP, due to the contributions of stimulation ( P < 0.05). BP was the dominant contributor to the initial peaked CBFv response in all paradigms with no significant differences between paradigms, while the contribution of the stimulus explained the plateau phase and extended duration of the CBFv responses. Separating the neural activation contribution from the influences of other covariates, it was possible to detect differences between three paradigms often used to assess disease-related NVC. Apparently similar CBFv responses to different motor-cognitive paradigms can be misleading due to the contributions from peripheral covariates and could lead to inaccurate assessment of NVC, particularly during MI.

Multivariate modeling of cognitive-motor stimulation on neurovascular coupling: transcranial Doppler used to characterize myogenic and metabolic influences

2012 ◽  
Vol 303 (4) ◽  
pp. R395-R407 ◽  
Author(s):  
Ronney B. Panerai ◽  
Michelle Eyre ◽  
John F. Potter
Keyword(s):  
Transcranial Doppler ◽  
Cerebral Blood Flow Velocity ◽  
Moving Average ◽  
Neurovascular Coupling ◽  
Transcranial Doppler Ultrasound ◽  
Diagnostic Value ◽  
Cognitive Stimulation ◽  
Neural Activation ◽  
Arterial Blood ◽  
End Tidal

Neural activation induces changes in cerebral blood flow velocity (CBFV) with separate contributions from resistance-area product (VRAP) and critical closing pressure (VCrCP). We modeled the dependence of VRAP and VCrCP on arterial blood pressure (ABP), end-tidal CO2 (EtCO2), and cognitive stimulation to test the hypothesis that VRAP reflects myogenic activity while VCrCP reflects metabolic pathways. In 14 healthy subjects, CBFV was measured with transcranial Doppler ultrasound, ABP with the Finapres device and EtCO2 with infrared capnography. Two different paradigms (word or puzzle) were repeated 10 times (30 s on-off), and the corresponding square-wave signal was used, together with ABP and EtCO2, as inputs to autoregressive-moving average (ARMA) models, which allowed identification of the separate contributions of the three inputs to either VRAP or VCrCP. For both paradigms, the contribution of ABP was mainly manifested through VRAP ( P < 0.005 for word; P < 0.004 for puzzle), while stimulation mainly contributed to VCrCP ( P < 0.002 for word; P < 0.033, for puzzle). The contribution of EtCO2 was relatively small (<10%) with greater contribution to VCrCP ( P < 0.01 for puzzle; not significant for word). Separate step responses were also obtained for each of the three inputs. ARMA modeling of VRAP and VCrCP allows the separation of the effects of cerebral autoregulation and CO2 reactivity from the main effects of cognitive-motor stimulation and have the potential to improve the diagnostic value of neurovascular coupling testing in physiological and clinical studies.

scholarly journals Cerebrovascular effects of the thigh cuff maneuver

2015 ◽  
Vol 308 (7) ◽  
pp. H688-H696 ◽  
Author(s):  
R. B. Panerai ◽  
N. P. Saeed ◽  
T. G. Robinson
Keyword(s):  
Cerebral Blood Flow Velocity ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Strongly Correlated ◽  
Mean Values ◽  
Autoregressive Moving Average Model ◽  
Arterial Blood ◽  
Moving Average Model ◽  
Area Product ◽  
End Tidal

Arterial hypotension can be induced by sudden release of inflated thigh cuffs (THC), but its effects on the cerebral circulation have not been fully described. In nine healthy subjects [aged 59 (9) yr], bilateral cerebral blood flow velocity (CBFV) was recorded in the middle cerebral artery (MCA), noninvasive arterial blood pressure (BP) in the finger, and end-tidal CO2 (ETCO2) with nasal capnography. Three THC maneuvers were performed in each subject with cuff inflation 20 mmHg above systolic BP for 3 min before release. Beat-to-beat values were extracted for mean CBFV, BP, ETCO2, critical closing pressure (CrCP), resistance-area product (RAP), and heart rate (HR). Time-varying estimates of the autoregulation index [ARI( t)] were also obtained using an autoregressive-moving average model. Coherent averages synchronized by the instant of cuff release showed significant drops in mean BP, CBFV, and RAP with rapid return of CBFV to baseline. HR, ETCO2, and ARI( t) were transiently increased, but CrCP remained relatively constant. Mean values of ARI( t) for the 30 s following cuff release were not significantly different from the classical ARI [right MCA 5.9 (1.1) vs. 5.1 (1.6); left MCA 5.5 (1.4) vs. 4.9 (1.7)]. HR was strongly correlated with the ARI( t) peak after THC release (in 17/22 and 21/24 recordings), and ETCO2 was correlated with the subsequent drop in ARI( t) (19/22 and 20/24 recordings). These results suggest a complex cerebral autoregulatory response to the THC maneuver, dominated by myogenic mechanisms and influenced by concurrent changes in ETCO2 and possible involvement of the autonomic nervous system and baroreflex.

Contribution of arterial blood pressure and PaCO2 to the cerebrovascular responses to motor stimulation

2012 ◽  
Vol 302 (2) ◽  
pp. H459-H466 ◽  
Author(s):  
Ronney B. Panerai ◽  
Angela S. M. Salinet ◽  
Thompson G. Robinson
Keyword(s):  
Blood Pressure ◽  
Arterial Blood Pressure ◽  
Moving Average ◽  
Sudden Change ◽  
Elbow Flexion ◽  
Autoregressive Moving Average Model ◽  
Arterial Blood ◽  
Moving Average Model ◽  
Aged Subjects ◽  
End Tidal

Motor stimulation induces a neurovascular response that can be detected by continuous measurement of cerebral blood flow (CBF). Simultaneous changes in arterial blood pressure (ABP) and PaCO2 have been reported, but their influence on the CBF response has not been quantified. Continuous bilateral recordings of CBF velocity (CBFV), ABP, and end-tidal CO2 (ETCO2) were obtained in 10 healthy middle-aged subjects at rest and during 60 s of repetitive, metronome-controlled (1 Hz) elbow flexion. A multivariate autoregressive-moving average model was adopted to quantify the relationship between beat-to-beat changes in ABP, breath-by-breath ETCO2, and the motor stimulus, represented by the metronome on-off signal (inputs), and the CBFV response to stimulation (output). All three inputs contributed to explain CBFV variance following stimulation. For the ipsi- and contralateral hemispheres, ABP explained 20.3 ± 17.3% ( P = 0.0007) and 19.5 ± 17.2% ( P = 0.01) of CBFV variance, respectively. Corresponding values for ETCO2 and metronome signals were 22.0 ± 24.2% ( P = 0.008), 24.0 ± 24.1% ( P = 0.037), 32.7 ± 22.5% ( P = 0.0015), and 43.2 ± 25.1% ( P = 0.013), respectively. Synchronized population averages suggest that the initial sudden change in CBFV was largely due to ABP, while the influence of ETCO2 was more erratic. The component due to elbow flexion showed a well-defined pattern, with rise time slower than the main CBFV change but reaching a stable plateau after 15 s of stimulation. Identifying and removing the influences of ABP and PaCO2 to motor-induced changes in CBF should lead to more robust estimates of neurovascular coupling and better understanding of its physiological covariates.

scholarly journals Directional sensitivity of dynamic cerebral autoregulation in squat-stand maneuvers

2018 ◽  
Vol 315 (4) ◽  
pp. R730-R740 ◽  
Author(s):  
Ronney B. Panerai ◽  
Sam C. Barnes ◽  
Mintu Nath ◽  
Naomi Ball ◽  
Thompson G. Robinson ◽  
...  
Keyword(s):  
Cerebral Autoregulation ◽  
Moving Average ◽  
Nonlinear Behavior ◽  
Directional Sensitivity ◽  
Autoregressive Moving Average Model ◽  
Arterial Blood ◽  
Cerebral Blood Velocity ◽  
Young Subjects ◽  
End Tidal ◽  
Dynamic Cerebral Autoregulation

Dynamic cerebral autoregulation (CA), the transient response of cerebral blood flow (CBF) to rapid changes in arterial blood pressure (BP), is usually modeled as a linear mechanism. We tested the hypothesis that dynamic CA can display nonlinear behavior resulting from differential efficiency dependent on the direction of BP changes. Cerebral blood velocity (CBV) (transcranial Doppler), heart rate (HR) (three-lead ECG), continuous BP (Finometer), and end-tidal CO2 (capnograph) were measured in 10 healthy young subjects during 15 squat-stand maneuvers (SSM) with a frequency of 0.05 Hz. The protocol was repeated with a median (interquartile range) of 44 (35–64) days apart. Dynamic CA was assessed with the autoregulation index (ARI) obtained from CBV step responses estimated with an autoregressive moving-average model. Mean BP, HR, and CBV were different (all P < 0.001) between squat and stand, regardless of visits. ARI showed a strong interaction ( P < 0.001) of SSM with the progression of transients; in general, the mean ARI was higher for the squat phase compared with standing. The changes in ARI were partially explained by concomitant changes in CBV ( P = 0.023) and pulse pressure ( P < 0.001), but there was no evidence that ARI differed between visits ( P = 0.277). These results demonstrate that dynamic CA is dependent on the direction of BP change, but further work is needed to confirm if this finding can be generalized to other physiological conditions and also to assess its dependency on age, sex and pathology.

scholarly journals Multi-Channel Singular Spectrum Analysis on Geocenter Motion and Its Precise Prediction

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1403
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Solid Earth ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Autoregressive Moving Average Model ◽  
Seasonal Characteristics ◽  
Geocenter Motion ◽  
Moving Average Model

Geocenter is the center of the mass of the Earth system including the solid Earth, ocean, and atmosphere. The time-varying characteristics of geocenter motion (GCM) reflect the redistribution of the Earth’s mass and the interaction between solid Earth and mass loading. Multi-channel singular spectrum analysis (MSSA) was introduced to analyze the GCM products determined from satellite laser ranging data released by the Center for Space Research through January 1993 to February 2017 for extracting the periods and the long-term trend of GCM. The results show that the GCM has obvious seasonal characteristics of the annual, semiannual, quasi-0.6-year, and quasi-1.5-year in the X, Y, and Z directions, the annual characteristics make great domination, and its amplitudes are 1.7, 2.8, and 4.4 mm, respectively. It also shows long-period terms of 6.09 years as well as the non-linear trends of 0.05, 0.04, and –0.10 mm/yr in the three directions, respectively. To obtain real-time GCM parameters, the MSSA method combining a linear model (LM) and autoregressive moving average model (ARMA) was applied to predict GCM for 2 years into the future. The precision of predictions made using the proposed model was evaluated by the root mean squared error (RMSE). The results show that the proposed method can effectively predict GCM parameters, and the prediction precision in the three directions is 1.53, 1.08, and 3.46 mm, respectively.

scholarly journals Analysis and prediction of polar motion using MSSA method

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xin Jin ◽  
Xin Liu ◽  
Jinyun Guo ◽  
Yi Shen
Keyword(s):  
Polar Motion ◽  
Rotation Axis ◽  
Moving Average ◽  
Singular Spectrum Analysis ◽  
Autoregressive Moving Average ◽  
Rotational Axis ◽  
Autoregressive Moving Average Model ◽  
System Service ◽  
Moving Average Model ◽  
Polar Motion Prediction

AbstractPolar motion is the movement of the Earth's rotational axis relative to its crust, reflecting the influence of the material exchange and mass redistribution of each layer of the Earth on the Earth's rotation axis. To better analyze the temporally varying characteristics of polar motion, multi-channel singular spectrum analysis (MSSA) was used to analyze the EOP 14 C04 series released by the International Earth Rotation and Reference System Service (IERS) from 1962 to 2020, and the amplitude of the Chandler wobbles were found to fluctuate between 20 and 200 mas and decrease significantly over the last 20 years. The amplitude of annual oscillation fluctuated between 60 and 120 mas, and the long-term trend was 3.72 mas/year, moving towards N56.79 °W. To improve prediction of polar motion, the MSSA method combining linear model and autoregressive moving average model was used to predict polar motion with ahead 1 year, repeatedly. Comparing to predictions of IERS Bulletin A, the results show that the proposed method can effectively predict polar motion, and the improvement rates of polar motion prediction for 365 days into the future were approximately 50% on average.

scholarly journals A Set of State–Space Models at a High Disaggregation Level to Forecast Italian Industrial Production

J ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 508-560
Author(s):  
Riccardo Corradini
Keyword(s):  
State Space ◽  
Industrial Production ◽  
Forecast Error ◽  
Moving Average ◽  
Predictive Ability ◽  
State Space Models ◽  
Vector Autoregressive ◽  
Autoregressive Moving Average Model ◽  
New Strategy ◽  
Moving Average Model

Normally, econometric models that forecast the Italian Industrial Production Index do not exploit information already available at time t + 1 for their own main industry groupings. The new strategy proposed here uses state–space models and aggregates the estimates to obtain improved results. The performance of disaggregated models is compared at the same time with a popular benchmark model, a univariate model tailored on the whole index, with persistent not formally registered holidays, a vector autoregressive moving average model exploiting all information published on the web for main industry groupings. Tests for superior predictive ability confirm the supremacy of the aggregated forecasts over three steps horizon using absolute forecast error and quadratic forecast error as a loss function. The datasets are available online.

Sign in / Sign up

Export Citation Format

Share Document