Investigation Of Interaction Between Physiological Signals And fMRI Dynamic Functional Connectivity Using Independent Component Analysis.

The blood oxygen level dependent (BOLD) fMRI signal is influenced not only by neuronal activity but also by fluctuations in physiological signals, including respiration, arterial CO2 and heart rate/ heart rate variability (HR/HRV). Even spontaneous physiological signal fluctuations have been shown to influence the BOLD fMRI signal in a regionally specific manner. Consequently, estimates of functional connectivity between different brain regions, performed when the subject is at rest, may be confounded by the effects of physiological signal fluctuations. In addition, resting functional connectivity has been shown to vary with respect to time (dynamic functional connectivity - DFC), with the sources of this variation not fully elucidated. The effect of physiological factors on dynamic (time-varying) resting-state functional connectivity has not been studied extensively, to our knowledge. In our previous study, we investigated the effect of heart rate (HR) and end-tidal CO2 (PETCO2) on the time-varying network degree of three well-described RSNs (DMN, SMN and Visual Network) using mask-based and seed-based analysis, and we identified brain-heart interactions which were more pronounced in specific frequency bands. Here, we extend this work, by estimating DFC and its corresponding network degree for the RSNs, employing a data-driven approach to extract the RSNs (low-and high-dimensional Independent Component Analysis (ICA)), which we subsequently correlate with the characteristics of simultaneously collected physiological signals. The results confirm that physiological signals have a modulatory effect on resting-state, fMRI-based DFC.

Spontaneous physiological variability modulates dynamic functional connectivity in resting-state functional magnetic resonance imaging.

It is well known that the blood oxygen level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is influenced-in addition to neuronal activity-by fluctuations in physiological signals, including arterial CO2, respiration and heart rate/heart rate variability (HR/HRV). Even spontaneous fluctuations of the aforementioned physiological signals have been shown to influence the BOLD fMRI signal in a regionally specific manner. Related to this, estimates of functional connectivity between different brain regions, performed when the subject is at rest, may be confounded by the effects of physiological signal fluctuations. Moreover, resting functional connectivity has been shown to vary with respect to time (dynamic functional connectivity), with the sources of this variation not fully elucidated. In this context, we examine the relation between dynamic functional connectivity patterns and the time-varying properties of simultaneously recorded physiological signals (end-tidal CO2 and HR/HRV) using resting-state fMRI measurements from 12 healthy subjects. The results reveal a modulatory effect of the aforementioned physiological signals on the dynamic resting functional connectivity patterns for a number of resting-state networks (default mode network, somatosensory, visual). By using discrete wavelet decomposition, we also show that these modulation effects are more pronounced in specific frequency bands.

Multiple-input nonlinear modelling of cerebral haemodynamics using spontaneous arterial blood pressure, end-tidal CO2 and heart rate measurements.

In order to examine the effect of changes in heart rate (HR) upon cerebral perfusion and autoregulation, we include the HR signal recorded from 18 control subjects as a third input in a two-input model of cerebral haemodynamics that has been used previously to quantify the dynamic effects of changes in arterial blood pressure and end-tidal CO2upon cerebral blood flow velocity (CBFV) measured at the middle cerebral arteries via transcranial Doppler ultrasound. It is shown that the inclusion of HR as a third input reduces the output prediction error in a statistically significant manner, which implies that there is a functional connection between HR changes and CBFV. The inclusion of nonlinearities in the model causes further statistically significant reduction of the output prediction error. To achieve this task, we employ the concept of principal dynamic modes (PDMs) that yields dynamic nonlinear models of multi-input systems using relatively short data records. The obtained PDMs suggest model-driven quantitative hypotheses for the role of sympathetic and parasympathetic activity (corresponding to distinct PDMs) in the underlying physiological mechanisms by virtue of their relative contributions to the model output. These relative PDM contributions are subject-specific and, therefore, may be used to assess personalized characteristics for diagnostic purposes.

Arterial CO2 effects modulate dynamic functional connectivity in resting-state fMRI.

It is well known that the blood-oxygen level dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI) is influenced - in addition to neuronal activity - by fluctuations in physiological signals, including arterial CO2. For instance, even spontaneous CO2 fluctuations have been shown to influence the BOLD fMRI signal regionally. Related to this, studies of functional connectivity between different brain regions, performed when the subject is at rest, may be confounded by the effects of physiological noise. Moreover, resting functional connectivity has been shown to vary with respect to time (dynamic functional connectivity), with the sources of this variation not fully understood at present. In this context, in the present paper we examine the relation between dynamic functional connectivity patterns and the properties of the end-tidal CO2 signal (PETCO2) using resting-state fMRI measurements from 12 healthy subjects. The results demonstrate that there exists a modulatory effect of the correlation strength between PETCO2 and the BOLD signal on dynamic resting functional connectivity. The extent to which this effect was observed was found to be strongly dependent on the data analysis methodology.

The effect of remifentanil on respiratory variability, evaluated with dynamic modeling.

Opioid drugs disrupt signaling in the brain stem respiratory network affecting respiratory rhythm. We evaluated the influence of a steady-state infusion of a model opioid, remifentanil, on respiratory variability during spontaneous respiration in a group of 11 healthy human volunteers. We used dynamic linear and nonlinear models to examine the effects of remifentanil on both directions of the ventilatory loop, i.e., on the influence of natural variations in end-tidal carbon dioxide (Pet(CO(2))) on ventilatory variability, which was assessed by tidal volume (Vt) and breath-to-breath ventilation (i.e., the ratio of tidal volume over total breath time Vt/Ttot), and vice versa. Breath-by-breath recordings of expired CO(2) and respiration were made during a target-controlled infusion of remifentanil for 15 min at estimated effect site (i.e., brain tissue) concentrations of 0, 0.7, 1.1, and 1.5 ng/ml, respectively. Remifentanil caused a profound increase in the duration of expiration. The obtained models revealed a decrease in the strength of the dynamic effect of Pet(CO(2)) variability on Vt (the "controller" part of the ventilatory loop) and a more pronounced increase in the effect of Vt variability on Pet(CO(2)) (the "plant" part of the loop). Nonlinear models explained these dynamic interrelationships better than linear models. Our approach allows detailed investigation of drug effects in the resting state at the systems level using noninvasive and minimally perturbing experimental protocols, which can closely represent real-life clinical situations.

Modeling of nonlinear physiological systems with fast and slow dynamics. II. Application to cerebral autoregulation.

Dynamic autoregulation of cerebral hemodynamics in healthy humans is studied using the novel methodology of the Laguerre-Volterra network for systems with fast and slow dynamics (Mitsis, G. D., and V. Z. Marmarelis, Ann. Biomed. Eng. 30:272-281, 2002). Since cerebral autoregulation is mediated by various physiological mechanisms with significantly different time constants, it is used to demonstrate the efficacy of the new method. Results are presented in the time and frequency domains and reveal that cerebral autoregulation is a nonlinear and dynamic (frequency-dependent) system with considerable nonstationarities. Quantification of the latter reveals greater variability in specific frequency bands for each subject in the low and middle frequency range (below 0.1 Hz). The nonlinear dynamics are prominent also in the low and middle frequency ranges, where the frequency response of the system exhibits reduced gain.

Modeling of nonlinear physiological systems with fast and slow dynamics. I. Methodology.

Effective modeling of nonlinear dynamic systems can be achieved by employing Laguerre expansions and feedforward artificial neural networks in the form of the Laguerre-Volterra network (LVN). This paper presents a different formulation of the LVN that can be employed to model nonlinear systems displaying complex dynamics effectively. This is achieved by using two different filter banks, instead of one as in the original definition of the LVN, in the input stage and selecting their structural parameters in an appropriate way. Results from simulated systems show that this method can yield accurate nonlinear models of Volterra systems, even when considerable noise is present, separating at the same time the fast from the slow components of these systems effectively.