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Bidirectional interactions between neuronal and hemodynamic responses to transcranial direct current stimulation (tDCS): challenges for brain-state dependent tDCS.

Dutta A - Front Syst Neurosci (2015)

Bottom Line: This neurovascular coupling phenomenon led to the concept of neurovascular unit (NVU) that consists of the endothelium, glia, neurons, pericytes, and the basal lamina.Here, recent works suggest NVU as an integrated system working in concert using feedback mechanisms to enable proper brain homeostasis and function where the challenge remains in capturing these mostly nonlinear spatiotemporal interactions within NVU for brain-state dependent tDCS.In principal accordance, we propose EEG-NIRS-based whole-head monitoring of tDCS-induced neuronal and hemodynamic alterations during tDCS.

View Article: PubMed Central - PubMed

Affiliation: INRIA (Sophia Antipolis) - CNRS: UMR5506 - Université Montpellier Montpellier, France ; Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS: UMR5506 - Université Montpellier Montpellier, France.

ABSTRACT
Transcranial direct current stimulation (tDCS) has been shown to modulate cortical neural activity. During neural activity, the electric currents from excitable membranes of brain tissue superimpose in the extracellular medium and generate a potential at scalp, which is referred as the electroencephalogram (EEG). Respective neural activity (energy demand) has been shown to be closely related, spatially and temporally, to cerebral blood flow (CBF) that supplies glucose (energy supply) via neurovascular coupling. The hemodynamic response can be captured by near-infrared spectroscopy (NIRS), which enables continuous monitoring of cerebral oxygenation and blood volume. This neurovascular coupling phenomenon led to the concept of neurovascular unit (NVU) that consists of the endothelium, glia, neurons, pericytes, and the basal lamina. Here, recent works suggest NVU as an integrated system working in concert using feedback mechanisms to enable proper brain homeostasis and function where the challenge remains in capturing these mostly nonlinear spatiotemporal interactions within NVU for brain-state dependent tDCS. In principal accordance, we propose EEG-NIRS-based whole-head monitoring of tDCS-induced neuronal and hemodynamic alterations during tDCS.

No MeSH data available.


Related in: MedlinePlus

State modulation of the NVU with (tDCS) to facilitate a brain state, e.g., spatiotemporal alpha-rhythm state. F is the function to capture NVU system, W is the function to capture observations, ICA is independent component (IC) analysis is a linear decomposition method to transform EEG—NIRS data collected at single scalp channels to a spatially transformed “virtual channel” (i.e., a spatial filter on multi-channel EEG-NIRS data), empirical mode decomposition (EMD) is empirical model decomposition of the “virtual channel” observations that provide intrinsic mode functions (IMF) for proportional control (gain is G) of individual “virtual channel” activity or IC (e.g., posterior alpha band activity) with tDCS.
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Figure 2: State modulation of the NVU with (tDCS) to facilitate a brain state, e.g., spatiotemporal alpha-rhythm state. F is the function to capture NVU system, W is the function to capture observations, ICA is independent component (IC) analysis is a linear decomposition method to transform EEG—NIRS data collected at single scalp channels to a spatially transformed “virtual channel” (i.e., a spatial filter on multi-channel EEG-NIRS data), empirical mode decomposition (EMD) is empirical model decomposition of the “virtual channel” observations that provide intrinsic mode functions (IMF) for proportional control (gain is G) of individual “virtual channel” activity or IC (e.g., posterior alpha band activity) with tDCS.

Mentions: Based on these prior works, we recently proposed EEG-NIRS-based monitoring of neurovascular coupling functionality under perturbation with tDCS (Jindal et al., 2015b). Here, neuronal and hemodynamic responses measured with EEG-NIRS neuroimaging can be represented abstractly as the system response of the NVU to tDCS perturbation (see Figure 1) where presence of symmetry in the nonlinear network of NVU (see Figure 1) may decrease observability (Whalen et al., 2015). Since no real-world network has exact symmetries so with intelligent placement of EEG-NIRS sensors (e.g., to avoid systemic interference; Sood et al., 2015) along with system identification and parameter estimation techniques, it may be possible to track the spatiotemporal change of the states of the NVU. This observer model can then be used to drive multi-electrode tDCS (Dmochowski et al., 2011) for active spatiotemporal modulation of the brain states (e.g., posterior alpha-rhythm). Here, we base our discussions on the recent advances in Kalman filtering approaches to spatiotemporal nonlinear systems (Schiff and Sauer, 2008) and an understanding from group representation theory in controller or observer design by obtaining a modal decomposition into decoupled controllable and uncontrollable (observable and unobservable) subspaces (Whalen et al., 2015). Specifically, Schiff and Sauer (2008) showed the feasibility of unscented Kalman filter (UKF) for recursive estimation of system state for nonlinear systems, including unobserved variables and parameter tracking, in a spatiotemporal model of cortex where such a nonlinear system is controllable using an adaptive feedback electrical field. Here, discretization of the whole-brain detailed biophysical model of NVU (Jolivet et al., 2015), for example with Galerkin methods that are used quite robustly in fluid dynamics, will be necessary where each discrete element corresponds to a volume of tissue imaged as well as stimulated with the EEG-NIRS/tDCS unit (see Figure 1). As an alternative to a fundamental NVU model (Jolivet et al., 2015) for the volume of tissue imaged and stimulated with EEG-NIRS/tDCS unit, we tried (Dutta et al., 2015) to find an empirical model where we performed empirical mode decomposition (EMD) and the Hilbert spectrum (Huang et al., 1998) to model the system dynamics and found a negative cross-correlation between one of the intrinsic mode function (IMF) of the HbO2 time-series and log-transformed mean-power time-course of EEG primarily within 0.5–11.25 Hz frequency band (i.e., one of the EEG IMFs). In principal accordance, for whole-head monitoring, we propose independent component analysis (ICA) to transform multi-channel EEG-NIRS/tDCS unit imaging data to a spatially transformed “virtual channel” (i.e., a spatial filter; Jung et al., 2001). Then, the “virtual channel” activity (e.g., posterior alpha band activity) can be subjected to EMD (i.e., a temporal filter) to reduce the dimension of the observable dynamics (i.e., further observer model reduction) before developing the Kalman filter observer (Schiff and Sauer, 2008) using the IMFs. For EEG-NIRS-based monitoring of NVU under perturbation with tDCS, as shown in the Figure 2, the NVU dynamics is captured by the function F and the IMF observations by the function W. The UKF approach should match the nonlinear IMF dynamics up to the second order statistics where the feasibility remains to be tested experimentally in future studies. Furthermore, it may be possible to use the Kalman observer to calculate proportional control (see Figure 2) of the brain-state (e.g., cortical excitability; Jindal et al., 2015a) with tDCS. Here, intelligent placement of EEG-NIRS sensors and tDCS effectors is necessary to ensure observability and controllability (Whalen et al., 2015) where Whalen et al. (2015) suggested in general that more direct incoming connections into an observed node lead to higher observability and more direct outgoing connections from a controlled node lead to higher controllability. Furthermore, controllability may be enhanced with multi-modal non-invasive brain stimulation (NIBS), e.g., with direct electrical stimulation (Pulgar, 2015) and photobiostimulation (Gonzalez-Lima and Barrett, 2014), which needs to be investigated.


Bidirectional interactions between neuronal and hemodynamic responses to transcranial direct current stimulation (tDCS): challenges for brain-state dependent tDCS.

Dutta A - Front Syst Neurosci (2015)

State modulation of the NVU with (tDCS) to facilitate a brain state, e.g., spatiotemporal alpha-rhythm state. F is the function to capture NVU system, W is the function to capture observations, ICA is independent component (IC) analysis is a linear decomposition method to transform EEG—NIRS data collected at single scalp channels to a spatially transformed “virtual channel” (i.e., a spatial filter on multi-channel EEG-NIRS data), empirical mode decomposition (EMD) is empirical model decomposition of the “virtual channel” observations that provide intrinsic mode functions (IMF) for proportional control (gain is G) of individual “virtual channel” activity or IC (e.g., posterior alpha band activity) with tDCS.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4530593&req=5

Figure 2: State modulation of the NVU with (tDCS) to facilitate a brain state, e.g., spatiotemporal alpha-rhythm state. F is the function to capture NVU system, W is the function to capture observations, ICA is independent component (IC) analysis is a linear decomposition method to transform EEG—NIRS data collected at single scalp channels to a spatially transformed “virtual channel” (i.e., a spatial filter on multi-channel EEG-NIRS data), empirical mode decomposition (EMD) is empirical model decomposition of the “virtual channel” observations that provide intrinsic mode functions (IMF) for proportional control (gain is G) of individual “virtual channel” activity or IC (e.g., posterior alpha band activity) with tDCS.
Mentions: Based on these prior works, we recently proposed EEG-NIRS-based monitoring of neurovascular coupling functionality under perturbation with tDCS (Jindal et al., 2015b). Here, neuronal and hemodynamic responses measured with EEG-NIRS neuroimaging can be represented abstractly as the system response of the NVU to tDCS perturbation (see Figure 1) where presence of symmetry in the nonlinear network of NVU (see Figure 1) may decrease observability (Whalen et al., 2015). Since no real-world network has exact symmetries so with intelligent placement of EEG-NIRS sensors (e.g., to avoid systemic interference; Sood et al., 2015) along with system identification and parameter estimation techniques, it may be possible to track the spatiotemporal change of the states of the NVU. This observer model can then be used to drive multi-electrode tDCS (Dmochowski et al., 2011) for active spatiotemporal modulation of the brain states (e.g., posterior alpha-rhythm). Here, we base our discussions on the recent advances in Kalman filtering approaches to spatiotemporal nonlinear systems (Schiff and Sauer, 2008) and an understanding from group representation theory in controller or observer design by obtaining a modal decomposition into decoupled controllable and uncontrollable (observable and unobservable) subspaces (Whalen et al., 2015). Specifically, Schiff and Sauer (2008) showed the feasibility of unscented Kalman filter (UKF) for recursive estimation of system state for nonlinear systems, including unobserved variables and parameter tracking, in a spatiotemporal model of cortex where such a nonlinear system is controllable using an adaptive feedback electrical field. Here, discretization of the whole-brain detailed biophysical model of NVU (Jolivet et al., 2015), for example with Galerkin methods that are used quite robustly in fluid dynamics, will be necessary where each discrete element corresponds to a volume of tissue imaged as well as stimulated with the EEG-NIRS/tDCS unit (see Figure 1). As an alternative to a fundamental NVU model (Jolivet et al., 2015) for the volume of tissue imaged and stimulated with EEG-NIRS/tDCS unit, we tried (Dutta et al., 2015) to find an empirical model where we performed empirical mode decomposition (EMD) and the Hilbert spectrum (Huang et al., 1998) to model the system dynamics and found a negative cross-correlation between one of the intrinsic mode function (IMF) of the HbO2 time-series and log-transformed mean-power time-course of EEG primarily within 0.5–11.25 Hz frequency band (i.e., one of the EEG IMFs). In principal accordance, for whole-head monitoring, we propose independent component analysis (ICA) to transform multi-channel EEG-NIRS/tDCS unit imaging data to a spatially transformed “virtual channel” (i.e., a spatial filter; Jung et al., 2001). Then, the “virtual channel” activity (e.g., posterior alpha band activity) can be subjected to EMD (i.e., a temporal filter) to reduce the dimension of the observable dynamics (i.e., further observer model reduction) before developing the Kalman filter observer (Schiff and Sauer, 2008) using the IMFs. For EEG-NIRS-based monitoring of NVU under perturbation with tDCS, as shown in the Figure 2, the NVU dynamics is captured by the function F and the IMF observations by the function W. The UKF approach should match the nonlinear IMF dynamics up to the second order statistics where the feasibility remains to be tested experimentally in future studies. Furthermore, it may be possible to use the Kalman observer to calculate proportional control (see Figure 2) of the brain-state (e.g., cortical excitability; Jindal et al., 2015a) with tDCS. Here, intelligent placement of EEG-NIRS sensors and tDCS effectors is necessary to ensure observability and controllability (Whalen et al., 2015) where Whalen et al. (2015) suggested in general that more direct incoming connections into an observed node lead to higher observability and more direct outgoing connections from a controlled node lead to higher controllability. Furthermore, controllability may be enhanced with multi-modal non-invasive brain stimulation (NIBS), e.g., with direct electrical stimulation (Pulgar, 2015) and photobiostimulation (Gonzalez-Lima and Barrett, 2014), which needs to be investigated.

Bottom Line: This neurovascular coupling phenomenon led to the concept of neurovascular unit (NVU) that consists of the endothelium, glia, neurons, pericytes, and the basal lamina.Here, recent works suggest NVU as an integrated system working in concert using feedback mechanisms to enable proper brain homeostasis and function where the challenge remains in capturing these mostly nonlinear spatiotemporal interactions within NVU for brain-state dependent tDCS.In principal accordance, we propose EEG-NIRS-based whole-head monitoring of tDCS-induced neuronal and hemodynamic alterations during tDCS.

View Article: PubMed Central - PubMed

Affiliation: INRIA (Sophia Antipolis) - CNRS: UMR5506 - Université Montpellier Montpellier, France ; Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS: UMR5506 - Université Montpellier Montpellier, France.

ABSTRACT
Transcranial direct current stimulation (tDCS) has been shown to modulate cortical neural activity. During neural activity, the electric currents from excitable membranes of brain tissue superimpose in the extracellular medium and generate a potential at scalp, which is referred as the electroencephalogram (EEG). Respective neural activity (energy demand) has been shown to be closely related, spatially and temporally, to cerebral blood flow (CBF) that supplies glucose (energy supply) via neurovascular coupling. The hemodynamic response can be captured by near-infrared spectroscopy (NIRS), which enables continuous monitoring of cerebral oxygenation and blood volume. This neurovascular coupling phenomenon led to the concept of neurovascular unit (NVU) that consists of the endothelium, glia, neurons, pericytes, and the basal lamina. Here, recent works suggest NVU as an integrated system working in concert using feedback mechanisms to enable proper brain homeostasis and function where the challenge remains in capturing these mostly nonlinear spatiotemporal interactions within NVU for brain-state dependent tDCS. In principal accordance, we propose EEG-NIRS-based whole-head monitoring of tDCS-induced neuronal and hemodynamic alterations during tDCS.

No MeSH data available.


Related in: MedlinePlus