Assessment of whole-head magnetoencephalography during transcranial electric entrainment of brain oscillations

Application of non-invasive brain stimulation for perturbing brain activity is well established. Various forms of brain stimulation protocols have been effectively demonstrated to modulate behavior associated with the perturbed brain activity. However, the interaction of brain stimulation with ongoing brain activity has been challenging to characterize as the stimulation artifacts in the recordings of brain activity impedes such characterization. The proposed amplitude modulated transcranial alternating current stimulation (tACSAM) attenuates possible stimulation artifacts at the frequency of interest. This is possible by modulating the amplitude of high frequency transcranial alternating current (tACS) signal at a lower physiological frequency of interest to generate the tACSAM signal. Furthermore, application of tACSAM allows localization of the perturbed brain activity with millimeter precision by applying spatial filters on magnetoencephalography (MEG) recordings. For characterization of the tACSAM-perturbed brain activity, conventional spectral analysis may not be sufficient. Thus, power and PLV were compared between tACS and tACSAM in a phantom model and MEG data recorded from healthy human volunteers. The synchronization estimate, phase lock value (PLV), is a measure of circular variance between two signals calculated as a function of instantaneous phase difference between the ongoing brain activity and the applied stimulation signal. Even though, systematic linear phase shifts due to the applied tES signal occur in MEG sensors, mathematically such systematic linear phase shifts nullify while calculating PLV. Systematic evaluation of the MEG data acquired during tACSAM showed increased PLV compared to tACS indicating increased demodulation in such paradigm. Upon observing tACSAM-related increased demodulation, it was still unclear whether such perturbations of brain activity could modulate behavior. To address this question, twenty volunteers while engaging in a working memory paradigm received tACSAM or no stimulation. Working memory is associated with transient storage and processing of information. Increasing the difficulty of working memory paradigm increases the amplitude of brain activity in the theta band (4 – 8 Hz), while encoding the temporal order of the transient information in the phase of the theta activity. Thus, by targeting individual’s theta peak frequency using tACSAM, it was possible to modulate the accuracy in the working memory paradigm. The accuracy on a working memory parading of volunteers receiving tACSAM deteriorated compared to the participants who did not receive brain stimulation. Therefore, targeting brain activity in theta band using tACSAM interferes with execution of normal working memory processes, probably by interfering with the maintenance of temporal order of the transient information. Furthermore, tACSAM but not sham stimulation inhibited the increase in amplitude of theta activity during the n-back task, which is essential for working memory processes. Even though, it is possible to assess the brain activity recorded during tACSAM, presence of stimulation artifacts in the assessed brain activity cannot be excluded. However, it was possible to gather evidence that tACSAM is associated with demodulation. TACSAM-induced phase synchrony at the modulation frequency was larger compared to tACS even though the power during tACS is larger compared to tACSAM. This observation is in favor of possible functional interaction of tACSAM signal with neurons in the brain. However, currently it is not possible to distinguish between the contribution towards demodulation of tACSAM signal by non-linearities of the stimulation setup and functional interactions with neurons in the brain. In conclusion, tACSAM can alter cognitive function, such as working memory performance, possibly through entrainment. The results obtained from such investigations must be interpreted with great care, as the extent by which possible stimulation artifacts impact the MEG recordings is not entirely clear. Further investigations are necessary to develop quantitative assessment techniques for characterizing artifacts of the stimulation and eventually develop brain state dependent stimulation paradigms in real time as a research tool and therapeutic intervention

A computational model of neuro-glio-vascular loop interactions.

We present a computational, biophysical model of neuron-astrocyte-vessel interaction. Unlike other cells, neurons convey "hunger" signals to the vascular network via an intervening layer of glial cells (astrocytes); vessels dilate and release glucose which fuels neuronal firing. Existing computational models focus on only parts of this loop (neuron→astrocyte→vessel→neuron), whereas the proposed model describes the entire loop. Neuronal firing causes release of a neurotransmitter like glutamate which triggers release of vasodilator by astrocytes via a cascade of biochemical events. Vasodilators released from astrocytic endfeet cause blood vessels to dilate and release glucose into the interstitium, part of which is taken up by the astrocyticendfeet. Glucose is converted into lactate in the astrocyte and transported into the neuron. Glucose from the interstitium and lactate (produced from glucose) influx from astrocyte are converted into ATP in the neuron. Neuronal ATP is used to drive the Na(+)/K(+)ATPase pumps, which maintain ionic gradients necessary for neuronal firing. When placed in the metabolic loop, the neuron exhibits sustained firing only when the stimulation current is more than a minimum threshold. For various combinations of initial neuronal [ATP] and external current, the neuron exhibits a variety of firing patterns including sustained firing, firing after an initial pause, burst firing etc. Neurovascular interactions under conditions of constricted vessels are also studied. Most models of cerebral circulation describe neurovascular interactions exclusively in the "forward" neuron→vessel direction. The proposed model indicates possibility of "reverse" influence also, with vasomotion rhythms influencing neural firing patterns. Another idea that emerges out of the proposed work is that brain's computations may be more comprehensively understood in terms of neuro-glial-vascular dynamics and not in terms of neural firing alone

Schematic of biochemical signaling involved in neuron→astrocyte→vessel→neuron coupling.

<p>Schematic of biochemical signaling involved in neuron→astrocyte→vessel→neuron coupling.</p

(a) Neuronal membrane potential bound by reversal potential of sodium and potassium channel along with (b) corresponding change in extracellular [EET] and the vessel radius.

<p>(a) Neuronal membrane potential bound by reversal potential of sodium and potassium channel along with (b) corresponding change in extracellular [EET] and the vessel radius.</p

Regimes obtained for various combinations of stimulation current (I_S) and initial [ATP].

<p>(1) Bursting, (2)Transition phase from bursting to firing with initial pause, (3) Firing with initial pause and (4) Continuous firing.</p

Neuronal firing pattern observed for various kinds of stimulation pattern.

<p>Neuronal firing pattern observed for various kinds of stimulation pattern.</p

Model changes in astrocyte on application of 2 s pulses of glutamate (a) resulting change in <i>EET</i> concentration in extracellular space between astrocyte and smooth muscle cell, in the proposed model. (b) the corresponding result reported by Bennett et al., (2008) [<b>3</b>]. The lowest trace in (b) is comparable with the graph in (a).

<p>Model changes in astrocyte on application of 2 s pulses of glutamate (a) resulting change in <i>EET</i> concentration in extracellular space between astrocyte and smooth muscle cell, in the proposed model. (b) the corresponding result reported by Bennett et al., (2008) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048802#pone.0048802-Bennett1" target="_blank">[<b>3</b>]</a>. The lowest trace in (b) is comparable with the graph in (a).</p

Simulation results depicting variation of (a) Neuronal <i>Ca²</i>

<p> ^<b>+</b><b> concentration along with (b) as reported in </b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048802#pone.0048802-Lee1" target="_blank">[<b>25</b>]</a><b>.</b></p

(a) Neuronal membrane potential bound by reversal potential of sodium and potassium channel for a stimulation duration of 1 s (black bar) along with (b) corresponding change in extracellular [EET] and vessel radius.

<p>(a) Neuronal membrane potential bound by reversal potential of sodium and potassium channel for a stimulation duration of 1 s (black bar) along with (b) corresponding change in extracellular [EET] and vessel radius.</p