Synapse Geometry and Receptor Dynamics Modulate Synaptic Strength

Synaptic transmission relies on several processes, such as the location of a released vesicle, the number and type of receptors, trafficking between the postsynaptic density (PSD) and extrasynaptic compartment, as well as the synapse organization. To study the impact of these parameters on excitatory synaptic transmission, we present a computational model for the fast AMPA-receptor mediated synaptic current. We show that in addition to the vesicular release probability, due to variations in their release locations and the AMPAR distribution, the postsynaptic current amplitude has a large variance, making a synapse an intrinsic unreliable device. We use our model to examine our experimental data recorded from CA1 mice hippocampal slices to study the differences between mEPSC and evoked EPSC variance. The synaptic current but not the coefficient of variation is maximal when the active zone where vesicles are released is apposed to the PSD. Moreover, we find that for certain type of synapses, receptor trafficking can affect the magnitude of synaptic depression. Finally, we demonstrate that perisynaptic microdomains located outside the PSD impacts synaptic transmission by regulating the number of desensitized receptors and their trafficking to the PSD. We conclude that geometrical modifications, reorganization of the PSD or perisynaptic microdomains modulate synaptic strength, as the mechanisms underlying long-term plasticity

A quantitative physical model of the TMS-induced discharge artifacts in EEG.

The combination of Transcranial Magnetic Stimulation (TMS) with Electroencephalography (EEG) exposes the brain's global response to localized and abrupt stimulations. However, large electric artifacts are induced in the EEG by the TMS, obscuring crucial stages of the brain's response. Artifact removal is commonly performed by data processing techniques. However, an experimentally verified physical model for the origin and structure of the TMS-induced discharge artifacts, by which these methods can be justified or evaluated, is still lacking. We re-examine the known contribution of the skin in creating the artifacts, and outline a detailed model for the relaxation of the charge accumulated at the electrode-gel-skin interface due to the TMS pulse. We then experimentally validate implications set forth by the model. We find that the artifacts decay like a power law in time rather than the commonly assumed exponential. In fact, the skin creates a power-law decay of order 1 at each electrode, which is turned into a power law of order 2 by the reference electrode. We suggest an artifact removal method based on the model which can be applied from times after the pulse as short as 2 milliseconds onwards to expose the full EEG from the brain. The method can separate the capacitive discharge artifacts from those resulting from cranial muscle activation, demonstrating that the capacitive effect dominates at short times. Overall, our insight into the physical process allows us to accurately access TMS-evoked EEG responses that directly follow the TMS pulse, possibly opening new opportunities in TMS-EEG research

Phase-Amplitude Markers of Synchrony and Noise:A Resting-State and TMS-EEG Study of Schizophrenia

The electroencephalogram (EEG) of schizophrenia patients is known to exhibit a reduction of signal-to-noise ratio and of phase locking, as well as a facilitation of excitability, in response to a variety of external stimuli. Here, we demonstrate these effects in transcranial magnetic stimulation (TMS)-evoked potentials and in the resting-state EEG. To ensure veracity, we used 3 weekly sessions and analyzed both resting-state and TMS-EEG data. For the TMS responses, our analysis verifies known results. For the resting state, we introduce the methodology of mean-normalized variation to the EEG analysis (quartile-based coefficient of variation), which allows for a comparison of narrow-band EEG amplitude fluctuations to narrow-band Gaussian noise. This reveals that amplitude fluctuations in the delta, alpha, and beta bands of healthy controls are different from those in schizophrenia patients, on time scales of tens of seconds. We conclude that the EEG-measured cortical activity patterns of schizophrenia patients are more similar to noise, both in alpha- and beta-resting state and in TMS responses. Our results suggest that the ability of neuronal populations to form stable, locally, and temporally correlated activity is reduced in schizophrenia, a conclusion, that is, in accord with previous experiments on TMS-EEG and on resting-state EEG

Representative example of TMS artifacts on a human head and assessment of the artifact model.

<p>(A) Example of raw EEG traces displaying TMS-induced artifacts recorded from a human head. Magnetic stimulation was applied at time 0 at electrode Cz on a 64-electrode cap with 32 electrodes on the right hemisphere. The artifacts appear in every electrode trace at different strength or shape. Inset: On the uV scale of physiological brain waves, some traces exhibit an artifact duration of more than 100 ms. (B) The fast initial artifact dynamics related to the magnetic pulse. (C) Averaging out noise using five trials shows the long-lasting artifact decay to baseline. (D) On a log-log scale, the tails in the decay of the artifacts (from C) follow a power-law with an exponent on the order of 2 (red dashed lines). (E) TMS-EEG traces on a human knee. Shown are raw data (gray) of a single recording from 28 electrodes covering the knee following TMS. The artifacts reconstructed with the model are shown in green. (F) Log-log plot. (G) Data after subtraction of the reconstructed artifacts followed by subtraction of average of all traces to remove the common mode. As expected from TMS on a knee, the stimulation does not evoke neuronal activity, such that the artifact-removed traces are flat up to continuation of the typical very slow and small electrode drifts. The area shaded in gray indicates where the artifacts could not be reconstructed. (H) To assess the goodness of fit, we use the <i>χ</i>²-test with significance level <i>α</i> = 5%. Shown is the maximal time span for which the test accepts the fit. Beyond this time, the fit is rejected. Small electrode drifts and noise due to TMS-device recharging can shorten this time, however never below 20 ms. (I-J) Fits of sums of two exponentials. (K) Subtraction of the fits introduces both fast and slow distortions of the data in almost all traces. (L) Correspondingly, the fits are generally not accepted, except mainly in noisy and drifting electrodes. (M) The difference of data reconstructed by the model and by the sum of two exponentials. This equals the difference of the respective reconstructed artifacts. (N) For almost all fits by a sum of two exponentials, the quotients of the decay constants are approximately equal and have the same order of magnitude (<i>a</i>, <i>b</i>, <i>c</i>, <i>d</i> constants, <i>t</i> time).</p

Effect of changing the sampling rate.

<p>(A, B) Decreasing acquisition rate (16384 Hz, 8192 Hz, 4096 Hz, 2048 Hz, 1024 Hz) lead to a progressive time shift of the TMS artifacts and a decrease of TMS pulse artifact amplitude. The shifting time can be found by time-shifting the traces for each sampling rate backwards until they coincide with the 16 kHz traces. The optimal time is found when the sum of distances between these traces, evaluated directly after the pulse artifact, becomes minimal. The optimal times coincide for the watermelon and the human head (C). Note that time shifting will not change power law decay tails as can also be seen in (D).</p

The simulated voltage difference between a reference and the recording electrode.

<p>The initial voltage distribution at each electrode is a Gaussian of the form <i>g</i>(<i>σ</i>, <i>x</i> + <i>μ</i>) convolved with the box function <i>B</i>, where <i>g</i>(<i>t</i>, <i>x</i>) is the voltage impulse-response function of our model, and <i>B</i>(<i>x</i>) equals 1/<i>πb</i>² for |<i>x</i>| ≤ <i>b</i>. The parameters for the reference electrode are fixed to <i>σ</i>_<i>R</i> = 0.5 and <i>μ</i>_<i>R</i> = 0. The choice of <i>μ</i>_<i>R</i> implies that the voltage depends only on the length |<i>μ</i>| of <i>μ</i> and not on its direction. The electrode radius was <i>b</i> = 2. (A, B) Effect of varying <i>σ</i> from 0 to 1 in steps of 0.05 (inset: close-up for <i>σ</i> for half step size). (D, E) Effect of varying |<i>μ</i>| from 0 to 2 in steps of 0.1 (inset: close-up for |<i>μ</i>| for half step size). (C, F) The plot on a log-log scale demonstrates the power law. In comparison to data, we note the asymmetry of positive and negative voltage shapes (compare to Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006177#pcbi.1006177.g004" target="_blank">4A</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006177#pcbi.1006177.g007" target="_blank">7A</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006177#pcbi.1006177.g011" target="_blank">11A</a>) and the emergence a local extremum near 0 in some traces (compare to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006177#pcbi.1006177.g004" target="_blank">Fig 4A</a>). (G-L) The simulated voltage difference for electrodes approximated by points (<i>b</i> = 0).</p

Equivalent circuits for the electrode-gel-skin interface.

<p>(A) Description of the electrode-skin-gel interface as lumped element model. The resistance and capacitance of the skin incorporate spatially varying properties. (B) Distributed circuit model for the spatial extent of the dermal interface. (C) The topology of a single-layer infinite regular grid with edge length <i>ϵ</i>. A node <i>x</i> = (<i>x</i>₁, <i>x</i>₂) has four direct neighbors (<i>x</i>₁±<i>ϵ</i>, <i>x</i>₂), (<i>x</i>₁, <i>x</i>₂±<i>ϵ</i>). (D) The currents in direction <i>x</i>₁ and <i>x</i>₂ are denoted by <i>I</i>₁ and <i>I</i>₂, respectively. Note that the injected current <i>J</i> is required to scale with <i>ϵ</i>. (E) The voltages at the nodes are denoted by <i>V</i>.</p

TMS-EEG on phantom heads with non-human skin structure.

<p>(A, B) Log-log plots of TMS artifacts on a watermelon and on a muskmelon show artifact decay is very different from a human head in that it does not follow a power law of order 1 (dotted line) or 2 (dashed). (C) The artifact traces on the phantom qualitatively resemble the artifact on a human head even though they are much smaller and generally shorter-lasting. (D) Example of an artifact which contains an additional recharging artifact consisting of two waves at an interval of 20 ms, where the first wave appears within around 20 ms after the pulse. This artifact can appear when the TMS device is operated with two boosters. It is visible in around half of all trials at different intensities and is not affected by acquisition rate. Inset: Subtraction of the common average from the traces turns the recharging artifact into two ‘blips’.</p