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@ -167,7 +167,7 @@ Ion channels determine neuronal excitability and mutations that alter ion channe
Voltage-gated ion channels are vital in determining neuronal excitability, action potential generation and firing patterns \citep{bernard_channelopathies_2008, carbone_ion_2020}. In particular, the properties and combinations of ion channels and their resulting currents determine the firing properties of a neuron \citep{rutecki_neuronal_1992, pospischil_minimal_2008}. However, ion channel function can be disturbed, resulting in altered ionic current properties and altered neuronal firing behaviour \citep{carbone_ion_2020}. Ion channel mutations are a common cause of such channelopathies and are often associated with hereditary clinical disorders including ataxias, epilepsies, pain disorders, dyskinesias, intellectual disabilities, myotonias, and periodic paralyses among others \citep{bernard_channelopathies_2008, carbone_ion_2020}.
\notenk{Are there any obvious citations missing from the following section?}
The effects of channelopathies on ionic current kinetics are frequently assessed by transfection of heterologous expression systems without endogenous currents \citep{Balestrini1044, Noebels2017, Dunlop2008}, and are frequently classified as either a loss of function (LOF) or a gain of function (GOF) with respect to changes in the amount of ionic current \notejb{or: ... changes in the magnitude of ionic currents flowing through the channels ?} \citep{Musto2020, Kullmann2002, Waxman2011, Kim2021}. This classification of the effects on ionic currents is often directly used to predict the effects on neuronal firing \textcolor{red}{(papers?\citep{Niday2018, Wei2017, Wolff2017}?)}, which in turn is important for understanding the pathophysiology of these disorders and for identification of potential therapeutic targets \citep{Orsini2018, Yang2018}. Genotype-phenotype relationships are complex and the understanding of the relationships between these is still evolving \citep{Wolff2017, johannesen_genotype-phenotype_2021}. Experimentally, the effects of channelopathies on neuronal firing can be assessed using primary neuronal cultures \citep{Scalmani2006, Smith2018, Liu2019} or \textit{in vitro} recordings from transgenic mouse lines \citep{Mantegazza2019, Xie2010,Lory2020, Habib2015, Hedrich2019}.
The effects of channelopathies on ionic current kinetics are frequently assessed by transfection of heterologous expression systems without endogenous currents \citep{Balestrini1044, Noebels2017, Dunlop2008}, and are frequently classified as either a loss of function (LOF) or a gain of function (GOF) with respect to changes in the magnitude of ionic currents flowing through the channels \citep{Musto2020, Kullmann2002, Waxman2011, Kim2021}. This classification of the effects on ionic currents is often directly used to predict the effects on neuronal firing \textcolor{red}{(papers?\citep{Niday2018, Wei2017, Wolff2017}?)}, which in turn is important for understanding the pathophysiology of these disorders and for identification of potential therapeutic targets \citep{Orsini2018, Yang2018}. Genotype-phenotype relationships are complex and the understanding of the relationships between these is still evolving \citep{Wolff2017, johannesen_genotype-phenotype_2021}. Experimentally, the effects of channelopathies on neuronal firing can be assessed using primary neuronal cultures \citep{Scalmani2006, Smith2018, Liu2019} or \textit{in vitro} recordings from transgenic mouse lines \citep{Mantegazza2019, Xie2010,Lory2020, Habib2015, Hedrich2019}.
%However the effect of a given channelopathy on different neuronal types across the brain is often unclear and not feasible to experimentally obtain. This is especially true when large numbers of distinct mutations are present and personalized medicine approaches are desired.
@ -276,7 +276,7 @@ To examine the role of cell-type specific ionic current environments on the impa
\begin{figure}[tp]
\centering
\includegraphics[width=0.5\linewidth]{Figures/firing_characterization.pdf}
\notejb{Das mit den LOF, GOF und ? in B ist mal ein Vorschlag, der erstens noch verbessert werden kann, aber der auch gerne wieder rueckgaengig gemacht werden kann.}
\\\notejb{Das mit den LOF, GOF und ? in B ist mal ein Vorschlag, der erstens noch verbessert werden kann, aber der auch gerne wieder rueckgaengig gemacht werden kann.}\notenk{Ich wurde LOF, GOF und ? rueckgaengig machen weil wir argumentieren dass LOF und GOF fuer da Feuerverhalten nicht so geeignet sind}
\linespread{1.}\selectfont
\caption[]{Characterization of firing with AUC and rheobase. (A) The area under the curve (AUC) of the repetitive firing frequency-current (fI) curve. (B)
Changes in firing as characterized by \(\Delta\)AUC and \(\Delta\)rheobase occupy 4 quadrants separated by no changes in AUC and rheobase. Representative schematic fI curves in blue with respect to a reference fI curve (black) depict the general changes associated with each quadrant.}
@ -294,12 +294,12 @@ Neuronal firing is a complex phenomenon and a quantification of firing propertie
\end{figure}
Neuronal firing is heterogenous across the CNS and a set of neuronal models with heterogenous firing due to different ionic currents is desirable to reflect this heterogeneity. The set of neuronal models used here has considerable diversity as evident in the variability seen across neuronal models both in spike trains and their fI curves (\Cref{fig:diversity_in_firing}). The models chosen all fire tonically and do not exhibit bursting. Some models, such as Cb stellate and RS inhibitory models, display type I firing whereas others such as Cb stellate \(\Delta\)\Kv and STN models have type II firing. Type I firing is characterized by continuous fI curve (i.e. firing rate increases from 0 in a continuous fashion) generated through a saddle-node on invariant cycle bifurcation. Type II firing is characterized by a discontinuity in the fI curve (i.e. a jump occurs from no firing to firing at a certain frequency) due to sub-critical Hopf bifurcation \cite{ERMENTROUT2002, ermentrout_type_1996}. The other models used here lie on a continuum between these prototypical firing classifications. \notejb{The STN models could be a homoclinic bifurcation (long delay but type 2 like firing), maybe cite Izhikevic book for this.} Most neuronal models exhibit hysteresis with ascending and descending ramps eliciting spikes with different thresholds, however the STN +\Kv, STN \(\Delta\)\Kv, and Cb stellate \(\Delta\)\Kv models have large hysteresis (\Cref{fig:diversity_in_firing}, \Cref{fig:ramp_firing}).
Neuronal firing is heterogenous across the CNS and a set of neuronal models with heterogenous firing due to different ionic currents is desirable to reflect this heterogeneity. The set of neuronal models used here has considerable diversity as evident in the variability seen across neuronal models both in spike trains and their fI curves (\Cref{fig:diversity_in_firing}). The models chosen all fire tonically and do not exhibit bursting. Some models, such as Cb stellate and RS inhibitory models, display type I firing whereas others such as Cb stellate \(\Delta\)\Kv and STN models have type II firing. Type I firing is characterized by continuous fI curve (i.e. firing rate increases from 0 in a continuous fashion) generated through a saddle-node on invariant cycle bifurcation. Type II firing is characterized by a discontinuity in the fI curve (i.e. a jump occurs from no firing to firing at a certain frequency) due to sub-critical Hopf or homoclinic bifurcation \cite{ERMENTROUT2002, ermentrout_type_1996, Izhikevich2006}. The other models used here lie on a continuum between these prototypical firing classifications. \notejb{The STN models could be a homoclinic bifurcation (long delay but type 2 like firing), maybe cite Izhikevic book for this.}\notenk{I added ``or homoclinic'' and cited Izhikevich} Most neuronal models exhibit hysteresis with ascending and descending ramps eliciting spikes with different thresholds, however the STN +\Kv, STN \(\Delta\)\Kv, and Cb stellate \(\Delta\)\Kv models have large hysteresis (\Cref{fig:diversity_in_firing}, \Cref{fig:ramp_firing}).
\subsection*{Sensitivity Analysis}
Sensitivity analyses are used to understand how input model parameters contribute to determining the output of a model \citep{Saltelli2002}. In other words, sensitivity analyses are used to understand how sensitive the output of a model is to a change in input or model parameters. One-factor-a-time sensitivity analyses involve altering one parameter at a time and assessing the impact of this parameter on the output. This approach enables the comparison of given alterations in parameters of ionic currents across models. Changes in gating \(V_{1/2}\) and slope factor \(k\) as well as the maximum conductance affect AUC (\Cref{fig:AUC_correlation} A, B and C). Heterogeneity in the correlation between gating and conductance changes and AUC occurs across models for most ionic currents. In these cases some of the models display non-monotonic relationships or no relationship (\( |\text{Kendall} \tau | \approx 0\)). However, shifts in A-current activation \(V_{1/2}\), changes in \Kv activation \(V_{1/2}\) and slope factor \(k\), and changes in A-current conductance display consistent monotonic relationships across models (\( |\text{Kendall} \tau | \ne 0\)). The impact of a similar change in \(V_{1/2}\), slope factor \(k\), or conductance of different currents will impact firing behaviour differently not just within but also between models.
\notejb{the slope factor has a name (``slope factor'', but \(V_{1/2}\) not. How is this called, ``midpoint potential''?}
\notejb{the slope factor has a name (``slope factor'', but \(V_{1/2}\) not. How is this called, ``midpoint potential''?}\notenk{How about ``half-maximal potential''?}
\begin{figure}[tp]

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ref.bib
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@ -1,4 +1,20 @@
@Book{Izhikevich2006,
author = {Izhikevich, Eugene M.},
editor = {Sejnowski, Terrence J. and Poggio, Tomaso A.},
publisher = {MIT Press},
title = {Dynamical {Systems} in {Neuroscience}: {The} {Geometry} of {Excitability} and {Bursting}},
year = {2006},
address = {Cambridge, MA, USA},
isbn = {9780262090438},
month = jul,
series = {Computational {Neuroscience} {Series}},
abstract = {Explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition.},
language = {en},
shorttitle = {Dynamical {Systems} in {Neuroscience}},
}
@Article{Wolff2017,
author = {Wolff, Markus and Johannesen, Katrine M. and Hedrich, Ulrike B. S. and Masnada, Silvia and Rubboli, Guido and Gardella, Elena and Lesca, Gaetan and Ville, Dorothée and Milh, Mathieu and Villard, Laurent and Afenjar, Alexandra and Chantot-Bastaraud, Sandra and Mignot, Cyril and Lardennois, Caroline and Nava, Caroline and Schwarz, Niklas and Gérard, Marion and Perrin, Laurence and Doummar, Diane and Auvin, Stéphane and Miranda, Maria J. and Hempel, Maja and Brilstra, Eva and Knoers, Nine and Verbeek, Nienke and van Kempen, Marjan and Braun, Kees P. and Mancini, Grazia and Biskup, Saskia and Hörtnagel, Konstanze and Döcker, Miriam and Bast, Thomas and Loddenkemper, Tobias and Wong-Kisiel, Lily and Baumeister, Friedrich M. and Fazeli, Walid and Striano, Pasquale and Dilena, Robertino and Fontana, Elena and Zara, Federico and Kurlemann, Gerhard and Klepper, Joerg and Thoene, Jess G. and Arndt, Daniel H. and Deconinck, Nicolas and Schmitt-Mechelke, Thomas and Maier, Oliver and Muhle, Hiltrud and Wical, Beverly and Finetti, Claudio and Brückner, Reinhard and Pietz, Joachim and Golla, Günther and Jillella, Dinesh and Linnet, Karen M. and Charles, Perrine and Moog, Ute and Õiglane-Shlik, Eve and Mantovani, John F. and Park, Kristen and Deprez, Marie and Lederer, Damien and Mary, Sandrine and Scalais, Emmanuel and Selim, Laila and Van Coster, Rudy and Lagae, Lieven and Nikanorova, Marina and Hjalgrim, Helle and Korenke, G. Christoph and Trivisano, Marina and Specchio, Nicola and Ceulemans, Berten and Dorn, Thomas and Helbig, Katherine L. and Hardies, Katia and Stamberger, Hannah and de Jonghe, Peter and Weckhuysen, Sarah and Lemke, Johannes R. and Krägeloh-Mann, Ingeborg and Helbig, Ingo and Kluger, Gerhard and Lerche, Holger and Møller, Rikke S},
journal = {Brain},