From 8a8f0091cd5c2d49a37afbc48443c594052e645e Mon Sep 17 00:00:00 2001
From: nkoch1 <nils@ualberta.ca>
Date: Wed, 20 Jul 2022 21:47:08 -0400
Subject: [PATCH] Update after meeting about discussion and paper - before
 sending to Uli and Stephan

---
 Figures/simulation_model_comparison.pdf | Bin 42586 -> 42792 bytes
 Figures/simulation_model_comparison.py  |  19 ++--
 g_table.tex                             |   6 +-
 manuscript.tex                          | 121 ++++++++++++------------
 4 files changed, 71 insertions(+), 75 deletions(-)

diff --git a/Figures/simulation_model_comparison.pdf b/Figures/simulation_model_comparison.pdf
index b3ce0b0cee3e5723e3226575b215829c611ee464..a80ed8e737350c1e12850f0e5661390cf5b7a33e 100644
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diff --git a/Figures/simulation_model_comparison.py b/Figures/simulation_model_comparison.py
index 78449b7..f84d6be 100644
--- a/Figures/simulation_model_comparison.py
+++ b/Figures/simulation_model_comparison.py
@@ -144,27 +144,27 @@ def mutation_plot(ax, model='RS_pramidal'):
 
     mod = models.index(model)
     mut_names = AUC.index
-    ax.plot(rheo.loc[mut_names, model_names[mod]]*1000, AUC.loc[mut_names, model_names[mod]], linestyle='',
+    ax.plot(rheo.loc[mut_names, model_names[mod]]*1000, AUC.loc[mut_names, model_names[mod]]*100, linestyle='',
             markeredgecolor='grey', markerfacecolor='grey', marker=Marker_dict[model_display_names[mod]],
             markersize=2)
 
-    ax.plot(rheo.loc['wt', model_names[mod]], AUC.loc['wt', model_names[mod]], 'sk')
+    ax.plot(rheo.loc['wt', model_names[mod]], AUC.loc['wt', model_names[mod]]*100, 'sk')
 
     mut_col = sns.color_palette("pastel")
-    ax.plot(rheo.loc['V174F', model_names[mod]]*1000, AUC.loc['V174F', model_names[mod]], linestyle='',
+    ax.plot(rheo.loc['V174F', model_names[mod]]*1000, AUC.loc['V174F', model_names[mod]]*100, linestyle='',
             markeredgecolor=mut_col[0], markerfacecolor=mut_col[0], marker=Marker_dict[model_display_names[mod]],markersize=4)
-    ax.plot(rheo.loc['F414C', model_names[mod]]*1000, AUC.loc['F414C', model_names[mod]], linestyle='',
+    ax.plot(rheo.loc['F414C', model_names[mod]]*1000, AUC.loc['F414C', model_names[mod]]*100, linestyle='',
             markeredgecolor=mut_col[1], markerfacecolor=mut_col[1], marker=Marker_dict[model_display_names[mod]],markersize=4)
-    ax.plot(rheo.loc['E283K', model_names[mod]]*1000, AUC.loc['E283K', model_names[mod]], linestyle='',
+    ax.plot(rheo.loc['E283K', model_names[mod]]*1000, AUC.loc['E283K', model_names[mod]]*100, linestyle='',
             markeredgecolor=mut_col[2], markerfacecolor=mut_col[2], marker=Marker_dict[model_display_names[mod]],markersize=4)
-    ax.plot(rheo.loc['V404I', model_names[mod]]*1000, AUC.loc['V404I', model_names[mod]], linestyle='',
+    ax.plot(rheo.loc['V404I', model_names[mod]]*1000, AUC.loc['V404I', model_names[mod]]*100, linestyle='',
             markeredgecolor=mut_col[3], markerfacecolor=mut_col[5], marker=Marker_dict[model_display_names[mod]],markersize=4)
     ax.set_title(model_display_names[mod], pad=14)
     ax.set_xlabel('$\Delta$ Rheobase (pA)')
-    ax.set_ylabel('Normalized $\Delta$AUC')
+    ax.set_ylabel('Normalized $\Delta$AUC (%)')
     ax.spines['right'].set_visible(False)
     ax.spines['top'].set_visible(False)
-    ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0),useMathText=True)
+    # ax.ticklabel_format(axis="y", style="sci", scilimits=(0, 0),useMathText=True)
 
     xmin, xmax = ax.get_xlim()
     ymin, ymax = ax.get_ylim()
@@ -233,7 +233,8 @@ correlation_plot(axr0,df = 'rheo', title='$\Delta$ Rheobase', cbar=True)
 axs = [ax00, ax01,ax02, ax10, ax11, ax12, ax20, ax21, ax22]
 j=0
 for i in range(0,9):
-    axs[i].text(-0.48, 1.175, string.ascii_uppercase[i], transform=axs[i].transAxes, size=10, weight='bold')
+    # axs[i].text(-0.48, 1.175, string.ascii_uppercase[i], transform=axs[i].transAxes, size=10, weight='bold')
+    axs[i].text(-0.625, 1.25, string.ascii_uppercase[i], transform=axs[i].transAxes, size=10, weight='bold')
     j +=1
 axr0.text(-0.77, 1.1, string.ascii_uppercase[j], transform=axr0.transAxes, size=10, weight='bold')
 axr1.text(-0.77, 1.1, string.ascii_uppercase[j+1], transform=axr1.transAxes, size=10, weight='bold')
diff --git a/g_table.tex b/g_table.tex
index 34870b1..52b2411 100644
--- a/g_table.tex
+++ b/g_table.tex
@@ -10,11 +10,11 @@
     \begin{tabular}{cccccccccc}
     % in mS/cm^2
      \Xhline{1\arrayrulewidth}
-                    & \begin{tabular}[x]{@{}c@{}} RS\\Pyra-\\midal \end{tabular}  & \begin{tabular}[x]{@{}c@{}} RS\\Inhib-\\itory\end{tabular} & FS & \begin{tabular}[x]{@{}c@{}} Cb\\Stellate \end{tabular}& \begin{tabular}[x]{@{}c@{}}Cb\\Stellate\\+\Kv \end{tabular}  & \begin{tabular}[x]{@{}c@{}}Cb\\Stellate\\\(\Delta\)\Kv \end{tabular}     & STN  &\begin{tabular}[x]{@{}c@{}} STN\\+\Kv \end{tabular}  &\begin{tabular}[x]{@{}c@{}} STN\\\(\Delta\)\Kv \end{tabular} \\
+                    & \begin{tabular}[x]{@{}c@{}} RS\\Pyra-\\midal\\(+\Kv) \end{tabular}  & \begin{tabular}[x]{@{}c@{}} RS\\Inhib-\\itory\\(+\Kv)\end{tabular} & \begin{tabular}[x]{@{}c@{}}FS\\(+\Kv)  \end{tabular}& \begin{tabular}[x]{@{}c@{}} Cb\\Stellate \end{tabular}& \begin{tabular}[x]{@{}c@{}}Cb\\Stellate\\+\Kv \end{tabular}  & \begin{tabular}[x]{@{}c@{}}Cb\\Stellate\\\(\Delta\)\Kv \end{tabular}     & STN  &\begin{tabular}[x]{@{}c@{}} STN\\+\Kv \end{tabular}  &\begin{tabular}[x]{@{}c@{}} STN\\\(\Delta\)\Kv \end{tabular} \\
         \Xhline{1\arrayrulewidth}                    
        \(g_{Na}\)       &  \(56\)   &  \(10\)       &  \(58\)   & \(3.4\)       & \(3.4\)       &  \(3.4\)      & \(49\)    & \(49\)    & \(49\)    \\
-       \(g_{K}\)        & \(5.4\)   &  \(1.89\)     &  \(3.51\) &  \(9.0556\)   & \(8.15\)     &\(9.0556\)     & \(57\)    & \(56.43\) & \(57\)     \\ 
-       \(g_{K_V1.1}\)   & \(0.6\)   &  \(0.21\)     &  \(0.39\) & ---         & \(0.90556\) &   \(1.50159\) & ---     & \(0.57\)  & \(0.5\)    \\
+       \(g_{K}\)        & \(6\)   (\(5.4\))   & 2.1 (\(1.89\))     & 3.9 (\(3.51\)) &  \(9.0556\)   & \(8.15\)     &\(9.0556\)     & \(57\)    & \(56.43\) & \(57\)     \\ 
+       \(g_{K_V1.1}\)   & ---   (\(0.6\))   &   ---   (\(0.21\))    &  ---    (\(0.39\)) & ---         & \(0.90556\) &   \(1.50159\) & ---     & \(0.57\)  & \(0.5\)    \\
        \(g_{A}\)        &  ---    &   ---      & ---     &  \(15.0159\)  & \(15.0159\)   &  ---        & \(5\)    & \(5\)     & ---      \\
        \(g_{M}\)        & \(0.075\) &   \(0.0098\)  &\(0.075\)  & ---        &  ---        &  ---        & ---     & ---     & ---      \\
        \(g_{L}\)        & ---     &    ---      &   ---   & ---         & ---         &  ---        & \(5\)     & \(5 \)    & \(5\)      \\
diff --git a/manuscript.tex b/manuscript.tex
index a39d9fc..bdd087b 100644
--- a/manuscript.tex
+++ b/manuscript.tex
@@ -89,7 +89,7 @@
 
 \begin{document}
 
-\title{Loss or Gain of Function? Effects of Ion Channel Mutation on Neuronal Firing Depend on Cell Type}
+\title{Loss or Gain of Function? Neuronal Firing Effects of Ion Channel Mutations Depend on Cell Type}
 \vspace{-1em}
 \date{}
  
@@ -98,16 +98,17 @@
 
 \section*{Titlepage for eNeuro - will be put into Word file provided for submission}
 \subsection{Manuscript Title (50 word maximum)}
-Loss or Gain of Function? Effects of Ion Channel Mutation on Neuronal Firing Depend on Cell Type
+Loss or Gain of Function? Neuronal Firing Effects of Ion Channel Mutations Depend on Cell Type
 
 \subsection{Abbreviated Title (50 character maximum)}
 Effects of Ion Channel Mutation Depend on Cell Type
 
 \subsection{List all Author Names and Affiliations in order as they would appear in the published article}
-Nils A. Koch\textsuperscript{1,2}, Lukas Sonnenberg\textsuperscript{1,2}, Jan Benda\textsuperscript{1,2}
+Nils A. Koch\textsuperscript{1,2}, Lukas Sonnenberg\textsuperscript{1,2}, Ulrike B.S. Hedrich\textsuperscript{3}, Stephan Lauxmann\textsuperscript{1,3}, Jan Benda\textsuperscript{1,2}
 
 \textsuperscript{1}Institute for Neurobiology, University of Tuebingen, 72072 Tuebingen, Germany\\
-\textsuperscript{2}Bernstein Center for Computational Neuroscience Tuebingen, 72076 Tuebingen, Germany
+\textsuperscript{2}Bernstein Center for Computational Neuroscience Tuebingen, 72076 Tuebingen, Germany\\
+\textsuperscript{3} Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, 72076 Tuebingen, Germany\\
 
 \subsection{Author Contributions - Each author must be identified with at least one of the following: Designed research, Performed research, Contributed unpublished reagents/ analytic tools, Analyzed data, Wrote the paper.}
 \notenk{Adjust as you deem appropriate}\\
@@ -149,6 +150,12 @@ Authors report no conflict of interest.
 \linenumbers
 \doublespacing
 \sloppy
+\vspace{-2cm}
+Nils A. Koch\textsuperscript{1,2}, Lukas Sonnenberg\textsuperscript{1,2}, Ulrike B.S. Hedrich\textsuperscript{3}, Stephan Lauxmann\textsuperscript{1,3}, Jan Benda\textsuperscript{1,2}
+
+\textsuperscript{1}Institute for Neurobiology, University of Tuebingen, 72072 Tuebingen, Germany\\
+\textsuperscript{2}Bernstein Center for Computational Neuroscience Tuebingen, 72076 Tuebingen, Germany\\
+\textsuperscript{3} Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, 72076 Tuebingen, Germany
 
 \section*{Abstract (250 Words Maximum - Currently 232)}
 %\textit{It should provide a concise summary of the objectives, methodology (including the species and sex studied), key results, and major conclusions of the study.}
@@ -171,7 +178,7 @@ 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 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 \citep{Niday2018, Wei2017, Wolff2017} \textcolor{red}{(any other Papers?)}, 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.
@@ -182,7 +189,7 @@ The effects of channelopathies on ionic current kinetics are frequently assessed
 %General understanding of the effects of changes in current properties on neuronal firing may help to fill the need to understand the impacts of ion channel mutations on neuronal firing. 
 However the effect of a given channelopathy on firing behavior of different neuronal types across the brain is often unclear and not feasible to experimentally obtain. Different neuron types differ in their composition of ionic currents \citep{yao2021taxonomy, Cadwell2016, BICCN2021, Scala2021} and therefore likely respond differently to changes in the properties of a single ionic current.
 % \textcolor{red}{In the simplest case, the influence on the firing behaviour should correlate with the expression level of the affected gene \textcolor{red}{(cite Niko , other Papers)}. But if a \textcolor{red}{ kinetic parameter} is changed too much, it can have unforseen consequences. }
-  The expression level of an affected gene can correlate with firing behaviour in the simplest case \citep{Layer2021} \textcolor{red}{(cite other Papers?)} \notenk{Not sure if Lukas had some in mind}, however if gating kinetics are affected this can have complex consequences.
+  The expression level of an affected gene can correlate with firing behaviour in the simplest case \citep{Layer2021} \textcolor{red}{(any other Papers?)}, however if gating kinetics are affected this can have complex consequences.
 
 For instance, altering relative amplitudes of ionic currents can dramatically influence the firing behaviour and dynamics of neurons \citep{rutecki_neuronal_1992, pospischil_minimal_2008,Kispersky2012, golowasch_failure_2002, barreiro_-current_2012, Noebels2017, Layer2021}, however other properties of ionic currents impact neuronal firing as well. In extreme cases, a mutation can have opposite effects on different neuron types. For example, the R1629H SCN1A mutation is associated which increased firing in interneurons, but decreases pyramidal neuron excitability \citep{Hedrich14874,makinson_scn1a_2016}
 
@@ -223,14 +230,11 @@ All modelling and simulation was done in parallel with custom written Python 3.8
     % Linux 3.10.0-123.e17.x86_64.
     
 \subsection*{Different Cell Models}
-    A group of neuronal models representing the major classes of cortical and thalamic neurons including regular spiking pyramidal (RS pyramidal), regular spiking inhibitory (RS inhibitory), and fast spiking (FS) cells were used \citep{pospischil_minimal_2008}. To each of these models, a \Kv current (\IKv; \citealt{ranjan_kinetic_2019}) was added. A cerebellar stellate cell model from \citet{alexander_cerebellar_2019} is used (Cb stellate). This model was also used with a \Kv current \citep{ranjan_kinetic_2019} in addition to the A-type potassium current (Cb stellate +\Kv) or replacing the A-type potassium current (Cb stellate \(\Delta\)\Kv). A subthalamic nucleus neuron model as described by \citet{otsuka_conductance-based_2004} are used (STN) and with a \Kv current (\IKv; \citealp{ranjan_kinetic_2019}) in addition to the A-type potassium current (STN +\Kv) or replacing the A-type potassium current (STN \(\Delta\)\Kv). The properties and maximal conductances of each model are detailed in \Cref{tab:g} and the gating properties are unaltered from the original Cb stellate and STN models. For comparability to typical electrophysiological data fitting reported and for ease of further gating curve manipulations, a modified Boltzmann function
+    A group of neuronal models representing the major classes of cortical and thalamic neurons including regular spiking pyramidal (RS pyramidal), regular spiking inhibitory (RS inhibitory), and fast spiking (FS) cells were used \citep{pospischil_minimal_2008}. Additionally,  a \Kv current (\IKv; \citealt{ranjan_kinetic_2019}) was added to each of these models (RS pyramidal +\Kv, RS inhibitory +\Kv, and FS +\Kv respectively). A cerebellar stellate cell model from \citet{alexander_cerebellar_2019} is used (Cb stellate). This model was also used with a \Kv current \citep{ranjan_kinetic_2019} in addition to the A-type potassium current (Cb stellate +\Kv) or replacing the A-type potassium current (Cb stellate \(\Delta\)\Kv). A subthalamic nucleus neuron model as described by \citet{otsuka_conductance-based_2004} are used (STN) and with a \Kv current (\IKv; \citealp{ranjan_kinetic_2019}) in addition to the A-type potassium current (STN +\Kv) or replacing the A-type potassium current (STN \(\Delta\)\Kv). The properties and maximal conductances of each model are detailed in \Cref{tab:g} and the gating properties are unaltered from the original Cb stellate and STN models. For comparability to typical electrophysiological data fitting reported and for ease of further gating curve manipulations, a modified Boltzmann function
     \begin{equation}\label{eqn:Boltz}
          x_\infty = {\left(\frac{1-a}{1+{\exp\left[{\frac{V-V_{1/2}}{k}}\right]}} +a\right)^j} 
     \end{equation}
-with slope \(k\), voltage for half-maximal activation or inactivation (\(V_{1/2}\)), exponent \(j\), and persistent current \(0 \leq a \leq 1\) were fitted to the original formulism for RS pyramidal, RS inhibitory and FS models from \citet{pospischil_minimal_2008}. The properties of \IKv  were fitted to the mean wild type biophysical parameters of \Kv \citep{lauxmann_therapeutic_2021}.
-
-\notenk{add this?}
- \textcolor{red}{Each of the original single-compartment models used here can reproduce physiological firing behaviour of the neurons they represent \citep{pospischil_minimal_2008, alexander_cerebellar_2019, otsuka_conductance-based_2004} and capture key aspects of the dynamics of these cell types. }
+with slope \(k\), voltage for half-maximal activation or inactivation (\(V_{1/2}\)), exponent \(j\), and persistent current \(0 \leq a \leq 1\) were fitted to the original formulism for RS pyramidal, RS inhibitory and FS models from \citet{pospischil_minimal_2008}. The properties of \IKv  were fitted to the mean wild type biophysical parameters of \Kv \citep{lauxmann_therapeutic_2021}. Each of the original single-compartment models used here can reproduce physiological firing behaviour of the neurons they represent \citep{pospischil_minimal_2008, alexander_cerebellar_2019, otsuka_conductance-based_2004} and capture key aspects of the dynamics of these cell types. 
 
     
 \input{g_table}
@@ -238,7 +242,15 @@ with slope \(k\), voltage for half-maximal activation or inactivation (\(V_{1/2}
 \input{gating_table}
     
 \subsection*{Firing Frequency Analysis} 
-    The membrane responses to 200 equidistant two second long current steps were simulated using the forward-Euler method with a \(\Delta \textrm{t} = 0.01\)\,ms from steady state. Current steps ranged from 0 to 1\,nA for all models except for the RS inhibitory neuron models where a range of 0 to 0.35 nA was used to ensure repetitive firing across the range of input currents. For each current step, action potentials were detected as peaks with at least 50\,mV prominence, or the relative height above the lowest contour line encircling it, and a minimum interspike interval of 1\,ms. The interspike interval was computed and used to determine the instantaneous firing frequencies elicited by the current step. The steady-state firing frequency were defined as the mean firing frequency in 0.5\,s after the first action potential in the last second of the current step respectively and was used to construct frequency-current (fI) curves.
+    The membrane responses to 200 equidistant two second long current steps were simulated using the forward-Euler method with a \(\Delta \textrm{t} = 0.01\)\,ms from steady state. Current steps ranged from 0 to 1\,nA for all models except for the RS inhibitory neuron models where a range of 0 to 0.35 nA was used to ensure repetitive firing across the range of input currents. For each current step, action potentials were detected as peaks with at least 50\,mV prominence, or the relative height above the lowest contour line encircling it, and a minimum interspike interval of 1\,ms. The interspike interval was computed and used to determine the instantaneous firing frequencies elicited by the current step. The steady-state firing frequency were defined as the mean firing frequency in 0.5\,s after the first action potential in the last second of the current step respectively and was used to construct frequency-current (fI) curves. Alteration in current magnitudes can have different effects on rheobase and the initial slope of the fI curve \citep{Kispersky2012}.
+For this reason, we quantify neuronal firing with the rheobase as well as the area under the curve (AUC) of the initial portion of the fI curve as a measure of the initial slope of the fI curve.
+
+%\textcolor{red}{In a modelling study, it was found that altering the sodium conductance in 2 stomatogastric ganglion neuron models from a population models decreases rheobase in both models, however the initial slope of the fI curves (proportional to AUC of the fI-curve) is increased in one model and decreased in the other suggesting that the magnitude of other currents in these models (such as \(\textrm{K}_\textrm{d}\)) determines the effect of a change in sodium current \citep{Kispersky2012} \notels{I don't see this in the paper. As far as I understood, they start with one model type and then only work with the other and state that they behave qualitatively the same} \notenk{Yes you are right. I looked at the paper again and I'm not sure why I wrote that. I think the key thing I was trying to get at is that the effect of an increase in sodium conductan on the fI curve can be different at different parts of the fI curve, because at higher firing rates \(\textrm{K}_\textrm{d}\) plays a role. As such changes or heterogeneity in \(\textrm{K}_\textrm{d}\)  could alter the effect of such an increase in sodium conductance at these higher firing rates.}. \notenk{Do you think that this is a more accurate representation? ``In a modelling study, it was found that altering the sodium conductance in 2 stomatogastric ganglion neuron models from a population models decreases rheobase in both models, however the shape of the fI curves especially at high firing rates is altered due other currents in these models such as \(\textrm{K}_\textrm{d}\) \citep{Kispersky2012}.''} \notenk{Could move this to methods as a justification as to why we use rheobase and AUC as measures for firing}}
+
+%\notenk{add as justification as to why we use AUC and rheobase. Previously, changes in fI curves have shown differences ....}
+
+
+
 
     The smallest current at which steady state firing occurs was identified and the current step interval preceding the occurrence of steady state firing was simulated at higher resolution (100 current steps) to determine the current at which steady state firing began. Firing was simulated with 100 current steps from this current upwards for 1/5 of the overall current range.
     Over this range a fI curve was constructed and the integral, or area under the curve (AUC), of the fI curve over this interval was computed with the composite trapezoidal rule and used as a measure of firing rate independent from rheobase. 
@@ -250,7 +262,7 @@ with slope \(k\), voltage for half-maximal activation or inactivation (\(V_{1/2}
     
 % Sensitivity analyses enable investigation into how different sources of uncertainty in a model result in uncertainty in model outputs \citep{saltelli_sensitivity_2002} and provide  information on the relative impact of model inputs \citep{saltelli_why_2019}. We recently used a one-factor-at-a-time (OFAT) sensitivity analysis approach to evaluate the relative impacts of currents on neuronal firing and developed a scoring system for SCN8A mutations that correlated (p = 0.0077, r = 0.64) with the clinical severity of epilepsy in patients with these mutations \citep{johannesen_genotype-phenotype_2021}. This was done in an isolated neuronal model and suggests that even with disregard of network level effects of mutations, the single cell level outcomes of mutations are relevant to disease phenotypes. OFAT sensitivity analyses indicate which factors have or do not have influence, with uninfluential factors never detected as relevant \citep{saltelli_how_2010}. OFAT sensitivity analyses can be used to screen factors that are influential on model outcomes and provide a mechanism by which factors and their relative influence can be easily identified and used in predictive applications. 
     
-    Properties of ionic currents common to all models (\(\textrm{I}_{\textrm{Na}}\), \(\textrm{I}_{\textrm{K}}\), \(\textrm{I}_{\textrm{A}}\)/\IKv, and \(\textrm{I}_{\textrm{Leak}}\)) were systematically altered in a one-factor-at-a-time sensitivity analysis for all models. The gating curves for each current were shifted (\(\Delta V_{1/2}\)) from -10 to 10\,mV in increments of 1\,mV. The voltage dependence of the time constant associated with the shifted gating curve was correspondingly shifted. The slope (\(k\)) of the gating curves were altered from half to twice the initial slope. Similarly, the maximal current conductance (\(g\)) was also scaled from half to twice the initial value. For both slope and conductance alterations, alterations consisted of 21 steps spaced equally on a \(\textrm{log}_2\) scale.\textcolor{red}{We neglect of altered time constants for the practical reason that estimation and assessment of time constants and changes to them is not straightforward \citep{Clerx2019, Whittaker2020}.}
+    Properties of ionic currents common to all models (\(\textrm{I}_{\textrm{Na}}\), \(\textrm{I}_{\textrm{K}}\), \(\textrm{I}_{\textrm{A}}\)/\IKv, and \(\textrm{I}_{\textrm{Leak}}\)) were systematically altered in a one-factor-at-a-time sensitivity analysis for all models. The gating curves for each current were shifted (\(\Delta V_{1/2}\)) from -10 to 10\,mV in increments of 1\,mV. The voltage dependence of the time constant associated with the shifted gating curve was correspondingly shifted. The slope (\(k\)) of the gating curves were altered from half to twice the initial slope. Similarly, the maximal current conductance (\(g\)) was also scaled from half to twice the initial value. For both slope and conductance alterations, alterations consisted of 21 steps spaced equally on a \(\textrm{log}_2\) scale. We neglect variation of time constants for the practical reason that estimation and assessment of time constants and changes to them is not straightforward \citep{Clerx2019, Whittaker2020}.
 
 
 %Although a number of methods have been used to fit ionic currents including different approaches in estimate time constants either from summary data or from full current traces, and are limited by the available data \citep{Clerx2019, Whittaker2020}. On one hand, specialized equipment and great experimental care is often required to estimate time constants \citep{Whittaker2020}. As a result summary data is often not recorded for voltage ranges in which time constants are fast. On the other hand,  lack of availability of full current traces for each mutation limits the alternative current trace fitting approach. For these practical reasons, we neglect the effect of mutation altered time constants despite acknowledging that time constant changes are likely important in determining the outcome of a given mutation on firing.}
@@ -297,10 +309,9 @@ Neuronal firing is heterogenous across the CNS and a set of neuronal models with
         \centering
 %        \includegraphics[width=0.5\linewidth]{Figures/firing_characterization.pdf}
 	\includegraphics[width=0.5\linewidth]{Figures/firing_characterization_arrows.pdf}
-	\\\notenk{Re-work legend to be in line with new figure}
         \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.}
+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 red with respect to a reference fI curve (blue) depict the general changes associated with each quadrant.}
         \label{fig:firing_characterization}
 \end{figure}
 
@@ -322,18 +333,14 @@ Qualitative differences can be found, for example, when increasing the maximal c
 \begin{figure}[tp]
         \centering
         \includegraphics[width=\linewidth]{Figures/AUC_correlation.pdf}
-	\\\notenk{New legend for new figure}
         \linespread{1.}\selectfont
         \caption[]{Effects of altered channel kinetics on AUC in various neuron models. The fI curves corresponding to shifts in FS \(+\)\Kv model delayed rectifier K half activation \(V_{1/2}\) (A), changes \Kv activation slope factor \(k\) in the FS \(+\)\Kv model (B), and changes in maximal conductance of delayed rectifier K current in the STN \(+\)\Kv model (C) are shown. The \ndAUC of fI curves is plotted against delayed rectifier K half activation potential (\(\Delta V_{1/2}\); B), \Kv activation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the delayed rectifier K current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \ndAUC (C), slope factor k and \ndAUC (F) as well as maximal current conductances and \ndAUC (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\)  and \ndAUC for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively.}
         \label{fig:AUC_correlation}
 \end{figure}
 
-
-\notejb{Add more detailed step-by-step description what we did here, like for AUC.}\notenk{started doing this - what do you think of it?}
-
 Changes in gating half activation potential \(V_{1/2}\) and slope factor \(k\) as well as the maximum conductance \(g\) affect rheobase (\Cref{fig:rheobase_correlation}), however, in contrast to AUC, qualitatively consistent effects on rheobase across models are observed. Increasing, for example, the maximal conductance of the leak current in the Cb stellate model increases the rheobase (\Cref{fig:rheobase_correlation}~G). When these changes are plotted against the change in maximal conductance a monontonically increasing relationship is evident (thick teal line in \Cref{fig:AUC_correlation}~H). This monotonically increasing relationship is evident in all models (\( \text{Kendall} \ \tau \approx +1\)), but with different slopes (thin lines in \Cref{fig:rheobase_correlation}~H). Similarily, positive correlations are consistently found across models for maximal conductances of delayed rectifier K, \Kv, and A type currents, whereas the maximal conductance of the sodium current consistently is associated with negative correlations (\( \text{Kendall} \ \tau \approx -1\); \Cref{fig:rheobase_correlation}~I), i.e. rheobase is decreased with increasing maximum conductance in all models.
 
-Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlate with rheobase similarly across model there are some exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affect rheobase both with positive and negative correlations in different models (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occur in some models as a result of K-current activation  \(V_{1/2}\) and slope factor \(k\), \Kv-current inactivation slope factor \(k\), and A-current activation slope factor \(k\) in some models. Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. \notejb{This we just have said...} \notenk{Do you still find it repetitive after I expanded the section about rheobase above?}
+Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlate with rheobase similarly across model there are some exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affect rheobase both with positive and negative correlations in different models (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occur in some models as a result of K-current activation  \(V_{1/2}\) and slope factor \(k\), \Kv-current inactivation slope factor \(k\), and A-current activation slope factor \(k\) in some models. Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. 
 
 %The rheobase is also affected by changes in channel kinetics (\Cref{fig:rheobase_correlation}). In contrast to AUC, most alterations result in similar changes of rheobase in all models, but there are some noteable exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affect rheobase both with positive and negative correlations in different models (\Cref{fig:rheobase_correlation}~B). Departures from monotonic relationships occur in some models as a result of K-current activation, \Kv-current inactivation, and A-current activation in some models \notejb{which parameters are changed?}. Maximum conductance affects rheobase similarly across models (\Cref{fig:rheobase_correlation}~C). However, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. \notejb{This we just have said...}
 
@@ -342,7 +349,6 @@ Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) ge
 \begin{figure}[tp]
         \centering
         \includegraphics[width=\linewidth]{Figures/rheobase_correlation.pdf}
-	\\\notenk{New legend for new figure}
         \linespread{1.}\selectfont
         \caption[]{Effects of altered channel kinetics on rheobase.  The fI curves corresponding to shifts in FS \(+\)\Kv model \Kv activation \(V_{1/2}\) (A), changes \Kv inactivation slope factor \(k\) in the Cb stellate \(+\)\Kv model (B), and changes in maximal conductance of the leak current in the Cb stellate model (C) are shown. The \drheo of fI curves is plotted against \Kv half activation potential (\(\Delta V_{1/2}\); B), \Kv inactivation slope factor \(k\) (k/\(\textrm{k}_{WT}\); E) and maximal conductance \(g\) of the leak current (g/\(\textrm{g}_{WT}\); H) for all models (thin lines) with relationships from the fI curve examples (A, D, G respectively) highlighted by thick lines with colors corresponding to the box highlighting each set of fI curves. The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in half maximal potential \(V_{1/2}\) and \drheo (C), slope factor k and \drheo (F) as well as maximal current conductances and \drheo (I) for each model and current property is computed. The relationships between \(\Delta V_{1/2}\), k/\(\textrm{k}_{WT}\), and g/\(\textrm{g}_{WT}\)  and \drheo for the Kendall rank correlations highlighted in the black boxes are depicted in (B), (E) and (H) respectively..}
         \label{fig:rheobase_correlation}
@@ -354,11 +360,8 @@ Mutations in \Kv are associated with episodic ataxia type~1 (EA1) and have been
 \begin{figure}[tp]
         \centering
         \includegraphics[width=\linewidth]{Figures/simulation_model_comparison.pdf}
-        \\\notejb{Font sizes...}\notenk{better?}
-	\\\notenk{What do you think of the light grey dashed lines?}
-	\\\notenk{Check legend is in line with figure}
         \linespread{1.}\selectfont
-        \caption[]{Effects of episodic ataxia type~1 associated \Kv mutations on firing.  Effects of \Kv mutations on AUC (\(AUC_{contrast}\)) and rheobase (\(\Delta\)rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models. V174F, F414C, E283K, and V404I mutations are highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \Kv mutations on rheobase and on AUC are shown in J and K respectively. Marker shape is indicative of model/firing type.}
+        \caption[]{Effects of episodic ataxia type~1 associated \Kv mutations on firing.  Effects of \Kv mutations on AUC (percent change in normalized \(\Delta\)AUC) and rheobase (\(\Delta\)Rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models. V174F, F414C, E283K, and V404I mutations are highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \Kv mutations on rheobase and on AUC are shown in J and K respectively. Marker shape is indicative of model/firing type, and grey dashed lines denote the quadrants of firing characterization (see \Cref{fig:firing_characterization}).}
         \label{fig:simulation_model_comparision}
 \end{figure}
 
@@ -366,11 +369,8 @@ Mutations in \Kv are associated with episodic ataxia type~1 (EA1) and have been
 \section*{Discussion (3000 Words Maximum - Currently 1871)}
 % \textit{The discussion section should include a brief statement of the principal findings, a discussion of the validity of the observations, a discussion of the findings in light of other published work dealing with the same or closely related subjects, and a statement of the possible significance of the work. Extensive discussion of the literature is discouraged.}\\
 
-\notels{change AUC to "fI-AUC" or "AUC of the fI-curve"} \notenk{Done for Discussion, not sure if this makes sense to do in the results as well}
-
 To compare the effects of changes to properties of ionic currents on neuronal firing of different neuron types, a diverse set of conductance-based models was simulated. Changes to single ionic current properties, as well as known episodic ataxia type~1 associated \Kv mutations showed consistent effects on the rheobase across cell types, whereas the effects on AUC of the steady-state fI-curve depend on cell type. Our results demonstrate that LOF and GOF on the biophysical level cannot be uniquely transfered to the level of neuronal firing. The effects depend on the properties of the other currents expressed in a cell and are therefore depending on cell type.
 
-
 %Using a set of diverse conductance-based neuronal models, the effects of changes to properties of ionic currents on neuronal firing were determined to be heterogenous for the AUC of the steady state fI curve but more homogenous for rheobase. For a known channelopathy, episodic ataxia type~1 associated \Kv mutations, the effects on rheobase are consistent across model cell types, whereas the effects on AUC depend on cell type. Our results demonstrate that LOF and GOF on the biophysical level cannot be uniquely transfered to the level of neuronal firing. The effects depend on the properties of the other currents expressed in a cell and are therefore depending on cell type.
 
 \subsection*{Neuronal Diversity}
@@ -386,47 +386,38 @@ Diversity across neurons is not limited to gene expression and can also be seen
  
 %Taken together, the nervous system consists of a vastly diverse and heterogenous collection of neurons with variable properties and characteristics including diverse combinations and expression levels of ion channels which are vital for neuronal firing dynamics. 
 
-\notels{with our models we tried to get this diversity and it's relevant}
-To capture the diversity in neuronal ion channel expression and its relevance in the outcome of ion channel mutations, multiple neuronal models with different ionic currents and underlying firing dynamics were used here.
+To capture the diversity in neuronal ion channel expression and its relevance in the outcome of ion channel mutations, we used multiple neuronal models with different ionic currents and underlying firing dynamics here.
 
 
     
 \subsection*{Ionic Current Environments Determine the Effect of Ion Channel Mutations}
 
-To our knowledge, no comprehensive evaluation of how ionic current environment and cell type affect the outcome of ion channel mutations have been reported. However, comparisons between the effects of such mutations between certain cell types were described. For instance, the R1628H mutation in SCN1A results in selective hyperexcitability of cortical pyramidal neurons, but causes hypoexcitability of adjacent inhibitory GABAergic neurons
-% mutations in the SCN1A gene encoding \(\textrm{Na}_{\textrm{V}}\textrm{1.1}\) result in epileptic phenotypes by selective hypoexcitability of inhibitory but not excitatory neurons in the cortex resulting in circuit hyperexcitability 
- \citep{Hedrich14874}. In CA3 of the hippocampus, the equivalent mutation in SCN8A, R1648H, increases excitability of pyramidal neurons and decreases excitability of parvalbumin positive interneurons
-% mutation of \(\textrm{Na}_{\textrm{V}}\textrm{1.6}\) similarly results in increased excitability of pyramidal neurons and decreased excitability of parvalbumin positive interneurons 
- \cite{makinson_scn1a_2016}. Additionally, the L858H mutation in \(\textrm{Na}_\textrm{V}\textrm{1.7}\), associated with erythermyalgia, has been shown to cause hypoexcitability in sympathetic ganglion neurons and hyperexcitability in dorsal root ganglion neurons \citep{Waxman2007, Rush2006}. The differential effects of L858H \(\textrm{Na}_\textrm{V}\textrm{1.7}\) on firing is dependent on the presence or absence of another sodium channel \(\textrm{Na}_\textrm{V}\textrm{1.8}\) \citep{Waxman2007, Rush2006}. In a modelling study, it was found that altering the sodium conductance in 2 stomatogastric ganglion neuron models from a population models decreases rheobase in both models, however the initial slope of the fI curves (proportional to AUC of the fI-curve) is increased in one model and decreased in the other suggesting that the magnitude of other currents in these models (such as \(\textrm{K}_\textrm{d}\)) determines the effect of a change in sodium current \citep{Kispersky2012} \notels{I don't see this in the paper. As far as I understood, they start with one model type and then only work with the other and state that they behave qualitatively the same} \notenk{Yes you are right. I looked at the paper again and I'm not sure why I wrote that. I think the key thing I was trying to get at is that the effect of an increase in sodium conductan on the fI curve can be different at different parts of the fI curve, because at higher firing rates \(\textrm{K}_\textrm{d}\) plays a role. As such changes or heterogeneity in \(\textrm{K}_\textrm{d}\)  could alter the effect of such an increase in sodium conductance at these higher firing rates.}. \notenk{Do you think that this is a more accurate representation? ``In a modelling study, it was found that altering the sodium conductance in 2 stomatogastric ganglion neuron models from a population models decreases rheobase in both models, however the shape of the fI curves especially at high firing rates is altered due other currents in these models such as \(\textrm{K}_\textrm{d}\) \citep{Kispersky2012}.''} \notenk{Could move this to methods as a justification as to why we use rheobase and AUC as measures for firing}
-These findings, in concert with our findings emphasize that the ionic current environment in which a channelopathy occurs is vital in determining the outcomes of the channelopathy on firing.
+To our knowledge, no comprehensive evaluation of how ionic current environment and cell type affect the outcome of ion channel mutations have been reported. However, comparisons between the effects of such mutations between certain cell types were described. For instance, the R1628H mutation in SCN1A results in selective hyperexcitability of cortical pyramidal neurons, but causes hypoexcitability of adjacent inhibitory GABAergic neurons \citep{Hedrich14874}. In CA3 of the hippocampus, the equivalent mutation in SCN8A, R1648H, increases excitability of pyramidal neurons and decreases excitability of parvalbumin positive interneurons \cite{makinson_scn1a_2016}. Additionally, the L858H mutation in \(\textrm{Na}_\textrm{V}\textrm{1.7}\), associated with erythermyalgia, has been shown to cause hypoexcitability in sympathetic ganglion neurons and hyperexcitability in dorsal root ganglion neurons \citep{Waxman2007, Rush2006}. The differential effects of L858H \(\textrm{Na}_\textrm{V}\textrm{1.7}\) on firing is dependent on the presence or absence of another sodium channel \(\textrm{Na}_\textrm{V}\textrm{1.8}\) \citep{Waxman2007, Rush2006}.  These findings, in concert with our findings emphasize that the ionic current environment in which a channelopathy occurs is vital in determining the outcomes of the channelopathy on firing.
 
 Cell type specific differences in ionic current properties are important in the effects of ion channel mutations, however within a cell type heterogeneity in channel expression levels exists and it is often desirable to generate a population of neuronal models and to screen them for plausibility to biological data in order to capture neuronal population diversity \citep{marder_multiple_2011}. The models we used here are originally generated by characterization of current gating properties and by fitting of maximal conductances to experimental data \citep{pospischil_minimal_2008, ranjan_kinetic_2019, alexander_cerebellar_2019, otsuka_conductance-based_2004}. This practice of fixing maximal conductances based on experimental data is limiting as it does not reproduce the variability in channel expression and neuronal firing behaviour of a heterogeneous neuron population \citep{verma_computational_2020}. For example, a model derived from the mean conductances in a sub-population of stomatogastric ganglion "one-spike bursting" neurons fires 3 spikes instead of 1 per burst due to an L shaped distribution of sodium and potassium conductances \citep{golowasch_failure_2002}.  
 Multiple sets of current conductances can give rise to the same patterns of activity also termed degeneracy and differences in neuronal dynamics may only be evident with perturbations \citep{marder_multiple_2011, goaillard_ion_2021}. 
 Variability in ion channel expression often correlates with the expression of other ion channels \citep{goaillard_ion_2021} and neurons whose behaviour is similar may possess correlated variability across different ion channels resulting in stability in neuronal phenotype \citep{lamb_correlated_2013, soofi_co-variation_2012, taylor_how_2009}. 
 The variability of ion currents and degeneracy of neurons may account, at least in part, for the observation that the effect of toxins within a neuronal type is frequently not constant \citep{khaliq_relative_2006, puopolo_roles_2007, ransdell_neurons_2013}.
+% mutations in the SCN1A gene encoding \(\textrm{Na}_{\textrm{V}}\textrm{1.1}\) result in epileptic phenotypes by selective hypoexcitability of inhibitory but not excitatory neurons in the cortex resulting in circuit hyperexcitability 
+% mutation of \(\textrm{Na}_{\textrm{V}}\textrm{1.6}\) similarly results in increased excitability of pyramidal neurons and decreased excitability of parvalbumin positive interneurons 
 
 \subsection*{Effects of KCNA1 Mutations}
+Changes in delayed rectifier potassium currents, analogous to those seen in \Kv mutations, change the underlying firing dynamics of the Hodgkin Huxley model result in reduced thresholds for repetitive firing and thus contribute to increased excitability \citep{hafez_altered_2020}. Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases in excitability seen by \citet{hafez_altered_2020} are in line with our simulation-based predictions of the outcomes of \Kv mutations.  LOF KCNA1 mutations generally increase neuronal excitability, however the varying susceptibility on rheobase and different effects on AUC of the fI-curve of KCNA1 mutations across models are indicative that a certain cell type specific complexity exists. Increased excitability is seen experimentally with \Kv null mice \citep{smart_deletion_1998, zhou_temperature-sensitive_1998}, with pharmacological \Kv block \citep{chi_manipulation_2007, morales-villagran_protection_1996} and by \citet{hafez_altered_2020} with simulation-based predictions of KCNA1 mutations. Contrary to these results,  \citet{zhao_common_2020} predicted \textit{in silico} that the depolarizing shifts seen as a result of KCNA1 mutations broaden action potentials and interfere negatively with high frequency action potential firing, however they varied stimulus duration between different models and therefore comparability of firing rates is lacking in this study.     
+
+In our simulations, different current properties alter the impact of KCNA1 mutations on firing in our simulations as evident in the differences seen in the impact of \(\textrm{I}_\textrm{A}\) and \IKv in the Cb stellate and STN model families on KCNA1 mutation firing. This highlights that knowledge of the biophysical properties of a current and its neuronal expression is vital for holistic understanding of the effects of a given ion channel mutation both at a single cell and network level. 
+
+%LOF KCNA1 mutations generally increase neuronal excitability, however the different effects of KCNA1 mutations across models on AUC are indicative that a certain cell type specific complexity exists. 
 %Moderate changes in delayed rectifier potassium currents change the bifurcation structure \notels{firing dynamics} of Hodgkin Huxley model, with changes analogous to those seen with KV1.1 mutations resulting
 %in increased excitability due to reduced thresholds for repetitive firing \citep{hafez_altered_2020}. Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases
 %in excitability seen are in line with both score-based and simulation-based predictions
 %of the outcomes of KCNA1 mutations.
 
-Changes in delayed rectifier potassium currents, analogous to those seen in \Kv mutations, change the underlying firing dynamics of the Hodgkin Huxley model result in reduced thresholds for repetitive firing and thus contribute to increased excitability \citep{hafez_altered_2020}. Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases in excitability seen by \citet{hafez_altered_2020} are in line with our simulation-based predictions of the outcomes of \Kv mutations.  
-LOF KCNA1 mutations generally increase neuronal excitability, however the varying susceptibility on rheobase and different effects on AUC of the fI-curve of KCNA1 mutations across models are indicative that a certain cell type specific complexity exists.
-%LOF KCNA1 mutations generally increase neuronal excitability, however the different effects of KCNA1 mutations across models on AUC are indicative that a certain cell type specific complexity exists. 
-Increased excitability is seen experimentally with \Kv null mice \citep{smart_deletion_1998, zhou_temperature-sensitive_1998}, with pharmacological \Kv block \citep{chi_manipulation_2007, morales-villagran_protection_1996} and by \citet{hafez_altered_2020} with simulation-based predictions of KCNA1 mutations. Contrary to these results,  \citet{zhao_common_2020} predicted \textit{in silico} that the depolarizing shifts seen as a result of KCNA1 mutations broaden action potentials and interfere negatively with high frequency action potential firing, however they varied stimulus duration between different models and therefore comparability of firing rates is lacking in this study.     
-
-In our simulations, different current properties alter the impact of KCNA1 mutations on firing in our simulations as evident in the differences seen in the impact of \(\textrm{I}_\textrm{A}\) and \IKv in the Cb stellate and STN model families on KCNA1 mutation firing. This highlights that knowledge of the biophysical properties of a current and its neuronal expression is vital for holistic understanding of the effects of a given ion channel mutation both at a single cell and network level. 
-
 \subsection*{Loss or Gain of Function Characterizations Do Not Fully Capture Ion Channel Mutation Effects on Firing}
 The effects of changes in current properties depend in part on the neuronal model in which they occur and can be seen in the variance of correlations (especially in AUC of the fI-curve) across models for a given current property change. Therefore, relative conductances and gating properties of currents in the ionic current environment in which an alteration in current properties occurs plays an important role in determining the outcome on firing. The use of loss of function (LOF) and gain of function (GOF) is useful at the level of ion channels and whether a mutation results in more or less ionic current, however the extension of this thinking onto whether mutations induce LOF or GOF at the level of neuronal firing based on the ionic current LOF/GOF is problematic due to the dependency of neuronal firing changes on the ionic current environment. Thus the direct leap from current level LOF/GOF characterizations to effects on firing without experimental or modelling-based evidence, although tempting, should be refrained from and viewed with caution when reported. This is especially relevant in the recent development of personalized medicine for channelopathies, where a patients specific channelopathy is identified and used to tailor treatments \citep{Weber2017, Ackerman2013, Helbig2020, Gnecchi2021, Musto2020, Brunklaus2022}. However, the effects of specific ion channel mutations are often characterized in expression systems and classified as LOF or GOF to aid in treatment decisions \citep{johannesen_genotype-phenotype_2021, Brunklaus2022, Musto2020}.  Interestingly, both LOF and GOF \(\textrm{Na}_{\textrm{V}}\textrm{1.6}\) mutations can benefit from treatment with sodium channel blockers \citep{johannesen_genotype-phenotype_2021}, suggesting that the relationship between effects at the level of ion channels and effects at the level of firing and therapeutics is not linear or evident without further contextual information. Therefore, this approach should be used with caution and the cell type which expressed the mutant ion channel may provide valuable insight into the functional consequences of an ion channel mutation. Where experimental assessment of the effects of a patient's specific ion channel mutation \textit{in vivo} is not feasible at a large scale, modelling approaches investigating the effects of patient specific channelopathies provides an alternative bridge between characterization of changes in biophysical properties of ionic currents and the firing consequences of these effects. In both experimental and modelling investigation of firing level effects of channelopathies cell-type dependency should be considered.
 
 %Accordingly, for accurate modelling and predictions of the effects of mutations on neuronal firing, information as to the type of neurons containing the affected channel, and the properties of the affected and all currents in the affected neuronal type is needed. When modelling approaches are sought out to overcome the limitations of experimental approaches, care must be taken to account for model dependency and the generation of relevant cell-type or cell specific populations of models should be standard in assessing the effects of mutations in specific neurons.
 
-\notels{move small sentences down here}
-\notels{Conclusion, ionic current composition defines how changes in ionic current properties affect neurons, personalized medicin could benefit from simulations of simulating cell types}
-
-The effects of altered ion channel properties on firing is generally influenced by the other ionic currents in the cell. In channelopathies the effect of a given ion channel mutation on neuronal firing therefore depends on the cell type in which those changes occur. Although certain complexities of neurons such as differences in cell-type sensitivities to current property changes, interactions between ionic currents, cell morphology and subcellular ion channel distribution are neglected here, it is likely that this increased complexity \textit{in vivo} would contribute to the cell-type dependent effects on neuronal firing. Cell-type dependent firing effects of channelopathies may underlie shortcomings in treatment approaches in patients with channelopathies and accounting for cell-type dependent firing effects may provide an opportunity to further the efficacy and precision in personalized medicine approaches.
+The effects of altered ion channel properties on firing is generally influenced by the other ionic currents in the cell. In channelopathies the effect of a given ion channel mutation on neuronal firing therefore depends on the cell type in which those changes occur \citep{Hedrich14874, makinson_scn1a_2016, Waxman2007, Rush2006}. Although certain complexities of neurons such as differences in cell-type sensitivities to current property changes, interactions between ionic currents, cell morphology and subcellular ion channel distribution are neglected here, it is likely that this increased complexity \textit{in vivo} would contribute to the cell-type dependent effects on neuronal firing. Cell-type dependent firing effects of channelopathies may underlie shortcomings in treatment approaches in patients with channelopathies and accounting for cell-type dependent firing effects may provide an opportunity to further the efficacy and precision in personalized medicine approaches.
 
 
 
@@ -472,6 +463,7 @@ The effects of altered ion channel properties on firing is generally influenced
 
 
 \selectlanguage{english}
+\newpage
 \FloatBarrier
 \section*{References}\sloppy
 % \textit{Only published references should appear in the reference list at the end of the paper. The latest information on in-press references should be provided. In the case of in-press references (i.e., accepted for publication in a specific journal or book) the paper, which must be relevant for reviewers to see in order to make a well-informed evaluation should be included as a separate document text file along with the submitted manuscript. In this case, the authors recognize the loss of anonymity. “Submitted” references should be cited only in text and in the following form: (unpublished observations). If the paper is accepted, the authors can then add their names: A. B. Smith, C. D. Johnson, and E. Green, unpublished observations). The form for personal communications is similar: (F. G. Jackson, personal communication). Authors are responsible for all personal communications and must obtain written approval from persons cited before submitting the paper to eNeuro. Proof of such approval may be requested by eNeuro.
@@ -514,39 +506,42 @@ The effects of altered ion channel properties on firing is generally influenced
 % Remove top and right borderlines that to not contain measuring metrics from all graph/histogram figure panels (i.e., do not box the panels in). Do not include any two-bar graphs/histograms; instead state those values in the text.
 % All illustrations documenting results must include a bar to indicate the scale. All labels used in a figure should be explained in the legend. The migration of protein molecular weight size markers or nucleic acid size markers must be indicated and labeled appropriately (e.g., “kD”, “nt”, “bp”) on all figure panels showing gel electrophoresis.}
 
-\setcounter{figure}{0}
-\captionof{figure}{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.}
-
-\captionof{figure}{Diversity in Neuronal Model Firing. Spike trains (left), frequency-current (fI) curves (right) for Cb stellate (A), RS inhibitory (B), FS (C), RS pyramidal (D), RS inhibitory +\Kv (E), Cb stellate +\Kv (F), FS +\Kv (G), RS pyramidal +\Kv (H), STN +\Kv (I), Cb stellate \(\Delta\)\Kv (J), STN \(\Delta\)\Kv (K), and STN (L) neuron models. Black marker on the fI curves indicate the current step at which the spike train occurs. The green marker indicates the current at which firing begins in response to an ascending current ramp, whereas the red marker indicates the current at which firing ceases in response to a descending current ramp.}
-
-\captionof{figure}{The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in \(V_{1/2}\) and AUC, slope factor k and AUC as well as current conductances and AUC for each model are shown on the right in (A), (B) and (C) respectively. The relationships between AUC and \(\Delta V_{1/2}\), slope (k) and maximal conductance (g) for the Kendall \(\tau\) coefficients highlights by the black box are depicted in the middle panel. The fI curves corresponding to one of the models are shown in the left panels.}
-
-\captionof{figure}{The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in \(V_{1/2}\) and rheobase, slope factor k and AUC as well as current conductances and rheobase for each model are shown on the right in (A), (B) and (C) respectively. The relationships between rheobase and \(\Delta V_{1/2}\), slope (k) and maximal conductance (g) for the Kendall \(\tau\) coefficients highlights by the black box are depicted in the middle panel. The fI curves corresponding to one of the models are shown in the left panels.}
-
-\captionof{figure}{Effects of episodic ataxia type~1 associated \Kv mutations on firing. Effects of \Kv mutations on AUC (\(AUC_{contrast}\)) and rheobase (\(\Delta\)rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models V174F, F414C, E283K, and V404I mutations are highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \Kv mutations on rheobase and on AUC are shown in J and K respectively.}
+add from manuscript text before submission
+%\setcounter{figure}{0}
+%\captionof{figure}{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.}
+%
+%\captionof{figure}{Diversity in Neuronal Model Firing. Spike trains (left), frequency-current (fI) curves (right) for Cb stellate (A), RS inhibitory (B), FS (C), RS pyramidal (D), RS inhibitory +\Kv (E), Cb stellate +\Kv (F), FS +\Kv (G), RS pyramidal +\Kv (H), STN +\Kv (I), Cb stellate \(\Delta\)\Kv (J), STN \(\Delta\)\Kv (K), and STN (L) neuron models. Black marker on the fI curves indicate the current step at which the spike train occurs. The green marker indicates the current at which firing begins in response to an ascending current ramp, whereas the red marker indicates the current at which firing ceases in response to a descending current ramp.}
+%
+%\captionof{figure}{The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in \(V_{1/2}\) and AUC, slope factor k and AUC as well as current conductances and AUC for each model are shown on the right in (A), (B) and (C) respectively. The relationships between AUC and \(\Delta V_{1/2}\), slope (k) and maximal conductance (g) for the Kendall \(\tau\) coefficients highlights by the black box are depicted in the middle panel. The fI curves corresponding to one of the models are shown in the left panels.}
+%
+%\captionof{figure}{The Kendall rank correlation (Kendall \(\tau\)) coefficients between shifts in \(V_{1/2}\) and rheobase, slope factor k and AUC as well as current conductances and rheobase for each model are shown on the right in (A), (B) and (C) respectively. The relationships between rheobase and \(\Delta V_{1/2}\), slope (k) and maximal conductance (g) for the Kendall \(\tau\) coefficients highlights by the black box are depicted in the middle panel. The fI curves corresponding to one of the models are shown in the left panels.}
+%
+%\captionof{figure}{Effects of episodic ataxia type~1 associated \Kv mutations on firing. Effects of \Kv mutations on AUC (\(AUC_{contrast}\)) and rheobase (\(\Delta\)rheobase) compared to wild type for RS pyramidal +\Kv (A), RS inhibitory +\Kv (B), FS +\Kv (C), Cb stellate (D), Cb stellate +\Kv (E), Cb stellate \(\Delta\)\Kv (F), STN (G), STN +\Kv (H) and STN \(\Delta\)\Kv (I) models V174F, F414C, E283K, and V404I mutations are highlighted in color for each model. Pairwise Kendall rank correlation coefficients (Kendall \(\tau\)) between the effects of \Kv mutations on rheobase and on AUC are shown in J and K respectively.}
 
-\newpage
+%\newpage
 \subsection*{Tables}
 % \textit{All tables must be numbered independently of figures, multimedia, and 3D models and cited in the manuscript. Do not duplicate data by presenting it both in the text and in a table.
 % Each table should include a title and legend; legends should be included in the manuscript file after the reference list. Legends should include sufficient detail to be intelligible without reference to the text and define all symbols and include essential information.
 % Each table should be double-spaced. Multiple-part tables (A and B sections with separate subtitles) should be avoided, especially when there are two [different] sets [or types] of column headings.
 % Do not use color or shading, bold or italic fonts, or lines to highlight information. Indention of text and sometimes, additional space between lines is preferred. Tables with color or shading in the table body will need to be processed as a figure.}
 \setcounter{table}{0}
-\input{g_table}
+%\input{g_table}
+add from manuscript text before submission
 
+%\newpage
 \subsection*{Extended Data}
-% \textit{A legend for the code file, labeled as “Extended Data 1,” should be at the end of the manuscript.\\}
-% The code files must be packaged into a single ZIP file, uploaded to the submission system as a “Multimedia/Extended Data” file type.}
-\captionof{Extended Data}{TODO: Caption for code in zip file.}
-
 \beginsupplement
 \begin{figure}[tp]%described
         \centering
         \includegraphics[width=\linewidth]{Figures/ramp_firing.pdf}
         \linespread{1.}\selectfont
+	\vspace{-2cm}
         \caption[]{Diversity in Neuronal Model Firing Responses to a Current Ramp. Spike trains for Cb stellate (A), RS inhibitory (B), FS (C), RS pyramidal (D), RS inhibitory +\Kv (E), Cb stellate +\Kv (F), FS +\Kv (G), RS pyramidal +\Kv (H), STN +\Kv (I), Cb stellate \(\Delta\)\Kv (J), STN \(\Delta\)\Kv (K), and STN (L) neuron models in response to a slow ascending current ramp followed by the descending version of the current ramp (bottom). The current at which firing begins in response to an ascending current ramp and the current at which firing ceases in response to a descending current ramp are depicted on the frequency current (fI) curves in \Cref{fig:diversity_in_firing} for each model.}
         \label{fig:ramp_firing}
 \end{figure}
 
+% \textit{A legend for the code file, labeled as “Extended Data 1,” should be at the end of the manuscript.\\}
+% The code files must be packaged into a single ZIP file, uploaded to the submission system as a “Multimedia/Extended Data” file type.}
+\captionof{Extended Data}{Code in zip file. Description needs to be added once code is ready.}
 \end{document}