started working after meeting with Stephan and Uli

Figures/AUC_correlation.py

@@ -332,7 +332,7 @@ def plot_AUC_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration='
     print(ystart, yend)
     start = (xstart, ystart * 1.0)
     end = (xend, ystart * 1.0)
-    ax = gradientaxis(ax, start, end, cmap, n=100, lw=2)
+    ax = gradientaxis(ax, start, end, cmap, n=200, lw=4)
     ax.spines['bottom'].set_visible(False)
     return ax
 

Figures/rheobase_correlation.py

@@ -344,7 +344,7 @@ def plot_rheo_alt(ax, model='FS', color1='red', color2='dodgerblue', alteration=
     print(ystart, yend)
     start = (xstart, ystart*1.0)
     end = (xend, ystart*1.0)
-    ax = gradientaxis(ax, start, end, cmap, n=100,lw=2)
+    ax = gradientaxis(ax, start, end, cmap, n=200,lw=4)
     ax.spines['bottom'].set_visible(False)
     # ax.set_ylim(ystart, yend)
 

manuscript.tex

@@ -157,49 +157,45 @@ Nils A. Koch\textsuperscript{1,2}, Lukas Sonnenberg\textsuperscript{1,2}, Ulrike
 \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
 
-\textcolor{red}{\noteuh{Viele verwenden mittlerweile variants anstelle von Mutations.  Könnte evtl. geändert werden.}}\newline
-\textcolor{red}{\notenk{Abbreviate models as model 1,2,3, etc.???}}\newline
-\textcolor{red}{\noteuh{Use model +\Kv or model + \Kv ?}}
-
 \section*{Abstract (250 Words Maximum - Currently )}
 %\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.}
-\textcolor{red}{\noteuh{Ich bin mit dem Abstract noch nicht so glücklich, sollte noch besser hervorgehoben werden, warum du das gemacht hast und v.a. wie. Das fehlt bisher komplett. Können wir aber ganz am Schluss noch mal umschreiben. }}\notenk{Tried to make it clear in the abstract that we used neuronal models and simulations} 
-
-Ion channels determine neuronal excitability and disruption in ion channel properties caused by mutations can result in neurological disorders called channelopathies. Often, mutations within one gene are associated with a specific channelepothy and the 
-%mutations are associated with a channelopathy \noteuh{Ist es das, was du mit dem Satz gemeint hast? War mir nicht ganz klar. }, and determination of the 
-effects of these mutations  on channel function, i.e. the gating current of the affected ion channel, are generally determined using heterologous expression systems.
-%are generally done at the level of currents.
-Nevertheless, the impact of such mutations on neuronal firing is essential not only for brain function, but also for selecting personalized treatment options for the affected patient. However, it is unclear whether the effect of a given mutation on firing can simply be inferred from the effects identified at the current level. The general impact of the ionic current environment in different neuronal cell types on the outcome of ion channel mutations is vital to the understanding of the impacts caused by ion channel mutations and the effective selection of personalized treatment options for affected patients.
-Using a diverse collection of computational neuronal models, the effects of changes in ion current properties on firing properties of different neuronal types were simulated systematically and for mutations in the \textit{KCNA1} gene encoding the \Kv potassium channel subtype associated with episodic ataxia type~1 (EA1). The effects of changes in ion current properties or changes due to mutations in the \Kv channel subtype on the firing of a neuron depends on the ionic current environment, or the neuronal cell type, in which such a change occurs in. Characterization of ion channel mutations as loss or gain of function is useful at the level of the ionic current. However, the effects of mutations causing channelopathies on the firing of a cell is dependent on the cell type and thus on the composition of different ion channels and subunits. To further the efficacy of personalized medicine in channelopathies, the effects of ion channel mutations must be examined in the context of the appropriate cell types in which these mutations occur.
+
+Ion channels determine neuronal excitability and disruption in ion channel properties caused by mutations can result in neurological disorders called channelopathies. Often, mutations within one gene are associated with a specific channelepothy and the effects of these mutations  on channel function, i.e. the gating current of the affected ion channel, are generally determined using heterologous expression systems. Nevertheless, the impact of such mutations on neuronal firing is essential not only for determining brain function, but also for selecting personalized treatment options for the affected patient. The effect of ion channel mutations on firing in different cell types has been mostly neglect and it is unclear whether the effect of a given mutation on firing can simply be inferred from the effects identified at the current level. Here we use a diverse collection of computational neuronal models to determine that ion channel mutation effects at the current level cannot be indiscriminantly used to infer firing effects without consideration of cell-type. In particular, systematic simulation and evaluation of the effects of changes in ion current properties on firing properties in different neuronal types as well as for mutations in the \textit{KCNA1} gene encoding the \Kv potassium channel subtype associated with episodic ataxia type~1 (EA1) was performed. The effects of changes in ion current properties generally and due to mutations in the \Kv channel subtype on the firing of a neuron depends on the ionic current environment, or the neuronal cell type, in which such a change occurs in. Thus, while characterization of ion channel mutations as loss or gain of function is useful at the level of the ionic current, this characterization should not be extended to the level of neuronal excitability as the effects of ion channel mutations on the firing of a cell is dependent on the cell type and the composition of different ion channels and subunits therein. To further the efficacy of personalized medicine in channelopathies, the effects of ion channel mutations must be examined in the context of the appropriate cell types in which these mutations occur.
+%Using a diverse collection of computational neuronal models, the effects of changes in ion current properties on firing properties of different neuronal types were simulated systematically and for mutations in the \textit{KCNA1} gene encoding the \Kv potassium channel subtype associated with episodic ataxia type~1 (EA1). The effects of changes in ion current properties or changes due to mutations in the \Kv channel subtype on the firing of a neuron depends on the ionic current environment, or the neuronal cell type, in which such a change occurs in. Characterization of ion channel mutations as loss or gain of function is useful at the level of the ionic current. However, the effects of mutations causing channelopathies on the firing of a cell is dependent on the cell type and thus on the composition of different ion channels and subunits. To further the efficacy of personalized medicine in channelopathies, the effects of ion channel mutations must be examined in the context of the appropriate cell types in which these mutations occur.
 \par\null
 
 \section*{Significant Statement (120 Words Maximum - Currently )}
 %\textit{The Significance Statement should provide a clear explanation of the importance and relevance of the research in a manner accessible to researchers without specialist knowledge in the field and informed lay readers. The Significance Statement will appear within the paper below the abstract.}
 
-Ion channels determine neuronal excitability and mutations that alter ion channel properties result in neurological disorders called channelopathies. Although the genetic nature of such mutations as well as their effects on the biophysical properties of an ion channel are routinely assessed experimentally, determination of the role in altering neuronal firing is more difficult. Computational modelling bridges this gap and demonstrates that the cell type in which a mutation occurs is an important determinant in the effects of neuronal firing. As a result, classification of ion channel mutations as loss or gain of function is useful to describe the ionic current but care should be taken when applying this classification on the level of neuronal firing.
+Ion channels determine neuronal excitability and mutations that alter ion channel properties result in neurological disorders called channelopathies. Although the genetic nature of such mutations as well as their effects on the biophysical properties of an ion channel are routinely assessed experimentally, determination of the role in altering neuronal firing is more difficult. In particular, cell-type dependency of ion channel mutations on firing has been observed experimentally, and should be accounted for. In this context, computational modelling bridges this gap and demonstrates that the cell type in which a mutation occurs is an important determinant in the effects of neuronal firing. As a result, classification of ion channel mutations as loss or gain of function is useful to describe the ionic current but should not be blindly extend to classification at the level of neuronal firing.
 \par\null
 
 
 \section*{Introduction (750 Words Maximum  - Currently )} 
 %\textit{The Introduction should briefly indicate the objectives of the study and provide enough background information to clarify why the study was undertaken and what hypotheses were tested.}
 
-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 subunits 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 behavior \citep{carbone_ion_2020}. Ion channel mutations are a common cause of such channelopathies \noteuh{Ist ja nicht nur common, sondern DER Grund für einen Kanalopathie} \notenk{Es gibt auch "acquired" Kanalopathie die aus verschiedene Gruenden (Droggen, Krankheiten, usw.) erstehen
-https://www.bmj.com/content/316/7138/1104} 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}.
+%\textcolor{red}{Over the last years, new technology such as next generation sequencing has led to the identification of a growing number of associated de novo genetic variants, providing the basis for subsequent pathophysiological studies. The ongoing joint effort of many research groups continues to contribute to an increasingly better understanding of underlying disease mechanisms. At the same time, the unfolding complexity of the pathophysiological landscape of DEE emerges as one likely reason for limited therapeutic success experienced with standard care.
+%Sodium channelopathies have initially been identified as one of the most frequent causes of genetic forms of epilepsy and more recent studies demonstrate their implication in DEE . SCN8A for example – the gene encoding the human NaV1.6 voltage gated sodium channel – gene has been recognized as an epilepsy-associated gene in 2012 (Veeramah et al., 2012).
+%  }
 
-The effects of mutations in ion channel genes on ionic current kinetics are frequently assessed using heterologous expression systems which do not express 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 gating of the altered ion 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}\notenk{Any other papers?} \textcolor{red}{\noteuh{Da gibe s ja viele dazu… drei sollten reichen, vielleicht noch ein review?}}, which in turn is important for understanding the pathophysiology of these disorders and for identification of potential therapeutic targets \citep{Orsini2018, Yang2018, Colasante2020, Yu2006}. 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 slices of transgenic mouse lines \citep{Mantegazza2019, Xie2010,Lory2020, Habib2015,  Hedrich2019} \noteuh{Hedrich 2019 ja ein review, würde vorne besser passen. Es gibt ja sehr viele Beispiele für Schnittableitungen…}. \notenk{Die reihenvolge ist alphabetisch}
+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 subunits 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, for instance through genetic alterations, resulting in altered ionic current properties and altered neuronal firing behavior \citep{carbone_ion_2020}.
 
-However, the effect of a given channelopathy on the 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. The expression level of an affected gene can correlate with firing behavior in the simplest case \citep{Layer2021}. However, if gating kinetics are affected this can have complex consequences on the firing behavior of a specific cell type and the network activity within the brain.
+In recent years, next generation sequencing has led to an increasing number of clinically relevant genetic mutations and has provided the basis for pathophysiological studies of genetic epilepsies, pain disorders, dyskinesias, intellectual disabilities, myotonias, and periodic paralyses \citep{bernard_channelopathies_2008, carbone_ion_2020}. Ongoing efforts of many research groups have contributed to the current understanding of underlying disease mechanism in channelopathies, however a complex pathophysiological landscape has emerged for many channelopathies and is likely a reason for limited therapeutic success with standard care.              
+
+% Ion channel mutations are the most 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 \citep{bernard_channelopathies_2008, carbone_ion_2020}.
 
-For instance, altering relative amplitudes of ionic currents can dramatically influence the firing behavior 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. Cell-type specific effects one firing can occur for instance increases inhibitory interneuron but not pyramidal neuron firing with R1648H mutation in \textit{SCN1A} \citep{Hedrich14874}. In extreme cases, a mutation can have opposite effects on different neuron types. For example, the R1627H \noteuh{R1629H ist glaube ich die Position in SCN8A}\notenk{R1627H in SCN8A in Makinson} \textit{SCN8A} mutation is associated which increased firing in interneurons, but decreases pyramidal neuron excitability \noteuh{Das stimmt nicht für die R1648H mutation in interneuronen. Das Makinson paper untersuchte doch die R1627H mutation in SCN8A. Da war aber die Erregbarkeit von Pyramidenzellen erhöht.}\notenk{Mein Fehler, ich habe den Paper falsch vestandend. Habe es im Text umgeschrieben} \citep{makinson_scn1a_2016}
+The effects of mutations in ion channel genes on ionic current kinetics are frequently assessed using heterologous expression systems which do not express 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 gating of the altered ion 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}, which in turn is important for understanding the pathophysiology of these disorders and for identification of potential therapeutic targets \citep{Orsini2018, Yang2018, Colasante2020, Yu2006}. 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 slices of transgenic mouse lines \citep{Mantegazza2019, Xie2010,Lory2020, Habib2015,  Hedrich2019}.
 
+However, the effect of a given channelopathy on the 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. The expression level of an affected gene can correlate with firing behavior in the simplest case \citep{Layer2021}. However, if gating kinetics are affected this can have complex consequences on the firing behavior of a specific cell type and the network activity within the brain.
 
+For instance, altering relative amplitudes of ionic currents can dramatically influence the firing behavior 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. Cell-type specific effects one firing can occur for instance increases inhibitory interneuron but not pyramidal neuron firing with R1648H mutation in \textit{SCN1A} \citep{Hedrich14874}. In extreme cases, a mutation can have opposite effects on different neuron types. For example, the R1627H  \textit{SCN8A} mutation is associated which increased firing in interneurons, but decreases pyramidal neuron excitability \citep{makinson_scn1a_2016}.
 
-Computational modelling approaches can be used to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}. \textcolor{red}{\notenk{added citation to make this clearer -  other papers not from us?}} \noteuh{Hiermit möchtest du sagen, dass man quasi den Block eines bestimmtem Kanaltyps auf das Feuern untersuchen kann? Oder was genau meinst du damit?} \notenk{Ja, nicht nur Mutation oder Block alleine, aber auch ob ein bestimmten Block das Feuern richtung Wildtyp bringen kann (bzw. Unser Paper mit Carbamazepine und Riluzul in KCNA1)}
+\textcolor{red}{Despite this evidence of cell-type specific effects of ion channel mutations on firing, the dependence of firing outcomes of ion channel mutations is generally not known. Cell-type specificity is likely vital for successful precision medicine treatment approaches. For example, Dravet syndrome was identified as the consquence of LOF mutations in \textit{SCN1A} \citep{Claes2001,Fujiwara2003,Ohmori2002}, however limited succes in treatment of Dravet syndrome persisted \citep{Claes2001,Oguni2001}. Once it became evident that only inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations alternative approaches, based on this understanding such as gene therapy, began to show promise \citep{Colasante2020, Yu2006}. Due to the high clinical relevance of understanding cell-type dependent effects of channelopathies, we use computationaly modelling approaches to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}.
+} 
 
-\textit{In this study, we therefore investigated the role that a specific neuronal cell type plays on the outcome of ionic current kinetic changes on firing \noteuh{Eher andresrum, oder? Es wurde untersucht, wie die Veränderungen in der Kinetik sich auf das Feuern unterschiedlicher Neuronentypen auswirken.} \notenk{Re-written below to make it clearer} by simulating the response of a repertoire of different neuronal models to changes in single current parameters as well as to more complex changes as they were observed for specific mutations. }
+%Computational modelling approaches can be used to assess the impacts of altered ionic current properties on firing behavior, bridging the gap between changes in the biophysical properties induced by mutations, firing and clinical symptoms. Conductance-based neuronal models enable insight into the effects of ion channel mutations with specific effects of the resulting ionic current as well as enabling \textit{in silico} assessment of the relative effects of changes in biophysical properties of ionic currents on neuronal firing. Furthermore, modelling approaches enable predictions of the effects of specific mutation and drug induced biophysical property changes \citep{Layer2021,Liu2019,johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021}. 
 
 In this study, we therefore investigated how the outcome of ionic current kinetic changes on firing depend on neuronal cell type by simulating the response of a repertoire of different neuronal models to changes in single current parameters as well as to more complex changes as they were observed for specific mutations. For this task we chose mutations in the \textit{KCNA1} gene, encoding  the potassium channel subunit \Kv, that are associated with episodic ataxia type~1 \citep{Browne1994, Browne1995, lauxmann_therapeutic_2021}.
-\noteuh{Warum hast du die UE den immer als „Formel“ eingefügt? (\Kv) Geht auch einfach als normaler Text. (Kv1.1) } \notenk{Ich habe die IUPHAR Nomenklatur mit "V" tiefgestellt benutzt}
 \par\null
 
 \section*{Materials and Methods}
@@ -222,23 +218,18 @@ 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  (stepsize 5\,pA) \noteuh{Wie groß war den die Step-size?}\notenk{Es gab 200 schritte (siehe erster Satz) also 0.005nA von 0 bis 1 nA }  \notenk{added step size for both to make it explicit} for all models except for the RS inhibitory neuron models where a range of 0 to 0.35\,nA (stepsize 1.75\,pA) 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 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  (step size 5\,pA) for all models except for the RS inhibitory neuron models where a range of 0 to 0.35\,nA (step size 1.75\,pA) 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.  
 
-To ensure a uniform time interval sampling across models, accurate firing frequencies at low firing rates, and reduced spike sampling bias steady-state firing was defined as the mean firing frequency in a 500\,ms window in the last second of the current steps starting at the inital action potential in this last second.
+To ensure accurate firing frequencies at low firing rates and reduced spike sampling bias, steady-state firing was defined as the mean firing frequency in a 500\,ms window in the last second of the current steps starting at the inital action potential in this last second.
 Firing characterization was performed in the last second of current steps to ensure steady-state firing is captured and adaptation processes are neglected in our analysis. Alteration in current magnitudes can have different effects on rheobase and the initial slope of the fI curve \citep{Kispersky2012}.
 For this reason, we quantified neuronal firing using 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 \Cref{fig:firing_characterization}A.
 
 The smallest current at which steady state firing occured 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. 
     
-To obtain the rheobase at a higher current resolution than the fI curve, the current step interval preceding the occurrence of action potentials was explored at higher resolution with 100 current steps spanning the interval (stepsizes of 0.05 pA and 0.0175 pA respectively) \notenk{To address the comment below I added the reason and step sizes to make it clearer why this was done}. Membrane responses to these current steps were then analyzed for action potentials and the rheobase was considered the lowest current step for which an action potential was elicited. \noteuh{Ich verstehe aber nicht so ganz, was dann der Unterschied zu oben ist? Ab wann hast du den steady-state definiert? Wie viele Aktionspotentiale mussten mindestens auftreten? Das ist mir unklar. Und für die Rheobas hast du dann geschaut, wann min. 1 AP aufgetreten ist, oder?} \notenk{Der Unterschied zu oben ist dass oben die Ziel Stromschrit gefunden wird  wo das Feuern anfaengt (z.B. 0.07  kein feuern, 0.08 feuern). Dieser Stromschrit wird dann wieder Simuliert mit kleinere schritte (z.B. 0.007, 0.00701, 0.00702, …, 0.0799, 0.08). Die Rheobas wird dann von diese kleiner schritte gefunden wo min 1 AP aufgreten ist (damit die Rheobase feiner aufgeloest ist)}
+To obtain the rheobase at a higher current resolution than the fI curve, the current step interval preceding the occurrence of action potentials was explored at higher resolution with 100 current steps spanning the interval (step sizes of 0.05 pA and 0.0175 pA respectively). Membrane responses to these current steps were then analyzed for action potentials and the rheobase was considered the lowest current step for which an action potential was elicited.
  
 All models exhibited tonic steady-state firing with default parameters. In limited instances, variations of parameters elicited periodic bursting, however these instances were excluded from further analysis.
 
-\notenk{Italics sections below are replaced above}
-\textit{The steady-state firing frequency was defined as the mean firing frequency 0.5\,s after the first action potential  in the last second of the current step respectively \textcolor{red}{to ensure a uniform time interval sampling across models and at low firing rates} and was used to construct frequency-current (fI) curves. }
-\textit{\noteuh{Warum nur in den ersten 0.5 s ? da würde man auch schon sehen, ob es zu einer spike frequency adaptation kommt? Aber das hattest du nicht angeschaut, oder? Ist ja auch wichtig für das Feuerverhalten unterschiedlichen Neurone. } \notenk{Die 0.5s ist in der letzte sekunde des steps, damit die adaption keine Rolle spielt. Wenn mann die ISIs hat fuer den ganzen Step, kann mann nicht einfach die letzte sekunde nehmen weil es nicht unbedingt (bzw fast nie) einen spike genau am Anfang oder am Ende gibt. Dies bedeutet das mann irgendwie nur ein Teil des erstes/letztes ISI im durchschnitt mit rechnen muss. Um das zu vermeiden habe ich nur 0.5s benutzt die vom ersten spike in der letzte sekund anfaengt. Ich hoffe das dass Sinn macht?} \textcolor{red}{\notenk{Reason: at low firing rates a fixed time interval can miss spikes (i.e. get F=0Hz when F is small}}}
-\textit{All models exhibited tonic firing and any instances of periodic bursting were excluded to simplify the characterization of firing. \noteuh{Die Pyramidenzellen haben auch nie am Anfang einen kurzen „burst“ gezeigt? Ich frage nur, weil das in Zellen bei einigen Pyramidenzellen der Fall ist. Die hast du auch alles ausgeschlossen, um die Variabilität zu minimieren?} \notenk{Mit bursting meine ich hier repetitiven bursts, bzw. Periodisches bursting mit interburst Intervale. Den Fall wo die Pyramidenzellen am Anfang einen kurzen "burst" haben ist vorgekommen, wird aber hier nicht analysiert weil wir das "steady state" feuern hier betrachten - added ``periodic'' to make this clear}}
-
     
 \subsection*{Sensitivity Analysis and Comparison of Models}
     
@@ -257,7 +248,7 @@ To assess whether the effects of a given alteration on \ndAUC  or \drheo were ro
 The Kendall's \(\tau\) coefficient, a non-parametric rank correlation, is used to describe the relationship between the magnitude of the alteration and AUC or rheobase values. A Kendall \(\tau\) value of -1 or 1 is indicative of monotonically decreasing and increasing relationships respectively.
     
 \subsection*{\textit{KCNA1} Mutations}\label{subsec:mut}
- Known episodic ataxia type~1 associated \textit{KCNA1} mutations and their electrophysiological characterization have been reviewed in \citet{lauxmann_therapeutic_2021}. The mutation-induced changes in \IKv amplitude and activation slope (\(k\)) were normalized to wild type measurements and changes in activation \(V_{1/2}\) were used relative to wild type measurements. Although initially described to lack fast activation, \Kv displays prominent inactivation at physiologically relevant temperatures \citep{ranjan_kinetic_2019}. The effects of a mutation were also applied to \(\textrm{I}_{\textrm{A}}\) when present as both potassium currents display prominent inactivation. \textcolor{red}{\noteuh{Allerdings trägt Kv1.1 eher zum D-current bei, und nur zu gewissen Teilen zum A-type (wenn mit Kv1.4 exprimiert). Ansonsten inaktiviert Kv1.1 eher langsam…}} \notenk{Bei hoehere bzw mehr physiologischen Temperaturen ist die Inaktivierung schneller und eher A-type als D. siehe: https://channelpedia.epfl.ch/wiki/ionchannels/1  https://www.frontiersin.org/articles/10.3389/fncel.2019.00358/full  Added sentence before this one to talk about inactivation of \Kv} In all cases, the mutation effects were applied to half of the \Kv or \(\textrm{I}_{\textrm{A}}\) under the assumption that the heterozygous mutation results in 50\% of channels carrying the mutation. Frequency-current curves for each mutation in each model were obtained through simulation and used to characterize firing behavior as described above. For each model the differences in mutation AUC to wild type AUC were normalized by wild type AUC (\ndAUC) and mutation rheobases were compared to wild type rheobase values (\drheo). Pairwise Kendall rank correlations (Kendall \(\tau\)) were used to compare the correlation in the effects of \Kv mutations on AUC and rheobase between models.
+ Known episodic ataxia type~1 associated \textit{KCNA1} mutations and their electrophysiological characterization have been reviewed in \citet{lauxmann_therapeutic_2021}. The mutation-induced changes in \IKv amplitude and activation slope (\(k\)) were normalized to wild type measurements and changes in activation \(V_{1/2}\) were used relative to wild type measurements. Although initially described to lack fast activation, \Kv displays prominent inactivation at physiologically relevant temperatures \citep{ranjan_kinetic_2019}. The effects of a mutation were also applied to \(\textrm{I}_{\textrm{A}}\) when present as both potassium currents display inactivation. In all cases, the mutation effects were applied to half of the \Kv or \(\textrm{I}_{\textrm{A}}\) under the assumption that the heterozygous mutation results in 50\% of channels carrying the mutation. Frequency-current curves for each mutation in each model were obtained through simulation and used to characterize firing behavior as described above. For each model the differences in mutation AUC to wild type AUC were normalized by wild type AUC (\ndAUC) and mutation rheobases were compared to wild type rheobase values (\drheo). Pairwise Kendall rank correlations (Kendall \(\tau\)) were used to compare the correlation in the effects of \Kv mutations on AUC and rheobase between models.
 
 
 
@@ -277,12 +268,12 @@ To examine the role of cell-type specific ionic current environments on the impa
         \centering
         \includegraphics[width=\linewidth]{Figures/diversity_in_firing.pdf}
         \linespread{1.}\selectfont
-        \caption[]{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 markers 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 (see \Cref{fig:ramp_firing}). \noteuh{Bricht das Feuern der Zellen wirklich schon bei einer Strominjektion von 0.3nA ab? Und die Feuerfrequenz ist so hoch? War das bei den bereits publizierten RS inhibitory neurons ebenfalls so?}\notenk{Ja das Feuern des Modells bricht von 0.3nA ab und feuert mit hoehe Feuerfrequenz. Das eine (RS inhibitory ist das original publizierte model (die von Experimentelle Daten gefitted wurde), und das RS inhibitory +\Kv hat niedriger Feuerfrequenz. }}
+        \caption[]{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 markers 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 (see \Cref{fig:ramp_firing}).}
         \label{fig:diversity_in_firing}
 \end{figure}
 
 \subsection*{Variety of model neurons}
-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 single-compartment, conductance-based 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 for this study all fire tonically and do not exhibit bursting (see methods for details and naming of the models). 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 exhibit type II firing. Type I firing is characterized by continuous fI curves (i.e. firing rate increases from 0 in a continuous fashion) whereas 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) \cite{ermentrout_type_1996, Rinzel_1998}. The other models used here lie on a continuum between these prototypical firing classifications. Most neuronal models exhibit hysteresis with ascending and descending ramps eliciting spikes at different current 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}). Different types of underlying current dynamics \textcolor{red}{\noteuh{wie zum Beispiel? Könnte dann in der Diskussion evtl. aufgegriffen werden}} are known to generate these different firing types and hysteresis \cite{ERMENTROUT2002, ermentrout_type_1996, Izhikevich2006}.
+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 single-compartment, conductance-based 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 for this study all fire tonically and do not exhibit bursting (see methods for details and naming of the models). 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 exhibit type II firing. Type I firing is characterized by continuous fI curves (i.e. firing rate increases from 0 in a continuous fashion) whereas 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) \cite{ermentrout_type_1996, Rinzel_1998}. The other models used here lie on a continuum between these prototypical firing classifications. Most neuronal models exhibit hysteresis with ascending and descending ramps eliciting spikes at different current 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}). Different types of underlying current dynamics are known to generate these different firing types and hysteresis \cite{ERMENTROUT2002, ermentrout_type_1996, Izhikevich2006}.
 
 \subsection*{Characterization of Neuronal Firing Properties}
 \begin{figure}[tp]
@@ -298,12 +289,12 @@ Neuronal firing is a complex phenomenon, and a quantification of firing properti
 
 Using these two measures we quantify the effects a changed property of an ionic current has on neural firing by the differences in both rheobase, \drheo, and in AUC, \(\Delta\)AUC, relative to the wild type neuron. \(\Delta\)AUC is in addition normalized to the AUC of the wild type neuron, see Eq.~\eqref{eqn:AUC_contrast}. Each fI curve resulting from an altered ionic current is a point in a two-dimensional coordinate system spanned by \drheo and \ndAUC (\Cref{fig:firing_characterization}B). An fI curve similar to the one of the wild type neuron is marked by a point close to the origin. In the upper left quadrant, fI curves become steeper (positive difference of AUC values: \(+\Delta\)AUC) and are shifted to lower rheobases (negative difference of rheobases: \(-\)\drheo), unambigously indicating an increased firing that clearly might be classified as a gain of function (GOF) of neuronal firing. The opposite happens in the bottom right quadrant where the slope of fI curves decreases (\(-\Delta\)AUC) and the rheobase is shifted to higher currents (\(+\)\drheo), indicating a decreased, loss of function (LOF) firing. In the lower left (\(-\Delta\)AUC and \(-\)\drheo) and upper right (\(+\Delta\)AUC and \(+\)\drheo) quadrants, the effects on firing are less clear-cut, because the changes in rheobase and AUC have opposite effects on neuronal firing. Changes in a neuron's fI curves in these two quadrants cannot uniquely be described as a gain or loss of excitability. 
 
- \notenk{Moved to discussion section ``Firing Frequency Analysis} \textit{In these cases, it depends on the regime the neuron is operating in. If it is in its excitable regime and only occasionally generates an action potential, then the effect on the rheobase matters more. If it is firing periodically with high rates, then the change in AUC might be more relevant. \textcolor{red}{\noteuh{Das sind ja eigentlich schon Hypothesen, sollte eher in die Diskussion oder dort zumindest noch mal aufgegriffen werden. Kommt ja vermutlich noch ;-)}}  \textcolor{red}{\notenk{Add to dicussion? As intro and explanation as to why we characterize firing?}}}
+% \notenk{Moved to discussion section ``Firing Frequency Analysis} \textit{In these cases, it depends on the regime the neuron is operating in. If it is in its excitable regime and only occasionally generates an action potential, then the effect on the rheobase matters more. If it is firing periodically with high rates, then the change in AUC might be more relevant. \textcolor{red}{\noteuh{Das sind ja eigentlich schon Hypothesen, sollte eher in die Diskussion oder dort zumindest noch mal aufgegriffen werden. Kommt ja vermutlich noch ;-)}}  \textcolor{red}{\notenk{Add to dicussion? As intro and explanation as to why we characterize 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.
 
-For example, when shifting the half activation voltage \(V_{1/2}\) of the delayed rectifier potassium current in the FS +\Kv model to more depolarized values, then the rheobase of the resulting fI curves shifted to lower currents \(-\)\drheo, making the neuron more sensitive to weak inputs, but at the same time the slope of the fI curves was reduced (\(-\)\ndAUC), which resulted in a reduced firing rate (\Cref{fig:AUC_correlation}~A) \textcolor{red}{\noteuh{Ich kann das auf Fig. 3 A nicht rauslesen…. Es ist aber auch nicht beschrieben, was dort genau gezeigt ist. Oder ich verstehe es gerade nicht....}}\notenk{The decrease in AUC and rheobase cause a decrease in firing (lower right  quadrant Figure 2B). I've added grey to black color scheme to to axis in B, D and H to help make it clearer} \textcolor{red}{\notenk{, but we could also change the figure legend?}}. As a result the effect of a depolarizing shift in the delayed rectifier potassium current half activation \(V_{1/2}\) in FS neurons is in the bottom left quadrant of \Cref{fig:firing_characterization}~B and characterization as LOF or GOF in excitability is not possible. Plotting the corresponding changes in AUC against the change in half activation potential \(V_{1/2}\) results in a monotonically falling curve (thick orange line in \Cref{fig:AUC_correlation}~B). For each of the many models we got a different relation between the changes in AUC and the shifts in half maximal potential \(V_{1/2}\) (thin lines in \Cref{fig:AUC_correlation}~B). To further summarize these different dependencies of the various models we characterized each of these curves by a single number, the \( \text{Kendall} \ \tau \) correlation coefficient\textsuperscript{a}. A monotonically increasing curve resulted in a \( \text{Kendall} \ \tau \) close to \(+1\)\textsuperscript{a}, a monotonously decreasing curve in \( \text{Kendall} \ \tau \approx -1 \)\textsuperscript{a}, and a non-monotonous, non-linear relation in \( \text{Kendall} \ \tau \) close to zero\textsuperscript{a} (compare lines in \Cref{fig:AUC_correlation}~B with dots in black box in panel C).
+For example, when shifting the half activation voltage \(V_{1/2}\) of the delayed rectifier potassium current in the FS +\Kv model to more depolarized values, then the rheobase of the resulting fI curves shifted to lower currents \(-\)\drheo, making the neuron more sensitive to weak inputs, but at the same time the slope of the fI curves was reduced (\(-\)\ndAUC), which resulted in a reduced firing rate (\Cref{fig:AUC_correlation}~A). As a result the effect of a depolarizing shift in the delayed rectifier potassium current half activation \(V_{1/2}\) in FS neurons is in the bottom left quadrant of \Cref{fig:firing_characterization}~B and characterization as LOF or GOF in excitability is not possible. Plotting the corresponding changes in AUC against the change in half activation potential \(V_{1/2}\) results in a monotonically falling curve (thick orange line in \Cref{fig:AUC_correlation}~B). For each of the many models we got a different relation between the changes in AUC and the shifts in half maximal potential \(V_{1/2}\) (thin lines in \Cref{fig:AUC_correlation}~B). To further summarize these different dependencies of the various models we characterized each of these curves by a single number, the \( \text{Kendall} \ \tau \) correlation coefficient\textsuperscript{a}. A monotonically increasing curve resulted in a \( \text{Kendall} \ \tau \) close to \(+1\)\textsuperscript{a}, a monotonously decreasing curve in \( \text{Kendall} \ \tau \approx -1 \)\textsuperscript{a}, and a non-monotonous, non-linear relation in \( \text{Kendall} \ \tau \) close to zero\textsuperscript{a} (compare lines in \Cref{fig:AUC_correlation}~B with dots in black box in panel C).
 
 Changes in gating half activation potential \(V_{1/2}\) and slope factor \(k\) as well as the maximum conductance \(g\) affected the AUC (\Cref{fig:AUC_correlation}), but how exactly the AUC was affected usually depended on the specific neuronal model. Increasing the slope factor of the \Kv activation curve for example increased the AUC in all models (\( \text{Kendall} \ \tau \approx +1\)\textsuperscript{a}), but with different slopes (\Cref{fig:AUC_correlation}~D,E,F). Similar consistent positive correlations can be found for shifts in A-current activation \(V_{1/2}\). Changes in \Kv half activation \(V_{1/2}\) and in maximal A-current conductance resulted in negative correlations with the AUC in all models (\( \text{Kendall} \ \tau \approx -1\)\textsuperscript{a}).
 
@@ -314,11 +305,11 @@ Qualitative differences can be found, for example, when increasing the maximal c
         \centering
         \includegraphics[width=\linewidth]{Figures/AUC_correlation.pdf}
         \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 (D), and changes in maximal conductance of delayed rectifier K current in the STN \(+\)\Kv model (G) are shown. The fI curves from the smallest and largest alterations are seen from grey to black respectively for (A,D, and G) in accordance to the x-axis in B, E, and H. The \ndAUC of fI curves is plotted against delayed rectifier K half activation potential (\(\Delta V_{1/2}\); D), \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. \noteuh{Den Unterschied zwischen den dicken und dünnen Linien kann man relativ schlecht sehen. Obwohl ja die dicke Linie auch der Farbe des Kastens entspricht. Aber wenn man B/W druckt, dann sieht man das halt schlecht…} \notenk{Added color gradient x-axis in B, E, H to indicate color scale in A,D,G, and added a tence to legend to make this clearer}}
+        \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 (D), and changes in maximal conductance of delayed rectifier K current in the STN \(+\)\Kv model (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. The \ndAUC of fI curves is plotted against delayed rectifier K half activation potential (\(\Delta V_{1/2}\); D), \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}
 
-Changes in gating half activation potential \(V_{1/2}\) and slope factor \(k\) as well as the maximum conductance \(g\) affected rheobase (\Cref{fig:rheobase_correlation}). However, in contrast to AUC, qualitatively consistent effects on rheobase across models could be observed. An increasing of the maximal conductance of the leak current in the Cb stellate model increased the rheobase (\Cref{fig:rheobase_correlation}~G). When these changes were plotted against the change in maximal conductance a monotonically increasing relationship was evident (thick teal line in \Cref{fig:rheobase_correlation}~H). \textcolor{red}{\noteuh{Den Unterschied zwischen den dicken und dünnen Linien kann man relativ schlecht sehen. Obwohl ja die dicke Linie auch der Farbe des Kastens entspricht. Aber wenn man B/W druckt, dann sieht man das halt schlecht…}}\notenk{decreased alpha of other lines to make thick line more obvious} This monotonically increasing relationship was evident in all models (\( \text{Kendall} \ \tau \approx +1\)\textsuperscript{a}), but with different slopes (thin lines in \Cref{fig:rheobase_correlation}~H). Similarly, positive correlations were consistently found across models for maximal conductances of delayed rectifier K, \Kv, and A type currents, whereas the maximal conductance of the sodium current was consistently associated with negative correlations (\( \text{Kendall} \ \tau \approx -1\)\textsuperscript{a}; \Cref{fig:rheobase_correlation}~I), i.e. rheobase decreased with increasing maximum conductance in all models.
+Changes in gating half activation potential \(V_{1/2}\) and slope factor \(k\) as well as the maximum conductance \(g\) affected rheobase (\Cref{fig:rheobase_correlation}). However, in contrast to AUC, qualitatively consistent effects on rheobase across models could be observed. An increasing of the maximal conductance of the leak current in the Cb stellate model increased the rheobase (\Cref{fig:rheobase_correlation}~G). When these changes were plotted against the change in maximal conductance a monotonically increasing relationship was evident (thick teal line in \Cref{fig:rheobase_correlation}~H). This monotonically increasing relationship was evident in all models (\( \text{Kendall} \ \tau \approx +1\)\textsuperscript{a}), but with different slopes (thin lines in \Cref{fig:rheobase_correlation}~H). Similarly, positive correlations were consistently found across models for maximal conductances of delayed rectifier K, \Kv, and A type currents, whereas the maximal conductance of the sodium current was consistently associated with negative correlations (\( \text{Kendall} \ \tau \approx -1\)\textsuperscript{a}; \Cref{fig:rheobase_correlation}~I), i.e. rheobase decreased with increasing maximum conductance in all models.
 
 Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlated with rheobase similarly across model there were some exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affected the rheobase both with positive and negative correlations in different models \textcolor{red}{\noteuh{Würde diese hier noch mal benennen, damit es klar wird. }}\notenk{Ich mache das ungern, weil ich für jedes (Na-current inactivation, \Kv-current inactivation, and A-current activation) 2 Liste habe (+ und - rheobase Aenderungen} (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occurred 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 \textcolor{red}{\noteuh{Auch hier die unterschiedlcihen betroffenen cell type models benennen, einfach in Klammer dahinter.}}\notenk{Hier mache ich das auch ungern, für ähnlichen Gründen}. 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. 
 
@@ -327,12 +318,12 @@ Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) ge
         \centering
         \includegraphics[width=\linewidth]{Figures/rheobase_correlation.pdf}
         \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 (D), and changes in maximal conductance of the leak current in the Cb stellate model (G) are shown. The fI curves from the smallest and largest alterations are seen from grey to black respectively for (A,D, and G) in accordance to the x-axis in B, E, and H. 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. \notenk{Added color gradient x-axis in B, E, H to indicate color scale in A,D,G and added a sentence to make it clearer.}}
+        \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 (D), and changes in maximal conductance of the leak current in the Cb stellate model (G) are shown. The fI curves from the smallest (grey) to the largest (black) alterations are seen for (A,D, and G) in accordance to the greyscale of the x-axis in B, E, and H. 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}
 \end{figure}
 
 \subsection*{\textit{KCNA1} Mutations}
-Mutations in \textit{KCNA1} are associated with episodic ataxia type~1 (EA1) and have been characterized biophysically (as reviewed by \citet{lauxmann_therapeutic_2021}). Here they were used as a case study \noteuh{Das hört sich so nach Klinik an…} in the effects of various ionic current environments on neuronal firing and on the outcomes of channelopathies. The changes in AUC and rheobase from wild type values for reported EA1 associated \textit{KCNA1} mutations were heterogeneous across models containing \Kv, but generally showed decreases in rheobase (\Cref{fig:simulation_model_comparision}A-I).  Pairwise non-parametric Kendall \(\tau\) rank correlations\textsuperscript{a} between the simulated effects of these \Kv mutations on rheobase were highly correlated across models (\Cref{fig:simulation_model_comparision}J) indicating that EA1 associated \textit{KCNA1} mutations generally decrease rheobase across diverse cell-types. However, the effects of the \Kv mutations on AUC were more heterogenous as reflected by both weak and strong positive and negative pairwise correlations between models (\Cref{fig:simulation_model_comparision}K), suggesting that the effects of ion-channel variant on super-threshold neuronal firing depend both quantitatively and qualitatively on the specific composition of ionic currents in a given neuron.
+Mutations in \textit{KCNA1} are associated with episodic ataxia type~1 (EA1) and have been characterized biophysically (as reviewed by \citet{lauxmann_therapeutic_2021}). Here they were used as a test case in the effects of various ionic current environments on neuronal firing and on the outcomes of channelopathies. The changes in AUC and rheobase from wild type values for reported EA1 associated \textit{KCNA1} mutations were heterogeneous across models containing \Kv, but generally showed decreases in rheobase (\Cref{fig:simulation_model_comparision}A-I).  Pairwise non-parametric Kendall \(\tau\) rank correlations\textsuperscript{a} between the simulated effects of these \Kv mutations on rheobase were highly correlated across models (\Cref{fig:simulation_model_comparision}J) indicating that EA1 associated \textit{KCNA1} mutations generally decrease rheobase across diverse cell-types. However, the effects of the \Kv mutations on AUC were more heterogenous as reflected by both weak and strong positive and negative pairwise correlations between models (\Cref{fig:simulation_model_comparision}K), suggesting that the effects of ion-channel variant on super-threshold neuronal firing depend both quantitatively and qualitatively on the specific composition of ionic currents in a given neuron.
 
 \begin{figure}[tp]
         \centering
@@ -345,9 +336,6 @@ Mutations in \textit{KCNA1} are associated with episodic ataxia type~1 (EA1) and
 
 \section*{Discussion (3000 Words Maximum - Currently )}
 % \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.}\\
-
-\textit{To compare the effects of alterations \noteuh{Welches changes? Du meinst vermutlich die Effekte unterschiedlicher Mutationen}\notenk{Ja die Effekte der Mutationen gehoeren dazu, aber es wird allgmein gemeint - wenn irgendwas anderes die Kanaleigenschaften aendern (auxillary subunits, etc) diese Aenderungen gehoeren auch dazu.} to properties of ionic currents on neuronal firing of different neuron types, a diverse set of conductance-based models was simulated.} \notenk{Re-written this section below hopefully makes this clearer and replaces italicized section} 
-
 Changes to single ionic current properties, as well as known episodic ataxia type~1 associated \textit{KCNA1} mutations showed consistent effects on the rheobase across cell types, whereas the effects on AUC of the steady-state fI-curve depended on the cell type. Our results demonstrate that loss of function (LOF) and gain of function (GOF) on the biophysical level cannot be uniquely transferred to the level of neuronal firing. Thus the effects caused by different mutations depend on the properties of the other ion channels expressed in a cell and are therefore depend on the channel ensemble of a specific cell type.
 
 To compare the effects ion channel mutations on neuronal firing of different neuron types, a diverse set of conductance-based models was simulated, by simulating the effect of changes in individual channel properties across conductance-based neuronal models and by simluating the effects of episodic ataxia type~1 associated (EA1) \textit{KCNA1} mutations. Changes to single ionic current properties, as well as known EA1 associated \textit{KCNA1} mutations showed consistent effects on the rheobase across cell types, whereas the effects on AUC of the steady-state fI-curve depended on the cell type. Our results demonstrate that loss of function (LOF) and gain of function (GOF) on the biophysical level cannot be uniquely transferred to the level of neuronal firing. Thus the effects caused by different mutations depend on the properties of the other ion channels expressed in a cell and are therefore depend on the channel ensemble of a specific cell type.
@@ -359,7 +347,7 @@ Although, firing differences can be characterized by an area under the curve of
 \subsection*{Neuronal Diversity}
 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.
 
-Advances in high-throughput techniques have enabled large-scale investigation into single-cell properties across the CNS \citep{Poulin2016} that have revealed large diversity in neuronal gene expression, morphology and neuronal types in the motor cortex \citep{Scala2021}, neocortex \cite{Cadwell2016, Cadwell2020}, GABAergic neurons in the cortex and retina \citep{Huang2019, Laturnus2020} \noteuh{Wie sind den hier die Unterschiede zwischen GABAergen Neuronen und Interneuronen festgelegt? Und von welchen Gehrinbereichen ist dann hier die Rede?} \notenk{Stimmt, habe ich schlecht geschrieben. Das sind Interneuronen vom neocortex in Huang und Paul und V1 und bipolar cells in der Retina in Laturnus}\notenk{Change to ``GABAergic neurons in the cortex and retina''}, cerebellum \citep{Kozareva2021}, spinal cord \citep{Alkaslasi2021}, visual cortex \citep{Gouwens2019} as well as the retina \citep{Baden2016, Voigt2019, Berens2017,  Yan2020a, Yan2020b}. 
+Advances in high-throughput techniques have enabled large-scale investigation into single-cell properties across the CNS \citep{Poulin2016} that have revealed large diversity in neuronal gene expression, morphology and neuronal types in the motor cortex \citep{Scala2021}, neocortex \cite{Cadwell2016, Cadwell2020}, GABAergic neurons in the cortex and retina \citep{Huang2019, Laturnus2020}, cerebellum \citep{Kozareva2021}, spinal cord \citep{Alkaslasi2021}, visual cortex \citep{Gouwens2019} as well as the retina \citep{Baden2016, Voigt2019, Berens2017,  Yan2020a, Yan2020b}. 
 
 Diversity across neurons is not limited to gene expression and can also be seen electrophysiologically \citep{Tripathy2017, Gouwens2018, Tripathy2015, Scala2021, Cadwell2020, Gouwens2019, Baden2016, Berens2017} with correlations existing between gene expression and electrophysiological properties \citep{Tripathy2017}. At the ion channel level, diversity exists not only between the specific ion channels the different cell types express but heterogeneity also exists in ion channel expression levels within cell types \citep{marder_multiple_2011,  goaillard_ion_2021,barreiro_-current_2012}.  As ion channel properties and expression levels are key determinents of neuronal dynamics and firing \citep{Balachandar2018, Gu2014, Zeberg2015, Aarhem2007, Qi2013, Gu2014a, Zeberg2010, Zhou2020, Kispersky2012} neurons with different ion channel properties and expression levels display different firing properties.
 
@@ -368,7 +356,7 @@ To capture the diversity in neuronal ion channel expression and its relevance in
    
 \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 R1648H mutation in SCN1A does not alter the excitability of cortical pyramidal neurons \noteuh{Das stimmt so allerdings nicht. Es wurde nicht geziegt, dass die Pyramidenzellen hyperexzitabel sind. Die Hypoexcitability der Interneurone wurde dagegen gezeigt und man geht davon aus, dass die reduzierte Inhibition zu einer Hyperexzitabilität des Netzwerks führt. Das war im Ergebnisteil auch schon etwas durcheinander.} \notenk{Mein Fehler, habe ich falsch von Paper vestandend. Habe es im Text umgeschrieben}, but causes hypoexcitability of adjacent inhibitory GABAergic neurons \citep{Hedrich14874}. In the CA3 region of the hippocampus, the equivalent mutation in \textit{SCN8A}, R1627H \noteuh{Bitte noch mal kontrollieren, ob das stimmt. Bin mir nicht sicher, ober es 1627 oder 1629 ist…} \notenk{1627 in \citep{makinson_scn1a_2016}}, increases the excitability of pyramidal neurons and decreases the 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, namely the \(\textrm{Na}_\textrm{V}\textrm{1.8}\) subunit \noteuh{Stimmt das dann beides so? Bitte noch mal kontrollieren.} \notenk{Ja, das stimmt. Habe ich kontrolliert - das stimmt beides, weil einer der grosse Unterschied zwischen diese Zelltypen ist Nav1.8} \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.
+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 R1648H mutation in SCN1A does not alter the excitability of cortical pyramidal neurons, but causes hypoexcitability of adjacent inhibitory GABAergic neurons \citep{Hedrich14874}. In the CA3 region of the hippocampus, the equivalent mutation in \textit{SCN8A}, R1627H, increases the excitability of pyramidal neurons and decreases the 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, namely the \(\textrm{Na}_\textrm{V}\textrm{1.8}\) subunit \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 \noteuh{Meinst du damit die “realen” Zellen oder die Modelle?}\notenk{Beides, ``realen'' Zellen und dann die Modelle die daraus entstehen} 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} \textcolor{red}{\notenk{add temperature sensitivity-> within cell-type heterogeneity exists - Marder paper?}}. 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 behavior of a heterogeneous neuron population \citep{verma_computational_2020}. For example, a model derived from the mean conductances in a neuronal sub-population within the stomatogastric ganglion, the so-called "one-spike bursting" neurons fire three spikes instead of one per burst due to an L-shaped distribution of sodium and potassium conductances \citep{golowasch_failure_2002}.  
 Multiple sets of 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}. 
@@ -376,17 +364,19 @@ The variability in ion channel expression often correlates with the expression o
 The variability of ionic 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}.
 
 \subsection*{Effects of \textit{KCNA1} Mutations}
-Changes in delayed rectifier potassium currents, analogous to those seen in LOF \textit{KCNA1} 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} \noteuh{Aber es kommt doch darauf an, welche Veränderungen. Wenn es zu einem GOF kommt, dann geht man ja eigentlich nicht von einer erhöhten Erregbarkeit aus. Oder gehst du hier nur von LOF Mutationen aus? Sollte dann evtl. noch mal klar gemacht werden, dass die analogen Mutationen in KCNA1 LOF Mutationen sind. } \notenk{Ja genau die KCNA1 mutationen are LOF, habe das im Text klargemacht}. Although the Hodgkin Huxley delayed rectifier lacks inactivation, the increases in excitability observed by \citet{hafez_altered_2020} are in line with our simulation-based predictions of the outcomes of \textit{KCNA1} mutations.  LOF  \textit{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 \textit{KCNA1} mutations. Contrary to these results,  \citet{zhao_common_2020} predicted \textit{in silico} that the depolarizing shifts seen as a result of \textit{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.     
+Changes in delayed rectifier potassium currents, analogous to those seen in LOF \textit{KCNA1} 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 observed by \citet{hafez_altered_2020} are in line with our simulation-based predictions of the outcomes of \textit{KCNA1} mutations.  LOF  \textit{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 \textit{KCNA1} mutations. Contrary to these results,  \citet{zhao_common_2020} predicted \textit{in silico} that the depolarizing shifts seen as a result of \textit{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  \textit{KCNA1} mutations on firing 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  \textit{KCNA1} mutation firing \noteuh{Dieser satz ist sehr verwirrend… }\notenk{removed second ``in our simulations'' to make it clearer}. This highlights that not only knowledge of the biophysical properties \textcolor{red}{\noteuh{Das sind ja die biophysical properties of a channel}} of a channel but also its neuronal expression and other neuronal channels present is vital for the holistic understanding of the effects of a given ion channel mutation both at the single cell and network level. 
+In our simulations, different current properties alter the impact of  \textit{KCNA1} mutations on firing 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  \textit{KCNA1} mutation firing. This highlights that not only knowledge of the biophysical properties of a channel but also its neuronal expression and other neuronal channels present is vital for the holistic understanding of the effects of a given ion channel mutation both at the 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 channel 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 \textcolor{red}{\noteuh{Möchtset du hier wirklich immer von den Current properties sprechen? Oder sollten wir eher channel properties schreiben, da ja eigentlich die Kanaleigenschaften und daher auch der Strom verändert ist? Vielleicht ist das „bei euch“ (;-)) so üblich, dann ist es natürlich in Ordnung }}\notenk{Koennen wir machen, da stimme ich zu das sind wirklich Kanaleigenschaften die as Stroemeigenschaft vorkommen} \textcolor{red}{\notenk{Could change to channel 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 to indicate whether a mutation results in more or less ionic current \noteuh{Was willst du damit sagen?}\notenk{``is useful at the level of ion channels and whether a mutation results in more or less ionic current'' to ``is useful at the level of ion channels to indicate 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 channel 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, Hedrich2021}. However, the effects of specific ion channel mutations are often characterized based on ionic currents in expression systems and classified as LOF or GOF to aid in treatment decisions \citep{johannesen_genotype-phenotype_2021, Brunklaus2022, Musto2020}.  Although positive treatment outcomes occur with sodium channel blockers in patients with GOF \(\textrm{Na}_{\textrm{V}}\textrm{1.6}\) mutations, patients with 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} \noteuh{Allerdings sprachen die GOF-carrier signifikant besser auf Na+ channel blocker an. Das solltest du auch bedenken!!!}\notenk{Satz geaendert damit das klar ist}, 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. 
+The effects of changes in channel 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 to indicate 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 channel 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, Hedrich2021}. However, the effects of specific ion channel mutations are often characterized based on ionic currents in expression systems and classified as LOF or GOF to aid in treatment decisions \citep{johannesen_genotype-phenotype_2021, Brunklaus2022, Musto2020}.  Although positive treatment outcomes occur with sodium channel blockers in patients with GOF \(\textrm{Na}_{\textrm{V}}\textrm{1.6}\) mutations, patients with 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 expresses 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 into the effects of ion channel mutations on neuronal firing the specific cell-type dependency should be considered.
 
 The effects of altered ion channel properties on firing is generally influenced by the other ionic currents present 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.
 
+\subsection*{Limitations}
+The models used here are simple and while they all capture key aspects of the firing dynamics for their respective cell, they fall short of capturing the complex physiology and biophysics of real cells. However, for the purpose of understanding how different cell-types, or current environments, contribute to diversity in firing outcomes of ion channel mutations, the fidelity of the models to the physiological cells they represent is of a minor concern and the variety in currents and dynamics across models is of utmost importance. Additionally, the development of more realistic models is a high priority and will enable cell-type specific predictions that may aid in precision medicine approaches. Thus, weight should not be put on any single predicted firing outcome here, but rather on the differences in outcomes that occur across the cell-type spectrum the models used here represent. 
 
 \par\null
 

ref.bib

@@ -1935,6 +1935,7 @@ SIGNIFICANCE: Bromide is most effective and is a well-tolerated drug among DS pa
   keywords  = {Biophysical models, Cellular neuroscience, Striate cortex},
   language  = {en},
   publisher = {Nature Publishing Group},
+  ranking   = {rank4},
 }
 
  
@@ -2204,4 +2205,78 @@ SIGNIFICANCE: Bromide is most effective and is a well-tolerated drug among DS pa
   language  = {English (US)},
 }
 
+ 
+@Article{Claes2001,
+  author   = {Claes, Lieve and Del-Favero, Jurgen and Ceulemans, Berten and Lagae, Lieven and Van Broeckhoven, Christine and De Jonghe, Peter},
+  journal  = {The American Journal of Human Genetics},
+  title    = {De {Novo} {Mutations} in the {Sodium}-{Channel} {Gene} {SCN1A} {Cause} {Severe} {Myoclonic} {Epilepsy} of {Infancy}},
+  year     = {2001},
+  issn     = {0002-9297},
+  month    = jun,
+  number   = {6},
+  pages    = {1327--1332},
+  volume   = {68},
+  abstract = {Severe myoclonic epilepsy of infancy (SMEI) is a rare disorder that occurs in isolated patients. The disease is characterized by generalized tonic, clonic, and tonic-clonic seizures that are initially induced by fever and begin during the first year of life. Later, patients also manifest other seizure types, including absence, myoclonic, andsimple and complex partial seizures. Psychomotor development stagnates around the second year of life. Missense mutations in the gene that codes for a neuronal voltage-gated sodium-channel α-subunit (SCN1A) were identified in families with generalized epilepsy with febrile seizures plus (GEFS+). GEFS+ is a mild type of epilepsy associated with febrile and afebrile seizures. Because both GEFS+ and SMEI involve fever-associated seizures, we screened seven unrelated patients with SMEI for mutations in SCN1A. We identified a mutation in each patient: four had frameshift mutations, one had a nonsense mutation, one had a splice-donor mutation, and one had a missense mutation. All mutations are de novo mutations and were not observed in 184 control chromosomes.},
+  doi      = {10.1086/320609},
+  file     = {:claes_novo_2001 - De Novo Mutations in the Sodium Channel Gene SCN1A Cause Severe Myoclonic Epilepsy of Infancy.html:URL},
+  language = {en},
+  urldate  = {2022-09-22},
+}
+
+ 
+@Article{Oguni2001,
+  author   = {Oguni, Hirokazu and Hayashi, Kitami and Awaya, Yutaka and Fukuyama, Yukio and Osawa, Makiko},
+  journal  = {Brain and Development},
+  title    = {Severe myoclonic epilepsy in infants – a review based on the {Tokyo} {Women}'s {Medical} {University} series of 84 cases},
+  year     = {2001},
+  issn     = {0387-7604},
+  month    = nov,
+  number   = {7},
+  pages    = {736--748},
+  volume   = {23},
+  abstract = {Severe myoclonic epilepsy in infants (SME) is one of the most malignant epileptic syndromes recognized in the latest classification of epileptic syndromes. The clinical details and electroencephalographic (EEG) characteristics have been elucidated by Dravet et al. The diagnosis of SME depends largely on the combination of clinical and EEG manifestations at different ages, of which the presence of myoclonic seizures appears to be the most important. However, because of the inclusion of different types of myoclonic attack and the lack of strict criteria for diagnosing SME, there has been some confusion as to whether patients without myoclonic seizures or myoclonus should be classified as SME, despite other identical clinical symptoms (SME borderlands (SMEB) group). Among the various clinical manifestations characterizing SME, special attention has been paid to seizures easily precipitated by fever and hot baths in Japan. We have demonstrated that the onset of myoclonic attack in these patients is very sensitive to the elevation of body temperature itself rather than its etiology. Using simultaneous EEG and rectal temperature monitoring during hot water immersion, we showed that epileptic discharges increased in frequency, and eventually developed into seizures at temperatures over 38°C. We believe that the unique fever sensitivity observed in SME is similar to, but more intense than that of febrile convulsions. We have also identified a group of cases who have had innumerous myoclonic and atypical absence seizures daily which were sensitive to the constant bright light illumination. In these cases, spike discharges increased or decreased depending on the intensity of constant light illumination. Although these cases form the most resistant SME group, they lost the constant light sensitivity with increasing age, leaving only relatively common types of fever-sensitive grand mal seizures (FSGM) at the age of around 5 years. In the long run, only convulsive seizures continue, while myoclonic or absence seizures and photosensitivity disappear with advancing age, thus it is conceivable that SMEB constitutes a basic epileptic condition underlying SME. There is a clinical continuum that extends from the mildest end of SMEB to the severest end of SME with constant light sensitivity, with intermediates of frequent or infrequent myoclonic and absence seizures in-between. This spectrum concept appropriately explains the clinical variabilities between SME and SMEB during early childhood.},
+  doi      = {10.1016/S0387-7604(01)00276-5},
+  file     = {:Oguni2001 - Severe Myoclonic Epilepsy in Infants – a Review Based on the Tokyo Women's Medical University Series of 84 Cases.html:URL},
+  keywords = {Severe myoclonic epilepsy in infants, Borderland group, Myoclonic seizures, Myoclonus, Fever sensitivity, Fever-sensitive grand mal, Constant light sensitivity},
+  language = {en},
+  series   = {West {Syndrome} and {Other} {Infantile} {Epileptic} {Encephalopathies}},
+  urldate  = {2022-09-22},
+}
+
+ 
+@Article{Fujiwara2003,
+  author   = {Fujiwara, Tateki and Sugawara, Takashi and Mazaki‐Miyazaki, Emi and Takahashi, Yukitoshi and Fukushima, Katsuyuki and Watanabe, Masako and Hara, Keita and Morikawa, Tateki and Yagi, Kazuichi and Yamakawa, Kazuhiro and Inoue, Yushi},
+  journal  = {Brain},
+  title    = {Mutations of sodium channel α subunit type 1 ({SCN1A}) in intractable childhood epilepsies with frequent generalized tonic–clonic seizures},
+  year     = {2003},
+  issn     = {0006-8950},
+  month    = mar,
+  number   = {3},
+  pages    = {531--546},
+  volume   = {126},
+  abstract = {A group of infant onset epilepsies manifest very frequent generalized tonic–clonic seizures (GTC) intractable to medical therapy, which may or may not be accompanied by minor seizures such as myoclonic seizures, absences and partial seizures. They include severe myoclonic epilepsy in infancy (SMEI) and intractable childhood epilepsy with GTC (ICEGTC). They are commonly associated with fever‐sensitivity, family history of seizure disorders and developmental decline after seizure onset. Mutations of the neuronal voltage‐gated sodium channel α subunit type 1 gene (SCN1A) were recently reported in SMEI patients. To clarify the genotypic differences in this group of epilepsies, we searched for SCN1A abnormalities in 25 patients with SMEI and 10 with ICEGTC, together with the family members of 15 patients. Frameshift mutations in SCN1A were observed in four patients, nonsense mutations in five patients, missense mutations in 21 patients, other mutations in two patients and no mutation in five patients. SMEI patients showed nonsense mutations, frameshifts, or missense mutations, while ICEGTC patients showed only missense mutations. Study of both parents of 11 patients revealed that the mutations in these patients were de novo. However, two mothers had the same missense mutations as their ICEGTC children, and they had generalized epilepsy with febrile seizures plus. Here we suggest that SMEI and ICEGTC represent a continuum with minor phenotypic and genotypic differences.},
+  doi      = {10.1093/brain/awg053},
+  file     = {:Fujiwara2003 - Mutations of Sodium Channel Α Subunit Type 1 (SCN1A) in Intractable Childhood Epilepsies with Frequent Generalized Tonic–clonic Seizures.html:URL},
+  urldate  = {2022-09-22},
+}
+
+ 
+@Article{Ohmori2002,
+  author   = {Ohmori, Iori and Ouchida, Mamoru and Ohtsuka, Yoko and Oka, Eiji and Shimizu, Kenji},
+  journal  = {Biochemical and Biophysical Research Communications},
+  title    = {Significant correlation of the {SCN1A} mutations and severe myoclonic epilepsy in infancy},
+  year     = {2002},
+  issn     = {0006-291X},
+  month    = jul,
+  number   = {1},
+  pages    = {17--23},
+  volume   = {295},
+  abstract = {To investigate the possible correlation between genotype and phenotype of epilepsy, we analyzed the voltage-gated sodium channel α1-subunit (SCN1A) gene, β1-subunit (SCN1B) gene, and γ-aminobutyric acidA receptor γ2-subunit (GABRG2) gene in DNAs from peripheral blood cells of 29 patients with severe myoclonic epilepsy in infancy (SME) and 11 patients with other types of epilepsy. Mutations of the SCN1A gene were detected in 24 of the 29 patients (82.7\%) with SME, although none with other types of epilepsy. The mutations included deletion, insertion, missense, and nonsense mutations. We could not find any mutations of the SCN1B and GABRG2 genes in all patients. Our data suggested that the SCN1A mutations were significantly correlated with SME (p{\textless}.0001). As we could not find SCN1A mutations in their parents, one of critical causes of SME may be de novo mutation of the SCN1A gene occurred in the course of meiosis in the parents.},
+  doi      = {10.1016/S0006-291X(02)00617-4},
+  file     = {:Ohmori2002 - Significant Correlation of the SCN1A Mutations and Severe Myoclonic Epilepsy in Infancy.html:URL},
+  keywords = {Neuronal voltage-gated sodium channel, SCN1A, SCN1B, GABRG2, Generalized epilepsy with febrile seizures plus, Sever myoclonic epilepsy in infancy},
+  language = {en},
+  urldate  = {2022-09-22},
+}
+
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