Update python header in .py files in ./Code

Code/Functions/Cb_stellate_fxns.py

@@ -2,10 +2,11 @@
 """
 Functions for Cb Stellate model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
+
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 
 import h5py

Code/Functions/Cb_stellate_fxns_Kv.py

@@ -1,10 +1,11 @@
 """
 Functions for Cb Stellate with Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
+
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 import h5py
 import scipy

Code/Functions/Pospischil_fxns.py

@@ -1,10 +1,11 @@
 """
 Functions for model from Pospischil et al. 2008 - RS Pyramidal, RS inhibitory, and FS
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
+
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 import gc
 import h5py

Code/Functions/STN_fxns.py

@@ -1,10 +1,11 @@
 """
 Functions for STN model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
+
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 import scipy
 import json

Code/Functions/STN_fxns_Kv.py

@@ -1,10 +1,10 @@
 """
 Functions for STN model with Kv1.1
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 import scipy
 import json

Code/Functions/Utility_fxns.py

@@ -1,10 +1,10 @@
 """
 General functions used throughout simulation scripts
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 
 import json

Code/KCNA1_mutations/mut_Cb_stellate.py

@@ -2,10 +2,10 @@
 """
 Script to simulate all KCNA1 mutations in Cb Stellate model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 import numpy as np
 from joblib import Parallel, delayed

Code/KCNA1_mutations/mut_Cb_stellate_Delta_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to simulate all KCNA1 mutations in Cb Stellate \Delta Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 
 import numpy as np

Code/KCNA1_mutations/mut_Cb_stellate_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to simulate all KCNA1 mutations in Cb Stellate +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 
 import numpy as np

Code/KCNA1_mutations/mut_FS_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to simulate all KCNA1 mutations in FS +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 
 import numpy as np

Code/KCNA1_mutations/mut_RS_inhib_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to simulate all KCNA1 mutations in RS Inhibitory +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 
 import numpy as np

Code/KCNA1_mutations/mut_RS_pyramidal_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to simulate all KCNA1 mutations in RS Pyramidal +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 
 import numpy as np

Code/KCNA1_mutations/mut_STN.py

@@ -2,10 +2,10 @@
 """
 Script to simulate all KCNA1 mutations in STN model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 import numpy as np
 from joblib import Parallel, delayed

Code/KCNA1_mutations/mut_STN_Delta_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to simulate all KCNA1 mutations in STN \Delta Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 import numpy as np
 from joblib import Parallel, delayed

Code/KCNA1_mutations/mut_STN_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to simulate all KCNA1 mutations in STN +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 import numpy as np
 from joblib import Parallel, delayed

Code/Neuron_models/Cb_stellate_Delta_Kv_model.py

@@ -2,10 +2,10 @@
 """
 Script to run Cb Stellate \Delta Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 
 import numpy as np

Code/Neuron_models/Cb_stellate_Kv_model.py

@@ -2,10 +2,10 @@
 """
 Script to run Cb Stellate +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 import os

Code/Neuron_models/Cb_stellate_model.py

@@ -2,10 +2,10 @@
 """
 Script to run Cb Stellate model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 import os

Code/Neuron_models/FS_Kv_model.py

@@ -2,10 +2,10 @@
 """
 Script to run FS +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Neuron_models/FS_model.py

@@ -2,10 +2,10 @@
 """
 Script to run FS model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Neuron_models/RS_inhib_Kv_model.py

@@ -2,10 +2,10 @@
 """
 Script to run RS Inhibitory +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Neuron_models/RS_inhib_model.py

@@ -2,10 +2,11 @@
 """
 Script to run RS Inhibitory model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
 import numpy as np
 from numba import types
 from numba.typed import Dict

Code/Neuron_models/RS_pyramidal_Kv_model.py

@@ -2,10 +2,10 @@
 """
 Script to run RS Pyramidal +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 
 import numpy as np

Code/Neuron_models/RS_pyramidal_model.py

@@ -2,10 +2,10 @@
 """
 Script to run RS Pyramidal model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Neuron_models/STN_Delta_Kv_model.py

@@ -2,10 +2,11 @@
 """
 Script to run STN \Delta Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
+
 import numpy as np
 import os
 from Code.Functions.Utility_fxns import capacitance, stimulus_init, init_dict

Code/Neuron_models/STN_Kv_model.py

@@ -2,10 +2,10 @@
 """
 Script to run STN +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 import os

Code/Neuron_models/STN_model.py

@@ -2,10 +2,10 @@
 """
 Script to run STN model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 import numpy as np
 import os

Code/Sensitivity_Analysis/SA_Cb_stellate.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for Cb Stellate model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Sensitivity_Analysis/SA_Cb_stellate_Delta_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for Cb Stellate \Delta Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Sensitivity_Analysis/SA_Cb_stellate_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for Cb Stellate +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Sensitivity_Analysis/SA_FS.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for FS model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Sensitivity_Analysis/SA_FS_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for FS +Kv model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Sensitivity_Analysis/SA_RS_inhib.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for RS Inhibitory model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Sensitivity_Analysis/SA_RS_inhib_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for RS Inhibitory +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Sensitivity_Analysis/SA_RS_pyramidal.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for RS Pyramidal model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from joblib import Parallel, delayed

Code/Sensitivity_Analysis/SA_RS_pyramidal_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for RS Pyramidal +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/Sensitivity_Analysis/SA_STN.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for STN model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 
 import numpy as np

Code/Sensitivity_Analysis/SA_STN_Delta_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for STN \Delta Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 
 import numpy as np

Code/Sensitivity_Analysis/SA_STN_Kv.py

@@ -2,10 +2,10 @@
 """
 Script to run sensitivity analysis for STN +Kv1.1 model
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
 """
+__author__ = "Nils A. Koch"
+__copyright__ = "Copyright 2022, Nils A. Koch"
+__license__ = "MIT"
 
 import numpy as np
 from numba import types

Code/csv_generation/Plotting_data_collection.py

@@ -1,11 +1,11 @@
 """
 Script to analyse and collect data from simulations
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
-"""
 
+"""
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 import pandas as pd
 import h5py
 import json

Code/csv_generation/SA_collection.py

@@ -1,10 +1,11 @@
 """
 Script to analyse and collect data from sensitivity analysis simulations
 
-__author__ = Nils A. Koch
-__copyright__ = Copyright 2022, Nils A. Koch
-__license__ = MIT
+
 """
+__author__ = "Nils A. Koch"
+__copyright__ = 'Copyright 2022, Nils A. Koch'
+__license__ = "MIT"
 
 import numpy as np
 import h5py

manuscript.tex

@@ -187,25 +187,25 @@ Clinically relevant mutations to voltage-gated  ion channels, called channelopat
 %Neuronal excitability is shaped by kinetics of ion channels 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 channelopathy. The effects of these mutations  on channel function, i.e. the ionic current conducted by the affected ion channels, are generally characterized 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. For increased efficiency and efficacy of personalized medicine approaches 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
+%\par\null
 
 \section*{Significance Statement (120 Words Maximum - Currently 119 )}
 %\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.}
 
 Although the genetic nature of ion channel mutations as well as their effects on the biophysical properties of an ion channel are routinely assessed experimentally, determination of their 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
+%\par\null
 
-\section*{Introduction (750 Words Maximum  - Currently 702)} 
+\section*{Introduction (750 Words Maximum  - Currently 704)} 
 %\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.}
 
 The properties and combinations of voltage-gated ion channels are vital in determining neuronal excitability \citep{bernard_channelopathies_2008, carbone_ion_2020, rutecki_neuronal_1992, pospischil_minimal_2008}. However, ion channel function can be disturbed, for instance through genetic alterations, resulting in altered neuronal firing behavior \citep{carbone_ion_2020}. In recent years, next generation sequencing has led to an increasing number of clinically relevant ion channel 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 variants are frequently classified in heterologous expression systems as either a loss of function (LOF) or a gain of function (GOF)  in the respective ionic current \citep{Musto2020, Kullmann2002, Waxman2011, Kim2021}. This LOF/GOF classification is often directly used to predict the effects on neuronal firing \citep{Niday2018, Wei2017, Wolff2017,Masnada2017}, which in turn is important for understanding the pathophysiology of these disorders and for identification of potential therapeutic targets \citep{Orsini2018, Yang2018, Colasante2020, Yu2006}. Experimentally, the effects of channelopathies on neuronal firing are 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, Hedrich14874} but are restricted to limited neuronal types. 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. Expression level of an affected gene \citep{Layer2021} and relative amplitudes of ionic currents \citep{rutecki_neuronal_1992, pospischil_minimal_2008,Kispersky2012, golowasch_failure_2002, barreiro_-current_2012} indeed dramatically influence the firing behavior and dynamics of neurons. Mutations in different sodium channel genes have been experimentally shown to affect firing in a cell-type specific manner based on differences in expression levels of the affected gene \citep{Layer2021}, but also on other cell-type specific mechanisms \citep{Hedrich14874, makinson_scn1a_2016}.
+Ion channel variants are frequently classified in heterologous expression systems as either a loss of function (LOF) or a gain of function (GOF)  in the respective ionic current \citep{Musto2020, Kullmann2002, Waxman2011, Kim2021}. This LOF/GOF classification is often directly used to predict the effects on neuronal firing \citep{Niday2018, Wei2017, Wolff2017,Masnada2017}, which in turn is important for understanding the pathophysiology of these disorders and for identification of potential therapeutic targets \citep{Orsini2018, Yang2018, Colasante2020, Yu2006}. Experimentally, the effects of channelopathies on neuronal firing are 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, Hedrich14874} but are restricted to limited number of neuronal types. 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. Expression level of an affected gene \citep{Layer2021} and relative amplitudes of ionic currents \citep{rutecki_neuronal_1992, pospischil_minimal_2008,Kispersky2012, golowasch_failure_2002, barreiro_-current_2012} indeed dramatically influence the firing behavior and dynamics of neurons. Mutations in different sodium channel genes have been experimentally shown to affect firing in a cell-type specific manner based on differences in expression levels of the affected gene \citep{Layer2021}, but also on other cell-type specific mechanisms \citep{Hedrich14874, makinson_scn1a_2016}.
 
- 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 success in treatment of Dravet syndrome persisted \citep{Claes2001,Oguni2001} until it became evident that inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations  \citep{Yu2006, Colasante2020}. 
+ 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 success in the treatment of Dravet syndrome persisted \citep{Claes2001,Oguni2001} until it became evident that inhibitory interneurons and not pyramidal neurons had altered excitability as a result of LOF \textit{SCN1A} mutations  \citep{Yu2006, Colasante2020}. 
 
-Taken together, these examples demonstrate the need to study the effects of ion channel mutations in many different cell types --- a daunting if not impossible experimental challenge. In the context of this diversity, simulations of conductance-based neuronal models are a powerful tool bridging the gap between altered ionic currents and firing in a systematic and efficient way. Furthermore, simlutions potentially allow to predict the effects of drugs needed to alleviate the pathophysiology of the respective mutation \citep{johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021, Bayraktar}.
+Taken together, these examples demonstrate the need to study the effects of ion channel mutations in many different cell types --- a daunting if not impossible experimental challenge. In the context of this diversity, simulations of conductance-based neuronal models are a powerful tool bridging the gap between altered ionic currents and firing in a systematic and efficient way. Furthermore, simlutions allow to predict the potential effects of drugs needed to alleviate the pathophysiology of the respective mutation \citep{johannesen_genotype-phenotype_2021, lauxmann_therapeutic_2021, Bayraktar}.
 
 In this study, we therefore investigated how the outcome of ionic current kinetic changes on firing depend on neuronal cell type by (1) characterizing firing responses with 2 measures, (2) simulating the response of a repertoire of different neuronal models to changes in single current parameters as well as (3) to more complex changes as they were observed for specific \textit{KCNA1} mutations that are associated with episodic ataxia type~1 \citep{Browne1994, Browne1995, lauxmann_therapeutic_2021}.
 
@@ -439,7 +439,7 @@ With this study we suggest that cell-type specific effects are vital to a full u
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 % \textit{Only published references should appear in the reference list at the end of the paper. The latest information on in-press references should be provided. In the case of in-press references (i.e., accepted for publication in a specific journal or book) the paper, which must be relevant for reviewers to see in order to make a well-informed evaluation should be included as a separate document text file along with the submitted manuscript. In this case, the authors recognize the loss of anonymity. “Submitted” references should be cited only in text and in the following form: (unpublished observations). If the paper is accepted, the authors can then add their names: A. B. Smith, C. D. Johnson, and E. Green, unpublished observations). The form for personal communications is similar: (F. G. Jackson, personal communication). Authors are responsible for all personal communications and must obtain written approval from persons cited before submitting the paper to eNeuro. Proof of such approval may be requested by eNeuro.
@@ -471,6 +471,7 @@ With this study we suggest that cell-type specific effects are vital to a full u
 
 
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@@ -482,10 +483,9 @@ With this study we suggest that cell-type specific effects are vital to a full u
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 % All illustrations documenting results must include a bar to indicate the scale. All labels used in a figure should be explained in the legend. The migration of protein molecular weight size markers or nucleic acid size markers must be indicated and labeled appropriately (e.g., “kD”, “nt”, “bp”) on all figure panels showing gel electrophoresis.}
 
-
 \begin{figure}[tp]
         \centering
-        \includegraphics[width=0.9\linewidth]{Figures/diversity_in_firing.pdf}
+        \includegraphics[width=0.95\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. Models are sorted qualitatively based on their fI curves. 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}

ref.bib

@@ -7,6 +7,7 @@
   issn     = {1476-5381},
   number   = {n/a},
   volume   = {n/a},
+  year = {In Press},
   abstract = {Background and Purpose Variants in SCN8A, the coding gene for NaV1.6 channels, are characterized by a variety of symptoms including, intractable epileptic seizures, psychomotor delay, progressive cognitive decline, autistic features, ataxia or dystonia. Standard anticonvulsant treatment has only a limited impact on the course of disease. Experimental Approach We investigated the therapeutic potential of S-Licarbazepine, an enhancer of slow inactivation of voltage gated sodium channels, on two variants with biophysical and neuronal gain-of-function (G1475R, M1760I) and one variant with biophysical gain-of-function but neuronal loss-of-function (A1622D) in neuroblastoma cells and in murine primary hippocampal neuron cultures. These three variants cover the broad spectrum of NaV1.6-associated disease and are linked to representative phenotypes of mild to moderate epilepsy (G1475R), developmental and epileptic encephalopathy (M1760I) and intellectual disability without epilepsy (A1622D). Key Results Similar to known effects on NaV1.6 wildtype channels, S-Licarbazepine predominantly enhances slow inactivation on all tested variants, irrespective of their particular biophysical mechanisms. However, beyond that, S-Licarbazepine exhibits variant-specific effects including a partial reversal of pathologically slowed fast inactivation dynamics (A1622D, M1760I) and a trend to reduce enhanced persistent Na+ current by A1622D variant channels. Furthermore, our data in primary transfected neurons reveal that not only variant-associated hyperexcitability (M1760I and G1475R) but also hypoexcitability (A1622D) can be modulated by S-Lic. Conclusion and Implications S-Licarbazepine not only owns substance- but also variant-specific effects. Personalized treatment regimens optimized to achieve such variant-specific pharmacological modulation may help to reduce adverse side effects and improve the overall therapeutic outcome of SCN8A-related disease.},
   doi      = {10.1111/bph.15981},
   file     = {Full Text PDF:https\://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/bph.15981:application/pdf},