nkoch/model_mutations_2022
2
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

more changes to discussion

Browse Source
This commit is contained in:
nkoch1 2022-06-19 21:42:06 -04:00
parent 0132812569
commit 86206005bb
2 changed files with 207 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

33
manuscript.tex
View File

@ -242,7 +242,7 @@ with slope \(k\), voltage for half-maximal activation or inactivation (\(V_{1/2}
To obtain the rheobase, the current step interval preceding the occurrence of action potentials was explored at higher resolution with 100 current steps spanning the interval. 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 exhibit tonic firing and any instances of bursting were excluded to simplify the characterization of firing. Firing characterization was performed on steady-state firing and as such adaptation processes are neglected in our analysis. \notenk{moved here from discussion}
All models exhibit tonic firing and any instances of bursting were excluded to simplify the characterization of firing. Firing characterization was performed on steady-state firing and as such adaptation processes are neglected in our analysis. \notenk{This last sentence moved here from discussion}
\subsection*{Sensitivity Analysis and Comparison of Models}
@ -285,7 +285,7 @@ To examine the role of cell-type specific ionic current environments on the impa
\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 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 have 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). 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}). \notejb{No bifurcations: How about this?} Different types of the underlying voltage and gating dynamics 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 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 have 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). 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}). \notejb{No bifurcations: How about this?} \notenk{I like it!} Different types of the underlying voltage and gating 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]
@ -299,7 +299,7 @@ Changes in firing as characterized by \(\Delta\)AUC and \(\Delta\)rheobase occup
\label{fig:firing_characterization}
\end{figure}
Neuronal firing is a complex phenomenon and a quantification of firing properties is required for comparisons across cell types and between conditions. Here we focus on two aspects of firing: rheobase, the smallest injected current at which the cell fires an action potential, and the shape of the frequency-current (fI) curve as quantified by the area under the curve (AUC) for a fixed range of input currents above rheobase (\Cref{fig:firing_characterization}A). \notenk{This enables a AUC measurement independent from rheobase.}\notejb{I added a few words to the next sentence. Would this be enough or should we make it more explicit by an extra sentence als Nils suggests it?} The characterization of firing by rheobase and AUC allows to characterize both a neuron's excitability in the sub-threshold regime (rheobase) and periodic firing in the super-threshold regime (AUC) by two independent measures. Note that AUC is essentially quantifying the slope of a neuron's fI curve.
Neuronal firing is a complex phenomenon and a quantification of firing properties is required for comparisons across cell types and between conditions. Here we focus on two aspects of firing: rheobase, the smallest injected current at which the cell fires an action potential, and the shape of the frequency-current (fI) curve as quantified by the area under the curve (AUC) for a fixed range of input currents above rheobase (\Cref{fig:firing_characterization}A). \notenk{This enables a AUC measurement independent from rheobase.}\notejb{I added a few words to the next sentence. Would this be enough or should we make it more explicit by an extra sentence als Nils suggests it?} \notenk{I think that this is enough - we can always expand if a reviewer asks} The characterization of firing by rheobase and AUC allows to characterize both a neuron's excitability in the sub-threshold regime (rheobase) and periodic firing in the super-threshold regime (AUC) by two independent measures. Note that AUC is essentially quantifying the slope of a neuron's fI curve.
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 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, 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. In these cases it depends on the regime the neuron is operating in. If it is in its excitable regime and only occasionaly 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.
@ -323,11 +323,11 @@ Qualitative differences can be found, for example, when increasing the maximal c
\end{figure}
\notejb{Add more detailed step-by-step description what we did here, like for AUC.}\notenk{started doing this}
\notejb{Add more detailed step-by-step description what we did here, like for AUC.}\notenk{started doing this - what do you think of it?}
Changes in gating half activation potential \(V_{1/2}\) and slope factor \(k\) as well as the maximum conductance \(g\) affect rheobase (\Cref{fig:rheobase_correlation}), however, in contrast to AUC, qualitatively consistent effects on rheobase across models are observed. Increasing, for example, the maximal conductance of the leak current in the Cb stellate model increases the rheobase (\Cref{fig:rheobase_correlation}~G). When these changes are plotted against the change in maximal conductance a monontonically increasing relationship is evident (thick teal line in \Cref{fig:AUC_correlation}~H). This monotonically increasing relationship is evident in all models (\( \text{Kendall} \ \tau \approx +1\)), but with different slopes (thin lines in \Cref{fig:rheobase_correlation}~H). Similarily, positive correlations are consistently found across models for maximal conductances of delayed rectifier K, \Kv, and A type currents, whereas the maximal conductance of the sodium current consistently is associated with negative correlations (\( \text{Kendall} \ \tau \approx -1\); \Cref{fig:rheobase_correlation}~I), i.e. rheobase is decreased with increasing maximum conductance in all models.
Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlate with rheobase similarly across model there are some exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affect rheobase both with positive and negative correlations in different models (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occur in some models as a result of K-current activation \(V_{1/2}\) and slope factor \(k\), \Kv-current inactivation slope factor \(k\), and A-current activation slope factor \(k\) in some models. Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. \notejb{This we just have said...}
Although changes in half maximal potential \(V_{1/2}\) and slope factor \(k\) generally correlate with rheobase similarly across model there are some exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affect rheobase both with positive and negative correlations in different models (\Cref{fig:rheobase_correlation}~F). Departures from monotonic relationships also occur in some models as a result of K-current activation \(V_{1/2}\) and slope factor \(k\), \Kv-current inactivation slope factor \(k\), and A-current activation slope factor \(k\) in some models. Thus, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. \notejb{This we just have said...} \notenk{Do you still find it repetitive after I expanded the section about rheobase above?}
%The rheobase is also affected by changes in channel kinetics (\Cref{fig:rheobase_correlation}). In contrast to AUC, most alterations result in similar changes of rheobase in all models, but there are some noteable exceptions. Changing the slope factor of Na-current inactivation, \Kv-current inactivation, and A-current activation affect rheobase both with positive and negative correlations in different models (\Cref{fig:rheobase_correlation}~B). Departures from monotonic relationships occur in some models as a result of K-current activation, \Kv-current inactivation, and A-current activation in some models \notejb{which parameters are changed?}. Maximum conductance affects rheobase similarly across models (\Cref{fig:rheobase_correlation}~C). However, identical changes in current gating properties such as the half maximal potential \(V_{1/2}\) or slope factor \(k\) can have differing effects on firing depending on the model in which they occur. \notejb{This we just have said...}
@ -363,16 +363,14 @@ Mutations in \Kv are associated with episodic ataxia type~1 (EA1) and have been
Using a set of diverse conductance-based neuronal models, the effects of changes to properties of ionic currents on neuronal firing were determined to be heterogenous for the AUC of the steady state fI curve but more homogenous for rheobase. For a known channelopathy, episodic ataxia type~1 associated \Kv mutations, the effects on rheobase are consistent across model cell types, whereas the effects on AUC depend on cell type. Our results demonstrate that LOF and GOF on the biophysical level cannot be uniquely transfered to the level of neuronal firing. The effects depend on the properties of the other currents expressed in a cell and are therefore depending on cell type.
\subsection*{Neuronal Diversity}
\notejb{Before we start questioning our models we should have a paragraph pointing out that neurons are diverse and differ in their ion channel composition. Cite for example those recent Nature/Science papers where Phillip Berens is part of on neuron types in cerebellum. Thomas Euler Retina ganglien cell types. Then the paper defining Regular/fast spiking interneurons. And many more... like Eve Marder as you have it in a paragraph further down.}
\notejb{Before we start questioning our models we should have a paragraph pointing out that neurons are diverse and differ in their ion channel composition. Cite for example those recent Nature/Science papers where Phillip Berens is part of on neuron types in cerebellum. Thomas Euler Retina ganglien cell types. Then the paper defining Regular/fast spiking interneurons. And many more... like Eve Marder as you have it in a paragraph further down.}\\
\notenk{Added this section - it needs more work, but what do you think of the direction I'm going?} \notenk{Also I'm not sure which regular/fast spiking interneuron paper you mean}\\
Advances in high-throughput techniques have enable 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 \citep{Huang2019} and interneruons \citep{Laturnus2020}, cerebellum \citep{Kozareva2021}, spinal cord \citep{Alkaslasi2021}, visual cortex \citep{Gouwens2019} as well as the retina \citep{Baden2016, Voigt2019, Berens2017, Yan2020, Yan2020a}. \notenk{Yan2020 and Yan2020a are not ``et al.'' - need to fix}
Advances in high-throughput techniques have enable 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 \citep{Huang2019} and interneruons \citep{Laturnus2020}, cerebellum \citep{Kozareva2021}, spinal cord \citep{Alkaslasi2021}, visual cortex \citep{Gouwens2019} as well as the retina \citep{Baden2016, Voigt2019, Berens2017, Yan2020a, Yan2020b}.
% Functional differences: reg/fat spiking, Ephys, models
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}.
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 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.
At the ion channel level, diversity exists not only between the specific ion channels cell types express but heterogeneity also exists in ion channel expression levels within cell types \citep{marder_multiple_2011, goaillard_ion_2021}.
Taken together, the nervous system consists of a vastly diverse and heterogenous collection of neurons with variable properties and characteristics including diverse combinations and expression levels of ion channels.
Taken together, the nervous system consists of a vastly diverse and heterogenous collection of neurons with variable properties and characteristics including diverse combinations and expression levels of ion channels which are vital for neuronal firing dynamics.
@ -382,11 +380,11 @@ Taken together, the nervous system consists of a vastly diverse and heterogenous
\subsection*{Validity of Neuronal Models}
\notels{should we move this to a less prominent position? How much of this part could be counted as common knowledge and be left out?, for example model complexity in terms of currents and compartments, I just think that this part might be too harsh on the models, even if the criticism doesn't apply for the main points of the paper}
\notenk{I think a large part of this section, although hgihlighting the problems with the models, helps make the case that there is a vast amount of complexity and heterogeneity not just in what we show with the models, but also unaccounted for by the models. That is to say that we are in a sense underestimating the amount of variability in responses of different cells to the same mutation. Perhaps it would be good to change this section to emphasize that perspective?}
\notenk{I think a large part of this section, although highlighting the problems with the models, helps make the case that there is a vast amount of complexity and heterogeneity not just in what we show with the models, but also unaccounted for by the models. That is to say that we are in a sense underestimating the amount of variability in responses of different cells to the same mutation. Perhaps it would be good to change this section to emphasize that perspective?}
\notejb{The following three paragraphs are rather technical and if possible should be shorter.} \notenk{shortened single vs multicompartment model paragraphs. We could remove the \Kv paragraph I've shortened - see below}
\notejb{The following three paragraphs are rather technical and if possible should be shorter.}\\ \notenk{shortened single vs multicompartment model paragraphs. We could remove the \Kv paragraph I've shortened - see below}\\
Our findings are based on simulations of a range of single-compartment conductance-based models. Single-compartment models do not take into consideration differential effects on neuronal compartments (i.e. axon, soma, dendrites), possible different spatial cellular distribution of channel expression across and within these neuronal compartments. More realistic models are more computationally expensive, and require knowledge of the distribution of conductances across the cell. However, each of the single-compartment models used here can reproduce physiological firing behaviour of the neurons they represent \citep{pospischil_minimal_2008, alexander_cerebellar_2019, otsuka_conductance-based_2004} and capture key aspects of the dynamics of these cell types.
%Many aspects of these models can be questioned.
@ -402,10 +400,11 @@ The \Kv model from \cite{ranjan_kinetic_2019} is based on expression of only \Kv
Despite all these shortcomings of the models we used in our simulations, they do not touch our main conclusion that the quantitative as well as qualitative effects of a given ionic current variant in general depend on the specific properties of all the other ionic currents expressed in a given cell.
\subsection*{Ionic Current Environments Determine the Effect of Ion Channel Mutations}
\notenk{We could add a brief discussion somewhere in this section about time constants and why we neglect them despite likely being important in determining the outcome of a mutation.} \notejb{\textcolor{red}{If we have citations for the time constant issue then yes, do it.}}
\notenk{I think that these 2 papers might be useful but it's late and I need to look at them with fresh eyes tomorrow}
https://doi.org/10.1016/j.bpj.2019.08.001 \\
https://doi.org/10.1002/wsbm.1482
\notenk{We could add a brief discussion somewhere in this section about time constants and why we neglect them despite likely being important in determining the outcome of a mutation.} \notejb{If we have citations for the time constant issue then yes, do it.}\\
\notenk{I'm not sure I like the section I wrote here. I would tend towards leaving it out.}
Although a number of methods have been used to fit ionic currents including different approaches in estimate time constants either from summary data or from full current traces, and are limited by the available data \citep{Clerx2019, Whittaker2020}. On one hand, specialized equipment and great experimental care is often required to estimate time constants \citep{Whittaker2020}. As a result summary data is often not recorded for voltage ranges in which time constants are fast. On the other hand, lack of availability of full current traces for each mutation limits the alternative current trace fitting approach.
For these practical reasons, we neglect the effect of mutation altered time constants despite acknowledging that time constant changes are likely important in determining the outcome of a given mutation on firing.
\notejb{Too technical, shorter! These aspects do not questions our result.} \notenk{Made a little shorter}
%One-factor-at-a-time (OFAT) sensitivity analyses such as the one performed here are predicated on assumptions of model linearity, and cannot account for interactions between factors \citep{czitrom_one-factor-at--time_1999, saltelli_how_2010}. OFAT approaches are local and not global (i.e. always in reference to a baseline point in the parameter space) and therefore cannot be generalized to the global parameter space unless linearity is met \citep{saltelli_how_2010}.

197
ref.bib
View File

@ -1,4 +1,41 @@
@Article{Clerx2019,
author = {Clerx, Michael and Beattie, Kylie A. and Gavaghan, David J. and Mirams, Gary R.},
journal = {Biophysical Journal},
title = {Four {Ways} to {Fit} an {Ion} {Channel} {Model}},
year = {2019},
issn = {0006-3495},
month = dec,
number = {12},
pages = {2420--2437},
volume = {117},
abstract = {Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example, to predict the risks and results of genetic mutations, pharmacological treatments, or surgical procedures. These safety-critical applications depend on accurate characterization of the underlying ionic currents. Four different methods can be found in the literature to fit voltage-sensitive ion channel models to whole-cell current measurements: method 1, fitting model equations directly to time-constant, steady-state, and I-V summary curves; method 2, fitting by comparing simulated versions of these summary curves to their experimental counterparts; method 3, fitting to the current traces themselves from a range of protocols; and method 4, fitting to a single current trace from a short and rapidly fluctuating voltage-clamp protocol. We compare these methods using a set of experiments in which hERG1a current was measured in nine Chinese hamster ovary cells. In each cell, the same sequence of fitting protocols was applied, as well as an independent validation protocol. We show that methods 3 and 4 provide the best predictions on the independent validation set and that short, rapidly fluctuating protocols like that used in method 4 can replace much longer conventional protocols without loss of predictive ability. Although data for method 2 are most readily available from the literature, we find it performs poorly compared to methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.},
doi = {10.1016/j.bpj.2019.08.001},
file = {ScienceDirect Full Text PDF:https\://www.sciencedirect.com/science/article/pii/S0006349519306666/pdfft?md5=116e21c18622fee37de1cd056c795e82&pid=1-s2.0-S0006349519306666-main.pdf&isDTMRedir=Y:application/pdf},
language = {en},
}
@Article{Balachandar2018,
author = {Balachandar, Arjun and Prescott, Steven A.},
journal = {The Journal of Physiology},
title = {Origin of heterogeneous spiking patterns from continuously distributed ion channel densities: a computational study in spinal dorsal horn neurons},
year = {2018},
issn = {1469-7793},
number = {9},
pages = {1681--1697},
volume = {596},
abstract = {Key points Distinct spiking patterns may arise from qualitative differences in ion channel expression (i.e. when different neurons express distinct ion channels) and/or when quantitative differences in expression levels qualitatively alter the spike generation process. We hypothesized that spiking patterns in neurons of the superficial dorsal horn (SDH) of spinal cord reflect both mechanisms. We reproduced SDH neuron spiking patterns by varying densities of KV1- and A-type potassium conductances. Plotting the spiking patterns that emerge from different density combinations revealed spiking-pattern regions separated by boundaries (bifurcations). This map suggests that certain spiking pattern combinations occur when the distribution of potassium channel densities straddle boundaries, whereas other spiking patterns reflect distinct patterns of ion channel expression. The former mechanism may explain why certain spiking patterns co-occur in genetically identified neuron types. We also present algorithms to predict spiking pattern proportions from ion channel density distributions, and vice versa. Abstract Neurons are often classified by spiking pattern. Yet, some neurons exhibit distinct patterns under subtly different test conditions, which suggests that they operate near an abrupt transition, or bifurcation. A set of such neurons may exhibit heterogeneous spiking patterns not because of qualitative differences in which ion channels they express, but rather because quantitative differences in expression levels cause neurons to operate on opposite sides of a bifurcation. Neurons in the spinal dorsal horn, for example, respond to somatic current injection with patterns that include tonic, single, gap, delayed and reluctant spiking. It is unclear whether these patterns reflect five cell populations (defined by distinct ion channel expression patterns), heterogeneity within a single population, or some combination thereof. We reproduced all five spiking patterns in a computational model by varying the densities of a low-threshold (KV1-type) potassium conductance and an inactivating (A-type) potassium conductance and found that single, gap, delayed and reluctant spiking arise when the joint probability distribution of those channel densities spans two intersecting bifurcations that divide the parameter space into quadrants, each associated with a different spiking pattern. Tonic spiking likely arises from a separate distribution of potassium channel densities. These results argue in favour of two cell populations, one characterized by tonic spiking and the other by heterogeneous spiking patterns. We present algorithms to predict spiking pattern proportions based on ion channel density distributions and, conversely, to estimate ion channel density distributions based on spiking pattern proportions. The implications for classifying cells based on spiking pattern are discussed.},
copyright = {© 2018 The Authors. The Journal of Physiology published by John Wiley \& Sons Ltd on behalf of The Physiological Society},
doi = {10.1113/JP275240},
file = {Full Text PDF:https\://onlinelibrary.wiley.com/doi/pdfdirect/10.1113/JP275240:application/pdf},
keywords = {spiking pattern, Spinal cord, dorsal horn, Neuronal excitability, classification, computational modeling},
language = {en},
shorttitle = {Origin of heterogeneous spiking patterns from continuously distributed ion channel densities},
}
@Article{Tripathy2017,
author = {Tripathy, Shreejoy J. and Toker, Lilah and Li, Brenna and Crichlow, Cindy-Lee and Tebaykin, Dmitry and Mancarci, B. Ogan and Pavlidis, Paul},
journal = {PLOS Computational Biology},
@ -1841,11 +1878,11 @@ SIGNIFICANCE: Bromide is most effective and is a well-tolerated drug among DS pa
}
@Article{Yan2020,
@Article{Yan2020b,
author = {Yan, Wenjun and Peng, Yi-Rong and van Zyl, Tavé and Regev, Aviv and Shekhar, Karthik and Juric, Dejan and Sanes, Joshua R.},
journal = {Scientific Reports},
title = {Cell {Atlas} of {The} {Human} {Fovea} and {Peripheral} {Retina}},
year = {2020},
year = {2020b},
issn = {2045-2322},
month = jun,
number = {1},
@ -1861,24 +1898,20 @@ SIGNIFICANCE: Bromide is most effective and is a well-tolerated drug among DS pa
@Article{Yan2020a,
author = {Yan, Wenjun and Laboulaye, Mallory A. and Tran, Nicholas M. and Whitney, Irene E. and Benhar, Inbal and Sanes, Joshua R.},
author = {Yan, Wenjun and Laboulaye, Mallory A. and Tran, Nicholas M. and Whitney, Irene E. and Benhar, Inbal and Sanes, Joshua R.},
journal = {Journal of Neuroscience},
title = {Mouse {Retinal} {Cell} {Atlas}: {Molecular} {Identification} of over {Sixty} {Amacrine} {Cell} {Types}},
year = {2020},
year = {2020a},
issn = {0270-6474, 1529-2401},
month = jul,
number = {27},
pages = {5177--5195},
volume = {40},
abstract = {Amacrine cells (ACs) are a diverse class of interneurons that modulate input from photoreceptors to retinal ganglion cells (RGCs), rendering each RGC type selectively sensitive to particular visual features, which are then relayed to the brain. While many AC types have been identified morphologically and physiologically, they have not been comprehensively classified or molecularly characterized. We used high-throughput single-cell RNA sequencing to profile {\textgreater}32,000 ACs from mice of both sexes and applied computational methods to identify 63 AC types. We identified molecular markers for each type and used them to characterize the morphology of multiple types. We show that they include nearly all previously known AC types as well as many that had not been described. Consistent with previous studies, most of the AC types expressed markers for the canonical inhibitory neurotransmitters GABA or glycine, but several expressed neither or both. In addition, many expressed one or more neuropeptides, and two expressed glutamatergic markers. We also explored transcriptomic relationships among AC types and identified transcription factors expressed by individual or multiple closely related types. Noteworthy among these were Meis2 and Tcf4, expressed by most GABAergic and most glycinergic types, respectively. Together, these results provide a foundation for developmental and functional studies of ACs, as well as means for genetically accessing them. Along with previous molecular, physiological, and morphologic analyses, they establish the existence of at least 130 neuronal types and nearly 140 cell types in the mouse retina. SIGNIFICANCE STATEMENT The mouse retina is a leading model for analyzing the development, structure, function, and pathology of neural circuits. A complete molecular atlas of retinal cell types provides an important foundation for these studies. We used high-throughput single-cell RNA sequencing to characterize the most heterogeneous class of retinal interneurons, amacrine cells, identifying 63 distinct types. The atlas includes types identified previously as well as many novel types. We provide evidence for the use of multiple neurotransmitters and neuropeptides, and identify transcription factors expressed by groups of closely related types. Combining these results with those obtained previously, we proposed that the mouse retina contains ∼130 neuronal types and is therefore comparable in complexity to other regions of the brain.},
chapter = {Research Articles},
copyright = {Copyright © 2020 the authors},
doi = {10.1523/JNEUROSCI.0471-20.2020},
keywords = {GABA, glycine, Meis2, neuropeptide, RNA-seq, TCF4},
language = {en},
pmid = {32457074},
publisher = {Society for Neuroscience},
shorttitle = {Mouse {Retinal} {Cell} {Atlas}},
}
@ -1920,4 +1953,152 @@ SIGNIFICANCE: Bromide is most effective and is a well-tolerated drug among DS pa
}
@Article{Gu2014,
author = {Gu, HuaGuang and Chen, ShengGen},
journal = {Science China Technological Sciences},
title = {Potassium-induced bifurcations and chaos of firing patterns observed from biological experiment on a neural pacemaker},
year = {2014},
issn = {1869-1900},
month = may,
number = {5},
pages = {864--871},
volume = {57},
abstract = {Changes of neural firing patterns and transitions between firing patterns induced by the introduction of external stimulation or adjustment of biological parameter have been demonstrated to play key roles in information coding. In this paper, bifurcation processes of bursting patterns were observed from an experimental neural pacemaker, through the adjustment of potassium parameter including ion concentration and calcium-dependent channel conductance. The adjustment of calcium-dependent potassium channel conductance was achieved by changing the extracellular tetraethylammonium concentration. The deterministic dynamics of chaotic bursting patterns induced by period-doubling bifurcation and intermittency, and lying between two periodic bursting patterns in a period-adding bifurcation process was investigated with a nonlinear prediction method. The bifurcations included period-doubling and period-adding bifurcations of bursting patterns. The experimental bifurcations and chaos closely matched those previously simulated in the theoretical neuronal model by adjusting potassium parameter, which demonstrated the simulation results of the theoretical model. The experimental results indicate that the potassium concentration and conductance of calcium-dependent potassium channel can induce bifurcations of the neural firing patterns. The potential role of these bifurcation structures in neural information coding mechanism is discussed.},
doi = {10.1007/s11431-014-5526-0},
file = {:Gu2014 - Potassium Induced Bifurcations and Chaos of Firing Patterns Observed from Biological Experiment on a Neural Pacemaker.pdf:PDF},
keywords = {bifurcation, neural firing pattern, chaos, potassium ion},
language = {en},
}
@Article{Zeberg2015,
author = {Zeberg, Hugo and Robinson, Hugh P. C. and Århem, Peter},
journal = {Journal of Neurophysiology},
title = {Density of voltage-gated potassium channels is a bifurcation parameter in pyramidal neurons},
year = {2015},
issn = {0022-3077},
month = jan,
number = {2},
pages = {537--549},
volume = {113},
abstract = {Several types of intrinsic dynamics have been identified in brain neurons. Type 1 excitability is characterized by a continuous frequency-stimulus relationship and, thus, an arbitrarily low frequency at threshold current. Conversely, Type 2 excitability is characterized by a discontinuous frequency-stimulus relationship and a nonzero threshold frequency. In previous theoretical work we showed that the density of Kv channels is a bifurcation parameter, such that increasing the Kv channel density in a neuron model transforms Type 1 excitability into Type 2 excitability. Here we test this finding experimentally, using the dynamic clamp technique on Type 1 pyramidal cells in rat cortex. We found that increasing the density of slow Kv channels leads to a shift from Type 1 to Type 2 threshold dynamics, i.e., a distinct onset frequency, subthreshold oscillations, and reduced latency to first spike. In addition, the action potential was resculptured, with a narrower spike width and more pronounced afterhyperpolarization. All changes could be captured with a two-dimensional model. It may seem paradoxical that an increase in slow K channel density can lead to a higher threshold firing frequency; however, this can be explained in terms of bifurcation theory. In contrast to previous work, we argue that an increased outward current leads to a change in dynamics in these neurons without a rectification of the current-voltage curve. These results demonstrate that the behavior of neurons is determined by the global interactions of their dynamical elements and not necessarily simply by individual types of ion channels.},
doi = {10.1152/jn.00907.2013},
file = {:Zeberg2015 - Density of Voltage Gated Potassium Channels Is a Bifurcation Parameter in Pyramidal Neurons.pdf:PDF},
keywords = {bifurcation, dynamic clamp, ion channel density, pyramidal neuron, threshold dynamics},
publisher = {American Physiological Society},
}
@Article{Aarhem2007,
author = {{\AA}rhem, Peter and Blomberg, Clas},
journal = {Biosystems},
title = {Ion channel density and threshold dynamics of repetitive firing in a cortical neuron model},
year = {2007},
issn = {0303-2647},
month = may,
number = {1},
pages = {117--125},
volume = {89},
abstract = {Modifying the density and distribution of ion channels in a neuron (by natural up- and down-regulation, by pharmacological intervention or by spontaneous mutations) changes its activity pattern. In the present investigation, we analyze how the impulse patterns are regulated by the density of voltage-gated channels in a model neuron, based on voltage clamp measurements of hippocampal interneurons. At least three distinct oscillatory patterns, associated with three distinct regions in the Na–K channel density plane, were found. A stability analysis showed that the different regions are characterized by saddle-node, double-orbit, and Hopf bifurcation threshold dynamics, respectively. Single strongly graded action potentials occur in an area outside the oscillatory regions, but less graded action potentials occur together with repetitive firing over a considerable range of channel densities. The presently found relationship between channel densities and oscillatory behavior may be relevance for understanding principal spiking patterns of cortical neurons (regular firing and fast spiking). It may also be of relevance for understanding the action of pharmacological compounds on brain oscillatory activity.},
doi = {10.1016/j.biosystems.2006.03.015},
file = {ScienceDirect Full Text PDF:https\://www.sciencedirect.com/science/article/pii/S0303264706002498/pdfft?md5=f5026b3f7f4298030a483889c8b4ac4d&pid=1-s2.0-S0303264706002498-main.pdf&isDTMRedir=Y:application/pdf},
keywords = {Ion channels, Hopf bifurcation, Saddle-node bifurcation, Hippocampal interneuron, Threshold dynamics},
language = {en},
series = {Selected {Papers} presented at the 6th {International} {Workshop} on {Neural} {Coding}},
}
@Article{Qi2013,
author = {Qi, Y. and Watts, A. L. and Kim, J. W. and Robinson, P. A.},
journal = {Biological Cybernetics},
title = {Firing patterns in a conductance-based neuron model: bifurcation, phase diagram, and chaos},
year = {2013},
issn = {1432-0770},
month = feb,
number = {1},
pages = {15--24},
volume = {107},
abstract = {Responding to various stimuli, some neurons either remain resting or can fire several distinct patterns of action potentials, such as spiking, bursting, subthreshold oscillations, and chaotic firing. In particular, Wilson’s conductance-based neocortical neuron model, derived from the Hodgkin–Huxley model, is explored to understand underlying mechanisms of the firing patterns. Phase diagrams describing boundaries between the domains of different firing patterns are obtained via extensive numerical computations. The boundaries are further studied by standard instability analyses, which demonstrates that the chaotic neural firing could develop via period-doubling and/or period- adding cascades. Sequences of the firing patterns often observed in many neural experiments are also discussed in the phase diagram framework developed. Our results lay the groundwork for wider use of the model, especially for incorporating it into neural field modeling of the brain.},
doi = {10.1007/s00422-012-0520-8},
file = {:Qi2013 - Firing Patterns in a Conductance Based Neuron Model_ Bifurcation, Phase Diagram, and Chaos.pdf:PDF},
keywords = {Action potential, Conductance-based neuron model, Linear stability analysis, Period-doubling/adding route to chaos},
language = {en},
shorttitle = {Firing patterns in a conductance-based neuron model},
}
@Article{Gu2014a,
author = {Gu, Huaguang and Pan, Baobao and Chen, Guanrong and Duan, Lixia},
journal = {Nonlinear Dynamics},
title = {Biological experimental demonstration of bifurcations from bursting to spiking predicted by theoretical models},
year = {2014},
issn = {1573-269X},
month = oct,
number = {1},
pages = {391--407},
volume = {78},
abstract = {A series of bifurcations from period-1 bursting to period-1 spiking in a complex (or simple) process were observed with increasing extra-cellular potassium concentration during biological experiments on different neural pacemakers. This complex process is composed of three parts: period-adding sequences of burstings, chaotic bursting to chaotic spiking, and an inverse period-doubling bifurcation of spiking patterns. Six cases of bifurcations with complex processes distinguished by period-adding sequences with stochastic or chaotic burstings that can reach different bursting patterns, and three cases of bifurcations with simple processes, without the transition from chaotic bursting to chaotic spiking, were identified. It reveals the structures closely matching those simulated in a two-dimensional parameter space of the Hindmarsh–Rose model, by increasing one parameter \$\$I\$\$and fixing another parameter \$\$r\$\$at different values. The experimental bifurcations also resembled those simulated in a physiologically based model, the Chay model. The experimental observations not only reveal the nonlinear dynamics of the firing patterns of neural pacemakers but also provide experimental evidence of the existence of bifurcations from bursting to spiking simulated in the theoretical models.},
doi = {10.1007/s11071-014-1447-5},
file = {:Gu2014a - Biological Experimental Demonstration of Bifurcations from Bursting to Spiking Predicted by Theoretical Models.pdf:PDF},
keywords = {Bifurcation, Neural firing, Chaos, Bursting, Spiking, Period-adding bifurcation},
language = {en},
}
@Article{Zeberg2010,
author = {Zeberg, Hugo and Blomberg, Clas and {\AA}rhem, Peter},
journal = {PLOS Computational Biology},
title = {Ion {Channel} {Density} {Regulates} {Switches} between {Regular} and {Fast} {Spiking} in {Soma} but {Not} in {Axons}},
year = {2010},
issn = {1553-7358},
month = apr,
number = {4},
pages = {e1000753},
volume = {6},
abstract = {The threshold firing frequency of a neuron is a characterizing feature of its dynamical behaviour, in turn determining its role in the oscillatory activity of the brain. Two main types of dynamics have been identified in brain neurons. Type 1 dynamics (regular spiking) shows a continuous relationship between frequency and stimulation current (f-Istim) and, thus, an arbitrarily low frequency at threshold current; Type 2 (fast spiking) shows a discontinuous f-Istim relationship and a minimum threshold frequency. In a previous study of a hippocampal neuron model, we demonstrated that its dynamics could be of both Type 1 and Type 2, depending on ion channel density. In the present study we analyse the effect of varying channel density on threshold firing frequency on two well-studied axon membranes, namely the frog myelinated axon and the squid giant axon. Moreover, we analyse the hippocampal neuron model in more detail. The models are all based on voltage-clamp studies, thus comprising experimentally measurable parameters. The choice of analysing effects of channel density modifications is due to their physiological and pharmacological relevance. We show, using bifurcation analysis, that both axon models display exclusively Type 2 dynamics, independently of ion channel density. Nevertheless, both models have a region in the channel-density plane characterized by an N-shaped steady-state current-voltage relationship (a prerequisite for Type 1 dynamics and associated with this type of dynamics in the hippocampal model). In summary, our results suggest that the hippocampal soma and the two axon membranes represent two distinct kinds of membranes; membranes with a channel-density dependent switching between Type 1 and 2 dynamics, and membranes with a channel-density independent dynamics. The difference between the two membrane types suggests functional differences, compatible with a more flexible role of the soma membrane than that of the axon membrane.},
doi = {10.1371/journal.pcbi.1000753},
file = {:Zeberg2010 - Ion Channel Density Regulates Switches between Regular and Fast Spiking in Soma but Not in Axons.pdf:PDF},
keywords = {Axons, Neurons, Hippocampus, Squids, Membrane potential, Action potentials, Ion channels, Behavior},
language = {en},
publisher = {Public Library of Science},
}
@Article{Zhou2020,
author = {Zhou, Xiuying and Xu, Ying and Wang, Guowei and Jia, Ya},
journal = {Cognitive Neurodynamics},
title = {Ionic channel blockage in stochastic {Hodgkin}–{Huxley} neuronal model driven by multiple oscillatory signals},
year = {2020},
issn = {1871-4099},
month = aug,
number = {4},
pages = {569--578},
volume = {14},
abstract = {Ionic channel blockage and multiple oscillatory signals play an important role in the dynamical response of pulse sequences. The effects of ionic channel blockage and ionic channel noise on the discharge behaviors are studied in Hodgkin–Huxley neuronal model with multiple oscillatory signals. It is found that bifurcation points of spontaneous discharge are altered through tuning the amplitude of multiple oscillatory signals, and the discharge cycle is changed by increasing the frequency of multiple oscillatory signals. The effects of ionic channel blockage on neural discharge behaviors indicate that the neural excitability can be suppressed by the sodium channel blockage, however, the neural excitability can be reversed by the potassium channel blockage. There is an optimal blockage ratio of potassium channel at which the electrical activity is the most regular, while the order of neural spike is disrupted by the sodium channel blockage. In addition, the frequency of spike discharge is accelerated by increasing the ionic channel noise, the firing of neuron becomes more stable if the ionic channel noise is appropriately reduced. Our results might provide new insights into the effects of ionic channel blockages, multiple oscillatory signals, and ionic channel noises on neural discharge behaviors.},
doi = {10.1007/s11571-020-09593-7},
file = {:Zhou2020 - Ionic Channel Blockage in Stochastic Hodgkin–Huxley Neuronal Model Driven by Multiple Oscillatory Signals.pdf:PDF},
keywords = {Ionic channel blockage, Stochastic Hodgkin–Huxley model, Multiple oscillatory signal, Discharge behavior},
language = {en},
}
@Article{Whittaker2020,
author = {Whittaker, Dominic G. and Clerx, Michael and Lei, Chon Lok and Christini, David J. and Mirams, Gary R.},
journal = {WIREs Systems Biology and Medicine},
title = {Calibration of ionic and cellular cardiac electrophysiology models},
year = {2020},
issn = {1939-005X},
number = {4},
pages = {e1482},
volume = {12},
abstract = {Cardiac electrophysiology models are among the most mature and well-studied mathematical models of biological systems. This maturity is bringing new challenges as models are being used increasingly to make quantitative rather than qualitative predictions. As such, calibrating the parameters within ion current and action potential (AP) models to experimental data sets is a crucial step in constructing a predictive model. This review highlights some of the fundamental concepts in cardiac model calibration and is intended to be readily understood by computational and mathematical modelers working in other fields of biology. We discuss the classic and latest approaches to calibration in the electrophysiology field, at both the ion channel and cellular AP scales. We end with a discussion of the many challenges that work to date has raised and the need for reproducible descriptions of the calibration process to enable models to be recalibrated to new data sets and built upon for new studies. This article is categorized under: Analytical and Computational Methods {\textgreater} Computational Methods Physiology {\textgreater} Mammalian Physiology in Health and Disease Models of Systems Properties and Processes {\textgreater} Cellular Models},
doi = {10.1002/wsbm.1482},
file = {Full Text PDF:https\://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/wsbm.1482:application/pdf},
keywords = {cardiac, electrophysiology, identification, inference, mathematical modeling, optimization, parameterization},
language = {en},
}
@Comment{jabref-meta: databaseType:bibtex;}