Abstract
Background
We recently reported a hyperexcitability phenotype displayed in dentate gyrus granule
neurons derived from patients with bipolar disorder (BD) as well as a hyperexcitability
that appeared only in CA3 pyramidal hippocampal neurons that were derived from patients
with BD who responded to lithium treatment (lithium responders) and not in CA3 pyramidal
hippocampal neurons that were derived from patients with BD who did not respond to
lithium (nonresponders).
Methods
Here we used our measurements of currents in neurons derived from 4 control subjects,
3 patients with BD who were lithium responders, and 3 patients with BD who were nonresponders.
We changed the conductances of simulated dentate gyrus and CA3 hippocampal neurons
according to our measurements to derive a numerical simulation for BD neurons.
Results
The computationally simulated BD dentate gyrus neurons had a hyperexcitability phenotype
similar to the experimental results. Only the simulated BD CA3 neurons derived from
lithium responder patients were hyperexcitable. Interestingly, our computational model
captured a physiological instability intrinsic to hippocampal neurons that were derived
from nonresponder patients that we also observed when re-examining our experimental
results. This instability was caused by a drastic reduction in the sodium current,
accompanied by an increase in the amplitude of several potassium currents. These baseline
alterations caused nonresponder BD hippocampal neurons to drastically shift their
excitability with small changes to their sodium currents, alternating between hyperexcitable
and hypoexcitable states.
Conclusions
Our computational model of BD hippocampal neurons that was based on our measurements
reproduced the experimental phenotypes of hyperexcitability and physiological instability.
We hypothesize that the physiological instability phenotype strongly contributes to
affective lability in patients with BD.
Keywords
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Article info
Publication history
Published online: February 04, 2020
Accepted:
January 24,
2020
Received in revised form:
January 8,
2020
Received:
April 18,
2019
Identification
Copyright
© 2020 Society of Biological Psychiatry.
ScienceDirect
Access this article on ScienceDirectLinked Article
- Can Computational Modeling Predict Disease Phenotype?Biological PsychiatryVol. 88Issue 2
- PreviewTo understand the cellular and molecular pathophysiology of mood disorders, including bipolar disorder (BD), has been an inherently challenging task. One of the primary barriers is the infeasibility of accessing live neuronal cells and tissue from patients to undertake experiments in the laboratory. Even though postmortem studies provide a great depth of knowledge, they cannot demonstrate precisely when during the neurodevelopmental trajectory the disease develops.
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