Conference PaperPDF Available

Spatio-temporal membrane potential and resistive current reconstruction from parallel multielectrode array and intracellular measurements in single neurons

Authors:
  • Institute of Cognitive Neuroscience
  • University Children's Hospital Zurich
  • Research Center for Natural Sciences, Budapest, Hungary

Abstract

Here we show, that based on parallel multichannel extracellular and single channel intracellular potential recordings, it is possible the reconstruct the spatio-temporal distribution of membrane potential with the spatial resolution of the extracellular recordings in single neurons. Moreover we show, that reconstruction of membrane potential made possible the distinction between the two components of the current source density (CSD): the resistive and the capacitive current. This distinction provides a clue to the clear interpretation of the CSD distribution, as resistive component corresponds to the active channel currents, both synaptic and voltage sensitive channelmembranecurrents, while capacitive current corresponds to the passive counter currents. The importance of this distinction is further emphasized by different properties of the resistive membrane current distribution and the CSD: as the CSD is the net membrane current, the sum of the CSD along a whole intact cell should be zero in each time moment, according to the charge conservation. In contrast to this, the sum of the resistive current is not necessarily zero, and it governs the membrane potential dynamics. Thus, estimation of the spatial distribution of the resistive membrane current makes possible the distinction between active and passive sinks and sources of theCSDmapand localization of the synaptic input currents, which makes the neuron fire.Wevalidate our reconstruction approach on simulations and demonstrate its application on parallel extracellular and intracellular recordings in CA1 region of hippocampal slice preparations in vitro.
Abstracts / IBRO Reports 6 (2019) S54–S345 S137
P05.05
New method for the detection of
neurocysticercosis cysts in MRI by image
processing
Laura Baquedano Santana1,, Javier Bustos2,
Gianfranco Arroyo3, Juan Chacaltana4, Manuel
Forero5, Hector Garcia2
1Universidad Peruana Cayetano Heredia, Lima, Peru
2Cysticercosis Unit, Instituto Nacional de Ciencias
Neurologicas, Lima, Peru
3School of Public Health and Administration,
Universidad Peruana Cayetano Heredia, Lima, Peru
4Department of Diagnostic Imaging, Instituto
Nacional de Ciencias Neurologicas, Lima, Peru
5Universidad de Ibagué, Ibague, Colombia
Neurocysticercosis (NCC) is the main cause of epilepsy in
endemic countries. This disease is caused by a parasite in the ner-
vous system. Symptoms depend on the location, size and number
of parasites in the brain, in addition to the inflammatory response
of each patient. Thus, brain magnetic resonance imaging (MRI)
becomes an indispensable tool in the diagnosis, treatment and fol-
low up of the neurocysticercosis, however, this technique is still
imprecise. Therefore, in this work we present a new semiautomatic
method based on the digital processing of magnetic resonance
imaging, which allows a better identification of the parasite in pig
brain with neurocysticercosis. For the study, five pigs with neuro-
cysticercosis were anesthetized and magnetic resonance imaging
of the head was acquired. The images were segmented using a
thresholding technique designed for this purpose to separate the
brain from the fundus and other structures of the head. Then was
followed by cleaning the mathematical morphology and labeling.
Technique achieved the detection, measurement and localization
of cysts semiautomatically in the brains of pigs. This new method
will allow us to study the evolution of cysts in NCC disease treated
and not treated with antiparasitic drugs.
https://doi.org/10.1016/j.ibror.2019.07.434
P05.06
Spatio-temporal membrane potential and
resistive current reconstruction from parallel
multielectrode array and intracellular
measurements in single neurons
Zoltán Somogyvári1,, Domokos Meszéna2,
Dorottya Cserpán1, Lucia Wittner2, István
Ulbert2
1Wigner Research Centre for Physics of Hungarian
Academy of Sciences, Budapest, Hungary
2Institute of Cognitive Neuroscience and Psychology,
Research Center for Natural Sciences of Hungarian
Academy of Sciences, Budapest, Hungary
Here we show, that based on parallel multichannel extracellular
and single channel intracellular potential recordings, it is possi-
ble the reconstruct the spatio-temporal distribution of membrane
potential with the spatial resolution of the extracellular record-
ings in single neurons. Moreover we show, that reconstruction of
membrane potential made possible the distinction between the
two components of the current source density (CSD): the resis-
tive and the capacitive current. This distinction provides a clue to
the clear interpretation of the CSD distribution, as resistive compo-
nent corresponds to the active channel currents, both synaptic and
voltage sensitive channel membrane currents, while capacitive cur-
rent corresponds to the passive counter currents. The importance of
this distinction is further emphasized by different properties of the
resistive membrane current distribution and the CSD: as the CSD
is the net membrane current, the sum of the CSD along a whole
intact cell should be zero in each time moment, according to the
charge conservation. In contrast to this, the sum of the resistive cur-
rent is not necessarily zero, and it governs the membrane potential
dynamics. Thus, estimation of the spatial distribution of the resis-
tive membrane current makes possible the distinction between
active and passive sinks and sources of the CSD map and localization
of the synaptic input currents, which makes the neuron fire. We val-
idate our reconstruction approach on simulations and demonstrate
its application on parallel extracellular and intracellular recordings
in CA1 region of hippocampal slice preparations in vitro.
https://doi.org/10.1016/j.ibror.2019.07.435
P05.07
Shaping brain signals with real-time fMRI:
Optimizing retrieval inducing neurofeedback
with simulations
Cindy Lor1,, Amelie Haugg2, Ronald Sladky1,
Gustavo Pamplona3, Frank Scharnowski1
1University of Vienna, Vienna, Austria
2University of Zurich, Zurich, Switzerland
3University of Lausanne, Lausanne, Switzerland
Functional MRI-based neurofeedback has significant therapeu-
tic potential for psychiatric disorders such as MDD, PTSD and
addiction. In a growing number of studies, successful control of
region-specific brain activity has even been followed by behavioral
improvement. To improve the efficacy of the procedure, it has been
proposed to replace the commonly used symbolic representation
of the feedback signal (e.g. a thermometer icon) with a dynamic
stimulus that inherently engages the brain regions to be trained.
This closed-loop between brain activity and parametric stimula-
tion allows for external shaping of spatially localized signals that
are also specific to the stimulus. Here, instead of relying exclusively
on the participant’s mental strategies to up or downregulate brain
signals, we want to capitalize on the plasticity window of memory
reconsolidation following retrieval by inducing exactly the activity
that the subject needs to modulate.
However, designing this dual closed-loop neurofeedback is
technically and conceptually challenging. Some system parame-
ters can be freely chosen but their effect on the system dynamics
has not been studied yet. In this project, we wanted to explicitly
formalize a negative feedback system that stabilizes the fMRI sig-
nal and describe quantitatively and qualitatively the effect of free
model parameters so that we can optimally choose them in future
closed-loop neurofeedback applications.
With the use of numerical simulations, we showed that the
mapping scale and resolution between the acquired signal and the
feedback stimulus plays a key role in determining the dynamics
of the oscillations provoked by the system whereas sampling rate
and frequency of feedback presentation had less impact on signal
stabilization. Finally, we provided a framework that can serve as
a basis to model closed-loop neurofeedback approaches and pro-
posed novel measures for evaluating neurofeedback success.
https://doi.org/10.1016/j.ibror.2019.07.436
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