ArticlePDF Available

Electrophysiological imaging of epileptic brain slices reveals pharmacologically confined functional changes

  • Hôpital ophtalmique Jules Gonin - Fondation Asile des aveugles

Abstract and Figures

Microelectrode arrays (MEAs) are employed to study extracellular electrical activity in neuronal tissues. Neverthe- less, commercially available MEAs provide a limited number of recording sites and do not allow a precise identifi- cation of the spatio-temporal characterization of the recorded signal. To overcome this limitation, high density MEAs, based on CMOS technology, were recently developed and validated on dissociated preparations (Ber- dondini et al. 2009). We show the platform capability to record extracellular electrophysiological signal from 4096 electrodes arranged in a squared area of 2.7 mm x 2.7 mm with inter-electrode distance of 21 μm at a sampling rate of 7.7 kHz/electrode. Here, we demonstrate the performances of these platforms for the acquisition chemi- cally evoked epileptiform activity from brain slices. Moreover the high spatial resolutions allow us to estimate the effect of drugs in spatially modulating Inter-Ictal ((I-IC) activity.
Content may be subject to copyright.
published: 14 November 2012
doi: 10.3389/fncir.2012.00080
Large-scale, high-resolution electrophysiological imaging
of field potentials in brain slices with microelectronic
multielectrode arrays
E. Ferrea1,A. Maccione1,L. Medrihan1,T. N i e u s 1,D. Ghezzi1,P. Baldelli1,2,F. Benfenati1,2 and
L. Berdondini1*
1Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa, Italy
2Department of Experimental Medicine, Università di Genova, Genoa, Italy
Edited by:
Rodolfo R. Llinas, New York
University School of Medicine, USA
Reviewed by:
Audrey Mercer, UCL School of
Pharmacy, UK
Hajime Hirase, RIKEN - Brain
Science Institute, Japan
L. Berdondini, NetS3Laboratory,
Department of Neuroscience and
Brain Technologies,
Neurotechnology Unit, Istituto
Italiano di Tecnologia,
Via Morego 30, 16163 Genova, Italy.
Multielectrode arrays (MEAs) are extensively used for electrophysiological studies on brain
slices, but the spatial resolution and field of recording of conventional arrays are limited
by the low number of electrodes available. Here, we present a large-scale array recording
simultaneously from 4096 electrodes used to study propagating spontaneous and evoked
network activity in acute murine cortico-hippocampal brain slices at unprecedented spatial
and temporal resolution. We demonstrate that multiple chemically induced epileptiform
episodes in the mouse cortex and hippocampus can be classified according to their
spatio-temporal dynamics. Additionally, the large-scale and high-density features of our
recording system enable the topological localization and quantification of the effects of
antiepileptic drugs in local neuronal microcircuits, based on the distinct field potential
propagation patterns. This novel high-resolution approach paves the way to detailed
electrophysiological studies in brain circuits spanning spatial scales from single neurons
up to the entire slice network.
Keywords: high-density electrode array, brain slices, functional imaging, local field potentials, epilepsy
Electrophysiological recordings in brain slices are used in a wide
range of studies to characterize neuronal circuits and signal-
ing mechanisms or to investigate pathogenic states and rescue
strategies in neuropharmacology. Currently available in vitro elec-
trophysiological methods allow neuroscientists to record either
the membrane potential of a single neuron or the extracellular
field potentials generated by the superposition of local trans-
membrane currents flowing through neuronal compartments
of multiple neurons (Nicholson and Freeman, 1975; Mitzdorf,
1985). However, these existing approaches have limitations in
their ability to accurately study the spread of activity in larger
brain circuits. To achieve large-scale high-resolution electrophys-
recording area whilst, at the same time, the spatio-temporal res-
olution of these recordings remains high, ensuring high fidelity
recording of the network dynamic under investigation (Chrobak
and Buzsaki, 1998). Interestingly, low frequency extracellular
signals reflect important global activity features in brain cir-
cuits that are still poorly understood. These local field potentials
(LFPs) reflect the activity of several neurons and they can propa-
gate between connected brain regions to influence the excitabil-
ity of local networks (Logothetis et al., 2001; Buzsaki, 2010;
Panzeri et al., 2010). Furthermore, characteristic LFP propaga-
tions are known to occur during epileptic seizures in cortico-
hippocampal circuits (Barbarosie and Avoli, 1997). In this respect,
acute cortico-hippocampal slices represent an established in vitro
model for the study of neurophysiological processes underlying
epileptogenesis (Cohen et al., 2002) and for screening potential
anticonvulsant drugs (Hill et al., 2010; Gonzalez-Sulser et al.,
2011). Conventional arrays of microelectrodes have been exten-
sively used to record network spiking and LFP activity in brain
slices (Shimono et al., 2000; Egert et al., 2002), but they are
limited in array size and electrode density due to technical con-
straints in individually routing each electrode. Recently, novel
electrode array technologies have opened new exciting opportu-
nities to design significantly larger arrays that enable recordings at
much higher resolution (Baker, 2010). These novel technologies
are taking advantage of specifically designed microelectronic cir-
cuits that are either used in hybrid configurations for connecting
large electrode arrays realized with conventional microfabrica-
tion methods, or for monolithic microelectronic chips integration
embedding thousands of electrodes with the adapted read-out cir-
cuits. Based on the hybrid approach, a 512-channel array has been
recently used to record at single cell resolution from the gan-
glion cell layer in the isolated primate retina (Field et al., 2010)
and a flexible device with 360 electrodes was demonstrated to be
effective in mapping cortical activity in vivo (Viventi et al., 2011).
Using the monolithic microelectronic approach, a chip enabling
to use 126 electrodes arbitrarily selected over an array of 11,011
microelectrodes has been used to record extracellular potentials
from Purkinje cells in acute cerebellar slices (Frey et al., 2009)
as well as to achieve sub-cellular resolution recordings in neu-
ronal cultures (Heer et al., 2007). As an alternative to arrays of
metallic electrodes, high-resolution field-effect-transistor (FET)
arrays have also been developed, potentially offering even higher
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |1
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
electrode integration densities (Hutzler et al., 2006). However,
so far none of these multielectrode array (MEA) designs pro-
vide large enough recordings areas in order to achieve high
enough spatial and temporal resolution to characterize fine-grain
properties of neural activity in large brain circuits.
Here, we demonstrate for the first time the feasibility of
large-scale electrical recordings of extracellular field potentials
in brain slices using a large and dense array consisting of 4096
microelectrodes. This system enables us to perform functional
electrophysiological imaging at unprecedented resolution to visu-
alize and quantify both spontaneous or evoked spiking activity
and LFPs from cortico-hippocampal slices. Our approach is based
on an active-pixel sensor multielectrode array system (APS-MEA
chip, Figure 1A) that records simultaneously from 4096 elec-
trodes at a sampling rate of 7.7kHz. The system provides 21 μm
inter-electrode distance and 21 ×21 μm2electrode size arranged
in a squared area of 2.6 ×2.6 mm2and it has been previously
established for extracellular recordings of spiking activity from
dissociated neuronal cultures (Berdondini et al., 2009). This plat-
form can be coupled with conventional electrode wire recordings
and fluorescence functional imaging for detailed electrophys-
iological studies in brain slices that take advantage from the
complementary recording resolution and sensing capabilities of
these methods (Figure 1B).
The large-scale active area of our high-resolution electrode
array chip was used to record extracellular activity in transverse
mouse brain slices encompassing the hippocampus (Figure 1C),
perirhinal and entorhinal cortices.
Upon electrical stimulation of the perforant path, we recorded
field excitatory postsynaptic potentials (fEPSPs) in the den-
tate gyrus (DG). Remarkably, the spatial distribution of the
fEPSP recorded from the DG upon electrical stimulation of the
perforant path perfectly matched the anatomical outline of the
DG (Figure 1C). The local polarity of the fEPSP corresponds to
current sinks and sources located in the dendritic, granule cell,
and axonal layers respectively (Figure 1D). Moreover, both spik-
ing and LFPs (Figures 2AF) during spontaneous and evoked
activities can be recorded, and viable brain slices were main-
tained for the entire duration of our experiments, up to 90 min.
This viability was validated by performing parallel APS-MEA
recordings and intracellular patch-clamp recordings from the tis-
sue contacting the electrode array and neurons located near the
upper side of the slice, respectively, and by synchronizing the
entire slice activity with the help of the voltage-gated potas-
sium blocker 4-aminopyridine (4AP) (Rutecki et al., 1987; Avoli
et al., 1996; D’Antuono et al., 2002). The patch-clamp responses
were both spatially and temporally tightly correlated with the
fEPSP observed with the APS-MEA, thus demonstrating that
our method yields accurate and reliable extracellular measures
(Figures 2E,F).
To further validate the recording quality of our chip, we com-
pared extracellular signals recorded using our microelectrodes
with those obtained using classical field electrode positioned in
the same slice. Voltage traces of electrically evoked responses
recorded with a single wire electrode and with the correspond-
ing electrode of the array, i.e., same (x, y) position, are shown
in Figure 3A. The stimulating electrode was kept in the same
position in the perforant path, whereas the recording single wire
electrode was moved from the outer molecular layer to the hilus in
order to correlate the evoked responses between five locations on
FIGURE 1 | Functional imaging of the dentate gyrus. (A) On the top,
Scanning Electron Microscopy (SEM) picture of a cross-section of an area
of the APS-MEA chip; on the bottom, schematics of the amplifying and
multiplexing circuitries integrated below each electrode. (B) APS-MEA
system coupled with an upright microscope for field electrode stimulation
and simultaneous VSD recordings. (C) A cortico-hippocampal slice over the
active area of the chip with superimposed color-coded fEPSP activity and
close-up on the activated area (the white tip indicates the site of
stimulation). (D) Electrophysiological traces of fEPSPs recorded by three
APS-MEA electrodes located in the dendritic layer of the dentate gyrus
(electrode 1), in the granular cell layer (electrode 2) and in the polymorphic
layer (electrode 3).
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |2
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
FIGURE 2 | Functional imaging of high frequency (action potentials)
and low frequency events (Inter-Ictals). (A) Hippocampal slice over the
recording area. (B) Absolute maximum values of an action potential train
(burst) at the various electrodes. (C) Burst event recorded on electrode
26–28. (D) Power spectral density for the same electrode. (E) Color-coded
map of maximum activity of spontaneous epileptic-form events. The
light-blue electrode is indicating the position of the patched cell whereas
the red circle is indicating the correspondent pixel. (F) Simultaneous
recording of extracellular activity with one pixel of the APS-MEA (top trace)
and intracellular activity with a patch electrode (bottom trace).
thesameplane(Figure 3B). This yielded responses with very sim-
ilar shapes and signal-to-noise ratio (Figures 3C,D), indicating
the high recording quality of our microelectrodes.
When compared with optical recordings such as voltage-
sensitive dyes (Grinvald and Hildesheim, 2004)(VSDs),conven-
tional MEAs are superior in signal quality and time resolution,
but they fail in terms of spatial resolution because of the small
number of electrodes and the large pitch between neighboring
channels (usually spaced 200 μm apart). Here, we demonstrate
simultaneous optical functional recordings with VSDs and elec-
trical APS-MEA recordings from an equivalent field of view of
1.5 ×1.5 mm2(Figure 4A). Upon stimulation of the perforant
path, paired recordings of APS-MEA with VSDs show that our
method can spatially and temporally resolve single-evoked fEP-
SPs with a much higher signal-to-noise ratio than single VSD
responses (Figures 4B,Cvs. Figures 4D,E).
By taking advantage of the high-density of electrodes integrated
over a large active area and the high sampling frequency for
each electrode, we have successfully recorded epileptiform activ-
ity at high spatio-temporal resolution through the entire slice,
encompassing the hippocampal circuit, and part of the con-
nected cortical areas (Figure 5A). We have used 4AP to induce
epileptiform activity in vitro that resembles Inter-Ictal events
(I-IC, Figure 5C, left) recorded in humans with EEG before or
after an epileptic seizure (de Curtis and Avanzini, 2001; Avoli and
de Curtis, 2011). During analysis, I-IC-like events were identi-
fied with a detection algorithm (see “Methods” section) and the
time course of the epileptiform activity during the whole exper-
iment is represented in a raster plot (Figure 5B). Noteworthy,
distinct anatomical structures show different inter-event laten-
cies. Furthermore, high frequency discharges resembling in vivo
Ictal (IC) discharges during seizures (Huberfeld et al., 2011)
(Figure 5C, right) were recorded after adding bicuculline (BIC).
Interestingly, I-ICs were observed in all the anatomical regions of
the cortico-hippocampal slice with different shapes (Figure 5C,
left) and contents in the spectral frequency band (Figure 5D,left)
whereas IC events were observed in perirhinal and entorhinal cor-
tices only. This is also confirmed by the different spectrograms
computed from recordings of electrodes located in these distinct
regions (Figure 5D,right).
Remarkably, the high-resolution of APS-MEAs allows for a
detailed description of the dynamic properties of each epilepto-
genic event (Figure 6). Similar to conventional electrode arrays,
the propagation dynamics can be estimated from the time shift of
the peak of I-IC activity between a few electrodes located in dif-
ferent brain regions (Boido et al., 2010)(Figure 6A). However,
this estimation does not fully describe the dynamics involved
in the propagation. Our high-resolution recordings can success-
fully visualize how activity propagates over the totality of the
neural network within the tissue coupled to the electrode array.
Extracellular field potentials are represented in 2D with a color-
coded scale and image sequences (or movies) are used to depict
their spatio-temporal dynamics. Interestingly, different propa-
gation patterns are observed over multiple recorded events. In
the example shown in Figure 6B (top row), a first type of I-IC
propagation originates in CA3 (t=0 s), propagates to the hilus
(t=30 ms), then to CA1 (90 ms) and finally to the entorhinal and
perirhinal cortices (t=350 ms, see also Supplementar y Video 1).
On the other hand, a second type of I-IC propagation (Figure 6B,
bottom raw) originates in the entorhinal cortex (t=0s),propa-
gates to the perirhinal cortex (t=30–150 ms), and finally invades
the hippocampus from the DG and CA1 (t=330–470 ms,
Figure 6B see also Supplementary Video 2). To summarize these
spatio-temporal dynamics, color-coded images of the propaga-
tion delays for each event can be computed for all the electrodes
in the array by considering a reference electrode located in the site
of origin of each event (Figure 6C).
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |3
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
FIGURE 3 | Validation of the LFP recorded by means of high-density
micro-electrode arrays through standard single field electrode
recordings. (A) The picture represents the adopted experimental
configuration. The hippocampal slice is placed on the electrode array
and two electrodes, one for extracellular stimulation the other for
extracellular recording are micropositioned on the top of the slice.
(B) Color-coded map of the maximal activation with superimposed
circles showing the positions of the extracellular recording electrode.
(C) In the same (x, y) position, the fEPSP recorded with one electrode
of the APS-MEA had a signal to noise ratio similar to the fEPSP
recorded with the conventional single extracellular electrode placed on
top of the slice. The APS-MEA trace was inverted to be visually
compared with the LFP recorded by a micropositioned extracellular
electrode. Signals have slightly different shapes because they recorded
at different heights in the brain slice (LFP at the top, APS-MEA at the
bottom). (D) Linear fitting of the relationship between max amplitudes
of the signal recorded with APS-MEA and with the conventional
extracellular electrode in the same position.
To identify patterns of propagating activities and to correlate
them with their corresponding anatomical regions, we developed
an analysis method adapted to our large-scale high-resolution
recordings (see “Method” section and Figure 7). This was neces-
sary since the spatial extension of I-IC events involve co-activated
anatomical regions and the spreading of activity from one region
to another follows complex propagation dynamics (Perreault and
Avoli, 1992) that cannot be described with a simple trajectory.
This propagation complexity is due to the cellular mechanisms
of I-IC propagation involving field interactions, gap junction-
mediated interactions, and ephaptic propagation (Jefferys, 1995;
Frohlich and McCormick, 2010). In our method, classes of
I-ICs recorded from each microelectrode were first identified to
map regions exhibiting similar waveform shapes, i.e., “Clustered
Activity Maps” (CAMs), representing the spatial distribution of
each I-IC in the slice (Figure 7C). Successively, to identify dis-
tinct I-IC spatio-temporal patterns over nrecorded events, we
classified the n-detected CAMs (Figure 7D).
Interestingly, our recordings from slices harvested from the
same area (from 3.96 to 3.16 mm from Bregma) in several
animals showed two major classes of I-ICs, corresponding to
distinct propagation patterns in the cortico-hippocampal circuit
(Figure 8A). The first class of propagations (Class 1) refers to I-IC
events that are generated in CA3 and propagate to the entorhi-
nal and perirhinal cortices (Figure 8B), whereas the second class
(Class 2) refers to I-IC events originating in the entorhinal cor-
tex and propagating to the hippocampus and perirhinal cortex
(Figure 8D).
High-resolution recordings combined with this analysis
method could be used to identify and quantify drug-induced
functional changes affecting specific neuronal populations. To
demonstrate this unique feature, we performed experiments
in the presence of 4,5,6,7-tetrahydroisoxazolo(5,4-c)pyridin-3-ol
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |4
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
FIGURE 4 | APS-MEA coupled with VSD recordings. (A) VSD
fluorescence background image of the dentate gyrus. (B) Color-coded
image of evoked fluorescence changes showing the maximal peak
amplitude of the VSD response (left) and a single pixel trace (right). To
increase the signal to noise ratio the image is scaled to a 26 ×26 pixel
array by averaging nine adjacent pixels. Normalization by a reference
frame and bleaching removal was performed on all pixels before
averaging. (C) Averaged fluorescence image of 20 responses after
electrical stimulation (left) and averaged single pixel trace (right).
(D) Color-coded voltage image showing the peak amplitude of the
APS-MEA electrodes after the same electrical stimulation (left) and single
electrode voltage trace (right). To compare with VSD recordings, (1) an
equivalent field of view is shown by cropping the full array recording, (2)
the voltage trace is inverted. (E) Color-coded voltage image showing the
averaged peak amplitude of 5 responses after electrical stimulation (left)
and averaged single electrode voltage trace (right).
(THIP), an agonist of the δ-subunit-containing GABAArecep-
tors. These receptors are known to be localized extrasynaptically,
and more particularly on DG granule neurons and in CA1
(Scimemi et al., 2005). Therefore, we expected to be able to selec-
tivity induce functional changes in these areas. The activation of
these receptors leads to a shift in the membrane potential to more
hyperpolarized values (Glykys et al., 2008) and this effect, known
as tonic inhibition, represents a protective mechanism against
hyperexcitability in the hippocampal network (Semyanov et al.,
2003). Interestingly, we found that THIP reduced the amplitude
of Class 1 I-ICs in the DG and CA1 regions (i.e., the regions
where there is higher expression of GABAA δ-subunit-containing
receptors), but not in the rest of the hippocampus (Figure 8C).
Moreover, THIP reduced the amplitude of Class 2 I-IC events in
DG, but not in CA1 (Figure 8E). These results suggest that the
extrasynaptic receptors are selectively recruited in the different
circuits involved in the event propagation.
The results presented in this study demonstrate that our 4096-
electrode array recording system can literally image brain slices
through extracellular field potentials visualized in a color-coded
scale, just like traditionally done so far for cellular imaging. As
opposed to other systems relying on currently available micro-
electronic MEA technology, our system was designed to provide
near-cellular resolution extracellular field potentials and spik-
ing activity in large-scale networks, allowing us to dissect global
network activity with unprecedented detail.
recorded network activity from brain slices encompassing the
hippocampus perirhinal and entorhinal cortices. These record-
ings were performed in combination with patch-clamp and field
electrodes as well as with voltage-sensitive-dye imaging. It has to
be highlighted that even though we performed experiments on
brain slices up to 90 min, we experienced to be able to main-
tain the viability of the slices for at least 3 or 4 h. In addition
to the demonstration of the superior quality of the recordings
achieved with our system, we have shown that this MEA sys-
tem can be combined with other electrophysiological methods.
This combined approach, providing complementary high-quality
large-scale network and single cell recordings provides unique
and novel opportunities to study how LFPs are affected by trans-
membrane currents of local neurons and vice versa, to understand
how these transmembrane currents are involved in shaping the
LFP (Buzsaki et al., 2012). In addition, our system allows inves-
tigating connectivity and plasticity of neuronal ensembles in
great detail, revealing the complex interactions between excita-
tory and inhibitory network drives. Such potential is illustrated
with the use of basic short-term plasticity protocols (Figure 9 and
Supplementary Video 3) such as paired and tetanic stimulation.
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |5
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
FIGURE 5 | Recording of epileptic like events. (A) Cortico-hippocampal
slice over the active chip area (black square). (B) Raster plot representation of
epileptic-like events induced in cortico-hippocampal slice by perfusion with
4-AP ([100 μM]) or 4-AP +BIC ([30 μM]). (C) I-IC events in the perirhinal
cortex (PC, blue trace), entorhinal cortex (EC, green trace), hippocampus
(CA3, red trace), and IC event recorded in the three distinct regions (see
Supplementary Video 5). (D) Power spectral densities estimated from the I-IC
(left) and spectrograms estimated from the IC traces (right).
Our electrode array has also been used for imaging phar-
macologically induced seizures in brain slices and for charac-
terizing spatio-temporal patterns over multiple recorded events.
The ability to image fast propagations involved in epileptogenic
events opens new perspectives to clarify how different synap-
tic and non-synaptic mechanisms are involved in the synchro-
nization of distinct neuronal populations (Jefferys et al., 2012)
and in the propagation of the IC and I-IC events. Indeed, the
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |6
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
FIGURE 6 | Functional imaging of an I-IC event propagation. (A) Two
distinct inter-ictal (I-IC) events are represented. Temporal traces
corresponding to four different electrodes for each event in different
regions: entorhinal cortex (EC), hilus, CA3 and CA1 in the hippocampus. For
both events, the red arrow indicates the direction of propagation: event 1 is
generated in CA3, propagates to the hilus and afterwards to the EC,
whereas event 2 is generated in the EC and propagates to the hilus, CA3
and CA1. (B) Time-lapse color-coded voltage maps of extracellular voltages
illustrating in detail the different dynamics of propagation for both events.
(C) Maps of the propagation delays computed for each event with respect
to a reference pixel in the region of origin of each event. These maps clearly
evidence the two distinct propagations.
I-ICs propagation dynamics clearly differs from the dynamics
of purely synaptic events, such as electrically evoked LFP or
spontaneous action potentials (APs). This important aspect can
be better appreciated by comparing functional images of I-IC
events (Figure 6 and Supplementary Video 1, 2) with electrically
evoked LFPs (Figure 1 and Supplementary Video 3) and with
APs (Figures 2A,Band Supplementary Video 4). Interestingly,
functional imaging of I-IC dynamics shows simultaneous acti-
vation of large areas due to the local synchronous firing of
many neurons that contribute in shaping the extracellular field
potential (McCormick and Contreras, 2001) and establishing a
distinct spatial distribution. Moreover, our recordings reveal that
areas recruited during spontaneous field potential propagation
involve adjacent functionally connected anatomical regions, but
the latencies in signal propagation from one region to another
not always directly correspond to specific synaptic connections.
On the contrary, LFPs evoked in the dendritic layer, granular
cell layer, and polymorphic layer through extracellular stimula-
tion of the perforant pathway appear to involve shorter activation
latencies, involving synaptic-mediated propagation. In the case
of APs recorded in the hippocampus at the level of the cell
bodies and axons, we observed propagations compatible with
synaptic transmission from CA2 to CA3, CA1, and DG dur-
ing bursting activity (Figures 2ADand Supplementary Video
4). Thus, given the complex connectivity involved in I-IC event
propagations, to identify patterns of I-IC propagations from
high-resolution recordings we have developed a novel analytical
approach consisting of pan-array mapping of the spatial distri-
bution of extracellular field potentials having a similar shape
in distinct anatomical regions and propagating through the cir-
cuit with the same sequential activation of local regions. This
analysis approach of network dynamics is possible only when
applied to high spatio-temporal resolution recordings such as
those provided by the APS-MEA. Indeed, since the local spa-
tial distribution of I-ICs varies depending on the propagation
pattern that locally activate a region, we can identify dissimilar
spatial distributions of local activity patterns in order to deter-
mine how different areas of the global network are affected by
different propagation patterns. This is an important aspect of
our analysis since it unmasks the existence of the relationship
between local activations and propagation patterns that has been
previously overlooked. Over multiple I-IC recorded events, we
have identified two distinct classes of I-ICs corresponding to two
distinct propagation patterns originating either from the hip-
pocampus or from the entorhinal cortex. This was achieved by
recording from brain slices harvested from several mice and cut
on the same plane to ensure reproducibility. However, when slices
were harvested from a different height (e.g., at 4.44 mm from
Bregma; Figure 10), we observed different classes with distinct
trigger focal points and propagation velocities, most probably
due to the diverse circuits present in these slices from different
anatomical locations.
Finally, our analysis method can effectively reveal and quan-
tify spatially confined functional changes induced by a drug.
To validate this capability, we used THIP to target δ-subunit
containing GABAAreceptors in the cortico-hippocampal circuit.
Interestingly, the spatial specificity of the effect of the compound
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |7
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
FIGURE 7 | Analysis method for classification of I-IC patterns. (A)
Raster plot showing the time course of 20 min of recording with dots
indicating each detected I-IC for each active electrode. Columns of points
therefore indicate single I-IC events recorded in all the active area of the
chip . (B) Color-map code showing the maximum amplitudes recorded for
two different events. The traces are representative of two pixels
highlighted with a black and a red circle, respectively. All the electrodes for
all the I-IC events were classified with the kmeans algorithm. (C) Results
of the previous classification are shown as spatial map of activation which
we referred as CAM. Representative traces are obtained by averaging all
recordings from electrodes located in the same region. The CAMs were
classified among them on the basis of a clustering criterion which
indicates the best number of cluster to fit the data. (D) According with the
silhouette coefficient, we found out that the optimal number of clusters for
all the experiments was 2 (top graph, see Methods section for details). In
the bottom panels the averages of all CAM belonging to the two different
classes are represented showing the different spatial activation profile of
the two classes.
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |8
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
FIGURE 8 | Classification of I-IC events. Classification of I-ICs from five
experiments reveals that two main classes of I-IC can be identified based on
the spatial dissection of the I-ICs (recordings of 20 min. per phase, 30 total
events per phase). Class 1 clusters events originating in CA3 and propagating
to other regions of the hippocampus and to the EC, whereas Class 2 clusters
events originating in the EC and propagating to the hippocampus. (A) On the
top, view of the cortico-hippocampal slice over the chip; on the bottom,
incidence of the two detected classes over the five different experiments
upon treatment with 4AP and THIP.(B, D) Average of the maximal amplitude
of I-ICs of Class 1 (B) and Class 2 (D) under 4-AP and THIP ([1μM]). The ratio
between THIP and 4AP images shows the spatially circumscribed modulation
of THIP on I-IC amplitudes. (C, E) Quantification of THIP-induced changes in
amplitude of the I-IC events of Class 1 (C) and Class 2 (E) for electrodes
located in DG, CA3, and CA1. The results show that the effect of the
compound was statistically significant in DG (24.08 ±4.84, % of reduction)
and CA1 (16.61 ±3.64, % of reduction) for Class 1 and in DG only
(8.94 ±2.83, % of reduction) for Class 2. p<0.05; ∗∗ p<0.01; paired
Student’s t-test, n=5 slices from 4 different mice.
could be separately evaluated for different classes of I-IC prop-
agations. Distinct THIP effects for different I-IC classes were
observed. For the first class, we found that the THIP effect was
remarkably confined to the molecular layer of the DG and to the
stratum pyramidal and stratum oriens of CA1. For the second
class the compound was found effective only in the molecular
layer of the DG, but not elsewhere. These differences might be
explained on one hand by the different number of THIP-targeted
receptors expressed in different regions of the brain slice, and
on the other hand, by the different propagation patterns involv-
ing diverse local neuronal connectivity. In particular, we observed
that I-IC events of Class 1 originating in CA3 propagate synapti-
cally to the DG with a delay lower than 30 ms (see Figures 6B,C
top raw), whereas I-IC events of Class2 originating in the EC
propagate in the DG with a delay greater than 300 ms (see
Figures 6B,Cbottom raw). The latter is unlikely to be synaptically
mediated and, given the large propagation delay, most probably
involves non-synaptic processes such as diffusion of extracellu-
lar potassium (Lian et al., 2001; Avoli et al., 2002). Overall, these
results show that our electrode array and analysis app roaches pro-
vide important information to spatially evaluate the effects of a
drug in specific regions, or even dynamically in the context of the
spreading activity. As far as both dynamic spreading activity and
mechanism(s) of propagation are concerned, parameters such as
speed of propagation or area of activation will vary from event to
event and will respond differently to pharmacological treatments,
depending on the class they belong to.
In summary, the novel recording and analytical approaches
that we have presented here to characterize epileptic events could,
in principle, be applied to investigate neuronal network dynam-
ics in many other systems. Furthermore, our method can be
uniquely combined with other experimental approaches such as
optogenetics or 3D fluorescence microscopy for detailed studies
spanning the spatial scale from single neurons up to the entire
circuit level (Helmchen and Denk, 2005).
All experiments were performed on C57BL/6J mice of either
sex aged 3 weeks to 6 months (Charles River Laboratories
International, Wilmington, MA, USA). All experiments were
carried out in accordance with the guidelines established
by the European Community Council (Directive 2010/63/EU
of September 22nd, 2010) and experimental protocols were
approved by the Italian Ministry of Health. Animals were anaes-
thetized with isofluoran prior to decapitation. Transverse hip-
pocampal slices (400 μm thick) were cut using a Microm HM
650 V microtome equipped with a Microm CU 65 cooling unit
(Thermo Fisher Scientific, Waltham, MA). Slices were cut at
2C in a high-sucrose protective solution containing (in mM):
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |9
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
FIGURE 9 | Spatial short-term plasticity in the dentate gyrus. (A) Two
fEPSPs recorded from a single electrode (white pixel) after a paired
stimulation of the lateral perforant path (t=100 ms) (top trace) and the
relative color-coded map of maximum values for the pixel array
showing activation of the dentate gyrus and hilus (bottom pictures).
(B) Superimposition of the first and the second fEPSP shown in (A) (top
trace) and color-coded spatial distribution of the ratio values recorded in the
array (bottom picture). (C) Electrical response from one electrode (white pixel
in the bottom image) during the first second of stimulation (top trace) of a
10s train @ 20 Hz and corresponding color-coded minimum peak amplitude
(sink in the dendritic layer) of the signal (bottom trace). (D) Electrical response
from the same electrode during the last second of stimulation (top trace) of
the train and corresponding color-coded minimum peak amplitude of the
signal (bottom trace). (E) Superimposition of the first (black trace) and the last
(red trace) fEPSP of the train and color-coded ratio between images in
(D) and (E), (bottom image).
87 NaCl, 25 NaHCO3,2.5KCl,0.5CaCl
75 sucrose, and saturated with 95% O2and 5% CO2.Sliceswere
incubated for 30–45 min at 35Candforatleastanotherhour
at room temperature in recording solution prior to being used
for recordings (see in “Patch-Clamp and Single Field Recording”
The APS-MEA system was extensively described in previous
papers (Imfeld et al., 2008). Briefly, it consists of a CMOS-based
CCD monolithic chip modified such that pixels are designed to
sense electrical voltage variations induced by electrogenic tis-
sues. The chip integrates amplification and analog multiplexing
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |10
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
FIGURE 10 | Classification algorithm applied to I-ICs in the hippocampus.
(A) Color-coded map of extracellular voltage showing the propagation of two
different I-IC events. (B) For the two events in (A) the CAM, the maps of the
delays and speeds of propagation e.g ., from dentate gyrus to hilus (black bars),
from hilus to CA3 (red bars), from CA3 t o CA1 (blue bars) are represe nted. (Event
1 speed: DG-Hilus =166.6±16 .6 mm/s; Hilus-CA3 =77.8±13 .5 mm/s;
CA3-CA1 =81.9±6.6 mm/s. Event 2 speed: DG-Hilus =48.5±1.1 mm/s;
Hilus-CA3 =24.0±3.5 mm/s; CA3-CA1 =17.0±1.1 mm/s).
circuits designed to provide simultaneous extracellular recordings
from 4096 electrodes at a sampling rate of 7.7 kHz per chan-
nel. Each square pixel measures 21 ×21 μm, and the array is
integrated with an electrode pitch (center-to-center) of 42μm.
Pixels are arranged in a 64 ×64 array configuration, yield-
ing an active area of 7.22 mm2with a pixel density of 567
pixel/mm2. The three on-chip amplification stages provide a
global gain of 60 dB, with a 0.1–5 kHz band-pass filter. This
bandwidth is adapted to record slow LFP signals as well as
fast APs. Acquisition is controlled by the software BrainWave
(3Brain Gmbh, Switzerland).
Both field and whole-cell patch-clamp recordings were performed
with a Multiclamp 700B/Digidata1440A system (Molecular
Devices, Sunnyvale, CA, USA) on an upright Olympus BX51WI
microscope (Olympus, Japan) equipped with Nomarski optics
and reflected light. Slices were bathed in artificial cerebrospinal
fluid (ACSF) containing (in mM): 125 NaCl, 25 NaHCO3,25glu-
cose, 2.5 KCl, 1.25 NaH2PO4, 2 CaCl2,and1MgCl
with 95% O2–5% CO2). The solution was perfuse at a rate
of 2.5 ml/min. For whole-cell patch-clamp recordings we used
borosilicate glass electrodes (Kimble Chase, Vineland, NJ, USA).
The patch electrode resistance was between 4 and 6M.
Recordings were done in selected granule neurons from the
granule layer of the DG in the presence of 4-aminopyridine
(4-AP, [100 μM]) and at a holding potential (Vh)of80 mV.
The intracellular solution contained (in mM): 126 K-gluconate,
2, 0.1 BAPTA, 15 Glucose, 5 HEPES,
3 ATP, and 0.1 GTP in which the pH was adjusted to 7.3
with KOH and the osmolarity to 290 mosmol·l1with sucrose.
For patch-clamp recordings the current traces were sampled
at 50 kHz, filtered at 10 kHz, and stored for off-line analysis.
Field recordings were performed in various areas of the hip-
pocampus and DG. For field recordings, we used borosilicate
glass electrodes (Kimble Chase, Vineland, NJ )withaninter-
nal resistance of 1–2 Mand filled with extracellular record-
ing solution (see composition above). The acquisition was
also performed with the Multiclamp 700B/Digidata1440A sys-
tem after a pre-amplification with a home-made pre-amplifier
of 40 dB. The total gain was set to 60 dB. For stimula-
tion experiments of the DG, we used a monopolar stimula-
tion electrode placed on the perforant path connected to an
external stimulator (A-M Systems, Sequim, WA). Unequivocal
evoked responses, well separated from the stimulation artifact,
were obtained using stimulation pulses of 200–400 μAlasting
30 μs.
The VSD RH-795 (4 μg/ml) was used to label cell membranes.
Hippocampal slices were incubated in a chamber containing
2 ml of ACSF and RH-795 for 15 min. After labeling, slices
were transferred to ACSF solution for 10 min to wash out
residual VSDs. A high-resolution (80 ×80 pixels) CCD camera
(RedShirtImaging, Tokyo, Japan) was used to sample membrane
potential depolarization at a frame rate of 2 kHz. Extracellular
stimulation was applied as described in the Patch-Clamp and
Single Field Recording section. Cells were imaged with wide
field epifluorescence using a 10×water immersion objective.
Baseline correction for bleaching, spatial averaging, and trace
averages were computed off-line with custom-written Matlab
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |11
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
Acute cortico-hippocampal slices were recorded for 20 min
per session (once activity had stabilized for at least 30 min).
Spontaneous LFPs were elicited by bath application of convul-
sant agent 4-AP ([100 μM]) (Avoli, 1990). Epileptiform dis-
charges were also modulated by the GABAAchannel blocker
BIC ([30 μM]) or THIP, ([1 μM]), an agonist of the δ-subunit-
containing GABAAreceptors.
All the analysis algorithms used in this study were developed
as Matlab scripts (MathWorks, ht tp:// )with
the exception of the field potential event detection that has been
implemented in C# language under Visual Studio (http://www.
microsof udio/ en-gb). The computational facility
for the most intensive computations (e.g., k-means classifica-
tion of signal shape among 4096 electrodes) was provided by
the parallel cluster CASPUR (Consorzio Interuniversitario per le
Applicazioni di Supercalcolo per Università e Ricerca -http://www., project STD11-499).
To evaluate evoked field responses and compare paired-pulse
responses (Figures 1,3,and9), we calculated the peak (abso-
lute value) of evoked extracellular signals. Raster plots (Figures 5
and 7) show the events detected by the previously described
Precision Timing Spike Detection (PTSD) algorithm (Maccione
et al., 2009). The algorithm, originally tailored to detect fast spik-
ing activity generated by a few neuronal units, has been adapted to
detect slower field potential events. To this purpose, the threshold
was set to 5 times the standard deviation of the noise, while the
refractory period and the peak lifetime period were set to 50 ms
and 40 ms respectively.
Color-coded maps of the time delays occurring during the prop-
agation of an I-IC event were computed by calculating the cross-
covariance of one electrode (taken as reference) selected in the
focus of activity (i.e., the area where the event originates) with
the cross-covariance peak was used to reconstruct the color-coded
maps of the time delays. Cross-covariance values falling below a
certain threshold value (e.g., 0.3) presumably did not represent
propagation and were discarded by setting the color to “black”
(data not shown). Velocities were calculated from time delays and
distances of 4 electrodes selected as: two electrodes representative
of the starting and ending regions, and two electrodes involved
in the propagation to better approximate the non-linearity of the
spatial and temporal distribution of the event. The spatial tra-
jectory covered by the event was estimated as the sum of the
four Euclidean distances between the consecutive selected elec-
trodes. Then, the distance was divided by the time delay of the
starting and ending electrode as calculated for the color-coded
maps. Propagation velocities were estimated by averaging 7 events
showing the same trajectory of propagation.
I-IC event classification presented in Figures 8B,D was calcu-
lated by following the procedure steps illustrated in Figure 7.I-IC
events were manually detected by identifying synchronous events
(Figure 7A) in the raster plot representation. The activity of a
single I-IC is visualized in a false color map, where the most
active electrodes are red colored. Figure 7B shows two examples
of different I-ICs where electrode channels (e.g., 31,12 and 52,32)
show different signal shapes. Each event was cut-off in a separate
subset of 1 s time windows. For all 4096 electrodes, the signals
were denoised using a band pass filter (1–100 Hz), corresponding
to the I-IC frequency band content (see Figure 5D). All further
steps were made by joining the dataset obtained under the two
treatments (4-AP and 4-AP +THIP).
For each event, we first classified the signal waveforms among
the electrodes in order to cluster events with similar waveforms
(Figure 7C). The classification process was based on k-means
algorithm (Xu and Wunsch, 2005). We initially set the max-
imum number of classes (i.e., in how many clusters signals
had to be separated) to 20. Clusters populated by less than
5 events (on average 3–4 clusters) and/or signals with ampli-
tudeslessthan20μV were discarded. Then, all clusters were
associated to a representative waveform (i.e., the averaged wave-
form of all electrode waveforms in the same cluster, as shown
in Figure 7C) and displayed in color code maps showing the
peak-to-peak amplitude of the their representative waveform
normalized to the highest peak-to-peak value between all repre-
sentative waveforms (two illustrative clusters and representative
waveforms named A and B, are indicated in Figure 7C). This
classification procedure yielded a color-coded CAM for each
I-IC event (Figure 7C, Classification Step I) that represents the
spatio-temporal distribution of signals based on their waveform
similarity. Importantly, even if k-means is a “blind” algorithm
(i.e., it does not consider the precise spatial arrangement of the
electrodes), the signals belonging to a cluster are almost all con-
tiguous and cluster distributions generally overlap well with the
anatomical organization of the slice. In a second classification
step, CAMs were computed under different experimental con-
ditions (i.e., 4AP and 4AP +THIP), collected and classified
(Figure 7D) following the same principle previously described
(Gandolfo et al., 2010). For this second classification step, we var-
ied the number of clusters between 2 and 15 and found that the
optimal number of clusters for all the experiments was 2, accord-
ing to the silhouette coefficient introduced by Kaufman and
Rousseeuw to test clustering efficiency (Kaufman and Rousseeuw,
To estimate functional changes occurring after chemical manip-
ulation of n=5 experiments, we identified three distinct
areas of the hippocampus, i.e., DG, CA3, and CA1 includ-
ing only the stratum pyramidale and the stratum oriens.For
each event, we averaged signals among electrodes belong-
ing to the identified area (Figures 8B,D). As an exam-
ple, histograms in Figures 8C,Eshow the ratio between
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |12
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
the averaged signal peak for events in 4-AP and for the events in
Statistical analysis was performed using Origin 8.0 (OriginLab
Corporation, Northampton, MA). We used paired Student’s
t-test to assess the effect of THIP in various hippocampal
regions. A p-value lower than 0.05 was considered statisti-
cally significant (p<0.05, ∗∗ p<0.01). Data throughout the
study are shown as mean ±SEM, where the means rep-
resent the average of the mean values calculated from each
individual slice.
E. Ferrea, L. Medrihan, A. Maccione, L. Berdondini designed and
carried out the experiments, developed the algorithms and ana-
lyzed the data. D. Ghezzi contributed with VSD experiments and
analysis. T. Nieus supported the analysis on the computing clus-
ter and contributed in writing the method section. P. Baldelli,
F. Benfenati gave conceptual advice and reviewed the manuscript.
E. Ferrea, L. Medrihan, A. Maccione, and L. Berdondini prepared
the manuscript.
The authors acknowledge support from the Istituto Italiano di
Tecnologia (IIT) and the parallel cluster CASPUR (Consorzio
Interuniversitario per le Applicazioni di Supercalcolo per
Universitàe Ricerca, project STD11-499). We are also grateful to
Evelyne Sernagor for critical reading of the manuscript.
The Supplementary Material for this article can be found online
Video S1 | Movie of a first type of recorded I-IC propagation originating in
CA3 (t=0 s), propagating to the hilus (t=30 ms), t hen to C A1 (90 ms) and
finally to the entorhinal and perirhinal cortices (t=350 ms). On the left,
raw traces of three selected electrodes located in the Hilus, Ent-CX, CA1. On
the right, color-coded extracellular activity.
Video S2 | Movie of a second type of recorded I-IC propagation originating
in the entorhinal cortex (t=0 s), propagating to the perirhinal cortex
(t=30–150ms) and finally invading the hippocampus from the DG and
CA1 (t=330–470 ms). On the left, raw traces of three selected electrodes
located in the Hilus, Ent-CX, CA1. On the right, color-coded extracellular activity.
Video S3 | Movie of evoked electrical response induced by paired pulse
stimulation (inter-pulse interval t=100 ms) in the dentate gyrus. On the
left, raw trace of one electrode located in the hilus. On the right, color-coded
extracellular activity.
Video S4 | Movie of recorded spontaneous spiking activity during a burst
(duration 150 ms). On the left, raw trace of one electrode located in CA2. On
the right, color-coded extracellular activity.
Video S5 | Movie of IC propagationoriginating in the perirhinal cortex and
propagating in the entorhinal cortex and sometimes invading the
hippocampus. The recorded duration is of 30 s.
Avoli, M. (1990). Epileptiform dis-
charges and a synchronous
GABAergic potential induced
by 4-aminopyridine in the rat
immature hippocampus. Neuros ci.
Lett. 117, 93–98.
Avoli, M., Barbarosie, M., Lucke,
A., Nagao, T., Lopantsev, V., and
Kohling, R. (1996). Synchronous
GABA-mediated potentials and
epileptiform discharges in the rat
limbic system in vitro.J. Neurosci.
16, 3912–3924.
Kohling, R., Biagini, G., Pumain,
R., et al. (2002). Network and
pharmacological mechanisms lead-
ing to epileptiform synchronization
in the limbic system in vitro.Prog.
Neuro biol. 68, 167–207.
Avoli, M., and de Curtis, M. (2011).
GABAergic synchronization in the
limbic system and its role in the
generation of epileptiform activity.
Prog. Neurobiol. 95, 104–132.
Baker, M. (2010). From promising to
practical: tools to study networks of
neurons. Nat. Methods 7, 877–883.
Barbarosie, M., and Avoli, M.
(1997). CA3-driven hippocampal-
entorhinal loop controls rather than
sustains in vitro limbic seizures.
J. Neurosci. 17, 9308–9314.
Berdondini, L., Imfeld, K., Maccione,
A., Tedesco, M., Neukom, S.,
Koudelka-Hep, M., et al. (2009).
Active pixel sensor array for
high spatio-temporal resolution
electrophysiological recordings
from single cell to large scale
neuronal networks. Lab Chip 9,
Boido, D., Farisello, P., Cesca, F.,
Ferrea, E., Valtorta, F., Benfenati, F.,
et al. (2010). Cortico-hippocampal
hyperexcitability in synapsin I/II/III
knockout mice: age-dependency
and response to the antiepileptic
drug levetiracetam. Ne uros cie nce
171, 268–283.
Buzsaki, G. (2010). Neural syntax: cell
assemblies, synapsembles, and read-
ers. Neuro n 68, 362–385.
Buzsaki, G., Anastassiou, C. A., and
Koch, C. (2012). The origin of extra-
cellular fields and currents - EEG,
ECoG, LFP and spikes. Nat. Rev.
Neuro sci . 13, 407–420.
Chrobak, J. J., and Buz saki, G.
(1998). Operational dynamics
in the hippocampal-entorhinal
axis. Neu ros ci. Bi obeha v. Rev. 22,
Cohen, I., Navarro, V., Clemenceau,
(2002). On the origin of inter-
ictal activity in human temporal
lobe epilepsy in vitro.Science 298,
D’Antuono, M., Benini, R., Biagini, G.,
D’Arcangelo, G., Barbarosie, M.,
Tancredi, V., et al. (2002). Limbic
network interactions leading to
hyperexcitability in a model of tem-
poral lobe epilepsy. J. Neurophysiol.
87, 634–639.
de Curtis, M., and Avanzini, G. (2001).
Interictal spikes in focal epilep-
togenesis. Prog. Neurobiol. 63,
Egert, U., Heck, D., and Aertsen, A.
(2002). Two-dimensional monitor-
ing of spiking networks in acute
brain slices. Exp. Brain Res. 142,
Field, G. D., Gauthier, J. L., Sher,
A., Greschner, M., Machado,
(2010). Functional connectivity
in the retina at the resolution
of photoreceptors. Nature 467,
Frey, U., Egert, U., Heer, F., Hafizovic,
S., and Hierlemann, A. (2009).
Microelectronic system for high-
resolution mapping of extracellular
electric fields applied to brain
slices. Biosens. Bioelectron. 24,
Frohlich, F., and McCormick, D. A.
(2010). Endogenous electric fields
may guide neocortical network
activity. Neuron 67, 129–143.
Gandolfo, M., Maccione, A., Tedesco,
M., Martinoia, S., and Berdondini,
L. (2010). Tracking burst patterns
in hippocampal cultures with high-
density CMOS-MEAs. J. Neural Eng.
7, 056001.
Glykys, J., Mann, E. O., and Mody,
I. (2008). Which GABA(A) recep-
tor subunits are necessary for tonic
inhibition in the hippocampus?
J. Neurosci. 28, 1421–1426.
Gonzalez-Sulser, A., Wang, J.,
Motamedi, G . K., Avoli, M.,
Vicini, S., and Dzakpasu, R. (2011).
The 4-aminopyridine in vitro
epilepsy model analyzed with a
perforated multi-electrode array.
Neuropharmacology 60, 1142–1153.
Grinvald, A., and Hildesheim, R.
(2004). VSDI: a new era in func-
tional imaging of cortical dynamics.
Nat. Rev. Neu rosc i. 5, 874–885.
Frey, U., Franks, W., Perriard, E.,
et al. (2007). Single-chip micro-
electronic system to interface with
living cells. Biosens. Bioelectron. 22,
Helmchen, F., and Denk, W.
(2005). Deep tissue two-photon
microscopy. Nat. Methods 2,
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |13
Ferrea et al. Electrophysiological imaging with large-scale electrode-array
B. J. (2010). Development of
multi-electrode array screening for
anticonvulsants in acute rat brain
slices. J. Neurosci. Methods 185,
Huberfeld, G., Menendez de la Prida,
Quyen, M., Adam, C., et al. (2011).
Glutamatergic pre-ictal discharges
emerge at the transition to seizure in
human epilepsy. Nat. Neurosc i. 14,
Hutzler, M., Lambacher, A.,
Eversmann, B., Jenkner, M.,
Thewes, R., and Fromherz, P.
(2006). High-resolution mul-
titransistor array recording of
electrical field potentials in cultured
brain slices. J. Neurophysiol. 96,
Imfeld, K., Neukom, S., Maccione, A.,
Bornat, Y., Martinoia, S., Farine,
P. A., et al. (2008). Large-scale,
high-resolution data acquisition
system for extracellular recording
of electrophysiological activity.
IEEE Trans. Biomed. Eng. 55,
Jefferys, J. G. (1995). Nonsynaptic
modulation of neuronal activity in
the brain: electric currents and
extracellular ions. Physiol. Rev. 75,
Jeffer ys, J. G., Menendez de la Prida,
M., Timofeev, I., et al. (2012).
Mechanisms of physiological and
epileptic HFO generation. Prog.
Neuro biol. 98, 250–264.
Kaufman, L., and Rousseeuw, P. J.
(eds.). (1990). Finding Groups in
Data. New York, NY: Wiley.
Lian, J., Bikson, M., Shuai, J., and
Durand, D. M. (2001). Propagation
of non-synaptic epileptiform activ-
ity across a lesion in rat hippocam-
pal slices. J. Physiol. 537, 191–199.
M., Trinath, T., and Oeltermann, A.
(2001). Neurophysiological investi-
gation of the basis of the fMRI sig-
nal. Natu re 412, 150–157.
Maccione, A., Gandolfo, M.,
Massobrio, P., Novellino, A.,
Martinoia, S., and Chiappalone,
M. (2009). A novel algorithm for
precise identification of spikes in
extracellularly recorded neuronal
signals. J. Neurosci. Methods 177,
McCormick, D. A., and Contreras, D.
(2001). On the cellular and network
bases of epileptic seizures. Annu.
Rev. Physiol. 63, 815–846.
Mitzdorf, U. (1985). Current source-
density method and application
in cat cerebral cortex: investi-
gation of evoked potentials and
EEG phenomena. Physiol. Rev. 65,
Nicholson, C., and Freeman, J. A.
(1975). Theory of current source-
density analysis and determination
of conductivity tensor for anu-
ran cerebellum. J. Neurophysiol. 38,
Panzeri, S., Brunel, N., Logothetis, N.
K., and Kayser, C. (2010). Sensory
neural codes using multiplexed tem-
poral scales. Tre n ds N eu ro s ci . 33,
Perreault, P., and Avoli, M. (1992).
4-aminopyridine-induced epilepti-
form activity and a GABA-mediated
long-lasting depolarization in the
rat hippocampus. J. Neurosci. 12,
Rutecki, P. A., Lebeda, F. J.,
and Johnston, D. (1987).
4-Aminopyridine produces epilep-
tiform activity in hippocampus
and enhances synaptic excitation
and inhibition. J. Neurophysiol. 57,
Scimemi, A., Semyanov, A., Sperk, G.,
Kullmann, D. M., and Walker, M. C.
(2005). Multiple and plastic recep-
tors mediate tonic GABAA recep-
tor currents in the hippocampus.
J. Neurosci. 25, 10016–10024.
Semyanov, A., Walker, M. C., and
Kullmann, D. M. (2003). GABA
uptake regulates cortical excitabil-
ity via cell type-specific tonic
inhibition. Nat . Neurosci. 6,
Lynch, G., and Taketani, M. (2000).
Origins and distribution of cholin-
ergically induced beta rhythms in
hippocampal slices. J. Neurosci. 20,
Viventi, J., Kim, D. H., Vigeland, L.,
Frechette, E. S., Blanco, J. A., Kim,
Y. S., et al. (2011). Flexible, foldable,
actively multiplexed, high-density
electrode array for mapping brain
activity in vivo.Nat. Neur osci. 14,
Xu, R., and Wunsch, D., 2nd. (2005).
Survey of clustering algorithms.
IEEE Trans. Neural Netw. 16,
Conflict of Interest Statement: The
authors declare that the research
was conducted in the absence of any
commercial or financial relationships
that could be construed as a potential
conflict of interest.
Received: 18 September 2012; accepted:
17 October 2012; published online: 14
November 2012.
Citation: Ferrea E, Maccione A,
Medrihan L, Nieus T, Ghezzi D, Baldelli
P, Benfenati F and Berdondini L (2012)
Large-scale, high-resolution electrophys-
iological imaging of field potentials in
brain slices with microelectronic multi-
electrode arrays. Front. Neural Circuits
6:80. doi: 10.3389/fncir.2012.00080
Copyright © 2012 Ferrea, Maccione,
Medrihan, Nieus, Ghezzi, Baldelli,
Benfenati and Berdondini. This is an
open-access article distributed under
the terms of the Creative Commons
Attribution License,whichpermits
use, distribution and reproduc-
tion in other forums, provided the
original authors and source are cred-
ited and subject to any copyright
notices concerning any third-party
graphics etc.
Frontiers in Neural Circuits November 2012 | Volume 6 | Article 80 |14
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources--including Na(+) and Ca(2+) spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations--can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.
Full-text available
Arrays of electrodes for recording and stimulating the brain are used throughout clinical medicine and basic neuroscience research, yet are unable to sample large areas of the brain while maintaining high spatial resolution because of the need to individually wire each passive sensor at the electrode-tissue interface. To overcome this constraint, we developed new devices that integrate ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors that are connected using fewer wires. We used this system to record spatial properties of cat brain activity in vivo, including sleep spindles, single-trial visual evoked responses and electrographic seizures. We found that seizures may manifest as recurrent spiral waves that propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface devices.
Full-text available
GABA is the main inhibitory neurotransmitter in the adult forebrain, where it activates ionotropic type A and metabotropic type B receptors. Early studies have shown that GABA(A) receptor-mediated inhibition controls neuronal excitability and thus the occurrence of seizures. However, more complex, and at times unexpected, mechanisms of GABAergic signaling have been identified during epileptiform discharges over the last few years. Here, we will review experimental data that point at the paradoxical role played by GABA(A) receptor-mediated mechanisms in synchronizing neuronal networks, and in particular those of limbic structures such as the hippocampus, the entorhinal and perirhinal cortices, or the amygdala. After having summarized the fundamental characteristics of GABA(A) receptor-mediated mechanisms, we will analyze their role in the generation of network oscillations and their contribution to epileptiform synchronization. Whether and how GABA(A) receptors influence the interaction between limbic networks leading to ictogenesis will be also reviewed. Finally, we will consider the role of altered inhibition in the human epileptic brain along with the ability of GABA(A) receptor-mediated conductances to generate synchronous depolarizing events that may lead to ictogenesis in human epileptic disorders as well.
Full-text available
A platform for high spatial and temporal resolution electrophysiological recordings of in vitro electrogenic cell cultures handling 4096 electrodes at a full frame rate of 8 kHz is presented and validated by means of cardiomyocyte cultures. Based on an active pixel sensor device implementing an array of metallic electrodes, the system provides acquisitions at spatial resolutions of 42 microm on an active area of 2.67 mm x 2.67 mm, and in the zooming mode, temporal resolutions down to 8 micros on 64 randomly selected electrodes. The low-noise performances of the integrated amplifier (11 microV (rms)) combined with a hardware implementation inspired by image/video processing concepts enable high-resolution acquisitions with real-time preprocessing capabilities adapted to the handling of the large amount of acquired data.
Interictal electroencephalography (EEG) potentials in focal epilepsies are sustained by synchronous paroxysmal membrane depolarization generated by assemblies of hyperexcitable neurons. It is currently believed that interictal spiking sets a condition that preludes to the onset of an ictal discharge. Such an assumption is based on little experimental evidence. Human pre-surgical studies and recordings in chronic and acute models of focal epilepsy showed that: (i) interictal spikes (IS) and ictal discharges are generated by different populations of neuron through different cellular and network mechanisms; (ii) the cortical region that generates IS (irritative area) does not coincide with the ictal-onset area; (iii) IS frequency does not increase before a seizure and is enhanced just after an ictal event; (iv) spike suppression is found to herald ictal discharges; and (v) enhancement of interictal spiking suppresses ictal events. Several experimental evidences indicate that the highly synchronous cellular discharge associated with an IS is generated by a multitude of mechanisms involving synaptic and non-synaptic communication between neurons. The synchronized neuronal discharge associated with a single IS induces and is followed by a profound and prolonged refractory period sustained by inhibitory potentials and by activity-dependent changes in the ionic composition of the extracellular space. Post-spike depression may be responsible for pacing interictal spiking periodicity commonly observed in both animal models and human focal epilepsies. It is proposed that the strong after-inhibition produced by IS protects against the occurrence of ictal discharges by maintaining a low level of excitation in a general condition of hyperexcitability determined by the primary epileptogenic dysfunction.
There is an enduring quest for technologies that provide – temporally and spatially – highly resolved information on electric neuronal or cardiac activity in functional tissues or cell cultures. Here, we present a planar high-density, low-noise microelectrode system realized in microelectronics technology that features 11,011 microelectrodes (3,150 electrodes per mm2), 126 of which can be arbitrarily selected and can, via a reconfigurable routing scheme, be connected to on-chip recording and stimulation circuits. This device enables long-term extracellular electrical-activity recordings at subcellular spatial resolution and microsecond temporal resolution to capture the entire dynamics of the cellular electrical signals. To illustrate the device performance, extracellular potentials of Purkinje cells (PCs) in acute slices of the cerebellum have been analyzed. A detailed and comprehensive picture of the distribution and dynamics of action potentials (APs) in the somatic and dendritic regions of a single cell was obtained from the recordings by applying spike sorting and spike-triggered averaging methods to the collected data. An analysis of the measured local current densities revealed a reproducible sink/source pattern within a single cell during an AP. The experimental data substantiated compartmental models and can be used to extend those models to better understand extracellular single-cell potential patterns and their contributions to the population activity. The presented devices can be conveniently applied to a broad variety of biological preparations, i.e., neural or cardiac tissues, slices, or cell cultures can be grown or placed directly atop of the chips for fundamental mechanistic or pharmacological studies.
High frequency oscillations (HFO) have a variety of characteristics: band-limited or broad-band, transient burst-like phenomenon or steady-state. HFOs may be encountered under physiological or under pathological conditions (pHFO). Here we review the underlying mechanisms of oscillations, at the level of cells and networks, investigated in a variety of experimental in vitro and in vivo models. Diverse mechanisms are described, from intrinsic membrane oscillations to network processes involving different types of synaptic interactions, gap junctions and ephaptic coupling. HFOs with similar frequency ranges can differ considerably in their physiological mechanisms. The fact that in most cases the combination of intrinsic neuronal membrane oscillations and synaptic circuits are necessary to sustain network oscillations is emphasized. Evidence for pathological HFOs, particularly fast ripples, in experimental models of epilepsy and in human epileptic patients is scrutinized. The underlying mechanisms of fast ripples are examined both in the light of animal observations, in vivo and in vitro, and in epileptic patients, with emphasis on single cell dynamics. Experimental observations and computational modeling have led to hypotheses for these mechanisms, several of which are considered here, namely the role of out-of-phase firing in neuronal clusters, the importance of strong excitatory AMPA-synaptic currents and recurrent inhibitory connectivity in combination with the fast time scales of IPSPs, ephaptic coupling and the contribution of interneuronal coupling through gap junctions. The statistical behaviour of fast ripple events can provide useful information on the underlying mechanism and can help to further improve classification of the diverse forms of HFOs.
A widely discussed hypothesis in neuroscience is that transiently active ensembles of neurons, known as "cell assemblies," underlie numerous operations of the brain, from encoding memories to reasoning. However, the mechanisms responsible for the formation and disbanding of cell assemblies and temporal evolution of cell assembly sequences are not well understood. I introduce and review three interconnected topics, which could facilitate progress in defining cell assemblies, identifying their neuronal organization, and revealing causal relationships between assembly organization and behavior. First, I hypothesize that cell assemblies are best understood in light of their output product, as detected by "reader-actuator" mechanisms. Second, I suggest that the hierarchical organization of cell assemblies may be regarded as a neural syntax. Third, constituents of the neural syntax are linked together by dynamically changing constellations of synaptic weights ("synapsembles"). The existing support for this tripartite framework is reviewed and strategies for experimental testing of its predictions are discussed.
Combinations of electrophysiology, two-photon microscopy and new tools for detecting neural activity show how neurons function in circuits.