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Development/Plasticity/Repair
Mesoscale Architecture Shapes Initiation and Richness of
Spontaneous Network Activity
XSamora Okujeni,
1,2
Steffen Kandler,
1,2
and XUlrich Egert
1,2
1
Bernstein Center Freiburg and
2
Biomicrotechnology, IMTEK—Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg,
Germany
Spontaneous activity in the absence of external input, including propagating waves of activity, is a robust feature of neuronal networks
in vivo and in vitro. The neurophysiological and anatomical requirements for initiation and persistence of such activity, however, are
poorly understood, as is their role in the function of neuronal networks. Computational network studies indicate that clustered connec-
tivity may foster the generation, maintenance, and richness of spontaneous activity. Since this mesoscale architecture cannot be system-
atically modified in intact tissue, testing these predictions is impracticable in vivo. Here, we investigate how the mesoscale structure
shapes spontaneous activity in generic networks of rat cortical neurons in vitro. In these networks, neurons spontaneously arrange into
local clusters with high neurite density and form fasciculating long-range axons. We modified this structure by modulation of protein
kinase C, an enzyme regulating neurite growth and cell migration. Inhibition of protein kinase C reduced neuronal aggregation and
fasciculation of axons, i.e., promoted uniform architecture. Conversely, activation of protein kinase C promoted aggregation of neurons
into clusters, local connectivity, and bundling of long-range axons. Supporting predictions from theory, clustered networks were more
spontaneously active and generated diverse activity patterns. Neurons within clusters received stronger synaptic inputs and displayed
increased membrane potential fluctuations. Intensified clustering promoted the initiation of synchronous bursting events but entailed
incomplete network recruitment. Moderately clustered networks appear optimal for initiation and propagation of diverse patterns of
activity. Our findings support a crucial role of the mesoscale architectures in the regulation of spontaneous activity dynamics.
Key words: clustering; network structure; neuronal networks; protein kinase C; spatiotemporal pattern; spontaneous activity
Introduction
A remarkable feature of neocortical circuitry is the generation of
rich spontaneous activity dynamics (Sanchez-Vives and McCor-
mick, 2000;Logothetis et al., 2009;Yanagawa and Mogi, 2009;
Sato et al., 2012), which are believed to play an important role in
cortical processing and development (Shatz, 1996;Wu et al.,
2008;Ringach, 2009;Kilb et al., 2011;Altwegg-Boussac et al.,
2014).
Computational network models predict that modular net-
work architectures with highly intrinsically connected subnet-
works (i.e., clustered networks) are optimal in generating and
sustaining network activity (Kaiser and Hilgetag, 2010;Klinshov
et al., 2014) and promote firing-rate variability and state transi-
tions (Litwin-Kumar and Doiron, 2012).
In a simplified view, cortical neurons are indeed arranged in
local clusters that have high intrinsic connectivity and connect to
other clusters by correlated long-range patchy projections, i.e.,
Received Aug. 11, 2016; revised Feb. 6, 2017; accepted Feb. 11, 2017.
Author contributions: S.O. and U.E. designed research; S.O. and S.K. performed research; S.O. analyzed data; S.O.
and U.E. wrote the paper.
This work was supported by BrainLinks-BrainTools, Cluster of Excellence funded by the German Research Foun-
dation (Grant EXC 1086), and Bernstein Focus: Neurotechnology Freiburg–Tuebingen (FKZ 01GQ0420). We thank
Sarah Jarvis, Ehsan Safavieh, and Oliver Weihberger for helpful discussions and gratefully acknowledge technical
assistance from Ute Riede, Patrick Pauli, Alexander Giffey, Hanna Kuhn, Nila Mo¨nig, and Patrick Ringwald.
Correspondence should be addressed to Samora Okujeni at the above address. E-mail:
okujeni@bcf.uni-freiburg.de.
S. Kandler’s present address: Neuro-Electronics Research Flanders, Imec Campus, Leuven, Belgium.
DOI:10.1523/JNEUROSCI.2552-16.2017
Copyright © 2017 the authors 0270-6474/17/373972-16$15.00/0
Significance Statement
Computational studies predict richer and persisting spatiotemporal patterns of spontaneous activity in neuronal networks with
neuron clustering. To test this, we created networks of varying architecture in vitro. Supporting these predictions, the generation
and spatiotemporal patterns of propagation were most variable in networks with intermediate clustering and lowest in uniform
networks. Grid-like clustering, on the other hand, facilitated spontaneous activity but led to degenerating patterns of propagation.
Neurons outside clusters had weaker synaptic input than neurons within clusters, in which increased membrane potential fluc-
tuations facilitated the initiation of synchronized spike activity. Our results thus show that the intermediate level organization of
neuronal networks strongly influences the dynamics of their activity.
3972 •The Journal of Neuroscience, April 5, 2017 •37(14):3972–3987
neurons in a cluster share common long-range target regions
(Ruthazer and Stryker, 1996;Voges et al., 2010). Functionally,
this has been linked to the association of neuronal ensembles with
similar function, for example the representation of orientation
selectivity in the visual cortex (Bosking et al., 1997;Chavane et al.,
2011). However, implications of this mesoscale network architec-
ture for spontaneous neuronal dynamics remain speculative.
We tested the theoretical predictions in readily accessible neu-
ronal networks in vitro by modifying the spatial distributions of
neurons and neurites. In an abstract way, these networks display
a mesoscale architecture with some resemblance to that of the
cortex. Cultured neurons spontaneously aggregate to form clus-
ters and grow fasciculated neurites connecting them (Kriegstein
and Dichter, 1983;Segev et al., 2003;Robert et al., 2012). Their
functional clustering is revealed by the spatial fragmentation of
the networks when excitatory synaptic transmission is dimin-
ished (Soriano et al., 2008). Functional network reconstruction
likewise indicates a mixture of locally clustered and long-range
connectivity (Stetter et al., 2012). These networks establish rich
spontaneous dynamics (Marom and Shahaf, 2002;van Pelt et al.,
2004;Wagenaar et al., 2006a) consisting of propagating synchro-
nized bursting events (SBEs), which are similar to patterns of
spontaneous activity characteristic for the developing neocortex
(Katz and Shatz, 1996;Wu et al., 2008;Golshani et al., 2009).
We modified the structure of these networks by pharmacological
modulation of protein kinase C (PKC), an enzyme regulating neu-
rite growth (Dent and Meiri, 1998), branching (Quinlan and Hal-
pain, 1996;Audesirk et al., 1997;Schrenk et al., 2002;Gundlfinger et
al., 2003;Metzger, 2010), fasciculation (Itoh et al., 1989), and cell
migration (Kumada and Komuro, 2004;Larsson, 2006). By exploit-
ing the formative effects of PKC modulation, we made networks
with more clustered respectively more uniform (defined as toward
randomness) arrangement of neurites and somata. Chronic PKC
activation (PKC
⫹
) promoted neuronal aggregation, neurite entan-
glement in clusters and axonal fasciculation, and reduced overall
neurite densities, suggesting more local and less long-range connec-
tivity. Inhibition of PKC (PKC
⫺
) diminished soma clustering and
axonal fasciculation and increased neurite densities, suggesting
more uniform connectivity.
Differences in the mesoscale architecture crucially affected the
generation and spatiotemporal structure of spontaneous activity. In
PKC
⫺
networks, spontaneous SBEs were elicited at significantly
lower rates even though these networks were highly excitable, as
indicated by high responsiveness to electrical stimulation. We sug-
gest that reduced clustering and fasciculation diminished recurrent
and convergent connectivity patterns that promote background ac-
tivity integration and SBE initiation. Neurons in clustered networks
and particularly those within clusters indeed received stronger syn-
aptic inputs, increasing membrane potential (V
m
) fluctuations.
Widely distributed burst initiation zones (BIZs) in clustered net-
works established a much richer repertoire of propagating waves
compared with uniform networks where SBEs were elicited only
from a few hotspots. However, clustering also resulted in incomplete
network recruitment during SBEs, leading to restricted activation
patterns and global desynchronization.
Our results support theoretical predictions that clustered net-
work topologies promote generation, maintenance, and richness
of spontaneous activity. A balance between local and long-range
connectivity furthermore seems necessary for the generation of
spontaneous activity dynamics that recruit large parts of the net-
work at the same time.
Materials and Methods
Cell culture techniques. Primary cortical cell cultures were prepared on
different microelectrode arrays (MEAs; Multi Channel Systems; elec-
trode grid layout/pitch distance: 8 ⫻8/200
m; 6 ⫻10/500
m; 16 ⫻
16/200
m; 32 ⫻32/300
m) and standard coverslips (12 mm diameter,
Carl Roth). All substrates were coated with polyethylene-imine (150
lof
0.2% aqueous solution; Sigma-Aldrich) for cell adhesion. Cell cultures
were prepared following Shahaf and Marom (2001). Cortical tissue was
prepared from brains of neonatal Wistar rat pups of either sex, minced
with a scalpel, and transferred into PBS (Invitrogen). Tissue pieces were
incubated with trypsin (isozyme mixture, 0.05%, 15 min at 37°C; Invit-
rogen) and proteolysis was subsequently stopped with horse serum
(20%; Invitrogen). DNase (type IV, 50
g/ml; Sigma-Aldrich) was added
to eliminate cell trapping in DNA strings if needed. Cells were dissociated
by trituration with a serological pipette, centrifuged (5 min, 617 ⫻g),
and resuspended in growth medium [Minimal Essential Medium
supplemented with 5% heat-inactivated horse serum, 0.5–1 mM
L-glutamine, 20 mMglucose, and 20
g/ml gentamycin (all from Invit-
rogen); 1 ml/pup]. Cells were counted with an automated cell counter
(CASY, Scha¨rfe Systems) and seeded at 300,000 cells per culture, result-
ing in a density of 1500 cells/mm
2
at1din vitro (DIV). Networks devel-
oped in 1 ml of growth medium in a humidified incubator (5% CO
2
,
37°C). Animal handling and tissue preparation were done in accordance
with the guidelines for animal research at the University of Freiburg.
PKC modulation and disinhibition. PKC inhibitor Go¨decke6976
(Go¨6976; 1
M; Sigma-Aldrich) and PKC agonist phorbol-12-myristate-
13-acetate (PMA; 1
M; Sigma-Aldrich) were dissolved in dimethylsulf-
oxide (DMSO; Sigma-Aldrich) and added to the culture medium directly
after cell preparation. The maximal concentration of DMSO in the
growth medium was 0.1%. GABAergic transmission was probed by acute
application of the noncompetitive GABA
A
receptor antagonist picro-
toxin (PTX; 10
M; Sigma-Aldrich) during electrophysiological record-
ings. Drug washout was performed by a complete medium exchange with
fresh medium after ⬃4 weeks. Networks growing without PKC modula-
tion (PKC
N
) were treated the same way to evaluate general washout-
induced effects. Cultures were subsequently recorded repeatedly within
the next days.
Morphological analyses. Cell adherence after seeding was documented
by phase contrast microscopy at 1 DIV to estimate initial cell densities.
Cell positions were determined in phase contrast micrographs by auto-
mated detection (2D convolution with a Mexican hat-shaped kernel with
an inner radius corresponding to the diameter of neuronal cell bodies
and subsequent thresholding of the resulting image). The spatial distri-
bution of neuronal cell bodies was reexamined after 21 DIV based on
immunocytochemical staining of neuronal nuclei (NeuN; rabbit-anti-
NeuN, 1:500; Abcam) and staining of all cellular nuclei (DAPI; Sigma-
Aldrich). Cellular nuclei were detected automatically (similar approach
as described above) and neurons were identified as the subset of cells with
NeuN immunoreactivity.
To assess minimal possible distances between neuronal somata, we calcu-
lated the distance between the centers of pairs of neurons by Delaunay tri-
angulation. Spatial clustering of cell bodies was evaluated by a modified
Clark–Evans aggregation index (Clark and Evans, 1954). The clustering in-
dex (CI) was calculated as the ratio between average observed and expected
(i.e., for random point patterns) nearest-neighbor distance. CI increases
from fully clustered (CI ⫽0 if the distance between nearest neighbors is
equal to the minimal distance for all neurons) to random (CI ⫽1) to grid-
like distributions of neurons (CI ⬎1, depending on the cell density). Note
that CI is insensitive to a potentially grid-like arrangement of clusters (i.e.,
the next level of structural organization), which we do not analyze in this
study. Under realistic circumstances, the distances between neurons must be
a least the diameter of cell bodies, which leads to longer average nearest-
neighbor distances and an underestimation of clustering. To account for
this, we simulated random distributions with equal density using the average
cell body diameter (10
m) as minimal possible distance by successively
adding neurons to randomly drawn positions and rejecting neurons that
would overlap with others (Galli-Resta et al., 1999). We further extended the
Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity J. Neurosci., April 5, 2017 •37(14):3972–3987 • 3973
analysis to assess cluster sizes by calculating the n
th
nearest-neighbor dis-
tances in networks and simulated random distributions.
Neurite morphology was examined by immunocytochemical staining
of microtubule-associated protein 2 (MAP2; chicken-anti-MAP2; 1:500;
Abcam) and phosphorylated neurofilament 200 kDa (rabbit-anti-neuro-
filament; 1:10; Abcam) to visualize dendritic and axonal compartments,
respectively. To detect axons and dendrites, images for the respective
channels were high-pass filtered. In a second step, a peak detection was
performed for each row of the resulting image (peaks corresponded to
neurites; detection resolution of 2
m intervals between intersections).
To avoid a bias due to strongly orientated neurites, the procedure was
repeated for different orientations (in steps of 30°; implemented by im-
age rotation). Neurite counts and intervals of all scans were pooled.
Axodendritic intersections were marked at the center-of-mass of patches
of colocalized axon-positive and dendrite-positive pixels (at any angle),
allowing deviation of 1 pixel width (0.645
m). To determine the degree
of neurite fasciculation, we took the mean interneurite intervals divided
by the SD as fasciculation index (FI). This provided a measure similar to
the CI for the cell body distribution: FI increases from fully fasciculated
(FI ⫽0; minimal distance between neurites) to random (FI ⫽1) to
grid-like neurite arrangements (FI ⬎1).
Dendritic morphology was examined in sparse networks by Sholl anal-
ysis (Sholl, 1953). All morphological analyses were made with Matlab.
Significance is specified by pvalues determined with the Student’s ttest
(p
stt
) for independent samples.
Patch-clamp recording and analysis. Patch pipettes [6.3 ⫾1.4 M⍀
(mean and SEM)] were filled with a intracellular solution containing the
following: potassium D-gluconate (125 mM; Sigma-Aldrich), KCl (20
mM; Sigma-Aldrich), EGTA (5 mM; Carl Roth), Na
2
-ATP (2 mM; Carl
Roth), HEPES (10 mM; Carl Roth), MgCl
2
(2 mM; Sigma-Aldrich), CaCl
2
(0.5 mM; Sigma-Aldrich), and biocytin (10 mg/ml, Invitrogen), adjusted
with KOH to pH 7.4, and with sucrose to 320 mOsm. Patch-clamp re-
cordings in whole-cell configuration were conducted at 37°C (PH01 per-
fusion heating, Multi Channel Systems; TC02 temperature controller,
Multi Channel Systems) and perfusion with carbogenated (95% O
2
and
5% CO
2
; Air Liquide) culture medium without horse serum and without
Go¨6976 and PMA. Data were sampled at 25 kHz (Micro1401 amplifier
and Spike2 software; Cambridge Electronics Design). Up to four neurons
were recorded per network for ⬃30 min each. Input resistances were
determined with hyperpolarizing current pulses (⫺50 pA). Datasets of
ⱖ20 min were analyzed with Matlab. The SD of V
m
was averaged for time
bins of 1 s. The first step to detect EPSPs was to bootstrap an EPSP
template from the data. Putative events were detected with a threshold set
manually for V
m
slope. Snippets surrounding detection time points t
D
were classified by hierarchical clustering and the EPSP template was sub-
sequently derived as the average of a manually chosen cluster resembling
the voltage time course of typical isolated EPSPs. Based on this template,
EPSPs were detected by correlation of V
m
with the onset phase of the
template (⫺5 to 10 ms around t
D
) and a correlation threshold of 0.5.
Extracellular recording and analyses. Multiunit spike activity was
recorded from microelectrode arrays (MEAs; MEA1060-BC, USB-
MEA256-System, and MEA 30-1024-System amplifiers; Multi Channel
Systems, 25 kHz sampling frequency, 12 bit) under culture conditions
(37°C, 5% CO
2
) and acquired with MCRack software (Multi Channel
Systems; versions 3.3– 4.0). Recordings of individual networks lasted
ⱖ1 h. Action potentials (APs) were detected with a threshold set to ⫺5
SDs of the high-pass-filtered baseline signal (Butterworth second-order
high-pass filter, 200 Hz cutoff; detection dead time, 2 ms). Stimulation
was controlled using a programmable stimulus generator (STG2004,
Multi Channel Systems). A single stimulus consisted of a monophasic
negative pulse with 400
s width and 0.6 V amplitude. Stimulation elec-
trode sequences were controlled by MEABench (version 1.0.16; Wage-
naar and Potter, 2004). After a baseline recording period of ⱖ30 min of
spontaneous activity, all array electrodes were stimulated consecutively
at interstimulus intervals of either 6, 4, or 2 s. The sequence was repeated
20 times in succession.
Raw data from MEA recordings were imported into Matlab using
MEA-Tools (Egert et al., 2002) and the Find toolbox (Meier et al., 2008).
Spontaneous SBEs were detected as follows: series of spikes with consec-
utive interspike intervals shorter than a threshold value (100 ms) were
detected as bursts. SBEs were defined as periods in which a predefined
fraction of electrodes showed simultaneous bursts (10% of active sites,
minimally 3, maximally 20 to keep criteria comparable between small
and large MEAs). To account for buildup and fading phases of SBEs,
spikes within a time windows of 25 ms before and following this SBE core
were included into the SBE. Network activity was characterized by the
following parameters: the rate of SBEs in the recording period, burst
strength as the average number of APs per SBE and active site, average
firing rate (AFR) as the gross average firing rate across all active sites, and
SBE recruitment as the average fraction of active sites participating in
SBEs. For experiments with PTX, we defined as control period the last 1 h
section before application of PTX and excluded the first 10 min after
application from the analysis to avoid transients. In stimulation experi-
ments, we excluded the first 10 ms after stimuli to blank stimulation
artifacts.
Propagation pattern analysis. To analyze spatiotemporal propagation
patterns, SBEs were classified by first-spike rank order, i.e., electrodes
were ranked by the relative timing of the first spike recorded at a given
electrode during SBEs. Similarity between SBEs was calculated as the
Spearman correlation of the resulting electrode sequences. Groups of
similar patterns were identified by correlation-matrix-based hierarchical
clustering (complete linkage) using one minus the correlation value as
the distance measure (Liu et al., 2012). Distances ⬍1 indicate correlated
patterns. Distances ⬎1 indicate anticorrelated patterns. To assess the
richness of patterns, we determined the number of classes yielded at a
given distance threshold between 0.02 and 2. As the contribution of
individual classes, we calculated the cumulative fraction of SBEs ac-
counted for by the most frequent pattern classes for series of 100 SBEs.
SBE initiation zones. BIZs were identified in recordings from large
MEAs (16 ⫻16 and 32 ⫻32 electrode grid layouts) that spanned almost
the entire network area. On these arrays, the median xand ycoordinates
of the first 10 active sites that fired during SBEs were used to localize the
spatial SBE onset position. Note that this approach produced a slight bias
of BIZs toward the array center. The spatiotemporal structure of SBE
initiation process was further analyzed as firing rate relative to SBE onset
time (2 ms bins) and distance to respective BIZs (300
m bins, i.e.,
electrode pitch) averaged across 500 consecutive SBEs.
Results
PKC promotes cell migration and clustering of neuronal
cell bodies
Neuronal networks were prepared from neonatal rat cortex with
⬃600 – 800 neurons/mm
2
surviving at 20 DIV. This variability is
unlikely to be relevant for the overall level of network activity
(Biffi et al., 2013). Following intense neurite outgrowth, these
networks developed spontaneous activity with SBEs as early as
5 DIV.
Under PKC
N
conditions (i.e., with normal PKC activity),
neuronal somata migrated and aggregated, establishing a soma
density landscape with high-density clusters within 3 weeks (Fig.
1A). To find the minimal distance between neurons, we calcu-
lated the Euclidean distance distribution for the edges derived
from a Delaunay triangulation of neuron positions. In all phar-
macological conditions, we found a minimal distance of ⬃10
m
between cell body centers, which corresponds to their soma di-
ameter (Fig. 1B). The degree of clustering after development was
quantified following the Clark–Evans aggregation index (Clark
and Evans, 1954), i.e., the ratio of the observed mean nearest-
neighbor distance in the network to that expected for a Poisson
point process with the same spatial density, modified to take into
account the minimal possible distance of 10
m between soma
centers (Fig. 1C).ACIof⬍1 denotes clustering and a CI ⬎1
denotes dispersion of neuronal somata relative to the expecta-
tion. PKC
N
networks were strongly clustered at 20 DIV com-
pared with almost random spatial distributions in networks
3974 •J. Neurosci., April 5, 2017 •37(14):3972–3987 Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity
shortly after seeding at 1 DIV (CI mean ⫾SEM: 20 DIV, 0.78 ⫾
0.03; 1 DIV, 1.0 ⫾0.02; n⫽6, p
stt
⫽1.3*10
⫺12
). We further
calculated the cluster index for the n
th
nearest neighbors to assess
the average number of neurons in clusters. At 20 DIV, CI for the
n
th
nearest neighbors (CI
n
) was significantly reduced approxi-
mately up to the 45
th
nearest neighbor (p
stt
⬍0.01, pairwise
testing), indicating typical cluster sizes in this range (Fig. 1D).
To modify the degree of clustering and to investigate its rele-
vance for the spatiotemporal structure of activity in these net-
works, we increased respectively inhibited PKC activity during
network development. Increasing PKC activity by PMA (1
M;
PKC
⫹
) during network development promoted neuronal aggre-
gation compared with PKC
N
networks (CI, 0.67 ⫾0.02; n⫽5,
p
stt
⫽1.1*10
⫺4
) and resulted in networks with strikingly regu-
larly spaced, well delineated clusters at 20 DIV (Fig. 1A). Al-
though clustering was increased in PKC
⫹
networks (Fig. 1C),
typical cluster sizes were only slightly larger than in PKC
N
net-
works, with CI
n
significantly reduced for the first 50 nearest
neighbors (p
stt
⬍0.01 for pairwise testing; Fig. 1D). In contrast,
chronic PKC inhibition by Go¨6976 diminished cell migration
and led to more homogeneously distributed neuronal somata
(Fig. 1A) with a significantly lower degree of clustering (CI,
0.90 ⫾0.04; n⫽5; p
stt
⫽3.6*10
⫺4
;Fig. 1C) compared with
PKC
N
networks and PKC
⫹
networks. Remaining cell aggregation
resulted in areas with slightly increased density but with weak
contrast to the background density of neurons. Again, these
denser areas contained ⬃50 neurons (Fig. 1D).
PKC promotes fasciculation and local density of neurites
Substantial neurite outgrowth started within hours after seeding.
We quantified the density and arrangement of neurites in mature
networks at 20 DIV based on immunohistochemical staining for
MAP2 in dendrites and neurofilament in axons (Fig. 2A). Neurite
densities were determined by counting neurite intersections with
straight lines drawn across the network (Fig. 2B,C).
PKC
N
networks had high dendrite densities (125.3 ⫾9.3 den-
drites/mm, mean ⫾SEM, n⫽6; Fig. 3A) with relatively homo-
geneous coverage (FI, 0.84 ⫾0.01; Fig. 3B). The density of axons
(61 ⫾8 axons/mm; Fig. 3C) was lower than that of dendrites, and
axons also formed bundles between clusters (FI, 0.73 ⫾0.09,
mean ⫾SEM; Fig. 3D). Note that this does not mean that axons
connected exactly two adjacent clusters. Some axons spanned
distances of several millimeters and could pass through several
clusters.
Following the clustering of somata, increasing PKC activity
had a profound impact on the arrangement of neurites. The over-
all density of dendrites decreased in PKC
⫹
networks (99 ⫾9
dendrites/mm, p
stt
⫽9.8*10
⫺4
,n⫽5 networks). Furthermore,
dendrites were densely aggregated in neuron clusters, leaving re-
gions with lower dendrite density in between clusters (Fig. 2B).
Fasciculation of dendrites was significantly increased in PKC
⫹
networks (FI, 0.78 ⫾0.04; p
stt
⫽2.7*10
⫺4
). The density of axons
likewise was significantly reduced compared with PKC
N
net-
works (49 ⫾5 axons/mm, p
stt
⫽3.3*10
⫺2
,n⫽5). As in PKC
N
networks, axons fasciculated and formed bundles between soma
Figure 1. Generic network structure and activity dynamics in networks of cultured cortical neurons. A, Staining of neuronal nuclei (NeuN; red) and all cellular nuclei (DAPI; blue) in networks at 20
DIVthathaddevelopedunderinhibited(PKC
⫺
),normal(PKC
N
),andenhancedPKC(PKC
⫹
)activity.Neuronsweredetected(whitecircles)and analyzed for their spatial distributions. Scale bars, 100
m. B, Distributions of minimal distances between soma centers of pairs of neighboring neurons. Distances were ⱖ10
m, indicating that the soma diameter was ⬃10
m (dashed line).
C, Clark–Evans CI for nearest-neighbor distances indicates significantly weaker clustering in PKC
⫺
and stronger clustering in PKC
⫹
networks compared with PKC
N
networks. D, CIs ⬍1uptothen
th
nearestneighborindicatetypical cluster sizes in the range of ⱕ60neurons.Boxplotsshowmedian, 25
th
,and75
th
percentilesandminimaland maximal values (excluding outliers). ***p
stt
⫽0.001.
Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity J. Neurosci., April 5, 2017 •37(14):3972–3987 • 3975
clusters (FI, 0.69 ⫾0.05). Within clusters, axons strongly rami-
fied and formed local loops.
In contrast to chronic PKC stimulation, developmental PKC
inhibition significantly increased dendrite and axon densities
(141 ⫾3 dendrites/mm, p
stt
⫽1.2*10
⫺2
; 131 ⫾7 axons/mm, p
stt
⫽6.5*10
⫺7
,n⫽4) compared with PKC
N
networks.
To validate the effect at the single-neuron level, we further ana-
lyzed the extent of dendritic fields by Sholl analysis in sparse cultures
Figure 2. Manipulating PKC activity changes the mesoscopic network architecture. A, Staining of axons (anti-neurofilament; green) and dendrites/cell bodies (anti-MAP2; red) and NeuN (blue).
The dashed square indicates the panel positions in Figure 1Aand white circles indicate the detected neuronal somata. Under PKC
N
conditions, axons and dendrites fasciculated and cell bodies
clustered. PKC inhibition resulted in more homogeneous axon and dendrite coverage, and soma distributions. Enhanced PKC activity resulted in well delineated clusters with strongly entangled
dendrites and axons that interconnected clusters in bundles and looped within clusters. B,C, To quantify fasciculation and density, dendrites (B) and axons (C) were analyzed in high-pass filtered
images of red and green channels, respectively, by detecting intensity peaks in image rows. Neurite detections for one row are shown in red. The FI was calculated based on the distribution of
intervals between peaks. Note that neurite densities decrease from PKC
⫺
over PKC
N
to PKC
⫹
.D, Intersections (small white dots) of axons and dendrites provide a lower bound estimate for putative
synaptic sites. The spatial distribution of intersections was concordant to the distributed or clustered arrangement of cell bodies (large circles) and neurites. Scale bars, 100
m.
3976 •J. Neurosci., April 5, 2017 •37(14):3972–3987 Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity
at 24 DIV (Fig. 3E). Chronic PKC inhibition increased dendrite
branching and extent, resulting in significantly increased total den-
drite length (PKC
N
, 1449 ⫾647
m; N⫽58 neurons; PKC
⫺
,
2132 ⫾816
m; N⫽78 neurons; p
stt
⫽5.6*10
⫺7
). In addition to
increasing neurite densities, PKC inhibition significantly diminished
dendritic (FI, 0.92 ⫾0.01, p
stt
⫽2.5*10
⫺3
) and axonal (FI, 0.92 ⫾
0.002, p
stt
⫽4.2*10
⫺3
) fasciculation, leading to a much more homo-
geneous neurite coverage than in PKC
N
networks.
Synaptic connectivity
As a lower estimate of the number of potential synapse sites in
different network types, we detected intersections between axons
and dendrites (Fig. 2D). PKC
N
networks yielded ⬃40,000 inter-
sections/mm
2
, corresponding to ⬃100 sites per neuron. Note
that due to the limited spatial resolution of the analysis, in
particular in clusters and neurite bundles, this number con-
siderably underestimated the real den-
sity of synapses but enabled estimates
over large areas of the network. The
more homogeneous arrangement of neu-
rites in PKC
⫺
networks entailed a corre-
sponding distribution of axodendritic
intersections. In PKC
⫺
networks, we de-
termined the highest number of axoden-
dritic intersections of ⬃70,000 sites/mm
2
,
much higher than in PKC
N
networks and
consistent with the high neurite density.
In contrast, the overall density in PKC
⫹
networks of ⬃30,000 sites/mm
2
was
slightly lower than in PKC
N
networks. As
a consequence of the local tangles of neu-
rites, axodendritic intersections were spa-
tially clustered in PKC
⫹
networks.
Changes in the distribution of axoden-
dritic contact sites likely influence the
connectivity of the network and thus the
synaptic input to individual neurons.
Such changes could be critical for the ini-
tiation and propagation of spontaneous
network activity. We thus analyzed synap-
tic dynamics in whole-cell patch-clamp
recordings during spontaneous network
activity between 18 and 30 DIV for
neurons grown in different PKC activity
conditions (number of neurons and net-
works: PKC
N
n⫽45 neurons, 16 net-
works; PKC
⫺
n⫽45 neurons, 21
networks; PKC
⫹
n⫽20 neurons, 10
networks).
Neurons in PKC
⫺
and PKC
N
networks
had comparable input resistances (PKC
N
,
233 ⫾14 M⍀, mean ⫾SEM; PKC
⫺
,
225 ⫾16 M⍀). In PKC
⫹
networks, how-
ever, input resistances were significantly
lower (PKC
⫹
, 136 ⫾19 M⍀;p
stt
⫽
6.5*10
⫺4
vs PKC
N
). Resting potentials
varied across neurons and were signifi-
cantly more negative in PKC
⫺
networks
(PKC
⫹
,⫺50.4 ⫾1.7 mV, mean ⫾SEM;
p
stt
⫽0.08 vs PKC
N
; PKC
N
,⫺53.8 ⫾1.0
mV; PKC
⫺
,⫺57.6 ⫾1.2 mV, p
stt
⫽0.02
vs PKC
N
). Average thresholds of AP initi-
ation did not differ significantly between
network types (PKC
N
,⫺42.5 ⫾0.7 mV; PKC
⫺
,⫺41.9 ⫾1.4 mV;
PKC
⫹
,⫺39.3 ⫾2.0 mV). To measure EPSP activity under com-
parable conditions, the resting potential of all neurons was set to
⬃⫺65 mV by constant current injection.
In all networks, V
m
fluctuated around the resting V
m
between
SBEs and underwent strong depolarization for several hundred
milliseconds, during which APs were generated (Fig. 4A). These
up states corresponded to SBEs measured extracellularly (Fig.
5A–C). V
m
fluctuation between SBEs increased from PKC
⫺
to
PKC
N
to PKC
⫹
networks (SD of V
m
: PKC
⫺
, 0.95 ⫾0.12 mV;
PKC
N
, 1.97 ⫾0.13 mV; p
stt
⫽8.2*10
⫺8
; PKC
⫹
, 2.25 ⫾0.43 mV).
To further determine how the embedding of individual neu-
rons into the network affected V
m
variability, we classified neu-
rons according to their membership in clusters. This revealed
that V
m
of neurons within clusters fluctuated more than that of
Figure 3. Distribution and density of neurites in different PKC activity conditions. A, Dendrite densities in networks decreased
significantly with increasing PKC activity. B, Neurite fasciculation increased with PKC activity. C,D, Changes of PKC activity levels
had the same and even stronger effects on axons. E, Dendrites were analyzed by Sholl analysis in sparse cultures stained with
antibodies against MAP2 at 24 DIV. PKC inhibition increased the average number of dendritic branches at any distance from the
soma, thereby producing larger dendrites. Boxplots show median, 25
th
, and 75
th
percentiles and minimal and maximal values
(excluding outliers). *p
stt
⫽0.05; **p
stt
⫽0.01; ***p
stt
⫽0.001.
Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity J. Neurosci., April 5, 2017 •37(14):3972–3987 • 3977
neurons in sparse regions in PKC
N
networks (SD of V
m
: sparse,
1.60 ⫾0.14 mV; n⫽14; cluster, 2.44 ⫾0.27 mV; n⫽16; p
stt
⫽
5.9*10
⫺3
) and PKC
⫹
networks (sparse, 1.19 ⫾0.18 mV; n⫽6;
cluster, 2.87 ⫾0.60 mV; n⫽13). PKC
⫺
networks had no clear
clusters to assess such dependence.
As a potential source of these differences, we assessed the
effective strength of synaptic inputs in different network types
by analyzing identifiable EPSPs during interburst periods. In
PKC
N
networks, EPSPs occurred at 8.1 ⫾0.6 Hz (mean ⫾
SEM). EPSPs amplitudes were approximately lognormally dis-
tributed (2.3 ⫾0.2 mV; Fig. 4 B,C). Occasional fast subthresh-
old excursions of V
m
⬎10 mV possibly corresponded to
multisynaptic input from the same neuron. In PKC
⫺
net-
works, EPSPs rates in between SBEs were significantly lower
(5.0 ⫾0.5 Hz, p
stt
⫽7.8*10
⫺5
) with much lower EPSP ampli-
tudes (1.5 ⫾0.1 mV, p
stt
⫽6.1*10
⫺4
). In contrast, in the
strongly clustered PKC
⫹
networks, EPSPs rates were 9.1 ⫾0.6
Hz (p
stt
⫽3.7*10
⫺6
vs PKC
⫺
) with an average amplitude of
2.7 ⫾0.5mV(p
stt
⫽2.2*10
⫺3
vs PKC
⫺
networks).
Since synaptic properties could depend on the local embed-
ding of neurons into the network, we again classified neurons
according to their position within or outside a cluster. In neu-
rons outside of clusters, EPSP amplitude distributions and the
average EPSP amplitudes were similar in all network types
(PKC
N
,1.8⫾0.2 mV; PKC
⫺
,1.5⫾0.1 mV; PKC
⫹
,1.5⫾0.1
mV; Fig. 4D,F). Neurons located within clusters, however,
had higher average EPSP amplitudes (PKC
N
,2.8⫾0.4 mV;
p
stt
⫽0.02; PKC
⫹
, 3.3 ⫾0.6 mV; Fig. 4E,F), whereas EPSP
Figure 4. Intracellular recordings from neurons in different mesoscopic network architectures. A,V
m
traces from neurons recorded in PKC
⫺
, PKC
N
, and PKC
⫹
networks at 20 DIV display EPSP
activity (black arrows) and strong depolarizations corresponding to SBEs. The AP threshold ⬃⫺40 mV is indicated by the dashed line. Insets, Average shape of nonoverlapping EPSPs in between
SBEs. B,C, Average amplitude distributions across neurons reveal a larger fraction of smaller EPSPs in PKC
⫺
networks than in PKC
N
networks. Strongly clustered PKC
⫹
networks had a bimodal
amplitude distribution with smaller and large EPSPs. D, Neurons were classified according to their local neuronal neighborhood. EPSP amplitude distributions for neurons in sparse regions did not
differ significantly between conditions. E,F, Clustered neurons in PKC
N
and PKC
⫹
networks had larger EPSP amplitudes than neurons in sparse regions. Boxplots show median, 25
th
, and 75
th
percentiles and minimal and maximal values (excluding outliers). *p
stt
⫽0.05; ***p
stt
⫽0.001.
3978 •J. Neurosci., April 5, 2017 •37(14):3972–3987 Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity
rates in clustered and nonclustered neurons did not differ
significantly.
Clustering promotes spontaneous bursting activity
V
m
dynamics in individual neurons reflect the ongoing AP dy-
namics in recurrent networks subsampled by their particular af-
ferent population. To assess the spontaneous network-wide
dynamics, we recorded extracellular spike activity over extended
time periods with MEAs. SBEs corresponded to up states mea-
sured intracellularly (Fig. 5A–C) and represented the dominating
spontaneous activity pattern, clearly standing out from the un-
correlated background activity in all network types. At onset,
SBEs typically propagated across the network with a traveling
wave front (Fig. 5F). In all networks, typically ⬎80% of spikes
were part of SBEs (Fig. 6A; recording at 25–35 DIV; PKC
N
,
95 ⫾1%; n⫽22; PKC
⫺
,85⫾3%, n⫽18; PKC
⫹
,97⫾1%, n⫽
7). Consistent with the higher rate of V
m
up states observed in-
tracellularly, the frequency of SBEs increased with the degree of
clustering in networks (Fig. 6B).
SBE rates increased from PKC
⫺
networks (2.0 ⫾0.3 SBE/min;
p
stt
⫽1.4*10
⫺8
vs PKC
N
) and PKC
N
networks (13.0 ⫾1.4 SBE/
min) to PKC
⫹
networks (29.5 ⫾5.0 SBE/min, p
stt
⫽1.8*10
⫺4
vs
PKC
N
).
Furthermore, the temporal variability of SBE initiation signif-
icantly increased with the degree of clustering in networks (Fig.
6F). In some networks, SBE rates fluctuated strongly, with peri-
ods of strongly increased bursting comparable to superbursts de-
scribed by Wagenaar et al. (2006a). These superbursts occurred
in almost all PKC
⫹
networks, occasionally in the moderately
clustered PKC
N
networks, and rarely in the PKC
⫺
networks.
SBEs typically did not involve the entire network. The fraction
of the network recruited in individual SBEs decreased with the
degree of clustering (Fig. 6E). In PKC
⫺
networks, a significantly
higher fraction of all active sites participated in individual SBEs,
compared with the clustered PKC
N
and PKC
⫹
networks (PKC
N
,
67 ⫾4%; PKC
⫺
,86⫾3%; p
stt
⫽1.8*10
⫺3
; PKC
⫹
,54⫾5%;
p
stt
⫽1.8*10
⫺3
vs PKC
⫺
).
Gross AFRs (Fig. 6D) across recording sessions of ⱖ1 h were
significantly lower in PKC
⫺
networks (0.35 ⫾0.05 Hz, p
stt
⫽
6.6*10
⫺4
) compared with PKC
N
networks (1.50 ⫾0.28 Hz).
Despite their much higher SBE rate, AFRs in PKC
⫹
networks
(1.49 ⫾0.15 Hz) were not significantly higher than in PKC
N
networks. This was due to an inverse correlation between SBE rate
and burst strength, i.e., the average number of spikes per SBE and
site. In consequence, high SBE rates entailed weaker bursts (PKC
⫺
,
10.9 ⫾1.9 spikes/site; PKC
N
, 7.8 ⫾1.3 spikes/site; PKC
⫹
, 3.5 ⫾0.7
spikes/site; p
stt
⫽0.026 vs PKC
⫺
;Fig. 6B). The bursting strength was
more variable in clustered networks where individual sites displayed
varying numbers of spikes during SBEs (Fig. 6G).
Network metastructure influences the richness of
activity patterns
The higher SBE variability in terms of burst size, network recruit-
ment, SBE intervals, and the occurrence of superbursts in PKC
N
and PKC
⫹
networks suggests that clustering promoted the rich-
ness of activity, which in this context refers to the spectrum of
temporal and spatial patterns of spike activity and their pathways
of propagation within a network. Based on MEA recordings, we
identified SBE propagation patterns as the rank order of burst
onset at individual electrodes (delay of first spike after SBE on-
set). Such order-based representation of SBE activity, compared
with temporal representations that preserve exact delays, was
demonstrated to be more robust for reconstructing SBE origins
and less dependent on detecting the true first spike of a pattern
(Shahaf et al., 2008).
SBEs typically spread with a traveling wave front, which was
irregular in PKC
N
networks, but smooth and regular in the more
homogeneous PKC
⫺
networks as well as in the strongly clustered
PKC
⫹
networks. Propagation pattern similarity was determined
as the Spearman correlation of first-spike rank-order sequences
Figure 5. Paired intracellular and MEA recordings. A,B,V
m
traces from two neurons near an MEA electrode. C, EPSPs are indicated by vertical ticks beneath the traces and the dashed horizontal
line indicates the spiking threshold at ⬃⫺40 mV. Baseline EPSP activity is interrupted by V
m
up states, during which these neurons generated spikes. D,E,V
m
up states coincide with network-wide
SBEs recorded with the MEA. F, Propagation patterns were defined from the order of the first spikes at each electrode after detection of SBE onset, shown here for these two consecutive SBEs. Green
line in Eindicates MEA electrode marked in C.
Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity J. Neurosci., April 5, 2017 •37(14):3972–3987 • 3979
across SBEs (Fig. 7A). In PKC
N
networks, correlation-matrix-
based hierarchical clustering (Liu et al., 2012) did not reveal con-
spicuous pattern classes. In contrast, PKC
⫺
as well as PKC
⫹
networks showed clear clusters of highly similar propagation pat-
terns (Fig. 7A–C) with a high intraclass correlation and low or
negative (distances ⬎1) interclass correlation.
To compare the diversity of these propagation patterns across
networks, we determined the number of classes with patterns
correlated above a given threshold (Fig. 7D). Intermediate dis-
tance thresholds between 0.02 and 0.8 (correlation coefficient,
0.2– 0.98), yielded significantly fewer classes in PKC
⫺
networks
than in PKC
N
networks (p⬍0.01), indicating that SBE propa-
gation patterns clustered in few groups with high intragroup sim-
ilarity in PKC
⫺
networks. While this might suggest that diversity
increases with clustering, the more strongly clustered network
structure found in PKC
⫹
networks did not further promote the
Figure6. SpontaneousSBEactivityat 30 DIV in networks recorded with MEAs. A,Inallnetworkarchitectures, spontaneous network activity consisted of SBEs and lowlevelsofspikingin between.
Right, Zoom into SBEs marked by the arrow on the left. The spatiotemporal structure of SBE activity differed remarkably in networks with different structures. Spontaneous SBE rates, gross activity
levels, and spatiotemporal variability increased with the degree of clustering in networks from PKC
⫺
to PKC
⫹
networks. The strength of neuronal recruitment during SBEs and the bursting strength
decreased along this axis. B, Spontaneous SBE rates. C, Bursting strength, i.e., the mean number of APs per site and SBE. D, AFRs, i.e., the average AP frequency across all site with activity in the 1 h
recording session. E, SBE recruitment, i.e., the average fraction of active sites recruited during SBEs. F, The irregularity of SBE initiation given as the coefficient of variance (CV) of inter-SBE intervals.
G, Variability in the intensity of neuronal firing during SBEs as average CV of bursts strengths (#APs/SBE) determined for individual sites. Boxplots show median, 25
th
, and 75
th
percentiles and
minimal and maximal values (excluding outliers). *p
stt
⫽0.05; **p
stt
⫽0.01; ***p
stt
⫽0.001.
3980 •J. Neurosci., April 5, 2017 •37(14):3972–3987 Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity
diversity of propagation patterns. Instead pattern diversity was
reduced and more similar to PKC
⫺
patterns with respect to their
intraclass similarity, although this was statistically significant
only for low classification thresholds between 0.02 and 0.16 (cor-
relation coefficient, 0.84 – 0.98; average number of classes result-
ing from classification of 100 SBEs with a threshold of 0.2: PKC
N
,
59.3 ⫾2.7; N⫽33; PKC
⫺
, 17.5 ⫾2.8; p
stt
⫽3.6*10
⫺13
;N⫽17;
PKC
⫹
, 46.4 ⫾8.0; N⫽8). In PKC
⫺
networks, SBE activity was
dominated by a smaller number of pattern classes than in PKC
N
and PKC
⫹
networks (Fig. 7E). On average, the five most frequent
classes accounted for 89.1 ⫾2.8% (p
stt
⫽5.5*10
⫺11
vs PKC
N
)of
the SBEs in PKC
⫺
networks but accounted for only 44.5 ⫾3.5%
in PKC
N
and 53.8 ⫾8.6% in PKC
⫹
networks.
Clustering promotes spatial variability of SBE initiation
SBEs typically initiated locally in BIZs, here defined by the elec-
trodes at which spikes assigned to a burst were detected first (Fig.
8A). With large MEAs that covered networks almost fully, we
identified the position of such BIZs. In all networks, BIZs clus-
tered spatially, indicating hot spots of SBE initiation, and had a
propensity to be located at the network boundary (Fig. 8B). Con-
sistent with the low diversity of SBE propagation patterns, BIZs
were more clustered in PKC
⫺
networks than in PKC
N
and PKC
⫹
networks. BIZs alternated in eliciting SBE (Fig. 8B, right), with
clusters of BIZs (hotspots) dominating SBE initiation in particu-
lar in PKC
⫺
networks. In all network types, spiking activity in-
creased in the vicinity (⬃1.5 mm) of the BIZ before SBE onset
(Fig. 8C). Interestingly, in PKC
⫹
networks, activity could persist
between SBEs in particular in the BIZ.
Influence of inhibition on spontaneous dynamics
The influence of inhibition in a network depends on the connec-
tivity of inhibitory neurons and thus may differ in networks with
different architecture. To test the influence of inhibition on spon-
Figure 7. Diversity in SBE propagation patterns. A, Spearman correlation matrices for first-spike rank-order sequences in SBEs (N⫽100 SBEs, 1 network per condition). B, Corresponding
dendrogramsforcompletelinkageclustering. PKC
⫺
networkstypicallydisplayedafew distinct propagation pattern classes, while PKC
N
networksweremarkedbya continuum of gradually differing
patterns. Strongly clustered PKC
⫹
networks had many distinct pattern classes. C, Average propagation patterns for classes for a threshold distance of 0.2 (corresponding to a Spearman correlation
coefficient of 0.8; B, red line). D, The number of SBE classes depended on the distance threshold applied to group SBEs. PKC
⫺
and PKC
⫹
networks formed significantly fewer classes for low and
intermediate distance thresholds. E, Fraction of all SBEs of the N-most frequently occurring SBE classes. In PKC
⫺
networks a significantly smaller number of pattern classes dominated the activity.
Bars above the panel indicates the range of pairs with p
stt
⬍0.01 tested versus PKC
N
.
Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity J. Neurosci., April 5, 2017 •37(14):3972–3987 • 3981
taneous activity dynamics, we disinhibited networks with PTX
applied at a concentration blocking ionotropic GABAergic trans-
mission (10
M;Krishek et al., 1996). PTX application increased
spontaneous activity (AFRs) in all network types with the stron-
gest effect in PKC
⫺
networks (Student’s ttest for paired samples:
PKC
N
,⫹119 ⫾33%; N⫽18; p⫽4.9*10
⫺3
; PKC
⫺
,⫹496 ⫾
108%; N⫽18; p⫽1.1*10
⫺5
; PKC
⫹
,⫹88 ⫾45%; N⫽8; p⫽
1.1*10
⫺2
;Fig. 9A). More detailed analysis showed that this was
due to significantly stronger bursting (spikes/site) during SBEs
(PKC
N
,⫹980 ⫾378%; p⫽5.1*10
⫺4
; PKC
⫺
,⫹598 ⫾96%; p⫽
4.6*10
⫺7
; PKC
⫹
,⫹724 ⫾101%; p⫽0.069; Fig. 9B). In a result
counterintuitive to the expectation that increased excitability fol-
lowing disinhibition promotes SBE initiation, SBE rates signifi-
cantly decreased in PKC
N
and PKC
⫹
networks after adding PTX,
and remained at the low baseline levels in PKC
⫺
networks
(PKC
N
,⫺62 ⫾4%, p⫽2.7*10
⫺5
; PKC
⫺
,⫺1⫾12%; p⫽0.16;
PKC
⫹
,⫺72 ⫾6%; p⫽1.1*10
⫺2
;Fig. 9C).
These observations suggest that burst strength-dependent
network depression and recovery could limit SBE rates. Lower
activity, more negative resting V
m,
, and the pronounced increase
of SBE strength upon disinhibition in PKC
⫺
networks could re-
sult from increased or more effective inhibition compared with
PKC
N
networks. This then would also predict that their excitabil-
ity should be lower. To investigate whether PKC
⫺
networks were
limited by reduced excitability, we stimulated PKC
⫺
and PKC
N
networks electrically.
PKC
ⴚ
networks support high activity levels
To test whether PKC
⫺
networks can support higher SBE rates, we
stimulated a separate set of networks electrically with interstimu-
lus intervals of either 6, 4, or2satalternating stimulation sites,
corresponding to the range of spontaneous SBE activation inter-
vals and the alternating BIZs in PKC
N
networks. In PKC
⫺
net-
works, electrical stimulation significantly increased AFRs (6 s
interval ⫹583 ⫾245%, p⫽0.030; 4 s interval ⫹757 ⫾332%, p⫽
0.023; 2 s interval ⫹859 ⫾344%, p⫽0.011; N⫽3; mean ⫾SEM;
Student’s ttest for paired samples; Fig. 9D) and SBE rates (6 s
interval, ⫹762 ⫾327%; p⫽6.1*10
⫺4
; 4 s interval, ⫹1117 ⫾
466%; p⫽6.1*10
⫺4
; 2 s interval, ⫹1978 ⫾755%; p⫽9.6*10
⫺4
;
spontaneous and evoked SBEs; Fig. 9F) beyond the level of spon-
taneous activity in PKC
N
networks. Moreover, PKC
⫺
networks
showed almost complete entrainment of the SBE activity to the
stimulation with very high response probability (evoked re-
sponses within 100 ms for 91.3, 87.6, and 81.4% of the stimuli
given in series of 1200 stimuli at 6, 4, and 2 s intervals, respec-
tively; N⫽4) and highly reproducible propagation patterns
(similar to spontaneous propagation patterns). Stimulation only
slightly increased gross average activity levels in PKC
N
networks
(6 s interval, ⫹31 ⫾6%; 4 s interval, ⫹44 ⫾17%; 2 s interval,
⫹73 ⫾25%; differences were not statistically significant; N⫽3;
Fig. 9D) and SBE rate (6 s interval, ⫹76 ⫾39%; 4 s interval,
⫹95 ⫾57%; 2 s interval, ⫹194 ⫾112%; differences were not
statistically significant; Fig. 9F). PKC
N
networks were less re-
Figure 8. BIZs and network hotspots. A, SBEs were typically elicited in local areas of the network and spread with a traveling wave front. Note the smoother wave fronts in PKC
⫺
and PKC
⫹
networks compared with those in PKC
N
networks. In these recordings, the MEA covered most of the network. PKC
⫺
and PKC
N
networks were recorded with 1000 electrodes PKC
⫹
networks were
recorded with 256 electrodes. Color indicates rank order of the first spikes recorded for an SBE at a given electrode. Left, One sample pattern. Right, Samples of typical patterns placed according to
their BIZ, regardless of their frequency of occurrence (downsampled to 8 ⫻8 tiles with 8 ⫻8 and 4 ⫻4 electrodes). B, SBE initiation in the PKC
⫺
networks was typically dominated by few BIZs at
thenetworkboundary.Right, The trajectory depicts the temporal sequence ofBIZselicitingSBEs (300 SBEs) by connecting BIZ of successiveSBEs.InPKC
N
andPKC
⫹
networks,SBEswereelicited from
manydistributedandalternating BIZs. Scale bars: A,B, 100
m.C,Spatiotemporalfiring-rate histogram relative to SBE onset(0ms)and BIZ (0 mm). In all networktypes,activityaccumulated within
a distance of 1–2 mm to the BIZ before SBE onset (averages of 500 consecutive SBEs at 30 DIV).
3982 •J. Neurosci., April 5, 2017 •37(14):3972–3987 Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity
sponsive to stimulation (response probabilities: 6 s interval,
48.8%; 4 s interval, 48.4%; 2 s interval, 45.6%) and responses
were much more variable. Additional SBEs started spontaneously
between stimuli. Eliciting additional SBEs by stimulation re-
duced SBE strength in both network types but differences be-
tween control and stimulation sessions were not statistically
significant for PKC
N
(PKC
N
: 6 s interval, ⫺22 ⫾12%; 4 s inter-
val, ⫺21 ⫾12%; 2 s interval, ⫺32 ⫾13%; PKC
⫺
: 6 s interval,
⫺21 ⫾3%; p⫽0.009; 4 s interval, ⫺31 ⫾5%; p⫽0.018; 2 s
interval, ⫺55 ⫾6%; p⫽0.003; Fig. 9E). In summary, SBE rates in
PKC
⫺
networks were neither limited by their capacity to sustain
higher rates nor by GABAergic inhibition. Electrically driven ac-
tivity showed a dynamical trade-off between SBE rate and SBE
strength.
Removal of PKC modulators after chronic exposure
To identify direct effects of the PKC modulators on the network
activity, we replaced the medium with fresh drug-free medium
after 4 weeks in all three PKC activity conditions (Fig. 10).
Washout-induced changes of burst activity were similar in all
groups. Relative differences in spontaneous activity between PKC
activity conditions were preserved for ⱖ3 DIV after drug re-
moval. This supports the idea that the findings above were related
to changes in network structure rather than to direct drug effects.
Discussion
Computational network models suggest that clustered connec-
tivity is beneficial for generation, maintenance (Kaiser and
Hilgetag, 2010;Stetter et al., 2012;Klinshov et al., 2014), and
variability of spontaneous activity dynamics in neuronal net-
works (Litwin-Kumar and Doiron, 2012). We addressed this is-
sue in cortical networks in vitro in which we manipulated the
degree of clustering and neurite bundling.
Manipulating mesoscale network architecture by
PKC modulation
Networks in vitro establish community structures (Girvan and
Newman, 2002;Sporns, 2013) exhibiting local neuron clusters
with strongly entangled neurites, neurite bundles interconnect-
ing clusters, and long-range axons (Kriegstein and Dichter, 1983;
Shefi et al., 2002;Bettencourt et al., 2007;Stetter et al., 2012). We
manipulated this architecture by modulating PKC activity. The
control of cell migration and neurite outgrowth by PKC has been
discussed previously (Itoh et al., 1989;Dent and Meiri, 1998;
Kumada and Komuro, 2004;Larsson, 2006;Metzger, 2010). Di-
minishing PKC activity during network development pro-
duced remarkably uniform networks with more randomly
distributed somata and less fasciculated neurites than in
PKC
N
networks. The opposite effects resulted from stimula-
Figure 9. Effects of disinhibition and electrical stimulation. A–C, Blocking GABA receptors (10
MPTX) in PKC
N
networks produced stronger bursts at the expense of lowered SBE rates. AFRs
significantly increased despite of this trade-off. PTX application likewise increased SBE strength in PKC
⫺
networks did not affect SBE rates. In consequence, AFRs significantly increased. D–F,To
assess the maximal activity level supported by a network, we electrically stimulated at alternating sites. PKC
⫺
networks were highly responsive, producing SBE rates close to the rate of stimulation
(dashed line). Higher SBE rates (spontaneous or evoked) were at the cost of weaker bursts with fewer APs per site and SBE but still resulted in significantly increased AFRs. PKC
N
networks were less
responsive to stimulation than PKC
⫺
networks and showed no significant increase of AFRs. Boxplots show median, 25
th
, and 75
th
percentiles and minimal and maximal values (excluding outliers).
**p⫽0.01; ***p⫽0.001.
Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity J. Neurosci., April 5, 2017 •37(14):3972–3987 • 3983
tion of PKC, which promoted spatial aggregation of somata
and local neurite sprouting within regularly spaced and well
delineated clusters.
As with cerebellar Purkinje cells (Metzger, 2010), PKC inhibi-
tion increased neurite fields and neurite densities. This presum-
ably increases the probability of a synaptic connection between
any pair of neurons, predicting increased availability of long-
range connections. Conversely, lower neurite and, in particular,
lower axon densities in PKC
⫹
networks should reduce long-
range connectivity. Pronounced tangles of neurites within clus-
ters, in turn, likely promote highly recurrent local connectivity.
In addition, axon bundles formed mainly between neighboring
clusters.
We therefore hypothesize that diminished PKC activity shifted
the network toward uniform connectivity, whereas increasing PKC
activity promoted recurrent connectivity within and convergent
connectivity between local clusters at the expense of long-range
connectivity.
Mesoscale network architecture and synaptic dynamics
Anatomical analyses in vivo showed that statistically approxi-
mately a quarter of the contact sites between axon and dendrites
are realized as functional connections (Stepanyants et al., 2002).
Neurite architecture thus is a strong determinant of the realized
synaptic connectivity. In our networks, synapses were indeed
more uniformly distributed in PKC
⫺
networks. Based on these
observations and reports that the strength of synapses inversely
scales with their number in vitro (Wilson et al., 2007) and in vivo
(Turrigiano, 2008), smaller average EPSP amplitudes in PKC
⫺
networks might be explained by the higher neurite density
resulting in more synaptic connections. Although PKC has been
implicated in synaptic plasticity (Saito and Shirai, 2002;Boehm et
al., 2006), it is apparently not essential for activity-dependent
synaptic plasticity due to other functionally redundant kinases
(Wang and Kelly, 1996;Herring and Nicoll, 2016). If so, neurons
should retain activity-dependent synaptic plasticity even with re-
duced PKC activity.
Large EPSP amplitudes in neurons associated with clusters are
consistent with findings by Cohen et al. (2008) that in isolated
clusters with ⬍10 neurons, the stimulation of single neurons is
sufficient to activate the entire ensemble while this is not possible
in larger networks. EPSP amplitudes measured in neurons out-
side of clusters were indeed comparable across conditions, sug-
gesting that their effective synaptic weights were not determined
by the respective overall changes in network structure but re-
flected their local network embedding.
The larger EPSPs in clusters could also be explained by a
higher propensity for multiple synaptic contacts between neu-
ron pairs and the highly correlated presynaptic firing attrib-
uted to clusters (Helias et al., 2014). The precise synchrony
among such connections, however, makes it impossible to dis-
tinguish between unitary EPSPs from single EPSPs and com-
pound EPSPs from sister release sites.
Figure 10. Effects of drug washout following chronic treatment. A–F, Complete medium exchange with fresh drug-free medium after 30 d of chronic treatment with PKC modulators produced
no systematic changes of the activity dynamics. Changes in PKC
N
networks indicate an effect of the washout itself, likely due to the strong perturbation or the replacement with fresh medium.
Changes in PKC-modulated networks were in the same range or smaller than those in PKC
N
networks. See Figure 6 for further details.
3984 •J. Neurosci., April 5, 2017 •37(14):3972–3987 Okujeni et al. •Mesoscale Architecture Shapes Spontaneous Activity
Clustering promotes spontaneous SBE generation
V
m
fluctuations strongly determine neuronal firing rates
(Kuhn et al., 2004), which in a recurrent network translate
back again into neuronal input statistics. Giugliano et al.
(2004) hypothesized that SBE initiation is a threshold-gated
process following activity integration on the network level
analogous to the AP generation in neurons. Network architec-
tures that increase background firing-rate fluctuations should
thus promote SBE initiation. Along with larger EPSPs and
higher EPSP frequencies, average V
m
and V
m
fluctuations in-
creased with the degree of clustering in networks. Neurons
consequently were more likely to reach the spiking threshold,
which can be sufficient to activate an entire local cluster (Co-
hen et al., 2008). Fasciculation of axons that extend from
clusters conveys highly correlated output to common target
clusters, promoting focused long-range excitation. Neuronal
clusters could thus act as local amplifiers or relay stations that
promote generation of spontaneous activity and reliable
transmission of activity within a network. Consistent with
this, SBE rates significantly increased with the degree of clus-
tering in networks. Furthermore, SBEs were elicited at widely
distributed sites in clustered networks, suggesting that many
local subnetworks exist that trigger SBEs. PKC
⫺
networks typ-
ically had few hotspots of SBE initiation. We propose that
diminished neurite fasciculation fostered divergent rather
than convergent connectivity patterns in these networks, re-
ducing correlated input. Interestingly, hotspots were mostly
located at the network boundary, which was most prominent
in more uniform PKC
⫺
networks. In agreement with Gritsun
et al. (2012), the boundary may enforce inward connections,
thus promoting recurrent and convergent connectivity. Clus-
ters may introduce additional BIZs by pronounced locally re-
current connectivity.
How changes in the network architecture affect overall inhibition
levels and activity dynamics is difficult to predict. In PKC
⫺
networks
excitation–inhibition balance could be more homogeneous than in
clustered networks, where this could vary locally. This would intro-
duce hot (and cold) spots that could further contribute to SBE initi-
ation. Although recent studies find that homeostatic synaptic
plasticity or spike-frequency adaptation could compensate for local
excitation–inhibition imbalance (Barral and D Reyes, 2016;Landau
et al., 2016), it is not clear whether this would fully flatten the exci-
tation–inhibition landscape.
Global versus local synchrony
SBEs dominated in all networks, regardless of their particular
mesoscale structure. However, SBE rate modulations and the oc-
currence of super bursts (Wagenaar et al., 2006b) increased with
the degree of clustering and neurite fasciculation, which is con-
sistent with network simulations showing that clustered topolo-
gies promote firing-rate variability (Litwin-Kumar and Doiron,
2012). In PKC
⫺
networks, activity reliably recruited most neu-
rons once SBEs were initiated. In the strongly clustered PKC
⫹
networks, however, SBEs often remained confined to local net-
work areas, as was observed in heavily clustered networks form-
ing without adhesive growth substrates (Teller et al., 2014). We
suggest that strong local dynamics in clusters and weak long-
distance coupling entails global desynchronization analogous to
effects described for weakly coupled oscillators (Strogatz and Mi-
rollo, 1991;Park et al., 2006). Global synchrony thus should de-
pend on the balance between local and global connectivity.
Moderately clustered PKC
N
networks were optimal in terms of
allowing high SBE rates conjointly with strong network recruit-
ment in SBEs and seem to possess sufficient recurrent connectiv-
ity to sustain activity as well as sufficient long-range connectivity
to recruit distant parts of the network.
SBE initiation: a buildup or release process?
The rate of SBE succession may depend on recovery from synap-
tic depression and a buildup process that involves the amplifica-
tion of activity in recurrent networks beyond a critical threshold
(Tabak et al., 2010). To gain insight into the underlying mecha-
nism, we tested networks by disinhibition and electrical stimula-
tion. As described earlier (Weihberger et al., 2013), disinhibition
only slightly increased overall spontaneous activity levels in
PKC
N
networks, since longer bursts were counterbalanced by
lower SBE rates. This trade-off suggests that PKC
N
networks op-
erate within a regime of activity that exploits most resources. In
agreement with this, overall activity levels could not be increased
much by external stimulation. In PKC
⫺
networks, amplified
bursting through disinhibition was not balanced by lower SBE
rates, suggesting rate limitation by the initiation process rather
than by synaptic resources. Consistent with this assumption,
electrical stimulation significantly increased activity levels and
SBE rates.
Clustering promotes richness of activity patterns
In accordance with Soriano et al. (2008), we found discrete network
areas that had a high propensity to trigger SBEs. These BIZs were
frequent and widely distributed in clustered networks and less abun-
dant in homogenous PKC
⫺
networks, which is consistent with the
idea that convergent and recurrent connectivity patterns promote
burst initiation. In all networks, SBEs spread with approximately
circular wave fronts. Interestingly, PKC
⫹
networks displayed more
regular wave fronts than PKC
N
networks, rather similar to those
observed in PKC
⫺
networks. One could argue that this is because
regular spacing of clusters in PKC
⫹
networks likewise introduces
uniformity at the mesoscale level. Since the local direction of prop-
agation depended mainly on the BIZ location, distributed BIZs in
clustered networks fostered a rich repertoire of different spatiotem-
poral propagation patterns.
Conclusion
We showed that modulation of PKC during network development
can change the balance between locally clustered and long-range
connectivity. Consistent with network simulations, our data indicate
that clustering promotes local activity generation but leads to re-
duced global synchrony. Intriguingly, the richness of structure and
activity patterns in PKC
N
networks are suggestive of small-world
and scale-free topologies with their computational advantages. We
propose that the coevolution of spontaneous activity levels, richness
of activity patterns, and community network structure reflects a
fundamental principle of neuronal self-organization.
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