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Modulation of Large Rhythmic Depolarizations in
Human Large Basket Cells by Norepinephrine and
Acetylcholine
Dirk Feldmeyer
Research Centre Jülich https://orcid.org/0000-0002-1716-8972
Danqing Yang
Research Center Juelich, Institute of Neuroscience and Medicine, INM-10
Guanxiao Qi
Jonas Ort
RWTH Aachen University Hospital
Victoria Witzig
RWTH Aachen University Hospital https://orcid.org/0000-0002-1141-6736
Aniella Bak
RWTH Aachen University Hospital https://orcid.org/0000-0003-2449-9689
Daniel Delev
Department of Neurosurgery, RWTH University of Aachen, Aachen, Germany
Henner Koch
RWTH Aachen University Hospital
Article
Keywords:
Posted Date: July 12th, 2024
DOI: https://doi.org/10.21203/rs.3.rs-2888711/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
Additional Declarations: There is NO Competing Interest.
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Abstract
Rhythmic brain activity is critical to many brain functions and is sensitive to neuromodulation, but so far
very few studies have investigated this activity on the cellular level
in vitro
in human brain tissue
samples. This study reveals and characterizes a novel rhythmic network activity in the human neocortex.
Using intracellular patch-clamp recordings of human cortical neurons, we identify large rhythmic
depolarizations (LRDs) driven by glutamate release but not by GABA. These LRDs are intricate events
made up of multiple depolarizing phases, occurring at ~ 0.3 Hz, have large amplitudes and long decay
times. Unlike human tissue, rat neocortex layers 2/3 exhibit no such activity under identical conditions.
LRDs are mainly observed in a subset of L2/3 interneurons that receive substantial excitatory inputs and
are likely large basket cells based on their morphology. LRDs are highly sensitive to norepinephrine (NE)
and acetylcholine (ACh), two neuromodulators that affect network dynamics. NE increases LRD
frequency through β-adrenergic receptor activity while ACh decreases it via M4 muscarinic receptor
activation. Multi-electrode array recordings show that NE enhances and synchronizes oscillatory network
activity, whereas ACh causes desynchronization. Thus, NE and ACh distinctly modulate LRDs, exerting
specic control over human neocortical activity.
Introduction
Rhythmic brain activity plays a pivotal role in numerous brain functions, from sensory processing to
memory consolidation 1–4. Over the past few decades, several studies have shown that resected human
cortical tissue can sustain rhythmic network activities across various frequency bands 5–9. Apart from
oscillations elicited by pharmacological application or electrical stimulation 6,8,10, spontaneous
synchronous events were found in slices prepared from human epileptic neocortex and were suspected
to be epilepsy-related 5–7,9. Intriguingly, comparable rhythmic activity has been observed in the healthy
monkey hippocampus and non-epileptic neocortex 9,11,12, suggesting that these events may indeed be
correlated with physiological network activity independent of epileptogenic processes. It is worth noting
that most of these rhythmic brain activity were recorded using an articial cerebrospinal uid (ACSF)
with a higher concentration of K+ to increase the overall excitability of the brain slices. The use of a
similar ACSF in the ferret, mouse, and rat neocortex have been linked to the induction of in vitro slow
oscillations, known as ‘Up’ states, leading to discharges in neuronal populations 13–15. Consequently, the
diverse characteristics of in vitro brain oscillations present challenges in pinpointing the functional
mechanisms that drive them.
Although spontaneous synchronous activity has been previously described in human cortical slices,
these activities were usually measured using multiple-electrode array or sharp microelectrode
techniques, making it hard to attain high resolutions for deciphering their temporal structures and their
associations with non-synchronous events 5,6,9,12. Additionally, the specic morphology of individual
neurons participating in these network activities has been largely unexplored. Unraveling whether
particular neuronal cell types are disproportionately active in these networks is essential, as they may be
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instrumental in mediating state transitions in the brain, with potential signicant implications for
behavior and cognition.
Norepinephrine (NE) and acetylcholine (ACh) are neuromodulators historically known to inuence
network dynamics across varying behavioral states 16–18. By stimulation of brainstem noradrenergic and
cholinergic nuclei or the basal forebrain, NE and ACh release is known to modulate cortical oscillations
19–22. Yet, our understanding of how NE and ACh modulate the human neocortex remains limited 23,24,
because our current knowledge about the neuromodulation of the neocortex by NE and ACh is derived
almost exclusively from
in vitro
/
in vivo
experiments in rodents. Given that primates, including humans,
exhibit a higher density of neuromodulatory afferents in the neocortex than rodents 25,26, it is essential to
investigate the neuromodulation of neuronal network activity in human neocortical tissue to elucidate
the mechanisms at play. Here, we report that in acute human brain slices, spontaneous large rhythmic
depolarizations (LRDs) were detected in neocortex in the absence of pharmacological manipulation or
external stimulation. These LRDs are complex network events that depend on presynaptic glutamatergic
release but are independent of GABAergic release. LRDs were more frequent in human interneurons than
in pyramidal cells; these were not observed in rat brain slices under similar conditions. Further
characterization revealed that human interneurons exhibiting LRDs (LRD+) have distinct, broad dendritic
and axonal arborization patterns, and are of the large basket cell type. This is in stark contrast to
interneurons that show no LRD activity (LRD-). Moreover, LRDs are highly sensitive to noradrenergic and
cholinergic modulation with NE enhancing and ACh reducing LRD frequency. MEA recordings revealed a
clear increase in neuronal spiking synchronization by NE while ACh drove neurons towards a more
desynchronized ring pattern. This implies that synchronous brain activity is under antagonistic
neuromodulatory control of NE and ACh in the human brain.
Results
Large rhythmic depolarizations (LRDs) are observed in a
subset of human cortical L2/3 neurons
Acute brain slices were prepared from tissue blocks following brain surgery (resection of epileptic and
tumor foci) in 24 patients aged 8–75 years. The neocortical tissue used predominantly came from the
temporal or frontal cortex, with exceptions including one case from the occipital and another from the
parietal cortex (Supplementary Tab. S1). The tissue was removed during surgical access to the
pathological brain region and was suciently distant from the tumor and/or epileptogenic focus. Whole-
cell current clamp recordings were made in human cortical L2/3 neurons with simultaneous biocytin
lling, which allowed for post hoc identication of their morphology. 82 pyramidal cells (PCs) and 64
interneurons (INs) were identied by their electrophysiological properties and morphological features
(Fig.1e). In a subset of human L2/3 neurons (24 out of 146 neurons), large rhythmic depolarizations
(LRDs) were observed against a background of excitatory postsynaptic potentials (EPSPs). In some
neurons (9 out of 17 L2/3 LRD + interneurons), the depolarization triggered direct action potential (AP)
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discharges (Fig.1a). LRDs were not associated with a specic brain region, gender, age or
pathophysiology (Supplementary Tab. S1). For each neuron exhibiting LRDs, we analyzed the excitatory
postsynaptic activity (continuous current-clamp recording, 100 s). LRDs and EPSPs were analyzed
separately. In L2/3 interneurons, LRDs exhibited an average amplitude of 10.2 ± 2.9 mV (n = 535 events in
17 neurons), which was signicantly larger than that of unitary EPSPs (2.0 ± 1.0 mV, n = 17213 events in
17 neurons). Dramatic differences in dynamic properties were also observed between LRDs and EPSPs.
The decay time was signicantly longer for LRDs (152.5 ± 43.3 ms) than for EPSPs (39.9 ± 13.6 ms)
(Fig.1b&d). LRDs occurred at a low frequency ranging between 0.08 and 0.70 Hz (mean = 0.29 ± 0.18
Hz). In contrast, EPSPs had a much higher frequency of 9.7 ± 5.7 Hz (Fig.1c&d) suggesting that the
functional mechanisms underlying LRD generation are fundamentally different from those of EPSPs.
LRDs were observed more frequently in L2/3 interneurons than in PCs (26.6% or 17 out of 64 for
interneurons vs. 8.5% or 7 out of 82 for PCs; Fig.1f). LRD frequency and rise time in human L2/3 PCs
and interneurons were similar, however, L2/3 PCs exhibited a smaller LRD amplitude and a prolonged
decay time (Supplementary Fig. S1). No spontaneous AP ring was triggered by LRDs in all 7 PCs
showing LRDs. We found that LRDs were detected only within the rst 6 hours after slicing but not
thereafter (see also Supplementary Fig. S2).
To investigate whether LRDs were also present in rodents, recordings were performed from L2/3
neurons in acute brain slices of adult rat prefrontal and temporal cortex. No LRDs activity was detected
in L2/3 PCs (n = 18, Fig.1f & Supplementary Fig. S1). We observed sporadic LRD-like events in 2 out of
21 L2/3 interneurons but these events showed no rhythmicity or persistency and therefore were
markedly different from typical LRDs observed in human neurons (Supplementary Fig. S1). Our data
suggest that the human brain is more likely to generate LRDs than the rat brain under the same recording
conditions.
It has been reported that slow rhythmic activity is initiated and prominent in infra-granular layers and
then propagates to supra-granular layers 13. To investigate network activity across all neocortical layers,
we performed extracellular recordings on cultured human cortical brain slices using a 256 channel multi-
electrode array (MEA). Local eld potentials (LFPs) were detected both in L2/3 and L5/6 (Supplementary
Fig. S3a&b). Whole-cell recordings were carried out in human L6 cortical neurons; typical LRD activity
was also observed in L6 interneurons (Supplementary Fig. S3c&d). Most of the network events detected
on the MEA originated in deep layers (L5/6, 73.1%), with the remaining events originating in L2/3 (26.9%)
(Supplementary Fig. S3e). The spatial activity pattern of the events propagates in both horizontal (within
layers) and vertical directions (across layers) in a complex manner (Supplementary Fig. S3f&g). Taken
together, LRDs are not conned to L2/3 but occur in different neocortical layers.
Previous studies suggested that human L2/3 PCs form stronger, more reliable connections compared to
rodents 27,28. Such strong connections make a direct postsynaptic AP more likely and could contribute to
synchronized AP ring in the local neuronal microcircuitry. Contrary to spontaneous unitary synaptic
inputs triggered by individual presynaptic APs, we hypothesize that LRDs are induced by near-
synchronous ring in presynaptic glutamatergic neurons (Fig.2a). As a rst test of this hypothesis, we
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performed a time-frequency analysis of the spontaneous synaptic activity in LRD + interneurons. Low
frequency LRDs and high frequency EPSPs can be clearly discriminated (Fig.2b). The onset of a LRD
was always accompanied by a superposition of small amplitude events, indicating that LRDs are
network-driven synchronous oscillations (Fig.2b). To reveal the temporal structure of LRDs, we
performed voltage clamp recordings in neurons that exhibited LRDs. During the course of an LRD, we
observed a predominance of EPSCs with a minimal contribution from IPSCs (Fig.2c&d).
To investigate the presynaptic mechanisms underlying LRD generation, either 0.5 µM TTX or 10 µM
CNQX was bath-applied. Both TTX (n = 2) and CNQX (n = 3) completely eliminated LRDs, suggesting that
LRDs are AP-dependent synaptic events relying on presynaptic glutamate release (Fig.2e). Previous
studies have reported so-called ‘giant depolarizing events’ for rat hippocampus and neocortex during the
rst postnatal week, and were elicited by γ-aminobutyric acid (GABA) which is a depolarizing transmitter
at early developmental stages because of a high intracellular Cl- concentration 29–32. In our experiments
bath application of gabazine (1 µM, n = 4) did not alter the LRD amplitude, frequency or dynamic
properties (Fig.2e and Supplementary Fig. S4), indicating that GABAergic synaptic transmission do not
play a causal role in LRD generation.
Since LRD + neurons might represent a group of highly connected cells in the neuronal network, we
examined the timing and magnitude of EPSPs in L2/3 interneurons that did and did not show LRDs.
Notably, LRD + interneurons exhibited a much higher EPSP frequency compared to LRD- interneurons
(11.0 ± 7.7 vs. 2.9 ± 2.4 Hz, P < 0.001). In addition, the amplitude of spontaneous EPSP was on average
1.5 times larger in LRD + interneurons compared to LRD- interneurons (LRD + INs: 2.1 ± 0.9 mV, LRD - INs:
1.4 ± 0.5 mV, P < 0.05) (Fig.2f-h). In summary, our data demonstrates that LRD + interneurons receive
robust excitatory input and participate in neuronal network activity. This supports the notion that the
human neocortex is more predisposed to produce LRDs than the rodent neocortex, likely due to the
notably strong excitatory-to-inhibitory connectivity present in the human L2/3 neocortex 28,33.
It has been reported that
in vitro
slow oscillations can be induced in ferret visual and prefrontal cortices
when perfused with a high K+/low Ca2+ ACSF 13. We performed intracellular recordings in L2/3 human
neurons using an ACSF containing 3.5 mM K+/1 mM Ca2+. In 5 out of 7 neurons we identied typical ‘Up’
states comprised a mix of inhibitory and excitatory inputs (Supplementary Fig. S5a). These ‘Up’ states
had durations in the second range and occurred less frequently than LRDs (0.02 ± 0.01 vs. 0.32 ± 0.18 Hz,
P < 0.001) (Supplementary Fig. S5b1&b2, d). In one PC we observed the simultaneous occurrence of
LRDs and ‘Up’ states reinforcing the notion that LRDs signify a unique and novel type of network behavior
(Supplementary Fig. S5b3&c). A detailed comparison between LRDs and ‘Up’ states is presented in
Supplementary Fig. S5d.
Interneurons with LRD are Large Basket Cells
Neocortical interneurons can be divided into several distinct subtypes based on their
electrophysiological, morphological and transcriptional properties 34–37. To investigate whether LRDs
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were observed in a specic interneuron subtype, we analyzed the electrophysiological properties and
performed 3D morphological reconstructions of L2/3 interneurons with and without LRDs. No clear
correlation between LRD occurrence and neuronal ring pattern was found: both LRD + and LRD-
interneurons exhibit diverse ring patterns comprising typical fast-spiking (FS) and non-fast spiking (nFS,
including adapting, late spiking, etc.) (Fig.2i; Supplementary Tab. S2) 38,39. However, after detailed
analysis of passive and active properties, LRD + interneurons were found to have a smaller input
resistance compared to LRD- interneurons (192.4 ± 85.1 vs. 298.5 ± 117.9 MΩ, P < 0.05). We next studied
the morphology of LRD + and LRD- L2/3 interneurons. Notably, LRD + interneurons were mostly large
basket cells with dense and broad dendritic and axonal domains. In contrast, LRD- interneurons
comprised multiple morphological subtypes including small basket, bipolar, double bouquet, and
neurogliaform cells (Fig.2i) 37. LRD + interneurons showed signicantly longer dendrites and axons than
LRD- interneurons (4.2 ± 1.2 vs. 2.1 ± 1.1 mm for dendritic length, P < 0.05; 40.8 ± 8.2 vs. 12.5 ± 5.4 mm
for axonal length, P < 0.001). The horizontal dendritic and axonal eldspan was wider for LRD + than for
LRD- interneurons (546 ± 176 µm vs. 212 ± 104 µm for dendrites and 1228 ± 427 µm vs. 604 ± 405 µm
for axons, respectively; Fig.2j). Moreover, a larger vertical dendritic eldspan was also observed for LRD
+ neurons (652 ± 194 vs. 308 ± 217 µm, P < 0.01). More details regarding morphological properties and
statistical comparisons are given in Supplementary Tab. S2.
Parvalbumin (PV)-expressing GABAergic interneurons are known to exert perisomatic inhibition onto PCs
and contribute to cortical network oscillations 40. A large proportion of basket cells in the human
neocortex are PV-expressing neurons displaying a FS ring pattern 41,42. To identify the expression of PV
in human L2/3 interneurons, we performed whole-cell recordings with simultaneous lling of biocytin
and the biocytin-conjugated uorescent Alexa Fluor 594 dye and subsequent immunolabelling. We found
that FS interneurons showing LRDs were PV-positive, while nFS interneurons showing LRDs were PV-
negative (Supplementary Fig. S6). This indicates that, although LRD + interneurons show uniform
morphologies, they contain more than one transcriptional type of basket cells including FS PV-positive
cells and also nFS, possibly cholecystokinin (CCK)-expressing interneurons 43,44.
Norepinephrine (NE) induces LRDs or enhances their
frequency via β-adrenergic receptors
In a subset of human L2/3 interneurons (n = 7), LRDs were induced following bath-application of 30 µM
NE (Supplementary Fig. S7). The morphological and functional properties of these neurons suggest that
they are large basket cells exhibiting high-frequency background EPSPs, similar to those L2/3
interneurons with spontaneous LRDs under control conditions. Thus, they were included in the statistical
analysis of electrophysiological and morphological properties of LRD + interneurons in Fig.2 and
Supplementary Tab. S2. Furthermore, in L2/3 interneurons with spontaneous LRDs, 30 µM NE
signicantly increased LRD frequency. The NE-induced changes in LRD frequency were reversible
following washout (Fig.3a). A similar increase of the LRD frequency was also observed in the presence
of only 10 µM NE (Supplementary Fig. S8). To examine whether NE specically altered the intrinsic
membrane properties in LRD + interneurons, we measured NE-induced changes in the resting membrane
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potential (Vm) but found no signicant effect in both LRD + and LRD- interneurons (Supplementary
Fig.3b).
LRDs and background EPSPs were analyzed separately for control conditions and in the presence of 30
µM NE (Fig.3C). Our results demonstrate that NE differentially modulates LRDs and EPSPs in LRD +
human L2/3 interneurons. Spectral analysis of the spontaneous activity in LRD + interneurons revealed
that NE signicantly increased the frequency of the LRDs (from 0.13 ± 0.17 Hz to 0.39 ± 0.23 Hz, n = 12
neurons, P < 0.001) without affecting the frequency of the EPSPs (10.16 ± 8.53 vs. 10.86 ± 6.52 Hz, P =
0.5796, Fig.3d). In addition, while NE slightly reduced the amplitude of LRDs (from 11.92 ± 4.07 to 10.02
± 3.53 mV, P < 0.05), it increased that of EPSPs (from 1.69 ± 0.65 to 2.11 ± 1.02 mV, P < 0.05) (Fig.3e&f).
To determine the specic adrenergic receptor type mediating the NE-induced increase in LRD frequency,
we applied either 2 µM prazosin (an α1-adrenergic receptor antagonist) or 20 µM propranolol (a β-
adrenergic receptor antagonist) together with 30 µM NE, following a bath-application of NE alone. While
prazosin had no effect on the adrenergic response, propranolol completely blocked the NE effect on LRD
frequency. The LRD frequency increased from 0.11 ± 0.09 to 0.28 ± 0.04 Hz during the NE application and
returned to control level (0.09 ± 0.10 Hz) following co-application of NE and propranolol (Fig.3g&h).
These experiments indicate that the increase in LRD frequency due to NE is mediated by β-
adrenoreceptors activation.
Effect of NE on Global Cortical Network Activity
To assess the effect of NE on overall cortical network activity, we made recordings in human cortical
brain slice cultures using a 256 channel multi-electrode array (MEA) ) both before and after the bath-
application of NE and ACh. Under control conditions the analyzed slices (n = 25, 6–16 days in vitro (DIV))
exhibited spontaneous network activity (Fig.3i&j and 4 i&j) with an average ring rate across all channels
of 10.5 ± 15.4 Hz. There were 23.4 ± 42.7 (out of 256) active channels and 5.7 ± 8.6 channels with
detected local eld potentials (LFPs; see methods for details). As a summated signal of concurrent
neuronal activity close to the recording site, LFP illustrates the dynamic progression of network activity.
The average amplitude of the negative LFP was − 57.8 ± 46.7 µV. The bath application of 30 µM NE (n =
13) resulted in a marked increase in the global ring rate (from 10.5 ± 17.2 Hz to 14.8 ± 12.8 Hz, *p <
0.05), paired with a signicant surge in the negative amplitude of the detected LFPs (from − 53.2 ± 45.5
µV to -76.3 ± 59.4 µV, *p < 0.05) (Fig.3j-l).
We used graph analysis to quantify the synchronicity degree of neuronal network activity, calculating the
degree of centrality for channels with simultaneous spiking activity as a surrogate measure for
synchronicity. The mean degree centrality (MDC) for slices treated with NE rose from 0.09 ± 0.09 to 0.16
± 0.15 (*p < 0.05, Fig.3l and Supplementary Fig. S9e). The increased activity was mainly noted at
electrodes that were already active under control conditions or those nearby (Fig.3j). No signicant
difference in the number of active channels with an LFP was observed (Fig.3l). The increase in ring rate
following bath-application of NE was statistically signicant for electrodes located in both L2/3 and
L5/6.
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Acetylcholine (ACh) suppresses LRDs via the activation of M 4 Rs.
ACh shifts cortical dynamics from a synchronous to asynchronous state and improves the signal-to-
noise ratio of sensory signaling 19,21. To elucidate cholinergic effects on human L2/3 interneurons, 30
µM ACh was bath applied and ACh-induced changes in Vm were compared between interneurons with
and without LRDs. ACh persistently depolarized all tested human L2/3 interneurons, an effect that was
reversible on washout with ACSF. LRD + and LRD- L2/3 interneurons were depolarized by 4.0 ± 3.5 mV (n
= 10) and 3.1 ± 2.6 mV (n = 12), respectively (Fig.4b). No signicant difference in depolarization
amplitude was observed.
We found that ACh application resulted in a marked suppression of both spontaneous (Fig.4a) and NE-
induced LRDs (Supplementary Fig. S7). To systematically study cholinergic modulation of spontaneous
activity, we analyzed LRDs and background spontaneous EPSPs from continuous current-clamp
recordings during 100 s epochs in each LRD + interneuron under control conditions and in the presence
of ACh (Fig.4c). Spectral analysis of spontaneous activity in LRD + interneurons indicates that ACh
blocked large amplitude events but increased the frequency of small amplitude events (Fig.4d). In fact,
ACh either decreased (n = 6) or completely blocked (n = 4) the occurrence of LRDs, resulting in a
reduction of LRD frequency from 0.40 ± 0.17 Hz to 0.13 ± 0.13 Hz. Of note, ACh at a lower concentration
of 10 µM similarly reduced LRD frequency (Supplementary Fig. S8). In contrast, 30 µM ACh signicantly
increased the frequency of EPSPs from 11.18 ± 6.74 Hz to 14.12 ± 7.99 Hz (Fig.4e&f). Our data suggests
that ACh modulates unitary synaptic and LRD activity in a distinctive fashion, leading to a
desynchronization of synaptic potentials thereby reducing LRD frequency or abolishing LRD occurrence
altogether. Additionally, ACh slightly decreased LRD amplitude from 12.74 ± 2.94 mV to 10.37 ± 4.24 mV
but had no effect on the amplitude of EPSPs (1.78 ± 0.80 vs. 1.78 ± 0.65 mV, P = 0.9856) (Fig.4e&f). It
has been reported that by blocking K+ conductances, ACh terminates ‘Up’ states via muscarinic
receptors 20,45. Here we found that the cholinergic modulation of LRDs was blocked by administration of
a muscarinic M4 receptor (M4R) antagonist, tropicamide, but not by mecamylamine, a general nicotinic
antagonist. The LRD frequency, which was reduced by ACh, increased to control level in the presence of
1 µM tropicamide (from 0.07 ± 0.06 to 0.28 ± 0.13, P < 0.05). Likewise, the ACh-induced decrease of the
LRD amplitude was reversed as well (from 6.44 ± 3.65 to 8.16 ± 3.54 mV, P < 0.05, Fig.4g&h). These
results suggest that ACh suppresses LRDs mainly via the activation of M4Rs.
Effect of ACh on Cortical Network Activity
To further understand how ACh inuences cortical network activity, we assessed its effect in human
cortical slice cultures using the MEA recording system. Bath application of ACh (15–30 µM, n = 12)
resulted in a substantial increase in the average AP ring rate (from 9.7 ± 13.7 Hz to 50.1 ± 62.2 Hz, **p <
0.01, Fig.4l). Yet, this increased ring rate appeared patchy and disjunct (Fig.4j). Additionally, the
temporal and spatial correlation observed under control conditions was disrupted in the presence of ACh
(Fig.4j&k). Moreover, the rise in ring rate did not coincide with an increase in LFP amplitude (which
would represent synchronous network events). Instead, there was a signicant reduction in the number
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of active electrodes showing LFPs (Fig.4l). The graph analysis to quantify the synchronicity degree of
neuronal network activity revealed a drop in the degree of centrality from 0.17 ± 0.18 to 0.12 ± 0.11 (*p <
0.05) after ACh application (Fig.4l and Supplementary Fig. S9f). We observed no notable difference
between the ring rate increase in electrodes situated in L2/3 or L5/6 following the ACh application. To
further probe how AP ring was modulated by NE and ACh, 30 µM NE and 30 µM ACh were applied on
L2/3 interneurons showing LRD-induced AP ring. We found that NE increased the LRD frequency and in
turn enhanced the LRD-induced AP ring rate. In contrast, application of ACh strongly depolarized
neurons and thus increase the overall AP ring rate. However, AP ring was no longer correlated, thereby
preventing the generation of LRDs (Supplementary Fig. S10). This indicates that NE and ACh enhance AP
ring rates in L2/3 interneurons through distinct mechanisms.
Discussion
In this study, we uncovered and characterized a rhythmic network event, revealing cell type-specic
network activity in layer 2/3 of human neocortex, here coined large rhythmic depolarization (LRD). LRDs
appeared in a low frequency range (0.1–0.7 Hz), displayed large amplitudes, long decay times and
sometimes triggered AP ring. Although L2/3 PCs occasionally showed LRDs, they were predominantly
observed in a subset of L2/3 interneurons exhibiting dendritic and axonal morphologies similar to those
of large basket cells 46. In contrast to human neocortex, LRDs were not observed in layer 2/3 of rat
frontal or temporal cortex under identical recording conditions. Our data suggest that LRDs are triggered
by near-synchronous presynaptic AP ring in glutamatergic neurons. Furthermore, NE and ACh
differentially modulate synchronous network events and asynchronous unitary synaptic inputs. NE
increased LRD frequency via β-adrenergic receptors activation without affecting the frequency of EPSPs.
Conversely, ACh decreased LRD frequency via activation of M4 muscarinic receptors but increased EPSP
frequency. Data obtained from MEA recordings further demonstrated that NE enhanced near-
synchronous AP ring whereas ACh desynchronized network activity. The differential modulation of such
activity by NE and ACh suggests specic modulatory mechanisms in the human neocortex and sheds
light on mechanisms of synchronized neuronal activity in the human neocortex which is associated with
different behavioral states.
‘Giant depolarizing potentials (GDPs)’ have been described as network-driven synaptic events in the
immature rodent hippocampus and neocortex. Their initiation requires excitatory GABAergic
transmission which promotes voltage-dependent AP bursts in immature pyramidal neurons 32,47. The
LRDs discovered and characterized in the present work were recorded in L2/3 of human neocortex and
are dependent on glutamatergic transmission and not affected by gabazine, application suggesting a
distinct mechanism in adult human neocortex. In the acute ferret, mouse and rat neocortex, rhythmic
network events can be triggered by perfusing ACSF containing a high K+/low Ca2+ ion concentration.
These events have been characterized as slow oscillations that persist in vitro 13–15. When this modied
ACSF was applied, ‘Up’ states, with both depolarizing and hyperpolarizing components, were observed in
L2/3 neurons of acute human slices. Contrary to LRDs, which were identied in a limited subset of
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neurons, ‘Up’ states were induced in the majority of the recorded neurons. The duration of these ‘Up’
states lasted several seconds, noticeably longer than LRDs, consistent with earlier studies 13–15.
Spontaneous sharp waves similar to the time course of LRDs, were observed in neocortical slices from
both epileptic 5,6,9 and non-epileptic tissue 9,12. However, unlike LRDs, which are always depolarising and
show only minor contribution of inhibitory inputs, intracellular recordings of neurons involved in
spontaneous sharp wave generation revealed a wider range of potentials, including depolarizing,
hyperpolarizing, or a combination of both 5,6,9. In addition, spontaneous sharp waves were effectively
suppressed by blockade of GABAA receptors, whereas LRDs remained unaffected. To our knowledge, the
LRDs described in this study appear to represent a novel form of brain oscillation, as they show
signicant differences from previously reported in vitro network activity.
In this study, the occurrence of LRDs showed no correlation with patient age, gender, specic neocortical
subregion. The cases were carefully selected, so that the surgical approach tissue was considered
according to anatomical, microscopic, imaging, and neuropathological criteria. We acquired spared
human cortex samples generated during the surgical approach of patients undergoing operations due to
epilepsy or brain tumors. It worth noting that the majority of patients diagnosed with epilepsy or tumor
exhibited preoperative seizures. In our experiments, we exclusively used brain tissue located more than
10 mm away from the lesion to ensure the non-pathological nature of the samples. However, the
possibility remains that these tissues may had been affected by the widespread of seizures prior to
surgery. Interpreting LRD as epileptiform-like activity is dicult, as LRD consists solely of EPSPs without
IPSP inputs, whereas typical epileptic activity involves both 48. Nevertheless, the excitatory-to-inhibitory
balance of the circuitry in resected human tissue might have been altered by preoperative seizures,
potentially facilitating the generation of rhythmic network activity. While we did observe LRDs in a patient
without preoperative seizures, further investigation with additional seizure-free samples is required to
conclusively demonstrate that LRDs are unrelated to seizure attacks.
Our preliminary recordings of L2/3 neurons in peritumoral area of human brain slices showed that
neurons in this area displayed high input resistance, low rheobase current and discontinued AP ring
when compared with neurons recorded in tumor-free healthy tissue. Neurons included in our dataset
neither showed abnormalities in ring patterns or in other electrophysiological nor in morphological
properties when compared to previous description of human cortical neurons 49–52. LRDs were only
observed within a short time window (6 h) after slice preparation. In this study, we were able to begin
preparation of neocortical slices within 10 min after resection of human tissue. This is likely to
contribute to the maintenance of the rhythmic network activity in acute human brain slices studied here.
The diversity of cortical interneurons has posed a major challenge to classify and characterize their
dening properties. Recent research on the human brain has shed light on this complexity by mapping
the transcriptomically dened cell types within human cortical taxonomy 37,53. However, the synaptic
activity were not investigated in these studies, as they used a recording ACSF that contained blockers for
both glutamatergic and GABAergic receptors. Here, we described for the rst time LRDs that were
Page 11/32
frequently observed in a specic morphological type of interneurons, namely large basket cells. Large
basket cells showing LRDs display more frequent and larger EPSPs, resulting in a greater membrane
depolarization and in turn a higher probability of the neuron reaching the threshold for AP ring.
However, these interneurons show a substantially lower input resistance compared to those without LRD
activity, indicating that they are less excitable and require a higher current inux to generate APs. It is
therefore likely that the synchronous network events, LRDs, are critical for eliciting APs in these large
basket cells and in turn trigger feedforward/feedback inhibition of connected postsynaptic neurons.
Previous studies showed that rat PV-positive basket cells have a low input resistance 44,54,55. By
providing perisomatic inhibition to pyramidal cells, PV basket cells have been implicated in the
generation of gamma oscillations (30–120 Hz) through feedforward and feedback inhibition 1,56. Apart
from this, inhibition of PV-positive interneurons disrupts learning-induced augmentation of delta (0.5-4
Hz) and theta oscillations (4–10 Hz) in mouse hippocampus 57. During slow oscillations (< 1 Hz), PV-
interneurons were the most active interneuron subtype during cortical ‘Up’ states in rodent neocortex 15.
Apart from PV-positive chandelier cells, many PV-expressing interneurons show ‘basket cell’-like
morphology 41. We found that LRD + interneurons not only comprise PV-positive FS interneurons but
also nFS interneurons which do not express PV. It is conceivable that these interneurons are CCK-
positive interneurons with an nFS ring pattern. Although they target the same somatodendritic
compartments of pyramidal cells as PV-expressing interneurons, CCK- and PV-positive interneurons play
complementary roles in network oscillations 58,59. In mouse prefrontal and temporal cortex, PV- and CCK-
expressing neurons constitute approximately 30–40% of all GABAergic interneurons, a percentage
similar to the LRD + neurons we recorded among all the interneurons 60,61. Interneurons showing LRDs
have signicantly broader and longer dendrites and axons than LRD- interneurons. The broad and dense
axonal arborizations of PV- and CCK- basket cells makes them a powerful inhibitory force in human layer
2/3 and may therefore play a dominant role in synchronized network oscillations.
We propose that the initiation of LRDs is driven by a summation of near-synchronous unitary EPSPs.
Neurons that show LRDs were found to display a larger mean EPSP amplitude and higher frequency of
spontaneous unitary EPSPs. With higher EPSP amplitude and/or more synapses converging on a
specic postsynaptic neuron, EPSP summation to a critical level and hence the initiation of LRDs
becomes more likely. This could be a contributing factor why LRDs were not observed in rat neocortex as
human pyramidal cells establish stronger and more reliable synaptic connections in the local neuronal
circuitry 33. Recent studies revealed that human cortex has a much higher fraction of interneurons
(approximately 2.5-fold) than rodent neocortex 62. Considering LRDs are more prominently observed in
interneurons rather than PCs, human neocortex is therefore more prone to generate synchronous
network activity than rodent neocortex under the same conditions.
In this study we demonstrated for the rst time that NE can initiate or increase the frequency of rhythmic
brain oscillations. This adrenergic modulation of LRDs may be attributed to several potential
mechanisms. NE may increase the EPSP amplitude in L2/3 large basket cells of human neocortex which
in turn thus promoting the emergence of LRDs. NE might potentially trigger persistent ring by activating
Page 12/32
α2 receptor-gated HCN channels which could contribute in the synchronization of neuronal activity as
demonstrated in layer 2/3 of rat prefrontal cortex 63. However, this is in contrast to the present nding
that the NE-induced enhancement of LRD frequency is mediated by β-adrenergic receptor activation.
It has been reported that the activation of muscarinic ACh receptors by stimulating the brainstem
cholinergic nucleus abolishes slow oscillations 20,45. We hypothesise that through activation of Gs
protein-coupled β-adrenergic receptors, NE regulates LRD frequency in a way opposite to ACh via
modulation of Ca2+ and K+ conductance. In accordance with this hypothesis, we found that ACh can
block either spontaneous or NE-induced LRDs by activating Gi/o protein-coupled M4Rs. Moreover, ACh
increased the overall frequency of unitary EPSPs, possibly via nAChR activation, suggesting that ACh
causes desynchronization of LRDs into unitary EPSPs and a decorrelation of responses between
neurons. In summary, LRD generation was promoted or suppressed by NE and ACh, respectively. The
adrenergic and cholinergic modulation of LRDs drives temporal dynamics of cortical activity and controls
cortical information processing and transitions between brain states.
Conclusions
To our knowledge, the study discovered and characterized a new form of rhythmic network activity in the
human neocortex called Large Rhythmic Depolarizations (LRDs), predominantly observed in a subset of
L2/3 interneurons. Unlike previously described activities, LRDs are independent of GABAA receptors,
relying instead solely on glutamatergic transmission; they demonstrate distinct features from other
known in vitro network activities. While prevalent in human neocortex samples, LRDs were notably
absent in rat frontal or temporal cortex. Modulation studies revealed distinct and differential impacts of
norepinephrine (NE) and acetylcholine (ACh) on synchronous and asynchronous network events, offering
insights into specic modulatory mechanisms in the human neocortex.
Methods
Patients and animals
All patients underwent neurosurgical resections because of pharmaco-resistant epilepsy or tumor
removal. Written informed consent to use spare neocortical tissue acquired during the surgical approach
was obtained from all patients. The study was reviewed and approved by the local ethic committee
(EK067/20). All ethical regulations relevant to human research participants were followed. For this study,
we collected data from 21 patients (14 females, 7 males; age ranging from 8 to 75 years old)
(Supplementary Tab. S1). The cases were meticulously selected to fulll two main criteria: 1) availability
of spare tissue based on the needed surgical approach; and 2) normal appearance of the tissue
according to radiological and intraoperative criteria (absence of edema, absence of necrosis, and
sucient distance to any putative intracerebral lesion). In addition, samples from tumor cases were
Page 13/32
neuropathologically reviewed to rule out the presence of tumor cells in the examined neocortical
specimen.
All experimental procedures involving animals were performed in accordance with the guidelines of the
Federation of European Laboratory Animal Science Association, the EU Directive 2010/63/EU, and the
German animal welfare law. In this study, Wistar rats (Charles River, either sex) aged 40–55 postnatal
days were anesthetized with isourane and then decapitated. Rats were obtained from Charles River and
kept under a 12-h light–dark cycle, with food and water available ad libitum.
Slice preparation
Human cortex was carefully micro-dissected and resected with minimal use of bipolar forceps to ensure
tissue integrity. Resected neocortical tissue from the temporal or frontal cortex was directly placed in an
ice-cold articial cerebrospinal uid (ACSF) containing (in mM): 110 choline chloride, 26 NaHCO3, 10 D-
glucose, 11.6 Na-ascorbate, 7 MgCl2, 3.1 Na-pyruvate, 2.5 KCl, 1.25 NaH2PO4, and 0.5 CaCl2) (325
mOsm/l, pH 7,45) and transported to the laboratory. Slice preparation commenced within 10 min after
tissue resection. The pia was carefully removed from the human tissue block using forceps and the pia-
white matter (WM) axis was identied. 300 µm thick slices were prepared using a Leica VT1200
vibratome in ice-cold ACSF solution containing 206 mM sucrose, 2.5 mM KCl, 1.25 mM NaH2PO4, 3mM
MgCl2, 1 mM CaCl2, 25 mM NaHCO3, 12mM N-acetyl-L-cysteine, and 25 mM glucose (325 mOsm/l, pH
7,45). During slicing, the solution was constantly bubbled with carbogen gas (95% O2 and 5% CO2). After
cutting, slices were incubated for 30 min at 31–33℃ and then at room temperature in ACSF containing
(in mM): 125 NaCl, 2.5 KCl, 1.25 NaH2PO4, 1 MgCl2, 2 CaCl2, 25 NaHCO3, 25 D-glucose, 3 myo-inositol, 2
sodium pyruvate, and 0.4 ascorbic acid (300 mOsm/l; 95% O2 and 5% CO2). To maintain adequate
oxygenation and a physiological pH level, slices were kept in carbogenated ACSF (95% O2 and 5% CO2)
during the transportation.
Rat brains were quickly removed and placed in an ice-cold sucrose containing ACSF. The experimental
procedures used here have been described in detail previously 64. 300 µm thick coronal slices of the
prelimbic medial prefrontal cortex (mPFC) and temporal association cortex were cut and incubated
using the same procedures and solutions as described above for human slices.
Organotypic slice cultures of human neocortex
Preparation and cultivation of slice cultures of human neocortex followed previously published protocols
65. In brief, the neocortex was carefully micro-dissected and resected with only minimal use of bipolar
forceps to ensure tissue integrity, directly transferred into ice-cold articial cerebrospinal uid (ACSF) (in
mM: 110 choline chloride, 26 NaHCO3, 10 D-glucose, 11.6 Na-ascorbate, 7 MgCl2, 3.1 Na-pyruvate, 2.5
KCl, 1.25 NaH2PO4, and 0.5 CaCl2) equilibrated with carbogen (95% O2, 5% CO2) and immediately
transported to the laboratory. Tissue was kept always submerged in cool and carbogenated ACSF. After
removal of the pia, tissue blocks were trimmed perpendicular to the cortical surface and 250 µm thick
slices were prepared using a live tissue vibratome. After the cortical tissue was sliced as described
Page 14/32
above, slices were cut into several evenly sized pieces. Subsequently, slices were transferred onto
culture membranes (uncoated 30 mm Millicell-CM tissue culture inserts with 0.4 µm pores, Millipore)
and kept in six-well culture dishes (BD Biosciences). For the rst hour following the slicing procedure,
slices were cultured on 1.5 ml intermediate step HEPES media (48% DMEM/F-12 (Life Technologies),
48% Neurobasal (Life Technologies), 1x N-2 (Capricorn Scientic), 1x B-27 (Capricorn Scientic), 1x
Glutamax (Life Technologies), 1x NEAA (Life Technologies) + 20 mM HEPES before changing to 1.5 ml
hCSF per well without any supplements. No antibiotics or antimycotics were used during cultivation. The
plates were stored in an incubator (MCO-170AICUVH-PE, PHC Corporation) at 37°C with 5% CO2 and
100% humidity. For MEA recordings, slice cultures were transferred into the recording chamber of a MEA
Setup (described below).
Whole-cell recordings
Whole cell recordings were performed in acute slices 30 hours at most after slice preparation for human
brain tissues and within 8 hours for rat brains. During whole-cell patch-clamp recordings, human or rat
slices were continuously perfused (perfusion speed ∼ 5 ml/min) with ACSF bubbled with carbogen gas
and maintained at 30–33℃. Patch pipettes were pulled from thick wall borosilicate glass capillaries and
lled with an internal solution containing (in mM): 135 K-gluconate, 4 KCl, 10 HEPES, 10
phosphocreatine, 4 Mg-ATP, and 0.3 GTP (pH 7.4 with KOH, 290–300 mOsm). Neurons were visualized
using either Dodt gradient contrast or infrared differential interference contrast microscopy. Human L2/3
neurons were identied and patched according to their somatic location (300–1200 µm from pia) 65. In
rat acute prelimbic cortical slices, layer 2 is clearly distinguishable as a thin dark band that is densely
packed with neuron somata. Layer 3 is about 2–3 times wider than layer 2 and has about the same width
as layer 1. According to previous publications, layer 2/3 was located at a depth of 200 to 550 µm from
the pia 66. Putative PCs and interneurons were differentiated on the basis of their intrinsic action
potential (AP) ring pattern during recording and after post hoc histological staining also by their
morphological appearance.
Whole-cell patch clamp recordings of human or rat L2/3 neurons were made using an EPC10 amplier
(HEKA). During recording, slices were perfused in ACSF at 31–33℃ containing (in mM): 125 NaCl, 2.5
KCl, 1.25 NaH2PO4, 1 MgCl2, 2 CaCl2, 25 NaHCO3, 25 D-glucose, 3 myo-inositol, 2 sodium pyruvate, and
0.4 ascorbic acid. In a subset of experiments designed to induce ‘Up’ states, slices were perfused in a
modied ACSF at 31–33℃ containing (in mM): 125 NaCl, 3.5 KCl, 1.25 NaH2PO4, 1 MgCl2, 1 CaCl2, 25
NaHCO3, 25 D-glucose, 3 myo-inositol, 2 sodium pyruvate, and 0.4 ascorbic acid. Signals were sampled
at 10 kHz, ltered at 2.9 kHz using Patchmaster software (HEKA), and later analyzed oine using Igor
Pro software (Wavemetrics). Recordings were performed using patch pipettes of resistance between 5
and 10 MΩ. Biocytin was added to the internal solution at a concentration of 3–5 mg/ml to stain
patched neurons. A recording time > 15 min was necessary for an adequate diffusion of biocytin into
dendrites and axons of patched cells 67.
Multi-electrode array (MEA) recordings
Page 15/32
To perform the MEA recordings of the human cortical cultures, the brain slice was excised from the
insert with the slice still attached to the culturing membrane. Subsequently, the slice was moved to the
MEA chamber and placed onto the electrodes of the MEA Chip with the slice surface facing down. For
xation and improved contact with the electrodes, the slice was xed in place by a weighted, close-
meshed harp (ALA-HSG MEA-5BD, Multi Channel Systems MCS GmbH). Slices equilibrated at least 30
min on the chip with constant carbogenated ACSF (same as used for acute slices) perfusion at 30–33°C
before MEA recordings were started. MEA recordings were performed using a 256–MEA (16 × 16 lattice)
with electrode diameter of 30 µm and electrode spacing of 200 µm, thus covering a recording area of
∼3.2 × 3.2 mm2 (USB-MEA 256-System, Multi Channel Systems MCS GmbH). Recordings with the 256-
MEA were performed at a sampling rate of 10–25 kHz using the Multi-Channel Experimenter (Multi
Channel Systems MCS GmbH).
Drug application
NE (10 µM or 30 µM) and ACh (10 µM, 15 µM or 30 µM) were bath applied for 150–300 s through the
perfusion system during whole-cell patch clamp or MEA recordings. In a subset of human neurons,
propranolol (20 µM), tropicamide (TRO, 1 µM), tetrodotoxin (TTX, 0.5 µM), cyanquixaline (CNQX, 10µM),
gabazine (1 µM), mecamylamine (1 µM) or prazosin (2 µM) were bath applied for 200–600 s to study the
underlying pharmacological mechanisms. Drugs were purchased from Sigma-Aldrich or Tocris.
Histological staining
After recordings, brain slices containing biocytin-lled neurons were xed for at least 24 h at 4 ℃ in 100
mM phosphate buffer solution (PBS, pH 7.4) containing 4% paraformaldehyde (PFA). After rinsing
several times in 100 mM PBS, slices were treated with 1% H2O2 in PBS for about 20 min to reduce any
endogenous peroxidase activity. Slices were rinsed repeatedly with PBS and then incubated in 1% avidin-
biotinylated horseradish peroxidase (Vector ABC staining kit, Vector Lab. Inc.) containing 0.1% Triton X-
100 for 1 h at room temperature. The reaction was catalyzed using 0.5 mg/ml 3,3-diaminobenzidine
(DAB; Sigma-Aldrich) as a chromogen. Subsequently, slices were rinsed with 100 mM PBS, followed by
slow dehydration with ethanol in increasing concentrations, and nally in xylene for 2–4 h. After that,
slices were embedded using Eukitt medium (Otto Kindler GmbH).
In a subset of experiments, we tried to identify the expression of the molecular marker - a calcium-
binding protein parvalbumin (PV) in human layer 2/3 interneurons. To this end, during
electrophysiological recordings, Alexa Fluor 594 dye (1:500, Invitrogen) was added to the internal
solution for post hoc identication of patched neurons. After recording, slices (300 µm) were xed with
4% PFA in 100 mM PBS for at least 24 h at 4 ℃ and then permeabilized in 1% milk powder solution
containing 0.5% Triton X-100 and 100 mM PBS. Primary and secondary antibodies were diluted in the
permeabilization solution (0.5% Triton X-100 and 100 mM PBS) shortly before the antibody incubation.
For single-cell PV staining, slices were incubated overnight with Rabbit-anti-PV primary antibody (1:120,
ab11427, Abcam) at 4 ℃ and then rinsed thoroughly with 100 mM PBS. Subsequently, slices were
treated with Donkey-anti-Rabbit Alexa Fluor secondary antibodies (1:400, A21207, Invitrogen) for 2–3 h
Page 16/32
at room temperature in the dark. After rinsing with 100 mM PBS, the slices were embedded in
Fluoromount. Fluorescence images were taken using the Olympus CellSens platform. The position of the
patched neurons was identied by the biocytin conjugated Alexa dye so that the expression of PV could
be examined in biocytin-stained neurons. After acquiring uorescent images, slices were incubated in
100 mM PBS overnight and then used for subsequent histological processing as described above.
Morphological 3D reconstructions
Using NEUROLUCIDA® software (MBF Bioscience, Williston, VT, USA), morphological reconstructions of
biocytin lled human layer 2/3 interneurons were made at a magnication of 1000-fold (100-fold oil-
immersion objective and 10-fold eyepiece) on an upright microscope. Neurons were selected for
reconstruction based on the quality of biocytin labelling when background staining was minimal.
Neurons with major truncations due to slicing were excluded. Embedding using Eukitt medium reduced
fading of cytoarchitectonic features and enhanced contrast between layers 67. This allowed the
reconstruction of different layer borders along with the neuronal reconstructions. Furthermore, the
position of soma and layers were conrmed by superimposing the Dodt gradient contrast or differential
interference contrast images taken during the recording. The tissue shrinkage was corrected using
correction factors of 1.1 in the x–y direction and 2.1 in the z direction 67. Analysis of 3D-reconstructed
neurons was done with NEUROEXPLORER® software (MBF Bioscience, Williston, VT, USA).
Data analysis
Single-cell recording data analysis
Custom written macros for Igor Pro 6 (WaveMetrics) were used to analyze the recorded
electrophysiological signals. The resting membrane potential (Vm) of the neuron was measured directly
after breakthrough to establish the whole-cell conguration with no current injection. The input
resistance was calculated as the slope of the linear t to the current–voltage relationship. For the
analysis of single spike characteristics such as threshold, amplitude and half-width, a step size
increment of 10 pA for current injection was applied to ensure that the AP was elicited very close to its
rheobase current. The spike threshold was dened as the point of maximal acceleration of the
membrane potential using the second derivative (d2V/dt2), which is, the time point with the fastest
voltage change. The spike amplitude was calculated as the difference in voltage from AP threshold to
the peak during depolarization. The spike half-width was determined as the time difference between
rising phase and decaying phase of the spike at half-maximum amplitude.
The spontaneous activity was analyzed using the program SpAcAn
(https://www.wavemetrics.com/project/SpAcAn). EPSPs and LRDs were distinguished by dramatic
differences in event amplitude and decay time. A threshold of 0.2 mV was set manually for detecting
EPSP events while a threshold of 3 mV was set for detecting LRDs. Recordings were not ltered to
reduce noise before data analysis. When marking EPSPs, small EPSPs distributing in decay phrase but
not rising phrase of LRDs were included into analysis. To study oscillatory network activities, we
Page 17/32
computed time-frequency representations of the signals by performing wavelet analysis using the Time-
Frequency Toolkit (https://www.wavemetrics.com/project/TFPlot). Morlet wavelets were used for
decomposition of recording signals as they provide an ideal compromise between time and frequency
resolution 68.
MEA data analysis
MEA recordings were analyzed using custom-written programs in Python, detecting and quantifying the
mean ring rate, number of active channels, bursting channels, and network bursts. First, the raw signal
was ltered using a band-pass lter (Butterworth 2nd order). The spike identication was performed
according to a threshold-based method using median absolute deviation (MAD) / 0.6745 x -5. Signal
deviations were detected and aligned to the next minimum of the signal with a 1 ms dead time. The ring
rate for each recording was dened as the number of recorded spikes divided by the duration of the
recording (in s). Bursting channels were calculated using a modied adaptive network-wide cumulative
moving average (CMA) approach in which all the electrodes of one MEA in multiple measurement time
points were analyzed together 69.
In short, all inter-spike intervals (ISIs) were calculated and grouped into 5 ms bins for the whole
recording. Next, a CMA over the histogram of the bins was calculated and a burst threshold was
determined, therefore adapting the detection of bursts to the basic activity of the entire slice 69. The
detection sensitivity was further increased by applying a minimum and maximum threshold of 60 ms
and 140 ms, respectively. Whenever the threshold was undercut by at least three consecutive spikes, it
was dened as a single-channel burst.
For a quantitative analysis of spiking synchronization, we used a graph theoretical approach to identify
the degree of centrality of active channels 70–72. Specically, we designated MEA contacts as nodes and
the shared spiking time as edges to construct a graph representation of the recorded network activity. To
construct edges, we grouped spike trains for each channel into 200 ms long bins and dened two
channels as connected by an edge only if they both recorded spikes are in the same bin. By using this
approach, we were able to examine the functional connectivity and activity patterns in the neuronal
network.
We used mean degree centrality as a measure of the connectivity of nodes in the network (Python library
NetworkX). Mean degree centrality (MDC) quanties the average number of edges connecting a node to
other nodes in the network. The degree of each node was dened by the number of edges connected to
that node, i.e. the number of other MEA contacts that share a spiking time with that contact within a 200-
ms bin. Finally, MDC was calculated by summing the degree of all nodes and dividing it by the total
number of nodes in the network, which is
MDC
=2 ×
numberoftotaledges
numberoftotalnodes
Page 18/32
where the factor 2 is introduced because each edge is counted twice (once for each node it connects).
By calculating the MDC for the whole MEA recording, we were able to assess the overall connectivity of
the network and identify how nodes connectivity (used as a surrogate for synchronicity) changed upon
application of NE and ACh compared to baseline.
Detection of LFPs was performed after low-pass ltering the signal of each channel (Butterworth 2nd
order with a Nyquist frequency of 0.5 x sampling rate and a cut-off of 100 Hz); as a threshold, standard
deviation of the low pass ltered signal multiplicated by three was used. Any deviation above or below
this threshold with a minimum duration of 30 ms was dened as a LFP. The respective maximum and
minimum deviation was dened as the amplitude of respective LFP.
All Python scripts are available on GitHub page
(https://github.com/jonasort/MEA_analysis/tree/main/modied_common_script).
Statistical analysis
Data was either presented as box plots (n ≥ 10) or as bar histograms (n < 10). For box plots, the
interquartile range (IQR) is shown as box, the range of values within 1.5∗IQR is shown as whiskers and
the median is represented by a horizontal line in the box; for bar histograms, the mean ± SD is given.
Wilcoxon Mann-Whitney U test was performed to access the difference between individual clusters.
Statistical signicance was set at P < 0.05, and n indicates the number of neurons/slices analyzed.
Declarations
Author contributions
D.F., D.Y., G.Q. and H.K. designed the experiments. D.Y. and G.Q. carried out the patch-clamp recording
experiments from human and rat slices and electrophysiological data analysis. D.Y. performed
Neurolucida reconstructions and performed morphological analysis. D.D performed surgeries on human
patients. D.Y., A.B. and H.K. prepared acute human brain slices and A.B. prepared the human slice
cultures. J.O. and V.W. performed MEA recordings. J.O., H.K. and D.D. analyzed MEA data. D.Y. and D.F.
wrote the manuscript with the inputs from all authors. All authors have given approval for the nal
version of the manuscript.
Acknowledgement
We would like to thank Werner Hucko and Birgit Gittel for excellent technical assistance. We thank Dr.
Karlijn van Aerde for custom-written macros in Igor Pro software. We are grateful for funding support
from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the
Framework Partnership Agreement No. 650003 (HBP FPA) to D.F., funding from DFG FOR2715, Chan
Zuckerberg Initiative DAF (2020-221779) to H.K. and funding from BMBF (German Ministry of Education
and Research, project number 031L0260B) to D.D..
Page 19/32
Data Availability
The authors declare that the data supporting the ndings of this study are available within the paper and
its Supplementary Data les. Supplementary Data 1–4 provided with this study are source data for
Figs.1–4, respectively. Should any raw data les be needed in another format they are available from the
corresponding author upon reasonable request. Source data are provided with this paper.
Code Availability
All the custom code used in the study is available from the corresponding author on reasonable request.
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Figures
Figure 1
LRDs are identied in a subset of human neocortical L2/3 neurons.
a Whole-cell intracellular recordings from a L2/3 interneuron in the human neocortex. Top trace: a typical
LRD in the temporal resolution of 2 s. The same LRD is shown in the middle trace on an expanded time
scale. Excitatory postsynaptic potentials (EPSPs) are marked by blue while LRDs are marked by red
arrowheads. Middle trace: continuous 30 s recording of spontaneously, rhythmically occurring LRDs.
Bottom trace: spontaneous LRDs triggering action potential discharge.
Page 25/32
b Top: Mean and individual LRDs (n = 29) are superimposed and given in dark and light red, respectively.
Bottom: Mean and individual EPSPs (n = 559) are superimposed and given in dark and light blue,
respectively. Events were extracted and analyzed from 100 s continuous recordings.
c A 300 s recording was obtained from the same neuron in (A) and (B) and interval histograms of events
are shown. The histograms of LRDs and EPSPs were constructed with 33 ms and 5 ms bins, respectively.
Inset, EPSP histogram at an expanded time scale.
d Box plots comparing event frequency, amplitude and decay time for LRDs and EPSPs. n = 17 neurons
for each group; *** P < 0.001 for the Wilcoxon Mann–Whitney U test.
e Top: Representative ring patterns of a human L2/3 PC and an interneuron exhibiting spontaneous
LRDs. The inset shows the rst AP elicited by rheobase current at high temporal resolution. Bottom:
Corresponding morphological reconstructions of the neurons shown above. The somatodendritic
domain is given in a darker, the axons in a lighter shade.
f Number of LRD+ and LRD- neurons in L2/3 of human and rat cortex. Numbers above bar graphs
indicate the percentage of LRD+ neurons. P = 0.0035 for χ2 test.
Page 26/32
Figure 2
LRDs are complex network events depending on glutamatergic transmission and human L2/3
interneurons showing LRDs are large basket cells.
a Left: Synaptic wiring scheme between human L2/3 interneurons and cortical PCs. Right: Diagram
summarizing the possible mechanism of the generation of EPSPs and a LRD. Presynaptic asynchronous
Page 27/32
APs are shown in light green while synchronous APs are in dark green.
b Time-frequency representation of excitatory spontaneous activities in a LRD+ human L2/3 interneuron.
Top plot: Original current clamp recordings in time from a human L2/3 interneuron. Central plot: A time-
frequency decomposition of the recording shown above. Squared event amplitude is depicted by heat
map colors. Right plot: Amplitude spectrum of excitatory spontaneous activities. The spectrum is shown
vertically in frequency (0.05-100 Hz), the red line represents baseline noise.
c Continuous current (top) and voltage clamp (bottom) recordings of the same LRD+ neuron.
d Left, ve consecutive LRDs recorded in current clamp mode. Right, ve consecutive large rhythmic
network activities recorded in voltage clamp mode in the same LRD+ neuron sh.
e Representative current clamp recordings showing block of LRDs in human L2/3 interneurons by TTX
(0.5 µM, top trace) and CNQX (10 µM, middle trace) but no effect of gabazine (1 µM, bottom trace) on
LRD frequency.
f Representative continuous recordings from LRD+ (top trace) and LRD- (bottom trace) human L2/3
interneurons. EPSPs are marked in blue while LRDs are marked in red.
g EPSPs were extracted and analyzed from the same LRD+ interneuron (top) and LRD- interneuron
(bottom) in d. The average and individual EPSP traces are superimposed and given in black and blue,
respectively.
h Box plots comparing the frequency and amplitude of EPSPs for LRD+ and LRD- interneurons. n = 14 for
LRD+ interneurons and n = 17 for LRD- interneurons. *P < 0.05, *** P < 0.001 for the Wilcoxon Mann–
Whitney U test.
i Representative morphological reconstructions and the corresponding ring patterns of seven LRD+
(top) and seven LRD- (bottom) interneurons. The somatodendritic domain is shown in black and axons
are shown in gray.
j Histograms comparing several morphological properties of LRD+ and LRD- human L2/3 interneurons. n
= 8 for each group. *P < 0.05, *** P < 0.001 for the Wilcoxon Mann–Whitney U test.
Page 28/32
Figure 3
NE increases LRD frequency via activating β-adrenergic receptors.
a Representative LRD+ (LRD+) interneuron (IN; top trace) and LRD- (LRD-) interneuron (bottom trace)
showing depolarizing responses following the bath application of 30 μM NE. The area in the dashed
boxes is enlarged in c.
Page 29/32
b Summary plots showing the resting membrane potential (Vm) of human L2/3 interneurons under
control conditionsand in the presence of NE (n = 10 for LRD+ interneurons and n = 12 for LRD-
interneurons). ns (not signicant); Wilcoxon signed-rank test.
c 80 s recording from the same LRD+ interneuron under control (top) and in the presence of NE (bottom).
Excitatory postsynaptic potentials (EPSPs) are marked in blue while LRDs are marked in red.
d Amplitude spectrum of excitatory spontaneous activities analyzed from recording traces in c. The
spectrums are shown vertically in frequency (0.05–100 Hz) and the black lines represent baseline noise.
e Cumulative distributions of inter-event intervals and amplitudes of excitatory spontaneous activity
recorded in LRD+ human L2/3 interneurons under control and NE conditions.
f Box plots summarizing the NE effect on frequency and amplitude of LRDs and EPSPs in LRD+ L2/3
human interneurons (n = 12). ns (not signicant), *P < 0.05, ***P < 0.001; Wilcoxon signed-rank test.
g Representative current-clamp recordings, following the bath application of NE, showing an increase of
LRD frequency in a human L2/3 interneuron. The effect is blocked by the β-adrenoreceptor antagonist
propranolol (20µM).
h Summary histograms of LRD frequency and amplitude under control, NE and propranolol conditions. ns
(not signicant), *P < 0.05 for paired student t-test.
i Representative voltage traces from 16 channels of the MEA under control conditions (top) and 30 µM
NE (bottom) showing an increase in LFP amplitude. Red insets show an enlarged view of the same three
electrodes in both conditions.
j Heatmap of the average ring rateover the MEA grid from a ve-minute recording in control condition
and in the presence of NE showing an increase synchronous ring in L2/3 and deep layers.
k Raster plots of the detected APs over a ve-minute recording period under control condition and in the
presence of NE showing an increase in synchronous ring.
l Box plots displaying the group effects of NE compared to control on the global ring rate (n = 13),
degree of centrality (n = 7), theaverage negative amplitude of the LFPs (n = 13) and the number of
channels with detected LFPs (n = 13). Ns (not signicant), *P < 0.05 for Wilcoxon signed-rank test.
Page 30/32
Figure 4
ACh suppresses LRDs via activating M4Rs.
a Representative LRD+ interneuron (top trace) and LRD- interneuron (bottom trace) showing depolarizing
responses following bath application of 30 μM ACh. The area in the dashed boxes is enlarged in c.
Page 31/32
b Summary plots showing the resting membrane potential (Vm) under control conditions and in the
presence of ACh in human L2/3 interneurons. n = 10 for each group. **P < 0.01; Wilcoxon signed-rank
test.
c A 80 s recording from a LRD+ interneuron under control (top) and ACh conditions (bottom),
respectively. Spontaneous EPSPs are marked in blue while LRDs are marked in red.
d Amplitude spectra of excitatory spontaneous activity analyzed from recording traces in c. Spectra are
shown vertically in frequency (0.05–100 Hz), black lines represent baseline noise.
e Cumulative distributions of inter-event interval and amplitude of spontaneous excitatory activity in
LRD+ human L2/3 interneurons under control conditions and in the presence of ACh.
f Box plots summarizing the ACh effect on frequency and amplitude of LRDs and EPSPs in LRD+ L2/3
human interneurons (n = 10). ns (not signicant), *P < 0.05, **P < 0.01; Wilcoxon signed-rank test.
g Representative current-clamp recordings with bath application of ACh showing a decrease of LRD
frequency in a human L2/3 interneuron. The effect is reversed by 1 μM tropicamide.
h Summary histograms of LRD frequency and amplitude under control conditions, in the presence ACh
alone and together with the M4 mAChR antagonist tropicamide. *P < 0.05; Wilcoxon signed-rank test.
i Representative voltage traces from 16 channels of the MEA under control conditions (top) and 30 µM
ACh (bottom) showing block of LFPs by ACh. Red inserts show a detailed view of the same three
electrodes in both conditions; note the switch from a synchronized ring pattern under control condition
to a desynchronized pattern in the presence of ACh.
j Heatmap of the average ring rateover the MEA grid from a ve-minute recording in control condition
and in the presence of ACh showing a decrease in temporal and spatial correlation of global AP ring.
k Raster plots of the detected action potentials over the ve-minute recording period under control l Box
plots displaying the group effects of ACh compared to control on the global ring rate (n = 12), degree of
centrality (n = 10), theaverage negative amplitude of the LFPs (n = 12) and the number of channels with
detected LFPs (n = 12). * P < 0.05, **P < 0.01 for Wilcoxon signed-rank test.
Supplementary Files
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SupplementaryData1.xlsx
SupplementaryData2.xlsx
SupplementaryData3.xlsx
