Oscillations in the cerebral cortex: mechanisms
of control and alterations in a transgenic model
of Down syndrome
Marcel Ruiz Mejías
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J Neurophysiol 106: 2910 –2921, 2011.
First published August 31, 2011; doi:10.1152/jn.00440.2011.
Slow and fast rhythms generated in the cerebral cortex of the anesthetized mouse
Marcel Ruiz-Mejias,1 Laura Ciria-Suarez,1 Maurizio Mattia,2 and Maria V. Sanchez-Vives1,3
1
IDIBAPS, Barcelona, Spain; 2Istituto Superiore di Sanità, Rome, Italy; and 3ICREA, Barcelona, Spain
Submitted 13 May 2011; accepted in final form 31 August 2011
oscillations; in vivo recording; ketamine; up states; sleep; spontaneous
activity
SLOW OSCILLATIONS (⬍1 Hz) occurring during slow-wave sleep
and ketamine anesthesia were originally characterized in the
neocortex of the cat (Steriade et al. 1993). During this rhythmic
activity, the cortical network switches between depolarized,
active periods or up states and silent periods or down states.
During up states, the recurrency within the network maintains
persistent activity with a functional structure that often reproduces that occurring during the cortical processing in the
awake state (Destexhe et al. 2007; Luczak et al. 2007; MacLean et al. 2005; Sakata and Harris 2009; Steriade and
Timofeev 2003; Tsodyks et al. 1999). One of these features is
the synchronization of activity in beta and gamma bands during
up states (Compte et al. 2008; Hasenstaub et al. 2005; Steriade
et al. 1996). In vivo rhythmic cortical slow oscillations and
their propagation have been observed and characterized in
different species including humans (Csercsa et al. 2010; Massimini et al. 2004; Nir et al. 2011), cats (Chauvette et al. 2010;
MacLean et al. 2005; Volgushev et al. 2006), ferrets (Haider et
al. 2006), and rats (Sakata and Harris 2009). Up and down
Address for reprint requests and other correspondence: M. V. Sanchez-Vives,
IDIBAPS, Villarroel 170, 08036 Barcelona, Spain (e-mail: msanche3@clinic.
ub.es).
2910
states have been studied as well in the brain of mice in vitro
(Ikegaya et al. 2004; Rigas and Castro-Alamancos 2007) and in
vivo, both in cortex (Fellin et al. 2009; Petersen et al. 2003)
and thalamus (Zhu et al. 2006). However, there has not been a
systematic characterization across different cortical areas, despite this being interesting for sleep and memory studies
(Diekelmann and Born 2010; Vyazovskiy et al. 2006) as well
as for establishing a baseline for cortical function in genetically
modified mice.
Here we present a detailed characterization of rhythmic
spontaneous activity across different cortical areas in the anesthetized mouse, including patterns of propagation and highfrequency content. We find that prefrontal cortex presents
differences with respect to all other studied areas (primary
visual, somatosensory, and motor). An analysis of the firing of
single units during up states is also included.
EXPERIMENTAL PROCEDURES
In vivo extracellular recordings. Mice were cared for and treated in
accordance with Spanish regulatory laws (BOE 256; 25-10-1990),
which comply with the European Union guidelines on protection of
vertebrates used for experimentation (Strasbourg 3/18/1986). All
experiments were approved by the Ethics Committee from the Hospital Clinic (Barcelona, Spain). Adult C57BL6/SJL mice 3– 6 mo old
were used for extracellular recordings (n ⫽ 40). Anesthesia was
induced with intraperitoneal injection of ketamine (75 mg/kg) and
medetomidine (1 mg/kg). Atropine (0.3 mg/kg) was administered to
prevent respiratory secretions. Tracheotomy was performed to increase stability during recordings. After this procedure and administration of a maintaining dose of ketamine (37.5 mg/kg ip), the animal
was placed in a stereotaxic frame and air enriched with oxygen was
delivered through a thin silicon tube placed at 0.5–1 cm from the
tracheal cannula. A continuous infusion of ketamine at 40
mg · kg⫺1 · h⫺1 was delivered subcutaneously to maintain a constant
level of anesthesia. Methylprednisolone (30 mg/kg) was also administered to prevent inflammation. Body temperature was maintained at
36 –37.5°C. Bilateral craniotomies were made at 4 sites: AP 2.3 mm
from bregma, L 0.4 mm or AP 2.5 mm, L 0.5 mm (medial prefrontal
cortex); AP 0.5 mm, L 1.5 mm (primary motor cortex); AP ⫺1.5 mm,
L 2.5 mm (primary somatosensory cortex); and AP ⫺2.5 mm, L 2.5
mm (primary visual cortex) (following Franklin and Paxinos 2008).
Extracellular slow wave recordings were obtained with tungsten
electrodes with impedances of 1–2 M⍀. Electrodes were placed in
infragranular layers (0.4 lateral and 1.0 –1.2 mm deep in prefrontal
cortex, 0.9 –1.1 mm in somatosensory cortex, 0.7– 0.9 mm in visual
cortex, 1.0 –1.2 mm in motor cortex). Extracellular recordings were
usually obtained bilaterally, amplified with either a NeuroLog (Digitimer) or a multichannel system (Multi Channel Systems). The signal
was digitized at 20 KHz with a CED acquisition board and Spike 2
software (Cambridge Electronic Design).
In vivo slow wave propagation. Arrays of M ⫽ 16 aligned electrodes separated by 125 ␮m (Neuronexus) were used to record activity
propagation. We computed the speed of up state propagation across the
cortex, relying on the time lags between consecutive detected up state
onsets from multiunit activity (MUA) of different electrodes. We selected
0022-3077/11 Copyright © 2011 the American Physiological Society
www.jn.org
Ruiz-Mejias M, Ciria-Suarez L, Mattia M, Sanchez-Vives MV.
Slow and fast rhythms generated in the cerebral cortex of the anesthetized mouse. J Neurophysiol 106: 2910 –2921, 2011. First published August 31, 2011; doi:10.1152/jn.00440.2011.—A characterization of the oscillatory activity in the cerebral cortex of the mouse was
realized under ketamine anesthesia. Bilateral recordings were obtained from deep layers of primary visual, somatosensory, motor, and
medial prefrontal cortex. A slow oscillatory activity consisting of up
and down states was detected, the average frequency being 0.97 Hz in
all areas. Different parameters of the oscillation were estimated across
cortical areas, including duration of up and down states and their
variability, speed of state transitions, and population firing rate.
Similar values were obtained for all areas except for prefrontal cortex,
which showed significant faster down-to-up state transitions, higher
firing rate during up states, and more regular cycles. The wave
propagation patterns in the anteroposterior axis in motor cortex and
the mediolateral axis in visual cortex were studied with multielectrode
recordings, yielding speed values between 8 and 93 mm/s. The firing
of single units was analyzed with respect to the population activity.
The most common pattern was that of neurons firing in ⬎90% of the
up states with 1– 6 spikes. Finally, fast rhythms (beta, low gamma, and
high gamma) were analyzed, all of them showing significantly larger
power during up states than in down states. Prefrontal cortex exhibited
significantly larger power in both beta and gamma bands (up to 1
order of magnitude larger in the case of high gamma) than the rest of
the cortical areas. This study allows us to carry out interareal comparisons and provides a baseline to compare against cortical emerging
activity from genetically altered animals.
RHYTHMS IN MOUSE CEREBRAL CORTEX
J Neurophysiol • VOL
All of the MUA off-line estimates and analyses were implemented
in MATLAB (The MathWorks, Natick, MA). Data are displayed as
means ⫾ SE in all error bars in plots.
Single-unit recordings were analyzed with Spike2 software (Cambridge Electronic Design). Firing patterns were obtained through
perievent time histograms (PETHs) triggered to the onset of up states
and a bin size of 20 ms. The length of the analyzed periods for the
PETHs was 300 s.
To analyze the fast components of oscillations, up and down states
were detected and their respective power spectra were calculated.
Power spectrum density analysis was carried out by Welch’s method
with 50% overlapped windows of 2,000 samples. To compare power,
an average relative power was calculated by averaging within three
frequency bands (beta 15–30 Hz, low gamma 30 – 60 Hz, and high
gamma 60 –90 Hz) resulting from the quotient between the power in
the up states and in the down states.
Statistical analysis. Unless otherwise stated, all comparisons between means were performed with one-way ANOVA followed by
Fischer least significant difference post hoc tests, and their degrees
of freedom and F values are shown in Supplemental Tables S1
and S2.1
RESULTS
The spontaneous rhythmic activity generated in different regions of the mouse cortex (primary visual, somatosensory, and
motor cortex and medial prefrontal) was recorded under ketamine
anesthesia. Under these conditions, both slow oscillations and fast
rhythms are generated in the thalamocortical network (Steriade et
al. 1993, 1996). The slow oscillation (Fig. 1) consisted of periods
of neuronal firing or up states (Fig. 1A, bottom) interspersed with
rather silent periods or down states. The recordings showed the
low-frequency nature of the slow waves (ⱕ1 Hz). During up
states different frequencies were generated, including highfrequency fluctuations (Fig. 1A, top), as described in detail below.
Such up states propagate across the cortical tissue (Luczac et al.
2007; Massimini et al. 2004; Sanchez-Vives and McCormick
2000), and several parameters of these waves were analyzed to
describe the features of these oscillations and to compare across
different cortical areas.
General description of slow oscillation in the anesthetized
mouse. To characterize the slow waves in the anesthetized
mouse, extracellular recordings from primary visual, somatosensory, motor, and medial prefrontal cortex were obtained by
means of tungsten electrodes placed in the deep cortical layers.
The recordings were bilateral, and up states usually occurred
concurrently in both hemispheres with a small time lag measurable at the central peak of the waveform cross-correlation
(Fig. 1, B and C). The distribution of time lags between left and
right hemispheres is represented in Supplemental Fig. S1 (n ⫽
29 bilateral recordings; n ⫽ 16 mice). It illustrates that there
was no significant bias toward one hemisphere to initiate the
activity. The average absolute value of the time lag of the peak
(mean ⫾ SD) was 7.1 ⫾ 10.9 ms in visual cortex (n ⫽ 7),
7.9 ⫾ 9.4 ms in somatosensory cortex (n ⫽ 7), 5.0 ⫾ 4.9 ms
in motor cortex (n ⫽ 6) , and 6.4 ⫾ 3.8 ms in prefrontal cortex
(n ⫽ 9).
To characterize the emergent rhythmic activity, nine parameters of the oscillatory activity were quantified as in
Sanchez-Vives et al. (2010): frequency of oscillation, up
state duration, down state duration, slope of down-to-up
1
Supplemental Material for this article is available online at the Journal
website.
106 • DECEMBER 2011 •
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time series containing at least N ⫽ 300 up states. The average of the onset
times of the same traveling up state in the multielectrode array was taken
as reference for each detected up state to compute the array of relative
time lags. In other words, if Ts,c is the onset time of the sth up state in the
M
cth electrode, the array of relative time lags is ⌬Ts,c ⫽ Ts,c ⫺ 冱c⫽1
Ts,c/M.
Absolute values ⌬Ts,c ⬎ 150 ms were not considered, because these
were very likely due to a failed up state detection. We explored the
possible existence of different patterns of activity propagation by sorting
the time lag arrays ⌬Ts of the detected up states in the low-dimensional
space resulting from principal component analysis (PCA) (Jackson
1991). We found that the first three principal components always represented ⬎50% of the total variance. In this three-dimensional subspace
each detected up state is a point. We worked out the “principal axis” of
the point cloud, minimizing in the least-squares sense the sum of the
distances of the points from this axis. Finally, the points are projected on
such a principal axis in order to associate a scalar quantity to each up state
and to rank them. Sorting ⌬Ts= with such ranking, we found gradual but
significant changes in the activity propagation patterns. We then pooled
the up states in five equally sized groups (at least 60 time lag arrays per
N/5
group), such that the first pool was ⌬T(1) ⫽ {⌬Ts=}s=⫽1
, the second pool
(2)
2N/5
was ⌬T ⫽ {⌬Ts=}s=⫽N/5⫹1, and so on. For each group of arrays we
N/5
(n)
carried out the average time lag ⬍⌬T(n)
c ⬎ ⫽ 冱s⫽1⌬Ts, c/(N/5) and its
standard error (SE) for each electrode c. Finally, the speed was computed
in those recordings showing a monotonic change with electrode position
of the average time lag within each propagation pattern. It was carried out
by dividing the distance D(n) between the electrodes showing the maximum and minimum average time lags by their time lag difference:
(n)
D(n)/[maxc⬍⌬T(n)
c ⬎ ⫺ minc⬍⌬Tc ⬎].
In vivo single unit with extracellular recordings. Glass recording
electrodes (8 –15 M⍀) were pulled on a Sutter Instruments P-97
micropipette puller (Novato, CA) from medium-walled glass and
filled up with saline. Electrodes were placed in the primary motor,
medial prefrontal cortex, and somatosensory cortex. A tungsten electrode similar to those described above was placed nearby the glass
electrode, at ⬍200 ␮m, to obtain a recording from the local network
in the vicinity. Signals were digitized and acquired at 50 kHz for
single-unit recordings and at 10 kHz for extracellular recordings. Agar
at 4% was used to prevent the cortex from desiccation and to stabilize
electrophysiological recordings.
Data analysis. MUA was estimated as the power change in the
Fourier components at high frequencies of the extracellular recordings
(Reig et al. 2010; Sanchez-Vives et al. 2010). We assume that the
normalized MUA spectrum provides a good estimate of the population firing rate, because normalized Fourier components at high
frequencies have densities proportional to the spiking activity of the
involved neurons (Mattia and Del Giudice 2002). MUAs were logarithmically scaled in order to balance the large fluctuations of the
nearby spikes. Up and down states were singled out by setting a
threshold in the log(MUA) time series. The threshold was set to 60%
of the interval between the peaks in the bimodal distributions of
log(MUA) corresponding to the up and down states. The peak related
to the down state was used as reference, setting there log(MUA) ⫽ 0.
Singled-out sets of up and down state durations from each recording
were used to estimate the nine parameters reported in Fig. 3. Up and
down state durations (Fig. 3, B and C) were the averages across such
sets. Frequency in Fig. 3A was the inverse of the average duration of
the whole up-down cycles. Down-to-up and up-to-down transitions in
Fig. 3, D and E, were estimated as the slope of the average profile of
log(MUA) around a small time interval around the transitions ([⫺10,
25] ms and [⫺25, 10] ms for upward and downward transitions,
respectively). The maximum relative firing rate in Fig. 3F was the
maximum average log(MUA) following the down-to-up transition.
The coefficient of variation (CV) in Fig. 3, G–I, was the fraction
between the standard deviation and the mean value of the durations of
up and down states and of the whole up-down cycles singled out for
each recording, respectively.
2911
2912
RHYTHMS IN MOUSE CEREBRAL CORTEX
state transition, slope of up-to-down state transition, maximum relative firing rate, CV of the up state duration, CV of
the down state duration, and CV of the up state– down state
period. An operational definition of each one of these parameters is included in EXPERIMENTAL PROCEDURES.
Figure 2 illustrates representative recordings of all four
recorded cortical areas and the analytical methods used to
quantify their activity. We were interested in the comparative
evaluation of these parameters across cortical areas, and therefore box plots were produced for primary visual (n ⫽ 7),
primary somatosensory (n ⫽ 7), primary motor (n ⫽ 7), and
medial prefrontal (n ⫽ 10), displaying the mean and median
values (Fig. 3). These numbers of observations refer to the
number of recordings included. Given that only one recording
was obtained per area in each experiment, the number of
recordings per area is equivalent to the number of animals. No
significant differences in any of the analyzed parameters were
found across hemispheres for the same cortical area.
The average oscillatory frequency of oscillation across all
areas was 0.97 Hz; 54.8% of the recordings displayed frequenJ Neurophysiol • VOL
106 • DECEMBER 2011 •
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Fig. 1. Oscillatory activity in the mouse cerebral cortex during ketamine
anesthesia. A, top: filtered signal of the recording (band pass 10 –100 Hz)
illustrating the occurrence of high frequencies in the up state. Middle: local
field potential (LFP) in the motor cortex. Bottom: single-unit recording of a
neuron firing in the up states. B: simultaneous bilateral extracellular recording
in the prefrontal cortex. L, left hemisphere; R, right hemisphere. C: waveform
cross-correlation of the 2 signals in B.
cies between 0.6 and 1 Hz, while 45.8% oscillated at frequencies over 1 Hz. No significant differences across areas were
observed between the average duration of either up (Fig. 3B;
0.28 s) or down (Fig. 3C; 0.8 s) states.
Comparing across areas, prefrontal cortex was the area that
exhibited larger differences (P ⬍ 0.05) compared with the
other areas across a variety of parameters. The slope of downto-up transitions in prefrontal recordings was significantly
faster than in the other three cortical regions. The population
firing rate was also higher in prefrontal cortex than in the other
cortical areas (Fig. 3F). Another property of the rhythm in the
prefrontal cortex was the higher regularity in oscillations,
reflected in a lower CV of the duration of up states compared
with the other three areas (Fig. 3G).
Some other specific differences between areas were also
detected, such as a faster transition from up to down state in
motor and prefrontal cortex than in the two primary sensory
areas. Further information about the statistics of this section is
given in Supplemental Table S1.
Firing properties of cortical neurons during slow waves.
Recordings of single units (Fig. 4) were obtained by singling
out 37 single neurons from motor, 9 from somatosensory, and
43 from prefrontal cortex. Their firing patterns were analyzed
with respect to the population activity in the same location
recorded as the local field potential (LFP). Almost all spikes
were found to occur during up states, with a few exceptions
that are described below. Figure 4, A–D, illustrates two different firing patterns that were observed: tonic firing during
the whole duration of the up state (mean firing rate 24.7 Hz;
n ⫽ 19 neurons) versus a sparse firing (mean firing rate 5.2
Hz; n ⫽ 70).
We were interested to determine the number of spikes that
single neurons fire per up state. No differences were observed
between neurons from motor and somatosensory cortex; thus
we grouped them together. Of those 46 neurons, 52.2% fired in
⬎90% of the up states, while 37.0% fired in 50 –90% of up
states and only 10.9% fired in ⬍50% of active states (Fig. 4F).
On the other hand, most of the neurons had a sparse firing with
⬍2 spikes per up state in 47.8% of the neurons; 26.1% fired
2– 6 spikes, and the same percentage had tonic firing and fired
trains of ⬎6 spikes during up states (Fig. 4E).
Neurons from prefrontal cortex showed some differences
with neurons from primary cortices, and we report these
separately. The difference was that prefrontal cortex neurons
fired in more up states and with more spikes per up state. This
is represented in Fig. 4, G and H; 72% of prefrontal cortex
neurons fired in all up states, while only 3% fired in less than
half the up states. On the other hand, 50% of neurons had a
discharge of 2– 6 spikes per up state.
Taking all neurons together, we conclude that most of the
neurons participate with a relatively sparse firing in most of the
up states when participating in slow oscillations.
We were interested to know how the firing of individual
neurons was distributed during up states. Therefore, the firing
rate of all 89 units was analyzed within up states, and PETHs
were built around the onset of up states. We observed a
different distribution of single units’ firing during up states
between primary cortices and prefrontal cortex neurons, so
they appear separated in the next considerations. The PETHs
RHYTHMS IN MOUSE CEREBRAL CORTEX
2913
J Neurophysiol • VOL
106 • DECEMBER 2011 •
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Fig. 2. Analysis of population activity in 4 cortical areas. A: prefrontal cortex: from top to bottom: relative firing rate, LFP, raster plots of 100
aligned up states, and waveform average of the
relative firing rate. This average is the one used
for the calculation of the down-to-up and up-todown slopes and the maximum firing rate of the
analyzed periods. The shade corresponds to the
SD. The red boxes represent the automatically
detected up and down states. B–D: same as A but
in motor, somatosensory, and visual cortices,
respectively.
2914
RHYTHMS IN MOUSE CEREBRAL CORTEX
revealed two main groups of firing patterns: 1) neurons with a
maximum firing rate at the initiation of the up state (primary
cortices n ⫽ 15; prefrontal cortex n ⫽ 23) (Fig. 5, A and D) and
2) neurons with the maximum firing rate toward the center of
the up state (primary cortices n ⫽ 28; prefrontal cortex n ⫽ 15)
(Fig. 5, B and E). Of the remaining three neurons from primary
cortices, one had a tonic firing evenly distributed during the
duration of the up state (e.g., Fig. 5C) and two showed an
increased firing rate preceding the termination of the up state.
Of the remaining prefrontal cortex single units, three fired
at the end of up states and two had rather a tonic discharge
(Fig. 5F).
To compare across neurons and to exclude the variations in
up state duration, up states were divided into five even windows (Fig. 5, G–I), and the firing rate of the unit was estimated
for each fifth of the up state. In this way, a grand average of the
PETHs for 46 neurons from primary cortices and 43 prefrontal
cortex neurons was calculated (see Fig. 5G, inset). The average
distribution of somatosensory and motor single units’ firing
J Neurophysiol • VOL
showed a preferential increase of firing rate toward the center
of the up state, given that the most common neuronal pattern
was that in Fig. 5B and E. Prefrontal cortex single units fired
toward the beginning of the up state (Fig. 5G, inset).
To further take into account differences in up state duration, all
the up states recorded for each neuron (n ⫽ 100 –200) were split
into three groups according to length: short, medium, and large.
When the firing rate distribution of single neurons was represented
for these three groups (Fig. 5, G–I) it showed that the predominance of motor and somatosensory single-unit firing toward the
center of the up state was not dependent on the duration of the up
states. The same was the case for prefrontal cortex single units,
which independently of the duration of up states tended to fire
toward the beginning of up states (Fig. 5, G–I). In this way the
single-unit firing rate distribution was evaluated in a total of 6,764
up states in the case of the primary cortices and 7,695 in the case
of the prefrontal cortex.
A small number of neurons (3 of 46) had spikes in the 50-ms
window preceding up states. These were the only spikes
106 • DECEMBER 2011 •
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Fig. 3. Box plots representing the statistical values of the 9 parameters (A–I) of the slow oscillations in visual, somatosensory, motor, and prefrontal cortex.
A: frequency of the oscillation. B: up state duration. C: down state duration. D: down-to-up state transition slope. E: up-to-down state transition slope. F: peak
of relative firing rate. G: coefficient of variation (CV) of up state duration. H: CV of down state duration. I: CV of the oscillatory period. Each box represents
50% of the data (from 25% to 75%), the small square inside depicts the mean, and the divisory line is the median. Whiskers represent the 5–95% interval of
the data, and asterisks represent 1% and 99% values. Recordings from the right hemisphere were used for the averages.
RHYTHMS IN MOUSE CEREBRAL CORTEX
2915
observed during down states. When analyzing these spikes
preceding up states, we should take into account a number of
possible sources of error. One of these is the fact that single
units and LFP were analyzed with two closely located different
electrodes. The maximum separation between the electrodes
was 100 ␮m. If we consider that some up states could be
detected earlier in one electrode than in the other, and we
consider the slowest detected propagation speed (10 mm/s), we
conclude that it would take 10 ms to travel from one electrode to the other, so the firing occurring earlier than 10 ms has
to be considered as preceding the up state. Another possible
source of error could be the method of detection of the up state
in the population. Any method of up state detection requires
the determination of a threshold. In the network, though, the up
state starts building up before the threshold is reached. We
estimate that on average we could consider that up to 30 ms
preceding the threshold there could be activity building up.
Thus, if we consider both confounds together, there can be a
window of 40 ms during which the firing of single units could
be considered as occurring during up states. Those occurring
earlier are probably spikes actually preceding up states. From
this we conclude that the spikes detected in these two neuJ Neurophysiol • VOL
rons that appear to precede up states are probably part of the
building up of the activity.
Propagation of slow waves in the mouse cortex. Wave propagation was evaluated with an array of 16 electrodes separated
from each other by 125 ␮m. The electrode was placed at 1 ⫾
0.2-mm depth and parallel to the midline in the motor cortex
and orthogonal to the midline in the visual cortex (Fig. 6, A and
B, top insets). We included in the analysis 20 recordings of
wave propagation, 12 from motor and 8 from visual cortex, and
the propagation of at least 300 consecutive up states was evaluated.
Up states were detected with the same threshold algorithm
for the spectral estimated MUA used previously to determine
the properties of slow oscillatory activity (see EXPERIMENTAL
PROCEDURES for details; Sanchez-Vives et al. 2010). The relative
time lags between up state onsets singled out from the 16
electrodes were used to identify different patterns of activity
propagation. Therefore, we performed a PCA that allowed the
projection of the time lag patterns of different up states into a
subspace suitable for sorting them and capable of retaining the
most variance. These ranked up states were pooled into five
equally sized groups. The average time lags within each group
106 • DECEMBER 2011 •
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Fig. 4. Firing of single units during up states.
A: LFP (top) and single-unit recording from
motor cortex (bottom) both obtained through
closely located (⬍200 ␮m apart) tungsten and
glass electrodes, respectively. B: recording
from motor cortex from a different animal;
rest same as in A. Neurons fired either tonically during up states (e.g., A) or just a few
spikes (e.g., B). C and D: as A and B, but the
recording was obtained from prefrontal cortex. Note that the neuron in C fires more
spikes per up state than the neuron in D.
E: histograms of single units from somatosensory and motor cortex according to the number of spikes fired within up states. F: histograms of single units from somatosensory and
motor cortex according to % of up states
where they displayed spike activity. G: histograms of single units from prefrontal cortex
according to the number of spikes fired within
up states. H: histograms of single units from
prefrontal cortex according to % of up states
where they displayed spike activity.
2916
RHYTHMS IN MOUSE CEREBRAL CORTEX
are shown in Fig. 6 for three typical recordings: the different
symbols and related connecting lines clearly display significantly different modes of propagation within the same recording session, which occur randomly in time.
Six of twelve cases showed a clear pattern of propagation
from front to back in motor cortex, in agreement with the
predominant direction of propagation of oscillations during
slow-wave sleep in humans (Massimini et al. 2004). In visual
cortex, the majority of cases (6 of 8) showed a monotonic
variation of the average time lag along the multielectrode
array.
While the direction of the propagation along the axis of the
electrode array was constant, the estimated speed of different
waves varied continuously within a range of 8 –93 mm/s. This
is illustrated for both motor and visual recordings in Fig. 6, A
and B. All 12 cases were highly similar to those illustrated
here. The motor and visual distributions of velocities (Fig. 6, A
and B, bottom insets) across time lag patterns and recordings
are not significantly different (2-tailed Kolmogorov-Smirnov
J Neurophysiol • VOL
test, P ⫽ 0.42), although the two electrode arrays were placed
in orthogonal positions.
The remaining seven cases showed more heterogeneity in
the direction of propagation (see, e.g., Fig. 6C from a motor
cortex recording). These patterns were compatible with
waves propagating from other areas, e.g., with lateral propagation or with colliding waves coming from different directions. In these cases we could not confidently estimate
the component of the speed of propagation along the axis of
the electrode array.
Fast components of mouse neocortical slow oscillations.
The frequency content of the LFP in the recordings from the
four studied cortical areas (visual, somatosensory, motor, and
prefrontal cortex) was analyzed. Spectrogram analysis confirmed the presence of fast rhythms in the up states (Fig. 7A).
To study frequency content during up and down states in a
systematic manner, a power spectrum analysis was carried out
on recordings from prefrontal cortex (n ⫽ 10 mice; Fig. 7, B
and C) and somatosensory (n ⫽ 7; Fig. 7E), motor (n ⫽ 6; Fig.
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Fig. 5. Distribution of single units’ firing rate during up states. A: perievent time histogram (PETH) of a motoneuron with the firing rate peak toward the initiation
of the up state. B: PETH of a motoneuron that reached the peak of its firing rate toward the center of the up state. C: example of a motoneuron that fired rather
tonically during the duration of the up state, increasing its firing toward the end of the up state. In all cases 200 s has been analyzed and the firing rate in 100 –200
up states averaged for each neuron. D–F: same as A–C but for prefrontal cortex. G: distribution of single spikes during up states for “short” (see below) up states.
Black, data corresponding to motor and somatosensory units; gray, data corresponding to prefrontal cortex units. Inset: grand average of the firing rate of single
units (n ⫽ 46 for somatosensory and motor cortex, n ⫽ 43 for prefrontal cortex) after dividing each analyzed up state into 5 equal time windows. H: distribution
of single spikes during up states for “medium” (see below) up states. I: distribution of single spikes during up states for “long” (see below) up states. Up states
recorded for 200 s in each one of 46 neurons for somatosensory and motor cortex (gray traces) or 43 neurons in prefrontal cortex (black traces) were clustered
in short, medium, and long durations (33.3% of analyzed up states for each neuron were classified in each group). Error bars correspond to SE. Note that the
distribution of firing rate of single neurons along the up states was independent of their duration.
RHYTHMS IN MOUSE CEREBRAL CORTEX
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7D), and visual (n ⫽ 7; Fig. 7F) areas. The analysis focused on
the occurrence of fast rhythms that were divided into beta
(15–30 Hz), low gamma (30 – 60 Hz), and high gamma (60 –90
Hz). The power of fast rhythms was significantly larger during
up than during down states (Fig. 7, B and D–F). Interestingly,
for all the studied frequency bands, beta, low gamma and high
gamma, power was consistently higher in prefrontal cortex
than in the rest of the areas (Fig. 8, A, B, and C, respectively).
The absolute values of beta and gamma power in up (Fig. 8,
D–F) and down (Fig. 8, G–I) states are illustrated in Fig. 8,
D–I, while the relative beta and gamma power in up versus
down states is represented in Fig. 8, A–C. Figure 8 shows that
the higher beta and gamma power in prefrontal cortex is the
result of a specific increase during up states and not of other
reasons like a lower power during down states in prefrontal
cortex.
The beta band power was 3.5- to 7.2-fold higher in motor,
somatosensory, and visual cortex in up with respect to down
states. The beta power ratio was 13.2 times larger in prefrontal
cortex (P ⬍ 0.005 with respect to motor cortex, P ⬍ 0.0001
with respect to somatosensory cortex, and P ⬍ 0.0001 with
respect to visual cortex). Prefrontal cortex showed a markedly
higher relative power in the gamma band (Fig. 7, B and C). We
divided the gamma band into high gamma (60 –90 Hz) and low
gamma (30 – 60 Hz). In the low gamma range, the relative
power in prefrontal cortex was 42.2, significantly larger than in
other areas (P ⬍ 0.001 with respect to motor cortex, P ⬍
0.0001 with respect to somatosensory cortex, and P ⬍ 0.001
with respect to visual cortex), where values ranged from 3.6 to
J Neurophysiol • VOL
5.8. Finally, in the high gamma band prefrontal cortex showed
a relative power value of 124, which was significantly different
from motor (P ⬍ 0.005), somatosensory (P ⬍ 0.005), and
visual (P ⬍ 0.005) cortices, where the values ranged from 8.9
to 14. Further information on the statistics of this section is
given in Supplemental Table S2.
DISCUSSION
This study is a systematic characterization of the emergent
cortical activity in the mouse cerebral cortex during ketamine
anesthesia. Continuous infusion of subcutaneous ketamine allowed us to obtain stable anesthesia levels reflected in a regular
slow oscillatory frequency along the duration of the experiment
(6 –7 h). Under these conditions, up and down states were
spontaneously generated by the cortical network, and nine
parameters quantifying these were compared across visual,
somatosensory, motor, and prefrontal cortices. We also explored the firing of individual neurons with respect to the
network activity, wave propagation, and generation of beta and
gamma synchronization during up states. This information
should be valuable for understanding cortical network emerging activity in the mouse model. It is also useful as a baseline
of spontaneous activity generated in the mouse, allowing us to
compare against experimental or genetic manipulations. Given
that slow-wave sleep has a role in memory consolidation
(Diekelmann and Born 2010; Marshall et al. 2006), it is also
relevant to have a quantitative study as a point of reference for
behavioral studies.
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Fig. 6. Speed of propagation of up state onsets in 2 cortical areas (motor and visual) of the mouse. A: average time lags of up state onsets recorded with arrays
of 16 electrodes in primary motor cortex. Top inset: location and position of the recording array (discontinuous line). Time lags were grouped in 5 different pools
with similar patterns of activity propagation (each with different symbols and gray levels) obtained with a principal component analysis (see text for details).
Bottom inset: distribution of speeds estimated from the up state pools with similar time lag patterns in the motor cortex recordings, illustrating a monotonic
propagation from front to back (n ⫽ 27, 6 of 12 recordings). Vertical dashed line is the average propagation speed: 30.0 ⫾ 3.9 mm/s. B: same as in A for
recordings in the visual cortex. Note that in this cortical area the array was placed in the coronal plane (see top inset), thus orthogonal to that in A. Bottom inset:
distribution of speeds for the visual cortex recordings showing a monotonic decrease of time lags from medial to lateral positions (n ⫽ 28, 6 of 8 recordings).
Average speed: 23.4 ⫾ 2.1 mm/s. C: another motor cortex example showing complex patterns of activity propagation.
2918
RHYTHMS IN MOUSE CEREBRAL CORTEX
Power 10log(mV2/Hz)
Slow oscillations recorded in primary sensory cortices (visual and somatosensory) and primary motor cortex were highly
similar, and most of the differences found in this study were
observed in prefrontal cortex. Interestingly, the mean frequency of occurrence was the same in all areas, coherent with
a traveling wave across the cortical network. However, the
regularity of this frequency (1/CV of the cycle) was maximal
in prefrontal cortex. During slow-wave sleep in humans it has
been described that waves preferentially travel from frontal to
occipital areas (Massimini et al. 2004). Even though any point
in the cortical network can potentially start a new wave, the
frontal cortex appears to be the most common initiator of a new
wave (Massimini et al. 2004). Our multiple recordings following an anteroposterior alignment also found a preferential
propagation from front to back in the mouse (Fig. 6A). The
preferential origin of the rhythmic activity in the prefrontal
cortex could be due to some of the distinctive features of this
area that are discussed next.
In this study we found that up state duration, the down-to-up
state transition slope, the maximum firing rate, and the CV of
up state duration were significantly different in prefrontal
cortex compared with the other three studied regions. These
parameters were not statistically different between motor, somatosensory, and visual cortices, a fact perhaps due to all of
these being primary areas and thus sharing some structural
properties. Not only did we find higher population firing rate in
prefrontal cortex, but also single-unit recordings revealed
higher firing rates in prefrontal cortex during up states. Differences in the prefrontal cortical structure or in its connectivity
with cortical and subcortical areas could translate functionally
into some of the functional differences detected here. InterestJ Neurophysiol • VOL
ingly, some of the differences observed in prefrontal cortex
activity (higher population firing rate during up states, faster
down-to-up state transition) are compatible with a higher
interneuronal connectivity and recurrence in this area. The
transition from down to up state reflects the recruitment of the
local network for the cortical activation. When the recruitment
is faster, there is a steeper transition from down to up state, as
is the case when inhibition is progressively decreased (Sanchez-Vives et al. 2010) or temperature increased (Reig et al.
2010). That the prefrontal network activates faster than the
primary cortices could be explained by different possible
mechanisms: higher local connectivity and more efficient reverberation of activity, higher excitability, or less inhibition
with respect to primary cortices. The finding of relatively more
spinous neurons in prefrontal cortex in macaques and humans
is suggestive of higher synaptic connectivity in this area
(Elston 2000; Elston et al. 2001).
The down-to-up transition was then faster in prefrontal than
in visual, somatosensory, and motor cortex. The transition
from the up to down state, the silencing of the local network,
was significantly faster in prefrontal and motor cortex than in
visual and somatosensory cortex. This means that in prefrontal
cortex, and partially in motor cortex, the initiation and termination of up states is more synchronized than in primary
sensory cortices. We have observed a correlation between the
down-to-up and up-to-down slopes previously, for example, in
situations with increasingly blocked inhibition (Sanchez-Vives
et al. 2010) or with increasing temperatures (Reig et al. 2010).
The link between down-to-up and up-to-down slope is population firing rate during the up state. A fast depolarizing
recruitment of the network usually leads to a high firing rate in
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Fig. 7. Oscillatory activity during slow waves in 4 (prefrontal, motor, somatosensory, and visual) cortical areas of the mouse. A: spectrogram showing the
occurrence of fast rhythms during the up states in prefrontal cortex. B: power spectrum of activity in the prefrontal cortex after separating up (red with circles)
and down (dark blue with circles) states. C: relative power in up vs. down states calculated from the power spectra. D–F: power spectra of oscillatory activity
in motor, somatosensory, and visual cortex, respectively.
RHYTHMS IN MOUSE CEREBRAL CORTEX
2919
Fig. 8. Fast rhythms in the mouse cerebral cortex during slow oscillations in 4 cortical areas. A–C: comparison of relative power in 15–30 Hz (beta), 30 – 60 Hz
(low gamma), and 60 –90 Hz (high gamma) frequency bands across the 4 cortical areas (prefrontal, visual, motor, and somatosensory cortex). D–F: comparison
of absolute power in up states in the beta, low gamma, and high gamma frequency bands across cortical areas. G–I: comparison of absolute power across areas
in down states for the same frequency bands. Error bars are SE. *Significant difference P ⬍ 0.01.
the population. A high firing rate efficiently recruits the potassium currents that could be terminating up states and that are
activity dependent (Compte et al. 2003; Cunningham et al.
2006; Sanchez-Vives et al. 2010). Indeed, prefrontal and motor
cortex have significantly higher firing rates than primary sensory cortices (Fig. 3F). In our previous observations, fast
down-to-up and up-to-down slopes were concurrent not only
with higher firing rates during up states but also with shorter up
states. However, we do not observe in our statistics illustrated
in Fig. 3B any significant differences in up state duration across
areas.
Our data from prefrontal cortex are in agreement with the
complementary hypothesis of an increased dynamical stability
of up states, for example due to a more effective recurrent
GABAergic and glutamatergic synaptic coupling (Amit and
Brunel 1997; Brunel and Wang 2001). Stronger stability means
J Neurophysiol • VOL
that changes in time of the firing rates are compelled by
“restoring forces” toward the low and high firing states, making them more robust to intrinsic fluctuations in the firing
activity. A strong local synaptic reverberation capable of making the high-firing regime a preferred dynamical state of the
network is a way to implement strong restoring forces. Such
forces induce the neuronal activity to have highly nonlinear
dynamics that could explain why both the drop from the up
states and the chain reaction eliciting high-frequency reverberation from down to up states are faster in prefrontal than in
primary sensory cortices. This is an attractive scenario that
suggests the existence along the cortex of a hierarchy of
excitability and characteristics of the local circuitry: starting
from “caudal” peripheral areas rapidly adapting to fast sensorial stimuli and motor actions and ending with the “rostral”
associative areas whose integrative role has to be more stim-
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RHYTHMS IN MOUSE CEREBRAL CORTEX
J Neurophysiol • VOL
and gamma frequencies have been functionally associated to
cognitive functions like attention (for a review see Wang
2010), these frequencies also emerge spontaneously during up
or activated states. This is the case not only during slow-wave
sleep and anesthesia (Steriade et al. 1996) but also in cortical
slices in vitro (Compte et al. 2008). We find that in the mouse,
the power of beta, and particularly that of gamma, is strikingly
higher in prefrontal than in motor and sensory primary cortical
areas (Fig. 8). We can only speculate about the cellular or
network basis for these differences, but they suggest that the
prefrontal circuitry has specific properties to efficiently generate gamma rhythms. These properties may involve networks of
fast-spiking inhibitory neurons or specific excitatory-inhibitory
loops (Compte et al. 2008; Freund 2003; Hasenstaub et al.
2005; Paik et al. 2009; Tamas et al. 2000; Whittington et al.
1995). Gamma synchronization would then emerge out of the
prefrontal circuit during up states, probably being upregulated
in the awake, attentive animal. This trend for the prefrontal
circuit to generate beta and gamma frequencies matches its role
as the origin of top-down influences during perceptual, attentive, or memory tasks (Engel et al. 2001; Gregoriou et al. 2009;
Lachaux et al. 2008; Palva et al. 2010; Womelsdorf et al.
2007).
ACKNOWLEDGMENTS
We thank Maria Perez-Zabalza and David Robbe for their suggestions
regarding data analysis.
GRANTS
This work has been funded by the Spanish Ministry of Science and
Innovation (BFU2008-01371/BFI to M. V. Sanchez-Vives).
DISCLOSURES
No conflicts of interest, financial or otherwise, are declared by the author(s).
AUTHOR CONTRIBUTIONS
Author contributions: M. R.-M., M. M., and M. V. S.-V. conception and
design of research; M. R.-M. performed experiments; M. R.-M., L. C.-S.,
M. M., and M. V. S.-V. analyzed data; M. R.-M., L. C.-S., M. M., and
M. V. S.-V. interpreted results of experiments; M. R.-M., L. C.-S., and M. M.
prepared figures; M. R.-M. and M. V. S.-V. drafted the manuscript; M. M. and
M. V. S.-V. edited and revised the manuscript; M. V. S.-V. approved the final
version of the manuscript.
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