ORIGINAL ARTICLE
Amyloid-b disrupts ongoing spontaneous activity in sensory cortex
Shlomit Beker • Miri Goldin • Noa Menkes-Caspi •
Vered Kellner • Gal Chechik • Edward A. Stern
Received: 21 May 2014 / Accepted: 8 December 2014
� Springer-Verlag Berlin Heidelberg 2014
Abstract The effect of Alzheimer’s disease pathology on
activity of individual neocortical neurons in the intact
neural network remains obscure. Ongoing spontaneous
activity, which constitutes most of neocortical activity, is
the background template on which further evoked-activity
is superimposed. We compared in vivo intracellular
recordings and local field potentials (LFP) of ongoing
activity in the barrel cortex of APP/PS1 transgenic mice
and age-matched littermate Controls, following significant
amyloid-b (Ab) accumulation and aggregation. We foundthat membrane potential dynamics of neurons in Ab-bur-dened cortex significantly differed from those of non-
transgenic Controls: durations of the depolarized state were
considerably shorter, and transitions to that state frequently
failed. The spiking properties of APP/PS1 neurons showed
alterations from those of Controls: both firing patterns and
spike shape were changed in the APP/PS1 group. At the
population level, LFP recordings indicated reduced
coherence within neuronal assemblies of APP/PS1 mice. In
addition to the physiological effects, we show that mor-
phology of neurites within the barrel cortex of the APP/PS1
model is altered compared to Controls. These results are
consistent with a process where the effect of Ab onspontaneous activity of individual neurons amplifies into a
network effect, reducing network integrity and leading to a
wide cortical dysfunction.
Keywords Alzheimer’s disease � Membrane potential �Synaptic summation � Plaques � LFP � Firing patterns
Introduction
Alzheimer’s disease (AD), the major cause of dementia in
the western world, results in progressive dysfunction of
memory and higher cognitive functions. It has been linked
to several deficits in sensory processing, most of which are
either visual (Grienberger et al. 2012; Trick and Silverman
1991) or olfactory (Cao et al. 2012; Devanand et al. 2000).
A major theory related to the etiology of AD is the Amy-
loid Hypothesis (Hardy and Selkoe 2002). It postulates that
abnormally folded protein amyloid-b (Ab) accumulating inthe brain is the primary factor driving AD pathogenesis. Abaccumulation, in both soluble and insoluble forms, has
been associated with synaptic loss (Hardy and Selkoe
2002), neuronal and dendritic loss (Spires et al. 2005),
spine instability (Spires-Jones et al. 2007), and disruption
of hypercolumnar organization in the neocortex (Beker
et al. 2012).
In recent years, the effects of AD pathology on prop-
erties of cellular functioning have been well-studied (Bero
et al. 2011, 2012; Busche et al. 2008; Grienberger et al.
2012; Gurevicius et al. 2012; Kamenetz et al. 2003; Palop
et al. 2007). Ab accumulation was also associated withaltered neuronal function in the neocortex in response to
electrical stimuli in vivo (Stern et al. 2004). However, the
ways in which AD pathology interacts with ongoing, sub-
threshold neuronal activity have not been directly
measured.
S. Beker � M. Goldin � N. Menkes-Caspi � V. Kellner �G. Chechik � E. A. Stern (&)Gonda Brain Research Center, Bar-Ilan University,
52900 Ramat Gan, Israel
e-mail: [email protected]
E. A. Stern
Department of Neurology, MassGeneral Institute for
Neurodegenerative Disease, Massachusetts General Hospital,
Charlestown, MA, USA
123
Brain Struct Funct
DOI 10.1007/s00429-014-0963-x
Spontaneous ongoing activity occurs even in the
absence of environmental inputs, and is a critical deter-
minant of information processing by neocortical neurons
(Haider and McCormick 2009; Chorev et al. 2007). Sen-
sory and other incoming synaptic information are super-
imposed on, and interact with ongoing activity in the
background (Petersen et al. 2003b). When recorded during
slow wave sleep or under anesthesia in vivo, the sub-
threshold membrane potential of neocortical neurons
spontaneously fluctuates between a quiescent, resting state
(‘Down state’) and a depolarized state (‘Up state’), from
which action potentials arise (Cowan and Wilson 1994;
Steriade et al. 1993; Stern et al. 1997). The ‘Up’–‘Down’
fluctuations result from coherent afferent synaptic inputs to
the neuron filtered by the nonlinear neuronal membrane
properties (Stern et al. 1997), and are a general property of
the activity of neocortical pyramidal neurons (Steriade
et al. 1993). These fluctuations critically determine the
firing patterns and functional properties of these cells
(Chorev et al. 2007; Haider and McCormick 2009).
Although many morphological and functional deficits
have been associated with Ab accumulation in the cortex,this pathology has only recently been linked to ongoing
activity patterns in frontal areas (Kellner et al. 2014).
However, it has not been yet related to any specific
ongoing subthreshold membrane potential and spike
activity patterns in sensory areas. It is important to measure
the effects of Ab accumulation in an area of early stage ofcortical processing, such as the primary sensory area. Abcould affect the activity of neocortical neurons by two
mechanisms: changing the patterns of synaptic inputs, and
changing the actual integration properties of the neurons. It
is therefore of use to measure the effects of Ab on corticalcellular activity in those areas receiving information in the
early stages of the feed-forward cortical information
pathways. Ab accumulation in these areas may have aspecific detrimental effect on the patterns and/or coherence
of ongoing subthreshold activity and firing patterns. If
patterns of activity of a single neuron in the presence of Abare altered from the activity patterns of healthy neurons,
the dysfunction may propagate to downstream neurons
within the recurrent network, become amplified over a
larger area and lead to network-wide functional deficits. In
a recurrent manner, these network deficits may affect the
integration of afferent inputs within individual neurons. In
sensory areas, such a process could affect the critical
activity patterns necessary for consequent actions.
Spontaneous intracellular activity has been found to be
highly correlated with local field potentials (LFP) in cor-
tical areas, showing fluctuation in similar frequencies
(\1 Hz) (Okun et al. 2010; Saleem et al. 2010), suggestinglocal synchronization (Lampl et al. 1999). LFP fluctua-
tions, like those of membrane potentials, are mostly due to
synaptic activity (Mitzdorf 1991, 1994; Okun et al. 2010).
In addition, the LFP at a given location can be well pre-
dicted by the spiking activity of neurons recorded in the
area surrounding the field potential electrode (Nauhaus
et al. 2009).
To measure possible pathophysiology of the intracellu-
lar and network spontaneous activity, we recorded spon-
taneous intracellular activity of neocortical neurons and
LFP in the barrel cortex of APP/PS1 AD model mice, a
strain with an early onset of amyloid deposition in the
cortex (Jankowsky et al. 2004). These measurements
allowed us to compare the spontaneous firing patterns of
APP/PS1 and Control neurons, as well as the patterns of
inputs, represented by the subthreshold activity. In addi-
tion, we measured differences in network activity by
comparing LFPs of Control and APP/PS1 animals. Finally,
to measure morphological alterations of neuronal popula-
tions in the transgenic animals, we measured curvature
indices of neurites in the barrel cortex of APP/PS1 animals.
Since the barrel cortex has a well-defined anatomy and
connectivity, it was chosen here as a locus for examining
the background activity underlying sensory information
processing, in a diseased cortex with AD pathology.
Materials and methods
Animals
In all experiments, we used B6C3 APP/PS1 dE9 APP/PS1
(APP/PS1) strain developed by Jankowsky et al. (2004),
and age-matched nontransgenic littermate Control mice
(Controls). This strain expresses human presenilin1 (PS1,
A246E variant) and a chimeric amyloid precursor protein
(APPswe), and develops amyloid pathology much earlier
than do models overexpressing only APP. Ab plaquesaccumulate in an age-dependent manner, and are abun-
dant in the cortex by 9 months of age (Jankowsky et al.
2004). For the intracellular recordings, we used 19 cells
from 18 animals. For the LFP recordings, we used 23
animals, from the two genotypes. Animals in all experi-
ments were 9–19 months old, an age when the cortex of
the APP/PS1 transgenic mice is largely filled with plaques
(Jankowsky et al. 2004). No statistical effect of age was
found for different age groups, between the APP/PS1 and
controls, for any of the experiments (see Table 1 in
Appendix).
All procedures were approved by the Bar-Ilan Univer-
sity Animal Care and Use Committee and performed in
accordance with Israeli Ministry of Health and US National
Institutes of Health (NIH) guidelines. All animals were
housed on a 12:12 h light/dark cycle and had ad libitum
access to food and water.
Brain Struct Funct
123
Surgery
Prior to anesthesia, animals were placed in a custom-built
stereotaxic device. Body temperature was kept at 37.5 �Cusing a heating blanket and a rectal thermometer (Harvard
apparatus, Holliston, MA, USA). Animals were anesthe-
tized with ketamine–xylazine solution (13:1), and given
supplemental intramuscular injections once per hour as
needed to maintain anesthesia level. Anesthesia was
monitored by electrocorticogram (ECoG) recording elec-
trodes placed over the cerebellum and cortex, and by
reaction to limb-pinch. During the surgery, a cranial win-
dow (2 9 2 mm) was prepared over the left primary
somatosensory barrel field (coordinates as in Petersen et al.
2003a: bregma—1.5 mm, 3.5 mm lateral to midline), a
part of the skull was exposed, dura was removed, and
electrodes were inserted.
Intracellular recordings
Intracellular recordings were performed using the standard
‘‘blind’’ technique. We have used sharp electrodes, pulled
from borosilicate micropipettes (outer and inner diameters:
1.5 and 0.86 mm, respectively; A-M Systems), with a P-97
micropipette puller (PE-21, Narishige). The pipettes were
filled with 1 M potassium acetate and had a resistance of
30–100 MX. The recording electrodes were aligned so thatthe tips would meet the central area of the Barrel cortex.
After recording electrodes were inserted, the exposed
cortex was covered with a low-melting-point paraffin wax
to reduce brain pulsations. Recordings were made using an
active bridge amplifier and then filtered and digitized at a
rate of 10 kHz. Neurons that had membrane potentials
more negative than -55 mV and action potentials more
positive than 0 mV were included in the sample. The
median ± MAD depth of electrode location was
300 ± 100 lm.
LFP
For the LFP recordings we used tungsten electrodes, hav-
ing 0.5–1 MX impedance at 1 kHz (Cygnus Technology).The tungsten electrode was inserted into a glass tube, in
one side of the barrel cortex, at a depth of 200–400 lmbelow the pia. The reference electrode was placed a few
hundred micrometers from the recording electrode,
encompassing the barrel field area.
ECoG
We used recordings of ECoG for monitoring the anesthesia
of the animals. Further analysis was done on ECoG
recorded simultaneously with LFP. Electrodes consisted of
Teflon-insulated silver wire with 1 mm insulation
removed. Small holes (1 mm) were drilled for the elec-
trodes over the barrel cortex and cerebellum. Electrodes
were placed above the dura and cemented in place. ECoG
was monitored continuously from the time of electrode
placement to monitor depth of anesthesia.
Curvature ratio
Histochemistry was done on five APP/PS1 mice and five
Control littermates between the ages 10–11 months. To
identify neurites trajectories in vicinity of plaques, the
brain were perfused, sectioned and stained as following:
After perfusions with saline and then 4 % PFA, brains were
flattened in order to achieve optimal position of barrel
cortex slices. Brains were post-fixed in 4 % PFA for at
least 24 h, in sucrose buffer for at least 48 h, in 4 �C.Brains were then frozen in -80 �C for another 24 h Sec-tions of 50 lm were cut on a freezing microtome andimmunostained with primary antibodies to SMI312 and
SMI32 (mouse monoclonal, 1:200; Sternberger Monoclo-
nals, Baltimore, MD) and secondary anti-mouse conjugated
to Cy3 or Cy5 (1:200; Jackson ImmunoResearch, West
Grove, PA). Sections were counterstained with 0.05 %
thioflavine S (ThioS) (Sigma–Aldrich) in 50 % ethanol to
label dense plaques. Observation was made using a Nikon
Eclipse E400 Microscope (Tokyo, Japan). Images of layer
IV of the barrel cortex were captured using a camera
attached to the microscope (Nikon digital camera DXM
1200F, Tokyo, Japan). Analysis and tracking of neurites
and plaques were done using the microscopy program
ImageJ (NIH, Bethesda, MD). Overall, 2,778 neurites were
traced and measured. Curvature ratio was defined as the
Table 1 Distribution of ages of animals for all experiments
Exp. type Intracellular
recordings
LFP recordings Histology
Ages (months) 9–14 15–19 12–14 15–16 10–11
Control 11 2 6 6 5
APP/PS1 2 4 9 2 5
No age 9 physiological marker effect was found for four physio-
logical markers of either control (failures rate: v2 = 2.64; df = 12;p = 0.1 ns; proportion of time in Up state: v2 = 1.09; df = 9;p = 0.29, ns; Up state duration: v2 = 0.35; df = 12; p = 0.55 ns; ISIv2 = 3.16; df = 12; p = 0.08 ns) or APP/PS1 transgenic mice (fail-ures rate: v2 = 0.86; df = 5; p = 0.35 ns; proportion of time in Upstate: v2 = 0; df = 5; p = 1, ns; Up state duration: v2 = 0; df = 5;p = 1 ns; ISI v2 = 0; df = 5; p = 1, ns). For LFP recordings, as forthe intracellular recordings, we divided the data to two subgroups of
ages (12–14; 15–16). We compared variance of troughs voltages of
LFP between these age groups. As with the intracellular data, no
age 9 physiological effect was found for control (Mann–Whitney
U = 29; p = 0.93, ns) or APP/PS1 transgenic mice (Mann–Whitney
U = 37; p = 0.82, ns)
Brain Struct Funct
123
ratio between the end-to-end distance, and the trace
distance.
Analysis
Numerical and statistical analysis of all recordings and
histology data was performed using custom software
written in MATLAB R2011 (MathWorks).
Analysis of subthreshold activity
Each voltage trace was analyzed individually. For state
analysis, spikes were removed from the traces. For each
trace, all-points histogram of the voltage was computed,
showing a bimodal distribution. State transitions were
detected using Gaussians mixture model (GMM) with
two means and two variance parameters. Bimodality of
each voltage distribution was verified with Kolmogorov–
Smirnov tests. All traces were significantly bimodal
(p \ 0.001). Two thresholds were defined for each trace:transition to an Up state at � of the distance between themeans of the two Gaussians, and transition to Down state at
� of the distance between those means (see Fig. 6 inAppendix for example). These values were selected by
manually studying classification into states, and were lar-
gely robust. Membrane voltages that fell between the two
thresholds were referred to as ‘‘Between state’’. A full
transition from one state to another was defined as a tran-
sition that crosses the two thresholds. A failure was defined
as a transition that crossed one threshold only, and returned
to its previous state without crossing the other threshold.
For instance, a transition from Down state to Between,
followed by transition back to Down, was considered a
‘‘failure-to-Up’’. Proportion of the failures in each trace
was defined as the ratio between total number of failures in
a trace to all transitions in that trace, that is, both failures
and successful transitions to a state.
Analysis of spiking activity
Spikes peaks were detected by local maxima, from a
threshold of -30 mV. The spike times were stored for the
analysis of inter-spike intervals (ISI) and post-up time
histograms (PUTH). Spikes shapes were defined from 15
samples before and 25 samples after the spikes peaks, for
analysis of spike transition rate. PUTH—The distribution
of spike latencies was calculated for each Up state, nor-
malized over states for each neuron, and averaged for each
group. To Control for higher firing rate at the earlier por-
tion of the Up state, the histograms were normalized by
dividing each bin by number of Up states included in that
bin. For quantifying changes in firing rate along the Up
state, each Up state was individually divided to early and
late portions, in its middle. Firing rate was then calculated
on each portion.
Analysis of LFP
To remove slow drifts, traces were digitally high-pass fil-
tered above 1/3 Hz offline. To observe slow oscillations
and identification of LFP troughs, traces were low-passed
filtered below 30 Hz. LFP troughs were detected by finding
local minima below a threshold tuned for each trace
individually.
Results
We quantified differences between neurons in amyloid-bburdened barrel cortex of APP/PS1 mice and in age-mat-
ched Control mice at three regimes of functional activity,
each characterizing different aspects of the system. Sub-
threshold activity is analyzed focusing on the patterns of
the Up and Down state dynamics of membrane potential.
Analysis of suprathreshold activity (spiking activity) is
focusing on differences in firing patterns, which are par-
tially determined by the subthreshold dynamics. Third,
patterns of LFP were measured as a characteristic of net-
work activity in the APP/PS1-burdened neocortex. Finally,
comparison of neuritic curvature in the barrel cortex
between APP/PS1 and Control animals revealed a signifi-
cant alteration of neuritic morphology in plaque-burdened
barrel cortex.
Subthreshold activity of APP/PS1 neurons is impaired
We recorded intracellular spontaneous activity of APP/PS1
mice and age-matched littermates as Controls. All record-
ings showed spontaneous subthreshold membrane potential
fluctuations between a depolarized ‘‘Up state’’ and a hy-
perpolarized ‘‘Down state’’. Figure 1a, b shows examples
of spontaneous activity, in which those states are apparent.
Most of the time, the membrane potential resides in one of
the two states, as apparent when plotting the all-point
bimodal voltage histograms of the traces (Fig. 1a, b, left).
All traces showed a bimodal voltage distribution (see
‘‘Materials and methods’’).
To characterize the differences in subthreshold activity
patterns between Ab neurons and Controls, we first seg-mented each recording into a sequence of states, each state
being one of Up state, Down state, and Between state,
where the membrane potential is in transition between the
two states. Segmentation was performed using a GMM (see
‘‘Materials and methods’’), and allowed us to characterize
the dynamics of subthreshold membrane potential, and to
quantify the statistics of transitions between the states. We
Brain Struct Funct
123
then quantified the dynamics of transitions between states.
When computing the fraction of time that each cell spent in
each of the three states (Fig. 1c), we found that the relative
proportion of time spent in the three states differed sig-
nificantly between the APP/PS1 and the Control group
(v2 = 117; df = 2; p \ 0.001). Specifically, cells in theAPP/PS1 group spent in the Up state only 60 % of the time
that was spent by cells in the Control group (Mann–
Whitney U = 25; p \ 0.01), and, consequently, also spentsignificantly more time in the Down state (Mann–Whitney
U = 74; p \ 0.05). These results reveal a fundamentaldifference in the typical subthreshold membrane potential
activity patterns between the two groups. Since action
potentials arise only in the Up state, if the firing rate is
maintained within Up states, the decreased proportion of
time of the membrane potential spent in the Up state should
lead to lower probability of information transmission from
the neuron to its targets.
To rule out confounding recording artifacts, we compared
voltage levels in the Up state, Down state, and spike threshold
between APP/PS1 and Controls neurons. Figure 1d shows the
mean and SD of these three voltage types, suggesting that the
two populations of neurons do not differ significantly in these
three parameters (see Table 2 in Appendix).
The shorter overall time spent in the Up state could result
either from fewer Up state occurrences or from shorter
average duration of the Up states. To test which of these two
options can explain the overall reduced Up state duration, we
compared the average duration of Up and Down states in the
two groups (see colored bars in Fig. 2a, b). We found that
while no difference was found in number of occurrences of
Up state (Mann–Whitney U = 61, p = 0.96, ns), the dura-
tion of Up states were significantly shorter in APP/PS1
neurons than in Control neurons (APP/PS1: median ±
MAD = 0.13 ± 0.08 s; Controls: median ± MAD =
0.21 ± 0.41 s; Mann–Whitney U = 24.2e ? 04, p \ 0.01;
1 Sec.
10m
v
-120
-80
-40
0Spike
Threshold Up Down
Control APP/PS1
A
B D
0
0.25
0.5
0.75
1
Control APP/PS1
C
Vol
tage
( mv)
Con
trol
AP
P/P
S1
Up
BetweenDown P
ropo
rtion
Fig. 1 Subthreshold activity differences. Examples of spontaneousactivity of Control (a) and B6C3 APP/PS1 APP/PS1 (b) barrel cortexneurons both show fluctuations of ‘Up state’ and ‘Down state’
(dashed lines). All-points-histograms are shown in left. c The twogroups have different dynamics pattern (v2 = 117; df = 2,p \ 0.001). Time spent in Up state was shorter among APP/PS1neurons (median ± MAD = 0.25 ± 0.05 s. n = 6) comparing to
Controls (median ± MAD = 0.42 ± 0.08 s. n = 10; Mann–Whitney
U = 25; p \ 0.01). Time spent in Down state was longer amongAPP/PS1 neurons (median ± MAD = 0.59 ± 0.05 s. n = 6) than
Controls (median ± MAD = 0.43 ± 0.08 s. n = 10; Mann–Whitney
U = 74; p \ 0.05). d Similar voltage differences between states andspike threshold are seen in Control and APP/PS1 groups (Controls:
Up state mean ± SD = -58.4 ± 7.8 mV; Down state mean ± SD =
-65.95 ± 8.45 mV; Spike threshold mean ± SD = -48.78 ±
9.76 mV. APP/PS1: Up state mean ± SD = -62.57 ± 8.56 mV;
Down state mean ± SD = -70.33 ± 7.24 mV; Spike threshold
mean ± SD = -53.21 ± 5.66 mV. t test Up state: t = 0.26;
df = 7, p = 0.79, ns. Down state: t = 0.07; df = 7; p = 0.94, ns.
Spike threshold: t = -0.06; df = 7; p = 0.95, ns). Error bars
represent SD
Table 2 Membrane potential properties of Control and APP/PS1mice (in mV)
Control APP/PS1
Up -58.4 ± 7.8 -62.5 ± 8.5
Down -65.9 ± 8.4 -70.3 ± 7.2
Spike threshold -48.8 ± 9.7 -53.2 ± 5.6
N 13 6
No difference was found between Up State, Down State, or Spike
threshold between the two groups (t test Up state: t = 0.26; df = 7,
p = 0.79, ns. Down state: t = 0.07; df = 7; p = 0.94, ns. Spike
threshold: t = -0.06; df = 7; p = 0.95, ns)
Brain Struct Funct
123
Fig. 2c). No difference was found in the typical duration of
Down states (APP/PS1: medians ± MAD = 0.16 ± 0.27 s;
Control: medians ± MAD = 0.13 ± 0.53 s; Mann–Whit-
ney U = 5.97e ? 05, p = 0.77, ns).
To test if synaptic inputs deficit is reflected in other
properties of membrane potential dynamics of the APP/PS1
neurons, we characterized patterns of voltage trajectories in
the Between state. In the healthy cortex, voltage trajecto-
ries between Up and Down states are stereotypical in any
given neuron (Stern et al. 1997), and once the membrane
potential starts transitioning out of a given state it com-
pletes the transition. In some cases, however, the mem-
brane potential may leave the Down state but fail to reach
the threshold for the Up state, falling back to the Down
state (green arrows on Fig. 2b). Figure 2d shows that the
proportion of such ‘failure to transition to Up state’ among
all Up states transitions was more than nine times larger
in APP/PS1 than in Control neurons (Mann–Whitney
U = 90; p \ 0.01; see ‘‘Materials and methods’’ for defi-nition of ‘failure’).
Spiking patterns of APP/PS1 neurons are altered
The above results reveal significant changes in the sub-
threshold membrane potential fluctuation patterns between
APP/PS1 and Control neurons. Since subthreshold activity
is the nonlinear summation of afferent synaptic inputs
integrated by the postsynaptic neuron (Stern et al. 1997),
those changes reflect synaptic properties and how the
inputs are summed by the neuron. As spikes arise only
when the membrane potential is in the Up state, the dif-
ferences in synaptic inputs described above will influence
the neuron’s output, with or without additional intrin-
sic cellular mechanisms that are affected by the AD
pathology. Figure 3a shows examples of spikes within Up
states.
A
B
1 Sec.
10mv
DC
Control APP/PS10
50
100
150
200
250
Up
Sta
te d
urat
ions
(m
sec)
Control APP/PS1
0
0.1
0.2
0.3P
roba
bilit
y of
Fai
lure
s
l ort
noC
1SP/
PPA
**
300
0.4
Fig. 2 Subthreshold activity dynamics is altered for APP/PS1neurons. a, b Example of spontaneous membrane potential dynamicsof APP/PS1 neuron (b) exhibits shorter Up state durations (a;median ± MAD = 0.13 ± 0.08 s.) than Controls (median ±
MAD = 0.21 ± 0.41 s; Mann–Whitney U = 24.2e ? 04, p \ 0.01,see boxplots in c). No difference was found in Down state duration(APP/PS1: medians ± MAD = 0.16 ± 0.27 s; Control: medians ±
MAD = 0.13 ± 0.53 s; Mann–Whitney U = 5.97e ? 05, p = 0.77,
ns). In addition, APP/PS1 spontaneous activity exhibits higher
probability of failures to Up state (median ± MAD = 0.28 ± 0.05)
than Controls (median ± MAD = 0.03 ± 0.09; marked in arrows.
Mann–Whitney U = 90, p = 0.006, see statistics in d). Error barsrepresent SEM
Brain Struct Funct
123
We first quantified differences in spike patterns of neurons
in the two groups, by computing the distribution of ISI.
Figure 3b shows that throughout the recording, the average
ISI in APP/PS1 neurons is about twice as long as that of
Controls (Mann–Whitney U = 8.28e ? 05; p \ 0.001). Inaddition, the coefficient of variation (CV) of the ISI (for Up
state episodes only) was much closer to 1 for the APP/PS1
group (median ± MAD = 0.99 ± 0.15) than Controls
(median ± MAD = 0.61 ± 0.35; Mann–Whitney U = 66;
p \ 0.05). This implies that the spike trains of APP/PS1neurons have different timing pattern than the Controls.
Firing rate calculated within Up states did not differ between
the two groups (APP/PS1: median ± MAD = 6.46 ± 6.18
spikes/s; Control: median ± MAD = 8 ± 6.39 spikes/s;
Mann–Whitney U = 47; p = 0.42, ns).
To quantify the relation between spikes and subthresh-
old membrane potential, we calculated the distributions of
action potential intervals, as measured from the time of
transition-to-Up state (see ‘‘Materials and methods’’). This
PUTH analysis can be thought of as a modification of the
post-stimulus time histogram (PSTH), where the reference
point from which action potentials latencies are measured
is the time of transition-to-Up state, instead of the time of
stimulus presentation. We measured all spike latencies
following the transition to the Up state. The firing distri-
bution differs significantly between groups. The latency to
spikes during the Up state of APP/PS1 neurons is about
half of that of Controls (Mann–Whitney U = 1.69e ? 06;
p \ 0.001; Fig. 3c). In addition, while Control neuronsmaintained sustained firing following the beginning of Up
state, the initial transient firing rate seen in the APP/PS1
neurons was not maintained over the Up state. To quantify
these differences, each Up state was divided to Early and
Late portions (see ‘‘Materials and methods’’). While Con-
trol group shows slightly higher firing rate at the late,
compared to early Up state portion (early Up state,
mean ± SD = 12 spikes/s; late Up state, mean ± SD =
13 spikes/s; t test t = -2.3, df = 4,228, p \ 0.05), APP/PS1 group shows larger differences, in which firing rate in
early portion is significantly higher (early Up state, mean ±
SD = 11 ± 24 spikes/s; late Up state, mean ± SD = 4 ± 9
spikes/s; t test t = 8.16, df = 1,890, p \ 0.001).Previous studies have shown that spiking activity in the
cortical network is largely governed by coordinated syn-
chronous presynaptic activity (Destexhe and Pare 1999;
Leger et al. 2005). This suggests, again, that either lack of
synaptic sufficiency needed to generate constant firing,
and/or altered intrinsic properties play a role in the path-
ological tissue.
In addition to affecting the temporal spiking patterns,
Ab may affect some properties of the action potentialsthemselves. Changes in the shape of spikes could imply an
intrinsic mechanism of the APP/PS1 neurons that is altered
by Ab overexpression. To test this hypothesis and comparethe parameters of the action potentials between groups, we
superimposed all spikes from each of the two groups.
Figure 3e shows that the rate in which the membrane
depolarizes at the spike threshold is higher for APP/PS1
(lower) than Controls (upper). When quantifying the
depolarizing rate of the membrane potential using the
values of second derivative at the spike threshold, we found
that transitions were faster (larger second derivative)
among APP/PS1 neurons than in Controls (Mann–Whitney
U = 8.16e ? 05; p \ 0.001; Fig. 3d). Other waveformshape parameters, such as the peak voltage, the peak height
and the width of mid-point amplitude did not differ
between the groups (Mann–Whitney U [ 0.1 for allbetween-groups comparisons; see Table 3 in Appendix).
Since spiking activity is the input of downstream neu-
rons, the changes in spiking properties will, in turn, influ-
ence the network. This positive-feedback loop could
theoretically underlie functional decline in cortical infor-
mation processing in the course of the disease.
LFP of APP/PS1 mice show increased network
variability
The above results show impaired patterns of activity in the
cellular level. Due to the amplification of the effect seen in
individual neurons over larger population, these changes
should be reflected in population activity. To test this
hypothesis, we recorded spontaneous ongoing LFPs from the
barrel cortex of another set of mice consisting of both APP/
PS1 and littermates as Controls. Figure 4a, b shows exam-
ples of LFP recordings from the barrel cortex of Control
(a) and APP/PS1 (b) mice. A key characteristic of LFP
recordings is a series of negative deflections, noted by col-
ored dots in Fig. 4a, b. These sharp hyperpolarizations, or
‘‘troughs’’, have been shown to reflect synchronous mem-
brane potential transition to an Up state, occurring in neurons
of the underlying population recorded in the LFP (Okun et al.
2010; Saleem et al. 2010). LFP is often viewed as the sum-
mation of all synaptic currents within a local region. Since
LFP oscillations are commonly attributed to synchronized
neuronal firing (Denker et al. 2011), it is likely that lack of
membrane potential transition synchronization among neu-
rons within the local network could be reflected in their
negative deflections. Figure 4a, b show troughs in examples
of Control and APP/PS1 LFP recordings.
To find an indication for a lower synchronization among
APP/PS1 neuron assemblies comparing with Controls, we
measured the variability of the voltage levels measured at
the LFP troughs. In order to overcome a possible effect of
absolute voltage on variability, we used coefficient of vari-
ation (CV) of the troughs voltages. Voltage levels of APP/
PS1 neurons vary more than in Controls (Mann–Whitney
Brain Struct Funct
123
Time (msec.)
Freq
uenc
y
B
C
E
2 1 0
-60
-50
-40
Volta
ge (m
v)
prob.
Volta
ge (m
v )
0.01
0.02
0.03
10 mv1 ms
-60
-50
-40
-2 -1 0
A
20 mv
0.2 Sec.
0
2 6 10 14
0.04
0.08
0.12
0.16
Freq
uenc
y
d2v/d t2 (mv/0.1 msec.2)
Control APP/PS1
D(i)
1S
P/P
PA
l ort
noC
0.05
100 200 300
0.01
0.02
0.03
0.04
0
100 200 300 400
0.1
0.2
0.3
0.4
12
Time (msec.)
Freq
uenc
y
D(ii)
D(iii)
Control APP/PS1
0.01
0.02
0.03
prob.
18
-2
Time before spike (msec.)
0.5
Trans. to spike
Time before spike (msec.) -1
Control
APP/PS1
Control
APP/PS1
Fig. 3 Suprathreshold activity of APP/PS1 cortical neurons showsdifferent patterns in comparison with Controls. a Examples of spikingactivity of Control (left) and APP/PS1 (right) neurons, within the Up
state. Up state durations are marked in colored bars. b Histogramsshow shorter inter-spike intervals (ISI) for APP/PS1 (median ±
MAD = 0.87 ± 0.91 s) than Controls (median ± MAD = 0.38 ±
0.2 s; Mann–Whitney U = 8.28e ? 05; p \ 0.001). The coefficientof variation (CV) of the ISI (for Up state episodes only) was much
closer to 1 for the APP/PS1 group (median ± MAD = 0.99 ± 0.15)
than Controls (median ± MAD = 0.61 ± 0.35; Mann–Whitney
U = 66; p = 0.026). c Post-up time histogram shows earlier spikingpattern among APP/PS1 neurons (median ± MAD = 0.07 ± 0.06 s)
than Controls (median ± MAD = 0.14 ± 0.1 s; Mann–Whitney
U = 1.69e ? 06; p \ 0.001). Mean ± SD of firing rate of Controlgroup in early Up state = 12 ± 20 spikes/s. In late Up state =
13 ± 22 spikes/s; t test t = -2.3, df = 4,228, p \ 0.05; Mean ± SDof firing rate of APP/PS1 group in early Up state: Mean ± SD =
11 ± 24 spikes/s. In late Up state: Mean ± SD = 4 ± 9 spikes/s;
t test t = 8.16, df = 1,890, p \ 0.001). d Transition to spike points(d(i)) have different pattern between the superimposed spikes of
Control (d(ii)) and APP/PS1 (d(iii)) neurons, showing faster transition
to spike of APP/PS1 neurons (Spikes at d(i) and black dashed lines in
d(ii) and d(iii) represent median spike of each group). e Histograms ofrate of transition to spike show higher depolarizing rate for action
potentials of APP/PS1 neurons (Mann–Whitney U = 8.16e ? 05;
p \ 0.001)
Brain Struct Funct
123
U = 88; p = 0.007; Fig. 4c). To study the timing patterns
of the LFP recording of the APP/PS1, we measured fre-
quency, CV and CV2 of troughs timing. Frequency of
troughs was found to be higher in the APP/PS1 group
(median ± MAD = 0.8 ± 0.18 troughs/s) than in Controls
(median ± MAD = 0.57 ± 0.2 troughs/s; Mann–Whitney
U = 168, p = 0.027; Fig. 4d). While the CV of timing did
not significantly differ between the groups (Mann–Whitney
U = 124, p = 0.6, ns), CV2 significantly differ (Controls
CV2 median ± MAD = 0.36 ± 0.19; APP/PS1 CV2 med-
ian ± MAD = 0.53 ± 0.16; Mann–Whitney U = 3.76e07,
p \ 0.001). Such a difference in CV2 implies higher vari-ability of events over time for the APP/PS1 recordings (Holt
et al. 1996; see Fig. 7 in Appendix for examples of CV2 of
APP/PS1 and Controls).
In order to have insight on the spectral dynamics of the
extracellular activity, we quantified the power at low fre-
quencies, of both LFP and simultaneously recorded ECoG
signals. We were specifically interested in the frequency
range of delta (*1–3 Hz), reflecting the slow oscillationsof Up-Down states. Due to the higher frequency of troughs
among the APP/PS1 recordings, possibly reflecting the
noisier Up-Down transitions of their membrane potential,
we expect that there will be lower power of the early delta
band (1–2 Hz) for the APP/PS1, and/or higher power of the
late delta band (2.5–3.5 Hz) for APP/PS1 comparing to
controls. Although these trends exist in the power spectrum
of the LFP, area under the curve in neither the early
(Mann–Whitney U = 117, p = 0.37, ns) nor the late
(Mann–Whitney U = 147, p = 0.37, ns) delta bands differ
significantly. Interestingly, when we made the same ana-
lysis on ECoG recordings, differences between areas were
more apparent, and statistically significant for both early
delta band (Mann–Whitney U = 90, p = 0.01) and late
delta band (Mann–Whitney U = 173, p = 0.01). Spectral
analysis is shown in Fig. 4e–g.
Being the extracellular correlate of state transition in the
membrane potential, variance in potentials of LFP troughs
could imply an irregularity of state transitions, resulting
from different assemblies of APP/PS1 neurons inputs par-
ticipating in every Up state, or from reduced synchrony in
the population of these input neurons. Morphological
changes in the neuronal network, caused by the Abpathology, could be related to a fragmented neuronal net-
work leading to such effects, by dividing spatiotemporal
organization of the neuronal input population to subunits.
Some of the candidates for such morphological changes
that affect the neuronal network are related to the structure
of the neuronal routes in the pathological brain.
The network morphology in barrel cortex of APP/PS1
mice is distorted
We suggest above, based on both intracellular and LFP
recordings, that the changes in subthreshold, ongoing activity
of neocortical neurons result from changes in function of
afferent inputs to these neurons. This claim raises the question
of whether such changes are reflected in the structural mor-
phology of the network. In other APP transgenic mouse
models (D’Amore et al. 2003), as well as in human AD tissue
(Knowles et al. 1999), neuritic curvature ratio, defined as the
ratio between the length of the neurite and its end-to-end
length, has been found to be lower than in Controls, indi-
cating a morphological distortion of the neurites. It has been
suggested that these changes may cause disruption of cortical
activity in AD (Knowles et al. 1998; Le et al. 2001). Such
changes, however, have not been measured specifically in the
barrel cortex, nor in the APP/PS1 AD mouse model. To
measure if such morphological changes are evident in the
barrel cortex of our model, we compared neuritic curvature
between Control and APP/PS1 mice at ages when the cortex
is burdened with plaques (see ‘‘Materials and methods’’).
Examples of curvature traces are shown in Fig. 5a, b. We
found that curvature ratio of the neurons in the APP/PS1
brains was significantly lower than those in Controls (APP/
PS1: median ± MAD = 0.89 ± 0.08; n = 1,331; Control:
median ± MAD = 0.93 ± 0.05; n = 1,447; Mann–Whitney
U = 15.6e ? 05; p \0.001, see Fig. 5c). These resultsreveal that the fibers of primary sensory cortical neurons are
significantly distorted in the APP/PS1 mouse model. Since
the distortion is not equal among all fibers, the possibility
exists that the synchrony of neuronal propagation through the
APP/PS1 network may be affected.
Discussion
In this study, we measured dynamics of ongoing activity in
the sensory neocortex of APP/PS1 APP/PS1 mice, in which
amyloid-b has accumulated and aggregated. We comparedthese activity patterns to those measured in cortical neurons
of healthy Control mice at several levels: subthreshold
membrane potential dynamics, which are the summed
inputs to the neuron (Stern et al. 1998); spiking activity,
which is the output of the neuron; and the LFP which
represents the activity of a population of neurons in the
local network. In addition, we measured morphological
changes in neuritic structures associated with the neuronal
pathology. Together these data provide a comprehensive,
Table 3 Median and MAD of spike shape properties
Peak amplitude Peak height Half-Amp. width
Control 53.78 ± 8.31 12.12 ± 9.56 14 ± 3.73
APP/PS1 67.83 ± 9.45 12.84 ± 5.25 10.5 ± 2.55
p [ 0.1 for all between groups comparisons
Brain Struct Funct
123
5µv
1 Sec.
2µv
1 Sec.
A C
B
Con
trol
APP/
PS1
APP/PS1Control
Pow
er/F
requ
ency
(dB/
Hz)
LFP
E
F
50 0. 5 1 1. 5 2 2. 5 3 3. 5 4 4. 5
0.01
0.03
0.05
0.07
Frequency (Hz)
Pow
er/F
requ
ency
(dB/
Hz)
ECoG
G
2
4
6
10
12
Freq
uenc
y (T
roug
hs/S
ec.)
Control APP/PS1
8
D
0.07
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0.01
0.03
0.05
Frequency (Hz)
0
0.2
0.4
0.6
Early δ Late δ Late δ
LFP
APP/PS1Control
AUC
Early δ Late δ
Early δ Late δ
APP/PS1Control
*
*
*
*
ECoG
0.15
0.2
0.3
0.35
0.4
0.45
Volta
ge C
V (S
D/m
ean)
0.25
Control APP/PS1
Early δ
13
0.5
0.09
0.09
Fig. 4 Examples of LFP spontaneous recording from the APP/PS1Barrel cortex. Voltages of recording troughs are more variable at
APP/PS1 (n = 11; median ± MAD = 0.28 ± 0.07 lv; marked inorange in b than Controls (n = 12; median ± MAD = 0.21 ±0.06 lv; marked in cyan in a; Mann–Whitney U = 88; p = 0.007c. Normalized voltage variance measured by coefficient of variation(CV: SD/mean) is higher for APP/PS1 LFP troughs than Controls.
Error bars represent SEM. d Frequency of LFP troughs was higherfor the APP/PS1 recordings (Controls: median ± MAD = 5.7e-03 ±
2.2e-03 troughs/s; APP/PS1: median ± MAD = 8e-03 ± 1.8e-03
troughs/s; Mann–Whitney U = 168, p = 0.027). Error bars represent
SEM. e Spectral analysis does not show difference in AUC for LFP inneither the early (1–2 Hz), nor the late (2.5–3.5 Hz) Delta bands
(Early: Mann–Whitney U = 117, p = 0.37, ns; Late: Mann–Whitney
U = 147, p = 0.37, ns). f Spectral analysis showed differences inAUC at Delta bands for the ECoG in both the early and late bands
(see bar graph in 4G; AUC at early Delta band: Control:
median ± MAD = 0.49 ± 0.05; APP/PS1: median ± MAD =
0.41 ± 0.04; Mann–Whitney U = 90, p = 0.01; AUC at late Delta
band: Control: median ± MAD = 0.08 ± 0.03; APP/PS1: med-
ian ± MAD = 0.13 ± 0.03; Mann–Whitney U = 173, p = 0.01)
Brain Struct Funct
123
multi-level, view of the effects of Ab on the structure andfunction of the cortical neural network. Finding changes in
the earliest stage of cortical information processing is
crucial for understanding the changed patterns of neuronal
activity in the AD model brain.
Our measurements of ongoing subthreshold membrane
potential fluctuations reveal a series of dramatic differences
in the synaptic input background activity between the APP/
PS1 and the Control neurons. First, the overall proportion of
time spent in Up state is reduced almost by half in the APP/
PS1 neurons. Second, the durations of individual Up states
are also significantly reduced in the APP/PS1 neurons.
Third, the dynamics of transition between states is altered:
the membrane potential of the APP/PS1 neurons frequently
fails to transition from a Down state to an Up state.
The shorter overall time spent in the Up state could result
either from fewer Up state occurrences or from shorter
average duration of the Up states. Fewer occurrences of the
Up state could reflect intrinsic mechanisms such as a
decrease in strength of the inward rectifying conductance
present in the Down state. Shorter durations of Up state
occurrences could reflect changes in synaptic currents
maintaining the Up state. Number of occurrences was not
different between the two groups; however, durations of Up
states of APP/PS1 group were found to be shorter, which
implies that the synaptic barrage generating an Up state fails
to generate enough current to maintain the voltage of the Up
state. This could result from either a desynchronization
among the synaptic inputs, and/or a lesser number of syn-
aptic afferents. The net result of these two possibilities
would be similar, since both mechanisms lead to a shortfall
in synaptic inputs necessary to maintain an Up state.
We define the degree of synaptic innervation and syn-
chrony necessary to initiate and maintain the Up state as
synaptic sufficiency, a reduction of which will cause a
reduction in dynamics of the subthreshold membrane
potential fluctuations. This reduction could affect addi-
tional characteristics of the membrane potential, other than
Up state maintenance, including the dynamics in the tran-
sitions between states. In these portions of the voltage
traces, of APP/PS1 neurons, we found a significantly
higher probability of unsuccessful transitions to Up states,
which we refer to as ‘‘failures’’. A failure-to-Up state is a
noisy, unstable membrane potential, which can result from
either insufficient synaptic input to reach the depolarized
state, and/or from changes in the nonlinear electrical
A(i)
0.6 0.8 1
0.04
0.08
0.12
Curvature Ratio
Prob
abilit
y
A(ii)
B
APP/PS1
Conrol
Control
APP/PS1
APP/PS1
25 µm
C0.16
Fig. 5 Counterstaining of smi-32 and Thioflavin-S showing exam-ples of Barrel Cortex slices with neurites of APP/PS1 (a(i, ii)) and
Control (b). Arrowheads following outerline of routes of neuritesshow curvier neurites with a lower curvature ratio (end-to-end route/
neurite route) in the APP/PS1 slice (a(i) left to right: 0.89, 0.85. a(ii)
from top neurite and clockwise: 0.63, 0.33, 0.8) than the Control one
(left to right: 0.99, 0.98). c Calculation of all neurites in the twogroups show that neurites are curvier in the APP/PS1 (median ±
MAD = 0.89 ± 0.08; n = 1,331; than Controls (median ± MAD =
0.93 ± 0.05; n = 1,447; Mann–Whitney U = 15.6e ? 05; p \ 0.001)
Brain Struct Funct
123
properties of the cell. Although our current study does not
differentiate between the two possible mechanisms, we
propose that the reduction in synaptic sufficiency described
above plays at least a partial role in the frequent failures to
generate a full Up state in the APP/PS1 neurons.
When comparing spiking patterns in the two groups, we
observed longer ISI and higher coefficient of variation
(CV) of ISI among the APP/PS1 neurons. Timing patterns
of spontaneous spiking of APP/PS1 neurons are signifi-
cantly different than those of Control neurons. Regular
spiking has been associated with a rhythmic motion of the
whiskers during whisking activity (Ahissar et al. 1997).
Studies of sensory information processing in rodents
showed that along the whisker-to-barrels pathway, sensory
inputs are coded with a high degree of temporal precision
around whisking frequencies (Ahissar et al. 1997; Desch-
enes et al. 2003). The increased irregularity we observe in
spontaneous spike trains of the diseased neurons is con-
sistent with the view that their spike trains are noisier, and
as a result, the precise temporal precision that is crucial for
coding of whisker-evoked sensory input may be damaged.
Looking more closely into the firing pattern of the APP/PS1
neurons, we found that the increased irregularity is partially
due to higher firing rate in the early portion of the Up state,
accompanied by a reduction in the sustained firing rate in the
later portion. The increase in transient firing may contribute to
the network hyperexcitability observed in AD mouse models
(Gurevicius et al. 2012; Palop et al. 2007), and the reduction in
sustained firing is consistent with our finding of failures in
generating and maintaining Up states: a study based on
intracellular recordings found short-lasting depolarization
before spikes, suggesting that considerable synchronization
among inputs is required to bring a neuron to fire a spike
(Leger et al. 2005). Based on this study and similar findings
(Abeles et al. 1994; Azouz and Gray 2000; Destexhe and Pare
1999; Stern et al. 1997), we suggest that the irregular patterns
of spiking is partially caused by the lack of synaptic suffi-
ciency together with the dynamics of subthreshold activity.
We suggest that all of these effects are caused by a common
mechanism: a shortfall in synaptic input that is necessary to
initiate and maintain an Up state. In a diseased network, the sum
of synaptic inputs is often not sufficient for a transition to an Up
state, which leads to the increased number of failures to Up that
we observe. Even when the sum of synaptic inputs is sufficient
for a transition, inputs often persist to a short duration only,
leading to significantly shorter Up states in the diseased net-
work. Since no difference was found between firing rate of
APP/PS1 and Control groups, the decreased proportion of time
spent in the Up state should lead to a reduced probability of
information transmission between the neurons.
Our results show differences both at the level of sub-
threshold membrane potential and at the level of spiking
patterns: These two effects are highly consistent, and may
strengthen each other. Cells that suffer from shorter dura-
tions of Up States and failures to transition to an Up state are
likely to fail to emit some spikes, since spikes can only be
created when the cell is in an Up state. In addition, the
temporal precision of the spikes may be damaged by the
same mechanism of lack of synaptic sufficiency. At the same
time, a cell receiving inputs that are more variable in time
from diseased neighboring cells, may fail to transition to an
Up state. There is therefore a positive feedback between the
two effects, which is likely to lead to a catastrophic failure of
information processing in the circuit.
The altered patterns of spontaneous firing of individual
neurons seen in the Ab-burdened cortex area are amplifiedover larger cortical areas, as shown in the LFP results. The
higher variability in the LFPs of the APP/PS1 neural
assemblies, compared with Controls, indicate that the
changes in activity patterns in the presence of Ab accu-mulation arise at least partially from changes in the neu-
ronal network, rather than the mere changes in cellular
properties of the individual neurons. These results are
confirmed by the intracellular data, in which a primary
difference between the APP/PS1 and Control recordings
reflects different synaptic inputs to the neurons, which
determine the state transitions and durations. The changes
observed in the subthreshold activity strongly suggest
changes in the synchrony of the inputs to the neurons. At the
network level, these changes are reflected in the increased
variability of the LFP troughs, which are caused by the non-
synchronous transitions of multiple neurons to the Up state.
The reduction of synchrony in the inputs is possibly linked
to the changes in the structural integrity of the network.
Our histology of brain slices from the APP/PS1 animals
revealed morphological distortion that is indicated by a
higher curvature index of neurites in the barrel cortex. A
model based on similar morphological effects in human AD
post-mortem brains predicted conduction of several milli-
seconds over an average plaque. This, when summed over
thousands of cortical plaques, is hypothesized to disrupt the
precise temporal firing patterns in the network, and con-
tribute to neural system failure (Knowles et al. 1999).
Another study that recorded intracellularly from an AD
mouse model (Stern et al. 2004), related this curvature to the
impaired evoked neuronal response to transcallosal stimuli,
and to a response jitter occurring in the evoked response of
the neurons from plaque-burdened APP/PS1 mice. It was
suggested that in the presence of substantial plaque accu-
mulation, for a given signal to be reliably transmitted, a
relatively large number of inputs must arrive at the neuron
within a narrow time window (Stern et al. 2004).
We propose that our physiological findings over all levels
point to the same set of underlying mechanisms: they are all
indicative of a lack of synaptic sufficiency, i.e., shortage in the
amount or synchrony of synaptic inputs that are necessary for
Brain Struct Funct
123
the normal maintenance of both subthreshold and spiking
activity. Such shortage may sum, over populations of neurons,
to local network desynchronization, which is reflected in the
higher variance in negative deflections that we observed in the
LFP recordings. The altered spontaneous activity patterns we
found could be due to a jitter in convergent inputs to the
afferent, recorded neuron, leading to lack of synaptic activity,
which is needed for transition from Down to Up state, for
maintaining an Up state, and eventually for generating action
potentials in optimal temporal pattern for sensory processing.
Another factor that might be related to the effects seen
above is change in the balance of excitatory and inhibitory
inputs to the neuron (Salinas and Sejnowski 2001). A pro-
gressive removal of inhibition in a slice preparation induced
a gradual shortening of up states (Sanchez-Vives et al.
2010). It is possible that amyloid-b, in one or more of itsforms, preferentially reduces inhibitory neuronal firing in a
way that affects the excitatory–inhibitory balance and
reduces the ability of the neurons to maintain the Up state
and sustained firing. This is consistent with previous findings
of progressive decline of neuronal function among hyper-
active neurons in AD model mice (Grienberger et al. 2012).
Our study does not address possible differential effects
of different species of amyloid-b: the various forms ofsoluble amyloid-b, and various types of plaques may eachcause specific neuronal dysfunctions. We specifically chose
an age at which all forms of amyloid-b are elevated, tomeasure the effects of the neuropathology on cellular
function. The changes observed in ongoing activity mea-
sured in our study may be specifically caused by one or
more forms of the abnormal protein.
We suggest that the effects of amyloid-b on neuronalactivity are bidirectional between individual neuronal mal-
function, and impaired network integrity. The altered neuronal
properties seen in intracellular activity can be partially due to
the effects of Ab on cellular electrical properties, which, ifaffecting enough neurons, will impact global activity of the
network. The network dysfunction could lead to further dis-
ruption of the activity of the individual neurons, by mechanisms
such as a lowering of input synchrony, which would reduce
synaptic summation. The lack of global input synchrony is seen
in the increased variability of the LFP troughs, which could be
attributed to network fragmentation caused by lowering of
input synchrony. This in turn could be at least partially caused
by the morphological effects of Ab accumulation and aggre-gation on neuritic structure.
In a recent study (Beker et al. 2012), we proposed a model in
which lateral inhibition between cortical columns (in our case,
barrels) is specifically reduced by selective plaque aggregation
in the septae. In the current research, we found changes in
several parameters of cortical neuronal activity in the APP/PS1
mice, which may cause an overall reduction in global afferent
synaptic inputs. It may be that the reduction of lateral inhibitory
input is balanced by a compensatory reduction in excitatory
input. This is consistent with our data, as we found no signifi-
cant differences between Up state voltages of Control and APP/
PS1 neurons. Since, from the Down state, both excitatory and
inhibitory inputs are depolarizing, reduction of both inputs
could cause the failures to transition to the Up state and the
reduced durations of the Up state cycles in the neurons of APP/
PS1. The differences we found in regularity and rhythmicity of
spontaneous spiking in the barrel cortex neurons of APP/PS1
mouse model may result from the reduced subthreshold
activity, and may be amplified to global deficiency in a recur-
rent manner and may eventually affect whisker movement
encoding parameters. Those alterations may reflect conse-
quences of plaque accumulation on cortical sensory informa-
tion processing in the cortex of this mouse model.
Acknowledgments This work was supported by the NationalInstitute on Aging at the National Institute on Health (Grant Number
AG024238); the Legacy Heritage Bio-Medical Program of the Israel
Science Foundation (Grant Number 688/10); and Marie Curie Euro-
pean Reintegration Grant within the 7th European Community
Framework Programme (Grant Number PERG03-GA-2008-230981).
We thank Profs. Israel Nelken and Moshe Abeles for their helpful
suggestions on this manuscript.
Appendix
See Tables 1, 2, 3 and Figs. 6, 7.
Fig. 6 Example of all-points voltage histogram recorded from aControl barrel cortex neuron. The histogram was segmented to Up
and Down states using Gaussian mixture models. Colored vertical
bars indicate means and transitions of the states. Transitions were
calculated at � and � of the difference between the means
Brain Struct Funct
123
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Amyloid- beta disrupts ongoing spontaneous activity in sensory cortexAbstractIntroductionMaterials and methodsAnimalsSurgeryIntracellular recordings LFPECoGCurvature ratioAnalysisAnalysis of subthreshold activity Analysis of spiking activityAnalysis of LFP
ResultsSubthreshold activity of APP/PS1 neurons is impairedSpiking patterns of APP/PS1 neurons are alteredLFP of APP/PS1 mice show increased network variabilityThe network morphology in barrel cortex of APP/PS1 mice is distorted
DiscussionAcknowledgmentsAppendixReferences