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: sterned@mail.biu.ac.il

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