Brain Struct Funct DOI 10.1007/s00429-014-0963-x

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 Ó 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 found that membrane potential dynamics of neurons in Ab-burdened cortex significantly differed from those of nontransgenic 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 morphology 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 on spontaneous activity of individual neurons amplifies into a

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

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 Amyloid Hypothesis (Hardy and Selkoe 2002). It postulates that abnormally folded protein amyloid-b (Ab) accumulating in the brain is the primary factor driving AD pathogenesis. Ab accumulation, 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 properties 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 with altered 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, subthreshold neuronal activity have not been directly measured.

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Spontaneous ongoing activity occurs even in the absence of environmental inputs, and is a critical determinant of information processing by neocortical neurons (Haider and McCormick 2009; Chorev et al. 2007). Sensory and other incoming synaptic information are superimposed 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 subthreshold 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 of cortical processing, such as the primary sensory area. Ab could 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 cortical cellular activity in those areas receiving information in the early stages of the feed-forward cortical information pathways. Ab accumulation in these areas may have a specific 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 Ab are 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 cortical areas, showing fluctuation in similar frequencies (\1 Hz) (Okun et al. 2010; Saleem et al. 2010), suggesting local synchronization (Lampl et al. 1999). LFP fluctuations, like those of membrane potentials, are mostly due to

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synaptic activity (Mitzdorf 1991, 1994; Okun et al. 2010). In addition, the LFP at a given location can be well predicted 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 intracellular and network spontaneous activity, we recorded spontaneous 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 addition, we measured differences in network activity by comparing LFPs of Control and APP/PS1 animals. Finally, to measure morphological alterations of neuronal populations 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 plaques accumulate in an age-dependent manner, and are abundant 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 experiments 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 University 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 Table 1 Distribution of ages of animals for all experiments Exp. type

Intracellular recordings

LFP recordings

Histology

Ages (months)

9–14

12–14

10–11

15–19

15–16

Control

11

2

6

6

5

APP/PS1

2

4

9

2

5

No age 9 physiological marker effect was found for four physiological 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; ISI v2 = 3.16; df = 12; p = 0.08 ns) or APP/PS1 transgenic mice (failures rate: v2 = 0.86; df = 5; p = 0.35 ns; proportion of time in Up state: 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 for the 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)

Surgery Prior to anesthesia, animals were placed in a custom-built stereotaxic device. Body temperature was kept at 37.5 °C using a heating blanket and a rectal thermometer (Harvard apparatus, Holliston, MA, USA). Animals were anesthetized 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 electrodes placed over the cerebellum and cortex, and by reaction to limb-pinch. During the surgery, a cranial window (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 that the 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, having 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 lm below 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 electrodes 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 Sections of 50 lm were cut on a freezing microtome and immunostained with primary antibodies to SMI312 and SMI32 (mouse monoclonal, 1:200; Sternberger Monoclonals, 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

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ratio between the end-to-end distance, and the trace distance.

late portions, in its middle. Firing rate was then calculated on each portion.

Analysis

Analysis of LFP

Numerical and statistical analysis of all recordings and histology data was performed using custom software written in MATLAB R2011 (MathWorks).

To remove slow drifts, traces were digitally high-pass filtered 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.

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 the means of the two Gaussians, and transition to Down state at ’ of the distance between those means (see Fig. 6 in Appendix for example). These values were selected by manually studying classification into states, and were largely 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 transition 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, normalized over states for each neuron, and averaged for each group. To Control for higher firing rate at the earlier portion 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

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Results We quantified differences between neurons in amyloid-b burdened barrel cortex of APP/PS1 mice and in age-matched Control mice at three regimes of functional activity, each characterizing different aspects of the system. Subthreshold 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 partially determined by the subthreshold dynamics. Third, patterns of LFP were measured as a characteristic of network activity in the APP/PS1-burdened neocortex. Finally, comparison of neuritic curvature in the barrel cortex between APP/PS1 and Control animals revealed a significant 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 recordings showed spontaneous subthreshold membrane potential fluctuations between a depolarized ‘‘Up state’’ and a hyperpolarized ‘‘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 segmented 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

C Control

Proportion

A

1 0.75

Up Between

0.5

Down

0.25 0

Control

B

D

0 Down

Voltage (mv)

APP/PS1

APP/PS1

Up

Spike Threshold

-40 Control APP/PS1

-80

10 mv

-120

1 Sec.

Fig. 1 Subthreshold activity differences. Examples of spontaneous activity of Control (a) and B6C3 APP/PS1 APP/PS1 (b) barrel cortex neurons both show fluctuations of ‘Up state’ and ‘Down state’ (dashed lines). All-points-histograms are shown in left. c The two groups have different dynamics pattern (v2 = 117; df = 2, p \ 0.001). Time spent in Up state was shorter among APP/PS1 neurons (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 among APP/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 and spike 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

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 significantly between the APP/PS1 and the Control group (v2 = 117; df = 2; p \ 0.001). Specifically, cells in the APP/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 spent significantly more time in the Down state (Mann–Whitney U = 74; p \ 0.05). These results reveal a fundamental difference 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).

Table 2 Membrane potential properties of Control and APP/PS1 mice (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)

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 duration 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;

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Brain Struct Funct

A Control

B

10 mv

APP/PS1

1 Sec.

D

*

*

250

0.4

Probability of Failures

Up State durations (msec)

C 300

200 150 100 50

0.3 0.2 0.1 0

0 Control

APP/PS1

Control

APP/PS1

Fig. 2 Subthreshold activity dynamics is altered for APP/PS1 neurons. a, b Example of spontaneous membrane potential dynamics of 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 bars represent SEM

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–Whitney 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 trajectories 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 completes the transition. In some cases, however, the membrane 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 definition of ‘failure’).

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Spiking patterns of APP/PS1 neurons are altered The above results reveal significant changes in the subthreshold 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 differences in synaptic inputs described above will influence the neuron’s output, with or without additional intrinsic cellular mechanisms that are affected by the AD pathology. Figure 3a shows examples of spikes within Up states.

Brain Struct Funct

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). In addition, 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/PS1 neurons 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 subthreshold 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 distribution 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 neurons maintained 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 Control 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 synchronous 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 pathological tissue. In addition to affecting the temporal spiking patterns, Ab may affect some properties of the action potentials themselves. 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 compare the 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 waveform shape 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 all between-groups comparisons; see Table 3 in Appendix). Since spiking activity is the input of downstream neurons, the changes in spiking properties will, in turn, influence the network. This positive-feedback loop could theoretically underlie functional decline in cortical information 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 examples 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 colored dots in Fig. 4a, b. These sharp hyperpolarizations, or ‘‘troughs’’, have been shown to reflect synchronous membrane 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 summation 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 neurons 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 variation (CV) of the troughs voltages. Voltage levels of APP/ PS1 neurons vary more than in Controls (Mann–Whitney

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Brain Struct Funct

A

D(i)

Control APP/PS1

Control APP/PS1

10 mv

20 mv

1 ms

0.2 Sec.

Trans. to spike

B

D(ii)

0.5 Control

Frequency

0.3 0.2

0.03

-40

Control

Voltage (mv)

APP/PS1

0.4

0.02 -50 0.01

0.1

-60

0

100

200

300

2-2

400

prob.

0

Time before spike (msec.)

Time (msec.)

C

-11

D(iii)

0.05

-40 0.03

Voltage (mv)

0.03 0.02

-50 0.02 -60

APP/PS1

Frequency

0.04

0.01

0.01

0

100

200

prob.

-1 0 -2 Time before spike (msec.)

300

Time (msec.)

E

0.16

Frequency

Control APP/PS1

0.12

0.08 0.04

2

6

10

14

18

d2v/dt2 (mv/0.1 msec.2)

Fig. 3 Suprathreshold activity of APP/PS1 cortical neurons shows different patterns in comparison with Controls. a Examples of spiking activity of Control (left) and APP/PS1 (right) neurons, within the Up state. Up state durations are marked in colored bars. b Histograms show 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 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 = 0.026). c Post-up time histogram shows earlier spiking pattern among APP/PS1 neurons (median ± MAD = 0.07 ± 0.06 s) than Controls (median ± MAD = 0.14 ± 0.1 s; Mann–Whitney

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U = 1.69e ? 06; p \ 0.001). Mean ± SD of firing rate of Control group 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 ± SD of 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 of rate 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 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

U = 88; p = 0.007; Fig. 4c). To study the timing patterns of the LFP recording of the APP/PS1, we measured frequency, 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 median ± MAD = 0.53 ± 0.16; Mann–Whitney U = 3.76e07, p \ 0.001). Such a difference in CV2 implies higher variability 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 frequencies, of both LFP and simultaneously recorded ECoG signals. We were specifically interested in the frequency range of delta (*1–3 Hz), reflecting the slow oscillations of 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 analysis 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 participating in every Up state, or from reduced synchrony in the population of these input neurons. Morphological changes in the neuronal network, caused by the Ab pathology, could be related to a fragmented neuronal network 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 morphology 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, indicating 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 results reveal 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 compared these 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,

123

Brain Struct Funct

C

A

0.5

*

5 µv

Control

Voltage CV (SD/mean)

0.45 0.4 0.35 0.3 0.25 0.2

1 Sec.

0.15 Control

D

13

APP/PS1 2 µv 1 Sec.

10 8 6 4 2 Control

E

G

0.09

Power/Frequency (dB/Hz)

Late δ

Control APP/PS1

Control APP/PS1

0.4 AUC

Early δ

0.07

APP/PS1

*

0.6

LFP

APP/PS1

*

12

Frequency (Troughs/Sec.)

B

0.05

0.2

0.03

0

* Early δ

Late δ LFP

Early δ Late δ ECoG

0.01 0

0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

Frequency (Hz)

F

0.09

Power/Frequency (dB/Hz)

ECoG Early δ

0.07

Late δ

Control APP/PS1

0.05

0.03

0.01 0

0.5 1

1.5 2

2.5 3

3.5 4

4.5 5

Frequency (Hz)

Fig. 4 Examples of LFP spontaneous recording from the APP/PS1 Barrel cortex. Voltages of recording troughs are more variable at APP/PS1 (n = 11; median ± MAD = 0.28 ± 0.07 lv; marked in orange in b than Controls (n = 12; median ± MAD = 0.21 ± 0.06 lv; marked in cyan in a; Mann–Whitney U = 88; p = 0.007 c. 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 higher for 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

123

SEM. e Spectral analysis does not show difference in AUC for LFP in neither 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 in AUC 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: median ± MAD = 0.13 ± 0.03; Mann–Whitney U = 173, p = 0.01)

Brain Struct Funct

A(i)

B

APP/PS1

Conrol

25 µm

A(ii)

C 0.16

Probability

APP/PS1

0.12

Control APP/PS1

0.08

0.04

0.6

0.8

1

Curvature Ratio Fig. 5 Counterstaining of smi-32 and Thioflavin-S showing examples of Barrel Cortex slices with neurites of APP/PS1 (a(i, ii)) and Control (b). Arrowheads following outerline of routes of neurites show 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 two groups 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)

multi-level, view of the effects of Ab on the structure and function 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 synaptic 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 synchrony 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 additional characteristics of the membrane potential, other than Up state maintenance, including the dynamics in the transitions 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

123

Brain Struct Funct

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 significantly 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; Deschenes et al. 2003). The increased irregularity we observe in spontaneous spike trains of the diseased neurons is consistent 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 sufficiency 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 network. 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 subthreshold membrane potential and at the level of spiking patterns: These two effects are highly consistent, and may

123

strengthen each other. Cells that suffer from shorter durations 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 amplified over 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 accumulation arise at least partially from changes in the neuronal 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 nonsynchronous 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 milliseconds 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 contribute 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 accumulation, 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

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 progressive 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 its forms, 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 hyperactive 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 of soluble amyloid-b, and various types of plaques may each cause specific neuronal dysfunctions. We specifically chose an age at which all forms of amyloid-b are elevated, to measure the effects of the neuropathology on cellular function. The changes observed in ongoing activity measured 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 neuronal activity are bidirectional between individual neuronal malfunction, 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, if affecting enough neurons, will impact global activity of the network. The network dysfunction could lead to further disruption 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 aggregation 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 significant 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 recurrent manner and may eventually affect whisker movement encoding parameters. Those alterations may reflect consequences of plaque accumulation on cortical sensory information processing in the cortex of this mouse model. Acknowledgments This work was supported by the National Institute 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 European 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 a Control 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

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Brain Struct Funct

Fig. 7 Examples of CV2 values of LFP troughs timing for Controls (a–d) and APP/PS1 (e–h). Two main characteristics are observed. First: a downward shift of the cloud of CV2 values among the Controls, creating mean of values that is farther from the upper bound

in the Control group, than the APP/PS1 (black line in the examples). Second, the spread of values is more even along the Y axis among the APP/PS1. These characteristics imply more perturbed, variable recordings of APP/PS1 (Holt et al. 1996)

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