Rescue of long-range circuit dysfunction in Alzheimer's disease models (original) (raw)

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Acknowledgements

We thank C. Tischbirek for assistance with graphics, A. Beazley for help with data analysis and M. Staufenbiel for discussions and comments. This work was funded by an Advanced European Research Council grant to A.K., the European Union FP7 program (Project Corticonic) and the Deutsche Forschungsgemeinschaft (RTG 1373 and SFB870). M.A.B. was supported by the Langmatz Stiftung. I.N. was supported by a grant from the Israel Science Foundation and by the Deutsche Forschungsgemeinschaft (SFB870).

Author information

Authors and Affiliations

  1. Institute of Neuroscience, Technische Universität München, Munich, Germany
    Marc Aurel Busche, Maja Kekuš, Helmuth Adelsberger, Takahiro Noda & Arthur Konnerth
  2. Department of Psychiatry and Psychotherapy, Technische Universität München, Munich, Germany
    Marc Aurel Busche & Hans Förstl
  3. Munich Cluster for Systems Neurology (SyNergy) and Center for Integrated Protein Science Munich (CIPSM), Munich, Germany
    Marc Aurel Busche & Arthur Konnerth
  4. Department of Neurobiology, Silberman Institute of Life Sciences and Edmond and Lily Safra Center for Brain Sciences, Hebrew University of Jerusalem, Jerusalem, Israel
    Israel Nelken

Authors

  1. Marc Aurel Busche
  2. Maja Kekuš
  3. Helmuth Adelsberger
  4. Takahiro Noda
  5. Hans Förstl
  6. Israel Nelken
  7. Arthur Konnerth

Contributions

M.A.B. and A.K. designed the study; M.A.B., M.K., H.A. and T.N. performed the experiments; M.A.B., M.K., H.A., H.F., I.N. and A.K. performed the analysis; M.A.B., I.N. and A.K. wrote the manuscript.

Corresponding authors

Correspondence toMarc Aurel Busche or Arthur Konnerth.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Large-scale calcium imaging in vivo.

(a) Schematic illustration of the in vivo camera imaging set-up for large-scale calcium imaging. (b) High-magnification two-photon image from a region in layer 2/3 of the frontal cortex and representative activity traces from neurons (1 – 3), neuropil and the entire image frame (integral).

Supplementary Figure 2 Correlations across brain regions.

(a) Schematic representation of the cortical area that was stained with fluorescent calcium indicator OGB-1 AM (left panel). The cortical subregions were identified by using a mouse brain atlas (right panel). Note that midline structures (cingulate and retrosplenial cortex) were not labeled by the calcium indicator. (b) Regions of interest for cortical imaging. (c) Regions for simultaneous recordings from cortex and contra-lateral hippocampus. (d) Regions for simultaneous recordings from cortex and contra-lateral thalamus.

Supplementary Figure 3 Average correlation matrix from all calcium imaging experiments.

Mean correlation coefficient plotted for all pairs of cortical areas in wild-type (WT, black) and APP23 x PS45 (Tg, red) mice. Occ. = occipital, som. = somatosensory, mot. = motor and fro. = frontal cortex. Error bars correspond to one standard error of the mean. Note that pairs of areas that have the same distance from each other (e.g. occipital and somatosensory vs. motor and frontal, both being adjacent pairs, or occipital and motor vs. somatosensory and frontal, both pairs having one intervening area between them) also have about the same correlation values (the correlation matrices have approximately constant diagonals).

Supplementary Figure 4 Impairment of the correlation between brain areas occurs predominantly in the slow wave and delta range, but is present in all frequency bands.

(a-d) The mean correlation coefficients of the fluorescent calcium signals are plotted as a function of cortical distance in wild-type (WT, black) and APP23 x PS45 (Tg, red) mice. Panel a presents the same data as in Supplementary Fig. 3 (the correlations calculated from the broadband signal). Instead of plotting the data in 3-d, the correlation coefficients from the four areas (six coefficients in total) are plotted separated by the cortical distance between the areas (these are the diagonal of the correlation matrix), demonstrating the dependence of the correlation on cortical distance. Near = two nearby cortical domains (e.g., occipital and somatosensory cortex), mid = two domains separated by one region (e.g., occipital and motor cortex), far = two domains separated by two regions (always occipital and frontal cortex). Panels b-d show the correlations, plotted in the same way, for the different frequency bands. The slow wave and delta correlation values (0.1 – 3 Hz, b) are essentially the same as the broadband (0.1 – 30 Hz, a) values, because this is the frequency range with the highest power (see also Supplementary Fig. 12). However, in all frequency bands, the correlations are higher in the WT than in the Tg animals.

Supplementary Figure 5 Impairment of correlation between brain areas is reflected in phase consistency in all frequency bands.

The average pairwise phase consistency (PPC; Vinck, M., et al. (2010) NeuroImage 51, 112-122) is plotted in the format as in Supplementary Fig. 4. The PPC was calculated for 32 s long segments of calcium recording, as elsewhere in the paper, using a multitaper method (Mitra, P.P. & Pesaran, B. (1999) Biophys. J. 76, 691-708) with NW = 16 (32 tapers). The PPC was averaged over frequency bands in order to be comparable to the correlation coefficients displayed in the other figures. For all anatomical distances and all frequency bands, the PPC in the WT animals (black) is larger than in the Tg animals (red). The PPC was analyzed using a linear mixed effect model with anatomical distance and genetic background as fixed factors, and with mouse and hemisphere within mouse as random factors. The effect of transgenic background was highly significant in all frequency bands. Effect of genetic background on PPC in the slow-wave frequency range was highly significant: F(1,450) = 65.8, P = 4.8e-15. In the theta range, the effect of genetic background was not significant. In the alpha range, there was a significant interaction between genetic background and anatomical distance, with phase consistency decreasing faster with distance in the Tg animals: F(2,450) = 6.09, P = 0.0025. The lack of significance of genetic background in the theta band, and its presence in the alpha band, were also found for LFP recordings in unanesthetized animals (see Supplementary Fig. 18).

Supplementary Figure 6 Similar impairment of long-range coherence of slow waves in APP23 × PS45 and APP23 mice.

(a) Summary graph displaying the average cross-correlation coefficients and standard errors plotted against the cortical distance (near = two nearby cortical domains, mid = two cortical domains separated by one region, far = two domains separated by two regions) in wild-type (WT, black, n = 9), APP23 (blue, n = 3) and APP23 x PS45 (red, n = 9) mice, respectively. (b) Bar graph shows the average correlation coefficients between cortical and hippocampal activity for WT (n = 5), APP23 (n = 3) and APP23 x PS45 (n = 5) mice, respectively. WT and APP23 x PS45: two-sample _t_-test, t(108) = 17, P = 2.7e-32 (these are the same data as shown in Fig. 3i of the main text); WT and APP23: t(75) = 21.6, P = 5.7e-34. Error bars denote SEM.

Supplementary Figure 7 LFP recordings in anesthetized animals yielded results that were similar to those obtained with calcium fluorescence imaging.

(a) Scheme illustrating the positions of the LFP electrodes. (b) Examples of LFP recordings in a wild-type (WT, top panel) and an APP23 x PS45 (Tg, bottom panel) mouse. Superimposed traces are from the shaded areas. (c) Bar graph shows the mean correlation coefficient between occipital and frontal cortex (ctx-ctx), frontal cortex and hippocampus (ctx-hc) as well as frontal cortex and thalamus (ctx-thl) for WT (gray bars) and Tg (green bars) mice (n = 18 animals), respectively. Two-way ANOVA on genotype (WT vs. Tg) and correlation type (ctx-ctx, ctx-hc and ctx-thl): main effect of genotype F(1,14) = 197, P = 1.2e-9; main effect of correlation type: F(2,14) = 0.27, P = 0.77; interaction: F(2,14) = 0.09, P = 0.91. Thus, all three correlation types are equally depressed in Tg relative to WT mice. Error bars denote SEM.

Supplementary Figure 8 Correlation between calcium transients and local field potentials in the hippocampus.

Simultaneous recording of the local field potential (LFP was inverted, blue) and camera-fluorescence signals (black) in the dorsal hippocampus.

Supplementary Figure 9 Correlation between calcium transients and local field potentials in the thalamus.

Simultaneous recording of the local field potential (LFP was inverted, blue) and camera-fluorescence signals (black) in the thalamus.

Supplementary Figure 10 Cortico-thalamic correlation maps.

Correlation maps using a seed-pixel in cortical regions (top and middle) or in the thalamus (bottom) in a wild-type (WT, left panel) and an APP23 x PS45 (Tg, right panel) mouse, respectively. The white asterisks denote the location of the seed pixel.

Supplementary Figure 11 Histological identification of local field potential electrode position.

(a, b) Example sections showing the electrode location in the hippocampus (a, top panel) and in the thalamus (b, bottom panel). Red: DiI-labeled electrode track. White arrows indicate the inferred location of the electrode tip.

Supplementary Figure 12 Power spectra of calcium imaging data.

Power spectra of cortical calcium fluorescence time series in wild-type (WT) and APP23 x PS45 (Tg) mice. While all power spectra show clear peaks at 0.3 Hz, the peak bandwidth increased substantially in the Tg mice, reflecting the higher rates of calcium transients in the Tg animals (see Fig. 2h of the main text). Note that, in the Tg mice, there is a greater increase in bandwidth in the frontal cortex (solid red line) compared to the occipital cortex (solid blue line). This finding is consistent with the relatively larger increase in the calcium wave rate in these areas (see Fig. 2h of the main text).

Supplementary Figure 13 The glutamate receptor antagonist APV has no effect on impaired slow-wave activity in APP23 × PS45 mice.

Superimposed traces of slow-wave activity in the frontal (red) and the occipital (black) cortex of an APP23 x PS45 mouse before (control), during and after (wash-out) application of 50 µM APV.

Supplementary Figure 14 Correlation maps of the pharmacological experiments.

(a-d) Correlation maps of the pharmacological experiments shown in Figs. 5 and 6 of the main text (topical midazolam application in Fig. 5a, topical gabazine application in Fig. 5b, topical Aβ application in Fig. 6a and rescue of topical Aβ application with midazolam in Fig. 6b). White asterisks mark the seed pixel that was used for the generation of the maps.

Supplementary Figure 15 Aβ1–40 and Aβ1–42 lead to similar impairments of coherent activity.

(a, b) Summary graph showing the average cross-correlation coefficients and standard errors plotted against the cortical distance (near = two nearby cortical domains, mid = two cortical domains separated by one region, far = two domains separated by two regions) before (black), during (red) and after (gray) wash-in of amyloid-β (Aβ) 1-40 in a or Aβ 1-42 in b in WT mice (n = 6 animals). For statistics see Supplementary Table 1.

Supplementary Figure 16 Determination of sleep and wake states in unanesthetized animals.

(a, b) Representative traces of surface EMG (top), cortical LFP (middle) and power spectral density of the LFP (bottom) under sleep (left) and wake (right) states in a wild-type (WT, a) and APP23 x PS45 (Tg, b) mouse, respectively. Sleep was defined as the prolonged periods with immobility of the animal, absence of EMG activity, closed eyes, and large-amplitude, low frequency activity in the LFP recordings. Wakefulness was characterized by periods with high EMG activity, occasional locomotion, opened eyes, as well as lower amplitude and higher frequency LFP activity. (c,d) Median power spectra of 200-sec LFP traces during sleep (left) and waking (right) in WT (c) and Tg (d) mice (n = 8 animals). Shaded error bands represent upper and lower quartile of the power spectra.

Supplementary Figure 17 Impairment of long-range coherence of slow-wave activity in naturally sleeping APP23 × PS45 mice.

(a, b) Example traces of LFP recordings from the frontal (red) and the occipital (black) cortex in unanesthetized wild-type (WT, a) and APP23 x PS45 (Tg, b) mice, respectively. (c) Example traces of LFP recordings from the frontal (red) and occipital (black) cortex in a Tg mouse before and 10 min after intraperitoneal injection of a low-dose of clonazepam. (d) Bar graph shows that the mean correlation between frontal and occipital cortex in the slow wave and delta range was significantly lower in Tg compared to WT mice (n = 10 animals; t(10) = 2.69, P = 0.023, two-sample _t_-test), and that, in the Tg mice, treatment with clonazepam increased this correlation (t(5) = 4.83, P = 0.0048, two-sample _t_-test).

Supplementary Figure 18 Impairment of long-range coherence in non-anesthetized animals extends to higher frequency bands.

Bar graph displays the mean correlation coefficients between LFP recordings from frontal and occipital cortex at different frequency bands in naturally sleeping wild-type (WT) and APP23 x PS45 (Tg) mice. Note that in the Tg mice (red bars) the correlation coefficients are considerably smaller compared to WT mice (gray bars), and that a low-dose of clonazepam (blue bars) increased the correlations. For the differences between WT and Tg, one-way nested ANOVA with animals nested within genotype, F(1,3) = 40.7, 11.6, 7.23, 13.2, 10.2 (P = 0.0078, 0.042, 0.074, 0.036, 0.049) for the broadband, slow wave and delta, theta, alpha and beta ranges. For the differences before and after clonazepam, two-sample _t_-test, t(5) = 4.07, 4.83, 0.35, 3.82, 4.30 (P = 0.0096, 0.0048, 0.74, 0.012, 0.0077) in the same ranges. The data in the slow wave and delta range is the same as in Supplementary Fig. 17. Error bars indicate SEM.

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Busche, M., Kekuš, M., Adelsberger, H. et al. Rescue of long-range circuit dysfunction in Alzheimer's disease models.Nat Neurosci 18, 1623–1630 (2015). https://doi.org/10.1038/nn.4137

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