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. 2017 Feb;20(2):251-259.
doi: 10.1038/nn.4457. Epub 2016 Dec 12.

A cortical-hippocampal-cortical loop of information processing during memory consolidation

Gideon Rothschild  1 , Elad Eban  2 , Loren M Frank  1   3
Affiliations

A cortical-hippocampal-cortical loop of information processing during memory consolidation

Gideon Rothschild et al. Nat Neurosci. 2017 Feb.

Abstract

Hippocampal replay during sharp-wave ripple events (SWRs) is thought to drive memory consolidation in hippocampal and cortical circuits. Changes in neocortical activity can precede SWR events, but whether and how these changes influence the content of replay remains unknown. Here we show that during sleep there is a rapid cortical-hippocampal-cortical loop of information flow around the times of SWRs. We recorded neural activity in auditory cortex (AC) and hippocampus of rats as they learned a sound-guided task and during sleep. We found that patterned activation in AC precedes and predicts the subsequent content of hippocampal activity during SWRs, while hippocampal patterns during SWRs predict subsequent AC activity. Delivering sounds during sleep biased AC activity patterns, and sound-biased AC patterns predicted subsequent hippocampal activity. These findings suggest that activation of specific cortical representations during sleep influences the identity of the memories that are consolidated into long-term stores.

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Conflict of interest statement

COMPETING FINANCIAL INTERESTS

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1
Auditory cortical reactivation during hippocampal SWRs in training-interleaved sleep. (a) Daily experimental schedule. Each day included 3–4 20-min training sessions, interleaved with 20–30-min rest sessions in the rest box (silent sleep). In addition, at the beginning and end of each day, a sound protocol was presented while the animals were in the rest box (sound sleep). (b) Track and task. Rats (n = 4) initiated each trial by nose-poking in the home well and receiving a reward. In ~75% of trials the rat then had to go to the silent well for the next reward. In a pseudorandom ~25% of trials, 5 s after poking in the home well, a sound series was emitted from a speaker, indicating the rat had to go to the sound well for the next reward. The speaker was placed at the end of the sound arm in the first days of training and moved to the center junction after rats displayed consistent correct choices in more than ~70% of trials. (c) Behavioral performance on the task. Error bars indicate s.e.m. across animals. Dotted line indicates chance level of 50% correct. (d) Tetrode targeting. Seven tetrodes targeted the hippocampal CA1 region and seven tetrodes targeted primary auditory cortex in each animal. (e) Example ensemble spiking activity in auditory cortex (green) and CA1 region of the hippocampus (orange) during sleep. Each tick marks a spike from one cell. Top two traces show broadband local field potentials (LFP; 1–400 Hz) in CA1 and ripple-band filtered LFP (150–250 Hz) in CA1. Cyan shading denotes SWRs. (f) SWR-aligned rasters of example neurons and corresponding time histograms. Top examples are significantly SWR-modulated AC neurons; bottom examples are significantly SWR-modulated CA1 neurons. Gray lines mark the range (mean ± 2 s.d.) of firing rates in the preceding −1,000 to −500 ms time window. FR, firing rate. (g) Population reactivation time series of an awake pattern from an example sleep epoch. SWR events shown in cyan. Inset: SWR-triggered reactivation histogram. (h) Mean z-scored SWR-triggered reactivation histogram across all sleep epochs that showed significant reactivation (n = 69 epochs). Error bars show s.e.m. Gray trace shows result for same data when SWR timing was randomly shuffled.
Figure 2
Figure 2
AC modulation precedes and predicts CA1 firing around SWRs. (a) z-scored SWR-triggered spiking histograms of all SWR-modulated AC neurons, sorted by timing of peak firing in −250 ms to 250 ms window. (b) Same as a for CA1 neurons. (c) Mean z-scored SWR-triggered spiking histogram across all SWR-modulated neurons for AC (green) and CA1 (orange). Shaded area indicates s.e.m. (d) Activity of an example AC–CA1 neuronal pair around SWR times. (di): overlaid SWR-triggered spike rasters of simultaneously recorded AC (green) and CA1 (orange) neurons; (dii): enlarged view of highlighted region in di; (diii): peri-SWR time histograms (PSTHs) of the AC (left) and CA1 (right) neurons from di separated by spike count of CA1 cell (≥1 spike, black; 0 spikes, dashed gray). (div): same as above, but for the same CA1 cell paired with a different AC cell. (dv): Same as above but for same AC cell as in diii but a different CA1 cell. (e) Example dataset used for generalized linear model (GLM) prediction analysis. Left matrix denotes spike counts in the pre-SWR time window (−200 to 0 ms relative to SWR onset) of 6 AC cells across SWRs. Right shows spike counts in the subsequent SWR time window (0 to 200 ms) of one CA1 cell. Spike counts of all cells are sorted by spike counts of the CA1 cell. Data corresponds to the data in d: rightmost column of the AC matrix corresponds to ‘AC cell 1’ in d, fifth column corresponds to ‘AC cell 2’ in d. Color scale denotes spikes per bin; max is 8 for AC cells and 12 for CA1 cell. (f) Prediction illustration aligned to the data in e. Blue and red edges denote individually positively and negatively predictive relationships, respectively. (g) Prediction of CA1 single-cell spiking during SWRs from ensemble spiking patterns in AC across varying time windows (n = 107 predicted CA1 cells). Black error bars indicate mean ± s.e.m. prediction gain for real data. Gray error bars indicate mean ± s.e.m. prediction gain for shuffled data. Columns represent varying time windows used as predictor data; predicted data is always the SWR time window (0–200 ms). CA1 spiking during SWRs could be predicted significantly better than shuffled data from AC ensemble spiking patterns in the −400 to −200 ms window (z = 3.45, P = 0.0006), −200 to 0 ms window (z = 4.69, P = 2.69 × 10−6) and 0 to 200 ms window (z = 6.75, P = 1.5 × 10−11) but not from the −600 to −400 ms window (z = 0.84, P = 0.40; all tests using a two-tailed rank-sum test compared to shuffled). (h) Prediction of AC single-cell spiking during SWRs from ensemble spiking in CA1 (n = 152 predicted AC cells). AC spiking during SWRs could not be significantly predicted from pre-SWR CA1 ensemble spiking patterns but could be predicted from CA1 spiking during the SWR (−600 to −400: z = 1.02, P = 0.31; −400 to −200: z = 1.58, P = 0.115; −200 to 0: z = 0.49, P = 0.627; 0 to 200: z = 6.04, P = 1.56 × 10−9, two-tailed rank-sum test compared to shuffled). (i) Prediction of post-SWR time window from SWR spiking. AC ensemble spiking patterns during SWRs could not predict CA1 firing in post-SWR window (left; n = 108 predicted CA1 cells, z = 1.18, P = 0.237, two-tailed rank-sum test compared to shuffle), but CA1 ensemble spiking patterns during SWRs significantly predicted AC firing in post-SWR window (right; n = 150 predicted AC cells, z = 4.37, P = 1.25 × 10−5, two-tailed rank-sum test compared to shuffled). (j) CA1 spiking in the pre-SWR time window could not be significantly predicted from AC ensemble spiking patterns in any time window (n = 101 predicted CA1 cells, −800 to −600: z = 0.28, P = 0.775; −600 to −400: z = 0.28, P = 0.78; −400 to −200: z = 1.73, P = 0.083; −200 to 0: z = −0.64, P = 0.523, two-tailed rank-sum test compared to shuffle). (k) AC spiking in pre-SWR time windows could not be significantly predicted from CA1 ensemble spiking patterns in any time window (n = 152 predicted AC cells, −800 to −600: z = −0.54, P = 0.59; −600 to −400: z = 1.66, P = 0.097; −400 to −200: z = 0.34, P = 0.737; −200 to 0: z = 0.43, P = 0.668, two-tailed rank-sum test compared to shuffle). ***P < 0.001 for GLM prediction beta values.
Figure 3
Figure 3
Sound-biased AC ensembles predict CA1 reactivation during SWRs. (a) Illustration of sound-series presentation and intermingled SWRs during sleep. Each sound series consisted of 15 pairs of sound pips of one of four sounds. Each sound pip lasted 200 ms, and pips within a pair were separated by 50 ms. Sound pip pairs within a series were presented at 1 Hz. Sound series were separated by 15–30 s. BBN, broadband noise. (b) Sound-triggered rasters (top) and corresponding PSTHs (bottom) from an example AC neuron’s responses to the four auditory stimuli. FR, firing rate. (c) Population average of z-scored PSTHs of all AC cells that were defined in both the first and last daily sound sleep epochs (n = 496 PSTHs from 248 cells) to the different sound stimuli. (d) Example ensemble spiking activity in AC (green) and CA1 (orange) during a sound series. Pink bars indicate sounds; cyan bars indicate detected SWRs. Top black trace is ripple-filtered LFP in CA1. (e) Decoding preceding sound identity from AC pre-SWR patterns within sound series. Top: decoding cartoon. Bottom: histogram of sound decoding gain across sound sleep epochs (n = 26 epochs). Decoding gain for each epoch is defined as the logarithm of the average rate of correctly decoded data (meancorrect) divided by the average rate of correctly decoded shuffled data (meanshuf). The mean of the distribution is significantly larger than 0 (t25 = 3.33, P = 0.0013, cross-validated linear discriminant analysis, population test for mean larger than 0 using one-tailed t-test). (f) Using sound-biased pre-SWR AC patterns to predict CA1 firing during SWRs. CA1 SWR-firing could be significantly predicted (n = 163 predictor-ensemble–predicted-cell combinations across sound epochs, 96 unique predicted CA1 cells, z = 3.4, P = 0.00068, two-tailed rank-sum test compared to shuffled). Error bars indicate s.e.m. **P < 0.01, ***P < 0.001.
Figure 4
Figure 4
Sounds bias AC patterns for seconds after sound termination. (a) Illustration of sound series presentation and interseries SWRs during sleep; colors as in Figure 3a. (b) Example ensemble spiking activity in AC (green) and CA1 (orange) during and following a sound series. Pink bars indicate sounds; cyan bars indicate detected SWRs. Top black trace is ripple-filtered LFP in CA1. (c) Similarity between sound-evoked patterns and pre-SWR patterns. Top: similarity cartoon depicting directions for forward and backward similarity. Bottom: distribution of the logarithm of forward similarity divided by backward similarity (Online Methods). Sound patterns were more similar to pre-SWR patterns that occurred after a sound series compared to those that occurred before (n = 55 epochs, t54 = 2.49, P = 0.0079, one-tailed t-test of mean vs. 0). (d) Decoding identity of preceding sound from AC pre-SWR patterns after sound series. Top: decoding cartoon. Bottom: histogram of sound decoding gain across sound sleep epochs (n = 17). Decoding gain for each epoch is defined as the logarithm of the average rate of correctly decoded data (meancorrect) divided by the average rate of correctly decoded shuffled data (meanshuf). The distribution is significantly larger than 0 (t16 = 1.84, P = 0.0418, population test for mean larger than 0 with one-tailed t-test). (e) Predicting CA1 firing during SWRs from sound-biased pre-SWR AC patterns that occurred after sound series. CA1 SWR-firing (n = 162 predictor-ensemble–predicted-cell combinations across sound epochs, 97 unique predicted CA1 cells) could be significantly predicted (z = 2.72, P = 0.0066, two-tailed rank-sum test compared to shuffled). Error bars indicate s.e.m. *P < 0.05, **P < 0.01.
Figure 5
Figure 5
Learning-related and sound-specific changes in SWR density. (a) SWR density is lower during sound presentation as compared to during silence (n = 30,782 stimuli bins and 39,899 no-stimuli bins, z = 19.85, P = 1.07 × 10−87, two-tailed rank-sum test). Error bars indicate s.e.m. (b) SWR density during presentation of the different sound series; colors as in Figure 3a. Data are separated into first (1–3) or later (≥4) d of training and denote whether it was the first or last daily sound-sleep epoch. SWR density differed significantly between stimuli in all learning phases (first days–first epochs: χ23, 3,504 = 40.56, P = 8.1 × 10−9, P(Target,S1) = 0.95, P(Target,S2) = 0.4, P(Target,S3) = 8.2 × 10−8, P(S1,S2) = 0.73, P(S1,S3) = 1.1 × 10−6, P(S2,S3) = 0.0001; first days–last epochs: χ23, 4,292 = 17.13, P = 6.7 × 10−4, P(Target,S1) = 0.97, P(Target,S2) = 0.84, P(Target,S3) = 0.001, P(S1,S2) = 0.98, P(S1,S3) = 0.004, P(S2,S3) = 0.015; later days–first epochs: χ23, 10,948 = 54.36, P = 9.4 × 10−12, P(Target,S1) = 0.98, P(Target,S2) = 0.0035, P(Target,S3) = 2 × 10−8, P(S1,S2) = 0.0007, P(S1,S3) = 4.9 × 10−9, P(S2,S3) = 0.056; later days–last epochs: χ23, 12,022 = 55.87, P = 4.5 × 10−12, P(Target,S1) = 0.0019, P(Target,S2) = 3.5 × 10−5, P(Target,S3) = 3.8 × 10−9, P(S1,S2) = 0.75, P(S1,S3) = 0.0005, P(S2,S3) = 0.02; Kruskal-Wallis test, stimulus-specific SWR count across stimuli: n = 823, 864, 948 and 873; 1,080, 1,156, 1,119 and 941; 2,719, 2,820, 2,720 and 2,693; and 3,142, 3,165, 2,958 and 2,761, respectively for each sound in each learning phase). Across learning, higher SWR rates occurred during presentation of target sound as compared to the control sounds. *P < 0.05, **P < 0.01, ***P < 0.001; Kruskal-Wallis test with Tukey-Kramer post hoc test. Error bars indicate s.e.m.
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