Laminar Organization of Encoding and Memory Reactivation in the Parietal Cortex

Abstract

Egocentric neural coding has been observed in parietal cortex (PC), but its topographical and laminar organization is not well characterized. We used multi-site recording to look for evidence of local clustering and laminar consistency of linear and angular velocity encoding in multi-neuronal spiking activity (MUA) and in the high-frequency (300-900 Hz) component of the local field potential (HF-LFP), believed to reflect local spiking activity. Rats were trained to run many trials on a large circular platform, either to LED-cued goal locations or as a spatial sequence from memory. Tuning to specific self-motion states was observed and exhibited distinct cortical depth-invariant coding properties. These patterns of collective local and laminar activation during behavior were reactivated in compressed form during post-experience sleep and temporally coupled to cortical delta waves and hippocampal sharp-wave ripples. Thus, PC neuron motion encoding is consistent across cortical laminae, and this consistency is maintained during memory reactivation.

Keywords: delta wave; high-frequency local field potential; hippocampus; memory reactivation; modular organization; movement decoding; multi-unit activity; parietal cortex; posterior parietal cortex; template matching.

Figure 1. Self-Motion tuning in parietal cortex is invariant across cortical laminae. See also Figs. S1-5, and Tables S1-2

A. Left Two Plots. Multi-unit activity (MUA) recorded for a single day’s recording session and from a single tetrode were classified as having a preferred-self motion state if the self-motion maps for two behavioral sessions (from the whole day recording session) were significantly positively correlated. Self-motion maps from two behavioral sessions and corresponding correlation value are shown for one MUA example. Right. The shuffled distribution and critical r-value corresponding to the 99^th percentile. B. Left. The random shuffle distribution for the within-session correlation values from A (black bars) but with a different bin-size to better match the frequency counts across histograms shown here. The full distribution of random shuffle critical values (red bars with 70% opacity) and the full distribution of significant within-session correlation values from all data analyzed for the present paper (blue bars). Right. Example of a self-motion map with lower (r=0.31) within-session stability. Example of a self-motion map with higher within-session stability is shown in A. C. Same as in A; however, data came from two separate recording sessions obtained when the tetrode was at two depths (700µm above and 1400µm below). Black outline on lower motion rate map illustrates that for cross-depth comparisons behavior can vary considerably, and this analysis is limited to common data points. D. The sorted correlation value for MUA for each pair of depths where the tetrode was moved at least 100µm and the session data for each depth met the significance criteria described in A. Pairs of depths with significantly correlated motion maps were colored red (calculated as described in A, but across depth as in C). Thus, self-motion tuning is consistent across cortical depths for a particular tetrode. Data comes from all tetrodes that met this criteria from all rats.

Figure 2. Illustration of patchy modularity of multi-unit activity (MUA) behavioral correlates in parietal cortex. See also Fig. S9

A. Rows A and B represent behavioral self-motion activity rate maps for three nearby electrodes in left and right hemispheres respectively from rat 1. Row C is from the left hemisphere of rat 2. The relative positions of the electrodes are shown in B, where the minimum distance between recording locations is ~300µm. In Row A, the self-motion map change abruptly from position to position, whereas in the Row B, the self-motion rate maps are quite similar across position. Row C illustrates a combination of these effects. The rate maps were highly consistent in the laminar dimension for each location (Fig. 1). For illustration, the session with the most significant behavior map was selected for each electrode. Thus, sessions were different for the examples in Rows A and B (and thus map shape varied), but was the same for the examples in Row C. Note, some MUA data sets show tuning to multiple motion states (e.g., C2 and C3), possibly as a result of recording from a region that bridges multiple regions each with a distinct tuning states. The depth correlations for Row A ranged from 0.20–0.30 (left), 0.14–0.21 (middle), and was 0.24 (right). For Row B, no depth correlations (left), 0.23–0.50 (middle), and 0.25–0.36 (right). For Row C, 0.16 (left), 0.48 (middle), and 0.48 (right). Numbers in red the top right of each plot indicate the r-value for the within session map correlation. B. Placement of the tetrodes used to record the data in A is shown on a surface view in the horizontal plane (outlines indicate the 95% confidence interval for each region; adapted from: Zilles, 1985). The topography of the various tuning states suggests that functional cell types are not segregated into different anatomical regions. Occipital cortex, area 2, mediomedial part (Oc2MM). Occipital cortex, area 2, mediolateral part (Oc2ML). Occipital cortex, area 1, monocular part (Oc1M). Agranular retrosplenial cortex (RSA).

Figure 3. Recording locations in parietal cortex

A. Nissl-stained coronal section (top) showing the marking lesion from a tetrode in rat 1 (black arrowhead). This tetrode tract is an example of the most medial tetrode placement and demonstrates that recordings did not encroach on the retrosplenial cortex. Scale bar=1mm. Nissl-stained sagittal section (bottom) showing an example of a tetrode tract in rat 2 (black arrowheads). Scale bar=500µm. B. Coronal sections throughout the anterior (top) to posterior (bottom) extent of the rat parietal cortex (Paxinos and Watson, 1998) color coded by rat (15 blue tracts-rat 1, 16 red tracts-rat 2, and 10 green tracts-rat 3). Each tract indicates the profile for a tetrode that recorded at least one session with significant within-session stability. Note, this represents nearly every tetrode that was in parietal cortex during the experiment: 41/44 tetrodes. Distance posterior to bregma is listed for each slice (lower right). Secondary visual cortex, lateral area (V2L), medolateral area (V2ML) and mediomedial area (V2MM). Parietal association cortex (PtA).

Figure 4. Compressed modular sequence reactivation. See also Fig. S7 and Table S3-4

A. Mean (±SEM) match percentage [(n matches/n time bins)*100] across the compression factors for multi-unit activity (MUA) templates during slow-wave sleep. Template matching increases between pre- (blue) and post-task-sleep (red) for compressed data, but not for ‘no-compression’ (nc). Match percentage varied significantly across compression factors (F_(4,44)=11.18, p<0.0001) and reactivation peaked at 4x compression. B. Example of MUA showing the match percentage (2min bins) for ‘no-compression’ (left) and 4x compression (right), over the pre-task-sleep, task and post-task-sleep. For ‘no-compression’, there is a high match percentage during the task, but not post-task-sleep. For the 4x compression (right), match percentage is higher in post-task-sleep, relative to both pre-task-sleep and task. C. Mean of the pre-task-sleep, task and post-task-sleep as was calculated for each session and averaged across time bins that are shown in B. Then a mean for all sessions was calculated for each rat. Finally, the mean (±SEM) of the rat mean data shows that across rats reactivation is stronger in post-task rest when a 4x compression factor is applied, but not for nc. To avoid the possible contribution of awake reactivation to template matching during task, only the contiguous movement periods (>5cm/s) longer than 2s were used for template matching. D. Normalized match percentage (match percentage/peak match percentage for that session) across ‘no-compression’ (left) and 4x (right) compression factors for MUA templates for each session for each of 3 rats. Reactivation consistently increases between pre- and post-task-sleep for compressed data, but not for ‘no compression’. Datapoints are normalized to pre-task-sleep values. A-D. Only the slow-wave sleep (SWS) periods are used from sleep and n is the number of data sets (n=12). * p<0.05.

Figure 5. Modular sequence reactivation of multiunit spiking activity (MUA) templates in parietal cortex are enhanced around cortical delta wave troughs (DWTs) and hippocampal sharp-wave ripples (SWRs). See also Fig. S7 and Table S3

A. Event-triggered average template matching Z-score (Mean ± SEM) for delta wave trough (DWT left) and sharp wave ripple (SWR right) for post-task-sleep (red), relative to pre-task-sleep (blue). A prominent dip in Z-score occurs 100–300ms after DWT, preceded by a larger peak (200–400ms) and followed by a smaller peak (300–500ms). SWR-triggered Z-score has a different profile, characterized by a dip 300–400ms before SWR and peak 50–150ms after SWR. B. Left. SWR probability density (Mean±SEM) centered on DWT. Larger peak in SWR probability density before DWT coincides with larger peak in DWT-triggered Z-score. Right. SWR-triggered average Z-score for SWRs preceded or not preceded by delta wave. The presence of delta wave prior to SWR is strongly predictive of SWR-triggered Z-score profile. Specifically, there is a large dip before SWRs that are preceded by delta wave (green), but small deflection when the SWRs are not preceded by delta wave (orange), both followed by comparable peak. C. Z-scored sum of event-triggered multi-unit spiking (Mean±SEM) on all tetrodes for DWTs (left) and SWRs preceded or not preceded by delta wave (right). DWTs are associated with down states, periods of widespread cortical neuronal silence and consequently low Z-scores. SWRs are generally coupled with increase in cortical spiking. Presence of large dip in spiking prior to SWR depends on the presence (green) or absence (orange) of a delta wave. D. Maximum event-triggered Z-score (Mean±SEM) across compression factors for DWTs (left) and SWRs (right). Peak amplitudes varied significantly across compression factors (Fs_(4,44) >3.01, p<0.05). A-D. n is the number of data sets (n=12). For both DWTs and SWRs, maximum event-triggered Z-score amplitudes were significantly higher in post-task-sleep (red), relative to pre-task-sleep (blue) baseline. Only slow-wave sleep periods were included in the analysis. * p<0.05. ** p<0.001. *** p<0.0001.

Figure 6. Apparatus, reference frames, and learned motion sequence

Left. Apparatus. Rats 1 and 2 were trained to run a random spatial sequence to 32 light locations. This task requires the rat to learn to execute a stereotyped motion sequence. Middle. Single segment from the random lights task (red) overlaid on 99% transparent random subset (1/5^th) of the trials from that session (blue). The route to the goal becomes a series of stereotyped motion sequences. Right. Schematic of the spatial sequence task. The rat starts at zone 12 and continued to zone 28 8 20 4 28 8 20 & 12. Rat 3 learned to execute this spatial sequence from memory (without cueing).

Figure 7. Template matching method. See also Fig. S8

A. Shuffling procedure. Above. Example original template. Multiunit activity (MUA) or high-frequency (HF) amplitude on each tetrode (row) is averaged over all trials, binned at 100ms, and Z-scored. Below. Template shuffling procedure. Position of each column (instantaneous MUA or HF amplitude over all the tetrodes) was randomly permuted in order to produce 100 shuffled templates while preserving the overall MUA or HF amplitude levels, as well as the instantaneous correlation between tetrodes. B. Template matching procedure. Above. MUA data segment from post-task-sleep. Activity during sleep is sparse, so samples are more variable. Activity on each tetrode (row) was binned (bin size=100ms/compression factor) and Z-scored. Below Left. Example match window from the sleep epoch (above) and template (below). Pearson correlation coefficient was calculated between the template and equally sized slow-wave sleep (SWS) segments of the sleep session, which were produced by sliding the template window over the sleep epoch with a 1-bin step size. This creates a correlation matrix (n templates x n SWS time bins). Each column of the correlation matrix is Z-scored; this value reflects the degree of similarity of a given template to sleep activity at given sleep time window, relative to the distribution across the original and all shuffled templates. Bottom right: Example original template matching trace. Z-scores >3 are considered matches.