Many cognitive processes require communication between the neocortex and the hippocampus. However, coordination between large-scale cortical dynamics and hippocampal activity is not well understood, partially due to the difficulty in simultaneously recording from those regions. In the present study, we developed a flexible, insertable and transparent microelectrode array (Neuro-FITM) that enables investigation of cortical–hippocampal coordinations during hippocampal sharp-wave ripples (SWRs). Flexibility and transparency of Neuro-FITM allow simultaneous recordings of local field potentials and neural spiking from the hippocampus during wide-field calcium imaging. These experiments revealed that diverse cortical activity patterns accompanied SWRs and, in most cases, cortical activation preceded hippocampal SWRs. We demonstrated that, during SWRs, different hippocampal neural population activity was associated with distinct cortical activity patterns. These results suggest that hippocampus and large-scale cortical activity interact in a selective and diverse manner during SWRs underlying various cognitive functions. Our technology can be broadly applied to comprehensive investigations of interactions between the cortex and other subcortical structures.
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The codes for ripple detection, two-stage TCA and the pairwise decoding of cortical patterns are available at https://github.com/xinliuucsd/hippocampus-cortex.
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We thank Q. Chen, O. Arroyo and L. Hall for technical assistance, and members of the Kuzum and Komiyama labs for discussions. This research was supported by grants from the Office of Naval Research (grants N000142012405 and N00014162531), the National Science Foundation (NSF; grants ECCS-2024776, ECCS-1752241 and ECCS-1734940) and the NIH (grants R21 EY029466, R21 EB026180 and DP2 EB030992) to D.K., and grants from the NIH (grants R01 NS091010A, R01 EY025349, R01 DC014690, R21 NS109722 and P30 EY022589), Pew Charitable Trusts and David & Lucile Packard Foundation to T.K. Fabrication of the electrodes was performed at the San Diego Nanotechnology Infrastructure of UCSD, a member of the National Nanotechnology Coordinated Infrastructure, which is supported by the NSF (grant ECCS-1542148).
The authors declare no competing interests.
Peer review information Nature Neuroscience thanks Benjamin Scott and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Microscope image of the recording tip of 32 channel Neuro-FITM array with 20 μm spacing. b, Same as (a), but for 64 channel Neuro-FITM array with 20 μm spacing. c, Picture of the whole probe (left), the microscope pictures of the recording tip of 32 channel Neuro-FITM array with 100 μm spacing (middle) and 20 μm spacing (right) for recording in rats. d, Same as c, but for 32 channel Neuro-FITM array with 100 μm spacing and 50 μm spacing for recording in primates.
Extended Data Fig. 2 Testing the multimodal recording setup using Neuro-FITM and standard silicon probes under both the wide-field and 2-photon imaging systems.
a, A picture of the probes tested in the multimodal recording setup. b, Pictures of the side view under the 2-photon imaging system. Neuro-FITM can be completely bent to the side as shown with the blue dashed line. Both the Neuronexus probes and the Neuropixel probe prevent the lowering of microscope objective (total rigid part indicated by red double arrow). The right column are the 2-photon images of the array surface, showing the thin Au wires, the boundary of the array substrate, and the penetration point. c, Pictures of the experimental setup (top), the zoom-in side view (middle), and the field of view (bottom) under wide-field imaging system, showing the blocking of field of view (Neuronexus probes) and preventing the lowering of microscope objective (Neuropixel probe). Wide-field image shows that mostly transparent Neuro-FITM does not block the field of view or generate shadows.
Extended Data Fig. 3 Implantation of Neuro-FITM array to hippocampus in in vivo experiments and the spike waveforms of example neurons.
a, Surgical setup of array implantation in actual experiments. Note that the array shank is largely invisible. The edge of the shank is marked by yellow dashed lines. b, The staining results of 6 mice, showing the successful penetration to the CA1 pyramidal layer. Arrowheads: trajectory in CA1 pyramidal layer. c, The spike waveforms of a few example neurons recorded from different animals. Single neurons can be detected in multiple adjacent channels, each exhibiting different waveform amplitudes.
a, Averaged cortical activity aligned to SWR onset in each animal. In all animals, the cortex exhibited broad activation around SWRs with the cortical activity rising before SWR onset. b, Mean activity in each cortical region aligned to SWR onset (mean ± s.e.m., across SWR events). Black dashed lines: SWR onset.
Extended Data Fig. 5 The distribution of time differences between SWR onset and activity onset in each cortical region.
The time differences (SWR onset-cortical activity onset: positive = cortex precedes SWR) formed a continuum around cortical activity onset. Note that the distribution was skewed to positive side in posterior cortical regions, suggesting cortical activity onset in posterior regions preceded SWR onset in a larger fraction of SWR events. Black lines: cortical activity onset.
a, Schematic of algorithm flow. b, Reconstruction error (rec. error) under different ranks of TCA model. c, The adjacency matrix before and after clustering. The 1,500 TCA patterns were obtained by the 100 runs of 15th order TCA with random initialization. Corr.: correlation. d, Number of assigned patterns in each cluster. Note that only the first 8 clusters had number of assigned patterns > 1. e, Reconstruction error (rec. error) of the original TCA algorithm with random initialization and the two-stage TCA algorithm with refined initialization (rank = 8). The reconstruction error given by the two-stage TCA model is smaller than that of the original TCA algorithm with random initialization (two-tailed rank-sum test, P=1.38×10−11, n = 100 repetitions for each algorithm), indicating that our two-stage TCA better captured the dynamics of cortical activity. f, Randomly selected 20 TCA patterns in each cluster for clusters 1-8. Patterns within each cluster exhibited similar spatiotemporal properties.
Extended Data Fig. 7 The two-stage TCA result and the cortical activation timing analysis for two patterns.
a, Factors generated by two-stage TCA algorithm. The high-dimensional data of SWR-associated activity from 16 cortical regions was decomposed into 3 factors. The region factors and time factors describe the spatial and temporal dynamics of cortical patterns respectively and the event factors measure the weighting of a given SWR event on the established set of patterns. b, Cortical activation timing for pattern 2 and pattern 5. Shown in each row are the pattern template (left), the average cortical activity for the events assigned to the pattern (middle), and the P-value maps (right) for all the cortical regions at [-1 s, 2 s] time interval aligned to SWR onset, showing significantly higher activity than baseline (-1 s) for most cortical regions.
Extended Data Fig. 9 Discriminant neurons in decoding cortical pattern identity and the fraction of distinguishable pairs using different neuron populations.
a, Discriminant neurons selected by feature elimination algorithm in decoding for each pattern pair. Note that the decoding often requires information from multiple hippocampal neurons, and all hippocampal neurons contributed to the decoding of some pattern pairs. b, The decoding results of cortical patterns using both the PYR and INT, the PYR only, and the INT only. Gray lines: the chance level fraction with P < 0.05. The chance level number of decodable pattern pairs (nc) was computed from the inverse of binomial cumulative distribution with probability 0.95 (one-sided binomial test, n = 28 pattern pairs). The chance level fraction was obtained by dividing nc with n = 28, the number of pattern pairs on which decoding was performed. PYR: pyramidal neurons, INT: interneurons. For PYR + INT, the p-values for mouse 1-6 are 2.24E-10, 5.10E-32, 5.10E-32, 2.60E-14, 9.17E-26, 8.42E-30. For PYR only, the p-values for mouse 1-6 are 1.26E-11, 8.42E-30, 9.63E-16, 0.16, 5.56E-7, 2.60E-14. For INT only, the p-values for mouse 1-6 are 0.76, 0.0023, 2.60E-14, 5.56E-7, 4.92E-5, 4.92E-5.
Extended Data Fig. 10 Different cortical activity patterns associated with distinct hippocampal neuronal activity patterns during all SWRs.
a, Raster plots (spikes) and the peri-event time histograms of example hippocampal neurons. b, Decoding accuracy of all cortical pattern pairs from all 6 animals. Cortical pattern pairs that are significantly distinguishable based on hippocampus activity are marked by asterisks (shuffled 2,000 times, one-tailed, *P < 0.05, **P < 0.01, ***P < 0.001, see Methods for exact p values). B. acc.: balanced accuracy. c, Fraction of distinguishable cortical pattern pairs in each animal. Gray lines: the chance level fraction with P < 0.05. The p-values for mouse 1-6 are 6.13×10−13, 1.99×10−34, 1.00×10−27, 2.60×10−14, 4.73×10–8, 9.17×10–26, n = 28 pattern pairs. d, Preference index and decoding accuracy between anterior (A)-posterior (P) and early (E) - late (L) pattern pairs. Left: preference index of discriminant hippocampus neurons between A-P pairs (pattern 1 vs. 4, 2 vs. 5, and 3 vs. 6) or between E-L patterns (pattern 1 vs. 2, 1 vs. 3, 2 vs. 3, 4 vs. 5, 4 vs. 6, and 5 vs. 6). Posterior patterns were associated with higher firing counts of discriminant neurons than the anterior patterns (two-tailed bootstrap test, 10,000 times, ***P(A-P)= 0.0005, n = 16 pattern pairs) while no significant differences were detected between early and late patterns (P(E-L) = 0.4380, n = 27 pattern pairs). Gray circles: preference index averaged over all neurons for each pair within each animal. Middle: same as Left but for individual discriminant neurons (two-tailed bootstrap test, 10,000 times, ***P(A-P) = 0, n = 71 neurons, P(E-L) = 0.3591, n = 129 neurons). Gray dots: preference index of individual discriminant neurons. Right: Decoding accuracy between A-P and E-L pairs was similar (two-tailed bootstrap test, 10,000 times, P = 0.4745, n = 16 pattern pairs for A-P, n = 27 pattern pairs for E-L). All error bars are s.e.m. Gray circles: decoding accuracy for each pair.
The impedance versus deposition time data, EIS result for the electrode and the noise versus impedance data.
The SNR data for spikes, ripples and sharp waves.
The average ΔF/F activity aligned to ripple onset, the time difference between ripple onset and cortical activation, and the fraction of ripple events relative to cortical activity onset.
The balanced accuracy for different pattern pairs and their associated P values for mouse 2. The preference index values for A–P and E–L pairs. The balanced accuracy for A–P and E–L pairs.
The reconstruction error between random init and refined init of TCA model.
The balanced accuracy for different pattern pairs and their associated P values for all six mice.
Number of decodable pairs using PYR + INT, PYR and INT neurons.
The balanced accuracy for different pattern pairs and their associated P values for mouse 2, considering all ripple events. The preference index values for A–P and E–L pairs. The balanced accuracy for A–P and E–L pairs.
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Liu, X., Ren, C., Lu, Y. et al. Multimodal neural recordings with Neuro-FITM uncover diverse patterns of cortical–hippocampal interactions. Nat Neurosci 24, 886–896 (2021). https://doi.org/10.1038/s41593-021-00841-5
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