DOI: 10.5061/dryad.fqz612jzz
title: Data from: Spindle oscillations in communicating axons within a reconstituted hippocampal formation are strongest in CA3 without thalamus

description: Spindle-shaped waves of oscillations emerge in EEG scalp recordings during
 human and rodent non-REM sleep.  The association of
 these 10-16 Hz oscillations with events during prior wakefulness suggests
 a role in memory consolidation.  Human and rodent depth
 electrodes in the brain record strong spindles throughout the cortex and
 hippocampus, with possible origins in the
 thalamus.  However, the source and targets of the
 spindle oscillations from the hippocampus are unclear. Here, we
 employed an in vitro reconstruction of four subregions of the
 hippocampal formation with separate microfluidic tunnels for single axon
 communication between subregions assembled on top of a microelectrode
 array.  We recorded
 spontaneous 400-1000-ms-long spindle waves at 10-16 Hz in single
 axons passing between subregions, as well as from individual neurons in
 those subregions.  The highest amplitudes and most
 frequent occurrences suggest origins in the feedback axons from CA3 to DG.
 Spindle dissociation from spiking activity and recording in single axons
 from isolated hippocampal neurons suggests that spindle mechanisms are
 independent of action potentials and that consolidation of
 declarative-cognitive memories in the hippocampus may be separate from the
 more easily accessible consolidation of memories related to thalamic motor
 function.
description: Data was collected from the experiments of Vakilna (2021) using
 four PDMS compartments of primary neurons connected by microfluidic
 tunnels over a 120-electrode array (MEA) (Figure
 1A<strong>)</strong>.  Briefly, recordings were made using a
 Multichannel Systems MEA120 1100× amplifier (Multichannel Systems,
 Reutlingen, Germany). This study focused only on five minutes of
 spontaneous activity recordings, which were collected with the MC_Rack
 software at a sampling rate of 25 kHz at 37°C for 5 min, in humidified 5%
 CO<sub>2</sub>, 9% O<sub>2</sub>, as in culture.
 The recording begins after three weeks of culture in NbActiv4 media
 (Thermo-Fisher, Carlsbad, CA). Arrays with poor growth or less than 80%
 active electrodes were rejected (Vakilna 2021).  The four subregions
 contained neurons micro-dissected from the subregions of the hippocampal
 formation of postnatal day 4 Sprague-Dawley rats, namely the entorhinal
 cortex (EC), dentate gyrus (DG), CA3, and CA1+subiculum in order. 
 Fifty-one microfluidic tunnels of 3 x 10 x 400 µm allowed axons to connect
 to adjacent compartments.  Five of these tunnels per subregion were
 aligned over two 30 µm diameter electrodes to measure the direction of
 action potential propagation.  Recordings from nine arrays are
 reported. <strong>Spike and Spindle
 detection</strong> All the computations were
 performed using custom scripts written in MATLAB® 2022a. Spikes were
 detected using the method discussed in Vakilna et al. (2021) and the
 updated automation improvements (Lassers et al. 2023). A fourth-order
 elliptic bandpass filter from 300 Hz to 3000 Hz was applied to 5 min of
 raw data sampled at 25 kHz, which was used to cluster spikes in Wave_Clus
 (Chaure et al., 2018). In order to adequately capture large spikes in
 tunnels, two thresholds were used for the detection of the negative peak:
 5 to 50 SD and 50.1 to 500 SD. Previous analyses of these tunnel devices
 showed ~63% of tunnels contained only one axon (Narula et al. 2017).
 Therefore, spikes could be combined into one cluster, and axons could
 reliably be found after normalized matching indexing (NMI) between the two
 tunnel electrodes to find conduction time delays, and by extension, the
 direction of transmission. Peaks in the NMI histogram distribution of
 conduction times can then be used to identify axons.
 Spindle detection followed the filter methods of Silversmith
 2020, Kim et al, and Sela et al. The time series data recorded from the
 tunnels was downsampled from 25 kHz to 1 kHz after applying a Kaiser
 window finite impulse response (FIR) anti-aliasing filter
 (50<sup>th</sup> order) in Matlab. Spindles were detected by
 applying an 8-16 Hz bandpass filter (6th-order high-pass and 8th-order
 low-pass Butterworth infinite impulse response filter). Afterward, the
 envelope of the signal was extracted by computing the magnitude of the
 analytic signal from the Hilbert transform. A time window was classified
 as a spindle if the upper envelope exceeded a threshold of 2.5 standard
 deviations above the mean once and the lower envelope crossed a threshold
 of 1.5 standard deviations below the mean for more than 300 ms. Multiple
 detections of the same spindle were eliminated by combining the spindles
 that were less than 500 ms apart (Silversmith et al., 2020; Schimicek et
 al., 1994). The area under the curve, spindle duration, and inter-spindle
 interval were calculated for each spindle. The amplitude of each spindle
 was then calculated by dividing the area by the length. Phase locking of
 spikes with each spindle cycle was estimated by computing the polar
 histogram of phases and the phase angles of the spindle wave when the
 spike occurred. Time-frequency decomposition of the spindle was generated
 by computing a continuous wavelet transform using an analytic Morse
 wavelet ( = 3, = 60)  (Lilly, J. M., and S. C. Olhede, 2012).
 <strong>Statistics</strong> Each
 spindle-length quantity was fitted with a log-normal distribution, and the
 parameters were estimated by fittinga  Gaussian distribution to the
 log-transformed histogram. Means and standard deviations were computed for
 each histogram. Recordings with only one spindle oscillation in 5 min or
 spindles less than 0.4 sin length were not included. To measure
 oscillatory relationships of waveforms from two recording sites, the
 non-spindle regions of the recording were set to zero. Then, Pearson
 correlations (r) were calculated using the xcorr function of MATLAB,
 normalized, a nd unscaled for the entire five-minute recording. The
 significance ofthe difference in means was estimated by computing one-way
 analysis of variance (ANOVA), followed by Tukey’s HSD test.
description: # Data from: Spindle oscillations in communicating axons within a
 reconstituted hippocampal formation are strongest in CA3 without thalamus
 [https://doi.org/10.5061/dryad.fqz612jzz](https://doi.org/10.5061/dryad.fqz612jzz) MC Rack output files in the form of MCD file types and the associated h5 files. Recordings are 5-minute MEA120 recordings of a self-wiring hippocampus with axonal tunnels. Axonal tunnels are as follows: A6-A7 B6-B7 C6-C7 D6-D7 E6-E7 F1-G1 F2-G2 F3-G3 F4-G4 F5-G5 H6-H7 J6-J7 K6-K7 L6-L7 M6-M7 F8-G8 F9-G9 F10-G10 F11-G11 F12-G12 Standard MC_Rack raw data outputs used in the MCD files. The h5 files are the hierarchical datafile conversions of the MCD files which are actually used in MATLAB. The names are preserved between the files. Matlab files labeled FIDX_stim associate a file ID used within the code to a file name via loading two MATLAB variables named "FID" and "filename" as a double and a string, respectively. They are associated as listed below: FID1=ECDGCA3CA1_19908_150729_150823_d25_5minspont0001 MCD and H5 FID2=ECDGCA3CA1_19908_160518_160610_d22_5minspont0001 MCD and H5 FID3=ECDGCA3CA1_19911_160518_160610_d22_5minspont0001 MCD and H5 FID4=ECDGCA3CA1_19914_150805_150828_d25_5minspont0001 MCD and H5 FID5=ECDGCA3CA1_19914_160127_160217_d21_5minspont0001 MCD and H5 FID6=ECDGCA3CA1_19914_160127_160303_d37_5minspont0001 MCD and H5 FID7=ECDGCA3CA1_24088_160127_160302_d36_5minspont0001 MCD and H5 FID8=ECDGCA3CA1_24574_160127_160303_d37_5minspont0001 MCD and H5 FID9=ECDGCA3CA1_24574_160727_160818_d22_5minspont0001 MCD and H5 More information on the data layout can be found here: [https://www.multichannelsystems.com/sites/multichannelsystems.com/files/documents/data\\_sheets/120MEA\\_Layout.pdf](https://www.multichannelsystems.com/sites/multichannelsystems.com/files/documents/data\\_sheets/120MEA\\_Layout.pdf) Arrays are cultured to include subregions of the hippocampus from the trisynaptic loop. Clockwise arrays have the EC in the upper left quadrant between the tunnels, DG is in the upper right, CA3 is in the lower right, CA1 is in the lower left. Counterclockwise arrays have the EC in the upper left, DG in the lower left, CA3 in the lower right, and CA1 in the upper right. The directions.txt file lists which array FID is cultured in each direction. Analysis files can be found here: [https://github.com/Brewer-Neurolab/Wang-Spindles-in-Axons](https://github.com/Brewer-Neurolab/Wang-Spindles-in-Axons) [https://github.com/Brewer-Neurolab/Lassers-2023-Axon-Flow](https://github.com/Brewer-Neurolab/Lassers-2023-Axon-Flow) ## Description of the data and file structure Data is structured from MultiChannel Systems structures. The MCD filetypes can be opened with MC_Rack. MC_Rack: [https://www.multichannelsystems.com/software/mc-rack](https://www.multichannelsystems.com/software/mc-rack) The MCD data files were converted to h5 files using the multichannel systems [DataManager](https://www.multichannelsystems.com/software/multi-channel-datamanager) application. The resulting h5 files can be opened into MATLAB using the [McsMatlabDataTools](https://www.mathworks.com/matlabcentral/fileexchange/54976-mcsmatlabdatatools) wrapper. Upon opening the h5 data, only the raw analog data has been recorded off the MEA. No preprocessing steps are applied and all analysis is done in MATLAB. Initially loading the h5 file into MATLAB shows five categories: FileName, McsHdf5Version, McsHdf5Type, Data, and Recording. The Data file is a structure that gives the recording details including the array type recorded on. The Recording structure contains a cell array that includes voltage read at time steps for each electrode (120x7500000 MATLAB array) and which row belongs to each electrode. In the Github repositories included below, we have written a [pipeline](https://github.com/Brewer-Neurolab/Lassers-2023-Axon-Flow) to automatically convert these files into .mat files for use in [additonal analysis scripts also provided](https://github.com/Brewer-Neurolab/Wang-Spindles-in-Axons).