DOI: 10.5061/dryad.5qfttdz6t
title: Ripples reflect a spectrum of synchronous spiking activity in human anterior temporal lobe

description: Direct brain recordings have provided important insights into how
 high-frequency activity captured through intracranial EEG (iEEG) supports
 human memory retrieval. The extent to which such activity is comprised of
 transient fluctuations that reflect the dynamic coordination of underlying
 neurons, however, remains unclear. Here, we simultaneously record iEEG,
 local field potential (LFP), and single unit activity in the human
 temporal cortex. We demonstrate that fast oscillations within the
 previously identified 80–120 Hz ripple band contribute to broadband
 high-frequency activity in the human cortex. These ripple oscillations
 exhibit a spectrum of amplitudes and durations related to the amount of
 underlying neuronal spiking. Ripples in the macro-scale iEEG are related
 to the number and synchrony of ripples in the micro-scale LFP, which in
 turn are related to the synchrony of neuronal spiking. Our data suggest
 that neural activity in the human temporal lobe is organized into
 transient bouts of ripple oscillations that reflect underlying bursts of
 spiking activity.
description: Twenty-one participants with drug resistant epilepsy underwent a
 surgical procedure in which platinum recording contacts were implanted on
 the cortical surface as well as within the brain parenchyma. In each case,
 the clinical team determined the placement of the contacts to localize
 epileptogenic regions. In all the participants investigated here, the
 clinical region of investigation was the temporal lobes.
 For research purposes, in six of these participants (4 female;
 34.8 ± 4.7 years old) we placed one or two 96-channel microelectrode
 arrays (MEA; 4 × 4 mm, Cereplex I; Blackrock Microsystems, Inc, Salt Lake
 City, UT) in the anterior temporal lobe (ATL) in addition to the subdural
 contacts. <strong>Intracranial EEG
 recordings</strong> We collected intracranial EEG
 (iEEG) data from a total of 1660 subdural and depth recording
 contacts. Subdural contacts were arranged in both grid and strip
 configurations with an inter-contact spacing of 10 mm. We captured iEEG
 signals sampled at 1000 Hz. The recorded raw iEEG signals used for
 analyses were referenced to the system hardware reference, which was set
 by the recording amplifier (Nihon Kohden, Irvine CA) as the average of two
 intracranial electrode channels. We applied a local detrending procedure
 to remove slow fluctuations (≤ 2 Hz) from the time series of each
 electrode and a regression-based approach to remove line noise at 60 Hz
 and 120 Hz. We quantified spectral power and phase in
 the iEEG signals by convolving the voltage time series with 200 linearly
 spaced complex valued Morlet wavelets between 2 and 200 Hz (wavelet number
 6). We extracted data from all retrieval periods, beginning 4 s preceding
 vocalization to 1 s following vocalization and included a 1000 ms buffer
 on both sides of the clipped data. We squared and log-transformed the
 continuous-time wavelet transform to generate a continuous measure of
 instantaneous power for each frequency.
 <strong>Microelectrode recordings</strong>
 In six participants, we additionally captured spiking activity
 and micro-scale local field potentials (LFP) from the MEAs implanted in
 the anterior temporal lobe. Microelectrodes were arranged in a 10 × 10
 grid with each electrode spaced 400 μm apart and extending 1.5 mm into the
 cortical surface (1.0 mm for one participant). Post-operative paraffin
 blocks of the resected tissue demonstrated that the electrodes extended
 approximately halfway into the 3-mm-thick gray matter. We digitally
 recorded microelectrode signals at 30 kHz using the Cereplex I and a
 Cerebus acquisition system (Blackrock Microsystems), with 16-bit precision
 and a range of ± 8 mV. To extract unit spiking
 activity, we re-referenced each electrode’s signal offline by subtracting
 the mean signal of all the electrodes in the MEA, and then used a second
 order Butterworth filter to bandpass the signal between 0.3 and 3 kHz.
 Using a spike-sorting software package (Plexon Offline Sorter, Dallas, TX,
 USA), we identified spike waveforms by manually setting a negative or
 positive voltage threshold depending on the direction of putative action
 potentials. The voltage threshold was set to include noise signals used in
 calculating unit isolation quality (see below). Waveforms (duration, 1.067
 ms; 32 samples per waveform) that crossed the voltage threshold were
 stored for spike sorting. Spike clusters were manually identified by
 viewing the first two principal components, and the difference in
 peak-to-trough voltage (voltage versus time) of the waveforms. We manually
 drew a boundary around clusters of waveforms that were differentiable from
 noise throughout the experimental session. In this manner, we identified a
 total of 989 putative single units across all sessions (average of 72 ± 21
 units per participant). The average spike rate across all units was 2.82 ±
 .01 Hz. In addition to the spiking data, we also used a 500 Hz low pass
 filter to extract the LFP signals from each microelectrode, down-sampled
 to 1000 Hz, and then performed a similar line noise removal and channel
 selection procedure to that used for the iEEG channels to exclude
 artifacts related to epileptiform activity or other system level noise.
 Across the six participants, after pre-processing we retained recordings
 from 78 ± 27 MEA electrodes for further analysis.
description: There are accompanying data files loaded by four Matlab scripts
 to show representative examples for four main figures.
 There are four data files for Figure 1, includes iEEG signals
 from medial temporal lobe in one representative participant.
 There is one data file for Figure 2, includes iEEG signals from
 temporal lobe and simultaneous LFP and spiking data in one representative
 participant. There are two data files for Figure 3,
 includes simultaneous iEEG signals and simultaneous spike phase locking
 data for one representative participant. There is one
 data file for Figure 4, includes spiking-LFP phase locking data for one
 representative participant.