Recordings were collected using four tetrodes. Each tetrode consisted of four polyimide-coated nichrome wires (12.5 µm diameter; Sandvik) gold plated to 0.2–0.4 MΩ impedance. Electrical signals were amplified and recorded using the Digital Lynx S multichannel acquisition system in conjunction with Cheetah data acquisition software (Neuralynx). Tetrode depths, estimated by calculating the rotation of the screw affixed to the shuttle holding the tetrode (one rotation = ~250 µm), were adjusted ~75 µm between recording sessions to sample independent populations of neurons across sessions. Offline spike sorting and cluster quality analysis was performed using MCLUST software (MClust-4.0, A.D. Redish et al.) in MATLAB. Briefly, single units were isolated by manual clustering based on features of the sampled waveforms (amplitude, energy, and the first principal component normalized by energy). Clusters with L-ratio <0.75 and isolation distance >12 were deemed single units (Schmitzer-Torbert et al., 2005 (link)), which resulted in excluding 30% of clusters. Although units were clustered blind to inter-spike interval (ISI), clusters with ISIs <1 ms were excluded.
Digital lynx s
Manufactured by Neuralynx
The Digital Lynx S is a high-performance data acquisition system designed for neuroscience research. It provides simultaneous recording of multiple neural signals, including electrophysiology, local field potentials, and other physiological data. The system features a modular and scalable architecture, allowing for flexible configuration to meet the needs of various research applications.
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3 protocols using digital lynx s
1
Multichannel Neural Recording Methodology
2
Multimodal neural data acquisition
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Neural data (putative spikes and local field potentials) were acquired using a 16-channel headstage that was connected to the electrode interface board and which relayed the signals to the data acquisition system (Digital Lynx S, Neuralynx) running Cheetah acquisition software (Neuralynx). The animal’s x and y position in the T-maze was also recorded using a light-emitting diode mounted on the head stage and digitized at 25 Hz using the same clock as for the electrophysiological data (Cheetah, Neuralynx). To extract putative spikes, neural signals were bandpass filtered between 0.6 and 6 kHz, and waveforms that passed a threshold were digitized at 30 kHz. Waveforms were then sorted offline into single-unit clusters using SPIKESORT3D (Neuralynx). To extract local field potential (LFP) activity, the same signals were bandpass filtered between 1 and 1000 Hz and digitized at 2 kHz. Subsequent analysis of neural and behavioral data was performed using scripts custom-written in MATLAB (MathWorks).
3
Optogenetic Inhibition of mPFC Neurons
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In order to examine the efficacy and selectivity of optogenetic inhibition, we recorded the activity of mPFC neurons during light delivery using electrodes attached to the optical fibers. This was done under two conditions. On the one hand, we recorded while delivering brief light pulses (500 ms duration, 3 s inter-pulse interval) when animals were in their home cage at the end of a testing session. On the other hand, we examined the responses of mPFC neurons to light delivery during the SWM task. In both cases, neural data were acquired using a 16-channel headstage (HS-18, Neuralynx) that was connected to the electrode interface board and which relayed the signals to the data acquisition system (Digital Lynx S, Neuralynx) running Cheetah acquisition software (Neuralynx). The animal's x and y position in the T-maze was simultaneously recorded using a small light-emitting diode attached to the headstage and digitized at 25 Hz using the same system as for the electrophysiological data. To extract putative spikes, neural signals were bandpass filtered between 0.6 and 6 kHz, and waveforms that passed a threshold were digitized at 30 kHz. Waveforms were then sorted offline into single-unit clusters using SpikeSort 3D (Neuralynx). Subsequent analysis of neural and behavioral data was performed using scripts custom-written in MATLAB (MathWorks).
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