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. Author manuscript; available in PMC: 2025 Oct 1.
Published in final edited form as: Neuropharmacology. 2024 Jun 6;257:110030. doi: 10.1016/j.neuropharm.2024.110030

Decoupling of cortical activity from behavioral state following administration of the classic psychedelic DOI

Randall J Olson 1, Lowell Bartlett 1, Alex Sonneborn 1, Russell Milton 1, Zachary Bretton-Granatoor 4, Ayesha Firdous 4, Alexander Z Harris 4,5, Atheir I Abbas 1,2,3,*
PMCID: PMC11260522  NIHMSID: NIHMS2003668  PMID: 38851531

Abstract

Administration or consumption of classic psychedelics (CPs) leads to profound changes in experience which are often described as highly novel and meaningful. They have shown substantial promise in treating depressive symptoms and may be therapeutic in other situations. Although research suggests that the therapeutic response is correlated with the intensity of the experience, the neural circuit basis for the alterations in experience caused by CPs requires further study. The medial prefrontal cortex (mPFC), where CPs have been shown to induce rapid, 5-HT2A receptor-dependent structural and neurophysiological changes, is believed to be a key site of action. To investigate the acute neural circuit changes induced by CPs, we recorded single neurons and local field potentials in the mPFC of freely behaving male mice after administration of the 5-HT2A/2C receptor-selective CP, 2,5-Dimethoxy-4-iodoamphetamine (DOI). We segregated recordings into active and rest periods in order to examine cortical activity during desynchronized (active) and synchronized (rest) states. We found that DOI induced a robust decrease in low frequency power when animals were at rest, attenuating the usual synchronization that occurs during less active behavioral states. DOI also increased broadband gamma power and suppressed activity in fast-spiking neurons in both active and rest periods. Together, these results suggest that the CP DOI induces persistent desynchronization in mPFC, including during rest when mPFC typically exhibits more synchronized activity. This shift in cortical dynamics may in part underlie the longer-lasting effects of CPs on plasticity, and may be critical to their therapeutic properties.

Keywords: Psychedelic, hallucinogen, prefrontal cortex, serotonin, DOI, cortex, gamma, delta, behavioral state, desynchronization

Introduction

Classic psychedelics (CPs) induce profound and multifaceted effects on experience after consumption (Nichols, 2004). The resulting experience is often described as “mystical”, and for many is rated as highly meaningful (Griffiths et al., 2008; Haijen et al., 2018). In recent years, there has been rekindled interest in the potential of CPs, particularly psilocybin, as possible therapeutic agents for treatment-resistant depression and other psychiatric disorders. This work has added to longstanding evidence that CPs can chronically alleviate depressive symptomatology in a dose-dependent manner mediated by the intensity of the experience (Johansen et al., 2022; Muttoni et al., 2019; Romeo et al., 2021; Vollenweider and Preller, 2020).

Classic psychedelics exert their main psychoactive effects through interaction with Gq-coupled 5-HT2A receptors (5-HT2ARs) (Nichols, 2016), and pretreatment with the selective 5-HT2AR antagonist ketanserin greatly reduces the subjective effects of the CPs psilocybin and LSD (Becker et al., 2022; Kometer et al., 2013; Quednow et al., 2012). Expression of these receptors is enriched in medial prefrontal cortex (mPFC) where roughly half of all excitatory neurons and 20-30% of all GABAergic interneurons express 5-HT2ARs (De Almeida and Mengod, 2007; Jakab and Goldman-Rakic, 2000, 1998; Santana et al., 2004). Their activation by both CPs and non-psychedelic 5-HT2AR agonists induces a diverse range of downstream molecular actions that do not clearly differentiate CPs from non-psychedelic 5-HT2AR agonists (Slocum et al., 2022). The most consistent long-term effect across compounds seems to be a potent initiation of structural and functional dendritic plasticity (Lukasiewicz et al., 2021), which is blocked by application of ketanserin (Ly et al., 2018). However, the rapid, circuit-level neurophysiological effects which precede this plasticity are less well understood. Thus, investigating how CPs affect neural circuit function might help determine how they acutely modulate experience and aid in identifying key neural activity which confers superior long-lasting therapeutic properties.

Previous ex vivo electrophysiology studies in the mPFC have identified complex acute effects of 5-HT2AR activation on the intrinsic excitability and synaptic activity of different neuron subtypes. Whole-cell recordings from layer 5 GABAergic neurons have shown that 5-HT2ARs modulate activity most strongly in fast-spiking interneurons (Athilingam et al., 2017), the main inhibitory subtype on which they are expressed (Weber and Andrade, 2010). More mixed effects were found in pyramidal neurons of rat mPFC, in which 5-HT2ARs were found to either hyperpolarize (Hu et al., 2016) or depolarize (Araneda and Andrade, 1991; Béïque et al., 2007; Tanaka and North, 1993) membrane potential. Synaptic activity impinging onto mPFC pyramidal neurons is also modified by 5-HT2ARs, which increase the frequency and amplitude of spontaneous and evoked inhibitory (Zhou and Hablitz, 1999) and excitatory (Aghajanian and Marek, 1997) postsynaptic currents. Furthermore, 5-HT2AR activation has been shown to bias these synapses toward a more asynchronous mode of evoked neurotransmitter release (Aghajanian and Marek, 1999), which can alter the timing of communication between neurons.

Taken together, these acute ex vivo effects of 5-HT2AR activation would be expected to alter local field potentials (LFPs), spiking, and synchrony in mPFC circuits in vivo. However, to our knowledge only a few studies have investigated these phenomena in freely behaving rodents. In the dorsal anterior cingulate subregion of the mPFC, systemic administration of the 5-HT2A/2C-selective CP 2,5-Dimethoxy-4-iodoamphetamine (DOI) to rats altered the firing rate of a subset of neurons and led to a broad decrease in gamma power (30-90 Hz) (Wood et al., 2012), while administration of psilocybin to head-fixed mice on a treadmill decreased low frequency power and increased gamma power (35-55 Hz) (Golden and Chadderton, 2022). More ventrally, in the prelimbic and infralimbic cortices, CPs increased low frequency power (delta and theta) and gamma power (30-80 Hz) (Riga et al., 2018), or decreased low frequency power while increasing gamma power (30-100 Hz) (Hansen et al., 2019), implying subregion-specific mechanisms of CPs in the mPFC. Two studies have demonstrated an increase in very high frequency oscillations (100-160 Hz; range varies slightly by study) in prelimbic and infralimbic cortices after administration of DOI to freely moving rats (Brys et al., 2023; Hansen et al., 2019) and another found an increase in high frequency oscillations in orbitofrontal cortex (OFC), but not prelimbic cortex, after 25C-NBOMe (Yu et al., 2023). Effects were in some cases reported to be significantly modulated by the behavioral state of the animal (Brys et al., 2023; Hansen et al., 2019; Vejmola et al., 2021) , which suggests a more complex interaction between CPs and cortical activity than previously thought. Studying these behavioral state-dependent effects of CPs on neural network activity is especially intriguing in more ventral mPFC, which is proposed to be analogous to the human subgenual and pregenual ACC (Bittar and Labonté, 2021), two brain regions known to be dysregulated in major depressive disorder (Drevets et al., 2008; Jing et al., 2020). Importantly though, only one prior study recorded a handful of single neurons in more ventral mPFC (Brys et al., 2023). Learning more about how CPs affect spiking and LFPs in mPFC would provide a more complete picture of the complex network effects that may contribute to their therapeutic properties.

To that end, we recorded single units and LFPs in the mPFC of freely behaving male mice before and after administration of DOI, with the goal of studying its effect on spiking, brain rhythms, and population activity patterns. We predicted that DOI would lead to a behavioral state-dependent shift in synchronized population dynamics and associated brain rhythms. To assess this, we segregated recordings by behavioral state – active versus rest – and compared pre- and post-DOI mPFC rhythmic power, single neuron firing, and measures of population dynamics. We found that activity in fast-spiking neurons and power at multiple frequencies were all disrupted after administration of DOI in a manner that depended on behavioral state.

Methods

Animals

All procedures were conducted in accordance with the US NIH Guide for the Care and Use of Laboratory Animals and approved by the New York State Psychiatric Institute Institutional Animal Care and Use Committee at Columbia University and the VA Portland Institutional Animal Care and Use Committee. Eight adult male (age 12-24 weeks) C57BL/6J (Jackson Labs, stock number 000664) experimental mice were used for DOI experiments. For saline experiments a subset of the DOI mice (N = 4) received saline injections one week following DOI administration, with another six adult male mice (age 12-24 weeks) C57BL/6J (Jackson Labs, stock number 000664) receiving saline only.

Surgical procedures

Male mice were anesthetized with 1%–3% vaporized isoflurane in oxygen (1 L/min) and placed in a stereotaxic apparatus. A craniotomy was made to allow for implantation of a 28 microwire bundle (14 stereotrodes; 13 micron tungsten wire, California Fine Wire) implanted in left mPFC (−0.35 ML, +1.85 AP, 1.3 below brain surface). A ground screw was placed over the cerebellum and a reference screw was placed over the orbitofrontal cortex. Electrodes were connected to a 32-channel Omnetics electrode interface board using gold pins (Neuralynx). Electrode placements were confirmed using an electrolytic lesion (5 mA, 10 s) followed by light microscopy to visualize the location of the lesion. Mice were allowed to recover for one week post-surgery before behavioral testing and were monitored closely during recovery.

Behavior

Male mice were recorded in a novel open field environment and were allowed to move freely. After 15 minutes of baseline recording male mice received a 5 mg/kg i.p. injection of racemic 2,5-dimethoxy-4-iodoamphetamine (DOI) or saline and were then placed back in the open field environment to freely behave for 60 min. Male mice were used due to the fact that the 3-4 g weight of the neural implant reduced locomotor activity in the lighter female mice. Due to the higher weight of our implants we also wanted to confirm that locomotion was not impaired in male mice. Therefore, we calculated the average velocity (cm/min) for all male mice during the entire recording session, which we found to be 186.26 cm/min. We then consulted the literature and calculated the average velocity (cm/min) in non-implanted mice which ranged from 65 cm/min to 300 cm/min (Juczewski et al., 2020; Seibenhener and Wooten, 2015), confirming that our mice exhibit locomotion within a range consistent with that of non-implanted mice.

Neural recording

A Digital Lynx system (Neuralynx, Bozeman, MT) was used to amplify, band-pass filter (1-1000 Hz for LFPs and 600-6000 Hz for spikes), and digitize the electrode recordings. LFP sampling rates were 2 kHz and spikes were collected at 32 kHz. The six saline only mice were recorded using a Blackrock CerePlex Direct system (continuous recordings at 30 kHz), with the same filtering used to pre-process the data. Single units were clustered based on the first two principal components (peak and energy) from each channel using Klustakwik (Ken Harris) and visualized in SpikeSorter3D (Neuralynx). Clusters were then visually inspected and included or eliminated based on waveform appearance, inter-spike interval distribution, isolation distance, and L-ratio.

Segregation by behavioral state

All data analyses were done using custom scripts in MATLAB (MathWorks) unless otherwise specified. Video was recorded at 30 fps and synchronized to neural data (Digital Lynx-associated camera or White Matter e3Vision). Videos were then imported into DeepLabCut (DLC, (Mathis et al., 2018), https://github.com/DeepLabCut), and positional markers were manually set for head, body and tail for 200 randomly selected frames throughout the entire recording. DLC was then run and average position data from the head, body and tail was calculated per frame for each animal. About 2-4% of frames would not identify the mouse’s position and instead place the positional marker outside the open field or in some other random location. Therefore, any points that were outside of the physical space the animal could move in the open field were interpolated using the previous position value. To confirm accuracy of the DLC position of the mouse, DLC position and the original video were overlayed and inspected. Any large discrepancies in DLC position and true mouse position resulted in retraining of DLC and added frames being used to train if necessary. Velocity was than calculated from the positional data. Velocity outliers were removed via a Hampel identifier and smoothed via a moving window of 5 fps. Each velocity trace was then synchronized to the existing video and manually inspected. Upon further inspection, large outliers that were not identified by the Hampel identifier were removed using a threshold value of 120-fold greater than the threshold value for active/rest. The active/rest threshold velocity value was identified based on visual inspection of velocity trace and video agreement across all animals, and one threshold for all animals was assigned. This threshold was then applied to the velocity trace resulting in a binary vector, 0 denoting rest and 1 denoting active. This vector still resulted in 0 or 1 periods that only last a few frames. These micro rest/active states were too small to perform further analyses on. As a result, sequential 5 second windows were used to identify the consensus of 0s or 1s in each window, changing to either all zeros or all ones for a given 5 second window. `

Local field potential analyses

Power:

LFP data was segregated for the baseline window (−15 min to −5 min prior to DOI or saline injection) and for the experimental window (DOI or saline, +25 min to +60 min after injection). For both the baseline window and the experimental window, bins corresponding to the “active” and “rest” periods were segregated and respectively concatenated into 4 conditions, (1) baseline active, (2) baseline rest, (3) experimental active, and (4) experimental rest, where experimental refers to DOI or saline. Each condition was normalized by the root mean square of the whole signal, and the analytic signal was calculated via the continuous 1-D wavelet transform (CWT using Morse wavelet) with frequency limits 1-120 Hz, symmetry parameter gamma (γ) = 3, time-bandwidth product equal to 60, and 10 voices per octave. CWT uses L1 normalization. We calculated the power of the LFP by taking the square of the absolute value of the analytic signal. The power was then segregated into canonical frequency bands: delta (2-4 Hz), theta (6-10 Hz), beta (12-25 Hz), low gamma (30-50 Hz) and high gamma (50-90 Hz). For spectrograms in Figures 1, 2 and 3 the entire non-segregated signal was used with which power was calculated as described above, with the smoothed non-binarized velocity trace. For quantification of LFP, each animal’s power was averaged across conditions and then compared using Wilcoxon signed-rank tests.

Figure 1: LFP power is behavioral state-dependent.

Figure 1:

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A) Left: example image of electrode bundle placement in mPFC with electrolytic lesion outlined in red. Right: Nissl (left) and anatomical annotations (right) from the Allen Mouse Brain Atlas and Allen Reference Atlas – Mouse Brain, superimposed with placements of electrolytic lesions for all mice. Allen Mouse Brain Atlas, mouse.brain-map.org and atlas.brain-map.org. B) Top: Raw power spectrogram from 30-120 Hz of an example animal during baseline (15 min prior to injection (red) of DOI until injection of DOI). Bottom: Same as top but for lower frequencies (1-12 Hz). Black trace above spectrograms is velocity of animal. Purple and green panels denote labeled behavioral state (rest vs. active). C) Left: Average normalized power by frequency for 1-12 Hz for baseline rest and baseline active averaged across all animals shows increased delta power during rest and increased theta during active periods. Right: Average power by animal for baseline rest is greater than baseline active in the delta frequency (2-4 Hz), p=0.0156 and lower for theta frequency (6-10 Hz), p=0.0156. C) Average normalized power by frequency for 40-120 Hz for baseline rest and baseline active averaged across all animals. D) Average power by animal for baseline rest vs. baseline active in the gamma frequency (50-90 Hz), p=0.1094.

Figure 2: DOI decreases power of low frequency oscillations in mPFC during rest periods.

Figure 2:

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A) Top: Raw power spectrogram from 1-12 Hz of an example animal from 15 min prior to injection (red) of DOI to 30 min post injection of DOI. Bottom: Same as top but with saline. Black traces above spectrograms are velocity of animal. B) Top Left: Average normalized power by frequency (1-12 Hz) for baseline rest and DOI rest averaged across all animals. Average power by animal for baseline rest is higher than DOI rest in the delta frequency (2-4 Hz), p=0.0234. Top Right: Average normalized power by frequency (1-12 Hz) for baseline active and DOI active averaged across all animals. Average power by animal for baseline and DOI active in the delta frequency (2-4 Hz) shows no significant difference, p=0.1484. Bottom Left: Average normalized power by frequency (1-12 Hz) for baseline rest and saline rest averaged across all animals. Average power by animal for baseline and saline rest in the delta frequency (2-4 Hz) shows no significant difference, p=0.7695. Bottom Right: Average normalized power by frequency (1-12 Hz) for baseline active and saline active averaged across all animals. Average power by animal for baseline and saline active in the delta frequency (2-4 Hz) shows no significant difference, p=0.9219.

Figure 3: DOI increases power of high frequency oscillations in mPFC irrespective of movement.

Figure 3:

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A) Top: Raw power spectrogram from 30-120 Hz of an example animal from 15 min prior to injection (red) of DOI to 30 min post injection of DOI. Bottom: Same as top but with saline. Black traces above spectrograms are velocity of animal. B) Top Left: Average normalized power by frequency (40-120 Hz) for baseline rest and DOI rest averaged across all animals. Average power by animal shows that DOI increases gamma frequency power during rest periods (50-90 Hz), p=0.0078. Top Right: Average normalized power by frequency (40-120 Hz) for baseline active and DOI active averaged across all animals. Average power by animal shows that DOI increases gamma frequency power during active periods (50-90 Hz), p=0.0078. Bottom Left: Average normalized power by frequency (40-120 Hz) for baseline rest and saline rest averaged across all animals. Average power by animal for baseline and saline rest in the gamma frequency shows no difference (50-90 Hz), p=0.3335. Bottom Right: Average normalized power by frequency for 50-90 Hz for baseline active and saline active averaged across all animals. Average power by animal for baseline and saline active in the gamma frequency shows no difference (50-90 Hz), p=0.3750.

Single unit analyses

Firing Rate:

Spike times sampled at 32 kHz were imported into MATLAB, each neuron having an associated spike train with spike times given to the nearest microsecond. Spikes were binned in 1 ms bins, and we calculated the baseline firing rate in Hz using the same LFP baseline window. Neurons with baseline firing rates below 0.5 Hz were omitted from further analysis. The remaining neurons were classified as either “high” or “low” firing rate neurons based on their average baseline firing rates, with 5 Hz being the cutoff between the two groups. We used firing rate cutoffs based on previous work showing that cortical parvalbumin interneurons largely exhibit firing rates >5 Hz, with many having a firing rate of 5-10 Hz (Moore and Wehr, 2013). Very few pyramidal neurons had a firing rate >10 Hz. Average firing rate for each of the same four conditions used in the LFP (1) baseline active, (2) baseline rest, (3) experimental active, and (4) experimental rest was calculated, where experimental refers to DOI or saline. Single units were not recorded in saline only animals (n=6). Wilcoxon signed-rank tests were used to compare firing rates of neurons across conditions.

Firing Rate Changes:

A wide range of responses to drug administration were observed in neuronal activity in the form of firing rate changes. To categorize neurons as increasing their firing rate relative to baseline or decreasing their firing rate relative to baseline, referred to as “increasers” or “decreasers”, spike trains were bootstrapped to ensure the observed change in activity was statistically significant. Bins from the baseline and experimental period were concatenated into a single spike train, which was used to generate 1,000 shuffled spike trains by randomly selecting a number of bins equivalent to either the length of the baseline spike train or the length of the experimental spike train to create a shuffled baseline or experimental spike train, respectively. The difference in these spike trains was calculated for each shuffle, and the observed difference was compared to the difference of the collection of shuffled spike trains. If the observed difference was greater or less than 97.5% of the shuffled differences, the cell was labeled as an increaser or decreaser, respectively. Neurons not significantly different from the shuffled spike train were labeled as no change. To test proportions of increasers and decreasers across conditions we bootstrapped the proportions 1,000 times.

Absolute Magnitude Change of Firing Rate:

To assess the firing rate dynamics in experimental conditions and examine firing rate changes from baseline, spikes were binned into 60 second bins. Firing rates in each bin for each neuron were Z-scored relative to the neuron’s mean firing rate across the 10-minute baseline period. Z-scored firing rates were averaged separately across the population of low- and high-firing rate neurons, respectively, and then the absolute value was taken. Average absolute value Z-scored firing rates after DOI were then compared against firing rates after saline for both populations of neurons using Wilcoxon signed-rank tests.

Results

The CP DOI modulates LFP power in a behavioral state-dependent manner

To examine the interaction between behavioral state and the effect of the CP DOI on cortical activity, we implanted mice with 14 tungsten stereotrodes in the left mPFC (see Figure 1A for placements) which are superimposed on a coronal section from the Allen Mouse Brain Atlas (Daigle et al., 2018; Harris et al., 2019; Lein et al., 2007; Oh et al., 2014). Electrodes were connected to an electronic interface board that facilitated the acquisition of synchronized electrical and video recordings of mPFC neural activity using a neural acquisition system (see Methods for further details). Mice were placed in a box and allowed to freely explore for a 15-30 minute baseline period, after which they were injected intraperitoneally (i.p.) with a highly efficacious dose of 5 mg/kg of DOI (Abbas et al., 2009; Wood et al., 2012) or saline. Mice continued free exploration for another 60 min as recordings continued.

Because the relationship between mouse medial PFC LFP and behavioral state is not well characterized, we first used DeepLabCut (Mathis et al., 2018) to calculate the position and velocity of the animal throughout the recording session to segregate the animal’s behavioral state into active and rest periods (see Methods for details), as LFP is known to be modulated by behavioral state (Hoy and Niell, 2015; Milton et al., 2020; Zagha et al., 2013). To confirm that segregation by DeepLabCut was effective, instantaneous velocities during all active and rest behavioral states were then compared as distributions to confirm that these distributions were significantly different using a two-sample Kolmogorov-Smirnov test, p<0.001), and that active and rest distributions had minimal overlap (19.74%, data not shown). Alignment of velocity traces to wavelet power spectra of mPFC LFP recordings during baseline (Figure 1B) revealed the typical relationship of higher delta (2-4 Hz) and lower theta (6-10 Hz) power during rest states and subsequent reversal of this relationship during active states (Figure 1C). We did not see state-dependent modulation of gamma power (Figure 1D); however, this is not surprising given that our behavioral state classification segregates between lighter rest periods (very low velocity but still interacting with the environment) and active periods. Deeper rest and sleep are associated with decreased gamma power (Castro-Zaballa et al., 2019; Harris and Thiele, 2011; McCormick et al., 2015), but we rarely captured deep rest periods as the mice were mostly engaged during recording. After classifying behavioral state-dependent dynamics, we examined the effect the CP DOI had on these dynamics. We confirmed that our active versus rest state segregation after DOI was similar to that during the baseline period, with 20.1% overlap in DOI-treated segments versus 19.74% in baseline. We then confirmed these overlaps where statistically indistinguishable from each other via permutation testing (1000 permutations, p>0.05).

DOI-treated animals revealed that the typical increase in low frequency delta (2-4 Hz) power which occurs during rest periods, a hallmark of cortical synchronization (Sachdev et al., 2015), is significantly decreased approximately 5 min after DOI injection, but not after saline injection (Figure 2A). Averaging normalized low frequency power spectra across animals showed that delta power was significantly decreased after DOI injection (Figure 2B) but not saline injection. Alignment of velocity traces with higher frequency spectrograms also revealed that systemic injection of DOI led to a significant increase in gamma power (50-90 Hz) in the mPFC (Figure 3A). The DOI-induced increase in gamma power occurred during both rest and active periods (Figure 3B). No increase in gamma power was seen after saline injection (Figure 3B). We also found the typical increase in theta power during active states was decreased only after DOI but not saline injection (Figure 4A-B). Neither DOI nor saline affected beta power (Figure 4C-D). Taken together, the CP DOI disrupts the typical behavioral state-dependent modulation of oscillatory activity in mPFC.

Figure 4: DOI decreases LFP theta power only during active periods and beta LFP power remains unchanged.

Figure 4:

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A) Left: Average normalized power by frequency (1-12 Hz) for baseline rest and DOI rest averaged across all animals, then quantified for the theta frequency band (6-10 Hz), p=0.3125. Right: Average normalized power by frequency (1-12 Hz) for baseline active and DOI active averaged across all animals, then quantified for the theta frequency band (6-10 Hz) shows that DOI decreases theta during active periods, p=0.0156. B) Left: Average normalized power by frequency (1-12 Hz) for baseline rest and saline rest averaged across all animals, then quantified for the theta frequency band (6-10 Hz), p=0.6250. Right: Average normalized power by frequency (1-12 Hz) for baseline active and saline active averaged across all animals, then quantified for the theta frequency band (6-10 Hz), p=0.6953. C) Left: Average normalized power by frequency (12-30 Hz) for baseline rest and DOI rest averaged across all animals, then quantified for the beta frequency band (12-25 Hz), p=0.8438. Right: Average normalized power by frequency (12-30 Hz) for baseline active and DOI active averaged across all animals, then quantified for the beta frequency band (12-25 Hz), p=0.2500. D) Left: Average normalized power by frequency (12-30 Hz) for baseline rest and saline rest averaged across all animals, then quantified for the beta frequency (12-25 Hz), p=0.4922. Right: Average normalized power by frequency (12-30 Hz) for baseline active and saline active averaged across all animals, then quantified for the beta frequency (12-25 Hz), p=0.0825.

DOI preferentially modulates high firing rate neurons

To investigate effects of DOI on the activity of individual neurons in mPFC, we recorded single units before and after injection of DOI or saline. In an effort to more carefully examine population activity, neurons were separated into high (>5 Hz) and low (<5 Hz) firing rate groups in accordance with their baseline activity (see methods for details). As a result, we initially compared average firing rates of these groups during rest and active periods but found that average firing rate did not vary as a function of behavioral state after DOI or saline (data not shown). We then sorted single units within each group (high/low) by the magnitude of the change in firing rate after DOI or saline and generated heat-maps of sorted neuron firing rates over the course of the experiment (Figure 5A). The mean firing rate of high firing rate neurons was significantly lower after DOI compared to the baseline period, while there was no significant change in mean firing rate seen with low firing rate neurons (Figure 5B). There was no significant change in mean firing rate seen with either low firing rate or high firing rate neurons in saline (Figure 5B).

Figure 5: DOI preferentially modulates high firing rate neurons.

Figure 5:

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A) Z-scored firing rate relative to baseline period (−15 min to injection) of all neurons, sorted by firing rate during post injection period, segregated by low firing rate (<5 Hz during baseline) and high firing rate (>5 Hz during baseline) from −15 min prior to injection to 60 min post injection. Active and rest periods were collapsed as they were not significantly different. B) Firing rate changes for high firing and low firing rate neurons between baseline and DOI and baseline and saline averaged over the time window of +25 min to +60 min post injection. Low firing rate neurons showed no difference between baseline and DOI, p=0.1129 or between baseline and saline, p=0.2077. High firing rate neurons showed a significant decrease in DOI from baseline, p=0.0044, there was no significant difference between baseline and saline, p=0.4978. C) Percentage of neurons which increase, decrease, or do not change in DOI compared to baseline, separated by firing rate (low vs. high) and by behavioral state (active vs. rest). The percentage of low firing rate increasers are significantly higher in active compared to rest, p=0.0047. The percentage of low firing rate decreasers is significantly higher in rest than active, p=0.0151. The percentage of low firing rate no-change neurons did not change between rest and active, p=0.6259. D) Left: Absolute magnitude changes of Z-scored firing rate, irrespective of direction of change, averaged across all low firing rate neurons between DOI and saline, p=0.004. Right: Absolute magnitude changes of Z-scored firing rate averaged, irrespective of direction of change, across all high firing rate neurons between DOI and saline, p<0.001.

Breaking the high/low firing rate groups down further, we noticed that a larger proportion of high firing rate neurons decreased after DOI as compared to low firing rate neurons (Figure 5C). Interestingly, the relative changes in firing rate for low firing neurons were dependent on behavioral state, showing a significantly higher proportion of cells increasing their firing rate during active periods, suggesting a complex relationship between circuit dynamics and behavioral state (Figure 5C). Upon further inspection of the heatmaps, it looked like DOI disproportionately affected the high firing rate group of neurons regardless of whether neurons in that group increased or decreased their firing rate from baseline. To illustrate this finding, we took the absolute value of all high/low firing rate neurons to examine the absolute magnitude of change in firing rate of high and low firing rate groups (Figure 5D). We found that the magnitude of the change caused by DOI on each neuron, irrespective of direction of change, was significantly larger for high firing rate neurons, even after rescaling by Z-scoring (Figure 5D). It is important to note that low firing rate neurons also significantly changed their overall magnitude of firing (Figure 5D, left). However, because the population of low firing rate neurons is heterogeneous as to direction of firing rate changes, the average firing rate does not change (Figure 5B). Overall, DOI has a predominantly inhibitory effect that is larger in terms of the proportion of neurons inhibited and the magnitude of inhibition in high firing rate neurons relative to low firing rate neurons. Furthermore, that effect of DOI on single neuron spiking is modulated by behavioral state, albeit in a complex way.

Discussion

In this study, we recorded mPFC single units and LFP before and after administering the psychedelic DOI during freely moving behavior, which was segregated into rest and active periods. We saw three key circuit-level changes in mPFC after DOI. First, the usual rest-related synchronization that typically occurs in rodents (Sabri and Arabzadeh, 2018), which is associated with an increase in delta power, was attenuated after DOI. Second, we observed an increase in high gamma power (50-90 Hz), suggestive of cortical desynchronization, irrespective of whether the mouse was active or at rest. Third, there was a significant decrease in the firing rate of fast-spiking neurons, which also occurs regardless of behavioral state. Overall, our findings suggest that DOI induces aberrant desynchronization that persists into rest periods, when brain activity is typically more synchronized.

Our results showing decreased delta power in mPFC during rest agree with other studies using the CP DOI in anesthetized (Celada et al., 2008; Puig et al., 2003) and freely moving rats (Hansen et al., 2019), and with human EEG studies using DMT (Riba et al., 2003) and psilocybin (Kometer et al., 2015). DOI also induced an increase in gamma power, another effect reported elsewhere in mice (Riga et al., 2018) and rats (Hansen et al., 2019), though in some studies an increase in gamma power has been reported at much higher frequencies (100-160 Hz) (Brys et al., 2023; Hansen et al., 2019). This is in contrast to Wood et al., who observed a decrease in gamma power (Wood et al., 2012). However, their recordings were done in the anterior cingulate cortex, which is known to be functionally and anatomically different from more ventral mPFC (Seamans et al., 1995). A more recent study in the anterior cingulate cortex, which performed experiments in head fixed mice, similarly found reduced cortical synchrony but a general increase in population firing (Golden and Chadderton, 2022). Furthermore, our data showing decreased fast-spiking activity after DOI is inconsistent with previously published results showing the opposite (Puig et al., 2010). The reason for this discrepancy may be due to that study being done in anesthetized rats. Furthermore, the chloral hydrate used to anesthetize animals during recording is known to directly affect the serotonergic system (Heym et al., 1984).

Different behavioral states are canonically characterized by oscillatory activity in distinct frequency bands (Sabri and Arabzadeh, 2018). In rodent and monkey cortex, rest and quiet wakefulness are normally associated with synchronized, low-frequency oscillations, which transitions to higher-frequency, desynchronized activity during more active engagement with the environment (Milton et al., 2020; Zagha et al., 2013). Fast-spiking inhibitory neurons are known to organize the higher-frequency gamma rhythms (Abbas et al., 2018; Buzsáki and Wang, 2012), and blocking the firing of these neurons desynchronizes cortical networks and increases gamma power (Guyon et al., 2021). Therefore, it is plausible that the aberrant desynchronized activity that we see during rest periods is partly due to DOI disrupting the dynamics of fast-spiking neurons, which prevents the normal transition to more synchronized brain states during rest/quiet wakefulness.

More active cortical states with decreased delta power and increased gamma power normally reflect a shift toward more local computations (Harris and Thiele, 2011; Solomon et al., 2017). These enhanced gamma power states are theorized to be optimal for inducing synapse-specific long-term plasticity (Singer, 1993), the precision of which relies on feed-forward inhibition involving parvalbumin positive, fast-spiking interneurons (van Versendaal and Levelt, 2016). Since DOI seems to be modulating fast-spiking neurons, the observed desynchronized rest state could lead to long-lasting changes in associative plasticity (Winnubst et al., 2015), which may underlie some of the experiential or therapeutic effects of CPs. This desynchronized state induced by DOI is consistent with the theory that psychedelics effectively enable neural networks to escape their strongest previously entrained patterns of activity, leading to a more labile and dynamic state (Carhart-Harris et al., 2023; Carhart-Harris and Friston, 2019).

As a precedent for this idea, several studies have shown long-lasting 5-HT2AR-dependent plasticity following CP administration. An in vitro study showed that when exposed to CPs, rat cortical neurons displayed an increase in dendritic arbor complexity, dendritic spine growth, and new functional synapse formation (Ly et al., 2018). In vivo, a single dose of psilocybin resulted in dendritic growth and synaptic activity in layer 5 apical dendrites of the mouse mPFC (Shao et al., 2021). Both of these findings persisted for at least 24 hours after drug removal and were dependent on 5-HT2ARs. Slice studies report a more rapid effect of 5-HT2AR activation in which DOI strengthens NMDA currents but weakens AMPA currents within minutes, while gating the induction of spike-timing dependent depression in mPFC at thalamocortical synapses (Barre et al., 2016; Berthoux et al., 2019). These long-lasting neuroplastic changes induced by CPs may be caused at least in part by the rapid circuit changes occurring after CP administration reported in this article.

It is apparent that the circuit level effects of DOI are complex, and our results highlight the need for future in vivo work on this subject in awake, freely behaving animals (Smausz et al., 2022). Studying the effect of DOI during behaviors that depend on circuits and cell subpopulations expressing 5-HT2A receptors will be particularly valuable. The effect of DOI on high broadband gamma is especially intriguing for future study, as the same phenomenon is seen in humans with ketamine (Hong et al., 2010) and other CPs like psilocybin (Tyls et al., 2016). This could be related to the known effectiveness of ketamine and several CPs for alleviating treatment-resistant depression, which supports the idea that elevated broadband gamma power in the mPFC may be an early translational neurophysiological correlate of antidepressant efficacy.

Furthermore, as stated previously, this increased gamma power may reflect a shift from global to more local computations (Harris and Thiele, 2011; Solomon et al., 2017). This shift may imply decreased integrative/associative processing, which is posited in the cortico-striato-thalamo-cortical (CSTC) model of psychedelic action to underlie the subjective effects of the psychedelic state via increased bottom-up and decreased top-down processing (Vollenweider and Preller, 2020). This overall shift from integrative processing to a more desynchronized state is also supported by the cortico-claustro-cortical (CCC) model of the psychedelic state, where disruption and disintegration of brain wide networks is primarily driven by a psychedelic-induced decrease in claustrum activity (Barrett et al., 2020; Doss et al., 2021). The CCC model is especially intriguing as the claustrum has been shown to disproportionally synapse onto inhibitory neurons in the mPFC (Jackson et al., 2018) and has recently shown strong evidence for the ability to synchronize large brain networks in low frequency bands (Narikiyo et al., 2020; Norimoto et al., 2020). Follow up work will require more detailed experiments to definitively determine which neuronal subpopulations are most affected by DOI and explore whether these effects are being driven predominantly locally or due to effects on one or more long range inputs.

While our results provide foundational information to expand our understanding of how CPs like DOI affect higher order regions like the mPFC, it is important to address some limitations. First, while there was great care to separate behavioral states into rest and active, segregation is imperfect and likely does not include deeper rest periods. Freely behaving environments with no behavioral task, or time locked events, increase the uncertainty in separating clear behavioral states, and we did not consider behaviors like sniffing, grooming and rearing. Ideally, a more granular analysis of behavior would be used and would be a great next step as automated behavioral segregation is improving rapidly (Hu et al., 2023). Second, due to the weight of our implants, only male mice were used. With the advent of smaller and more lightweight implants, further studies will need to include females to see how CPs like DOI affect circuits in vivo. This is especially important for clinical translation as males and females present stark differences in depression and other psychiatric disorders that CPs, like DOI, may help treat (Bangasser and Cuarenta, 2021; Eid et al., 2019; Moderie et al., 2022; Mohammadi et al., 2023).

Conclusion

Overall, our results suggest that classic psychedelics like DOI induce rapid circuit level changes in the mPFC. These circuit changes fundamentally alter the typical dynamics associated with different behavioral states, favoring persistent cortical desynchronization. This shift toward desynchronization may be a correlate of the experiential effects of psychedelics and of a more labile neural state which drives the subsequent neuroplastic changes observed after psychedelic administration.

Highlights.

  • DOI disrupts the typical increase in 1-4 Hz delta power that occurs when mice are at rest

  • DOI increases 50-90 Hz gamma power irrespective of behavioral state

  • DOI decreases the firing rate of fast-spiking neurons irrespective of behavioral state

  • DOI increases the proportion of low-spiking neurons which increase their firing rate when mice are active

Acknowledgments

Research was supported by the OHSU Physician Scientist Grant (AIA, LB), 32DA007262 (RJO), T32MH018870-29 (AIA), 2T32AA007468-36 (AS, RM), and R01MH124998-01 (AZH), D-22-OD-0001 (AIA), ADA12013 (AIA), CDER-20-I-0546 (AIA), and PVARF (AIA). The opinions expressed are the authors’ own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, US Department of Veterans Affairs, US Department of Justice, Drug Enforcement Administration, Food and Drug Administration, or the United States Government.

Footnotes

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CRediT authorship contribution statement

Randall J. Olson: Formal Analysis, Visualization, Writing – original draft, review and editing. Lowell Bartlett: Formal analysis, Visualization. Alex Sonneborn: Writing – original draft, review and editing. Russell Milton: Investigation, Writing, Review and Editing. Zachary Bretton-Granatoor: Investigation. Ayesha Firdous: Investigation. Alexander Z. Harris: Conceptualization, Writing – review and editing. Atheir I. Abbas: Conceptualization, Formal analysis, Investigation, Visualization, Writing – original draft, review and editing.

Declaration of competing interests

The authors have no competing interests to declare.

Data availability

Data analyzed in this study is available upon request to the corresponding author.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data analyzed in this study is available upon request to the corresponding author.

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