Aversive 22-kHz ultrasonic vocalization playback reveals differences in affective (dys)function following early life adversity in male and female juvenile rats.

Aversive 22-kHz ultrasonic vocalization playback reveals differences in affective (dys)function following early life adversity in male and female juvenile rats - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Behav Brain Res . Author manuscript; available in PMC: 2026 May 31. Published in final edited form as: Behav Brain Res. 2026 May 14;511:116272. doi: 10.1016/j.bbr.2026.116272 Aversive 22-kHz ultrasonic vocalization playback reveals differences in affective (dys)function following early life adversity in male and female juvenile rats Sydney M Bonauto 1 , Piper L Wilson 1 , Jennifer A Honeycutt 1, * Author information Article notes Copyright and License information 1 Research in Affective and Translational Neuroscience Laboratory, Department of Psychology and Program in Neuroscience, Bowdoin College, Brunswick, ME, 04011 USA * Corresponding Author: Jennifer A. Honeycutt, PhD, 6900 College Station, Brunswick, ME 04011, USA, jhoneycu@bowdoin.edu Issue date 2026 Aug 5. PMC Copyright notice PMCID: PMC13222100  NIHMSID: NIHMS2178842  PMID: 42140498 The publisher's version of this article is available at Behav Brain Res Abstract Early life adversity (ELA), such as childhood neglect, is a significant risk factor for the development of anxiety and other disorders characterized by affective dysfunction, with worse outcomes in women/girls. Rodent models of ELA, such as maternal separation, show comparable behavioral and neural correlates to patient populations. Despite this, many behavioral assays aimed at understanding affective processing in rodents lack species-specific relevance and are therefore limited in translational application. To address this, we utilized an ethologically relevant rat-specific cue of potential threat - aversive 22-kHz ultrasonic vocalization (USV) playback - within the open field test (OFT) to probe changes in affective processing in juvenile male and female rats following ELA. In control reared rats, measures of locomotion and center exploration revealed significant anxiogenic effects of 22-kHz USV playback compared to silence. These effects were exacerbated by ELA, where ELA reared females demonstrated even greater indices of USV-evoked anxiety-like behavior than standard-reared controls. As prior research has indicated a role for parvalbumin (PV) containing inhibitory neurons in regulating affective behaviors, we quantified changes in PV cell recruitment (via c-Fos co-expression) in two regions important for processing threat: prefrontal cortex (PFC) and basolateral amygdala (BLA). Results showed region-specific effects of ELA and 22-kHz USV playback on the recruitment of PV neurons within the BLA and infralimbic PFC, which differed between males and females. Together, these findings suggest that 22-kHz USVs as a probe is a sensitive measure of ELA-induced affective dysfunction, and that these behavioral changes may be associated with PV activity within the infralimbic PFC and BLA. Keywords: Ultrasonic vocalization, 22-kHz, early life adversity, basolateral amygdala, prefrontal cortex, sex differences, anxiety 1. Introduction Experiencing adversity in early life has numerous detrimental consequences to health and wellbeing [ 1 – 3 ]. Cumulative stress from early adversity can lead to dysregulation of stress systems to maladaptive extent, especially once the stressor is removed [ 4 ]. Thus, individuals that experience early life adversity (ELA) have an increased risk and incidence of affective disorders and emotional dysregulation [ 2 , 5 , 6 ]. Adversity also results in dysregulation at the neurological level. Children with histories of ELA have amygdala hyperreactivity (greater blood oxygen level-dependent response) to negative stimuli [ 7 , 8 ], and altered engagement of complementary regulatory regions such as the prefrontal cortex (PFC) [ 9 ]. Despite the clear neurobiological effects of ELA, much remains to be uncovered about differences between male and female outcomes as well as cell-type specific dysfunction. Investigation into these effects may give insight into disorder etiology and individual outcomes. Rodent models of ELA, such as maternal separation (e.g., [ 10 – 14 ]) or limited bedding and nesting (e.g., [ 15 , 16 ]) parallel aspects of the human experience such as psychosocial deprivation and resource scarcity, respectively. The ELA maternal separation paradigm increases anxiety-like behaviors in male and female rats in a variety of assays [ 17 , 18 ], and shows functional, cellular, molecular and epigenomic changes (e.g., [ 13 , 19 – 22 ]) comparable to those observed in humans with a history of adversity [ 22 – 27 ]. However, most studies examining the effects of ELA in preclinical rodent models have only included male subjects until recently (for reviews see [ 28 , 29 ]). ELA exposure influences a wide-range of neural outcomes including the alteration of PFC excitatory/inhibitory balance through aberrant functionality and/or presence of fast-spiking inhibitory parvalbumin (PV)-containing interneurons [ 30 , 31 ]. Additionally, previous work highlighted PFC inputs from the basolateral amygdala (BLA) as a mediator of ELA-induced sex-specific dysfunction in rats [ 13 ], expanding upon functional connectivity studies in humans observing precocial maturation of this circuit following adversity [ 23 ]. Further examination of these regions and the PV cell type may enhance understanding of the sex-specific and adversity-influenced mechanisms of anxiety disorders. Historically, negative affect and anxiety have been difficult to assess in preclinical models as many lack high translational validity [ 32 ]. In the present study, we used aversive 22-kHz rat ultrasonic vocalization (USV) playback to probe for affective (dys)regulation following ELA exposure. Rat USVs are emitted with the purpose of conspecific communication within underground burrow systems [ 33 ]. Adult rats primarily vocalize at frequencies around 22-kHz (18–32 kHz in negatively valenced contexts such as restraint stress [ 34 ], foot shock [ 35 – 39 ], and/or exposure to predator odor [ 33 , 40 , 41 ]) or 50-kHz (38–80 kHz in positively valenced contexts such as play [ 34 , 42 , 43 ] and food anticipation [ 44 ]). Importantly, this frequency-specific social communication is innate [ 45 ]. While previous work suggested that prior experience associating self-emitted USVs with negative events may be necessary for effects of 22-kHz USV playback, recent evidence shows that devocalized and naive rats also react to 22-kHz USV playback with behavioral inhibition [ 45 , 46 ]. Previous work has shown that playback of recorded calls elicits alterations in behavioral and neural responses in typically developing adult rats [ 34 , 47 ], with subtle sex differences in responses depending on USV valence [ 48 ]. In rodents, aversive 22-kHz USV playback recruits brain regions involved in affective processing including the PFC, BLA, bed nucleus of the stria central amygdala, periaqueductal gray [ 34 , 38 , 47 – 49 ]. Importantly, these regions’ overlap in evoking activity in brain regions recruited in the fearful face task in humans [ 50 ]. Thus, 22-kHz USV playback in rats may be a valuable translational tool to evoke anxiety-like behaviors, particularly in pathological models of adversity. In the present study, we leveraged 22-kHz USV playback to probe affective processing in response to an innate, ethological cue in juvenile male and female Sprague-Dawley rats with and without a history of ELA. We assessed open field behavior including locomotion and exploration, as well as measures of neural activity via immunofluorescence. Following 22-kHz USV playback, we quantified basolateral amygdala (BLA), and prelimbic (PL) and infralimbic (IL) PFC PV neuron recruitment to elucidate rearing-dependent changes in these regions implicated in dysregulated behavioral output. We hypothesized that juvenile ELA males and females would display greater anxiety-like behavior in response to USV playback compared to controls. Further, we predicted that ELA females would exhibit more recruitment of the BLA and PFC during 22-kHz USV playback than controls, with males showing no or modest effects. This was predicted due to previously observed precocial maturation of the BLA-PFC circuit in juvenility in female rats following ELA, but not in males [ 13 ]. We also expected that 22-kHz USV playback-evoked PV-specific activity would be less in ELA females in both the BLA and PFC, potentially indicating less inhibitory influence on neural responses to threat cues. Our data show that 22-kHz USV playback as a probe within the OFT reveals robust behavioral and neural effects in ELA rats, with females showing more marked outcomes, supporting our hypotheses. Taken together, this work highlights the utility of aversive 22-kHz USV playback to detect deficits in affective processing and to interrogate potential mechanisms in a rat model of adversity-induced risk. 2. Methods 2.1. Animals and Early Life Adversity Paradigm Pregnant Sprague-Dawley rat dams (Charles River Laboratories, Wilmington, MA) arrived at Bowdoin College on gestational day (GD) 15 and were housed individually (cages measuring 26.7 cm W × 48.3 cm L × 20.3 cm H) in a temperature- and humidity-controlled vivarium (approx. 23 ± 2°C; 30–70% relative humidity) with a 12 h light on/off cycle (lights on at 0700 h). Parturition was designated as postnatal day 0 (P0), and litters were culled to 12 pups maximum per litter on P1. Culled pups were selected based on sex to achieve equal ratios of males and females wherever possible. Control (CON) or early life adversity (ELA) conditions were assigned to litters and counterbalanced based on litter size and sex ratio (CON litters n = 10, ELA litters n = 12). Litter size for CON ranged 6–12 with mean number of pups 10.3, median number of pups 11.5, mean percent of males 44.8%. Litter size for ELA ranged 9–12 with mean number of pups 10.9, median number of pups 11.5, and mean percent of males 45.8%. Pups in ELA and CON litters received toe clips on P6 for longitudinal identification. Rearing timeline information can be seen in Figure 1A and has been previously described (e.g., [ 10 , 11 , 13 , 51 , 52 ]). From P2 to P10, ELA pups were removed from the vivarium for 3.5 h each day (approx. 0930–1300 h) and placed into individual heated (37°C) plastic cups with bedding containing the dam’s scent. From P11 to P20, ELA pups were separated in individual small cages (19 cm W × 29.2 cm L × 12.7 cm H) containing bedding with the dam’s scent for 4 h each day (approx. 0900–1300 h). Of note, all ELA litters were separated in a different room during the 4 h period so as not to introduce potential stress to the colony with retrieval calls to the dam. Control litters were undisturbed through rearing except for husbandry, toe clips, weighing, and handling habituation twice on days preceding weaning. Upon weaning (P21), offspring were pair-housed in same-sex, same-condition pairs whenever possible (cages measuring 26.7 cm W × 48.3 cm L × 20.3 cm H). Rats had ad libitum access to food (Lab Diet 5P00 Prolab RMH 3000) and water. Behavioral testing was conducted on P25–26, with tissue collection occurring 90 min after testing. Figure 1. Timeline and Methodology. Open in a new tab Experimental timeline can be seen in ( A ). Litters assigned to the early life adversity (ELA) condition were subjected to a maternal separation protocol daily from postnatal day 2 (P2) until weaning at P21. Control (CON) litters were undisturbed except for identifying toe clips at P6, handling/habituation, and all pups were weighed at P9 and P21. Behavioral testing occurred during juvenility (P25-P26) with all rats receiving an initial open field test (OFT) baseline assessment for 5 min, followed by either and additional 15 min of silence or 15 min of 22-kHz ultrasonic vocalization (USV) playback, during which the first 5 min (baseline) and second 5 min (test) were analyzed for behavioral assessment ( B ). Brain tissue was collected 90 min after the onset of testing to assess for markers of activity (c-Fos) during testing within the basolateral amygdala (BLA), prelimbic (PL) and infralimbic (IL) PFC. An example spectrogram of a small portion of the 22-kHz USV playback probe can be seen in ( C ). Of note, the playback probe consisted of a full 5 min continuous ethological recording. Control and ELA offspring were divided into a 2×2×2 experimental design with rearing (ELA, CON), sex (male, female), and USV playback (Silence, 22-kHz USV) as experimental variables ( n = 8–10/experimental group). To avoid litter effects, no more than two animals from each litter were assigned to any experimental group. Additional rats from litters were used in other ongoing studies. All experiments were performed with approval of Bowdoin College’s Institutional Animal Care and Use Committee in accordance with the Guide for the Care and Use of Laboratory Animals (NIH). 2.2. Ultrasonic Vocalization (USV) Recording and 22-kHz USV Playback Aversive 22-kHz ultrasonic vocalizations (USVs) were previously obtained from an adult male Sprague-Dawley rat (see [ 34 , 47 , 48 ]). Briefly, these USVs were recorded with an ultrasonic microphone (Avisoft-Bioacoustics CM16/CMPA) and processed with Avisoft RECORDER USGH Bioacoustics software (Glienick, Germany). To elicit 22-kHz USVs, a DecapiCone (Braintree Scientific) was used to restrain the animal during exposure to cat urine in the presence of an anesthetized conspecific [ 34 , 53 ]. The five-minute recording was high pass filtered at 15 kHz and low pass filtered at 40 kHz to reduce background noise. The recording contained 119 individual 22-kHz calls made up of 118 long 22-kHz USVs (defined as longer than 300 ms [ 93 ] and ranging from approx. 320 – 3700 ms in length) and one short USV (270 ms). A representative spectrogram of calls can be seen in Figure 1C . The recording was played three times on loop to produce a total of 15 min of USV playback to generate a sufficient c-Fos expression. All recordings were played via an ultrasonic speaker (Avisoft Bioacoustics, Ultrasonic Dynamic Speaker Vifa, part #60108) from 130 cm above the open field at a volume such that the sound levels in the floor of the open field reached 65 dB. This was verified prior to each testing day with an ultrasonic microphone (Condenser Microphone CM16/CMPA, Avisoft Bioacoustics UltraSoundGate Model 116Hb). USV Playback was confirmed during testing with an external monitor (Anabat Monitor, Product AAAB007, Titley Scientific). 2.3. Open Field Test (OFT) and 22-kHz USV Probe Baseline behavior in the open field test (OFT) and testing with USV playback in the OFT was conducted on P25–26 in a sound attenuated room. For testing, each animal was placed in the corner of a 60 × 60 cm × 40 cm arena (Maze Engineers, Skokie, IL). Baseline OFT behavior was recorded for 5 min. After baseline testing, rats were probed with either 22-kHz aversive USV playback or silence for 15 min, which was recorded for behavioral analysis by an overhead camera (Logitech C922 Pro Stream Webcam, 30 fps). Graphical depiction of baseline and USV playback testing timeline can be seen in Figure 1B . Behavior in the OFT was analyzed using Ethovision XT (Noldus Information Technology) software during baseline (0–5 min) and the first 5 minutes after baseline (5–10 min; testing session). A virtual center zone of 24 cm × 24 cm was created to quantify distance traveled (cm), time spent not moving (s), and center duration (s). The 15 min total duration of 22-kHz USV playback (or maintained silence) was necessary to maximize c-Fos expression for later analyses (e.g., [ 34 ]). The OFT was cleaned with 50% ethanol between trials. All testing occurred in the light cycle between 0745 h and 1600 h. 2.4. Tissue Collection and Immunohistochemistry 2.4.1. Perfusion and Tissue Collection Animals were sacrificed 90 min after the start of the OFT to allow for expression of the immediate early gene c-Fos in response to USV playback [ 54 ]. Rats were deeply anesthetized via CO 2 immersion followed by transcardial perfusion of ice cold physiological (0.9%) saline and 4% paraformaldehyde in 0.02 M phosphate buffered saline (PBS) at pH = 7.4. After fixation, rats were decapitated, and brains were collected. Brains were stored in 4% paraformaldehyde at 4°C for 1 week to postfix before being transferred to 30% sucrose in PBS (0.02 M; pH = 7.4)) at 4°C for an additional week for cryoprotection. Cryoprotected brains were serially sectioned on a freezing microtome (Leica Biosystems, Wetzlar, Germany) into 40 μm coronal sections, reserving those containing PFC and BLA, and stored in anti-freeze solution (10% 0.2 M phosphate buffer, 30% dH 2 O, 30% ethylene glycol, 30% glycerol) at −20°C until processing. 2.4.2. Immunohistochemistry Following storage, tissue was washed 3 × 5 min in 0.02 M PBS (pH 7.4). Tissue was placed in blocking solution (5% normal goat serum (Jackson Immuno, 005-000-121) in 0.02 M PBS with 0.1% Triton-x100 (PBST) for 2 h on a shaker at room temperature (~22°C). Following blocking, rabbit anti-c-Fos (1:1000, abcam190289) and guinea pig anti-parvalbumin (1:1333, Synaptic Systems, 195 004) primary antibodies were added to blocking solution and sections were transferred to primary antibody solution for 19 h at 4°C on an orbital shaker (100 rpm). After primary incubation, tissue was washed 3 × 5 min in PBS, then transferred to secondary antibody solution (2.5% normal goat serum in 0.1% PBST; 1:1000 goat anti-rabbit conjugated to Cy5 ab6564, 1:500 goat anti-guinea pig conjugated to Cy3 A21435) for 2 h at room temperature on the shaker (100 rpm). Following secondary incubation, sections were washed 3 × 10 min in PBS, mounted on glass slides, and coverslipped with ProLong Glass antifade mounting medium (Invitrogen P36930 ). 2.4.3. Imaging and Quantification Regions of interest were imaged at 20x magnification on a BZ-X810 fluorescence microscope (Keyence, Osaka, Japan). These regions included the prelimbic prefrontal cortex (PL PFC), infralimbic prefrontal cortex (IL PFC), and the basolateral amygdala (BLA). Two to four images per region were collected for each animal based on tissue quality and were at least 120 μm apart within the target region. The Quick Full Focus function in the microscope image acquisition software (BZ-X810 Viewer, Keyence, Osaka, Japan) was used to focus all cells within the section into a single focal plane and images were collected at 1920 × 1680 pixels (high resolution). Images of stained cells were quantified using ImageJ [ 55 ] and were in line with previously defined methods [ 34 ]. To quantify cells without bias, cell size and brightness parameters were standardized across all sections during imaging and processing. In c-Fos positive cell quantification, the cell size was set to 250–5000 pixels, and circularity was set to 0.0–1.0. For PV neuron quantification, cell size was 500–5000 pixels and circularity was 0.05–1.0. Following color filtering to black and white with the Color Threshold function, the Analyze Particles function was used to count all cells that met these criteria. These parameters were chosen to avoid counting background staining, and to account for relative sizes of c-Fos staining in the nucleus versus PV staining in the soma. 2.5. Statistical analyses 2.5.1. General Statistical Approach Data for behavioral and neural measures were imported to Prism 9 (GraphPad) data processing software. All data was subjected to groupwise Grubbs Outlier Testing (GraphPad Software). Analyses were performed using ANOVA for dependent variables where both normality (verified via Shapiro-Wilk testing on residuals) and homogeneity of variance assumptions (verified via Brown-Forsythe testing) were met. Significant main effects or interactions ( p < 0.05) in datasets that met normality and variance assumptions were followed up with Šídák testing for multiple comparisons. Significant ( p < 0.05) and trending ( p < 0.07) multiple comparisons are reported. When ANOVA assumptions were violated, data appeared positively skewed and were analyzed using a generalized linear model (GLM) with a Tweedie distribution (power parameter = 1.5) and log link function in IBM SPSS Statistics (Version 31). Model fit was evaluated using log-likelihood, dispersion statistics (Pearson χ 2 /df), and examination of residuals. A common power parameter was selected based on fit tests and significant effects remained unchanged regardless of power parameter, indicating robust model choice for data analysis. Fixed effects included sex, rearing, playback, and their interaction used across behavioral and neural measures as described below. Model effects were evaluated using Type III Wald χ 2 tests ( p = 0.05). Pairwise multiple comparisons were conducted using estimated marginal means derived from the GLM. Šídák correction was applied across the full set of pairwise comparisons within each model. Comparisons were performed on the original scale of the dependent variable. Summary data are presented as mean ± SEM. 2.5.2. OFT Baseline and 22-kHz Testing Outliers were excluded from analyses group-wise; as such, statistical analyses were run with sample sizes no less than n = 7–10 per group. Baseline behavioral data to determine the effect of ELA on OFT behavioral measures was evaluated using a 2-way ANOVA (sex x rearing), or using a GLM with a Tweedie distribution when ANOVA assumptions were violated. As there were significant main effects of sex across all measures at baseline, 22-kHz probe testing outcomes were split by sex. To determine the effect of 22-kHz USV playback on OFT behavioral outcomes, the 5-min 22-kHz test session data was split by sex such that two (male, female) 2-way ANOVAs (rearing x USV playback), or GLM with Tweedie, were conducted for each behavioral outcome (distance traveled, center duration, time not moving). 2.5.3. Neural Outcomes Three neural outcomes were assessed for each region of interest (BLA, PL, IL): PV cell counts, c-Fos cell counts, and percent of PV neurons colocalized with c-Fos. Outliers were excluded from analyses group-wise such that final sample sizes no less than n = 6–10 per group (BLA samples) and no less than n = 8–10 per group (PFC samples). For PV, cell counts were averaged across images collected for each region for each animal and analyzed using 2-way ANOVAs (sex x rearing). c-Fos cell counts were averaged across the images collected per region for each animal. Percent of PV cells colocalized with c-Fos was calculated ((colocalized count/PV cell count)*100) and averaged across images for each region within each animal. As prior work suggests sex differences in c-Fos outcomes following USV playback (e.g., [ 48 ]), two separate (male, female) 2-way ANOVAs (rearing x USV playback) were conducted for c-Fos cell counts and percent PV colocalized with c-Fos measures. 3. Results 3.1. Baseline Open Field Testing Behavior was quantified within the first 5 minutes of the OFT to quantify sex and rearing differences in anxiety-like behavior. There were significant main effects of sex across all three OFT measures: distance traveled ( F (1,65) = 12.89, p < 0.001; Figure 2A ), time spent not moving (Wald χ 2 (1) = 8.99, p = 0.003; Figure 2B ), and center duration (Wald χ 2 (1) = 11.78, p < 0.001; Figure 2C ). Follow-up multiple comparisons for distance traveled revealed the main effect of sex was largely driven by CON rats where females traveled significantly further than males ( p = 0.012; Figure 2A ). Multiple comparisons following up on the main effect of sex for time spent not moving was driven by CON males spending more time not moving than CON females ( p = 0.032; Figure 3B ). The main effect of sex in center duration was largely driven by ELA rats, as females spent significantly more time in the center than males ( p = 0.035; Figure 2C ). Figure 2. Sex, but not adversity, drives behavioral outcomes in the traditional open field test (OFT). Open in a new tab All behavioral measures observed during baseline OFT showed significant main effects of sex, with no effects of rearing condition (control (CON) or early life adversity (ELA)). CON females exhibited significantly longer distance traveled than CON males ( A ). In line with these findings, CON males spent more time not moving in the apparatus than CON females ( B ). For center duration, ELA females spent more time in the center than ELA males ( C ). Together, these data show that female rats generally have higher levels of locomotor and center oriented behaviors than males in the OFT, which does not appear to be significantly associated with rearing. Males are represented as black bars with open circles, and females are represented as white bars with solid circles. All data are presented as mean ± SEM. * p < 0.05; ** p < 0.01 Figure 3. 22-kHz USV playback reveals deficits induced by adversity and is a sensitive assay for affective dysfunction. Open in a new tab While both males and females showed an overall change in behavior in response to an aversive 22-kHz ultrasonic vocalization (USV) playback probe in the OFT, only females showed both a main effect of playback as well as main effects/interactions with rearing. Male ELA rats traveled significantly less ( A ) and spent less time moving ( B ) during 22-kHz USV playback compared to silence. Female ELA rats also traveled a smaller distance ( A ), spent less time moving ( B ) and spent less time in the center ( C ) during 22-kHz USV playback compared to rats in the silence condition. Additionally, female ELA rats were the only group to show robust differences in behavior from their CON counterparts as a result of 22-kHz USV playback in measures of distance traveled ( A ) and time spent moving in the apparatus ( B ). Together, these findings suggest that female ELA rats are sensitive to aversive 22-kHz USV alarm call playback across measures, though males are also notably impacted. Control (CON) is represented as dark grey bars and solid circles, while early life adversity (ELA) is represented as light grey bars and open circles. All data are presented as mean ± SEM. * p < 0.05; ** p < 0.01 3.2. Open Field Testing with 22-kHz USV Playback To test the effects of a 22-kHz USV affective probe, playback initiated after the 5-min OFT baseline in rats assigned to the 22-kHz USV playback condition and behavior was quantified. Omnibus analysis of sex x playback x rearing revealed no main effects of sex; thus males and females were analyzed separately to distinguish effects of rearing and/or playback. Distance Traveled: A 2-way ANOVA (rearing x USV playback) in males revealed a main effect of USV playback ( F (1,33) = 14.13, p < 0.001). Multiple comparisons showed that ELA males traveled significantly less distance during 22-kHz USV playback compared to silence ( p = 0.001). 2-way ANOVA (rearing x USV playback) in females showed a significant main effect of 22-kHz USV playback ( F (1,28) = 7.85, p = 0.009) and a significant main effect of rearing ( F (1,28) = 6.18, p = 0.019). Multiple comparisons revealed that during 22-kHz USV playback, ELA females traveled significantly less distance than CON ( p = 0.032). Further, ELA females traveled significantly less distance during 22-kHz USV playback compared to silence ( p = 0.019). Summary data can be seen in Figure 3A . Time Not Moving: Males showed a main effect of USV playback (Wald χ 2 (1) = 12.84, p < 0.001), with ELA males spending significantly more time not moving during 22-kHz USV playback compared to silence ( p = 0.017). In females, 2-way ANOVA revealed a main effect of USV playback ( F (1,28) = 7.68, p = 0.002) and a main effect of rearing ( F (1,28) = 7.68, p < 0.01). During 22-kHz USV playback, ELA females spent significantly more time not moving compared to CON ( p = 0.016). Further, ELA females had significantly greater time not moving during 22-kHz USV playback compared to ELA females in the silent condition ( p = 0.005). Summary data can be seen in Figure 3B . Center Duration: In males, analyses revealed a significant main effect of USV playback (Wald χ 2 (1) = 8.65, p = 0.003). Additionally, there was a main effect of rearing in males (Wald χ 2 (1) = 4.17, p = 0.041); however, multiple comparisons testing showed no significant differences between groups. In females, main effects of rearing (Wald χ 2 (1) = 7.04, p = 0.008) and playback (Wald χ 2 (1) = 10.86, p < 0.001) were found. There was a significant playback x rearing interaction (Wald χ 2 (1) = 12.77, p < 0.001), such that ELA females spent significantly less time in the center during 22-kHz USV playback compared to silence ( p = 0.047). Summary data can be seen in Figure 3C . 3.3. Neural Outcomes Two-way ANOVAs (sex x rearing) were conducted on PV cell counts to determine the effects of rearing and sex on PV outcomes within each region of interest (ROI). To determine the effects of playback condition, sex, and rearing condition on neuronal activity during behavioral testing, initial 3-way ANOVAs were conducted for each ROI (BLA, PL and IL PFC) and followed up with 2-way ANOVAs split by sex. Basolateral Amygdala (BLA): Within the BLA ( Figure 4A , [ 56 ]), there was a significant main effect of sex on PV cell counts ( F (1,59) = 8.66, p = 0.005). Multiple comparison analyses revealed that in CON groups, males had significantly more PV cells than females ( p = 0.027; Figure 4B ). c-Fos analyses revealed no significant main effects or interactions ( Figure 4C ). In contrast, when assessing the percentage of PV cells that co-expressed c-Fos, there was a main effect of USV playback ( F (1,55) = 14.48, p < 0.001). Follow-up 2-way ANOVAs split by sex showed a significant main effect of USV playback in males (F(1,28) = 7.77, p = 0.009). In females, there was also a main effect of USV playback ( F (1,27) = 7.72, p <0.01), such that ELA females had a significantly greater percentage of active PV cells during 22-kHz USV playback compared to silence ( p = 0.040; Figure 4D ). Figure 4. Percentage of active (c-Fos+) parvalbumin (PV) cells in the BLA is greater during 22-kHz USV playback than silence in female ELA rats. Open in a new tab Immunohistochemistry images were obtained from the basolateral amygdala (BLA) in order to quantify PV and c-Fos cell counts, as well as to determine the percentage of PV cells colocalized with c-Fos, indicating that these cells were recently active ( A ). There were no overall effects of rearing on PV cell count, though a significant effect of sex was revealed such that CON males had more PV cells in the BLA than CON females ( B ). There were no significant differences observed in overall c-Fos counts within the BLA ( C ). However, when looking at the percentage of PV cells that colocalized with c-Fos (% active PV), there was a significant main effect of 22-kHz ultrasonic vocalization (USV) playback in both males and females ( D ), with ELA females showing greater in PV recruitment during USV playback compared to silence (SIL). Males are represented by black bars and females are represented by white bars in ( B ). Control (CON) are represented by dark grey bars and early life adversity (ELA) are represented by light grey bars in ( C ) and ( D ). BLA Atlas adapted from Swanson (2018). All data are presented as mean ± SEM. * p < 0.05; ns (not significant) Prelimbic Prefrontal Cortex (PL PFC): Analyses of PV cell counts within the PL PFC ( Figure 5A , [ 56 ]) revealed no significant main effects or interactions ( Figure 5B ). c-Fos analyses showed no significant main effects or interactions in c-Fos cell counts ( Figure 5C ). There were also no significant main effects or interactions in the percentage of active PV cells (those colocalized with c-Fos) in the PL PFC ( Figure 5D ). Figure 5. The infralimbic PFC shows less parvalbumin (PV) activity during a 22-kHz USV playback probe in female CON rats, with already lower baseline PV recruitment in ELA females. Open in a new tab Immunohistochemistry images were obtained from the prelimbic (PL) ( A ) and infralimbic (IL) ( E ) PFC in order to quantify PV and c-Fos cell counts, as well as to determine the percentage of PV cells colocalized with c-Fos, indicating that these cells were active during behavioral testing. Within the PL PFC, there were no significant differences in PV cell counts ( B ), c-Fos counts ( C ), or in the percentage of colocalized PV/c-Fos cells ( D ) between any groups. Similarly, there were no significant effects observed in PV cell counts within the IL PFC ( F ). However, in the IL PFC females generally showed higher levels of cell activity, represented by more c-Fos labelled cells compared to males ( G ). In CON females, there was a significantly lower percentage of PV cells colocalized with c-Fos during 22-kHz ultrasonic vocalization (USV) playback, suggesting less local inhibition during potential threat ( H ). With no USV playback (silence condition), ELA females also showed less PV activity compared to CON females, suggesting an already disinhibited local tone that did not further change with USV playback. Males are represented by black bars and females are represented by white bars in ( B,F ). Control (CON) are represented by dark grey bars and early life adversity (ELA) are represented by light grey bars in ( C,G ) and ( D,H ). PFC atlas adapted from Swanson (2018). All data are presented as mean ± SEM. # p < 0.07; * p < 0.05; ns (not significant). Infralimbic Prefrontal Cortex (IL PFC): Analyses of PV cell counts within the IL PFC ( Figure 5E ) revealed no significant main effects or interactions ( Figure 5F ). There was a significant main effect of sex in c-Fos cell counts (F(1,61) = 5.36, p = 0.024). Follow-up 2-way ANOVAs split by sex revealed no significant main effects or interactions in males or females ( Figure 5G ). Analysis of percentage of active PV cells (colocalized with c-Fos) in the IL PFC revealed a significant USV playback x rearing interaction ( F (1,58) = 6.35, p = 0.015). Follow-up 2-way ANOVAs split by sex revealed no significant main effects or interactions in males; however, in females, there was a significant USV playback x rearing interaction ( F (1,29) = 5.53, p = 0.026). CON females had significantly less active PV cells during 22-kHz USV playback than during silence ( p = 0.023; Figure 5H ). Further, there was a trending difference in percentage of active PV cells during silence, such that ELA females appear to show less PV recruitment than CON reared females ( p = 0.065). 4. Discussion This study sought to investigate the utility of species-specific aversive social communication (22-kHz USVs) in probing underlying, subtle affective dysfunction following ELA experience in male and female juvenile rats. We show that playback of rat USVs at 22-kHz can elicit behavioral and neural recruitment differences in rats exposed to ELA, with patterns that are mostly present in females. Notably, the effects of ELA were not seen during baseline OFT testing and were only observed when probed with the 22-kHz aversive USV playback, suggesting that this approach may be a more sensitive assay of affective processing in rats. The present data shows that ELA history increased vulnerability in juvenile females to the behavioral effects of 22-kHz USV playback. Both males and females exhibited blunted locomotor behavior in response to 22-kHz USV playback; however, this lack of exploration was enhanced when females with an ELA history were exposed to 22-kHz USV playback. Additionally, ELA females exposed to 22-kHz USV playback demonstrated robust anxiety-like behavior with much less time in the center of the OFT compared to ELA females in silence. Previous work exploring the utility of 22-kHz USV playback has showed it is capable of transmitting information about potential threat and/or inducing negative affective states as measured through changes in anxiety-like behaviors [ 38 , 39 , 43 , 48 , 57 ] and inhibition of locomotion [ 58 – 60 ]. While prior work illustrated that the outcomes of 22-kHz and 50-kHz USV playback in typically developing adult male and female rats is subtle [ 48 ], this work in an ELA model highlights 22-kHz USV playback as a species-specific ethological probe for affective dysregulation following ELA. Here, we highlight how baseline testing in the traditional OFT was not sufficient to reveal effects of rearing on behavior, but the addition of 22-kHz USV playback produced an observable difference between rearing conditions. Studying behavior through the lens of negative valence systems is challenging, as behavioral assays often lack translatability to the human condition [ 32 , 61 , 62 ], especially for sex differences [ 63 ]. Indeed, many behavioral assays, including the OFT, were validated in and/or considered in the context of male animals [ 64 , 65 ]. This is consistent with the lack of information when it comes to evaluating potential sex differences in anxiety-like behavior in models of adversity [ 29 ]. The integration of 22-kHz USV playback - an ethologically relevant rat alarm call - in the OFT within a model of affective dysfunction (ELA) allowed for an enhanced response to potential threat in an otherwise threat neutral environment. Previous work from our group and others have used similar 22-kHz USV playback paradigms in typically developing naive rats, with results showing only subtle effects of USV playback (e.g., [ 34 , 47 , 48 ]). Likely, these subtle effects were due to the rats being able to successfully regulate behavioral responses to distal threat in the absence of additional threat cues. In the present work, we utilized a model system that is associated with deficits in affective regulation and threat responsivity [ 29 , 66 ]. Thus, the presentation of a 22-kHz potential threat cue revealed deficits in threat processing within ELA rats, with the most robust changes seen in females. Notably, previous studies have used 22-kHz USV playback to assess effects of other disease/pathological models on behavioral outputs. For instance, Wöhr and colleagues demonstrated that 22-kHz USV playback induced elevated avoidance behaviors in male rats with CACNA1C haploinsufficiency, a risk gene for bipolar disorder, schizophrenia, and major depressive disorder [ 41 ]. Bigelow and colleagues probed effects of early life seizure on behavior in the 8-arm radial maze, finding approach behavior during 22-kHz USV playback was unaffected by seizure experience [ 60 ]. Further, work by Shukla and Charratji examined the effects of prior stress on subsequent reaction to 22-kHz USV playback [ 39 ]. This group showed that prior chronic immobilization stress inhibited avoidance of 22-kHz USV playback yet increased conditioned fear responses, thus illuminating USV playback as a unique tool for assessing processing of socially-relevant stimuli [ 39 ]. While these studies use USV playback paradigms in tandem with markedly different disease models from the one used in the study here (ELA), they support the utility of 22-kHz USV playback to test social and affective processing. The present work expands upon these studies by utilizing USV playback as a probe to assess affective dysregulation following ELA. To quantify potential sex and rearing differences in neural recruitment during 22-kHz USV playback exposure, we measured c-Fos immunoreactive cells in the BLA and PFC along with the percentage of PV interneurons co-expressing c-Fos to determine potential differences in local inhibitory/excitatory balance. We show that PV cells were recruited (PV colocalized with c-Fos) significantly more in the BLA of 22-kHz USV playback-exposed animals, especially ELA females. In the BLA, PV cells are critical regulators of gamma oscillations that orchestrate plasticity and fear learning [ 67 ], and gate amygdala excitability [ 68 ], positioning them to effectively regulate responses to proximal or potential threat via feedback inhibition [ 69 , 70 ]. Overall neuronal activity in the BLA increases in response to direct or potential threat across several paradigms, including 22-kHz aversive USV playback [ 34 ] and predator odor [ 71 ]. Supporting this, Amir and colleagues [ 72 ] showed that BLA neurons increase activity when there was expectation of a predator, but not necessarily predator presence. Other work directly evaluating PV activity suggests that PV cells in the BLA show greater activity in response to an unexpected aversive unconditioned stimulus [ 73 ], and during foot shock [ 74 ]. Our findings in the BLA are in line with these reports, as both males and females have a greater percentage of active (c-Fos positive) PV cells as a result of unconditioned, ambiguous, aversive 22-kHz USV playback. Notably, this relationship appears exacerbated by ELA experience in females, perhaps suggesting increased sensitivity of the BLA in this group to potential threat cues. Indeed, evidence suggests that ELA accelerates BLA development [ 75 ] as well as BLA-PFC circuit maturation in females [ 13 , 76 ], which likely underlie the behavioral manifestations observed here in response to the potential threat cue assessed via 22-kHz USV playback. Similar to the BLA, the inhibitory/excitatory balance of activity in the PFC plays a role in mediating anxiety-like behavioral output [ 77 , 78 ]. In rats, the PFC mediates executive function and is an important analogue to study the human PFC [ 79 ]. Typically, the PFC develops earlier in females compared to males [ 80 ; for review see 81 ] and has been implicated in the regulation of anxiety-like behavioral output in rodents [ 82 ]. Precocial innervation of the PFC by BLA afferents is seen in female rats exposed to ELA [ 13 ], likely leading to local changes within the PFC, contributing to observed affective dysfunction [ 83 ]. The prelimbic (PL) and infralimbic (IL) subregions of the PFC have distinct roles with the PL being active in the behavioral output of fear [ 84 ] while the IL PFC is involved in the suppression of fear [ 85 , 86 ]. Both the IL and PL PFC were examined in the current study due to their role in behavioral output and malleability due to ELA experience. In the present study, the IL PFC exhibited 22-kHz USV playback-induced PV activity changes that were exclusive to females. Specifically, while ELA females exhibited less PV recruitment than controls during the OFT without USV playback, control females exposed to 22-kHz USV playback had less recruitment of PV cells, comparable to the level of ELA. This indicates that aversive threat cues such as 22-kHz USV playback are sufficient to shape IL PFC inhibitory response. PV-expressing cells in the IL PFC are recruited by aversive stimuli such as foot shock [ 87 ]. Additionally, these neurons are likely involved in threat assessment as chemogenetic excitation of IL PFC PV neurons during extinction facilitates later retrieval [ 87 ]. Recent work corroborates this by demonstrating the role of the IL PFC in inhibiting fear memories in unstressed animals [ 88 ]. These studies did not observe sex differences between males and females, and their assays utilized fear conditioning, a vastly different assay for threat than USV playback. Further, the role of IL PFC and BLA reciprocal circuitry is well established in fear conditioning and in response to aversive stimuli [ 89 , 90 ]. These data suggest that cross talk between these regions, in addition to local inhibitory/excitatory balance facilitated by PVs, is likely dysregulated by ELA and can be directly observed in the OFT with the addition of an ethologically relevant 22-kHz USV playback probe. While not directly evaluated here, potential causal roles of PV-directed feedback within BLA-PFC circuitry in a potential threat assay should be further investigated in future work. 4.1. Limitations This study demonstrates the use of USV playback as a compelling tool for uncovering ELA-induced affective dysregulation. Though this work highlights PV neurons in the IL PFC and BLA as prominent candidates for contributing to observed behavioral effects, the evidence presented here does not explicitly support a causal relationship. Further, the quantification of c-Fos and PV positive cells does not account for changes in the activity of these neurons across the duration of the assay, as activity for each cell likely changes over the course of the testing period. Indeed, this approach likely captures both initial activity changes in these ranges in response to 22-kHz USV playback, as well as temporally delayed activity initiated by feedback both locally and from other brain regions. Future studies should consider the mechanisms underlying PV plasticity and/or recruitment during and after ELA and subsequent responses to affective stimuli such as USV playback. Additionally, further work should incorporate circuit manipulation to delineate the relationship between the IL PFC and BLA in aversive 22-kHz USV playback paradigms. It is worth noting that the present study lacks an acoustic control stimulus (i.e., a neutral tone or other socially relevant USV control) and only utilizes silence or 22-kHz USV playback. Thus, we cannot completely rule out that our findings may be a general result of hearing an anxiogenic stimulus and not necessarily driven by the socially communicated threat of the 22-kHz USV playback. Some artificial tones or alarming stimuli (i.e., white noise) can also induce anxiety-like or defensive behaviors [ 39 ]. Indeed, previous work has shown that artificially produced 20-kHz single tone stimulus playback is capable of altering defensive behaviors (both active and passive) in adult listening rats [ 91 , 92 ], with some strain-dependent effects [ 92 ]. The present work was conducted in juvenile Sprague Dawley rats which may also have a different behavioral profile in response to ultrasonic acoustic stimuli than adults or other strains [ 50 ]. Despite this lack of auditory controls in the present study, prior work supports the alarming function of ethologically generated 22-kHz playback as selectively influencing defensive behaviors in comparison to scrambled or phase-shifted control stimuli [ 40 ]. Prior work from our group has shown that a 22-kHz computer-generated tone did not evoke changes in brain activity comparable to naturalistic 22-kHz playback in awake rats within the fMRI [ 47 ]. Studies utilizing 50-kHz appetitive USV playback as a USV comparison group have also shown differing behavioral responses in the open field [ 34 , 48 ]. These latter findings lend support to our interpretation that the 22-kHz USV playback used here did indeed drive observed changes in the brain and behavioral output. However, future research should consider and incorporate acoustic controls to determine whether findings are driven by the innate valence of 22-kHz USV playback or by alarming acoustic features of auditory stimuli. 5. Conclusions The present work highlights the utility of 22-kHz USV playback as a useful, sensitive, and translational assay for uncovering emotional dysregulation following stress. We demonstrate that 22-kHz USV playback generates anxiety-like behavior in ELA, but not control, juvenile females, and induces blunted locomotion across all juvenile males regardless of rearing history. Importantly, these behavioral effects coincide with differences in neural recruitment, specifically of PV interneurons in the IL PFC and the BLA, regions implicated in affective regulation. These regions are excellent candidates for further investigation of their roles in this species-specific ethological stress paradigm. Further, we emphasize that the consideration of sex as a biological variable is critical for understanding the effects of ELA, especially within the context of USV playback paradigms. Taken together, we show that a 22-kHz USV playback probe within the OFT is a sensitive measure to evoke and capture changes in affective processing in a rat model of affective dysfunction, showing translational promise for the use of this approach in future research. Highlights. Adversity and 22-kHz vocalization playback alter parvalbumin activity profiles 22-kHz vocalization playback reduces locomotor behavior following adversity Adversity drives an increase in amygdala parvalbumin recruitment in females Naive females show less prefrontal parvalbumin activity with 22-kHz vs. silence 22 kHz USV playback is a sensitive assay of adversity-induced affective dysfunction Acknowledgements: The authors would like to sincerely thank Erin McCue and Adelaide Hocking, both of whom meaningfully contributed to obtaining behavioral data, helped with tissue collection, and/or assisted with imaging. We would also like to thank Marko Melendy and his team for assisting with caring for the rats and maintenance of the vivarium space. Funding This work was supported by the Maine IDeA Network for Biomedical Research Excellence (INBRE) with a subaward to J.A.H. Maine INBRE and this work is supported by the National Institutes of General Medical Sciences of the National Institutes of Health under Award Number P20GM103423. This work was also supported in part through Bowdoin College’s Paller Neuroscience Research Fellowship (awarded to S.M.B.) and Bowdoin College’s Grua/O’Connell Academic Year Research Awards (awarded to S.M.B.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funders. Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. CRediT Authorship Contribution Statement Sydney M. Bonauto : Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Funding Acquisition, Data Curation. Piper L. Wilson : Writing – review & editing, Writing – original draft, Visualization, Investigation, Data Curation. Jennifer A. Honeycutt : Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project Administration, Methodology, Investigation, Funding Acquisition, Formal Analysis, Conceptualization. References 1. Heim C, Binder EB. Current research trends in early life stress and depression: Review of human studies on sensitive periods, gene–environment interactions, and epigenetics. Exp Neurol. 2012;233(1):102–111. doi: 10.1016/j.expneurol.2011.10.032 [ DOI ] [ PubMed ] [ Google Scholar ] 2. Callaghan BL, Tottenham N. The Stress Acceleration Hypothesis: effects of early-life adversity on emotion circuits and behavior. Curr Opin Behav Sci. 2016;7:76–81. doi: 10.1016/j.cobeha.2015.11.018 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 3. Lähdepuro A, Savolainen K, Lahti-Pulkkinen M, et al. The Impact of Early Life Stress on Anxiety Symptoms in Late Adulthood. Sci Rep. 2019;9(1):4395. doi: 10.1038/s41598-019-40698-0 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 4. Cicchetti D Annual Research Review: Resilient functioning in maltreated children – past, present, and future perspectives. J Child Psychol Psychiatry. 2013;54(4):402–422. doi: 10.1111/j.1469-7610.2012.02608.x [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 5. Dich N, Hansen ÅM, Avlund K, et al. Early life adversity potentiates the effects of later life stress on cumulative physiological dysregulation. Anxiety Stress Coping. 2015;28(4):372–390. doi: 10.1080/10615806.2014.969720 [ DOI ] [ PubMed ] [ Google Scholar ] 6. Hoppen TH, Chalder T. Childhood adversity as a transdiagnostic risk factor for affective disorders in adulthood: A systematic review focusing on biopsychosocial moderating and mediating variables. Clin Psychol Rev. 2018;65:81–151. doi: 10.1016/j.cpr.2018.08.002 [ DOI ] [ PubMed ] [ Google Scholar ] 7. Hein TC, Goetschius LG, McLoyd VC, et al. Childhood violence exposure and social deprivation are linked to adolescent threat and reward neural function. Soc Cogn Affect Neurosci. 2020;15(11):1252–1259. doi: 10.1093/scan/nsaa144 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 8. McLaughlin KA, Peverill M, Gold AL, Alves S, Sheridan MA. Child Maltreatment and Neural Systems Underlying Emotion Regulation. J Am Acad Child Adolesc Psychiatry. 2015;54(9):753–762. doi: 10.1016/j.jaac.2015.06.010 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 9. Krugers HJ, Arp JM, Xiong H, et al. Early life adversity: Lasting consequences for emotional learning. Neurobiol Stress. 2017;6:14–21. doi: 10.1016/j.ynstr.2016.11.005 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 10. Grassi-Oliveira R, Honeycutt JA, Holland FH, Ganguly P, Brenhouse HC. Cognitive impairment effects of early life stress in adolescents can be predicted with early biomarkers: Impacts of sex, experience, and cytokines. Psychoneuroendocrinology. 2016;71:19–30. doi: 10.1016/j.psyneuen.2016.04.016 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 11. Coley EJL, Demaestri C, Ganguly P, et al. Cross-Generational Transmission of Early Life Stress Effects on HPA Regulators and Bdnf Are Mediated by Sex, Lineage, and Upbringing. Front Behav Neurosci. 2019;13:101. doi: 10.3389/fnbeh.2019.00101 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 12. Köhler JC, Gröger N, Lesse A, et al. Early-Life Adversity Induces Epigenetically Regulated Changes in Hippocampal Dopaminergic Molecular Pathways. Mol Neurobiol. 2019;56(5):3616–3625. doi: 10.1007/s12035-018-1199-1 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 13. Honeycutt JA, Demaestri C, Peterzell S, et al. Altered corticolimbic connectivity reveals sex-specific adolescent outcomes in a rat model of early life adversity. eLife. 2020;9:e52651. doi: 10.7554/eLife.52651 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 14. Drastichova Z, Rudajev V, Pallag G, Novotny J. Proteome profiling of different rat brain regions reveals the modulatory effect of prolonged maternal separation on proteins involved in cell death-related processes. Biol Res. 2021;54(1):4. doi: 10.1186/s40659-021-00327-5 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 15. Molet J, Maras PM, Avishai-Eliner S, Baram TZ. Naturalistic rodent models of chronic early-life stress: Chronic Early Postnatal Stress Model. Dev Psychobiol. 2014;56(8):1675–1688. doi: 10.1002/dev.21230 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 16. Walker CD, Bath KG, Joels M, et al. Chronic early life stress induced by limited bedding and nesting (LBN) material in rodents: critical considerations of methodology, outcomes and translational potential. Stress. 2017;20(5):421–448. doi: 10.1080/10253890.2017.1343296 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 17. Jin S, Zhao Y, Jiang Y, et al. Anxiety-like behaviour assessments of adolescent rats after repeated maternal separation during early life. NeuroReport. 2018;29(8):643–649. doi: 10.1097/WNR.0000000000001010 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 18. Wang D, Levine JLS, Avila-Quintero V, Bloch M, Kaffman A. Systematic review and meta-analysis: effects of maternal separation on anxiety-like behavior in rodents. Transl Psychiatry. 2020;10(1):174. doi: 10.1038/s41398-020-0856-0 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 19. Chocyk A, Bobula B, Dudys D, et al. Early-life stress affects the structural and functional plasticity of the medial prefrontal cortex in adolescent rats. Eur J Neurosci. 2013;38(1):2089–2107. doi: 10.1111/ejn.12208 [ DOI ] [ PubMed ] [ Google Scholar ] 20. Maccari S, Krugers HJ, Morley-Fletcher S, Szyf M, Brunton PJ. The Consequences of Early-Life Adversity: Neurobiological, Behavioural and Epigenetic Adaptations. J Neuroendocrinol. 2014;26(10):707–723. doi: 10.1111/jne.12175 [ DOI ] [ PubMed ] [ Google Scholar ] 21. Nishi M, Horii-Hayashi N, Sasagawa T. Effects of early life adverse experiences on the brain: implications from maternal separation models in rodents. Front Neurosci. 2014;8. doi: 10.3389/fnins.2014.00166 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 22. Murthy S, Gould E. How Early Life Adversity Influences Defensive Circuitry. Trends Neurosci. 2020;43(4):200–212. doi: 10.1016/j.tins.2020.02.001 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 23. Gee DG, Gabard-Durnam LJ, Flannery J, et al. Early developmental emergence of human amygdala–prefrontal connectivity after maternal deprivation. Proc Natl Acad Sci. 2013;110(39):15638–15643. doi: 10.1073/pnas.1307893110 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 24. Berens AE, Jensen SKG, Nelson CA. Biological embedding of childhood adversity: from physiological mechanisms to clinical implications. BMC Med. 2017;15(1):135. doi: 10.1186/s12916-017-0895-4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 25. Malave L, Van Dijk MT, Anacker C. Early life adversity shapes neural circuit function during sensitive postnatal developmental periods. Transl Psychiatry. 2022;12(1):306. doi: 10.1038/s41398-022-02092-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 26. Waters RC, Gould E. Early Life Adversity and Neuropsychiatric Disease: Differential Outcomes and Translational Relevance of Rodent Models. Front Syst Neurosci. 2022;16:860847. doi: 10.3389/fnsys.2022.860847 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 27. Chan SY, Ngoh ZM, Ong ZY, et al. The influence of early-life adversity on the coupling of structural and functional brain connectivity across childhood. Nat Ment Health. 2024;2(1):52–62. doi: 10.1038/s44220-023-00162-5 [ DOI ] [ Google Scholar ] 28. Bath KG. Synthesizing Views to Understand Sex Differences in Response to Early Life Adversity. Trends Neurosci. 2020;43(5):300–310. doi: 10.1016/j.tins.2020.02.004 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 29. Ellis SN, Honeycutt JA. Sex Differences in Affective Dysfunction and Alterations in Parvalbumin in Rodent Models of Early Life Adversity. Front Behav Neurosci. 2021;15:741454. doi: 10.3389/fnbeh.2021.741454 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 30. Holland FH, Ganguly P, Potter DN, Chartoff EH, Brenhouse HC. Early life stress disrupts social behavior and prefrontal cortex parvalbumin interneurons at an earlier time-point in females than in males. Neurosci Lett. 2014;566:131–136. doi: 10.1016/j.neulet.2014.02.023 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 31. Ohta K ichi, Suzuki S, Warita K, et al. The effects of early life stress on the excitatory/inhibitory balance of the medial prefrontal cortex. Behav Brain Res. 2020;379:112306. doi: 10.1016/j.bbr.2019.112306 [ DOI ] [ PubMed ] [ Google Scholar ] 32. Honeycutt JA, Young JW, Porcu A, Sabariego M. Editorial: Negative valence systems. Front Syst Neurosci. 2022;16:1014745. doi: 10.3389/fnsys.2022.1014745 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 33. Blanchard RJ, Blanchard DC, Agullana R, Weiss SM. Twenty-two kHz alarm cries to presentation of a predator, by laboratory rats living in visible burrow systems. Physiology & Behavior. 1991;50(5):967–972. Doi: 10.1016/0031-9384(91)90423-L [ DOI ] [ PubMed ] [ Google Scholar ] 34. Demaestri C, Brenhouse HC, Honeycutt JA. 22 kHz and 55 kHz ultrasonic vocalizations differentially influence neural and behavioral outcomes: Implications for modeling anxiety via auditory stimuli in the rat. Behav Brain Res. 2019;360:134–145. doi: 10.1016/j.bbr.2018.12.005 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 35. Kim EJ, Kim ES, Covey E, Kim JJ. Social transmission of fear in rats: the role of 22-kHz ultrasonic distress vocalization. PLoSOne. 2010; 5(12): e15077. doi: 10.1371/journal.pone.0015077 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 36. Wöhr M, Schwarting RKW. Affective communication in rodents: ultrasonic vocalizations as a tool for research on emotion and motivation. Cell Tissue Res. 2013; 354:81–97. doi: 10.1007/s00441-013-1607-9 [ DOI ] [ PubMed ] [ Google Scholar ] 37. Bang SJ, Allen TA, Jones LK, Boguszewski P, Brown TH. Asymmetrical stimulus generalization following differential fear conditioning. Neurobiology of Learning and Memory. 2008; 90:200–216. doi: 10.1016/j.nlm.2008.02.009 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 38. Sadananda M, Wöhr M, Schwarting RK. Playback of 22-kHz and 50-kHz ultrasonic vocalizations induces differential c-fos expression in rat brain. Neurosci Lett. 2008;435(1):17–23. [ DOI ] [ PubMed ] [ Google Scholar ] 39. Shukla A, Chattarji S. Stressed rats fail to exhibit avoidance reactions to innately aversive social calls. Neuropsychopharmacology. 2022;47(6):1145–1155. DOI: 10.1038/s41386-021-01230-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 40. Fendt M, Brosch M, Wernecke EA, Willadsen M, Wöhr M. Predator odour but not TMT induces 22-kHz ultrasonic vocalizations in rats that lead to defensive behaviours in conspecifics upon replay. Scientific Reports. 2018;8:11041. doi: 10.1038/s41598-018-28927-4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 41. Wöhr M, Willadsen M, Kisko TM, Schwarting RKW, Fendt M. Sex-dependent effects of Cacna1c haploinsufficiency on behavioral inhibition evoked by conspecific alarm signals in rats. Prog Neuropsychopharmacol Biol Psychiatry. 2020; 99:109849. doi: 10.1016/j.pnpbp.2019.109849 [ DOI ] [ PubMed ] [ Google Scholar ] 42. Knutson B, Burgdorf J, Panksepp J. Ultrasonic vocalizations as indices of affective states in rats. Psychol Bull. 2002;128(6):961–977. doi: 10.1037/0033-2909.128.6.961 [ DOI ] [ PubMed ] [ Google Scholar ] 43. Burgdorf J, Kroes RA, Moskal JR, Pfaus JG, Brudzynski SM, Panksepp J. Ultrasonic vocalizations of rats (Rattus norvegicus) during mating, play, and aggression: Behavioral concomitants, relationship to reward, and self-administration of playback. J Comp Psychol. 2008;122(4):357. [ DOI ] [ PubMed ] [ Google Scholar ] 44. Opiol H, Pavlovski I, Michalik M, Mistlberger R. Ultrasonic vocalizations in rats anticipating circadian feeding schedules. Behav Brain Res. 2015; 284:42–50. doi: 10.1016/j.bbr.2015.02.003 [ DOI ] [ PubMed ] [ Google Scholar ] 45. Seffer D, Rippberger H, Schwarting RKW, Wöhr M. Pro-social 50-kHz ultrasonic communication in rats: post-weaning by not post-adolescent social isolation leads to social impairments-phenotypic rescue by re-socialization. Front Behav Neurosci. 2015;9:102. doi: 10.3389/fnbeh.2015.00102 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 46. Calub CA, Furtak SC, Brown TH. Revisiting the autoconditioning hypothesis for acquired reactivity to ultrasonic alarm calls. Physiol Behav. 2018. 194:380–386. doi: 10.1016/j.physbeh.2018.06.029 [ DOI ] [ PubMed ] [ Google Scholar ] 47. Granata LE, Chang A. Shaheed H, Shinde A, Kulkarni P, Satpute A, Brenhouse HC, Honeycutt JA. Examining brain activity responses during rat ultrasonic vocalization playback: Insights from a novel fMRI translational paradigm. eNeuro, 2025;11(10). doi: 10.1523/eneuro.0179-23-2024 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 48. Bonauto SM, Patel KA, Honeycutt JA. 22 and 50 kHz rat ultrasonic vocalization playback reveals sex differences in behavior and c-Fos in brain regions associated with affective processing. Behav Brain Res. 2025;478:115326. doi: 10.1016/j.bbr.2024.115326 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 49. Ouda L, Jílek M, Syka J. Expression of c-Fos in rat auditory and limbic systems following 22-kHz calls. Behav Brain Res. 2016;308:196–204. [ DOI ] [ PubMed ] [ Google Scholar ] 50. Bonauto SM, Greuel OM, Honeycutt JA. Playback of rat 22-kHz ultrasonic vocalizations as a translational assay of negative affective states: An analysis of evoked behavior and brain activity. Neurosci Biobehav Rev. 2023;153:105396. doi: 10.1016/j.neubiorev.2023.105396 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 51. Ganguly P, Honeycutt JA, Rowe JR, Demaestri C, Brenhouse HC. Effects of early life stress on cocaine conditioning and AMPA receptor composition are sex-specific and driven by TNF. Brain Behav Immun. 2019; 78:41–51. doi: 10.1016/j.bbi.2019.01.006 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 52. Wieck A, Andersen SL, Brenhouse HC. Evidence for a neuroinflammatory mechanism in delayed effects of early life adversity in rats: relationship to cortical NMDA receptor expression. Brain Behav Immun. 2013; 28:218–226. doi: 10.1016/j.bbi.2012.11.012 [ DOI ] [ PubMed ] [ Google Scholar ] 53. Seagraves KM, Arthur BJ, Egnor SER. Evidence for an audience effect in mice: male 22 khzsocial partners alter the male vocal response to female cues. J Exp Biol. 2016;219(10):1437–1448. doi: 10.1242/jeb.129361 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 54. Morgan JI, Cohen DR, Hempstead JL, Curran T. Mapping Patterns of c-fos Expression in the Central Nervous System After Seizure. Science. 1987;237(4811):192–197. doi: 10.1126/science.3037702 [ DOI ] [ PubMed ] [ Google Scholar ] 55. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9(7):671–675. doi: 10.1038/nmeth.2089 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 56. Swanson LW. Brain maps 4.0—Structure of the rat brain: An open access atlas with global nervous system nomenclature ontology and flatmaps. J Comp Neurol. 2018;526(6):935–943. doi: 10.1002/cne.24381 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 57. Schönfeld LM, Zech MP, Schäble S, Wöhr M, Kalenscher T. Lesions of the rat basolateral amygdala reduce the behavioral response to ultrasonic vocalizations. Behav Brain Res. 2020;378:112274. [ DOI ] [ PubMed ] [ Google Scholar ] 58. Brudzynski SM, Chiu EM. Behavioural responses of laboratory rats to playback of 22 kHz ultrasonic calls. Physiol Behav. 1995;57(6):1039–1044. doi: 10.1016/0031-9384(95)00003-2 [ DOI ] [ PubMed ] [ Google Scholar ] 59. Olszyński KH, Polowy R, Małż M, Boguszewski PM, Filipkowski RK. Playback of alarm and appetitive calls differentially impacts vocal, heart-rate, and motor response in rats. Iscience. 2020;23(10):101577. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 60. Bigelow LJ, Fiset C, Jarvis JHM, et al. Early-life seizures modify behavioral response to ultrasonic vocalization playback in adult rats. Epilepsy Behav. 2022;127:108494. doi: 10.1016/j.yebeh.2021.108494 [ DOI ] [ PubMed ] [ Google Scholar ] 61. Ramos A Animal models of anxiety: do I need multiple tests? Trends Pharmacol Sci. 2008;29(10):493–498. doi: 10.1016/j.tips.2008.07.005 [ DOI ] [ PubMed ] [ Google Scholar ] 62. Gencturk S, Unal G. Rodent tests of depression and anxiety: Construct validity and translational relevance. Cogn Affect Behav Neurosci. 2024;24(2):191–224. doi: 10.3758/s13415-024-01171-2 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 63. Börchers S, Krieger JP, Asker M, Maric I, Skibicka KP. Commonly-used rodent tests of anxiety-like behavior lack predictive validity for human sex differences. Psychoneuroendocrinology. 2022;141:105733. doi: 10.1016/j.psyneuen.2022.105733 [ DOI ] [ PubMed ] [ Google Scholar ] 64. Hall CS. Emotional behavior in the rat. I. Defecation and urination as measures of individual differences in emotionality. J Comp Psychol. 1934;18(3):385–403. doi: 10.1037/h0071444 [ DOI ] [ Google Scholar ] 65. Shansky RM. Sex differences in behavioral strategies: avoiding interpretational pitfalls. Curr Opin Neurobiol. 2018;49:95–98. doi: 10.1016/j.conb.2018.01.007 [ DOI ] [ PubMed ] [ Google Scholar ] 66. Brenhouse HC. Points of divergence on a bumpy road: early development of brain and immune threat processing systems following postnatal adversity. Mol Psychiatry. 2023;28(1):269–283. doi: 10.1038/s41380-022-01658-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 67. Cattani A, Arnold DB, McCarthy M, Kopell N. Basolateral amygdala oscillations enable fear learning in a biophysical model. eLife. 2024;12:RP89519. doi: 10.7554/eLife.89519.4 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 68. Ryazantseva M, Liiwand M, Shteinikov V, Kunnari AJ, Sulku J, Lauri SE. Parvalbumin interneurons gate amygdala excitability and response to chronic stress via kainate receptor-driven tonic GABAB receptor-mediated inhibition. Mol Psychiatry. Published online June 28, 2025. doi: 10.1038/s41380-025-03093-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 69. Duvarci S, Pare D. Amygdala Microcircuits Controlling Learned Fear. Neuron. 2014;82(5):966–980. doi: 10.1016/j.neuron.2014.04.042 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 70. Krabbe S, Gründemann J, Lüthi A. Amygdala Inhibitory Circuits Regulate Associative Fear Conditioning. Biol Psychiatry. 2018;83(10):800–809. doi: 10.1016/j.biopsych.2017.10.006 [ DOI ] [ PubMed ] [ Google Scholar ] 71. Takahashi LK. Olfactory systems and neural circuits that modulate predator odor fear. Front Behav Neurosci. 2014;8. doi: 10.3389/fnbeh.2014.00072 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 72. Amir A, Kyriazi P, Lee SC, Headley DB, Paré D. Basolateral amygdala neurons are activated during threat expectation. J Neurophysiol. 2019;121(5):1761–1777. doi: 10.1152/jn.00807.2018 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 73. Yau JOY, Chaichim C, Power JM, McNally GP. The Roles of Basolateral Amygdala Parvalbumin Neurons in Fear Learning. J Neurosci. 2021;41(44):9223–9234. doi: 10.1523/JNEUROSCI.2461-20.2021 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 74. Asim M, Wang H, Waris A, He J. Basolateral amygdala parvalbumin and cholecystokinin-expressing GABAergic neurons modulate depressive and anxiety-like behaviors. Transl Psychiatry. 2024;14(1):418. doi: 10.1038/s41398-024-03135-z [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 75. Guadagno A, Belliveau C, Mechawar N, Walker CD. Effects of Early Life Stress on the Developing Basolateral Amygdala-Prefrontal Cortex Circuit: The Emerging Role of Local Inhibition and Perineuronal Nets. Front Hum Neurosci. 2021;15:669120. doi: 10.3389/fnhum.2021.669120 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 76. Cody CR, Artur De La Villarmois E, Miguelez Fernandez A, et al. Effects of early life adversity and adolescent basolateral amygdala activity on corticolimbic connectivity and anxiety behaviors. Published online March 29, 2024. doi: 10.1101/2024.03.26.586708 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 77. Jinks AL, McGregor IS. Modulation of anxiety-related behaviours following lesions of the prelimbic or infralimbic cortex in the rat. Brain Res. 1997;772(1–2):181–190. doi: 10.1016/S0006-8993(97)00810-X [ DOI ] [ PubMed ] [ Google Scholar ] 78. Ferguson BR, Gao WJ. PV Interneurons: Critical Regulators of E/I Balance for Prefrontal Cortex-Dependent Behavior and Psychiatric Disorders. Front Neural Circuits. 2018;12:37. doi: 10.3389/fncir.2018.00037 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 79. Kesner RP, Churchwell JC. An analysis of rat prefrontal cortex in mediating executive function. Neurobiol Learn Mem. 2011;96(3):417–431. doi: 10.1016/j.nlm.2011.07.002 [ DOI ] [ PubMed ] [ Google Scholar ] 80. Lenroot RK, Giedd JN. Sex differences in the adolescent brain. Brain Cogn. 2010;72(1):46–55. doi: 10.1016/j.bandc.2009.10.008 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 81. Perry CJ, Campbell EJ, Drummond KD, Lum JS, Kim JH. Sex differences in the neurochemistry of frontal cortex: Impact of early life stress. J Neurochem. 2021;157(4):963–981. doi: 10.1111/jnc.15208 [ DOI ] [ PubMed ] [ Google Scholar ] 82. Likhtik E, Stujenske JM, A Topiwala M, Harris AZ, Gordon JA. Prefrontal entrainment of amygdala activity signals safety in learned fear and innate anxiety. Nat Neurosci. 2014;17(1):106–113. doi: 10.1038/nn.3582 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 83. Goodpaster CM, Christensen CR, Alturki MB, DeNardo LA. Prefrontal cortex development and its implications in mental illness. Neuropsychopharmacology. Published online July 3, 2025. doi: 10.1038/s41386-025-02154-8 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 84. Sharpe M, Killcross S. The prelimbic cortex uses contextual cues to modulate responding towards predictive stimuli during fear renewal. Neurobiol Learn Mem. 2015;118:20–29. doi: 10.1016/j.nlm.2014.11.005 [ DOI ] [ PubMed ] [ Google Scholar ] 85. Chang C hui, Maren S. Medial prefrontal cortex activation facilitates re-extinction of fear in rats. Learn Mem. 2011;18(4):221–225. doi: 10.1101/lm.2070111 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 86. Giustino TF, Maren S. The Role of the Medial Prefrontal Cortex in the Conditioning and Extinction of Fear. Front Behav Neurosci. 2015;9. doi: 10.3389/fnbeh.2015.00298 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 87. Binette AN, Liu J, Bayer H, et al. Parvalbumin-Positive Interneurons in the Medial Prefrontal Cortex Regulate Stress-Induced Fear Extinction Impairments in Male and Female Rats. J Neurosci. 2023;43(22):4162–4173. doi: 10.1523/JNEUROSCI.1442-22.2023 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 88. Bayer H, Hassell JE, Oleksiak CR, et al. Pharmacological stimulation of infralimbic cortex after fear conditioning facilitates subsequent fear extinction. Neuropsychopharmacology. 2024;49(13):1951–1957. doi: 10.1038/s41386-024-01961-9 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 89. Hong J, Kim D. Freezing response-independent facilitation of fear extinction memory in the prefrontal cortex. Sci Rep. 2017;7(1):5363. doi: 10.1038/s41598-017-04335-y [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 90. Bukalo O, Nonaka M, Weinholtz CA, Mendez A, Taylor WW, Holmes A. Effects of optogenetic photoexcitation of infralimbic cortex inputs to the basolateral amygdala on conditioned fear and extinction. Behav Brain Res. 2021;396:112913. doi: 10.1016/j.bbr.2020.112913 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 91. Beckett SR, Aspley S, Graham M, Marsden CA. Pharmacological manipulation of ultrasound induced defence behaviour in the rat. Psychopharmacology (Berl). 1996;127(4):384–390. doi: 10.1007/s002130050102 [ DOI ] [ PubMed ] [ Google Scholar ] 92. Neophytou SI, Graham M, Williams J, Aspley S, Marsden CA, Beckett SR. Strain differences to the effects of aversive frequency ultrasound on behaviour and brain topography of c-fos expression in the rat. Brain Res. 2000; 854(1–2):158–164. doi: 10.1016/s0006-8993(99)02334-3 [ DOI ] [ PubMed ] [ Google Scholar ] 93. Brudzynski SM, Bihari F, Ociepa D, Fu X-W. Analysis of 22 kHz ultrasonic vocalization in laboratory rats: Long and short calls. Physiology & Behavior. 1993;54(2):215–221. doi: 10.1016/0031-9384(93)90102-L [ DOI ] [ PubMed ] [ Google Scholar ] ACTIONS View on publisher site PDF (1.2 MB) Cite Collections Permalink RESOURCES Similar articles Cited by other articles Links to NCBI Databases Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top

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