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Characterization of neuronal oscillations in the prelimbic cortex, nucleus accumbens and CA1 hippocampus during object retrieval task in rats predisposed to early life stress

Behavioral and Brain Functions volume 20, Article number: 34 (2024) Cite this article

Abstract

Background

Early life stress (ELS) during the stress hypo-responsive period (SHRP) alters the curiosity-like behavior later during adolescence. Previous studies have shown maternal separation (MS) stress-induced heightened curiosity and associated risk-taking behavior in the object retrieval task (ORT). However, the neural correlates of curiosity in adolescent rats predisposed to early life stress remain unexplored. Hence, the present study aimed to investigate the neural oscillatory patterns and network characteristics in the regions implicated in curiosity behavior, such as the Prelimbic cortex (PL), Nucleus Accumbens (NAc), and CA1 of the Hippocampus. The local field potentials data were analysed to understand the neural activity patterns in these areas during the risky zone crossing and object retrieval phase of the ORT in MS rats and compared with the normal control (NC) group.

Results

In comparison to NC, MS rats showed a reduction in the theta power at 8–12 Hz, beta power at 12–20 Hz, and gamma power at 20–40 Hz range in the PL during risky zone crossing time. MS rats also showed reduced cross-correlation between PL-CA1 and reduced theta coherence between NAc-CA1 during risky zone crossing. During the object retrieval phase, the MS rats showed reduced peak cross-correlation between PL-CA1 and PL-NAc. Behaviourally, MS rats displayed an increased preference for the curiosity platform and retrieved more hidden objects, thus accounting for a higher curiosity index than controls.

Conclusion

In summary, a reduced synchronization between the PL, NAc, and CA1 during the object retrieval task indicates how early MS stress during a critical developmental period impacts the limbic circuit connectivity. This corresponded with enhanced curiosity index in adolescent MS rats, predicting an altered intrinsic motivation and hence a higher susceptibility to substance use disorders during adolescence.

Introduction

Curiosity is the desire to explore and acquire new information due to intrinsic motivation [1,2,3] and thus is essential for enhancing learning and memory especially during childhood and adolescence [4, 5]. Several neuroimaging studies using curiosity dependent task have shown that optimal level of curiosity enhances activity in the brain areas related to memory and reward like hippocampus (HPC), prefrontal cortex (PFC) and ventral striatum (VS) [6,7,8,9,10,11,12]. Lack of curiosity may have detrimental effects on learning and memory, while heightened curiosity results in increased risk-taking, impulsive decision-making, and curiosity-driven sensation-seeking behaviors, including substance use, especially during adolescence when the brain is still under construction [13,14,15]. Intense curiosity for specific information might even weaken the encoding process, especially when less exciting but meaningful content is presented in close temporal proximity [16].

Brain undergoes dynamic changes and especially during adolescence, PFC and associated limbic structures undergo reorganization, by increasing myelination [17], synaptogenesis and subsequent pruning away of irrelevant connections in the activity dependent manner [18,19,20,21]. As the mesocorticolimbic structures are forming functional connectivity immensely during adolescence, there will be changes in excitatory and inhibitory synapses and altered excitation-inhibition (E-I) balance [22]. Increase in midbrain dopaminergic projections to the cortex along with elevated levels of dopamine (DA) acts as the potent neuromodulator during adolescence [23, 24]. As a result, adolescents demonstrate heightened exploration, novelty seeking, experimental use of substance and impulsivity which may lead to risk taking behavior [25].

The curiosity-like behavior gradually grows from childhood through adolescence to young adulthood. The differential curiosity level among children is further associated with the maturation trajectories and functional connectivity between lateral PFC, anterior cingulate cortex (ACC) and HPC [4]. Proper development and maturation of curiosity promoting areas may be crucial for learning [5]. Thus, the early childhood environment becomes an integral part of the brain maturation process in shaping adolescent curiosity-like behavior, learning, and memory. Studies have shown the detrimental effects of early life stress (ELS) on brain maturation and functions in the later stages of life [26, 27]. Maternal Separation (MS) stress during stress hypo responsive period (SHRP) causes disruption of cortico-limbic connectivity leading to altered affective behaviors and cognition that varies across different age groups [28, 29].

Our previous studies have demonstrated that rats that were exposed to early MS stress during the postnatal day (PND) 4–14 showed an increased the curiosity-like behavior in the object retrieval task (ORT) during early adolescence [30], increased risk-taking behaviour in risky decision-taking task (RDTT) in late adolescence and young adulthood [31] as well as an increased reward seeking behaviour in 5-CSRTT [32]. Prediction–appraisal–curiosity–exploration (PACE) framework [33] explained the role of HPC, ACC and PFC maturation in eliciting curiosity and memory during childhood and adolescence [4]. Also, decisions aimed at satisfying curiosity and fulfilling food-related rewards, even at the risk of electric shock, could elicit striatal activity [9]. Considering the above facts, we hypothesized that MS stress during SHRP could alter neural activity and synchronous connectivity between the brain areas like the Prelimbic cortex (PL), Nucleus accumbens (NAc) and CA1 region of the hippocampus, resulting in heightened curiosity during adolescence.

The brain oscillations derived from the local field potential (LFP) recordings provided extensive evidence for the functional connectivity between mPFC, HPC, Amygdala and ventral striatum (VS) during exploratory and affective behaviours. More specifically, theta oscillations (4–12 Hz) in the HPC were associated with exploratory behaviour, spatial navigation, learning and memory [34, 35]. While, rhythmic oscillations at theta frequency ranges in cortico-limbic-hippocampal circuitry were associated with fear memory retrieval [36, 37] and REM sleep [28]. Furthermore, studies in rodents have shown synchronised oscillations between ventral hippocampus and mPFC with increased anxiety in an anxiety-driven exploration paradigm [38] and during free exploration in the unknown environment [39]. An increased theta (7–9 Hz) and Gamma (50–60 Hz) power in PFC and NAc during CSRTT in rats exhibiting impulsive traits [40] may, thus, indicate heightened brain attention, with an increased arousal and perception [41, 42].

However, there are no studies yet to delineate the characteristics of neural oscillations in limbic brain areas and how early life stress affects the oscillatory patterns during the curiosity-like behaviour in adolescence age. Accordingly, the present study investigated the neural oscillatory patterns in the PL, NAc and CA1 region of hippocampus while the rats performed the ORT due to curiosity. More specifically, the study has examined the LFP power at different frequency bands and synchrony during the two determining stages of the ORT predicting the curiosity-like behavior in adolescence age – while crossing the risky zone filled with water and secondly, during the object retrieval phase of the ORT – in the rats predisposed to early MS stress during SHRP.

Materials and methods

Ethical approval

All experiments were done after obtaining the approval by the Institutional Animal Ethics Committee (AEC/72/474/N.P.) and were carried out according to the guidelines of the Committee for the Purpose of Control and Supervision of Experiments on Animals (CPCSEA), Government of India. All efforts were made to minimize pain and suffering for the experimental animals.

Animals

Female Sprague Dawley (SD) rats on their 18–19 day of gestation were procured from the central animal research facility (CARF), NIMHANS, Bengaluru. They were housed individually in polypropylene cages with controlled environmental conditions of temperature 250C and 12 h:12 h light-dark cycles (lights on at 6:00 AM, lights off at 6:00 PM.) around 200–250 lx light intensity during daytime. Ad libitum food and water was provided. Day of delivery of pups was considered as PND 0 and pups were weaned from the mother on PND 21 and male and female pups were housed in separate polypropylene cages (22.5 cm x 35.5 cm x 15 cm) thereafter. All experiments were performed on the male offsprings from PND 30, between 10am-6pm after habituating to laboratory conditions.

Experimental groups

Dams were randomly assigned to two groups, Normal control group (NC) and Maternal Separation (MS) Stress group. NC pups were groomed with the dam in a standard housing condition till weaning. They were handled only during bedding change. In MS stress group, the pups were separated from mother and the littermates during SHRP. Totally 18 male offsprings including 8 for NC and 10 rats for MS were used in the study.

Maternal separation (MS) stress procedure

MS stress procedure was carried out for 11 days during SHRP as explained previously [30]. During PND 4–14, rat pups were separated from the dam and subsequently isolated from their littermates. Isolated rat pups were kept in separate mice cages containing corncob bedding and lid covered on top. The stress protocol was carried out for 6 h daily, from 10:00 AM to 4:00 PM, after which pups and dam were reunited. Maternal separation stress protocol was applied only after confirming that the pups were well fed and the milk pouch was full. After completion of the MS stress protocol, the pups were returned and maintained in the standard housing condition until weaning on day 22.

Stereotaxic implantation of electrodes

The stereotaxic implantation of electrodes was carried out in a sterile environment. To ensure the sterile condition, all surgical equipment, screws, depth electrodes, cotton, gelatin sheet, and tissue rolls were UV sterilized for 5 min and arranged on the surgery table prior to the commencement of the surgery. The active electrodes used were coated stainless steel wires with a bare diameter of 0.005 inches (125 μm) and a total diameter including the coating of 0.008 inches (203 μm). The coating thickness was approximately 0.0015 inches (38.1 μm) (A-M Systems, Inc., USA). The stereotaxic frames and tables were thoroughly cleaned with 70% alcohol before the surgery. The rat was weighed and deeply anesthetized using ketamine (80 mg/kg body weight, intraperitoneal injection (I.P), Aneket®, Neon Laboratories Pvt. Ltd., India) and xylazine (10 mg/kg body weight, I.P injection, Xylaxin® by Indian Immunologicals Ltd, India) before being carefully positioned in a stereotaxic apparatus (Stoelting Co., USA). The withdrawal reflex of the rat was checked by gently pinching the toe. The rat head was shaved and cleaned with betadine solution during the pre-surgery preparation. The head was then fixed using blunt ear bars on the sides and to the incisor bar in front. Wet cotton was placed over the eyes throughout the surgery to prevent them from drying. Subsequently, 2% lidocaine (SPM Drugs Pvt. Ltd., India) was injected (s.c.) as local anesthesia. A straight midline incision was made on the head with a sterile scalpel blade (blade size no. 22; Glassvan, Niraj Industries Pvt. Ltd., India) exposing the underlying connective tissue, muscles, and skull. The skull was exposed, and the periosteum was gently scraped away by carefully scraping over the skull until the bregma point was visible. The skull was dried using absorbent gel to keep the surface dry. Bregma is the junction between the sagittal and coronal sutures at the top of the skull, which served as a landmark and was identified and marked. From this point, coordinates for different regions were marked using anteroposterior and mediolateral measurements.

The recording electrodes were implanted stereotaxically using the rat brain atlas [43] in the right hemisphere of the (a) PL (Layer V) (anteroposterior: +3.2 mm, mediolateral: +0.5 mm and dorsoventral: -3.0 mm); (b) NAc (Shell) (anteroposterior: -2.2 mm, mediolateral: +1.0 and dorsoventral: -7.4 mm); (c) CA1 region of HPC (stratum pyramidale) (anteroposterior: -3.0 mm, mediolateral: +1.5 mm and dorsoventral: -2.6 mm). The two subdural screw electrodes were placed bilaterally in the cerebellum - one in the left hemisphere served as the ground, and another one in the right served as the reference. Anaesthesia was maintained throughout the surgery using 2% isoflurane in oxygen. The electrode was fixed onto the skull surface using dental acrylic cement. The opposite ends of the electrode were soldered to an IC socket, and to ensure proper fixation, the assembly was encased in dental acrylic above the rat’s head, preventing the electrodes from being exposed. To maintain cleanliness and minimize the risk of infection, 10% Povidone-iodine solution was applied along the wound’s edges. Finally, the incision was carefully sutured using Vicryl 3.0 absorbable sutures. Meloxicam (10 mg/kg, Intas Pharmaceuticals LTD, India) was given subcutaneously and 0.9% saline (Pharma Impex Pvt.Ltd, India) was injected (I.P.) to prevent dehydration following surgical blood loss. The rats were kept under observation in a heated chamber until they show recovery from motor functions and begin to eat and drink.

Rats were allowed to recover from the surgery for a period of 7–10 days. Neosporin topical antibiotic ointment (Glaxosmithkline Pharmaceuticals Limited, India) was applied on the surgical wound for three or more days to prevent infections and aid better healing. Entire procedure was conducted under strict aseptic precautions. Care was taken to minimize pain or distress during and after stereotaxic surgery. After the complete recovery, rats were subjected to Object Retrieval Task.

Object retrieval task (ORT) for curiosity behaviour

To assess the neural correlates of curiosity-like behaviour, rats were first habituated to the ORT chamber and after 24 h, the rats were tested in level 1 of ORT protocol [30] with a simultaneous recordings of the Local field potential (LFP) from PL, NAc and CA1. This task drives the animal to engage in the retrieval of hidden objects based on its inherent motivation and curiosity, in the presence of associated risk-like behavioural phenotypes by crossing the water zone. The timeline of the experimental study and paradigm is depicted in Fig. 1A.

Fig. 1
figure 1

Experimental design: (a) experimental timeline (b) Object retrieval task (ORT) paradigm to assess the curiosity-like behaviour in rats

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Apparatus

The apparatus for ORT (Fig. 1B) consisted of a custom-designed glass chamber measuring 3ft x 3ft, constructed from 10 mm thick toughened glass. Basic lighting conditions was used in the experiment with the illumination in ORT apparatus was maintained less than 50 lx which is considered non-anxiogenic for rats. Two white polypropylene platforms measuring 3ft x 1ft were positioned inside the chamber, with their heights being adjustable. These platforms were separated by an empty middle arena, which was filled with 2-inch water level. The platforms were inclined obliquely in the middle arena to prevent the rats from escaping underneath them.

The chamber comprised of three distinct platforms:

  1. (1)

    Entry (E) platform, served as the safe entry platform for the rats into the chamber.

  2. (2)

    Water zone (W), which is unsafe and risky zone for rats, and.

  3. (3)

    Curiosity (Q) platform, that contained a tray with the objects hidden in the bedding material.

Curiosity platform (Q) contained a plastic tray containing corncob bedding material was placed at the centre. The bedding material had a height of 2-inches and concealed five odourless and tasteless toy insects positioned at specific locations, including the four corners and the centre.

On the day of recording, to assess the level of curiosity, each rat was allowed individually to explore both Entry (E) and curiosity (Q) platforms. Rat had to traverse across the water zone, which was filled with 2 inches of water to reach the Q platform. The Q platform consisted of a rectangular plastic tray wherein novel objects were hidden. The rat had to retrieve the objects within 20 min of testing duration. After the completion of recording, rats were returned to home cage where they were provided with ad libitum supply of water. The entire apparatus was cleaned using 70% alcohol between each session.

ORT for 20 min was video recorded for offline analysis. The behavioral data were analysed manually offline using the recorded video footage. The coders were blinded to the experimental conditions during this process. Several behavioural parameters were analysed during the experiment, including the time spent on platform Q (Q preference) and number of objects retrieved. Then Curiosity Index was calculated using the following formula:

$$\begin{aligned}&\:Curiosity\:Index\:\left(CI\right)\\ & \quad =\frac{Q\:preference\:\left(\%\right)+number\:of\:objects\:retrieved}{total\:recording\:time}\end{aligned}$$

Local field recording during object retrieval task

Representative video of rat performing ORT and simultaneous recording of LFP is provided as Additional file 1. LFPs were recorded using video-EEG system enabled BESS data acquisition system (Axxonet system technologies, India). The data acquisition process included signal amplification, digitization, and storage for subsequent analysis. The LFP signals were amplified with a gain of 1000 and digitized at a sampling rate of 1024 Hz and 24-bit amplitude resolution (0.01–512 Hz bandwidth). Recordings were viewed at the voltage scale of 7.0 µV/mm, display filters applied (50 Hz and 100 Hz notch filters, and band pass filter of 0.1–100 Hz). The behavior of the animals was recorded using a video camera with a sampling rate of 30 frames per second (FPS) through the AXXONET BESS software. Events were marked offline after the recording. Start and end times were marked for the transitions across the water and during the retrieval of objects. Time of first crossing of water from E platform to Q platform was considered as latency to cross water (LTW). Time at which first object was retrieved was considered as latency to retrieve object (LTR). After the event marking the raw data was exported and stored in EDF format for further analysis.

Histology for electrode confirmation

At the end of electrophysiological experiments, rats were deeply anesthetized using isoflurane (Aerrane, Baxter Healthcare Corporation) and then transcardially perfused with 0.9% saline followed by 4% paraformaldehyde in 0.1 M phosphate buffer, pH 7.4 (v/v). The brains were shelled out and immersed in the same fixative for 24 h. Brain coronal sections of 60 μm thickness were taken using vibratome (Leica VT 1000 S, Leica Biosystems™, Wetzlar, Germany) at the level of PL, NAc and CA1. Electrode locations were histologically verified by 0.1% cresyl violet staining (Fluka Chemika; Sigma-Aldrich, Germany) in comparison to rat brain stereotaxic coordinates. Data from animals with electrodes placed outside the desired area were not used for analysis. Figure 2A-C depicts the brain sections confirming the electrode placements in layer V of PL, shell of NAc and pyramidale layer of CA1.

Fig. 2
figure 2

Histological confirmation of electrode placement in (c) PL, (d) NAc and (e) CA1 Hippocampus

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Data analysis

The data acquisition for LFP recording was conducted using BESS software (Axxonet System Technologies, India). LFP data were extracted and processed using Spike-2 software version 6.0. The EDF file was imported to Spike-2 and converted to SMR file. Channel processing for SMR file includes DC removal and Smoothening with time constant 0.0097s. The data analysis was carried out using Spike 2 software, as well as custom codes using the Brainstorm toolbox (version July 2023) and the EEGLAB toolbox (version 2023) in MATLAB 2023a. To synchronise the behavioural recordings with the LFP recordings, TTL pulses were generated during the recordings to ensure the precise data alignment for offline analysis.

The oscillatory activities in the PL, CA1 and NAc regions were analysed offline from the two critical events of the object retrieval task: (i) Risky zone crossing phase: 1-second prior to the risky zone crossing is the time interval where rat was actively involved in the quick decision-making and (ii) Object retrieval phase: 5-s time interval where rats were actively exploring the objects. For LFP analysis, 2-s prior to the curiosity-driven object exploration and 3-s during the initial object retrieval phase was considered. To evaluate if theta-beta-gamma oscillations were modulated during risky zone crossing and object retrieval phase, a power spectral density (PSD), waveform correlation, coherence and Granger Causality analysis of LFP signals from PL, CA1 and NAc was performed.

Power spectral analysis

Power Spectrum for risky zone crossing and object retrieval phase was analysed, using fast Fourier transformation (FFT) with the Hanning window which gave the frequency range from 0 to 512 Hz in 512 bins with the sampling rate of 1024 Hz. Frequency band was divided into Delta (0–4 Hz), low theta (4–8 Hz), high theta (8–12 HZ), Beta (15–30 Hz), Gamma low (30–45 Hz), Gamma high (60–80 Hz) [39], . The relative power (RP) is then computed as the ratio of the absolute power in each frequency as a percentage P(f) of the maximum absolute power of the total LFP data, P (fmax).

$$\:Relative\:Power\:\left(\%\right)=\frac{\text{P}\left(f\right)}{\text{P}\left(fmax\right)}X100$$

Waveform correlation and coherence

Autocorrelation and cross correlation were measured using 1s epoch during risky zone crossing and also 5 s epoch during object retrieval phase, from 3 regions of interest - PL, NAc and CA1 wherein 2s width and 1s offset was set for the analysis. Second peak of autocorrelation and peak cross correlation were calculated in the time series analysis. Additionally, using power spectral data, theta coherence (4–8 Hz and 8–12 Hz) was analysed which measures the correlation between two signals within a specific frequency band between the brain regions while the rat was performing aforementioned tasks in the ORT.

Granger causality

To delineate the directed interactions among neural time series, Granger Causality in spectral domain was applied onto the LFP data. In the present analysis, the magnitude of the Granger causality is given by the ratio of the variance prediction-error terms of the reduced and full regressions, at each frequency level.

Statistical analysis

Statistical analysis and graph generation for both behavioral and electrophysiological experiments were performed using GraphPad Prism 9 (Graph-Pad Software Inc., CA, USA). The Shapiro–Wilk test and Kolmogorov-Smirnov Test were used to check the normality of the data sets before applying parametric tests. Behavioural data was analysed using student’s t test. For LFP data, two-way ANOVA with repeated measures followed by Sidak’s post hoc test was used to test changes in relative power values across the groups at different frequencies (groups as the column factor and various frequencies as row factor). One-way ANOVA followed by Tukey’s multiple comparison test was performed for PSD, coherence and granger causality considering different frequency bands between MS and NC groups. All data are expressed as mean\(\:\pm\:\)SEM and two-tailed test for the probability of P < 0.05 was considered statistical significance.

Results

Heightened curiosity index in rats exposed to MS stress

In rodents, the curiosity-like behaviour was assessed in the ingeniously designed object retrieval task (ORT) paradigm based on their natural tendency to explore objects even when they are not rewarding in a familiar environment. During the test phase, the objects were hidden, which triggers curiosity so that adolescent rats spontaneously retrieve the non-rewarding but novel objects after crossing the risky zone. Similar to our previous findings [30], in the present study, the MS rats showed increased curiosity-like behaviour by retrieving significantly more number of objects when compared to NC during the test phase (Fig. 3). Student t-test was applied to compare between groups showing an increased curiosity index (CI) in MS group as compared to NC rats (t1,16=2.345, p < 0.03) (Fig. 3A). An increase in the curiosity index was mainly based on the Q preference, i.e., significantly increased time spent in the curiosity platform (t1,16=2.304, p < 0.03) (Fig. 3B.) and increased trend in the number of objects retrieved (t1,16=1.685, p < 0.11) (Fig. 3C) between NC and MS rats.

Fig. 3
figure 3

Object Retrieval task to examine the curiosity-like behaviour. (a) curiosity index; (b) preference for curiosity platform (%); (c) number of objects retrieved. Unpaired t-test was applied to compare between the groups. Data represented as Mean ± SEM from NC and MS rats. *p < 0.05 vs. NC. NC = 8, MS = 10

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Neural correlates of curiosity-like behaviour in the object retrieval task

The hypothesis is that an active interaction between PL and CA1 with NAc are important in enhancing the cognitive learning driven by intrinsic motivation. To establish this hypothesis, the local field potentials (LFPs) from these brain regions in adolescent NC and MS rats were recorded during ORT. The LFP data were analysed after histological verification of the electrode placement in a rat brain sections at PL, NAc and CA1 areas.

PSD analysis from PL, NAc and CA1 hippocampus during risky zone crossing phase of the object retrieval task

Figure 4 shows the representative example of the LFPs from PL, NAc and CA1 during risky zone crossing phase from NC (Fig. 4A) and MS (Fig. 4B) rats. The oscillatory wave patterns in three brain regions – PL, NAc and CA1 were classified into 1–4 Hz, 4–8 Hz, 8–12 Hz, 12–20 Hz and 20–40 Hz. The analysed power spectral density data were further subjected to ordinary one-way ANOVA which revealed a significant effect of MS stress on groups (F9,80=40.14, p < 0.0001). As shown in Fig. 4 C, Tukey’s multiple comparison test revealed a significant reduction in PSD values of PL at higher theta (8–12 Hz) (p < 0.0001), beta (12–20 Hz) (p < 0.0001) and gamma (20–40 Hz) (p < 0.0001) frequency range when compared to NC.

Fig. 4
figure 4

Representative examples of local field potential (LFP) and spectrograms during Risky zone crossing phase. (A) NC and (B) MS rats from NAc, CA1 and PL during Risky zone crossing phase (1s) (please refer to the inset) of ORT. Time–frequency spectrogram depicting a reduction in theta/beta activity in PL of MS rats compared to NC. t0-t1 is the 1 s epoch which aligns to the beginning of risky zone crossing phase. Lower panel: Averaged PSD (RP) data from PL, NAc and CA1 during Risky zone crossing phase of ORT. Note a significant reduction in the PSD (RP) values in (C) PL at 8–12 Hz, 12–20 Hz and 20–40 Hz frequency bands of MS rats compared to NC. One-way ANOVA followed by Tukey’s multiple comparison test. Data is represented as Mean ± SEM. ****p < 0.0001 Vs NC was considered as statistically significant. NC = 8, MS = 10

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On the other hand, the oscillatory pattern in the NAc during the risky zone crossing phase was significantly different between groups with the one-way ANOVA analysis (F9,80=17.57, p < 0.0001). However, the multiple comparison with Tukey’s test did not reveal any significant differences in the band-specific frequency range (Fig. 4D).

As shown in Fig. 4E, the power spectral density values in the CA1 during risky zone crossing period was statistically significant based on one-way ANOVA analysis F9,80=34.37, p < 0.0001, but Tukey’s multiple comparison test did not reveal any band specific differences between groups.

MS stress and the power spectral density in PL-NAc-CA1 during object retrieval phase in the ORT

The changes in delta-theta-beta-gamma power during the object retrieval period in ORT paradigm was calculated. Figure 5 shows the representative example of the LFPs from PL, NAc and CA1 during object retrieval phase from NC (Fig. 5A) and MS (Fig. 5B) rat groups. The statistical analysis with the ordinary one-way ANOVA has revealed a significant effect between groups (F9, 80=49.29. P < 0.0001) in MS compared to NC rats. However, the Tukey’s multiple comparison test did not show any significant differences in PSD values in PL at different frequency bands such as in 1–4 Hz, 4–8 Hz, 8–12 Hz, 12–20 Hz and 20–40 Hz (Fig. 5C).

Fig. 5
figure 5

Representative local field potential (LFP) and spectrograms during object retrieval phase. From (a) NC and (b) MS rats from NAc, CA1 and PL during object retrieval phase, 2-s prior to the curiosity-driven exploration and 3-s from the beginning of the object retrieval phase. tx-ty is the 5 s epoch where tx represents 2-s prior to the curiosity-driven exploration and ty represents 3-s from the beginning of the object retrieval phase. Lower panel: Averaged PSD (RP) data from PL, NAc and CA1 during Object retrieval phase (5s) of ORT. During Object retrieval phase of ORT, NC and MS groups did not show any significant difference in the PSD (RP) at any frequency bands in (C) PL, (D) NAc and (E) CA1 hippocampus. One-way ANOVA followed by Tukey’s multiple comparison test. Data is represented as Mean ± SEM. ****p < 0.0001 Vs NC was considered as statistically significant. NC = 8, MS = 10

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The LFP data from the NAc was analysed from both NC and MS rats while exploring the non-rewarding objects and found that the PSD values were significantly altered between groups (F8,90=35.09, P < 0.0001). As shown in Fig. 5D, the power spectral density values in the NAc during object retrieval phase was not significantly between the groups at all frequency bands.

Similar to NAc, the LFP data from CA1 hippocampus was also subjected to the frequency band-wise analysis and revealed significant effect on the groups (F8,90=33.57, P < 0.0001) but with no significant interactions between the groups at all frequency bands in Tukey’s multiple comparison test (Fig. 5E).

The impact of early MS stress on the functional connectivity between PL-NAc-CA1 during risky zone crossing and object retrieval phase in the ORT

In the waveform correlation analysis, autocorrelation and cross-correlation analysis were performed. The synchronized oscillatory activities in PL, NAc and CA1 during risky zone crossing and object retrieval phase was significantly different between NC and MS rats (Additional file 2 A-F). The functional connectivity between PL, NAc and CA1 hippocampus during the risky zone crossing period and object retrieval task phase was evaluated using waveform cross correlation analysis. The results showed that the synchronized oscillations between PL-NAc, NA-CA1 and PL-CA1 during risky zone crossing period was significantly different between NC and MS rats (Fig. 6). Specifically, we found a significant difference between groups in the peak cross-correlation level in PL-CA1 (t1,16= 2.028, p < 0.05) (Fig. 6A and D) and with a significant trend in NAc-CA1 (t1,16= 1.904, p = 0.07) (Fig. 6B and E). However, no change in correlation level in PL-NAc (Fig. 6C and F) in MS rats compared to NC during risky zone crossing phase suggesting that the reduced risky zone crossing by MS rats could be correlated to a reduced functional connectivity between PL-CA1 (Fig. 6A) and NAc-CA1 (Fig. 6B).

Fig. 6
figure 6

Averaged waveform correlation analysis during risky zone cross phase of ORT. Cross-correlation between (A) PL vs. CA1, (B) NAc vs. CA1, and (C) PL vs. NAc. Note a significant reduction in peak cross correlation level of LFP between (D) PL vs. CA1 of MS rats compared to NC. Similar trend in reduced waveform correlation was observed between (E) NAc vs. CA1 but no difference in (F) PL vs. NAc. Unpaired t-test was used to compare the averaged peak values between the groups. *p < 0.05 Vs NC was considered as statistically significant. NC = 8, MS = 10

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The waveform correlation analysis during object retrieval phase indicated a reduced peak cross correlation level between PL-CA1 (t1,16= 2.139, p < 0.05) (Fig. 7A and D) and no change in correlation level in NAc-CA1 (Fig. 7B and E) but with a significant trend in PL-NAc (t1,16= 1.925, p < 0.07) in MS when compared to NC rats (Fig. 7C and F).

Fig. 7
figure 7

Averaged waveform correlation analysis during object retrieval phase of ORT. Cross-correlation between (A) PL vs. CA1, (B) NAc vs. CA1, (C) PL vs. NAc. No significant difference in peak cross correlation level of LFP between (D) PL vs. CA1, (E) NAc vs. CA1 and (F) PL vs. NAc during object retrieval phase. NC = 8, MS = 10

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Effect of early MS stress on theta coherence in PL-CA1, NAc-CA1 and PL-NAc during the risky zone crossing and object retrieval phase in the ORT

To assess whether the synchronous activity between PL-NAc-CA1 regions during the risky zone crossing and object retrieval phase of the ORT was altered following early MS stress, the coherence analysis was carried out. The coherence analysis in the theta range (4–8 Hz and 8–12 Hz) during the risky zone crossing period was carried out using ordinary one-way ANOVA analysis and results revealed no significant differences between PL-CA1 (Fig. 8A). A significant effect on groups was observed between NAc-CA1 (F3,34=4.179, p = 0.01) showing a significant reduction in theta coherence at 4–8 Hz between NC and MS group (p < 0.05), but not at 8–12 Hz (Fig. 8B). No such significant interactions were observed at theta coherence was observed between PL-NAc (Fig. 8C).

Fig. 8
figure 8

Coherence analysis between PL, NAc and CA1 during (A) risky zone crossing (B) object retrieval phase of ORT. The coherence analysis at various theta frequency range is shown between (A) PL-CA1, (B) NAc-CA1, (C) PL-NAc, during risky zone crossing; (D) between PL-CA1, (E) NAc-CA1 and (F) PL-NAc during Object retrieval phase of ORT. Note a significance between NAc-CA1 and with similar trend in PL-CA1 regions at 4–8 Hz frequency during Risky zone crossing phase. One-way ANOVA followed by Tukey’s multiple comparison test. Data represented as Mean ± SEM. *p < 0.05 Vs NC was considered as statistically significant. NC = 8, MS = 10

Full size image

On the other hand, during the object retrieval phase, there was no significant changes in the coherence level between PL-CA1 (Fig. 8D), NAc-CA1 (Fig. 8E) and PL-NAc (Fig. 8F) at the theta frequency range between NC and MS.

Granger causality

The directional causal interactions between PL, NAc and CA1 network during two different events of ORT was investigated using Granger Causality (GC) analysis. To compare the effect of MS on directional flow of information between PL, NAc and CA1 during risky zone crossing and object retrieval phase of ORT, two-way ANOVA followed by Sidak’s multiple comparison tests were carried out. There were no significant differences in GC values at 4–8 Hz and 8–12 Hz frequency range between MS and NC rats (Additional file. 3.).

Discussion

In the current study, we have studied the functional connectivity in the PL-NAc-CA1 network during the ORT to investigate the role of these brain areas in intrinsically driven curiosity-like behavior. The study was carried out in the adolescent rats predisposed to MS stress during SHRP and compared that with the age-matched normal control curiosity behavior. The main findings from the study indicates that the dynamic functional connectivity between PL-NAc-CA1 areas is associated with adolescence age-driven curiosity behavior in the ORT. The increased curiosity behavior in MS rats as compared to NC was associated with the following changes in PL-NAc-CA1 connectivity during the risky zone crossing phase: (a) reduced theta power in PL, (b) increased theta (lower range) power, while decreased theta (higher frequency range) power in NAc, (c) increased theta-gamma power in CA1 (d) reduced waveform correlation between PL-CA1, and NAc-CA1 and (e) reduced coherence in the PL-NAc circuit. On the contrary, during the object retrieval period, peak cross- correlation level was decreased predominantly in PL-CA1 and PL-NAc circuit.

Adolescent rats exhibit elevated curiosity-like behavior as a result of MS stress during SHRP

Simultaneous LFP recordings, along with the curiosity task, suggested that MS rats showed impulsivity while crossing the unsafe water zone, which was indicated by decreased latency in crossing water. MS rats spent significantly more time in the curiosity platform and retrieved more objects than normal controls. LTW was negatively correlated with the Curiosity Index, while Q preference and NOR were positively correlated with the Curiosity Index. This is in line with our previous study, where we had demonstrated in 30 30-minute ORT task that the MS rats showed decreased LTW, decreased retrieval latency, Increased Q preference (%), higher number of objects retrieved, and, in turn, higher Curiosity Index (CI) compared to normal controls [30]. Another study using a Risky decision-making task (RDTT) for food reward showed decreased latency in crossing the unsafe water zone in MS rats, which is considered as high risk-taking behavior or decreased risk assessment [31]. MS stress during SHRP had increased anxiety-like behavior in light-dark tests and increased plasma corticosterone and dopamine levels at P30 in both male and female rats, indicating hyperactivity of the HPA axis [30]. This supports the findings that where increased plasma DA levels may be complementary to better cue detection abilities and compulsive reward-seeking behaviors in MS rats in 5-CSRTT paradigm [32]. Adolescent rats exhibited reduced freezing during fear retention and enhanced freezing to CS + during the extinction recall phase. The increased fear extinction learning during adolescence could be due to MS stress-induced hypersensitivity of the HPA axis, leading to elevated corticosterone levels [29]. In contrast, in the fear conditioning paradigm, adult MS rats showed heightened anxiety, increased freezing, and fear memory retrieval [28, 44]. Altogether, the behavioural data suggest that MS stress has a more impact on the corticolimbic circuitry, increasing the intrinsic motivation to perform the curiosity-driven task, which may also possess survival value. However, the decreased latency to cross the risky zone may contribute to impulsive and risky decision-making during adolescence.

Early MS stress induces oscillatory changes during both the risky zone crossing and object retrieval phases of the ORT

The ORT is primarily designed to assess the curiosity-like behavior, driven by the intrinsic motivation to explore the novel arena for the non-rewarding objects under risky conditions. The elevated level of risky decision-taking behaviour with an increased curiosity-like behavior in adolescence age may be due to an elevated intrinsic drive. The PL is the sub-region of the medial prefrontal cortex (mPFC) involved in the reward-seeking behavior in a specific contextual environment. The curiosity in adolescence age, many times, does not depend upon the food reward but mainly on the intrinsic drive state. Hence it is important to delineate whether NAc plays a role in enhancing the curiosity like behavior under risky environment. It is also important to know how PL is involved in the cognitive control over intrinsic drive state. To elucidate the neural basis of such curiosity-driven performance, the present study has chosen two significant events of ORT: (a) the initial crossing of water, representing an active “approach” form of motivation indicative of cognitive control on the impulsive decision-making behavior, and (b) the retrieval of hidden objects, reflecting curiosity as a motivational state that prompts active object exploration and information-seeking behavior.

The curiosity is the motivational state driving active exploration through the intrinsic drive [8, 45,46,47]. It is suggested that the activation of striatal regions, such as NAc is strongly required during the anticipatory phase of the curiosity based on trivia questions [7, 8], particularly by triggering the risk-taking behaviour [9]. In addition to the striatal regions, several other brain regions are involved in enhancing the intrinsic drive state, such as the lateral prefrontal cortex, anterior cingulate cortex, and anterior insula [8, 33].

In a study based on PACE framework, the presence of an information gap triggers an appraisal process in the prefrontal cortex, subsequently driving exploration, supported by hippocampal memory processes and neural mechanisms for motivation, such as dopaminergic circuits [8, 12]. From a neurophysiological perspective, heightened curiosity may be associated with altered functional connectivity within key areas of the reward circuitry, leading to changes in neural activity in these regions. Therefore, in the current study, local field potential (LFP) signals were recorded from three key regions: the prelimbic cortex (PL), nucleus accumbens (NAc), and CA1 region of the hippocampus. Increased synchrony at multiple frequencies was observed in these regions. Low frequency rhythms such as theta oscillations are the important determinants of the functional connectivity between the long-distance cell assembles during the cognitive processes. During risky zone crossings in the ORT, we found that MS rats exhibited reduced theta power in PL but increased theta power in NAc and CA1, which was associated with reduced functional connectivity between PL-CA1 and PL-NAc. The gamma power in CA1 was increased in CA1 but not in PL and NAc. Studies have also reported an increased theta and gamma power in CA1 during decision making and attentional processes [34, 48, 49], while an increased theta power in NAc might be associated with an increased intrinsic drive for the non-rewarding object [48]. The role of mPFC in cognitive decision making behavior is well known [49]. Therefore, the decreased theta power in mPFC during decision-making could indicate poor cognitive processing [50], which might enhance risky decisions, resulting in elevated curiosity behavior.

The intense urge to explore novel environments, especially those involving risky elements, driven by curiosity, is influenced by various motivational factors, such as novelty seeking, sensation seeking, learning, and uncertainty resolution. The co-occurrence of decreased activity in the prefrontal cortex and increased power in NAc and CA1 theta activity suggests that multiple modalities influence the emergence of exploration behaviour. This variation in curiosity-driven exploration reflects distinct behavioral traits and processing modes during risky situations and object retrieval. At a network level, synchronous oscillations may facilitate spatial and temporal communication within and between brain regions, potentially supporting affective and cognitive functions [51,52,53]. No significant changes in the Granger causality analysis during the ORT indicates that the changes in synchronized oscillations at theta frequency range between PL, NAc and CA1 may not be due to the specific directional flow, rather could be due to changes in power spectral density in the PL-NAc-CA1 circuit. Therefore, the oscillation-mediated neural synchrony may be relevant to understanding the MS-induced changes in curiosity-like behavior in the presence of risky situations.

Theta activity in the CA1 region of the hippocampus has been extensively associated with spatial navigation [54, 55]. The medial prefrontal cortex (mPFC) is implicated in reward-directed actions [56, 57] and the regulation of anxiety and aversive states [38, 58,59,60]. Studies have shown that the prefrontal cortex exerts greater control over decision-making during potentially risky outcomes [61]. The synchronized activity between the PFC and hippocampus has been studied in various exploration tasks, including the center-periphery phenomenon of the open field and spatial learning and memory tasks [62, 63].

The nucleus accumbens (NAc), also known as the ventral striatum, is implicated in reward and reinforcement actions during both natural and drug rewards [64]. For instance, low gamma oscillations are more closely associated with reward processing than high gamma oscillations [65]. As the animal reaches the reward points, there was an increased low gamma power in NAc, while an increased high gamma power as the animal approaches the salient points [66, 67] thus, may be attributed to an increased dopamine release into the NAc in response to external rewards. Thus, the increased intrinsic motivation and curiosity in rats predisposed to early MS stress could be attributed to the dynamically changing intrinsically connected network in NAc, CA1, and PL during the object retrieval task.

Conclusion

Overall, the present study has characterized the dynamic interactions between PL-NAc-CA1 connectivity during the curiosity-driven object retrieval task performance in both controls and in rats predisposed to early MS stress. The study found that impulsive, risky decision-making with a high curiosity index in MS rats was characterized by [1] reduced theta power in the PL [2], reduced cross-correlation between PL-CA1 and [3] reduced theta coherence between NAc-CA1, during risky zone crossing [4]. Similarly, during the object retrieval phase, MS rats displayed reduced theta synchrony between PL-CA1 and PL-NAc, with a similar trend between NAc-CA1. Based on the above findings, it is evident that curiosity is one of the hallmark behavioral phenotypes of adolescence, and early adverse experiences are known to affect such behavior in enhancing the risk for substance abuse disorders later in life. Thus, identifying the neural circuits underlying the risky decision-making under triggered curiosity becomes a critical determining factor in identifying the risk for substance abuse disorders.

Data availability

Data is provided within the manuscript or supplementary information files.

Abbreviations

ELS:

Early life stress

SHRP:

Stress Hyporesponsive Period

MS:

Maternal separation stress

NC:

Normal control

ORT:

Object retrieval task

PL:

Prelimbic cortex

NAc:

Nucleus accumbens

HPC:

Hippocampus

PFC:

Prefrontal Cortex

VS:

Ventral striatum

DA:

Dopamine

ACC:

Anterior Cingulate cortex

PND:

Post-natal day

RDTT:

Risky decision taking task

5-CSRTT:

5- Choice serial reaction time task

PACE:

Prediction–appraisal–curiosity–exploration

REM:

Rapid eye movement

LFP:

Local field Potential

LTW:

latency to cross water

LTR:

Latency to retrieve object

CI:

Curiosity Index

NOR:

Number of objects retrieved

FFT:

Fast Fourier Transformation

PSD (RP):

Power spectral density (Relative Power)

GC:

Granger causality

CPCSEA:

Committee for the purpose of control and supervision of experiments on animals

CARF:

Central animal research facility

References

  1. Berlyne DE. Curiosity and exploration. Science. 1966;153(3731):25–33.

    Article  CAS  PubMed  Google Scholar 

  2. Reeve J, Nix G. Expressing intrinsic motivation through acts of exploration and facial displays of interest. Motivation Emot. 1997;21:237–50.

    Article  Google Scholar 

  3. Ryan RM, Deci EL. Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp Educ Psychol. 2000;25(1):54–67.

    Article  CAS  PubMed  Google Scholar 

  4. Gruber MJ, Fandakova Y. Curiosity in childhood and adolescence — what can we learn from the brain. Curr Opin Behav Sci. 2021;39:178–84.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Fandakova Y, Gruber MJ. States of curiosity and interest enhance memory differently in adolescents and in children. Dev Sci. 2021;24(1):e13005.

    Article  PubMed  Google Scholar 

  6. Charpentier CJ, Bromberg-Martin ES, Sharot T. Valuation of knowledge and ignorance in mesolimbic reward circuitry. Proc Natl Acad Sci. 2018;115(31):E7255–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jepma M, Verdonschot RG, Van Steenbergen H, Rombouts S, Nieuwenhuis S. Neural mechanisms underlying the induction and relief of perceptual curiosity. Front Behav Neurosci. 2012;6(5):103389.

    Google Scholar 

  8. Kang MJ, Hsu M, Krajbich IM, Loewenstein G, McClure SM, Wang JT et al. yi,. The wick in the candle of learning: Epistemic curiosity activates reward circuitry and enhances memory. Psychological science. 2009;20(8):963–73.

  9. Lau JKL, Ozono H, Kuratomi K, Komiya A, Murayama K. Shared striatal activity in decisions to satisfy curiosity and hunger at the risk of electric shocks. Nat Hum Behav. 2020;4(5):531–43.

    Article  PubMed  Google Scholar 

  10. Ligneul R, Mermillod M, Morisseau T. From relief to surprise: dual control of epistemic curiosity in the human brain. NeuroImage. 2018;181:490–500.

    Article  PubMed  Google Scholar 

  11. Oosterwijk S, Snoek L, Tekoppele J, Engelbert L, Steven Scholte H. Choosing to view morbid information involves reward circuitry. BioRxiv, 795120. 2019.

  12. Gruber MJ, Gelman BD, Ranganath C. States of Curiosity Modulate Hippocampus-Dependent Learning via the Dopaminergic Circuit. Neuron. 2014;84(2):486–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Heinz DE, Schöttle VA, Nemcova P, Binder FP, Ebert T, Domschke K, et al. Exploratory drive, fear, and anxiety are dissociable and independent components in foraging mice. Transl Psychiatry. 2021;11(1):1–12.

    Article  Google Scholar 

  14. Voon V, Irvine MA, Derbyshire K, Worbe Y, Lange I, Abbott S, et al. Measuring waiting impulsivity in substance addictions and binge eating disorder in a novel analogue of rodent serial reaction time task. Biol Psychiatry. 2014;75(2):148–55.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Dalley JW, Fryer TD, Brichard L, Robinson ES, Theobald DE, Lääne K, et al. Nucleus accumbens D2/3 receptors predict trait impulsivity and cocaine reinforcement. Science. 2007;315(5816):1267–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Keller NE, Salvi C, Leiker EK, Gruber MJ, Dunsmoor JE. States of epistemic curiosity interfere with memory for incidental scholastic facts. npj Sci Learn. 2024;9(1):22.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci. 1999;2(10):861–3.

    Article  CAS  PubMed  Google Scholar 

  18. Uylings HB, van Eden CG. Qualitative and quantitative comparison of the prefrontal cortex in rat and in primates, including humans. Prog Brain Res. 1991;85:31–62.

    Article  Google Scholar 

  19. Sowell ER, Thompson PM, Holmes CJ, Jernigan TL, Toga AW. In vivo evidence for post-adolescent brain maturation in frontal and striatal regions. Nat Neurosci. 1999;2(10):859–61.

    Article  CAS  PubMed  Google Scholar 

  20. Sowell ER, Thompson PM, Tessner KD, Toga AW. Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: inverse relationships during postadolescent brain maturation. J Neurosci. 2001;21(22):8819–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zuo Y, Lin A, Chang P, Gan WB. Development of long-term dendritic spine stability in diverse regions of cerebral cortex. Neuron. 2005;46(2):181–9.

    Article  CAS  PubMed  Google Scholar 

  22. Uhlhaas PJ, Singer W. Oscillations and neuronal dynamics in schizophrenia: the search for basic symptoms and translational opportunities. Biol Psychiatry. 2015;77(12):1001–9.

    Article  PubMed  Google Scholar 

  23. Rosenberg DR, Lewis DA. Postnatal maturation of the dopaminergic innervation of monkey prefrontal and motor cortices: a tyrosine hydroxylase immunohistochemical analysis. J Comp Neurol. 1995;358(3):383–400.

    Article  CAS  PubMed  Google Scholar 

  24. Lambe EK, Krimer LS, Goldman-Rakic PS. Differential postnatal development of catecholamine and serotonin inputs to identified neurons in prefrontal cortex of rhesus monkey. J Neurosci. 2000;20(23):8780–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Spear LP. The adolescent brain and age-related behavioral manifestations. Neurosci Biobehavioral Reviews. 2000;24(4):417–63.

    Article  CAS  Google Scholar 

  26. Nishi M. Effects of early-life stress on the brain and behaviors: implications of early maternal separation in rodents. IJMS. 2020;21(19):7212.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Park AT, Tooley UA, Leonard JA, Boroshok AL, McDermott CL, Tisdall MD, et al. Early childhood stress is associated with blunted development of ventral tegmental area functional connectivity. Dev Cogn Neurosci. 2021;47:100909.

    Article  PubMed  Google Scholar 

  28. Sampath D, Sabitha KR, Hegde P, Jayakrishnan HR, Kutty BM, Chattarji S, et al. A study on fear memory retrieval and REM sleep in maternal separation and isolation stressed rats. Behav Brain Res. 2014;273:144–54.

    Article  PubMed  Google Scholar 

  29. Mishra PK, Kutty BM, Laxmi TR. The impact of maternal separation and isolation stress during stress hyporesponsive period on fear retention and extinction recall memory from 5-week-to 1-year-old rats. Exp Brain Res. 2019;237:181–90.

    Article  PubMed  Google Scholar 

  30. Sharma SS, Srinivas Bharath MM, Doreswamy Y, Laxmi TR. Effects of early life stress during stress hyporesponsive period (SHRP) on anxiety and curiosity in adolescent rats. Exp Brain Res. 2022;240(4):1127–38.

    Article  PubMed  Google Scholar 

  31. Chowdhury A, Sharma SS, Arjun BS, Pandya HJ, Shankaranarayana Rao BS, Laxmi TR. Risky decision-taking task: a novel paradigm to assess the risk-taking behaviour in rats predisposed to early-life stress. J Neurosci Methods. 2023;392:109864.

    Article  PubMed  Google Scholar 

  32. Kambali MY, Anshu K, Kutty BM, Muddashetty RS, Laxmi TR. Effect of early maternal separation stress on attention, spatial learning and social interaction behaviour. Exp Brain Res. 2019;237(8):1993–2010.

    Article  PubMed  Google Scholar 

  33. Gruber MJ, Ranganath C. How curiosity enhances Hippocampus-dependent memory: the Prediction, Appraisal, Curiosity, and Exploration (PACE) Framework. Trends Cogn Sci. 2019;23(12):1014–25.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Buzsáki G. Theta oscillations in the hippocampus. Neuron. 2002;33(3):325–40.

    Article  PubMed  Google Scholar 

  35. Buzsáki G, Draguhn A. Neuronal oscillations in cortical networks. Science. 2004;304(5679):1926–9.

    Article  PubMed  Google Scholar 

  36. Hegde P, O’Mara S, Laxmi TR. Extinction of contextual fear with timed exposure to enriched environment: a differential effect. Annals Neurosciences. 2017;24(2):90–104.

    Article  Google Scholar 

  37. Seidenbecher T, Laxmi TR, Stork O, Pape HC. Amygdalar and hippocampal theta rhythm synchronization during fear memory retrieval. Science. 2003;301(5634):846–50.

    Article  CAS  PubMed  Google Scholar 

  38. Adhikari A, Topiwala MA, Gordon JA. Synchronized activity between the ventral Hippocampus and the Medial Prefrontal cortex during anxiety. Neuron. 2010;65(2):257–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dong W, Chen H, Sit T, Han Y, Song F, Vyssotski AL, et al. Characterization of exploratory patterns and hippocampal–prefrontal network oscillations during the emergence of free exploration. Sci Bull. 2021;66(21):2238–50.

    Article  Google Scholar 

  40. Donnelly NA, Holtzman T, Rich PD, Nevado-Holgado AJ, Fernando ABP, Van Dijck G et al. Oscillatory Activity in the Medial Prefrontal Cortex and Nucleus Accumbens Correlates with Impulsivity and Reward Outcome. Tort ABL, editor. PLoS ONE. 2014;9(10):e111300.

  41. Samerphob N, Issuriya A, Cheaha D, Chatpun S, Jensen O, Kumarnsit E. Beta and gamma synchronous oscillations in neural network activity in mice-induced by food deprivation. Neurosci Lett. 2019;709:134398.

    Article  CAS  PubMed  Google Scholar 

  42. Wróbel A. BETA ACTIVITY: A CARRIER FOR VISUAL ATTENTION.

  43. Paxinos G, Watson C. The rat brain in stereotaxic coordinates. Academic; 2013. p. 162.

  44. Laxmi TR, Kutty DS, Vaishnavi B, Durgalakshmi S. Long-term effects of early maternal separation and isolation stress on adulthood behaviour of female rats. Curr Sci. 2010;99:1811–5.

    Google Scholar 

  45. Berlyne DE. Curiosity and exploration: animals spend much of their time seeking stimuli whose significance raises problems for psychology. Science. 1966;153(3731):25–33.

    Article  CAS  PubMed  Google Scholar 

  46. Berlyne DE. Novelty and curiosity as determinants of exploratory Behaviour1. Br J Psychol Gen Sect. 1950;41(1–2):68–80.

    Article  Google Scholar 

  47. Loewenstein G. The psychology of curiosity: a review and reinterpretation. Psychol Bull. 1994;116(1):75–98.

    Article  Google Scholar 

  48. Gruber AJ, Hussain RJ, O’Donnell P. The Nucleus Accumbens: a switchboard for goal-Directed behaviors. PLoS ONE. 2009;4(4):e5062.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Euston DR, Gruber AJ, McNaughton BL. The role of medial prefrontal cortex in memory and decision making. Neuron. 2012;76(6):1057–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Myroshnychenko M, Seamans JK, Phillips AG, Lapish CC. Temporal dynamics of hippocampal and medial prefrontal cortex interactions during the Delay period of a Working Memory-guided foraging Task. Cereb Cortex. 2017;27(11):5331–42.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Fries P. Rhythms for cognition: communication through coherence. Neuron. 2015;88(1):220–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kim Y, Wood J, Moghaddam B. Coordinated activity of ventral tegmental neurons adapts to appetitive and aversive learning. PLoS ONE. 2012;7(1):e29766.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Lee E, Rhim I, Lee JW, Ghim JW, Lee S, Kim E, et al. Enhanced neuronal activity in the Medial Prefrontal Cortex during Social Approach Behavior. J Neurosci. 2016;36(26):6926–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. O’Keefe J, Recce ML. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus. 1993;3(3):317–30.

    Article  PubMed  Google Scholar 

  55. Vanderwolf CH. Hippocampal electrical activity and voluntary movement in the rat. Electroencephalogr Clin Neurophysiol. 1969;26(4):407–18.

    Article  CAS  PubMed  Google Scholar 

  56. Powell NJ, Redish AD. Representational changes of latent strategies in rat medial prefrontal cortex precede changes in behaviour. Nat Commun. 2016;7(1):12830.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Rich EL, Shapiro M. Rat prefrontal cortical neurons selectively Code Strategy switches. J Neurosci. 2009;29(22):7208–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kumar S, Hultman R, Hughes D, Michel N, Katz BM, Dzirasa K. Prefrontal cortex reactivity underlies trait vulnerability to chronic social defeat stress. Nat Commun. 2014;5(1):4537.

    Article  CAS  PubMed  Google Scholar 

  59. Likhtik E, Stujenske JM, Topiwala A, Harris M, Gordon AZ. Prefrontal entrainment of amygdala activity signals safety in learned fear and innate anxiety. Nat Neurosci. 2014;17(1):106–13.

    Article  CAS  PubMed  Google Scholar 

  60. Park J, Wood J, Bondi C, Del Arco A, Moghaddam B. Anxiety evokes hypofrontality and disrupts rule-relevant Encoding by Dorsomedial Prefrontal Cortex Neurons. J Neurosci. 2016;36(11):3322–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Park J, Moghaddam B. G Schoenbaum editor 2017 Risk of punishment influences discrete and coordinated encoding of reward-guided actions by prefrontal cortex and VTA neurons. eLife 6 e30056.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Jones MW, Wilson MA. Theta rhythms coordinate hippocampal–prefrontal interactions in a spatial memory Task. PLoS Biol. 2005;3(12):e402.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Siapas AG, Wilson MA. Coordinated interactions between hippocampal ripples and cortical spindles during slow-Wave Sleep. Neuron. 1998;21(5):1123–8.

    Article  CAS  PubMed  Google Scholar 

  64. Spanagel R, Weiss F. The dopamine hypothesis of reward: past and current status. Trends Neurosci. 1999;22(11):521–7.

    Article  CAS  PubMed  Google Scholar 

  65. Kalenscher T, Lansink CS, Lankelma JV, Pennartz CMA. Reward-Associated Gamma oscillations in ventral striatum are regionally differentiated and modulate local firing activity. J Neurophysiol. 2010;103(3):1658–72.

    Article  PubMed  Google Scholar 

  66. Van Der Meer MAA, Redish AD. Low and high gamma oscillations in rat ventral striatum have distinct relationships to behavior, reward, and spiking activity on a learned spatial decision task. Front Integr Neurosci. 2009.

  67. van der Meer MAA, Kalenscher T, Lansink CS, Pennartz CMA, Berke JD, Redish AD. Integrating early results on ventral striatal gamma oscillations in the rat. Front Neurosci. 2010;4:300.

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We sincerely acknowledge Council for Scientific and Industrial Research (CSIR), India for the research grant (27(0369)/20/EMR-II) and National Institute of Mental Health And Neuro Sciences (NIMHANS), Bengaluru, India for providing the infrastructure.

Funding

This study was supported by Council for Scientific and Industrial Research (CSIR), India, research grant (27(0369)/20/EMR-II).

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  1. Arun Sasidharan & D. Yoganarasimha

    Present address: Department of Neurophysiology, National Institute of Mental Health and Neuro Sciences, Bengaluru, 560 029, India

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  1. Department of Neurophysiology, National Institute of Mental Health and Neuro Sciences, Bengaluru, 560 029, India

    Shruthi S. Sharma & T. R. Laxmi

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Contributions

S.S.S: Data acquisition, data analysis, preparing figures, preparing manuscript; A.S: MATLAB programing; D.Y: manuscript proofreading and supervision; T.R.L: Study conceptualization and design, data analysis, preparing figures, preparing manuscript, revisions and supervisions.

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Correspondence to T. R. Laxmi.

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Sharma, S.S., Sasidharan, A., Yoganarasimha, D. et al. Characterization of neuronal oscillations in the prelimbic cortex, nucleus accumbens and CA1 hippocampus during object retrieval task in rats predisposed to early life stress. Behav Brain Funct 20, 34 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12993-024-00255-w

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