Introduction: Developmental windows in which experiences can elicit long-lasting effects on brain circuitry and behavior are called “sensitive periods” and reflect a state of heightened plasticity. The classic example of a sensitive period comes from studies of sensory systems, like the visual system, where early visual experience is required for normal wiring of primary visual cortex and proper visual functioning. At a mechanistic level, loss of incoming visual input results in a decrease in activity in thalamocortical neurons representing the affected eye, resulting in an activity-dependent reduction in the representation of those inputs in the visual cortex and loss of visual perception in that eye. While associative cortical regions like the medial prefrontal cortex (mPFC) do not receive direct sensory input, recent findings demonstrate that changes in activity levels experienced by this region during defined windows in early development may also result in long-lasting changes in prefrontal cortical circuitry, network function, and behavior. For example, we recently demonstrated that decreasing the activity of mPFC parvalbumin-expressing (PV) interneurons during a period of time encompassing peripuberty (postnatal day P14) to adolescence (P50) led to a long-lasting decrease in their functional inhibition of pyramidal cells, as well as impairments in cognitive flexibility. While the effects of manipulating mPFC PV interneuron activity were selective to development, and not adulthood, the exact timing of the sensitive period for this manipulation remains unknown. Methods: To refine the sensitive period in which inhibiting mPFC PV cell activity can lead to persistent effects on prefrontal functioning, we used a chemogenetic approach to restrict our inhibition of mPFC PV activity to two distinct windows: (1) peripuberty (P14–P32) and (2) early adolescence (P33–P50). We then investigated adult behavior after P90. In parallel, we performed histological analysis of molecular markers associated with sensitive period onset and offset in visual cortex, to define the onset and offset of peak-sensitive period plasticity in the mPFC. Results: We found that inhibition of mPFC PV interneurons in peripuberty (P14–P32), but not adolescence (P33–P50), led to an impairment in set-shifting behavior in adulthood manifest as an increase in trials to reach criterion performance and errors. Consistent with a pubertal onset of sensitive period plasticity in the PFC, we found that histological markers of sensitive period onset and offset also demarcated P14 and P35, respectively. The time course of expression of these markers was similar in visual cortex. Conclusion: Both lines of research converge on the peripubertal period (P14–P32) as one of heightened sensitive period plasticity in the mPFC. Further, our direct comparison of markers of sensitive period plasticity across the prefrontal and visual cortex suggests a similar time course of expression, challenging the notion that sensitive periods occur hierarchically. Together, these findings extend our knowledge about the nature and timing of sensitive period plasticity in the developing mPFC.

Developmental windows in which experiences can elicit long-lasting effects on brain circuitry and behavior are called “sensitive periods” and reflect a state of heightened plasticity. The classic example of how experience can refine brain circuitry during a sensitive period to persistently impact adult behavior comes from seminal studies in the visual system. In the visual system, exposure to visual stimuli during a defined early postnatal window is required for proper maturation of visual cortical circuitry. For example, loss of visual input from one eye during this period (e.g., due to eyelid suture in the animal or cataracts in a human) results in a permanent loss of visual acuity in that eye if visual input from that eye is not restored before the end of the critical period (CP) [1]. At a mechanistic level, loss of incoming visual input results in a decrease in activity in thalamocortical neurons representing the affected eye, resulting in an activity-dependent reduction in the representation of those inputs in the visual cortex and loss of visual perception in that eye [1‒4]. While associative cortical regions like the prefrontal cortex do not receive direct sensory input, recent findings demonstrate that changes in activity levels experienced by this region during defined windows in early development may also result in long-lasting changes in prefrontal cortical circuitry, network function, and behavior [5‒11]. For example, we recently demonstrated that decreasing the activity of medial prefrontal parvalbumin (PV)-expressing interneurons during a period of time encompassing peripuberty (postnatal day P14) to adolescence (P50) led to a long-lasting decrease in their functional inhibition of pyramidal cells, as well as impairments in cognitive flexibility [5]. While the effects of manipulating prefrontal PV interneuron activity were selective to development, and not adulthood [5], the exact timing of the sensitive period for this manipulation remains unknown.

The developmental window identified by our original study encompasses both peripuberty and early adolescence, distinct stages in the lifespan of the animal, based on various behavioral and hormonal criteria [12‒14]. These time periods are also distinguished by several physiological and anatomical changes occurring to PV cells in the medial prefrontal cortex (mPFC). During peripuberty (P14–P32), PV cells become the dominant source of GABAergic inhibition on pyramidal cells, taking over from somatostatin interneurons [15]. Their synaptic strength and connection probability also increase during this time [15]. During the early adolescent window (P33–P50), the ability of PV interneurons to provide GABAergic inhibition is heightened as they now fire at a higher frequency. Therefore, understanding the timing of the sensitive period for manipulations of PV cell activity will give insight into the mechanisms underlying the long-lasting effects on cortical circuitry.

To refine the sensitive period in which manipulating PV cell activity alters adult mPFC function and behavior, we used a chemogenetic approach [16] to restrict our manipulation of mPFC PV activity to two distinct windows: (1) peripuberty (P14–P32) and (2) early adolescence (P33–P50). We then investigated adult behavior after P90. In parallel, we used molecular markers to define the onset and offset of peak-sensitive period plasticity in the mPFC. Specifically, we examined PV cell number as a marker for the onset of sensitive period plasticity and intensity of perineuronal nets (PNNs) as a marker for the closure of sensitive period plasticity. These markers were established and validated in primary visual cortex (V1) [17‒20] and then investigated in the mPFC at five different timepoints spanning postnatal days 14–90.

Both lines of research converge on the peripubertal period (P14–P32) as one in which sensitive period plasticity is greatest in the mPFC. Further, our direct comparison of markers of sensitive period plasticity across the prefrontal and visual cortex suggests a similar time course of expression, challenging the notion that sensitive periods occur hierarchically. Together, these findings extend our knowledge about the nature and timing of sensitive period plasticity in the developing mPFC.

Animals

C57BL/6 (Jackson Labs, Stock #000664) mice were bred in house with PV-Cre (Jackson Stock #008069) mice to produce heterozygous PV-Cre mice used for the PV inhibition experiments. C57Bl/6 mice were used for histological studies, either bred in house (for studies of P14, P21, and P35 animals) or ordered from Jackson Laboratories (for studies of P50 and P90 animals). Both male and female mice were used for all experiments. All animals were fed ad libitum and reared under normal lighting conditions (12/12 light/dark cycle).

Chemogenetic Manipulation

Animals underwent viral injection surgery at P12–P16 (the P16 surgeries were for pups meant to receive CNO injections at P33). Pups were anesthetized with a mixture of ketamine (40 mg/kg) and xylazine (5 mg/kg; 0.01 mL of 4 mg/mL ketamine/0.6 mg/mL xylazine per 1 g of mouse). AAV5-hSyn-DIO-hM4DGi-mCherry (Addgene cat. 50459, lot: v120251, 2.4 × 1013 GC/mL titer) or AAV5-hSyn-DIO-mCherry (Addgene cat. 44362, lot: v63478, 8.4 × 1012 GC/mL titer) was injected bilaterally into the mPFC at coordinates: (AP) +0.92, (ML) ±0.13, (DV) −1.45 (relative to bregma). 0.25 μL of virus was injected at each site over the course of 2 min, followed by a 2-min wait prior to withdrawing the injection pipette. Injection pipettes were pulled from 0.53 mm internal diameter glass capillaries with a P-97 puller (Sutter Instruments).

Four experimental groups were formed: animals expressing hM4DGi-mCherry and receiving CNO from P14 to P32 (Peripuberty Inhibition), animals expressing hM4DGi-mCherry and receiving CNO from P33 to P50 (Early Adolescent Inhibition), animals expressing mCherry and receiving CNO from P14 to P32 (Peripuberty Control), and animals expressing mCherry and receiving CNO from P33 to P50 (Early Adolescent Control). Animals from each litter were assigned to these groups with efforts made to balance the sexes across groups. Animals injected with AAV5-hSyn-DIO- hM4DGi-mCherry and AAV5-hSyn-DIO-mCherry were cohoused. 2 mg/kg of CNO (in 0.9% saline) was administered via intraperitoneal injection daily P14–P32 or P33–P50 (1 mg/kg 2× per day from Monday to Friday and 2 mg/kg 1× per day on Saturday and Sunday). The solution was kept at room temperature, protected from light when not in use, and made new every 4 days. The stock CNO powder was stored at −20°C.

Attentional Set-Shifting

Attentional set-shifting (SS) was performed as previously described [5, 21]. Briefly, mice were food deprived until they reached 80–85% of their baseline body weight. They received 1 day of habituation to the SS enclosure (24″ L × 11.5″ W × 12″ H), which consisted of 10-min foraging for pieces of Honey Nut Cheerios-brand sweetened oat cereal distributed around the enclosure as a reward. They were habituated to digging bowls (terracotta cups, 3″ diameter, 0.75″ height) filled with the bedding media (corn cob or torn paper, Vitakraft, Amazon) and the reward (Cheerios), which was buried in the bedding media. Habituation was performed overnight in the home cage. The next 2 days, mice received training days in which they were presented with two pots filled with either unscented corn cob or torn paper bedding, both baited with a hidden Cheerio reward. On both days, the animals received 5 trials in which they were required to dig to obtain the reward from both pots. After 2 days of shaping, if the animal was consistently finding the buried Cheerio, they began behavioral testing the next day. In the first phase of testing, initial acquisition (IA), the animal was presented with two pots each filled with a compound stimulus. The compound stimulus was composed of a bedding (corn cob or torn paper) and scent (cinnamon or paprika). The animal was required to learn that one dimension of the stimulus (in this case, the cinnamon scent) predicted the presence of a Cheerio reward. For the first five trials, the animal was allowed to dig in both pots to aid in rule acquisition, but the outcome of the trial was scored based on the first pot they dug in. After the first five trials, the animal was only allowed to dig in one pot. If they attempted to dig in the other pot after digging in the first, they were removed from the enclosure. IA finished when the animal performed 8 out of 10 consecutive trials correctly. Immediately after IA finished, the SS portion of the task began. The rule then changed, so the opposite stimulus dimension (in this case, paper bedding) indicated the presence of a Cheerio, irrespective of the scent with which it was paired. The animal was only allowed to dig in one pot for all trials of SS. SS ended once the animal had performed 8 out of 10 consecutive trials correctly. During SS, errors resulting from the mouse making the choice that would have been rewarded in IA were called “perseverative errors.” All other errors were called “random errors.”

Perfusions

Mice were deeply anesthetized with a mixture of ketamine and xylazine prior to transcardial perfusion. Postnatal day 14 (P14) mice were anesthetized with a mixture of 40 mg/kg ketamine and 5 mg/kg xylazine. Older mice were anesthetized with 100 mg/kg ketamine and 5 mg/kg xylazine. All animals were perfused with phosphate-buffered saline (PBS) followed by 4% paraformaldehyde (PFA) in PBS to fix the tissue. P14 mice were perfused using a 10-cc syringe due to their smaller size and older mice using a pump. Brains were dissected out and postfixed in 4% paraformaldehyde overnight before being transferred to 1% PBS for long-term storage.

Histology

For Plasticity Markers

Using a vibratome, 100 μm sections containing the mPFC and primary visual cortex (V1) were collected for each animal. In order to detect PNNs and PV-containing interneurons, slices were first permeabilized by incubation with 0.1% Triton X-100 (TX-100) in PBS and then blocked by incubation with 5% normal donkey serum in PBS with 0.1% TX-100, followed by incubation with wisteria floribunda agglutinin (WFA) conjugated to biotin (Vector Labs, B-1355) and an antibody to detect PV (Sigma, P3088-100UL), diluted at 1:1,000 in blocking buffer. Primary antibody incubation was 48 h at 4°C after which sections were rinsed three times in PBS with 0.1% TX-100. Subsequently, sections were incubated with Streptavidin-488 (Invitrogen, A10036) and Donkey anti-mouse-546 (Biotium, 29034) diluted at 1:1,000 in 5% normal donkey serum in PBS with 0.1% TX-100 for 1 h at room temperature. Sections were then rinsed 3 times in PBS, mounted onto glass slides and coverslipped using Vectashield mounting media without DAPI. Reagent concentrations were chosen based on pilot experiments to maximize visualization of the PNN and PV signals while minimizing nonspecific binding and reducing background fluorescence.

To Confirm Viral Targeting

Using a vibratome, 100 μm sections containing the mPFC were stained for the mCherry fluorophore. First, slices were first permeabilized by incubation with 0.1% TX-100 in PBS and then blocked by incubation with 5% fetal calf serum (FCS) in PBS with 0.1% TX-100. This was followed by incubation with an anti-dsRed antibody made in rabbit (Takara cat: 632496), diluted at 1:1,000 in 5% FCS in PBS with 0.1% TX-100. Select slices were also incubated with an anti-PV antibody produced in mouse (Sigma P3088) diluted at 1:1,000 in 5% FCS in PBS with 0.1% TX-100. Sections were incubated in the primary antibody solution for 48 h at 4°C on a shaker after which the sections were rinsed four times with PBS with 0.1% TX-100. Afterward, the sections were incubated in donkey anti-rabbit-546 (Invitrogen cat: A10040) diluted at 1:1,000 in 5% FCS in PBS with 0.1% TX-100 for 1 h at room temperature. Slices meant for PV cells were also incubated in the same step with donkey anti-mouse-488 (Invitrogen cat: A21202) diluted at 1:1,000 in 5% FCS in PBS with 0.1% TX-100. The sections were washed three times for 10 min in PBS and then washed in Tris 7.4 buffer and mounted on glass slides and coverslipped with Vectashield mounting medium without DAPI. 10× tile scan images were taken of each slice using a Leica confocal microscope.

Image Acquisition

For Plasticity Markers

Images were acquired with a 63× oil objective (1.40 numerical aperture) on a Leica Sp8 confocal microscope. To identify the appropriate anatomical location within the mPFC and V1 to take the 63× image, 10× (0.4 numerical aperture) tile scans of the entire brain were used to navigate to the appropriate location. The focal plane for 63× confocal image was chosen based on the plane in which PV cells were best in focus. Acquisition settings for PV and WFA were held constant for all images.

To Confirm Viral Targeting

10× (0.4 numerical aperture) tile scans of each slice were taken with a Leica Sp8 confocal microscope. Each tile had a pixelation density of 1,024 × 1,024 pixels. Acquisition settings were held constant for all images.

Analysis

For Markers of Plasticity

Two images (1 per hemisphere) of each brain section (using at least 2 separate sections per mouse) containing either mPFC or V1 were acquired using Leica software, exported as tifs, and analyzed using Image J software to assess the number of PV cells per image and the intensity of the WFA staining encapsulating the PV-expressing cells. The images were acquired with an area of 184.71 × 184.71 μm and a pixelation density of 1,024 × 1,024 pixels and were under the same settings with no significantly different background fluorescence. Each 63× image comprised a single confocal plane with one fluorescence channel containing the image of PV staining and another fluorescence channel containing the image of WFA/PNN staining. Using the PV images, all visible PV-expressing cells were manually traced and identified as regions of interest (ROIs). These ROIs were used to calculate the average number of PV cells per image. The ROIs were then enlarged outward in all directions by 2 µm on a 2-D image and transferred to the corresponding PNN images. By transferring the traced PV cells into the PNN images, the region to quantify the PNNs was standardized across images and cells. The intensity within this outline was quantified in the PNN image as a measure of PNN staining. Background tissue autofluorescence was not subtracted from these measurements. All measurements were averaged by animal and compared by age and by region of cortex using a 2-way ANOVA. One animal from P35 and another one from P21 had a laceration in V1 and mPFC regions, respectively, and thus were excluded from the results. Additionally, as no PV cells were visible in the mPFC at P14 except for a single cell identified for one animal, that animal was included for PV cell count analysis, but excluded for the WFA staining intensity analysis, where our inability to average that value (because no other PV cells were visible) would falsely skew the results. Statistical analyses were performed using Prism (GraphPad Software, CA, USA).

To Confirm Viral Targeting

All experimental animals injected with AAV-hSyn-DIO- hM4DGi-mCherry were put into one of three categories based on the following analysis done in ImageJ. The outline of the mPFC (infralimbic and prelimbic combined) for each hemisphere was taken based on anatomical landmarks and saved as an ROI for each slice. We noted that some viral staining extends beyond these boundaries. A small rectangular ROI was taken on the bottom of each hemisphere (e.g., the olfactory bulb in early slices) and another was taken outside of the slice to take background measurements accounting for tissue autofluorescence or any other sources of fluorescence outside of the slice. To properly measure the area covered by and the level of intensity of viral expression fluorescence, a fluorescence threshold was assigned to each image. Thresholds were empirically determined for a subset of animals and then were assigned to all images as a function of the mean gray value of the tissue background. All measurements taken are a function of the pixels whose brightness is higher than the assigned threshold. There was a trimodal distribution of animals’ %area fluorescence (%area of pixels over threshold, summed over two hemispheres and averaged over the three slices). Analysis of the distribution of these values suggested three distinct clusters: (1) no expression (online suppl. Fig. S1 No [for all online suppl. material, see https://doi.org/10.1159/000539584], Peripuberty Inhibition: mean ± SEM: 1.242 ± 0.1536%, range = 0.959–1.65, n = 4; Early Adolescent Inhibition: mean ± SEM: 0.3518 ± 0.07724%, range = 0.1750–0.5380, n = 4); (2) low expression (online suppl. Fig. S1 Low, Peripuberty Inhibition: mean ± SEM: 9.883 ± 0.7362%, range = 7.830–11.30%, n = 4; Early Adolescent Inhibition: mean ± SEM: 6.71 ± 3.22%, range = 3.490–9.930%, n = 2); and (3) high expression (online suppl. Fig. S1 High, Peripuberty Inhibition: mean ± SEM: 30.65 ± 7.880%, range = 20.90–54.20%, n = 4; Early Adolescent Inhibition: mean ± SEM: 27.64 ± 3.832%, range = 19.90–41.30%, n = 5). One-way ANOVA with post hoc analysis confirmed that the percent area of expression in the high expression group indeed differed from the others (Peripuberty Inhibition: one-way ANOVA, Sidak’s multiple comparisons No vs. Low p = 0.5146, No vs. High p = 0.0042, Low vs. High p = 0.0315; Early Adolescence Inhibition: one-way ANOVA, Sidak’s multiple comparisons No vs. Low p = 0.6193, No vs. High p = 0.0006, Low vs. High p = 0.012). Importantly, the percent area of expression differences between the groups reflected a difference in the intensity of viral expression and not a difference in the area of the PFC analyzed as there was no significant difference in the ROI area between any of the expression groups (online suppl. Fig. S1d, Peripuberty Inhibition, No: mean ± SEM: 756,353 ± 23,919 px2; Low: mean ± SEM: 766,691 ± 27,210 px2; High: 743,183 ± 6,683 px2; one-way ANOVA, Sidak’s multiple comparisons No vs. Low p = 0.9822, No vs. High p = 0.9647, Low vs. High p = 0.8377) (online suppl. Fig. S1e, Early Adolescence, No: mean ± SEM: 756,236 ± 21,278 px2; Low: mean ± SEM: 778,512 ± 19,979 px2; High: 760,902 ± 10,404 px2; one-way ANOVA, Sidak’s multiple comparisons No vs. Low p = 0.8337, No vs. High p = 0.9955, Low vs. High p = 0.8989). Therefore, only those mice in the high expression group were included in main behavioral analyses.

Inhibiting mPFC PV Cells in Peripuberty, but Not Early Adolescence, Leads to Impaired Adult Cognitive Flexibility

To test whether reversibly inhibiting PV interneurons in the mPFC during either developmental window had long-lasting effects on adult behavior, we expressed the DREADD receptor, hM4DGi, in mPFC PV cells of mouse pups and systemically administered CNO in either peripuberty (P14–P32) (termed “Peripuberty Inhibition”) or early adolescence (P33–P50) (termed “Early Adolescence Inhibition”). Controls expressed the fluorophore mCherry in their mPFC PV cells and received CNO in either window (termed “Control”). Once the animals reached adulthood (P90, at least 40 days after the last CNO injection), we initiated an odor and texture-based attentional SS task (Fig. 1a, c). The task (the rodent equivalent of the Wisconsin Card Sorting task) is designed to test cognitive flexibility by initially requiring the animal to learn that one dimension of a compound odor and texture-based stimulus (e.g., odor) indicates the presence of a food reward buried in a pot (IA). After the animal learns the initial rule, the rule is switched, so now the opposite stimulus dimension (e.g., texture) indicates the presence of reward. This rule switch is referred to as extradimensional set-shifting (ED Set-Shift) (Fig. 1c).

Fig. 1.

Peripubertal inhibition of mPFC PV interneurons results in long-term effects in adult cognitive flexibility. a Experimental timeline. Animals expressing hM4DGi or the control fluorophore mCherry in mPFC PV interneurons were given CNO during either peripuberty (P14–P32) or early adolescence (P33–P50). ED SS behavior was assessed in adulthood (P90+). b Immunofluorescence micrograph illustrates viral expression in the mPFC at P19 (red depicts mCherry and blue depicts DAPI). c Diagram of the SS task. Animals are initially required to learn that one dimension of a multidimensional stimulus (e.g., odor not texture) indicates the presence of a buried reward in a pot (IA). After performing 8 out 10 consecutive trials correctly, the rule is shifted, so now a different dimension (e.g., texture) indicates the presence of a buried reward (ED Set-Shift). O = odor; T = texture. The groups do not differ significantly on the number of trials it takes them to reach criterion d or the number of errors they make during IA e. During the ED Set-Shift phase of the task, the Peripuberty Inhibition group takes significantly more trials to reach criterion (one-way ANOVA, Sidak multiple comparison Control vs. Peripuberty Inhibition p = 0.0044) f and makes significantly more errors (one-way ANOVA, Sidak multiple comparison Control vs. Peripuberty Inhibition p = 0.0230) g than the control group. *p < 0.05; **p < 0.01.

Fig. 1.

Peripubertal inhibition of mPFC PV interneurons results in long-term effects in adult cognitive flexibility. a Experimental timeline. Animals expressing hM4DGi or the control fluorophore mCherry in mPFC PV interneurons were given CNO during either peripuberty (P14–P32) or early adolescence (P33–P50). ED SS behavior was assessed in adulthood (P90+). b Immunofluorescence micrograph illustrates viral expression in the mPFC at P19 (red depicts mCherry and blue depicts DAPI). c Diagram of the SS task. Animals are initially required to learn that one dimension of a multidimensional stimulus (e.g., odor not texture) indicates the presence of a buried reward in a pot (IA). After performing 8 out 10 consecutive trials correctly, the rule is shifted, so now a different dimension (e.g., texture) indicates the presence of a buried reward (ED Set-Shift). O = odor; T = texture. The groups do not differ significantly on the number of trials it takes them to reach criterion d or the number of errors they make during IA e. During the ED Set-Shift phase of the task, the Peripuberty Inhibition group takes significantly more trials to reach criterion (one-way ANOVA, Sidak multiple comparison Control vs. Peripuberty Inhibition p = 0.0044) f and makes significantly more errors (one-way ANOVA, Sidak multiple comparison Control vs. Peripuberty Inhibition p = 0.0230) g than the control group. *p < 0.05; **p < 0.01.

Close modal

Animals included in the analysis of the task were older than P90 at the time of the task and those in the inhibition groups also were confirmed to have a high level of viral expression. Postmortem analysis of viral expression yielded a trimodal distribution, with average levels of expression between the highest-expressing group and the other two categories differing statistically (see online suppl. Methods and online suppl. Fig. S1). Therefore, only those in the high expression group (online suppl. Fig. S1) were included in behavioral analyses. All groups contained both males and females (Control: 7 males, 5 females; Peripuberty Inhibition: 1 male, 3 females; Early Adolescence Inhibition: 3 males, 2 females).

We found that all groups performed equally well in IA. They took the same number of trials to reach criterion (defined as attaining 8 correct out of 10 consecutive trials) (Fig. 1d) (Control: mean ± SEM: 11 ± 0.6629 trials, n = 12 mice; Peripuberty Inhibition: 10 ± 0.4082 trials, n = 4 mice; Early Adolescence Inhibition: 11.4 ± 1.503 trials, n = 5 mice) and made the same number of errors (Fig. 1e) (Control: mean ± SEM: 2.333 ± 0.3553 errors, n = 12 mice; Peripuberty Inhibition: 2.000 ± 0.4082 errors, n = 4 mice; Early Adolescence Inhibition: 2.200 ± 0.5831 errors, n = 5 mice). However, in the ED Set-Shift, the Peripuberty Inhibition group took significantly more trials to reach criterion (Fig. 1f) (Control: mean ± SEM: 10.08 ± 0.3580 trials, n = 12 mice; Peripuberty Inhibition: 14.5 ± 1.658 trials, n = 4 mice; Early Adolescence Inhibition: 11.40 ± 1.122 trials, n = 5 mice; one-way ANOVA, Sidak multiple comparison, Control vs. Peripuberty Inhibition: p = 0.0044) and made significantly more errors than the Control group (Fig. 1g, Control: mean ± SEM: 2.083 ± 0.3580 errors, n = 12 mice; Peripuberty Inhibition: 4.500 ± 0.8660 errors, n = 4 mice; Early Adolescence Inhibition: 2.600 ± 0.6782 errors, n = 5 mice; one-way ANOVA, Sidak multiple comparison, Control vs. Peripuberty Inhibition: p = 0.0230), while the Early Adolescent Inhibition group did not (Fig. 1f, one-way ANOVA, Sidak multiple comparison, Control vs. Early Adolescent Inhibition: p = 0.5638; Fig. 1g, Control vs. Early Adolescent Inhibition: p = 0.8720).

Further support that PV inhibition in peripuberty, but not early adolescence, relates to SS impairments in adulthood comes from the analysis showing a positive correlation between the extent of viral expression and trials to reach criteria during set shifting among mice where CNO was administered during peripuberty (online suppl. Fig. S1f, simple linear regression, Y = 1.609*X − 4.846, R-squared = 0.1050, p = 0.3041), but not early adolescence (online suppl. Fig. S1g, simple linear regression, Y= −1.127*X + 27.53, R-squared = 0.04899, p = 0.5130). Although neither correlation was significant, only with peripubertal inhibition was greater viral expression associated with poorer cognitive performance (the opposite directionality was detected with adolescent inhibition). Additionally, there was visually one outlier in the peripubertal inhibition correlation analysis; if that animal was removed, the positive correlation became highly significant (simple linear regression, Y = 2.377*X − 17.83, R-squared = 0.71, p = 0.001). No correlation was observed between viral expression and trials to reach criterion in the IA portion of the task, in either group of mice (online suppl. Fig. S1h, i), consistent with the finding that this aspect of behavior is not affected by developmental PV inhibition.

Developmental Expression Dynamics of PV and WFA in V1 Serve as Markers for Critical Period Plasticity

Prior work demonstrates that the onset of CP plasticity in the visual cortex is triggered by rapidly increasing levels of inhibition provided by PV-expressing interneurons [18]. Closure of this plasticity is at least partially triggered by maturation of PNNs encapsulating PV cells [19, 20]. Therefore, we asked whether we could use developmental expression dynamics of PV and WFA staining as markers for timing of CP plasticity.

To address this question, we quantified the number of PV-expressing interneurons and the intensity of WFA staining encapsulating these cells in primary visual cortex (V1) and the prefrontal cortex (mPFC) at several timepoints spanning early development through adulthood (P14, P21, P35, P50, and P90). The number of PV-expressing cells per section was averaged for each region within each mouse and we conducted a one-way ANOVA to examine the main effects of animal age within each brain region. We first examined expression dynamics in V1 and found a highly significant main effect of age. The number of PV-expressing GABAergic interneurons increased most dramatically between P14 and P21 (Fig. 2c, one-way ANOVA: mean ± SEM: P14, 3.95 ± 0.496 cells/section, n = 5 mice; P21, 7.95 ± 0.5208 cells/section, n = 5 mice; P35, 5.875 ± 0.4993 cells/section, n = 7 mice; P50, 5.9 ± 0.292 cells, n = 8; P90, 4.969 ± 0.752, n = 8; p < 0.0001; Sidak post hoc P14 vs. P21, p < 0.0001). PV cell number significantly decreased between P21 and P35 (p = 0.0072) and did not change between P35 and P50 and P50 and P90. This is consistent with the known onset of CP plasticity in this time [18, 20, 22].

Fig. 2.

Expression of markers of CP plasticity in primary visual cortex. a Experimental design. Sections from mice ranging in age from P14 to P90 that encompassed V1 were stained for PV and WFA and imaged on a confocal microscope. b Example images of PV and WFA staining in V1 across developmental ages. c The number of PV cells/section was averaged for all sections from a given animal and compared as a function of developmental age using a one-way ANOVA followed by a Sidak post hoc. The most dramatic increase in PV cells/section occurred between P14 and P21 (Sidak post hoc P14 vs. P21, p < 0.0001). PV cell number significantly decreased between P21 and P35 (p = 0.0072) and did not change between P35 and P50 and P50 and P90. d The intensity of WFA staining encapsulating identified PV cells was averaged for all cells for a given animal and compared for each developmental age against levels in the adult (P90) animal with a one-way ANOVA followed by a Sidak post hoc. Levels of WFA significantly differed between P14 and P90, P21 and P90 and P35 and P90 (Sidak post hoc: P14 vs. P90, p = 0.0019; P21 vs. P90, p = 0.0289; P35 vs. P90, p = 0.0374), but not between P50 and P90 (p > 0.9999). *p < 0.05, **p < 0.01, ***p < 0.001.

Fig. 2.

Expression of markers of CP plasticity in primary visual cortex. a Experimental design. Sections from mice ranging in age from P14 to P90 that encompassed V1 were stained for PV and WFA and imaged on a confocal microscope. b Example images of PV and WFA staining in V1 across developmental ages. c The number of PV cells/section was averaged for all sections from a given animal and compared as a function of developmental age using a one-way ANOVA followed by a Sidak post hoc. The most dramatic increase in PV cells/section occurred between P14 and P21 (Sidak post hoc P14 vs. P21, p < 0.0001). PV cell number significantly decreased between P21 and P35 (p = 0.0072) and did not change between P35 and P50 and P50 and P90. d The intensity of WFA staining encapsulating identified PV cells was averaged for all cells for a given animal and compared for each developmental age against levels in the adult (P90) animal with a one-way ANOVA followed by a Sidak post hoc. Levels of WFA significantly differed between P14 and P90, P21 and P90 and P35 and P90 (Sidak post hoc: P14 vs. P90, p = 0.0019; P21 vs. P90, p = 0.0289; P35 vs. P90, p = 0.0374), but not between P50 and P90 (p > 0.9999). *p < 0.05, **p < 0.01, ***p < 0.001.

Close modal

As expected, WFA intensity demarking PNNs encapsulating PV cells increased more slowly than PV cell number in V1 (Fig. 2d) consistent with its role in the closure of CP plasticity in V1 at this time [20]. To identify when WFA intensity reached mature levels, we performed a one-way ANOVA to examine the effect of age on WFA intensity, followed by a post hoc analysis comparing the intensity at each age point to that at P90 (the mature level of WFA). In V1, WFA intensity surrounding PV cells only significantly differed between P14 and P90, P21 and P90 and P35 and P90 (Fig. 2d, one-way ANOVA: mean ± SEM: P14, 17.770 ± 2.902 AU/cell, n = 5 mice; P21, 28.200 ± 4.652 AU/cell, n = 5 mice; P35, 31.78 ± 4.372 AU/cell, n = 7 mice; P50, 55.84 ± 9.792 AU/cell, n = 8; P90, 56.54 ± 5.163 AU/cell, n = 8; Sidak post hoc: P14 vs. P90, p = 0.0019; P21 vs. P90, p = 0.0289; P35 vs. P90, p = 0.0374), but not between P50 and P90 (p > 0.9999). This suggests that a mature level of WFA staining is seen by sometime between P35 and P50 at which point the staining intensity does not change from that at P90.

Markers of CP Plasticity Display a Similar Temporal Course in V1 and mPFC

We performed a similar analysis of PV and WFA expression across the developing and adult prefrontal cortex (mPFC). For analysis of the P14 mPFC animals, all but one of the animals showed no PV staining whatsoever, with the remaining animal having a single identifiable cell (Fig. 3c, mean ± SEM: P14, 0.050 ± 0.050 cells/section, n = 5 mice; P21, 4.938 ± 0.825 cells/section, n = 4 mice; P35, 3.578 ± 0.2724 cells/section, n = 8 mice; P50, 3.953 ± 0.4494 cells, n = 8; P90, 3.385 ± 0.2726, n = 8). We found a highly significant main effect of age in mPFC, where PV cell number increased dramatically between P14 and P21 (Fig. 3c, one-way ANOVA, Sidak post hoc P14 vs. P21, p < 0.0001, P21 vs. P35, P35 vs. P50, P50 vs. P90, p > 0.05). As the onset of CP plasticity is signaled by a period of rapid development of PV cell inhibition, this analysis is consistent with an onset of CP plasticity also starting around P14. Similar to V1, in the mPFC WFA intensity surrounding PV cells only differed significantly from adult (P90) levels at P14 and P21 (Fig. 3d, one-way ANOVA: mean ± SEM: P14, 0 ± 0 AU/cell, n = 4 mice; P21, 7.739 ± 1.13 AU/cell, n = 4 mice; P35, 10.48 ± 1.026 AU/cell, n = 8 mice; P50, 14.31 ± 2.397 AU/cell, n = 8; P90, 17.00 ± 2.334 AU/cell, n = 8; p < 0.0001, followed by Sidak post hoc: P14 vs. 90, p < 0.0001; P21 vs.90, p = 0.0246). There was also a strong trend-level difference in WFA intensity between P35 and P90 (p = 0.0638) but no difference between P50 and P90 (p = 0.7594). This result indicates that WFA intensity encapsulating PV cells reaches adult levels sometime between P35 and P50 in both mPFC and V1.

Fig. 3.

Expression of markers of CP plasticity in prefrontal cortex. a Experimental design. Sections from mice ranging in age from P14 to P90 that encompassed the mPFC were stained for PV and WFA and imaged on a confocal microscope. b Example images of PV and WFA staining in mPFC across developmental ages. c The number of PV cells/section was averaged for all sections from a given animal and compared as a function of developmental age using a one-way ANOVA followed by a Sidak post hoc. The most dramatic increase in PV cells/section occurred between P14 and P21 (Sidak post hoc P14 vs. P21, p < 0.0001, P21 vs. P35, P35 vs. P50, P50 vs. P90, p > 0.05). d The intensity of WFA staining encapsulating identified PV cells was averaged for all cells for a given animal and compared for each developmental age against levels in the adult (P90) animal with a one-way ANOVA followed by a Sidak post hoc. Levels of WFA significantly differed between P14 and P90 and P21 and P90 (Sidak post hoc, P14 vs. P90, p < 0.0001; P21 vs.90, p = 0.0246). There was also a strong trend-level difference in WFA intensity between P35 and P90 (p = 0.0638) but no difference between P50 and P90 (p = 0.7594). *p < 0.05, **p < 0.01, ***p < 0.001, #p < 0.1.

Fig. 3.

Expression of markers of CP plasticity in prefrontal cortex. a Experimental design. Sections from mice ranging in age from P14 to P90 that encompassed the mPFC were stained for PV and WFA and imaged on a confocal microscope. b Example images of PV and WFA staining in mPFC across developmental ages. c The number of PV cells/section was averaged for all sections from a given animal and compared as a function of developmental age using a one-way ANOVA followed by a Sidak post hoc. The most dramatic increase in PV cells/section occurred between P14 and P21 (Sidak post hoc P14 vs. P21, p < 0.0001, P21 vs. P35, P35 vs. P50, P50 vs. P90, p > 0.05). d The intensity of WFA staining encapsulating identified PV cells was averaged for all cells for a given animal and compared for each developmental age against levels in the adult (P90) animal with a one-way ANOVA followed by a Sidak post hoc. Levels of WFA significantly differed between P14 and P90 and P21 and P90 (Sidak post hoc, P14 vs. P90, p < 0.0001; P21 vs.90, p = 0.0246). There was also a strong trend-level difference in WFA intensity between P35 and P90 (p = 0.0638) but no difference between P50 and P90 (p = 0.7594). *p < 0.05, **p < 0.01, ***p < 0.001, #p < 0.1.

Close modal

Highlighting Peripuberty as an Activity-Dependent Postnatal Sensitive Period for mPFC PV Interneuron-Related Adult Behavior

Our past findings demonstrate that decreasing mPFC PV interneuron activity during a broad window of postnatal development (P14–P50) led to a long-lasting decrease in their functional inhibition of pyramidal cells, as well as impairments in cognitive flexibility [5]. We sought to refine the timing of plasticity for these effects as our original window encompasses both peripuberty and early adolescence, distinct life stages distinguished by behavioral and hormonal criteria [12] as well as changes in mPFC PV interneuron electrophysiology and anatomy [15, 23‒25]. Here, we show that chemogenetically inhibiting mPFC PV interneurons during peripuberty (P14–P32), but not early adolescence (P33–P50), is sufficient to impair adult cognitive flexibility. In this time window, PV interneurons undergo dramatic changes in intrinsic electrophysiological properties (membrane capacitance, resting membrane potential, synaptic strength, etc.) as well as the connections they receive (for example from the mediodorsal thalamus [26]) and those they make [15, 23‒25], suggesting multiple aspects of mPFC PV maturation that may be altered by manipulation of their activity in this time. Notably, while changes in cortical activity can affect PV programmed cell death, the window for this to occur is earlier than the window of our manipulation of activity [6, 27, 28].

Other work looking into long-lasting effects of developmental manipulations of mPFC activity has used a variety of approaches and focused on a broad range of time windows. Most similar to the present study, chemogenetic inhibition (using hM4DGi) of the mediodorsal thalamus from P20 to P50 resulted in a similar impairment in cognitive flexibility assessed with an ED SS task in adulthood [7]. Inhibition of thalamo-prefrontal inputs during this early window was associated with reduced excitatory drive and thalamocortical projections to the mPFC in adulthood [7]. As the P20–P50 window includes the majority of our P14–P32 window of interest, some of the long-term effects produced by the reduction in the thalamocortical drive in the early part of this window may result from decreased activation of PV cells in this time. Alternatively, both manipulations may exert their effects in nonoverlapping ways, suggesting that this period is one of heightened plasticity for the refinement of both connections into, and within, the prefrontal cortex, with implications for adult prefrontal network functioning and cognitive behavior.

Work by Bitzenhofer et al. [6] identified a sensitive period in very early life (P7–P11) in which increasing mPFC activity by optically exciting pyramid neurons results in impaired working memory in adolescence. Notably, they found that these behavioral effects likely result from increased PV cell density. While we did not examine PV cell number following peripubertal or adolescent inhibition of PV cells, our prior work inhibiting PV cells during both peripuberty and adolescence found that the PV cell number in adulthood was unchanged. This finding is consistent with evidence that programmed cell death occurs primarily in very early postnatal development and is complete by peripuberty [29]. Therefore, there may be an earlier sensitive period in the mPFC in which alterations in overall cortical network activity affect interneuron apoptosis.

Finally, chemogenetic activation of mPFC PV interneurons during late adolescence (P60–P75) resulted in long-lasting rescue of deficits in adult cognition in LgDel+/− mice (a genetic model of schizophrenia) [10]. Adult LgDel+/− mice took significantly more trials to learn a new rule during an intra-dimensional SS task, although performance in the ED portion remained intact. This again supports a role for mPFC PV cells in adult cognitive flexibility, though in a later window that we did not address. Alternatively, it is possible that LgDel+/− mice and other genetic models may have altered (e.g., delayed) sensitive period timing relative to animals not carrying this genetic mutation.

Identifying Molecular Markers of Critical Period Onset and Offset in the Visual Cortex

Consistent with our observation that peripuberty represents a time of heightened plasticity for manipulations of mPFC PV cell activity to influence long-term cognitive functioning, we found that molecular markers associated with the onset and offset of sensitive period plasticity in visual cortex also demarcate this peripubertal time window in mPFC. Specifically, we first demonstrated that PV cells increase most dramatically in number between P14 and P21 in V1, consistent with the known onset of activity-dependent sensitive period plasticity in that structure at this time. This finding is also consistent with prior work, demonstrating that an increase in GABA tone is essential for triggering the onset of activity-dependent sensitive period plasticity in V1 [19, 20, 22]. Based on these observations, we can confirm that the rate of change of PV cell number in our study was an effective indicator of CP onset.

We also found that PNNs encapsulating PV cells reached mature levels sometime between P35 and P50 in V1, consistent with prior work indicating that WFA intensity peaks in V1 at P42, as well as the known slowing of activity-dependent sensitive period plasticity at this time [17]. Increases in the PNN encapsulation of PV cells are known to suppress sensitive period plasticity in V1 as enzymatic degradation of PNNs reopens this plasticity [19, 20]. Therefore, maturation of PNN encapsulation of PV cells is an appropriate molecular marker for the offset of sensitive period plasticity.

Like V1, we found that PV cell number increased dramatically in the mPFC between P14 and P21 and PNN encapsulation of mPFC PV cells reached mature levels between P35 and P50. As previously noted, this finding is consistent with a sensitive period for activity-dependent plasticity in the prepubertal period of P14–P32 we had previously identified through our chemogenetic experiments. The timing of changes in PV cell number we observed is similar to that reported in Ueda et al. [30] where levels of PV transcript in the mPFC increase dramatically between P14 and P21, leveling off thereafter. Notably, in the same publication, the authors find that PV cell number increases most dramatically between P21 and P35, rather than between P14 and P21. As they identify PV cells using a GFP reporter driven by activity of the PV promoter, rather than endogenous levels of PV protein, it is possible that this delayed increase in detectable PV cells is the result of the delay in the buildup of GFP expression required to identify them, following activity of the promoter.

The results of our study indicate that the timing of plasticity within the mPFC is highly synchronized with that of V1. This result differs from theories that have predicted that sensitive period timing may proceed hierarchically across cortical regions, occurring later for associative regions, than for primary sensory ones [31]. However, it is possible that we would have seen subtle differences in the timing of maximal changes in marker expression between regions if we had probed more tightly spaced time points. Future studies should address this, as well as whether manipulating the onset of sensitive period plasticity in one region (e.g., via dark rearing to delay sensitive period onset in V1 influences the onset of sensitive period plasticity in the mPFC).

In conclusion, our work highlights peripuberty (∼P14–P35) as a sensitive period for mPFC development, implicating PV cell activity in this time as key for the proper development of the structure and future adult cognition. Furthermore, our direct comparison of sensitive period plasticity markers across the prefrontal and visual cortex suggests a similar time course of expression, challenging the notion that sensitive periods occur hierarchically. Future studies should build on this work to identify the molecular, cellular, and anatomical consequences of manipulating PV activity in this window as well as to explore how manipulations of activity in other cell populations may produce similar or divergent consequences. Further, it will be important to understand both intrinsic and external factors that regulate mPFC PV cell activity in this time. Along with our findings, these studies could extend our knowledge about the nature and timing of sensitive period plasticity in the developing prefrontal cortex.

This study protocol was reviewed and approved by the New York Psychiatric Institute IACUC Committee (Approval No. 1618).

The authors have no conflicts of interest to declare.

This study was supported by funding from the National Institute of Mental Health (R01 01MH128277 to S.E.C.).

G.M.S. and T.D.D.: investigation, methodology, data curation, formal analysis, investigation, writing – original draft, writing – review and editing, and visualization; A.A. and A.H.: investigation, methodology, data curation, formal analysis, and writing – review and editing; S.E.C.: conceptualization, data curation, formal analysis, funding acquisition, project administration, resources, supervision, writing – original draft, and writing – review and editing.

All data generated or analyzed during this study are included in this article and its supplementary material files. Further inquiries can be directed to the corresponding author.

1.
Wiesel
TN
,
Hubel
DH
.
Single-cell responses in striate cortex of kittens deprived of vision in one eye
.
J Neurophysiol
.
1963
;
26
(
6
):
1003
17
.
2.
Antonini
A
,
Stryker
MP
.
Plasticity of geniculocortical afferents following brief or prolonged monocular occlusion in the cat
.
J Comp Neurol
.
1996
;
369
(
1
):
64
82
.
3.
Hubel
DH
,
Wiesel
TN
,
LeVay
S
.
Functional architecture of area 17 in normal and monocularly deprived macaque monkeys
.
Cold Spring Harb Symp Quant Biol
.
1976
;
40
:
581
9
.
4.
Shatz
CJ
,
Stryker
MP
.
Ocular dominance in layer IV of the cat’s visual cortex and the effects of monocular deprivation
.
J Physiol
.
1978
;
281
(
1
):
267
83
.
5.
Canetta
SE
,
Holt
ES
,
Benoit
LJ
,
Teboul
E
,
Sahyoun
GM
,
Ogden
RT
, et al
.
Mature parvalbumin interneuron function in prefrontal cortex requires activity during a postnatal sensitive period
.
Elife
.
2022
;
11
:
e80324
.
6.
Bitzenhofer
S
,
Pöpplau
J
,
Chini
M
,
Marquardt
A
,
Hanganu-Opatz
I
.
A transient developmental increase in prefrontal activity alters network maturation and causes cognitive dysfunction in adult mice
.
Neuron
.
2021
;
24
:
109
.
7.
Benoit
LJ
,
Holt
ES
,
Posani
L
,
Fusi
S
,
Harris
AZ
,
Canetta
S
, et al
.
Adolescent thalamic inhibition leads to long-lasting impairments in prefrontal cortex function
.
Nat Neurosci
.
2022
;
25
(
6
):
714
25
.
8.
Chini
M
,
Hanganu-Opatz
IL
.
Prefrontal cortex development in Health and disease: lessons from rodents and humans
.
Trends Neurosci
.
2021
;
44
(
3
):
227
40
.
9.
Pöpplau
JA
,
Schwarze
T
,
Dorofeikova
M
,
Pochinok
I
,
Günther
A
,
Marquardt
A
, et al
.
Reorganization of adolescent prefrontal cortex circuitry is required for mouse cognitive maturation
.
Neuron
.
2024
;
112
(
3
):
421
40.e7
.
10.
Mukherjee
A
,
Carvalho
F
,
Eliez
S
,
Caroni
P
.
Long-lasting rescue of network and cognitive dysfunction in a genetic schizophrenia model
.
Cell
.
2019
;
178
(
6
):
1387
402.e14
.
11.
Banerjee
T
,
Pati
S
,
Tiwari
P
,
Vaidya
VA
.
Chronic hM3Dq-DREADD-mediated chemogenetic activation of parvalbumin-positive inhibitory interneurons in postnatal life alters anxiety and despair-like behavior in adulthood in a task- and sex-dependent manner
.
J Biosci
.
2022
;
47
(
4
):
68
.
12.
Piekarski
DJ
,
Johnson
CM
,
Boivin
JR
,
Thomas
AW
,
Lin
WC
,
Delevich
K
, et al
.
Does puberty mark a transition in sensitive periods for plasticity in the associative neocortex
.
Brain Res
.
2017
;
1654
(
Pt B
):
123
44
.
13.
Piekarski
DJ
,
Boivin
JR
,
Wilbrecht
L
.
Ovarian hormones organize the maturation of inhibitory neurotransmission in the frontal cortex at puberty onset in female mice
.
Curr Biol
.
2017
;
27
(
12
):
1735
45.e3
.
14.
Schneider
M
.
Adolescence as a vulnerable period to alter rodent behavior
.
Cell Tissue Res
.
2013
;
354
(
1
):
99
106
.
15.
Miyamae
T
,
Chen
K
,
Lewis
DA
,
Gonzalez-Burgos
G
.
Distinct physiological maturation of parvalbumin-positive neuron subtypes in mouse prefrontal cortex
.
J Neurosci
.
2017
;
37
(
19
):
4883
902
.
16.
Roth
BL
.
DREADDs for neuroscientists
.
Neuron
.
2016
;
89
(
4
):
683
94
.
17.
Pizzorusso
T
,
Medini
P
,
Berardi
N
,
Chierzi
S
,
Fawcett
JW
,
Maffei
L
.
Reactivation of ocular dominance plasticity in the adult visual cortex
.
Science
.
2002
;
298
(
5596
):
1248
51
.
18.
Hensch
TK
.
Critical Period mechanisms in developing visual cortex
. In:
Current topics in developmental biology [internet]
.
Academic Press
;
2005
[cited 2023 Apr 26]. p.
215
37
. (Neural Development; vol. 69). Available from: https://www.sciencedirect.com/science/article/pii/S0070215305690084
19.
Kobayashi
Y
,
Ye
Z
,
Hensch
TK
.
Clock genes control cortical critical period timing
.
Neuron
.
2015
;
86
(
1
):
264
75
.
20.
Krishnan
K
,
Wang
BS
,
Lu
J
,
Wang
L
,
Maffei
A
,
Cang
J
, et al
.
MeCP2 regulates the timing of critical period plasticity that shapes functional connectivity in primary visual cortex
.
Proc Natl Acad Sci USA
.
2015
;
112
(
34
):
E4782
91
.
21.
Canetta
S
,
Bolkan
S
,
Padilla-Coreano
N
,
Song
L
,
Sahn
R
,
Harrison
N
, et al
.
Maternal immune activation leads to selective functional deficits in offspring parvalbumin interneurons
.
Mol Psychiatry
.
2016
;
21
(
7
):
956
68
.
22.
Huang
ZJ
,
Kirkwood
A
,
Pizzorusso
T
,
Porciatti
V
,
Morales
B
,
Bear
MF
, et al
.
BDNF regulates the maturation of inhibition and the critical period of plasticity in mouse visual cortex
.
Cell
.
1999
;
98
(
6
):
739
55
.
23.
Goldberg
EM
,
Jeong
HY
,
Kruglikov
I
,
Tremblay
R
,
Lazarenko
RM
,
Rudy
B
.
Rapid developmental maturation of neocortical FS cell intrinsic excitability
.
Cereb Cortex
.
2011
;
21
(
3
):
666
82
.
24.
Micheva
KD
,
Kiraly
M
,
Perez
MM
,
Madison
DV
.
Extensive structural remodeling of the axonal arbors of parvalbumin basket cells during development in mouse neocortex
.
J Neurosci
.
2021
;
41
(
45
):
9326
39
.
25.
Okaty
BW
,
Miller
MN
,
Sugino
K
,
Hempel
CM
,
Nelson
SB
.
Transcriptional and electrophysiological maturation of neocortical fast-spiking GABAergic interneurons
.
J Neurosci
.
2009
;
29
(
21
):
7040
52
.
26.
Rios
O
,
Villalobos
J
.
Postnatal development of the afferent projections from the dorsomedial thalamic nucleus to the frontal cortex in mice
.
Brain Res Dev Brain Res
.
2004
;
150
(
1
):
47
50
.
27.
Southwell
DG
,
Paredes
MF
,
Galvao
RP
,
Jones
DL
,
Froemke
RC
,
Sebe
JY
, et al
.
Intrinsically determined cell death of developing cortical interneurons
.
Nature
.
2012
;
491
(
7422
):
109
13
.
28.
Wong
FK
,
Bercsenyi
K
,
Sreenivasan
V
,
Portalés
A
,
Fernández-Otero
M
,
Marín
O
.
Pyramidal cell regulation of interneuron survival sculpts cortical networks
.
Nature
.
2018
;
557
(
7707
):
668
73
.
29.
Blanquie
O
,
Yang
JW
,
Kilb
W
,
Sharopov
S
,
Sinning
A
,
Luhmann
HJ
.
Electrical activity controls area-specific expression of neuronal apoptosis in the mouse developing cerebral cortex
.
Elife
.
2017
;
6
:
e27696
.
30.
Ueda
S
,
Niwa
M
,
Hioki
H
,
Sohn
J
,
Kaneko
T
,
Sawa
A
, et al
.
Sequence of molecular events during the maturation of the developing mouse prefrontal cortex
.
Mol Neuropsychiatry
.
2015
;
1
(
2
):
94
104
.
31.
Takesian
AE
,
Hensch
TK
.
Balancing plasticity/stability across brain development
.
Prog Brain Res
.
2013
;
207
:
3
34
.