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. 2026 Apr 30;86:e70033. doi: 10.1002/dneu.70033

Early Isoflurane Exposure Is Associated With Delayed Adolescent Neurobehavioral Deficits, Accompanied by Dysregulation of FOXG1 and EB3

Ke Chen 1, Mengdi Xia 1, Changhong Li 1, Zhenhua Ren 1,✉, Xiaohui Li 1,✉
PMCID: PMC13133545  PMID: 42063249

ABSTRACT

Early‐life exposure to general anesthetics has been linked to long‐term neurodevelopmental risks, yet the underlying mechanisms remain unclear. Using a mouse model, we show that neonatal isoflurane exposure at Postnatal Day 7 leads to age‐dependent behavioral alterations, changes in cortical neuronal abundance and layer‐specific neuronal density, and dysregulation of cytoskeleton‐associated proteins. Isoflurane‐exposed mice displayed increased exploratory behavior during early adolescence but developed marked recognition memory deficits by late adolescence. Histological analyses revealed layer‐specific disruption of neuronal distribution in the sensory cortex, characterized by reduced neuronal density in layers II–IV and age‐dependent changes in inhibitory interneuron composition. Mechanistically, isoflurane reduced the expression of the microtubule plus‐end tracking protein EB3 and the transcription factor FOXG1. FOXG1 co‐localized with EB3 and β‐tubulin in the cytoplasm, supporting a noncanonical, cytoskeleton‐associated role. Together, these findings indicated that early isoflurane exposure is associated with altered neuronal density and dysregulation of the cytoskeleton‐associated proteins FOXG1 and EB3, suggesting a potential link between anesthesia exposure and long‐term neurodevelopmental alterations.

Keywords: cognitive dysfunction, cortical layer II–IV, cytoskeletal, EB3, FOXG1, isoflurane

1. Introduction

The early postnatal period represents a critical window for cortical development, during which neuronal migration, synaptogenesis, and circuit refinement are highly dynamic (Chen et al. 2023; Evsyukova et al. 2013; Nakajima et al. 2021). Accumulating evidence suggests that general anesthetics such as isoflurane, widely used in pediatric practice, may interfere with these developmental processes and increase the risk of long‐term cognitive impairment (Gascoigne et al. 2021; Jevtovic‐Todorovic et al. 2003; Wilder et al. 2009). However, the cellular and molecular mechanisms linking neonatal anesthesia exposure to later behavioral dysfunction remain poorly understood.

Adolescence represents another sensitive stage of cortical maturation, marked by refinement of excitatory‐inhibitory balance and remodeling of local circuits (Larsen and Luna 2018; Paus et al. 2008; Stranahan et al. 2013). Although most cortical projection neurons complete radial migration during embryonic and early postnatal stages (Dehay and Kennedy 2007; Qian et al. 2000; Rash et al. 2018; Sur and Rubenstein 2005; Vinopal et al. 2023), accumulating evidence indicates that postnatal development and adolescence are characterized by local positional adjustments, delayed maturation, and circuit integration within cortical networks (Lei et al. 2022; Menon 2013; Uhlhaas et al. 2009). These processes are particularly relevant for interneuron subtypes such as somatostatin (SST)‐positive cells, which continue to undergo refinement of laminar positioning and functional incorporation into local circuits rather than long‐distance migration during this period (Kim and Paredes 2021; Mòdol et al. 2024; Munguba et al. 2023; Nadarajah and Parnavelas 2002). Disruption of these processes may impair cortical layering, compromise sensory integration, and contribute to neuropsychiatric vulnerability (Hanganu‐Opatz et al. 2021; Liang et al. 2023; Valiente and Marín 2010).

Experimental studies have shown that anesthetic exposure during neonatal stages can interfere with neuronal migration, dendritic development, and glial signaling (Agarwal et al. 2022; Deng et al. 2024; Dong et al. 2022; Speigel et al. 2022). In the neocortex, such disturbances may manifest as abnormal laminar positioning, reductions in neuronal density within layers II–IV, and delayed refinement of excitatory‐inhibitory networks (Liang et al. 2023). Because layer IV serves as the primary thalamic input relay and layers II/III mediate local computation and inter‐areal communication, disruption of these superficial layers is expected to exert lasting effects on sensory processing and cognition (Bragg‐Gonzalo et al. 2024; Cheetham and Fox 2010; Jie et al. 2025; Kubota et al. 2016; Putman et al. 2024; Scheuer et al. 2024). However, whether these alterations reflect immediate neurotoxicity or progressive dysregulation of developmental programs remains incompletely resolved.

Beyond neuronal positioning, adolescence is characterized by extensive synaptogenesis and circuit refinement, particularly within superficial cortical layers (Huttenlocher 1979; Huttenlocher and Dabholkar 1997; Koss et al. 2014). Upper‐layer excitatory neurons in layers II–IV undergo pronounced dendritic growth and synaptic remodeling during this period, processes essential for sensory integration and cognitive function (Skibinska et al. 2000). The transcription factor CUX1 is selectively enriched in upper‐layer cortical neurons and has been shown to regulate dendritic complexity, synapse formation, and experience‐dependent circuit refinement, making it a key molecular marker of cortical synaptogenesis and maturation. Disruption of CUX1‐associated developmental programs has been linked to impaired cortical circuit formation and long‐term behavioral deficits (Cubelos et al. 2010, 2015; Platzer et al. 2018).

The transcription factor FOXG1, a core regulator of neocortical development, dynamically governs neuronal migration, positioning, and differentiation (Ba et al. 2023; Cargnin et al. 2018; Liu et al. 2022; Miyoshi and Fishell 2012; Miyoshi et al. 2024; Yang et al. 2017). Alterations in FOXG1 expression have been associated with abnormalities in cortical organization and connectivity. In parallel, the precise regulation of neuronal morphology and synapse formation relies on cytoskeletal dynamics. The microtubule plus‐end tracking protein EB3 (MAPRE3) contributes to dendritic development and synapse formation by regulating microtubule assembly, stability, and interactions with the cellular cortex (Jaglin and Chelly 2009; Jaworski et al. 2009; Pchitskaya et al. 2022; Penazzi et al. 2016; Straube and Merdes 2007). Together, FOXG1 and EB3 represent transcriptional and cytoskeletal regulators that may participate in coordinated developmental processes in the cortex.

Against this backdrop, we hypothesize that early‐life isoflurane exposure persistently disrupts cortical development in the sensory cortex and is associated with alterations in neuronal density and dysregulation of cytoskeleton‐related proteins, including FOXG1 and EB3, during postnatal brain maturation. To test this hypothesis, we employed a neonatal mouse model of isoflurane exposure and systematically evaluated behavioral outcomes from early to late adolescence. By integrating behavioral analyses with histological assessments and molecular characterization of CUX1, EB3, and FOXG1, this study aims to explore potential links between early anesthetic exposure and long‐term neurodevelopmental alterations.

2. Materials and Methods

2.1. Animal Model and Grouping

On Postnatal Day 7 (P7), C57BL/6 mice were randomly assigned to either the isoflurane group or the control group. For each experimental time point, each group comprised 12 mice with equal numbers of males and females (n = 6 per sex). Isoflurane exposure was performed at P7 for all experimental animals, and independent cohorts of mice were used for subsequent behavioral and histological analyses at P42 and P56.

Mice in the isoflurane group were placed in a sealed induction chamber, where anesthesia was induced using a mixed gas containing 5% isoflurane delivered in oxygen (flow rate: 1.5 L/min) for approximately 5 min until loss of spontaneous movement and the righting reflex was observed. Following induction, the isoflurane concentration was reduced to 1.5% and maintained for 2 h, with oxygen delivered at a flow rate of 2 L/min. Isoflurane was administered via a chamber‐based delivery system; no physical restraints were used during the exposure period. The respiratory rate and toe‐pinch reflex were monitored every 15 min to ensure stable anesthesia, and no anesthesia‐related complications or technical issues were observed. Control mice were exposed to room air under identical conditions. After anesthesia, animals were returned to their cages once the righting reflex had recovered and were reared under standard conditions. All mice were weaned at P21 and housed four to five per cage under a 12‐h light/dark cycle (lights on at 07:00 a.m.) at 22 ± 1°C, with ad libitum access to food and water. All experimental procedures were approved by the Animal Ethics Committee of Anhui Medical University (LLSC20241480).

2.2. Behavioral Tests

2.2.1. Open‐Field Test

Mice were transferred to the behavioral testing room the night before testing for acclimatization. On the test day, the mice were placed within a temporary, clean, neutral container (not the open field apparatus) and allowed to habituate to the testing room for 5 min. Each mouse was then placed in the center of a gray PVC open‐field arena (40 × 40 × 40 cm) and allowed to explore freely for 5 min. The open‐field session served both as an assessment of locomotor and exploratory‐related behavior and as the habituation phase (Day 1) of the novel object recognition (NOR) paradigm. Behavior was recorded using an overhead camera and analyzed with the open‐field test (OFT) module of VisuTrack software. After each trial, the arena was cleaned with 70% ethanol. Testing alternated between control and isoflurane‐exposed mice, as well as between male and female mice. All OFT sessions were conducted in the morning, starting at 09:00 a.m., under uniform, subdued lighting with minimal background noise. The following parameters were quantified: total distance traveled, mean speed, time spent in the center zone, number of center entries, and the ratio of distance traveled in the center versus peripheral zones.

2.2.2. NOR Test

Mice were transferred to the behavior testing room on the evening prior to the first day of testing for acclimatization. The NOR test was conducted in the same gray PVC arena used for the OFT (40 × 40 × 40 cm) over three consecutive days. On Day 1 (habituation), the mice were allowed to freely explore the empty arena for 10 min. On Day 2 (familiarization), two identical objects were placed symmetrically in the arena, and the mice were allowed to explore for 10 min. On Day 3 (test phase), one of the familiar objects was replaced with a novel object of a different shape, and the mice were again allowed to explore for 10 min. The arena and objects were thoroughly cleaned with 70% ethanol between trials. Behavior was recorded using an overhead camera and analyzed with the NOR module of VisuTrack software. Testing alternated between control and isoflurane‐exposed mice, as well as between male and female mice. All NOR sessions were conducted in the morning, starting at 09:00 a.m., under uniform lighting conditions with minimal background noise. Object exploration was defined as directing the nose toward the object at a close distance. The recognition index (RI) was calculated as RI = time exploring novel object/total exploration time.

2.3. Tissue Processing and Section Preparation

At designated time points, mice were deeply anesthetized and transcranially perfused with 0.9% saline followed by 4% paraformaldehyde (PFA). The brains were carefully removed, postfixed in 4% PFA, dehydrated through graded ethanol solutions, cleared, and embedded in paraffin. Serial sections (4‐µm thickness) were cut using a paraffin microtome in both coronal and sagittal orientations and mounted on glass slides for subsequent histological analyses.

2.4. Cortical Region Selection and Histological Quantification

Quantitative analyses were performed on coronal sections collected from comparable rostrocaudal levels of the primary somatosensory cortex (SSp). This region was selected because of its well‐defined laminar organization and its critical role in sensory processing and cortical circuit integration. In addition, preliminary post‐behavioral histological observations indicated that structural alterations were most evident in sensory cortical regions.

Coronal sections corresponding approximately to AP −1.35 to −2.16 mm relative to bregma were selected for analysis, encompassing the SSp, including the barrel field region (SSp‐bfd) as defined in the Allen Mouse Brain Common Coordinate Framework (CCFv3; Q. Wang et al. 2020). The analyzed cortical region was located dorsal to the hippocampal CA1 region. Sections from anatomically matched rostrocaudal levels were selected across animals to minimize regional variability.

Quantification was performed exclusively on coronal sections. Images were generally obtained from the same hemisphere across animals; however, when tissue quality differed between hemispheres, the hemisphere with better structural preservation and clearer immunostaining was selected. To ensure that hemisphere selection did not introduce bias, paired comparisons were performed in sections containing intact bilateral hemispheres, confirming that no significant differences existed between the left and right hemispheres for the parameters analyzed.

Cortical layers II–IV were delineated according to the laminar cytoarchitecture defined in the Allen Mouse Brain Atlas at the corresponding stereotaxic levels. Layer boundaries were identified based on relative differences in neuronal density and morphological characteristics. Quantification was performed using these relative laminar boundaries within each individual section to account for minor interindividual variability in cortical thickness or tissue processing. For each mouse, multiple sections were analyzed as specified below for each staining protocol.

2.5. Nissl Staining

Paraffin‐embedded mouse brain sections (4 µm) were dewaxed in xylene, rehydrated through graded ethanol solutions, and rinsed in distilled water. Sections were then fully immersed in Nissl staining solution for 5 min, followed by rinsing in distilled water for 3 min. Differentiation was performed using glacial acetic acid for 10 s, after which sections were rinsed again in distilled water for 3 min. Sections were subsequently dehydrated through graded ethanol solutions, cleared in xylene, and placed in a 65°C oven for 2 h. Sections were then mounted with neutral resin and air‐dried horizontally in a ventilated area. Images were acquired using an Olympus BX63 microscope. Neuronal density was quantified on Nissl‐stained sections by manually counting neurons using ImageJ software and calculating the number of neurons per unit area. Quantification was performed across cortical layers II–VI. For each mouse, six coronal sections were analyzed, and the mean value was used for statistical analysis.

2.6. Immunohistochemistry

Paraffin‐embedded mouse brain sections (4 µm) were deparaffinized, rehydrated, and subjected to antigen retrieval in sodium citrate buffer (pH 9.0). Endogenous peroxidase was blocked with 3% H2O2, followed by blocking with 10% goat serum for 30 min at room temperature. Sections were incubated overnight at 4°C with primary antibodies: EB3/MAPRE3 rabbit mAb (ABclonal, A19768, dilution 1:100), FOXG1 rabbit pAb (ABclonl, A1685, dilution 1:100), and CUX1 polyclonal antibody (Proteintech, 11733‐1‐AP dilution 1:100). Then, the cells were washed with PBS after rewarming and incubated with HRP‐conjugated secondary antibodies for 1 h (Rabbit 2‐Step Assay Kit (Rabbit Enhanced Polymer Assay System) (ZSGB‐BIO, PV‐9001). Signals were visualized with DAB, counterstained with hematoxylin, and mounted. Images were acquired with an Olympus BX63 microscope and quantified using ImageJ.

2.7. Immunofluorescence

Paraffin‐embedded mouse brain sections (4 µm) were deparaffinized, rehydrated, and subjected to antigen retrieval. After permeabilization with 0.3% Triton X‐100 for 10 min, sections were blocked with 10% goat serum for 30 min and incubated overnight at 4°C with primary antibodies: NeuN (PT0847R) PT rabbit mAb (Immunoway, YM8616, dilution 1:100), SST rabbit mAb (ABclonal, A20617, dilution 1:100), parvalbumin/PVALB rabbit mAb (ABclonal, A19098, dilution 1:100), CUX1 polyclonal antibody (Proteintech, 11733‐1‐AP dilution 1:100), EB3/MAPRE3 rabbit mAb (ABclonal, A19768, dilution 1:100), FOXG1 rabbit pAb (ABclonal, A16851, dilution 1:100), β‐actin (PT0519R) PT rabbit mAb (Immunoway, YM8343, dilution 1:100), and tubulin beta antibody (Affinity, AF7011, dilution 1:100). After washing, the sections were incubated with Alexa Fluor‐conjugated secondary antibodies for 1 h in the dark. Nuclei were counterstained with DAPI. Images were captured using an Olympus BX63 fluorescence microscope or a Zeiss LSM980 confocal microscope.

NeuN immunofluorescence (IF) was used to assess neuronal proportion, calculated as the ratio of NeuN+ cells to DAPI+ nuclei, and PV‐ and SST‐positive interneurons were quantified by cell counting. Quantification was performed across layers II–VI. For each mouse, six sections were analyzed for NeuN quantification, and three sections were analyzed for PV and SST IF. Fluorescence intensity and cell counts were analyzed using ImageJ software.

Appropriate negative controls were included for all immunohistochemistry (IHC) and IF experiments. These controls included omission of the primary antibody to assess nonspecific secondary antibody binding. No specific staining was observed under these conditions. Identical imaging acquisition and processing parameters were applied across all experimental groups.

2.8. Western Blot

Cortical tissues were homogenized in RIPA lysis buffer containing protease and phosphatase inhibitors. The protein concentration was determined using the BCA assay. Equal amounts of protein were separated by 10% SDS‐PAGE and transferred to PVDF membranes. After blocking with 5% nonfat milk, the membranes were incubated overnight at 4°C with primary antibodies against EB3/MAPRE3 rabbit mAb (ABclonal, A19768, dilution 1:1000), FOXG1 rabbit pAb (ABclonal, A16851, dilution 1:1000), and GAPDH (PT0582R) PT rabbit mAb (Immunoway, YM8394, dilution 1:20,000), followed by HRP‐conjugated secondary antibodies. Bands were visualized using ECL detection reagents, and densitometric analysis was performed with ImageJ. The intensity of each target protein band was normalized to the corresponding GAPDH band from the same sample. Normalized values were subsequently expressed relative to the mean value of the age‐matched control group, which was set to 1.0 for quantitative comparison.

2.9. Statistical Analysis

All data are presented as the mean ± standard error of the mean (SEM). Statistical analyses were performed using GraphPad Prism 9.0 (GraphPad Software, San Diego, CA, USA). Data normality for all variables (including OFT, NOR performance, protein expression levels of EB3 and FOXG1, proportion of CUX1‐, EB3‐, and FOXG1‐positive cells, and PV‐ and SST‐positive neuron counts) was assessed using the Shapiro‒Wilk test, and all datasets met normality assumptions. For data visualization, some figures display multiple variables (including time, treatment group, and cortical layer) together for intuitive comparison. However, not all variables were simultaneously included as factors in a single one‐way or two‐way ANOVA model. The specific statistical models applied are detailed below:

Comparisons between the control (CON) and isoflurane (ISO) groups at each individual age (P42 and P56) were conducted using ordinary one‐way ANOVA, followed by Tukey's multiple comparisons test when appropriate. For neuronal density and proportional distribution across cortical layers II–VI, two‐way ANOVA was performed separately at each time point (P42 and P56) to assess the main effects of treatment and cortical layer, with Tukey's multiple comparisons test applied for post hoc analysis.

Independent cohorts of mice were used at P42 and P56; therefore, repeated‐measures ANOVA was not applicable. Sample size and sex distribution are provided in the Methods section and figure legends. A p < 0.05 was considered statistically significant.

3. Results

3.1. Early‐Life Isoflurane Exposure Induces Age‐Dependent Behavioral Alterations

To examine the long‐term behavioral impact of neonatal isoflurane exposure, C57BL/6 mice were anesthetized with 1.5% isoflurane for 2 h at Postnatal Day 7 (P7) (Agarwal et al. 2022; Feng et al. 2016; Z. Wang et al. 2018). Behavioral assessments were conducted during early (P42) and late adolescence (P56) (Figure 1A).

FIGURE 1.

FIGURE 1

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Behavioral assessment following neonatal isoflurane exposure. (A) Experimental timeline. Mice were exposed to isoflurane or room air at Postnatal Day 7 (P7). Behavioral assessments, including the open‐field test (OFT) and novel object recognition (NOR), were performed during adolescence at P42 and P56. (B) OFT performance at P42 and P56. Quantified parameters included total distance traveled, mean movement speed, time spent in the central zone, number of center entries, and the ratio of distance traveled in the center versus peripheral zones. (C) NOR performance at P42 and P56. Cognitive performance was assessed using the recognition index (RI), calculated as the time spent exploring the novel object divided by the total object exploration time. For all behavioral analyses, each group comprised 12 mice with equal numbers of males and females (control: n = 12, male/female = 1:1; isoflurane: n = 12, male/female = 1:1). Data are presented as the mean ± SEM. Statistical significance was assessed using one‐way ANOVA followed by Tukey's multiple‐comparisons test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

At P42, isoflurane‐exposed mice showed significantly increased exploratory activity in the OFT, as evidenced by increased time spent in the central zone, higher average movement speed, greater numbers of center entries, a higher center/periphery distance ratio, and increased total distance traveled compared with control mice (Figure 1B). In contrast, no significant differences were observed between groups in the NOR test at this age, indicating preserved recognition memory during early adolescence at P42 (Figure 1C). By P56, OFT parameters, including time spent in the central zone, average movement speed, number of center entries, center/periphery distance ratio, and total distance traveled, were comparable between groups (Figure 1B), whereas recognition memory was significantly impaired in isoflurane‐exposed mice relative to controls (Figure 1C).

These results indicate that neonatal isoflurane exposure produces age‐dependent behavioral effects, with transient alterations in exploratory activity during early adolescence and delayed deficits in recognition memory.

3.2. Early Isoflurane Exposure Induces Layer‐Specific Alterations in Neuronal Distribution

To investigate the effects of early isoflurane exposure on behavioral performance and cortical neuronal organization, we first observed significant abnormalities in mice through behavioral experiments and hypothesized that they might be related to developmental disorders of cortical neurons after anesthetic intervention. To test this hypothesis, we performed histological analyses of the sensory cortex of isoflurane‐exposed and control mice. We performed quantitative analysis of cortical neuronal density in mice at the P28 postnatal stage and found no significant differences between the two groups at this time point (Figure S1). Based on behavioral performance, we shifted our focus to the P42 and P56 stages.

Preliminary examination of NeuN IF staining on sagittal brain sections at P56 revealed an apparent difference in NeuN signal distribution within the sensory cortex between the isoflurane‐exposed and control groups (Figure 2A). As this analysis was intended for qualitative visualization only, no quantitative measurements were performed for NeuN staining in this panel. To further explore whether these qualitative observations were associated with potential changes in neuronal organization, we performed Nissl staining on both coronal and sagittal brain sections (Figure 2B). Visual inspection of Nissl‐stained sections suggested an apparent difference in neuronal packing density between groups, with changes more prominently observed in the superficial cortical layers of the sensory cortex. Fine anatomical examination indicated that these differences were mainly localized to cortical layers II–IV. Because the observations in Figure 2A,B were based on qualitative assessment, we subsequently conducted systematic quantitative analyses using NeuN IF and Nissl staining across cortical layers II–VI at defined developmental stages, as described below (Figure 2C,D).

FIGURE 2.

FIGURE 2

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Analysis of neuronal abundance and layer‐specific neuronal density in the sensory cortex. (A) Representative NeuN immunofluorescence images of the sensory cortex from control and isoflurane‐exposed mice at P56. Images show sagittal brain sections (4‐µm thickness). NeuN‐positive neurons are shown in red and nuclei in blue (DAPI). The upper panels display low‐magnification views of the cortex, and the lower panels show higher‐magnification images of the boxed regions. Scale bar = 2000 µm (low magnification), 100 µm (high magnification). (B) Representative Nissl‐stained images of the sensory cortex from control and isoflurane‐exposed mice at P56. Both sagittal and coronal brain sections (4‐µm thickness) are shown. The upper panels display low‐magnification views, and the lower panels show higher‐magnification images highlighting cortical layers. Scale bar = 2000 µm (low magnification), 100 µm (high magnification). (C) Quantitative analysis of NeuN immunofluorescence across cortical layers II–VI in control and isoflurane‐exposed mice at P42 and P56. Analyses were performed on coronal sections (4‐µm thickness). NeuN‐positive cells (red) were quantified relative to total nuclei stained with DAPI (blue). Control group: n = 6; isoflurane group: n = 6. Scale bar = 100 µm. (D) Quantitative analysis of neuronal density based on Nissl staining across cortical layers II–VI in control and isoflurane‐exposed mice at P42 and P56. Analyses were performed on coronal sections (4‐µm thickness). Control group: n = 6; isoflurane group: n = 6. Scale bar = 100 µm. Data are presented as the mean ± SEM. Statistical significance was assessed using two‐way ANOVA followed by Tukey's multiple‐comparisons test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

NeuN IF analysis revealed that at both P42 and P56, the proportion of NeuN‐positive cells in layers II–IV was significantly lower in isoflurane‐exposed mice than in controls, whereas no significant differences were observed in layers V and VI at either time point (Figure 2C). Consistent with these findings, Nissl staining demonstrated a significant reduction in neuronal density within layers II–IV at P42 in the isoflurane‐exposed group, with no significant differences detected in deeper layers V–VI. By P56, Nissl staining revealed significantly lower neuronal density in the isoflurane‐exposed group across layers II–VI (Figure 2D).

Together, these data indicate that the effects of early isoflurane exposure on neuronal distribution are layer‐dependent and age‐dependent, emerging initially in superficial cortical layers during early adolescence (P42) and becoming more widespread by late adolescence (P56). This temporal pattern supports the presence of a delayed and progressive impact of early anesthetic exposure on cortical neuronal organization.

Given that layers II–IV are key regions for sensory information integration and processing (Adesnik and Naka 2018; C. C. Petersen and Crochet 2013; Voelcker et al. 2022), a decrease in their neuronal density is likely to lead to behavioral abnormalities. We further focused on this region and detected the expression of the layer‐specific neuronal marker CUX1. The results showed that CUX1 expression was significantly lower in the isoflurane‐exposed group than in the control group at both the P42 and P56 time points, consistent with the decrease in neuronal density (Figure 3A,B). In addition, we analyzed changes in the expression of the inhibitory interneuron markers SST and parvalbumin (PV). SST expression was found to increase with age in both groups, and there was no significant difference between groups at P42; however, by P56, SST expression was significantly higher in the isoflurane‐exposed group than in the control group (Figure 3C). In contrast, PV expression did not exhibit significant age‐dependent or group‐dependent changes (Figure 3D). These findings indicate that early isoflurane exposure is associated with a selective, delayed alteration of SST‐positive interneurons, while PV‐positive interneuron populations remain relatively preserved.

FIGURE 3.

FIGURE 3

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Analysis of neuronal subtypes in cortical intermediate layers. (A) Representative immunofluorescence images showing CUX1 expression in the sensory cortex of control and isoflurane‐exposed mice at P42 and P56. Images were acquired from coronal paraffin sections (4‐µm thickness). CUX1 immunoreactivity is shown in red, with nuclei counterstained with DAPI (blue). Scale bar = 100 µm. (B) Quantitative analysis of CUX1 immunohistochemical staining in cortical layers II–IV of control and isoflurane‐exposed mice at P42 and P56. Analyses were performed on coronal sections (4µm thickness). Control group: n = 3; isoflurane group: n = 3. Scale bar = 100 µm. (C) Quantification of somatostatin (SST)‐positive interneurons in the sensory cortex of control and isoflurane‐exposed mice at P42 and P56 based on immunofluorescence staining. SST‐positive cells are shown in red, and nuclei are shown in blue (DAPI). Analyses were performed on coronal sections (4‐µm thickness). Control group: n = 3; isoflurane group: n = 3. Scale bar = 100 µm. (D) Quantification of parvalbumin (PV)‐positive interneurons in the sensory cortex of control and isoflurane‐exposed mice at P42 and P56 based on immunofluorescence staining. PV‐positive cells are shown in red and nuclei in blue (DAPI). Analyses were performed on coronal sections (4‐µm thickness). Control group: n = 3; isoflurane group: n = 3. Scale bar = 100 µm. Data are presented as the mean ± SEM. Statistical significance was assessed using one‐way ANOVA followed by Tukey's multiple‐comparisons test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

3.3. Early Isoflurane Exposure Is Associated With Altered Expression of Neuronal Morphology‐Related Proteins

To explore molecular alterations that may underlie the age‐dependent changes in neuronal abundance and layer‐specific neuronal density described above, we examined the expression of the microtubule‐associated protein EB3 and the transcription factor FOXG1, both of which are known regulators of neuronal morphogenesis and cytoskeletal dynamics. IF staining showed that FOXG1 and EB3 were widely expressed in cortical tissues (Figure 4A), but there was a significant difference in expression levels between the control and isoflurane‐exposed groups (Figure 4B). Further co‐localization analysis showed that FOXG1 and EB3 were significantly co‐localized in the neuronal cytoplasm (Figure 4C), indicating a close intracellular association between these two proteins. β‐Tubulin IF revealed altered cytoskeletal organization in isoflurane‐exposed mice, with reduced signal continuity and altered filament distribution in cortical layers II–IV (Figure S2).

FIGURE 4.

FIGURE 4

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Expression and co‐localization of FOXG1 and EB3 in the cortex. (A) Representative immunofluorescence images showing FOXG1 and EB3 expression in the sensory cortex of control and isoflurane‐exposed mice at P56. Images were acquired from paraffin sections (4‐µm thickness). For FOXG1, the left panels show sagittal sections (scale bar = 1000 µm), and the right panels show coronal sections (scale bar = 500 µm). EB3 expression is presented in the corresponding right‐hand panels. FOXG1 and EB3 immunoreactivity are shown in red. (B) Quantitative analysis of FOXG1 and EB3 immunofluorescence intensity in cortical layers II–IV of control and isoflurane‐exposed mice at P42 and P56. Analyses were performed on coronal sections (4‐µm thickness). EB3 is shown in green, FOXG1 in red, and nuclei are counterstained with DAPI (blue). Control group: n = 3; isoflurane group: n = 3. Scale bar = 100 µm. (C) Co‐localization analysis of FOXG1 and EB3 in cortical neurons based on high‐magnification immunofluorescence images. EB3 is shown in green, FOXG1 in red, and nuclei in blue (DAPI). Co‐localization was quantified using Pearson's correlation coefficient (Pearson's R = 0.92). Scale bar = 20 µm. Data are presented as the mean ± SEM. Statistical significance was assessed using one‐way ANOVA followed by Tukey's multiple‐comparisons test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

To clarify the effect of anesthesia on the dynamics of FOXG1 and EB3 expression, we performed IHC and protein immunoblotting (WB) analysis at two time points: P42 and P56. Immunohistochemical results showed that at both P42 and P56, the proportion of FOXG1‐ and EB3‐positive cells quantified across the entire cortical column was significantly lower in the isoflurane‐exposed group than in the control group (Figure 5A,B). It is noteworthy that the expression of these two factors increased gradually over time in both groups, but the increase in the isoflurane‐exposed group lagged behind, suggesting that anesthesia may have delayed the alterations in neuronal morphology‐related protein expression.

FIGURE 5.

FIGURE 5

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Temporal analysis of FOXG1 and EB3 expression in the cortex. (A) Quantitative analysis of EB3 immunohistochemical staining in cortical layers II–IV of control and isoflurane‐exposed mice at P42 and P56. Analyses were performed on coronal paraffin sections (4‐µm thickness). Control group: n = 3; isoflurane group: n = 3. Scale bar = 100 µm. (B) Quantitative analysis of FOXG1 immunohistochemical staining in cortical layers II–IV of control and isoflurane‐exposed mice at P42 and P56. Analyses were performed on coronal paraffin sections (4‐µm thickness). Control group: n = 3; isoflurane group: n = 3. Scale bar = 100 µm. (C) Western blot analysis and densitometric quantification of FOXG1 and EB3 protein expression in cortical tissue from control and isoflurane‐exposed mice at P42 and P56. Band intensities were quantified using ImageJ software and normalized to the corresponding GAPDH loading control for each sample. Normalized values were expressed relative to the mean of the age‐matched control group, which was set to 1.0. Control group: n = 3; isoflurane group: n = 3. Data are presented as the mean ± SEM. Statistical significance was assessed using one‐way ANOVA followed by Tukey's multiple‐comparisons test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

This conclusion was further supported by the WB results: at P42, the protein expression of FOXG1 and EB3 in the isoflurane‐exposed group was significantly lower than that in the control group; by P56, there was no significant difference in the expression levels between the two groups (Figure 5C). This dynamic pattern of change suggests that anesthesia does not completely inhibit the final expression levels of FOXG1 and EB3, but rather alters the normal temporal profile of their developmental regulation.

Together, these results indicate that neonatal isoflurane exposure is associated with delayed, layer‐associated dysregulation of proteins involved in cytoskeletal organization and neuronal morphogenesis. Such temporal perturbations may contribute to the age‐dependent alterations in neuronal distribution and inhibitory interneuron composition observed in the sensory cortex and may underlie the emergence of cognitive and behavioral deficits during late adolescence.

4. Discussion

In this study, we show that neonatal exposure to isoflurane is associated with age‐dependent behavioral alterations, progressive changes in cortical neuronal distribution, and dysregulation of cytoskeleton‐related proteins, including EB3 and FOXG1. These findings provide mechanistic insight into potential mechanisms by which early‐life anesthesia may contribute to long‐term neurodevelopmental alterations.

Behaviorally, isoflurane exposure enhanced exploratory activity during early adolescence but led to delayed recognition memory impairments by late adolescence (Figure 1), suggesting that anesthesia induced dynamic and delayed disruption of cortical loop function. This temporal pattern aligns with previous reports showing that early anesthetic exposure does not uniformly affect cognitive function immediately but can produce delayed deficits (Dai et al. 2020; Perouansky 2008; Sinner et al. 2014; Xie et al. 2020). In contrast, some studies report minimal behavioral changes following similar exposures, highlighting the influence of exposure duration, anesthetic concentration, and genetic background (Shen et al. 2013; Ward and Loepke 2012; Wilder et al. 2009).

Histological analyses revealed layer‐dependent and age‐dependent alterations in neuronal density, with superficial cortical layers (II–IV) showing significant reductions during adolescence, while deeper layers were relatively preserved at earlier stages (Figure 2). These results suggest that early‐life anesthesia may influence neuronal distribution and circuit integration rather than causing immediate cell death. Previous studies have reported reduced neuronal density or dendritic complexity after neonatal anesthesia, but few have described this delayed, layer‐specific trajectory, underscoring the importance of temporal profiling in developmental studies (Neudecker et al. 2023; Schaefer et al. 2020).

With respect to inhibitory interneurons, we observed a selective, age‐dependent increase in SST‐positive interneurons at P56, whereas PV‐positive interneuron populations remained largely unchanged (Figure 3). This suggests that early isoflurane exposure does not uniformly affect all inhibitory neuron subtypes but may preferentially influence SST‐positive neurons involved in dendritic inhibition and cortical circuit modulation (Hosseini Fin et al. 2025; Jiang et al. 2025; Mòdol et al. 2024). These changes are interpreted as maturation‐dependent, altered survival, or circuit‐level alterations rather than postnatal neuronal migration.

Our observation of a significant divergence in SST‐positive cell distribution between P42 and P56 in the sensory cortex promotes re‐evaluation of the regional developmental timeline. Conventional models suggest that SST interneuron maturation and connectivity in primary sensory areas plateau by P30 (Jiang et al. 2025; A. Wang et al. 2024). In contrast, more protracted remodeling is typically associated with higher‐order regions such as the medial prefrontal cortex (mPFC) (Du et al. 2018; D. Petersen et al. 2024). One possibility is that early isoflurane exposure perturbs circuits that are normally relatively stable in the sensory cortex during this developmental window, resulting in delayed changes in interneuron marker expression or circuit composition. Alternatively, it remains possible that SST interneuron maturation and circuit integration in the sensory cortex continue to undergo subtle refinement during late juvenile stages that are not yet well characterized in the current literature.

Taken together, these considerations suggest that the SST alterations observed at P56 may reflect anesthesia‐associated disruption or delay of late‐stage interneuron maturation and circuit stabilization. Further studies incorporating lineage tracing or longitudinal circuit analyses will be required to determine whether these changes arise from altered survival, delayed maturation, or modifications in circuit remodeling dynamics.

We also acknowledge that other interneuron subtypes, such as VIP‐positive interneurons (Ferguson et al. 2023; Furutachi et al. 2024; Qiu et al. 2020; Rachel et al. 2025), also play important roles in cortical inhibitory networks. As VIP‐positive cells were not examined in this study, potential effects on this population cannot be excluded, representing a limitation that should be addressed in future work.

At the molecular level, early isoflurane exposure was associated with delayed and layer‐associated dysregulation of cytoskeleton‐related proteins, identifying EB3 and FOXG1 as convergent targets. These changes do not provide evidence for large‐scale anatomical reorganization or postnatal neuronal migration but instead reflect age‐dependent alterations in protein expression that coincide with quantitative changes in neuronal abundance and layer‐specific neuronal density.

EB3 regulates microtubule dynamics, while FOXG1, traditionally considered a transcription factor, localizes predominantly in the cytoplasm and co‐localizes with EB3 and β‐tubulin (Figure 4), supporting a potential noncanonical, cytoskeleton‐associated role. Consistent with this interpretation, β‐tubulin IF revealed altered cytoskeletal organization in the sensory cortex of isoflurane‐exposed mice, particularly within layers II–IV (Figure S2), indicating disruption of microtubule integrity rather than changes in gross cortical architecture.

While previous work has emphasized the transcriptional roles of FOXG1 in cortical development (Cargnin et al. 2018; Hou et al. 2020; Liu et al. 2022; Miyoshi and Fishell 2012), our observations suggest a possible cytoplasmic, cytoskeleton‐associated role of FOXG1 and a spatial association with EB3. We therefore interpret the altered expression of EB3 and FOXG1 as reflecting temporal dysregulation of neuronal density and cytoskeletal stabilization during sensitive developmental windows, which may indirectly contribute to the age‐dependent changes in neuronal distribution and circuit composition observed in this study.

Our findings both align with and extend previous literature. Consistent with prior reports, early‐life anesthesia alters cortical structure and cognitive function. However, rather than invoking persistent postnatal neuronal migration defects, our data support a model in which developmental timing and layer‐specific vulnerability determine the long‐term impact of anesthesia. The dynamic, layer‐specific trajectory of neuronal alterations further emphasizes that immediate post‐exposure assessments may underestimate long‐term effects.

Future studies should examine the mechanisms regulating FOXG1 nucleocytoplasmic shuttling and its impact on EB3‐mediated cytoskeletal dynamics. It will also be important to test whether pharmacological stabilization of EB3 or modulation of FOXG1 may influence neuronal density and circuit refinement. Furthermore, a key limitation of the current study is that sex‐specific statistical analyses were not performed for cellular and molecular endpoints, despite no significant sex differences in behavioral outcomes. Future studies with larger, sex‐stratified sample sizes will therefore be necessary to comprehensively evaluate the potential sex‐specific effects of neonatal isoflurane exposure on cortical neuronal development and function. Ultimately, clarifying the critical developmental windows and vulnerable molecular nodes involved in anesthetic neurotoxicity will help guide the establishment of safer pediatric anesthetic strategies and promote the development of targeted therapeutic interventions to protect the developing brain.

In summary, neonatal isoflurane exposure is associated with alterations in cortical development, accompanied by reduced expression of FOXG1 and EB3 and delayed cognitive deficits. These findings highlight the potential role of cytoskeleton‐related regulatory processes during cortical maturation and provide a framework for understanding how early‐life anesthesia exposure may contribute to long‐term neurodevelopmental alterations. Importantly, the present findings should not be interpreted as evidence of postnatal neuronal migration or large‐scale cortical structural reorganization but rather as reflecting delayed and layer‐associated alterations in neuronal marker expression and cytoskeleton‐related developmental programs following early isoflurane exposure.

Author Contributions

Ke Chen: conceptualization, methodology, experimental procedures, data analysis, writing – original draft, writing – review and editing, visualization. Mengdi Xia: experimental procedures, data analysis, writing – review and editing. Changhong Li: experimental procedures, data analysis, writing – review and editing. Xiaohui Li: conceptualization, methodology, resources, supervision, writing – review and editing, funding acquisition. Zhenhua Ren: conceptualization, methodology, resources, supervision, writing – review and editing. All authors have read and approved the final manuscript.

Funding

This work was supported by the Basic and Clinical Improvement Program of Anhui Medical University (2023xkjT013) and the Natural Science Research Project of Anhui Provincial Department of Education (2025AHGXZK40056).

Ethics Statement

All animal experiments were conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee of Anhui Medical University (Protocol no. LLSC20241480).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1. Analysis of layer‐specific neuronal density in the sensory cortex during P28. (A) Quantitative analysis of neuronal density based on Nissl staining across cortical layers II–VI in control and isoflurane‐exposed mice at P28. Analyses were performed on coronal sections (4‐µm thickness). Control group: n = 6; isoflurane group: n = 6. Scale bar = 100 µm. Data are presented as the mean ± SEM. Statistical significance was assessed using two‐way ANOVA followed by Tukey's multiple‐comparisons test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure S2. Alterations in neuronal cytoskeleton organization. (A) Representative immunofluorescence images showing β‐tubulin staining in cortical layers II–IV of control and isoflurane‐exposed mice. Images were acquired from coronal paraffin sections (4‐µm thickness). β‐Tubulin immunoreactivity is shown in green, with nuclei counterstained with DAPI (blue). Scale bar = 50 µm. (B) Representative immunofluorescence images of the higher magnification view. Scale bar = 20 µm.

DNEU-86-0-s001.docx (1.1MB, docx)

Contributor Information

Zhenhua Ren, Email: renzhenhua@ahmu.edu.cn.

Xiaohui Li, Email: lixiaohui@ahmu.edu.cn.

Data Availability Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Adesnik, H. , and Naka A.. 2018. “Cracking the Function of Layers in the Sensory Cortex.” Neuron 100, no. 5: 1028–1043. 10.1016/j.neuron.2018.10.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Agarwal, S. , Schaefer M. L., Krall C., and Johns R. A.. 2022. “Isoflurane Disrupts Postsynaptic Density‐95 Protein Interactions Causing Neuronal Synapse Loss and Cognitive Impairment in Juvenile Mice via Canonical NO‐Mediated Protein Kinase‐G Signaling.” Anesthesiology 137, no. 2: 212–231. 10.1097/aln.0000000000004264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ba, R. , Yang L., Zhang B., et al. 2023. “FOXG1 Drives Transcriptomic Networks to Specify Principal Neuron Subtypes During the Development of the Medial Pallium.” Science Advances 9, no. 7: eade2441. 10.1126/sciadv.ade2441. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bragg‐Gonzalo, L. , Aguilera A., González‐Arias C., et al. 2024. “Early Cortical GABAergic Interneurons Determine the Projection Patterns of L4 Excitatory Neurons.” Science Advances 10, no. 19: eadj9911. 10.1126/sciadv.adj9911. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cargnin, F. , Kwon J. S., Katzman S., Chen B., Lee J. W., and Lee S. K.. 2018. “FOXG1 Orchestrates Neocortical Organization and Cortico‐Cortical Connections.” Neuron 100, no. 5: 1083–1096.e5. 10.1016/j.neuron.2018.10.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cheetham, C. E. , and Fox K.. 2010. “Presynaptic Development at L4 to L2/3 Excitatory Synapses Follows Different Time Courses in Visual and Somatosensory Cortex.” Journal of Neuroscience 30, no. 38: 12566–12571. 10.1523/jneurosci.2544-10.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chen, Y. , Xu Z., Chen J., et al. 2023. “Coronin 2B Regulates Neuronal Migration via Rac1‐Dependent Multipolar‐Bipolar Transition.” Journal of Neuroscience 43, no. 2: 211–220. 10.1523/jneurosci.1087-22.2022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cubelos, B. , Briz C. G., Esteban‐Ortega G. M., and Nieto M.. 2015. “Cux1 and Cux2 Selectively Target Basal and Apical Dendritic Compartments of Layer II‐III Cortical Neurons.” Developmental Neurobiology 75, no. 2: 163–172. 10.1002/dneu.22215. [DOI] [PubMed] [Google Scholar]
  9. Cubelos, B. , Sebastián‐Serrano A., Beccari L., et al. 2010. “Cux1 and Cux2 Regulate Dendritic Branching, Spine Morphology, and Synapses of the Upper Layer Neurons of the Cortex.” Neuron 66, no. 4: 523–535. 10.1016/j.neuron.2010.04.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Dai, C. L. , Li H., Hu X., et al. 2020. “Neonatal Exposure to Anesthesia Leads to Cognitive Deficits in Old Age: Prevention With Intranasal Administration of Insulin in Mice.” Neurotoxicity Research 38, no. 2: 299–311. 10.1007/s12640-020-00223-y. [DOI] [PubMed] [Google Scholar]
  11. Dehay, C. , and Kennedy H.. 2007. “Cell‐Cycle Control and Cortical Development.” Nature Reviews Neuroscience 8, no. 6: 438–450. 10.1038/nrn2097. [DOI] [PubMed] [Google Scholar]
  12. Deng, L. , Song S.‐Y., Zhao W.‐M., et al. 2024. “Triggering Receptor Expressed on Myeloid Cells 2 Alleviated Sevoflurane‐Induced Developmental Neurotoxicity via Microglial Pruning of Dendritic Spines in the CA1 Region of the Hippocampus.” Neuroscience Bulletin 40, no. 9: 1215–1229. 10.1007/s12264-024-01260-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dong, R. , Han Y., Jiang L., et al. 2022. “Connexin 43 Gap Junction‐Mediated Astrocytic Network Reconstruction Attenuates Isoflurane‐Induced Cognitive Dysfunction in Mice.” Journal of Neuroinflammation 19, no. 1: 64. 10.1186/s12974-022-02424-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Du, X. , Serena K., Hwang W. J., et al. 2018. “Prefrontal Cortical Parvalbumin and Somatostatin Expression and Cell Density Increase During Adolescence and Are Modified by BDNF and Sex.” Molecular and Cellular Neuroscience 88: 177–188. 10.1016/j.mcn.2018.02.001. [DOI] [PubMed] [Google Scholar]
  15. Evsyukova, I. , Plestant C., and Anton E. S.. 2013. “Integrative Mechanisms of Oriented Neuronal Migration in the Developing Brain.” Annual Review of Cell and Developmental Biology 29, no. 1: 299–353. 10.1146/annurev-cellbio-101512-122400. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Feng, C. , Liu Y., Yuan Y., et al. 2016. “Isoflurane Anesthesia Exacerbates Learning and Memory Impairment in Zinc‐Deficient APP/PS1 Transgenic Mice.” Neuropharmacology 111: 119–129. 10.1016/j.neuropharm.2016.08.035. [DOI] [PubMed] [Google Scholar]
  17. Ferguson, K. A. , Salameh J., Alba C., et al. 2023. “VIP Interneurons Regulate Cortical Size Tuning and Visual Perception.” Cell Reports 42, no. 9: 113088. 10.1016/j.celrep.2023.113088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Furutachi, S. , Franklin A. D., Aldea A. M., Mrsic‐Flogel T. D., and Hofer S. B.. 2024. “Cooperative Thalamocortical Circuit Mechanism for Sensory Prediction Errors.” Nature 633, no. 8029: 398–406. 10.1038/s41586-024-07851-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gascoigne, D. A. , Serdyukova N. A., and Aksenov D. P.. 2021. “Early Development of the GABAergic System and the Associated Risks of Neonatal Anesthesia.” International Journal of Molecular Sciences 22, no. 23: 12951. 10.3390/ijms222312951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hanganu‐Opatz, I. L. , Butt S. J. B., Hippenmeyer S., et al. 2021. “The Logic of Developing Neocortical Circuits in Health and Disease.” Journal of Neuroscience 41, no. 5: 813–822. 10.1523/jneurosci.1655-20.2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hosseini Fin, N. S. , Yip A., Scott J. T., Teo L., Homman‐Ludiye J., and Bourne J. A.. 2025. “Developmental Dynamics of Marmoset Prefrontal Cortical SST and PV Interneuron Networks Highlight Primate‐Specific Features.” Development 152, no. 10: dev204254. 10.1242/dev.204254. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hou, P. S. , hAilín D., Vogel T., and Hanashima C.. 2020. “Transcription and Beyond: Delineating FOXG1 Function in Cortical Development and Disorders.” Frontiers in Cellular Neuroscience 14: 35. 10.3389/fncel.2020.00035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Huttenlocher, P. R. 1979. “Synaptic Density in Human Frontal Cortex—Developmental Changes and Effects of Aging.” Brain Research 163, no. 2: 195–205. 10.1016/0006-8993(79)90349-4. [DOI] [PubMed] [Google Scholar]
  24. Huttenlocher, P. R. , and Dabholkar A. S.. 1997. “Regional Differences in Synaptogenesis in Human Cerebral Cortex.” Journal of Comparative Neurology 387, no. 2: 167–178. 10.1002/(sici)1096-9861(19971020)387:2<167::aid-cne1>3.0.co;2-z. [DOI] [PubMed] [Google Scholar]
  25. Jaglin, X. H. , and Chelly J.. 2009. “Tubulin‐Related Cortical Dysgeneses: Microtubule Dysfunction Underlying Neuronal Migration Defects.” Trends in Genetics 25, no. 12: 555–566. 10.1016/j.tig.2009.10.003. [DOI] [PubMed] [Google Scholar]
  26. Jaworski, J. , Kapitein L. C., Gouveia S. M., et al. 2009. “Dynamic Microtubules Regulate Dendritic Spine Morphology and Synaptic Plasticity.” Neuron 61, no. 1: 85–100. 10.1016/j.neuron.2008.11.013. [DOI] [PubMed] [Google Scholar]
  27. Jevtovic‐Todorovic, V. , Hartman R. E., Izumi Y., et al. 2003. “Early Exposure to Common Anesthetic Agents Causes Widespread Neurodegeneration in the Developing Rat Brain and Persistent Learning Deficits.” Journal of Neuroscience 23, no. 3: 876–882. 10.1523/jneurosci.23-03-00876.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Jiang, Q. , Wu S. J., Fishell G., and Chen C.. 2025. “Reciprocal Interaction Between Cortical SST and PV Interneurons in Top‐Down Regulation of Retinothalamic Refinement.” Proceedings of the National Academy of Sciences of the United States of America 122, no. 25: e2504224122. 10.1073/pnas.2504224122. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Jie, H. , Petrus E., Pothayee N., and Koretsky A. P.. 2025. “Reactivated Thalamocortical Plasticity Alters Neural Activity in Sensory‐Motor Cortex During Post‐Critical Period.” Progress in Neurobiology 247: 102735. 10.1016/j.pneurobio.2025.102735. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Kim, J. Y. , and Paredes M. F.. 2021. “Implications of Extended Inhibitory Neuron Development.” International Journal of Molecular Sciences 22, no. 10: 5113. 10.3390/ijms22105113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Koss, W. A. , Belden C. E., Hristov A. D., and Juraska J. M.. 2014. “Dendritic Remodeling in the Adolescent Medial Prefrontal Cortex and the Basolateral Amygdala of Male and Female Rats.” Synapse 68, no. 2: 61–72. 10.1002/syn.21716. [DOI] [PubMed] [Google Scholar]
  32. Kubota, J. , Mikami Y., Kanemaru K., Sekiya H., Okubo Y., and Iino M.. 2016. “Whisker Experience‐Dependent mGluR Signaling Maintains Synaptic Strength in the Mouse Adolescent Cortex.” European Journal of Neuroscience 44, no. 3: 2004–2014. 10.1111/ejn.13285. [DOI] [PubMed] [Google Scholar]
  33. Larsen, B. , and Luna B.. 2018. “Adolescence as a Neurobiological Critical Period for the Development of Higher‐Order Cognition.” Neuroscience & Biobehavioral Reviews 94: 179–195. 10.1016/j.neubiorev.2018.09.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lei, T. , Liao X., Chen X., et al. 2022. “Progressive Stabilization of Brain Network Dynamics During Childhood and Adolescence.” Cerebral Cortex 32, no. 5: 1024–1039. 10.1093/cercor/bhab263. [DOI] [PubMed] [Google Scholar]
  35. Liang, X. , Jiang M., Xu H., et al. 2023. “Maternal Sevoflurane Exposure Increases the Epilepsy Susceptibility of Adolescent Offspring by Interrupting Interneuron Development.” BMC Medicine 21, no. 1: 510. 10.1186/s12916-023-03210-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Liu, J. , Yang M., Su M., et al. 2022. “FOXG1 Sequentially Orchestrates Subtype Specification of Postmitotic Cortical Projection Neurons.” Science Advances 8, no. 21: eabh3568. 10.1126/sciadv.abh3568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Menon, V. 2013. “Developmental Pathways to Functional Brain Networks: Emerging Principles.” Trends in Cognitive Sciences 17, no. 12: 627–640. 10.1016/j.tics.2013.09.015. [DOI] [PubMed] [Google Scholar]
  38. Miyoshi, G. , and Fishell G.. 2012. “Dynamic FoxG1 Expression Coordinates the Integration of Multipolar Pyramidal Neuron Precursors Into the Cortical Plate.” Neuron 74, no. 6: 1045–1058. 10.1016/j.neuron.2012.04.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Miyoshi, G. , Ueta Y., Yagasaki Y., et al. 2024. “Developmental Trajectories of GABAergic Cortical Interneurons Are Sequentially Modulated by Dynamic FoxG1 Expression Levels.” Proceedings of the National Academy of Sciences of the United States of America 121, no. 16: e2317783121. 10.1073/pnas.2317783121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Mòdol, L. , Moissidis M., Selten M., Oozeer F., and Marín O.. 2024. “Somatostatin Interneurons Control the Timing of Developmental Desynchronization in Cortical Networks.” Neuron 112, no. 12: 2015–2030.e2015. 10.1016/j.neuron.2024.03.014. [DOI] [PubMed] [Google Scholar]
  41. Munguba, H. , Nikouei K., Hochgerner H., et al. 2023. “Transcriptional Maintenance of Cortical Somatostatin Interneuron Subtype Identity During Migration.” Neuron 111, no. 22: 3590–3603.e5. 10.1016/j.neuron.2023.07.018. [DOI] [PubMed] [Google Scholar]
  42. Nadarajah, B. , and Parnavelas J. G.. 2002. “Modes of Neuronal Migration in the Developing Cerebral Cortex.” Nature Reviews Neuroscience 3, no. 6: 423–432. 10.1038/nrn845. [DOI] [PubMed] [Google Scholar]
  43. Nakajima, C. , Sawada M., and Sawamoto K.. 2021. “Postnatal Neuronal Migration in Health and Disease.” Current Opinion in Neurobiology 66: 1–9. 10.1016/j.conb.2020.06.001. [DOI] [PubMed] [Google Scholar]
  44. Neudecker, V. , Perez‐Zoghbi J. F., Miranda‐Domínguez O., et al. 2023. “Early‐in‐Life Isoflurane Exposure Alters Resting‐State Functional Connectivity in Juvenile Non‐Human Primates.” British Journal of Anaesthesia 131, no. 6: 1030–1042. 10.1016/j.bja.2023.07.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Paus, T. , Keshavan M., and Giedd J. N.. 2008. “Why Do Many Psychiatric Disorders Emerge During Adolescence?” Nature Reviews Neuroscience 9, no. 12: 947–957. 10.1038/nrn2513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Pchitskaya, E. , Rakovskaya A., Chigray M., and Bezprozvanny I.. 2022. “Cytoskeleton Protein EB3 Contributes to Dendritic Spines Enlargement and Enhances Their Resilience to Toxic Effects of Beta‐Amyloid.” International Journal of Molecular Sciences 23, no. 4: 2274. 10.3390/ijms23042274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Penazzi, L. , Bakota L., and Brandt R.. 2016. “Microtubule Dynamics in Neuronal Development, Plasticity, and Neurodegeneration.” International Review of Cell and Molecular Biology 321: 89–169. 10.1016/bs.ircmb.2015.09.004. [DOI] [PubMed] [Google Scholar]
  48. Perouansky, M. 2008. “General Anesthetics and Long‐Term Neurotoxicity.” In Handbook of Experimental Pharmacology, edited by Schüttler J. and Schwilden H., 143–157. Springer. 10.1007/978-3-540-74806-9_7. [DOI] [PubMed] [Google Scholar]
  49. Petersen, C. C. , and Crochet S. . 2013. “Synaptic Computation and Sensory Processing in Neocortical Layer 2/3.” Neuron 78, no. 1: 28–48. 10.1016/j.neuron.2013.03.020. [DOI] [PubMed] [Google Scholar]
  50. Petersen, D. , Raudales R., Silva A. K., Kellendonk C., and Canetta S.. 2024. “Adolescent Thalamoprefrontal Inhibition Leads to Changes in Intrinsic Prefrontal Network Connectivity.” eNeuro 11, no. 8: ENEURO.0284–24.2024. 10.1523/eneuro.0284-24.2024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Platzer, K. , Cogné B., Hague J., et al. 2018. “Haploinsufficiency of CUX1 Causes Nonsyndromic Global Developmental Delay With Possible Catch‐Up Development.” Annals of Neurology 84, no. 2: 200–207. 10.1002/ana.25278. [DOI] [PubMed] [Google Scholar]
  52. Putman, J. N. , Watson S. D., Zhang Z., et al. 2024. “Pre‐ and Postsynaptic MEF2C Promotes Experience‐Dependent, Input‐Specific Development of Cortical Layer 4 to Layer 2/3 Excitatory Synapses and Regulates Activity‐Dependent Expression of Synaptic Cell Adhesion Molecules.” Journal of Neuroscience 44, no. 45: e0098242024. 10.1523/jneurosci.0098-24.2024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Qian, X. , Shen Q., Goderie S. K., et al. 2000. “Timing of CNS Cell Generation: a Programmed Sequence of Neuron and Glial Cell Production From Isolated Murine Cortical Stem Cells.” Neuron 28, no. 1: 69–80. 10.1016/s0896-6273(00)00086-6. [DOI] [PubMed] [Google Scholar]
  54. Qiu, F. , Mao X., Liu P., et al. 2020. “microRNA Deficiency in VIP+ Interneurons Leads to Cortical Circuit Dysfunction.” Cerebral Cortex 30, no. 4: 2229–2249. 10.1093/cercor/bhz236. [DOI] [PubMed] [Google Scholar]
  55. Rachel, J. , Möck M., Daigle T. L., Tasic B., Witte M., and Staiger J. F.. 2025. “VIP‐to‐SST Cell Circuit Motif Shows Differential Short‐Term Plasticity Across Sensory Areas of Mouse Cortex.” Journal of Neuroscience 45, no. 13: e0949242025. 10.1523/jneurosci.0949-24.2025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Rash, B. G. , Micali N., Huttner A. J., Morozov Y. M., Horvath T. L., and Rakic P.. 2018. “Metabolic Regulation and Glucose Sensitivity of Cortical Radial Glial Cells.” Proceedings of the National Academy of Sciences of the United States of America 115, no. 40: 10142–10147. 10.1073/pnas.1808066115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Schaefer, M. L. , Perez P. J., Wang M., et al. 2020. “Neonatal Isoflurane Anesthesia or Disruption of Postsynaptic Density‐95 Protein Interactions Change Dendritic Spine Densities and Cognitive Function in Juvenile Mice.” Anesthesiology 133, no. 4: 812–823. 10.1097/aln.0000000000003482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Scheuer, K. S. , Jansson A. M., Zhao X., and Jackson M. B.. 2024. “Inter and Intralaminar Excitation of Parvalbumin Interneurons in Mouse Barrel Cortex.” PLoS One 19, no. 6: e0289901. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Shen, X. , Dong Y., Xu Z., et al. 2013. “Selective Anesthesia‐Induced Neuroinflammation in Developing Mouse Brain and Cognitive Impairment.” Anesthesiology 118, no. 3: 502–515. 10.1097/ALN.0b013e3182834d77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sinner, B. , Becke K., and Engelhard K. . 2014. “General Anaesthetics and the Developing Brain: An Overview.” Anaesthesia 69, no. 9: 1009–1022. 10.1111/anae.12637. [DOI] [PubMed] [Google Scholar]
  61. Skibinska, A. , Glazewski S., Fox K., and Kossut M. . 2000. “Age‐Dependent Response of the Mouse Barrel Cortex to Sensory Deprivation: A 2‐Deoxyglucose Study.” Experimental Brain Research 132, no. 1: 134–138. 10.1007/s002210000341. [DOI] [PubMed] [Google Scholar]
  62. Speigel, I. A. , Patel K., and Hemmings H. C. Jr.. 2022. “Distinct Effects of Volatile and Intravenous Anaesthetics on Presynaptic Calcium Dynamics in Mouse Hippocampal GABAergic Neurones.” British Journal of Anaesthesia 128, no. 6: 1019–1028. 10.1016/j.bja.2022.01.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Stranahan, A. M. , Erion J. R., and Wosiski‐Kuhn M.. 2013. “Reelin Signaling in Development, Maintenance, and Plasticity of Neural Networks.” Ageing Research Reviews 12, no. 3: 815–822. 10.1016/j.arr.2013.01.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Straube, A. , and Merdes A.. 2007. “EB3 Regulates Microtubule Dynamics at the Cell Cortex and Is Required for Myoblast Elongation and Fusion.” Current Biology 17, no. 15: 1318–1325. 10.1016/j.cub.2007.06.058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Sur, M. , and Rubenstein J. L.. 2005. “Patterning and Plasticity of the Cerebral Cortex.” Science 310, no. 5749: 805–810. 10.1126/science.1112070. [DOI] [PubMed] [Google Scholar]
  66. Uhlhaas, P. J. , Roux F., Singer W., Haenschel C., Sireteanu R., and Rodriguez E.. 2009. “The Development of Neural Synchrony Reflects Late Maturation and Restructuring of Functional Networks in Humans.” Proceedings of the National Academy of Sciences of the United States of America 106, no. 24: 9866–9871. 10.1073/pnas.0900390106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Valiente, M. , and Marín O.. 2010. “Neuronal Migration Mechanisms in Development and Disease.” Current Opinion in Neurobiology 20, no. 1: 68–78. 10.1016/j.conb.2009.12.003. [DOI] [PubMed] [Google Scholar]
  68. Vinopal, S. , Dupraz S., Alfadil E., et al. 2023. “Centrosomal Microtubule Nucleation Regulates Radial Migration of Projection Neurons Independently of Polarization in the Developing Brain.” Neuron 111, no. 8: 1241–1263.e16. 10.1016/j.neuron.2023.01.020. [DOI] [PubMed] [Google Scholar]
  69. Voelcker, B. , Pancholi R., and Peron S. . 2022. “Transformation of Primary Sensory Cortical Representations From Layer 4 to Layer 2.” Nature Communications 13, no. 1: 5484. 10.1038/s41467-022-33249-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Wang, Q. , Ding S.‐L., Li Y., et al. 2020. “The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas.” Cell 181, no. 4: 936–953.e20. 10.1016/j.cell.2020.04.007.</bib. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Wang, A. , Ferguson K. A., Gupta J., Higley M. J., and Cardin J. A.. 2024. “Developmental Trajectory of Cortical Somatostatin Interneuron Function.” Preprint, bioRxiv, March 7, 10.1101/2024.03.05.583539. [DOI]
  72. Wang, Z. , Meng S., Cao L., Chen Y., Zuo Z., and Peng S. . 2018. “Critical Role of NLRP3‐Caspase‐1 Pathway in Age‐Dependent Isoflurane‐Induced Microglial Inflammatory Response and Cognitive Impairment.” Journal of Neuroinflammation 15, no. 1: 109. 10.1186/s12974-018-1137-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Ward, C. G. , and Loepke A. W. . 2012. “Anesthetics and Sedatives: Toxic or Protective for the Developing Brain?” Pharmacological Research 65, no. 3: 271–274. 10.1016/j.phrs.2011.10.001. [DOI] [PubMed] [Google Scholar]
  74. Wilder, R. T. , Flick R. P., Sprung J., et al. 2009. “Early Exposure to Anesthesia and Learning Disabilities in a Population‐Based Birth Cohort.” Anesthesiology 110, no. 4: 796–804. 10.1097/01.anes.0000344728.34332.5d. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Xie, L. , Liu Y., Hu Y., et al. 2020. “Neonatal Sevoflurane Exposure Induces Impulsive Behavioral Deficit Through Disrupting Excitatory Neurons in the Medial Prefrontal Cortex in Mice.” Translational Psychiatry 10, no. 1: 202. 10.1038/s41398-020-00884-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Yang, Y. , Shen W., Ni Y., Su Y., Yang Z., and Zhao C.. 2017. “Impaired Interneuron Development After Foxg1 Disruption.” Cerebral Cortex 27, no. 1: 793–808. 10.1093/cercor/bhv297. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Figure S1. Analysis of layer‐specific neuronal density in the sensory cortex during P28. (A) Quantitative analysis of neuronal density based on Nissl staining across cortical layers II–VI in control and isoflurane‐exposed mice at P28. Analyses were performed on coronal sections (4‐µm thickness). Control group: n = 6; isoflurane group: n = 6. Scale bar = 100 µm. Data are presented as the mean ± SEM. Statistical significance was assessed using two‐way ANOVA followed by Tukey's multiple‐comparisons test. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Figure S2. Alterations in neuronal cytoskeleton organization. (A) Representative immunofluorescence images showing β‐tubulin staining in cortical layers II–IV of control and isoflurane‐exposed mice. Images were acquired from coronal paraffin sections (4‐µm thickness). β‐Tubulin immunoreactivity is shown in green, with nuclei counterstained with DAPI (blue). Scale bar = 50 µm. (B) Representative immunofluorescence images of the higher magnification view. Scale bar = 20 µm.

DNEU-86-0-s001.docx (1.1MB, docx)

Data Availability Statement

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Articles from Developmental Neurobiology are provided here courtesy of Wiley

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