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. 2024 Aug 25;169(1):e16212. doi: 10.1111/jnc.16212

Poly (I:C)‐induced maternal immune activation generates impairment of reversal learning performance in offspring

Eva Munarriz‐Cuezva 1,2,✉, Jose Javier Meana 1,2,3
PMCID: PMC11657921  PMID: 39183542

Abstract

Maternal immune activation (MIA) induces a variety of behavioral and brain abnormalities in offspring of rodent models, compatible with neurodevelopmental disorders, such as schizophrenia or autism. However, it remains controversial whether MIA impairs reversal learning, a basic expression of cognitive flexibility that seems to be altered in schizophrenia. In the present study, MIA was induced by administration of a single dose of polyriboinosinic‐polyribocytidylic acid (Poly (I:C) (5 mg/kg i.p.)) or saline to mouse pregnant dams in gestational day (GD) 9.5. Immune activation was monitored through changes in weight and temperature. The offspring were evaluated when they reached adulthood (8 weeks) using a touchscreen‐based system to investigate the effects of Poly (I:C) on discrimination and reversal learning performance. After an initial pre‐training, mice were trained to discriminate between two different stimuli, of which only one was rewarded (acquisition phase). When the correct response reached above 80% values for two consecutive days, the images were reversed (reversal phase) to assess the adaptation capacity to a changing environment. Maternal Poly (I:C) treatment did not interfere with the learning process but induced deficits in reversal learning compared to control saline animals. Thus, the accuracy in the reversal phase was lower, and Poly (I:C) animals required more sessions to complete it, suggesting impairments in cognitive flexibility. This study advances the knowledge of how MIA affects behavior, especially cognitive domains that are impaired in schizophrenia. The findings support the validity of the Poly (I:C)‐based MIA model as a tool to develop pharmacological treatments targeting cognitive deficits associated with neurodevelopmental disorders.

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Keywords: maternal immune activation, poly (I:C), reversal learning, schizophrenia, touchscreen

Poly (I:C) administration (5 mg/kg i.p.) to a pregnant dam in GD9.5 induces a variety of behavioral and brain abnormalities in offspring of rodent models of schizophrenia. However, it remains controversial whether MIA impairs reversal learning. The offspring were evaluated using a touchscreen‐based system to investigate the discrimination and reversal learning performance. Mice were trained to discriminate between two stimuli, of which only one was rewarded. When the correct response reached above 80% values, the images were reversed to assess the adaptation capacity to a changing environment. Maternal Poly (I:C) treatment did not interfere with the learning process but induced deficits in reversal learning, suggesting impairments in cognitive flexibility.

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Abbreviations

ABET II

animal behavior environment test II

ANOVA

analysis of variance

dsRNA

synthetic double‐stranded RNA

F t

time, within groups

F tr

treatment, between groups

F i

treatment × time, interaction

GD

gestational day

MATRICS

measurement and treatment research to improve cognition in schizophrenia

MIA

maternal immune activation

NHPs

non‐human primates

PD

pairwise (visual) discrimination

PFC

prefrontal cortex

Poly (I:C)

polyriboinosinic‐polyribocytidylic acid

RRID

Research Resource Identifier

S+

rewarded stimulus

S−

non‐rewarded stimulus

SEM

standard error of the mean

TLR3

toll‐like receptor 3

1. INTRODUCTION

Aberrant value learning and decision‐making have long been considered cognitive behaviors of several neuropsychiatric conditions, including schizophrenia, autism, obsessive‐compulsive disorder, and substance use disorders (Izquierdo et al., 2017).

Schizophrenia is characterized by positive (hallucinations, delusions), negative (apathy, social withdrawal, anhedonia), and cognitive (attention, memory, executive functioning) symptom clusters. Whereas antipsychotic treatments are efficacious on positive symptoms, current pharmacological treatments are largely ineffective in treating negative and cognitive disturbances (Nuechterlein et al., 2004; Barch & Ceaser, 2012; Khan & Keefe, 2013; Lett et al., 2014). Increasing evidence supports a relationship between cognitive performance and overall functional outcomes in patients with schizophrenia (Green et al., 2004; Khan & Keefe, 2013). Cognitive impairment associated with schizophrenia has become a pharmacologically unmet clinical need that requires the development of new translational approaches (Javitt, 2023).

In the last decades, the hypotheses about schizophrenia pathogenesis have dramatically changed, moving from a neurodegenerative process occurring in early adult life to a neurodevelopmental disorder starting before birth. Currently, this idea is supported by modern techniques of neuroimaging, genetic advances, and, more in general, neurodevelopmental research (De Berardis et al., 2021).

Alterations in the innate immune system, caused by endogenous or exogenous factors, are considered stressful events during embryogenesis and are hypothesized to have consequences on fetal brain development (Bergdolt & Dunaevsky, 2019; Han et al., 2021; Meyer et al., 2009; Reisinger et al., 2015; Vlasova et al., 2021). According to several retrospective studies on the offspring of mothers with viral or bacterial infections during the early‐to‐middle stages of pregnancy, it has been described that these stressful events may contribute to the manifestation of mental illnesses later in life, including schizophrenia (Boksa, 2008; Brown & Derkits, 2010; Leza et al., 2015; Patterson, 2009; Winter et al., 2009).

Schizophrenia is in fact uniquely human and modeling of neuropsychiatric disorders in animals is limited. Disease animal models should be derived from risk factors or causative agents of human disease (construct validity) or else exhibit a substantial degree of neural or behavioral pathology that corresponds to the human disorder (face validity). Furthermore, the animal model should respond to treatments in a way that predicts the effects of those treatments in humans (predictive validity; Nestler & Hyman, 2010).

In this context of modeling neurodevelopmental diseases, prenatal exposure to polyriboinosinic‐polyribocytidylic acid [Poly (I:C)] mimics a viral infection, triggers an innate immune response in the mother causing maternal immune activation (MIA), and consequently induces schizophrenia‐like phenotypes in the offspring during adulthood (Haddad et al., 2020; Hanson et al., 2022; Wolff & Bilkey, 2008; Zuckerman et al., 2003; Zuckerman & Weiner, 2005). Poly (I:C) is a synthetic double‐stranded RNA (dsRNA) that acts through the Toll‐like Receptor 3 (TLR3). In pregnant dams, Poly (I:C) administration reduces litter size, maternal weight, and placenta and brain weight (Mueller et al., 2019, 2021). A single dose of Poly (I:C) is sufficient to cause behavioral and histological abnormalities in the offspring of maternal‐infected mothers (Shi et al., 2009; Smith et al., 2007). These phenotypes display alterations in the dopaminergic pathways and prefrontal cortex (PFC; Brown & Meyer, 2018; Pérez‐Palomar et al., 2023; Rahman et al., 2020; Smucny et al., 2023; Weinstein et al., 2017). Behavioral studies in rodents have revealed several abnormalities (Meyer et al., 2005), including sensorimotor gating impairment of the acoustic startle response (Ballendine et al., 2015; Prades et al., 2017; Wolff & Bilkey, 2008, 2010) and anxiety (Chen et al., 2024), in agreement with previous human studies (Aleman et al., 2003; McGrath et al., 2004), as well as sociability alterations (Bitanihirwe et al., 2010; Hui et al., 2018; Lins et al., 2018), cognitive impairments (Ballendine et al., 2015; Kleinmans & Bilkey, 2018; MacDowell et al., 2017; Pérez‐Palomar et al., 2023; Prades et al., 2017; Vlasova et al., 2021; Zhao et al., 2021; Zuckerman & Weiner, 2005), and alterations in executive function (Zhang et al., 2012). Therefore, administration of Poly (I:C) efficiently mimics the acute inflammatory response to viral infection (Meyer & Feldon, 2012), and it is considered an MIA animal model for neurodevelopmental disorders with relatively high construct and face validity (Bauman & Van de Water, 2020; Brown & Meyer, 2018; Haddad et al., 2020).

Due to the lack of effective treatments for negative and cognitive symptoms of schizophrenia, Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) initiative developed a consensus on the core cognitive deficits suffered by schizophrenia patients. According to the MATRICS classification, a battery of preclinical behavioral test has been developed to guide the translational research of cognitive domains in animal models of schizophrenia (Young et al., 2009). Reversal learning paradigms are among the most widely used tests for cognitive flexibility evaluation. Cognitive flexibility is defined as the ability to rapidly change behavior in response to changing circumstances. Reversal learning tests are considered validated models of the reasoning and problem‐solving MATRICS cognitive domains and show high translational conclusions to humans. Cognitive flexibility assessment by reversal learning in MIA models has previously been studied through different approaches. Lever‐ and touchscreen‐based tasks in operant conditioning chambers have revealed apparent controversial results of Poly (I:C) effects on reversal learning in adult offspring (Zhang et al., 2012; Ballendine et al., 2015;Lins et al., 2018; Gogos et al., 2020; Zhao et al., 2021; Nakamura et al., 2022). Evaluations based in spatial vs visual discrimination tasks also introduced a source of variability (Amodeo et al., 2019; Kleinmans & Bilkey, 2018; Meyer et al., 2006; Savanthrapadian et al., 2013; Schroeder et al., 2019). Despite some differences between species in the paradigms, touchscreen technology based on visual discrimination testing and a reversal learning task is widely used in mice, rats, non‐human primates (NHPs), and humans to test cognitive flexibility and problem solving ability (Izquierdo et al., 2017).

In a touchscreen‐based reversal learning test, subjects are trained to discriminate between two visual stimuli, one of which is rewarded each time it is chosen, and the other is not rewarded and is accompanied with an operant chamber lit for 5 s, as a kind of punishment. After a successful discrimination learning (also called the acquisition stage) by reaching the criterion level of performance (normally 80% correct responses), the outcomes associated with the two stimuli are reversed, and mice are tested until they reach this new performance criterion (Izquierdo et al., 2017; Mar et al., 2013).

A high degree of standardization and minimal experimenter involvement are some of the advantages of using a touchscreen system. Touch‐screen automated paradigms have become increasingly used to test rodent models of numerous neuropsychiatric disorders (Copping et al., 2017; Donegan et al., 2018; Leach et al., 2016; Marquardt et al., 2014; Yang et al., 2015). Furthermore, as these paradigms closely model tools utilized in the clinical assessment, they may increase the translational potential of preclinical studies (Hvoslef‐Eide et al., 2016; Mar et al., 2013; Marquardt et al., 2017; Talpos & Steckler, 2013).

In the current study, we examined whether a single administration of poly (I:C) in pregnant dams generates impairments in the cognitive flexibility of the adult offspring. Mouse cognitive flexibility was measured using a reversal learning task based on visual discrimination performed in an automated touchscreen system.

2. MATERIALS AND METHODS

2.1. Animals

Experiments were conducted using indistinctly C57BL/6J male and female mice. The reversal learning task was performed completely with 19 saline animals (11 males and 8 females) and 10 Poly (I:C) animals (4 males and 6 females) from three different rounds of Poly (I:C) administration (Table 1). Pregnancy outcomes and the number of animals completed each phase are shown in Table 1. The three cohorts/rounds were generated under identical experimental conditions, and the success rates in reaching the goal in each phase and progressing to the next stage were similar in all rounds. Animals were maintained at a temperature of 22°C (± 2°C) with free access to food (commercial diet for rodents A04, Panlab, Barcelona, Spain) and water. Animals were housed in standard laboratory cages; each one containing groups of six individuals maximum. All experiments were carried out between 08:00 and 16:00 h. All the experimental protocols were approved by the Committee of Ethics for Animal Experimentation of the University of the Basque Country UPV/EHU (CEAA: M20‐2017‐166). All procedures were performed in accordance with European Ethical Standards (6106/10‐EEC) and Spanish Law (RD 53/2013).

TABLE 1.

Pregnancy outcomes and number of saline and Poly (I:C) mice completing acquisition and reversal phases in three different rounds of experiments.

Offspring Acquisition phase Reversal phase
Administration date Pregnants Males Females Total Males Females Total Males Females Total
Experiment round 1
Saline 2 5 8 13 5 2 7 1 1 2
Poly (I:C) 4 3 10 13 3 7 10 1 3 4
Experiment round 2
Saline 2 2 3 5 1 3 4 1 3 4
Poly (I:C) 3 5 2 7 3 2 5 3 2 5
Experiment round 3
Saline 2 10 4 14 9 4 13 9 4 13
Poly (I:C) 1 0 2 2 0 2 2 0 1 1
Total
Saline 6 17 15 32 15 9 24 11 8 19
Poly (I:C) 5 mg/kg 8 8 14 22 6 11 17 4 6 10
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Bold values indicate the total of animals.

2.2. Drug administration

Pregnant C57BL/6J females (Envigo, Spain) (RRID:MGI:3028467) were injected with a single dose of Polyinosinic–polycytidylic acid sodium salt (Poly (I:C) (5 mg/kg, i.p.)) (Cat. No: P1520; Batch: #095M4049V; Sigma‐Aldrich, St. Louis, USA) or vehicle (0.9% NaCl solution) on gestational day 9.5 (GD9.5; Holloway et al., 2013; MacDowell et al., 2017). At postnatal day 21, animals were sexed, and the experiments were carried out when the pups reached early adulthood (8 weeks).

No randomization was performed to allocate subjects in the study. The animals were classified into each group depending on the administration of saline or Poly(I:C) to their mothers. The MIA model reporting guidelines checklist (based on Kentner et al., 2019) is resumed in Table S1 with ARRIVE reporting guidelines and recommendations (Table S1).

Weight and rectal temperature of pregnant females were monitored during GD8.5 to GD11.5 (24 h before and 0, 3, 6, 24, and 48 h after injection) with the purpose of assuring that the administration of Poly (I:C) had any effect in these parameters (Figure 1) and the subsequent immune activation.

FIGURE 1.

FIGURE 1

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Changes in percentage of (a) weight and (b) rectal temperature monitoring of pregnant dams during 48 h after administration in GD9.5. Points represent mean ± SEM value. (Saline mice: White circles in continuous line, n = 6; Poly (I:C) mice: Black squares in discontinuous line, n = 8). Asterisks denote statistical differences (two‐way ANOVA followed by Bonferroni's multiple comparison tests). *p < 0.05 and ***p < 0.001.

2.3. Behavioral test: Reversal learning

2.3.1. Procedure

Mice were trained as described by Instruction manual of the system (89540‐ Pairwise (Visual) Discrimination (PD) Task for Mouse Touch Screen Systems and ABET II; Cambridge, U.K.).

Before the touchscreen training and testing, mice were food deprived to increase motivation to liquid reward (10% condensed milk, Nestle™, Vevey, Switzerland) and were weighted daily to avoid losing more than 85% of their free weight (weight without food deprivation). Water was available ad libitum. Following the instructions of the manufacturer (Campdem Instruments, A Lafayette Instrument Company; Cambridge, U.K.), the food restriction consisted in a general rule of providing the animals with the 8–10% of their free weight in food. For example, if there were four animals in a cage with a total weight of 100 g, the corresponding weight of food was 8–10 g. Moreover, animals had access to liquid reward in their home cages (0.625 mL per animal approximately) with the purpose of increasing motivation.

2.3.2. Pretraining

The pretraining phases included 10 min of habituation to the operant chamber, Initial Touch Training stage, Must‐Touch Stimuli stage, Must Initiate stage, and Punish Incorrect phase. In the Initial Touch Training stage, the stimulus was displayed for 30 s in one of the two windows each time and was followed by liquid reward delivery. The volume of delivered reward was tripled if the animal touched the correct window, while it was presented the stimulus (random images displayed in the touchscreen). The criterion to pass to the following stage was to complete 30 trials in a session of 60 min. In the Must Touch or Mouse Touch Stimuli stage (MTS), the stimulus was presented in one of the two windows randomly until the animal nose‐poked correctly. After that, there was a reward delivery, but touches in the other window did not deliver liquid reward. The criterion to pass to the following phase was to complete 30 trials in a session of 60 min in two consecutive days. In the Must Initiate stage (MI), a free reward was delivered, and the mouse had to nose poke and exit the reward tray before a stimulus was displayed randomly on the screen. In these stages, correct responses were accompanied by a tone to facilitate the learning process and light stimulus appeared after 5 s delay. In the Punish Incorrect (PI) stage and in previous phases, when the blank window (without stimulus) was touched, the house light switched on for 5 s, and there was no reward. The animal had to complete a correct trial (the stimulus and place maintained) and repeat the same trial until a correct response was done (called Correction trial). The criterion in this stage was 23/30 correct trials (70%) in 30 min (Correction trials not included) during two consecutive days.

In these different stages, the number of sessions needed for reaching each phase in both saline and Poly (I:C) groups were similar (See Figure S1).

2.3.3. Discrimination training: Acquisition with correction trials

In the acquisition period, trials started with two new stimuli (defined as “marbles” and “star”) on the touchscreens. One of them was rewarded (S+), and the other one was considered incorrect (S−). The position of the rewarded stimulus was pseudorandomized, but it was not in the same place three times in a row. If the animal nose‐poked in incorrect stimulus, there was no reward (accompanied with a 5 s period of lit of the operant chamber) and started a period of 5 s of time‐out before the opportunity to complete a correction trial. Correction trials occurred until the animal choose rewarded stimulus. A correction trial consisted in re‐presentation of stimulus in the same localization and was not included in percentage of correct figures. The criterion to pass to the following stage was 24/30 trials (80% correct choices) in 60 min (not including correction trials) during two consecutive days (Figure 2a).

FIGURE 2.

FIGURE 2

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Representation of discrimination training. (a) Control and Poly (I:C) mice were trained on the acquisition time of a pairwise visual discrimination task. (b) Percentage of correct responses in acquisition phase, (c) number of correction trials, (d) correct touch latency, and (e) correct reward latency. Control saline mice are shown with white points with continuous line, n = 19; and Poly (I:C) mice are shown with black squares with discontinuous line, n = 10. Note that representations only include those animals that reached the end of the full protocol. Points represent mean ± SEM values.

2.3.4. Reversal learning

Once the animals reached the criterion of the acquisition phase, the reversal stage started. The protocol was the same as in the previous phase, but the stimuli were inverted: the correct stimulus or image in the acquisition stage, in this phase, was incorrect and punished. The position of the rewarded stimulus was pseudorandomized, but it was not in the same place three times in a row. The protocol and criterion were the same that followed in the acquisition phase [24/30 trials (80% correct choices) in 60 min (not including correction trials) during two consecutive days] (Figure 3a).

FIGURE 3.

FIGURE 3

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Representation of reversal learning. (a) Control and Poly (I:C) mice were trained on the reversal time of a pairwise visual discrimination task. (b) Percentage of correct responses in reversal phase, (c) number of correction trials, (d) correct touch latency, and (e) correct reward latency. Control saline mice are shown with white points with continuous line, n = 19; and Poly (I:C) mice are shown with black squares with discontinuous line, n = 10. Note that representations only include those animals that reached the end of the full protocol. Points represent mean ± SEM values. Asterisks denote statistical differences (two‐way ANOVA followed by Bonferroni's multiple comparison tests). *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

When the protocol was finished and the established criteria were reached, the animals were sacrificed by cervical dislocation.

The only exclusion criteria in the study was the inability to reach the criterion or death. The rate of animals that were not able to finish all the protocol was 59.38% of saline animals (19 mice out of 32; the rest were not able to reach the criterion) and 45.45% of Poly (I:C) mice (10 out of 22; one died and the rest were not able to reach the criterion). In any case, the animals were not replaced.

2.4. Data analysis

Data were compiled and analyzed using the ABET II Software, while statistical procedures were performed using GraphPad Prism™ version 10.0.2 (Graph Pad Software Inc., San Diego, CA, USA). No sample size was estimated a priori. Grubb's test was conducted on the data for outliers. No further test for normality was performed.

Results are expressed as the mean ± standard error of the mean (SEM) of n individual animals. In the case of acquisition and reversal phase data, two‐way analysis of variance (ANOVA) with repeated measures followed by Bonferroni's post hoc test between control saline and Poly (I:C) animals was assessed for statistical analysis. Variances were tested for sphericity, and degrees of freedom were corrected for this condition. F values were expressed as F t (time, within groups), F tr (treatment, between groups), or F i (treatment × time, interaction). A comparison of the number of sessions between control saline and Poly (I:C) groups was performed by Student's t‐test. Data divided by sex are not presented due to the small number of animals. All the results were considered statistically significant for p‐values below 0.05.

3. RESULTS

3.1. Dams monitoring

The administration of Poly (I:C) to pregnant females (n = 8) showed a statistical reduction of their weight after 24 h (5%) compared with controls (n = 6). This decrease was not completely recovered after 48 h (F t[2,24] = 12.61, p = 0.0002; F tr[1,12] = 34.77, p < 0.0001; F i[2.24] = 21.19, p < 0.0001) (Bonferroni's multiple comparison tests: 24 h, p = 0.0007; 48 h, p = 0.0196) (Figure 1a). The average weight before initiating the protocol was similar between both groups (saline: 23.2 ± 0.04 g and Poly (I:C): 24.4 ± 1.20 g). The temperature monitoring of pregnant females displayed no differences between both groups across the time (F t[4,40] = 15.27, p < 0.0001; F tr[1,70] = 1.220, p = 0.2732; F i[4,70] = 2.0.38, p = 0.0984; Figure 1b). Also in this case, the average temperature at the administration time was similar (saline: 37.4 ± 0.2°C and Poly (I:C): 37.7 ± 0.1°C).

3.2. Visual discrimination and reversal learning

Saline and Poly (I:C) mice were trained on a pairwise visual discrimination task in two stages: Acquisition phase (S+: “marbles” and S−: “star”; Figure 1a) and reversal phase (S+: “star” and S−: “marbles”; Figure 2a).

In the acquisition phase, there were no statistical differences in the percentage of rewarded responses between saline and Poly (I:C) groups over the time. Both groups learned the task and discriminated between rewarded stimulus (S+) and non‐rewarded stimulus (S−) similarly (F t[3,85] = 14.34, p < 0.0001; F tr[1,27] = 0.5115, p = 0.4806; F i[6162] = 0.6972, p = 0.6522; Figure 2b). The percentages of mice that completed the acquisition phase were similar in both groups, 75% of saline and 77% of Poly (I:C) mice, suggesting that both groups could learn equally. The number of correction trials is shown in Figure 2c, which shows that there were no differences between both groups (F t[3.111,84.00] = 8.082, p < 0.0001; F tr[1,27] = 1.002, p = 0.3257; F i[6162] = 0.7707, p = 0.5940). Assessing correct touch latencies demonstrated that there were effects of time and treatment but not interaction between them (F t[3.494,94.34] = 16.59, p < 0.0001; F tr[1,27] = 7.720, p = 0.0098; F i[6.162] = 2.027, p = 0.0649; Figure 2d). Finally, there was a statistical effect of interaction between time and treatment in correct Reward latency analysis in the acquisition phase (F t[2.868,77.43] = 8.958, p < 0.0001; F tr[1,27] = 7.848, p < 0.0093; F i[6162] = 3.643, p = 0.0020; Bonferroni's multiple comparison tests did not show differences between groups at any sessions) (Figure 2e).

When both saline and Poly (I:C) mice reached more than 80% correct responses, the stimuli were inverted (S−: “marbles” and S+: “star”), and the reversal phase started with the schema described in Figure 3a. In the first few days, saline and Poly (I:C) mice were not able to reach 30 trials, and, according to protocol, the accuracy of these days was not evaluated.

During the reversal phase of the task, there were statistical differences in the percentage of correct responses between saline and Poly (I:C) groups. Saline mice showed more accuracy than Poly (I:C) animals (F t[3.674,99.19] = 48.17, p < 0.0001; F ir[1,27] = 5.548, p = 0.0260; Fi[10, 270] = 4.395, p < 0.0001) in 7–10 sessions (Bonferroni's multiple comparison tests: session 7, p = 0.0011; session 8, p = 0.0011, session 9, p = 0.0264; session 10, p < 0.0001; Figure 3b). When the percentage of animals in each group that completed the reversal phase (more than 80% accuracy) was estimated, 59% of saline and 46% of Poly (I:C) mice completed all the task, suggesting deficits in adaptation to the new conditions in Poly (I:C) animals. When comparing number of correction trials between both groups, Poly (I:C) mice behaved differently to saline animals (F t[4.984134.6] = 17.57, p < 0,0001; F tr[1,27] = 1.640, p = 0.2112; Fi[10270] = 2.116, p = 0.0235) (Bonferroni's multiple comparison tests did not show differences between groups at any sessions) (Figure 3c). In the correct touch latency, there was the effect of time and treatment but no interaction among them (F t[3.971107.2] = 4.931, p = 0.0011; F tr(1,27) = 16.64, p = 0.0004; F i(10270) = 1.694, p = 0.0821) (Bonferroni's multiple comparison tests: session 9, p < 0.0001; session 10, p < 0.0001) (Figure 3d). Finally, there was only an effect of treatment in correct reward latency in the reversal phase (F t[5.626151.9] = 1.273, p = 0.2752; F tr[1,27] = 9.005, p = 0.0057; F i[10270] = 0.4522, p = 0.9191) (Bonferroni's multiple comparison tests did not show differences between groups at any sessions) (Figure 3e).

The number of sessions required for control saline (n = 19) and Poly (I:C) mice (n = 10) to acquire the visual pairwise discrimination task were similar (t = 1.043, df = 27; p = 0.3062; Figure 4). However, when the rewarded stimulus was changed, Poly (I:C) mice required a greater number of sessions to complete this Reversal phase of the task as compared with saline mice (t = 2.119, df = 27; p = 0.0434), pointing to impairment in cognitive flexibility (Figure 4).

FIGURE 4.

FIGURE 4

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Representation of number of sessions to complete acquisition (left) and reversal (right) phases. Control saline animals are expressed by gray columns with black points (n = 19), and Poly(I:C) animals are expressed by gray columns with white squares (n = 10) mice. Note that representations only include those animals that reached the end of the full protocol. Bars represent mean ± SEM values. Asterisks denote statistical differences (Student t‐test). *p < 0.05.

4. DISCUSSION

The present findings demonstrate that administration of a single dose of Poly (I:C) to pregnant mice induces in the adult offspring impairments of cognitive flexibility when measured in a reversal learning task. Both saline and Poly (I:C) mice performed equally during discrimination until performance reached more than 80% accuracy in two consecutive days, and there were no changes in the number of correction trials, correct touch latency, or correct reward latency. These data show that Poly (I:C) administration does not change the ability to develop discrimination learning (acquisition phase) and both groups need a similar number of sessions to reach the goal. In addition, the percentage of animals that completed the acquisition phase was similar in the control and Poly (I:C) groups. The finding that Poly (I:C) animals learn the basic task structure without alterations is consistent with previous studies using this model (Amodeo et al., 2019). In contrast, when the rewarded stimulus was changed in the reversal phase, Poly (I:C) mice showed less accuracy than controls and needed more sessions to reach the goal, indicating potential impairment of cognitive flexibility. Furthermore, there were no changes in the number of correction trials or correct reward latency, but there was an increase in correct touch latency of Poly (I:C) mice, indicating that it needed more time to choose a correct response. These data suggest deficits in adaptation when the environmental conditions changed, as observed in schizophrenia patients (Sampedro et al., 2019; Waltz, 2017). Furthermore, the percentage of animals in Poly (I:C) group that completed the reversal phase across the time was lower compared with saline mice, supporting the idea of that MIA model of schizophrenia shows face validity. The present findings are in agreement with previous studies reporting disrupted performance in working memory, executive function, and cognitive flexibility in this MIA model (Amodeo et al., 2014; Gogos et al., 2020; Lins et al., 2018; Meehan et al., 2017; Meyer et al., 2010; Murray et al., 2017; Pérez‐Palomar et al., 2023; Wallace et al., 2014; Zhao et al., 2021). Other evaluations of cognitive responses by reversal learning task based on spatial instead of the visual discrimination used here also led to similar findings to those reported in the present study (Amodeo et al., 2019; Kleinmans & Bilkey, 2018; Zuckerman & Weiner, 2005). Differential reversal learning responses between male and female rodents have been described (Gogos et al., 2020; Nakamura et al., 2022; Schroeder et al., 2019; Zhang et al., 2012; Zhao et al., 2021). In the present study, separated data by sex are not presented due to the small number of animals, which represents a clear limitation of the conclusions.

The literature on cognition and MIA is highly variable among studies, with enhancements (Su et al., 2022), deficits (Han et al., 2011; Pérez‐Palomar et al., 2023; Savanthrapadian et al., 2013), or no effects of Poly(I:C) administration on cognitive abilities of offspring (Gogos et al., 2020; Stollenwerk & Hillard, 2021). The reasons for these discrepancies are unclear but may be due to the factors such as the use of different species, different administration days (Nakamura et al., 2022), vendor, within‐group variability (Lorusso et al., 2022; Mueller et al., 2021), assessment methodology (Lins et al., 2018), sex, and other variables (Gogos et al., 2020). Despite methodological variability, changes in social development associated with species behavior have emerged as a common feature of many rodent MIA models (Kentner et al., 2019). Thus, in general, MIA rodents tend to have deficits in associative learning tasks (Gray et al., 2019; Lipina et al., 2013), although some studies have reported no differences in learning between control and treatment groups (Abazyan et al., 2010). In concordance with the results presented in this work by using a touchscreen visual discrimination task, other tests based on simple odor discrimination, set shifting, and trial‐unique nonmatching‐to‐location tasks (TUNL) have also reported performance decreases in MIA offspring (Breach et al., 2021; Gogos et al., 2020; Lins et al., 2018). On the contrary, some studies showed an equal or greater performance compared with control animals (Bates et al., 2018; Deane et al., 2021). Standardization of the research design and test batteries (for example previous research with lever‐equipped operant conditioning chambers offers many of the same standardization benefits as touchscreen‐equipped chambers); and implementation of common guidelines could contribute to clarify and improve the reproducibility of the poly (I:C)‐based translational model (Kentner et al., 2019; Young et al., 2009).

In reversal learning tasks using touchscreen systems (Table 2), the timing of gestational poly(I:C) exposure is critical in determining what outcomes are observed later in life. Previous findings are not concordant, likely due to other interexperimental factors such as route of administration of poly(I:C), dosage, husbandry methodology, housing, and sub‐strain lineage. For example, Nakamura et al., observed differences in female mice when mothers were administered with Poly (I:C) earlier (GD9‐11) whereas when the administration was carried out in GD13‐15, the alterations were observed in males (Nakamura et al., 2022) (Table 2). Better performance in reversal learning was obtained by Zhao et al. in female mice (Zhao et al., 2021) under single conditions and with an associated second hit (Poly (I:C) administration and social isolation) (Table 2).In rats, there is high variability in late administration (GD15), with no effects of Poly (I:C) in males and females (Gogos et al., 2020) or more sessions requirement in Sprague–Dawley rats (Lins et al., 2018) (Table 2). In this work, GD9.5 was chosen because this period in mice represents a critical moment of brain development with events that in human gestation occur in the middle/end of the first trimester (Clancy et al., 2001; Schepanski et al., 2018).

TABLE 2.

Poly (I:C)‐induced MIA model and changes in reversal learning evaluated in a touchscreen system.

Authors Year Specie Strain Second Hit Administration day Results in reversal learning
Lins et al. 2018 Rat Sprague–Dawley No GD15 More days to reach the criterion in reversal phase
Gogos et al. 2020 Rat Long Evans No GD15 No changes (males and females)
Zhao et al. 2021 Mouse C57BL/6J Social isolation GD12 Fewer correction trials and errors in females
Nakamura et al. 2022 Mouse C57Bl/6J No GD9‐11 Changes in percentage choice in females
GD13‐15 Changes in percentage choice in males
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In this context, the advantage contribution of this work is the standardization carried out by a touchscreen system with its closed protocol. The touchscreen methodology represents a progressively more used translational approach to cognitive flexibility evaluation, as previously displayed. It can be used in humans, reliable, and very reproducible. Furthermore, reversal learning in adult offspring of pregnant mice dams treated with Poly (I:C) at early gestational age (GD9.5) has not been tested previously.

In conclusion, the present data demonstrated that Poly (I:C) mice showed behavioral alterations in cognitive performance. Despite these mice were able to learn at a similar speed and perform the task as accurately as saline mice, they required more sessions to reach a flexible goal. The Poly (I:C) animals showed cognitive impairments and signs of deficits in adaptation when the basic task was reversed, suggesting the existence of impairments in adapting to new environmental conditions. Furthermore, this MIA model based on Poly (I:C) administration represent a valuable translational tool that could help to evaluate new therapeutic strategies for schizophrenia patients.

AUTHOR CONTRIBUTIONS

Eva Munarriz‐Cuezva: Methodology; writing – review and editing; writing – original draft; formal analysis; investigation; validation; visualization; software; conceptualization. Jose Javier Meana: Investigation; funding acquisition; writing – review and editing; formal analysis; project administration; supervision; conceptualization; resources.

FUNDING INFORMATION

This work was supported by the Spanish Ministry of Science, Innovation and Universities and European ERDF Funds (SAF2013/48586‐R and 2017/88126‐R), the Basque Government (IT‐1211‐19; IT‐1512‐22; IKUR Neurobiosciences Strategy), and the CIBER‐Consorcio Centro de Investigación Biomédica en Red‐Instituto de Salud Carlos III.

CONFLICT OF INTEREST STATEMENT

J. Javier Meana received unrestricted funds from Janssen. Eva Munarriz‐Cuezva declares no conflict of interest.

Supporting information

Figure S1.

Figure S2.

Table S1.

JNC-169-0-s001.pdf (255.7KB, pdf)

ACKNOWLEDGMENTS

This work was supported by the Spanish Ministry of Science, Innovation and Universities and European ERDF Funds (SAF2013/48586‐R and 2017/88126‐R), the Basque Government (IT‐1211–19; IT‐1512–22; IKUR Neurobiosciences Strategy), and the CIBER‐Consorcio Centro de Investigación Biomédica en Red‐Instituto de Salud Carlos III.

Munarriz‐Cuezva, E. , & Meana, J. J. (2025). Poly (I:C)‐induced maternal immune activation generates impairment of reversal learning performance in offspring. Journal of Neurochemistry, 169, e16212. 10.1111/jnc.16212

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

REFERENCES

  1. Abazyan, B. , Nomura, J. , Kannan, G. , Ishizuka, K. , Tamashiro, K. L. , Nucifora, F. , Pogorelov, V. , Ladenheim, B. , Yang, C. , Krasnova, I. N. , Cadet, J. L. , Pardo, C. , Mori, S. , Kamiya, A. , Vogel, M. W. , Sawa, A. , Ross, C. A. , & Pletnikov, M. V. (2010). Prenatal interaction of mutant DISC1 and immune activation produces adult psychopathology. Biological Psychiatry, 68(12), 1172–1181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Aleman, A. , Kahn, R. S. , & Selten, J. P. (2003). Sex differences in the risk of schizophrenia: Evidence from meta‐analysis. Archives of General Psychiatry, 60(6), 565–571. [DOI] [PubMed] [Google Scholar]
  3. Amodeo, D. A. , Jones, J. H. , Sweeney, J. A. , & Ragozzino, M. E. (2014). Risperidone and the 5‐HT2A receptor antagonist M100907 improve probabilistic reversal learning in BTBR T+tf/J mice. Autism Research, 7(5), 555–567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Amodeo, D. A. , Lai, C. Y. , Hassan, O. , Mukamel, E. A. , Behrens, M. M. , & Powell, S. B. (2019). Maternal immune activation impairs cognitive flexibility and alters transcription in frontal cortex. Neurobiology of Disease, 125, 211–218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Ballendine, S. A. , Greba, Q. , Dawicki, W. , Zhang, X. , Gordon, J. R. , & Howland, J. G. (2015). Behavioral alterations in rat offspring following maternal immune activation and ELR‐CXC chemokine receptor antagonism during pregnancy: Implications for neurodevelopmental psychiatric disorders. Progress in Neuro‐Psychopharmacology and Biological Psychiatry, 57(3), 155–165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Barch, D. M. , & Ceaser, A. (2012). Cognition in schizophrenia: Core psychological and neural mechanisms. Trends in Cognitive Sciences, 16(1), 27–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bates, V. , Maharjan, A. , Millar, J. , Bilkey, D. K. , & Ward, R. D. (2018). Spared motivational modulation of cognitive effort in a maternal immune activation model of schizophrenia risk. Behavioral Neuroscience, 132(1), 66–74. [DOI] [PubMed] [Google Scholar]
  8. Bauman, M. D. , & Van de Water, J. (2020). Translational opportunities in the prenatal immune environment: Promises and limitations of the maternal immune activation model. Neurobiology of Disease, 141, 1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bergdolt, L. , & Dunaevsky, A. (2019). Brain changes in a maternal immune activation model of neurodevelopmental brain disorders. Progress in Neurobiology, 175, 1–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bitanihirwe, B. K. , Peleg‐Raibstein, D. , Mouttet, F. , Feldon, J. , & Meyer, U. (2010). Late prenatal immune activation in mice leads to behavioral and neurochemical abnormalities relevant to the negative symptoms of schizophrenia. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 35(12), 2462–2478. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Boksa, P. (2008). Maternal infection during pregnancy and schizophrenia. Journal of Psychiatry & Neuroscience: JPN, 33(3), 183–185. [PMC free article] [PubMed] [Google Scholar]
  12. Breach, M. R. , Dye, C. N. , Joshi, A. , Platko, S. , Gilfarb, R. A. , Krug, A. R. , Franceschelli, D. V. , Galan, A. , Dodson, C. M. , & Lenz, K. M. (2021). Maternal allergic inflammation in rats impacts the offspring perinatal neuroimmune milieu and the development of social play, locomotor behavior, and cognitive flexibility. Brain, Behavior, and Immunity, 95, 269–286. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Brown, A. S. , & Derkits, E. J. (2010). Prenatal infection and schizophrenia: A review of epidemiologic and translational studies. The American Journal of Psychiatry, 167(3), 261–280. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Brown, A. S. , & Meyer, U. (2018). Maternal immune activation and neuropsychiatric illness: A translational research perspective. The American Journal of Psychiatry, 175(11), 1073–1083. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Chen, T. , Meng, H. , Fang, N. , Shi, P. , Chen, M. , Liu, Q. , Lv, L. , & Li, W. (2024). Age‐related changes in behavior profile in male offspring of rats treated with poly I:C‐induced maternal immune activation in early gestation. Animal Models and Experimental Medicine, in press. 10.1002/ame2.12417 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Clancy, B. , Darlington, R. B. , & Finlay, B. L. (2001). Translating developmental time across mammalian species. Neuroscience, 105(1), 7–17. [DOI] [PubMed] [Google Scholar]
  17. Copping, N. A. , Berg, E. L. , Foley, G. M. , Schaffler, M. D. , Onaga, B. L. , Buscher, N. , Silverman, J. L. , & Yang, M. (2017). Touchscreen learning deficits and normal social approach behavior in the Shank3B model of Phelan–McDermid syndrome and autism. Neuroscience, 345, 155–165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. De Berardis, D. , De Filippis, S. , Masi, G. , Vicari, S. , & Zuddas, A. (2021). Neurodevelopment approach for a transitional model of early onset schizophrenia. Brain Sciences, 11, 275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Deane, A. R. , Potemkin, N. , & Ward, R. D. (2021). Mitogen‐activated protein kinase (MAPK) signalling corresponds with distinct behavioural profiles in a rat model of maternal immune activation. Behavioural Brain Research, 396, 112876. [DOI] [PubMed] [Google Scholar]
  20. Donegan, J. J. , Boley, A. M. , & Lodge, D. J. (2018). Embryonic stem cell transplants as a therapeutic strategy in a rodent model of autism. Neuropsychopharmacology, 43(8), 1789–1798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Gogos, A. , Sbisa, A. , Witkamp, D. , & van den Buuse, M. (2020). Sex differences in the effect of maternal immune activation on cognitive and psychosis‐like behaviour in long Evans rats. European Journal of Neuroscience, 52(1), 2614–2626. [DOI] [PubMed] [Google Scholar]
  22. Gray, A. , Tattoli, R. , Dunn, A. , Hodgson, D. M. , Michie, P. T. , & Harms, L. (2019). Maternal immune activation in mid‐late gestation alters amphetamine sensitivity and object recognition, but not other schizophrenia‐related behaviours in adult rats. Behavioural Brain Research, 356, 358–364. [DOI] [PubMed] [Google Scholar]
  23. Green, M. F. , Kern, R. S. , & Heaton, R. K. (2004). Longitudinal studies of cognition and functional outcome in schizophrenia: Implications for MATRICS. Schizophrenia Research, 15, no. 72 (1), 41–51. [DOI] [PubMed] [Google Scholar]
  24. Haddad, F. L. , Patel, S. V. , & Schmid, S. (2020). Maternal immune activation by poly I:C as a preclinical model for neurodevelopmental disorders: A focus on autism and schizophrenia. Neuroscience and Biobehavioral Reviews, 113, 546–567. [DOI] [PubMed] [Google Scholar]
  25. Han, V. X. , Patel, S. , Jones, H. F. , Nielsen, T. C. , Mohammad, S. S. , Hofer, M. J. , Gold, W. , Brilot, F. , Lain, S. J. , Nassar, N. , & Dale, R. C. (2021). Maternal acute and chronic inflammation in pregnancy is associated with common neurodevelopmental disorders: A systematic review. Translational Psychiatry, 11(71), 1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Han, X. , Li, N. , Meng, Q. , Shao, F. , & Wang, W. (2011). Maternal immune activation impairs reversal learning and increases serum tumor necrosis factor‐α in offspring. Neuropsychobiology, 64(1), 9–14. [DOI] [PubMed] [Google Scholar]
  27. Hanson, K. L. , Grant, S. E. , Funk, L. H. , Schumann, C. M. , & Bauman, M. D. (2022). Impact of maternal immune activation on nonhuman primate prefrontal cortex development: Insights for schizophrenia. Biological Psychiatry, 92(6), 460–469. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Holloway, T. , Moreno, J. L. , Umali, A. , Rayannavar, V. , Hodes, G. E. , Russo, S. J. , & González‐Maeso, J. (2013). Prenatal stress induces schizophrenia‐like alterations of serotonin 2A and metabotropic glutamate 2 receptors in the adult offspring: Role of maternal immune system. Journal of Neuroscience, 33(3), 1088–1098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hui, C. W. , St‐Pierre, A. , El Hajj, H. , Remy, Y. , Hebert, S. S. , Luheshi, G. N. , Srivastava, L. K. , & Tremblay, M. E. (2018). Prenatal immune challenge in mice leads to partly sex‐dependent Behavioral, microglial, and molecular abnormalities associated with schizophrenia. Frontiers in Molecular Neuroscience, 11, 1–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hvoslef‐Eide, M. , Nilsson, S. R. O. , Saksida, L. M. , & Bussey, T. J. (2016). Cognitive translation using the rodent touchscreen testing approach. Current Topics in Behavioral Neurosciences, 28, 423–447. [DOI] [PubMed] [Google Scholar]
  31. Izquierdo, A. , Brigman, J. L. , Radke, A. K. , Rudebeck, P. H. , & Holmes, A. (2017). The neural basis of reversal learning: An updated perspective. Neuroscience, 345, 12–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Javitt, D. C. (2023). Cognitive impairment associated with schizophrenia: From pathophysiology to treatment. Annual Review of Pharmacology and Toxicology, 20(63), 119–141. [DOI] [PubMed] [Google Scholar]
  33. Kentner, A. C. , Bilbo, S. D. , Brown, A. S. , Hsiao, E. Y. , McAllister, A. K. , Meyer, U. , Pearce, B. D. , Pletnikov, M. V. , Yolken, R. H. , & Bauman, M. D. (2019). Maternal immune activation: Reporting guidelines to improve the rigor, reproducibility, and transparency of the model. Neuropsychopharmacology, 44(2), 245–256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Khan, R. S. , & Keefe, R. S. E. (2013). Schizophrenia is a cognitive illness. Time for a change in focus. JAMA Psychiatry, 70(10), 1107–1112. [DOI] [PubMed] [Google Scholar]
  35. Kleinmans, M. , & Bilkey, D. K. (2018). Reversal learning impairments in the maternal immune activationrat model of schizophrenia. Behavioural Neuroscience, 132(6), 520–525. [DOI] [PubMed] [Google Scholar]
  36. Leach, P. T. , Hayes, J. , Pride, M. , Silverman, J. L. , & Crawley, J. N. (2016). Normal performance of Fmr1 mice on a touchscreen delayed nonmatching to position working memory task. eNeuro, 3(1), ENEURO.0143‐15.2016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lett, T. A. , Voineskos, A. N. , Kennedy, J. L. , Levine, B. , & Daskalakis, Z. J. (2014). Treating working memory deficits in schizophrenia: A review of the neurobiology. Biological Psychiatry, 75(5), 361–370. [DOI] [PubMed] [Google Scholar]
  38. Leza, J. C. , Garcia‐Bueno, B. , Bioque, M. , Arango, C. , Parellada, M. , Do, K. , O'Donnell, P. , & Bernardo, M. (2015). Inflammation in schizophrenia: A question of balance. Neuroscience and Biobehavioral Reviews, 55, 612–626. [DOI] [PubMed] [Google Scholar]
  39. Lins, B. R. , Hurtubise, J. L. , Roebuck, A. J. , Marks, W. N. , Zabder, N. K. , Scott, G. A. , Greba, Q. , Dawicki, W. , Zhang, X. , Rudulier, C. D. , Gordon, J. R. , & Howland, J. G. (2018). Prospective analysis of the effects of maternal immune activation on rat cytokines during pregnancy and behavior of the male offspring relevant to schizophrenia. eNeuro, 5(4), ENEURO.0249‐18.2018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lipina, T. V. , Zai, C. , Hlousek, D. , Roder, J. C. , & Wong, A. H. C. (2013). Maternal immune activation during gestation interacts with Disc1 point mutation to exacerbate schizophrenia‐related behaviors in mice. Journal of Neuroscience, 33(18), 7654–7666. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lorusso, J. M. , Woods, R. M. , McEwan, F. , Glazier, J. D. , NeilL, J. C. , Harte, M. , & Hager, R. (2022). Clustering of cognitive phenotypes identifies susceptible and resilient offspring in a rat model of maternal immune activation and early‐life stress. Brain, Behavior, & Immunity – Health, 25, 100514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. MacDowell, K. S. , Munarriz‐Cuezva, E. , Caso, J. R. , Madrigal, J. L. M. , Zabala, A. , Meana, J. J. , García‐Bueno, B. , & Leza, J. C. (2017). Paliperidone reverts toll‐like receptor 3 signaling pathway activation and cognitive deficits in a maternal immune activation mouse model of schizophrenia. Neuropharmacology, 116, 196–207. [DOI] [PubMed] [Google Scholar]
  43. Mar, A. C. , Horner, A. E. , Nilsson, S. R. O. , Alsiö, J. , Kent, B. A. , Kim, C. H. , Holmes, A. , Saksida, L. M. , & Bussey, T. J. (2013). The touchscreen operant platform for assessing executive function in rats and mice. Nature Protocols, 8(10), 1985–2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Marquardt, K. , Sigdel, R. , & Brigman, J. L. (2017). Touch‐screen visual reversal learning is mediated by value encoding and signal propagation in the orbitofrontal cortex. Neurobiology of Learning and Memory, 139, 179–188. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Marquardt, K. , Sigdel, R. , Caldwell, K. , & Brigman, J. L. (2014). Prenatal ethanol exposure impairs executive function in mice into adulthood. Alcoholism: Clinical and Experimental Research, 38(12), 2962–2968. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. McGrath, J. , Saha, S. , Welham, J. , El Saadi, O. , MacCauley, C. , & Chant, D. (2004). A systematic review of the incidence of schizophrenia: The distribution of rates and the influence of sex, urbanicity, migrant status and methodology. BMC Medicine, 2(13), 1–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Meehan, C. , Harms, L. , Frost, J. D. , Barreto, R. , Todd, J. , Schall, U. , Shannon Weickert, C. , Zavitsanou, K. , Michie, P. T. , & Hodgson, D. M. (2017). Effects of immune activation during early or late gestation on schizophrenia‐related behaviour in adult rat offspring. Brain, Behavior, and Immunity, 63, 8–20. [DOI] [PubMed] [Google Scholar]
  48. Meyer, U. , & Feldon, J. (2012). To poly(I:C) or not to poly(I:C): Advancing preclinical schizophrenia research through the use of prenatal immune activation models. Neuropharmacology, 62(3), 1308–1321. [DOI] [PubMed] [Google Scholar]
  49. Meyer, U. , Feldon, J. , & Fatemi, S. H. (2009). In‐vivo rodent models for the experimental investigation of prenatal immune activation effects in neurodevelopmental brain disorders. Neuroscience and Biobehavioral Reviews, 33(7), 1061–1079. [DOI] [PubMed] [Google Scholar]
  50. Meyer, U. , Feldon, J. , Schedlowski, M. , & Yee, B. K. (2005). Towards an immuno‐precipitated neurodevelopmental animal model of schizophrenia. Neuroscience and Biobehavioral Reviews, 29(6), 913–947. [DOI] [PubMed] [Google Scholar]
  51. Meyer, U. , Knuesel, I. , Nyffeler, M. , & Feldon, J. (2010). Chronic clozapine treatment improves prenatal infection‐induced working memory deficits without influencing adult hippocampal neurogenesis. Psychopharmacology, 208(4), 531–543. [DOI] [PubMed] [Google Scholar]
  52. Meyer, U. , Nyffeler, M. , Engler, A. , Urwyler, A. , Schedlowski, M. , Knuesel, I. , Yee, B. K. , & Feldon, J. (2006). The time of prenatal immune challenge determines the specificity of inflammation‐mediated brain and behavioral pathology. The Journal of Neuroscience, 26(18), 4752–4762. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Mueller, F. S. , Richetto, J. , Hayes, L. H. , Zambon, A. , Pollak, D. D. , Sawa, A. , Meyer, U. , & Weber‐Stadlbauer, U. (2019). Influence of poly(I:C) variability on thermoregulation, immune responses and pregnancy outcomes in mouse models of maternal immune activation. Brain, Behavior, and Immunity, 80, 406–418. [DOI] [PubMed] [Google Scholar]
  54. Mueller, F. S. , Scarborough, J. , Schalbetter, S. M. , Richetto, J. , Kim, E. , Couch, A. , Yee, Y. , Lerch, J. P. , Vernon, A. C. , Weber‐Stadlbauer, U. , & Meyer, U. (2021). Behavioral, neuroanatomical, and molecular correlates of resilience and susceptibility to maternal immune activation. Molecular Psychiatry, 26(2), 396–410. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Murray, B. G. , Davies, D. A. , Molder, J. J. , & Howland, J. G. (2017). Maternal immune activation during pregnancy in rats impairs working memory capacity of the offspring. Neurobiology of Learning and Memory, 141, 150–156. [DOI] [PubMed] [Google Scholar]
  56. Nakamura, J. P. , Schroeder, A. , Gibbons, A. , Sundram, S. , & Hill, R. A. (2022). Timing of maternal immune activation and sex influence schizophrenia‐relevant cognitive constructs and neuregulin and GABAergic pathways. Brain, Behavior, and Immunity, 100, 70–82. [DOI] [PubMed] [Google Scholar]
  57. Nestler, E. J. , & Hyman, S. E. (2010). Animal models of neuropsychiatric disorders. Nature Neuroscience, 13(10), 1161–1169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Nuechterlein, K. H. , Barch, D. M. , Gold, J. M. , Goldberg, T. E. , Green, M. F. , & Heaton, R. K. (2004). Identification of separable cognitive factors in schizophrenia. Schizophrenia Research, 72(1), 29–39. [DOI] [PubMed] [Google Scholar]
  59. Patterson, P. H. (2009). Immune involvement in schizophrenia and autism: Etiology, pathology and animal models. Behavioural Brain Research, 204(2), 313–321. [DOI] [PubMed] [Google Scholar]
  60. Pérez‐Palomar, B. , Erdozain, A. M. , Erkizia‐Santamaría, I. , Ortega, J. E. , & Meana, J. J. (2023). Maternal immune activation induces cortical catecholaminergic hypofunction and cognitive impairments in offspring. Journal of Neuroimmune Pharmacology, 18, 348–365. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Prades, R. , Munarriz‐Cuezva, E. , Urigüen, L. , Gil‐Pisa, I. , Gómez, L. , Mendieta, L. , Royo, S. , Giralt, E. , Tarragó, T. , & Meana, J. J. (2017). The prolyl oligopeptidase inhibitor IPR19 ameliorates cognitive deficits in mouse models of schizophrenia. European Neuropsychopharmacology, 27(2), 180–191. [DOI] [PubMed] [Google Scholar]
  62. Rahman, T. , Weickert, C. S. , Harms, L. , Meehan, C. , Schall, U. , Todd, J. , Hodgson, D. M. , Michie, P. T. , & Purves‐Tyson, T. (2020). Effect of immune activation during early gestation or late gestation on inhibitory markers in adult male rats. ScientificReports, 6(1), 1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Reisinger, S. , Khan, D. , Kong, E. , Berger, A. , Pollak, A. , & Pollak, D. D. (2015). The poly(I:C)‐induced maternal immune activation model in preclinical neuropsychiatric drug discovery. Pharmacology & Therapeutics, 149, 213–226. [DOI] [PubMed] [Google Scholar]
  64. Sampedro, A. , Peña, J. , Ibarretxe‐Bilbao, N. , Sánchez, P. , Iriarte‐Yoller, N. , Ledesma‐González, S. , Tous‐Espelosin, M. , & Ojeda, N. (2019). Mediating role of cognition and social cognition on creativity among patients with schizophrenia and healthy controls: Revisiting the shared vulnerability model. Psychiatry and Clinical Neurosciences, 74(2), 149–155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Savanthrapadian, S. , Wolff, A. R. , Logan, B. J. , Eckert, M. J. , Bilkey, D. K. , & Abraham, W. C. (2013). Enhanced hippocampal neuronal excitability and LTP persistence associated with reduced behavioral flexibility in the maternal immune activation model of schizophrenia. Hippocampus, 23(12), 1395–1409. [DOI] [PubMed] [Google Scholar]
  66. Schepanski, S. , Buss, C. , Hanganu‐Opatz, I. , & Arck, P. C. (2018). Prenatal immune and endocrine modulators of offsprings's brain development and cognitive functions later in life. Frontiers in Immunology, 26(9), 2168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Schroeder, A. , Nakamura, J. P. , Hudson, M. , Jones, N. C. , Du, X. , Sundram, S. , & Hill, R. A. (2019). Raloxifene recovers effects of prenatal immune activation on cognitive task‐induced gamma power. Psychoneuroendocrinology, 110, 104448. [DOI] [PubMed] [Google Scholar]
  68. Shi, L. , Smith, S. E. , Malkova, N. , Tse, D. , Su, Y. , & Patterson, P. H. (2009). Activation of the maternal immune system alters cerebellar development in the offspring. Brain, Behavior, and Immunity, 23(1), 116–123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Smith, S. E. , Li, J. , Garbett, K. , Mirnics, K. , & Patterson, P. H. (2007). Maternal immune activation alters fetal brain development through interleukin‐6. The Journal of neuroscience: the official journal of the Society for Neuroscience, 27(40), 10695–10702. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Smucny, J. , Vlasova, R. M. , Lesh, T. A. , Rowland, D. J. , Wang, G. , Chaudhari, A. J. , Chen, S. , Iosif, A. M. , Hogrefe, C. E. , Bennett, J. L. , Shumann, C. M. , Van de Water, J. A. , Maddock, R. J. , Styner, M. A. , Geschwind, D. H. , McAllister, A. K. , Bauman, M. D. , & Carter, C. S. (2023). Increased striatal presynaptic dopamine in a nonhuman primate model of maternal immune activation: A longitudinal neurodevelopmental positron emission tomography study with implications for schizophrenia. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 8(5), 505–513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Stollenwerk, T. M. , & Hillard, C. J. (2021). Adolescent THC treatment does not potentiate the Behavioral effects in adulthood of maternal immune activation. Cells, 10(12), 3503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Su, Y. , Lian, J. , Hodgson, J. , Zhang, W. , & Deng, C. (2022). Prenatal Poly I:C challenge affects behaviors and neurotransmission via elevated neuroinflammation responses in female juvenile rats. International Journal of Neuropsychopharmacology, 25(2), 160–171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Talpos, J. , & Steckler, T. (2013). Touching on translation. Cell and Tissue Research, 354(1), 297–308. [DOI] [PubMed] [Google Scholar]
  74. Vlasova, R. M. , Iosif, A. M. , Ryan, A. M. , Funk, L. H. , Murai, T. , Chen, S. , et al. (2021). Maternal immune activation during pregnancy alters postnatal brain growth and cognitive development in nonhuman primate offspring. The Journal of Neuroscience, 41(48), 9971–9987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Wallace, J. , Marston, H. M. , McQuade, R. , & Gartside, S. E. (2014). Evidence that aetiological risk factors for psychiatric disorders cause distinct patterns of cognitive deficits. European Neuropsychopharmacology, 24(6), 31–42. [DOI] [PubMed] [Google Scholar]
  76. Waltz, J. A. (2017). The neural underpinnings of cognitive flexibility and their disruption in psychotic illness. Neuroscience, 345, 203–217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Weinstein, J. J. , Chohan, M. O. , Slifstein, M. , Kegeles, L. S. , Moore, H. , & Abi‐Dargham, A. (2017). Pathway‐specific dopamine abnormalities in schizophrenia. Biological Psychiatry, 81(1), 31–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Winter, C. , Djodari‐Irani, A. , Sohr, R. , Morgenstern, R. , Feldon, J. , Juckel, G. , & Meyer, U. (2009). Prenatal immune activation leads to multiple changes in basal neurotransmitter levels in the adult brain: Implications for brain disorders of neurodevelopmental origin such as schizophrenia. The International Journal of Neuropsychopharmacology, 12(4), 513–524. [DOI] [PubMed] [Google Scholar]
  79. Wolff, A. R. , & Bilkey, D. K. (2008). Immune activation during mid‐gestation disrupts sensorimotor gating in rat offspring. Behavioural Brain Research, 190(1), 156–159. [DOI] [PubMed] [Google Scholar]
  80. Wolff, A. R. , & Bilkey, D. K. (2010). The maternal immune activation (MIA) model of schizophrenia produces pre‐pulse inhibition (PPI) deficits in both juvenile and adult rats but these effects are not associated with maternal weight loss. Behavioural Brain Research, 213(2), 323–327. [DOI] [PubMed] [Google Scholar]
  81. Yang, M. , Lewis, F. C. , Sarvi, M. S. , Foley, G. M. , & Crawley, J. N. (2015). 16p11.2 deletion mice display cognitive deficits in touchscreen learning and novelty recognition tasks. Learning and Memory, 22(12), 622–632. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Young, J. W. , Powell, S. , Risbrough, V. , Marston, H. M. , & Geyer, M. A. (2009). Using the MATRICS to guide development of a preclinical cognitive test battery for research in schizophrenia. Pharmacology & Therapeutics, 122(2), 150–202. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Zhang, Y. , Cazkoff, B. N. , Thai, C. A. , & Howland, J. G. (2012). Prenatal exposure to a viral mimetic alters behavioural flexibility in male, but not female, rats. Neuropharmacology, 62(3), 1299–1307. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Zhao, X. , Tran, H. , DeRosa, H. , Roderick, R. C. , & Kentner, A. C. (2021). Hidden talents: Poly (I:C)‐induced maternal immune activation improves mouse visual discrimination performance and reversal learning in a sex‐dependent manner. Genes, Brain and Behavior, 20(7), e12755. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Zuckerman, L. , Rehavi, M. , Nachman, R. , & Weiner, I. (2003). Immune activation during pregnancy in rats leads to a postpubertal emergence of disrupted latent inhibition, dopaminergic hyperfunction, and altered limbic morphology in the offspring: A novel neurodevelopmental model of schizophrenia. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 28(10), 1778–1789. [DOI] [PubMed] [Google Scholar]
  86. Zuckerman, L. , & Weiner, I. (2005). Maternal immune activation leads to behavioral and pharmacological changes in the adult offspring. Journal of Psychiatry Research, 39(3), 311–323. [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.

Figure S2.

Table S1.

JNC-169-0-s001.pdf (255.7KB, pdf)

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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