Europe PMC
Nothing Special   »   [go: up one dir, main page]

Europe PMC requires Javascript to function effectively.

Either your web browser doesn't support Javascript or it is currently turned off. In the latter case, please turn on Javascript support in your web browser and reload this page.

This website requires cookies, and the limited processing of your personal data in order to function. By using the site you are agreeing to this as outlined in our privacy notice and cookie policy.

Abstract 


In order to develop better treatments for autism spectrum disorder (ASD) it is critical to understand the developmental trajectory of the disorder and the accompanying brain changes. This study used the valproic acid (VPA) model to induce ASD-like symptoms in rodents. Prior studies have demonstrated that VPA animals are impaired on executive function tasks, paralleling results in humans with ASD. Here, VPA adolescent female rats were impaired on a set-shifting task and had enlarged frontal cortices compared to control females. The deficits observed in the VPA female rats mirrors results in females with ASD. In addition, adolescent VPA females with enlarged frontal cortices performed the worst across the entire task. These brain changes in adolescence are also found in adolescent humans with ASD. These novel findings highlight the importance of studying the brain at different developmental stages.

Free full text 


Logo of nihpaLink to Publisher's site
Brain Res. Author manuscript; available in PMC 2024 Feb 1.
Published in final edited form as:
PMCID: PMC9835202
NIHMSID: NIHMS1857081
PMID: 36509128

Adolescent female valproic acid rats have impaired extra-dimensional shifts of attention and enlarged anterior cingulate cortices

Abstract

In order to develop better treatments for autism spectrum disorder (ASD) it is critical to understand the developmental trajectory of the disorder and the accompanying brain changes. This study used the valproic acid (VPA) model to induce ASD-like symptoms in rodents. Prior studies have demonstrated that VPA animals are impaired on executive function tasks, paralleling results in humans with ASD. Here, VPA adolescent female rats were impaired on a set-shifting task and had enlarged frontal cortices compared to control females. The deficits observed in the VPA female rats mirrors results in females with ASD. In addition, adolescent VPA females with enlarged frontal cortices performed the worst across the entire task. These brain changes in adolescence are also found in adolescent humans with ASD. These novel findings highlight the importance of studying the brain at different developmental stages.

Keywords: VPA, ASD, cognitive flexibility, set-shifting, brain volume

1.1. Introduction

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with social, communication and repetitive behavior deficits. Frequently humans with ASD also exhibit problems with executive functions1-3. Clinical work has found that females with ASD have a different symptom profile than males with ASD4,5 and that the error types in cognitive testing may also differ6. One way to begin to understand these differences is to use animal models of ASD. The valproic acid (VPA) model of ASD has been widely used and has both face and construct validity7. Different labs have established the timeline for VPA administration8, and found social and repetitive behavioral deficits9-12 in the offspring. The mechanisms of VPA exposure leading to ASD symptomology are still being researched, but hypotheses regarding changes in synaptic excitation and inhibition balance, HDAC inhibition as well as alterations to various neurotransmitter systems are being examined13,14. In addition, adult human females that have been taking medications with VPA compounds have children that develop ASD at a higher rate than the general population15-17. Although the VPA model is environmental, only 40-50% of the variance of developing ASD is unexplained by genetic risks, which means more than one model is needed to understand the various types of ASD18-20.

The rodent attentional set-shifting task (ASST) requires that animals learn that a particular cue leads to reward, while ignoring unnecessary cues in the environment. After the animal learns the specific rule such as odor (i.e., lemon) or digging medium (i.e., shredded paper) leads to reward and reaches criterion, then the rule is changed. The task was developed to model some of the deficits observed in humans that use the Wisconsin Card Sorting Task (WCST)21. There are different phases of the task. The intra-dimensional shift (ID) is when new exemplars are given to the subject, but the same rule applies (i.e., if odor was the rewarded cue, then the new correct cue will also be an odor cue). For the extra-dimensional shift, the attentional domain shifts from the odor cue to the digging cue. Past research has indicated that multiple ID phases results in the formation of an attentional set22,23, meaning that the ED is more difficult to perform compared to ID phases. For this experiment multiple ID phases were used to induce the ED effect24. The ASST relies on a wide neural network of brain regions for correct performance, with medial prefrontal regions mediating ED shifts24,25 and the anterior cingulate mediating ID shifts26,27.

A recent publication demonstrates that VPA rats were impaired when performing the set-shifting task, which was designed to capture executive function deficits observed in humans25. This paper also found that VPA females were significantly impaired across the set-shifting task, compared to female controls and VPA males. In humans, executive function deficits are more common in females with ASD28-33, which the set-shifting task measures and which supports the hypothesis that females with ASD may present different symptomology than males with ASD34. Recent research has found that adult VPA animals were impaired on an attentional set-shifting task35, however since ASD is a developmental disorder it is important to study cognitive changes at earlier developmental ages.

In adolescent humans with ASD, it has been found that frontal regions of the brain, which are critical for cognitive flexibility, are enlarged with more cortical thickness compared to age matched controls36-38. Cortical thickness also decreases at an accelerated rate throughout the lifespan for ASD36,39-41. The VPA model has induced cellular changes within the medial prefrontal cortex, where pyramidal cells are less excitable than in controls42,43. This project asked if adolescent VPA rats would be impaired on the ED shift of the ASST and hypothesized that adolescent VPA rats would be impaired on the set-shifting task by taking more trials to complete the ED phase compared to control animals. Figure 1 has a layout of the task and timeline of the experiments. It was also predicted that increased volumetric measurements of the VPA animals within the medial prefrontal cortices would occur, figure 2. These results would support the hypothesis that these structural brain changes are contributing to altered executive function and could indicate that earlier interventions to boost plasticity may improve cognition. It is necessary to know when brain volumes are impacted within development so that targeted treatments can be made before cognitive deficits occur. Children with ASD are impaired at similar tasks 44,45 and also show alterations within the frontal cortex 46. Regression analysis between brain volume and performance on the task were conducted in order to examine if excessive frontal cortex volume was related to decreased task performance.

An external file that holds a picture, illustration, etc.
Object name is nihms-1857081-f0001.jpg

A) Timeline of experimental events (P (postnatal day), G (gestational day). B) Set-shifting apparatus viewed from above. Flower pots depicted as blue circles. C) Example of phases for a rat. Underlined or colored circle indicates rewarded and correct choice. (Note: For any phase of the task there is another pairing of pots to offer all combinations. For example, for the CD on different trials the Cumin/Cloth pads vs Marjoram/ Cigarette filters combinations would be offered.) CD= compound discrimination, ID=Intra-dimensional shift, ED= Extra-dimensional shift, SD= simple discrimination

An external file that holds a picture, illustration, etc.
Object name is nihms-1857081-f0002.jpg

MRI of frontal cortex region. A) coronal view at +2.16 from Bregman showing start of ACC and, B) shows end of ACC at −.60 mm from bregma.

2.0. Results

ASST Performance

Male and female data were analyzed separately based on data suggesting females were worse on set-shifting compared to males35. On trials to criterion for the females there was a significant interaction between phase and condition (F6,72=2.34, p=0.03, VPA= 7, saline=8); and the LSD posthoc found that the ED phase between controls and VPA rats was different (p=0.04), with VPA rats having more trials to criterion, (Figure 3). On trials to criterion for males there were no significant condition or interaction effects, (VPA= 7, saline=10). Female control rats improved across the ID phases (fewer trials to criterion for each phase (mean performance: ID1=15, ID2= 14, ID3=11, ID 4= 10), whereas for the VPA females there was a brief improvement for ID2=9, but worsened performance on ID3=12 and ID4=17. The impairments on ID4 and the ED phases for the VPA rats are not due to basic learning issues, motor problems or fatigue as there was no group difference on the simple discrimination after the ED.

An external file that holds a picture, illustration, etc.
Object name is nihms-1857081-f0003.jpg

Trial to criterion data for set-shifting task. Panel A) VPA female rats were worse at the ED shift compared to control females (*p<0.05); VPA= 7, saline=8). Panel B) Male VPA rats were not different compared to control males (VPA= 7, saline=10).

One of the reasons the four phases of ID were used in this task, is that female rats in the prior studies failed to form an attentional set, including control females35. The four ID phases are meant to drive set-formation22,47. Here, the control females still failed to form an attentional set as defined by completing more trials on the ED compared to the mean across the ID phases. Whereas the VPA females did show set-formation as demonstrated by having a shift cost (more trials on ED compared to mean of ID phases), Figure 4. Both VPA and control males demonstrated a shift cost as well and there was no difference in the shift cost between control and VPA males, Figure 3. There were no significant group differences for latency data.

An external file that holds a picture, illustration, etc.
Object name is nihms-1857081-f0004.jpg

Mean shift costs for A) females and B) males. A) VPA females had a shift cost, that is it required more trials to criterion for the ED shift compared to the mean across the 1-4 ID phases. B) Both control and VPA males also showed this shift cost.

2.1. Total Brain Volumes

There were no significant differences between condition for male or female rats for total body weights at the time of brain collection, Control males (107 g mean, 4.4 sd); VPA males (99 g mean, 4.5 sd); Control females (97 g mean, 3.7 sd); VPA females (98 g mean, 5.1 sd). There were no condition differences for total brain volume (Control males (1728 mm3 mean, 114 sd); VPA males (1666 mm3 mean, 65.8 sd); Control females (1676 mm3, 59.5 sd); VPA females (1621 mm3 mean, 69 sd).

2.2. Frontal Cortex Volumes

The mPFC was not significantly altered in the female VPA animals compared to female control rats. The ACC of female VPA rats was enlarged compared to control rats (t13=2.05, p=.03), Figure 5A). The total PFC of the VPA rats was marginally larger than the control rats (t13=1.60, p=.06). The regression results indicated that volume predicted performance for VPA animals, R2=.58, F(1,5) =7.10, p=0.04 with the equation for best fit (Y = 2.139*X + 9.023), Figure 6. For controls, it was not significant (F(1,5)=6.08, p=0.05), but followed a similar relationship indicating that even for control females it is possible that enlarged frontal cortices during this stage of adolescence could impact behavioral performance on a cognitive task. There were no significant group differences for male rats and frontal cortex regions, mPFC, ACC or the total PFC, Figure 5B. However, for male rats a regression analysis found that there was a significant relationship between performance on the first ID shift phase and anterior cingulate volume, Figure 7. For control animals, R2=.48 which was significant (F(1,7) =6.68, p=0.03) and for VPA animals R2=.62, which was also significant (F(1,7) =8.40, p=0.03). The equation for best fits were control (Y = −1.450*X + 58.29), VPA (Y = −0.6973*X + 33.01). This relationship suggests that during adolescence in male rats that ACC volume predicted performance on the first ID phase of the task, where larger volumes were linked with better performance.

An external file that holds a picture, illustration, etc.
Object name is nihms-1857081-f0005.jpg

Total Prefrontal Volumes for A) females and B) males. A) Graphs for mPFC, ACC, and total PFC volume are shown (combined mPFC and ACC volumes). The VPA females had significantly increased volumes of the ACC compared to control females, (p<0.05). There were no significant condition differences for male volumes.

An external file that holds a picture, illustration, etc.
Object name is nihms-1857081-f0006.jpg

PFC volume and total trial performance in female rats. A regression analysis found that for VPA females (N=7) enlarged total PFC volumes were predictive of worse task performance (as measured by more trials to complete the task). Control rats had a similar trend (N=7). This suggests that frontal overgrowth in female VPA animals impaired task performance.

An external file that holds a picture, illustration, etc.
Object name is nihms-1857081-f0007.jpg

ACC volume and performance on the first ID shift. In male rats there was no condition difference for ACC volume. However, a regression analysis found that for both groups (Control, N=9; VPA, N=7) rats with larger volumes of the ACC, regardless of treatment condition, led to better performance on the first ID phase of the task.

2.3. Marble burying task

Adolescent rats (PND 28-29) performed a marble burying task. Behaviors were video coded for bouts and percent of time spent digging, grooming, exploring the arena, and interacting with the marble. Table 2 has means for each behavior. There was a significant effect of condition for the number of grooming bouts (F1,31=6.17, p=.01) demonstrating VPA rats spent less time than controls grooming, Figure 8A. There was a significant main effect of sex on time spent interacting with marbles (F1,31=5.39, p=.02), with females spending more time interacting with the marbles than males, Figure 8B. VPA males buried (mean= 9.75, N=8) more marbles than control males (mean =7.1, N=10) and VPA females buried (7.4 vs 5.6 means) for control females; while this relationship did not reach statistical significance it supports that repetitive behaviors were observed in VPA animals, figure 8C. There was a trend for a significant effect of condition on time spent being inactive (F1,31=3.96, p=.05, Figure 8D) where VPA rats spent less time being inactive.

An external file that holds a picture, illustration, etc.
Object name is nihms-1857081-f0008.jpg

Behaviors scored during marble burying. A) Control male rats explored the area significantly more than VPA males, VPA females or control females. B) Both male and female VPA rats engaged in less self-grooming during this task compared to controls. C) VPA rats spent more time interacting with marbles compared to controls, which suggests repetitive like behavior in the VPA animals. D) Number of marbles buried. (*p<0.05); (N= 10 control males, N=8 VPA males, N=10 control females, N=7 VPA females).

Table 2:

The scored behaviors that occurred during marble burying are listed with the means followed by standard deviations, row 1 time spent, row 2 bouts.

MalesDiggingInactiveGroomingExplorationMarble
Interaction
ControlVPAControlVPAControlVPAControlVPAControlVPA
Time spent (s) mean (sd) 7.1
(4.8)
9.7
(5.1)
11.1
(4.6)
9.37
(3.5)
12
(6.4)
8.3
(5.5)
3.9
(1.9)
2.87
(1.8)
21.1
(4.5)
17.8
(8.4)
Bouts mean (sd) 5.8
(1.8)
10.7
(5.8)
20.2
(11.3)
15.2
(10.7)
10.3
(5.9)
13.1
(4.9)
42.9
(16.1)
35.5
(10.2)
7.23
(4.6)
10.5
(3.2)
 
FemalesDiggingInactiveGroomingExplorationMarble
Interaction
ControlVPAControlVPAControlVPAControlVPAControlVPA
Time spent (s) mean (sd) 9.1
(4.9)
16.6
(12.9)
7.0
(4.7)
8.4
(6.1)
24.2
(14.9)
12.1
(12.3)
13
(7.9)
14.7
(11.2)
32.3
(5.3)
34.5
(22.60)
Bouts mean (sd) 5.6
(3.8)
7.7
(5.6)
8.8
(4.9)
7.5
(4.5)
11.3
(5.4)
9.4
(5.4)
5.0
(2.1)
2.5
(2.4)
22
(10.3)
5.5
(2.0)

3.0. Discussion

The main findings demonstrate that adolescent female VPA rats have impaired cognitive function as measured by the ASST. The impairment on this task aligns with similar data in humans with ASD where females performed worse on the Wisconsin Card Sorting Task28 and is consistent with the prior findings in adult VPA females48. The second important result was that increased frontal cortex volumes in female VPA rats were associated with worse overall task performance. This is a novel finding for the VPA model and parallels the overgrowth seen in adolescent humans with ASD49. The VPA males were not impaired on the set-shifting task compared to male controls, which aligns with past findings in adult VPA males35. Male VPA rats have been found to be impaired on different aspects of attention50 and male rats in general are more impulsive than females51. Prior research has indicated that adolescent rats have more cognitive rigidity compared to adults and suggested that immature frontal cortex was a contributing factor52; the current data supports this hypothesis.

3.1. Translational relevance of sex differences

The behavioral deficit and change in brain volumes occurring in female rats is an important result. The clinical community has recognized that a different behavioral phenotype is presented in females with ASD compared to males with ASD53,54. These results implicate changes in cognition and brain anatomy that align with those observed in adolescents with ASD. For instance, researchers have suggested that those with ASD have weaker long range connections that impact executive functions55. Loss of grey matter has also been found in twin females with ASD and the greater loss of volume in temporal lobes was associated with more symptoms 56. Females with ASD have alterations in the default mode and central executive networks, whereas males have greater differences in salience networks57, these differences could also contribute to altered executive functions in females with ASD. The default mode network is associated with executive function operation and in females with ASD there was greater functional connectivity between networks57. Other research has found females with ASD had more perseverative errors31 compared to males with ASD, which is similar to the ED deficit observed in this study. In this study there were no frontal cortex volume differences between male control and male VPA rats. This was unexpected but could be due to power issues of sample size and also linked to different maturation and/or pruning rates occurring in males at PND 4058-60. Future studies could examine changes in hormones, maturation and pruning rates longitudinally in the animal model and possible combine that with histological techniques to better understand how sex impacts brain changes within ASD.

The shift-cost analysis suggests that VPA females, VPA males and control males formed an attentional set. This provides evidence that the multiple ID phases did drive set formation for most of the groups. Female controls failed to form an attentional set, even with the multiple ID phases. This could partly be driven by the fact that some control females had enlarged frontal cortices and also performed poorly across the entire task. Future studies could assess changes in cognition across developmental ages with different types of tasks. It would be beneficial to observe neural activity in critical regions such as the mPFC and ACC during task performance. Recording potentials during task performance could reveal how these regions represent rule information when an animal identifies the correct strategy61. In addition, differences could relate to synaptic pruning or other developmental changes occurring within this critical stage of development.

For male control and VPA rats there was a significant relationship between performance on phase ID1 and anterior cingulate volume, suggesting that rats with larger volumes (but not overgrown) led to improved performance for this first exposure to the ID shift. There were no volume differences between control and VPA groups within the anterior cingulate which indicates this observation is independent of treatment condition. Prior data has found that enlarged volumes of lobule VI of the cerebellum is associated with impaired ID performance62, and lobule VI is connected to the anterior cingulate cortex63. Therefore, it would be interesting to track developmental changes occurring in both frontal and cerebellar regions and compare how those interactions could impact cognitive development. It is possible that rats that have pruned networks or refined white matter pathways are the rats that are performing better at the first ID phase of the task. Future studies could examine white matter pathways with diffusion tensor imaging at different developmental stages to address this question.

3.2. Brain changes within ASD and parallels in the VPA model

Structurally it has been demonstrated that there is an excessive amount of grey matter within frontal areas in adolescent humans with ASD38,49. In addition, dysregulation of these frontal regions has been associated with changes in cognition in humans with ASD 64-66, including during adolescence 67. Finding increases in frontal volumes in this study supports that the VPA model is capturing another component of the disorder, with the increases in PFC volume being greater in the VPA females compared to control females. Although the mPFC volumes did not reach statistical significance, this could be resolved in future studies with more animals, which may increase statistical power to verify this relationship.

Human longitudinal studies suggest that frontal cortex and other cortical regions with overgrowth are then followed by periods where grey matter declines at a faster rate in those with ASD36,39,68. One important factor to consider with excessive volumes is which cell type or process has contributed to the overgrowth. In human postmortem studies there have been reports of excessive levels of gliosis and enhanced levels of microglia69,70. During adolescence it has been found that those with ASD have increased levels of microglia within the ACC and orbital frontal cortex71. Some research suggests these differences in volume may relate to a delay in pruning mechanisms72. Future studies could examine glial cell ratios of cortex regions in VPA rodents and also examine markers of pruning or synaptic changes at different developmental ages throughout adolescence73,74. If humans with ASD have more rapid pruning across age then longitudinal scans could be informative in the animal model. Understanding the brain changes in the model system provides a way to test interventions to improve or reduce increased pruning, such as exercise. Future studies could use interventions at earlier stages and test for improvements in volume in adult animals.

The marble burying task demonstrated an effect of condition on grooming behavior, where VPA animals spent less time grooming, Figure 8A. In addition, VPA rats buried more marbles and were less inactive compared to control rats. Female rats, regardless of condition, were more likely to interact with the marbles. Past research has found that long-evans rats had reduced marble interactions compared to other strains of rats, but that work was conducted in male rats only75. It would be interesting to see if sex differences are influenced by age and if the physical size of objects changes these behaviors. Female rats interacted with the marbles more than males, regardless of condition. Lastly, although it did not reach statistical significance, the fact that both VPA males and VPA females buried more marbles than controls, and were less inactive, supports the hypothesis that there was an increase in some repetitive behavior.

3.3. Conclusion

In summary, VPA adolescent females were impaired on the set-shifting task and had enlarged frontal cortices. These enlarged cortices were correlated to worse task performance. Both of the behavioral and frontal overgrowth results have corresponding outcomes in the adolescent ASD clinical population. These results provide evidence suggesting that neurobiological brain changes in adolescent are impacting cognition. Future research can clarify the type of cellular changes that are occurring across adolescence and may assist with creation of future treatments for autism.

4.0. Methods and Materials

4.1. Subjects

Pregnant dams (Long-Evans) rats were shipped from Charles River on gestational day 6 to the research facility. Dams were injected on gestational day 12 with a single dose of saline or VPA (sodium valproate (Sigma), 250mg/ml, mixed in saline, 600 mg/kg). Dams were briefly anesthetized on isoflurane gas to administer a less stressful I.P. injection. The isoflurane administration (required by the IACUC) was very brief lasting only a few minutes and should not have adversely effected the pups, as more prolonged exposure is required to see adverse effects in offspring 76. All procedures were conducted in accordance with the Kansas State University IACUC guidelines. Rats were given free access to food except for when undergoing preparation for the set-shifting task. Lights were on starting at 7 am- 7 pm. Rats were reared with litter mates until weaning, then were pair housed with a same sex littermate. One male and one female from each litter was used for each experimental condition to account for the litter effect77.

4.2. Video equipment

All videos were collected and analyzed with the Limelight software camera system (Actimetrics, IL). The limelight camera runs at 30 frames per second.

4.3. Marble burying

Rats (PND 28) were placed in a doublewide home cage (40 W x 43 L x 20 H cm) filled 3 cm deep with regular pine bedding. Light was regular white light. They were allowed to habituate for 5 minutes then placed in their home cage briefly while an arrangement of 20 marbles (2.5 cm diameter of the marble) in a 4 x 5 pattern were placed on the bedding in the double wide cage. Rats were then placed back in the double-wide cage and had 10 minutes to bury marbles. Rats were videotaped and a picture was taken at the end of the session. The container was emptied, wiped down with Peroxigard cleaner, dried and then refilled for the next session. Marbles buried by greater than 2/3 were considered buried. Additional behaviors, coded by blind to condition reviewers, were self-grooming, digging, and inactive using definitions from a prior study 75. Behaviors were coded for the percent of time spent performing that behavior and the number of bouts.

4.4. Attentional set-shifting task and apparatus

4.4.1. Apparatus and stimuli

A Plexiglas box (50 x 37.5 x 25 cm) with a black divider was used as the testing arena. The sliding door allowed access to the flowerpot. All media stimuli were mixed and recycled between rats to allow any odor cues to be disseminated. However, once a pot had been scented with the blotting paper it was only ever scented with the same scent. Crushed Honey Nut Cheerio powder was applied to all digging media to prevent reward odor from serving as a digging cue. Media were shredded manila folders, aspen shavings, foam rubber, felt, burlap ribbon, silk ribbon, plastic beads, plastic jewels, cloth string, wire string, cigarette filters, and shredded cotton pads. Odors were rum, vanilla, lemon, almond, cinnamon, anise, watermelon, grapefruit, strawberry, blueberry, marjoram, and cumin. A pilot study demonstrated that rats could discriminate these odors.

4.4.2. Training

For the set-shifting task rats were given basic digging training with plain bedding for 1-2 days. Basic training consisted of shaping digging behavior in the testing arena. Rats were allowed to obtain a small 1/3 piece of cereal on top of the bedding of the flowerpots. During successive trials the cereal was buried deeper until the rat was successfully digging to retrieve it at about 3.5 cm deep. Then 1-2 days of training to learn with one odor pair (tea tree oil vs lavender) and one media pair (shredded paper vs pine shavings) with unscented pots. Since prior work has found that adolescent rats were impaired on the CD phase52, compared to adults, basic training was extended to ensure rats could complete the full set-shifting task. After reaching 7 correct trials on the odor and media pairs they performed the full set-shifting task the next day on PND 37-40. All rats that completed the set-shifting task were euthanized on PND 39-40. Honey Nut Cheerios (General Mills, MN) and a flowerpot were introduced into the home cage the night before training began. Pots themselves were not scented, rather blotting paper was taped to the inner lip of the ceramic pot and scented with a pipettor (2.5 microliters). The paper was re-scented before every training session.

4.4.3. Set-shifting task

Rats were trained in the set-shifting task with 7 stages: compound discrimination (CD), intra-dimensional shift 1-4 (ID), extra-dimensional shift (ED), and a (simple discrimination (SD). The SD was included as a control, and no condition differences were expected. Rats underwent food restriction for around 7 days, obtaining around 90% of their body weight. In all sessions the first four trials were discovery trials; the rat could sample the other pot in these trials only. After these trials, once the rat initiated a dig (defined as moving the medium with nose or paw), it was scored, and rats were allowed to finish digging in incorrect pots but not allowed to go back to the correct pot. After reaching criterion on the basic digging training, rats completed the set-shifting task CD, ID 1-4, ED, and SD phases. Trials to criterion was set to 7 trials as the subjects were adolescents78. Discovery trials were included in the 7 trials to criterion. All stimuli were counterbalanced with a Latin square design and half of the rats were trained odor to medium shifts, half medium to odor shifts, Table 1.

Table 1:

The volumes of the prefrontal cortex and standard deviations are listed. There was a significant difference between the ACC of female control and VPA animals, (*=p<0.05).

mPFCACCtotal PFC
Control females13.30 (2.4)19.77 (2.1)*39.25 (2.7)
VPA females14.17 (4.1)22.86 (3.7)*43.11 (6.4)
Control males12.49 (2.3)29.00 (3.4)40.84 (3.6)
VPA males13.36 (3.4)26.85 (3.3)40.21 (6.3)

4.5. MRI volumetric measurements

Volumetric measurements were collected via magnetic resonance imaging (MRI) with a Bruker 600 MHz that had a bottom loading probe, 30mm x 40 mm. They were placed in a flat bottom glass tube and covered with Fomblin (a safe, nontoxic, inert perfluoropolyether oil which is MRI silent and often used to increase signal-to-noise ratios and decrease magnetic susceptibility distortions79), and secured with a pronged glass stabilizer specially made to ensure sample position consistency. Scans were be performed with Rapid Acquisition with Refocused Echoes (RARE) pulse sequences in ~1 hour and 45 minutes with one repetition, a repetition time of 2500 ms, echo time of 30.23 ms, and a RARE factor of 16. The pulse sequence used a flip angle of 90°, acquired two averages, with slices acquired in an interlaced pattern. Image resolution was approximately 100 x 100 x 300 μm with matrix sizes, field of view, and number of slices varying according to brain size. The use of Fomblin reduces the prevalence of susceptibility artifacts, which are often present in whole brain ex vivo imaging. Additionally, a pronged glass sample holder was made to ensure consistent orientation and sample position throughout imaging as well as between samples. The utilization of adaptable parameters for imaging allows for maximal signal-to-noise and contrast-to-noise ratios dependent upon sample size.

Volume of the medial prefrontal cortex (areas: A32D, A32V) and the anterior cingulate cortex (ACC) (areas: A24a, A24b) from Paxinos and Watson 7th edition were segmented with ITK SNAP software by blind to condition researchers. The mPFC and ACC were chosen because of their importance to set-shifting performance 25-27. Segmentation was screened for consistency through a 3-D modeling in ITK-SNAP before evaluation of total volume. Total brain volume was analyzed using the active contour segmentation mode. Each ROI was outlined based on anatomical landmarks and the volume for each lobule and frontal region was measured.

4.6. Data analysis

Behavioral data for set-shifting was separated by sex based on prior research findings 35. Trials to criterion data were analyzed with a repeated measure ANOVA (phase- repeated measure) x condition as the between-subject factor followed by LSD posthocs after a significant interaction. Behaviors coded by reviewer’s blind to condition during the marble bury task included digging, inactive time, grooming, exploring, and marble interaction time. They were analyzed with 2-way ANOVAs (Condition x Sex). Brain volumes were separated by sex and examined with unpaired one-tailed t-tests based on the prior prediction of enlarged frontal cortex occurring in the treatment group. Total body weights and total brain volumes were examined with t-tests. Linear regression was used to assess relationships between brain volumes predicting behavioral task outcomes.

Highlights:

Adolescent valproic acid (VPA) female rats had enlarged frontal cortices compared to control females

VPA female rats were impaired on the extra-dimensional shift phase of the task compared to control females

Overgrowth of the prefrontal cortex regions was associated with reduced accuracy in an attentional set-shifting task

Footnotes

Declarations of interest: none

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

1. Demetriou EA et al. Autism spectrum disorders: a meta-analysis of executive function. Mol. Psychiatry (2017) 10.1038/mp.2017.75 [doi]. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
2. Ozonoff S, Pennington BF & Rogers SJ Executive function deficits in high-functioning autistic individuals: relationship to theory of mind. J. Child Psychol. Psychiatry 32, 1081–1106 (1991). [Abstract] [Google Scholar]
3. Hughes C, Russell J & Robbins TW Evidence for executive dysfunction in autism. Neuropsychologia 32, 477–493 (1994). [Abstract] [Google Scholar]
4. Lai MC et al. A behavioral comparison of male and female adults with high functioning autism spectrum conditions. PLoS One 6, (2011). [Europe PMC free article] [Abstract] [Google Scholar]
5. Chen C & Van Horn JD Developmental neurogenetics and multimodal neuroimaging of sex differences in autism. Brain Imaging Behav. 11, 38–61 (2017). [Europe PMC free article] [Abstract] [Google Scholar]
6. Memari AH et al. Cognitive flexibility impairments in children with autism spectrum disorders: Links to age, gender and child outcomes. Res. Dev. Disabil 34, 3218–3225 (2013). [Abstract] [Google Scholar]
7. Mabunga DF, Gonzales EL, Kim JW, Kim KC & Shin CY Exploring the Validity of Valproic Acid Animal Model of Autism. Exp. Neurobiol 24, 285–301 (2015). [Europe PMC free article] [Abstract] [Google Scholar]
8. Kim KC et al. The critical period of valproate exposure to induce autistic symptoms in Sprague-Dawley rats. Toxicol. Lett 201, 137–143 (2011). [Abstract] [Google Scholar]
9. Kerr DM, Downey L, Conboy M, Finn DP & Roche M Alterations in the endocannabinoid system in the rat valproic acid model of autism. Behav. Brain Res 249, 124–133 (2013). [Abstract] [Google Scholar]
10. Edalatmanesh MA, Nikfarjam H, Vafaee F & Moghadas M Increased hippocampal cell density and enhanced spatial memory in the valproic acid rat model of autism. Brain Res. 1526, 15–26 (2013). [Abstract] [Google Scholar]
11. Schneider T & Przewlocki R Behavioral alterations in rats prenatally exposed to valproic acid: animal model of autism. Neuropsychopharmacology 30, 80–90 (2005). [Abstract] [Google Scholar]
12. Markram K, Rinaldi T, La Mendola D, Sandi C & Markram H Abnormal fear conditioning and amygdala processing in an animal model of autism. Neuropsychopharmacology 33, 901–913 (2008). [Abstract] [Google Scholar]
13. Nicolini C & Fahnestock M The valproic acid-induced rodent model of autism. Exp. Neurol (2017) S0014-4886(17)30110-3 [pii]. [Abstract] [Google Scholar]
14. Tartaglione AM, Schiavi S, Calamandrei G & Trezza V Prenatal valproate in rodents as a tool to understand the neural underpinnings of social dysfunctions in autism spectrum disorder. Neuropharmacology 159, 107477 (2019). [Abstract] [Google Scholar]
15. Chomiak T, Turner N & Hu B What We Have Learned about Autism Spectrum Disorder from Valproic Acid. Patholog. Res. Int 2013, 712758 (2013). [Europe PMC free article] [Abstract] [Google Scholar]
16. Christensen J et al. Prenatal valproate exposure and risk of autism spectrum disorders and childhood autism. Jama 309, 1696–1704 (2013). [Europe PMC free article] [Abstract] [Google Scholar]
17. Dean JC et al. Long term health and neurodevelopment in children exposed to antiepileptic drugs before birth. J. Med. Genet 39, 251–260 (2002). [Europe PMC free article] [Abstract] [Google Scholar]
18. Modabbernia A, Velthorst E & Reichenberg A Environmental risk factors for autism: an evidence-based review of systematic reviews and meta-analyses. Mol. Autism 8, 1–16 (2017). [Europe PMC free article] [Abstract] [Google Scholar]
19. Deng W et al. The Relationship among Genetic Heritability, Environmental Effects, and Autism Spectrum Disorders. J. Child Neurol 30, 1794–1799 (2015). [Abstract] [Google Scholar]
20. Hallmayer J et al. Genetic heritability and shared environmental factors among twin pairs with autism. Arch. Gen. Psychiatry 68, 1095–1102 (2011). [Europe PMC free article] [Abstract] [Google Scholar]
21. Owen Adrian M, Roberts AC, Poley CE, Sahakian BJ, R. T. VERSUS INTRA-DIMENSIONAL SET SHIFTING PERFORMANCE FOLLOWING FRONTAL LOBE EXCISIONS , TEMPORAL LOBE EXCISIONS OR AMYGDALO- HIPPOCAMPECTOMY IN MAN. Neuropsychologia 29, 993–1006 (1991). [Abstract] [Google Scholar]
22. Chase EA, Tait DS & Brown VJ Lesions of the orbital prefrontal cortex impair the formation of attentional set in rats. Eur. J. Neurosci 36, 2368–2376 (2012). [Abstract] [Google Scholar]
23. Lindgren HS, Wickens R, Tait DS, Brown VJ & Dunnett SB Lesions of the dorsomedial striatum impair formation of attentional set in rats. Neuropharmacology 71, 148–154 (2013). [Abstract] [Google Scholar]
24. Tait DS, Bowman EM, Neuwirth LS & Brown VJ Assessment of intradimensional/extradimensional attentional set-shifting in rats. Neurosci. Biobehav. Rev 89, 72–85 (2018). [Abstract] [Google Scholar]
25. Birrell JM & Brown VJ Medial frontal cortex mediates perceptual attentional set shifting in the rat. J. Neurosci 20, 4320–4325 (2000). [Europe PMC free article] [Abstract] [Google Scholar]
26. Bubb EJ, Aggleton JP, Mara SMO & Nelson AJD ORIGINAL ARTICLE Chemogenetics Reveal an Anterior Cingulate–Thalamic Pathway for Attending to Task-Relevant Information. Cereb. Cortex 1–18 (2020) 10.1093/cercor/bhaa353. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
27. Ng CW, Noblejas MI, Rodefer JS, Smith CB & Poremba A Double dissociation of attentional resources: prefrontal versus cingulate cortices. J. Neurosci 27, 12123–12132 (2007). [Europe PMC free article] [Abstract] [Google Scholar]
28. Memari AH et al. Cognitive flexibility impairments in children with autism spectrum disorders: links to age, gender and child outcomes. Res. Dev. Disabil 34, 3218–3226 (2013). [Abstract] [Google Scholar]
29. Hjelmquist E Nydn2000. 185, 180–185 (2000). [Abstract] [Google Scholar]
30. Lemon JM, Gargaro B, Enticott PG & Rinehart NJ Brief report: Executive functioning in autism spectrum disorders: A gender comparison of response inhibition. J. Autism Dev. Disord 41, 352–356 (2011). [Abstract] [Google Scholar]
31. Kiep M & Spek AA Executive functioning in men and women with an autism spectrum disorder. Autism Res. 10, 940–948 (2017). [Abstract] [Google Scholar]
32. White EI et al. Sex differences in parent-reported executive functioning and adaptive behavior in children and young adults with autism spectrum disorder. Autism Res. 10, 1653–1662 (2017). [Europe PMC free article] [Abstract] [Google Scholar]
33. Baio J et al. Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014. Morb. Mortal. Wkly. report.Surveillance Summ. (Washington, D.C. 2002) 67, 1–24 (2018). [Europe PMC free article] [Abstract] [Google Scholar]
34. Halladay AK et al. Sex and gender differences in autism spectrum disorder: Summarizing evidence gaps and identifying emerging areas of priority. Mol. Autism 6, 1–5 (2015). [Europe PMC free article] [Abstract] [Google Scholar]
35. McKinnell ZE, Maze T, Ramos A, Challans B & Plakke B Valproic acid treated female Long-Evans rats are impaired on attentional set-shifting. Behav. Brain Res 397, 112966 (2021). [Europe PMC free article] [Abstract] [Google Scholar]
36. Khundrakpam BS, Lewis JD, Kostopoulos P, Carbonell F & Evans AC Cortical Thickness Abnormalities in Autism Spectrum Disorders Through Late Childhood, Adolescence, and Adulthood: A Large-Scale MRI Study. Cereb. Cortex 27, 1721–1731 (2017). [Abstract] [Google Scholar]
37. Courchesne E et al. The ASD Living Biology: from cell proliferation to clinical phenotype. Mol. Psychiatry 24, 88–107 (2019). [Europe PMC free article] [Abstract] [Google Scholar]
38. Van Rooij D et al. Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: Results from the ENIGMA ASD working group. Am. J. Psychiatry 175, 359–369 (2018). [Europe PMC free article] [Abstract] [Google Scholar]
39. Braden BB & Riecken C Thinning faster? Age-related cortical thickness differences in adults with autism spectrum disorder. Res. Autism Spectr. Disord 64, 31–38 (2019). [Europe PMC free article] [Abstract] [Google Scholar]
40. Laidi C et al. Decreased Cortical Thickness in the Anterior Cingulate Cortex in Adults with Autism. J. Autism Dev. Disord 49, 1402–1409 (2019). [Abstract] [Google Scholar]
41. Mensen VT et al. Development of cortical thickness and surface area in autism spectrum disorder. NeuroImage Clin. 13, 215–222 (2017). [Europe PMC free article] [Abstract] [Google Scholar]
42. Lauber E, Filice F & Schwaller B Prenatal valproate exposure differentially affects parvalbumin-expressing neurons and related circuits in the cortex and striatum of mice. Front. Mol. Neurosci 9, 1–16 (2016). [Europe PMC free article] [Abstract] [Google Scholar]
43. Rinaldi T, Silberberg G & Markram H Hyperconnectivity of local neocortical microcircuitry induced by prenatal exposure to valproic acid. Cereb. cortex (New York, N.Y. 1991) 18, 763–771 (2008). [Abstract] [Google Scholar]
44. Yerys BE et al. Set-shifting in children with autism spectrum disorders: reversal shifting deficits on the Intradimensional/Extradimensional Shift Test correlate with repetitive behaviors. Autism 13, 523–539 (2009). [Europe PMC free article] [Abstract] [Google Scholar]
45. Reed P, Watts H & Truzoli R Flexibility in young people with autism spectrum disorders on a card sort task. Autism 17, 162–172 (2013). [Abstract] [Google Scholar]
46. Postema MC et al. Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets. Nat. Commun 10, 4958 (2019). [Europe PMC free article] [Abstract] [Google Scholar]
47. Wright NF, Vann SD, Aggleton JP & Nelson AJ A critical role for the anterior thalamus in directing attention to task-relevant stimuli. J. Neurosci 35, 5480–5489 (2015). [Europe PMC free article] [Abstract] [Google Scholar]
48. McKinnell ZE, Maze T, Ramos A, Challans B & Plakke B Valproic acid treated female Long-Evans rats are impaired on attentional set-shifting. Behav. Brain Res 397, 112966 (2020). [Europe PMC free article] [Abstract] [Google Scholar]
49. Uddin LQ et al. Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism. Biol. Psychiatry 70, 833–841 (2011). [Europe PMC free article] [Abstract] [Google Scholar]
50. Anshu K et al. Altered attentional processing in male and female rats in a prenatal valproic acid exposure model of autism spectrum disorder. Autism Res. (2017) 10.1002/aur.1852 [doi]. [Abstract] [CrossRef] [Google Scholar]
51. Bayless DW, Darling JS, Stout WJ & Daniel JM Sex differences in attentional processes in adult rats as measured by performance on the 5-choice serial reaction time task. Behav. Brain Res 235, 48–54 (2012). [Abstract] [Google Scholar]
52. Newman LA & Mcgaughy J Adolescent rats show cognitive rigidity in a test of attentional set shifting. Dev. Psychobiol 53, 391–401 (2011). [Europe PMC free article] [Abstract] [Google Scholar]
53. de Giambattista C, Ventura P, Trerotoli P, Margari F & Margari L Sex Differences in Autism Spectrum Disorder: Focus on High Functioning Children and Adolescents. Front. Psychiatry 12, 1–13 (2021). [Europe PMC free article] [Abstract] [Google Scholar]
54. Rynkiewicz A & Lucka I Autism spectrum disorder (ASD) in girls. Co-occurring psychopathology. Sex differences in clinical manifestation. Psychiatr. Pol 52, 629–639 (2018). [Abstract] [Google Scholar]
55. Tavares V, Fernandes LA, Antunes M, Ferreira H & Prata D Sex Differences in Functional Connectivity Between Resting State Brain Networks in Autism Spectrum Disorder. J. Autism Dev. Disord (2021) 10.1007/s10803-021-05191-6. [Europe PMC free article] [Abstract] [CrossRef] [Google Scholar]
56. Cauvet É et al. Sex differences along the autism continuum: A twin study of brain structure. Cereb. Cortex 29, 1342–1350 (2019). [Europe PMC free article] [Abstract] [Google Scholar]
57. Lawrence KE et al. Sex differences in functional connectivity of the salience, default mode, and central executive networks in youth with asd. Cereb. Cortex 30, 5107–5120 (2020). [Europe PMC free article] [Abstract] [Google Scholar]
58. Koshibu K, Levitt P & Ahrens ET Sex-specific, postpuberty changes in mouse brain structures revealed by three-dimensional magnetic resonance microscopy. Neuroimage 22, 1636–1645 (2004). [Abstract] [Google Scholar]
59. Brouwer RM et al. White matter development in early puberty: A longitudinal volumetric and diffusion tensor imaging twin study. PLoS One 7, 1–10 (2012). [Europe PMC free article] [Abstract] [Google Scholar]
60. Baloch S et al. Quantification of brain maturation and growth patterns in C57BL/6J mice via computational neuroanatomy of diffusion tensor images. Cereb. Cortex 19, 675–687 (2009). [Europe PMC free article] [Abstract] [Google Scholar]
61. Bryden DW, Johnson EE, Tobia SC, Kashtelyan V & Roesch MR Attention for learning signals in anterior cingulate cortex. J. Neurosci 31, 18266–18274 (2011). [Europe PMC free article] [Abstract] [Google Scholar]
62. Payne M et al. Increased volumes of lobule VI in a valproic acid model of autism are associated with worse set-shifting performance in male Long-Evan rats. Brain Res. 1765, 147495 (2021). [Europe PMC free article] [Abstract] [Google Scholar]
63. Coffman KA, Dum RP & Strick PL Cerebellar vermis is a target of projections from the motor areas in the cerebral cortex. Proc. Natl. Acad. Sci. U. S. A 108, 16068–16073 (2011). [Europe PMC free article] [Abstract] [Google Scholar]
64. Agam Y, Joseph RM, Barton JJ & Manoach DS Reduced cognitive control of response inhibition by the anterior cingulate cortex in autism spectrum disorders. Neuroimage 52, 336–348 (2010). [Europe PMC free article] [Abstract] [Google Scholar]
65. Barttfeld P et al. State-dependent changes of connectivity patterns and functional brain network topology in autism spectrum disorder. Neuropsychologia 50, 3653–3662 (2012). [Abstract] [Google Scholar]
66. Schmitz N et al. Neural correlates of executive function in autistic spectrum disorders. Biol. Psychiatry 59, 7–17 (2006). [Abstract] [Google Scholar]
67. Yerys BE et al. Neural Correlates of Set-Shifting in Children With Autism. Autism Res. 8, 386–398 (2015). [Europe PMC free article] [Abstract] [Google Scholar]
68. Braden BB et al. Executive function and functional and structural brain differences in middle-age adults with autism spectrum disorder. Autism Res. 10, 1945–1960 (2017). [Abstract] [Google Scholar]
69. Morgan JT et al. Microglial activation and increased microglial density observed in the dorsolateral prefrontal cortex in autism. Biol. Psychiatry 68, 368–376 (2010). [Abstract] [Google Scholar]
70. Petrelli F, Pucci L & Bezzi P Astrocytes and microglia and their potential link with autism spectrum disorders. Front. Cell. Neurosci 10, 1–8 (2016). [Europe PMC free article] [Abstract] [Google Scholar]
71. Suzuki K et al. Microglial activation in young adults with autism spectrum disorder. Arch. Gen. Psychiatry 70, 49–58 (2013). [Abstract] [Google Scholar]
72. Zielinski BA et al. Longitudinal changes in cortical thickness in autism and typical development. Brain 137, 1799–1812 (2014). [Europe PMC free article] [Abstract] [Google Scholar]
73. Cheadle L et al. Sensory Experience Engages Microglia to Shape Neural Connectivity through a Non-Phagocytic Mechanism. Neuron 108, 451–468.e9 (2020). [Europe PMC free article] [Abstract] [Google Scholar]
74. Schuldiner O & Yaron A Mechanisms of developmental neurite pruning. Cell. Mol. Life Sci 72, 101–119 (2015). [Europe PMC free article] [Abstract] [Google Scholar]
75. Ku KM, Weir RK, Silverman JL, Berman RF & Bauman MD Behavioral phenotyping of juvenile long-evans and sprague-dawley rats: Implications for preclinical models of autism spectrum disorders. PLoS One 11, 1–25 (2016). [Europe PMC free article] [Abstract] [Google Scholar]
76. Andropoulos DB Effect of Anesthesia on the Developing Brain : Infant and Fetus. 77030, 1–11 (2018). [Abstract] [Google Scholar]
77. Lazic SE & Essioux L Improving basic and translational science by accounting for litter-to-litter variation in animal models. BMC Neurosci. 14, (2013). [Europe PMC free article] [Abstract] [Google Scholar]
78. Cain RE, Wasserman MC, Waterhouse BD & McGaughy JA Atomoxetine facilitates attentional set shifting in adolescent rats. Dev. Cogn. Neurosci 1, 552–560 (2011). [Europe PMC free article] [Abstract] [Google Scholar]
79. Li R et al. Restoring susceptibility induced MRI signal loss in rat brain at 9.4 T: A step towards whole brain functional connectivity imaging. PLoS One 10, (2015). [Europe PMC free article] [Abstract] [Google Scholar]

Funding 


Funders who supported this work.

Kansas State University

    NIGMS

      NIGMS NIH HHS (1)