Comparison of the spatial-cognitive functions of dorsomedial striatum and anterior cingulate cortex in mice

Tine Pooters; Annelies Laeremans; Ilse Gantois; Ben Vermaercke; Lutgarde Arckens; Rudi D’Hooge

doi:10.1371/journal.pone.0176295

Abstract

Neurons in anterior cingulate cortex (aCC) project to dorsomedial striatum (DMS) as part of a corticostriatal circuit with putative roles in learning and other cognitive functions. In the present study, the spatial-cognitive importance of aCC and DMS was assessed in the hidden-platform version of the Morris water maze (MWM). Brain lesion experiments that focused on areas of connectivity between these regions indicated their involvement in spatial cognition. MWM learning curves were markedly delayed in DMS-lesioned mice in the absence of other major functional impairments, whereas there was a more subtle, but still significant influence of aCC lesions. Lesioned mice displayed impaired abilities to use spatial search strategies, increased thigmotaxic swimming, and decreased searching in the proximity of the escape platform. Additionally, aCC and DMS activity was compared in mice between the early acquisition phase (2 and 3 days of training) and the over-trained high-proficiency phase (after 30 days of training). Neuroplasticity-related expression of the immediate early gene Arc implicated both regions during the goal-directed, early phases of spatial learning. These results suggest the functional involvement of aCC and DMS in processes of spatial cognition that model associative cortex-dependent, human episodic memory abilities.

Citation: Pooters T, Laeremans A, Gantois I, Vermaercke B, Arckens L, D’Hooge R (2017) Comparison of the spatial-cognitive functions of dorsomedial striatum and anterior cingulate cortex in mice. PLoS ONE 12(5): e0176295. https://doi.org/10.1371/journal.pone.0176295

Editor: Alexandra Kavushansky, Technion Israel Institute of Technology, ISRAEL

Received: October 31, 2016; Accepted: April 7, 2017; Published: May 3, 2017

Copyright: © 2017 Pooters et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are included within the manuscript and its Supporting Information files.

Funding: This work was supported by an interdisciplinary research grant from the University of Leuven—Research Council (IDO/06/004, https://admin.kuleuven.be/raden/en/research-council) and a GOA research program (to L.A. and R.D.). T.P. and A.L. were both supported by a PhD fellowship grant from the Flanders Agency for Innovation in Science and Technology (IWT, http://www.iwt.be/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The Morris water maze (MWM) is an established rodent model to investigate spatial learning and memory in laboratory animals [1]. The task requires rodents to associate a configuration of distal environmental cues with the position of a hidden escape platform [2–4]. During training, the animals gradually learn to swim away from the walls, and adopt more proficient search strategies [1,5,6]. They usually progress from non-spatial or repetitive strategies to cognitively demanding, but more efficient, spatial strategies [7,8]. Apart from the established hippocampal dependence of this task, striatal and prefrontal telencephalic regions have been implicated in MWM learning as well, although their involvement in the spatial-cognitive aspects of the task is still debated [9,10].

Both neural activation and lesion studies have shown that dorsal striatum, and more specifically its medial part, is crucial during the early phase of MWM training. Woolley and colleagues [10] reported increased activation of dorsomedial striatum (DMS) in mice during the first three days of training, and lesion studies showed that rats with DMS damage needed more time to acquire the hidden platform position [5,6,11,12]. Impaired performance in these animals might be, at least partly, attributed to disinclination or inability to acquire spatial search strategies. Increased tendency to keep on swimming along the wall of the pool (thigmotaxis) was indeed reported in rats with striatal lesions [5], rats with striatal dopamine depletion [13], PDE10A knockout mice with altered medium spiny neuron activity [14], and parkin-deficient mice [15].

There are indications that DMS interacts with other telencephalic structures to control spatial-cognitive functions. Notably, DMS is innervated by neurons in medial prefrontal cortex (mPFC), most prominently those in anterior cingulate cortex (aCC), as part of a distinct corticostriatal network [9,16–22]. Studies about aCC involvement in spatial learning and memory found that this brain region may play a role in storage and retrieval of remote memories (appr. 30 days after encoding) [23,24], but not in the acquisition of spatial information as aCC lesions failed to impair place learning [24,25,26]. However, reports failed to compare the involvement of mPFC and DMS in MWM learning [27–30]. A more recent study of Woolley and colleagues [10] reported that mPFC was activated in the mouse brain during early MWM place learning (the first 3 days of training), but not during late place learning. This early mPFC activation coincided with DMS activation, which definitely supports the involvement of the corticostriatal circuit in rodent and human spatial learning [10,31].

In the present study, we used complementary techniques to examine further the spatial-cognitive importance of aCC and DMS. In experiment 1, we compared the different effects of aCC and DMS lesions on specific, previously unexamined spatial-cognitive parameters, since lesion techniques remain the most established way to study regional brain function in mice [24,32,33]. In view of previous studies, DMS damage might impair MWM performance more profoundly than aCC lesions. Detailed analysis of the swimming paths allowed us to compare the involvement of each brain region in the use of cognitively demanding spatial search strategies. However, lesion studies are often confounded by functional compensation by other brain areas. In experiment 2, we therefore determined the involvement of these brain areas during early and late learning by visualizing regional expression of a known plasticity marker. Immediate early genes (IEG), in general, and particularly Arc (activity-regulated cytoskeleton-associated protein [34]; also known as Arg 3.1 [35]), are considered valid imaging tools to examine the neural substrate of learning and memory [36,37]. The localization of Arc mRNA and protein in activated dendrites [38–40], and their requirement for long-term potentiation maintenance and memory consolidation [41,42], implements this IEG in synaptic plasticity and memory [41,43–46]. To define the time dependence of this involvement, we compared regional activation between mice following short (2 days and 3 days, acquisition phase) and extended (30 days, overtrained phase) training. Quantified regional expression of Arc will be a read-out for neuroplasticity-related brain activation. More specifically, we used learning-dependent changes in Arc expression to visualize aCC and DMS involvement during MWM learning.

Materials and methods

Animals

Female C57BL/6J mice, 8 weeks of age, were purchased from Janvier Labs (Le-Genest-Saint-Isle, France) and group housed (6–9 mice per cage; cage dimensions: 46 cm x 27 cm floor, 23 cm high). Females were preferred for these experiments, despite possible influences of fluctuations in oestrus hormones [47–49], because in our experience, territorial fights have a more profound impact on behavioural read-outs in group-housed males. Food and water were available ad libitum, and mice were handled for 1 week (tail colouring) before the start of behavioural testing. Animals were housed in a temperature-controlled environment (22°C ± 2°C) that was maintained at 30–40% humidity, and kept on a 12-h light–dark cycle (lights on at 8:00 AM). Mice were tested behaviourally from 12 weeks of age and experiments were performed during the light phase. Study design and procedures approved according to European guidelines by Animal Ethics Committee of KU Leuven: https://admin.kuleuven.be/raden/en/animals-ethics-committee.

Morris water maze protocol

Spatial learning was tested in the Morris water maze (MWM) with hidden platform. The maze consisted of a circular pool (150 cm diameter, 33 cm high), which was filled with opaque water (25 ± 1°C) to a depth of 16 cm, and an escape platform (15 cm diameter, 15 cm high) hidden 1 cm below the water surface in the middle of a fixed quadrant. The pool was situated in a room enriched with distal visual cues. Each trial began at one of four randomized starting locations by placing the mouse at the edge of the pool facing its centre. During trials, the experimenter remained seated at a fixed location. When a trial was not completed within 2 min, the mouse was guided to the platform and remained there for 15s. A training session consisted of four swimming trials during which the animal needed to find the position of the hidden platform. Trials in each session were separated by a 15-min break, and when two sessions were performed on a single day they were separated by 2h. Five consecutive training days were followed by 2 resting days. MWM navigation was recorded using Ethovision video tracking equipment and software (Noldus Information Technology, Wageningen, The Netherlands).

Experiment 1: Effects of aCC and DMS lesions on MWM learning

Brain lesion procedure.

Animals used in the lesion experiments (n = 34) were randomly assigned to one of the experimental groups: aCC lesions (n = 11), DMS lesions (n = 11), and a sham control group (aCC sham, n = 6; DMS sham, n = 6). Mice were anaesthetized by intraperitoneal injection of 5% chloral hydrate (1% of body weight) and positioned on a stereotaxic frame (Narishige scientific instruments, Tokyo, Japan). Corneal drying was prevented by ophthalmic ointment and 0.05 ml lidocaine (20 mg/ml) was injected subcutaneously before making an incision in the skin above the skull. Next, holes were drilled into the skull and bilateral electrolytic lesions were produced by applying direct current (0,5 mA for 30 s) through a tungsten-coated 0.2 mm needle (Teflon insulated Tungsten wire, advent research materials, Oxford, UK). Sham animals were submitted to the same surgical procedure but no current was delivered after placement of the electrode. Coordinates relative to Bregma were: (1) aCC, +1.0 mm anteroposterior (AP), ±0.2 mm mediolateral (ML), -1.5 mm dorsoventral (DV); (2) DMS, +0.2 and +1.2 mm AP, ±1.5 mm ML, -2.5 and -3.0 mm DV [50]. Post-surgery, each animal was treated with 5% glucose saline (10% of body weight) and placed on a heat pad in the recovery cage. Paracetamol (30 mg/ml) was administered in drinking water 24-h pre- and post-surgery. Animals were given 2 weeks of post-surgery recovery before starting the behavioural experiments.

Following behavioural testing animals were sacrificed by cervical dislocation. Brains were removed, snap-frozen in isopentane (Sigma-Aldrich, Germany) and stored at -80°C for further processing. Coronal sections (25 μm thickness) were cut using a cryostat (Microm HM 500 OM, Waldorf, Germany) and mounted on poly-L-lysine coated glass slides. Brain sections were Nissl stained with 5% thionin acetate (Alfa Aesar, Karlsruhe, Germany) followed by microscopic determination of lesion size and location (Optech Biostar microscopes, Germany).

Additional behavioural assessment.

All animals were tested for motor defects and working memory impairments before starting MWM training. Motor coordination and balance were measured on an accelerating rotarod (MED Associates Inc., St. Albans, Vermont, US). Mice were first trained at a constant speed (4 rotations per minute, rpm; 2 min) followed by four test trials (3 min inter-trial interval, ITI). During test trials, rotation speed was increased from 4 to 40 rpm over 5 min (10 min ITI), and time on the rod before falling was recorded [51].

The Y-maze spontaneous alternation (SA) task assessed working memory functions. The animal was placed at the centre of three enclosed arms (30 cm long x 6 cm wide x 31 cm high) and was allowed to freely explore the arms during 10 min. Alternation behaviour was confirmed as soon as the mouse entered three different arms consecutively. The percentage of spontaneous alternations (%SA) was calculated by the formula [(sum alterations / sum arm entries– 2) x 100] [52,53], and the number of total arm entries was considered to reflect spontaneous locomotor activity [54].

Morris water maze training.

DMS-, aCC-, and sham-lesioned mice received a total 15 days of training. Overall task performance was evaluated by calculating the time required to find the hidden platform (latency in s), average distance between the mouse and the hidden platform (mean distance to platform, cm), swimming speed (velocity, cm/s) and time spent in the 15% outer rim of the pool (thigmotaxis, %). Swimming trials were further categorized into differential search strategies according to previously described methods (see [33] for a detailed description). Briefly, three different categories of search strategy were identified ranging from proper spatial strategies (i.e., swimming directly to the platform or with one small explorative loop) to strategies that involved systematic scanning of the pool without relying on spatial information (i.e., non-spatial strategies), or those that merely consisted of repetitive looping (i.e. swimming in tight circles). Probe trials were performed after each 5 days of training (i.e. probe 1 was performed on day 6, probe 2 on day 11 and probe 3 on day 16, before continuing acquisition training) to determine whether mice actually showed a preference for the platform area as a demonstration of spatial memory. During these trials, the platform was removed from the pool, animals were allowed to swim freely during 100 s, and time spent in each quadrant was recorded.

Statistical analysis.

All behavioural data are presented as means with standard error of the mean (SEM). Differences between mean values were determined using analysis of variance (ANOVA) procedures with Tukey test for post-hoc evaluation. Trials or trial blocks over several days between groups were analysed using repeated-measures day x group ANOVA (RM-ANOVA). Paired t-tests were used to determine learning differences between specific days within groups and one-sample t-tests when comparing the results to chance levels. Statistical analyses were performed using SPSS version 19. All statistical tests were performed with α = 0.05 (*p < 0.05, **p < 0.01 and ***p < 0.001).

Experiment 2: DMS and aCC activation during MWM learning

Morris water maze training.

Mice used for in situ hybridization experiments were trained in MWM for 2 days (2d_T, n = 7, 1 daily training session), 3 days (3d_T, n = 7, 1 daily training session) or 30 days (30d_T, n = 8, 2 daily training sessions). Free-swimming control mice (2d_SC, n = 4; 3d_SC, n = 4; and 30d_SC, n = 4) explored the same environment except that hidden platform and distal cues were removed (by placing white cardboard around the pool). Experimental (trained) and free-swimming groups were matched with respect to average time spent swimming on each trial and the total number of trials performed. Non-swimming (caged) control mice (CC, n = 5) were included that did not receive any MWM training, but were transferred between housing and training rooms together with the other mice. Heat plots depicting spatial occupancy of the pool were created by summating the swimming paths of individual mice, using custom-made MATLAB software [10,55]. These heat plots used an intensity-based pseudo-color scale to indicate spatial occupancy or dwell during a specific trial (blue low vs. red high occupancy). Notably, Arc expression levels in aCC and DMS were significantly correlated (Pearson r = 0.84, p < 0.001).

Quantitative in situ hybridization.

In situ Arc hybridization was performed using previously established methods [56]. Briefly, a series of 25 μm brain sections, covering the entire rostrocaudal extent of the striatum/anterior cingulate, were collected and kept at -30°C. Tissue was postfixed in 4% (vol/vol) paraformaldehyde in 0.12 M phosphoric acid in PBS (0.1 M, pH 7.4, 30 min, 4°C; 0.9% NaCl), dehydrated (50%, 70%, 98%, 100% (vol/vol), 5 min), and delipidated (100% vol/vol chloroform, 10 min). Mouse-specific synthetic Arc (probe: 5’-cttgacccagcgctccaggttggcgatggtctcctggcagcggca-3’) was end-labelled with 33P-dATP (New England Nuclear) using terminal deoxynucleotidyl transferase (Invitrogen). Unincorporated nucleotides were removed using mini Quick Spin columns (Roche Diagnostics). The radioactive labelled probe was mixed with a hybridization mixture [50% (vol/vol) formamide, 4× standard saline citrate, 1× Denhardt’s solution, 10% (wt/vol) dextran sulfate, 100 μg/mL Herring sperm DNA, 250 μg/mL tRNA, 60 mM DTT, 1% (wt/vol) N-lauryl-sarcosine, and 26 mM NaHPO₄ (pH 7.4)], applied to a series of dehydrated sections and incubated overnight at a temperature of 37°C. The next day, sections were rinsed in 1× standard saline citrate buffer at 42°C, air-dried, and apposed to an autoradiographic film (Kodak) together with a [¹⁴C] microscale (GE Healthcare). Films were developed 2.5 wk later in Kodak D19 developing solution and fixed in Rapid fixer (Ilford Hypam).

Autoradiographic images were scanned (CanoScan LiDE 600F; Canon), and optical densities (mean grey value per pixel) were quantified with ImageJ software (image processing and analysis in Java; National Institutes of Health). Optical density was measured in three brain sections per mouse along the rostrocaudal axis for each target region. Striatal and aCC slices were collected at +1.10 mm to +0.38 mm relative to Bregma [50]. Within striatum, we targeted the dorsomedial subregion. Mean grey values were averaged across hemispheres and brain slices, resulting in a single data point per animal for each subregion.

Statistical analysis.

Learning-specific changes in IEG expression were evaluated by single-factor design (one-way ANOVA) in which the main effect of day of treatment (i.e., 2 days, 3 days or 30 days) on IEG expression was independently evaluated in MWM trained mice and free-swimming controls. Fisher’s LSD post-hoc tests were used for pairwise comparisons. For each subregion, learning-specific changes in IEG expression were defined as changes in IEG expression over the course of the training period. An unpaired t-test was used for the direct comparison of IEG expression present in trained and free-swimming control groups at different levels of the factor days of treatment. One-way between-groups ANOVA was used to test differences in IEG expression between the non-swimming caged control group and all experimental and control groups. Statistical analyses were performed using SigmaStat 3.1 (SYSTAT software). For all analysis, ∞ was set at 0.05 (*p < 0.05, **p < 0.01 and ***p < 0.001).

Results

Experiment 1

Brain lesion location.

Fig 1 shows coronal brain sections illustrating the extend of the electrolytically induced bilateral aCC (Fig 1A) and DMS (Fig 1B) lesions. ACC lesions extended between +0.98 mm and 0.00 mm, and +1.42 mm and -0.74 mm (AP to Bregma). DMS lesions extended between +1.18 mm and -0.34 mm, and +1.42 mm and -0.58 mm (AP to Bregma). Lesions were consistently between these coordinates, except for 2 animals of the DMS group, which were discarded from further analysis. Lesions in DMS were comparable in size and location with our previous work [33]. It should be noted that we aimed for comparable relative volumes of DMS and aCC damage, and that DMS lesions corresponded to the corticostriatal projection of aCC (based on Allen Mouse Brain Connectivity Atlas, available: http://connectivity.brain-map.org).

Download:

Fig 1. Histological verification and lesion size determination.

Representative coronal sections of bilateral (A) aCC and (B) DMS lesions. The left, middle and right pictures represent small, big and sham control lesions, respectively. All lesions were restricted to the region of interest. The bilateral lesions are encircled by a dotted line.

https://doi.org/10.1371/journal.pone.0176295.g001

Additional behavioural assessment.

Motor capacity and balance were tested on an accelerating rotarod device. All animals learned to stay longer on the rod (RM-ANOVA, trial, F_{3, 87} = 7.28, p < 0.001) and no differences were found between lesion groups (trial x group, F_{96, 87} = 0.12, p = 0.99; group, F_{2, 29} = 2.50, p = 0.10; Fig 2A), indicating that neither aCC, nor DMS lesions caused major motor impairment. Spatial working memory was assessed by spontaneous alternation (SA) in the Y-maze. No differences in % SA were observed, which shows that spatial working memory was not impaired in the different lesion groups (ANOVA, F_{2, 29} = 0.11, p = 0.89; Fig 2B). Furthermore, no differences in total number of arm entries were reported between the different lesion groups (group, F_{2, 29} = 0.94, p = 0.40; Fig 2C), suggesting unchanged spontaneous locomotor activity.

Download:

Fig 2. Motor performance and working memory.

(A) Average fall latencies over 4 consecutive trials on the accelerated rotarod in aCC- (n = 11; white bar), DMS- (n = 9; black bar) and sham control-lesioned mice (n = 12; grey bar) indicated no motor deficits. (B) Percentage of spontaneous alternations in the y-maze showed no differences in spatial working memory between the groups, and (C) total number of arm entries showed unchanged spontaneous locomotor activity.

https://doi.org/10.1371/journal.pone.0176295.g002

MWM learning in lesioned mice.

The effects of lesions in aCC and DMS on spatial memory were assessed over 15 training days in the hidden platform MWM. Over the course of training, significant differences in latency decline were observed between the different groups (latency, Fig 3A, RM-ANOVA, day x group, F_{28, 406} = 4.90, p < 0.001; day, F_{14, 406} = 37.48, p < 0.001; group, F_{2, 29} = 14.86, p < 0.001). Post-hoc analysis revealed that DMS-lesioned mice required significantly more time to locate the hidden platform from day 3 onwards compared to the sham lesion group (all p-values < 0.01), and that this difference increased as training progressed (day 15; p < 0.001). All animals reached asymptotic performance around 10–15 days (day, F_{4, 116} = 0.16, p = 0.96; group, F_{2, 29} = 15.34, p < 0.001; day x group, F_{8, 116} = 1.40, p = 0.21), therefore training was limited to 15 days. Consequent to the main group and interaction effects, we compared both lesion groups against the control group. First, we found that no learning occurred in the DMS group over 15 days of acquisition training (day, F_{14, 112} = 1.11, p = 0.36). On the other hand, when we compared the latency curves of animals with aCC lesions and sham control animals, we did observe differences in latency curves between those groups. Both aCC-lesioned animals and sham controls were able to learn the position of the hidden platform, whereas animals with damage in aCC needed more time to do so (day, F_{14, 294} = 62.53, p < 0.001; group, F_{1, 21} = 5.33, p < 0.05; day x group, F_{14, 294} = 0.77, p = 0.70).

Download:

Fig 3. Spatial learning in the Morris water maze test.

Animals with lesions in DMS (n = 9; filled circles, black bar) (A) required significantly more time to locate the hidden platform, (B) spent more time along the walls of the pool, (C) searched further away from the location of the hidden, and (D and E) swam slower from the second day of training compared to sham control group (n = 12; filled squares, grey bar). No overall differences between aCC (n = 11; empty circles, white bar) and sham control group were reported. Represented data are expressed as means ±SEM. * indicates significant differences between the sham control and lesion groups: *p < 0.05, **p < 0.01, ***p < 0.001.

https://doi.org/10.1371/journal.pone.0176295.g003

The significant interaction effect of thigmotaxis indicated that not all animals showed a typical decrease in time spent along the wall of the pool, as training progressed (Fig 3B, day x group, F_{28, 406} = 8.08, p < 0.001; day, F_{14, 406} = 13.89, p < 0.001; group, F_{2, 29} = 15.15, p < 0.001). Post-hoc comparison between groups indicated that animals in the DMS group spent overall more time along the wall of the pool compared to animals of the other groups (all p-values < 0.001), which differed significant from sham controls from day 2 onwards (p < 0.05). Again in pairwise comparison, we found that the aCC group also showed more thigmotaxic behaviour compared to sham animals (day, F_{14, 294} = 38.39, p < 0.001; group, F_{1, 21} = 4.92, p < 0.05; day x group, F_{14, 294} = 0.46, p = 0.95).

Not all animals learned to focus their searching more accurately on the platform location (distance to platform, Fig 3C, day x group, F_{28, 406} = 5.04, p < 0.001; day, F_{14, 406} = 29.30, p < 0.001; group, F_{2, 29} = 14.00, p < 0.001). The DMS group consistently searched farther from the location of the hidden platform compared to the other groups (post-hoc, all p-values < 0.01). This difference was significant compared to the sham control group from day 4 onwards (p < 0.05). Pairwise comparisons revealed that animals with aCC lesions on the other hand were swimming closer to the platform location compared to sham control animals (day, F_{14, 294} = 45.19, p < 0.001; group, F_{1, 21} = 5.56, p < 0.05; day x group, F_{14, 294} = 0.35, p = 0.99).

The ability of animals to locate the hidden platform significantly affected their swimming speed (day x group, F_{28, 406} = 6.46, p < 0.001; velocity, Fig 3D, day, F_{14, 406} = 5.78, p < 0.001; group, F_{2, 29} = 18.74, p < 0.001). Animals in the aCC and sham control group significantly increased their swimming speed by the end of training, whereas swimming speed in the DMS group decreased (day 1 compared to day 15, all p-values < 0.05). Note that the difference in velocity between DMS and sham control group was only present from day 2 (p < 0.05). No differences in swimming speed were present between aCC-lesioned and sham control animals when removing the DMS group from analysis (day, F_{14, 294} = 12.22, p < 0.001; group, F_{1, 21} = 0.33, p = 0.57; day x group, F_{14, 294} = 1.05, p = 0.40). We also calculated the mean velocity of the animals in 10 s time bins to evaluate swimming speed throughout the acquisition trials (Fig 3E). Complete data sets were analysed for the first three days only. These analyses further confirmed that all animals showed comparable swimming during the first day of acquisition training (time bin, F_{11, 319} = 32.58, p < 0.001; group, F_{2, 29} = 3.27, p = 0.05; time bin x lesion, F_{22, 319} = 1.08, p = 0.37; Fig 3E, left). Differences between DMS-lesioned animals and the other groups only arise from day 2 (Fig 3E, middle). All animals started trial swimming comparably (time bin, F_{11, 319} = 17.88, p < 0.001; group, F_{2, 29} = 7.09, p < 0.01; time bin x lesion, F_{22, 319} = 1.56, p > 0.05), although animals with DMS lesions remained slower throughout the rest of the trial compared to the sham control group (post-hoc, p < 0.01). This difference was even bigger from day 3 onward (time bin, F_{11, 319} = 14.45, p < 0.001; group, F_{2, 29} = 8.70, p ≤ 0.001; time bin x lesion, F_{22, 319} = 1.31, p = 0.16), supporting the overall decrease in swimming speed that was found after lesions in DMS.

Swimming paths of the animals during acquisition training were categorized according to 3 main search strategies (Table 1; spatial, non-spatial or repetitive). On day 5, aCC- (48%) and DMS-lesioned animals (53%) displayed a preference for repetitive looping, whereas sham control animals rather mixed the 3 main search strategies. By day 10, both the aCC (75%) and sham (75%) group developed a preference for spatial search strategies to locate the hidden platform, which further increased to 85% at the end of training for sham animals, but even dropped slightly (to 66%) in animals with aCC damage. Animals with DMS lesions on the other hand completely failed to display such a preference (D10 = 22%; D15 = 25%) and continued to rely on repetitive looping strategies (D10 = 67%; D15 = 61%). More specifically, DMS-lesioned animals primarily relied on peripheral looping strategies (Table 2; 64%) on the first day of training and progressed to circling strategies after the first week of training (D5 = 39%; D10 = 51%; D15 = 53%).

Download:

Table 1. Search strategies to locate the hidden platform.

https://doi.org/10.1371/journal.pone.0176295.t001

Download:

Table 2. Development of repetitive looping strategies over the course of training.

https://doi.org/10.1371/journal.pone.0176295.t002

Next, we examined how spatial strategy use changed with time. When comparing the results of day 5, 10 and 15 (Fig 4), deployment of spatial search strategies over the course of training differed between lesion groups (Fig 4A; day x group, F_{4, 58} = 3.47, p < 0.05; group, F_{2, 29} = 5.46, p < 0.05; day, F_{2, 58} = 29.57, p < 0.001). No differences were present between aCC or sham controls at any time point (all p-values ≥ 0.26), whereas the DMS group used significantly less spatial strategies compared to the other groups from day 10 (all p-values < 0.001). In fact, spatial strategy use in the DMS group increased slightly, but non-significantly (p = 0.14) from 11% on day 5 to 25% on day 15, further confirming that this group of animals was severely impaired in their ability to learn to use spatial information to locate the hidden platform. Logically, deployment of spatial strategies coincided with decreasing use of non-spatial (group, F_{2, 29} = 0.004, p = 0.99; day, F_{2, 58} = 5.67, p < 0.01; day x group, F_{4, 58} = 1.15, p = 0.34; Fig 4B) and repetitive strategies (day x group, F_{4, 58} = 10.30, p < 0.001; group, F_{2, 29} = 14.08, p < 0.001; day, F_{2, 58} = 10.29, p < 0.001; Fig 4C). Except in the DMS group, which continued to rely predominantly on repetitive looping strategies compared to aCC and sham controls (all p-values < 0.05).

Download:

Fig 4. Employment of search strategies to locate the hidden platform.

The strategy to search for the hidden platform location changed over the course of training and differed between lesion groups. The DMS group (black bar) failed to deploy a preference for the spatial search strategy, and instead relied on a repetitive search strategy. Represented data are expressed as means ±SEM. * indicates significant differences between the sham control and lesion groups: *p < 0.05, **p < 0.01, ***p < 0.001.

https://doi.org/10.1371/journal.pone.0176295.g004

After each 5 days of acquisition training, spatial accuracy was studied by removing the platform from the pool during probe trials (Fig 5A). The first probe trial did not show differences between groups for time spent in the target quadrant (ANOVA, F_{2, 29} = 1.11, p = 0.34; Fig 5A), and student’s t-test (Bonferroni correction) indicated that none of the groups spent significantly more than 25% of the time (i.e., chance level) in this quadrant (all p-values > 0.34). Differences between the different groups were present from the second probe trial (probe 2, F_{2, 29} = 5.88, p < 0.01; probe 3, F_{2, 29} = 8.07, p < 0.01). Post-hoc analysis indicated that the DMS group spent less time in the target quadrant compared to the aCC and sham control group in both probe trials (all p-values < 0.01). Animals with lesions in the DMS also failed to spend more than 25% search time in this quadrant (probe 2, t₈ = -1.14, p = 0.29), even after 15 days of training (probe 3, t₈ = -.93, p = 0.38). Furthermore, swimming speed during probe trials differed between lesion groups from the first probe trial onwards (probe 1, F_{2, 29} = 17.62, p < 0.001; probe 2, F_{2, 29} = 16.26, p < 0.001; probe 3, F_{2, 29} = 18,72, p < 0.001; Fig 5B), verifying that animals with lesions in DMS overall showed slower swimming speed compared to aCC and sham controls (all p-values < 0.001).

Download:

Fig 5. Probe trial performance in the Morris water maze test.

(A) No preference for the target quadrant was present during the first probe trial (compared to 25% chance level, dotted line). During the second probe trial all groups, except animals with DMS lesions (n = 9; black bar), spent significantly more time in the target quadrant, which increased even more during the third probe trial for the sham control (n = 12; grey bars) and aCC lesion group (n = 11; white bars). (B) Animals with DMS lesions showed slower swimming speed during all probe trials. Represented data are expressed as means ±SEM. * indicates significant target preference compared to chance level and significant differences between the sham control and lesion groups: *p < 0.05, **p < 0.01, ***p < 0.001.

https://doi.org/10.1371/journal.pone.0176295.g005

Experiment 2

MWM learning in WT mice.

All animals were trained for 2, 3 or 30 days. Obviously, MWM performance in the 2-day group (2d_T) was characterized by an unfocused search pattern that covered the entire search area (Fig 6A), whereas the search pattern of the 3-day group (3d_T) was more goal-directed, but still variable. In contrast, the search pattern of the 30-day (30d_T) extensively trained group was highly focused on the hidden platform location. Similar to experiment 1, latency (Fig 6A) decreased during the early learning phase between day 1 and 2 in the 2-day group (t_1,6 = 3.97, p < 0.01) and between day 1 and 3 in the 3-day group (F_2,12 = 4.06, p < 0.05). Furthermore, time to locate the hidden platform decreased significantly over the course of training (day, F_29,203 = 33.82, p < 0.001), reaching asymptotic performance around 10–15 days. Comparing between the training groups showed that the 30-day group obviously performed much better than the 2-day and 3-day groups (F_2,19 = 27.24, p < 0.001), whereas no significant change occurred between 2 and 3 days. These results confirm that 2-day and 3-day trained groups represent the flexible, early learning phase, whereas the 30-day group reflects more stable performance typical of the habitual phase.

Download:

Fig 6. Learning-dependent changes in Arc expression.

(A) Learning curves, demonstrating task acquisition as a decrease in latency (s) before reaching the hidden platform, of 2 days (2d_T), 3 days (3d_T) and 30 days (30d_T) trained mice are light grey, medium grey and black, respectively. Heat plots (blue and red indicating minimum and maximum search time spent, respectively) during early learning in the 2d_T and 3d_T group indicate that the overall search area remains variable and covers most of the environment with some focus towards the platform quadrant after 3 days of learning. During the late learning phase search patterns in the 30d_T group are highly focused on the hidden platform. Black circle represents the hidden platform. (B) Coronal sections displaying Arc expression in trained mice during early learning (2d_T and 3d_T) and late learning (30d_T) group. The left hemisphere shows the original autoradiogram in grey scale and the right hemisphere shows its matched pseudo-colour counterpart (dark green to white indicating no signal to maximum signal, respectively). (C) Arc expression in aCC increased from 2-day to 3-day trained group and decreased upon overtraining. In free-swimming controls, a reduction in Arc expression was present from the early to the late phase. At 3 days, trained mice demonstrated significantly higher Arc expression compared to free-swimming controls. (D) During the early learning phase Arc expression in DMS of trained mice was upregulated, albeit borderline significant, while extensive training resulted in a decreased Arc expression level. Free-swimming controls only showed a reduction in Arc expression from the early to the late phase. At 3 days, Arc expression in trained mice was significantly higher than in free-swimming controls. Dotted line represents baseline Arc expression level of cage control animals. Black and grey bars represent experimental (trained, T) and free-swimming control (SC) groups, respectively. Represented data are expressed as means ±SEM. * indicates significant differences between groups: **p < 0.01, ***p < 0.001.

https://doi.org/10.1371/journal.pone.0176295.g006

Arc immediate early gene expression.

Arc expression in aCC (T, F_2,18 = 12.46, p < 0.001; SC, F_2,9 = 40.20, p < 0.001; Fig 6B) and DMS (T, F_2,18 = 7.55, p < 0.01; SC, F_2,9 = 7.23, p < 0.05; Fig 6C) was upregulated above the non-trained baseline level. Arc expression in aCC (Fig 6B and 6C) showed a significant increase in 2- and 3-days trained mice (p < 0.01) that was absent in free-swimming controls (post-hoc test, 2d_SC vs 3d_SC, p = 0.12), resulting in significantly higher Arc expression in trained compared to free-swimming mice at 3 days (t_1,9 = 5.16, p < 0.001). Arc expression declined in the late phase in both trained mice (post-hoc test, 3d_T vs 30d_T, p < 0.001) and free-swimming controls (post-hoc tests, 2d_SC vs 30d_SC and 3d_SC vs 30d_SC, p < 0.001).

Similarly, Arc expression in DMS (Fig 6B and 6D) was upregulated in trained groups during the early learning phase (post-hoc test, 2d_T vs 3d_T, p = 0.07), but not in the free-swimming controls (post-hoc test, 2d_SC vs 3d_SC, p = 0.13). As a result, Arc expression at 3 days was significantly higher in trained compared to free-swimming animals (t_1,9 = 4.91, p < 0.001). Following extended training, both trained (post-hoc tests, 2d_T vs 30d_T, p < 0.01; 3d_T vs 30d_T, p = 0.06) and free-swimming mice (post-hoc test, 2d_SC vs 30d_SC, p < 0.01) displayed a decrease in Arc expression.

Discussion

We examined the involvement of aCC and DMS in spatial cognition, employing the MWM task that has been the dominant paradigm in rodents for over three decades [2–4,9]. Although this test is mostly viewed as a hippocampus-dependent task [1,57], other telencephalic regions such as mPFC and dorsal striatum (part of a functional corticostriatal system) also appear to play a crucial, but less understood role in this type of learning [10]. To compare aCC and DMS involvement directly, we performed electrolytic lesions that selectively targeted these brain regions, and demonstrated that DMS lesions severely impaired MWM performance. DMS-lesioned animals displayed impaired learning, increased thigmotaxis, and decreased search precision. Also, these animals failed to show preference for the target quadrant relative to the other quadrants during the probe trials.

Even though dorsal striatum has been classically related to motor control [58], it seems unlikely that the effects of DMS lesions observed here were essentially caused by sensorimotor impairment. Firstly, several studies reported that sensorimotor functions were affected by lesions in dorsolateral striatum (DLS), rather than DMS [5,6,10,59,60]. Furthermore, our results demonstrate that DMS lesions did not affect spontaneous locomotor activity as measured in the Y-maze, nor affected motor coordination and equilibrium in the rotarod task. They even showed a tendency to stay longer on the rotating rod compared to sham controls. Moreover, there was no difference in swimming speed in DMS-lesioned mice during the first day of MWM training. Reduced swimming speed at a later phase might have resulted from their inability to find the platform, and ensuing extinction of active escape strategies.

In accordance with our previous findings [33], animals with DMS lesions showed increased thigmotaxis and more dispersed searching for the hidden platform. Devan and colleagues [5] also reported thigmotaxis in rats, but only during early trials, whereas Lee and colleagues [32] failed to find thigmotaxic swimming in mice. We, on the other hand, observed thigmotaxis during the entire course of training, possibly due to the fact that, in our study, the medial part of the striatum was specifically targeted. Thigmotaxis has been implicated as a symptom of altered search strategy and impaired goal-directed learning [5,33]. Indeed, our search strategy analyses confirm that DMS-lesioned mice were unable to employ goal-directed learning strategies, and relied predominantly on repetitive looping instead of spatial searching. Consequently, these animals failed to display preference for the platform position during the probe trials, even after 15 days of training.

On the other hand, aCC damage affected spatial cognition more subtly, but notwithstanding conflicting literature reports, we observed significantly impaired spatial cognition in aCC-lesioned mice using detailed behavioural analysis of MWM performance. We observed slightly slower learning curves, increased thigmotaxis, and decreased search precision in this group compared to controls. Search strategy analyses were consistently inflexible and erratic deployment of goal-directed spatial search strategies in these mice. Whilst sham controls flexibly switched between the different strategies, aCC animals preferred more repetitive strategies during early learning. Furthermore, although the aCC group showed a preference for spatial search strategies from the second week, they were less able than control animals to maintain stable use of these strategies. These detailed analyses qualify previous lesion studies that failed to report aCC involvement in spatial task acquisition [24–26]. We therefore conclude that both DMS and aCC lesions impair the cognitively demanding deployment of spatial search strategies, although to a different extent.

In addition, elevated Arc expression in both aCC and DMS during early MWM learning indicate that both brain regions are involved in goal-directed, early spatial learning. Anatomical connectivity between these regions has been established (Allen Mouse Brain Connectivity Atlas, available: http://connectivity.brain-map.org), whereas our present observations suggest that these brain regions also play a joint role in spatial cognition. Consistent with our observation that aCC lesions affect MWM learning in a different way from DMS lesions, studies using different methodology indicated that this involvement might in fact be functionally dissociated. Indeed, aCC has been suggested to have a more general, executive function and several studies implicated this brain structure in effort-based decision making [61–64]. For example, it has been suggested that aCC controls cost-benefit decisions about the possible course of action. Consequently, Walton and Mars [65] observed a higher firing rate in aCC during actions that maximized overall reward gain by encoding a cumulative history of recent rewards. Such a function of aCC could definitely be convergent with our observations that aCC is important for deployment of efficient navigation strategies. During the early stages of spatial learning, mPFC areas such as aCC might be involved in cost-benefit choices between competing strategies. Spatial strategies are definitely the most cognitively effortful ones (high cost), but could deliver the most economical yield in terms of successful platform situation (high benefit), compared to strategies with lower success rates, which is also in accordance with the putative error prevention role of the aCC [66,67] and the role of mPFC in task difficulty [68].

The current study aimed to investigate the differential role of the anatomically connected aCC and DMS in early phases of learning and memory. Brain lesion experiments demonstrated impaired learning curves and inability to deploy spatial search strategies in mice with DMS damage in the absence of other major motor or working memory impairments, whereas aCC lesions had a more subtle effect. In addition, expression of the neuroplasticity-related, IEG Arc suggested the involvement of aCC and DMS in goal-directed, early phases of MWM learning. It must be noted that more studies are needed to further investigate the significance of aCC activation in relation to DMS. To determine if early spatial learning and memory depends on the integrity of circuits connecting aCC and DMS, one might investigate whether increased Arc expression in aCC is also observed in trained DMS-lesioned animals, or use disconnection lesions or reversible activation studies to disrupt communication between brain regions [69,70].

Supporting information

S1 Dataset. Dataset.

https://doi.org/10.1371/journal.pone.0176295.s001

(XLS)

Acknowledgments

This work was supported by an interdisciplinary research grant from the KU Leuven (IDO/06/004, https://admin.kuleuven.be/raden/en/research-council) and a GOA research program (to L.A. and R.D.). T.P. and A.L. were both supported by a PhD fellowship grant from the Flanders Agency for Innovation in Science and Technology (IWT, http://www.iwt.be). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors furthermore wish to thank Samme Vreysen for his help with artwork.

Author Contributions

Conceptualization: TP RD LA IG.
Data curation: TP.
Formal analysis: TP AL.
Funding acquisition: RD LA.
Investigation: TP AL.
Methodology: TP AL RD LA IG.
Project administration: RD LA.
Resources: RD LA.
Software: TP BV.
Supervision: RD LA.
Validation: TP BV.
Visualization: TP.
Writing – original draft: TP AL.
Writing – review & editing: TP RD AL LA.

References

1. Morris R. Developments of a water-maze procedure for studying spatial learning in the rat. J Neurosci Methods. 1984;11: 47–60. pmid:6471907
- View Article
- PubMed/NCBI
- Google Scholar
2. D’Hooge R, De Deyn PP. Applications of the Morris water maze in the study of learning and memory. Brain Res Rev. 2001;36: 60–90. pmid:11516773
- View Article
- PubMed/NCBI
- Google Scholar
3. Vorhees CV, Williams MT. Morris water maze: procedures for assessing spatial and related forms of learning and memory. Nat Protoc. 2006;1: 848–858. pmid:17406317
- View Article
- PubMed/NCBI
- Google Scholar
4. Vorhees CV, Williams MT. Value of water mazes for assessing spatial and egocentric learning and memory in rodents basic research and regulatory studies. Neurotoxicol Teratol. 2014;45: 75–90. pmid:25116937
- View Article
- PubMed/NCBI
- Google Scholar
5. Devan BD, McDonald RJ, White NM. Effects of medial and lateral caudate-putamen lesions on place- and cue guided behaviors in the water maze: relation to thigmotaxis. Behav Brain Res. 1999;100: 5–14. pmid:10212049
- View Article
- PubMed/NCBI
- Google Scholar
6. Devan BD, White NM. Parallel information processing in the dorsal striatum: relation to hippocampal function. J Neurosci. 1999;19: 2789–2798. pmid:10087090
- View Article
- PubMed/NCBI
- Google Scholar
7. Janus C. Search strategies used by APP transgenic mice during navigation in the Morris water maze. Learn Mem. 2004;11: 337–346. pmid:15169864
- View Article
- PubMed/NCBI
- Google Scholar
8. Stover KR, O’Leary TP, Brown RE. A computer-based application for rapid unbiased classification of swim paths in the Morris water maze. In: Spink AJ, Grieco F, Krips OE, Loijens LWS, Noldus LPJJ, Zimmerman PH, editors. Proceedings of Measuring Behavior. Eighth International Conference on Methods and Techniques in Behavioral Research; 2012. pp. 353–357.
9. Pooters T, Van der Jeugd A, Callaerts-Vegh Z, D’Hooge R. Telencephalic neurocircuitry and synaptic plasticity in rodent spatial learning and memory. Brain Res. 2015;1621: 294–308. pmid:25619550
- View Article
- PubMed/NCBI
- Google Scholar
10. Woolley DG, Laeremans A, Gantois I, Mantini D, Vermaercke B, Op de Beeck H, et al. Homologous involvement of striatum and prefrontal cortex in rodent and human water maze learning. Proc Natl Acad Sci U S A. 2013;110: 3131–3136. pmid:23382228
- View Article
- PubMed/NCBI
- Google Scholar
11. Furtado JC, Mazurak MF. Behavioral characterization of quinolinate-induced lesions of the medial striatum: Relevance for Huntington’s disease. Exp Neurol. 1996;138: 158–168. pmid:8593891
- View Article
- PubMed/NCBI
- Google Scholar
12. Whishaw IQ, Mittleman G, Bunch ST, Dunnett SB. Impairments in the acquisition, retention and selection of spatial navigation strategies after medial caudate-putamen lesions in rats. Behav Brain Res. 1987;24: 125–138. pmid:3593524
- View Article
- PubMed/NCBI
- Google Scholar
13. Mura A, Feldon J. Spatial learning in rats is impaired after degeneration of the nigrostriatal dopaminergic system. Mov Disord. 2003;18: 860–871. pmid:12889075
- View Article
- PubMed/NCBI
- Google Scholar
14. Piccart E, Gantois I, Laeremans A, de Hoogt R, Meert T, Vanhoof G, et al. Impaired appetitively as well as aversively motivated behaviors and learning in PDE10A-deficient mice suggest a role for striatal signaling in evaluative salience attribution. Neurobiol Learn Mem. 2011;95: 260–269. pmid:21130175
- View Article
- PubMed/NCBI
- Google Scholar
15. Zhu XR, Maskri L, Herold C, Bader V, Stichel CC, Güntürkün O, et al. Non-motor behavioural impairments in parkin-deficient mice. Eur J Neurosci. 2007;26: 1902–1911. pmid:17883413
- View Article
- PubMed/NCBI
- Google Scholar
16. Devinsky O, Morrell MJ, Voght BA. Contributions of anterior cingulate cortex to behaviour. Brain. 1995;118: 279–306. pmid:7895011
- View Article
- PubMed/NCBI
- Google Scholar
17. Heidbreder CA, Groenewegen HJ. The medial prefrontal cortex in the rat: evidence for a dorso-ventral distinction based upon functional and anatomical characteristics. Neurosci Biobehav Rev. 2003;27: 555–579. pmid:14599436
- View Article
- PubMed/NCBI
- Google Scholar
18. Reep RL, Cheatwood JL, Corwin JV. The associative striatum: organization of cortical projection to the dorsocentral striatum in rats. J Comp Neurol. 2003;467: 271–292. pmid:14608594
- View Article
- PubMed/NCBI
- Google Scholar
19. White NM. Some highlights of research on the effects of caudate nucleus lesions over the past 200 years. Behav Brain Res. 2009;199: 3–23. pmid:19111791
- View Article
- PubMed/NCBI
- Google Scholar
20. Van De Werd HJ, Rajkowska G, Evers P, Uylings HB. Cytoarchitectonic and chemoarchitectonic characterization of the prefrontal cortical areas in the mouse. Brain Struct Funct. 2010;4: 339–353.
- View Article
- Google Scholar
21. Pan WX, Mao T, Dudman JT. Inputs to the dorsal striatum of the mouse reflect the parallel circuit architecture of the forebrain. Front Neuroanat. 2010; 4: 147. pmid:21212837
- View Article
- PubMed/NCBI
- Google Scholar
22. Oh SW, Harris JA, Ng L, Winslow B, Cain N, Mihalas S, et al. A mesoscale connectome of the mouse brain. Nature. 2014;508: 207–214. pmid:24695228
- View Article
- PubMed/NCBI
- Google Scholar
23. Lopez J, Herbeaux K, Cosquer B, Engeln M, Muller C, Lazarus C, et al. Context-dependent modulation of hippocampal and cortical recruitment during remote spatial memory retrieval. Hippocampus. 2012;22: 827–841. pmid:21542054
- View Article
- PubMed/NCBI
- Google Scholar
24. Teixeira CM, Pomedli SR, Maei HR, Kee N, Frankland PW. Involvement of the anterior cingulate cortex in the expression of remote spatial memory. J Neurosci. 2006;26: 7555–7564. pmid:16855083
- View Article
- PubMed/NCBI
- Google Scholar
25. St-Laurent M, Petrides M, Sziklas V. Does the cingulate cortex contributes to spatial conditional associative learning in the rat? Hippocampus. 2009;19: 612–622. pmid:19123251
- View Article
- PubMed/NCBI
- Google Scholar
26. Sutherland RJ, Whishaw IQ, Kolb B. Contributions of cingulate cortex and two forms of spatial learning and memory. J Neurosci. 1988;8: 1863–1872. pmid:3385478
- View Article
- PubMed/NCBI
- Google Scholar
27. Churchwell JC, Morris AM, Musso ND, Kesner R. Prefrontal and hippocampal contributions to encoding and retrieval of spatial memory. Neurobiol Learn Mem. 2010;93: 415–421. pmid:20074655
- View Article
- PubMed/NCBI
- Google Scholar
28. Deacon RM, Penny C, Rawlins JN. Effects of medial prefrontal cortex cytotoxic lesions in mice. Behav Brain Res. 2003;139: 139–155. pmid:12642185
- View Article
- PubMed/NCBI
- Google Scholar
29. de Bruin JP, Moita MP, de Brabander HM, Joosten RN. Place and response learning of rats in a Morris water maze: differential effects of fimbria fornix and medial prefrontal cortex lesions. Neurobiol Learn Mem. 2001;75: 164–178. pmid:11222058
- View Article
- PubMed/NCBI
- Google Scholar
30. Lacroix L, White I, Feldon J. Effect of excitotoxic lesions of rat medial prefrontal cortex on spatial memory. Behav Brain Res. 2002;133: 69–81. pmid:12048175
- View Article
- PubMed/NCBI
- Google Scholar
31. Di Filippo M, Picconi B, Tantucci M, Ghiglieri V, Bagetta V, Sgobio C, et al. Short-term and long-term plasticity at corticostriatal synapses: Implication for learning and memory. Behav Brain Res. 2009;199: 108–118. pmid:18948145
- View Article
- PubMed/NCBI
- Google Scholar
32. Lee AS, André JM, Pittenger C. Lesions of the dorsomedial striatum delay spatial learning and render cue-based navigation inflexible in a water maze task in mice. Front Behav Neurosci. 2014;8: 1–9.
- View Article
- Google Scholar
33. Pooters T, Gantois I, Vermaercke B, D’Hooge R. Inability to acquire spatial information and deploy spatial search strategies in mice with lesions in dorsomedial striatum. Behav Brain Res. 2016;298: 134–141. pmid:26548360
- View Article
- PubMed/NCBI
- Google Scholar
34. Lyford GL, Yamagata K, Kaufmann WE, Barnes CA, Sanders LK, Copeland NG, et al. Arc/Arg3.1, a growth factor and activity-regulated gene, encodes a novel cytoskeleton-associated protein that is enriched in neuronal dendrites. Neuron. 1995;14: 433–445. pmid:7857651
- View Article
- PubMed/NCBI
- Google Scholar
35. Link W, Konietzko U, Kauselmann G, Krug M, Schwanke B, Frey U, et al. Somatodendritic expression of an immediate early gene is regulated by synaptic activity. Proc Natl Acad Sci U S A. 1995;92: 5734–5738. pmid:7777577
- View Article
- PubMed/NCBI
- Google Scholar
36. Guzowski JF, Setlow B, Wagner EK, McGaugh JL. Experience-dependent gene expression in the rat hippocampus following spatial learning: a comparison of the immediate-early genes Arc, c-fos, and zif268. J Neurosci. 2001;21: 5089–5098. pmid:11438584
- View Article
- PubMed/NCBI
- Google Scholar
37. Vazdarjanova A, Ramirez-Amaya V, Insel N, Plummer TK, Rosi S, Chowdhury S, et al. Spatial exploration induces ARC, a plasticity-related immediate-early gene, only in calcium/calmodulin-dependent protein kinase II-positive principal excitatory and inhibitory neurons of the rat forebrain. J Comp Neurol. 2006;498: 317–329. pmid:16871537
- View Article
- PubMed/NCBI
- Google Scholar
38. Jansen RP. mRNA localization: Message on the move. Nat Rev Mol Cell Biol. 2001;2: 247–256. pmid:11283722
- View Article
- PubMed/NCBI
- Google Scholar
39. Moga DE, Calhoun ME, Chowdhury A, Worley P, Morrison JH, Shapiro ML. Activity-regulated cytoskeletal-associated protein is localized to recently activated excitatory synapses. Neuroscience. 2004;125: 7–11. pmid:15051140
- View Article
- PubMed/NCBI
- Google Scholar
40. Steward O, Wallace CS, Lyford GL, Worley PF. Synaptic activation causes the mRNA for the IEG Arc/Arg3.1 to localize selectively near activated postsynaptic sites on dendrites. Neuron. 1998;21: 741–751. pmid:9808461
- View Article
- PubMed/NCBI
- Google Scholar
41. Guzowski JF, Lyford GL, Stevenson GD, Houston FP, McGaugh JL, Worley PF, et al. Inhibition of activity-dependent Arc protein expression in the rat hippocampus impairs the maintenance of long-term potentiation and consolidation of long-term memory. J Neurosci. 2000;20: 3993–4001. pmid:10818134
- View Article
- PubMed/NCBI
- Google Scholar
42. Kubik S, Miyashita T, Guzowski JF. Using immediate-early genes to map hippocampal subregional functions. Learn Mem. 2007;14: 758–770. pmid:18007019
- View Article
- PubMed/NCBI
- Google Scholar
43. Robertson HA. Immediate-early genes, neuronal plasticity, and memory. Biochem Cell Biol. 1992;70: 729–737. pmid:1482550
- View Article
- PubMed/NCBI
- Google Scholar
44. Kaczmarek L. Molecular biology of vertebrate learning: is c-fos a new beginning? J. Neurosci Res. 1993;34: 377–381. pmid:8474140
- View Article
- PubMed/NCBI
- Google Scholar
45. Dragunow M. A role for immediate-early transcription factors in learning and memory. Behav Genet. 1996;26: 293–299. pmid:8754252
- View Article
- PubMed/NCBI
- Google Scholar
46. Tischmeyer W, Grimm R. Activation of immediate early genes and memory formation. Cell Mol Life Sci. 1999;55: 564–574. pmid:10357227
- View Article
- PubMed/NCBI
- Google Scholar
47. Korol DL, Malin EL, Borden KA, Busby RA, Couper-Leo J. Shifts in preferred learning strategy across the estrous cycle in female rats. Horm Behav. 2004;45: 330–338. pmid:15109907
- View Article
- PubMed/NCBI
- Google Scholar
48. Zurkovsky L, Brown SL, Korol DL. Estrogen modulates place learning through estrogen receptors in the hippocampus. Neurobiol Learn Mem. 2006;86: 336–343. pmid:16979357
- View Article
- PubMed/NCBI
- Google Scholar
49. Zurkovsky L, Brown SL, Boyd SE, Fell JA, Korol DL. Estrogen modulates learning in female rats by acting directly at distinct memory systems. Neuroscience. 2007;144: 26–37. pmid:17052857
- View Article
- PubMed/NCBI
- Google Scholar
50. Franklin KJ, Paxinos G. The mouse brain in stereotaxic coordinates. 3rd ed. San Diego: Academic Press; 2008.
51. Stroobants S, Gantois I, Pooters T, D’Hooge R. Increased gait variability in mice with small cerebellar cortex lesions and normal rotarod performance. Behav Brain Res. 2013;241: 32–37. pmid:23219967
- View Article
- PubMed/NCBI
- Google Scholar
52. Hiramatsu M, Sasaki M, Nabeshima T, Kameyama T. Effects of dynorphin A (1–13) on carbon monoxide-induced delayed amnesia in mice. Pharmacol Biochem Behav. 1997;56: 73–79. pmid:8981612
- View Article
- PubMed/NCBI
- Google Scholar
53. Sarter M, Bodewitz G, Stephens DN. Attenuation of scopolamine-induced impairment of spontaneous alternation behaviour by antagonist but not inverse agonist and agonist beta-carbolines. Psychopharmacology. 1988;94: 491–495. pmid:2836875
- View Article
- PubMed/NCBI
- Google Scholar
54. Hooper N, Fraser C, Stone TW. Effects of purine analogues on spontaneous alternation in mice. Psychopharmacology. 1996;123: 250–257. pmid:8833418
- View Article
- PubMed/NCBI
- Google Scholar
55. Van der Jeugd A, Vermaercke B, Derisbourg M, Lo AC, Hamdane M, Blum D, et al. Progressive age-related cognitive decline in tau mice. J Alzheimers Dis. 2013;37: 777–788. pmid:23948912
- View Article
- PubMed/NCBI
- Google Scholar
56. Laeremans A, Sabanov V, Ahmed T, Nys J, Van de Plas B, Vinken K, et al. Distinct and simultaneously active plasticity mechanisms in mouse hippocampus during different phases of Morris water maze training. Brain Struct Funct. 2015;220: 1273–1290. pmid:24562414
- View Article
- PubMed/NCBI
- Google Scholar
57. Moser E, Moser MB, Andersen P. Spatial learning impairments parallels the magnitude of dorsal hippocampal lesions, but is hardly present following ventral lesions. J Neurosci. 1993;13: 3916–3925. pmid:8366351
- View Article
- PubMed/NCBI
- Google Scholar
58. Graybiel AM. The basal ganglia, Trends Neurosci. 1995;18: 60–62. pmid:7537409
- View Article
- PubMed/NCBI
- Google Scholar
59. McDonald RJ, White NM. Parallel information processing in the water maze: Evidence for independent memory systems involving dorsal striatum and hippocampus. Behav Neural Biol. 1994;61: 260–270. pmid:8067981
- View Article
- PubMed/NCBI
- Google Scholar
60. Thorn CA, Atallah H, Howe M, Graybiel AM. Differential dynamics of activity changes in dorsolateral and dorsomedial striatal loops during learning. Neuron. 2010;66: 781–795. pmid:20547134
- View Article
- PubMed/NCBI
- Google Scholar
61. Walton ME, Bannerman DM, Rushworth MF. The role of rat medial prefrontal cortex in effort-based decision making. J Neurosci. 2002;22: 10996–11003. pmid:12486195
- View Article
- PubMed/NCBI
- Google Scholar
62. Walton ME, Bannerman DM, Alterescu K, Rushworth MF. Functional specialization within medial prefrontal cortex of the anterior cingulate for evaluating effort-related decisions. J Neurosci. 2003;23: 6475–6479. pmid:12878688
- View Article
- PubMed/NCBI
- Google Scholar
63. Hillman KL, Bilkey DK. Neurons in the rat anterior cingulate cortex dynamically encode cost-benefit in a spatial decision-making task. J Neurosci. 2010;30: 7705–7713. pmid:20519545
- View Article
- PubMed/NCBI
- Google Scholar
64. Khani A, Kermani M, Hesam S, Haghparast A, Argandona EG, Rainer G. Activation of cannabinoid system in anterior cingulate cortex and orbitofrontal cortex modulates cost-benefit decision making. Psychopharmacology. 2015;232: 2097–2112. pmid:25529106
- View Article
- PubMed/NCBI
- Google Scholar
65. Walton ME, Mars RB. Probing human and monkey anterior cingulate cortex in variable environments. Cogn Affect Behav Neurosci. 2007;7: 413–422. pmid:18189014
- View Article
- PubMed/NCBI
- Google Scholar
66. Carter CS, Botvinick MM, Cohen JH. The contribution of the anterior cingulate cortex to executive processes in cognition. Rev Neurosci. 1999;10: 49–57. pmid:10356991
- View Article
- PubMed/NCBI
- Google Scholar
67. Carter CS, Macdonald AM, Botvinick M, Ross LL, Stenger VA, Noll D, et al. Parsing executive processes: strategic vs. evaluative functions of the anterior cingulate cortex. Proc. Natl. Acad. Sci. U S A. 2000;97: 1944–1948. pmid:10677559
- View Article
- PubMed/NCBI
- Google Scholar
68. Hok V, Save E, Lenck-Santini PP, Poucet B. Coding for spatial goals in the prelimbic/infralimbic area of the rat frontal cortex. Proc Natl Acad Sci U S A. 2005;102: 4602–4607. pmid:15761059
- View Article
- PubMed/NCBI
- Google Scholar
69. Christakou A, Robbins TW, Everitt BJ. Functional disconnection of a prefrontal cortical-dorsal striatal system disrupts choice reaction time performance: implication for attentional function. Beh Neurosci. 2001;115: 812–825.
- View Article
- Google Scholar
70. Dunnett SB, Meldrum A, Muir JL. Frontal-striatal disconnection disrupts cognitive performance of the frontal-type in the rat. Neuroscience. 2005;135: 1055–1065. pmid:16165288
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Morris R. Developments of a water-maze procedure for studying spatial learning in the rat. J Neurosci Methods. 1984;11: 47–60. pmid:6471907
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. D’Hooge R, De Deyn PP. Applications of the Morris water maze in the study of learning and memory. Brain Res Rev. 2001;36: 60–90. pmid:11516773
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Vorhees CV, Williams MT. Morris water maze: procedures for assessing spatial and related forms of learning and memory. Nat Protoc. 2006;1: 848–858. pmid:17406317
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Vorhees CV, Williams MT. Value of water mazes for assessing spatial and egocentric learning and memory in rodents basic research and regulatory studies. Neurotoxicol Teratol. 2014;45: 75–90. pmid:25116937
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Devan BD, McDonald RJ, White NM. Effects of medial and lateral caudate-putamen lesions on place- and cue guided behaviors in the water maze: relation to thigmotaxis. Behav Brain Res. 1999;100: 5–14. pmid:10212049
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Devan BD, White NM. Parallel information processing in the dorsal striatum: relation to hippocampal function. J Neurosci. 1999;19: 2789–2798. pmid:10087090
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Janus C. Search strategies used by APP transgenic mice during navigation in the Morris water maze. Learn Mem. 2004;11: 337–346. pmid:15169864
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Stover KR, O’Leary TP, Brown RE. A computer-based application for rapid unbiased classification of swim paths in the Morris water maze. In: Spink AJ, Grieco F, Krips OE, Loijens LWS, Noldus LPJJ, Zimmerman PH, editors. Proceedings of Measuring Behavior. Eighth International Conference on Methods and Techniques in Behavioral Research; 2012. pp. 353–357.

[ref9] 9. Pooters T, Van der Jeugd A, Callaerts-Vegh Z, D’Hooge R. Telencephalic neurocircuitry and synaptic plasticity in rodent spatial learning and memory. Brain Res. 2015;1621: 294–308. pmid:25619550
View Article
PubMed/NCBI
Google Scholar

[31] View Article

[32] PubMed/NCBI

[33] Google Scholar

[ref10] 10. Woolley DG, Laeremans A, Gantois I, Mantini D, Vermaercke B, Op de Beeck H, et al. Homologous involvement of striatum and prefrontal cortex in rodent and human water maze learning. Proc Natl Acad Sci U S A. 2013;110: 3131–3136. pmid:23382228
View Article
PubMed/NCBI
Google Scholar

[35] View Article

[36] PubMed/NCBI

[37] Google Scholar

[ref11] 11. Furtado JC, Mazurak MF. Behavioral characterization of quinolinate-induced lesions of the medial striatum: Relevance for Huntington’s disease. Exp Neurol. 1996;138: 158–168. pmid:8593891
View Article
PubMed/NCBI
Google Scholar

[39] View Article

[40] PubMed/NCBI

[41] Google Scholar

[ref12] 12. Whishaw IQ, Mittleman G, Bunch ST, Dunnett SB. Impairments in the acquisition, retention and selection of spatial navigation strategies after medial caudate-putamen lesions in rats. Behav Brain Res. 1987;24: 125–138. pmid:3593524
View Article
PubMed/NCBI
Google Scholar

[43] View Article

[44] PubMed/NCBI

[45] Google Scholar

[ref13] 13. Mura A, Feldon J. Spatial learning in rats is impaired after degeneration of the nigrostriatal dopaminergic system. Mov Disord. 2003;18: 860–871. pmid:12889075
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref14] 14. Piccart E, Gantois I, Laeremans A, de Hoogt R, Meert T, Vanhoof G, et al. Impaired appetitively as well as aversively motivated behaviors and learning in PDE10A-deficient mice suggest a role for striatal signaling in evaluative salience attribution. Neurobiol Learn Mem. 2011;95: 260–269. pmid:21130175
View Article
PubMed/NCBI
Google Scholar

[51] View Article

[52] PubMed/NCBI

[53] Google Scholar

[ref15] 15. Zhu XR, Maskri L, Herold C, Bader V, Stichel CC, Güntürkün O, et al. Non-motor behavioural impairments in parkin-deficient mice. Eur J Neurosci. 2007;26: 1902–1911. pmid:17883413
View Article
PubMed/NCBI
Google Scholar

[55] View Article

[56] PubMed/NCBI

[57] Google Scholar

[ref16] 16. Devinsky O, Morrell MJ, Voght BA. Contributions of anterior cingulate cortex to behaviour. Brain. 1995;118: 279–306. pmid:7895011
View Article
PubMed/NCBI
Google Scholar

[59] View Article

[60] PubMed/NCBI

[61] Google Scholar

[ref17] 17. Heidbreder CA, Groenewegen HJ. The medial prefrontal cortex in the rat: evidence for a dorso-ventral distinction based upon functional and anatomical characteristics. Neurosci Biobehav Rev. 2003;27: 555–579. pmid:14599436
View Article
PubMed/NCBI
Google Scholar

[63] View Article

[64] PubMed/NCBI

[65] Google Scholar

[ref18] 18. Reep RL, Cheatwood JL, Corwin JV. The associative striatum: organization of cortical projection to the dorsocentral striatum in rats. J Comp Neurol. 2003;467: 271–292. pmid:14608594
View Article
PubMed/NCBI
Google Scholar

[67] View Article

[68] PubMed/NCBI

[69] Google Scholar

[ref19] 19. White NM. Some highlights of research on the effects of caudate nucleus lesions over the past 200 years. Behav Brain Res. 2009;199: 3–23. pmid:19111791
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref20] 20. Van De Werd HJ, Rajkowska G, Evers P, Uylings HB. Cytoarchitectonic and chemoarchitectonic characterization of the prefrontal cortical areas in the mouse. Brain Struct Funct. 2010;4: 339–353.
View Article
Google Scholar

[75] View Article

[76] Google Scholar

[ref21] 21. Pan WX, Mao T, Dudman JT. Inputs to the dorsal striatum of the mouse reflect the parallel circuit architecture of the forebrain. Front Neuroanat. 2010; 4: 147. pmid:21212837
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref22] 22. Oh SW, Harris JA, Ng L, Winslow B, Cain N, Mihalas S, et al. A mesoscale connectome of the mouse brain. Nature. 2014;508: 207–214. pmid:24695228
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref23] 23. Lopez J, Herbeaux K, Cosquer B, Engeln M, Muller C, Lazarus C, et al. Context-dependent modulation of hippocampal and cortical recruitment during remote spatial memory retrieval. Hippocampus. 2012;22: 827–841. pmid:21542054
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref24] 24. Teixeira CM, Pomedli SR, Maei HR, Kee N, Frankland PW. Involvement of the anterior cingulate cortex in the expression of remote spatial memory. J Neurosci. 2006;26: 7555–7564. pmid:16855083
View Article
PubMed/NCBI
Google Scholar

[90] View Article

[91] PubMed/NCBI

[92] Google Scholar

[ref25] 25. St-Laurent M, Petrides M, Sziklas V. Does the cingulate cortex contributes to spatial conditional associative learning in the rat? Hippocampus. 2009;19: 612–622. pmid:19123251
View Article
PubMed/NCBI
Google Scholar

[94] View Article

[95] PubMed/NCBI

[96] Google Scholar

[ref26] 26. Sutherland RJ, Whishaw IQ, Kolb B. Contributions of cingulate cortex and two forms of spatial learning and memory. J Neurosci. 1988;8: 1863–1872. pmid:3385478
View Article
PubMed/NCBI
Google Scholar

[98] View Article

[99] PubMed/NCBI

[100] Google Scholar

[ref27] 27. Churchwell JC, Morris AM, Musso ND, Kesner R. Prefrontal and hippocampal contributions to encoding and retrieval of spatial memory. Neurobiol Learn Mem. 2010;93: 415–421. pmid:20074655
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref28] 28. Deacon RM, Penny C, Rawlins JN. Effects of medial prefrontal cortex cytotoxic lesions in mice. Behav Brain Res. 2003;139: 139–155. pmid:12642185
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref29] 29. de Bruin JP, Moita MP, de Brabander HM, Joosten RN. Place and response learning of rats in a Morris water maze: differential effects of fimbria fornix and medial prefrontal cortex lesions. Neurobiol Learn Mem. 2001;75: 164–178. pmid:11222058
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref30] 30. Lacroix L, White I, Feldon J. Effect of excitotoxic lesions of rat medial prefrontal cortex on spatial memory. Behav Brain Res. 2002;133: 69–81. pmid:12048175
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref31] 31. Di Filippo M, Picconi B, Tantucci M, Ghiglieri V, Bagetta V, Sgobio C, et al. Short-term and long-term plasticity at corticostriatal synapses: Implication for learning and memory. Behav Brain Res. 2009;199: 108–118. pmid:18948145
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref32] 32. Lee AS, André JM, Pittenger C. Lesions of the dorsomedial striatum delay spatial learning and render cue-based navigation inflexible in a water maze task in mice. Front Behav Neurosci. 2014;8: 1–9.
View Article
Google Scholar

[122] View Article

[123] Google Scholar

[ref33] 33. Pooters T, Gantois I, Vermaercke B, D’Hooge R. Inability to acquire spatial information and deploy spatial search strategies in mice with lesions in dorsomedial striatum. Behav Brain Res. 2016;298: 134–141. pmid:26548360
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref34] 34. Lyford GL, Yamagata K, Kaufmann WE, Barnes CA, Sanders LK, Copeland NG, et al. Arc/Arg3.1, a growth factor and activity-regulated gene, encodes a novel cytoskeleton-associated protein that is enriched in neuronal dendrites. Neuron. 1995;14: 433–445. pmid:7857651
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref35] 35. Link W, Konietzko U, Kauselmann G, Krug M, Schwanke B, Frey U, et al. Somatodendritic expression of an immediate early gene is regulated by synaptic activity. Proc Natl Acad Sci U S A. 1995;92: 5734–5738. pmid:7777577
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref36] 36. Guzowski JF, Setlow B, Wagner EK, McGaugh JL. Experience-dependent gene expression in the rat hippocampus following spatial learning: a comparison of the immediate-early genes Arc, c-fos, and zif268. J Neurosci. 2001;21: 5089–5098. pmid:11438584
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref37] 37. Vazdarjanova A, Ramirez-Amaya V, Insel N, Plummer TK, Rosi S, Chowdhury S, et al. Spatial exploration induces ARC, a plasticity-related immediate-early gene, only in calcium/calmodulin-dependent protein kinase II-positive principal excitatory and inhibitory neurons of the rat forebrain. J Comp Neurol. 2006;498: 317–329. pmid:16871537
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref38] 38. Jansen RP. mRNA localization: Message on the move. Nat Rev Mol Cell Biol. 2001;2: 247–256. pmid:11283722
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref39] 39. Moga DE, Calhoun ME, Chowdhury A, Worley P, Morrison JH, Shapiro ML. Activity-regulated cytoskeletal-associated protein is localized to recently activated excitatory synapses. Neuroscience. 2004;125: 7–11. pmid:15051140
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref40] 40. Steward O, Wallace CS, Lyford GL, Worley PF. Synaptic activation causes the mRNA for the IEG Arc/Arg3.1 to localize selectively near activated postsynaptic sites on dendrites. Neuron. 1998;21: 741–751. pmid:9808461
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref41] 41. Guzowski JF, Lyford GL, Stevenson GD, Houston FP, McGaugh JL, Worley PF, et al. Inhibition of activity-dependent Arc protein expression in the rat hippocampus impairs the maintenance of long-term potentiation and consolidation of long-term memory. J Neurosci. 2000;20: 3993–4001. pmid:10818134
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref42] 42. Kubik S, Miyashita T, Guzowski JF. Using immediate-early genes to map hippocampal subregional functions. Learn Mem. 2007;14: 758–770. pmid:18007019
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref43] 43. Robertson HA. Immediate-early genes, neuronal plasticity, and memory. Biochem Cell Biol. 1992;70: 729–737. pmid:1482550
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref44] 44. Kaczmarek L. Molecular biology of vertebrate learning: is c-fos a new beginning? J. Neurosci Res. 1993;34: 377–381. pmid:8474140
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref45] 45. Dragunow M. A role for immediate-early transcription factors in learning and memory. Behav Genet. 1996;26: 293–299. pmid:8754252
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref46] 46. Tischmeyer W, Grimm R. Activation of immediate early genes and memory formation. Cell Mol Life Sci. 1999;55: 564–574. pmid:10357227
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref47] 47. Korol DL, Malin EL, Borden KA, Busby RA, Couper-Leo J. Shifts in preferred learning strategy across the estrous cycle in female rats. Horm Behav. 2004;45: 330–338. pmid:15109907
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref48] 48. Zurkovsky L, Brown SL, Korol DL. Estrogen modulates place learning through estrogen receptors in the hippocampus. Neurobiol Learn Mem. 2006;86: 336–343. pmid:16979357
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref49] 49. Zurkovsky L, Brown SL, Boyd SE, Fell JA, Korol DL. Estrogen modulates learning in female rats by acting directly at distinct memory systems. Neuroscience. 2007;144: 26–37. pmid:17052857
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

[ref50] 50. Franklin KJ, Paxinos G. The mouse brain in stereotaxic coordinates. 3rd ed. San Diego: Academic Press; 2008.

[ref51] 51. Stroobants S, Gantois I, Pooters T, D’Hooge R. Increased gait variability in mice with small cerebellar cortex lesions and normal rotarod performance. Behav Brain Res. 2013;241: 32–37. pmid:23219967
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref52] 52. Hiramatsu M, Sasaki M, Nabeshima T, Kameyama T. Effects of dynorphin A (1–13) on carbon monoxide-induced delayed amnesia in mice. Pharmacol Biochem Behav. 1997;56: 73–79. pmid:8981612
View Article
PubMed/NCBI
Google Scholar

[198] View Article

[199] PubMed/NCBI

[200] Google Scholar

[ref53] 53. Sarter M, Bodewitz G, Stephens DN. Attenuation of scopolamine-induced impairment of spontaneous alternation behaviour by antagonist but not inverse agonist and agonist beta-carbolines. Psychopharmacology. 1988;94: 491–495. pmid:2836875
View Article
PubMed/NCBI
Google Scholar

[202] View Article

[203] PubMed/NCBI

[204] Google Scholar

[ref54] 54. Hooper N, Fraser C, Stone TW. Effects of purine analogues on spontaneous alternation in mice. Psychopharmacology. 1996;123: 250–257. pmid:8833418
View Article
PubMed/NCBI
Google Scholar

[206] View Article

[207] PubMed/NCBI

[208] Google Scholar

[ref55] 55. Van der Jeugd A, Vermaercke B, Derisbourg M, Lo AC, Hamdane M, Blum D, et al. Progressive age-related cognitive decline in tau mice. J Alzheimers Dis. 2013;37: 777–788. pmid:23948912
View Article
PubMed/NCBI
Google Scholar

[210] View Article

[211] PubMed/NCBI

[212] Google Scholar

[ref56] 56. Laeremans A, Sabanov V, Ahmed T, Nys J, Van de Plas B, Vinken K, et al. Distinct and simultaneously active plasticity mechanisms in mouse hippocampus during different phases of Morris water maze training. Brain Struct Funct. 2015;220: 1273–1290. pmid:24562414
View Article
PubMed/NCBI
Google Scholar

[214] View Article

[215] PubMed/NCBI

[216] Google Scholar

[ref57] 57. Moser E, Moser MB, Andersen P. Spatial learning impairments parallels the magnitude of dorsal hippocampal lesions, but is hardly present following ventral lesions. J Neurosci. 1993;13: 3916–3925. pmid:8366351
View Article
PubMed/NCBI
Google Scholar

[218] View Article

[219] PubMed/NCBI

[220] Google Scholar

[ref58] 58. Graybiel AM. The basal ganglia, Trends Neurosci. 1995;18: 60–62. pmid:7537409
View Article
PubMed/NCBI
Google Scholar

[222] View Article

[223] PubMed/NCBI

[224] Google Scholar

[ref59] 59. McDonald RJ, White NM. Parallel information processing in the water maze: Evidence for independent memory systems involving dorsal striatum and hippocampus. Behav Neural Biol. 1994;61: 260–270. pmid:8067981
View Article
PubMed/NCBI
Google Scholar

[226] View Article

[227] PubMed/NCBI

[228] Google Scholar

[ref60] 60. Thorn CA, Atallah H, Howe M, Graybiel AM. Differential dynamics of activity changes in dorsolateral and dorsomedial striatal loops during learning. Neuron. 2010;66: 781–795. pmid:20547134
View Article
PubMed/NCBI
Google Scholar

[230] View Article

[231] PubMed/NCBI

[232] Google Scholar

[ref61] 61. Walton ME, Bannerman DM, Rushworth MF. The role of rat medial prefrontal cortex in effort-based decision making. J Neurosci. 2002;22: 10996–11003. pmid:12486195
View Article
PubMed/NCBI
Google Scholar

[234] View Article

[235] PubMed/NCBI

[236] Google Scholar

[ref62] 62. Walton ME, Bannerman DM, Alterescu K, Rushworth MF. Functional specialization within medial prefrontal cortex of the anterior cingulate for evaluating effort-related decisions. J Neurosci. 2003;23: 6475–6479. pmid:12878688
View Article
PubMed/NCBI
Google Scholar

[238] View Article

[239] PubMed/NCBI

[240] Google Scholar

[ref63] 63. Hillman KL, Bilkey DK. Neurons in the rat anterior cingulate cortex dynamically encode cost-benefit in a spatial decision-making task. J Neurosci. 2010;30: 7705–7713. pmid:20519545
View Article
PubMed/NCBI
Google Scholar

[242] View Article

[243] PubMed/NCBI

[244] Google Scholar

[ref64] 64. Khani A, Kermani M, Hesam S, Haghparast A, Argandona EG, Rainer G. Activation of cannabinoid system in anterior cingulate cortex and orbitofrontal cortex modulates cost-benefit decision making. Psychopharmacology. 2015;232: 2097–2112. pmid:25529106
View Article
PubMed/NCBI
Google Scholar

[246] View Article

[247] PubMed/NCBI

[248] Google Scholar

[ref65] 65. Walton ME, Mars RB. Probing human and monkey anterior cingulate cortex in variable environments. Cogn Affect Behav Neurosci. 2007;7: 413–422. pmid:18189014
View Article
PubMed/NCBI
Google Scholar

[250] View Article

[251] PubMed/NCBI

[252] Google Scholar

[ref66] 66. Carter CS, Botvinick MM, Cohen JH. The contribution of the anterior cingulate cortex to executive processes in cognition. Rev Neurosci. 1999;10: 49–57. pmid:10356991
View Article
PubMed/NCBI
Google Scholar

[254] View Article

[255] PubMed/NCBI

[256] Google Scholar

[ref67] 67. Carter CS, Macdonald AM, Botvinick M, Ross LL, Stenger VA, Noll D, et al. Parsing executive processes: strategic vs. evaluative functions of the anterior cingulate cortex. Proc. Natl. Acad. Sci. U S A. 2000;97: 1944–1948. pmid:10677559
View Article
PubMed/NCBI
Google Scholar

[258] View Article

[259] PubMed/NCBI

[260] Google Scholar

[ref68] 68. Hok V, Save E, Lenck-Santini PP, Poucet B. Coding for spatial goals in the prelimbic/infralimbic area of the rat frontal cortex. Proc Natl Acad Sci U S A. 2005;102: 4602–4607. pmid:15761059
View Article
PubMed/NCBI
Google Scholar

[262] View Article

[263] PubMed/NCBI

[264] Google Scholar

[ref69] 69. Christakou A, Robbins TW, Everitt BJ. Functional disconnection of a prefrontal cortical-dorsal striatal system disrupts choice reaction time performance: implication for attentional function. Beh Neurosci. 2001;115: 812–825.
View Article
Google Scholar

[266] View Article

[267] Google Scholar

[ref70] 70. Dunnett SB, Meldrum A, Muir JL. Frontal-striatal disconnection disrupts cognitive performance of the frontal-type in the rat. Neuroscience. 2005;135: 1055–1065. pmid:16165288
View Article
PubMed/NCBI
Google Scholar

[269] View Article

[270] PubMed/NCBI

[271] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Animals

Morris water maze protocol

Experiment 1: Effects of aCC and DMS lesions on MWM learning

Brain lesion procedure.

Additional behavioural assessment.

Morris water maze training.

Statistical analysis.

Experiment 2: DMS and aCC activation during MWM learning

Morris water maze training.

Quantitative in situ hybridization.

Statistical analysis.

Results

Experiment 1

Brain lesion location.

Additional behavioural assessment.

MWM learning in lesioned mice.

Experiment 2

MWM learning in WT mice.

Arc immediate early gene expression.

Discussion

Supporting information

S1 Dataset. Dataset.

Acknowledgments

Author Contributions

References