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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Arch Gen Psychiatry. Author manuscript; available in PMC 2010 July 6.
Published in final edited form as:
PMCID: PMC2897252
NIHMSID: NIHMS98590

A reverse translational study of dysfunctional exploration in psychiatric disorders: from mice to men

Abstract

Context

Bipolar mania and schizophrenia are recognized as separate disorders but share many commonalities, raising the question of whether they are in fact the same disorder on different ends of a continuum. The lack of distinct endophenotypes of bipolar mania and schizophrenia has complicated the development of animal models that are specific to these disorders. Exploration is fundamental to survival and is dysregulated in these two disorders. Although exploratory behavior in rodents has been widely studied, surprisingly little work has examined this critical function in humans.

Objective

We used a novel human open field paradigm, the human Behavioral Pattern Monitor (BPM), to quantify exploratory behavior of individuals with bipolar mania and schizophrenia and to identify distinctive phenotypes of these illnesses.

Design

Static group comparison.

Setting

Psychiatric hospital.

Participants

15 bipolar mania and 16 schizophrenia subjects were compared to 26 healthy volunteers in the human BPM. The effects of amphetamine, the selective dopamine transporter (DAT) inhibitor GBR12909, and genetic knockdown of the DAT were compared to controls in the mouse BPM.

Measures

The amount of motor activity, spatial patterns of activity, and exploration of novel stimuli were quantified in both the human and mouse BPMs.

Results

Bipolar manic subjects demonstrated a unique exploratory pattern, characterized by high motor activity and increased object exploration. Schizophrenia subjects did not show the expected habituation of motor activity. Selective genetic or pharmacological inhibition of the DAT matched the mania phenotype better than the “gold standard” model of mania (amphetamine).

Conclusion

These findings validate the human open field paradigm and identify defining characteristics of bipolar mania that are distinct from schizophrenia. This cross-species study of exploration calls into question an accepted animal model of mania and should help to develop more accurate human and animal models, which are essential to identify neurobiological underpinnings of neuropsychiatric disorders.

Traditionally, bipolar mania and schizophrenia were considered as unrelated conditions. More recent evidence indicates that these disorders share genetic1, symptom2, and epidemiological3 characteristics and that both respond to medications that act via blockade of dopamine receptors4. These similarities prompted some to suggest that bipolar mania and schizophrenia are one illness that varies along a continuum of mood and psychotic symptoms5.

Animal models are of great utility in understanding the neurobiological substrates of psychiatric conditions. The unique features of bipolar mania and schizophrenia have, however, been difficult to model, largely because the symptoms that reflect the cardinal features of these conditions are challenging to produce and measure in laboratory animals. The lack of distinctive animal models of bipolar mania has limited the ability to identify the neurobiological underpinnings and potential treatments for this condition6. Paradigms that can be used in both humans and animals are essential to elucidating the neurobiology of neuropsychiatric diseases. We recently developed and utilized a “reverse-translational”7 paradigm to characterize exploratory activity in humans that parallels our work using the rodent Behavioral Pattern Monitor (BPM). Exploratory behavior is the act of making the unknown known and is a fundamental function found among all living species8. Measures of exploratory motor activity in rodents in open fields have a rich tradition that has revealed the complexity of the behavior and its utility in fostering drug discovery efforts912. The BPM is an elaboration of the traditional rodent open field and provides measures of the three fundamental dimensions of exploratory behavior: the amount of motor activity; the sequential structure of this activity; and the exploration of novel stimuli1315. In rodents, the multivariate behavioral profiles assessed in the BPM have enabled us to distinguish and quantify effects of different pharmacological, neurobiological, and genetic manipulations1618. Multivariate assessment of motor activity has been used to identify optimal doses of medication for the treatment of neuropsychiatric disorders19. Nevertheless, surprisingly little experimental assessment of human exploratory behavior has been conducted (but see10,20). This paucity of research is particularly astounding given that abnormal exploratory behavior is characteristic of a number of major neuropsychiatric conditions, such as excessive activity observed in bipolar mania, and prominent inactivity and withdrawal as observed in schizophrenia. Furthermore, a growing literature suggests that overactivity, i.e., increased motor behavior, is a core criterion for manic states and as important as elevated and/or irritable mood for the diagnosis of mania21,22.

The aims of this study were to validate our human BPM (hBPM), establish quantifiable and distinct endophenotypes of bipolar mania and schizophrenia, and empirically validate corresponding animal models. Specifically, we tested acutely hospitalized bipolar manic and schizophrenia subjects and healthy volunteers in the hBPM to determine whether these psychiatric disorders exhibit distinctive signature patterns of exploratory behavior. In parallel, we tested potential candidate rodent models of these disorders in the mouse BPM (mBPM). Because amphetamine has been the ‘gold standard’ animal model of bipolar mania23,24 and has also been used to model aspects of schizophrenia2527 we examined the behavioral profile of mice treated with amphetamine. Furthermore, given that mutations and other abnormalities relevant to the function of the dopamine transporter (DAT) have been linked to both bipolar disorder2832 and schizophrenia30,31 and amphetamine preferentially inhibits the norepinephrine transporter33, we assessed the potential of genetic and selective pharmacological manipulations of the DAT to mimic the patterns exhibited by manic and/or schizophrenia subjects. Specifically, we assessed the behavioral profiles of mice tested in the mBPM after administration of the selective DAT inhibitor GBR12909 and in DAT knockdown mice (KD) having 90% reduced expression of the DAT34.

Methods

Humans

Acutely hospitalized subjects with either SCID (Structured Clinical Interview for DSM-IV) diagnosed DSM-IV Bipolar Disorder, Current Episode Manic (n=15,9 M), or Schizophrenia (n=16,10 M) between the ages of 18 to 55 participated in this study. Healthy volunteers who had never met criteria for an Axis I Disorder as determined by the SCID were recruited from the community (n=26,13 M). Participants were excluded if they had abused or been dependent on alcohol or substances within the past month, had a positive result on a urine toxicology screen, had a neurological condition, or had a condition that impaired motor functioning. Table 1 lists demographic and illness factors.

Table 1
Demographic and illness factors.

All bipolar mania and all but three schizophrenia subjects were prescribed psychotropic medication during the time of testing; the bipolar mania subjects were typically treated with a combination of mood-stabilizing and atypical antipsychotic medications while the schizophrenia subjects were prescribed an antipsychotic medication alone. The most common antipsychotic medication prescribed was risperidone, and the most common mood stabilizers prescribed were lithium and valproate.

After consenting to the study, the Brief Psychiatric Rating Scale (BPRS)35 and the Young Mania Rating Scale (YMRS)36 was administered to both patient groups. Subjects were then fitted with an ambulatory monitoring device in the form of a wearable upper-body garment that resembles a sleeveless vest37. Electrocardiogram (ECG) leads were placed on the subject’s sternum and connected to the LifeShirt for the measurement of cardiac parameters. A two-axis accelerometer is embedded in the fabric of the garment. Once the subject was fitted with the LifeShirt, s/he was directed into the hBPM and told that s/he would see the experimenter in a short period of time and to wait in the room. No other instructions were provided.

The hBPM is a 2.7 × 4.3 m room to which the subject has not been previously exposed. It contains several items of furniture placed along the periphery of the room, including a desk, small and large filing cabinets, and two sets of bookshelves. There is no chair in the room. Dispersed evenly on items of furniture are 11 small objects. These objects were chosen using the criteria that they be safe, colorful, tactile, manipulable, and likely to invite human exploration38. Subjects were left in the hBPM for 15 minutes. Subjects were monitored continuously by a digital video camera embedded in the ceiling; on two occasions a subject approached the door but was immediately met by the research team and asked to remain in the room.

Data in the hBPM reflect three sources of measurement: physiologic data, namely motor activity of the subject’s torso, using the accelerometer embedded in the LifeShirt device; x-y coordinates of the subject’s spatial location in the room, extracted from digital video recording; and experimenter scoring of the video recordings to count exploratory events such as interactions with objects, drawers, or window blinds. These measurements are the human analogs to the three dimensions of exploratory activity described in our rodent work1315.

Accelerometer data from the LifeShirt were sampled in digital units at 10 Hz, stored in an on-board PDA during data collection, and extracted and analyzed using Vivologic Software37. The number of acceleration movements was assessed for each 5-minute epoch. In addition, the continuous high-frequency sampling of motor activity data enabled the calculation of dynamical entropy, h, which measures the degree to which behavior is observed along a continuum between complete order and disorder39. To calculate h, a given sequence of activity is compared to similar preceding sequences, and this comparison is conducted for varying sequence lengths. Lower values of h (low entropy) suggest highly predictable or ordered sequences of motor activity across time, while higher values (high entropy) suggest a greater variety, or disorder, in the structure of motor activity across time.

To generate x-y coordinates, the digitized video images are stored at 30 frames per second (Figure 1) and subjected to frame-by-frame analysis with proprietary software40 that generates x-y-coordinates of the subject’s successive locations. The x-y coordinates can provide a measure of the amount of motor activity, quantified by counts, which are defined as the number of discrete instances of movement or the smallest measured change in x-y coordinates. From the x-y coordinates, the spatial scaling exponent d is also calculated. Spatial d measures the hierarchical and geometric organization of behavior. Specifically, d is based on the principles of fractal geometry and describes the degree to which the path taken within an enclosure by the subject is one-dimensional or two-dimensional. To obtain spatial d, the distance traveled is plotted against the number of counts using a double-logarithmic coordinate system, and a line of fit between these two variables is generated39,41. Spatial d typically varies between 1 (a straight line) and 2 (a filled plane), with values closer to 1 reflecting straight movements and values closer to 2 reflecting highly circumscribed, local movements. At both ends of this spectrum, the geometric pattern of movement around the BPM is highly predictable but exhibits either an almost straight-line movement or a highly circumscribed geometrical pattern, respectively42.

Figure 1
The human Behavioral Pattern Monitor and mouse Behavioral Pattern Monitor and sample x-y tracings.

In addition, the digitized video images enable detailed assessments of the subject’s interactions with the 11 objects placed in the room, in analogy to the rodent’s investigatory behavior directed toward the 11 holes placed in the walls and floors of the rodent BPM chambers. The number of discrete interactions with objects, drawers, or window blinds was hand-counted and recorded by experimenters who were blind to group membership. In many cases, several experimenters independently rated videotapes in order to ensure reliable ratings.

Statistics-Humans

Mean values for acceleration, entropy h, spatial d, counts, and object interactions were calculated for each of the three 5-minute epochs of the 15-minute hBPM session. Group differences on the hBPM measures were tested using a multivariate analysis of variance (MANOVA), with epoch as a repeated measure. The MANOVA was followed up by individual ANOVAs for each hBPM measure. Age and years of education were used as covariates in the ANOVAs, due to group differences in these variables. Pearson r correlation coefficients were used to test for relationships between illness-related factors and hBPM measures, and between symptom rating scores and hBPM variables. Items from the BPRS were clustered into five domains43: negative symptoms; depression/anxiety; hostility/uncooperativeness; positive symptoms; and mania. These five domains, as well as the YMRS total score, were entered into a discriminant function analysis to classify the schizophrenia and bipolar mania subjects according to diagnosis. A second discriminant analysis was then conducted using just hBPM measures.

Animals

DAT KD mice were generated by inserting embryonic stem cells of the 129 SvJ mouse strain in C57BL/6J blastocysts. One chimera was mated with 129 SvJ females to generate heterozygous mutants on a 129 SvJ genetic background34. The DAT KD breeders were originally sent to our laboratory from Columbia University. All subsequent mice were derived from breeding heterozygous mice at the vivarium at the University of California, San Diego (UCSD). The DAT KD and WT littermates assessed here were from the 11th generation. At the time of first testing, the mice were between 5–6 months old and weighed approximately 20–40 g. To assess the pharmacological models using amphetamine or GBR12909 to inhibit the DAT, C57BL/6J mice were bought and were tested at approximately 4 months old and weighed between 20–40 g.

All mice were housed in groups of maximum four per cage with a reversed day-night cycle (lights on at 8.00 PM, off at 8.00 AM) with unlimited access to water and food (Harlan, Madison, WI), except during testing. Before testing, all mice were brought to the testing room for an acclimation period of at least 60 minutes. Testing occurred between 9.00 AM and 6.00 PM. All procedures were approved by the Institutional Animal Care and Use Committee. Mice were maintained in an animal facility at UCSD that meets all federal and state requirements for animal care.

Drugs

d-amphetamine sulfate (Sigma, St Louis, MO) was dissolved in saline and injected at 5 ml/kg. GBR12909 dihydrochloride (Sigma, St Louis, MO) was dissolved in saline after sonication for 2–4 hours at 40°C. Due to the poor solubility of GBR12909 in saline, the injection volume was increased to 10 ml/kg. Drugs were administered by intraperitoneal injection.

Mouse Behavioral Pattern Monitor

Spontaneous locomotor and exploratory behavior was examined for 60 min (unless otherwise stated) as described previously using the mouse BPM17 (Figure 1). The mBPM consists of a 30.5 × 61 × 38 cm Plexiglas chamber equipped with 3 floor holes and 8 wall holes that serve as objects that engender frequent investigation by burrowing animals such as mice (Figure 1). The mouse’s location is monitored by a grid of 12 × 24 infrared photobeams 1 cm above the floor and recorded every 55 msec. Each hole is also equipped with an infrared photobeam to detect holepokes. Rearing behavior is detected by an array of 16 infrared photobeams placed 2.5 cm above the floor and aligned with the long axis of the chamber. All mice were habituated to the test chambers (see below) prior to drug administration. The primary measures of interest were activity (transitions and distance traveled), exploratory behavior (holepoking and rearing), and spatial d (described above).

Statistics-Animals

Data from each experiment were analyzed using two- or three- way ANOVAs, with sex, genotype, or treatment as between subject factors and time as a within subject factor. When no sex effect or interaction with sex was observed, data were collapsed across sex and reanalyzed. Significant main effects were analyzed using Tukey post hoc analyses. The alpha level was set to 0.05. Data were analyzed in 10-minute time bins using the Biomedical Data Programs software (Statistical Solutions Inc., Saugus, MA).

Mouse studies in the mBPM

DAT KD mice: Naïve male DAT KD (n=21) plus WT littermate mice (n=16) and female DAT KD (n=20) plus WT mice (n=14) were assessed in the mBPM for 40 min. These mice were bred inhouse from heterozygote matings.

Amphetamine

This study was conducted to determine a dose response effect of d-amphetamine in C57BL/6J male mice at 0 (n=6), 0.5 (n=7), 1.25 (n=7), 2.5 (n=7), 5 (n=7), and 10 (n=7) mg/kg.

GBR12909

A dose response study of GBR12909 was performed on C57BL/6J mice in the mBPM. Eighty male C57BL/6J mice were assessed in the BPM after acute administration of GBR12909 at 0 (n=14), 2.85 (n=14), 5 (n=13), 9 (n=13), 16 (n=13), or 28.5 mg/kg (n=13). The mice were drug naïve and had one previous mBPM exposure 10 weeks earlier.

Results-Humans

Although bipolar mania and schizophrenia groups did not differ on BPRS total scores [t(29)=1.4, ns], bipolar mania subjects had higher YMRS scores than schizophrenia subjects [t(29)=2.5, p<0.02]. The overall MANOVA comparing the three groups on all of the hBPM measures across all three epochs was significant [time × group interaction F(4,106)=3.3, p<0.01]. Examination of overall activity (acceleration) showed that bipolar mania subjects were more active than schizophrenia subjects initially and more active than healthy volunteers throughout the test [F(2,52)=7.6, p<0.001; time × group interaction F(4,104)=3.4, p<0.01] (Figure 2A). Moreover, the sequential accelerometer patterns of both bipolar mania and schizophrenia subjects were less predictable than those of healthy volunteers using a measure of dynamical entropy h39 [F(2,52)=6.2, p<0.05] (Figure 2B).

Figure 2
Bipolar mania (BD) and schizophrenia (SCZ) subjects compared to healthy volunteers (HV) in the hBPM. Acceleration (A), entropy h (B), counts (C), spatial d (D), and object interactions (E) are presented. Values represent mean ± standard error. ...

Reconstruction of each individual’s two-dimensional (x-y) movements using a video-tracking device confirmed that manic subjects moved around more than healthy volunteers early in the test session but also habituated more quickly over time. Healthy volunteers moved around the room at a relatively constant pace throughout the session, while schizophrenia subjects slightly increased their activity over the course of the session [time × group interaction F(4,104)=3.4, p<0.01] (Figure 2C). Relative to healthy volunteers, the spatial patterns of manic individuals were characterized by more direct straight paths, as evidenced by a decrease in the spatial scaling exponent d15,42[F(2,52)=5.1, p<0.01] (Figure 2D). Visual inspection and scoring of the video recordings revealed that bipolar mania subjects interacted more with objects in the room than either schizophrenia subjects or healthy volunteers throughout the entire session [F(2,52)=18.5, p<0.001] (Figure 2E). Whereas object interactions by manic subjects decreased as the hBPM session progressed, schizophrenia subjects and healthy volunteers interacted with objects in the room at a relatively constant but low level [time × group interaction F(4,104)=5.2, p<0.001] (Figure 2E).

Schizophrenia subjects with higher scores on the BPRS negative symptoms domain engaged in more predictable sequential movement patterns (r=−0.56, p<0.03), whereas schizophrenia subjects with higher scores on the BPRS manic symptoms domain (symptoms of excitability and agitation) engaged in more unpredictable movement patterns (r=0.69, p<0.003). These correlations were not significant for the manic subjects. Higher YMRS total scores were associated with increased object interactions (r=0.50, p<0.002). There were no relationships between hBPM measures and variables such as age of illness onset, duration of illness, and treatment duration.

To assess the potential impact of medications on the hBPM measures, we divided subjects based upon whether they were taking or not taking the most commonly prescribed medications of risperidone, valproate, and lithium. Subjects taking risperidone did not have significant differences in hBPM measures compared to subjects not taking risperidone [all t(29)<1.0, ns]; the same was true for lithium [all t(29)<1.0, ns]. Subjects taking valproate had significantly more object interactions than those not taking valproate [t(29)=1.5, p<0.02].

Finally, we conducted two discriminant function analyses to classify bipolar and schizophrenia subjects, one using symptom domain scores from the BPRS as well as YMRS total scores and the second using the hBPM measures (acceleration, entropy, spatial d, and object interactions). Symptom domain scores correctly classified bipolar subjects with a sensitivity of 73% and a specificity of 75% and correctly classified schizophrenia subjects with a sensitivity of 75% and a specificity of 73%. The hBPM measures correctly classified bipolar subjects with a sensitivity of 80% and a specificity of 75% and correctly classified schizophrenia subjects with a sensitivity of 75% and a specificity of 80%.

Results-Animals

Using the mBPM17, we tested C57BL/6J mice for one hour after administration of various doses of d-amphetamine. As expected41, amphetamine increased activity [F(5,34)=15.31, p<0.0001] and decreased spatial d [F(5,34)=4.27, p<0.005], but also significantly reduced hole interactions [F(5,34)=11.03, p<0.0001] and had no effect on rearings (Figure 3A–D). Thus, across a wide dose range, amphetamine treatment failed to mimic the most striking of the three dimensions of abnormal exploration seen in bipolar mania, exaggerated investigatory behavior. Since amphetamine acts at multiple monoamine transporters, we conducted an identical study of mice treated with a more specific DAT inhibitor, GBR12909. Across multiple doses, GBR12909 increased activity (F(5,67)=12.68, p<0.0001) and rearings [F(5,67)=2.73, p<0.05], while decreasing spatial d [F(5,67)=6.83, p<0.0001], with the most consistent effects being observed after 16 mg/kg (presented) (Figure 3A–D). Hole interactions were significantly affected [F(5,67)=3.17, p<0.05], due to a trend increase at 16 mg/kg and a reduction at the highest dose of GBR12909 (28.5 mg/kg) that was similar to the effect of all active doses of amphetamine and may not be selective for DAT over NET. This profile of behavior matches that seen in the bipolar group in that corresponding changes were seen in all three of the independent dimensions of exploratory behavior. To evaluate whether this profile of behavior seen after an acute inhibition of the DAT could be mimicked in mice having a genetically determined diminution of DAT, we tested DAT KD mice34. We previously reported that DAT KD mice exhibit motor hyperactivity in an open field that is attenuated by the mood stabilizer valproic acid18. Relative to wildtype littermates, DAT KD mice exhibited increases in activity [F(1,65)=29.31, p<0.0001], hole interactions (F(1,65)=5.65, p<0.05), a non-significant increase in rearings, and robust decreases in spatial d [F(1,67)=9.26, p<0.0001] (Figure 3A–D).

Figure 3
Mouse models in the mBPM: amphetamine (AMP); DAT KD (DAT); and GBR12909 (GBR) Activity levels (A), locomotor patterns (B), hole interactions (C) and rearing (D) behaviors are presented. Data are presented as mean + standard error for control (vehicle, ...

Comment

In the present study, we demonstrate that the hBPM can characterize exploration and motor activity in subjects with bipolar mania and can differentiate these subjects from schizophrenia patients. Additionally, the behavioral patterns exhibited by the bipolar mania or schizophrenia subjects in the hBPM paradigm clearly differed from those of healthy volunteers. Furthermore, each patient group exhibited a unique behavioral profile, quantitatively distinct from each other. The hBPM however, was considerably more effective in demonstrating behavioral abnormalities in mania than in schizophrenia subjects, relative to healthy volunteers. Motoric hyperactivity is widely expected as a feature of bipolar mania22,44,45. The present work extends and complements early reports on actigraphy in psychiatric populations46,47 and provides an objective quantification of hyperactivity in manic subjects. In addition, the hBPM revealed that bipolar mania subjects are characterized by abnormalities in all three main dimensions of exploratory behavior that have been described in rodent studies. Factor analyses converging with extensive pharmacological studies in rodents demonstrate that exploratory behavior is comprised of three independent domains: the amount of activity; the structure of the activity; and investigatory behavior15,41. The present study reveals that manic subjects exhibit not only increased levels of activity, but also abnormally straight and predictable patterns of activity, and excessive specific investigatory activity as reflected by object interactions. It appears that such investigatory exploration most sensitively differentiated individuals with bipolar disorder from the other groups. Specifically, manic subjects gathered up multiple objects and most picked up and wore a face-mask that was among the objects placed in the room, in contrast to the controls or schizophrenia patients. Thus, the hBPM provides an objective means to assess overt disinhibition, a hallmark of bipolar disorder, as the act of entering a novel environment and engaging with a stranger’s belongings reflects a failure of behavioral filtering processes that modulate appropriate social conduct. In contrast to this multidimensional profile of abnormalities in bipolar subjects, schizophrenia subjects exhibited normal levels of object interactions and differed from healthy volunteers primarily in their failure to exhibit the normal habituation of motor activity across time in the novel environment. This abnormality is consistent with previous reports of failures of habituation in schizophrenia in other behavioral paradigms4850.

One criterion for the usefulness of this reverse-translational approach is its ability to classify individuals into different diagnostic groups. The discriminant functional analyses revealed that the hBPM measures correctly classified bipolar and schizophrenia patients better than did symptom domain scores. Bipolar disorder and schizophrenia share many symptoms, particularly during highly symptomatic states when psychosis is a prominent feature of both illnesses. Still, there are cardinal features that help to differentiate these two disorders, namely the hypermotoric behavior of people with bipolar mania and the prominent withdrawal observed among schizophrenia patients. Surprisingly, this distinction has been understudied. The current data support the position that the disorders share many symptoms, as indicated by the similar BPRS scores, but that bipolar mania patients have distinct characteristics of hyper-exploration and increased motor activity. It remains unclear whether these features are an enduring feature of BD, or whether it is specific to the manic state. Therefore, an obvious next question is whether this signature of inhibitory deficits persists during other phases of bipolar disorder, e.g., during euthymic, depressed, and hypomanic states.

Because almost all the bipolar and schizophrenia subjects in this study were treated with a variety of medications, potential medication effects on the hBPM measures are difficult to quantify, which is a limitation of this investigation. Nevertheless, treatment with antipsychotics or mood stabilizers did not appear to reduce activity for either bipolar manic or schizophrenia subjects. The observation that subjects on valproate had increased object interactions is likely due to the fact that these were the subjects with bipolar mania. The increase in object interactions was not seen in subjects on lithium, which may be cause for some speculation that lithium may have had more of a “dampening” effect on activity than did valproate. This speculation is tempered, however, by the fact that manic subjects were tested after an average of two days of treatment, before steady state blood levels of lithium were reached. Medication effects on exploratory behavior can be better addressed in longitudinal studies where patients are tested in an unmedicated state and re-tested after several weeks of treatment; such studies are ongoing in our laboratory.

With respect to the candidate mouse models tested, the genetically induced lack of DAT function produced a behavioral profile that matched that seen after pharmacological inhibition of DAT function in mice. Furthermore, both of these multivariate profiles were consistent with that observed for bipolar mania. In particular, the similarity between bipolar mania and these two models in the spatial patterns of locomotion was striking (Figure. 4). The shared mechanism of these two rodent models therefore suggests possible neurobiological underpinnings of bipolar disorder, and a putative target for developing novel therapeutics. It has been suggested that DAT KD mice may be suitable as a model for attention deficit hyperactivity disorder51. Based on the present data and their hypersensitivity to stimulants52 (although see34) however, DAT KD mice may prove to be more suitable as a model of BD mania. These data provide support for studies that have implicated DAT polymorphisms53 and lower levels of DAT binding28 as being important for the bipolar disorder phenotype.

Figure 4
X-Y plots of mice and men – tracking movements in the hBPM and mBPM. Case examples of a healthy volunteer, a patient with Bipolar Disorder, a DAT wildtype mouse, and a DAT knockdown mouse.

Importantly, the multivariate profile of novel environment exploration that characterized schizophrenia subjects was not reproduced by any of the animal models assessed here. Although this observation may reflect the complexity and multifactorial etiology of schizophrenia, it remains surprising given the widespread use of amphetamine-induced hyperactivity in animal models of schizophrenia25,27. Nonetheless, it should be recognized that amphetamine-induced motor hyperactivity remains a highly predictive model that identifies existing antipsychotic treatments that act as antagonists at dopamine receptors. Considering that antipsychotics are used to treat both schizophrenia and bipolar disorder, the amphetamine hyperactivity model may therefore be seen more as applicable to studying antipsychotics, and less as mimicking schizophrenia or mania specifically. Hence this cross-species characterization of exploratory behavior has allowed us to empirically assess and challenge the widely used animal model of mania/schizophrenia, amphetamine administration, and suggest that more specific genetic or pharmacological manipulations of the DAT might provide more appropriate models.

In contrast to the typical approach that starts from the human and attempts to model behaviors in animals, we adopted the reverse strategy that has been fruitful in other contexts such as the virtual water maze for humans54. Translating animal behavior models to humans has allowed us to differentiate groups of neuropsychiatric subjects through the unique signatures of their exploration. Compared to healthy volunteers and schizophrenia subjects, bipolar mania subjects exhibit hyper-exploration but do so in a highly predictable fashion. This finding is not surprising given that, clinically, manic individuals do not classically demonstrate aimless overactivity, but rather are driven by a persistent and highly focused attraction to novel, often risky situations22. Furthermore, despite relatively modest sample sizes, the hBPM successfully distinguished between manic and schizophrenia subjects better than the symptom presentation of these two groups. This distinction between bipolar mania and schizophrenia in humans is consistent with the parallel studies in mice. Consequently, the quantification of behavior in the hBPM provides a behavioral marker of psychiatric disorders that can be directly modeled in animals. These markers may provide quantifiable indicators of the efficacy of putative therapeutic treatments that are likely to be more readily predicted by preclinical rodent screens than are standard clinical measures based on symptom rating scales55. The present findings also support the use of the hBPM in identifying differences between patients with bipolar mania and schizophrenia. While these disorders appear to share many commonalities from risk factors, pathophysiology, and genetics to clinical manifestation56, there are clear behavioral manifestations that have eluded empirical characterization. Thus, future use the hBPM may assist in the discovery of novel therapeutics via guided research, as opposed to current treatments of bipolar disorder which have been discovered largely via serendipity or by testing medications previously approved for other disorders57.

Acknowledgments

This study was supported by NIH grant R01-MH071916 and by the Veteran’s Administration VISN 22 Mental Illness Research, Education, and Clinical Center. We thank Rebecca Wershba and Andrew Goey for their research assistance. Dr. Minassian has a family-based interest in VivoMetrics. Dr. Geyer has an equity interest in San Diego Instruments, Inc.

References

1. Pope HG, Jr, Yurgelun-Todd D. Schizophrenic individuals with bipolar first-degree relatives: analysis of two pedigrees. J Clin Psychiatry. 1990 Mar;51(3):97–101. [PubMed]
2. Pini S, de Queiroz V, Dell’Osso L, et al. Cross-sectional similarities and differences between schizophrenia, schizoaffective disorder and mania or mixed mania with mood-incongruent psychotic features. Eur Psychiatry. 2004 Feb;19(1):8–14. [PubMed]
3. Buckley PF. Update on the treatment and management of schizophrenia and bipolar disorder. CNS Spectr. 2008 Feb;13(2 Suppl 1):1–10. quiz 11–12. [PubMed]
4. Citrome L, Goldberg JF, Stahl SM. Toward convergence in the medication treatment of bipolar disorder and schizophrenia. Harv Rev Psychiatry. 2005 Jan-Feb;13(1):28–42. [PubMed]
5. Craddock N, O’Donovan MC, Owen MJ. Genes for schizophrenia and bipolar disorder? Implications for psychiatric nosology. Schizophr Bull. 2006 Jan;32(1):9–16. [PMC free article] [PubMed]
6. Gould TD, Einat H. Animal models of bipolar disorder and mood stabilizer efficacy: a critical need for improvement. Neurosci Biobehav Rev. 2007;31(6):825–831. [PMC free article] [PubMed]
7. Young JW, Minassian A, Paulus MP, Geyer MA, Perry W. A reverse-translational approach to bipolar disorder: Rodent and human studies in the Behavioral Pattern Monitor. Neurosci Biobehav Rev. 2007;31(6):882–896. [PMC free article] [PubMed]
8. Berlyne DE. Curiosity and exploration. Science. 1966 Jul 1;153(731):25–33. [PubMed]
9. Drai D, Kafkafi N, Benjamini Y, Elmer G, Golani I. Rats and mice share common ethologically relevant parameters of exploratory behavior. Behav Brain Res. 2001 Nov 1;125(1–2):133–140. [PubMed]
10. Eilam D, Zor R, Szechtman H, Hermesh H. Rituals, stereotypy and compulsive behavior in animals and humans. Neurosci Biobehav Rev. 2006;30(4):456–471. [PubMed]
11. Lat J. The spontaneous exploratory reactions as a tool for psychopharmacological studies. A contribution towards a theory of contradictory results in psychopharmacology. In: Mikhelson M, Longo V, Votava Z, editors. Pharmacology of conditioning, learning and retention. Oxford: Pergamon Press; 1965.
12. Schiorring E. An open field study of stereotyped locomotor activity in amphetamine-treated rats. Psychopharmacology (Berl) 1979;66(3):281–287. [PubMed]
13. Geyer MA, Russo PV, Masten VL. Multivariate assessment of locomotor behavior: pharmacological and behavioral analyses. Pharmacol Biochem Behav. 1986 Jul;25(1):277–288. [PubMed]
14. Paulus MP, Dulawa SC, Ralph RJ, Geyer MA. Behavioral organization is independent of locomotor activity in 129 and C57 mouse strains. Brain Res. 1999 Jul 17;835(1):27–36. [PubMed]
15. Paulus MP, Geyer MA. Three independent factors characterize spontaneous rat motor activity. Behav Brain Res. 1993 Feb 26;53(1–2):11–20. [PubMed]
16. Geyer MA, Paulus MP. Multivariate and nonlinear approaches to characterizing drug effects on the locomotor and investigatory behavior of rats. NIDA Res Monogr. 1992;124:203–235. [PubMed]
17. Risbrough VB, Masten VL, Caldwell S, Paulus MP, Low MJ, Geyer MA. Differential contributions of dopamine D1, D2, and D3 receptors to MDMA-induced effects on locomotor behavior patterns in mice. Neuropsychopharmacology. 2006 Nov;31(11):2349–2358. [PubMed]
18. Ralph-Williams RJ, Paulus MP, Zhuang X, Hen R, Geyer MA. Valproate attenuates hyperactive and perseverative behaviors in mutant mice with a dysregulated dopamine system. Biol Psychiatry. 2003 Feb 15;53(4):352–359. [PubMed]
19. Teicher MH, Polcari A, McGreenery CE. Utility of objective measures of activity and attention in the assessment of therapeutic response to stimulants in children with attention-deficit/hyperactivity disorder. J Child Adolesc Psychopharmacol. 2008 Jun;18(3):265–270. [PMC free article] [PubMed]
20. Zor R, Hermesh H, Szechtman H, Eilam D. Turning order into chaos through repetition and addition of elementary acts in obsessive-compulsive disorder (OCD) World J Biol Psychiatry. 2007 Jul 13;:1–8. [PubMed]
21. Angst J, Gamma A, Benazzi F, Ajdacic V, Eich D, Rossler W. Diagnostic issues in bipolar disorder. Eur Neuropsychopharmacol. 2003 Aug;13(Suppl 2):S43–50. [PubMed]
22. Benazzi F. Testing new diagnostic criteria for hypomania. Ann Clin Psychiatry. 2007 Apr-Jun;19(2):99–104. [PubMed]
23. Einat H. Modelling facets of mania--new directions related to the notion of endophenotypes. J Psychopharmacol. 2006 Sep;20(5):714–722. [PubMed]
24. Machado-Vieira R, Kapczinski F, Soares JC. Perspectives for the development of animal models of bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2004 Mar;28(2):209–224. [PubMed]
25. Ellenbroek BA, Cools AR. Animal models with construct validity for schizophrenia. Behav Pharmacol. 1990;1(6):469–490. [PubMed]
26. Lisman JE, Coyle JT, Green RW, et al. Circuit-based framework for understanding neurotransmitter and risk gene interactions in schizophrenia. Trends Neurosci. 2008 May;31(5):234–242. [PMC free article] [PubMed]
27. Stone JM, Morrison PD, Pilowsky LS. Glutamate and dopamine dysregulation in schizophrenia--a synthesis and selective review. J Psychopharmacol. 2007 Jun;21(4):440–452. [PubMed]
28. Amsterdam JD, Newberg AB. A preliminary study of dopamine transporter binding in bipolar and unipolar depressed patients and healthy controls. Neuropsychobiology. 2007;55(3–4):167–170. [PubMed]
29. Horschitz S, Hummerich R, Lau T, Rietschel M, Schloss P. A dopamine transporter mutation associated with bipolar affective disorder causes inhibition of transporter cell surface expression. Mol Psychiatry. 2005 Dec;10(12):1104–1109. [PubMed]
30. Inada T, Sugita T, Dobashi I, et al. Dopamine transporter gene polymorphism and psychiatric symptoms seen in schizophrenic patients at their first episode. Am J Med Genet. 1996 Jul 26;67(4):406–408. [PubMed]
31. Kelsoe JR, Sadovnick AD, Kristbjarnarson H, et al. Possible locus for bipolar disorder near the dopamine transporter on chromosome 5. Am J Med Genet. 1996 Nov 22;67(6):533–540. [PubMed]
32. Meisenzahl EM, Schmitt GJ, Scheuerecker J, Moller HJ. The role of dopamine for the pathophysiology of schizophrenia. Int Rev Psychiatry. 2007 Aug;19(4):337–345. [PubMed]
33. Han DD, Gu HH. Comparison of the monoamine transporters from human and mouse in their sensitivities to psychostimulant drugs. BMC Pharmacol. 2006;6:6. [PMC free article] [PubMed]
34. Zhuang X, Oosting RS, Jones SR, et al. Hyperactivity and impaired response habituation in hyperdopaminergic mice. Proc Natl Acad Sci U S A. 2001 Feb 13;98(4):1982–1987. [PubMed]
35. Overall JR, Gorham DR. The brief psychiatric rating scale. J Oper Psychiatry. 1980;11:48–64.
36. Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: Reliability, validity and sensitivity. Br J Psychiatry. 1978;133:429–435. [PubMed]
37. Vivometrics. The LifeShirt System. Ventura, CA: 2002.
38. Pierce K, Courchesne E. Evidence for a cerebellar role in reduced exploration and stereotyped behavior in autism. Biological Psychiatry. 2001;49:655–664. [PubMed]
39. Paulus MP, Geyer MA, Gold LH, Mandell AJ. Application of entropy measures derived from the ergodic theory of dynamical systems to rat locomotor behavior. Proc Natl Acad Sci U S A. 1990 Jan;87(2):723–727. [PubMed]
40. Top Scan Version 1.0 [computer program]. Version. Washington D.C: Clever Systems, Inc; 1999.
41. Ralph RJ, Paulus MP, Geyer MA. Strain-specific effects of amphetamine on prepulse inhibition and patterns of locomotor behavior in mice. J Pharmacol Exp Ther. 2001 Jul;298(1):148–155. [PubMed]
42. Paulus MP, Geyer MA. A temporal and spatial scaling hypothesis for the behavioral effects of psychostimulants. Psychopharmacology (Berl) 1991;104(1):6–16. [PubMed]
43. Shafer A. Meta-analysis of the brief psychiatric rating scale factor structure. Psychol Assess. 2005 Sep;17(3):324–335. [PubMed]
44. Akiskal HS, Azorin JM, Hantouche EG. Proposed multidimensional structure of mania: beyond the euphoric-dysphoric dichotomy. J Affect Disord. 2003 Jan;73(1–2):7–18. [PubMed]
45. Angst J, Gamma A, Benazzi F, Ajdacic V, Eich D, Rossler W. Toward a re-definition of subthreshold bipolarity: epidemiology and proposed criteria for bipolar-II, minor bipolar disorders and hypomania. J Affect Disord. 2003 Jan;73(1–2):133–146. [PubMed]
46. Teicher MH. Actigraphy and motion analysis: new tools for psychiatry. Harv Rev Psychiatry. 1995 May-Jun;3(1):18–35. [PubMed]
47. Teicher MH, Lawrence JM, Barber NI, Finklestein SP, Lieberman H, Baldessarini RJ. Altered locomotor activity in neuropsychiatric patients. Prog Neuropsychopharmacol Biol Psychiatry. 1986;10(6):755–761. [PubMed]
48. Geyer MA, Braff DL. Habituation of the Blink reflex in normals and schizophrenic patients. Psychophysiology. 1982 Jan;19(1):1–6. [PubMed]
49. Meincke U, Light GA, Geyer MA, Braff DL, Gouzoulis-Mayfrank E. Sensitization and habituation of the acoustic startle reflex in patients with schizophrenia. Psychiatry Res. 2004 Apr 15;126(1):51–61. [PubMed]
50. Bernstein AS, Frith CD, Gruzelier JH, et al. An analysis of the skin conductance orienting response in samples of American, British, and German schizophrenics. Biol Psychol. 1982 May-Jun;14(3–4):155–211. [PubMed]
51. van der Kooij MA, Glennon JC. Animal models concerning the role of dopamine in attention-deficit hyperactivity disorder. Neurosci Biobehav Rev. 2007;31(4):597–618. [PubMed]
52. Tilley MR, Cagniard B, Zhuang X, Han DD, Tiao N, Gu HH. Cocaine reward and locomotion stimulation in mice with reduced dopamine transporter expression. BMC Neurosci. 2007;8:42. [PMC free article] [PubMed]
53. Greenwood TA, Schork NJ, Eskin E, Kelsoe JR. Identification of additional variants within the human dopamine transporter gene provides further evidence for an association with bipolar disorder in two independent samples. Mol Psychiatry. 2006 Feb;11(2):125–133. 115. [PubMed]
54. Shipman SL, Astur RS. Factors affecting the hippocampal BOLD response during spatial memory. Behav Brain Res. 2008 Mar 5;187(2):433–441. [PubMed]
55. Markou A, Chiamulera C, Geyer MA, Tricklebank M, Steckler T. Removing Obstacles in Neuroscience Drug Discovery: The Future Path for Animal Models. Neuropsychopharmacology. 2009;34:74–89. [PMC free article] [PubMed]
56. Shao L, Vawter MP. Shared gene expression alterations in schizophrenia and bipolar disorder. Biol Psychiatry. 2008 Jul 15;64(2):89–97. [PMC free article] [PubMed]
57. Gould TD, Quiroz JA, Singh J, Zarate CA, Manji HK. Emerging experimental therapeutics for bipolar disorder: insights from the molecular and cellular actions of current mood stabilizers. Mol Psychiatry. 2004 Aug;9(8):734–755. [PubMed]