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We examined striatal dopamine transporter (DAT) distribution volume ratio (DVR) values in subjects with unipolar or bipolar major depressive episode (versus non-depressed healthy volunteers) using the selective DAT radioligand [99mTc]TRODAT-1 and single photon emission computed tomography (SPECT). We hypothesized that striatal DVR values would be greater in depressed versus non-depressed subjects, and that greater DVR values may represent a possible clinical biomarker of depression.
[99mTc]TRODAT-1 SPECT images were acquired from 39 depressed and 103 non-depressed drug-free subjects. The primary outcome measure was the DVR value of [99mTc]TRODAT-1 binding for the putamen region and the combined putamen plus caudate region.
DVR values were significantly correlated across all striatal regions within both subject groups (p<0.005). Depressed subjects had significantly greater DVR values (versus non-depressed subjects) in the putamen (p<0.0005) and the combined putamen plus caudate (p<0.0005) regions. There was no difference in DVR values between unipolar (n=24) and bipolar (n=15) depressed subjects, and no difference in DVR values for depressed subjects with or without prior antidepressant exposure. The predictive probability of the putamen or combined putamen plus caudate DVR value to distinguish depressed from non-depressed subjects was significant (p<0.0005).
– DAT values could potentially be influenced by age, gender, diagnosis, prior psychotropic dug exposure, illness length, or symptom severity.
– Results confirm prior observations of greater striatal DAT density in depressed versus non-depressed subjects, and suggest that greater DVR values may possibly represent a potential diagnostic biomarker for distinguish depressed from non-depressed individuals.
Striatal dopamine may play an important role in the pathophysiology of depression by modulating emotional and motor symptoms (Rogers et al., 1998; Byrum et al., 1999; Newberg et al., 2007). Cortical dopamine pathways have been identified that project to the striatum, globus pallidus, substantia nigra, and back to the cortex (Rogers et al., 1998). Some studies have suggested that dopamine transporter (DAT) activity may reflect the general state of dopamine function in the brain, and that alterations in DAT activity may reflect abnormalities in dopamine function (Jaber et al., 1997). Several anatomic imaging studies have reported reduced basal ganglia volume in depression (Husain et al., 1991; Parashos et al., 1998), and this observation has been confirmed in at least one postmortem study (Baumann et al., 1999). In addition, several magnetic resonance imaging studies have found greater signal intensities in the striatum of depressed subjects (Greenwald et al., 1996; Lenze et al., 1999).
A large number of single photon emission computed tomography (SPECT) and positron emission tomography (PET) studies were reviewed by Soares and Mann (1997) and generally found reduced regional cerebral blood flow and reduced glucose metabolism, respectively, in the basal ganglia in depressed subjects - although these observations have not been universally confirmed. The authors of this review suggested that the findings in the basal ganglia, in conjunction with generally decreased cerebral blood flow and metabolism in the frontal regions, limbic structures and the thalamus, indicate an overall network of structures involved in the pathophysiology of mood disorders. Further, given the important role of the striatum, it is likely that the dopaminergic system is an important mediator of mood disorders.
The potential role of the dopamine system in depression has been studied using a variety of imaging studies. For example, abnormalities in the striatal dopamine (D2/D3) receptor binding in depression has been reported as being either greater (D’haenen & Bossuyt, 1994; Shah te al., 1997) or no different than that seen in non-depressed subjects (Klimke et al., 1999). Several SPECT studies have examined striatal DAT density in depression. Laasonen-Balk et al. (1999) found approximately 20% greater striatal DAT density in 15 depressed subjects (versus 18 non-depressed subjects) using [123I]β-CIT (a radioligand with mixed serotonin transporter and DAT binding affinity). In contrast, Malison et al. (1998) reported no difference in striatal [123I]β-CIT binding in 15 depressed versus 15 non-depressed subjects. Similarly, a postmortem study found no difference in striatal DAT density of depressed suicide victims versus non-depressed subjects, using the non-selective radioligand, [3H]GBR12935 (Bowden et al., 1997).
We have utilized the selective DAT radioligand, [99mTc]TRODAT-1, to quantify striatal DAT density in depressed versus non-depressed subjects (Brunswick et al., 2003; Weintraub et al., 2005; Amsterdam & Newberg 2007; Newberg et al., 2007). [99mTc]TRODAT-1 is highly selective for DAT sites, utilizes a less expensive isotope (i.e., 99mTc), and has excellent imaging characteristics. In a study of DAT density of emotional affect in 73 healthy, non-depressed volunteer subjects, we observed greater striatal DAT density in individuals with higher self-report scores of depressive affect on the Profile of Mood Scale (p=0.02) (Newberg et al., 2007). In another study of 15 depressed versus 46 non-depressed subjects, we observed significantly greater age and gender adjusted striatal [99mTc]TRODAT-1 uptake (i.e., DAT density) in depressed subjects in the right anterior putamen (p<0.001), right posterior putamen (p<0.001), left posterior putamen (p<0.05), and left caudate (p<0.05) (Brunswick et al., 2003). These finding were subsequently replicated by Yang et al. (2008) using [99mTc]TRODAT-1 in 10 depressed (versus 7 non-depressed) subjects.
In the current study, we examined a new cohort of depressed subjects in an effort to validate our earlier observation of greater striatal DAT density in depressed (versus non-depressed) subjects using [99mTc]TRODAT-1, and to test the hypothesis that greater striatal DAT density may represent a putative biomarker to distinguish depressed from non-depressed subjects.
Depressed subjects were outpatients recruited from the Depression Research Unit at the University of Pennsylvania. All subjects were ≥18 years old and had a DSM IV-TR Axis I diagnosis of major depressive episode. The clinical diagnosis was verified using the Structured Clinical Interview for DSM-IV format (First et al., 2001). Subjects were off prior psychotropic medication ≥6 months, and had a 17-item Hamilton Depression Rating (HAM-D) (Williams, 1988) score ≥16. Subjects had a physical examination and laboratory evaluation (including electrolyte, hepatic, renal, and thyroid panels, urinalysis, illicit drug screen, and electrocardiogram). Subjects were in good physical health and had no meaningful laboratory abnormalities. Women of child-bearing potential had a negative pregnancy test. Subjects were excluded from the study if they had any of the following: primary Axis I diagnosis other than major depressive episode such as schizophrenia, schizoaffective disorder, anxiety disorders, or attention disorders; history of mania or psychosis; actively suicidal; substance abuse or dependence within the preceding 3 months; positive screen for illicit drugs; use of psychotropic medication within the preceding 6 months (subjects were not tapered off their medications in order to participate in this study, but were required to be off medications for 6 months); unstable medical condition; pregnant or nursing; history of transient ischemic attack, cerebral infarction, hypertensive encephalopathy, intracranial hemorrhage, head trauma with loss of consciousness, encephalitis, exposure to neurotoxin, dementia, normal pressure hydrocephalus, brain tumor, basal ganglia disease, polyneuropathy, or unable to provide informed consent.
Non-depressed, healthy volunteer subjects were recruited as part of a NIH-funded [99mTc]TRODAT-1 ‘normative’ database project (Mozley et al., 2001). Healthy volunteers were ≥18 years old and had no clinically meaningful medical illness or laboratory abnormalities. Data from healthy volunteers were collected using the same procedures as those used for depressed subjects. Psychiatric status was ascertained using the SCID format, and none had a DSM IV Axis I disorder. A medical history, physical examination, and laboratory evaluation were obtained. DAT density measurements were acquired using an identical [99mTc]TRODAT-1 SPECT protocol as that employed in the current study (; Mozley et al., 2000, 2001; Amsterdam & Newberg 2007; Newberg et al., 2007).
Subjects provided written informed consent in accordance with the ethical standards of the Institutional Review Board of the University of Pennsylvania. The study was conducted under IND #64,205 for [99mTc]TRODAT-1 using Good Clinical Practice guidelines with oversight by the local Office of Human Research and an independent Data & Safety Monitoring Board.
[99mTc]TRODAT-1 740 MBq (20 mCi) was injected through an indwelling venous catheter with SPECT images acquired over a one hour period (between 3 and 4 hours after [99mTc]TRODAT-1 injection) on a triple-head gamma camera equipped with ultra-high resolution fan-beam collimators (Picker 3000; Picker International, Cleveland, OH) and the images were analyzed using validated parameters (Mozley et al., 2001; Brunswick et al., 2003; Weintraub et al., 2005; Amsterdam & Newberg 2007; Newberg et al., 2007). The acquisition parameters included a continuous mode with 40 projection angles over a 120° arc to obtain data in a 128×128 matrix with a pixel width of 2.11 mm and a slice thickness of 3.56 mm. The center of rotation was 14 cm. The system spatial resolution is 8–10 mm. The low pass filter was based on a systematic analysis of parameters that produced the best signal to noise characteristics in the images. Chang’s first order attenuation correction (coefficient of 0.11 cm−1) was also applied.
Manual demarcation of the regions of interest (ROIs) was then performed. Standardized templates containing ROIs were fit on each scan. The ROIs used have been previously developed specifically for TRODAT scans since the binding of the tracer is almost exclusively in the striatum. Thus, ROIs were placed upon the primary structures of the caudate and putamen, on both the left and right, in which the TRODAT binding occurs. Within the x–y plane, ROIs in the template were smaller than the actual structures they represent in order to minimize resolution induced problems with ill defined edges. To reduce the effects of volume averaging in the axial direction, the small ROIs were not placed on the slices that contained the upper most and lower most portions of the structures they represent. This limits the small ROIs to the central aspect of structures they represented. This results in a high degree of quantitative accuracy with test-retest reliability typically less than 6%. Whole brain boundaries were drawn by hand on slices located 12 mm above the highest slice that included the basal ganglia. The primary imaging outcome measure was the distribution volume ratio (DVR) at 3 to 4 hours post injection, when the distribution of [99mTc]TRODAT-1 approached a near equilibrium state that reflected the ratio of k3/k4, which was related to [99mTc]TRODAT-1 binding potential (Acton et al., 1999, 20000). The DVR value was calculated as the ROI ÷ reference region (where the reference region was the supratentorial ROI consisting of non-specific binding.
Analyses were implemented with the realization that the limited sample size may only allow for the detection of large differences between groups. All analyses were conducted in Stata 11.0 (College Station, TX) with two-sided tests. Bonferroni correction was applied to the criterion for statistical significance so that the criterion for significance was <0.05÷45=0.001.
Initial analyses were descriptive and stratified according to ROI. Analyses included means, medians, ranges, and standard deviation (SD) of continuous covariates (e.g. age) and DVR values. The ‘sktest’ procedure in Stata was used to assess the normality of DVR values at each ROI. The intra-subject association of DVR values was estimated using Spearman rank correlation coefficients for each ROI. Student t-tests and linear regression (adjusted for age and gender) were used to compare DVR values for each ROI in depressed versus non-depressed subjects, unipolar versus bipolar depressed subjects, and depressed subjects with or without prior psychotropic medication exposure.
Logistic regression was used to examine the predictive association between DVR values and the odds of having a diagnosis of depression (versus non-depression). Logistic models included depression (1=yes; 0=no) as the outcome variable, and DVR for each ROI as the covariate. Additional models were age and gender adjusted. The significance level associated with each DVR value was calculated in both sets of logistic regression models, while the odds ratio associated with each DVR value was inflated in the adjusted models. Receiver operator characteristic curves (ROC) were constructed for each model and the area under the curve (AUC) was obtained for each model. The ROC curves plot sensitivity versus one minus specificity for tests that use each estimated probability as a cut-off value for depression. AUC values closer to 1.0 indicate better ability of the DVR value to predict depression. There is typically a trade-off between specificity and sensitivity for a fixed sample size, so that as sensitivity values increase, the specificity values become smaller in value. The inverse relationship between sensitivity and specificity follows from the fact that stricter tests with higher cut-off values for depression may be more likely to correctly classify depressed subjects (with a high sensitivity), but be less likely to correctly classify non-depressed subjects (with a low specificity). We calculated the distance between each (sensitivity, specificity) value and (1.1), as a means of ordering the probability and associated (sensitivity, specificity) values with respect to being simultaneously closer to a value of 1. Hosmer-Lemeshow goodness of fit test was applied to each model to test the hypothesis of adequate fit. A p value >0.05 indicated that the hypothesis of good fit was not rejected.
53 depressed subjects with a mean (SD) age of 44.2 (11.1) (range 20–63) years were enrolled: 22 women (41%) and 37 Caucasian (69.8%). There were 14 screen failures: 8 lost to follow up or unable to complete baseline procedures, 5 who withdrew consent, and 1 discontinued for noncompliance. Thirty-nine subjects (73.6%) completed all study procedures: 23 men and 16 women, mean (SD) age 41.4 (11.8) (range 20–63) years. Of these subjects, more detailed psychiatric evaluation revealed that 24 had unipolar depression and 15 had bipolar type II depression, 21 (53.8%) had prior exposure to a mean (SD) of 3.1 (2.4) (range 1–9) psychotropic medications, and 18 (46.2%) had never taken psychotropic medication. The mean (SD) illness length was 24.0 (10.9) (range 3–41) years and the mean (SD) current depressive episode duration was 19.4 (20.9) (range 1–96) months.
Eighty-four non-depressed, healthy volunteer subjects with a mean (SD) age of 37.5 (14.5) (range 20.2–64.8) years were included: 41 women (53.5%), and 59 Caucasian (69.4%). There were no significant differences in gender distribution (p=0.20; χ2 test), mean age (p=0.60; Students t-test), or racial distribution (p=0.74; χ2 test) between subject groups.
Table 1 displays the intra-subject correlation coefficient of DVR values for striatal ROIs within depressed and non-depressed subjects. DVR values were highly correlated within all striatal ROIs in depressed (p<0.005) and non-depressed (p<0.005) subject groups.
Depressed subjects had significantly greater mean (SD) DVR values (versus non-depressed subjects) for the right putamen (p<0.003) and left putamen (p<0.0005) (Table 2).
There were no significant differences in mean DVR values for unipolar versus bipolar II subjects, or for depressed subjects with or without prior psychotropic medication exposure (Table 3).
Figure 1 displays the ROC curve for the total putamen plus caudate region with an AUC value of 0.79. The ROC curve plots the sensitivity versus 1 minus the specificity for tests based on all possible predicted probability values from the logistic regression models (where each probability value is used as a cutoff point for distinguishing depressed from non-depressed subjects). As sensitivity increases in value, specificity decreases in value.
Table 4 displays the estimated probabilities with associated sensitivity and specificity values for the first ten points along the ROC curve, when the values are ordered according to the distance between each (sensitivity, specificity) pair and the point (1,1).
The Hosmer-Lemeshow test indicates that the fit of the regression models are good for the left putamen, right putamen, total putamen, and total putamen plus caudate regions (Table 5). The predictive ability of each ROI to distinguish depressed from non-depressed subjects is high because the AUC values are close to 1.0.
The dopamine system may be involved in the pathophysiology of depression (Rogers et al., 1998; Klimke et al., 1999; Amsterdam & Newberg 2007; Newberg et al., 2007). Several studies have suggested that DAT density may be greater in depression (Jaber et al., 1997; Laasonen-Balk et al., 1999; Brunswick et al., 2003; Yang et al., 2008). For example, Laasonen-Balk et al. (1999) measured striatal DAT density in 15 depressed and 18 non-depressed subjects using [123I]β-CIT and found greater DAT density in depressed subjects. These investigators speculated that this alteration may represent a key mechanism by which dopamine is modulated in depression. Similarly, we previously reported a greater age and gender adjusted DAT density using [99mTc]TRODAT-1 in 15 depressed (versus 46 non-depressed) subjects (Brunswick et al., 2003). In that study, we found that [(99m)Tc]TRODAT-1 binding was significantly higher in the right anterior putamen (23%), right posterior putamen (36%), left posterior putamen (18%), and left caudate nucleus (12%) of the patients with depression than in control subjects. This finding was subsequently replicated by Yang et al. (2008) in 10 depressed (versus 7 non-depressed) subjects. In contrast, Malison et al. (1998) reported no difference in DAT density using [123I]β-CIT in 15 depressed (versus 15 non-depressed) subjects, while Weintraub et al., (2005) found reduced DAT density in subjects with co-morbid depression plus Parkinson’s disease which also correlated with affect scores.
The current study validates our prior observation of greater striatal DAT density in depression (Brunswick et al., 2003). Moreover, the current observation also suggest that greater DAT density may represent a putative biomarker of altered dopamine function in depression with the ability to distinguish depressed from non-depressed subjects. This has implications for the clinical evaluation of patients with depression as the DAT binding may ultimately be useful to evaluate depression in patients or to help determine prognosis and response to various interventions. However, future studies will have to be performed in order to better evaluate the potential clinical utility of DAT imaging in patients with depression.
While the mechanism of increased DAT density in depression is not known, it is possible that the greater DAT density may compensate for a diminished extra-neuronal dopamine concentration. It is also possible that the greater DAT density acts as a compensatory clearing mechanism for excessive central dopamine concentrations, or compensates for reduced synaptic D2/D3 receptor function. However, several studies have examined the relationship of D2/D3 receptor function on DAT density in depression and have reported no difference in D2/D3 receptor activity in depression (Klimke et al., 1999; Kimmel et al., 2001; Yang et al., 2008). Altered DAT density may also represent a neuronal reflection of psychomotor or affective changes that occur in depression (Byrum et al., 1999; Caligiuri & Ellwanger, 2000; Meyer et al., 2001, 2006; Newberg et al., 2007). In this framework, the putamen is part of a complex system in which cortical pathways project to the striatum (along with neuronal inputs from the thalamus and amygdala) and then project back to the cortex. Several of these pathways involve circuits that modulate mood and behavior, and alterations in striatal dopamine could impair the cortico-striatal circuits disrupting limbic input back to the cortex in depression (Byrum et al., 1999; Drevets, 2000). Thus, the abnormalities observed in our study of DAT binding in depression may represent striatal dopamine mediated dysregulation of the cortical and limbic structures.
Several caveats should be considered in the interpretation of the current findings. For example, DAT density may be influenced by gender (Lammers et al., 1999; Koch et al., 2007) and age (Malison et al., 1998; Laasonen-Balk et al., 1999; Mozley et al., 2000, 2001). In the current study, women were included without regard to menopausal status or menstrual cycle phase. This may have influenced DVR values (Lammers et al., 1999). Similarly, the broad age range of our subjects could have influenced DVR values - although analyses were adjusted for age and gender.
It is possible that diagnostic heterogeneity may have influenced DVR values. We included subjects with unipolar or bipolar II major depression – although subjects with prior mania (i.e., bipolar I disorder) were specifically excluded. Prior examination of [99mTc]TRODAT-1 binding in 10 unipolar versus 5 bipolar II depressed subjects found no significant difference in putamen DVR values between diagnostic groups (Amsterdam & Newberg, 2007) suggesting that the increased DAT binding in both groups compared to controls is more likely a biomarker of depression. This appears to be confirmed in the current study which found no difference in DVR values between unipolar and bipolar II subjects. Thus, there appears to be a specific relationship between depression and higher DAT binding suggesting that there is altered dopamine function in depression.
It is possible that prior antidepressant exposure may have influenced DVR values, despite the fact that subjects in the current study were drug free for ≥6 months. While we found no influence of prior psychotropic medication exposure on DVR values, Kugaya et al. (2003) found that citalopram increased striatal DAT density by 15% after 8 days of administration and they reported increased DAT density during paroxetine administration. Similarly, Shang et al. (2007) reported increased [123I]β-CIT binding to DAT sites with venlafaxine, while Pirker et al. (1995) found no change in DAT binding during antidepressant administration.
It is possible that differences in illness length, episode duration, symptom severity, or psychosocial stress may have influenced DVR values. Differences in stamina, daily activity level, sleep, and circadian rhythms may influence DVR values. Finally, the extent of tobacco and social alcohol consumption was not specifically controlled in the current study. It is possible that smoking and social alcohol use may have influenced DVR values (Middleton et al., 2004; Cosgrove et al., 2009).
We used [99mTc]TRODAT-1 and SPECT imaging to measure striatal DAT density in depressed versus non-depressed subjects. The finding of greater striatal DAT density in depressed subjects validates our prior observation, and suggests that depression may be associated with an increase in DAT density. Moreover, greater striatal DAT density may represent a putative biomarker of depression with the predictive ability to distinguish depressed from non-depressed individuals. Future studies will be needed to confirm these observations.
This work was supported in part by NIH grants MH-070753, AG-17524, DA-09469, NS-18509, and the Jack Warsaw Endowment for Research in Biological Psychiatry, Depression Research Unit, University of Pennsylvania Medical Center.
CONTRIBUTION OF EACH AUTHOR TO THE MANUSCRIPTDr. Amsterdam served as study Principal Investigator. He designed and wrote the study protocol, implemented the study procedures, recruited study subjects, oversaw study conduct, oversaw data monitoring and double data entry, assisted in data analysis, and prepared the manuscript.
Sr. Newberg served as study co-investigator. He performed all SPECT scan procedures and scan analyses. He assisted in designing and writing the study protocol, recruited study subjects, oversaw study conduct, assisted in data analysis, and assisted in the writing and preparation of the manuscript.
Ms. Soeller served as study co-investigator. She assisted in implementation of the study procedures and recruitment of study subjects.
Dr. Shults served as biostatistician on the study and performed all statistical analyses on the data. Dr. Shults assisted with the design and writing of the study protocol, and in the writing and preparations of the manuscript.
Dr. Amsterdam, Dr. Newberg, and Dr. Shults had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the data analysis.
Within three (3) years of beginning the work on this project, Dr. Amsterdam received grant support from NIH/NIMH; NIH/NCCAM; Stanley Medical Research Institute; Lilly Research Laboratories; Forest Laboratories; Novartis, Inc.; and Sanofi-Aventis, Inc. He was a member of the Wyeth and Bristol-Myers-Squibb speakers bureau. He received an honorarium from Cephalon, Inc. for participation on a scientific advisory panel, from the NIMH for participation on a scientific review group, and from Princeton Medical Center for a grand rounds presentation. He was not a member of any pharmaceutical industry-sponsored advisory board and has had no significant financial interest in any pharmaceutical company.
Dr. Newberg received grant support from NIH/NIMH. He was not a member of any industry-sponsored advisory board or speaker’s bureau, and has had no significant financial interest in any pharmaceutical company.
Ms. Soeller received support from NIH/NIMH and NIH/NCCAM. She was not a member of any pharmaceutical industry-sponsored advisory board or speaker’s bureau, and has had no significant financial interest in any pharmaceutical company.
Dr. Shults received grant support from the NIMH. She was not a member of any industry-sponsored scientific advisory board or speaker’s bureau, and has had no significant financial interest in any pharmaceutical company.
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