Regional Cerebral Glucose Metabolic Abnormalities in Bipolar II Depression

Regional Cerebral Glucose Metabolic Abnormalities in Bipolar II Depression Linda Mah, Carlos A. Zarate Jr, Jaskaran Singh, Yu-Fei Duan, David A. Luckenbaugh, Husseini K. Manji, and Wayne C. Drevets Background: Functional neuroimaging studies of bipolar disorder (BD) performed in conjunction with antidepressant treatment trials generally require that patients remain on mood stabilizers to reduce the risk of inducing mania; yet, it is unknown whether the metabolic abnormalities evident in unmedicated BD depressives remain detectable in patients receiving mood stabilizers. This study investigated whether cerebral metabolic abnormalities previously reported in unmedicated BD subjects are evident in depressed bipolar disorder type II (BD II) subjects receiving lithium or divalproex. Methods: Using [18F]-fluorodeoxyglucose-positron-emission tomography, cerebral glucose metabolism was compared between 13 depressed BD II subjects on therapeutic doses of lithium or divalproex and 18 healthy control subjects. Regional metabolism was compared between groups in predefined regions of interest. Results: Metabolism was increased in the bilateral amygdala, accumbens area, and anteroventral putamen, left orbitofrontal cortex and right pregenual anterior cingulate cortex in depressives versus control subjects. Post hoc exploratory analysis additionally revealed increased metabolism in left parahippocampal, posterior cingulate, and right anterior insular cortices in depressives versus control subjects. Correlational analyses showed multiple limbic-cortical-striatal interactions in the BD sample not evident in the control sample, permitting sensitive and specific classification of subjects by discriminant analysis. Conclusions: These results confirm previous reports that bipolar depression is associated with abnormally increased metabolism in the amygdala, ventral striatum, orbitofrontal cortex, anterior cingulate, and anterior insula, and extend these results to bipolar disorder type II depressives on lithium or divalproex. They also implicate an extended functional anatomical network known to modulate visceromotor function in the pathophysiology of BD II depression. Key Words: Cerebral metabolism, bipolar disorder, depression, striatum, prefrontal cortex, amygdala, mood stabilizers

B

ipolar disorder (BD) is a chronic, disabling condition with a reported 12-month prevalence of 1% to 3% (Kessler et al 2005). Much of the morbidity and mortality associated with BD is attributable to the depressive phase (Calabrese et al 2003), which manifests a substantially more chronic and severe course and higher suicide rate in bipolar disorder type II (BD II), as compared with bipolar disorder type I (BD I) depression (Judd et al 2003). Yet, little is known about the pathophysiology of BD II. Functional neuroimaging studies of mood disorders have shown that physiological activity is abnormal in limbic and paralimbic structures such as the amygdala, ventral anterior cingulate cortex (ACC), and anatomically related areas of the orbitofrontal cortex (OFC), striatum, and thalamus during major depressive episodes, but the majority of these studies focused on unipolar depression. Few neuroimaging studies have assessed neurophysiological activity in unmedicated subjects with bipolar depression, and none of the studies specifically have limited the study sample to BD II (reviewed in Ketter and Drevets 2002).

From the Section on Neuroimaging in Mood and Anxiety Disorders (LM, Y-FD, WCD), Molecular Imaging Branch, and Mood and Anxiety Disorders Program (CAZ, JS, DAL, HKM), National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland. Address reprint requests to Linda Mah, M.D., Kunin-Lunenfeld Applied Research Unit, Posluns Building, Room 738, Baycrest Centre for Geriatric Care, 3560 Bathurst St., Toronto, Ontario, Canada M6A 2E1. E-mail: [email protected]. Received January 3, 2006; revised June 9, 2006; accepted June 10, 2006.

0006-3223/07/$32.00 doi:10.1016/j.biopsych.2006.06.009

Previous positron-emission tomography (PET) studies of unmedicated bipolar depressives that combined BD I and BD II cases demonstrated abnormally elevated metabolic activity in the amygdala, ventral striatum, and anatomically related limbic and paralimbic cortices. Drevets et al (1995a, 2002) found significantly increased normalized (regional/global) metabolism in the left amygdala, with a trend toward hypermetabolism in the right amygdala and the right ventral striatum in unmedicated bipolar depressives (n ⫽ 7, 4 BD II) relative to healthy control subjects. The same BD sample had decreased flow and metabolism in the subgenual ACC, an area where decreased perfusion also had been identified in an independent sample of bipolar depressives (Drevets et al 1997). In a larger sample of unmedicated, treatment-resistant, bipolar depressives (n ⫽ 17, 13 BD II) who were moderately to severely depressed, Ketter et al (2001) also found increased normalized metabolism in the right amygdala, bilateral ventral striatum, medial thalamus, and the medial cerebellum relative to healthy control subjects. Ketter et al (2001) also reported that in mildly depressed BD cases (n ⫽ 16, 9 BD II), normalized metabolism was increased in the posterior cingulate cortex, left ventrolateral prefrontal cortex (PFC), left middle and superior frontal gyri, left insula, hippocampus, left postcentral gyrus, left transverse temporal gyrus, and cerebellum, and decreased in the right inferior and middle temporal gyri. This study also reported regional differences in absolute metabolism, but the interpretation of these data was confounded by a significant reduction in whole brain metabolism in BD subjects relative to control subjects. Moreover, it was noteworthy that studies of unmedicated bipolar depressives identified abnormally increased normalized metabolism in many of the same regions where metabolism was elevated in unmedicated unipolar depressives, including the amygdala, ventral striatum, medial thalamus, ventrolateral PFC, OFC, pregenual ACC, BIOL PSYCHIATRY 2007;61:765–775 © 2007 Society of Biological Psychiatry

766 BIOL PSYCHIATRY 2007;61:765–775 anterior insula, posterior cingulate cortex, and medial cerebellum (reviewed in Drevets 2000). Insofar as these abnormalities constitute biologically relevant targets for treatment, the ability to assess how they are affected by antidepressant treatments is an important goal in investigations aimed at elucidating therapeutic mechanisms. However, the risk that such treatments would trigger hypomanic or manic conversion in BD cases generally requires that such subjects receive mood stabilizers during antidepressant treatment trials. It remains unclear, however, whether the metabolic abnormalities described above would be evident in subjects receiving moodstabilizing agents. Antidepressant, antipsychotic, and antianxiety treatments have been reported to reduce cerebral blood flow and metabolism in some frontal, parietal, and temporal lobe regions, such that many studies of patients on these medications have not detected areas of abnormally elevated metabolism or instead have reported areas of reduced flow or metabolism not evident in studies of unmedicated samples (reviewed in Drevets 2000). Preliminary data in the study conducted by Drevets et al (2002) suggested that mood stabilizer treatment reduced amygdalar metabolic activity toward normative levels in a small sample of clinically remitted subjects with BD but mood stabilizer treated subjects who remained depressed were not studied. More recently, Bauer et al (2005) found that currently depressed BD patients (n ⫽ 10, 1 BD II) being medicated with a combination of antidepressants and mood stabilizers exhibited increased relative metabolism versus control subjects in the subgenual ACC, right amygdala, right hippocampus, right ventral striatum, left thalamus, and cerebellar vermis, and decreased metabolism in the middle frontal gyri bilaterally. However, the variability in type, combination, and dosage of medications and the lack of blood drug concentrations (to ensure that therapeutic levels were achieved) limited the interpretability of these data. The current study used [18F]-fluorodeoxyglucose (18FDG) PET to assess cerebral metabolic rates for glucose (CMRglu) in BD II patients who remained depressed while receiving therapeutic doses of mood stabilizers. Based on previous PET studies of unmedicated depressed patients and preclinical studies of lithium and divalproex sodium (Bauer et al 2005; Drevets et al 2002; Du et al 2003; Ketter et al 2001), BD II depressives on therapeutic levels of mood stabilizers in the current study were expected to exhibit abnormally elevated metabolic activity in similar regions as found in unmedicated depressed samples, including the amygdala and anatomically related areas of the orbital and medial PFC and the ventral striatum. Associations between regional glucose metabolism and depression and anxiety ratings were examined post hoc.

Methods and Materials Subjects Patients (n ⫽ 13; 11 female subjects) met DSM-IV criteria for BD type II and for a current major depressive episode. Healthy control subjects (n ⫽ 18, 15 female subjects) with no history of psychiatric illness also participated. Diagnoses were established by an unstructured interview with a psychiatrist and the Structured Clinical Interview for DSM-IV-Patient Edition (SCID-P) with a second clinician. Patients were included if they had an initial score of ⱖ20 on the Montgomery-Asberg Depression Rating Scale (MADRS) (Montgomery and Asberg 1979). Severity of anxiety symptoms also was rated using the Hamilton Anxiety Rating Scale (HAM-A) (Hamilton 1959). All ratings were obtained on the day of the PET scan. Exclusion criteria included current www.sobp.org/journal

L. Mah et al psychotic features, serious suicidal risk, substance abuse within 90 days, substance dependence within 5 years, rapid cycling course within 12 months, major medical or neurological disorders, pregnancy, and lactating female subjects. Patients were recruited as part of a clinical trial studying the effect of adding an adjunct antidepressant medication to mood stabilizer treatment and were required to take a stable dose of either divalproex sodium or lithium for at least 4 weeks, during which two weekly blood drug levels fell within the therapeutic range before imaging (plasma valproic acid concentrations of 50 to 125 ug/mL; serum lithium concentrations of .6 –1.2 mEq/L). Other psychotropic medications and all medications likely to influence cerebral physiology, perfusion, or metabolism were not permitted within 2 weeks of scanning. Subjects provided written informed consent, as approved by the National Institute of Mental Health Institutional Review Board (NIMH-IRB). Image Acquisition Quantitative whole brain and regional metabolic measures were obtained using a technique that combined left cardiac ventricular chamber time-activity curve data with venous blood sampling to yield the input function for calculating CMRGlu. This method previously was validated against methods that employed arterial blood sampling to generate the 18FDG input function (Moore et al 2003). Positron-emission tomography images were acquired using a GE Advance PET scanner (GE Medical Systems, Waukesha, Wisconsin) (35 contiguous slices 4.25 mm thick; axial resolution ⫽ 4.9 and 5.3 mm full-width at half maximum [FWHM] in two-dimensional [2-D] and three-dimensional [3-D] modes, respectively). Subjects received 4.5 mCi of 18FDG following a fasting period of at least 6 hours. Following an initial transmission scan of the chest to permit measured attenuation correction of the cardiac emission scan, a 35-minute dynamic emission scan of the heart was acquired in 2-D mode (10 30-second frames and 10 3-minute frames), with concurrent serial venous blood sampling beginning 15 minutes posttracer injection. At 45 minutes posttracer injection, a 10-minute static emission scan of the brain was acquired in 3-D mode, which immediately was followed by an 8-minute transmission scan of the head to perform measured attenuation-correction of the emission scan. An anatomical magnetic resonance image (MRI) was obtained for each subject using a 3.0 Tesla GE Signa Scanner (GE Medical Systems, Waukesha, WI) and a 3-D MPRAGE (echo time [TE] ⫽ 2.982 milliseconds, repetition time [TR] ⫽ 7.5 milliseconds, inversion time ⫽ 725 milliseconds, voxel size ⫽ .9 ⫻ .9 ⫻ 1.2 mm) for co-registration of PET images. Image Analysis To quantify whole brain and regional CMRGlu from 18FDG emission images, the cardiac input function was derived by combining left cardiac ventricular chamber time-activity curve data with venous blood sampling, as detailed in Moore et al (2003). Briefly, cardiac slices were reconstructed and five left ventricular slices were identified for region-of-interest (ROI) placement. The cardiac image frames acquired from 0 to 5 minutes initially were averaged to allow localization of the left ventricular blood pool, while the frames acquired between 25 and 35 minutes permitted identification of myocardial wall 18 FDG uptake. Circular ROIs of 2 cm diameter were positioned over the left ventricular chamber on difference images obtained by subtracting the left ventricular myocardial 18FDG uptake from the blood pool image to minimize spillover of radioactivity from the myocardium. An average left ventricular time-activity curve

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L. Mah et al was computed from the time-activity curves obtained from the ROI in each of the five slices. The ratio of the average radioactivity concentration in venous blood samples taken at 25, 30, and 35 minutes to the average radioactivity concentration in the left ventricular blood pool from 25 to 35 minutes was calculated. This ratio was used to scale the 50-minute venous sample concentration, which was then appended to the left ventricular curve to complete the input function used to generate parametric images of regional cerebral metabolic rates for glucose (rCMRGlu) (Brooks 1982). An image of quantitative glucose metabolism was derived from the regional tissue radioactivity in the cerebral emission scan and the input function as described in Moore et al (2003). This image was aligned to the magnetic resonance (MR) image, which was resliced to yield images with the same slice thickness and voxel size as the PET images using AIR (Ahmanson-Lovelace Brain Mapping Center, UCLA, Los Angeles, California; Woods et al 1993). The whole brain metabolism was measured as the average CMRGlu within a template defined using the co-registered MRI image to define the brain edge (Drevets et al 2002). For analysis of regional metabolism, each subject’s MRI image and co-registered PET image were spatially normalized to the standard stereotaxic space of the Montreal Neurological Institute (MNI) template using SPM99 (Wellcome Department of Imaging Neuroscience, London, United Kingdom). Spatially normalized images were resampled to a voxel size of 2 ⫻ 2 ⫻ 2 mm. Metabolic images were filtered using a 12 ⫻ 12 ⫻ 12 mm Gaussian smoothing kernel within SPM99. Nonspecific effects of global activity were removed by proportional scaling. The a priori hypotheses were tested by comparing the normalized regional metabolism between depressives and control subjects within 12 predefined ROIs: the amygdala, OFC, ACC, and three striatal regions: the accumbens area, caudate and putamen in the left and right hemispheres. These regions were delimited using standard anatomical criteria (Breiter et al 1997; Drevets et al 2001; Duvernoy 1999; Szeszko et al 1999, 2002; Talairach and Tournoux 1988) on a single MNI template and applied to all normalized brains at the group level contrasts. The boundaries of the amygdala were defined anteriorly, laterally, and ventrally by the temporal lobe white matter, posteriorly by the temporal horn of the lateral ventricle and the alveus, dorsally by the basal ganglia, and medially by the cerebrospinal fluid (CSF) space. The boundaries of the ACC ROI were (anterior, posterior, ventral, dorsal) tip of the cingulate sulcus, connection of the superior and precentral sulci, callosal sulcus, and cingulate sulcus. The boundaries of the OFC ROI were (anterior, posterior, lateral, and medial) last appearance of the anterior horizontal ramus, last appearance of the olfactory sulcus, anterior horizontal ramus or circular sulcus of insula, and the olfactory sulcus. The accumbens area encompassed the gray matter ventral to the ventral tip of the internal capsule and was delimited dorsally by a line connecting the ventral aspect of the lateral ventricle and the ventral tip of the internal capsule and posteriorly by the anterior commissure. The caudate ROI extended from the anterior aspect of the caudate head posteriorly to the coronal plane containing the lateral geniculate nucleus. The putamen was outlined from the posterior boundary of the accumbens ROI dorsally and posteriorly as delimited laterally by the external capsule, medially by the internal capsule and the globus pallidus. Within each ROI, the mean normalized metabolism for the BD and control groups was compared voxel-by-voxel using unpaired t tests to identify voxels with t-values corresponding to p ⬍ .05. Correction for multiple comparisons was applied for

each ROI using the cluster test, which tests the probability of type I error based on the spatial extent of clusters of adjacent voxels for which the voxel t-values correspond to p ⬍ .05 (Poline et al 1997). The number of voxels used to compute the corrected p-values in the cluster-level comparisons was limited using the Small Volume Correction option within SPM99. To adjust for the number of ROIs searched, the resulting p-values also were divided by 12 (the number of ROIs), following the method described by Bauer et al (2005) for applying Bonferroni correction to multiple ROIs assessed by the Small Volume Correction approach (but see Discussion). To address the probability of type II error, the whole brain was searched post hoc using an exploratory voxelwise analysis to identify clusters of voxels with t-values corresponding to puncorrected ⬍ .001 that also exceeded the “minimum expected cluster size” (as calculated by SPM99 based on Gaussian random field theory and the smoothness of the image data and listed in the SPM99 output file) (Friston et al 1994). The significance threshold was set at p ⬍ .001 to reduce the likelihood of type I error because of the larger number of statistical comparisons performed for the whole brain analysis. Another post hoc analysis was performed to assess the covariance of regional metabolism across the areas where mean metabolism differed between groups within the predefined ROI. The normalized metabolism was extracted for each subject from the voxel containing the peak t-value within each region (the final spatial resolution of such voxel measurements after reconstruction and filtering was much larger than that of individual voxels), and the covariance between metabolic activity across regions was assessed by computing Pearson’s product moment correlation coefficients using SPSS Inc. (Chicago, Illinois). To test the significance of differences in intercorrelations between regional metabolism in patients versus control subjects, z tests on correlations transformed using Fisher’s z transformation were performed (Rosenthal 1991). Because these analyses were considered exploratory, the significance threshold for correlations performed within groups and the z tests comparing correlations across groups was set at p ⬍ .05.

Table 1. Demographic and Clinical Characteristics of Depressed and Control Samples Bipolar II Depressed Number Gender (M/F) Agea (Mean Years ⫾ SD) IQb MADRS (Mean ⫾ SD) HAM-A (Mean ⫾ SD) Age of Onset (Mean Years ⫾ SD) Length of Illness (Mean Years ⫾ SD) Length of Current Episode (Mean Months ⫾ SD)

13 2/11 43 (⫾ 8.4) 117 (⫾ 6.6) 32.2 ⫾ 4 Range (26–39) 17.2 (⫾ 3.9) Range (10–24) 20 (⫾ 10.5) 22.9 (⫾ 12.0)

Control Subjects 18 5/13 39 (⫾ 8.0) 116 (⫾ 9.1)

4.2 (⫾ 2.0)

IQ, intelligence quotient; MADRS, Montgomery-Asberg Depression Rating Scale; HAM-A, Hamilton Anxiety Rating Scale. a No significant difference in age between patients and control subjects [t (29) ⫽ 1.1, p ⫽ .28]. b No significant difference in IQ between patients and control subjects [t (22) ⫽ .26, p ⫽ .80]. Data unavailable for 3/18 control subjects and 4/13 patients.

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To optimize the pattern of regions where the covariance matrix of metabolic activities provided the greatest discrimination between individual depressed cases versus control subjects, a stepwise discriminant function analysis was performed using SPSS. Normality was examined using the Kolmorgorov-Smirnov test and visual review of histograms and boxplots. Outlying values with potential for having undue influence on the outcome of any statistics were adjusted by reducing the values to within one unit above the next highest value or one unit below the next lowest unit, depending on the end of the scale concerned (Tabachnick and Fidell 1989). Stepwise entry criteria were set at p ⬍ .05 with removal at p ⬎ .10 using Wilks lambda. A leave-one-out crossvalidation method was used to verify the sensitivity and specificity of the model. Following the stepwise procedure, the variables not entering the model were added individually to determine whether they would improve the sensitivity and specificity of the model. To identify areas where metabolism correlated with illness severity, linear regression analyses were performed post hoc using SPM99 to assess relationships between regional metabolism and depression or anxiety ratings. The statistical significance threshold was set at a voxelwise probability level of p ⬍ .01 within the predefined ROIs and p ⱕ .005 for other regions.

Results Demographic and clinical characteristics of the subjects appear in Table 1. The depressed and control samples were similar in gender composition, mean age, and intelligence quotient (IQ). With respect to comorbid conditions in the BD sample, two subjects met DSM-IV criteria for past alcohol dependence (in remission ⬎ 5 years), one for past polysubstance abuse (in remission ⬎ 3 months), one for obsessive-compulsive disorder, and one for eating disorder not otherwise specified. Three depressives had mild medical conditions including chronic sinusitis (n ⫽ 1), chronic low back pain (n ⫽ 1), and irritable bowel syndrome (n ⫽ 1). The control subjects were medically healthy and had no personal or family history of psychiatric illness. Patients were receiving lithium or divalproex only. Serum lithium levels (n ⫽ 8, mean ⫽ .73 mEq/L, range .6 –.9 mEq/L) and plasma divalproex levels (n ⫽ 5, mean ⫽ 75 ug/mL, range 54 –90 ug/mL) were within the therapeutic range. Control subjects were unmedicated. Whole Brain Metabolism The mean whole-brain CMRGlu did not differ between groups [BD II: .0560 ⫾ .015 mg/min/mL; control subjects: .0558 ⫾ .0068

Table 2. Comparison of Regional Metabolism in Bipolar II Depressed Subjects Versus Control Subjects the Regions of Interest Defined A Priori Stereotaxic Coordinatesa Region of Interest Amygdala Left Right Orbitofrontal Cortex Left

Right Anterior Cingulate Left Right Accumbens Area Left Right Caudate Left Right Putamen Left Right

Tb

Voxel-Level Uncorrected p-Value

Cluster-Level Corrected p-Valuec

x

y

z

⫺24 22

⫺1 ⫺1

⫺20 ⫺18

2.12 1.78

.02 .04

.041d .032d

⫺18 ⫺30 ⫺22 55 16

17 30 34 35 34

⫺14 ⫺13 ⫺17 ⫺3 ⫺18

2.93 2.59 2.40 2.12 2.11

.003 .007 .01 .02 .02

.003d

⫺6 ⫺4 12

48 18 47

⫺9 40 5

2.23 ⫺2.70 2.62

.03 .006 .007

.096 .14 .009d

⫺14 16

11 11

⫺7 ⫺6

3.24 3.12

.001 .002

⬍.001d ⬍.001d

⫺10 14

10 10

1 5

3.26 2.97

.001 .003

.037d .061

⫺22 26 30 18

8 8 4 8

⫺2 ⫺2 0 5

4.35 4.18 4.09 3.45

⬍.001 ⬍.001 ⬍.001 ⬍.001

⬍.001d ⬍.001d

.32 .31

a Stereotaxic coordinates locate peak voxel t-values by their distance in mm from the anterior commissure, with positive x and y indicating right and anterior, respectively, and positive z dorsal to a plane containing both the anterior and posterior commissures. Voxel coordinates were transformed from Montreal Neurological Institute to Talairach space using the Talairach Daemon database (http://ric.uthscsa.edu). b The voxel-level t-value for the voxel containing the peak difference. Positive t-values indicate greater regional metabolism in bipolar II subjects relative to control subjects. Negative t-values indicate lower regional metabolism in bipolar II subjects relative to control subjects. c The cluster-level corrected p-value represents the probability of obtaining any cluster with a spatial extent this large or larger (number of voxels) within the region of interest search volume after applying corrections for multiple comparisons (Poline et al 1997). d Significant after correction for multiple comparisons using the cluster test.

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Regions-of-Interest Analysis Table 2 summarizes the differences in regional metabolism between groups within the predefined ROI. Metabolism was increased (pcorrected ⬍ .05 after application of the cluster test) in the depressives versus the control subjects in areas within the left and right amygdala, left OFC, right ACC, left and right accumbens area, left and right putamen, and the left caudate. In other areas, metabolism appeared increased in the right OFC, left ventral ACC, and right caudate, and decreased in the left dorsal ACC, at a voxel-level significance threshold of puncorrected ⬍ .05, but these differences were not significant after applying the cluster test to correct for multiple comparisons within the ROI. After additionally applying Bonferroni adjustment to correct for multiple comparisons across the 12 ROI after Bauer et al (1995), only the differences in the left OFC, bilateral accumbens area, and bilateral putamen would have remained significant (but see Discussion). Whole-Brain Exploratory Analyses Figure 1 and Table 3 present the results of the post hoc, voxelwise analysis comparing metabolism between groups across the entire brain. The depressives had greater metabolism than control subjects in the anteroventral portion of the putamen bilaterally, right caudate, left posterior cingulate cortex/lingual gyrus, left OFC (anterior to the predefined OFC-ROI), right anterior insula, and left parahippocampal gyrus (Table 3). No areas were identified where metabolism was decreased significantly in the depressives versus the control subjects. Exploratory Analyses of Relationships between Regional Metabolism and Clinical Ratings Results from the exploratory, voxelwise regression analyses are summarized in Table 4. Depression severity ratings (MADRS scores) correlated positively with metabolism in the left lateral Table 3. Post Hoc Voxelwise Analysis of Regional Metabolism Between Bipolar II Depressed Subjects and Control Subjects Stereotaxic Coordinatesb Regiona

Figure 1. Image sections from the statistical parametric mapping (SPM) analysis showing regions with significantly greater metabolic activity in the bipolar disorder type II depressives versus the healthy control subjects. The SPM images demonstrate voxel t-values corresponding to a significance threshold set at puncorrected ⬍.001. The horizontal sections run parallel to the horizontal plane containing both the anterior and posterior commissures and are located at the bicommissural plane (upper panel) and 16 mm (lower panel) ventral to this bicommissural plane. The coronal and sagittal planes run orthogonal to the horizontal planes and are located at 8 and 44 mm anterior to the anterior commissure and 22 and 24 mm to the left of the midline in the upper and lower panels, respectively. This post hoc whole brain analysis showed elevated metabolism in left ventral striatum (A), right ventral striatum (B), left posterior cingulate (C), left orbitofrontal cortex (D), left parahippocampal cortex (E), and right insula/claustrum (see Table 2 for coordinates and significance levels).

mg/min/mL, t (29) ⫽ ⫺.063, p ⫽ .95]. Consequently, for the regional analyses, local metabolic values were normalized to whole brain metabolism to reduce the variability associated with nonspecific global fluctuations across subjects.

Depressives ⬎ Control Subjects Left Anteroventral Putamen Right Anteroventral Putamen Right Caudate Head Left Posterior Cingulate Cortex

Left Anterior Orbitofrontal Cortex Left Parahippocampal Gyrus Right Anterior Insula Control Subjects ⬎ Depressives No Suprathreshold Clusters

x

Y

z

Tc

⫺22 26 16 ⫺20 ⫺22 ⫺12d ⫺24 ⫺22 34

8 8 10 ⫺72 ⫺64 ⫺77 44 ⫺35 2

⫺2 ⫺2 3 0 1 7 ⫺16 ⫺7 2

4.35 4.18 3.70 4.20 4.14 3.48 3.95 3.72 3.99

Areas where mean metabolism was increased in bipolar II subjects relative to control subjects indicated by “Depressives ⬎ Control Subjects.” No areas were identified where metabolism was decreased at p ⬍ .001 in the depressives versus the control subjects. a Stereotaxic coordinates locate peak voxel t-values corresponding to p ⬍ .001 that are part of clusters of voxels that exceeded the minimum expected cluster size of 36 voxels per cluster (defined by SPM99). b Coordinates interpreted as in Table 2. c The voxel-level t-value for the voxel containing the peak difference. d Left posterior cingulate cortex region lying along the calcarine and parieto-occipital sulci.

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Table 4. Regions Where Metabolism Correlated with Depression and Anxiety Severity in Bipolar Subjects Positive Correlation

Negative Correlation

Stereotaxic Coordinatesa Brain Regiona Regions Where Metabolism Correlated with Depression Severity Left Superior Frontal Gyrus Left Precentral Gyrus Right Superior Temporal Cortex Right Inferior Parietal Lobule Regions Where Metabolism Correlated with Anxiety Severity Left OFC

x

y

⫺18 ⫺44 ⫺26 65 44

35 4 ⫺24 0 ⫺42

⫺20

40

Stereotaxic Coordinatesa Tb

pa

39 35 64 0 44

3.84 3.77 3.45 3.69 2.85

.001 .002 .003 .002 .003

Left Anterior Insula Left Lateral OFC

⫺34 ⫺34

⫺24

2.67

.01

Right Putamen

24 18 20 ⫺20 ⫺12 8 ⫺20 ⫺16 ⫺24 ⫺32 ⫺38 ⫺22 ⫺8 ⫺55 48

z

Brain Regiona

Left Putamen Left Caudate Right Medial Frontal Gyrus Left ACC Left Frontopolar Cortex Left OFC Left Anterior Insula Left Posterior Insula Left Superior Parietal Lobule Left Postcentral Gyrus Left Superior Temporal Gyrus Right Occipital Cortex

x

z

Tb

24 34

14 ⫺10

3.75 2.66

.002 .01

⫺7 1 10 4 8 ⫺23 40 57 34 14 ⫺28 ⫺57 ⫺32 ⫺40 ⫺70

15 13 4 3 5 53 24 5 ⫺12 14 14 62 64 18 5

5.44 4.56 3.34 2.95 2.93 4.48 3.21 3.96 3.77 3.58 3.07 4.57 3.06 3.18 3.40

⬍.001 ⬍.001 .003 .007 .007 ⬍.001 .004 .001 .002 .002 .005 ⬍.001 .005 .004 .003

y

pa

Depression severity rated by the Montgomery-Asberg Rating Scale; anxiety severity rated by the Hamilton Anxiety Rating Scale. OFC, orbitofrontal cortex; ACC, anterior cingulate cortex. a Stereotaxic coordinates, interpreted as in Table 2, locate peak voxel t-values corresponding to puncorrected ⱕ .01 within predefined regions of interest and p ⱕ .005 for other regions. b The voxel-level t-value for the voxel containing the peak t-value.

aspect of the superior frontal gyrus (dorsal anterolateral PFC), left precentral gyrus, right superior temporal gyrus, and right inferior parietal cortex but negatively with metabolism in the left anterior insula and left lateral OFC. Anxiety ratings (HAM-A) correlated positively with metabolism in the left medial OFC but negatively with metabolism in the left lateral OFC, bilateral putamen, left caudate, left ACC, left anterior and posterior insula, left frontal polar cortex, left postcentral gyrus, superior parietal lobule, left superior temporal cortex, and right occipital cortex (Table 4B). None of these clinical correlations would have remained significant after correction for multiple comparisons. Exploratory Correlational Analyses of the Metabolic Values Across Regions Within the striatal ROI, the metabolism in each accumbens, caudate, and putamen ROI correlated positively with that in the homologous ROI in the contralateral hemisphere in both depressives and control subjects (Table 5). The left accumbens metabolism correlated positively with metabolism in the left caudate in both groups but correlated (positively) with activity in the left ACC, left OFC, bilateral amygdala, and bilateral putamen only in the depressives. The strength of the correlation coefficients differed across groups only for the association between left accumbens and left ACC (p ⬍ .01). The right accumbens metabolism correlated positively with metabolism in the bilateral amygdala, bilateral putamen, right caudate, left OFC, and left www.sobp.org/journal

ACC only in the depressives, but the strength of these correlations differed between groups only for the left OFC and bilateral putamen (p ⬍ .05). Metabolism in the putamen also correlated positively with metabolism in the amygdala bilaterally in the depressives, but the strength of these correlations did not differ significantly between groups. The left and right amygdala metabolic values were positively correlated in the depressives, and the strength of this correlation differed between groups (p ⬍ .05). In the depressives, right amygdala metabolism correlated with metabolism in the right ACC and OFC bilaterally, but the strength of these correlations differed between groups only for the right OFC (p ⬍ .05). Furthermore, the depressives showed correlations between left OFC activity and activity in the left putamen and ACC, and the difference in the correlation coefficients between groups reached significance for the putamen. Finally, in the depressives, the left ACC metabolism correlated with left putamen metabolism and the right ACC metabolism correlated with right OFC metabolism, but only the intercorrelation between left ACC and putamen differed between groups (p ⬍ .05). Discriminant Analysis The stepwise discriminant analysis allowed the right putamen and right OFC to enter the model to differentiate patients and control subjects (Wald ␹2 ⫽ 18.42, df ⫽ 2, p ⬍ .0001). Table 6 shows the sensitivity and specificity of this model together with the cross-validated values. The right ACC and left OFC were

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Table 5. Intercorrelations Between Regional Metabolic Values for the Bipolar II Depressed and Control Subjects L Amygdala

L OFC

.82a,f

.53c .52c

.31

.24 .59c

R OFC

.34 .58c,f .23

⫺.05 ⫺.22 .02

L Cingulate

.50d .55d .68b .02

.08 .33 .38 ⫺.20

R Cingulate

.31 .60c,g .35 .70b,g .45

.06 ⫺.03 .20 .14 .31

OFC, orbitofrontal cortex. Correlation is significant at the .001 level (two-tailed). Correlation is significant at the .01 level (two-tailed). c Correlation is significant at the .05 level (two-tailed). d Trend, p ⬍ .1 (two-tailed). e Difference in strength of correlation coefficient between depressives and control subjects, p ⬍ .01. f Difference in strength of correlation coefficient between depressives and control subjects, p ⬍ .05. g Difference in strength of correlation coefficient between depressives and control subjects, .05 ⬍ p ⬍ .1. a

b

L Accumbens

.71b .66c .73b .29 .69b .46

.25 .36 .41d .16 ⫺.24 ⫺.17

R Accumbens

.68b,g .72b,g .67c,f .50d .56c,g .33 .77b

.06 .21 .01 .00 ⫺.16 .05 .64b

L Caudate

R Caudate

.55d .39 .34 .41 .19 .41 .55d .45

.35 .38f .40f .29 .32 .14 .45 .63c .66c

.21 ⫺.24 ⫺.08 .22 ⫺.24 ⫺.01 .47c .39

.08 ⫺.40d ⫺.37 ⫺.08 ⫺.04 .10 ⫺.11 .14 .74a

L Putamen

.60a .52d .69b,f .32 .64c,f .45g .77b,g .78b,f .71b .66c

.16 ⫺.07 ⫺.04 ⫺.31 ⫺.05 ⫺.21 .26 .03 .47c .40d

R Putamen

.62c,g .57c .43 .44f .53d,g .47f .80a,g .83b,f .67c .60c .84a

⫺.06 .09 ⫺.10 ⫺.38 ⫺.18 ⫺.32 .38 .17 .23 .10 .80a

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BIOL PSYCHIATRY 2007;61:765–775 771

Bipolar II Depressed Subjects (n ⫽ 13) L Amygdala R Amygdala L Orbital Cortex R Orbital Cortex L Cingulate R Cingulate L Accumbens R Accumbens L Caudate R Caudate L Putamen R Putamen Healthy Control Subjects (n ⫽ 18) L Amygdala R Amygdala L Orbital Cortex R Orbital Cortex L Cingulate R Cingulate L Accumbens R Accumbens L Caudate R Caudate L Putamen R Putamen

R Amygdala

772 BIOL PSYCHIATRY 2007;61:765–775

L. Mah et al

Table 6. Predictive Ability of Models from Discriminant Analysis Putamen (Right) ⫹ Cingulate ⫹ Orbital Frontal Orbital Frontal (Right) (Right) (Left) Observed Wilk’s Lambda Sensitivity Specificity % Correct Cross-Validation Sensitivity Specificity % Correct

.518 84.6 72.2 77.4

.483 84.6 88.9 87.1

.465 84.6 94.4 90.3

84.6 72.2 77.4

84.6 83.3 83.9

69.2 88.9 80.6

added successively to the stepwise model to determine whether they would improve the discriminative capability of the model. Each region improved the specificity and sensitivity for the model based on the observed values. However, the sensitivity for the model using the cross-validated values decreased with the addition of the left OFC. Thus, the model including right putamen, right OFC, and right ACC provided the best discriminative ability overall.

Discussion This study demonstrates for the first time that the abnormal increases in metabolic activity in limbic and paralimbic regions previously reported in unmedicated, depressed BD samples also are evident in depressed BD II subjects receiving mood stabilizers. In regions defined a priori, metabolism was elevated in the amygdala, accumbens area and anteroventral putamen bilaterally, left OFC, right pregenual ACC, and the left caudate in the BD subjects versus the control subjects (Table 2). Post hoc analyses additionally revealed increases in metabolism in depressives versus control subjects in the left parahippocampal cortex, posterior cingulate cortex, and right anterior insula (Table 3). Mean whole-brain CMRglu did not differ significantly between BD and control samples, justifying the comparison of regional values normalized to global metabolism to reduce nonspecific variability and ensuring that the regional results were not attributable to widespread increases in metabolism in BD. The major goal of this study was to determine whether metabolic abnormalities previously identified in unmedicated, depressed BD subjects also would be evident in depressed BD subjects currently taking mood stabilizers. Consequently, a priority of the hypothesis testing was to avoid type II error, and intergroup differences that replicated previous findings were reported in Table 2 if they survived correction for multiple comparisons within an ROI. Nevertheless, even when applying Bonferroni adjustment to correct for multiple comparisons across the 12 ROI after Bauer et al (2005), the differences in left OFC, bilateral accumbens area, and bilateral putamen remained significant. The validity of applying this method for correcting across the 12 regions searched by SPM99 was limited, however, because the metabolic measures across many of the regions were highly correlated (and thus nonindependent; Table 5). In addition, the sizes of the area searched using the small volume concentration analyses differed across ROI both herein and in Bauer et al (2005). Thus, important evidence that the abnormalities reported in Tables 2 and 3 did not constitute type I error was provided by their consistency with previous findings in BD (excepting the novel finding that metabolism was elevated in the parahippocampal gyrus in the BD II sample). www.sobp.org/journal

Although few previous functional imaging studies examined specifically the depressed phase of BD and none focused exclusively on BD II, it is noteworthy that the regional abnormalities reported herein in mood stabilizer treated, BD II depressives generally appear consistent with those reported in studies of unmedicated depressed samples that combined BD I and II depressives, combined BD and major depressive disorder (MDD) depressives, or included only MDD depressives. Specifically, the abnormal elevations of regional metabolism found in our sample replicated the findings of other studies of unmedicated BD depressives imaged at rest or during a continuous performance task in the right amygdala (Bauer et al 2005; Drevets et al 2002; Ketter et al 2001), left amygdala (Drevets et al 2002), ventral striatum (Ketter et al 2001; Drevets et al 1995a), and right ventral ACC (Bauer et al 2005). Our finding of increased metabolism in the posterior OFC situated on the left inferior frontal gyrus was consistent with the findings of Ketter et al (2001) in unmedicated, “mildly depressed” bipolar patients (BD I and BD II combined) and of Cohen et al (1992) in unmedicated, depressed patients (BD II and MDD combined). The mildly depressed BD sample of Ketter et al (2001) also showed increased normalized metabolism in the posterior cingulate and anterior insular cortices, consistent with the findings of our post hoc whole brain analysis (Table 3). Each of these abnormalities also has been reported in unmedicated depressed patients with MDD (reviewed in Drevets 2000). Finally, the area of increased metabolism in the left parahippocampal cortex in our BD II depressives (Table 3) is noteworthy in light of a report that metabolism in this region correlated positively with depression severity in MDD (Drevets et al 2002). Similarly, the absence of significant differences in whole brain metabolism between mood stabilizer medicated BD II patients and control subjects was consistent with the previous findings of Drevets et al (1997, 2002) in samples that included both BD I and BD II depressives. In contrast, Ketter et al (2001) reported that metabolism was reduced 7.7 % in a combined sample of BD I and BD II patients with moderate to severe depression. Other differences in the patient samples may have accounted for this apparent discrepancy across studies, however, as Ketter et al (2001) selected only treatment-resistant patients who in most cases also were rapid-cycling. Of the subjects studied herein and the BD subjects previously studied by Drevets et al (1997, 2002), none were rapid cycling and treatment refractoriness was not established. The current results suggest that lithium or divalproex do not produce the widespread reductions in metabolism that occur in response to benzodiazepine and some antipsychotic agents (reviewed in Drevets et al 2004). Moreover, the substantial overlap between the regional findings of the current study and those of previous studies conducted in unmedicated samples (reviewed above) suggests that the differences found herein between bipolar depressives and control subjects (Tables 2 and 3) were not attributable to effects of mood stabilizer treatment. Instead, these data suggest that currently depressed BD II subjects exhibit elevated neural transmission through the limbiccortical-striatal circuitry (Drevets et al 1992), which remains uncorrected by mood stabilizers in cases whose depressive symptoms persist in spite of mood stabilizer monotherapy. In contrast, mood stabilizer treatment that successfully alleviates the depressive syndrome may attenuate the hypermetabolism in these regions, as Drevets et al (2002) previously reported preliminary evidence that amygdala metabolism decreases to the normative range in BD patients whose depressive symptoms remitted during mood stabilizer monotherapy.

L. Mah et al One noteworthy difference between the metabolic results obtained in symptomatic BD patients on mood stabilizers versus those reported in unmedicated BD depressives involved the subgenual ACC. Metabolic activity was abnormally decreased in this region in unmedicated depressed subjects with BD (Drevets et al 1997), a finding not observed herein. The reduction in metabolism in unmedicated BD samples was associated with a prominent reduction in gray matter (Drevets et al 1997; Hirayasu et al 1999; Ongur et al 1998), which appeared to account for the apparent reduction in metabolism via partial volume averaging effects (Drevets 2005). Lithium treatment resulted in increased gray matter volume in specific PFC regions where abnormal reductions in gray matter were found in the untreated state, including the subgenual ACC (Moore et al, unpublished data, 2004), consistent with evidence from preclinical studies indicating that lithium and divalproex exert neurotrophic and neuroprotective effects in the frontal cortex of experimental animals (Du et al 2003). Potentially consistent with this hypothesis, Bauer et al (2005) found that in currently depressed BD I patients taking mood stabilizers (predominantly lithium or divalproex), metabolism was abnormally increased in the subgenual ACC (as well as in the right amygdala, ventral striatum, left thalamus, and cerebellar vermis). The previous finding of reduced metabolic activity in BD in the subgenual ACC thus may be significantly altered by lithium therapy, if the increase in subgenual ACC tissue is sufficient to eliminate the partial volume averaging effect in PET images. Longitudinal imaging studies acquired both pre- and post-treatment with mood stabilizers are needed to adequately characterize these relationships between morphometry and metabolism. The significant, positive correlations found between regional metabolic activity across the regions studied (Table 5) are consistent with their known anatomical connections with each other (Ongur et al 2003) because regional glucose metabolism largely reflects the energy utilization associated with synaptic transmission in neuronal terminal fields (Shulman et al 2004). For example, in the striatum, the positive correlations between the metabolism of the left and right side accumbens, caudate, and putamen are consistent with the known interhemispheric projections connecting these regions with their homologous structures in the contralateral hemisphere. Moreover, the specific areas of the striatum where metabolism was increased in the depressed BD sample, namely the accumbens, ventromedial caudate, and anteroventral putamen (Tables 2 and 3; Figure 1), are the specific targets of extensive afferent anatomical projections from the amygdala and orbital and medial PFC regions also implicated herein (Ongur et al 2003). Further, the limbic (amygdala, ventral striatum, anterior and posterior cingulate cortex) and paralimbic regions (medial and orbital PFC, anterior insula, parahippocampal gyrus) implicated in the current study participate in an extensively interconnected, “visceromotor” network of structures that modulate endocrine, autonomic, behavioral, and experiential aspects of emotional behavior (Ongur et al 2003). The pattern of intercorrelated, abnormal metabolic increases across the structures within this circuit in the depressed group suggests that these regions function in concert to mediate, modulate, and/or adapt to the pathophysiology underlying the bipolar depression phenotype. The absence of similar intercorrelations in the control group suggests that this circuit may not be physiologically activated in the normative, resting state (Shulman et al 2004). The significant covariation between the metabolic values across these regions in the BD group but not the control group

BIOL PSYCHIATRY 2007;61:765–775 773 also was reflected in the results of the discriminant analysis, which allowed relatively specific and sensitive classification of individual subjects into correct diagnostic groups. Notably, the structures identified by this analysis as providing the greatest discriminating capability were predominantly lateralized to the right hemisphere. In a previous, similar discriminant analysis involving unipolar depressives, left-sided structures within the same circuit (amygdala, lateral OFC, medial thalamus, and ventral ACC) provided the greatest discrimination of unipolar depressives from control subjects, as well as from bipolar depressives (Drevets et al 1995b). These data support hypotheses previously based on studies of secondary mood disorders associated with cerebrovascular lesions proposing that frontal-striatal dysfunction within the right hemisphere is involved more specifically in the pathophysiology of bipolar mood syndromes, whereas frontal-striatal dysfunction on the left side is more relevant to the development of unipolar depression (Robinson et al 1988). Post hoc exploratory analyses provided preliminary evidence of associations between regional metabolism and symptom ratings. Depression severity correlated positively with metabolism in the dorsal anterolateral PFC. This region reportedly showed reduced metabolism in both unipolar and bipolar depression (Baxter et al 1989) and reductions in glial cell counts and neuronal size in MDD (Rajkowska et al 1999). Depression ratings also correlated positively with metabolism in the right superior temporal cortex, a region that forms part of the extended visceromotor network (Ongur et al 2003) and is reduced in volume in depressed BD subjects (Nugent et al 2006). The inverse correlation found between depression severity and metabolism in the left OFC and anterior insula is consistent with studies of unmedicated unipolar depressives imaged at rest (Drevets et al 1992, 1995b) or during a continuous performance task (Kimbrell et al 2002). Notably, lesions within the left OFC increase the risk for developing late-onset depression (MacFall et al 2001). Taken together, these data suggest that left lateral OFC/anterior insular function exerts a modulatory or inhibitory effect on depressive symptomatology (Drevets et al 2004). A complex relationship was observed between anxiety severity and metabolism in the left medial OFC, such that anxiety correlated negatively with metabolism in one left medial OFC region but positively in another that was situated more ventrally and anteriorly. These observations are notable, given evidence that two adjacent regions in the medial PFC exert opposing effects on amygdala activity and fear-related behavior in rats (Vidal-González et al, unpublished data). Anxiety severity also correlated inversely with metabolism in the left frontal polar cortex and left anterior insula, compatible with previous reports that metabolism in these regions correlated inversely with depressive symptoms rated by the Hamilton Depression Rating Scale, which also assesses multiple anxiety symptoms (Drevets et al 1992). In addition, anxiety ratings correlated negatively with metabolism in the left superior temporal cortex and left posterior insula, consistent with the previous findings of Osuch et al (2000) in BD subjects imaged across a variety of mood states, and in the caudate, similar to reports that right caudate activity correlated negatively with symptom severity in posttraumatic stress disorder (Lucey et al 1997). Finally, the observation that anxiety symptoms correlated inversely with metabolism in the anteroventral putamen was notable, given evidence that in healthy humans, anxiety symptoms associated with amphetamine administration correlated inversely with the magnitude www.sobp.org/journal

774 BIOL PSYCHIATRY 2007;61:765–775 of dopamine release in the anteroventral striatum (Drevets et al 2001). In summary, during mood stabilizer treatment, persistently depressed subjects with BD II show increased metabolism in the amygdala and other limbic and paralimbic structures. The presence of elevated activity within this extended functional anatomical network that modulates visceromotor function (Ongur et al 2003) thus may depend primarily on the current mood state when considered together with evidence that metabolism in these regions decreases following effective antidepressant treatment in MDD subjects (reviewed in Drevets et al 2004) but increases during tryptophan depletioninduced depressive relapse in recovered MDD subjects (Neumeister et al 2004). These results confirm the feasibility of studying the metabolic effects of adjunctive antidepressant therapy in patients receiving mood stabilizers to prophylax against the development of hypomania. In addition, the convergence of findings between bipolar disorder type II depression and unipolar depression suggests that the functional anatomical correlates of the major depressive syndrome constitute a neurophysiological phenotype that, to some extent, appears independent of mood disorder subtype. This research was supported by the Intramural Research Program of the Naitonal Institutes of Health (NIH), National Institute of Mental Health (NIMH) and was presented to fulfill thesis requirements toward a Masters in Health Sciences degree through the Duke-NIH Clinical Research Training Program. We thank examining committee members David C. Steffens, M.D., M.HSc., and David M. DeLong, Ph.D., for their helpful comments, the NIH PET Staff, and Allison C. Nugent, Ph.D., and Stephen J. Fromm, Ph.D., for advice regarding scientific development, image processing, and analysis. Bauer M, London ED, Rasgon N, Berman SM, Frye MA, Altshuler LL, et al (2005): Supraphysiological doses of levothyroxine alter regional cerebral metabolism and improve mood in bipolar depression. Mol Psychiatry 10:456 – 469. Baxter LR Jr, Schwartz JM, Phelps ME, Mazziotta JC, Guze BH, Selin CE, et al (1989): Reduction of prefrontal cortex glucose metabolism common to three types of depression. Arch Gen Psychiatry 46:243–250. Breiter HC, Gollub RL, Weisskoff RM, Kennedy DN, Makris N, Berke JD, et al (1997): Acute effects of cocaine on human brain activity and emotion. Neuron 19:591– 611. Brooks RA (1982): Alternative formula for glucose utilization using labeled deoxyglucose. J Nucl Med 23:538 –539. Calabrese JR, Hirschfeld RM, Reed M, Davies MA, Frye MA, Keck PE, et al (2003): Impact of bipolar disorder on a U.S. community sample. J Clin Psychiatry 64:425– 432. Cohen RM, Gross M, Nordahl TE, Semple WE, Oren DA, Rosenthal N (1992): Preliminary data on the metabolic brain pattern of patients with winter seasonal affective disorder. Arch Gen Psychiatry 49:545–552. Drevets WC (2000): Neuroimaging studies of mood disorders. Biol Psychiatry 48:813– 829. Drevets WC (2005): Brain structural abnormalities in mood disorders. In: Zorumski CF, Rubin EH, editors. Psychopathology in the Genome and Neuroscience Era. Washington, DC: American Psychiatric Publishing Inc., 119 –152. Drevets WC, Gadde K, Krishnan R (2004): Neuroimaging studies of depression. In: Charney DS, Nestler EJ, Bunney BJ, editors. The Neurobiological Foundation of Mental Illness, 2nd ed. New York: Oxford University Press, 461– 490. Drevets WC, Gautier C, Price JC, Kupfer DJ, Kinahan PE, Grace AA, et al (2001): Amphetamine-induced dopamine release in human ventral striatum correlates with euphoria. Biol Psychiatry 49:81–96. Drevets WC, Price JL, Bardgett ME, Reich T, Todd RD, Raichle ME (2002): Glucose metabolism in the amygdala in depression: Relationship to

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