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. Author manuscript; available in PMC: 2011 Nov 1.
Published in final edited form as: J Affect Disord. 2010 May 7;126(3):395–401. doi: 10.1016/j.jad.2010.04.004

MRI Signal Hyperintensities and Treatment Remission of Geriatric Depression

Faith M Gunning-Dixon *, Michael Walton *, Janice Cheng *, Jessica Acuna *, Sibel Klimstra *, Molly E Zimmerman ‡, Adam M Brickman ^, Matthew J Hoptman †,+, Robert C Young *, George S Alexopoulos *
PMCID: PMC2946967  NIHMSID: NIHMS205371  PMID: 20452031

Abstract

Background

White matter abnormalities may interfere with limbic-cortical balance and contribute to chronic depressive syndromes in the elderly. This study sought to clarify the relationship of SH to treatment response. We hypothesized that patients who failed to remit during a 12-week controlled treatment trial of escitalopram would exhibit greater SH burden than patients who remitted.

Methods

The participants were 42 non-demented individuals with non-psychotic major depression and 25 elderly comparison subjects. After a 2-week single blind placebo period, subjects who still had a Hamilton Depression Rating Scale (HDRS) of 18 or greater received escitalopram 10 mg daily for 12 weeks. Remission was defined as a HDRS score of 7 or below for 2 consecutive weeks. FLAIR sequences were acquired on a 1.5 Tesla scanner and total SH were quantified using a semi-automated thresholding method.

Results

The patient sample consisted of 22 depressed patients who achieved remission during the study and 20 depressed patients who remained symptomatic. ANCOVA, with age and gender as covariates, revealed that depressed subjects had greater total SH burden relative to non-depressed controls. Furthermore, patients who failed to remit following escitalopram treatment had significantly greater SH burden than both patients who remitted and elderly comparison subjects, whereas SH burden did not differ between depressed patients who remitted and elderly comparison subjects.

Limitations

Patients were treated with a fixed dose of antidepressants and the index of SH is an overall measure that does not permit examination of the relationship of regional SH to treatment remission.

Discussion

SH may contribute to a “disconnection state” both conferring vulnerability to and perpetuating late-life depression.

Introduction

A common finding in older adults is the presence of signal hyperintensities (SH) on magnetic resonance imaging (MRI). SH are areas of increased intensity appearing on T2-weighted images and are thought to reflect damage to the white matter and subcortical nuclei. With the exception of those that appear as smooth “rims” or “caps” along the surface of the lateral ventricles, SH primarily appear to be ischemic in nature and reflect rarefaction of myelin, breakdown of vessel endothelium, and microvascular disease (Fazekas et al., 1993; Thomas et al., 2003). SH are more frequent and severe in older depressed individuals than age-matched controls, mainly occur in subcortical regions and frontal white matter projections (Coffey et al., 1990; Greenwald et al., 1996; 1998; Krishnan 1993; Lenze et al., 1999; MacFall et al., 2006; O’Brien et al., 1996; O’Brien et al., 2006; Sheline et al., 2008; Taylor et al., 2003a; Thomas et al., 2003), and are often associated with select cognitive deficits (Boone et al., 1992; Kramer-Ginsberg et al.. 1999; Lesser et al., 1996; Potter et al., 2007).

In addition to the frequent presence of SH in depressed elders, functional neuroimaging studies suggest that depression is associated with abnormal, mostly increased metabolism, in limbic regions, including the amygdala (Drevets et al., 2002), the subgenual and rostral anterior cingulate (Kennedy et al., 2001; Mayberg et al., 1999; Smith et al., 1999) the lateral orbitofrontal cortex (Drevets et al., 2002) and the posterior cingulate (Drevets et al., 2002). In contrast, the dorsolateral prefrontal cortex and the dorsal anterior cingulate often demonstrate reduced blood flow during depressive states (Biver et al., 1994; Drevets et al., 2002; Kennedy et al., 2001). Relative to elderly controls, elderly depressed patients exhibit reduced activation of dorsal anterior cingulate, dorsolateral prefrontal cortex, and the hippocampus in response to neurobehavioral probes (Aizenstein et al., 2005; de Asis et al., 2001; Takami et al., 2007; Wang et al., 2009). Hyperactivity of limbic regions, particularly the amygdala, appears to reflect sustained reactivity to negatively valenced features of incoming stimuli and this sustained activity may be related to a tendency to ruminate (Siegle et al., 2002). Furthermore, improvement of depression is often associated with at least partial normalization of these patterns (Aizenstein et al., 2009; Brassen et al., 2008; Buchsbaum et al., 1997; Drevets et al. 2002; Kennedy et al., 2001; Kennedy et al., 2007; Saxena et al., 2002).

It is plausible that the presence of SH may contribute to a disconnection state that not only confers vulnerability to the development of late-life depression but may prevent remission by interfering with the efficiency of networks needed for normalization of abnormal cerebral activity patterns that contribute to depression. However, to date, the existence of a relationship between SH and response to antidepressant treatment is controversial. A number of studies indicated that white matter hyperintensity (WMH) burden and subcortical gray matter hyperintensities were associated with response to both ECT (Hickie et al., 1995; Steffens et al., 2002) and antidepressant pharmacotherapy (Hickie et al., 1995; Patankar et al., 2007; Simpson et al., 1998). However, some disagreement exists (Janssen et al., 2007). In particular, the failure to detect a relationship between SH severity and antidepressant treatment response in two controlled monotherapy antidepressant trials casts doubt on the existence of a relationship (Salloway et al., 2002; Sneed et al., 2007).

It is possible that methodological differences between studies have contributed to the inconclusive findings regarding SH severity and treatment response. First, the uncontrolled nature of many of the treatment interventions as well as inclusion of individuals with early dementia and/or psychotic symptoms likely contributes to the mixed findings. Furthermore, some studies of SH have been limited by the use of qualitative visual rating scales. For example, although the Salloway (2002) study was a placebo lead-in, controlled treatment trial, SH were examined using a visual rating scale. Recent image analysis techniques have been developed that enable the quantitative analysis of SH volumes (Firbank et al., 2004; Gurol et al., 2006; MacFall et al., 2006; Sheline et al., 2008; Wu et al., 2006) and direct comparison of visual rating scales and volumetric analysis of SH revealed that volumetric analysis was more sensitive than three commonly used visual rating scales to differences between clinical groups as well as relationships with cognitive performance (van Straaten et al., 2006).

The purpose of the study was to examine the relationship of SH to treatment response. To clarify the existing conflicting findings, this study addresses some of the critical methodological limitations that have characterized previous investigations. That is, to our knowledge, this is the first treatment response study in geriatric depression to report the use of a quantitative volumetric method to examine SH within the context of a placebo-lead in controlled antidepressant treatment trial. We hypothesized that elderly depressed patients would exhibit greater SH burden than non-depressed controls subjects. Furthermore, we predicted that patients who fail to remit with treatment would exhibit greater SH burden than those patients who remit with treatment.

Patients and Methods

Participants

The depressed participants were 42 (> 60 years) patients from a university-based geriatric psychiatry clinic who were recruited for an escitalopram treatment trial. Scans were performed during a 2-week single blind drug washout/placebo lead-in phase. Participants met DSM-IV-TR criteria and Research Diagnostic Criteria for unipolar major depression and had a score > 18 on the 24-item Hamilton Depression Rating Scale (Williams 1988). Exclusion criteria were 1) major depression with psychotic features (according to DSM-IV-TR); 2) history of other psychiatric disorders (except personality disorders) before the onset of depression; 3) severe medical illness (i.e., metastatic cancer, brain tumors, unstable cardiac, hepatic, or renal disease, myocardial infarction, or stroke) within the 3 months preceding the study; 4) neurological disorders (i.e., dementia or delirium according to DSM-IV criteria, history of head trauma, Parkinson’s disease, and multiple sclerosis); 5) conditions often associated with depression (i.e., endocrinopathies other than diabetes, lymphoma, and pancreatic cancer); 6) drugs causing depression (i.e., steroids, α-methyl-dopa, clonidine, reserpine, tamoxifen, and cimetidine); 7) Mini-Mental State Examination (Folstein et al., 1975) score < 25; and 8) contraindications to MRI scanning. These criteria resulted in a group of elderly patients with non-psychotic unipolar major depression of mild to moderate severity without a diagnosable dementing disorder.

Depressive symptoms were assessed using the (HDRS). Side effects of escitalopram were monitored with the UKU side effect scale (Lingjaerde et al., 1987). Baseline cognitive impairment was rated with the Mini-Mental State Examination.

Comparison Participants

An elderly group of community-dwelling volunteers were recruited by advertisements using newspaper announcements. Additional exclusion criteria were current or history of Axis I Disorders as well as current or previous psychiatric treatment.

The Weill Cornell Medical College and Rockland Psychiatric Center/Nathan S. Kline Institute (NKI) for Psychiatric Research Institutional Review Boards approved all procedures. After complete description of the study to subjects, written informed consent was obtained.

Treatment

Depressed patients were informed that they would receive placebo at some point during their 14-week trial. After a 2-week drug wash-out and single blind placebo lead-in, subjects who still met DSM-IV-TR criteria for major depression and had an HDRS score of 18 or greater received controlled treatment with escitalopram 10 mg daily for 12 weeks. Escitalopram was given in a single dose in the morning. Subjects received their medication in one-week supply blisters that permitted dispensation of their daily dosage separately.

During the treatment phase, the subjects were followed weekly beginning with the placebo lead-in and until the 12th week of treatment with escitalopram. During each follow-up meeting, a research assistant administered the HDRS, the UKU, obtained vital signs, questioned the subjects about medication adherence, and counted the remaining tablets. The meeting with the research assistant was followed by a brief session with a research psychiatrist, to assess the risk of continuing the treatment trial. The session followed a medication clinic model consisting of a review of symptoms, explanations related to the need for treatment, and encouragement of treatment adherence. No subject received psychotherapy during the study. The subjects were considered in remission if they no longer met DSM-IV-TR criteria for depression and had an HDRS score of 7 or below for two consecutive weeks.

MRI

Scanning took place on a 1.5T Siemens Vision scanner (Erlangen, Germany) housed at NKI’s Center for Advanced Brain Imaging. Scans were performed during a 2-week single blind drug washout/placebo lead-in phase of the treatment trial. FLAIR images were acquired with echo time TE=119ms, inversion time TI=2400ms, and repetition time TR=9000ms at 4mm, with the interslice gap set at 1mm, 240mm field of view, and matrix size 210 × 256. Slices were positioned in an oblique axial plane parallel to the anterior commissure-posterior commissure axis.

Image Analysis

FLAIR sequences were converted to Analyze format using MRIcro (www.mricro.com) for computer-assisted determination of SH volume. Volumetric measures of SH were obtained on the axial FLAIR images in native space. The approach for quantification of SH was similar to the protocol detailed by Gurol and colleagues (2006). First, based on a priori knowledge of the distribution of voxel intensities and visual inspection of each image, a semi-automated procedure was used to determine an intensity threshold for each scan to automatically label voxels that fell within the “hyperintense” distribution. Second, regions of interest (ROIs) were manually traced on each slice to define gross regions containing hyperintense signal while excluding non-SH areas that were labeled in the first step (e.g., orbital and dermal fat). Finally, the intersection of the automatically labeled voxels and the manually defined ROIs yielded the volume of SH in cm3. SH were included in the measurement if they were located either in the white matter or in subcortical nuclei, whereas SH in cortical gray matter were excluded from the total SH measurement. Scans were rated by one rater (FGD) who was blind to subjects’ diagnosis and remission status. Intrarater reliability conducted on 10 scans was r = .98 for total SH ratings and intrarater reliability conducted between 2 raters on 10 scans was r = .96.

Data Analysis

Group comparisons of demographic variables were conducted using univariate analysis of variance (ANOVA). The relationship of diagnostic group (Controls, NonRemitters, Remitters) to SH was examined using analysis of covariance (ANCOVA) with diagnostic group as a between subjects independent variables, age and gender as covariates, and total SH as the dependent variable. In these models, age was used as a covariate because of the documented relationship between age and SH severity. All ANCOVA’s were completed using an intent-to-treat model, which is most analogous to treatment of real patients, and therefore estimates the effect of treatment as offered. The intent-to-treat principle has been mandated by the FDA as the primary design and analysis strategy for industry clinical trials. Our analysis compares outcome data for all participants regardless of their status of adherence to treatment. Two-tailed significance levels are reported. Data analysis was performed with SPSS 14.0 (SPSS, Inc.).

Results

A total of 54 patients met study eligibility criteria, completed the required MRI sequences, and entered the 2-week single-blind, placebo lead-in period. Of these, 42 met symptom severity criteria after the 2-week placebo phase and entered the 12-week escitalopram treatment phase. Of these 42 subjects, 34 completed the 12-week treatment trial. Of the remaining 8, 2 had 4 weeks of treatment (both exited due to worsening of their depression), 2 had 7 weeks of treatment (1 exited because he found the treatment ineffective and 1 withdrew because she developed hyponatremia), 1 had 8 weeks of treatment and exited because he found the treatment ineffective, 1 had 9 weeks of treatment and exited because of worsening depression and 2 had 11 weeks of treatment with escitalopram and exited because they found the treatment ineffective). All but one patient who exited prior to 12 weeks of treatment had failed to achieve remission.

The sample consisted of depressed patients who achieved remission during the study (Remitters, N = 22), depressed patients who failed to achieve remission (Non-Remitters, N = 20), and non-depressed controls (Controls, N = 25). The groups did not differ in age, gender, years of education, baseline MMSE performance, or history of hypertension. None of the subjects had a lifetime history of stroke. 41% of the sample had a history of hypertension. As expected, the three groups differed in their ratings on the baseline HDRS (F (2, 67) = 288.51, p < .001), with both patient groups having higher baseline HDRS ratings than comparison subjects. Although at baseline HDRS did not differ between Remitters and Non-Remitters, at study exit Remitters had significantly lower HDRS ratings than did Non-Remitters (t (40) = 6.12, p < .001). None of the subjects had a lifetime history of stroke. 41% of the sample had a history of hypertension.

ANCOVA with age and gender as covariate and diagnosis as a between subjects variable revealed that diagnosis is a significant predictor of SH (F (1, 63) = 5.67, p < .05). That is, depressed patients had a significantly greater burden of SH (Mean = 6.66, SD = 7.91) than the Controls (Mean = 3.11, SD = 2.84). To examine the relationship of remission to SH, ANCOVA was performed with age and gender as covariates and treatment classification as a between subjects factor (Controls, Remitters, Non-Remitters). Results of the ANCOVA revealed that there was a main effect of treatment classification (F (2, 62) = 6.35, p < .01). Post hoc analysis using Least Square Difference revealed that the Non-Remitters (Mean = 9.50, SD = 9.19) exhibited greater SH than both Controls (p <. 001) and Remitters (Mean = 4.08, SD = 5.59, p < .05). In contrast, SH did not differ between Controls and Remitters (p = .40).

To explore whether the group differences between Remitters and Non-Remitters remained when the influence of gross cognitive status was taken into account, ANCOVA was performed with age, gender, and total performance on the Mattis Dementia Rating Scale as covariates, and treatment classification as a between subjects factor (Controls, Remitters, Non-Remitters). Results of the ANCOVA revealed that there was a main effect of treatment classification (F (2, 61) = 7.41, p < .01). Post hoc analysis using Least Square Difference revealed that the Non-Remitters exhibited greater SH than both Controls (p < .001) and Remitters (p < .01).

To examine the potential influence of the presence of hypertension on group differences in SH burden, another ANOCOVA was performed with age, gender, and presence of hypertension as covariates, and treatment classification as a between subjects factor. Results of the ANCOVA revealed that the main effect of treatment classification remained significant (F (2, 61) = 6.23, p < .01). Post hoc analysis using Least Square Difference revealed that the Non-Remitters exhibited greater SH than both Controls (p < .001) and Remitters (p < .01).

Discussion

The chief finding of this study is that depressed patients exhibit greater SH burden than non-depressed elderly comparison subjects, and this relationship is principally accounted for by the greater SH burden in patients who failed to achieve symptom remission in a prospective pharmacologic treatment trial. In contrast, patients who achieve remission do not differ significantly from elderly non-psychiatric comparison subjects in SH severity.

To our knowledge, this is the first study to examine the relationship of SH to treatment response in non-demented, elderly depressed patients using a quantitative, volumetric measure of SH within the context of a placebo lead-in, controlled treatment trial of escitalopram. Volumetric measures of SH appear to be more sensitive than visual rating scales to subtle group differences and relationships with clinical variables. For example, a previous report that compared three commonly used visual rating scales with a semi-automated volumetric approach indicated that the volumetric approach allowed for more sensitive and reliable detection of relationships between SH and cognition (van Straaten et al. 2006). Another study demonstrated the benefit of semi-automated volumetric analysis relative to a visual rating scale, finding greater sensitivity and reliability in an examination of longitudinal changes in SH (van den Heuvel et al. 2006). The superiority of the volumetric approach is likely enhanced by reducing user interference, strengthening intra- and inter-rater reliability, and avoiding ceiling effects that are inherent in many visual rating scales.

Our findings are consistent with findings that abnormalities of cerebral white matter and subcortical nuclei are associated with poor outcomes of late-life depression (Heiden et al. 2005; Hickie et al. 1995b; Simpson et al. 1998; Steffens et al. 2001; Steffens et al. 1999). For example, a recent study noted that basal ganglia hyperintensities predicted failure to respond to antidepressant monotherapy with a sensitivity of 80% and a specificity of 62% (Patankar et al. 2007). Along with short-term treatment outcomes, percent increase in white matter hyperintensities predicts two-year outcome of depression (Taylor et al. 2003b), and lesions in subcortical grey matter predict poor outcome of geriatric depressed patients over a period extending up to 64 months (Steffens et al. 2005). Furthermore, our diffusion tensor imaging work suggests that microstructural white matter abnormalities, including those in frontostriatal and limbic networks, predict failure to achieve remission after treatment with escitalopram (Alexopoulos et al. 2008).

A model of the neurobiology of emotion based on neuroimaging findings identifies two neuroanatomical systems, one ventral and one dorsal, that appear critical to the processing of emotional stimuli (Phillips et al. 2003a). The ventral system, which includes the amygdala, insula, ventral striatum, ventral anterior cingulate, and orbitofrontal cortex, is important for the evaluation of the emotional significance of incoming stimuli and generation of an affective response (Phillips et al. 2003a). The dorsal system, comprised of the dorsal anterior cingulate, dorsolateral prefrontal cortex, and the hippocampus, is critical for the cognitive regulation of affective responses. The ventral and dorsal systems have reciprocal connections, and dysfunction in either network may result in poor emotional regulation. Depression is believed to be characterized by increased negative bias in the assessment of incoming stimuli within the ventral system, accompanied by decreased regulation of the affective response by the dorsal system (Phillips et al. 2003b).

fMRI studies of elderly depressed patients suggest that abnormal activation in select frontolimbic regions is state-related (Aizenstein et al., 2009; Wang et al., 2008). For example in elderly depressed patients hypoactivation of the ventromedial prefrontal cortex in response to the emotional evaluation of negatively-valenced words, normalized after several months of uncontrolled antidepressant treatment (Brassen et al., 2008). Furthermore, in a controlled, antidepressant treatment trial, during a cognitive control task elderly depressed patients demonstrated hypoactivation in the dorsolateral prefrontal cortex and diminished functional connectivity between the dorsolateral prefrontal cortex and dorsal ACC prior to treatment (Aizenstein et al., 2009). Although the hypoactivity in the right dorsolateral prefrontal cortex subsided after successful antidepressant treatment, the reduced functional connectivity between the dorsal ACC and the DLPFC persisted.

The above observations suggest that metabolic changes occur during depression in networks critical to emotional functioning and that improvement of depression is associated with restoration of some of these changes. Thus, one plausible interpretation of our findings is that SH interfere with the reciprocal regulation of dorsal cortical-ventral limbic networks critical to the experience of emotions and lead to a “disconnection state” associated with poor antidepressant response. Evidence from a DTI study of microstructural white matter integrity provides converging evidence for the important contribution of lower white matter integrity to resistance to treatment with antidepressants, although some disagreement exists (Taylor et al., 2009). Of course multiple biological (e.g., medical illness, genetic influences) and nonbiological factors (e.g., poor social support) likely contribute to poor antidepressant response in late-life depression with the presence of a disconnection state being only one plausible contributor (Alexopoulos, 2005). This is highlighted by the fact that approximately half of the patients who failed to remit in this study exhibited SH burden that was within the same range as the nondepressed comparison subjects.

The findings of this study should be viewed in the context of its limitations. These include the subject selection bias inherent in imaging studies (i.e., healthier patients may be preferentially included), the fixed dose of the antidepressant, and the absence of long-term follow-up). It is plausible that some patients may have remitted if treated with higher dosages of escitalopram and for longer periods of time. However, 6 out of 8 non-remitted subjects who exited the trial had 7–11 weeks of escitalopram treatment and 2 had 4 weeks of treatment. Another limitation of the study is that use of a global measure of SH that does not allow the examination of the relationship of SH in specific anatomical regions to treatment remission. Thus, the results from this study cannot directly speak to earlier findings that subcortical gray and frontal WMH may be of particular importance in the presence and course of late-life depression (Greenwald et al., 1998; Patankar et al., 2007; Steffens et al., 2005). Finally, the study screening and recruitment procedures yielded a subject of elderly patients with major depression of mild to moderate severity and these results may not generalize patients with more severe depression.

In conclusion, this study indicates that SH burden increases the risk for chronicity of geriatric depression. Along with microstructural white matter abnormalities (Alexopolous et al., 2008), SH may contribute to a “disconnection state” predisposing and perpetuating late-life depression by interfering with the regulation of limbic-cortical balance. These findings set the stage to examine the relationship of SH in specific regional locations to treatment outcomes as well as to explore the relationship of SH to treatment response within the context of a functional neuroimaging study of geriatric depression. The identification of specific structure/function network abnormalities associated with treatment response can generate investigations of specifically targeted novel therapeutic interventions.

Figure 1.

Figure 1

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Signal Hyperintensities in Controls, Remitters, and Non-Remitters

TABLE 1.

Baseline demographic and clinical characteristics of depressed study subjects and normal controls.

Controls (N=25) Remitters (N= 22) Non-Remitters (N= 20) Analysis
Variable Mean SD Mean SD Mean SD F p
Age (years) 70.68 5.82 69.61 4.71 71.18 6.95 0.39 .67
Gender (% men) 36.0 - 36.4 - 45.0 - .64
Education (years) 16.40 2.84 16.36 2.72 16.65 2.41 0.07 .88
Baseline HDRS 1.72 1.82 22.29 4.01 22.10 4.04 288.51 .00
Hypertension % 40 45 40 chi2=0.16 .91
Age of Onset N/A N/A 57.0 15.33 58.0 16.80 .0.03 .86
Number of Previous N/A N/A 2.67 3.23 2.71 1.45 0.01 .96
Episodes
Baseline MMSE 28.64 0.91 28.68 1.39 28.06 1.26 1.69 .19
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Note: HDRS = 24-item Hamilton Depression Rating Scale; MMSE = Mini Mental State Exam

Acknowledgments

Role of Funding Sources

This work was supported by National Institute of Mental Health grants P30 MH68638 (GSA), RO1 MH65653 (GSA), T32 MH19132 (GSA), and K23 MH074818 (FGD), the Sanchez Foundation, the TRU Foundation, and Forest Pharmaceuticals. The NIMH, Forest Pharmaceuticals, the Sanchez Foundation, and the TRU Foundation had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

The authors thank Raj Sangoi RT (R) MR for his work as chief MR technologist.

Footnotes

Contributors

Dr. Alexopoulos designed and oversaw the conduct of the study. Drs. Zimmerman and Brickman developed the signal hyperintensity measurement protocol and assisted in writing Method section. Drs. Hoptman oversaw acquisition of MRI data and performed image processing. Drs Klimstra and Young conducted treatment protocol with depressed patients. Under the supervision of Drs. Gunning-Dixon and Dr. Hoptman, Ms. Cheng and Ms. Acuna performed processing of MRI data. Dr. Walton performed literature search and wrote draft of introduction and portions of results and discussion. Dr. Gunning-Dixon performed signal hyperintensity ratings, performed statistical analyses, and wrote majority of first draft of the manuscript. All authors contributed to and have approved the final manuscript.

Disclosures/Conflicts of Interest

Dr. Alexopoulos has received a research grant by Forest Pharmaceuticals, Inc. for support for this study and is a consultant for Forest Pharmaceuticals. In addition, Dr Alexopoulos has the following financial interests to disclose for professional services that are not directly related to this work: A research grant from Cephalon. Financial compensation as a speaker for Cephalon, Forest, Sanofi-Aventis, Novartis, Lilly, Bristol Meyers Squibb, Glaxo-Smith Kline, Pfizer, and Janssen; Financial compensation as a consultant for Sanofi-Aventis, and Novartis. Dr. Brickman has received financial compensation from ePharmaSolutions, ProPhase Training Group, University of Toronto, and City University of New York for professional service unrelated to the work presented here. Dr. Sibel Klimstra has received financial compensation from Eisai Medical Research Inc. for professional service unrelated to the work presented here. Dr. Walton has received financial compensation from Educational Testing and Assessment Systems (ETAS), publisher of the Psychiatry-in-Review Study Guide which is distributed with compliments of Astra Zeneca. Dr. Young has received financial compensation as a speaker for Astra Zeneca and receives research support from AstraZeneca and Glaxo-Smith Kline. Drs. Gunning-Dixon, Zimmerman, Hoptman and Ms. Cheng and Ms. Acuna have no financial interests to disclose.

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