Imaging Alzheimer Pathology in Late-Life Depression With PET and Pittsburgh Compound-B

doi:10.1097/WAD.0b013e31816c92bf

Alzheimer Dis Assoc Disord. Author manuscript; available in PMC 2009 February 5.

Published in final edited form as:

Alzheimer Dis Assoc Disord. 2008; 22(3): 261–268.

doi: 10.1097/WAD.0b013e31816c92bf

PMCID: PMC2636843

NIHMSID: NIHMS90119

Imaging Alzheimer Pathology in Late-Life Depression With PET and Pittsburgh Compound-B

Meryl A. Butters, PhD,^* William E. Klunk, MD, PhD,^*^†^‡ Chester A. Mathis, PhD,^‡^§ Julie C. Price, PhD,^‡^|| Scott K. Ziolko, BS,^‡ Jessica A. Hoge, BS,^‡ Nicholas D. Tsopelas, MD,^* Brian J. Lopresti, BS,^‡ Charles F. Reynolds, III, MD,^*^†^¶ Steven T. DeKosky, MD, PhD,^*^†^¶ and Carolyn C. Meltzer, MD^*^†^#^**^††

^* Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA

^† Department of Neurology, University of Pittsburgh, Pittsburgh, PA

^‡ Department of Radiology, University of Pittsburgh, Pittsburgh, PA

^§ Department of Pharmacology, University of Pittsburgh, Pittsburgh, PA

^|| Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA

^¶ Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA

^# Department of Radiology, Emory University, Atlanta, GA

^** Department of Neurology, Emory University, Atlanta, GA

^†† Department of Psychiatry and Behavioral Science, Emory University, Atlanta, GA

Reprints: Meryl A. Butters, PhD, Department of Psychiatry, University of Pittsburgh School of Medicine, Western Psychiatric Institute and Clinic, 3811 O’Hara St., Pittsburgh, PA 15213 (e-mail: buttersma/at/upmc.edu)

The publisher's final edited version of this article is available at Alzheimer Dis Assoc Disord

See other articles in PMC that cite the published article.

Abstract

There is increasing evidence for an empiric link between late-life depression and Alzheimer disease (AD). The neuropathology of AD, previously only confirmed at autopsy, may now be detectable in vivo using selective imaging ligands for β-amyloid. Positron emission tomography (PET) with [¹¹C] 6-OH-BTA-1 [Pittsburgh Compound-B (PiB)] has shown high tracer retention in cortical areas in patients with clinical diagnoses of probable AD and low retention in age-matched controls. We also previously reported variable PiB retention in patients with mild cognitive impairment (MCI). In this study, we used PiB-PET to evaluate whether amyloid is present in elders with treated major depression, many of whom have persistent cognitive impairment. We evaluated 9 subjects with remitted major depression [3M: 6F, mean (SD) age=71.8(5.7) y]. Seven of the 9 depressed subjects also met criteria for the diagnosis of MCI. PiB-PET data from healthy elders [n=8; mean (SD) age=71.5(3.0) y] were used for comparison. PET was acquired with arterial sampling and PiB retention was quantified using magnetic resonance imaging-guided cortical regions and graphical analysis of time-activity data; arterial line failure led to exclusion of 1 depressed subject. The data demonstrated variably elevated PiB retention. PiB retention in the 2 depressed subjects with normal cognitive ability was in the range of nondepressed cognitively normal subjects. PiB retention in 3 of the 6 depressed subjects with MCI fell in the range of subjects with AD. PiB retention in the remaining 3 depressed subjects with cooccurring MCI was variable and generally was intermediate to the other subjects. Our findings are consistent with and supportive of the hypothesis that depression may herald the development of AD in some individuals.

Keywords: depression, Alzheimer disease, amyloid, brain, emission tomography

There is clear and increasing evidence of an association between late-life depression and Alzheimer disease (AD). Reported rates of dementia among late-life depression patients vary considerably, from 10% to nearly 90%.¹^–³ A meta-analysis of the results from 7 case-control and 6 prospective studies suggests that depression in late life approximately doubles an individual’s dementia risk.⁴ A more recent meta-analysis performed by Ownby and colleagues⁵ came to the same conclusion, that is, that a history of depression at any point in life approximately doubles one’s risk of developing AD. Data from the multi-institutional research in Alzheimer’s Genetic Epidemiology (MIRAGE) study suggest that depression occurring up to 25 years before the onset of AD may still serve as a predisposing factor for dementia.⁶ Conversely, AD patients also have a high prevalence of coexisting depression, ranging from 10% to 50% and occurring in both mild and later stages of dementia.⁷^–¹⁰ The frequency with which depression and dementia coexist in the elderly supports a shared mechanistic and/or etiologic foundation between the 2 conditions.

Indeed, depression may be the initial sign of neurodegenerative disease, and alone may be regarded as a risk factor for the later development of dementia. The presence of cognitive impairment may further increase the risk of developing AD among depressed elders. The risk of dementia is increased nearly 5-fold in depressed elders with reversible cognitive dysfunction over that observed in patients with depression without cognitive impairment.¹¹ Further, 20% of individuals in the Cardiovascular Health Study who had mild cognitive impairment (MCI) and 32% of those with dementia also exhibited depressive symptoms.¹² The risk of dementia in patients with MCI also seems to be greater in those individuals who also exhibit neuropsychiatric symptoms, particularly depression.¹²

Petersen and colleagues¹³ initially described a form of MCI in which there is a singular, substantial deficit in episodic memory, accompanied by otherwise normal levels of function in other cognitive domains and no measurable functional impairment in daily life. There is broad agreement that most individuals with amnestic MCI have subclinical AD, and most longitudinal studies have been limited to this subtype.¹³^–¹⁶ Evolution of the MCI concept recognizes heterogeneity among individuals. ¹⁷ Lopez and colleagues¹⁸ have defined a multiple cognitive domain (MCI-MCD) subtype, in which subjects experience either a singular deficit in a nonmemory domain (eg, in visuospatial, executive, memory, etc.) or very mild impairment in MCDs (which may or may not include memory). In studies by Butters et al¹⁹ and Bhalla et al,²⁰ approximately one half of elderly depression patients (regardless of whether they were depressed or in remission) met cognitive criteria for MCI, with most of them exhibiting the MCI-MCD subtype. The National Alzheimer’s Coordinating Center Uniform Data Set very recently implemented a new diagnostic system for use by Alzheimer Disease Research Centers, which contains 4 MCI subtypes that maximize the distinction between those who do and those who do not, have a deficit in episodic memory: (1) amnestic MCI–memory impairment only, (2) amnestic MCI–impairment in memory plus 1 or more other domains, (3) nonamnestic MCI–impairment in a single nonmemory domain, and (4) nonamnestic MCI–impairment in multiple nonmemory domains. As most longitudinal studies have employed the original Petersen criteria of MCI, the prognostic significance of amnestic MCI accompanied by impairment in additional domains and nonamnestic MCI is less clear.

Brain β-amyloid accumulation, believed to be due to an imbalance between its production and clearance, is a key pathologic underpinning of AD.²¹ Although the bulk of our knowledge of the pathophysiology of neurodegenerative disorders is based on postmortem studies, which are biased toward end-stage disease, there is evidence to suggest that amyloid deposition may occur as early as a decade before clinical symptoms.²² This evidence is further supported by studies that show that subjects who die at the stage of MCI have sufficient plaques and tangles to meet criteria for AD.²³^,²⁴ The recent development of high-affinity positron emission tomography (PET) imaging ligands for amyloid now permits the evaluation of the neuropathologic link among depression, cognitive impairment, and AD in vivo.²⁵^,²⁶ In initial human studies with Pittsburgh Compound-B (PiB)-PET imaging, we demonstrated variable accumulation of β-amyloid in the brains of nondepressed individuals with MCI.²⁷^–²⁹ Several very recent studies suggest that 52% to 62% of individuals with MCI are PiB-positive, or exhibit PiB retention well above the mean of cognitively normal individuals and in the range of individuals with AD.³⁰^–³² The current study will extend these findings to perform the first in vivo human investigation of whether amyloid neuropathology is present in elders with major depression.

METHODS AND MATERIALS

Subject Selection

We studied a total of 9 subjects over age 65 with treated late-life major depression [3 men, 6 women; mean (SD) age=71.8 (5.7) y] (Table 1). All of these subjects presented to a late-life depression clinic and remitted with escitalopram as part of an ongoing clinical trial of maintenance therapy in geriatric depression. After successful antidepressant therapy, subjects underwent detailed neuropsychiatric evaluation for dementia and MCI that included neuropsychologic assessment and other clinical measures.

TABLE 1

Subject Characteristics

Subject evaluation included a psychiatric interview using the structured clinical interview for Diagnostic and Statistical Manual of Mental Disorders (DSM-IV).³³ The Mini-Mental State Examination (MMSE)³⁴ was administered to evaluate general cognitive status. Before antidepressant treatment, subjects underwent thorough psychiatric evaluation at our late-life depression clinic and met DSM-IV criteria for current major depressive episode (nonpsychotic),³⁵ and consensus expert evaluation confirmed the diagnosis of depression [mean (SD) Hamilton Depression Rating Scale Scores: pretreatment: 18.2 (2.5); posttreatment: 7.1(1.5)]. Individuals with unstable medical illness, substance abuse over the past year, history of neurologic disorder or significant head trauma (defined as loss of consciousness for >30 min), and/or active malignancy were not studied.

To aid in diagnostic adjudication of cognitive status, a comprehensive neuropsychologic assessment³⁶^–⁴⁹ was performed by 2 examiners who were highly trained and closely supervised by a senior neuropsychologist with expertize in geriatric neuropsychiatric disorders (M.A.B.). In all the cases, subjects were tested within a few weeks after responding to antidepressant treatment. The neuropsychologic test results along with the other relevant data [eg, results from psychiatric and neurologic evaluations, magnetic resonance (MR) imaging, activities of daily living evaluation, etc] were forwarded to the University of Pittsburgh Alzheimer Disease Research Center (ADRC), where they were used for diagnostic adjudication.

PiB-PET data from healthy elders [n=8; 2 men, 6 women; mean (SD) age 71.5 (3.0) y] were used for comparison. Data from 6 of these nondepressed controls were previously included in other published data.²⁷^–²⁹ For these healthy control subjects, additional exclusion criteria included a history of neurologic or psychiatric disease and/or a first-degree relative with a psychiatric or neurodegenerative disorder.

All subjects provided written informed consent before study entry, as approved by the University of Pittsburgh Institutional Review Board and Radioactive Drug Research Committee.

Imaging Acquisition

The radiosynthesis of PiB was performed as initially detailed byMathis and colleagues⁵⁰ and using a simplified synthesis described by Price et al.²⁷

PET scans were acquired using an emission computerized axial tomography high resolution+ (ECAT HR+) PET scanner (Siemens Medical Solutions, Erlangen, Germany) in 3-dimensional⁵¹ mode [63 transaxial planes, 2.4-mm thickness; in-plane resolution=4.1mm full-width at half-maximum over a 15.2-cm field-of-view]. Subjects were positioned parallel to the canthomeatal line and the entire brain was included in the scan. After a 10- minute transmission scan acquired using rotating rods of 68Ge/68Ga, emission imaging immediately followed intravenous injection of 14.8 ± 1.6mCi high-specific activity (approximately 1000mCi/μmol) PiB. PET scanning was performed for 90 min (except in 1 subject who was only able to cooperate for the acquisition of a total of 70 min of emission data). A Neuro-insert (CTI PET Systems, Knoxville, TN) placed in the camera gantry was used to reduce random coincidences.⁵¹ Head movements were minimized by the use of a thermoplastic mask and headholder system. PET data were also corrected for radioactive decay and scatter using a model-based approach.⁵² PET image reconstruction was performed using filtered back-projection (Fourier rebinning, 2D backprojection, Hann filter: kernel full-width at half-maximum= 3 mm) for a final reconstructed image resolution of about 6mm (transverse and axial, in-house measurements, data not shown).

Dynamic arterial blood sampling was performed during emission scanning. In 1 depressed subject, arterial line failure caused loss of PET data. Plasma data were corrected for the presence of radiolabeled metabolites as determined by high-performance liquid chromatography or extraction analyses as detailed by Price et al²⁷ and Lopresti et al.²⁸

MR imaging was successfully performed in all subjects for the dual purpose of guiding region-of-interest (ROI) placement and performing partial volume correction. MR images were acquired using a Signa 1.5 Tesla scanner (GE Medical Systems, Milwaukee, WI) with a standard head coil. PET analysis focused on T1-weighted volumetric spoiled gradient-recalled (SPGR) MR images. The SPGR sequence (echo time=5, repetition time=25, flip angle=40 degrees, number of excitations=1; section thickness=1.5mm with no intersection gap) was acquired in the coronal plane. PET-MR registration was accomplished using automated image registration.⁵³ Pixels corresponding to scalp and calvarium were removed from the SPGR MR images⁵⁴ to facilitate registration with the PET images. Standard clinical T2-weighted and fluid-attenuated inversion recovery (FLAIR) axial images were also acquired to exclude unexpected pathology.

Image Analysis

ROIs were hand-drawn on the coregistered MR images according to anatomic landmarks using guidelines established within the laboratory and transferred to the dynamic PET data for regional sampling. We have previously assessed and reported high interrater reliability (intraclass correlation coefficient ≥0.90) for measurements of ROIs (eg, 27). Coauthor Jessica A. Hoge was the sole rater for this study and we did not assess her intrarater reliability. ROIs were sampled on multiple consecutive MR images on which the structure was visualized and right and left regions were combined to reduce noise. Regions sampled included posterior cingulate, parietal cortex, frontal cortex, sensory motor cortex, occipital cortex, subcortical white matter, pons, mesial temporal cortex (including amygdala, hippocampus, and a portion of the parahippocampal gyrus), and cerebellum.

Time-activity data (0 to 90 min postinjection) were generated from the dynamic image data for each ROI. Data were analyzed using a Logan graphical⁵⁵^,⁵⁶ (35 to 90 min postinjection interval) approach. Regional PiB distribution volume (DV) values were normalized to the PiB reference (cerebellum) to yield DV ratios (DVRs). The DVR was chosen, rather than the binding potential (BP),⁵⁷ as the primary outcome measure [DVR=BP+1]. For the controls with null levels of PiB retention, the average regional PiB BP value varied about 0, yielding both positive and negative BP values. On the basis of earlier work comparing compartmental and other approaches, the DVR has been shown to provide a robust and physiologically tenable non-negative outcome measure across control, MCI, and AD subjects.²⁷^,²⁸ The DVR measure can be directly related to the free binding site pool (Bmax) and the ligand dissociation constant (K_D).

Regional DVR values were corrected for partial volume effects because of differential cerebral volume loss among the elderly subjects. For this purpose, we applied a previously validated 2-component MR-based atrophy correction algorithm,⁵⁸ as modified and routinely used in our laboratory.⁵⁹

Data analyses were performed using the Statistical Package for the Social Sciences version 14.0.⁶⁰ The Wilcoxon rank-sum test was applied to assess differences in age, educational level, and cognitive function between patients and healthy control subjects. A 2-way repeated measures analysis of variance was used to determine whether the fraction of metabolites in plasma at 2, 10, 30, 60, 75, and 90 minutes postinjection differed between patients and control subjects. For all analyses, statistical significance was set at the P<0.05 level.

RESULTS

There were no significant differences in age (P = 0.64) or education (P = 0.08) between the patients and healthy control subject groups (Table 1). Although the MMSE scores tended to be higher in controls [mean (SD)=29.1 (1.1)] than patients [mean (SD) 28.2 (1.8)], this difference was not significant (P = 0.33).

Subjects’ scores on the neuropsychologic test battery are presented in Table 2. Performance is expressed in Z-scores based on the mean and standard deviation of nondepressed control subjects recruited into our late-life mood disorders research center. The characteristics and test performance of these control subjects have been published previously.¹⁹^,²⁰ Cognitive status of depressed patients (including MCI subtype, if applicable) and impaired domains (if applicable) are listed in Table 3 for each subject. Of the 9 depressed subjects whose depression remitted with escitalopram, detailed neuropsychologic evaluation showed that 2 had no cognitive abnormalities and 7 qualified for a diagnosis of MCI. Of the 7 with MCI, 3 met criteria for amnestic MCI–memory impairment plus 1 or more other domains, 2 met criteria for nonamnestic MCI–single domain, and 2 met criteria for nonamnestic MCI–multiple domains (PET data not available in 1 of these latter subjects because of arterial line failure) (Table 3). In summation, of all 9 depressed MCI subjects evaluated, 7 had MCI; 3 had amnestic MCI and 4 had nonamnestic MCI.

TABLE 2

Cognitive Evaluation in Depressed Subjects

TABLE 3

Depressed Subjects’ Neuropsychologic Performance

In a comparative analysis of 5 subjects with 90- minute emission data analyzed using the Logan graphical method with the 35 to 70 minutes postinjection interval and the 35 to 90-minutes postinjection interval, there was no systematic difference (ie, directional bias) in regional DVR values and variability was small (within 6%). Therefore, we elected to include the 1 subject with 70 minutes of emission data in the final analysis.

There was no significant difference between controls and depressed subjects in PiB retention in the cerebellar reference region [mean (SD)=3.78 (0.64)] and control [mean (SD)=3.88(0.68)] groups (P=0.64). Among control subjects, regional PiB DVR values in areas where amyloid accumulation is expected in AD (eg, posterior cingulate/precuneus, parietal cortex, frontal cortex) were within the range previously published for healthy subjects. These subjects’ values were also similar for those regions with little or no amyloid accumulation in AD (sensory motor cortex, subcortical white matter, pons).

Figure 1 shows representative PET images in depressed subjects with a range of cortical PiB accumulation including “control-like” in 2 subjects, mild-to-moderate uptake in 2 subjects, and extensive “AD-like” uptake in 1 subject. Regional DVR values for the depressed group spanned from similar to the control group to within the range observed in subjects with probable AD (Fig. 2).²⁸ Interestingly, in the areas where amyloid deposition is typically prominent in AD, 2 of the 3 depressed subjects with amnestic MCI and 1 of the 3 with nonamnestic MCI had PiB retention in the AD range. In contrast, PiB retention in the 2 cognitively normal depressed subjects fell in the range of control subjects. Therefore, 3 of 6 depressed subjects with MCI who were successfully scanned showed PiB retention that was clearly above control levels in at least 1 cortical area, indicating the presence of amyloid accumulation. In the frontal cortex, 2 depressed subjects without cognitive impairment showed PiB retention similar to control values (Fig. 3). Although not statistically significant possibly because of small sample sizes, frontal PiB DVR values were higher among amnestic (n=3) versus nonamnestic MCI (n=3) subjects.

FIGURE 1

Representative Logan parametric PiB-PET images in 5 subjects with late-life depression who also meet criteria for MCI. A range of PiB retention in cortical areas is noted across subjects. MCI indicates mild cognitive impairment; PET, positron emission (more ...)

FIGURE 2

Scatter plot of Logan DVR values for several cortical regions with high amyloid retention in AD [frontal (FRT), lateral temporal cortex (LTC) parietal (PAR), precuneus (PRC)], areas affected relatively later in the disease [mesial temporal cortex (MTC; (more ...)

FIGURE 3

Box plot showing PiB DVR values (mean, SD, and individual data points) in frontal cortex of all 8 successfully scanned depressed subjects grouped by cognitive diagnosis. Although sample sizes are small, there was a trend toward higher PiB uptake in those (more ...)

No effect of subject group was observed in the rate of metabolism of PiB in plasma (P=0.82). At 6 postinjection intervals, similar mean fractions of the parent compound (2 min: 82.7% ± 10.6% SD, 83.9% ± 5.2% SD; 10 min: 31.3% ± 10.0% SD, 32.8% ± 8.2% SD; 30 min: 10.3% ± 4.2% SD, 32.8% ± 2.4% SD; 60 min: 7.7% ± 2.0% SD, 7.3% ± 2.1% SD; 75 min: 7.1% ± 2.4% SD, 6.6% ± 1.6% SD; 90 min: 6.7% ± 1.4% SD, 5.6% ± 2.1% SD) were measured in plasma in the depressed and control subjects, respectively.

DISCUSSION

This initial imaging study of AD neuropathology in elders with major depression supports growing clinical and epidemiologic data that late-life depression may be either a substantial risk factor or in some cases a potential early manifestation of AD. Among 9 nondemented subjects with treated depression and variable cognitive impairment (7 with MCI), approximately one-half demonstrated PiB retention indicative of brain β-amyloid accumulation in cortical areas in a pattern characteristic of early AD (3 of 8 with arterial lines were definitely PiB-positive, 1 of the remaining 5 had intermediate levels of PiB). Thal and colleagues⁶¹ described 5 distinct, sequential phases of brain amyloid deposition, where phase I demonstrates exclusively neocortical involvement (frontal, parietal, temporal, occipital) and later phases progressively show β-amyloid deposits in the entorhinal region and insula, thalamus, putamen, caudate nucleus, cholinergic nuclei of the basal forebrain, and brainstem nuclei. The latest phases indicate β-amyloid in the cerebellum and additional brainstem nuclei. Although both β-amyloid deposition and altered tau metabolism leading to neurofibrillary tangle formation are hallmarks of AD, it is pathologically defined by the presence of β-amyloid–containing neuritic plaques.⁶² The bulk of our knowledge of the pathophysiology of neurodegenerative disorders is based on postmortem studies, which are biased toward end-stage disease. However, a postmortem study in Down syndrome, in which early AD is common, supports the hypothesis that β-amyloid deposition may occur as early as a decade before clinical symptoms.²² In vivo data with PiB-PET imaging further support the accumulation of β-amyloid in the brains of individuals with only mild cognitive dysfunction.³⁰^–³² Demonstration of this new capability to detect the presence of AD pathologic changes in vivo has important implications for patient prognosis and differential management of individuals with late-life depression.

The clinical relevance of identifying those individuals at greatest risk of developing AD is growing as new therapeutic approaches directed at modifying AD progression evolve. Among subjects with MCI in memory disorders clinics, the overall rate of progression to dementia seems to be approximately 15% per year across several studies.¹³^–¹⁶ The Alzheimer’s Disease Cooperative Study Group reported an annual rate of progression to AD of 16% among 769 MCI subjects followed over 3 years.⁶³ Approximately 50% of individuals with MCI progress to develop AD over a 5-year period, indicating that a diagnosis of MCI alone is not a sufficient predictor of dementia prognosis.¹⁴^,¹⁶^,⁶⁴^,⁶⁵ On the basis of recent reports of individuals labeled with MCI reverting to normal, Gauthier and Touchon⁶⁶ argue against the use of MCI as a clinical entity or “predementia stage of AD.” Notably, a recent study by Forsberg et al³⁰ reported that 7 of 21 MCI subjects converted to AD after 2 years, and moreover, at baseline (when adjudicated as having MCI), all 7 had PiB retention levels similar to AD subjects.

Most of the focus on MCI as a high-risk AD group has focused on the amnestic (memory impaired only) form of the disorder, originally described by Petersen and colleagues.¹³ However, depressed elders often have prominent deficits in other domains such as executive functioning or mild impairment in MCDs that may or may not include memory. In a study by Bhalla and colleagues,²⁰ approximately one-half of elderly depression patients in remission met the cognitive criteria for MCI, with most of them exhibiting the MCI-MCD subtype, some with and some without memory impairment. One may hypothesize that MCI-MCD may represent a combination of the cognitive deficits associated with late-life depression and those owing to superimposed AD neuropathology. Notably, if we examine the relative PiB retention in frontal cortex in the depressed subjects in this study by cognitive diagnosis, there is a trend toward higher PiB DVR values among those with amnestic-MCI relative to nonamnestic MCI (Fig. 3). Larger future studies will be needed to further evaluate the relationship between cognitive impairment subtyping and amyloid binding measures in elders treated for major depression.

A recent study by Rapp and colleagues⁶⁷ demonstrated that the brains of patients with AD and lifetime history of depression showed higher levels of both plaque and tangles in the hippocampus than AD patients without depression. Those AD patients with depression also had a more rapid cognitive decline than nondepressed demented subjects. Our in vivo findings are consistent with evidence that late-life depression may be either a risk factor for AD—in cases where depression may present many years before cognitive impairment—and/or a potential AD prodromal state in some individuals. Indeed, postmortem studies by Sweet et al⁶⁸ have confirmed a predominance of AD neuropathology among well-characterized late-onset depressed patients with varying cognitive impairment who were followed longitudinally.

The heterogeneity of late-life depression and its association with cerebrovascular risk factors,⁶⁹ physiologic changes related to aging,⁷⁰ serotonergic dysfunction,⁷¹ structural brain changes,⁷² and medical burden⁷³ suggest that clinical expression of dementia may be related to a confluence of brain insults and diminished cognitive reserve. Future large prospective studies can address the relationship between the magnitude and distribution of amyloid load as measured by in vivo PET imaging and clinical and demographic factors that may contribute to the manifestation of cognitive impairment and clinical dementia. This information will likely shed further light on the complex neurobiologic mechanisms underlying depression and AD and guide future approaches to early intervention.

CONCLUSION

Our preliminary finding of heterogeneously elevated PiB retention in late-life depressed subjects with and without persistent cognitive impairment after antidepressant therapy is consistent with and supportive of the hypothesis that depression may share some common underlying neurobiologic mechanisms with the development of AD. Future studies will explore to what degree AD pathology may contribute to long-term prognosis, and also the course, and treatment response characteristics of depression in late life.

Acknowledgments

The authors thank Michele Bechtold, Shelley Hulland, Michelle Zmuda, Marianne Schlernitzauer, James Ruszkiewicz, Denise Ratica, and Patricia McGowen for their vital contributions to this work.

Supported by PHS grants MH072947, MH67602, MH59945, MH64625, AG25516, AG25204, AG05133, MH43832, MH01210, AG01039, MH71944, MH070729 and MH52247, as well as the John A. Hartford Foundation Center of Excellence in Geriatric Psychiatry and UPMC Endowment in Geriatric Psychiatry.

Footnotes

Dr Reynolds receives research support (pharmaceutical supplies) from Glaxo Smith-Kline, Forest, Pfizer, Lilly, and Bristol Myers Squibb.

GE Healthcare holds a license agreement with the University of Pittsburgh based on the technology described in this manuscript. Drs Klunk and Mathis are co-inventors of PiB and, as such, have a financial interest in this license agreement. GE Healthcare provided no grant support for this study and had no role in the design or interpretation of results or preparation of this manuscript. All other authors have no conflicts of interest with this work. Drs Butters, Meltzer, Klunk, Mathis, Price, and DeKosky as well as Mr Ziolko had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

References

1. Kral VA, Emery OB. Long-term follow-up of depressive pseudodementia of the aged. Can J Psychiatry. 1989;34:445–446. [PubMed]

2. Reynolds C, Kupfer D, Hoch C, et al. Two-year follow-up of elderly patients with mixed depression and dementia. Clinical and electro-encephalographic sleep findings. J Am Geriatr Soc. 1986;34:793–799. [PubMed]

3. Sachdev P, Smith J, Angus-Lepan H, et al. Pseudodementia twelve years on. J Neurol Neurosurg Psychiatry. 1990;53:254–259. [PMC free article] [PubMed]

4. Jorm AF. History of depression as a risk factor for dementia: an updated review. Aust N Z J Psychiatry. 2001;35:776–781. [PubMed]

5. Ownby R, Crocco E, Acevedo A, et al. Depression and risk for Alzheimer disease. Systemic review, meta-analysis, and meta regression analysis. Arch Gen Psychiatry. 2006;53:530–538. [PMC free article] [PubMed]

6. Green RC, Cupples LA, Kurz A, et al. Depression as a risk factor for Alzheimer disease: the MIRAGE Study. Arch Neurol. 2003;60:753–759. [PubMed]

7. Fischer P, Simanyi M, Danielczyk W. Depression in dementia of the Alzheimer type and in multi-infarct dementia. Am J Psychiatry. 1990;147:1484–1487. [PubMed]

8. Komahashi T, Ohmori K, Nakano T, et al. Epidemiological survey of dementia and depression among the aged living in the community in Japan. Jpn J Psychiatry Neurol. 1994;48:517–526. [PubMed]

9. Lopez OL, Becker JT, Sweet RA, et al. Psychiatric symptoms vary with the severity of dementia in probable Alzheimer’s disease. J Neuropsychiatry Clin Neurosci. 2003;15:346–353. [PubMed]

10. Payne J, Sheppard J, Steinberg M, et al. Incidence, prevalence, and outcomes of depression in residents of a long-term care facility with dementia. Int J Geriatr Psychiatry. 2002;17:247–253. [PubMed]

11. Alexopoulos G, Meyers B, Young R, et al. The course of geriatric depression with “reversible dementia”: a controlled study. Am J Psychiatry. 1993;150:1693–1699. [PubMed]

12. Lyketsos CG, Lopez O, Jones B, et al. Prevalence of neuropsychiatric symptoms in dementia and mild cognitive impairment: results from the cardiovascular health study. JAMA. 2002;288:1475–1483. [PubMed]

13. Petersen RC, Smith GE, Waring SC, et al. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999;56:303–308. [PubMed]

14. Bowen J, Teri L, Kukull W, et al. Progression to dementia in patients with isolated memory loss. Lancet. 1997;349:763–765. [PubMed]

15. Devanand DP, Folz M, Gorlyn M, et al. Questionable dementia: clinical course and predictors of outcome. J Am Geriatr Soc. 1997;45:321–328. [PubMed]

16. Yesavage JA, O’Hara R, Kraemer H, et al. Modeling the prevalence and incidence of Alzheimer’s disease and mild cognitive impairment. J Psychiatr Res. 2002;36:281–286. [PubMed]

17. Petersen RC, Doody R, Kurz A, et al. Current concepts in mild cognitive impairment. Arch Neurol. 2001;58:1985–1992. [PubMed]

18. Lopez O, Jagust W, DeKosky S, et al. Prevalence and classification of mild cognitive impairment in the Cardiovascular Health Study Cognition Study: part 1. Arch Neurol. 2003;60:1385–1389. [PubMed]

19. Butters M, Whyte E, Nebes R, et al. Neuropsychological functioning and clinical correlates of late-life depression. Arch Gen Psychiatry. 2004;61:587–595. [PubMed]

20. Bhalla R, Butters M, Mulsant B, et al. Persistence of neuropsychological deficits in the remitted state of late-life depression. Am J Geriatr Psychiatry. 2006;14:419–427. [PubMed]

21. Hardy J, Higgins G. Alzheimer’s disease: the amyloid cascade hypothesis. Science. 1992;256:184–185. [PubMed]

22. Hyman BT, West HL, Rebeck GW, et al. Quantitative analysis of senile plaques in Alzheimer disease: observation of log-normal size distribution and molecular epidemiology of differences associated with apolipoprotein E genotype and trisomy 21 (Down syndrome) Proc Nat Acad Sci U S A. 1995;92:3586–3590. [PubMed]

23. DeKosky ST, Ikonomovic MD, Styren SD, et al. Upregulation of choline acetyltransferase activity in hippocampus and frontal cortex of elderly subjects with mild cognitive impairment. Ann Neurol. 2002;51:145–155. [PubMed]

24. Davis P, Mohs R, Marin D, et al. Cholinergic markers in elderly patients with early signs of Alzheimer disease. J Am Med Assoc. 1999;281:1401–1406. [PubMed]

25. Klunk W, Engler H, Nordberg A, et al. Imaging brain amyloid in Alzheimer’s disease using the novel PET tracer, PIB. Ann Neurol. 2004;55:306–319. [PubMed]

26. Shoghi-Jadid K, Small GW, Agdeppa ED, et al. Localization of neurofibrillary tangles and beta-amyloid plaques in the brains of living patients with Alzheimer disease. Am J Geriatr Psychiatry. 2002;10:24–35. [PubMed]

27. Price J, Klunk W, Lopresti B, et al. Kinetic modeling of amyloid binding in humans using PET imaging and Pittsburgh Compound-B. J Cereb Blood Flow Metab. 2005;125:1528–1547. [PubMed]

28. Lopresti B, Klunk W, Mathis C, et al. Simplified quantification of Pittsburgh Compound-B amyloid imaging PET studies: a comparative analysis. J Nucl Med. 2005;6:1959–1972. [PubMed]

29. Ziolko S, Weissfeld L, Klunk W, et al. Evaluation of voxel-based methods for the statistical analysis of PIB PET amyloid imaging studies in Alzheimer’s disease. Neuroimage. 2006;33:94–102. [PubMed]

30. Forsberg A, Engler H, Almkvist O, et al. PET imaging of amyloid deposition in Ppatients with mild cognitive impairment. Neurobiol Aging. 2007 epub ahead of print.

31. Kemppainen NM, Aalto S, Wilson IA, et al. PET amyloid ligand [11C] PIB uptake is increased in mild cognitive impairment. Neurology. 2007;68:1603–1606. [PubMed]

32. Pike KE, Savage G, Villemagne VL, et al. beta-amyloid imaging and memory in non-demented individuals: evidence for pre-clinical Alzheimer’s disease. Brain. 2007;130:2837–2844. [PubMed]

33. First M, Spitzer R, Gibbon M, et al. Structured Clinical Interview for DSM-IV Axis 1 Disorders-Patient Edition (SCID-I/P) New York: New York Psychiatric Institute; 1995.

34. Folstein MF, Folstein SE, McHugh PR. Mini-mental state: a practical method grading the cognitive state of patients for the clinician. Psychiatr Res. 1975;12:189–198. [PubMed]

35. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 4. Washington, DC: American Psychiatric Association; 1994.

36. Wechsler D. Wechsler Adult Intelligence Scale III. San Antonio: The Psychological Corporation; 1996.

37. Matthews CG, Klove H. Instruction Manual for the Adult Neuropsychology Test Battery. Madison, WI: University of Wisconsin Medical School; 1964.

38. Reitan RM, Wolfson D. The Halstead-Reitan Neuropsychological Test Battery. Tucson: Neuropsychology Press; 1993.

39. Goodglass H, Kaplan E, Weintraub S. The Assessment of Aphasia and Related Disorders. 2. Philadelphia: Lea & Febiger; 1983.

40. Rouleau I, Salmon DP, Butters N, et al. Quantitative and qualitative analyses of clock drawings in Alzheimer’s and Huntington’s disease. Brain Cogn. 1992;18:70–87. [PubMed]

41. Royall DR, Mahurin RK, Gray KF. Beside assessment of executive cognitive impairment: the executive interview. J Am Geriatr Soc. 1992;40:1221–1226. [PubMed]

42. Trenerry MR, Crosson B, De Boe J, et al. The Stroop Neuropsychological Test. Odessa, FL: Psychological Assessment Resources; 1989.

43. Berg EA. A simple objective technique for measuring flexibility in thinking, using a sorting method. J Gen Psychol. 1948;39:15–22. [PubMed]

44. Borkowski JG, Benton AL, Spreen O. Word fluency and brain damage. Neuropsychologia. 1967;5:135–140.

45. Baddeley A, Emslie H, Smith IN. The Speed and Capacity of Language Processing (SCOLP) Test Manual. England: Bury St. Edmunds; 1992.

46. Wechsler D. Wechsler Memory Scale-III. San Antonio: The psychological corporation; 1996.

47. Osterreith PA. Le test de copie d’un figure complexe. Arch Psychologie. 1944;30:206–356.

48. Rey A. L’examinen psychologie dans les cas d’encephalopathie traumatique. Arch Psychologie. 1941;30:286–340.

49. Delis D, Kramer J, Kaplan E, et al. California Verbal Learning Test. New York: Psychological Corporation; 1987.

50. Mathis C, Wang Y, Holt D, et al. Synthesis and evaluation of 11Clabeled 6-substituted 2-aryl benzothiazoles as amyloid imaging agents. J Med Chem. 2003;46:2740–2755. [PubMed]

51. Weinhard K. Applications of 3D PET. In: Bendriem B, Townsend D, editors. The Theory and Practice of 3D PET. Boston: Kluwer Academic Publishers; 1998. pp. 133–167.

52. Watson C, Newport D, Casey M, et al. Evaluation of simulation-based scatter correction for 3D PET cardiac imaging. IEEE Trans Nucl Sci. 1997;44:90–97.

53. Woods RP, Mazziotta JC, Cherry SR. MRI-PET registration with automated algorithm. J Comput Assist Tomogr. 1993;17:536–546. [PubMed]

54. Smith S. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17:143–155. [PubMed]

55. Logan J, Fowler JS, Volkow ND, et al. Graphical analysis of reversible radioligand binding from time-activity measurements applied to [N-11C-methyl]-(−)- cocaine PET studies in human subjects. J Cereb Blood Flow Metab. 1990;10:740–747. [PubMed]

56. Logan J. Graphical analysis of PET data applied to reversible and irreversible tracers. Nucl Med Biol. 2000;27:661–670. [PubMed]

57. Kwong K, Belliveau J, Chesler D, et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A. 1992;89:5675–5679. [PubMed]

58. Meltzer CC, Leal JP, Mayberg HS, et al. Correction of PET data for partial volume effects in human cerebral cortex by MR imaging. J Comput Assist Tomogr. 1990;14:561–570. [PubMed]

59. Meltzer C, Drevets W, Price J, et al. Gender-specific aging reductions in [11C-carbonyl] WAY100635 binding: not an effect of atrophy. Neuroimage. 2000;11:S14.

60. SPSS Inc. SPSS Base 14.0 for Windows User’s Guide. Chicago IL: 2005.

61. Thal DR, Rub U, Orantes M, et al. Phases of A beta-deposition in the human brain and its relevance for the development of AD. Neurology. 2002;58:1791–1800. [PubMed]

62. Mirra S, Heyman A, McKeel D, et al. The consortium to establish a registry for Alzheimer’s disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology. 1991;41:479–486. [PubMed]

63. Petersen RC, Thomas R, Grundman M, et al. Vitamin E and donepezil for the treatment of mild cognitive impairment. New Engl J Med. 2005;352:2379–2388. [PubMed]

64. Hanninen T, Hallikainen M, Koivisto K, et al. A follow-up study of age-associated memory impairment: neuropsychological predictors of dementia. J Am Geriatr Soc. 1995;43:1007–1015. [PubMed]

65. Morris JC, Storandt M, Miller JP, et al. Mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol. 2001;58:397–405. [PubMed]

66. Gauthier S, Touchon J. Mild cognitive impairment is not a clinical entity and should not be treated. Arch Neurol. 2005;62:1164–1166. [PubMed]

67. Rapp M, Schnaider-Berri M, Grossman H, et al. Increased hippocampal plaques and tangles in patients with Alzheimer disease with a lifetime history of major depression. Arch Gen Psychiatry. 2006;63:161–167. [PubMed]

68. Sweet R, Hamilton R, Butters M, et al. Neuropathologic correlates of late-onset major depression. Neuropsychopharmacology. 2004;29:2224–2225.

69. Krishnan K, Hays J, Blazer D. MRI-defined vascular depression. Am J Psychiatry. 1997;154:497–501. [PubMed]

70. Deuschle M, Weber B, Colla M, et al. Effects of major depression, aging and gender upon calculated diurnal free plasma cortisol concentrations: a re-evaluation study. Stress. 1998;2:281–287. [PubMed]

71. Meltzer C, Price J, Mathis C, et al. Serotonin 1A receptor binding and treatment response in late-life depression. Neuropsychopharmacology. 2004;29:2258–2265. [PubMed]

72. Kumar A, Schweizer E, Jin Z, et al. Neuroanatomical substrates of late-life minor depression. A quantitative magnetic resonance imaging study. Arch Neurol. 1997;54:613–617. [PubMed]

73. Steffens DC, Hays J, Krishnan KR. Disability in geriatric depression. Am J Geriatr Psychiatry. 1999;7:34–40. [PubMed]