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Depression has been associated with higher conversion rates from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) and may be a potential clinical marker of prodromal AD that can be used to identify individuals with MCI who are most likely to progress to AD. Using tensor-based morphometry (TBM), we examined the longitudinal neuroanatomical changes associated with depressive symptoms in MCI.
243 MCI subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had brain MRI scans at baseline and 2-year follow-up were classified into depressed (DEP, n=44), non-depressed with other neuropsychiatric symptoms (OTHER, n=93), and no-symptom (NOSYMP, n=106) groups based on the Neuropsychiatric Inventory Questionnaire (NPI-Q). TBM was used to create individual 3D-maps of 2-year brain changes that were compared between groups.
DEP subjects had more frontal (p=0.024), parietal (p=0.030), and temporal (p=0.038) white matter atrophy than NOSYMP subjects. A subset of DEP subjects whose depressive symptoms persisted over 2-years also had higher conversion to AD and more decline on measures of global cognition, language abilities, and executive functioning compared to stable NOSYMP subjects. OTHER and NOSYMP groups exhibited no differences in rates of atrophy.
Depressive symptoms in MCI subjects were associated with greater atrophy in AD-affected regions, increased cognitive decline, and higher rates of conversion to AD. Depression in individuals with MCI may be associated with underlying neuropathological changes including prodromal AD. Thus, assessment of depressive symptoms may be a potentially useful clinical marker in identifying MCI patients who are most likely to progress to AD.
Mild cognitive impairment (MCI) (1) is conceptualized as a transitional state between normal aging and early Alzheimer’s disease (AD). In longitudinal studies, individuals meeting criteria for MCI are at increased risk for progressing to AD compared to age-matched controls (1,2). However, rates of conversion from MCI to AD are highly variable (3) because the cognitive deficits exhibited by these individuals may be related to a number of different pathologies. In an effort to detect AD in prodromal stages, there have been attempts to identify subgroups of MCI patients who are at highest risk for progression to AD. Many approaches focus on identifying early biological markers in structural (4) and functional (5) neuroimaging, and cerebrospinal fluid (6), but clinical tools, such as neuropsychological testing (7) have also been useful.
Another potential clinical marker for identifying MCI individuals at high risk of developing AD is the presence of neuropsychiatric symptoms. Depression, in particular, has been associated with increased risk of dementia (8,9). We previously demonstrated that depressive symptoms predicted progression to AD in MCI patients (10,11), but the neurobiological mechanism underlying this association is not yet fully understood. In several cross-sectional studies, depressed elderly appear to have underlying brain changes associated with AD, including reduced temporal lobe (12), hippocampal, and amygdala volume (13,14). As depressive symptoms may be a clinical marker of prodromal AD, we wanted to extend the findings in the existing literature and demonstrate that depressive symptoms would be associated with AD-related neuroanatomical changes, particularly in white matter regions.
Tensor-based morphometry (TBM) is a relatively novel computational approach that can compare longitudinally acquired images and visualize the spatial profile of brain atrophy over time, including estimates of tissue volume loss at each voxel in the brain (15). This approach has been successfully used to track longitudinal changes associated with normal brain aging and neurodegenerative disorders (16,17). Also, it may be more sensitive in detecting changes in white matter volume as it does not require a segmentation step, thus avoiding potential errors in accurate tissue classification. We applied TBM to compare patterns of brain atrophy in MCI patients with and without depressive symptoms. Specifically, we hypothesized that MCI patients with depressive symptoms would demonstrate greater brain atrophy over 2 years compared to those without depressive symptoms in regions specifically associated with AD pathology.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public-private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), and other biological markers can be combined to measure the progression of MCI and early AD. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials. The Principal Investigator of this initiative is Michael W. Weiner, MD, VA Medical Center and University of California-San Francisco. ADNI is the result of efforts of many co-investigators from a broad range of academic institutions and private corporations, and subjects have been recruited from over 50 sites across the U.S. and Canada. The initial goal of ADNI was to recruit 800 adults, ages 55 to 90, to participate in the research – approximately 200 cognitively normal older individuals to be followed for 3 years, 400 people with MCI to be followed for 3 years and 200 people with early AD to be followed for 2 years. For up-to-date information, see www.adni-info.org.
Baseline and 2-year follow-up MRI scans were downloaded from the ADNI public database (http://www.loni.ucla.edu/ADNI/Data/) on or before June 1, 2010, and reflect the status of the database at that point. Subjects were excluded if they had significant neurologic disease other than AD, abnormal baseline MRI scan or contraindications to MRI, psychiatric disorder, substance abuse or dependence within the last 2 years, and medical illnesses that could affect cognition or protocol compliance. Please refer to the ADNI protocol for detailed inclusion and exclusion criteria (18).
We analyzed baseline and 2-year follow-up MRI scans from 243 individuals (162 males; mean age at baseline: 75.1±6.9 years; age 55–90) who were diagnosed with amnestic MCI at baseline. The average length of time between baseline and follow-up scans was 2.09 years (SD=0.09; range=1.82–2.73 years). MCI diagnosis was made according to Petersen’s criteria (1) in that all MCI subjects demonstrated objective memory impairment but did not meet criteria for dementia. Specifically, they had a Mini-Mental State Examination (MMSE) (19) score of 24 or higher, a global Clinical Dementia Rating (CDR) (20) score of 0.5, a CDR memory score of 0.5 or higher, and an impaired score on delayed recall of Story A on the Wechsler Memory Scale-Revised (WMS-R) (21).
The study was conducted according to the Good Clinical Practice guidelines, the Declaration of Helsinki and U.S. 21 CFR Part 50— Protection of Human Subjects, and Part 56—Institutional Review Boards. Written informed consent was obtained from all participants before experimental procedures were performed.
All subjects underwent thorough clinical and neuropsychological assessment at the time of scan acquisition. Neurocognitive tests included the following domains and measures: Global cognitive functioning was assessed using the MMSE (19). The delayed recall trial of the Rey Auditory Verbal Learning Test (AVLT) (22) provided a measure of auditory verbal memory. The Wechsler Adult Intelligence Scale-Revised (WAIS-R) Digit Span subtest (23) was used to measure attention. Language abilities were assessed using the Boston Naming Test (24), which is a measure of object naming, and Animals and Vegetables (25), which are measures of semantic verbal fluency. The WAIS-R Digit Symbol subtest (23) and Trail Making Test (Trails A and Trails B) (26) are measures of psychomotor speed and visuospatial tracking. Trails B additionally assesses executive abilities including cognitive flexibility and divided attention. Complete details of the ADNI assessments are found in the ADNI Procedures Manual (http://www.adni-info.org/Scientists/Pdfs/adniproceduresmanual12.pdf).
Neuropsychiatric symptoms were assessed using the Neuropsychiatric Inventory Questionnaire (NPI-Q) (27), a caregiver-based instrument that measures the presence (1=yes, 0=no) and severity (1=mild, 2=moderate, 3=severe) over the prior month of 12 symptom domains: delusions, hallucinations, agitation, depression, anxiety, elation, apathy, disinhibition, irritability, aberrant motor behavior, nighttime disturbances, and eating disturbances. Study participants were divided into three groups based on baseline NPI-Q scores: individuals with depressive symptoms (DEP), defined as having a score of 1 on the Depression domain regardless of the presence or absence of other neuropsychiatric symptoms, individuals with a score of 0 on the Depression domain but a score of 1 on any of the other 11 domains (OTHER), and individuals with no psychiatric symptoms, or scores of 0 across all 12 domains (NOSYMP). Subjects meeting criteria for major depression were excluded from ADNI, thus any reported depressive symptoms are subsyndromal and unrelated to a premorbid psychiatric disorder.
All subjects were scanned with a standardized MRI protocol developed for ADNI (28). Briefly, high-resolution structural brain MRI scans were acquired at 59 sites using 1.5T MRI scanners. Although different scanner types (GE, Siemens, Philips) and various software platforms were used, a standardized MRI protocol was used to maximize cross-site comparability (28). A sagittal 3D-MPRAGE scanning protocol was used with the following acquisition parameters: repetition time (TR) of 2400ms, minimum full TE, inversion time (TI) of 1000ms, 8° flip angle, 24cm field of view, and 192×192×166 acquisition matrix in the x-, y-, and z-dimensions, yielding a voxel size of 1.25×1.25×1.2 mm3, later reconstructed to 1mm isotropic voxels.
Image corrections were applied using a processing pipeline at the Mayo Clinic, consisting of: (1) correction of geometric distortion due to gradient nonlinearity (29), i.e. “gradwarp”, (2) B1-correction for adjustment of image intensity inhomogeneity due to B1-nonuniformity (28), (3) N3 bias field correction for reducing residual intensity inhomogeneity (30), and (4) geometrical scaling for removing scanner and potential session-specific calibration errors using a phantom scan acquired for each subject (31) . All original image files as well as images with all of these corrections are available to the general scientific community at http://www.loni.ucla.edu/ADNI/Data/.
First, each subject’s follow-up scan was linearly registered to their baseline scan, with a 9-parameter (9P) transformation driven by a mutual information (MI) cost function (32), to adjust for global differences in position and scale across time. Second, to account for global brain shape and size differences across subjects, the mutually-aligned scan pairs were then linearly registered to the International Consortium for Brain Mapping template (ICBM-53) (33), applying the same 9P-transformation to both scans. Globally aligned images were resampled in an isotropic space of 220 voxels along x-, y- and z-dimensions with a final voxel size of 1mm3.
Jacobian maps were created for each individual by non-linearly warping follow-up scans to match baseline scans of the same individual using a non-linear inverse-consistent elastic intensity-based registration algorithm driven by a mutual information cost function (3DMI) (34). A color-coded map of the Jacobian determinants was computed from the gradient of the deformation field to illustrate regions of volume expansion (15) over the 2-year interval, yielding a map that estimates the amount of tissue volume change at each voxel. Jacobian maps were also spatially normalized across subjects by nonlinearly aligning all individual maps to a minimal deformation template (MDT), for regional comparisons and group statistical analysis. The MDT represents the average shape of 40 healthy elderly controls; the procedure to construct the MDT is detailed in Hua et al. (35). Average maps were computed by taking the mean at each voxel of the Jacobian maps across subjects.
The regions of interest (ROI), comprised of frontal, temporal, parietal, and occipital lobes, were manually hand-traced by a trained anatomist on the MDT to generate binary masks for each lobe, which were subsequently used to summarize brain atrophy at a regional level in each group. Within each lobe, tissue types were distinguished by creating maps of gray and white matter, CSF, and non-brain tissues using the partial volume classification (PVC) algorithm from the BrainSuite software package (36).
To illustrate systematic differences in atrophic rates between the DEP, OTHER, and NOSYMP groups we constructed voxel-wise statistical maps based on the Student’s t-statistic. The Jacobian maps were compared between groups using permutation-based two-sample t-tests to assess overall significance of group differences inside each ROI, corrected for multiple comparisons (37). In brief, a null distribution for the group differences in tissue volume change (Jacobian values) at each voxel was constructed using 10,000 random permutations of the data. For each test, the subjects’ group status (e.g., DEP vs. NOSYMP) was randomly permuted and voxel-wise t-tests were conducted to identify voxels more significant than p=.05. The volume of voxels inside a mask (i.e., temporal lobes) more significant than p=.05 was computed for the real experiment and for the random assignments. A ratio, describing the fraction of the time suprathreshold volume was more extreme in the randomized tests than the original test, was calculated to yield an overall p-value for the significance of the map (corrected for multiple comparisons by permutation).
For group comparisons of neuropsychological performance, a one-way analysis of variance (ANOVA) was performed, followed by Scheffe tests for post-hoc analysis of significant group differences. Group differences in rates of conversion from MCI to dementia were compared using chi-square analyses.
Of 243 MCI subjects, 44 were in the DEP group, 93 in the OTHER group, and 106 in the NOSYMP group. The three groups did not differ on any demographic characteristics (Table 1). The mean severity of depressive symptoms in the DEP group was 1.30 (SD = 0.51). Thirty-two (73%) were rated as mild in severity, 11 (25%) were rated as moderate, and 1 (2%) was rated as severe. Thirty-five of the 44 subjects in the DEP group also endorsed at least 1 other symptom on the NPI-Q. The most commonly endorsed co-morbid symptoms were Irritability (45.5%) and Anxiety (31.8%). Prevalence rates and mean severity scores for each NPI-Q domain are reported in Table S1 in the Supplement. Twenty-one (48%) subjects in the DEP group were on anti-depressant medications, 22 (50%) were not on anti-depressants, and 1 (2%) subject had no information regarding anti-depressant use. Anti-depressant use was not associated with differences in depression severity at baseline (F1,41=0.27, p=.61) or change in depression severity at 2-year follow-up (F1,18=1.96, p=.18).
At 2-year follow-up, 21 of the 44 DEP subjects remained depressed (DEP-stable), whereas 20 no longer reported depressive symptoms. NPI-Q data at 2-year follow-up was unavailable for the remaining 3 subjects. Of the 106 NOSYMP subjects, 51 continued to exhibit no psychiatric symptoms (NOSYMP-stable), 51 developed psychiatric symptoms, and 4 had no NPI-Q data at 2-year follow-up. Secondary analyses were performed on this subset of participants who had stable psychiatric symptoms and group status (i.e., DEP-stable or NOSYMP-stable) at 2-year follow-up. Demographic characteristics are reported in Table 1. The mean severity of depressive symptoms in the DEP-stable group was 1.38 (SD=0.59) at baseline and 1.29 (SD=0.46) at 2-year follow-up. At baseline, 14 DEP-stable subjects were rated as mild in severity, 6 were rated as moderate, and 1 was rated as severe. At follow-up, 15 were rated as mild in severity, and 6 were rated as moderate.
Individual Jacobian maps were averaged within each DEP, OTHER, and NOSYMP group to demonstrate the mean volume loss (in blue) and ventricular enlargement (in red) in each group (Figure 1), thus providing a voxel-wise estimate of the amount of atrophy over 2 years. The resulting statistical maps from direct group comparisons (Figure 2) revealed significantly more atrophy over 2 years in the frontal, parietal and temporal white matter regions of the DEP group relative to the NOSYMP group. Permutation tests (corrected for multiple comparisons) revealed that the DEP group had significantly more atrophy than the NOSYMP group over 2 years in the frontal (p=.024) and parietal (p=.030) white matter, left greater than right, and in the bilateral temporal white matter (p=.038). In contrast, statistical comparisons between the OTHER and NOSYMP groups revealed no significant regional differences in atrophy. Direct comparison of the DEP group to the OTHER group revealed greater atrophy of the frontal white matter, which was statistically significant on the left (p=.045) but not on the right (p=.107) after correcting for multiple comparisons.
Average Jacobian maps of the DEP-stable and NOSYMP-stable groups are shown in Figure 3. Voxel-wise statistical comparison (Figure 2D) and permutation tests revealed greater overall white matter atrophy (p=.044) in the DEP-stable group compared to the NOSYMP-stable group. Specifically, the DEP-stable group exhibited more frontal (p=.027), parietal (p=.048), and temporal (p=.026) white matter atrophy bilaterally than the NOSYMP-stable group.
Rates of conversion from MCI to AD are shown in Table 2. The DEP group had a 50% rate of conversion from MCI to AD within 2 years, which was higher than the OTHER (40%) and NOSYMP (34%) groups, though the difference was non-significant (χ2=3.39, p=.184). The DEP-stable group, however, demonstrated a significantly higher rate of conversion to AD (62%) compared to the NOSYMP-stable group (27%; χ2=7.53, p=.006).
At baseline, there were no significant differences (p>.05) in neuropsychological test performance among the three groups in any of the cognitive domains. At 2-year follow-up, there were no significant group differences in the relative change in test scores from baseline to follow-up, except for a measure of working memory (WAIS-R Digit Span-backwards; F2,234=3.76, p=.03). Specifically, Scheffe post-hoc tests revealed that the OTHER group (M=-0.74, SD=1.74) had greater decline on Digit Span-backwards compared to the NOSYMP group (M=−0.01, SD=1.94; p=.02). The DEP group did not differ significantly from either the NOSYMP or OTHER group on this task. Baseline scores and relative change in scores at follow-up for each test are provided in Table S2 in the Supplement.
The DEP-stable and NOSYMP-stable groups also performed similarly (p>.14) on all neuropsychological tests at baseline. At 2-year follow-up, the DEP-stable group demonstrated significantly more decline from baseline scores than the NOSYMP-stable group on the MMSE (F1,70=7.45, p=.01; DEP-stable M=-2.19, SD=3.75; NOSYMP-stable M=-0.12, SD=2.53), Boston Naming Test (F1,70=14.44, p<.01; DEP-stable M=−1.48, SD=2.91; NOSYMP-stable M=0.84, SD=2.09), Vegetables (F1,70=5.55, p=.02; DEP-stable M=-2.24, SD=2.47; NOSYMP-stable M=-0.20, SD=3.63), and Trails B (F1,70=6.40, p=.01; DEP-stable M=38.79 sec, SD=63.87; NOSYMP-stable M=0.92 sec, SD=52.45). Baseline scores and relative change in scores at follow-up of the DEP-stable and NOSYMP-stable groups are provided in Table S3 in the Supplement.
Using TBM, we demonstrated that MCI subjects with depressive symptoms exhibited significantly more atrophy over 2 years compared to those without any neuropsychiatric symptoms. This finding, coupled with the lack of detectable differences between the OTHER and NOSYMP groups, highlights the specificity of the relationship between depressive symptoms and the observed brain changes. Moreover, the DEP group demonstrated greater atrophy even when compared directly with the OTHER group. DEP subjects with persistent symptoms over 2 years also demonstrated more decline on select neuropsychological tests and had higher rates of conversion to AD compared to the stable NOSYMP subjects. Depression has previously been demonstrated to be a potentially useful clinical marker for identifying MCI subjects who are more likely to progress to AD (10,11). To our knowledge, this is the first study to map the neurobiological effects underlying the predictive function of depressive symptoms associated with increased progression to AD.
Prior literature on the neuroanatomical correlates of depression in healthy elderly have found reduced volume in the frontal lobes and anterior cingulate (38), as well as hippocampus and amygdala (13,14). Our findings were consistent with these past reports; however, we also observed significant white matter atrophy in parietal and temporal lobes, regions known to be affected in AD. Greater atrophy was observed on the left than the right. This asymmetry has also been observed in previous studies that found greater atrophy and metabolic dysfunction in the left hemisphere of AD patients (39,40), suggesting greater vulnerability of the left hemisphere to neurodegeneration in AD. A similar pattern has also been shown in MCI subjects who had higher levels of Pittsburgh Compound-B (PiB) retention on the left dorsal frontal cortex and sensorimotor cortex compared to the right (41). Hence, the pattern of atrophy in the DEP group may reflect shared underlying changes in the neuroanatomical correlates of both depressive symptoms and pathological changes associated with AD (42). This is consistent with our previous findings demonstrating that depressive symptoms represent a phenotypic marker related to the onset of AD and favorable response to donepezil therapy (10), along with other studies that have found increased AD-related pathology in depressed elderly with cognitive impairment, including elevated retention of PET radioligands for beta-amyloid (Aβ42) and tau (43,44) and higher burden of Aβ42-containingneuritic plaques and tau-containing neurofibrillary tangles at autopsy (45). Qiu and colleagues (46) found that depressed elderly demonstrated lower plasma-Aβ42 levels compared to nondepressed elderly. In our sample, post-hoc analysis of cerebrospinal fluid (CSF) biomarker concentrations available for a subgroup of participants (n=129) revealed no significant differences between the DEP, OTHER, and NOSYMP groups in mean concentrations of Aβ1-42, tau, or phosphorylated-tau or in the prevalence of subjects meeting AD-signature cutoff values (6) in any of these CSF biomarkers (Tables S4-S5 in the Supplement). However, the cohort studied by Qiu and colleagues had clinically significant levels of depression, whereas our DEP group was subclinical and, on average, mild in severity. Thus, differences in CSF biomarker concentrations associated with depression may be underestimated in our sample.
Despite the increased evidence of a possible shared pathophysiology between depression and AD, the mechanism underlying this relationship is not fully elucidated but seems to involve white matter. In our study, the brain changes associated with depressive symptoms were largely confined to white matter. Some investigators have found reduced white matter volume in depressed individuals (47), and recent investigations using diffusion tensor imaging (DTI) have identified lower fractional anisotropy (FA) in prefrontal regions, anterior cingulate, and temporal regions in depressed subjects (48). Severe depression and other neuropsychiatric disorders have also been associated with abnormal myelination (49,50) and reduced or abnormal oligodendrocytes (51), especially at older ages (52).
Beyond the association between white matter and depression, the white matter atrophy may be a direct consequence of AD pathology. Substantial theoretical and empirical evidence supports white matter pathology in AD (42,53). Myelin breakdown has also been observed at the MCI stage (54,55). Furthermore, depressive symptoms that were persistent over 2 years were associated not only with white matter atrophy, but also more cognitive decline on measures of global cognition, language, and executive abilities, as well as higher rates of conversion to AD compared to MCI subjects with no psychiatric symptoms. Thus, the white matter changes elucidated by the presence of depressive symptoms may be interpreted as a shared early pathological process of AD rather than representative of neuroanatomical changes associated with depression alone. Post-hoc analyses revealed that within the NOSYMP group, subsequent development of psychiatric symptoms at 2-year follow-up was associated with greater right frontal white matter atrophy (p=.033), a trend towards greater right temporal white matter atrophy (p=.053), and higher rate of conversion to AD (47% vs. 27%; χ2=4.19, p=.041). This finding lends further support to the hypothesis that the development of new psychiatric symptoms in MCI may be a symptom reflecting the underlying progression of AD pathology.
The strengths of this study include the prospective design in which each subject acts as his/her own control and measurement of intra-individual rates of change yield greater sensitivity to detect subtle brain changes over time. Several limitations should also be acknowledged. First, the acquisition of MRI scans from multiple centers raises the possibility of inter-scanner and software variability. However, a standardized MRI protocol was used across all 59 sites to maximize cross-site comparability (28). Second, although the current study focused on depressive symptoms, there was significant co-morbidity in the DEP group, with almost 80% of subjects endorsing at least 1 other symptom on the NPI-Q. Further, the ADNI protocol specifically excluded patients with clinically significant depressive symptoms constituting a diagnosis of major depression. However, the prevalence rates of depressive and other neuropsychiatric symptoms in our sample is comparable to those reported in other large MCI cohorts, including the National Alzheimer’s Coordinating Center (NACC) database (56) and the population-based Cardiovascular Health Study (57). Moreover, as these symptoms are now recognized to be prevalent in MCI patients and may have predictive value in identifying individuals at higher risk of progressing to AD, the differences we found may even be underestimated. Third, even though the NPI-Q has been demonstrated to provide adequate test-retest reliability and convergent validity for assessing a broad range of neuropsychiatric symptoms (27), it is not designed as a detailed or comprehensive measure of depressive symptoms; therefore, the inclusion of a more precise instrument is necessary for future studies of the depression risk for developing dementia. Fourth, the present study sample was predominantly Caucasian, male, and highly educated, which may limit the generalizability of the results to more demographically diverse community-based samples. Finally, future studies should examine subregions of the broad lobar atrophy reported here in order to further explore the neurobiological substrates of the complex relationship between depression and dementia in the elderly.
Analysis of serial MRI scans using TBM appears to be sensitive in tracking distinct patterns of brain changes within a population of MCI patients. Specifically, the presence of depressive symptoms was associated with greater atrophy in the frontal, temporal and parietal white matter compared to MCI patients without any neuropsychiatric symptoms. These regions of increased atrophy may represent shared white matter mechanisms and indicate a greater severity or faster progression of AD pathology. Findings from this study lend further support to the hypothesis that depression may be a symptom of prodromal AD and thus may be useful as a surrogate clinical marker to identify those MCI subjects who are most likely to progress to AD.
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. Further support for this work came from NIH grant K23-AG028727, a grant from the Alzheimer’s Association (NIRG-07-60424), the Alzheimer's Disease Research Center grant P50 AG-16570, and California Alzheimer's Disease Center. Algorithm development for this study was also funded by the NIA, NIBIB, the National Library of Medicine, and the National Center for Research Resources (AG016570, EB01651, LM05639, RR019771 to PT). Aspects of this work were presented at the Alzheimer’s Association International Conference, July 16-21, 2011, in Paris, France.
Dr. Bartzokis has received research funding from Janssen Pharmaceutical Inc. and Novartis and speaker honoraria from Bristol-Myers Squibb, Janssen Pharmaceutical Inc., Novartis, and Pfizer.
Dr. Jack serves as a consultant for Janssen, Eisai, GE, Johnson and Johnson, and Lilly and is involved in clinical trials sponsored by Allon and Baxter Inc.
Dr. Toga received a speaker honorarium from St. Jude Children's Hospital; serves in an editorial capacity for NeuroImage, InSight, The Cerebellum, Neuroimaging, Neuroinformatics, Anatomy & Embryology, Current Medical Imaging Reviews, Biology Image Library, Biomedical Computation Review, Brain Structure and Function, and Journal of Neural Regeneration Research; has served on scientific and/or external advisory boards for Wellcome Trust, Allen Institute for Brain Science, University of Texas at Austin, Oklahoma IDeA Network for Biomedical Research Excellence, Takeda Global Research & Development Center, and the University of Pittsburgh; and has received/receives research support from an Academic Excellence Grant–SUN Microsystems, and from High-Q Foundation and the National Multiple Sclerosis Society.
Dr. Weiner serves on scientific advisory boards for Bayer Schering Pharma, Eli Lilly and Company, CoMentis, Inc., Neurochem Inc, Eisai Inc., Avid Radiopharmaceuticals Inc., Aegis Therapies, Genentech, Inc., Allergan, Inc., Lippincott Williams & Wilkins, Bristol-Myers Squibb, Forest Laboratories, Inc., Pfizer Inc, McKinsey & Company, Mitsubishi Tanabe Pharma Corporation, and Novartis; has received funding for travel from Nestlé and Kenes International and to attend conferences not funded by industry; serves on the editorial board of Alzheimer's & Dementia; has received honoraria from the Rotman Research Institute and BOLT International; serves as a consultant for Elan Corporation; receives research support from Merck & Co. and Radiopharmaceuticals Inc.; and holds stock in Synarc and Elan Corporation.
Dr. Thompson serves on editorial advisory boards for IEEE Transactions on Medical Imaging, Human Brain Mapping, Medical Image Analysis, Cerebral Cortex, Current Medical Imaging Reviews, Inverse Problems and Imaging, and Translational Neuroscience. All other authors report no biomedical financial interests or potential conflicts of interest.
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