Individual regions of interest are displayed on brain surfaces along with intranetwork connections in each of the 5 networks analyzed in the current study. CON indicates executive control network; DAN, dorsal attention network; DMN, default mode network; SAL, salience network; and SMN, sensorimotor network.
Changes in intranetwork resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) composite scores for participants with ADAD and participants with LOAD as a function of the Clinical Dementia Rating (CDR) Scale. For both ADAD and LOAD, a stepwise loss of functional connectivity was seen for most RSNs with an increasing CDR. Whiskers extend to 1.5 × interquartile range. M− indicates mutation negative and M+, mutation positive.aP < .05.bP < .005.cP < .001.
Changes in internetwork composite scores for participants with ADAD and participants with LOAD as a function of Clinical Dementia Rating (CDR) status. A loss of between-network functional connectivity was seen for the default mode network–dorsal attention network and default mode network–sensorimotor network with an increasing CDR, although for executive control network–sensorimotor network, this pattern was only present in LOAD. Whiskers extend to 1.5 × interquartile range. M− indicates mutation negative and M+, mutation positive.aP < .05.bP < .005.cP < .001.
Intranetwork functional connectivity (and standard error bands) as a function of estimated years from symptom onset for all mutation positive (M+) and mutation negative (M−) individuals with autosomal dominant Alzheimer disease.
Internetwork functional connectivity (and standard error bands) as a function of estimated years from symptom onset for all mutation positive (M+) and mutation negative (M–) participants with autosomal dominant Alzheimer disease.
A fitted model predicts that whole-brain intranetwork (warm colors) and internetwork (cool colors) default mode functional connectivity declines with estimated years from symptom onset in M+ individuals (n = 57) and begins to disappear approximately at the anticipated age of symptom onset.
eTable. Mean (standard deviation) concentration of Aß42 in the CSF of each group.
Thomas JB, Brier MR, Bateman RJ, Snyder AZ, Benzinger TL, Xiong C, Raichle M, Holtzman DM, Sperling RA, Mayeux R, Ghetti B, Ringman JM, Salloway S, McDade E, Rossor MN, Ourselin S, Schofield PR, Masters CL, Martins RN, Weiner MW, Thompson PM, Fox NC, Koeppe RA, Jack CR, Mathis CA, Oliver A, Blazey TM, Moulder K, Buckles V, Hornbeck R, Chhatwal J, Schultz AP, Goate AM, Fagan AM, Cairns NJ, Marcus DS, Morris JC, Ances BM. Functional Connectivity in Autosomal Dominant and Late-Onset Alzheimer Disease. JAMA Neurol. 2014;71(9):1111-1122. doi:10.1001/jamaneurol.2014.1654
Copyright 2014 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.
Autosomal dominant Alzheimer disease (ADAD) is caused by rare genetic mutations in 3 specific genes in contrast to late-onset Alzheimer disease (LOAD), which has a more polygenetic risk profile.
To assess the similarities and differences in functional connectivity changes owing to ADAD and LOAD.
Design, Setting, and Participants
We analyzed functional connectivity in multiple brain resting state networks (RSNs) in a cross-sectional cohort of participants with ADAD (n = 79) and LOAD (n = 444), using resting-state functional connectivity magnetic resonance imaging at multiple international academic sites.
Main Outcomes and Measures
For both types of AD, we quantified and compared functional connectivity changes in RSNs as a function of dementia severity measured by the Clinical Dementia Rating Scale. In ADAD, we qualitatively investigated functional connectivity changes with respect to estimated years from onset of symptoms within 5 RSNs.
A decrease in functional connectivity with increasing Clinical Dementia Rating scores were similar for both LOAD and ADAD in multiple RSNs. Ordinal logistic regression models constructed in one type of Alzheimer disease accurately predicted clinical dementia rating scores in the other, further demonstrating the similarity of functional connectivity loss in each disease type. Among participants with ADAD, functional connectivity in multiple RSNs appeared qualitatively lower in asymptomatic mutation carriers near their anticipated age of symptom onset compared with asymptomatic mutation noncarriers.
Conclusions and Relevance
Resting-state functional connectivity magnetic resonance imaging changes with progressing AD severity are similar between ADAD and LOAD. Resting-state functional connectivity magnetic resonance imaging may be a useful end point for LOAD and ADAD therapy trials. Moreover, the disease process of ADAD may be an effective model for the LOAD disease process.
Late-onset Alzheimer disease (LOAD) is the leading cause of dementia worldwide, currently affecting more than 18 million people.1 Alzheimer disease (AD) is defined by the pathological accumulation of tau neurofibrillary tangles and amyloid β (Aβ) plaques.2 While AD is typically polygenetic and late onset (LOAD), in a small subset of individuals, AD is inherited as an autosomal dominant (ADAD) trait, which is typically early onset and caused by monogenetic mutations in the genes encoding presenilin 1, presenilin 2, or amyloid precursor protein. These mutations are approximately 100% penetrant and cause AD by affecting Aβ cleavage and folding.3
The discovery of ADAD mutations has enabled researchers to develop transgenic mouse models and cell lines that express these mutations.4 These experimental models have enabled the preclinical testing of potential antiamyloid AD therapies.5 By studying individuals with ADAD who will develop dementia at a predictable age, researchers can identify the temporal dynamics of changes in biomarker profiles before the development of clinical symptoms.6 However, questions remain concerning the extent to which findings in ADAD translate to LOAD.
Converging evidence from cerebrospinal fluid (CSF), amyloid imaging, and brain volumetric studies7,8 suggests that ADAD and LOAD have similar disease processes. However, biomarker differences exist between LOAD and ADAD. Specifically, individuals with ADAD may have greater amyloid plaque deposition in the basal ganglia compared with individuals with LOAD.9 Additionally, increased levels of CSF Aβ1-42 have been observed very early on in ADAD but not in LOAD.8
One biomarker of interest in LOAD that is relatively unestablished in ADAD is resting-state functional connectivity magnetic resonance imaging (rs-fcMRI).10,11 Functional connectivity measures the correlation structure of blood oxygen level–dependent (BOLD) signals between regions of interest (ROIs), collections that form resting state networks (RSNs).12,13 In LOAD, reduced functional connectivity has been observed with progressing clinical status (measured by the Clinical Dementia Rating [CDR] Scale)14 within the default mode network (DMN), an RSN composed of regions known to harbor Aβ15 and tau15 pathology. Default-mode network functional connectivity decreases have also been noted in presymptomatic individuals genetically at risk for LOAD.16 Recently, abnormalities in functional connectivity have been observed in the dorsal attention network (DAN), executive control network (CON), salience network (SAL), and sensorimotor network (SMN) that parallel deteriorating cognitive status.17
We measured functional connectivity in a cross-sectional cohort of asymptomatic and symptomatic participants with ADAD (mutation positive [M+; n = 54] and mutation negative [M–; n = 25]) and a cross-sectional cohort of individuals with LOAD (very mild AD dementia [n = 74], mild AD dementia [n = 27], and cognitively normal older adults [n = 343]). We show that functional connectivity changes with respect to CDR are similar for both ADAD and LOAD.17
The ADAD cohort was drawn from the international Dominantly Inherited Alzheimer Network and consisted of participants from families with ADAD, both individuals with mutations and individuals lacking mutations (Table 1). We excluded 26 M+ individuals and 7 M– individuals from the analysis who were scanned with inconsistent sequence parameters. We removed 1 additional M– participant with questionable clinical status. Cross-sectional data available as of February 2012 were included in this analysis.18 Only participants who passed quality control (described later) were included in the final analysis.
A separate cohort of participants was enrolled in a longitudinal study at the Knight Alzheimer Disease Research Center at Washington University in St Louis, designed to track individuals at risk for LOAD through the stages of cognitive decline.17 All participants from both cohorts provided written informed consent. Institutional review boards at their respective institutions provided approval. Each participant completed a general physical (including neurologic) examination, health and medication history, and clinical assessment for dementia.19 We used independent general linear mixed models to assess group differences in demographics.
Experienced clinicians conducted semistructured interviews of each participant and a knowledgeable collateral source. The CDR was used to determine and stage dementia severity.14 A score of CDR 0 indicates cognitive normality, CDR 0.5 corresponds to very mild dementia, and CDR 1 or more specifies mild and moderate dementia. In other studies, certain participants with CDR 0.5 may be classified as having mild cognitive impairment due to AD, depending on the staging criteria.20 Five participants from the ADAD cohort with a score of CDR 1 or more had more advanced disease (CDR 2 [n = 4]; CDR 3 [n = 1]). All participants with a score of CDR greater than 0 had a clinical diagnosis of AD dementia in accordance with standard criteria.21 Disease biomarkers such as Pittsburgh Compound B positron emission tomography imaging22 and CSF measures23 were not explicitly taken into account for the diagnosis of LOAD but were used to exclude participants with profiles inconsistent with AD, when available (eTable in the Supplement).
Within the Dominantly Inherited Alzheimer Network cohort, parent age at symptomatic onset was determined from semistructured interviews with the participant, a knowledgeable collateral source, and/or other informants familiar with the parental history of disease. The age at onset of the affected parent was determined by estimating the time of onset of symptoms (eg, memory/cognition, motor, or behavior). The anticipated age at symptomatic onset (AAO) for each individual was indexed to the AAO for that individual’s affected parent. The estimated years from symptom onset (EYO) for each individual from the Dominantly Inherited Alzheimer Network was defined as age at testing minus AAO.5
DNA was extracted from peripheral blood and apolipoprotein E (APOE) genotyping was conducted according to previously published methods.24 Individuals were defined as APOE ε4 positive if they had at least 1 ε4 allele.
For both cohorts, neuroimaging was performed using 3-T Tim Trio scanners (Siemens) equipped with the standard 12-channel head coil (eAppendix in the Supplement; Table 2). Structural images were acquired to allow alignment of rs-fcMRI images to atlas space.17
Initial preprocessing of all rs-fcMRI data (both ADAD and LOAD) followed conventional methods as previously described,17,25 which were modified to correct for nonoptimal order of operations26 (eAppendix in the Supplement). Spurious variance was reduced by a regression of nuisance time series derived from head-motion correction and extraction of BOLD activity from white matter, CSF regions, and the BOLD time series averaged over the whole brain (or global signal).27
The analyses of rs-fcMRI and quality control procedures for participants with ADAD and LOAD are described in the eAppendix of the Supplement.28 Participants with either outlier root mean squared movement or excessive frame removal (>40%) were excluded from further analysis.
For all participants, we extracted time series data from thirty-five 6-mm radius spherical brain ROIs distributed throughout 5 functionally defined RSNs, including the DMN, DAN, CON, SAL, and SMN (Figure 1). Briefly, intranetwork composite scores were obtained by averaging BOLD correlation values computed between ROIs belonging to a particular RSN and internetwork composite scores were obtained by averaging correlations from ROIs belonging to separate RSNs. Using a composite score for intranetwork and internetwork comparisons reduces the amount of data while reducing the potential impact of sampling error. We analyzed composite scores for 5 intranetwork (DMN, DAN, CON, SAL, and SMN) and 3 internetwork (DMN:DAN, DMN:SMN, and CON:SMN) composites, which we have previously shown are affected by LOAD.17
Generalized linear mixed models were used for each RSN composite to assess the fixed effects of CDR and AD type as well as their interaction. For ADAD, this model did not include the CDR 0 M– group to preserve the balance of the model between LOAD and ADAD. Differences between CDR 0 M+ and CDR 0 M– were assessed using the model that incorporates EYO, described later. However, we included the CDR 0 M– group in each figure for comparison purposes. We also included ADAD family membership as a random effect because it is likely that functional connectivity measures are correlated for members of a common family. The AD type factor is a single fixed factor accounting for differences in average age and scanner acquisition parameters between the LOAD and ADAD groups. We assessed significant pairwise effects (eg, between CDR 0 M+ and CDR 0.5 M+) by extracting individual contrasts from the omnibus model. We compared the pairwise effect size for different CDR stages between groups (eg, CDR 0.5-CDR 1 ADAD vs CDR 0.5-CDR 1 LOAD) using the Q test for effect size heterogeneity. We subsequently refit the preceding models adding factors in a stepwise fashion to account for the random effect of scanner and fixed effects of age and APOE ε4 status.
To analyze the effect of EYO on functional connectivity in the ADAD cohort, generalized linear mixed models were constructed for each RSN with EYO, quadratic effect of EYO, and (EYO2) and mutation status, as well as interactions among these factors. Family membership of ADAD was included as a random effect. Changes in RSN strength with respect to EYO were displayed using a locally weighted scatterplot smoothing.7 To protect the confidentiality of participants’ mutation status, individual data were not displayed.
To qualitatively assess whole-brain changes in DMN-associated functional connectivity with respect to EYO in the M+ ADAD group, we computed voxelwise correlations between a 6-mm ROI in the posterior cingulate cortex (an important node of the DMN) and each voxel in the brain for each individual. We then used a locally weighted scatterplot smoothing model to predict posterior cingulate cortex functional connectivity at each value of EYO in the range between −25 and 10 years for at 0.1-year increments for M+ individuals and displayed these predicted values using a movie. Each frame of the movie shows the predicted whole-brain average posterior cingulate cortex seed functional connectivity for a specific EYO value. Warm regions represent positive averages within DMN functional connectivity; cool regions represent negative between-network functional connectivity.
We used ordinal logistic regression to perform a cross-regression analysis that further elucidated similarities between ADAD and LOAD. We fit a regression to predict CDR using the 5 intranetwork and 3 internetwork composite values. We fit a separate model in each AD type and used this to predict CDR values for participants in the other AD type. We used the Spearman rank correlation to assess the similarity between actual and predicted CDR values.
Initially, we combined both cohorts to test for the main effect of CDR score on intranetwork functional connectivity (Figure 2). A mixed model (corrected for mean age and acquisition differences between cohorts as well as a random effect of ADAD family membership) showed a significant main effect of CDR for multiple RSNs including the DMN, DAN, and CON (Tables 3 and 4). Only the SAL and SMN did not show a significant effect of CDR. In general, pairwise comparison between CDR scores showed that functional connectivity was lower in a stepwise fashion for the ADAD cohort (Table 5). A similar pattern was observed for LOAD, although individual pairwise differences (from CDR 0 to CDR 0.5) were generally not significant (Table 6). Stepwise inclusion of additional factors that assessed fixed effects of age as a continuous covariate and APOE ε4 status, as well as a random effect of ADAD acquisition site, reduced the observed effect sizes but did not remove them (Tables 3 and 4).
Although the general patterns of intranetwork functional connectivity changes seen for ADAD and LOAD were similar, subtle differences were observed. When pairwise effect sizes (Cohen d) differed between ADAD and LOAD, the CDR effect was generally greater in ADAD compared with LOAD.
Internetwork functional connectivity was also decreased in magnitude with respect to CDR in both LOAD and ADAD (Figure 3). Internetwork (eg, DMN:DAN) BOLD correlations were typically negative in sign (ie, anticorrelations) in data preprocessed using global signal regression.27 As previously reported, LOAD cross-network anticorrelations were diminished (ie, closer to 0) with advancing CDR.17 A similar finding was observed in ADAD (Figure 2) where decreased anticorrelation magnitude was observed for DMN:DAN but not DMN:SMN or CON:SMN (Table 4). The stepwise inclusion of additional factors testing for fixed effects of age as a continuous covariate, APOE ε4 status, and the random effect of ADAD acquisition site reduced the effects but did not remove them (Tables 3 and 4).
To further characterize the similarity between AD types, we fit ordinal logistic regression models in ADAD and used these to predict CDR levels in LOAD (and vice versa). The model fit in ADAD was able to predict LOAD CDR levels much better than chance (t442 = 5.11; P < .001). The inverse process also allowed us to predict ADAD CDR levels based on LOAD data better than chance (t52 = 4.51; P < .001). Cross–AD type classification was unsuccessful for predicting genetic risk in the absence of clinical symptoms.
For ADAD, we showed how functional connectivity changes occur relative to EYO in all M+ individuals, including those destined to develop cognitive impairment and those who were already symptomatic. Figure 4 presents locally weighted scatterplot smoothing plots of RSN composites scores against EYO and demonstrates a qualitative decrease in the DMN several years prior to expected symptom onset. Figure 5 presents the same analysis for between-RSN data. The limited size of this cohort spread over many decades of EYO precludes statistical demonstration of this effect but suggests that functional connectivity may slightly precede cognitive symptoms.
We produced a video that demonstrates progressive loss of intranetwork and internetwork functional connectivity in the M+ group, using the posterior cingulate cortex as a seed. The fitted model predicted qualitative changes in functional connectivity in M+ participants prior to AAO (Video).
Both ADAD and LOAD manifest similar functional connectivity changes with respect to CDR. Moreover, regression models constructed in one cohort distinguished CDR stages in the other. This result demonstrates that functional connectivity changes manifest similarly in both types of AD. However, some differences exist between AD types in functional connectivity. A modestly greater effect of disease severity was seen for ADAD compared with LOAD. The available data suggest that ADAD may serve as an effective model to study LOAD pathophysiology, albeit with some reservations.
The first studies to investigate LOAD using rs-fcMRI detected changes in the DMN.29 Our group has reported decreased functional connectivity in a wider set of intranetwork and internetwork relationships.17 These results are recapitulated in our current study where we show similar effects of CDR on RSN connectivity in LOAD and ADAD. Similarities were also evident between ADAD and LOAD when a regression model was fit in each group using all analyzed RSNs as features and used these models to predict CDR levels in the other group. Our success fitting CDR models in the ADAD cohort and predicting CDR status for the LOAD cohort (and vice versa) further suggests similar widespread RSN changes in both AD types.
However, analysis of the certain RSN composites suggested a slightly more pronounced decline for ADAD compared with LOAD. The greater loss in functional connectivity seen in ADAD in certain networks may suggest that ADAD is a more aggressive process than LOAD.30,31 We previously hypothesized that internetwork correlations may reflect a mechanism by which pathology spreads from 1 functional network to the next in a cascading disease process.32 There may be a more rapid and dramatic accumulation of Aβ and tau neurofibrillary tangle pathology in ADAD compared with LOAD.33 Hence, the observed rapid decline both in and between certain RSNs possibly reflects a faster spread of pathology from the DMN across diseased connections in ADAD.
Biomarker profiles accrue with age along distinct intraindividual trajectories in LOAD and ADAD.6,34 In ADAD, we show evidence suggesting that functional connectivity decreases with EYO only in the M+ group. In individuals with M+, intraindividual changes in BOLD correlations within and between networks may serve as an effective biomarker of disease progression. Functional connectivity is a potentially useful biomarker in ADAD. However, we have only demonstrated qualitative differences between M+ and M– groups temporally proximate to the anticipated AAO, suggesting that gross changes in intranetwork functional connectivity likely occur later than changes in metabolism, hippocampal volume, and CSF Aβ and tau. Observed changes in BOLD correlations may reflect downstream pathophysiological processes.7 Ongoing longitudinal studies will assess the usefulness of functional connectivity in tracking preclinical AD.
Our results differ from previous results on 3 points. First, individual RSN composite scores were not significantly different for asymptomatic participants with genetic risk factors in either cohort. This conflicts with previous studies of LOAD that showed DMN functional connectivity changes within network in asymptomatic individuals with Aβ plaque deposits35 or a family history of LOAD.36 Second, we did not observe a transient increase in functional connectivity in the SAL for participants with ADAD, as was previously observed for LOAD.17,37 This suggests another possible difference between LOAD and ADAD. Finally, in contrast to a recent study by Chhatwal et al,11 we were unable to demonstrate at a statistically significant divergence between the individuals with M+ and individuals with M– prior to symptom onset, although qualitatively, our data are consistent with that finding. This difference possibly reflects the fact that Chhatwal et al analyzed ROI-level changes whereas here we analyzed network-level changes. We were able to demonstrate voxel-level DMN functional connectivity changes using a locally weighted scatterplot smoothing video. This qualitatively confirmed the Chhatwal et al results using an ROI. Indeed, the ADAD cohort reported by Chhatwal et al is the same cohort reported here, although we excluded several additional participants owing to scan parameter issues.
This study used network composite scores as a measure of functional connectivity strength,17 which have several advantages but also make 2 assumptions. First, composite scores are a data reduction strategy, reducing the burden of multiple comparisons. Second, they reduce sampling error of observing any single functional connectivity pair within an RSN. However, they assume that each functional connectivity pair in an RSN behaves similarly. This has been previously shown to be valid in LOAD but may obscure focal changes such as those previously seen in ADAD.11 In addition, composite scores assume that an ROI’s RSN membership does not change with disease, which could bias the measurement.
Several limitations occurred from the design of this study. First, there were scanning differences between cohorts. This complicated demonstration of average differences between cohorts but did not impact our ability to demonstrate similarities between AD types. Second, our LOAD cohort was significantly older than our ADAD cohort. This is an unavoidable confound in any study comparing LOAD with early-onset ADAD. We addressed this issue by correcting for age differences between the 2 cohorts. Finally, it has been argued that EYO might not be the best estimate of disease progression in participants with ADAD CDR 0.5. However, because individuals with CDR 0.5 are difficult to stage precisely, EYO is the most practical measure in a cross-sectional study. Larger longitudinal studies will be able to more fully characterize ADAD and LOAD functional connectivity changes and place them in temporal relation to other biomarkers (especially CSF tau, Aβ, positron emission tomography, volumetrics, and amyloid imaging). Volumetric comparisons are particularly important to this study because atrophy may influence the measured BOLD signal. Future studies directly comparing these 2 measures will be important.
Finally, this study used the global signal regression (GSR) preprocessing step. This procedure is controversial.27,38 It is algebraically true that GSR forces the mean of correlations across the brain to be 0 and can make negative correlations more apparent. However, correlations following GSR are essentially first-order partial correlations accounting for widely shared variance while correlations without GSR are canonical correlations. This makes correlations with and without GSR 2 fundamentally different statistical quantities reflecting different types of relationships. It is likely that some of the removed signal is of neural origin;39 however, a large fraction of the global signal is related to residual effects of head motion28 and fluctuations in the partial pressure of carbon dioxide.40 Thus, we viewed GSR as a necessary step for noise reduction in this cross-scanner multisite study. Beyond its noise reduction properties,41 GSR has been shown to increase the concordance between BOLD correlation-mapping and electrocorticography, particularly for negative correlations,42 indicating an important relationship to neurobiology.
This study demonstrates that functional connectivity changes owing to advanced AD are largely similar in LOAD and ADAD. This result supports the use of ADAD as a model of LOAD and compliments the emerging biomarker data supporting the similarity of these 2 entities.
Corresponding Author: Beau M. Ances, MD, PhD, Department of Neurology, Washington University in St Louis, 660 S Euclid Ave, PO Box 8111, St Louis, MO 63110 (firstname.lastname@example.org).
Accepted for Publication: June 10, 2014.
Published Online: July 28, 2014. doi:10.1001/jamaneurol.2014.1654.
Author Contributions: Dr Ances had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Thomas and Brier served as co–first authors, each with equal contribution to the manuscript.
Study concept and design: Thomas, Brier, Bateman, Snyder, Benzinger, Raichle, Holtzman, Mayeux, Masters, Hornbeck, Schultz, Marcus, Ances.
Acquisition, analysis, or interpretation of data: Thomas, Brier, Benzinger, Xiong, Sperling, Ghetti, Ringman, Salloway, McDade, Rossor, Ourselin, Schofield, Martins, Weiner, Thompson, Fox, Koeppe, Jack, Mathis, Oliver, Blazey, Moulder, Buckles, Chhatwal, Goate, Fagan, Cairns, Morris, Ances.
Drafting of the manuscript: Thomas, Brier, Bateman, Snyder, Raichle, Oliver, Blazey, Hornbeck, Ances.
Critical revision of the manuscript for important intellectual content: Thomas, Bateman, Benzinger, Xiong, Sperling, Mayeux, Ghetti, Ringman, Salloway, McDade, Rossor, Ourselin, Schofield, Masters, Martins, Weiner, Thompson, Fox, Koeppe, Jack, Mathis, Moulder, Buckles, Chhatwal, Schultz, Goate, Fagan, Marcus, Ances.
Statistical analysis: Thomas, Brier, Bateman, Snyder, Xiong, Holtzman, Salloway, Ourselin, Blazey, Chhatwal, Cairns, Morris, Ances.
Obtained funding: Bateman, Benzinger, Ghetti, Ringman, Masters, Martins, Weiner, Moulder, Buckles, Goate, Morris, Ances.
Administrative, technical, or material support: Thomas, Snyder, Benzinger, Raichle, Ghetti, Rossor, Schofield, Thompson, Fox, Koeppe, Jack, Oliver, Moulder, Buckles, Fagan, Cairns, Marcus, Ances.
Study supervision: Thomas, Bateman, Benzinger, Holtzman, Ourselin, Martins, Goat, Morris, Ances.
Conflict of Interest Disclosures: Dr Bateman receives funding from Fidelity Nonprofit Management Foundation, is a board member for EvVivo Scientific Advisory Board, and consults with C2N Diagnostics, Eisai Scientific Advisory Panel, Medtronics Scientific Advisory Panel, and Novartis; he is also funded or pending funding by the National Institutes of Health, Alzheimer’s Association, Glenn Foundation for Medical Research, AstraZeneca LP, Merck & Company Inc, Eli Lilly and Co, Pharma Consortium (Biogen Idec, Elan Pharmaceuticals Inc, Eli Lilly and Co, Hoffman-La Roche Inc, Genentech Inc, Janssen Alzheimer Immunotherapy, Mithridion Inc, Novartis Pharma AG, Pfizer Biotherapeutics R and D, and sanofi-aventis), The Ruth K Broad Biomedical Research Foundation, Elan and AstraZeneca, Washington University, and C2N Diagnostics. Dr Benzinger has received research funding from Avid Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly. Dr Fagan receives funding from the National Institutes of Health and is a member of the scientific advisory boards for IBL International and Roche. Dr Fox has received payment for consultancy or for conducting studies from Avid, Bristol-Myers Squibb, Elan Pharmaceuticals, Eisai, Lilly Research Laboratories, GE Healthcare, IXICO, Janssen Alzheimer Immunotherapy, Johnson & Johnson, Janssen-Cilig, Lundbeck, Neurochem Inc, Novartis Pharma AG, Pfizer Inc, sanofi-aventis, and Wyeth Pharmaceuticals; he has a National Institute for Health Research Senior Investigator award and receives support from the Wolfson Foundation, National Institute for Health Research Biomedical Research Unit (Dementia) at University College London, the EPSRC, Alzheimer’s Research United Kingdom, and the NIA; and receives no personal compensation for the activities mentioned above. Dr Ghetti consults for Piramal Imaging. Dr Goate receives funding from AstraZeneca, Genentech, Pfizer, Tau Consortium, and the DIAN Pharmaceutical Consortium: Biogen Idec, Eisai Inc, Elan Pharmaceuticals Inc, Eli Lilly and Company, FORUM Pharmaceuticals, Hoffman-La Roche Inc, Genetech Inc, Janssen Alzheimer Immunotherapy, Mithridion Inc, Novartis Pharm AG, Pfizer Biotherapeutics Research & Development, sanofi-aventis; she also consults for Amgen and Cognition Therapeutics and received payments for lectures including service on speaker bureaus from Pfizer, AstraZeneca, and Genentech; she receives royalties for IP licensed to Taconic. Dr Holtzman receives research funding from the National Institutes of Health, AstraZeneca, C2N Diagnostics, Cure Alzheimer’s Fund, and Tau Consortium; he serves on the scientific advisory board of C2N Diagnostics and consults for Bristol-Myers Squibb, Eli Lilly, and Genentech; he holds US patents 7,892,845, 7,892,545, 7,771,722, 7,195,761, 7,015,044, and 6,465,195. Dr Jack has provided consulting services for Janssen Research & Development, LLC, and Eli Lilly; receives research support from the Alexander Family Alzheimer's Disease Research Professorship of the Mayo Foundation. Dr Marcus consults with Avid Radiopharmaceuticals and has stock options with Radiologics Inc; he receives travel, accommodations, and meeting expenses unrelated to activities listed from BlueArc Inc. Dr Masters has advisory roles with Prana Biotechnology and the Eli Lilly Company. Dr Morris has participated or is currently participating in clinical trials of antidementia drugs sponsored by Janssen Immunotherapy and Pfizer; has served as a consultant for Lilly USA; and receives research support from Eli Lilly/Avid Radiopharmaceuticals. Dr Ourselin receives funding from GE Healthcare, Siemens, and Mirada Medical. Dr Ringman has received compensation for serving on scientific advisory boards for Takeda Pharmaceuticals and StemCells, Inc and has received research support from Janssen, Pfizer, Accera, Bristol-Myers Squibb, and Wyeth Pharmaceuticals. Dr Salloway is a consultant for Janssen AI, AstraZeneca, Avid-Lilly, GE, Baxter, Pfizer, Athena, Bristol-Myers Squibb, Biogen, and Merck. Dr Schofield receives funding from the Australian National Health and Medical Research Council, Australian Research Council, and Beyondblue; he is a board member of Neuroscience Research Australia, the Neuroscience Research Australia Foundation, and the Health Science Alliance; is an employee of Neuroscience Research Australia; and has received speaker fees from Janssen Pharmaceuticals Australia. Dr Schultz has served on an advisory board for Janssen Pharmaceuticals. Dr Sperling consults for Pfizer, Janssen, Eisai, Roche, and Bristol-Myers Squibb and receives payment for lectures including service on speakers bureaus from Pfizer, Janssen, and Eli Lilly. Dr Weiner has served on the scientific advisory boards for Pfizer, BOLT International, Neurotrope Bioscience, and Eli Lilly; has provided consulting to Synarc, Pfizer, Janssen, KLJ Associates, Easton Associates, Harvard University, University of California, Los Angeles, Alzheimer’s Drug Discovery Foundation, Avid Radiopharmaceuticals, Clearview Healthcare Partners, Perceptive Informatics, Smartfish AS, Decision Resources, Inc, Araclon, Merck, Defined Health, and Genentech; has served on the editorial boards for Alzheimer’s & Dementia and MRI; received honoraria from Pfizer, Tohoku University, and Danone Trading, BV; and has received research support from Merck, Avid, the Veterans Administration, and the Department of Defense. The following entities have provided funding for travel: Pfizer, Paul Sabatier University, MCI Group France, Travel eDreams, Inc, Neuroscience School of Advanced Studies, Danone Trading, BV, CTAD Ant Congres, Kenes, Intl, ADRC, University of California, Los Angeles, University of California, San Diego, sanofi-aventis Groupe, University Center Hospital, Toulouse, Araclon, AC Immune, Eli Lilly, New York Academy of Sciences, the National Brain Research Center, and India for Johns Hopkins Medicine.
Funding/Support: This work was funded by grants U19-AG032438 from the National Institute on Aging; K23MH081786 and R21MH099979 from the National Institute of Mental Health; R01NR014449, R01NR012657, and R01NR012907 from the National Institute of Nursing Research; P30NS048056, P50 AG05681, P01 AG03991, and P01 AG026276 from the National Institute of Neurological Disorders and Stroke; and G0601846 from the Medical Research Council. The study was also supported by the National Institute for Health Research Queen Square Dementia Biomedical Research Unit and the Washington University in St Louis Alzheimer’s Disease Research Center Genetics Core. Dr Jack received grants R01-AG011378, U01-HL096917, U01-AG024904, RO1 AG041851, R01 AG37551, R01AG043392, and U01-AG06786 from the National Institutes of Health. Dr Morris received grants P50AG005681, P01AG003991, P01AG026276, and U19AG032438 from the National Institutes of Health. Dr Ourselin received grants EP/H046410/1, EP/J020990/1, and EP/K005278 from the Engineering and Physical Sciences Research Council; MR/J01107X/1 from the the Medical Research Council; and FP7-ICT-2011-9-601055 from the EUFP7 project at the National Institute for Health Research University College London Hospitals Biomedical Research Centre High Impact Initiative.
Role of the Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Information: The authors are members of the Dominantly Inherited Alzheimer Network. For more information, visit http://www.dian-info.org/#.