Diaschisis-Like Association of Hippocampal Atrophy and Posterior Cingulate Cortex Hypometabolism in Cognitively Normal Elderly Depends on Impaired Integrity of Parahippocampal Cingulum Fibers

Abstract

Hippocampal atrophy and hypometabolism of the posterior cingulate cortex (PCC), early markers of Alzheimer’s disease (AD), have been shown to be associated in late mild cognitive impairment and early AD via atrophy of connecting cingulum fibers. Recently, a direct association of hippocampal atrophy and PCC hypometabolism has been shown in cognitively normal elderly. We aimed to investigate if this association might be modulated by partly non-hippocampogenic alterations of parahippocampal cingulum (PHC) integrity. 45 cognitively healthy elderly aged 59 to 89 years were included from the Alzheimer’s Disease Neuroimaging Initiative. Hippocampal volumes and PCC glucose metabolism were measured using MRI and FDG-PET. PHC fibers connecting the hippocampus and the PCC were reconstructed using diffusion weighted MRI and measures of diffusivity were calculated. Using robust linear regression, interaction effects of PHC diffusivity and hippocampal volume on PCC metabolism were calculated. For both hemispheres, significant interaction effects were found for PHC mean diffusivity. Interaction effects were such that the association of hippocampal volume and PCC metabolism was higher in subjects with increased mean diffusivity in PHC fibers. In cognitively normal elderly, compromised integrity of the PHC may increase the risk of PCC hypometabolism due to hippocampal atrophy. Spared PHC fiber integrity may protect against PCC hypometabolism due to hippocampal atrophy.

Keywords

Cingulum diffusion weighted MRI fiber tractography hippocampus interaction normal aging position emission tomography posterior cingulate cortex

INTRODUCTION

The posterior cingulate cortex (PCC) is a highly-connected hub region of the human connectome and part of the association cortex. It is functionally integrated within the well-studied default mode network [1] and structurally forms, together with the hippocampus and other structures, part of the Papez’ circuit that is involved in episodic memory retrieval and encoding [2 –4]. Consequently, the consistent finding of hypometabolism of the PCC in early Alzheimer’s disease (AD) is not surprising given the apparent increased vulnerability of hub regions in neurodegenerative diseases [5] and memory impairment being one of the earliest forms of cognitive impairment in AD. Interestingly, rather than local pathological processes, PCC hypometabolism in mild cognitive impairment (MCI) and AD seems to be caused to a certain extent by a diaschisis-like phenomenon, where hippocampal atrophy leads to deterioration of the parahippocampal cingulum (PHC) fibers directly connecting to the PCC [6 –8]. Furthermore, Teipel and Grothe recently demonstrated that the diaschisis-like association of hippocampal atrophy and PCC hypometabolism extends to the cognitively healthy elderly and is independent of local PCC pathology [9]. This may indicate that one driving factor for PCC hypometabolism could be the functional disconnection of the hippocampus from the PCC via impairment of the connecting PHC fibers. Indeed, Andrews-Hanna et al. have shown functional disconnection of the hippocampal formation from the PCC to be related to white matter (WM) impairment in aging [10]. However, WM integrity is not only generally affected negatively by age, but also by other age-associated factors showing considerable inter-individual variance such as cerebral amyloid-β load and WM lesions [11 –13]. This might mean that apart from hippocampal atrophy, impaired integrity of the PHC may be a risk factor to develop PCC hypometabolism in cognitively healthy elderly. To test this hypothesis, we aimed to investigate whether impaired PHC integrity is independently from hippocampal atrophy associated with PCC hypometabolism and/or modifies the association of hippocampal atrophy and PCC hypometabolism in a sample of cognitively healthy elderly. As PCC hypometabolism is viewed as a risk factor for the development of later dementia [14], the results of this study might contribute to the individual dementia risk assessment in the elderly.

METHODS

Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD.

Subjects

Subjects and their respective datapoints were selected from the database of the ADNI project according to the following criteria: baseline assessment during the ADNI 2 phase, classification as cognitively normal, availability of T1, FLAIR, diffusion weighted (DWI) MRI as well as florbetapir (AV45) and fluorodeoxyglucose (FDG) PET imaging at baseline with a maximum of 6 months between measurements. For details regarding clinical classification rating procedures please refer to the ADNI procedures manual: https://adni.loni.usc.edu/wp-content/uploads/2008/07/adni2-procedures-manual.pdf. All in all, the selection criteria yielded 45 subjects aged 59.8 to 89 years, with a mean age of 72.9 and standard deviation of 6.1 years. Of these, 24 were female and 21 male. Applying the ADNI recommended cutoff value of 1.11 to the AV45-PET data led to 12 subjects being classified as amyloid positive and 33 amyloid negative. For an overview of demographical information on the subjects please refer to Table 1.

Table 1

Demographical information on the study sample

	Female	Male	Total Sample
N	24	21	45
Age	72.4±5.4	73.5±6.9	72.9±6.1
Education (y)	14.8±2.3	17.8±2.3	16.2±2.7
Amyloid-β	1.13±0.14	1.03±0.08	1.08±0.13
MMSE	28.9±1.4	28.8±1.6	28.8±1.5
Left hippocampal volume	2.25±0.14	2.16±0.18	2.21±0.17
Right hippocampal volume	2.38±0.14	2.31±0.21	2.35±0.18
Left PCC metabolism	1.57±0.09	1.57±0.08	1.57±0.09
Right PCC metabolism	1.55±0.08	1.54±0.09	1.54±0.08
Left PHC FA	0.32±0.06	0.32±0.06	0.32±0.06
Right PHC FA	0.38±0.07	0.37±0.07	0.37±0.07
Left PHC MD	0.78±0.13	0.76±0.07	0.77±0.11
Right PHC MD	0.73±0.07	0.73±0.08	0.73±0.07

Amyloid-β, standardized uptake value ratio in the frontal, angular/posterior cingulate, lateral parietal, and temporal cortices; MMSE, Mini-Mental State Examination; Hippocampal volume, cm³; PCC, posterior cingulate cortex; PCC metabolism, standardized uptake value ratio; PHC, parahippocampal cingulum; FA, fractional anisotropy; MD, mean diffusivity.

Imaging data acquisition

DWI, FLAIR, and inversion-recovery spoiled gradient recalled (IR-SPGR) T1-weighted imaging data were acquired on several General Electric 3T scanners using scanner specific protocols. Briefly, DWI data was acquired with a voxel size of 1.37²×2.70 mm³, 41 diffusion gradients, and a b-value of 1000 s/mm². IR-SPGR data were acquired with a voxel size of 1.02²×1.20 mm³.

AV45 and FDG-PET imaging data were acquired on several types of scanners using different acquisition protocols. In order to increase data uniformity, the data underwent a standardized preprocessing procedure at the ADNI project.

All imaging protocols and preprocessing procedures are available at the ADNI website (http://adni.loni.usc.edu/methods/).

T1-weighted and FLAIR data processing

The T1-weighted IR-SPGR data was automatically tissue segmented and spatially normalized to MNI-space using SPM8 (http://www.fil.ion.ucl.ac.uk/spm/) and the VBM8-toolbox (http://www.neuro.uni-jena.de/vbm/). Additionally, inverse transformations from MNI to native T1 space were calculated.

Bilateral hippocampal volumes were calculated using a newly created mask in MNI space resulting from segmentations of the high resolution MNI152-template by 4 expert tracers following the newly established Harmonized Protocol [15]. The mask was designed as a consensus mask, wherein all voxels were included that had been segmented as hippocampal tissue on the MNI template by all 4 tracers [16]. The volumes were calculated as the sum of the modulated probability values of the gray matter (GM) segments of the VBM8 normalization in MNI space. Hippocampal volumes were normalized by dividing through the total intracranial volume (TIV) of the subject. The probability values were modulated based on the Jacobian of affine and nonlinear components of the normalization transformation to MNIspace [17].

TIV as well as WM hyperintensity volume were calculated at ADNI core laboratories from T1-weighted and FLAIR data using published tissue segmentation methods [18, 19].

PET data processing

Subjects’ cortical amyloid-β load and GM metabolism were calculated from AV45 PET and FDG PET images according to procedures established by the ADNI (http://adni.loni.usc.edu/methods/pet-analysis/). Briefly, cortical amyloid was calculated as the average of the AV45 uptake in the frontal, angular/posterior cingulate, lateral parietal, and temporal cortices normalized by dividing by the mean uptake in the cerebellum. GM glucose metabolism was calculated as the mean FDG uptake of the left and right angular and inferior temporal gyri normalized by the uptake in the pons/cerebellar vermis region [20]. To avoid statistical problems when controlling main analyses for cortical FDG, PCC uptake was excluded from the GM glucose metabolism average.

In order to calculate PCC metabolism, FDG-PET images were first corrected for partial volume effects using the method proposed by Müller-Gärtner [21]. The corrected FDG images were then coregistered to T1 images via SPM8. Using the unmodulated probability values of the GM segmentation image, the coregistered FDG images were then masked to exclude all voxels with a probability of being GM less than 50%. Finally, the mean metabolism in the PCC was obtained by averaging all non-zero voxels inside the PCC region-of-interest (ROI) of the Harvard Oxford Atlas as shipped with FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases), that was warped from MNI to native T1 space by using the inverse normalization transformations calculated from VBM8.

DWI data

DWI data were corrected for eddy currents and motion artefacts using the method of Rohde et al. as implemented in VistaSoft [22]; diffusion gradients were adjusted according to the resulting transformations. Additionally, DWI data were upsampled to 1 mm isotropic voxel size for further processing.Subsequently, diffusion tensors were fitted to the data using a weighted linear least squares approach as implemented in Mrtrix3 [23] and diffusion tensor metrics fractional anisotropy (FA) and mean diffusivity (MD) were calculated.

For fiber tractography, Anatomically Constrained Tractography (ACT) as implemented in MRtrix was employed [24]. This approach incorporates anatomical constraints based on tissue segmentations of T1 data. To this end, VBM8 tissue segmented data in T1 native space were coregistered using SPM8 to the upsampled DWI B0 images, and used in the subsequent ACT. Based on the tissue segmentation images, the ACT framework calculates an isocontour representing the interface of GM and WM for the seeding of fibers. Additionally, we masked this GM-WM interface with the hippocampus mask in order to allow seeding only from the GM-WM interface within the hippocampus.

In order to extract only the fibers connecting hippocampus and PCC, two respective standard ROIs in MNI space were used: the hippocampus ROI and the PCC ROI of the Harvard Oxford Atlas. Both were first warped to native T1 space using the inverse VBM8 normalization transformations and then transferred to the upsampled DWI space using the transformation estimated from the T1 to B0 coregistration.

Finally, tractography was conducted as follows: seed points were placed randomly along the GM-WM interface within the hippocampus ROI. Starting from these points, the probabilistic “ifod2” tractography algorithm was executed. Streamlines were accepted if they met the anatomical constraints of ACT [24], had a minimum length of 5 mm and a maximum length of 60 mm. Random seeding and tractography was repeated until 1000 streamlines were accepted for each subject. See Fig. 2 for an exemplary plot of one subject’s reconstructed parahippocampal cingulum. Subsequently, mean FA and MD along the tracts were calculated.

Fig.1

Scatterplots of interaction models with superimposed regression plane. Individual cases are colored according to weighting in the robust regression models, with red color indicating high weights and black low weights. The regression plane is plotted more translucent with increasing distance from the center of mass of cases. Intersections of the regression plane with the bounding box are highlighted to contrast the associations of hippocampal volume and posterior cingulate metabolisms at different extremes of parahippocampal cingulum mean diffusivity; green, low mean diffusivity; blue, high mean diffusivity. Hippocampal volume and parahippocampal cingulum mean diffusivity values are mean centered.

Fig.2

Exemplary plot of the bilateral parahippocampal cingulate bundle fibers.

Statistical analyses

All statistical analyses were carried out using R 3.0.2 including the package “robustbase” (https://cran.r-project.org/web/packages/robustbase/robustbase.pdf). We used robust regression analyses to account for possible outliers using an initial M-S estimator to accommodate the categorical variable gender in the interaction models [25] and subsequently the design adaptive scale estimator developed for small sample sizes [26] in order to investigate interaction effects of WM microstructure and hippocampal volumes on PCC metabolism. To this end, PCC metabolism was defined as the dependent variable, hippocampal volume, PHC diffusivity measures, FA or MD, as well as their interaction term formed by multiplication were set as independent variables. Global amyloid-β load and GM metabolism as well as age and gender were set as covariates [9].

A supplementary analysis was conducted in order to demonstrate the improved model fit due to the interaction term as well as the specificity of results to PCC metabolism. For model comparison, we recalculated those models who had shown a significant interaction term again but this time excluding the interaction term. Subsequently, corresponding models were compared using analysis of robust deviances [27] as well as adjusted R² scores. To demonstrate the specificity of main results for PCC metabolism, we recalculated models with significant interaction terms removing GM metabolism as covariate and putting it as dependent variable instead of PCC metabolism.

Every continuous predictor term except the interaction terms were demeaned to handle collinearity between the predictors. For the calculation of interaction terms, the base variables were demeaned before multiplication.

The significance threshold for all analyses was set to p≤0.05.

RESULTS

Interaction models including FA of the PHC did not show significant interaction terms (left: p = 0.388, right: p = 0.120). However, significant interaction effects where found for left and right PHC MD and hippocampal volumes on PCC metabolism (p = 0.043 for the left and p = 0.028 for the right hemisphere) (see Table 2 for model parameters). The interaction effect in both hemispheres was such that elevated MD of the PHC fibers connecting the hippocampus and the PCC was associated with higher association of hippocampal volume and PCC metabolism (see Fig. 1). Main effects of either PHC FA or MD could not be found. However hippocampal volume showed a significant main effect in the right hemisphere both in the model containing PHC FA (p = 0.014) as well as PHC MD (p = 0.026) such that higher hippocampal volume was associated with higher PHC metabolism.

Table 2

Standardized regression coefficients and p-values of the model terms of interaction analyses on posterior cingulate metabolism

Parahippocampal Cingulum	Model Term	Cerebral Hemisphere
Diffusivity Measure		Left		Right
		β	p-value	β	p-value
FA	Hippocampal Volume	0.088	0.466	0.422	0.014 ^*
	Parahippocampal Cingulum	0.098	0.378	–0.050	0.741
	Interaction	0.105	0.388	0.251	0.120
MD	Hippocampal Volume	0.070	0.480	0.339	0.026 ^*
	Parahippocampal Cingulum	0.075	0.514	0.016	0.913
	Interaction	0.232	0.043 ^*	0.334	0.028 ^*

FA, fractional anisotropy; MD, mean diffusivity; β, standardized regression coefficient; ^*, considered statistically significant.

Significant associations of PCC metabolism with covariates were found in the left hemisphere for age (p < 0.001) in the model containing PHC FA as well as for age (p < 0.001) and GM metabolism (p = 0.029) in the model containing PHC MD. In the right hemisphere, significant associations with covariates were found for age (p = 0.042) and GM metabolism (p = 0.011) in the model containing PHC FA and again for age (p = 0.028) and GM metabolism (p = 0.007) in the model containing PHC MD. All associations of PCC metabolism with covariates were positive.

Supplementary analyses, revealed a higher adjusted R² for models with a significant interaction term than their counterparts that did not contain an interaction term (see Table 3). Specifically, for models containing PHC MD adjusted R² values were 0.267 versus 0.346 for the left hemisphere and 0.149 versus 0.242 for the right hemisphere. According to analyses of robust deviances, these differences were statistically significant both for the left (p = 0.017) and the right hemisphere (p = 0.009). Finally, models with GM metabolism instead of PPC metabolism as dependent variable showed neither significant main effects nor interaction terms (all p-values >0.672, see Table 4).

Table 3

Comparison of models with and without interaction terms in models using mean diffusivity as diffusivity measure of the parahippocampal cingulum

Cerebral	Interaction	Adjusted R²	p-value
Hemisphere	term
Left	Excluded	0.267	0.017 ^*
	Included	0.346
Right	Excluded	0.149	0.009 ^*
	Included	0.242

p-value calculated by analysis of robust deviance; ^*, considered statistically significant.

Table 4

Standardized regression coefficients and p-values of the model terms of interaction analyses on grey matter metabolism excluding the posterior cingulate

Parahippocampal Cingulum	Model Term	Cerebral Hemisphere
Diffusivity Measure		Left		Right
		β	p-value	β	p-value
MD	Hippocampal Volume	–0.061	0.727	–0.020	0.908
	Parahippocampal Cingulum	0.072	0.720	–0.012	0.946
	Interaction	0.083	0.672	0.064	0.714

MD, mean diffusivity; β, standardized regression coefficient; ^*, considered statistically significant.

DISCUSSION

Our results indicate an interaction effect of hippocampal volume and PHC MD on PCC metabolism in elderly subjects with normal cognition in both hemispheres, such that hippocampal atrophy and increased parahippocampal cingulum fiber diffusivity in combination are associated with decreased PCC metabolism. However, an independent main effect was found only for hippocampal atrophy in the right hemisphere.

To our knowledge, no study has so far investigated the interaction effect of PHC diffusivity and hippocampal volume on PCC metabolism in cognitively healthy elderly. This study’s results are thus by design of a somewhat limited comparability to previous studies. However, we believe they are generally in agreement with previous findings.

Considering hippocampal volume, previous studies have shown a direct association with PCC metabolism, most recently in cognitively normal healthy elderly and patients of MCI [9, 28]. Our results indicate the same association for cognitively normal elderly at increased levels of PHC MD. However, an independent main effect of hippocampal atrophy on PCC metabolism was found only in the right hemisphere. One possible reason for this discrepancy with the work of Teipel and Grothe may be that due to the right >left hemispheric asymmetry in hippocampal volume, volume variations of the left hippocampus may be more subtle and thus difficult to detect in smaller samples such as in the presentstudy.

Considering the cingulum, studies have shown direct associations of cingulum integrity and PCC metabolism in AD before [6, 29]. However, whereas Bozoki et al. found decreased cingulum FA to be associated with PCC hypometabolism, we instead found increased MD to be associated with PCC hypometabolism at lower levels of hippocampal volume. Possible reasons for this discrepancy are the fact that Bozoki et al. reconstructed the whole cingulum in patients of MCI and AD, whereas we investigated only the cingulum fibers connecting the PCC to the hippocampus in cognitively normal elderly. Another possible reason for the fact that the interaction term including MD was significant whereas the interaction term including FA was not may be that MD represents the average of the axial diffusivity (DA) and the radial diffusivities (RDs), whereas FA is a relative measure quantifying their differences, which may alter only little if the increase of DA and RDs is proportional. Finally, the interpretation of DA and RD remains controversial [30]. However, RD is usually interpreted as referring to demyelination, whereas DA is viewed as a form of axonal injury [31].

Considering the joint influence of hippocampal volume and cingulum integrity on PCC metabolism, Villain et al. have shown a sequential relationship where hippocampal volume is associated with cingulum atrophy which in turn is associated with PCC hypometabolism in a longitudinal study with patients of amnestic MCI [7]. Our finding of an interaction effect of hippocampal volume and PHC MD in a cross-sectional design may be interpreted as in agreement with those of Villain et al. such that hippocampal atrophy will have an effect on PCC metabolism only if the connecting fibers have been affected as a consequence.

Teipel and Grothe have demonstrated a general association of hippocampal atrophy and PCC hypometabolism in cognitively normal subjects before and our finding of a main effect of hippocampal atrophy on PCC metabolism in the right hemisphere is in agreement with their results [9]. Originally, this diaschisis-like association was considered a feature of AD [6, 7]. Thus, in a sample of cognitively normal subjects, one would expect it to be restricted to subjects with higher cortical amyloid-β load indicative of preclinical AD according to currently proposed criteria [32]. However, the association of hippocampal atrophy and PCC hypometabolism was independent of cortical amyloid-β load both in the study of Teipel and Grothe as well as in the present study [9]. One might thus argue that the hippocampus–PCC diaschisis is a feature of normal aging that is exacerbated in AD. However, this view assumes that the association of hippocampal atrophy and PCC hypometabolism refers to the same pathological process across clinical stages. On the one hand, the results of Teipel and Grothe seem to support this view, as hippocampal volume and clinical group did not show an interaction predicting PCC metabolism [9]. On the other hand, Teipel and Grothe could not demonstrate a direct association of hippocampal volume decrease and PCC hypometabolism in a supplementary analysis within the subgroup of AD patients, whereas it was present in the control, early, and late MCI groups. One might interpret this discrepancy as a sign for a possibly different pathological process across clinical stages causing PCC hypometabolism. We believe that the finding of an interaction of hippocampal volume and PHC diffusivity in this study provides additional evidence of a different mechanism. In MCI and AD, Villain et al. have longitudinally demonstrated a sequential association previously, where hippocampal atrophy is associated with PHC atrophy, which in turn is associated with PCC hypometabolism [7]. Furthermore, other studies have demonstrated an association of hippocampal atrophy with deteriorated diffusivity measures of the cingulum in MCI and AD [33, 34]. In a supplementary analysis, we aimed to confirm these results in the context of this study’s findings by calculating a robust regression with PHC MD as dependent variable, hippocampal volume as predictor as well as age, gender, and cortical amyloid-β load as covariates. Contrary to the studies mentioned above, hippocampal volume was neither in the left (p = 0.698) nor in the right (p = 0.751) hemisphere a significant predictor of PHC diffusivity (data not shown). This may indicate a different underlying mechanism at clinical stages compared to cognitively healthy elderly. Based on the current data, we can only speculate about the nature of this difference. Considering the findings of Teipel and Grothe, we can assume PCC hypometabolism due to hippocampal atrophy in cognitively healthy refers more to decreased activity due to disrupted input from the atrophied hippocampus in cognitively healthy elderly, whereas at subsequent clinical stages local pathology becomes increasingly predictive of PCC hypometabolism [9]. One molecular pathologic agent that has received increasing attention in terms of AD etiology is the tau-molecule which spreads from the medial temporal lobe to the PCC among other regions of the neocortex as shown by histological studies [35]. Considering recent evidence demonstrating prion-like properties and synaptic propagation along WM fibers [36 –38], the PHC could be considered a candidate pathway for the spread of tau. This might be a possible explanation for the sequential association shown by Villain et al. [7] and thus a target for the design of future studies aiming to investigate the differences of the association of hippocampal atrophy and PCC hypometabolism between cognitively healthy and patients of MCIand AD.

Finally, considering that PCC hypometabolism is a known risk factor for cognitive impairment and a biomarker for AD dementia [1, 14], this study’s findings have interesting clinical implications as well: preserved PHC diffusivity may attenuate the negative effect of hippocampal atrophy on PCC metabolism in cognitively normal elderly, whereas increased PHC diffusivity may exacerbate it. As we found PHC diffusivity independent of hippocampal atrophy in our supplementary analysis (see above), it may be viewed as a resilience factor to the effect of hippocampal atrophy on PCC metabolism in aging and thus more generally to the risk of developing cognitive impairment and AD dementia.

This study has several limitations. First, the statistical analyses are based on a small sample. However, the estimators of the regression analysis were specifically designed for low sample size/predictor ratios [26]. Second, no correction for accumulated alpha error was conducted. However, main results and most other results were reproducible acrosshemispheres.

Conclusion

In the present study, we demonstrated an interaction effect of hippocampal volume and PHC MD on PCC metabolism such that with increasing PHC MD, hippocampal atrophy is increasingly associated with PCC hypometabolism in cognitively normal elderly subjects. Inversely, PCC hypometabolism was less associated with hippocampal atrophy in subjects with lower PHC MD. This might imply that spared PHC integrity is to some degree protective against PCC hypometabolism subsequent to hippocampal atrophy. Increased PHC MD could thus exacerbate or be a risk factor for PCC hypometabolism due to hippocampal atrophy. As PCC hypometabolism is associated with increased risk of cognitive decline, this finding has potential clinical relevancy [14].

Footnotes

ACKNOWLEDGMENTS

Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck &Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of SouthernCalifornia.

Authors’ disclosures available online ().

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