Nonlinear cerebral atrophy patterns across the Alzheimer's disease continuum: impact of APOE4 genotype

Accepted Manuscript Nonlinear cerebral atrophy patterns across the Alzheimer’s Disease continuum: Impact of APOE4 genotype J.D. Gispert, L. Rami, G. Sánchez-Benavides, C. Falcon, A. Tucholka, S. Rojas, J.L. Molinuevo PII:

S0197-4580(15)00339-5

DOI:

10.1016/j.neurobiolaging.2015.06.027

Reference:

NBA 9315

To appear in:

Neurobiology of Aging

Received Date: 5 January 2015 Revised Date:

26 June 2015

Accepted Date: 30 June 2015

Please cite this article as: Gispert, J.D, Rami, L, Sánchez-Benavides, G, Falcon, C, Tucholka, A, Rojas, S, Molinuevo, J.L, Nonlinear cerebral atrophy patterns across the Alzheimer’s Disease continuum: Impact of APOE4 genotype, Neurobiology of Aging (2015), doi: 10.1016/j.neurobiolaging.2015.06.027. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Nonlinear cerebral atrophy patterns across the Alzheimer’s

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Disease continuum: Impact of APOE4 genotype

Gispert JDa,b, Rami Lc,d, Sánchez-Benavides Ge, Falcon Ca,b, Tucholka Aa, Rojas Sa, Molinuevo JLa,c,d

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Author Affiliations:

a) BarcelonaBeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain

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b) Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 08036 Barcelona, Spain c) Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain d) Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain

Correspondence:

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e) Hospital del Mar Medical Research Institute. Barcelona, Spain

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Dr. José-Luis Molinuevo

BarcelonaBeta Brain Research Centre - Pasqual Maragall Foundation Dr. Aiguader, 88. Planta Baixa. Edifici PRBB.

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08003 Barcelona, Spain [email protected]

Phone: +34 93 316 0990

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Abstract

The progression of Alzheimer’s disease (AD) is characterized by complex trajectories of

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cerebral atrophy which, moreover, are affected by interactions with age and APOE4 status. In this article, we report the nonlinear volumetric changes in gray matter across the full biological spectrum of the disease, represented by the AD CSF Index. This index reflects the subject’s

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level of pathology and position along the AD continuum. We also evaluated the associated impact of the ApoE4 genotype.

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The atrophy pattern associated with the AD-CSF-Index was highly symmetrical and corresponded with the typical AD signature. Medial temporal structures showed different atrophy dynamics along the progression of the disease. The bilateral parahippocampal cortices and a temproparietal region extending from the middle temporal to the supramarginal gyrus

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presented an initial increase in volume which later reverted. Similarly, a portion of the precuneus presented a rather linear inverse association with the AD CSF Index whereas some other clusters did not show significant atrophy until Index values corresponding to positive CSF

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progression.

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tau values. APOE4 carriers showed steeper hippocampal volume reductions with AD

Overall, the reported atrophy patterns are in close agreement with previous findings.

However, the detected nonlinearities suggest that there may be different pathological processes taking place at specific moments in time during AD progression and reveal the impact of the APOE4 allele.

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Keywords: MRI, VBM, brain, biomarkers, neuroinflammation, amyloid, tau.

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Introduction

Alzheimer's disease (AD) is a chronic neurodegenerative disorder that is characterized by progressive neuropathology and cognitive decline. The characteristic progressive memory

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impairment in AD is related to a pattern of atrophy that starts in the entorhinal cortex, closely followed by the hippocampus, amygdala, and parahippocampus (Frisoni, et al., 2010). Other structures within the limbic lobe, such as the posterior cingulate, are also affected early on in

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the disease process. This pattern of atrophy then extends to the temporal neocortex, followed by all neocortical association areas, usually in a rather symmetrical fashion. This sequence of

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progression of atrophy on MRI most closely fits histopathological studies that have derived stages for the spread of neurofibrillary tangles (Johnson, et al., 2012). CSF concentrations of Aβ42 as well as total and phosphorylated tau (p-tau181p) have been shown to serve as in vivo proxy measures of the central neuropathological hallmarks of AD (Braak, et al., 2013).

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Therefore, studying the relationship between CSF biomarkers and regional changes on structural and functional MRI may contribute to understanding the pathological mechanisms of AD (Stricker, et al., 2012).

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In AD, elevations in CSF t-tau and p-tau correlate with neurofibrillary tangle burden at autopsy and, in vivo, with the typical AD MRI atrophy pattern including baseline hippocampal

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volume and rate of hippocampal atrophy (Apostolova, et al., 2010,Beckett, et al., 2010,de Leon, et al., 2006,de Souza, et al., 2012,Fortea, et al., 2014,Hampel, et al., 2005,Henneman, et al., 2009,Schuff, et al., 2009). On the other hand, reductions in Aβ42 can be detected decades before the onset of the symptoms and correlate with fibrillar Aβ deposits in autopsy studies and amyloid PET (Villemagne, et al., 2013). In contrast to tau findings, no such a strong relationship has been found between alterations in Aβ biomarkers and brain atrophy. Some cross-sectional studies which included cognitively impaired individuals as well as healthy controls did not find a significant relationship between Aβ alterations and hippocampal volume 4

ACCEPTED MANUSCRIPT (Becker, et al., 2011,de Souza, et al., 2012,Fagan, et al., 2009) whilst others support at least a weak one (Apostolova, et al., 2010,Bourgeat, et al., 2010, Chetelat, et al., 2010a, Dickerson, et al., 2009,Jack, et al., 2008). Longitudinal studies did not fully resolved these divergences since such an association was supported by (Beckett, et al., 2010,de Leon, et al., 2006,Schuff, et al.,

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2009) but not by (Henneman, et al., 2009). On top of this, the debate also affects to potential brain alterations in cognitively healthy subjects harboring amyloid pathology, i.e. in the preclinical stage of AD (Chetelat, et al., 2013,Fjell, et al., 2014). Again, while some cross-

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sectional studies found that amyloid positive nondemented subjects show decreased hippocampal volumes (Bourgeat, et al., 2010,Dickerson, et al., 2009,Mormino, et al.,

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2009,Storandt, et al., 2009), some other did not found significant differences the hippocampus (Jack, et al., 2010,Vemuri, et al., 2009) but did in whole brain volume (Fagan, et al., 2009), parietal, posterior cingulate cortex and precuneus (Becker, et al., 2011,Fortea, et al., 2014) or even increased volume in temporal regions including the hippocampus (Chetelat, et al., 2010b).

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Previous studies have also reported an association between the reduction of cortical thickness and Aβ deposition, prior to clinically evident cognitive impairment, in temporoparietal and posterior cingulate regions extending into the precuneus (Becker, et al., 2011,Fortea, et al.,

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2011). Longitudinal studies comparing cerebral atrophy rates have reported increased declines in whole brain, hippocampus, amygdala, posterior cingulate and in temporal parietal and

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frontal regions in preclinical AD (Dore, et al., 2013,Mattsson, et al., 2014a,Mattsson, et al., 2014b,Schott, et al., 2010,Storandt, et al., 2009). However, some others were unable to detect such accelerated atrophy rates (Driscoll, et al., 2011,Fotenos, et al., 2008). Several approaches have been used to establish the association between CSF

biomarkers and brain atrophy. The most frequently used method relies on linear models which assume that atrophy is associated with CSF biomarkers in the same way regardless these reach pathological values or not (Insel, et al., 2014). Another approach consists of dichotomizing the quantitative range of CSF measurements into ‘positive’ or ‘negative’ categories using 5

ACCEPTED MANUSCRIPT thresholds which are generally derived on the basis of their diagnostic capacity (Molinuevo, et al., 2013). This model generally assumes constant atrophy rates in each group with no transition between them. While these models allow for a simple interpretation, they are unlikely to represent the relationship between CSF biomarkers and brain atrophy realistically

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(Insel, et al., 2014). There is prior evidence that the trajectories of CSF biomarkers and cortical atrophy are nonlinear (Sabuncu, et al., 2011), complex and affected by interactions with age and APOE status (Jack, et al., 2011). Therefore, linear models are not the best suited to reveal

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and describe complex associations between biomarkers known to have nonlinear dynamics (Jack, et al., 2013). Several nonlinear methods have been to model the relationship between

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biomarkers and neuroimaging derived measurements. For example, polynomial regression has been used to model age-related atrophy in relation to CSF Aβ levels using a quadratic term (Fjell, et al., 2010). Other methods do not need to explicitly model the parametric form of the association: Generalized additive models have been used to model the effect of aging (Schuff,

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et al., 2012), splines to model the relationship between brain atrophy and CSF Aβ levels (Insel, et al., 2014) and local regression methods to track the evolution of several biomarkers in dominantly inherited AD as a function of the estimated years from expected symptom onset

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(Bateman, et al., 2012).

In order to track the CSF biomarker changes along the AD continuum, we developed

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the ‘AD CSF Index’ (Molinuevo, et al., 2013); an indicator composed by the sum of the normalized CSF concentrations of Aβ42 and t-tau which reflects the degree of pathology (Struyfs, et al., 2014) and determines where the patient is along the AD continuum. The AD CSF Index has shown higher diagnostic capability than the individual biomarkers and other progression indices in a multicenter validation study (Molinuevo, et al., 2013) and in autopsyconfirmed AD patients (Struyfs, et al., 2014). A major advantage of the AD CSF Index is that its diagnostic accuracy is preserved irrespective of the CSF analytical platform.

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ACCEPTED MANUSCRIPT Another relevant factor to this association is the role of the apolipoprotein E allele ε4 (APOE4) gene, the major genetic risk factor for sporadic AD. Elucidating the relationship between these three factors, CSF biomarkers, ApoE ε4, and regional brain atrophy, might provide important information about the vulnerability of the brain to AD (Desikan, et al., 2013).

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The presence of, at least one, ApoE ε4 allele is related to abnormal CSF biomarker concentrations (Glodzik-Sobanska, et al., 2009,Sunderland, et al., 2004), as well as, to higher rates of brain atrophy (Basso, et al., 2006,Fleisher, et al., 2005,Potkin, et al., 2009,Schuff, et al.,

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2009,Sluimer, et al., 2008,Tosun, et al., 2011,Tosun, et al., 2010). Healthy late-middle aged APOE4 carriers display AD-like brain changes such as, decreased glucose metabolism (Protas, et

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al., 2013,Reiman, et al., 1996,Small, et al., 2000), brain atrophy (Burggren, et al., 2008,Donix, et al., 2013,Donix, et al., 2010,Espeseth, et al., 2008,Fan, et al., 2010,Fennema-Notestine, et al., 2011,Julkunen, et al., 2010,Liu, et al., 2010) and other alterations in task-related and default mode network activity (Brown, et al., 2011,Trachtenberg, et al., 2012a,Trachtenberg, et al.,

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2012b). By the time AD symptoms occur, hippocampal atrophy seems to be a characteristic feature of ApoE ε4 allele carriers (Wolk and Dickerson, 2010). However, there is no consensus whether this is the case for asymptomatic carriers. For instance, (den Heijer, et al., 2002)

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reported reduced hippocampal volumes in older asymptomatic carriers, whereas (Lemaitre, et al., 2005) only found differences in homozygous, but (Protas, et al., 2013) did not.

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In this article, we aimed to study the cerebral atrophy patterns associated to ADrelated CSF biomarkers though the AD CSF Index across the entire disease spectrum. To account for the possibility of some brain areas displaying nonlinear dynamics along the progression through the AD continuum, we set up a nonlinear regression method and devised a color-coding to quantify the variance explained by the linear and nonlinear terms. This allowed us to detect different patterns of brain atrophy associated to the AD CSF Index. The impact of the presence of at least one copy of the APOE4 allele in the association between brain atrophy patterns and the AD CSF Index has also been assessed. 7

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Material and Methods Subjects A total of 129 participants (62 controls, 18 preclinical AD, 28 mild cognitive impairment due

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to AD and 21 AD patients) were recruited at the Alzheimer’s disease and other cognitive disorders unit, from the Hospital Clinic i Universitari, Barcelona. The study was approved by the local ethics committee and all participants gave written informed consent to participate in the

(LP), MRI scanning and CSF analysis at the local laboratory.

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study. All subjects underwent clinical and neuropsychological assessment, lumbar puncture

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An interdisciplinary clinical committee formed by two neurologists and one neuropsychologist established the diagnoses. Cognitive cut-off scores were determined accounting for the age and education of the subjects, and were considered abnormal if below 1.5 standard deviations from the mean. Controls presented no evidence of cognitive

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impairment on any of the administered neuropsychological tests and were CSF Aβ negative (over the 550 pg/mL). On the other hand, preclinical AD subjects had completely normal neuropsychological evaluation but presented positive CSF Aβ values (below 550 pg/mL).

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Cognitively healthy subjects with elevated t-tau (above 450 pg/mL) were excluded from the analysis. MCI were CSF Aβ positive and had an objective memory deficit, defined as an

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abnormal score on the total recall measure of the Free and Cued Selective Reminding Test (FCRST), impairment on one or more of the other cognitive tests or preserved activities of daily living, as measured by the Functional Activities Questionnaire (FAQ score <6). The NINCDSADRDA criteria were applied for probable AD diagnosis, taking into account clinical information and objective measures derived from the FAQ and neuropsychological results. All included AD patients were CSF Aβ positive and in the mild stages of the disease (Global Deterioration Scale = 4).

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ACCEPTED MANUSCRIPT Genomic DNA was extracted from peripheral blood of probands using the QIAamp DNA blood minikit (Qiagen AG, Basel, Switzerland). Apolipoprotein E genotyping was performed by

CSF sampling and calculation of the AD CSF Index

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polymerase chain reaction amplification and HhaI restriction enzyme digestion.

Subjects underwent lumbar puncture (LP) between 9:00-12:00 am. 10mL of CSF was

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collected in polypropylene tubes, with the samples being centrifuged and stored at -80ºC within an hour. Levels of Aβ1-42, t-tau and p-tau were measured by enzyme-linked

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immunosorbent assay (ELISA) kits (Innogenetics, Ghent, Belgium). The AD CSF Index, for each subject, was calculated as previously described (Molinuevo, et al., 2013) applying the following formula: applying the following formula:

Aβ max − Aβ 1− 42 ttau − ttau min + Aβ max − Aβ min ttau max − ttau min

(eq.

1)

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AD CSF Index (t − tau ) =

Where Aβmax and t-taumax represent the 95th percentile of the respective values, Aβmin and

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t-taumin represent the 5th percentile of the distribution values, and Aβ1-42, t-tau and p-tau represent the biomarker values for every individual. In addition, we compared the

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concentration of CSF biomarkers and the AD CSF Index between APOE4 carriers and noncarriers, by means of a two-sample t-test (p<0.05).

Image acquisition Subjects were examined on a 3T magnetic resonance imaging (MRI) scanner (Magnetom Trio Tim, Siemens Medical Solutions, Erlangen, Germany). A high-resolution threedimensional structural dataset (T1-weighted magnetization-prepared rapid gradient-echo, 9

ACCEPTED MANUSCRIPT repetition time = 2300 msec, echo time = 2.98 msec, 240 slices, field of view = 256 mm; matrix size = 256 × 256; slice thickness = 1 mm) was acquired for all the subjects. The mean time interval between the lumbar puncture and the MRI was 41 days and ranged between 1 and 134 days. Hippocampal volumes were automatically calculated for each subject with an in-house

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implementation for SPM8 of the Individual Atlases using Statistical Parametric Mapping toolbox (IBASPM; (Alemán-Gómez, et al., 2006)) using the Automated Anatomical Labelling Atlas

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(Tzourio-Mazoyer, et al., 2002).

Image preprocessing

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Images were processed and examined using the SPM8 software (Wellcome Department of Imaging Neuroscience Group, London, UK; http://www.fil.ion.ucl.ac.uk/spm), where we applied VBM implemented in the VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm.html) with default parameters. Briefly, T1 images were normalized to a referenced template using a

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high-dimensional DARTEL normalization and segmented into gray matter in the images. In order to avoid biasing the segmentation towards normality, segmentation is performed without tissue priors but using a Hidden Markov Random Field (HMRF) model (Cuadra, et al.,

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2005). As a part of the segmentation process, images are denoised with an adaptive non-local means (SANLM) algorithm (Manjon, et al., 2010). As a result, voxels are assigned a value

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between 0 and 1 according to their relative content of gray matter. Then, gray matter partitions are multiplied with the jacobian determinants of the spatial normalization deformation to modulate local intensity values with volumetric change. Only the non-linear part of the normalization was used for the modulation allowing the comparison of the absolute amount of gray matter corrected for individual brain size. Then, images were smoothed with a 6 mm FWHM kernel.

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ACCEPTED MANUSCRIPT Statistical Image analysis Images from all subjects were pooled together irrespective of their diagnostic classification. A regression model was set up entering age, sex, the AD CSF Index and the APOE4 genotype as regressors. Age was modeled as a second order polynomial to correct for

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nonlinear effects of aging on cerebral gray matter content. Similarly, AD progression was modeled by entering the AD CSF Index up to the third-order. This approach is possible in SPM thanks to the fact that the different polynomial terms are orthogonalized with regard to its

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preceding column. An F-contrast was applied looking for effects of interest regarding the three AD CSF Index terms.

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In order to assess the significance of the higher order terms beyond the linear one we also tested for their statistical significance after accounting for the linear one, and vice versa (i.e. in this latter case, the linear term was entered as the last column in the design matrix). Then, to evaluate the relative weighting of the linear and nonlinear terms across the brain regions

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showing a significant association with the AD CSD Index, we fused the thresholded F-map of the linear component to the equivalent map of the non-linear terms using different color scales.

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To describe the atrophy patterns in a model-free fashion, gray matter data corrected by age

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and sex, was regressed against the AD CSF Index using a robust locally weighted scatterplot smoothing (LOWESS) method as implemented in Matlab (The MathWorks, Natick, Massachusetts, USA) with a smoothing factor of 0.8. This factor was selected empirically in order the resulting fits to present a maximum of two inflexion points in accordance to the third-order parametric model in the SPM analysis. Post-hoc 95% confidence intervals were calculated by bootstrapping with 1000 samples. This procedure was used for descriptive purposes only. Statistical significance was exclusively determined by the SPM analysis.

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ACCEPTED MANUSCRIPT A second design matrix was implemented to look for volumetric differences associated to the APOE4 after correcting for age and gender. In this second model the AD CSF Index was not entered as covariate. Two one-sided Student t-test were implemented to look for increased and decreased volumes associated to APOE4 status. In order to evaluate the impact of APOE4

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on the trajectories of brain volume along the AD CSF Index, independent LOWESS regressions were performed for carriers and non-carriers, in brain regions showing statistically significant differences associated to the APOE4 genotype. In addition, CSF biomarkers were statistically

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compared between APOE4 carriers and non-carriers.

Statistical significance threshold for all SPM analyses was set to p < 0.001 uncorrected for

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multiple comparisons. Only clusters of over 100 supra-thresholded voxels were taken into consideration. According to (Hayasaka and Nichols, NeuroImage, 2003) the threshold of uncorrected p < 0.001 is conservative in rejecting type I errors for images smoothed at 6 mm FHWM. At this same smoothing and at the p<0.01 level, a cluster threshold of 100 voxels

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rejects 90% of the clusters under the null hypothesis. Unfortunately, no results are provided in that paper at the p<0.001 level, but of course they will be far more conservative for type I

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errors.

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Results The demographic characteristics of the participants are shown in Table 1. Control subjects were significantly younger than the rest of groups (p<0.05) and AD patients were younger than MCI ones (p=0.038). APOE4 was overrepresented in MCI and AD groups. Figure 1 shows the

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pattern of brain atrophy resulting from the third-order polynomial regression against the AD CSF index, after correction for the effects of age and sex. This pattern was highly symmetrical and comprised the bilateral hippocampus, amygdala, temporal cortex, posterior cingulate

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precuneus. To a lower extent, several clusters did also achieve statistical significance in the insula and other frontal and parietal areas. See Table 2 for a complete listing of the brain

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regions and associated statistics.

After accounting for the linear term of the polynomial regression, the nonlinear ones reached statistical significance in the bilateral parahippocampal cortices and in a right parietotemporal region extending from the middle temporal cortex to the angular and

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supramarginal giri (Fig 2a-b). Only in the left parahippocampal region the linear term was statistically significant after accounting for the nonlinear terms (Fig 2c).

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Figure 3 shows the relative contribution of the linear and nonlinear terms to the observed variance in gray matter volume in association with the AD CSF index. Regions in red display a

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predominant contribution of the linear term whereas the nonlinear ones dominate in regions in green. Those areas in which the contribution of the linear and nonlinear terms independently explain a significant percentage of the observed variance are shown in yellow. The regions in which the nonlinear terms reached statistical significance after accounting for the linear one (bilateral parahipocampal and right parietotemporal cortices) present such a behavior (Fig 3a-b).

Figure 4 shows the brain atrophy trajectory across the AD CSF Index for several representative brain regions. The linear component was predominant in the posterior 13

ACCEPTED MANUSCRIPT cingulate, precuneus, tail of the hippocampus and middle temporal cortex. Figure 4a shows the linear trajectory of brain volumetric association with the AD CSF Index in the posterior cingulate.

However, most of the brain regions which display a statistically significant association with

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the AD CSF Index show a behavior similar to that displayed by the hippocampal head in Figure 4b. In this case, the brain volume remains unaffected until Index values of 0.7, approximately.

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Then a rather linear atrophy pattern is observed with increasing Index values.

Those regions in which the nonlinear component achieved statistical significance, show

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trajectories as displayed in Fig 4c-d. In these cases, and initial increase with the Index can be observed. This increase reaches a plateau at Index values of about 0.6 and then decrease in a rather linear fashion. The bilateral parahippocampal cortices, the right parietotemporal region and some clusters in the middle and anterior cingulate cortex show this trajectory.

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Most of the brain areas displayed in green in the composite F-map show the trajectory in Fig 4e. In these instances, there are no variation of the gray matter volumes until values of the index of about 1.4 and, then, an inverse linear association with the index can be observed.

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trajectories.

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Some clusters in the precuneus, temporal, parietal and frontal regions present such

APOE4 carriers showed significantly lower CSF Aβ1-42 (608.87 ± 256.28 vs 431.74 ± 186.29;

p<0.001), higher t-tau (346.70 ± 282.70 vs. 624.92 ± 338.86; p<0.001), p-tau (65.37 ± 38.28 vs. 93.33; p<0.001) and AD CSF Index values (0.6958 ± 0.5089 vs 1.1933 ± 0.4732; p<0.001). Figure 5 shows brain regions with decreased gray matter volume in APOE4 carriers. The inverse contrast did not show any statistically significant differences. APOE4 carriers showed lower gray matter volume in the bilateral hippocampus, amygdala, parahippocampal cortex and temporal pole, the left angular and inferior parietal cortex, the right insula as well as in the posterior

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ACCEPTED MANUSCRIPT cingulate and precuneus. When the brain volume of carriers and non-carriers was independently fitted against the AD CSF Index, two different tendencies could be observed. Hippocampal regions present trajectories similar to that in Fig 6a in which no initial differences can be appreciated between carriers and noncarriers but, as the AD CSF Index increases,

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carriers show a steepest decrease in brain volume. In the other hand, the other cortical regions

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APOE4 carriers display somewhat lower volumes all along the AD CSF Index (Fig 6b).

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Discussion In this article, we describe non-linear trajectories of AD-related brain atrophy in a continuum ranging from normal cognition to mild AD. In order to track AD-related pathology we employed the AD CSF Index, which is built as a sum of the normalized CSF concentrations of

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Aβ42 and tau (total tay in this work). The AD CSF Index ranges from 0 (completely normal biomarkers) to 2 (most altered biomarkers). One major advantage of this normalized approach is that index values are comparable even in the event of CSF samples quantified using different

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analytical platforms (Molinuevo, et al., 2013,Struyfs, et al., 2014).

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Since we sought to identify brain atrophy patterns associated to CSF biomarkers ranging from completely normal to severily altered, we opted for using a nonlinear regression model rather a linear one. Linear models assuming constant atrophy rates are unlikely to represent the relationship between CSF biomarkers and brain atrophy realistically across the full AD pathological continuum (Insel, et al., 2014). In addition, subtle departures from linearity can

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provide more insight about the timing of the inflexion points associated to pathological processes along the AD continuum.

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To identify the atrophy pattern associated with the AD CSF Index, we used the standard VBM methodology and implemented a third-order polynomial model. The suitability of this

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approach is supported by the fact that the nonlinear terms reached statistical significance, after accounting for the linear one, in the bilateral parahippocampal cortices and a parietotemporal region (Fig 2a-b). Actually, in most of the regions showing a statistically significant association with the Index, the nonlinear terms explained significant percentages of variability in gray matter (Figures 3 and 4). Exceptions to this predominant trend were the posterior cingulate, precuneus, tail of the hippocampus and middle temporal cortex, in which the linear component was clearly dominant (Fig 4a). To describe the different atrophic trajectories in association with the AD CSF Index, we extracted the age and sex corrected

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ACCEPTED MANUSCRIPT values from the VBM-SPM model and applied a local smoothing in a similar fashion as in (Bateman, et al., 2012). This allowed us to obtain model-free smooth variations of gray matter volume across the full range of the AD CSF Index.

The atrophy pattern associated with the AD CSF Index showed the typical AD signature

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(Frisoni, et al., 2010). Four main atrophy patterns emerged from our analysis. The most preponderant showed no significant brain alterations until AD CSF Index values of about 0.8 (Fig 4b). Most of the controls had Index values below this value which is in close agreement

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with the optimal classification threshold in the diagnostic validation of the AD CSF Index in four European populations (Molinuevo, et al., 2013). This pattern was observed in most of the

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regions of the AD brain signature including the hippocampal heads. This finding is in agreement with the hippocampus being considered to be the best established imaging biomarker for the diagnosis of AD (Drago, et al., 2011,Teipel, et al., 2011, Hill, et al., 2014) along with previous findings in APOE4 carriers displaying hippocampal atrophy by the time AD symptoms occur

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(Wolk and Dickerson, 2010).

Nevertheless, different portions of the hippocampi followed various atrophy trajectories.

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Posterior horns showed a linear association with the Index (Fig 3c). Brain regions in which the nonlinear terms were statistically significant (Fig 2a-b), along with some focal areas in the

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middle and anterior cingulate cortex depicted in yellow in Figure 3, showed a non monotonous trajectory. It was characterized by an early increase in volume that peaked at index values around 0.6 and then followed a rather linear decline (Fig 4c-d). Such a departure from the linear behavior might not be consistently detected by linear regression methods. To some extent, this could explain the discrepancies in the literature with respect to the association between hippocampal volume and amyloid deposition. A similar inverse u-shaped relation was found between cortical thickness and CSF amyloid concentrations in these regions in cognitively healthy subjects (Fortea, et al., 2011). It should be noted that the dataset partially

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ACCEPTED MANUSCRIPT overlaps with the one in the present study (17 out of the 41 Normal Controls and 16 out of the 42 subjects with subjective memory complaints). Nevertheless, the robustness of this finding is reinforced by the fact that the same pattern was detected after applying very different procedures of analysis: volumetric changes in the present study vs cortical thickness in the

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former. Such an early increase in volume might be attributable to early neuroinflammatory processes.

Neuroinflammatory processes associated to microglial activation have been previouly

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reported in AD using positron emission tomography with 11C-PBR28 to measure translocator protein 18 kDa (Kreisl, et al., 2013). AD patients showed increased 11C-PBR28 binding in the

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parietal and temporal cortices which was inversely correlated with grey matter volume and, in parietal cortex and striatum, positively correlated with lower age of onset. After partial volume correction, they found an association between 11C-PBR28 and 11C-Pittsburgh Compound B binding. On the other hand, astrocyte activation, as measured by 11C-deprenyl PET, has also

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been reported with amyloid positive MCI patients displaying greater binding than AD ones (Carter, et al., 2012). In addition, astrocyte mediated neuroinflammation has shown to correlate with reduced gray matter density in the parahippocampus gyrus of amyloid-positive

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prodromal Alzheimer's patients (Choo, et al., 2014). In view of these previous reports, our finding of increased grey matter volume in parietal areas might be attributable to an early

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neuroinflammatory process associated to amyloid accumulation than reverts later on at the onset of neurodegenerative processes associated to tau accumulation. There is a large amount of data showing that brain atrophy in AD is much more related to tau pathology than to amyloid accumulation (Desikan, et al., 2011, Hyman, et al, 2011). However, the dataset in this study does not allow us to assess this possibility and, consequently, new studies are required to verify this end.

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ACCEPTED MANUSCRIPT The precuneus also presented complex trajectories as a cluster that also included the posterior and middle cingulate showed a rather linear association with disease progression whereas another cluster did not display any gray matter variations until Index values associated with high CSF tau values. Such departures from the linear model were also found by (Becker, et

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al., 2011) who modeled the cortical thickness as a function of PiB retention using a sigmoid function.

The impact of the APOE genotype was most evident in the hippocampus with carriers

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showing a steeper decline in gray matter volume after Index values approaching the diagnostic threshold (0.8). This finding is consistent with previous studies that showed that APOE4 carriers

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exhibit accelerated hippocampal atrophy (Liu, et al., 2010,Manning, et al., 2014,Shi, et al., 2014) and might be indicative of APOE4 carriers being more vulnerable to amyloid accumulation or other associated brain insults (Bookheimer and Burggren, 2009). Still, APOE4associated lower hippocampal volumes have been reported in cognitively normal older

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subjects (Burggren, et al., 2008,den Heijer, et al., 2002,Reiman, et al., 1998) and even in children and adolescent APOE4-carriers (Shaw, et al., 2007). On the other hand, in other cortical areas (Table 3), APOE4 carriers showed lower gray matter volume regardless of the AD

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CSF Index which might be associated to pre-existing differences which are similarly affected by

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the progression of the AD pathology.

By using a VBM approach and a non-linear parametric regression, our analysis method was

able to determine different voxelwise patterns of gray matter atrophy throughout the whole brain and cerebellum. This approach represents an advantage over methods that only study the cerebral cortex, are based on pre-defined regions of interest or are restricted to linear models. The main limitation of our study is that, due to its transversal design, it provides limited value to establishing the atrophy progression though the whole spectrum of AD. The use of a biological progression biomarker like the AD CSF Index is not intended to be a

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ACCEPTED MANUSCRIPT substitute of longitudinal studies with clinical follow-up. Indeed, please note that the confidence intervals in our model-free curves were estimated post-hoc the SPM analysis. Therefore, they should be regarded as confidence intervals of the expected average trajectories of brain atrophy versus the AD CSF Index without any predictive value at the individual level.

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In summary, we report different nonlinear dynamics of gray matter reduction associated to progression through the AD biological continuum, modulated by the APOE4 genotype. Even though the atrophy patterns detected were in close agreement with previous findings relying

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on linear methods, the subtle nonlinearities here described are suggestive of different potential pathological processes taking place at specific moments in time during AD

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progression. To some extent, the detected nonlinearities could explain the discrepancies in the literature with respect to the association between hippocampal atrophy and amyloid

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deposition and the impact of the APOE4 allele on this association.

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Acknowledgements Authors would like to acknowledge the contribution of Dr. Andreia Carvalho in the preparation of this manuscript. Juan D Gispert holds a ‘Ramón y Cajal’ fellowship (RYC-201313054) and Lorena Rami is part of the Programa de investigadores del sistema nacional Miguel

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Servet II (CPII/00023; IP: Lorena Rami). The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115568 (AETIONOMY), resources of which are composed of financial contribution from the

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European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. This publication has also been suported by the BIOMARKAPD project within

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the EU Joint Programme for Neurodegenerative Diseases (JPND) funded by the ISCIII (PI11/03023 PI José L Molinuevo; PI11/03022 PI: Juan D Gispert), Consolider-Ingenio 2010 (CSD 2010-00045 PI: José L Molinuevo), FIS-Fondo europeo de desarrollo regional, una manera de hacer Europa (PI11/01071 IP: Lorena Rami), proyecto IMSERSO (197/2011 IPD: Lorena Rami),

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Mapfre company grant (IP: Lorena Rami), Industex S.L. company grant (PI: Juan D Gispert),

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Research Grant from the Ayuntamento de Barcelona (PI: Juan D Gispert).

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Schuff, N., Woerner, N., Boreta, L., Kornfield, T., Shaw, L.M., Trojanowski, J.Q., Thompson, P.M., Jack, C.R., Jr., Weiner, M.W. 2009. MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers. Brain 132(Pt 4), 1067-77. Shaw, P., Lerch, J.P., Pruessner, J.C., Taylor, K.N., Rose, A.B., Greenstein, D., Clasen, L., Evans, A., Rapoport, J.L., Giedd, J.N. 2007. Cortical morphology in children and adolescents with different apolipoprotein E gene polymorphisms: an observational study. Lancet Neurol 6(6), 494-500.

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Shi, J., Lepore, N., Gutman, B.A., Thompson, P.M., Baxter, L.C., Caselli, R.J., Wang, Y. 2014. Genetic influence of apolipoprotein E4 genotype on hippocampal morphometry: An N = 725 surface-based Alzheimer's disease neuroimaging initiative study. Hum Brain Mapp 35(8), 3903-18. Shima, K., Matsunari, I., Samuraki, M., Chen, W.P., Yanase, D., Noguchi-Shinohara, M., Takeda, N., Ono, K., Yoshita, M., Miyazaki, Y., Matsuda, H., Yamada, M. 2012. Posterior cingulate atrophy and metabolic decline in early stage Alzheimer's disease. Neurobiol Aging 33(9), 2006-17. Sluimer, J.D., Vrenken, H., Blankenstein, M.A., Fox, N.C., Scheltens, P., Barkhof, F., van der Flier, W.M. 2008. Whole-brain atrophy rate in Alzheimer disease: identifying fast progressors. Neurology 70(19 Pt 2), 1836-41. Small, G.W., Ercoli, L.M., Silverman, D.H., Huang, S.C., Komo, S., Bookheimer, S.Y., Lavretsky, H., Miller, K., Siddarth, P., Rasgon, N.L., Mazziotta, J.C., Saxena, S., Wu, H.M., Mega, M.S., Cummings, J.L., Saunders, A.M., Pericak-Vance, M.A., Roses, A.D., Barrio, J.R., Phelps, M.E. 2000. Cerebral metabolic and cognitive decline in persons at genetic risk for Alzheimer's disease. Proc Natl Acad Sci U S A 97(11), 6037-42.

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ACCEPTED MANUSCRIPT Storandt, M., Mintun, M.A., Head, D., Morris, J.C. 2009. Cognitive decline and brain volume loss as signatures of cerebral amyloid-beta peptide deposition identified with Pittsburgh compound B: cognitive decline associated with Abeta deposition. Arch Neurol 66(12), 1476-81. Stricker, N.H., Dodge, H.H., Dowling, N.M., Han, S.D., Erosheva, E.A., Jagust, W.J. 2012. CSF biomarker associations with change in hippocampal volume and precuneus thickness: implications for the Alzheimer's pathological cascade. Brain Imaging Behav 6(4), 599609.

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Struyfs, H., Molinuevo, J.L., Martin, J.J., De Deyn, P.P., Engelborghs, S. 2014. Validation of the AD-CSF-index in autopsy-confirmed Alzheimer's disease patients and healthy controls. J Alzheimers Dis 41(3), 903-9.

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Sunderland, T., Mirza, N., Putnam, K.T., Linker, G., Bhupali, D., Durham, R., Soares, H., Kimmel, L., Friedman, D., Bergeson, J., Csako, G., Levy, J.A., Bartko, J.J., Cohen, R.M. 2004. Cerebrospinal fluid beta-amyloid1-42 and tau in control subjects at risk for Alzheimer's disease: the effect of APOE epsilon4 allele. Biol Psychiatry 56(9), 670-6.

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Teipel, S.J., Peters, O., Heuser, I., Jessen, F., Maier, W., Froelich, L., Arlt, S., Hull, M., Gertz, H.J., Kornhuber, J., Wiltfang, J., Thome, J., Rienhoff, O., Meindl, T., Hampel, H., Grothe, M. 2011. Atrophy outcomes in multicentre clinical trials on Alzheimer's disease: effect of different processing and analysis approaches on sample sizes. World J Biol Psychiatry 12 Suppl 1, 109-13. Tosun, D., Schuff, N., Shaw, L.M., Trojanowski, J.Q., Weiner, M.W. 2011. Relationship between CSF biomarkers of Alzheimer's disease and rates of regional cortical thinning in ADNI data. J Alzheimers Dis 26 Suppl 3, 77-90.

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Tosun, D., Schuff, N., Truran-Sacrey, D., Shaw, L.M., Trojanowski, J.Q., Aisen, P., Peterson, R., Weiner, M.W. 2010. Relations between brain tissue loss, CSF biomarkers, and the ApoE genetic profile: a longitudinal MRI study. Neurobiol Aging 31(8), 1340-54. Trachtenberg, A.J., Filippini, N., Ebmeier, K.P., Smith, S.M., Karpe, F., Mackay, C.E. 2012a. The effects of APOE on the functional architecture of the resting brain. Neuroimage 59(1), 565-72.

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Trachtenberg, A.J., Filippini, N., Mackay, C.E. 2012b. The effects of APOE-epsilon4 on the BOLD response. Neurobiol Aging 33(2), 323-34.

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Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M. 2002. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1), 273-89. doi:10.1006/nimg.2001.0978. Vemuri, P., Wiste, H.J., Weigand, S.D., Shaw, L.M., Trojanowski, J.Q., Weiner, M.W., Knopman, D.S., Petersen, R.C., Jack, C.R., Jr. 2009. MRI and CSF biomarkers in normal, MCI, and AD subjects: diagnostic discrimination and cognitive correlations. Neurology 73(4), 287-93. Villemagne, V.L., Burnham, S., Bourgeat, P., Brown, B., Ellis, K.A., Salvado, O., Szoeke, C., Macaulay, S.L., Martins, R., Maruff, P., Ames, D., Rowe, C.C., Masters, C.L. 2013. Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study. Lancet Neurol 12(4), 357-67. Wolk, D.A., Dickerson, B.C. 2010. Apolipoprotein E (APOE) genotype has dissociable effects on memory and attentional-executive network function in Alzheimer's disease. Proc Natl Acad Sci U S A 107(22), 10256-61. 28

ACCEPTED MANUSCRIPT Table 1: Demographic features of the studied subjects (Mean ± StdDev or percentages when appropriate).

21 65.47 ± 9.24 66.67%

14.52 %

38.89 %

60.71 %

52.38%

<0.001

763.6 ± 170.0 235.0 ± 77.8 52.4 ± 13.4 0.3859 ± 0.2139 2.88 ± 0.24 2.79 ± 0.26 14.67 ± 1.29 28.66 ± 1.34

371.4 ± 90.7 296.6 ± 167.0

348.4 ± 79.7 752.5 ± 305.9 110.2 ± 37.7 1.4221 ± 0.3027 2.63 ± 0.34 2.56 ± 0.36 5.81 ± 4.79 24.63 ± 2.87

331.8 ± 123.5 761.3 ± 363.8 109.4 ± 48.0 1.4521 ± 0.3311 2.68 ± 0.32 2.57 ± 0.27 6.29 ± 5.06 24.00 ± 3.59

<0.001

57.5 ± 25.7 0.9675 ± 0.2104 2.73 ± 0.24 2.63 ± 0.24 13.83 ± 2.04 27.61 ± 1.88

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P

<0.001 0.950

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AD

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62 61.68 ± 7.45 61.29 %

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MMSE

18 67.67 ± 6.63 66.67 %

MCI due to AD 28 70.07 ± 6.84 60.71 %

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R Hippocampal Vol (cm3) L Hippocampal Vol (cm3) FCSRT-TR

Pre-clin AD

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N Age (yr) Gender (% Female) ApoE ε4 (% Carriers) CSF Aβ42 (pg/mL) CSF t-tau (pg/mL) CSF p-tau (pg/mL) AD CSF Index

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<0.001 <0.001 <0.001 0.004 0.005 <0.001 <0.001

ACCEPTED MANUSCRIPT Table 2: Areas showing statistically significant atrophy in association with the AD CSF Index (p<0.001 unc; k>100 voxels). Brain Regions

406 1988

787

160 159 113 292 167 270

-6 3

-12 -20

-18 0 -6 2 52 60 57 42 42 -38 -40 -40 56 44 39 45 51 62 30 38 26 -28 57 46 -57

-6 -34 -24 -52 -28 -40 -21 11 11 20 9 42 4 8 18 -30 -22 -30 35 30 33 54 -15 -18 -34

-14 37 40 28 -5 3 -15 34 25 0 25 9 -2 0 1 49 43 30 31 30 -15 0 10 10 16

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130

7.75 6.64 5.74 5.24 6.07 5.88 5.64 5.31 4.21 4.96 4.76 4.53 4.80 4.26 3.99 4.47 3.64 4.47 4.37 3.76 4.22 4.15 4.07 3.99 4.06

20 -22

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R Hippocampus, Amygdala, Parahippocampal L Temporal Pole Sup L Hippocampus, Amygdala, Parahippocampal Cingulum Mid/Posterior Cingulum Mid Cingulum Post/Precuneus R Temporal Inf/Mid/Sup R Temporal Inf/Mid/Sup R Temporal Inf/Mid/Sup R Frontal Inf Operc R Frontal Inf Operc L Insula / Frontal Inf Frontal Inf Frontal Inf R Temporal Pole Sup R Insula / Frontal Inf R Insula / Frontal Inf R Postcentral / Parietal Inf R Postcentral / Precentral R Supramarginal R Frontal Mid/Inf R Frontal Mid/Inf R Frontal Orb L Frontal Sup R Rolandic Oper, Temporal Sup R Heschl, Rolandic Oper L Temp Sup/Mid, Supramarginal

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9724

<0.001 <0.001 <0.001 0.006 <0.001 <0.001 0.001 0.004 0.390 0.021 0.051 0.129 0.043 0.332 0.667 0.162 0.972 0.163 0.234 0.914 0.375 0.463 0.556 0.664 0.576

Inf Inf

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5744

<0.001 <0.001

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34834

Zequiv

Label

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PFWE-corr

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MNI Coords {mm} x y z

ACCEPTED MANUSCRIPT Table 3: Areas showing differential atrophy patterns according to the APOE4 status (p<0.001 unc; k>100 voxels).

Brain Regions

179 189

MNI Coords {mm} x y z 32 -22 -9 28 -13 -20 18 -10 -15 -15 -12 -15 -28 -10 -15 -30 -21 -15 -45 -64 48 50 -1 13 45 4 4 2 -49 21 -9 -49 24 0 -49 31

Label

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832

Zequiv 4.93 4.82 4.45 4.26 4.07 3.9 4.04 3.9 3.54 3.86 3.67 3.65

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R Hippocampus R Hippocampus R Hippocampus L Hippocampus L Hippocampus L Hippocampus L Angular R Rolandinc Operc R Insula R Precuneus L Precuneus L Cingulum Post

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1479

PFWE-corr 0.018 0.029 0.127 0.245 0.437 0.65 0.473 0.647 0.964 0.698 0.891 0.901

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kequiv 2679

ACCEPTED MANUSCRIPT Figure 1: Brain areas showing gray matter atrophy in association with the AD CSF Index.

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Colorbar indicates F-statistic.

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ACCEPTED MANUSCRIPT Figure 2: (A, B) Brain regions where the nonlinear terms reached statistical significance (p < 0.001 unc; k> 100 voxels) after accounting for the linear one. (C) Brain regions where the linear term reached statistical significance (p < 0.001 unc; k> 100 voxels) after accounting for the

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nonlinear ones.

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ACCEPTED MANUSCRIPT Figure 3: Additive fusion of the F-maps associated to the linear (red) and nonlinear (green)

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shown.

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components of the regression against the AD CSF index. Only statistically significant regions are

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Figure 4a: Gray matter volume vs the AD CSF index in the Right Precuneus. Top: spatial location (MNI coordinates: 2, -54, 26 mm). Bottom: Scatterplot of gray matter residuals against the AD CSF Index. The black solid line indicates the mean trajectory and the dotted ones, 95%

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confidence intervals. Dot colors denote diagnostic category (see legend).

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ACCEPTED MANUSCRIPT Figure 4b: Gray matter volume vs the AD CSF index in the Left Hippocampus. Top: spatial location (MNI coordinates: -16, -8, 14 mm). Bottom: Scatterplot of gray matter residuals against the AD CSF Index. The black solid line indicates the mean trajectory and the dotted ones, 95%

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confidence intervals. Dot colors denote diagnostic category (see legend).

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ACCEPTED MANUSCRIPT Figure 4c: Gray matter volume vs the AD CSF index in the Right Parahippocampal cortex. Top: spatial location (MNI coordinates: 24, -28, -12 mm). Bottom: Scatterplot of gray matter residuals against the AD CSF Index. The black solid line indicates the mean trajectory and the

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dotted ones, 95% confidence intervals. Dot colors denote diagnostic category (see legend).

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ACCEPTED MANUSCRIPT Figure 4d: Gray matter volume vs the AD CSF index in the Right Temporal Middle cortex. Top: spatial location (MNI coordinates: 60, -40, 3 mm). Bottom: Scatterplot of gray matter residuals against the AD CSF Index. The black solid line indicates the mean trajectory and the dotted

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ones, 95% confidence intervals. Dot colors denote diagnostic category (see legend).

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ACCEPTED MANUSCRIPT Figure 4e: Gray matter volume vs the AD CSF index in the right Precuneus. Top: spatial location (MNI coordinates: 2, -52, 52 mm). Bottom: Scatterplot of gray matter residuals against the AD CSF Index. The black solid line indicates the mean trajectory and the dotted ones, 95%

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confidence intervals. Dot colors denote diagnostic category (see legend).

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ACCEPTED MANUSCRIPT Figure 5: Significant areas of lower gray matter volume in APOE4 carriers vs. non-carriers along

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the AD CSF Index (p<0.001; k>100 voxels).

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ACCEPTED MANUSCRIPT Figure 6: Trajectories of brain atrophy according to APOE4 status. Solid lines indicate mean trajectories and dotted lines 95% confidence intervals. APOE4 carriers are shown in red and non-carriers in blue. A) In the right hippocampus, carriers and non-carriers have similar gray matter volumes with lower AD CSF Index values but with higher index values, carriers show

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greater reductions. B) In the precuneus, carriers show lower values than non-carriers

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irrespective of the AD CSF Index.

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Nonlinear cerebral atrophy patterns across the Alzheimer’s

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Disease continuum: Impact of APOE4 genotype

Gispert JDa,b, Rami Lc,d, Sánchez-Benavides Ge, Falcon Ca,b, Tucholka Aa, Rojas Sa, Molinuevo JLa,c,d

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Author Affiliations:

a) BarcelonaBeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain

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b) Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 08036 Barcelona, Spain c) Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain d) Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain

Correspondence:

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e) Hospital del Mar Medical Research Institute. Barcelona, Spain

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Dr. José-Luis Molinuevo

BarcelonaBeta Brain Research Centre - Pasqual Maragall Foundation Dr. Aiguader, 88. Planta Baixa. Edifici PRBB.

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08003 Barcelona, Spain [email protected]

Phone: +34 93 316 0990

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Abstract

The progression of Alzheimer’s disease (AD) is characterized by complex trajectories of

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cerebral atrophy which, moreover, are affected by interactions with age and APOE4 status. In this article, we report the nonlinear volumetric changes in gray matter across the full biological spectrum of the disease, represented by the AD CSF Index. This index reflects the subject’s

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level of pathology and position along the AD continuum. We also evaluated the associated impact of the ApoE4 genotype.

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The atrophy pattern associated with the AD-CSF-Index was highly symmetrical and corresponded with the typical AD signature. Medial temporal structures showed different atrophy dynamics along the progression of the disease. The bilateral parahippocampal cortices and a temproparietal region extending from the middle temporal to the supramarginal gyrus

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presented an initial increase in volume which later reverted. Similarly, a portion of the precuneus presented a rather linear inverse association with the AD CSF Index whereas some other clusters did not show significant atrophy until Index values corresponding to positive CSF

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progression.

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tau values. APOE4 carriers showed steeper hippocampal volume reductions with AD

Overall, the reported atrophy patterns are in close agreement with previous findings.

However, the detected nonlinearities suggest that there may be different pathological processes taking place at specific moments in time during AD progression and reveal the impact of the APOE4 allele.

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Keywords: MRI, VBM, brain, biomarkers, neuroinflammation, amyloid, tau.

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Introduction

Alzheimer's disease (AD) is a chronic neurodegenerative disorder that is characterized by progressive neuropathology and cognitive decline. The characteristic progressive memory

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impairment in AD is related to a pattern of atrophy that starts in the entorhinal cortex, closely followed by the hippocampus, amygdala, and parahippocampus (Frisoni, et al., 2010). Other structures within the limbic lobe, such as the posterior cingulate, are also affected early on in

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the disease process. This pattern of atrophy then extends to the temporal neocortex, followed by all neocortical association areas, usually in a rather symmetrical fashion. This sequence of

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progression of atrophy on MRI most closely fits histopathological studies that have derived stages for the spread of neurofibrillary tangles (Johnson, et al., 2012). CSF concentrations of Aβ42 as well as total and phosphorylated tau (p-tau181p) have been shown to serve as in vivo proxy measures of the central neuropathological hallmarks of AD (Braak, et al., 2013).

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Therefore, studying the relationship between CSF biomarkers and regional changes on structural and functional MRI may contribute to understanding the pathological mechanisms of AD (Stricker, et al., 2012).

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In AD, elevations in CSF t-tau and p-tau correlate with neurofibrillary tangle burden at autopsy and, in vivo, with the typical AD MRI atrophy pattern including baseline hippocampal

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volume and rate of hippocampal atrophy (Apostolova, et al., 2010,Beckett, et al., 2010,de Leon, et al., 2006,de Souza, et al., 2012,Fortea, et al., 2014,Hampel, et al., 2005,Henneman, et al., 2009,Schuff, et al., 2009). On the other hand, reductions in Aβ42 can be detected decades before the onset of the symptoms and correlate with fibrillar Aβ deposits in autopsy studies and amyloid PET (Villemagne, et al., 2013). In contrast to tau findings, no such a strong relationship has been found between alterations in Aβ biomarkers and brain atrophy. Some cross-sectional studies which included cognitively impaired individuals as well as healthy controls did not find a significant relationship between Aβ alterations and hippocampal volume 4

ACCEPTED MANUSCRIPT (Becker, et al., 2011,de Souza, et al., 2012,Fagan, et al., 2009) whilst others support at least a weak one (Apostolova, et al., 2010,Bourgeat, et al., 2010, Chetelat, et al., 2010a, Dickerson, et al., 2009,Jack, et al., 2008). Longitudinal studies did not fully resolved these divergences since such an association was supported by (Beckett, et al., 2010,de Leon, et al., 2006,Schuff, et al.,

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2009) but not by (Henneman, et al., 2009). On top of this, the debate also affects to potential brain alterations in cognitively healthy subjects harboring amyloid pathology, i.e. in the preclinical stage of AD (Chetelat, et al., 2013,Fjell, et al., 2014). Again, while some cross-

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sectional studies found that amyloid positive nondemented subjects show decreased hippocampal volumes (Bourgeat, et al., 2010,Dickerson, et al., 2009,Mormino, et al.,

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2009,Storandt, et al., 2009), some other did not found significant differences the hippocampus (Jack, et al., 2010,Vemuri, et al., 2009) but did in whole brain volume (Fagan, et al., 2009), parietal, posterior cingulate cortex and precuneus (Becker, et al., 2011,Fortea, et al., 2014) or even increased volume in temporal regions including the hippocampus (Chetelat, et al., 2010b).

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Previous studies have also reported an association between the reduction of cortical thickness and Aβ deposition, prior to clinically evident cognitive impairment, in temporoparietal and posterior cingulate regions extending into the precuneus (Becker, et al., 2011,Fortea, et al.,

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2011). Longitudinal studies comparing cerebral atrophy rates have reported increased declines in whole brain, hippocampus, amygdala, posterior cingulate and in temporal parietal and

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frontal regions in preclinical AD (Dore, et al., 2013,Mattsson, et al., 2014a,Mattsson, et al., 2014b,Schott, et al., 2010,Storandt, et al., 2009). However, some others were unable to detect such accelerated atrophy rates (Driscoll, et al., 2011,Fotenos, et al., 2008). Several approaches have been used to establish the association between CSF

biomarkers and brain atrophy. The most frequently used method relies on linear models which assume that atrophy is associated with CSF biomarkers in the same way regardless these reach pathological values or not (Insel, et al., 2014). Another approach consists of dichotomizing the quantitative range of CSF measurements into ‘positive’ or ‘negative’ categories using 5

ACCEPTED MANUSCRIPT thresholds which are generally derived on the basis of their diagnostic capacity (Molinuevo, et al., 2013). This model generally assumes constant atrophy rates in each group with no transition between them. While these models allow for a simple interpretation, they are unlikely to represent the relationship between CSF biomarkers and brain atrophy realistically

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(Insel, et al., 2014). There is prior evidence that the trajectories of CSF biomarkers and cortical atrophy are nonlinear (Sabuncu, et al., 2011), complex and affected by interactions with age and APOE status (Jack, et al., 2011). Therefore, linear models are not the best suited to reveal

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and describe complex associations between biomarkers known to have nonlinear dynamics (Jack, et al., 2013). Several nonlinear methods have been to model the relationship between

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biomarkers and neuroimaging derived measurements. For example, polynomial regression has been used to model age-related atrophy in relation to CSF Aβ levels using a quadratic term (Fjell, et al., 2010). Other methods do not need to explicitly model the parametric form of the association: Generalized additive models have been used to model the effect of aging (Schuff,

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et al., 2012), splines to model the relationship between brain atrophy and CSF Aβ levels (Insel, et al., 2014) and local regression methods to track the evolution of several biomarkers in dominantly inherited AD as a function of the estimated years from expected symptom onset

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(Bateman, et al., 2012).

In order to track the CSF biomarker changes along the AD continuum, we developed

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the ‘AD CSF Index’ (Molinuevo, et al., 2013); an indicator composed by the sum of the normalized CSF concentrations of Aβ42 and t-tau which reflects the degree of pathology (Struyfs, et al., 2014) and determines where the patient is along the AD continuum. The AD CSF Index has shown higher diagnostic capability than the individual biomarkers and other progression indices in a multicenter validation study (Molinuevo, et al., 2013) and in autopsyconfirmed AD patients (Struyfs, et al., 2014). A major advantage of the AD CSF Index is that its diagnostic accuracy is preserved irrespective of the CSF analytical platform.

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ACCEPTED MANUSCRIPT Another relevant factor to this association is the role of the apolipoprotein E allele ε4 (APOE4) gene, the major genetic risk factor for sporadic AD. Elucidating the relationship between these three factors, CSF biomarkers, ApoE ε4, and regional brain atrophy, might provide important information about the vulnerability of the brain to AD (Desikan, et al., 2013).

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The presence of, at least one, ApoE ε4 allele is related to abnormal CSF biomarker concentrations (Glodzik-Sobanska, et al., 2009,Sunderland, et al., 2004), as well as, to higher rates of brain atrophy (Basso, et al., 2006,Fleisher, et al., 2005,Potkin, et al., 2009,Schuff, et al.,

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2009,Sluimer, et al., 2008,Tosun, et al., 2011,Tosun, et al., 2010). Healthy late-middle aged APOE4 carriers display AD-like brain changes such as, decreased glucose metabolism (Protas, et

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al., 2013,Reiman, et al., 1996,Small, et al., 2000), brain atrophy (Burggren, et al., 2008,Donix, et al., 2013,Donix, et al., 2010,Espeseth, et al., 2008,Fan, et al., 2010,Fennema-Notestine, et al., 2011,Julkunen, et al., 2010,Liu, et al., 2010) and other alterations in task-related and default mode network activity (Brown, et al., 2011,Trachtenberg, et al., 2012a,Trachtenberg, et al.,

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2012b). By the time AD symptoms occur, hippocampal atrophy seems to be a characteristic feature of ApoE ε4 allele carriers (Wolk and Dickerson, 2010). However, there is no consensus whether this is the case for asymptomatic carriers. For instance, (den Heijer, et al., 2002)

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reported reduced hippocampal volumes in older asymptomatic carriers, whereas (Lemaitre, et al., 2005) only found differences in homozygous, but (Protas, et al., 2013) did not.

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In this article, we aimed to study the cerebral atrophy patterns associated to ADrelated CSF biomarkers though the AD CSF Index across the entire disease spectrum. To account for the possibility of some brain areas displaying nonlinear dynamics along the progression through the AD continuum, we set up a nonlinear regression method and devised a color-coding to quantify the variance explained by the linear and nonlinear terms. This allowed us to detect different patterns of brain atrophy associated to the AD CSF Index. The impact of the presence of at least one copy of the APOE4 allele in the association between brain atrophy patterns and the AD CSF Index has also been assessed. 7

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Material and Methods Subjects A total of 129 participants (62 controls, 18 preclinical AD, 28 mild cognitive impairment due

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to AD and 21 AD patients) were recruited at the Alzheimer’s disease and other cognitive disorders unit, from the Hospital Clinic i Universitari, Barcelona. The study was approved by the local ethics committee and all participants gave written informed consent to participate in the

(LP), MRI scanning and CSF analysis at the local laboratory.

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study. All subjects underwent clinical and neuropsychological assessment, lumbar puncture

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An interdisciplinary clinical committee formed by two neurologists and one neuropsychologist established the diagnoses. Cognitive cut-off scores were determined accounting for the age and education of the subjects, and were considered abnormal if below 1.5 standard deviations from the mean. Controls presented no evidence of cognitive

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impairment on any of the administered neuropsychological tests and were CSF Aβ negative (over the 550 pg/mL). On the other hand, preclinical AD subjects had completely normal neuropsychological evaluation but presented positive CSF Aβ values (below 550 pg/mL).

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Cognitively healthy subjects with elevated t-tau (above 450 pg/mL) were excluded from the analysis. MCI were CSF Aβ positive and had an objective memory deficit, defined as an

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abnormal score on the total recall measure of the Free and Cued Selective Reminding Test (FCRST), impairment on one or more of the other cognitive tests or preserved activities of daily living, as measured by the Functional Activities Questionnaire (FAQ score <6). The NINCDSADRDA criteria were applied for probable AD diagnosis, taking into account clinical information and objective measures derived from the FAQ and neuropsychological results. All included AD patients were CSF Aβ positive and in the mild stages of the disease (Global Deterioration Scale = 4).

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ACCEPTED MANUSCRIPT Genomic DNA was extracted from peripheral blood of probands using the QIAamp DNA blood minikit (Qiagen AG, Basel, Switzerland). Apolipoprotein E genotyping was performed by

CSF sampling and calculation of the AD CSF Index

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polymerase chain reaction amplification and HhaI restriction enzyme digestion.

Subjects underwent lumbar puncture (LP) between 9:00-12:00 am. 10mL of CSF was

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collected in polypropylene tubes, with the samples being centrifuged and stored at -80ºC within an hour. Levels of Aβ1-42, t-tau and p-tau were measured by enzyme-linked

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immunosorbent assay (ELISA) kits (Innogenetics, Ghent, Belgium). The AD CSF Index, for each subject, was calculated as previously described (Molinuevo, et al., 2013) applying the following formula: applying the following formula:

Aβ max − Aβ 1− 42 ttau − ttau min + Aβ max − Aβ min ttau max − ttau min

(eq.

1)

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AD CSF Index (t − tau ) =

Where Aβmax and t-taumax represent the 95th percentile of the respective values, Aβmin and

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t-taumin represent the 5th percentile of the distribution values, and Aβ1-42, t-tau and p-tau represent the biomarker values for every individual. In addition, we compared the

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concentration of CSF biomarkers and the AD CSF Index between APOE4 carriers and noncarriers, by means of a two-sample t-test (p<0.05).

Image acquisition Subjects were examined on a 3T magnetic resonance imaging (MRI) scanner (Magnetom Trio Tim, Siemens Medical Solutions, Erlangen, Germany). A high-resolution threedimensional structural dataset (T1-weighted magnetization-prepared rapid gradient-echo, 9

ACCEPTED MANUSCRIPT repetition time = 2300 msec, echo time = 2.98 msec, 240 slices, field of view = 256 mm; matrix size = 256 × 256; slice thickness = 1 mm) was acquired for all the subjects. The mean time interval between the lumbar puncture and the MRI was 41 days and ranged between 1 and 134 days. Hippocampal volumes were automatically calculated for each subject with an in-house

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implementation for SPM8 of the Individual Atlases using Statistical Parametric Mapping toolbox (IBASPM; (Alemán-Gómez, et al., 2006)) using the Automated Anatomical Labelling Atlas

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(Tzourio-Mazoyer, et al., 2002).

Image preprocessing

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Images were processed and examined using the SPM8 software (Wellcome Department of Imaging Neuroscience Group, London, UK; http://www.fil.ion.ucl.ac.uk/spm), where we applied VBM implemented in the VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm.html) with default parameters. Briefly, T1 images were normalized to a referenced template using a

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high-dimensional DARTEL normalization and segmented into gray matter in the images. In order to avoid biasing the segmentation towards normality, segmentation is performed without tissue priors but using a Hidden Markov Random Field (HMRF) model (Cuadra, et al.,

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2005). As a part of the segmentation process, images are denoised with an adaptive non-local means (SANLM) algorithm (Manjon, et al., 2010). As a result, voxels are assigned a value

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between 0 and 1 according to their relative content of gray matter. Then, gray matter partitions are multiplied with the jacobian determinants of the spatial normalization deformation to modulate local intensity values with volumetric change. Only the non-linear part of the normalization was used for the modulation allowing the comparison of the absolute amount of gray matter corrected for individual brain size. Then, images were smoothed with a 6 mm FWHM kernel.

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ACCEPTED MANUSCRIPT Statistical Image analysis Images from all subjects were pooled together irrespective of their diagnostic classification. A regression model was set up entering age, sex, the AD CSF Index and the APOE4 genotype as regressors. Age was modeled as a second order polynomial to correct for

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nonlinear effects of aging on cerebral gray matter content. Similarly, AD progression was modeled by entering the AD CSF Index up to the third-order. This approach is possible in SPM thanks to the fact that the different polynomial terms are orthogonalized with regard to its

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preceding column. An F-contrast was applied looking for effects of interest regarding the three AD CSF Index terms.

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In order to assess the significance of the higher order terms beyond the linear one we also tested for their statistical significance after accounting for the linear one, and vice versa (i.e. in this latter case, the linear term was entered as the last column in the design matrix). Then, to evaluate the relative weighting of the linear and nonlinear terms across the brain regions

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showing a significant association with the AD CSD Index, we fused the thresholded F-map of the linear component to the equivalent map of the non-linear terms using different color scales.

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To describe the atrophy patterns in a model-free fashion, gray matter data corrected by age

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and sex, was regressed against the AD CSF Index using a robust locally weighted scatterplot smoothing (LOWESS) method as implemented in Matlab (The MathWorks, Natick, Massachusetts, USA) with a smoothing factor of 0.8. This factor was selected empirically in order the resulting fits to present a maximum of two inflexion points in accordance to the third-order parametric model in the SPM analysis. Post-hoc 95% confidence intervals were calculated by bootstrapping with 1000 samples. This procedure was used for descriptive purposes only. Statistical significance was exclusively determined by the SPM analysis.

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ACCEPTED MANUSCRIPT A second design matrix was implemented to look for volumetric differences associated to the APOE4 after correcting for age and gender. In this second model the AD CSF Index was not entered as covariate. Two one-sided Student t-test were implemented to look for increased and decreased volumes associated to APOE4 status. In order to evaluate the impact of APOE4

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on the trajectories of brain volume along the AD CSF Index, independent LOWESS regressions were performed for carriers and non-carriers, in brain regions showing statistically significant differences associated to the APOE4 genotype. In addition, CSF biomarkers were statistically

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compared between APOE4 carriers and non-carriers.

Statistical significance threshold for all SPM analyses was set to p < 0.001 uncorrected for

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multiple comparisons. Only clusters of over 100 supra-thresholded voxels were taken into consideration. According to (Hayasaka and Nichols, NeuroImage, 2003) the threshold of uncorrected p < 0.001 is conservative in rejecting type I errors for images smoothed at 6 mm FHWM. At this same smoothing and at the p<0.01 level, a cluster threshold of 100 voxels

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rejects 90% of the clusters under the null hypothesis. Unfortunately, no results are provided in that paper at the p<0.001 level, but of course they will be far more conservative for type I

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errors.

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Results The demographic characteristics of the participants are shown in Table 1. Control subjects were significantly younger than the rest of groups (p<0.05) and AD patients were younger than MCI ones (p=0.038). APOE4 was overrepresented in MCI and AD groups. Figure 1 shows the

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pattern of brain atrophy resulting from the third-order polynomial regression against the AD CSF index, after correction for the effects of age and sex. This pattern was highly symmetrical and comprised the bilateral hippocampus, amygdala, temporal cortex, posterior cingulate

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precuneus. To a lower extent, several clusters did also achieve statistical significance in the insula and other frontal and parietal areas. See Table 2 for a complete listing of the brain

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regions and associated statistics.

After accounting for the linear term of the polynomial regression, the nonlinear ones reached statistical significance in the bilateral parahippocampal cortices and in a right parietotemporal region extending from the middle temporal cortex to the angular and

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supramarginal giri (Fig 2a-b). Only in the left parahippocampal region the linear term was statistically significant after accounting for the nonlinear terms (Fig 2c).

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Figure 3 shows the relative contribution of the linear and nonlinear terms to the observed variance in gray matter volume in association with the AD CSF index. Regions in red display a

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predominant contribution of the linear term whereas the nonlinear ones dominate in regions in green. Those areas in which the contribution of the linear and nonlinear terms independently explain a significant percentage of the observed variance are shown in yellow. The regions in which the nonlinear terms reached statistical significance after accounting for the linear one (bilateral parahipocampal and right parietotemporal cortices) present such a behavior (Fig 3a-b).

Figure 4 shows the brain atrophy trajectory across the AD CSF Index for several representative brain regions. The linear component was predominant in the posterior 13

ACCEPTED MANUSCRIPT cingulate, precuneus, tail of the hippocampus and middle temporal cortex. Figure 4a shows the linear trajectory of brain volumetric association with the AD CSF Index in the posterior cingulate.

However, most of the brain regions which display a statistically significant association with

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the AD CSF Index show a behavior similar to that displayed by the hippocampal head in Figure 4b. In this case, the brain volume remains unaffected until Index values of 0.7, approximately.

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Then a rather linear atrophy pattern is observed with increasing Index values.

Those regions in which the nonlinear component achieved statistical significance, show

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trajectories as displayed in Fig 4c-d. In these cases, and initial increase with the Index can be observed. This increase reaches a plateau at Index values of about 0.6 and then decrease in a rather linear fashion. The bilateral parahippocampal cortices, the right parietotemporal region and some clusters in the middle and anterior cingulate cortex show this trajectory.

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Most of the brain areas displayed in green in the composite F-map show the trajectory in Fig 4e. In these instances, there are no variation of the gray matter volumes until values of the index of about 1.4 and, then, an inverse linear association with the index can be observed.

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trajectories.

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Some clusters in the precuneus, temporal, parietal and frontal regions present such

APOE4 carriers showed significantly lower CSF Aβ1-42 (608.87 ± 256.28 vs 431.74 ± 186.29;

p<0.001), higher t-tau (346.70 ± 282.70 vs. 624.92 ± 338.86; p<0.001), p-tau (65.37 ± 38.28 vs. 93.33; p<0.001) and AD CSF Index values (0.6958 ± 0.5089 vs 1.1933 ± 0.4732; p<0.001). Figure 5 shows brain regions with decreased gray matter volume in APOE4 carriers. The inverse contrast did not show any statistically significant differences. APOE4 carriers showed lower gray matter volume in the bilateral hippocampus, amygdala, parahippocampal cortex and temporal pole, the left angular and inferior parietal cortex, the right insula as well as in the posterior

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ACCEPTED MANUSCRIPT cingulate and precuneus. When the brain volume of carriers and non-carriers was independently fitted against the AD CSF Index, two different tendencies could be observed. Hippocampal regions present trajectories similar to that in Fig 6a in which no initial differences can be appreciated between carriers and noncarriers but, as the AD CSF Index increases,

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carriers show a steepest decrease in brain volume. In the other hand, the other cortical regions

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APOE4 carriers display somewhat lower volumes all along the AD CSF Index (Fig 6b).

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Discussion In this article, we describe non-linear trajectories of AD-related brain atrophy in a continuum ranging from normal cognition to mild AD. In order to track AD-related pathology we employed the AD CSF Index, which is built as a sum of the normalized CSF concentrations of

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Aβ42 and tau (total tay in this work). The AD CSF Index ranges from 0 (completely normal biomarkers) to 2 (most altered biomarkers). One major advantage of this normalized approach is that index values are comparable even in the event of CSF samples quantified using different

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analytical platforms (Molinuevo, et al., 2013,Struyfs, et al., 2014).

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Since we sought to identify brain atrophy patterns associated to CSF biomarkers ranging from completely normal to severily altered, we opted for using a nonlinear regression model rather a linear one. Linear models assuming constant atrophy rates are unlikely to represent the relationship between CSF biomarkers and brain atrophy realistically across the full AD pathological continuum (Insel, et al., 2014). In addition, subtle departures from linearity can

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provide more insight about the timing of the inflexion points associated to pathological processes along the AD continuum.

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To identify the atrophy pattern associated with the AD CSF Index, we used the standard VBM methodology and implemented a third-order polynomial model. The suitability of this

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approach is supported by the fact that the nonlinear terms reached statistical significance, after accounting for the linear one, in the bilateral parahippocampal cortices and a parietotemporal region (Fig 2a-b). Actually, in most of the regions showing a statistically significant association with the Index, the nonlinear terms explained significant percentages of variability in gray matter (Figures 3 and 4). Exceptions to this predominant trend were the posterior cingulate, precuneus, tail of the hippocampus and middle temporal cortex, in which the linear component was clearly dominant (Fig 4a). To describe the different atrophic trajectories in association with the AD CSF Index, we extracted the age and sex corrected

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ACCEPTED MANUSCRIPT values from the VBM-SPM model and applied a local smoothing in a similar fashion as in (Bateman, et al., 2012). This allowed us to obtain model-free smooth variations of gray matter volume across the full range of the AD CSF Index.

The atrophy pattern associated with the AD CSF Index showed the typical AD signature

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(Frisoni, et al., 2010). Four main atrophy patterns emerged from our analysis. The most preponderant showed no significant brain alterations until AD CSF Index values of about 0.8 (Fig 4b). Most of the controls had Index values below this value which is in close agreement

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with the optimal classification threshold in the diagnostic validation of the AD CSF Index in four European populations (Molinuevo, et al., 2013). This pattern was observed in most of the

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regions of the AD brain signature including the hippocampal heads. This finding is in agreement with the hippocampus being considered to be the best established imaging biomarker for the diagnosis of AD (Drago, et al., 2011,Teipel, et al., 2011, Hill, et al., 2014) along with previous findings in APOE4 carriers displaying hippocampal atrophy by the time AD symptoms occur

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(Wolk and Dickerson, 2010).

Nevertheless, different portions of the hippocampi followed various atrophy trajectories.

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Posterior horns showed a linear association with the Index (Fig 3c). Brain regions in which the nonlinear terms were statistically significant (Fig 2a-b), along with some focal areas in the

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middle and anterior cingulate cortex depicted in yellow in Figure 3, showed a non monotonous trajectory. It was characterized by an early increase in volume that peaked at index values around 0.6 and then followed a rather linear decline (Fig 4c-d). Such a departure from the linear behavior might not be consistently detected by linear regression methods. To some extent, this could explain the discrepancies in the literature with respect to the association between hippocampal volume and amyloid deposition. A similar inverse u-shaped relation was found between cortical thickness and CSF amyloid concentrations in these regions in cognitively healthy subjects (Fortea, et al., 2011). It should be noted that the dataset partially

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ACCEPTED MANUSCRIPT overlaps with the one in the present study (17 out of the 41 Normal Controls and 16 out of the 42 subjects with subjective memory complaints). Nevertheless, the robustness of this finding is reinforced by the fact that the same pattern was detected after applying very different procedures of analysis: volumetric changes in the present study vs cortical thickness in the

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former. Such an early increase in volume might be attributable to early neuroinflammatory processes.

Neuroinflammatory processes associated to microglial activation have been previouly

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reported in AD using positron emission tomography with 11C-PBR28 to measure translocator protein 18 kDa (Kreisl, et al., 2013). AD patients showed increased 11C-PBR28 binding in the

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parietal and temporal cortices which was inversely correlated with grey matter volume and, in parietal cortex and striatum, positively correlated with lower age of onset. After partial volume correction, they found an association between 11C-PBR28 and 11C-Pittsburgh Compound B binding. On the other hand, astrocyte activation, as measured by 11C-deprenyl PET, has also

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been reported with amyloid positive MCI patients displaying greater binding than AD ones (Carter, et al., 2012). In addition, astrocyte mediated neuroinflammation has shown to correlate with reduced gray matter density in the parahippocampus gyrus of amyloid-positive

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prodromal Alzheimer's patients (Choo, et al., 2014). In view of these previous reports, our finding of increased grey matter volume in parietal areas might be attributable to an early

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neuroinflammatory process associated to amyloid accumulation than reverts later on at the onset of neurodegenerative processes associated to tau accumulation. There is a large amount of data showing that brain atrophy in AD is much more related to tau pathology than to amyloid accumulation (Desikan, et al., 2011, Hyman, et al, 2011). However, the dataset in this study does not allow us to assess this possibility and, consequently, new studies are required to verify this end.

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ACCEPTED MANUSCRIPT The precuneus also presented complex trajectories as a cluster that also included the posterior and middle cingulate showed a rather linear association with disease progression whereas another cluster did not display any gray matter variations until Index values associated with high CSF tau values. Such departures from the linear model were also found by (Becker, et

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al., 2011) who modeled the cortical thickness as a function of PiB retention using a sigmoid function.

The impact of the APOE genotype was most evident in the hippocampus with carriers

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showing a steeper decline in gray matter volume after Index values approaching the diagnostic threshold (0.8). This finding is consistent with previous studies that showed that APOE4 carriers

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exhibit accelerated hippocampal atrophy (Liu, et al., 2010,Manning, et al., 2014,Shi, et al., 2014) and might be indicative of APOE4 carriers being more vulnerable to amyloid accumulation or other associated brain insults (Bookheimer and Burggren, 2009). Still, APOE4associated lower hippocampal volumes have been reported in cognitively normal older

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subjects (Burggren, et al., 2008,den Heijer, et al., 2002,Reiman, et al., 1998) and even in children and adolescent APOE4-carriers (Shaw, et al., 2007). On the other hand, in other cortical areas (Table 3), APOE4 carriers showed lower gray matter volume regardless of the AD

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CSF Index which might be associated to pre-existing differences which are similarly affected by

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the progression of the AD pathology.

By using a VBM approach and a non-linear parametric regression, our analysis method was

able to determine different voxelwise patterns of gray matter atrophy throughout the whole brain and cerebellum. This approach represents an advantage over methods that only study the cerebral cortex, are based on pre-defined regions of interest or are restricted to linear models. The main limitation of our study is that, due to its transversal design, it provides limited value to establishing the atrophy progression though the whole spectrum of AD. The use of a biological progression biomarker like the AD CSF Index is not intended to be a

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ACCEPTED MANUSCRIPT substitute of longitudinal studies with clinical follow-up. Indeed, please note that the confidence intervals in our model-free curves were estimated post-hoc the SPM analysis. Therefore, they should be regarded as confidence intervals of the expected average trajectories of brain atrophy versus the AD CSF Index without any predictive value at the individual level.

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In summary, we report different nonlinear dynamics of gray matter reduction associated to progression through the AD biological continuum, modulated by the APOE4 genotype. Even though the atrophy patterns detected were in close agreement with previous findings relying

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on linear methods, the subtle nonlinearities here described are suggestive of different potential pathological processes taking place at specific moments in time during AD

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progression. To some extent, the detected nonlinearities could explain the discrepancies in the literature with respect to the association between hippocampal atrophy and amyloid

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deposition and the impact of the APOE4 allele on this association.

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Acknowledgements Authors would like to acknowledge the contribution of Dr. Andreia Carvalho in the preparation of this manuscript. Juan D Gispert holds a ‘Ramón y Cajal’ fellowship (RYC-201313054) and Lorena Rami is part of the Programa de investigadores del sistema nacional Miguel

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Servet II (CPII/00023; IP: Lorena Rami). The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115568 (AETIONOMY), resources of which are composed of financial contribution from the

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European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. This publication has also been suported by the BIOMARKAPD project within

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the EU Joint Programme for Neurodegenerative Diseases (JPND) funded by the ISCIII (PI11/03023 PI José L Molinuevo; PI11/03022 PI: Juan D Gispert), Consolider-Ingenio 2010 (CSD 2010-00045 PI: José L Molinuevo), FIS-Fondo europeo de desarrollo regional, una manera de hacer Europa (PI11/01071 IP: Lorena Rami), proyecto IMSERSO (197/2011 IPD: Lorena Rami),

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Mapfre company grant (IP: Lorena Rami), Industex S.L. company grant (PI: Juan D Gispert),

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Research Grant from the Ayuntamento de Barcelona (PI: Juan D Gispert).

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ACCEPTED MANUSCRIPT Table 1: Demographic features of the studied subjects (Mean ± StdDev or percentages when appropriate).

21 65.47 ± 9.24 66.67%

14.52 %

38.89 %

60.71 %

52.38%

<0.001

763.6 ± 170.0 235.0 ± 77.8 52.4 ± 13.4 0.3859 ± 0.2139 2.88 ± 0.24 2.79 ± 0.26 14.67 ± 1.29 28.66 ± 1.34

371.4 ± 90.7 296.6 ± 167.0

348.4 ± 79.7 752.5 ± 305.9 110.2 ± 37.7 1.4221 ± 0.3027 2.63 ± 0.34 2.56 ± 0.36 5.81 ± 4.79 24.63 ± 2.87

331.8 ± 123.5 761.3 ± 363.8 109.4 ± 48.0 1.4521 ± 0.3311 2.68 ± 0.32 2.57 ± 0.27 6.29 ± 5.06 24.00 ± 3.59

<0.001

57.5 ± 25.7 0.9675 ± 0.2104 2.73 ± 0.24 2.63 ± 0.24 13.83 ± 2.04 27.61 ± 1.88

EP 29

P

<0.001 0.950

RI PT

AD

SC

62 61.68 ± 7.45 61.29 %

AC C

MMSE

18 67.67 ± 6.63 66.67 %

MCI due to AD 28 70.07 ± 6.84 60.71 %

TE D

R Hippocampal Vol (cm3) L Hippocampal Vol (cm3) FCSRT-TR

Pre-clin AD

M AN U

N Age (yr) Gender (% Female) ApoE ε4 (% Carriers) CSF Aβ42 (pg/mL) CSF t-tau (pg/mL) CSF p-tau (pg/mL) AD CSF Index

CN

<0.001 <0.001 <0.001 0.004 0.005 <0.001 <0.001

ACCEPTED MANUSCRIPT Table 2: Areas showing statistically significant atrophy in association with the AD CSF Index (p<0.001 unc; k>100 voxels). Brain Regions

406 1988

787

160 159 113 292 167 270

-6 3

-12 -20

-18 0 -6 2 52 60 57 42 42 -38 -40 -40 56 44 39 45 51 62 30 38 26 -28 57 46 -57

-6 -34 -24 -52 -28 -40 -21 11 11 20 9 42 4 8 18 -30 -22 -30 35 30 33 54 -15 -18 -34

-14 37 40 28 -5 3 -15 34 25 0 25 9 -2 0 1 49 43 30 31 30 -15 0 10 10 16

AC C

130

7.75 6.64 5.74 5.24 6.07 5.88 5.64 5.31 4.21 4.96 4.76 4.53 4.80 4.26 3.99 4.47 3.64 4.47 4.37 3.76 4.22 4.15 4.07 3.99 4.06

20 -22

30

R Hippocampus, Amygdala, Parahippocampal L Temporal Pole Sup L Hippocampus, Amygdala, Parahippocampal Cingulum Mid/Posterior Cingulum Mid Cingulum Post/Precuneus R Temporal Inf/Mid/Sup R Temporal Inf/Mid/Sup R Temporal Inf/Mid/Sup R Frontal Inf Operc R Frontal Inf Operc L Insula / Frontal Inf Frontal Inf Frontal Inf R Temporal Pole Sup R Insula / Frontal Inf R Insula / Frontal Inf R Postcentral / Parietal Inf R Postcentral / Precentral R Supramarginal R Frontal Mid/Inf R Frontal Mid/Inf R Frontal Orb L Frontal Sup R Rolandic Oper, Temporal Sup R Heschl, Rolandic Oper L Temp Sup/Mid, Supramarginal

RI PT

9724

<0.001 <0.001 <0.001 0.006 <0.001 <0.001 0.001 0.004 0.390 0.021 0.051 0.129 0.043 0.332 0.667 0.162 0.972 0.163 0.234 0.914 0.375 0.463 0.556 0.664 0.576

Inf Inf

SC

5744

<0.001 <0.001

TE D

34834

Zequiv

Label

M AN U

PFWE-corr

EP

kequiv

MNI Coords {mm} x y z

ACCEPTED MANUSCRIPT Table 3: Areas showing differential atrophy patterns according to the APOE4 status (p<0.001 unc; k>100 voxels).

Brain Regions

179 189

MNI Coords {mm} x y z 32 -22 -9 28 -13 -20 18 -10 -15 -15 -12 -15 -28 -10 -15 -30 -21 -15 -45 -64 48 50 -1 13 45 4 4 2 -49 21 -9 -49 24 0 -49 31

Label

AC C

EP

TE D

M AN U

832

Zequiv 4.93 4.82 4.45 4.26 4.07 3.9 4.04 3.9 3.54 3.86 3.67 3.65

31

R Hippocampus R Hippocampus R Hippocampus L Hippocampus L Hippocampus L Hippocampus L Angular R Rolandinc Operc R Insula R Precuneus L Precuneus L Cingulum Post

RI PT

1479

PFWE-corr 0.018 0.029 0.127 0.245 0.437 0.65 0.473 0.647 0.964 0.698 0.891 0.901

SC

kequiv 2679

ACCEPTED MANUSCRIPT Disclosure: José L Molinuevo has provided scientific advice or has been a data monitoring board member in return for consultancy fees from Pfizer, Eisai, MSD, Merk, Janssen-Cilag, Novartis, Lundbeck, Roche, Bayer, Bristol-Myers Squibb, GE Health Care, GlaxoSmithKline and Innogenetics. No funding was provided to the author for the preparation of this manuscript.

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Funding: Juan D Gispert holds a ‘Ramón y Cajal’ fellowship (RYC-2013-13054) and Lorena Rami is part of the Programa de investigadores del sistema nacional Miguel Servet II (CPII/00023; IP: Lorena Rami). The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n°115568 (AETIONOMY), resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. This publication has also been suported by the BIOMARKAPD project within the EU Joint Programme for Neurodegenerative Diseases (JPND) funded by the ISCIII (PI11/03023 PI José L Molinuevo; PI11/03022 PI: Juan D Gispert), Consolider-Ingenio 2010 (CSD 2010-00045 PI: José L Molinuevo), FIS-Fondo europeo de desarrollo regional, una manera de hacer Europa (PI11/01071 IP: Lorena Rami), proyecto IMSERSO (197/2011 IPD: Lorena Rami), Mapfre company grant (IP: Lorena Rami), Industex S.L. company grant (PI: Juan D Gispert), Research Grant from the Ayuntamento de Barcelona (PI: Juan D Gispert). The data contained in the manuscript being submitted have not been previously published, have not been submitted elsewhere and will not be submitted elsewhere while under consideration at Neurobiology of Aging.

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The study was approved by the local ethics committee and all participants gave written informed consent to participate in the study.

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All authors have reviewed the contents of the manuscript being submitted, approve of its contents and validate the accuracy of the data