Part 1:Entorhinal Tau Pathology, Episodic Memory Decline, And Neurodegeneration in Aging
Mar 18, 2022
Contact: Audrey Hu audrey.hu@wecistanche.com
X Suzanne L. Baker,4 Gil D. Rabinovici,1,5 and X William J. Jagust1,4
1Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, California 94720, 2German Center for Neurodegenerative Diseases, Magdeburg 39120, Germany, 3Department of Internal Medicine, Division of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina 27157, 4Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, California 94720, and 5Memory and Aging Center, University of California San Francisco, San Francisco, California 94158
The medial temporal lobe (MTL) is an early site of tau accumulation and MTL dysfunction may underlie episodic-memory decline in aging and dementia.Postmortemdataindicatethattaupathologyinthetransentorhinalcortexiscommonbyage60, whereas spread to neocortical regions and worsening of cognition is associated with-amyloid(A). Weused[18F]AV-1451and[11C]PiBpositronemission tomography, structural MRI, and neuropsychological assessment to investigate how in vivo tau accumulation in temporal lobe regions, A,andMTLatrophycontributetoepisodicmemoryincognitivelynormalolderadults(n 83;age,77 6years;58%female). Stepwise regressions identified tau in MTL regions known to be affected in old age as the best predictor of episodic-memory performance independent of A status.TherewasnointeractiveeffectofMTLtauwithAonmemory.HigherMTLtauwasrelatedtohigherageinthe subjectswithoutevidenceofA.Amongtemporallobesubregions, episodic memory was most strongly related to tau-tracer uptake in the parahippocampal gyrus, particularly the posterior entorhinal cortex, which in our parcellation includes the transentorhinal cortex. In subjectswithlongitudinalMRIandcognitivedata(n 57),entorhinalatrophymirrored patternsoftaupathology and their relationship with memory decline.Our data are consistent with neuropathological studies and further suggest that entorhinaltaupathologyunderlies memory decline in old age even without A.
Keywords: -amyloid; aging; episodic memory; positron emission tomography; tau; transentorhinal cortex
Significance Statement Tautangles and -amyloid (A ) plaques are key lesions in Alzheimer’s disease (AD) but both pathologies also occur in cognitively normal older people. Neuropathological data indicate that tau tangles in the medial temporal lobe (MTL) underlie episodicmemory impairments in AD dementia. However, it remains unclear whether MTL tau pathology also accounts for memory impairments often seen in elderly people and how A affects this relationship. Using tau-specific and A -specific positron emission tomography tracers, we show that in vivoMTLtau pathology is associated with episodic memory performance and MTL atrophy in cognitively normal adults, independent of A . Our data point to MTL tau pathology, particularly in the entorhinal cortex, as a substrate of age-related episodic-memory loss.
Materials and Methods
Participants
Our sample comprised 83 cognitively normal OAs from the Berkeley Aging Cohort Study (BACS), a longitudinal study of normal aging. Aspects of memory function were recently reported in a subset of 33 subjects (Schll et al., 2016). Subjects underwent structural MRI, [ 18F]AV-1451, and [ 11C]PiB PET imaging, neuropsychological assessment, and standard laboratory blood tests including APOE genotyping (data missing for eight subjects).
Eligibility requirements for inclusion into BACS comprise the follow- ing: no MRI or PET contraindications, living independently in the community, Mini-Mental State Examination score (Folstein et al., 1975) 25, within 1.5 SD of normative values on the California Verbal Learn- ing Test (CVLT; Delis et al., 2000) and delayed recall from the visual reproduction (VR) test (Wechsler, 1997), absence of neurological or psychiatric illness, and lack of major medical illnesses and medications that affect cognition. Subjects who performed below the CVLT or VR cutoff in one follow-up session remained in the study as we were interested in biomarkers underlying age-related memory decline.
Data acquisition and preprocessing
Cognitive data. Cross-sectional neuropsychological data closest to the AV-1451 tau scan were used to calculate composite scores for episodic- memory, working memory, and executive function domains. Cognitive data were collected within 117 87 d of the tau PET scan. For 85% of the subjects,thetimelagwas6months.Foronlyonesubject, the time delay was 1 year (1.3 years). Z scores were calculated as the average of the Z-transformed individual test scores using mean and SD from the first cognitive session data of a larger sample of 164 BACS OA participants (age: 74 6 years; education: 17 2 years; 60% female) that also included the 83 OAs studied here.
The memory composite score comprised short-delay and long-delay (after 20 min) free recall of the CVLT and VR tests. The working-memory score included the WMS-III Digit Span test forward and backward total score. The executive function composite score comprised the Digit- Symbol test (Smith, 1982), number correct in 60 s in the Stroop Interference Test (Stroop, 1938), and “Trail B minus A” score from the Trail Making Test (Reitan, 1985; score inverted after Z-transformation).
Fifty-seven of 83 OAs had longitudinal MRI as well as cognitive data (2 scans/testing sessions). These subjects had between 2 and 10 cognitive testing sessions (mean, 5.6 2.2) over a period of 5.6 2.5 years with an average delay of 1.3 0.3 years between sessions. For these subjects with both available longitudinal MRI and cognitive data, we also assessed measures of episodic-memory change (using linear mixed-effects models to derive slopes).
MRI data. For all subjects, 1 1 1-mm-resolution T1-weighted magnetization prepared rapid gradient echo (MPRAGE) images were acquired at 1.5 T at Lawrence Berkeley National Laboratory (Schll et al., 2016). These images were used for the definition of brain regions of interest (ROIs) and for both cross-sectional and longitudinal measurement of MTL atrophy.
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Free Surfer output from the closest 1.5 T MRI scan was used to derive bilateral mean HC volume and entorhinal thickness as cross-sectional measures of MT Latrophy. MRIs cans were acquired within 65 128dof the tau PET scan. HC volumes were corrected for ICV (to account for differences in head size) via the following linear equation: Voladj Volraw(i) b(ICV(i) Mean ICV), where Voladj is the adjusted HC volume, Volraw(i) is the original volume for an individual, b is the slope of HC volume regressed on ICV, and Mean ICV is the sample mean of ICV (Raz et al., 2015).
Fifty-seven of 83 OAs had longitudinal 1.5 T MRI data, with 2–5 scans (mean, 2.8 0.9) over a period of4.5 2.6 years and an average delay of 2.3 1.4 years between two MRI scans. To extract reliable longitudinal volume and thickness estimates, these T1 images were processed with the longitudinal FreeSurfer stream (Reuter et al., 2012). Specifically, unbiased within-subject template space and image (Reuter and Fischl, 2011) are created using robust, inverse consistent registration (Reuter et al., 2010).
PET data. A detailed description of AV-1451 tau PET and PiB APET acquisition for BACS/UCSF (University of California San Francisco) has been published previously (Ossenkoppele et al., 2016; Scho¨ll et al., 2016). AV-1451 scans were collected within 49 91 d of PiB. PiB and AV-1451 PET images were reconstructed using an ordered subset expectation maximization algorithm with weight attenuation and smoothed with a 4 mm Gaussian kernel with scatter correction (calculated image resolution, 6.5 6.5 7.25 mm 3 using Hoffman phantom).
AV-1451 standardized uptake value ratio (SUVR) images were coregistered and resliced to the structural MRI closest in time, as mentioned previously. We created AV-1451 SUVR images based on mean uptake over 80–100 min postinjection (Shcherbinin et al., 2016; Baker et al., 2017b; Wooten et al., 2017) normalized by mean inferior cerebellar gray matter uptake. We excluded the superior portion of the cerebellar gray from our reference region as it showed frequent tracer binding (Baker et al., 2017a). We created the inferior cerebellar gray ROI from the reverse-normalized cerebellar SUIT (A Spatially Unbiased Atlas Template of the CerebellumandBrainstem)templateasdescribedindetailbyBakeretal. (2017a).
To derive Braak-ROI mean values consistent with our previously published preprocessing stream and Braak-based staging approach (Maass et al., 2017), SUVR images were smoothed with a 4.7 4.7 2.8 mm 3 FWHM kernel to achieve a resolution of 8 8 8mm 3 (i.e., resolution of Alzheimer’s Disease Neuroimaging Initiative data on which we validated our Braak thresholds). These smoothed SUVR images were partial volume (PV) corrected using the Geometric Transfer Matrix approach (Rousset et al.,1998)withFreeSurfer-derivedROIsasdescribedbyBaker et al. (2017a). The goals of the PVC were to correct for choroid plexus and basal ganglia signal bleeding into neighboring regions (such as the HC), toaccountforPVeffectsduetoatrophy, for and to correct for spillover from extracortical hotspots. PV-corrected ROI SUVR values were renormalized by PV-corrected inferior cerebellar gray. For analyses on temporal lobe subregional patterns of tau-tracer uptake, we used the unsmoothed SUVR images (as we wanted to keep the highest possible resolution), which were also PV-corrected after parcellation of the temporal lobe.
Individual PiB frames were realigned, coregistered, and resliced to the closest structural MRI. DVRs for PiB images were generated with Logan graphical analysis on PiB frames corresponding to 35–90 min postinjection using a cerebellar gray matter reference region (Logan et al., 1996; Price et al., 2005). The global cortical PiB DVR was calculated as a weighted meanacrossFreeSurfer-derived frontal, temporal, parietal, and posterior cingulate cortical regions. Participants were classified as PiB-positive if theirglobalPiBDVRwas 1.065,auto-adapted from previous thresh- olds developed in our laboratory (Mormino et al., 2012; Villeneuve et al., 2015). We only included OAs with full dynamic PiB data.
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AV-1451 uptake in Braak composite regions. We (Schll et al., 2016; Maass et al., 2017) along with other laboratories (Cho et al., 2016b; Schwarz et al., 2016; Hoenig et al., 2017) have proposed approaches to summarizing tracer uptake in ROIs that parallel the Braak staging approach. Specifically, we calculated weighted bilateral mean SUVR values after PVC in native space from three composite ROIs that approximate anatomical definitions of Braak stages I/II, III/IV, and V/VI. FreeSurfer indices for the different Braak ROIs can be found in baker et al. (2017a). We recently developed AV-1451 SUVR thresholds for each Braak ROI, based on data of AD patients and controls, to assign subjects to one of four stages (Scho¨lletal.,2016; Maass et al.,2017). The number of subjects classified to each stage is summarized in table 1.
Parcellation of the temporal lobe. To examine associations between temporal lobe regional tau patterns and memory,weusedtheFreeSurfer- derived ROIs of the HC, the EC, the parahippocampal cortex (PHC), the fusiform gyrus (FuG), the inferior temporal gyrus (ITG), and the middle temporal gyrus (MTG; Desikan et al., 2006) and subdivided those along the longitudinal axis of the temporal lobe. This is shown schematically in Figure 1A and for the group T1 image in MNI space in figure 1B. We used the most anterior, the middle, and the most posterior HC slice (Fig. 1, cut points 1–3) as landmarks to coronally slice each gyrus (PhG, FuG, ITG, and TG)into four segments(ant, med, post, post-HC). Slicing was performed for each hemisphere separately. These anatomical landmarks were chosen as they can be automatically determined (in Matlab) by use of the HC FreeSurfer segmentation.
The FreeSurfer-defined EC covers the anterior portion of the PhG, including the medial bank of the collateral sulcus, and thus also likely includes the transentorhinal region (BraakandBraak,1985,1991; Taylor and Probst, 2008), corresponding to Brodmann’s area (BA) 35 or to the medial perirhinal cortex (Kivisaari et al., 2013). A protocol for segmentation of the transentorhinal cortex at 7 T has been published recently (Berron et al., 2017). Rostral and caudal boundaries of the FreeSurfer- defined EC is the rostral end of the collateral sulcus and amygdala, respectively. The theFreeSurfer-labeled “parahippocampal cortex”(which is called “parahippocampal gyrus” in the original paper ByDesign et al., 2006) is the posterior portion of the PhG, which borders the EC. We merged FreeSurfer EC and PHC ROIs before parcellation to derive a continuous PhG ROI.
Moving from anterior to posterior, the “ant” segment of each gyrus starts at its FreeSurfer-defined anterior boundary and ends on the first (most anterior) slice of the HC. The “med” segment ends in the middle
slice of the HC (counting all coronal HC slices and dividing by 2). The “post” segment ends on the last (most posterior) coronal slice of the HC, which also corresponds to the most posterior slice of the FreeSurfer defined “parahippocampal cortex.” We called the part of FuG, ITG, and MTGposteriortotheHCthe“post HC.”ThePHCisposteriorlyadjoined by the lingual gyrus (LiG). Our PhGant segment covers the anterior portion of EC, whereas our PhGmed segment covers the posterior portion of EC. The PhG post segment corresponds to the PHC. Note that our boundary between the EC and PHC (middle of the HC) is more posterior than the FreeSurfer-defined landmark (end of amygdala), and approximately corresponded to the end of the HC head. We also note that BA36 or the lateralpartoftheperirhinalcortexiscoveredbytheFuG, in particular the FuGant and FuGmed. We also assessed mean SUVRs from the amygdala, which borders the HC anteriorly. Temporal lobe subregional SUVs were derived in individual (i.e., subject) space after the PVC but the same parcellation was also performed in MNI space without the PVC. Note that individual T1 images were coregistered and resliced (before parcellation) to the SPM-provided avg152T1.nii, which is anterior commissure–posterior commissure aligned.
AV-1451 data processing for analyses in MNI space. For voxelwise analyses, SUVR images (unsmoothed) were transformed into MNI152 space using DARTEL (diffeomorphic anatomical registration through exponentiated lie algebra). T1 images were segmented in SPM12. Native and DARTEL-imported gray and white matter segments were used to create a study-specific DARTEL template. The resulting flow fields served to normalize the SUVR images and the T1 images to MNI space (preserve concentration; resolution: 1.5 1.5 1.5 mm 3, no additional smoothing). We created a study-specific T1 group image by averaging across all warped T1 images. Similar to the processing of T1 images in individual space, we segmented the T1-group image by FreeSurfer (Desikan–Killiany atlas) to derive ROIs that were used for the temporal lobe parcellation in MNI space (Figure 1B).
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Experimental design and statistical analysis
ROI-based correlational analyses and regression models. First, we performed correlational analyses to describe the relationship between each cognitive measure and age, global PiB DVR, regional AV-1451 SUVR in Braak composite ROIs, bilateral entorhinal thickness, and bilateral HC volumes. Skipped Pearson correlation coefficients were obtained using the Robust Correlation Toolbox in Matlab (http://sourceforge.net/projects/ robust tool/) to limit the influence of outliers and data heteroscedasticity (Wilcox, 2004; Pernet et al., 2012). The toolbox (function skipped- _correlation.m) performs Pearson tests after removing bivariate outliers by taking into account the overall structure of the data and provides bootstrapped 95% confidence intervals (CIs). Figures depict outliers excluded from the skipped Pearson correlation in black. Correlations were considered significant if the 95% CI did not include zero. These correlational analyses were only descriptive and not corrected for multiple comparisons (note that we performed a stepwise regression to identify the best predictor for episodic memory). The toolbox does not allow users to control for the effects of additional covariates, such as gender or education. However, education was not significantly related to any cognitive score (Pearson correlation, all p’s 0.37, all r’s 0.10). Gender was only related
We finally assessed the relationship of entorhinal-thickness change to temporal lobe tau measures and episodic-memory change in a subgroup of 57 OAs with longitudinal MRI and cognitive data by means of robust correlations.
Whencomparingthestrengthbetweenrobustdependentcorrelations, we used a percentile bootstrapping approach as described by Wilcox (2016). A Matlab script implementing the procedure is available online (https://github.com/GRousselet/blog/tree/master/comp2dcorr).
Voxelwise regressions. We performed voxelwise regressions in MNI space (without PVC) to further examine the spatial pattern of episodic memory–tau associations in the whole brain.TheMNI-warped-1451 SUVRimageswereenteredintoamultiple-regressionanalysisinSPM12. We did not apply any explicit masking. Results are familywise error (FWE) corrected at the cluster level (p 0.05) with an uncorrected threshold of p 0.001 at the voxel level.
Linear mixed-effects models to derive slopes. To assess changes in entorhinal-thickness or episodic-memory performance over time, slopes were generated using a linear mixed-effects model in R (“lme4”). Entorhinal- thickness measures were derived by the longitudinal FreeSurfer pipeline. The following model, including random slopes and random intercepts, was fitted to the data, assuming that slopes and intercepts are independent: lmer[memory time (time 1 Subj) (1 Subj)], where time isthetimeoftheMRIscanorcognitivesessioninyearsrelativetothefirst MRI or first session, respectively. The following model with correlated slopes and intercepts revealed very similar results:[lmer(memory time (1-time Subj)].
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Results
Subject characteristics
The current sample included 83 cognitively normal OAs (age, 77 6 years), of whom 36 were A-positive (A; PiB DVR, 1.065). Sample characteristics for the whole group as well as for A-negative(A )and A subjects separately are summarized inTable 1.
A subject had significantly less education (16 2 years) than A subjects (17 2 years; t(81) 2.34,p 0.022, independent samples t-test) but did not differ in age, gender, or any structural measure (allp’s 0.88). However, we note that the age range was broader in the A (60–93 years) than the A (69–86 years) subjects. The proportion of carriers of the apolipoprotein E (APOE) 4 allele was significantly higher in the A group [2(1, N 75) 19.53, p 0.001, 2 test].
WecreatedcompositeZscoresforepisodicmemory, comprising short-delay and long-delay free recall in a verbal and a visual memory task, as well as for working-memory and executive-function domains (see Materials and Methods). A and A subjects performed similarly for all composite scores(allt’s1.65,allp’s0.10, independent sample t-test). A subjects performed worse on the CVLT short-delay free-recall test (t(81) 1.99,p 0.049).
