Serial position effects differ between Alzheimer’s and vascular features in mild cognitive impairment

CN and MCI characteristics

During the study period, 253 participants were recruited and completed study procedures. From there, a further 66 participants were dropped from analysis (37 with Mini-Mental State Exam (MMSE) scores <24, 29 with MRI images failing volumetric quality control due to excessive artifacts). The current study cohort comprised of the remaining 187 participants, of whom 177 were right handed. 68 participants were classified as cognitively normal (CN) and 119 were classified as MCI (Figure 1). CN participants were significantly younger than MCI participants [mean age 62.34 (SD 6.80) vs. 67.61 (SD 7.72) years; p<0.001], were more highly educated [mean duration 13.49 (SD 3.12) vs. 11.14 (SD 3.39) years; p<0.001], and were more likely to be employed at the time [48.5% vs. 28.6%; p=0.023; remainder of participants were retired or homemakers]. CN and MCI participants did not differ in terms of gender (55.9% vs. 48.7% female; p=0.347) or race (94.1% vs. 89.9% Han Chinese; p=0.652; remainder of participants were a mix of Malay, Indian, Eurasian, and others).

Of the 119 MCI participants, 40 (33.6% of MCI participants, 21.4% of cohort) were classified as amnestic MCI (aMCI). All participants performed better in the primacy and recency regions than in the middle region, and MCI participants performed worse than CN participants in all three regions while generally exhibiting the same recall patterns (Figure 2A). When considering amnestic status separately, aMCI participants have generally lower performance than CN and non-amnestic MCI (naMCI) participants while exhibiting a slightly increased recency performance over primacy performance (Figure 2B).

SPE performance comparison

67 (56.3%) of MCI participants were classified as Good SPE, and 52 (43.7%) were classified as Poor SPE (Figure 1). Compared to CN participants, both Good SPE and Poor SPE participants were significantly older and less educated, and only the Poor SPE group had a greater proportion of Apolipoprotein E (ApoE) ε4 carriers than CN or Good SPE participants. In cognitive assessment both Good SPE and Poor SPE participants performed worse than CN in all tests at Holm-Bonferroni corrected significance levels, with Poor SPE participants generally performing poorer than Good SPE participants in all assessments except Digit Span Forward, Boston Naming Test, and the ADAS-Cog Maze (Table 1).

CN, MCI – Good SPE, and MCI – Poor SPE participants differed significantly in total grey matter (GM) volumes, as well as the bilateral occipital lobes, temporal lobes, and hippocampi. Post-hoc analysis showed that only the Poor SPE group had significantly reduced volumes compared to CN participants, and no differences were detected with the Good SPE participants. Similarly, white matter hyperintensities (WMH) volumes were significantly different, with Poor SPE participants having greater total and periventricular WMH volumes than CN participants. Global white matter (WM) volumes did not differ across the three groups (Table 2). As statistical significance was not achieved for global WM volumes, further breakdown of regional WM volumes was not explored.

No significant differences between study conditions or (ApoE) polymorphism were found for GM volumes in the frontal lobe (Figure 3A) or temporal lobe (Figure 3B). There were study condition x ApoE status effects found for hippocampal GM and periventricular WMH (pvWMH) volumes. For hippocampal volumes in ApoE ε4 carriers, bilateral volumes were significantly lower in Poor SPE participants (p=0.001 for right, p=0.001 for left) and left hippocampus in Good SPE participants (p=0.096 for right, p=0.023 for left) compared to CN participants. In ApoE ε4 non-carriers, bilateral hippocampal volumes were lower in Poor SPE participants compared to CN (p=0.042 for right, p=0.012 for left), and hippocampal volumes did not differ between Good SPE and CN participants (p=0.259 for right p=0.516 for left) (Figure 3C). For pvWMH, volumes were significantly greater in ApoE ε4 negative Poor SPE participants compared to CN (p=0.002), but not for Good SPE participants (p=0.947) compared to CN. In ApoE ε4 carriers, pvWMH volumes in both Good SPE and Poor SPE were greater than CN, but this did not reach statistical significance (p=0.153 for Good SPE vs. CN; p=0.089 for Poor SPE vs. CN) (Figure 3D).

SPE profiles comparison

Of the Poor SPE participants, 14 (11.8% of all MCI participants) were classified as Low SPE, 25 (21.0%) were low primacy only SPE (LP-SPE), and 13 (10.9%) were low recency only SPE (LR-SPE) (Figure 1). Checking the performance of the three SPE regions (primacy, middle, and recency) across the five groups showed results that corresponded to their classification criteria. Although the middle region was not used to classify participants and was not the focus of this analysis, it was noted that middle region performance was similar between CN and Good SPE participants, and between Low SPE, LP-SPE, and LR-SPE. Also, even though CN participants were not selected based on their SPE performance, their performance in all three regions, including the middle region, was noted to be better than all other MCI groups (Figure 4).

In terms of cognitive performance, χ² tests showed significant differences across all study conditions for all cognitive measures (Table 3). Table 4 details p values from posthoc pairwise comparisons for cognitive assessments between CN, Good SPE, LP-SPE, LR-SPE, and Low SPE participants, and statistical significance was tested at Holm-Bonferroni corrected levels. For episodic memory, CN participants universally performed better in Word Recognition than the MCI groups regardless of SPE performance. Additionally, LP-SPE participants also performed worse than CN, Good SPE, and LR-SPE participants in Visual Reproduction. LP-SPE, LR-SPE, and Low SPE participants generally performed poorer than CN participants in attention tests. Working memory performance was only significantly better in CN participants compared to Good SPE, and also compared to LP-SPE when considering Digit Span Backwards. For executive function, all MCI SPE groups performed poorer than CN participants, with LP-SPE also notably poorer than Good SPE. LP-SPE were poorer than CN in language tests, with Low SPE additionally poorer than CN in fruit fluency. Finally, LP-SPE participants were poorer than CN and Good SPE participants in Block Design, a measure of visuospatial orientation. There were very few significant differences in cognitive performances amongst the SPE profiles, the main exceptions being between Good SPE and LP-SPE. After correcting for age, education, and employment status, Low SPE participants had reduced hippocampal GM volumes and significantly greater periventricular WMH volumes compared to CN participants (Table 5).

For the logistic regression analysis, all models started with age, secondary school completion status (equivalent to at least 10 years of formal education), total frontal, parietal, occipital, and temporal lobe GM volumes, bilateral hippocampal GM volumes, pvWMH volume, and ApoE ε4 status as candidate predictor variables before undergoing elimination, with scanner type left in as a locked covariate (Table 6). The ApoE ε4 allele, education, and pvWMH were identified to be significantly associated with the Low SPE condition (Model A). For LP-SPE participants (The AD type condition), ApoE ε4, education, and left hippocampal

GM volumes were significant (Model B). Only pvWMH and education were significant for the LR-SPE group (Model C), and only age and education were significant for the Good SPE group (Model D). Model fit was good for all models except Model D.

Subject recruitment

Participants were from a cross-sectional research study carried out at the National Neuroscience Institute, Singapore and Duke-NUS Medical School, Singapore that recruited individuals with MCI and healthy controls between July 2013 and March 2016. All participants were between 50 and 90 years of age at time of recruitment, were literate, had no major psychiatric or neurological comorbidities, and had no contraindications to high-field MRI. MCI participants were individuals that presented to a tertiary memory clinic for cognitive complaints, and were classified via clinical assessment using Petersen’s criteria [16] corroborated by scores in the Mini-Mental State Examination (MMSE) [17] and the Singaporean version of the Montreal Cognitive Assessment (MoCA-SG) [18]. Participants were dropped from this analysis if they had a Clinical Dementia Rating (CDR) [19] Global score greater than 0.5, MMSE score less than 24, met criteria for dementia, or were flagged for significant depressive symptoms on the 15-point Geriatric Depression Scale (GDS) [20]. Cognitively normal (CN) controls were recruited from the community via outreach programs for dementia awareness and snowball recruitment. CN participants had a CDR global score of 0, MMSE score of 27 or greater, were functionally independent, and did not meet criteria for MCI, dementia, or any other neurological or psychiatric disorder. Participants provided voluntary informed consent prior to collection of any research data.

Cognitive and SPE assessment

All participants attended a clinical interview session where demographic data, clinical measurements, and past medical history data was noted. Cognition was assessed with the MMSE [17], MoCA-SG [18], and the Alzheimer’s Disease Assessment Scale – Cognitive (ADAS-Cog) battery [21]. Furthermore, participants underwent a comprehensive cognitive assessment battery designed to assess the following cognitive subdomains: episodic memory via the fourth version of the Wechsler Memory Scale (WMS-IV) Visual Reproduction II subtest [22] and the ADAS-Cog Word Recognition test [21], attention via the fourth version of the Wechsler Adult Intelligence Scale (WAIS-IV) Symbol Search subtest [23] and the Color Trails Test (CTT) 1 subtest [24]; working memory via the WAIS-IV Digit Span Forward and Backward subtests [23], executive function via the CTT 2 subtest [24] and the Frontal Assessment Battery [25]; language via fruit semantic fluency and the Hong Kong version of the Boston Naming Test (BNT) [26]; and visuospatial orientation via the ADAS-Cog Maze subtest [21] and the WAIS-IV Block Design subtest [23].

SPE performance was assessed using the 10-word immediate word recall (IWR) task in the ADAS-Cog [21]. Participants were shown a list of 10 sequential words on flash cards at approximately two seconds per word. The words were one or two syllables long and were relatively common concrete nouns. Participants were told to read each word out loud and to memorize them. At the end of the exposure, participants were asked to repeat as many words as they can regardless of order, taking up to a maximum of two minutes. The trial exposure and recall challenge was then repeated two more times, each using the same 10 words in a pseudorandomized order. After approximately 5 minutes, during which a filler task is conducted (in this case, the ADAS-Cog Instructions and Constructional Praxis tasks), a delayed word recall (DWR) task is carried out. During DWR, participants are challenged to repeat as many of the 10 words from the immediate word with no repeat exposure prior to challenge.

In each IWR trial, the primacy region was defined as the first three words, and the recency region was defined as the last three words. The remaining four words were labeled as the middle region. A primacy score would then be generated using the total number of correctly recalled words in the primacy regions of all three trials, out a total possible score of 9. A score of <6 (<2 out of 3 words for each trial on average) denoted poor primacy performance. Recency scores, and classifications for poor recency performance, were similarly derived using the nine recency region words. These criteria were used to classify MCI participants into the various SPE groups as described below. Separately, MCI participants also were classified as amnestic (aMCI) if the standardized z-score of either episodic memory subtest (Visual Reproduction or Word Recognition) was >1.5 standard deviations below the mean, while other MCI participants were classified as non-amnestic (naMCI).

ApoE genotyping

Genomic DNA was extracted from peripheral blood samples using the QIAamp DNA Blood Maxi Kit (Qiagen GmbH, Hilden, Germany) according to manufacturer specifications. The TaqMan SNP genotyping assay [rs429358 (ABI assay ID: C_3084793_20) and rs7412 (ABI assay ID: C_904973_10)] on the ABI 7900HT PCR system (Applied Biosystems, Foster City, CA) was used for apolipoprotein E (ApoE) genotyping [27]. Participants with at least one copy of the ApoE ε4 allele, a risk factor for AD [28], were classified as ApoE ε4 carriers, while other participants were classified as non-carriers.

MRI protocols and volumetric analysis

Participants underwent MRI in a 3T Siemens Tim Trio (Siemens, Erlangen, Germany; for participants recruited up until July 2014) or 3T Siemens Prisma (Siemens, Erlangen, Germany; for participants recruited after July 2014) whole body MR system. 3D volumetric scans were obtained using a T1-weighted magnetization-prepared rapid gradient-echo (MPRAGE) sequence [repetition time (TR)=2300ms, echo time (TE)=2.98ms, matrix=192x256×256, flip angle=80, 180 slices, 1.0mm isotropic voxels] as per the Alzheimer’s Disease Neuroimaging Initiative (ADNI) protocols (http://adni.loni.usc.edu/). Voxels of white matter hyperintensities (WMH) were obtained using T2-weighted fluid attenuated inversion recovery (FLAIR) sequences (TR=8000ms, TE=336.86ms, matrix=256 x 256, slice thickness=2mm, flip angle=900, 180 slices), using the corresponding MPRAGE sequence as a template.

Voxel-based morphometry (VBM) was done using the Computational Anatomy Toolbox (CAT12) package for the Statistical Parametric Mapping 12 (SPM12) software (http://www.fil.ion.ucl.ac.uk/spm) in MATLAB. Volumetric MPRAGE sequences were converted from DICOM to 3D NIfTI format and manually oriented to be within the standard Montreal Neurological Institute (MNI) template space. Images were then segmented into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF) maps using a unified segmentation pipeline [29] that includes affine regularization to the International Consortium for Brain Mapping (ICBM) space template for East Asian brains, bias corrections, and affine and non-linear modulated normalization. The generated GM and WM maps were then smoothed (8mm full width at half maximum) in SPM12. CAT12 was used to estimate the total intracranial volume (TIV) for each participant, and the smoothed GM and WM maps were used to generate global volumes of GM and WM, and also regional volumes based on regions of interest (ROIs) defined using the Wake Forest University PickAtlas v3.0 software toolbox [30].

Volumetric analysis of WMH was done in SPM12 using an existing workflow [31]. Briefly, FLAIR sequences were converted into 3D SPM/Analyze data format, and coregistered with their corresponding MNI-normalized MPRAGE sequences. WMH maps were segmented from FLAIR sequences based on voxels identified as having an intensity of at least 1.4 times compared to the modal intensity of the surrounding white matter. ROIs extending 10mm from the lateral ventricles were used to define periventricular WMH (pvWMH).

Statistical analysis

MCI participants were classified into various groups according to their SPE performance, specifically based on if their recency and primacy recall were defined as good or poor as described above (see ‘Cognitive and SPE assessment’ subsection in Methods). MCI participants were classified as having Good SPE if they had high performance (≥6 out of 9 items correctly recalled) in both primacy and recency regions, or Poor SPE if they had poor performance (<6 out of 9 items correctly recalled) in either primacy and/or recency regions. Poor SPE participants were also further divided into low primacy and recency (Low SPE), low primacy and high recency (LP-SPE, the typical AD pattern of SPE), and high primacy and low recency (LR-SPE, the “inverse” phenotype to the AD pattern). Figure 1 details the recruitment breakdown and subclassification workflow for SPE subtypes. Subject groups were compared for differences in demographic profiles, clinical and vascular risk factors, cognitive performance, and neuroimaging variables including global and regional GM, WM, and WMH, correcting for age, education, and employment status. These volumes were also compared between CN, Good SPE, and Poor SPE participants separately for ApoE ε4 carriers and non-carriers. Logistic regression models were run to identify specific neuroimaging and risk factor features significantly associated with Low SPE (Model A), LP-SPE (Model B), LR-SPE (Model C), and Good SPE (Model D) compared to CN participants, while correcting for confounding factors, inclusive of scanner type.

All statistical analyses were done using Stata version 14 (StataCorp, College Station, TX, USA). Variables were tested for normality of distribution by appraising skewness and kurtosis, and via Shapiro-Wilk test. Univariate analysis was conducted via analysis of variance or Kruskal-Wallis test for continuous parametric and nonparametric variables respectively, and χ² test for categorical variables. Significance tests were two-tailed, and level of significance for univariate analysis and reverse elimination in regression models was set at p<0.05. Post-hoc pairwise comparisons were conducted using Mann-U-Whitney test and χ² test for continuous and categorical variables respectively using a significance level of p<0.05, adjusted via Holm-Bonferroni corrections to control for familywise error rate. All study procedures were carried out in accordance with institutional guidelines, and in compliance with the International Conference on Harmonization-Good Clinical Practice (ICH/GCP) and the Singapore Good Clinical Practice (SG-GCP) guidelines. The study was approved by the SingHealth Centralized Institutional Review Board (reference number 2013/267/A, dated 4^th April 2013).