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Behavioural Neurology
The recency ratio assessed by story recall is
associated with cerebrospinal fluid levels of
neurodegeneration biomarkers
Davide Bruno
a,*
, Ainara Jauregi Zinkunegi
a
, Gwendlyn Kollmorgen
b
,
Ivonne Suridjan
c
, Norbert Wild
b
, Cynthia Carlsson
d,e,f,g
,
Barbara Bendlin
e,f
, Ozioma Okonkwo
e,f
, Nathaniel Chin
d,e
,
Bruce P. Hermann
d,h
, Sanjay Asthana
d,e
, Henrik Zetterberg
i,j,k,l,m
,
Kaj Blennow
i,j
, Rebecca Langhough
d,e,f
, Sterling C. Johnson
d,e,f,g
and
Kimberly D. Mueller
d,e,n
a
School of Psychology, Liverpool John Moores University, UK
b
Roche Diagnostics GmbH, Penzberg, Germany
c
Roche Diagnostics International Ltd, Rotkreuz, Switzerland
d
Wisconsin Alzheimer's Institute, School of Medicine and Public Health, University of Wisconsin eMadison,
Madison, WI, USA
e
Wisconsin Alzheimer's Disease Research Center, School of Medicine and Public Health, University of Wisconsin e
Madison, Madison, WI, USA
f
Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA
g
Geriatric Research Education and Clinical Center, William S. Middleton Veterans Hospital, Madison, WI, USA
h
Department of Neurology, University of Wisconsin eMadison, Madison, WI, USA
i
Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy
at the University of Gothenburg, M€
olndal, Sweden
j
Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, M€
olndal, Sweden
k
Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
l
UK Dementia Research Institute at UCL, London, UK
m
Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
n
Department of Communication Sciences and Disorders, University of Wisconsin eMadison, Madison, WI, USA
article info
Article history:
Received 19 July 2022
Reviewed 3 November 2022
Revised 11 November 2022
Accepted 8 December 2022
Action editor Stefano Cappa
Published online 23 December 2022
abstract
Recency refers to the information learned at the end of a study list or task. Recency
forgetting, as tracked by the ratio between recency recall in immediate and delayed con-
ditions, i.e., the recency ratio (Rr), has been applied to list-learning tasks, demonstrating its
efficacy in predicting cognitive decline, conversion to mild cognitive impairment (MCI), and
cerebrospinal fluid (CSF) biomarkers of neurodegeneration. However, little is known as to
whether Rr can be effectively applied to story recall tasks. To address this question, data
were extracted from the database of the Alzheimer's Disease Research Center at the Uni-
versity of Wisconsin eMadison. A total of 212 participants were included in the study. CSF
*Corresponding author. Tom Reilly Building, Byrom Street, Liverpool, L3 3AF, UK.
E-mail address: d.bruno@ljmu.ac.uk (D. Bruno).
Available online at www.sciencedirect.com
ScienceDirect
Journal homepage: www.elsevier.com/locate/cortex
cortex 159 (2023) 167e174
https://doi.org/10.1016/j.cortex.2022.12.004
0010-9452/©2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.
org/licenses/by/4.0/).
Keywords:
Memory assessment
Story recall
Dementia
Serial position effects
Cerebrospinal fluid biomarkers
biomarkers were amyloid-beta (Ab) 40 and 42, phosphorylated (p) and total (t) tau, neu-
rofilament light (NFL), neurogranin (Ng), and a-synuclein (a-syn). Story Recall was
measured with the Logical Memory Test (LMT). We carried out Bayesian regression ana-
lyses with Rr, and other LMT scores as predictors; and CSF biomarkers (including the Ab42/
40 and p-tau/Ab42 ratios) as outcomes. Results showed that models including Rr consis-
tently provided best fits with the data, with few exceptions. These findings demonstrate
the applicability of Rr to story recall and its sensitivity to CSF biomarkers of neuro-
degeneration, and encourage its inclusion when evaluating risk of neurodegeneration with
story recall.
©2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC
BY license (http://creativecommons.org/licenses/by/4.0/).
1. Introduction
In memory research, serial position effects refer to better
retrieval of the information learned at the beginning of a study
list or task as compared to the information learned in the
middle (primacy effect), and better retrieval of the information
learned at the end of such study list or task as compared to the
information learned in the middle (recency effect; Murdock,
1962). Interestingly, while individuals with Alzheimer's dis-
ease (AD) typically present with poor primacy effects, a
recency effect is often still observed, particularly when testing
occurs right after the learning phase (Foldi et al., 2003). How-
ever, recency performance then tends to deteriorate if a delay
is placed between learning and test (Carlesimo et al., 1995). To
leverage this pattern whereby high recency is observed at
immediate recall whereas low recency is found at delayed
recall in AD, the recency ratio (Rr) was proposed, which is
operationally defined as the ratio between recency recall in
immediate compared to delayed recall conditions (Bruno
et al., 2016,2018).
Using Rr in list-learning tasks (e.g., Rey's Auditory Verbal
Learning Task), it has been shown that higher (i.e., worse)
scores predict cognitive decline in asymptomatic individuals
(Bruno et al., 2016), preclinical mild cognitive impairment
(MCI) from a healthy baseline (Bruno et al., 2016;Egeland,
2021), and amyloid-bpathology in individuals with MCI
(Bruno et al., 2019). Rr from list-learning tasks has also been
found to correlate with cerebrospinal fluid (CSF) levels of
neurogranin (Ng), a post-synaptic protein reflecting synaptic
dysfunction (Bruno, Reichert Plaska, et al., 2021), and to both
phosphorylated (p-) and total (t-) tau levels (Bruno et al., 2022).
Additionally, Rr scores have been found to discriminate be-
tween individuals diagnosed with AD versus other types of
dementia (Turchetta et al., 2018), and to identify successfully
individuals with MCI who are more likely to convert to AD
(Turchetta et al., 2020). All in all, in list-learning tasks, Rr
compares favourably to most conventional scores employed
to estimate memory ability in older individuals (Bock et al.,
2021).
However, little is known as to how successful Rr may be in
story recall tasks. Story recall tasks, unlike list-learning tasks,
present participants with a coherent story to learn, and then
typically ask them to recall it right after presentation and then
again after a delay. Owing to their semantic structure
(compared to a semantically unrelated list of words), story
recall tasks are typically thought to be less sensitive to serial
position effects, but this is not the case. Hall and Bornstein
(1991), for example, showed serial position effects in in-
dividuals with closed head injuries and controls, when using
the Logical Memory test (LMT), a common story recall task
(Wechsler, 1987). The LMT is also commonly used as a
screening tool for dementia, as demonstrated by its use in the
Alzheimer's Disease Neuroimaging Initiative (ADNI) and the
Australian Imaging, Biomarker &Lifestyle Flagship Study of
Ageing (AIBL). Aptly then, Bruno, Mueller, et al. (2021) exam-
ined LMT scores in late middle-age and older individuals with
MCI and people with no cognitive impairment, and observed
clear serial position effects. Moreover, they also highlighted
the role of primacy forgetting (billed there primacy ratio,
henceforth Pr) in predicting amyloid burden.
The aims of the present study were to examine the degree
to which process scores such as Rr and Pr may predict CSF
biomarkers of brain amyloidosis, tau pathology and neuro-
degeneration in middle age and older individuals. Moreover,
we also aimed to establish whether these process scores
performed better than traditional scores of immediate and
delayed recall performance in story recall. We achieved this
by analysing data from the Wisconsin Alzheimer's Disease
Research Center (WADRC), an ongoing longitudinal cohort
study based at the University of Wisconsin eMadison, which
included LMT data alongside CSF levels of biomarkers asso-
ciated with AD and neurodegeneration: amyloid-beta (Ab)40
and 42, p- and t-tau, neurofilament light (NFL), Ng, and a-
synuclein (a-syn).
2. Methods
We report how we determined our sample size, all data ex-
clusions, all inclusion/exclusion criteria, whether inclusion/
exclusion criteria were established prior to data analysis, all
manipulations, and all measures in the study. The ethical
regulations that govern the WADRC prevent unrestricted
public archiving of anonymised study data. Data can be
requested from the WADRC Executive Committee at: https://
www.adrc.wisc.edu/apply-resources. Data will be released to
internal and external investigators following confirmation of
IRB approval together with an evaluation by the WADRC of
scientific merit and resource availability.
cortex 159 (2023) 167e174168
2.1. Participants
Data were extracted from the WADRC database. After base-
line, WADRC participants complete regular follow-up visits at
1- or 2-year intervals, including neuropsychological tests,
clinical measurements (e.g., blood pressure, heart rate), and
health history. Participants were selected on the basis of
having completed at least two assessment visits: one for
cognitive evaluation, including LMT, and at least one lumbar
puncture visit for CSF extraction. Participants were classified
as either cognitively unimpaired, with MCI due to presumed
AD (MCI-AD), or with dementia due to presumed AD, via
consensus conference diagnosis, determined by a team that
included physicians, clinical neuropsychologists, and clinical
nurse practitioners, based on core clinical criteria developed
by the National Institute on Aging and the Alzheimer's Asso-
ciation (Albert et al., 2011;McKhann et al., 2011), and without
regard to AD biomarker status. From the total pool of 828
participants, 212 participants fit the inclusion criteria. Of
these, 156 were cognitively unimpaired, 26 had a diagnosis of
MCI-AD, and 30 had a diagnosis of AD. Moreover, 16 (10%)
cognitively unimpaired participants displayed biomarkers-
determined AD based on a CSF p-tau/Ab42 ratio cut-off of
.038: a low ratio identifies individuals without a biomarker-
based AD diagnosis, whereas a higher ratio identifies people
with a biomarker-based AD diagnosis (Van Hulle et al., 2021).
Similarly, 16 (62%) and 28 (93%) of MCI-AD and AD partici-
pants, respectively, displayed AD biomarkers. Only one
participant did not identify as white Caucasian. Participants'
cognitive data were taken from whichever visit was closest to
the visit where the lumbar puncture was performed. All ac-
tivities for this study were approved by the ethics committees
of the authors'universities and competed in accordance with
the Declaration of Helsinki. All participants provided
informed consent prior to testing. No part of the study pro-
cedures or analyses were pre-registered prior to the research
being conducted.
2.2. Cognitive assessment
The LMT was used to measure story recall performance. The
LMT is a subtest of the Weschler Memory Scale Revised (WMS-
R; Wechsler, 1987), comprising two stories, A and B, with 25
items (“idea units”) each, and representing different semantic
and lexical categories. Each story is read aloud to the partici-
pant and then the participant is asked to recall both stories
immediately and again after a 25e30-min delay. Scoring pro-
cedures from the WMS-R manual were applied. Although the
scoring criteria permits some alteration from the original item
(e.g., “slid off the table”is allowed instead of “fell off the
table”), certain items must be recalled verbatim, e.g., numer-
ical expressions or proper names. In order to slim down the
administered cognitive battery, story recall in the present
study was measured only with story A of the LMT. However, to
note, story A and story B have been found to be of comparable
memorability in a separate study (Mueller et al., 2022). Im-
mediate LMT and Delayed LMT recall scores were calculated
from adding all the correctly recalled items in the immediate
recall trial and delayed recall trial, respectively. Possible
scores for Immediate and Delayed Recall trials range from 0 to
25 for each, where higher scores reflect more items being
recalled. Finally, primacy and recency were defined as the first
and final eight idea units of the story, respectively, while
middle was defined as the middle nine units, following pre-
vious work (Bruno, Mueller, et al., 2021)ethe choice of idea
units per serial position is arbitrary. Immediate and delayed
recency scores were calculated as the number of correctly
recalled recency items in immediate and delayed recall trials,
respectively. Rr was obtained by dividing the recency scores in
the immediate recall trial by the corresponding scores in the
delayed recall trial. A correction also was applied ((immediate
recency score þ1)/(delayed recency score þ1)) to avoid
missing data due to zero scores (Bruno et al., 2018). Pr was
calculated following (Bruno, Mueller, et al., 2021) by dividing
delayed primacy by immediate primacy, with no adjustments.
While this is inconsistent with the way Rr is computed, and
should arguably be aligned, we opted here for maintaining the
original formula. Finally, to provide a non-serial position
based forgetting index that would account for memory loss,
we also computed a ratio score with Immediate LMT and
Delayed LMT ((Immediate LMT þ1)/(Delayed LMT þ1)), which
we dubbed the total ratio (Tr).
2.3. Biomarker determination
All CSF samples were assayed at the Clinical Neurochemistry
Laboratory, University of Gothenburg, under strict quality
control procedures. All CSF markers were measured using the
exploratory Roche NeuroToolKit assays, a panel of automated
robust prototype immunoassays (Roche Diagnostics Interna-
tional Ltd, Rotkreuz, Switzerland), not currently approved for
clinical use. Elecsys®Ab42, Ab40, p-tau (181P), and t-tau, were
performed on a cobas e 601 analyzer; a-syn, NFL, and Ng were
performed on a cobas e 411 analyzer, as previously described
(Van Hulle et al., 2021).
2.4. Genotyping
DNA was extracted from whole blood. Samples were aliquoted
on 96-well plates for determination of APOE genotypes. An
APOE risk score was calculated based on the odds ratios of the
e2/e3/e4 genotype, as previously reported (Darst et al., 2017).
2.5. Analysis plan
For each CSF outcome, we carried out Bayesian linear re-
gressions with Pr, Rr, Immediate LMT, Delayed LMT, and Tr as
predictors in the same analyses; age at the lumbar puncture,
time elapsed between lumbar puncture and memory assess-
ment, sex, years of education, APOE risk score, number of
overall cognitive assessment visits (to account for practice
effects), and whether they classified for biomarkers-
determined AD were used as control variables. Control vari-
ables formed the null models. CSF biomarkers were used as
outcomes, in separate analyses. Bayesian analyses allow for
the estimation of model plausibility, which permits compari-
son of models with different combinations of predictors, and
for the determination of effect sizes with credible intervals
(e.g., Teipel et al., 2021). In Supplementary Materials we also
report the outputs of Frequentist analyses. Note that diagnosis
cortex 159 (2023) 167e174 169
was not included in the analyses to avoid circularity since LMT
scores are evaluated in the consensus process. For all
Bayesian analyses, the model prior was set to Uniform, where
all models are a-priori equally likely, and the prior on param-
eters was set to the default Jeffreys-Zellner-Siow (JZS) prior
probability distribution, which allows the Bayes factor to be
the same regardless of unit of measurement. Credible in-
tervals were set to 95%. To address potential issues with non-
normally distributed residuals in the regressions, Markov
chain-Monte Carlo (MCMC) sampling to each analysis was
applied 1,000 times. The outcome variables were CSF levels of
Ab40, Ab42, p-tau, t-tau, Ng, NFL, and a-syn. Additionally, we
also examined models with Ab42/Ab40 and p-tau/Ab42 ratios
as outcomes, as these measures are commonly used as bio-
markers of neurodegeneration (Campbell et al., 2021;Li et al.,
2013). Control variables formed the null models in each
analysis. Analyses were conducted using JASP (0.16.2; https://
jasp-stats.org/).
3. Results
Table 1 reports means, standard deviations and range for the
demographic variables, age differences, APOE risk score, Pr,
Rr, Immediate LMT, Delayed LMT, and Tr recall scores by
cognitive status closest to lumbar puncture. Rr scores ranged
from .43 to 5 across participants. Note that while higher Pr
scores are preferable, the higher the Rr score, the worse.
Fig. 1a and b report serial position performance by delay in
controls and individuals with biomarkers-determined AD,
respectively. The values for primacy, middle and recency are
proportions out of eight, nine and eight, respectively, to allow
for direct comparison across serial positions. The plot displays
a slightly more pronounced curve for immediate recall than
for delayed recall in controls, and a substantial drop in
Table 1 eDemographics, CSF measures and memory tests scores (mean and standard deviation) for the study participants.
Elapsed time refers to time between cognitive testing and lumbar puncture, and it was calculated as an absolute value.
Statistical tests were also conducted to check for differences across cognitively unimpaired, MCI-AD and AD: pvalues are
reported. LP ¼lumbar puncture; CSF ¼cerebro-spinal fluid; Rr ¼recency ratio; Tr ¼total ratio; Pr ¼primacy ratio;
LMT ¼logical memory test. *Ab40 N¼211, p-tau N¼212, a-syn N¼212.
Characteristic Total (N¼212) Cognitively unimpaired (N¼156) MCI-AD (N¼26) AD (N¼30) pvalue
Sex (female) 129 (61%) 112 (72%) 6 (23%) 11 (37%) <.001
Education (years) 16.1 (2.6) 16.2 (2.4) 16.5 (2.8) 14.8 (3.0) .014
Age at LP (years) 65.1 (10.0) 62.1 (8.6) 74.6 (8.4) 72.7 (9.2) <.001
Elapsed time (years) .2 (.2) .2 (.2) .2 (.1) .1 (.1) .146
APOE risk score 1.4 (.9) 1.2 (.8) 1.6 (.8) 2.0 (1.1) <.001
CSF Ab42 (ng/L) 824.1 (390.8) 939.2 (376.7) 579.7 (214.6) 445.1 (199.3) <.001
CSF Ab40 (ng/L)*14286.0 (4648.0) 14284.9 (4591.5) 14496.9 (4666.2) 14108.3 (5062.2) .953
CSF Ab42/Ab40*(ng/L) .1 (.02) .06 (.0) .04 (.0) .03 (.0) <.001
CSF P-tau (ng/L) 21.3 (12.8) 17.0 (6.9) 29.0 (18.2) 36.7 (15.3) <.001
CSF P-tau/Ab42 (ng/L) .04 (.04) .02 (.0) .06 (.0) .09 (.0) <.001
CSF T-tau (ng/L) 232.8 (116.9) 195.1 (76.4) 301.2 (155.5) 366.8 (128.9) <.001
CSF a-synuclein*(ng/L) 172.7 (84.8) 155.4 (70.8) 206.4 (107.8) 232.2 (95.4) <.001
CSF NFL (ng/L) 120.1 (110.6) 91.5 (79.5) 179.6 (153.4) 215.5 (133.0) <.001
CSF Ng (ng/L) 818.9 (363.0) 753.6 (311.9) 917.8 (475.5) 1068.1 (382.1) <.001
Rr 1.1 (.5) 1.0 (.2) 1.3 (.8) 1.6 (.9) <.001
Immediate LMT 12.1 (5.2) 14.5 (3.2) 7.9 (4.9) 3.9 (2.8) <.001
Delayed LMT 11.0 (5.6) 13.6 (3.3) 5.4 (4.7) 2.0 (2.1) <.001
Tr 1.3 (.9) 1.1 (.1) 2.0 (1.7) 2.0 (1.4) <.001
Pr .7 (.4) .9 (.2) .3 (.3) .1 (.2) <.001
Fig. 1 ea. Serial position plot by delay in controls. Imm:
immediate recall; Del: delayed recall. b. Serial position plot
by delay in individuals with AD. Imm: immediate recall;
Del: delayed recall.
cortex 159 (2023) 167e174170
delayed primacy in biomarkers-determined AD. This pattern
is analogous to that reported already by Bruno, Mueller, et al.
(2021). It may be noted also that delayed recency is better in
our data with LMT than what traditionally expected with
word-list tests. While it is beyond the scope of this paper to
address theories of serial position, these findings do argue
against the recency boost being solely a consequence of short-
term memory processing in story recall.
CSF Ab42. The best fitting model was the null model. The
second best model had Immediate LMT alone (see Supple-
mentary information for full model comparisons and poste-
rior summaries). The Bayes Factor (BF
10
) that gives us the
relative predictive adequacy of this model compared to the
null model was .649, meaning that the observed data are .649
times more likely under this model than under the null model
(which includes all the covariates). BF
10
scores below 1, as in
this case, indicate that the null model is a better fit for the data
than the alternative models. Conventionally, also, BF
10
scores
below 3 are considered to provide only anecdotal evidence
over the null model, and are therefore not sufficiently strong
to draw firm conclusions.
CSF Ab40. In contrast, the best fitting model for CSF Ab40
combined Delayed LMT performance with Rr (BF
10
¼5.350;
moderate evidence). Both Delayed LMT and Rr were positively
associated with CSF Ab40 levels: Delayed LMT had a posterior
mean of 64.438 (SD ¼112.988), and 95% Credible Intervals (CIs)
ranged from 84.286 to 327.865; Rr had a posterior mean of
1067.043 (SD ¼844.836), and CIs ranged from 47.522 to
2539.844. The inclusion probability was much higher for Rr,
.762, than for Delayed LMT, .493, suggesting that Rr is the
better predictor of CSF Ab40 in these data.
CSF t-tau. The best fitting model with CSF t-tau included
only Rr (BF
10
¼3754.173; extreme evidence). Rr had a posterior
mean of 56.214 (SD ¼13.652; CIs from 31.435 to 83.779). The
inclusion probability for Rr was >.999, and adding Rr to the
model improved it by over 650 times (i.e. BF
inclusion
¼658.873).
CSF p-tau. The best fitting model with CSF p-tau also
included only Rr (BF
10
¼1552.309; extreme evidence). Rr had a
posterior mean of 5.490 (SD ¼1.428; CIs from 2.719 to 8.241).
The inclusion probability for Rr was ¼.998 (i.e.
BF
inclusion
¼231.593).
CSF Ng. The best fitting model with CSF Ng combined
Delayed LMT and Rr (BF
10
¼392.515; extreme evidence). Again,
the BF
inclusion
for Rr (62.712) trumped that for delayed LMT
(6.596). Higher Delayed LMT performance was, unexpectedly,
associated with higher levels of CSF Ng (posterior
mean ¼21.180, SD ¼17.387, CIs: 0 to 55.836), whereas as
predicted Rr was positively correlated with Ng: posterior
mean ¼184.119, SD ¼55.681, CIs: 84.228 to 299.589.
CSF NFL. With CSF NFL levels, the best fitting model
included Immediate LMT, Tr and Rr (BF
10
¼105.934; extreme
evidence). Immediate LMT was negatively associated with
NFL (posterior mean ¼4.492, SD ¼3.327, CIs: 11.267 to 0), Tr
also, and against expectations, was negatively correlated with
NFL levels (posterior mean ¼9.042, SD ¼11.231, CIs: 34.594
to 0). In contrast, Rr was positively correlated with NFL (pos-
terior mean ¼29.297, SD ¼21.281, CIs: 0 to 66.394), as ex-
pected. The inclusion probability was a little higher for
Immediate LMT, .817, BF
inclusion
¼4.192, compared to Rr, .790,
BF
inclusion
¼3.530, and Tr, .535, BF
inclusion
¼1.225.
CSF a-syn. The best fitting model with CSF a-syn was the
model with Rr alone (BF
10
¼50.318; very strong evidence). Rr
was positively correlated with a-syn (posterior mean ¼33.501,
SD ¼12.311, CIs: 12.009 to 59.343). The BF
inclusion
was 28.196.
CSF Ab42/Ab40. The best fitting model was the null model,
followed by a model with Rr alone (BF
10
¼.250).
CSF p-tau/Ab42. The best fitting model had Immediate LMT
and Rr together (BF
10
¼61,549.487; extreme evidence). Im-
mediate LMT had a posterior mean of .001 (SD <.001; CIs
.003 to 0); and Rr had a posterior mean of .012 (SD ¼.003; CIs
from .006 to .018). The best inclusion probability was for Rr,
>.999 (BF
inclusion
¼580.040), whereas Immediate LMT reached
.745 (BF
inclusion
¼2.636).
4. Discussion
The goal of this study was to establish whether process scores
from story recall, such as Rr, were as sensitive to amyloid and
tau proteinopathy, alongside other biomarkers of neuro-
degeneration, as it has been shown to be previously in list-
learning tasks. To test this claim, we analysed data from the
WADRC, comprising 212 participants who were either cogni-
tively unimpaired, with presumed MCI-AD, or with presumed
AD, and we correlated performance in the LMT, a popular
story recall test, with CSF levels of biomarkers associated with
AD, including measures of amyloid, tau, and neuro-
degeneration. Our Bayesian analyses clearly indicate that
cross-sectional Rr levels are associated with several CSF bio-
markers of neurodegeneration and AD, when controlling for
age, level of education and APOE risk. Rr also specifically
outperformed a ratio score introduced to measure total
memory loss from immediate to delayed story recall, which
we termed here total ratio. By and large, these results were
mirrored by frequentist analyses (see Supplementary
information, S1): Rr was the best predictor of t- and p-tau,
Ng, a-syn and the p-tau/Ab42 ratio, consistent with the
Bayesian results. Also consistent with the Bayesian results, Rr
was not correlated with Ab42, NFL and the Ab42/Ab40 ratio.
The only difference was with Ab40, where the Bayesian
analysis, but not the Frequentist analysis, found an associa-
tion with Rr.
A lack of association between Rr and CSF Ab42 is overall
consistent with a recent report (within an overlapping cohort)
using a list-learning task, where Rr was not found to predict
CSF Ab42 levels (Bruno et al., 2022). As CSF Ab42 levels are
thought to reflect closely brain amyloid deposition, the pre-
eminent pathological hallmark of AD, these findings may
suggest that Rr is not a specific cognitive marker of AD.
However, this notion is not consistent with the following ob-
servations: Rr is sensitive to the CSF levels of Ng (also reported
in Bruno, Reichert Plaska, et al., 2021), a neuron-specific
postsynaptic protein that has been linked specifically to AD
neurodegeneration (Wellington et al., 2016;Zetterberg &
Bendlin, 2021); and Rr was also sensitive to the levels of the
p-tau/Ab42 ratio, which has been shown to be as predictive of
brain amyloid pathology (Campbell et al., 2021). A final point to
consider is that Rr was also found to correlate with CSF a-syn
levels, partially consistent with the results of Bruno, Reichert
Plaska, et al. (2021). While a-syn, a pre-synaptic protein that
cortex 159 (2023) 167e174 171
can be found in cortical and sub-cortical areas, is typically
linked to Parkinson's disease and dementia with Lewy bodies
(Selnes et al., 2017), elevated CSF a-syn levels have also been
found in individuals on a trajectory to AD (Shim et al., 2020).
Both p- and t-tau were found to be associated with Rr
levels, suggesting that it is sensitive to neurofibrillary tangle
(tau) pathology and, in turn, neurodegeneration in the medial-
temporal lobe (Maass et al., 2019;Tennant et al., 2021). These
findings are consistent with a recent report using list-learning,
where Rr was also found to correlate positively with both p-
and t-tau in individuals with MCI and unimpaired cognition
(Bruno et al., 2022). A link between higher Rr scores and lower
volume of the hippocampus was also recently observed
(Jauregi et al., 2022) in overlapping participants, giving
credence to the suggestion that higher Rr scores may be a
consequence of combined loss of consolidation ability, which
would follow atrophy of the medial-temporal lobe (Wixted,
2004;Wixted &Cai, 2013), while reliance on phonological/
echoic short-term memory remains relatively intact (Bruno
et al., 2018;Turchetta et al., 2018). These observation may
also help explaining further the lack of association between Rr
and CSF Ab42 levels, as amyloid pathology does not specif-
ically target regions in the medial temporal lobe, unlike
neurofibrillary tangle pathology.
The different sex distribution across consensus diagnoses
should be noted. As per Table 1, 72% of unimpaired individuals
were female, while that percentage dropped drastically in
people with MCI (23%) or probable AD (37%). This finding is at
odds with the common observation that the majority of AD
cases tend to be women (Alzheimer's Association, 2017). While
a thorough examination of this issue is beyond the scope of
the present manuscript, we looked at story recall outputs
across sexes to see how they may vary. Interestingly, while
immediate and delayed LMT scores are significantly higher for
unimpaired females than males, with both parametric and
non-parametric tests, Rr tends not to vary in relation to sex in
this group. Finally, none of the memory scores differed across
sexes for people with MCI or AD.
Despite co-varying in the analysis whether participants
classified as AD positive according to a CSF p-tau/Ab42 ratio
threshold of .038 (Van Hulle et al., 2021), we also ran post hoc
regressions within the AD positive cohort only (see Supple-
mentary information for full results). These extra analyses
were consistent with what reported above. When also evalu-
ating inclusion probabilities, Rr is the best predictor for all
outcomes, except for Ab42, Ab42/Ab40, and NFL, as with the
full sample. To note, as these analyses were based on a
smaller sample, the findings should be interpreted with
increased caution.
Unlike Bruno, Mueller, et al. (2021), who showed that Pr was
predictive of amyloid load, as measured via Pittsburgh
compound-B (PiB) positron emission tomography (PET), we did
not observe an association between Pr and Ab42 or the Ab42/
Ab40 ratio. In this regards, we wish to make two observations.
First, while the data were drawn in both cases from studies
based at the University of Wisconsin eMadison, the actual
samples were different: in the Bruno, Mueller, et al. (2021)
paper, participants came from the Wisconsin Registry for
Alzheimer's Prevention; these volunteers are generally
younger and there is a higher proportion of cognitively
unimpaired individuals, compared to WADRC. Second, in the
Bruno, Mueller, et al. (2021) study, the outcome was discrete,
based on relevant PiB PET cut-points, whereas in the present
study we examined continuous CSF levels as outcomes: we
have noted that CSF and PiB PET markers will sometimes
show differential levels of sensitivity to different cognitive
(process) scores, and we plan on pursuing this observation
further in the near future.
Limitations of this research should be noted. First of all, the
sample sizes for MCI-AD and AD are significantly smaller than
for the cognitively unimpaired participants. Sample sizes
were dictated by availability, and future research with larger
groups of individuals with cognitive impairment, possibly also
including dementia pathologies other than AD, would be ideal
to further these research questions. A second limitation is that
the present sample nearly exclusively comprised individuals
that identified as white Caucasians. While this may be posi-
tive methodologically, as possible confounding variables
related to race are limited, many studies have highlighted the
importance of including a wider spectrum of ethnicities and
backgrounds in AD research (Manly et al., 2021;Morris et al.,
2019). As far as we are aware, at least with regards to pub-
lished works, Rr to date has only been tested primarily in
white Caucasian populations efuture research should
consider examining whether the same patterns observed here
would also extend to a more heterogeneous sample.
To conclude, this study showed that Rr, the ratio between
immediate and delayed performance scores at the recency
position is applicable to story recall, and sensitive to CSF levels
of Ab40, p-tau, t-tau, NFL, Ng and a-syn. Higher Rr scores,
showing disproportionate loss of recency recall from imme-
diate to delayed testing, were associated with worse bio-
markers profiles, when controlling for age, diagnosis and
APOE risk eand that the best predictors of biomarkers out-
comes tended to be Rr combined with lower levels of imme-
diate or delayed LMT performance. Therefore, we suggest the
following: 1) Rr is a worthwhile measure to add to the clini-
cian's battery (see also Egeland, 2021) when evaluating in-
dividuals suspected to be on a trajectory towards
neurodegeneration; and 2) serial position values should be
included in databases examining AD and other types of de-
mentia. Future research should consider also comparing the
relative predictive power of Rr when derived from word lists
versus story recall; and examine whether the neurocognitive
basis of Rr is different in word lists and story recall tasks.
Credit author statement
Davide Bruno: Conceptualisation; formal analysis; funding
acquisition; Writing eoriginal draft. Ainara Jauregi Zinkunegi:
formal analysis; Writing eoriginal draft. Gwendlyn Kollmor-
gen: Methodology. Ivonne Suridjan: Methodology. Norbert
Wild: Methodology. Cynthia Carlsson: Project administration.
Barbara Bendlin: Writing ereview &editing. Ozioma
Okonkwo: Writing ereview &editing. Nathaniel Chin: Writing
ereview &editing. Bruce P. Hermann: Writing ereview &
editing. Sanjay Asthana: Project administration. Henrik Zet-
terberg: Methodology; Writing ereview &editing. Kaj Blen-
now: Methodology; Writing ereview &editing. Rebecca
cortex 159 (2023) 167e174172
Langhough: Formal analysis; Writing ereview &editing.
Sterling C. Johnson: Project administration. Kimberly D.
Mueller: Funding acquisition; Writing ereview &editing.
Author notes
The ethical regulations that govern the WADRC prevent un-
restricted public archiving of anonymised study data. Data
can be requested from the WADRC Executive Committee at:
https://www.adrc.wisc.edu/apply-resources. Data will be
released to internal and external investigators following
confirmation of IRB approval together with an evaluation by
the WADRC of scientific merit and resource availability.
This secondary analysis of WADRC data was funded by a
NIH-NIA (R01 144 AAI8612) grant to KDM, in which DB and RL
are co-investigators.
Conflicts of interests and disclosure statement
IS is a full-time employee and shareholder of Roche Di-
agnostics International Ltd., GK is a full-time employee of
Roche Diagnostics GmbH, and NW is a full-time employee of
Roche Diagnostics GmbH. COBAS, COBAS E and ELECSYS are
trademarks of Roche. The Elecsys®b-Amyloid (1e42) CSF
assay, the Elecsys®Phospo-Tau (181P) CSF assay and the
Elecsys®Total-Tau CSF assay are not approved for clinical use
in the US. The NeuroToolKit robust prototype assays are for
investigational purposes and are not approved for clinical use.
HZ is a Wallenberg Scholar supported by grants from the
Swedish Research Council (#2018-02532), the European
Research Council (#681712 and #101053962), Swedish State
Support for Clinical Research (#ALFGBG-71320), the Alzheimer
Drug Discovery Foundation (ADDF), USA (#201809-2016862),
the AD Strategic Fund and the Alzheimer's Association
(#ADSF-21-831376-C, #ADSF-21-831381-C and #ADSF-21-
831377-C), the Olav Thon Foundation, the Erling-Persson
Family Foundation, Stiftelsen f€
or Gamla Tj€
anarinnor,
Hj€
arnfonden, Sweden (#FO2019-0228), the European Union's
Horizon 2020 research and innovation programme under the
Marie Skłodowska-Curie grant agreement No 860197 (MIR-
IADE), the European Union Joint Programme eNeurodegen-
erative Disease Research (JPND2021-00694), and the UK
Dementia Research Institute at UCL (UKDRI-1003).
KB is supported by the Swedish Research Council (#2017-
00915), the Alzheimer Drug Discovery Foundation (ADDF) USA
(#RDAPB-201809-2016615), the Swedish Alzheimer Foundation
(#AF-930351, #AF-939721 and #AF-968270), Hj€
arnfonden,
Sweden (#FO2017-0243 and #ALZ2022-0006), the Swedish state
under the agreement between the Swedish government and
the County Councils, the ALF-agreement (#ALFGBG-715986
and #ALFGBG-965240), the European Union Joint Program for
Neurodegenerative Disorders (JPND2019-466-236), the Na-
tional Institute of Health (NIH) USA (grant #1R01AG068398-01),
and the Alzheimer's Association 2021 Zenith Award (ZEN-21-
848495).
No other author reports any conflicts of interests or
disclosures.
Supplementary data
Supplementary data to this article can be found online at
https://doi.org/10.1016/j.cortex.2022.12.004.
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