Title page
Title: Hippocampal neurogenesis and pattern separation: a meta-analysis of behavioral data
Authors: Thiago F. A. França1; Alexandre M. Bitencourt1; Naiana R. Maximilla2; Daniela M.
Barros1,3; José M. Monserrat1,3.
Authoraffiliations:1Programa de Pós-graduação em Ciências Fisiológicas, Universidade
Federal do Rio Grande - FURG, Rio Grande, RS, Brazil. 2Curso de graduação em Ciências
Biológicas, Universidade Federal do Rio Grande -FURG, Rio Grande, RS, Brazil. 3Instituto
de Ciências Biológicas, Universidade Federal do Rio Grande (FURG), Rio Grande, RS, Brazil
Abbreviated title: Meta-analysis of neurogenesis and pattern separation
Number of pages: 36 pages of main text (with references), 21 pages of supplementary
material.
Number of figures: 7
Number of tables: 3
Correspondence information: Correspondence should be addressed to Thiago F. A. França.
Address: Instituto de Ciências Biológicas (ICB), Universidade Federal do Rio Grande FURG, Av Itália, Km 8 s/n– Rio Grande, RS, 96210-900, Brazil. Phone number: +55 53
984625616. Email: alvesfranca@outlook.com
Funding:TFAF and AMB receive masters scholarships from the Brazilian national council
for scientific and technological development (CNPq) and the Coordination for improvement
of higher education personnel (CAPES), respectively (grant number does not apply).
JMM receive a productivity research grant; Grant sponsor: CNPq. Grant number:
PQ307880/2013-3.
This article has been accepted for publication and undergone full peer review but has not been
through the copyediting, typesetting, pagination and proofreading process which may lead to
differences between this version and the Version of Record. Please cite this article as an
‘Accepted Article’, doi: 10.1002/hipo.22746
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DMB receive a productivity research grant; Grant sponsor: CNPq. Grant number:
305268/2015-5.
Keywords: Adult neurogenesis, hippocampal formation, dentate gyrus, behavioral pattern
separation, systematic review.
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Abstract
The generation of new neurons in the hippocampus of adult mammals has become a widely
accepted phenomenon, but the functional significance of the adult neurogenesis in the
hippocampus is not fully understood. One of the main hypotheses currently investigated
suggests that neurogenesis contributes to pattern separation in the dentate gyrus. Many
behavioral studies were conducted aiming to test this hypothesis using rodents as animal
model. In those studies, researches ablated neurogenesis in the animals and subsequently
evaluate them in tests of behavioral pattern separation, that is, behaviors that are thought to
rely on the computational process of pattern separation. The results of these studies are varied,
with most supporting a role for neurogenesis in pattern separation, but some others not. To
address this controversy it was performed a systematic review and meta-analysis of studies
evaluating the effect of neurogenesis ablation on behavioral pattern separation. Analysis
results indicated that most of the literature in the topic is surprisingly consistent and, although
there are two studies with divergent results, the bulk of the literature supports an effect of
hippocampal neurogenesis on behavioral pattern separation. We discuss those findings in light
of other behavioral effects of hippocampal neurogenesis ablation, limitations of behavioral
data and other lines of evidence about the effect of hippocampal neurogenesis in the dentate
gyrus.
1. Introduction
Hippocampal neurogenesis (HN) is the process by which new principal neurons are
generated in the dentate gyrus (DG) and are functionally integrated into the hippocampal
circuitry in the adult mammalian brain (Molina-Navarro & Garcia-Verdugo, 2016). Despite
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the enormous amount of research in this topic, there are still many open questions regarding
the functional significance of HN (Gonçalves et al., 2016). One of the hypotheses considers
that the functional role of HN is to aid in computational process called pattern separation.
Pattern separation consists in disambiguating similar inputs to produce dissimilar outputs
(Treves et al., 2008). Such computational process has long been implicated as a function of
the DG within the hippocampal formation, so it was suggested that HN played a role on the
disambiguation of inputs by the DG (Wiskott et al., 2006). Over the last decade, many
researchers have attempted to evaluate this hypothesis, ablating HN and submitting the
animals to tests that evaluate behavioral pattern separation. Those behavioral testes evaluate
the capacity of the animals to discriminate between similar spatial stimuli that demand
different behavioral responses. Such behavioral pattern separation is, in theory, dependent on
the computational process of pattern separation in such a way that impairment in the
computational pattern separation would be expected to cause a corresponding impairment in
behavioral pattern separation. Most of the results of studies evaluating the effect of HN
ablation on behavioral pattern separation have supported the pattern separation hypothesis and
as a consequence, the hypothesis gained increase acceptance in the field (Hvoslef-Eide
&Oomen, 2016). Despite that, some contradictory findings exist in the literature (Cushman et
al., 2012; Groves et al., 2013). Furthermore, an evaluation of the consistency of the results
regarding HN and pattern separation in hindered by a pervasive problem: the over reliance on
statistical p values (Nuzzo, 2014). When testing hypothesis, the criterion used to reject the
null hypothesis in most of the literature in biological sciences is the p value, but evaluating the
consistency of the literature based on the p value of the different studies is a treacherous task
because equal p-values does not implicate equal effects (Borenstein, 2009). In fact, one would
not expect a high level of consistency in the literature of HN and pattern separation, given the
variation between studies in many potentially relevant factors such the employed animal
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model (rat or mice), its strain, sex and age, methods of HN ablation and behavioral paradigm
used to access pattern separation.
To gain a better understanding of the role of HN on pattern separation it is necessary
to look beyond the individual studies and beyond the simple dichotomy of being or not being
statistically significant. It is important to look at the whole data, using more informative
measures about the effects found on each study and evaluate the consistency of those effects
across studies, as well as the patterns which may point to possible sources of variation
(Borenstein, 2009). In the present study, it was performed a systematic review and a metaanalysis of the studies evaluating the effect of HN ablation on behavioral pattern separation in
rodents. After the relevant studies were found through a systematic search of the literature, the
data from those studies were combined to yield a quantitative synthesis of the literature.
Moreover, since the metric used to combine the results was the effect size, not the p-value, it
was possible to better evaluate the consistency between results of different studies. It was
observed that, despite the presence of two outliers, the bulk of the data is surprisingly
consistent and indicate a strong effect of HN on behavioral pattern separation. The results are
discussed in the light of the other behavioral effects of HN ablation, the limitations of
behavioral tests other lines of evidence about the mechanistic basis of the effects of HN on
behavior.
2. Methods
Systematic literature search
The literature search was conducted on July 20th, 2016. Four databases were search:
PubMed, Web of Science, Scopus and Science Direct. The search string for each database was
elaborated based on the research question and using the tutorial elaborated by Leenaars et al.
(2012) as a general guideline. The research question was: “what is the effect of HN ablation
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on behavioral pattern separation in mammals?” Therefore, the search strings were based on
terms related to “adult neurogenesis”, “hippocampus”, “dentate gyrus”, “behavioral pattern
separation” and, specifically in the case of PubMed, there were terms regarding animal
models. There was also a term to exclude review articles and, in Scopus, there were terms to
exclude publications from unrelated research topics. The search was limited to publications
from 2009 onwards, since the first report of HN ablation and behavioral pattern separation
(Clelland et al., 2009) was published that year. Additionally, all studies in the four databases
that cited Clelland et al. (2009) were included for selection (except Science Direct, in which
the study from Clellandet al. (2009) was not indexed). The complete search string for each
database can be found inSupplementary Table 1 (Table S1).
Inclusion criteria
We included in the meta-analysis all studies which evaluated the effect of HN ablation on
behavioral tests of pattern separation in juvenile/adult healthy mammals. Additionally,
specific criteria were employed to decide on the inclusion of different ablation methods and
different behavioral tests of pattern separation as discussed below.
Regarding the method of HN ablation, we included studies which performed the ablation
by either focal X-ray irradiation of the hippocampus or by transgenic/pharmacogenetic
methods and excluded those who used cytostatic agents or whole brain irradiation. The
rationale for this choice was the fact that ablating HN using cytostatic agents or whole brain
irradiation has very low selectivity and widespread side effects (Drew et al., 2016a). It is true
that both focal X-ray irradiation and transgenic/pharmacogenetic methods also have side
effects, but those seem to be more restricted than the side effects of the excluded methods. For
example, comparing focal X-ray irradiation and whole brain irradiation, it is clear that the
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former has all the collateral effects of the latter plus other effects on brain regions not targeted
by focal X-ray irradiation. A similar point can be made regarding chemostatic agents and
genetic methods. Side effects of some genetic methods include ablation of neurogenesis in the
sub ventricular zone and ablation of gliogenesis (both of which occur in some, but not all
genetic models). Cytostatic agents in general can cause both those effects and also cause other
nonspecific DNA modifications and epigenetic alterations. Furthermore, as cytostatic agents
are usually administered systemically, they also affect many other systems throughout the
body of the animal, most notably the immune and digestive systems. Were also excluded
transgenic or knockout models that ablated neurogenesis but also model neuropathological
states (e.g., Guo et al., 2011; Park et al., 2015b).
The criteria for inclusion of behavioral tests was the accordance of the test design with the
general definitions of a pattern separation behavioral test given by Kirwan and Stark (2007)
and Hagen et al. (2014). We included behavioral tests which exposed the animals to two
highly similar contexts with small spatial changes between them, requiring the animal to
present a different behavioral response in each context. The reason for adopting such criteria
is that the goal of included studies should be to evaluate behavioral pattern separation, that is,
a behavior dependent on the computational process of pattern separation. Since computational
pattern separation consists in disambiguation of similar stimuli, allowing similar memories to
be stored without interference, the structure of behavioral tests described above is a logical
choice as it forces the animal to compare between similar contexts based on its memory and
to present a response specific to context in which the animal is inserted at the moment. Based
on this criterion and considering only the behavioral tests used in the studies that met the
other selection criteria, the included behavioral tests were: contextual fear discrimination,
delayed non-matching-to-place in the radial arms maze, spatial metric processing, and
spontaneous location recognition tests.
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Study selection
The references found through the systematic search were loaded into the reference
manager software JabRef 3.6 (JabRef development team, 2016). They were screened by two
independent investigators (TFAF and AMB or NRM) in three steps: (a) screening by title, (b)
screening by abstract and finally, (c) full text reading. In the first two steps, were excluded
papers which: (1) were off topic, (2) were not original papers, (3) did not perform in vivo
studies with mammals, (4) used animal models of disease, (5) evaluate the effects of drugs or
toxins or (6) did not report behavioral data. Therefore, studies selected for the third step were
those which dealt with behavioral effects of neurogenesis manipulation in healthy mammals.
Studies in which any of these criteria could not be evaluated based on the abstract were also
selected for full text reading. In the third step accordance with the specific inclusion criteria
regarding the behavioral tests and methods of HN ablation was evaluated.
Data extraction
After the selection process, relevant data from the selected studies were extracted to a
LibreOffice spreadsheet. Such data included sample sizes, means and standard
deviations/errors from the pattern separation behavioral tests, the sex, species, and strain of
the animals used in the study, the method of neurogenesis ablation, the behavioral test used,
the outcome measure, the time between ablation and behavioral testing and the duration of the
behavioral test. When any relevant data was not reported in the text, the data in question was
requested to the authors; in the absence of reply, the data was marked as unknown. In case of
means and standard deviation/error, when data was not presented in the text, it was extracted
from the graphs using the software PlotDigitizer (Rohatgi, 2016).
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Quality assessment
Included studies were submitted to risk of bias assessment by SYRCLE’s risk of bias tool
(Hooijmans et al, 2014). This tool consists of ten questions, each of which addresses a
different form of bias that may affect the reliability of the results from the study. The
questions were adapted from a risk of bias tool for evaluation of clinical trials. Although there
may be sources of bias specific for this area of research and even sources of bias specific for
each of the different methods used, the questions included in the risk of bias tool evaluate
sources of bias that are expected to be important for all animal studies. Therefore, the
SYRCLE’s risk of bias tool evaluates study quality based on certain common sources of bias,
almost universal within animal studies. This means that fulfilling the criteria evaluated by the
SYRCLE’s risk of bias tool may not guarantee absence of bias, but not fulfilling it would
implicate its presence. Importantly, during study evaluation, missing data was considered as
not performed (e.g. no mention of blinded animal caregivers was interpreted as not
performed).
The questions, as well as the kind of bias evaluated by each question (along with a brief
definition for each kind of bias), can be found in Table S2 on the Supplementary
information. More information about the risk of bias tool can be found at Hooijmans et al
(2014).
Meta-analysis
Given the differences in behavioral tests used to assess pattern separation, the chosen
measure of effect size was the standardized mean difference (SMD) calculated using Hedge’s
g (Hedges &Odkin, 1985). The effect size from the different studies was combined into a
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summary effect size (accompanied by confidence interval) using the inverse variance method
with the random effects model, with τ being estimated by the restricted maximum likelihood
estimator (Borenstein, 2009). Presence of heterogeneity was evaluated by the Cochran’s Q
method and the percentage of observed variation corresponding to true heterogeneity was
calculated using the I² measure (Vesterinenet al, 2014). To evaluate possible sources of
heterogeneity in the results, a stratified meta-analysis was planned a priori (Vesterinenet al,
2014). Factors considered in this analysis would be sex, species and strain of the animals used
in the study, the method of neurogenesis ablation, the behavioral test used, outcome measure,
time between ablation and behavioral testing, the duration of the behavioral test and the
methodological quality of studies. The stratified meta-analysis ended up not being performed
because a sensitivity analysis carried out before it was able to pinpoint the sources of
heterogeneity. The sensitivity analysis in question was not planned a priori, as its execution
was only suggested after a visual inspection of the results of the first analysis; it consisted in
the exclusion of two data sets followed by the calculation of new estimate of summary effect
and heterogeneity. Another sensitivity analysis (planned a posteriori, before the first metaanalysis but after the data extraction) was performed to evaluate the effect of the inclusion of
different time points in the meta-analysis. In this sensitivity analysis, we aggregated data,
when available, from the multiple days of behavioral testing within studies to compute a
combined effect size using the procedure outlined in Borenstein (2009b). As a conservative
measure, we assumed the correlation between multiple time points to be 1. All statistical
analysis was performed on R (R development core team, 2008) and all the codes used, as well
as the data used in the analysis, are in the Supplementary Information as Annex 1.
Publication bias
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The original plan was to assess the possibility of publication bias and its impact on the
results using funnel plot and funnel plot-based methods such as Egger regression and trimand-fill (Borenstein, 2009a). Unfortunately, those methods could not be applied due to the
small number of studies. As an alternative, Orwin’s fail-safe N (Orwin, 1983) was employed
to evaluate the robustness of results to the addition of hypothetical unpublished studies. This
method consists in calculating the number of hypothetical studies with a give effect size
needed to add so as to reduce the unweighted summary effect size to a given target effect size
(usually set at a value that would indicate a small or irrelevant effect).
It is important to highlight that the method works with the unweighted summary effect, or
equivalently, the summary effect obtained when all studies are given the same weight. This is
different from the summary effect in the main analysis – although not by much in this case,
since the variance of the studies included in the meta-analysis (and therefore their weights)
was similar.
3. Results
We identified a total of 13,317 articles through the literature search (PubMed=2,371 plus
323 that cited Clelland et al. (2009); Web of Science=3,187 plus 547 that cited Clelland et al.
(2009); Scopus=5,841 plus 592 that cited Clelland et al. (2009); Science Direct=456) (Figure
1). After removing the duplicates 10,706 articles remained for the first screening. After
carefully reading the titles, 10,192 articles were excluded for being off topic, being review
papers, not being in vivo studies, using animal models of disease or evaluating the effects of
drugs or toxins. The 514 articles left were then submitted to careful abstract reading and 428
articles were then excluded (156 were not original research papers, 24 did not dealt with
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mammals, 33 did not report behavioral data and 215 did not report neurogenesis ablation). We
then read the full text of the remaining 86 articles and 75 of those were excluded (39 did not
met the criteria for the pattern separation test and 36 did not met the criteria for the method of
HN ablation). Of the remaining 11 articles, two (Nakashiba et al., 2012 and Wu and Hen,
2014) were later excluded because they did not report a comparable outcome measure (see
details in the next section). The other nine articles were then included in the analysis; two of
those articles (Clelland et al., 2009 and Cushman et al., 2012) had two independent datasets
within them and those datasets were treated as separate studies in the meta-analysis, yielding a
total of 11 data sets.
Characteristics of included studies
The main characteristics of each selected study are summarized in Table 1. The general
structure of all the studies was the same, with animals with and without HN being submitted
to behavioral tests of pattern separation. Despite the generality of design, there were
differences between the studies in all the factors evaluated. For example, there were seven
different methods of HN ablation used, with varying side effects ranging from temporary
hippocampal inflammation in the case of focal X-ray irradiation (Monjeet al., 2003) to
collateral ablation of neurogenesis in the olfactory bulb (e.g., Pan et al., 2012; Kesner et al.,
2014) or ablation of gliogenesis (e.g., Groves et al., 2013) in some of the transgenic models.
Seven of the studies used contextual fear discrimination as the behavioral test, other four
studies used delayed non-matching-to-place in radial arms maze (DNMTP), and special
metric processing and spontaneous location recognition were used in one study each. The
outcome measure used to estimate the effect size was the percentage of correct responses in
the DNMTP test and the discrimination ratios in the other tests. Two of studies that used
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contextual fear discrimination to assess pattern separation (Nakashiba et al., 2012 and Wu and
Hen, 2014) did not report a discrimination ratio, reporting only the percentage of time the
animals spent freezing in each context. Given the nature of contextual fear discrimination test,
comparison of effects between studies was only possible with a single outcome measure that
encompassed the behavior of the animals in both contexts to which they were exposed, since
the capacity of the animal to discriminate between the different contexts can only be
measured by considering the behavior in one context relative to the behavior on the other
context. For that reason, that data from Nakashiba et al. (2012) and Wu and Hen (2014) were
excluded from the analysis. Importantly, the conclusion of those two studies, based on the %
of freezing time data, support a negative effect of HN ablation on behavioral performance in
tests of pattern separation and, therefore, are in accordance (at least in the direction of the
effect) with the results from the meta-analysis based on the remaining studies.
Most of the studies used behavioral tests with a duration of multiple days. Some of them
presented outcome data combined from all testing days, others presented data from individual
days or blocks of days and some presented data only for the last day of testing. In the studies
that reported data from individual days or blocks of days the discrimination ratios tended to
change over time, sometimes showing improvements consistent with learning (Sahay et al.,
2011; Cushman et al., 2012; Tronel et al., 2012). In fact, in those studies that reported data
from individual days but did not report discrimination ratios (Nakashiba et al., 2012; Wu and
Hen, 2014), it was possible to see a pattern of improvement in discrimination with time (as
indicated by increasingly different percentage freezing in the different contexts). There is
evidence that HN affects learning (reviewed in Deng et al., 2010) and, consistent with those
data, control animals seem to achieve the capacity to discriminate between contexts faster
than HN ablated animals (Nakashiba et al., 2012; Wu and Hen, 2014). This implies that, at
least in the contextual discrimination test (used by all the above mentioned studies), there is
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an effect of HN ablation on the time needed to learn to discriminate between the different
contexts. Of course, given the putative effect of HN on the computational process of pattern
separation it is expected that animals without HN find the task more difficult since the neural
representations of the different contexts stored in the hippocampus of those animals would
have a greater level of superposition. This greater superposition in the neural representations
that constitute the memories of the different contexts would cause those animals to take a
longer time to learn to discriminate between the two contexts (that is, would lead to an
impairment in behavioral pattern separation). However, learning deficits have also been found
on tasks that do not involve behavioral pattern separation (Winocur et al, 2006; Dupret et al,
2008), so they may be caused by mechanisms other than impairment in computational pattern
separation. Also, given the similarities in the structure of the other behavioral tests (since all
follow the general structure that serves as the inclusion criteria) it would be reasonable to
expect behavioral patterns consistent with learning impairment in other tests as well. To
minimize interference of a possible learning delay in HN ablated animals, whenever possible
we used only results from the last day of testing in the meta-analysis. Some of the studies did
not provide individualized data (Clelland et al, 2009; Groves et al, 2013), but fortunately such
studies have behavioral tests of very long duration, which may have dampened the delayed
learning effect. To evaluate the effect of this decision on the results, we performed a
sensitivity analysis in which the effect size of those studies that reported outcome from
multiple time points (Sahay et al., 2011; Cushman et al., 2012; Tronel et al., 2012) was
calculated from the aggregated data of all time point.
Study quality
The results of the application of SYRCLES’s risk of bias tool are summarized in Table 2.
As expected, both the percentage of qualified studies in most criteria and the unweighted
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score of individual studies were low. Two factors may have contributed to those results. One
is the fact that some criteria evaluated are still not common practice in animal studies, such as
the use of an algorithm for random allocation of the animals within the experimental groups
(Hooijmans et al, 2014), which in some cases may not even possible to carry out (e.g., when
studies make use of some genetic methods to ablate neurogenesis). The other factor is the
poor reporting quality in animal studies (Kilkenny et al., 2009). In most studies, there were
various criteria that could not be adequately judged due to lack of information in the material
and methods section. Since unreported qualitative missing data was presumed as not
performed, the overall results possibly underestimate the quality of studies.
Another relevant point is the presence of unclear answers to two of the questions in the
risk of bias tool. The first one asks if the animals used in the study were similar at relevant
baseline characteristics. This questions is impossible to address, since parameters such as the
natural levels of neurogenesis in the animals cannot be evaluated without killing them. Since
this is a methodological limitation, it was marked as unclear for all studies. Another question
which contained unclear responses was the one regarding justification for incomplete data. In
this question, studies were evaluated as unclear when there was doubt of whether there were
incomplete data in the study (e.g., in cases of uneven number of animals in control and
treatment groups, which may or may not reflect the exclusion of animals from the
experiment).
Interestingly, two of the studies had an unusually high individual score (Cushman et al.,
2012 and Kesner et al., 2014). This was due to the transgenic ablation method used by those
studies. In those studies, it was only possible to know whether an animal had reduced HN or
normal HN after histological analysis, which had to be done after behavioral testing.
Therefore, all procedures were carried out blind to the genotype of the animals, resulting in a
positive score in various criteria and thus a higher overall score.
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Effect of HN ablation on behavioral pattern separation
The results of the first meta-analysis are show in the forest plot in Figure 2. The
magnitude of the effect size was interpreted following Cohen’s general guidelines, defining a
standardized mean difference of 0.2, 0.5 and 0.8 as a small, medium, and large effect,
respectively (Cohen, 1988). The results show a moderate-to-strong negative effect of HN
ablation on behavioral performance (SMD = -0.718), but although the result reached
statistical significance (p = 0.0013), the 95% confidence interval was wide and spanned both
values which indicate a very strong effect (upper limit = -1.156) and values that pointed to
very weak effect (lower limit = -0.281). Furthermore, there was a moderate heterogeneity (p =
0.0078, I² = 58.2%) and the 95% prediction interval was extremely wide. Visual inspection of
the data shows nine of the eleven data points clustered on the left of the forest plot, with only
two data points further on the right, suggesting that those two results may be the cause of
heterogeneity in the data. A sensitivity analysis performed by excluding these two data points
confirmed this was in fact the case, as the results of the new analysis without the data from
Cushman et al (2012; male dataset) and Groves et al (2013) had no detectable heterogeneity
(p = 0.9223, I² = 0%) and the calculations became equivalent to a fixed effects model (Figure
3). The second analysis had a higher effect size (SMD = -0.999, p<0.0001) and a narrower
95% confidence interval (-1.305; -0.692), showing a strong and consistent negative effect of
HN ablation on behavioral pattern separation. Also, due to the presence of the outlying studies
the normal distribution model does not fit the data well (p=0.014 with Shapiro-Wilk test),
something that is also corrected by the removal of the two outliers (p=0.73 with Shapiro-Wilk
test). Importantly, removing the two leftmost data points do not eliminate detectable
heterogeneity (Figure 4; p = 0.0110, I²=59.6%) and does not lead to normality (p=0.006 with
Shapiro-Wilk test).
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To evaluate the effect in the results of the choice of time points to be included, we
performed a second sensitivity analysis. The results are shown in Figures 5, 6 and 7. Figure 5
shows the results with all eleven data sets included. The summary effect was slightly smaller
than the summary effect of the corresponding analysis with outcomes of the final day of
testing. Also, the confidence interval was slightly narrower (SMD= -0.667, with a 95%
confidence interval of -1.05 to -0.29, p = 0.0005) and the data met the normality criteria
(p=0.38 with Shapiro-Wilk test). The heterogeneity was also smaller, but still significant
(p=0.0499, I²= 45.44%), and the prediction interval was still wide. The qualitative assessment
is like the previous analysis: a moderate but variable effect with a fair degree of
heterogeneity. Visual inspection of the data in Figure 5 shows now three data points clustered
on the right of forest plot, the same two as in Figure 2, but now accompanied by the other
data set from Cushman et al. (2012). As expected, removing those data sets from the analysis
completely removes any detectable heterogeneity (Figure 6; p = 0.8131, I²= 0%; other details
in the legend) and produces a stronger and more consistent effect, very similar to the effect of
the analysis including only data from the final day of test (SMD= -0.9622, 95% confidence
interval from -1.29 to -0.64, p<0.0001). Importantly, removing the three leftmost data points
(Figure 7) do not fully removes detectable heterogeneity, as it becomes no longer statistically
significant (p = 0.1322) but remains detectable (I² = 37.15%), suggesting that there was
heterogeneity but the test did not have enough power to detect it at the significance level of
0.05.
To evaluate the possibility that the effects observed are due to publication bias, we
performed a series of variations of Orwin’s fail-safe N (Orwin, 1983). The results of the tests
are summarized in Table 3. The target effect size was set at -0.2 (which implies a very small
effect) and the result shows the number of studies with a given effect size that needs to be
added to reduce the summary effect to the target effect size. The number of studies with null
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effect sizes needed to achieve the target effect size is more than three times the number of
available studies in the literature in both the meta-analysis with data from final day of test and
meta-analysis with aggregated data. The smaller numbers of studies need to reach the target
effect were found when adding, instead of neutral studies, studies with effect sizes equal to
those of the excluded outliers. In the smallest of all fail-safe Ns, one would need five more
studies with the same extreme result as Cushman et al. (2012; male dataset) to reduce the
unweighted summary effect size of the analysis with outliers (i.e., already contain one such
extreme effect) to -0.2. Considering that in eleven studies with fourteen data sets only two
studies reported improvement in pattern separation with ablation of HN, and of those two,
only one dataset of one study had such an extreme effect size, it seems very unlikely that there
are another 5 or 7 results like that of Cushman et al. (2012) that were not published, especially
since this result was statistically significant, and therefore one would not expect that other
results alike remained unpublished. Overall, then, the results seem robust.
4. Discussion
The results show the literature supports a moderate-to-strong effect of HN ablation on
behavioral pattern separation, with most studies reporting a consistently strong effect, except
by two studies that reported effects in the other direction (that is, showing improvement in
behavioral pattern separation with HN ablation). Many questions arise from the data presented
here. First, what is the neurobiological significance of these results? This question involves
understanding the mechanisms underlying behavioral pattern separation and how the
behaviors evaluated in the studies analyzed here relate to other behaviors that are shown to be
impaired by HN ablation. Before discussing those questions, though, two important features
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of the results must be addressed: one is the inconsistency observed in part of the data and the
other is the unexpected consistency of the remaining data.
The outliers
One look at the characteristics of the studies (Table 1) whose data were inconsistent with
the rest of the literature (Cushman et al., 2012; Groves et al., 2013) reveals that there is no
pattern to be found regarding any of the factors that were to be evaluated as possible sources
of heterogeneity. Not only both studies share all their characteristics with at least one other
study, they also share almost no characteristics with each other. It is interesting to note that
the most extreme dataset (Cushman et al., 2012; male dataset) was the one that suffered the
greatest reduction in effect size when data was aggregated (from SMD=0.974 to
SMD=0.230), probably due to a strange fluctuation in the discrimination ratio of control and
HN ablated animals throughout the testing days (for the graphs regarding those data, see
Cushman et al., 2012). The significance of those fluctuations remains unknown, and a similar
evaluation of the data from Groves et al. (2013) could not be made since this study only show
the outcome combined across all 12 days of testing. Moreover, it is also interesting to see that
the female data set from Cushman et al. (2012) was consistent with the rest of the literature
(although with a smaller effect size than average), but became less consistent with the
leftmost studies (with an almost null effect size) when data from all time points were
aggregated. This was likely since discrimination ratios of control and HN ablated animals in
this experiment increased steadily in parallel, only becoming significantly different in the last
two days of testing, where the discrimination ratio of HN ablated animals showed a sudden
decrease (for the graphs regarding those data, see Cushman et al., 2012).
A possible explanation suggested by Groves et al. (2013) is that the unexpected result in
their test may be due to the test not being challenging enough, causing the performance of
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both groups not to be statistically different, with the result in the opposite direction being a
statistical fluctuation. Although that may be a satisfactory explanation for the small effect size
observed by Groves et al. (2013) and some consideration regarding the difficulty of the test
may also hold for the data on female mice in Cushman et al. (2012), it is harder to consider
this possibility regarding the data from male mice in Cushman et al. (2012), since their effect
size was substantially larger. The explanation proposed by Cushman et al. (2012) relies on a
role for HN encoding time into memories (Aimone et al., 2006). This hypothesis suggests that
memories formed close in time would be bound together by the activity of the cohort of adult
born neurons maturing at the time of encoding. Therefore, removing HN would remove this
source of similarity between the representations of the two similar contexts presented in the
test, resulting in improved discrimination by the animal. Although the hypothesis proposed by
Cushman et al. (2012) do make sense, it leads to an expectation of similar results in other
studies, at least in those using male mice (considering the possibility of a sex-specific effect).
The problem is that all the other studies that used male mice found results in the opposite
direction (Sahay et al., 2011; Tronel et al., 2012; Pan et al., 2012; Nakashiba et al., 2012; Wu
and Hen, 2014).
In summary, that is a lack of possibly informative patterns in the data and therefore a lack
of satisfactory hypotheses that could explain both data. As more studies are produced, it is
possible that other studies with similar results show up in the literature, allowing us to see
some pattern that may explain those results.
The consistent results
Given the various important differences between the studies included in the metaanalysis, one would not expect a high degree of consistency in the effect sizes reported in
each study. In fact, it is well known that different ablation methods differ in their collateral
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effects (Drew et al., 2016a) and that different rodent species and (at least in mice) different
strains have different natural levels of HN (Snyder et al., 2009; Kempermann et al., 1997).
Furthermore, it would be expected that different behavioral tests of pattern separation varied
in their sensibility and specificity in detecting behavioral correlates of pattern separation.
Variations within each experimental paradigm would also be expected to affect the outcome.
Yet, the results showed that seven out of nine studies included in the meta-analysis had no
detectable heterogeneity in their results, despite all the differences between them. Importantly,
this does not mean the studies are homogeneous, just that the heterogeneity was too small to
be detected in this study. Two factors that limit the power of the test to detect heterogeneity
are the number of studies and the precision of the individual studies (as it is easier to detect a
difference between two values when one has a precise estimate of what those values are).
Therefore, it is likely that there is some heterogeneity but it is not expected to be high as it
could not be detected based on the studies analyzed here. Publication of more studies
(preferably with higher statistical power) may allow the detection of influences from one or
more relevant study factors on the effect of HN on behavioral pattern separation.
In summary, the results point to a robust effect of HN on behavioral pattern separation.
Even if one includes the outliers the effect is still moderate-to-strong and although the
predictive value of the meta-analysis becomes reduced due to widening of the prediction
interval and changes in statistical distribution, it would still be expected that the mean effect
of a sufficiently large number of new studies to be moderate-to-strong.
The question remains as to the neurobiological significance of those results. For that
matter, the first step is to understand what is really being evaluated by behavioral pattern
separation tests.
The significance of behavioral pattern separation
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The behavioral tests of pattern separation where designed to be dependent on the
computational process of pattern separation. Of course, the behaviors evaluated in the tests do
not depend solely on computational pattern separation, but involve a myriad of other
computational and cognitive processes. Therefore, the results of such tests can only show
consistency with impairment in pattern separation but can never show unambiguously that
pattern separation was impaired (Aimone, 2016). The reason for this is that other cognitive
processes may be affected by a given intervention and thus impair the behavioral performance
of the animals. This creates confounding variables that hamper the interpretation of results at
a biological level. Here, the most clear confounding variable is memory. The behavioral tests
of pattern separation require that animals discriminate between similar memories. A
prerequisite to be able to discriminate between similar memories is, of course, to form those
memories in the first place. An animal cannot give the appropriate response to the appropriate
context if it does not remember the contexts or the responses. In fact, considering that the
difference in performance between the control group and HN ablated group increases with
time in at least part of the studies (as evidenced by the dilution of the effect size when data
from all time points are aggregated), it is possible the effects observed are caused by impaired
memory function in the HN ablated group as much as they are caused by a difference between
the two groups in their capacity to discriminate similar contexts using their memories. This
possibility is strengthened by various studies pointing to an effect of HN ablation on different
kinds of memory (reviewed in Deng et al., 2010), although the real effect of HN ablation on
memory is far from clear.
A meta-analysis performed a few years ago (Groves et al, 2013) evaluated the
literature regarding the effect of HN ablation on the performance at Morris water maze probe
trial and at cued and contextual fear conditioning. The results showed a moderate-to-strong
effect, although none of them reached statistical significance at the 0.0125 alfa level. The 95%
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confidence intervals reported ranged from moderate to very strong effects in all three
behavioral tests, but the prediction intervals were not reported (although they presumably
ranged from almost null effects to very strong effects). Therefore, the reason the effects did
not reach statistical significance is probably because the effect was too variable between
studies. In fact, heterogeneity was high for all three tests and the authors were not able to link
the heterogeneity to any of a number of factors evaluated. Furthermore, the biological
interpretation of those data is hampered by one additional factor: the authors included all kids
of HN ablation methods. As discussed before, although no HN ablation method is even close
to be completely selective, methods such as cytostatic agents and whole brain irradiation have
particularly low selectivity. Therefore, their inclusion may inflate the summary effect size and
its confidence interval, making the analysis less conservative, and they may also affect the
estimated heterogeneity. The conclusion that can be drawn from the analysis discussed above
is that the effect of HN ablation on memory dependent behaviors varies substantially across
the experimental literature and cannot be fully confirmed nor fully discarded yet.
The possibility of HN ablation affecting memory creates an unsolvable problem whose
root is impossibility to distinguish, based the behavior of the animals, which computational
processes is being impaired, pattern separation or memory formation. Even though
computational pattern separation at the DG is distinct from memory storage at CA3,
behavioral pattern separation is not easily dissociated from learning. Therefore, the data in the
present study raises the possibility of impaired computational pattern separation, but nothing
more than that.
Another point not always considered is the fact that some studies also report an effect
of HN ablation on anxiety-related behaviors. In fact, the meta-analysis from Groves et al
(2013) also evaluated behavioral tests of anxiety with findings highly similar to those of the
meta-analysis of behavioral tests of memory discussed above. Such possibility is relevant to
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this discussion because elevated anxiety may impair behavioral performance behavioral tests
involving memory (Harrison et al, 2009) and is conceivable that it may impair behavioral
performance in pattern separation tests as well. This is highly problematic as controlling for
anxiety levels is a treacherous task and behavioral tests to assess of anxiety may themselves
increase anxiety, which further complicates the situation.
Knowledge obtained from behavioral tests cannot advance this discussion much
further as it is not possible to disentangle the effects of each of the factors considered above.
To make any more progress in this discussion other sources of information are required.
Recent years have seen an accumulating amount of evidence at another line of investigation,
electrophysiology (Lacefield et al., 2012; Ikar et al., 2013; Park et al., 2015a; Drew et al.,
2016b), the results of which offer some insight to the question of whether or not there is a real
impairment in computational pattern separation contributing to the observed impairmentin
behavioral pattern separation.
Electrophysiology and pattern separation
Adult-born granule cells (abGCs) are known to pass through a maturation process with
duration of several weeks (reviewed in Gonçalves et al., 2016). In this process, that is a
critical time window in which abGC show high excitability and insensitivity to GABAergic
input. In this critical period the abGCs already have functional synapses, receiving input from
the perforant path and sending output to CA3 principal neurons and interneurons as well as to
interneurons within the DG. The abGC in this stage of the maturation process are also known
to be broadly tuned and this feature, along with the higher excitability of those cells, makes
the population of maturing abGCs proportionately more active than the population of mature
granule cells (GCs).
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There are many hypotheses regarding the mechanism that links the special properties
of abGCs to their effects on pattern separation, but one of them, known as the indirect
hypothesis, has gained popularity over the last few years due to increasing experimental
support (Johnston et al., 2016; Park et al., 2015a). The indirect hypothesis states that abGC
contribute to the function of the DG by modulating the sparsity of the DG network. Studies
have demonstrated that maturing abGC make synaptic contacts with Mossy cells and
inhibitory interneurons (Ikar et al., 2013) and that stimulation of abGC activates local
inhibitory circuits (Drew et al., 2016b). Moreover, increasing and ablating HN caused a
decreased and increase, respectively, in neuronal activity in the DG in response to electrical
stimulation (Ikar et al., 2013). Additionally, HN ablation increases spontaneous activity in the
DG of anesthetized mice (Lacefield et al., 2012). All those pieces of evidence point to
modulation of the sparsity of DG network as the physiological correlate of HN’s effect on
behavior. The reduced sparsity of DG network activity caused by HN ablation is thought to
increase the degree of superposition of neural representations, that is, decrease the efficiency
of computational pattern separation. This would lead to a difficulty in discriminating similar
memories, thus impairing behavioral pattern separation.Such interpretation provides
physiological and computational mechanisms for the effect o HN ablation on behavioral
pattern separation, but it does not explain the other behavioral effects observed in tests of
memory and anxiety. Although the effects of HN ablation on those behaviors are still under
debate, we would like to note that the mechanism for pattern separation described above could
be easily extended to account for all behavioral effects observed and even to suggest a
possible reason why those effects are inconsistent.
Mature GC activity is shaped by lateral inhibition (Sloviter and Brisman, 1995) and
feedback inhibition (Leutgeb et al., 2007; Danielsson et al., 2016), both dependent on the
inhibitory circuits of the DG. Therefore, at any given moment, only a small proportion of the
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GC population will be active. According to the indirect hypothesis, when abGCs are removed,
the activity of lateral and feedback inhibitory circuits is reduced, increasing the activity of the
DG (Piatti et al, 2013). Those effects would increase the number of active cells at any given
moment, thus increasing the overlap between the representations of similar inputs leading to
impaired pattern separation (both computational and behavioral). One additional consequence
of the reduction in the inhibitory tonus of DG is that any given pattern of activity forwarded to
CA3 would now contain a noise created by the increased activity in the network. The addition
of noise would be expected to decrease the amount of information the neural representation
has about the stimuli being represented (Borst and Theunissen, 1999). The formation of low
information content memories and the attempt to recover such memories from low
information representations is expected to impair not only behaviors dependent on fine
discrimination between similar memories, but also any behavior that relies on memory– but
only if the task is sufficiently demanding regarding the level of detail to remembered. Also, as
the activity of the ventral hippocampus seems to be associated with anxiety-related behaviors
(Fanselow and Dong, 2010), the generation of noise due to increased activity in the ventral
part of DG could generate anxiety-related behavioral effects. Such effects may vary in
severity depending on factors such as the levels of stress and anxiety imposed to the animals,
as higher stress load would lead to higher activity in the ventral DG which would be further
exacerbated by the decreased activity of inhibitory circuits (Glasper et al., 2012). In fact, other
authors have already argued for a dependence of the behavioral effect of HN ablation on the
difficulty of the behavioral test (Cameron and Glover, 2014) and there is also evidence for
dependence of the effect on the stress load (Snyder et al, 2011). Thus, we suggest the
mechanism discussed above may explain at least part of the variability found in the literature
regarding the effect of HN ablation on memory and anxiety as variations in the difficulty of
behavioral tasks and stress levels of the animals may modulate the severity of the behavioral
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effects. Importantly, the fact that no heterogeneity could be detected in this study may be
because the behavioral test used in the studies analyzed here is one of the most demanding on
memory and this may have been the dominating factor determining the severity of behavioral
impairment observed.
5. Concluding remarks
The results of this systematic review and meta-analysis demonstrated that, overall, the
literature point to a robust effect of HN in behavioral pattern separation. Nonetheless, we
must not forget the two studies with results in the opposite direction. Although no pattern
could be found to connect both studies and offer a possible explanation for their results, they
may still have important implications for role of HN on pattern separation and memory.
Additionally, their presence in the literature creates heterogeneity in the results, which is
reflected in the width of the prediction interval of the analyses with outliers (Figure 2and
Figure 5) compared to the corresponding analyses without the inconsistent results (Figure
3and Figure 6, respectively). Furthermore, specifically in the analysis of data from last day of
testing (Figure 2), the presence of the outlying results affects the distribution of the data in
such a way that it does not fit well with the normality assumption, making its estimate of
precision from that particular analysis even less predictive (although there are evidence of
meta-analysis methods being robust to violations of the normality assumption; Kontopantelis
and Reeves, 2010). Therefore, if one considers only the consistent results, next studies in this
topic would be expected to yield results within the narrow prediction intervals observed in
Figure 3and Figure 6, but when one considers the studies of Cushman et al. (2012) and
Groves et al. (2013), predictions about results of future studies in this topic become way less
certain and span an interval far wider. As more studies are performed, we might find some
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pattern that allow us to understand such sporadic discrepancies. There is also another factor
that limits the detection of heterogeneity sources, which is the low statistical power of primary
studies that leads to effect estimates with poor precision that may hide variation between
studies (Borrenstein, 2009a). Therefore, that is need not only for more studies, but also for
more precise studies, with larger sample sizes and higher statistical power. Moreover, given
the reports of effects of HN ablation on other types of behavioral tests and the limitations
intrinsic to any behavioral test, it is not possible to state with confidence that the impairment
observed in behavioral pattern separation is in fact caused by impairment in computational
pattern separation. Those considerations make it clear that to elucidate the mechanism behind
the effects of HN ablation on behavior, further studies using other approaches will also be
necessary. The results of such studies will have to be carefully integrated with behavioral data
if we want to achieve a satisfactory explanation for the mechanisms of HN’s effect on
behavior. In this line, we interpreted the behavioral data in light of electrophysiological
studies and argued here that behavioral pattern separation may not reflect the main effect of
HN ablation. When the phenomenon is analyzed at another level, behavioral pattern
separation seems to be just one of many behavioral manifestations of the same underlying
physiological process: the decreased activity of inhibitory circuits caused by HN ablation.
Thus, it raises the possibility that what seems to be a plethora of different effects at the
behavioral level may be the result of a single effect at the physiological level.
Acknowledgements
The authors would also like to thank the Brazilian National Council for Scientific and
Technological Development (CNPq) and the Coordination for Improvement of Higher
Education Personnel (CAPES) for financial support.
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References
1. Aimone, JB, Wiles J, Gage FH. 2006. Potential role for adult neurogenesis in the encoding
of time in new memories. Nat.Neurosci.9: 723–727.
2. Aimone, JB. 2016. Computational Modeling of Adult Neurogenesis. Cold Spring Harb.
Perspect. Biol. 8: a018960.
3. Bekinschtein P, Kent, BA, Oomen CA, Clemenson GD, Gage FH, Saksida LM, Bussey
TJ. 2014. Brain-derived neurotrophic factor interacts with adult-born immature cells in the
dentate gyrus during consolidation of overlapping memories: BDNF, adult neurogenesis
and overlapping memories. Hippocampus 24: 905–911.
4. Borenstein M. 2009a. Introduction to meta-analysis. Chichester, U.K.: John Wiley &
Sons. 421p.
5. Borenstein M. 2009b. Effect sizes for continuous data. In H. Cooper, L. V. Hedges, & J.
C. Valentine (Eds.), The handbook of research synthesis and meta analysis. New York:
Russell Sage Foundation. p. 279-293.
6. Borst A, Theunissen FE. 1999. Information theory and neural coding. Nat. Neurosci. 2:
947–957.
7. Cameron HA, Glover LR. 2015. Adult neurogenesis: Beyond learning and memory. Ann.
Rev. Psychol. 66 (1): 53–81.
8. Clelland CD, Choi M, Romberg C, Clemenson GD, Fragniere A, Tyers P, Jessberger S,
Saksida LM, Barker RA, Gage FH, Bussey TJ. 2009. A functional role for adult
hippocampal neurogenesis in spatial pattern separation. Science 325: 210–213.
9. Cohen, J. 1988. Statistical Power Analysis for the behavioral sciences. Hillside, NJ:
Lawrence Erlbaum Associates. p. 19-26.
10. Cushman JD, Maldonado J, Kwon EE, Garcia AD, Fan G, Imura T, Sofroniew MV,
Fanselow MS. 2012. Juvenile neurogenesis makes essential contributions to adult brain
structure and plays a sex-dependent role in fear memories. Front. Behav. Neurosci 6(3).
11. Danielson NB, Kaifosh P, Zaremba JD, Lovett-Barron M, Tsai J, Denny CA, Balough
EM, Goldberg AR, Drew LJ, Hen R, Losonczy A, Kheirbek MA. 2016. Distinct
contribution of adult-born hippocampal granule cells to context encoding. Neuron 90:
101–112.
12. Deng W, Aimone JB, Gage FH. 2010. New neurons and new memories: how does adult
hippocampal neurogenesis affect learning and memory? Nature Rev.: Neuroscience 11:
339-350.
13. Drew LJ, Fusi S, Hen R. 2016a. Adult neurogenesis in the mammalian hippocampus: Why the
dentate gyrus? Learning and Memory 20: 710-729.
14. Drew LJ, Kheirbek MA, Luna VM, Denny CA, Cloidt MA, Wu M., Jain S, Scharfman
HE, Hen R. 2016b. Activation of local inhibitory circuits in the dentate gyrus by adultborn neurons. Hippocampus 26: 763–778.
15. Dupret D, Revest JM, Koehl M, Ichas F, De Giorgi F. 2008. Spatial relational
memoryrequires hippocampal adult neurogenesis. PLoS One 3: e1959.
29
John Wiley & Sons
This article is protected by copyright. All rights reserved.
Hippocampus
Page 30 of 51
16. Glasper ER, Schoenfield TJ, Gould E. 2012. Adult neurogenesis: optimazing function to
suit the environment. Behavioral Brain Research 227:380-383.
17. Gonçalves JT, Schafer ST, Gage FH. 2016. Adult neurogenesis in the hippocampus: From
Stem cells to behavior. Cell 167: 897–914.
18. Groves JO, Leslie I, Huang GJ, McHugh SB, Taylor, Mott R, Munafò M, Bannerman
DM, Flint J. 2013. Ablating adult neurogenesis in the rat has no effect on spatial
processing: Evidence from a novel pharmacogenetic model. PLoS Genet 9: e1003718.
19. Guo W, Allan AM, Zong R., Zhang L, Johnson EB, Schaller EG, Murthy AC, Goggin SL,
Eisch AJ, Oostra BA, Nelson DL, Jin P, Zhao X. 2011. Ablation of Fmrp in adult neural
stem cells disrupts hippocampus-dependent learning. Nature Med. 17: 559–565.
20. Hagen BTJ, van Goethem NP, Lagatta DC, Prickaerts J. The object pattern separation
(OPS) task: A behavioral paradigm derived from the object recognition task. 2015. Behav.
Brain Res. 285: 44-52.
21. Harrison FE, Hosseini AH, McDonald MP. Endogenous anxiety and stress responses in
water maze and Barnes maze spatial memory tasks. 2009. Behav. Brain Res. 198(1): 247251.
22. Hedges LV, Olkin I. 1985. Statistical methods for meta-analysis. Orlando: Academic Press.
23. Hooijmans CR, Tillema A, Leenaares M, Hoitinga MR. 2010. Enhancing search
efficiency by means of a search filter for finding all studies on animal experimentation in
PubMed. Laboratory Animals. 44: 170-75.
24. Hooijmans CR, Rovers MM, Vries RBM, Leenaars M, Hoitinga MR, Langendam MW.
2014. SYRCLE's risk of bias tool for animal studies. BMC Medical Research
Methodology 14: a.43.
25. Hvoslef-Eide M, Oomen CA. 2016. Adult neurogenesis and pattern separation in rodents:
A critical evaluation of data, tasks and interpretation. Frontiers in Biology. 11(3): 168181.
26. Ikrar T, Guo N, He K, Besnard A, Levinson S, Hill A, Lee HK, Hen R, Xu X, Sahay A.
2013. Adult neurogenesis modifies excitability of the dentate gyrus. Front. Neural Circuits
7.
27. JabRef development team. JabRef 3.6., open source. Web: https://www.jabref.org/.
28. Johnston ST, Shtrahman M, Parylak S, Gonçalves JT, Gage FH. 2016. Paradox of pattern
separation and adult neurogenesis: A dual role for new neurons balancing memory
resolution and robustness. Neurobiology of Learning and Memory 129: 60–68.
29. Kempermann G, Kuhn HG, Gage FH. 1997. Genetic influence on neurogenesis in the
dentate gyrus of adult mice. Proc. Nat. Acad. Sci. USA94(19):10409-10414.
30. Kesner RP, Hui X, Sommer T, Wright C, Barrera VR, Fanselow MS. 2014. The role of
postnatal neurogenesis in supporting remote memory and spatial metric processing:
Neurogenesis and remote memory. Hippocampus 24: 1663–1671.
31. Kilkenny C, Parsons N, Kadyszewski E, Festing MF, Cuthill IC, Fry D, Hutton J, Altman
DG. 2009. Survey of the quality of experimental design, statistical analysis and reporting
of research using animals. PLoS One 4(11):e7824.
32. Kirwan CB, Stark CEL. Overcoming interference. 2007. An fMRI investigation of pattern
separation in the medial temporal lobe. Learning and Memory 14:625–633.
30
John Wiley & Sons
This article is protected by copyright. All rights reserved.
Page 31 of 51
Hippocampus
33. Kontopantelos E, Reeves D. 2010. Performance of statistical methods for meta-analysis
when true study effects are non-normally distributed: a simulation study. Statistical
Methods in Medical Research 21(4): 409-426.
34. Leenaars M, Hooijmans CR, Veggel NV, Riet GT, Leeflang M, Hooft L, Wilt GJVD,
Tillema A, Hoitinga MR. 2012. Laboratory Animals 46: 24–31.
35. LibreOffice.org.
LibreOffice
The
Document
Foundation.Available: https://www.libreoffice.org/. (Accessed 1st July 2016).
36.
37. Lacefield CO, Itskov V, Reardon T, Hen R, Gordon JA. 2012. Effects of adult-generated
granule cells on coordinated network activity in the dentate gyrus. Hippocampus 22: 106–
116.
38. Leutgeb JK, Leutgeb S, Moser MB, Moser EI. 2007. Pattern separation in the dentate
gyrus and CA3 of the hippocampus. Science 315: 961–966.
39. Molina-Navarro MM, Garcia-Verdugo JM. Neurobiology. In: Canales JJ. 2016. Adult
Neurogenesis in the Hippocampus. Academic Press. p.1-18.
40. Monje ML, Toda H, Palmer TD. 2003. Inflammatory blockade restores adult hippocampal
neurogenesis. Science302: 1760–1765.
41. Nakashiba T, Cushman JD, Pelkey KA, Renaudineau S, Buhl DL, McHugh TJ, Barrera
VR, Chittajallu R, Iwamoto KS, McBain CJ, Fanselow MS, Tonegawa S. 2012. Young
dentate granule cells mediate pattern separation, whereas old granule cells facilitate
pattern completion. Cell 149: 188–201.
42. Niibori, Y, Yu TS, Epp JR, Akers KG, Josselyn SA, Frankland PW. 2012. Suppression of
adult neurogenesis impairs population coding of similar contexts in hippocampal CA3
region. Nat. Commun. 3:1253.
43. Nuzzo R. 2014. Statistical errors. Nature 506: 150-152.
44. Orwin RG. 1983. Fail-safe N for effect size in meta-analysis. J. Ed. Stat. 8(2): 157-159.
45. Pan YW, Chan GCK, Kuo CT, Storm DR, Xia Z. 2012. Inhibition of adult neurogenesis
by inducible and targeted deletion of ERK5 mitogen-activated protein kinase specifically
in adult neurogenic regions impairs contextual fear extinction and remote fear memory. J.
Neurosci. 32: 6444–6455.
46. Park EH, Burghardt NS, Dvorak D, Hen R, Fenton AA. 2015a. Experience-dependent
regulation of dentate gyrus excitability by adult-born granule cells. J. Neurosci. 35:
11656–11666.
47. Park H, Yang J, Kim R, Li Y, Lee Y, Lee C, Park J, Lee D, Kim H, Kim E. 2015b. Mice
lacking the PSD-95–interacting E3 ligase, Dorfin/Rnf19a, display reduced adult
neurogenesis, enhanced long-term potentiation, and impaired contextual fear conditioning.
Sci. Rep. 5: 16410.
48. Piatti VC, Ewell LA, Leutgeb JK. 2013. Neurogenesis in the dentate gyrus: carrying the
message or dictating the tone. Frontiers in Neuroscience 7, 50:1-11.
49. R Development Core Team. 2008. R: A language and environment for statistical
computing. R Foundation for Statistical Computing Vienna, Austria. ISBN 3-900051-070, Web: http://www.R-project.org.
50. Rohatgi A. WebPlotDigitizer v.3.10. 2016. Web: http://arohatgi.info/WebPlotDigitizer.
31
John Wiley & Sons
This article is protected by copyright. All rights reserved.
Hippocampus
Page 32 of 51
51. Sahay A, Scobie KN, Hill AS, O’Carroll CM, Kheirbek MA, Burghardt NS, Fenton AA,
Dranovsky A, Hen R. 2011. Increasing adult hippocampal neurogenesis is sufficient to
improve pattern separation. Nature 472: 466–470.
52. Sloviter RS, Brisman JL, 1995. Lateral inhibition and granule cell synchrony in the rat
hippocampal dentate gyrus. J. Neurosci. 15:811–820.
53. Snyder JS, Soumier A, Brewer M, Pickel J, Cameron HA. 2011b. Adult hippocampal
neurogenesis buffers stress responses and depressive behaviour. Nature476(7361):458–61
54. Snyder JS, Choe JS, Clifford MA, Jeurling SI, Hurley P, Brown A, Kamhi JF, Cameron
HA. 2009. Adult-Born hippocampal neurons are more numerous, faster maturing, and
more involved in behavior in rats than in mice. J. Neurosci. 29: 14484–14495.
55. Treves A, Tashiro A, Witter ME, Moser EI. 2008. What is the mammalian dentate gyrus
good for? Neuroscience 154: 1155–1172.
56. Tronel, S., Belnoue, L., Grosjean, N., Revest, J.-M., Piazza, P.-V., Koehl, M., Abrous,
D.N., 2012. Adult-born neurons are necessary for extended contextual discrimination.
Hippocampus 22, 292–298. doi:10.1002/hipo.20895
57. Verestein HM, SenaES,lEgna KJ, Hist TC, Churolov L, Currie GL, Antonic A, Howells
DW, Macleod MR. 2014. Meta-analysis of data from animal studies: a practical guide.
Journal of Neuroscience Methods. 221: 92– 102.
58. Winocur G, Wojtowicz JM, Sekeres M, Snyder JS, Wang S. 2006. Inhibition of
neurogenesis interferes with hippocampus-dependent memory function. Hippocampus 16:
296–304.
59. Wiskott L, Rasch ML, Kempermann G. 2006. A functional hypothesis for adult
hippocampal neurogenesis: Avoidance of catastrophic interference in the dentate gyrus.
Hippocampus 16:329–343.
60. Wu, M.V., Hen, R. 2014. Functional dissociation of adult-born neurons along the
dorsoventral axis of the dentate gyrus: Adult neurogenesis in the dorsal vs. ventral dentate
gyrus. Hippocampus 24: 751–761.
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Figure and table legends
Figure 1. Flow chart representing the process of study selection.
Table 1. Summary of study characteristics. Note: the extent of ablation in different studies
was evaluated using different markers, which are indicated between parenthesis.
dnWnt:
dominant negative Wnt; rtTA: tetracycline controlled transativator; Cre: Cre recombinase;
CreER: tamoxifen dependent Cre recombinase; GFAP: glial fibrillary acidic protein; Cre;
DNMT1: DNA cytosine-methyltransferase 1; TK: thymidine kinase; DCX: doublecortin;
CldU: chlorodeoxyuridine; BrdU: bromodeoxyuridine.
Table 2. SYRCLE’s risk of bias tool results. Each criterion corresponds to a measure that
should have been taken to avoid a possible source of bias in the study (for details, see Table
S2 on the Supplementary information). A “yes” evaluation implies no risk of bias in the
criterion evaluated and a “no” evaluation implies risk of bias in that criterion. An “unclear”
evaluation, as its name suggests, implies that the risk of bias the criterion in question could
not be evaluated. The table also show the percentage of studies qualified in each criterion and
the unweighted score of each study, which would be used in the stratified meta-analysis. This
unweighted score is computed by the sum of the grades in each criterion, with a “yes”
counting as 1 point, a “no” counting as -1 and “unclear” counting as 0.
33
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Page 34 of 51
Figure 2. Forest plot of the first meta-analysis. The plot shows, from left to right, the study
IDs, their effect sizes (with squares proportional to the study weights), the study weight and
the SMD with its 95% confidence interval. Additional information: heterogeneity: Q(df = 10)
= 23.9096, p-value = 0.0078; I² = 59%; tau² = 0.32; 95% prediction interval from SMD = 1.910 to SMD = 0.474. Combined N=118 control and 113 experimental manipulation.
Figure 3. Forest plot for the sensitivity analysis. Forest plot for the meta-analysis without
the data from Cushman et al. (2012; male dataset) and Groves et al. (2013). Additional
information: heterogeneity: Q(df = 8) = 3.1839, p-value = 0.9223; I² = 0%; tau² = 0; 95%
prediction interval = 95% confidence interval (SMD = -1.305 to SMD = - 0.692). Combined
N=118 control and 113 experimental manipulation. Combined N=99 control and 93
experimental manipulation.
Figure 4. Forest plot for the sensitivity analyses. Forest plot for the meta-analysis without
the two leftmost data points (Niiboriet al., 2012; Beckinschteinet al., 2014). Additional
information: heterogeneity: Q(df = 8) = 19.8213, p-value = 0.0110; I² = 60.5%; tau² = 0.32;
95% prediction interval from SMD = -1.789 to SMD = 0.626. Combined N=99 control and
97 experimental manipulation.
Figure 5. Forest plot for the meta-analysis with aggregated data from all test days. Forest
plot for the meta-analysis with all studies included. Additional information: heterogeneity:
Q(df = 10) = 18.3125, p-value = 0.0499; I² = 45.4%; tau² = 0.184; 95% prediction interval
from SMD = -1.589 to SMD = 0.255). N=118 control and 113 experimental manipulation.
34
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Hippocampus
Figure 6. Forest plot for the meta-analysis with aggregated data from all test days. Forest
plot for the meta-analysis without data from Cushman et al. (2012) and Groves et al. (2013).
Additional information: heterogeneity: Q(df = 7) = 3.7050, p-value = 0.8131; I² = 0%; tau² =
0; 95% prediction interval equals 95% confidence interval (from SMD = -1.289 to SMD = 0.635). Combined N=87 control and 82 experimental manipulation.
Figure 7. Forest plot for the meta-analysis with aggregated data from all test days. Forest
plot for the meta-analysis without three leftmost data points (Clelland et al., 2009, IR dataset;
Niibori et al., 2012; Beckinschtein et al., 2014). Additional information: heterogeneity: Q(df=
7) = 11.1511, p-value = 0.1322; I² = 37.2%; tau² = 0.118; 95% prediction interval from SMD
= -1.223 to SMD = 0.332. Combined N=90 control and 87 experimental manipulation.
Table 3. Fail-safe summary. The table shows the results of Orwin’s fail-safe N for the metaanalysis with data from the last day of testing and with aggregated data from all testing days
(for the studies that reported such data), both with and without heterogeneous studies. The
values shown here represent the number of studies one would need to add to the analysis to
reduce the unweighted summary effect size to -0.2. Of note, the unweighted summary effects
were very like the summary effects obtained in the meta-analyses in all four cases.
Specifically, the unweighted summary effect sizes for the meta-analysis with last day data
were SMD=-0.736 with all studies and SMD=-1.052 without outliers. For the meta-analysis
with aggregated data, the unweighted summary effect sizes were SMD= -0.693 with all
studies and SMD=-1.016 without outliers.
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Figure 1. Flow chart representing the process of study selection.
142x187mm (600 x 600 DPI)
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Table 1 – part 1
Reference
n (C | T) Species Strain/Genetic background
Sex
Age at ablation
Method of
Extent of ablation
ablation
Clelland et al., 2009
9 | 10
mice
C57Bl/6
female
8 weeks
Focal X-ray IR
100% (DCX)
Clelland et al., 2009
14 | 12
mice
C57Bl/6
female
8 weeks
dnWnt
~56% (DCX)
Sahay et al., 2011
7|9
mice
Baxf/f (C57Bl/6 w/ 129SvEv)
male
10 weeks
Focal X-ray IR
~95% (DCX)
Tronel et al., 2012
15|15
mice
C57BL/6JxCBA
male
8 weeks
Nestin-rtTA/Bax
~33% (CldU)
Pan et al., 2012
10|10
mice
C57BL/6
male
10-12 weeks
Nestin-
~55% (NeuroD)
CreER/ERK5
Cushman et al., 2012
8|11
mice
C57Bl/6, BALB/C hybrid
male
At birth
mGFAP-Cre-
~90% (BrdU+NeuN)
DMNP1
Cushman et al., 2012
12|11
mice
C57Bl/6, BALB/C hybrid
female
At birth
mGFAP-Cre-
~90% (BrdU+NeuN)
DMNP1
Niibori et al., 2012
11|8
mice
C57Bl/6NTacfBr, 129Svev
both
8 weeks
hybrid
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TK+/Nestin
~95% (NeuroD)
Page 39 of 51
Hippocampus
Nakashiba et al., 2012
14|14
mice
C57Bl/6
male
9-12 weeks
Focal X-ray IR
~97% (BrdU)
Groves et al., 2013
11|9
rat
Sprague Dawley
--
8 weeks
TK+/GFAP
98.3% (DCX)
Bekinschtein et al.,
8|8
rat
Long-Evans
male
7-8 weeks
dnWnt
~40% (DCX)
Wu & Hen, 2014
28|28
mice
C57Bl6/J
male
8 weeks
Focal X-ray IR
100% (DCX)
Kesner et al., 2014
13|10
mice
C57Bl/6, BALB/C hybrid
male
At birth
mGFAP-Cre-
100% (BrdU)
2014
DMNP1
Table 1 – part2
Reference
Time between
Behavioral test
Outcome mesure
Duration of test
Outcome report
ablation and testing
Clelland et al., 2009
2 months
DNMP in Radial arms maze
% correct choice
15 days
Combined (all days)
Clelland et al., 2009
2 months
DNMP in Radial arms maze
% correct choice
15 days
Combined (all days)
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Sahay et al., 2011
4 months
Contextual fear discrimination
Page 40 of 51
Freezing ratio ((A-
5 days
Individual days
13 days
Individual days
B)/(A+B))
Tronel et al., 2012
9 weeks
Contextual fear discrimination
Freezing ratio
(A/(A+B))
Pan et al., 2012
10 weeks
DNMP in Radial arms maze
% correct choice
5 days
Last day only
Cushman et al., 2012
3-5 months
Contextual fear discrimination
Freezing ratio
17 days
Individual days
17 days
Individual days
(A/(A+B))
Cushman et al., 2012
3-5 months
Contextual fear discrimination
Freezing ratio
(A/(A+B))
Niibori et al., 2012
4 weeks
Contextual fear discrimination
Freezing ratio (A-B)
3 days
Last day (1day-test)
Nakashiba et al., 2012
6 weeks
Contextual fear discrimination
% freezing
17 days
Individual days
Groves et al., 2013
9 weeks
DNMP in Radial arms maze
% correct choice
12 days
Combined (all days)
Bekinschtein et al.,
4 weeks
Spontaneous location recognition
Discrimination ratio
8 days
Combined (all days)
14 days
Individual days
((A-B)/(A+B))
2014
Wu & Hen, 2014
6 weeks
Contextual fear discrimination
% freezing
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Kesner et al., 2014
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8-9 weeks
Metric spatial processing
Discrimination ratio
((A-B)/(A+B))
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1 day
Last day (1day-test)
Hippocampus
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Table 2 – part 1
Criteria
Reference
Random allocation
Animals similar at
Allocation
Random
Blinded
Random outcome
sequence
baseline
concealment
housing
investigators
assessment
Clelland et al, 2009
No
Unclear
No
No
No
Yes
Sahay et al, 2011
No
Unclear
No
No
No
No
Tronel etal, 2012
No
Unclear
No
No
No
No
Pan et al, 2012
No
Unclear
No
No
No
No
Cushman et al,
Yes
Unclear
Yes
Yes
Yes
Yes
Niibori et al, 2012
No
Unclear
No
No
No
No
Nakashiba et al,
No
Unclear
No
No
No
No
No
Unclear
No
No
No
No
2012
2012
Groves et al, 2013
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Bekinschetein et al,
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No
Unclear
No
No
No
No
Wu and Hen, 2014
No
Unclear
No
No
No
No
Kesner et al., 2014
No
Unclear
Yes
Yes
Yes
Yes
2014
Qualified studies
0
0
18.18181818
18.18181818
18.18181818
27.27272727
(%)
Table 2 – part 2
Criteria
Reference
Blinded result collection
Incomplete data
Unbiased conclusions
Other
justification
Unweighted study
score
Clelland et al, 2009
No
Yes
No
No
-5
Sahay et al, 2011
Yes
No
Yes
No
-5
Tronel etal, 2012
Yes
Unclear
Yes
No
-4
Pan et al, 2012
Yes
Unclear
Yes
No
-4
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Cushman et al, 2012
Yes
Unclear
No
No
2
Niibori et al, 2012
No
Unclear
Yes
No
-6
Nakashiba et al, 2012
Yes
Yes
Yes
No
-3
Groves et al, 2013
No
No
Yes
No
-7
Bekinschetein et al,
No
Yes
Yes
No
-5
Wu and Hen, 2014
Yes
No
Yes
No
-5
Kesner et al., 2014
Yes
Yes
Yes
No
5
2014
Qualified studies (%)
63.63636364
36.36363636
81.81818182
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0
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Figure 2. Forest plot of the first meta-analysis. The plot shows, from left to right, the study IDs, their effect
sizes (with squares proportional to the study weights), the study weight and the SMD with its 95%
confidence interval. Additional information: heterogeneity: Q(df = 10) = 23.9096, p-value = 0.0078; I² =
59%; tau² = 0.32; 95% prediction interval from SMD = -1.910 to SMD = 0.474. Combined N=118 control
and 113 experimental manipulation.
99x71mm (600 x 600 DPI)
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Figure 3. Forest plot for the sensitivity analysis. Forest plot for the meta-analysis without the data from
Cushman et al. (2012; male dataset) and Groves et al. (2013). Additional information: heterogeneity: Q(df
= 8) = 3.1839, p-value = 0.9223; I² = 0%; tau² = 0; 95% prediction interval = 95% confidence interval
(SMD = -1.305 to SMD = - 0.692). Combined N=118 control and 113 experimental manipulation. Combined
N=99 control and 93 experimental manipulation.
91x61mm (600 x 600 DPI)
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Figure 4. Forest plot for the sensitivity analyses. Forest plot for the meta-analysis without the two leftmost
data points (Niiboriet al., 2012; Beckinschteinet al., 2014). Additional information: heterogeneity: Q(df = 8)
= 19.8213, p-value = 0.0110; I² = 60.5%; tau² = 0.32; 95% prediction interval from SMD = -1.789 to
SMD = 0.626. Combined N=99 control and 97 experimental manipulation.
91x61mm (600 x 600 DPI)
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Figure 5. Forest plot for the meta-analysis with aggregated data from all test days. Forest plot for the metaanalysis with all studies included. Additional information: heterogeneity: Q(df = 10) = 18.3125, p-value =
0.0499; I² = 45.4%; tau² = 0.184; 95% prediction interval from SMD = -1.589 to SMD = 0.255). N=118
control and 113 experimental manipulation.
91x60mm (600 x 600 DPI)
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Figure 6. Forest plot for the meta-analysis with aggregated data from all test days. Forest plot for the metaanalysis without data from Cushman et al. (2012) and Groves et al. (2013). Additional information:
heterogeneity: Q(df = 7) = 3.7050, p-value = 0.8131; I² = 0%; tau² = 0; 95% prediction interval equals
95% confidence interval (from SMD = -1.289 to SMD = -0.635). Combined N=87 control and 82
experimental manipulation.
91x60mm (600 x 600 DPI)
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Figure 7. Forest plot for the meta-analysis with aggregated data from all test days. Forest plot for the metaanalysis without three leftmost data points (Clelland et al., 2009, IR dataset; Niibori et al., 2012;
Beckinschtein et al., 2014). Additional information: heterogeneity: Q(df= 7) = 11.1511, p-value = 0.1322;
I² = 37.2%; tau² = 0.118; 95% prediction interval from SMD = -1.223 to SMD = 0.332. Combined N=90
control and 87 experimental manipulation.
91x60mm (600 x 600 DPI)
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Table 3
Analysis with last day test outcome
All
No ouliers
studies
Adding neutral studies
30
39
Adding Groves et al., (2013)
10
13
5
7
Adding Cushman et al., (2012, male data
set)
Analysis with aggregated data
All
No ouliers
studies
Adding neutral studies
27
33
9
11
13
15
36
44
Adding Groves et al., (2013)
Adding Cushman et al., (2012, male data
set)
Adding Cushman et al., (2012, female data
set)
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