Rapid stimulation of human dentate gyrus function
with acute mild exercise
, Kyeongho Byun
, Kazuki Hyodo
, Zachariah M. Reagh
, Jared M. Roberts
, Kousaku Saotome
, Genta Ochi
, Takemune Fukuie
, Kenji Suzuki
, Yoshiyuki Sankai
Michael A. Yassa
, and Hideaki Soya
Laboratory of Exercise Biochemistry and Neuroendocrinology, Faculty of Health and Sport Sciences, University of Tsukuba, 305-8574 Ibaraki, Japan;
Neuroscience Division, Advanced Research Initiative for Human High Performance (ARIHHP), Faculty of Health and Sport Sciences, University of Tsukuba,
305-8574 Ibaraki, Japan;
Department of Neurobiology and Behavior, University of California, Irvine, CA 92697-3800;
Center for the Neurobiology of
Learning and Memory, University of California, Irvine, CA 92697-3800;
Center for Cybernics Research, University of Tsukuba, 305-8574 Ibaraki, Japan;
Department of Neurology, Ibaraki Prefectural University of Health Sciences, 300-0394 Ibaraki, Japan
Edited by Bruce McEwen, The Rockefeller University, New York, NY, and approved August 14, 2018 (received for review April 19, 2018)
Physical exercise has beneficial effects on neurocognitive function,
including hippocampus-dependent episodic memory. Exercise inten-
sity level can be assessed according to whether it induces a stress
response; the most effective exercise for improving hippocampal
function remains unclear. Our prior work using a special treadmill
running model in animals has shown that stress-free mild exercise
increases hippocampal neuronal activity and promotes adult neuro-
genesis in the dentate gyrus (DG) of the hippocampus, improving
spatial memory performance. However, the rapid modification, from
mild exercise, on hippocampal memory function and the exact
mechanisms for these changes, in particular the impact on pattern
separation acting in the DG and CA3 regions, are yet to be elucidated.
To this end, we adopted an acute-exercise design in humans, coupled
with high-resolution functional MRI techniques, capable of resolving
hippocampal subfields. A single 10-min bout of very light-intensity
VO2peak) results in rapid enhancement in pattern sepa-
ration and an increase in functional connectivity between hippocam-
pal DG/CA3 and cortical regions (i.e., parahippocampal, angular, and
fusiform gyri). Importantly, the magnitude of the enhanced functional
connectivity predicted the extent of memory improvement at an in-
dividual subject level. These results suggest that brief, very light
exercise rapidly enhances hippocampal memory function, possi-
bly by increasing DG/CA3−neocortical functional connectivity.
Physical exercise is an important lifestyle intervention for
promoting mental health, including hippocampal-dependent
memory. Wheel running has well-known effects on hippocampal
neural plasticity and memory in rodents (1); however, the most
effective exercise regimen (e.g., intensity level) for improving hip-
pocampal function remains an open question. Exercise intensity can
be assessed according to whether it induces a stress response based
on the lactate threshold (LT). Our recent studies using an animal
model of exercise that utilizes controlled treadmill running to dis-
tinguish stress-free mild exercise (below LT) from intense exercise
(above LT) have shown that mild exercise increases hippocampal
neuronal activity (2) and promotes adult neurogenesis in the den-
tategyrus(DG)(3–5), improving spatial memory performance (6).
Intriguingly, these effects were suppressed with intense exercise, i.e.,
follow a hormetic dose–response profile (3–6). Based on this evi-
dence, we hypothesized that very light-intensity exercise can stim-
ulate the human hippocampus, and improve episodic memory
through functional activation in the hippocampal network.
To test this hypothesis in humans, we used an acute-exercise
design based on our previous human studies (7–10), coupled with
high-resolution functional magnetic resonance imaging (fMRI)
techniques, capable of resolving hippocampal subfields, to ex-
amine the neural substrates of exercise-enhanced hippocampal
function. We specifically hypothesized that mild exercise will
enhance DG-mediated pattern separation, the process of
differentiating among similar experiences to keep stored memo-
ries distinct from one another (11). We recently reported that acute
moderate-intensity exercise (50% peak oxygen uptake [
improves mnemonic discrimination performance for highly similar
items in a task that is thought to rely on DG-mediated pattern
separation (12). Using the same experimental design, we con-
ducted two experiments to investigate whether even acute very
light, stress-free exercise similarly improves hippocampal memory,
and, if so, to identify the underlying neural mechanisms using
high-resolution fMRI of hippocampal subfields and cortical re-
gions during task performance.
In experiment 1, we assessed the effect of 10 min of acute mild
VO2peak; defined as “very light”by the American
College of Sports Medicine [ACSM]) on performance in the
mnemonic discrimination task. We set the exercise duration
to 10 min because our past work has shown that a minimum of
10 min of exercise improves cognitive performance (13). Healthy
young adults (SI Appendix, Table S1) were assessed under two
Our previous work has shown that mild physical exercise can
promote better memory in rodents. Here, we use functional
MRI in healthy young adults to assess the immediate impact of
a short bout of mild exercise on the brain mechanisms sup-
porting memory processes. We find that this brief intervention
rapidly enhanced highly detailed memory processing and
resulted in elevated activity in the hippocampus and the sur-
rounding regions, as well as increased coupling between the
hippocampus and cortical regions previously known to support
detailed memory processing. These findings represent a
mechanism by which mild exercise, on par with yoga and tai
chi, may improve memory. Future studies should test the long-
term effects of regular mild exercise on age-related memory
Author contributions: K. Suwabe, K.B., M.A.Y., and H.S. designed research; K. Suwabe,
K.H., A.M., K. Saotome, G.O., and T.F. performed research; Z.M.R., K. Suzuki, Y.S., and
M.A.Y. contributed new reagents/ analytic tools; K. Suwabe, K. B., K.H., Z.M.R., J.M.R.,
A.M., K. Saotome, and M.A.Y. analyzed data; and K. Suwabe, K.B., M.A.Y., and H.S. wrote
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Published under the PNAS license.
Data deposition: All neuroimaging data were deposited with XNAT CENTRAL and are
available at https://central.xnat.org (Acute Mild Exercise). The fMRI scripts were deposited
on GitHub and are available at https://github.com/yassalab/afni_proc_py_pipeline.
K. Suwabe and K.B. contributed equally to this work.
To whom correspondence may be addressed. Email: firstname.lastname@example.org or soya.hideaki.gt@
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
www.pnas.org/cgi/doi/10.1073/pnas.1805668115 PNAS Latest Articles
experimental conditions, control (CTL) and exercise (EX), on
separate days in randomized order (Fig. 1A). A within-subject
design was applied to increase power and reduce the effects of
intersubject variability in the response to exercise. In the EX
condition, participants performed 10 min of very light-intensity
exercise on a recumbent cycle ergometer, with an individualized
load corresponding to 30% of the participant’s
condition, participants sat quietly on the ergometer instead of
performing exercise. All other conditions were held constant. After
10 min in the EX or CTL condition, participants performed the
explicit version of the mnemonic discrimination task described
previously (14, 15). During the study phase, participants were shown
pictures of everyday objects and asked to indicate whether each
item was an indoor or an outdoor item. This was followed by a
recognition test in which participants were asked to identify each
item as either “old”(targets: previously seen items), “similar”
(lures: similar but not identical to previously viewed images), or
“new”(foils: new items not previously seen). The lure stimuli varied
in the degree of mnemonic similarity to the targets, thereby
allowing us to parametrically manipulate the level of interference
(12, 14). Parametric changes in discrimination performance de-
pendent on mnemonic interference levels are strongly associated
with age-related deficits (14), aerobic fitness-related memory im-
provement (16), and functional signals in the DG/CA3 (17). Thus,
the task and its corresponding lure discrimination measure are
appropriate for assessing changes in an individual’s capacity for
DG-mediated pattern separation. In addition, we assessed exercise-
induced psychological mood changes to examine whether an acute
bout of mild exercise leads to increased arousal levels, which may,
in turn, mediate improved hippocampal memory function.
In experiment 2, we assessed the neural substrates of the ob-
served behavioral effects using high-resolution fMRI. Partici-
pants performed a continuous recognition version (combining
the study and test sessions into one continuous session) of a
mnemonic discrimination task in the MRI scanner within ∼5 min
after a 10-min mild exercise session (SI Appendix, Fig. S1). We
compared neural activity during the critical pattern separation
contrast [lure correct rejections (CRs) minus lure false alarms
(FAs)] based on prior study (18) between the EX and CTL
conditions. Moreover, we assessed functional correlations be-
tween hippocampal subfields and cortical regions using psycho-
physiological interaction (PPI) analysis.
Physiological and Psychological Response to Acute Mild Exercise. In
both experiments, we first confirmed that mean heart rate (HR) at
the end of the EX session was within the range of very light in-
tensity according to the ACSM guidelines (SI Appendix,TableS1).
We measured salivary alpha amylase (sAA) and cortisol levels
throughout the experiment. A repeated measures two-way ANOVA
for sAA levels revealed a significant interaction between the
condition and time-point factors [F(2, 36) =6.73, P<0.01; SI
Appendix, Fig. S3C]. Bonferroni-corrected post hoc comparisons
revealed that sAA level in the EX condition was significantly
higher for the postexercise session [F(1, 18) =12.99, P<0.01].
Differences in salivary cortisol levels were not significant be-
tween conditions (SI Appendix, Fig. S3D).
We also measured psychological mood state (arousal and plea-
sure) by the Two-Dimensional Mood Scale. A repeated measures
two-way ANOVA for arousal levels revealed a significant in-
teraction between condition and time point [F(2, 38) =14.01, P<
0.001; SI Appendix,Fig.S3A]. Bonferroni-corrected post hoc com-
parisons revealed that arousal level in the EX condition was sig-
nificantly higher in the postexercise session [F(1, 19) =30.11, P<
0.001], and there was no significant difference between the preex-
ercise session [F(1, 19) =2.29, P=0.15] and the poststudy session
[F(1, 19) =3.74, P=0.07]. Pleasure levels did not differ significantly
between conditions and exhibited no interaction across time points
Mild Exercise Improves Discrimination Performance for Highly Similar
Objects. The response proportions of the mnemonic discrimina-
tion task for each condition in experiment 1 are shown in detail in
SI Appendix,Fig.S2A. The statistical analyses methods applied
were previously validated to extract response bias-corrected in-
dices of performance (12, 14, 15). The key measure of discrimi-
nation (the behavioral correlate of pattern separation) is the lure
discrimination index (LDI), which is defined as P(“similar”jlure)
minus P(“similar”jfoil), calculated separately for each level of
similarity/interference (Fig. 1B) (12). A repeated measures two-
way ANOVA for condition (EX, CTL) and similarity (high, me-
dium, and low similarity) revealed a significant main effect of
condition [F(1, 19) =5.07, P<0.05] and similarity [F(2, 38) =
32.96, P<0.001], and a significant interaction [F(2, 38) =3.80,
P<0.05]. Bonferroni-corrected post hoc comparisons revealed
that the LDI in the EX condition was significantly higher than the
LDI in the CTL condition for the high- [F(1, 19) =13.08, P<
0.01] and medium-similarity lures [F(1, 19) =5.04, P<0.05]. No
difference was detected between conditions, however, for the low-
similarity lures [F(1, 19) <0.01, P=0.93]. In addition, exercise-
induced arousal enhancement (SI Appendix,Fig.S3A) positively
correlated with the LDI improvement for high-similarity lures (r=
0.54, P<0.05), but not with medium- (r=0.05, P=0.83) or low-
(r=−0.36, P=0.12) similarity lures (adjusted significance
threshold using the Bonferroni method; Fig. 1C).
For experiment 2, in an orthogonal sample, we observed that
overall performance of the continuous version of the mnemonic
discrimination task in the MRI scanner was comparable to that
in experiment 1 (SI Appendix, Fig. S2C). In addition, perfor-
mance differed significantly between the EX and CTL conditions
[t(15) =2.61, P<0.05; SI Appendix, Fig. S2D], which served as
an independent replication of the findings in experiment 1.
(ISI:500 ms) 2,000 ms
Study Testindoor/outdoor ? old/similar/new ?
15 min 45 min
in high-sim lures
r = 0.62
h Medium Low
-10 0 10 20
Fig. 1. (A) Outline of the experimental procedures. Participants performed
10 min of exercise or rested (CTL) on different experimental days. After that,
the study phase of the mnemonic discrimination task was administered. Par-
ticipants waited ∼45 min before performing the test phase, an old−similar−new
judgment task using targets, foils, and similar lures to which hippocampal
pattern separation is particularly sensitive. (B) Discrimination performance
assessed by the LDI for high, medium, and low mnemonic similarity bins. Mild
exercise improved the LDI for the high- and medium-similarity bins compared
with the CTL condition. *P<0.05. (C) Increased psychological arousal levels
positively correlated with LDI improvement in high-similarity lures.
www.pnas.org/cgi/doi/10.1073/pnas.1805668115 Suwabe et al.
Exercise Enhances Pattern Separation Related Activity in the Hippocampal
Network. We further examined the critical contrast of fMRI signals
(lure CRs minus lure FAs) with a repeated measures ANOVA,
and limited our analysis of regions of interest (ROIs) to hippo-
campal subregions and the medial temporal lobe. We observed a
main effect for condition [F(1, 15) =18.88, P<0.001] and a
significant interaction between condition and region [F(7, 105) =
5.18, P<0.001]. Holm−Bonferroni-corrected post hoc compari-
sons revealed higher levels of activation in the EX condition
compared with the CTL condition across hippocampal subfields,
including the DG/CA3 [F(1, 15) =9.70, P<0.01], CA1 [F(1, 15) =
10.26, P<0.01], and subiculum [F(1, 15) =16.98, P<0.001; Fig.
2A]. We also observed similar increases in the entorhinal cortex
(EC) [F(1, 15) =10.92, P<0.01] and parahippocampal cortex
(PHC) [F(1, 15) =5.71, P<0.05; Fig. 2B].
Increase in Functional Connectivity Between DG/CA3 and Cortical
Regions. To assess whether exercise affected the functional con-
nectivity between hippocampal and neocortical sites, we conducted
a PPI analysis. Briefly, in both the EX and CTL conditions, we
extracted the time series of activity from seed regions (DG/CA3,
CA1, subiculum, and whole-hippocampus ROIs) during specific
trials (lure CRs and lure FAs). Specifically, we tested the hypothesis
that improved discrimination is mediated by increased functional
connectivity between the DG/CA3 region and extrahippocampal/
cortical regions involved in recall (see Materials and Methods for
details). Seeding the DG/CA3 subfield bilaterally during the lure
CRs condition, we found significant correlations with the left an-
gular gyrus, left fusiform gyrus, and left PHC (P<0.05 family-wise
error corrected; Fig. 3). We further detected a significant positive
correlation between the DG/CA3 and left primary visual cortex,
and a significant negative correlation between the DG/CA3 and
temporal pole (P<0.05 family-wise error corrected; SI Appendix,
Fig. S5). Conversely, when we seeded the whole hippocampus
(including CA1 and subiculum in addition to DG/CA3), we found a
significant correlation between the DG/CA3 region and bilateral
PHC during lure CRs, but not the angular gyrus or fusiform gyrus.
Neither seed region correlated significantly with the DG/CA3 re-
gion during lure FAs. Finally, seeding the CA1 or subiculum did not
reveal significant correlations that survived the corrected threshold.
Thus, the DG/CA3 appears to make somewhat specific contribu-
tions to cortical communication during lure CRs.
Enhanced Functional Connectivity Correlates with Memory Improvement.
We next evaluated whether these correlations were predictive of the
behavioral benefit observed in the EX condition. We calculated a
simple change in the performance metric from the behavioral data
change in the correlation between the PPI seed and target regions.
Behavioral improvement was predicted by higher correlations between
the DG/CA3 and the angular gyrus (r=0.64, P<0.01), fusiform gyrus
(r=0.57, P<0.05), and PHC (r=0.62, P<0.01) across participants
(adjusted significance threshold using the Holm−Bonferroni method;
Fig. 3). No significant correlations were detected between the DG/
CA3 and primary visual cortex or temporal pole. Additionally, no
significant correlations were detected when seeding the whole hip-
pocampus. Thus, specific correlations between the DG/CA3 and
cortical regions known to be involved in detailed forms of memory
seem to predict the exercise-related improvement in behavior.
The findings of the present study demonstrate that acute very
light exercise improves hippocampal memory function, especially
DG-mediated pattern separation. Furthermore, from the results of
high-resolution fMRI analysis, involvement of hippocampal
−cortical networks as an underlying neural basis of memory
improvement has emerged. Although there is a large literature
on exercise effects on the human brain (19, 20), including the
impact of long-term moderate-intensity exercise interventions on
hippocampal volume (21) and DG cerebral blood volume (22),
this study demonstrates rapid enhancement of hippocampal
memory function with acute very light exercise.
The results of experiment 1 revealed that 10 min of very light
VO2peak) exercise improved discrimination perfor-
mance for high- and medium-similarity lures, the more difficult
discrimination conditions. Because the DG/CA3 region is highly
sensitive to small changes in sensory input (23), a hallmark feature of
prior studies of pattern separation is the effect on discrimination
performance for high-, but not necessarily low-, similarity items (11).
The results of experiment 2 independently replicated the behavioral
effect observed in experiment 1 under scanning conditions while
using a continuous recognition variant of the same task. The short
bout (10 min) of mild exercise increased activity specifically in hip-
pocampal subregions, and in the entorhinal and parahippocampal
cortices. Other regions within the scanning field of view, such as the
perirhinal cortex, temporal pole, and amygdala, exhibited no change
in activation, suggesting that this effect is specific to certain brain
regions and not secondary to global brain changes induced by ex-
ercise. This particular network of brain subregions may be involved
in processing sensory input together with the hippocampus, or
representing recalled information as distinct from the current
. . .
DG/CA3 CA1 SUB EC PHC PRC TempPole Amy
Fig. 2. Neural activity profiles in (A) the hippocam-
pus and (B) other ROIs. Values indicate the critical
pattern separation contrast of fMRI signals (lure CRs
minus lure FAs). Of all hippocampal subfields, the EC
and PHC exhibited higher levels of activation during
the EX condition compared with the CTL condition.
*P<0.05. (C) Coronal view of ROI segmentation on a
custom group template. Representative slices are
shown from top to bottom in the anterior−posterior
direction, and ROI demarcations are represented
based on the color key displayed below. Note: PRC,
perirhinal cortex; SUB, subiculum; TempPole, tem-
Suwabe et al. PNAS Latest Articles
experience. Based on context-dependent PPI analysis, exercise in-
creased the functional connectivity between the DG/CA3 and
associated memory cortices (i.e., parahippocampal, angular, and
fusiform gyri) during correct rejection of lures, and the magni-
tude of the enhancement correlated positively with the magni-
tude of the improvement in discrimination performance.
These findings support the hypothesis that mild exercise im-
proves hippocampal memory by facilitating DG/CA3 communi-
cation with surrounding neocortical regions.
The behavioral results in experiment 1 support and extend our
previous findings that acute exercise at moderate intensity, which
is around the LT, positively affects hippocampal memory (12). Mild
exercise does not increase the release of lactate and adrenocorti-
cotropic hormone, and is therefore considered stress-free exercise
(24), as confirmed in the present study by the stable salivary cortisol
levels (SI Appendix,Fig.S3D). Mild exercise is highly practical and
feasible, especially for older adults and individuals with physical
disabilities and low levels of physical fitness. We have previously
shown that acute mild exercise positively improves prefrontal exec-
utive function (7), and here we provide evidence that mild exercise
also improves hippocampal pattern separation, and propose a
mechanistic account for this improvement at the level of hippo-
campal subfields and hippocampal−neocortical communication.
Importantly, the rapid form of plasticity observed is distinct from
previously reported neurogenesis-mediated effects of exercise in-
terventions, which operate on a much longer timescale (1). It is
possible that the increased connectivity we observed is associated
with synaptogenesis that may provide a suitable niche for the sub-
sequent integration of newborn granule cells. Past studies have
shown that a large proportion of newborn granule cells die within a
short period of time if not integrated within functional networks
(25). Perhaps increasing connectivity via exercise allows for this in-
The increased context-dependent functional connectivity ob-
served is consistent with the idea that detailed memories involve
strengthening of shared representations across the hippocampus
and neocortex (26). Correlations with the primary visual cortices,
the angular gyrus, the fusiform gyrus, and the PHC are impli-
cated in rich, vivid recollection processes of visual information,
including the well-known contextual reinstatement effect (27–29).
For instance, the angular gyrus is considered to be a convergence
point between multisensory inputs (serving as an integrator) and
top-down predictions, with a critical role in episodic memory re-
trieval, particularly during successful recollection (28, 30). The
fusiform gyrus is a part of the ventral visual stream (higher-order
visual cortex) and is a key region involved in functionally specialized
computations of higher-level visual features such as object recogni-
tion and face perception (31, 32). Similar processing of complex
features could be employed here in our object discrimination task,
where stimuli are processed holistically (33). The PHC is thought
to be part of a network of brain regions that processes contextual
associations, and is involved in associative memory (27, 29).
Interestingly, a negative correlation between DG/CA3 and the
temporal pole (left) with exercise was observed. The temporal pole is
implicated in general, scheme-based memory (34), and is often im-
plicated in false memory (35). Thus, this negative relationship could
also reflect a sharper, more accurate representation as a result of
exercise. Taken together, we suggest that these brain regions play a
role in representing high-precision memories, and enhanced com-
munication with the DG/CA3 may contribute to improve memory
Although the molecular, synaptic, and chemical bases of the
transient modulation of pattern separation by mild exercise
remain largely unclear, the observed correlation between the
change in psychological arousal and improved cognitive perfor-
mance, similar to our previous findings (7), suggests that mild
exercise-related activation of the arousal system improves hip-
pocampal memory. DG function is regulated by several neuro-
modulatory systems, including cholinergic input from the medial
septum (36). Cholinergic modulation is also thought to be in-
volved in switching hippocampal network modes between recall
and storage (36). Mild exercise such as treadmill walking in an-
imals increases hippocampal acetylcholine concentrations (37).
Up-regulation of acetylcholine by exercise may increase arousal
levels and improve DG-mediated pattern separation.
This study has several limitations. First, in the present experi-
mental design, the effect of exercise on encoding could not be
separated from the effect on storage/consolidation. A poststudy in-
tervention design (15) is required to assess distinct contributions of
exercise to facilitating encoding vs. storage mechanisms. Second, we
adopted a high-resolution blood-oxygen-level−dependent fMRI se-
quence focusing on the medial temporal lobe and posterior pari-
etal regions, and could therefore not adequately assess the activity
of other brain regions that may be involved, e.g., the frontal lobes
(38). Finally, the exercise intensity required to optimize this effect is
unknown. Previous findings in rodents showed that mild exercise
training, compared with intense exercise training, increases sur-
vival and maturation of newborn neurons, and induces the ex-
pression of a larger number of genes and proteins, suggesting that
mild exercise has more molecular effects than explored here (4).
Further studies are needed to evaluate these points.
In conclusion, the present study demonstrates that a single bout of
very light-intensity exercise, comparable to walking at slow pace or
traditional oriental bodywork such as yoga and tai chi, improves
hippocampal pattern separation, possibly by enhancing functional
activity levels across hippocampal subfields and bolstering DG/CA3-
neocortical communication. These transient responses to acute ex-
ercise are a potential basis for hippocampal adaptation to chronic
interventions observed in both humans and animals. This is of par-
ticular significance, since episodic memory loss is present in many
conditions, including Alzheimer’s disease, and much less is currently
known about the utility of milder interventions. Given physical ca-
pacity and activity limitations common to the elderly and vulnerable
populations, the use of mild exercise to slow down or stave off
cognitive decline is a crucial avenue of future exercise investigation.
Materials and Methods
For a full description of all materials and methods, see SI Appendix,SI Ma-
terials and Methods.
Participants. A total of 36 healthy young adults participated in the study; 20
(mean age 20.6 ±1.7 y, 8 women) participated in experiment 1, and 16
(mean age 21.1 ±2.0 y, 12 women) participated in experiment 2. None of
r = 0.64 r = 0.58 r = 0.62
Fig. 3. Results of PPI analyses. (Upper)Voxelswithincorticalregionswith
significantly higher context-dependent (lure CRs) correlations with the hip-
pocampal DG/CA3 in the EX condition compared with the CTL condition.
(Lower) Significant correlations between the extent of PPI connectivity of the
DG/CA3 with the specified cortical region and the enhancement in the LDI
resulting from exercise. These brain−behavior relationships are observed in
the left angular gyrus, left fusiform gyrus, and left parahippocampal gyrus.
www.pnas.org/cgi/doi/10.1073/pnas.1805668115 Suwabe et al.
the subjects reported a history of neurological or psychiatric disorders, or
had a disease requiring medical care. All participants had normal or
corrected-to-normal vision and normal color vision. All participants provided
written informed consent to participate in the study. The University of
Tsukuba Ethics Committee approved the study protocol, which conformed
to the ethical principles of the seventh revision (2013) of the Declaration of
Helsinki. Participants’demographic and physiological characteristics are
presented in SI Appendix, Table S1. Based on our previous studies (7–10, 12)
and sample size determination software G-power (39), 20 and 16 subjects
were considered sufficient to detect a significant difference (d
tween groups on a two-sided, 0.05 test of proportions (difference between
two dependent means [matched pairs]) with >80% power.
Experiment 1 Procedures. All participants underwent the CTL and EX experi-
ments on separate days in a randomized order (Fig. 1A). All experiments were
conducted at the same time of day for each participant, and the experiments
were started between the hours of 1200 and 1800. The two experimental days
were separated by at least 48 h. Participants were also asked to refrain from
exercise and consuming alcohol and caffeine for at least 24 h before the
experiment to control for outside factors that could affect cognitive function.
An outline of the experimental procedures is shown in Fig. 1A. Twenty minutes
after arrival, participants performed 10 min of mild exercise on a recumbent cycle
ergometer (Corival Recumbent; Lode), with an individualized load corresponding
to 30% of the subject’s
VO2peak in the EX condition. We previously reported that
a single 10-min bout of exercise enhances prefrontal activation and executive
function in young adults (7–9); thus we used the same parameters for this ex-
periment. Heart rate (HR) and Borg’s rating of perceived exertion (RPE) (40) were
recorded once every minute during exercise. In the CTL condition, participants sat
on the recumbent cycle ergometer for 10 min and did not pedal. Approximately
5 min after the 10-min exercise or rest period, participants began the encoding
phase of the discrimination task. After completing the encoding phase, the
participants rested for 45 min while they watched a movie (low-arousal stimulus)
without sound to avoid falling asleep. After the rest period, participants per-
formed the retrieval phase of the mnemonic discrimination task.
Mnemonic Discrimination Task. The task used in this study consisted of an
encoding and retrieval phase (Fig. 1A). In the encoding phase, participants
viewed a series of 196 color photographs of everyday objects on a white
background on a computer screen and were required to judge whether the
item displayed represented an indoor or outdoor object. In the retrieval
phase, participants viewed a series of 256 color photographs of various
objects and were asked to identify each item as “old,”“similar,”or “new”
by pressing buttons. Sixty-four (25%) of the presented items in the retrieval
phase were “old,”or exact repetitions of those presented in the encoding
phase (targets); 128 (50%) of the items were “similar”to those seen during
the encoding phase, but not identical (lures); and 64 (25%) were “new”
items not previously presented (foils). In both phases, each picture was
presented for 2 s with a 0.5-s interstimulus interval. All participants un-
derwent a practice session (four encoding items, eight retrieval items) to
confirm their understanding of the task instructions and procedures, using
photographs that were not included in the experimental task sets.
The task measures discrimination performance for lures with varying de-
grees of similarity. The lure stimuli were stratified into three bins, namely
high-, medium-,and low-similarity lures,based on the discriminationrating for
each similar object pair to the targets. The ratings were based on testing in
an orthogonal data set with n>100 adults to arrive at ratings that are highly
stable and reliable (41). This is superior to using a simple perceptual similarity
rating or an automated computer algorithm to determine similarity based on
features, as it takes into account the level of familiarity and “confusability”of
specific object classes. We have used the same approach in numerous studies
in the past, and it has been replicated across multiple laboratories (see ref. 42
for recent review). The LDI was calculated as the probability of correctly
responding “similar”when presented with similar lure objects minus the
probability of incorrectly responding “similar”when presented with novel foil
objects [p (similarjlure) −p(similarjnew)] for each similarity bin. Subtraction
was used to correct for any bias in selecting “similar”overall.
Experiment 2 Procedures. The overall experimental design and procedure was
the same as for experiment 1 (SI Appendix, Fig. S1). The recumbent er-
gometer was placed in the anteroom of the MRI scanner. After the 10-min
period of exercise or rest, the participants were quickly placed into the
scanner as instructed before the experiment. HR was calculated from the
continuous signal derived from an MRI-compatible pulse oximeter (4500 MRI
Pulse Oximeter; Invivo) placed over the left index finger (SI Appendix, Fig.
S4). Before beginning the memory task, 12 images of high-speed echoplanar
single shot (five images for coronal plane, seven images for sagittal plane)
were obtained to fix the imaging area of the functional echoplanar imaging
(EPI) scans. All participants started the task within ∼5 min after the end of the EX
or CTL session (mean 5 min 31 s ±17.2 s). The exercise−scan interval was set to 5
min because noncerebral hemodynamic variables such as middle cerebral artery
mean blood velocity and skin blood flow increase following 10 min of very light-
intensity exercise and return to basal levels within 5 min (43). Structural
magnetization-prepared rapid gradient echo (MPRAGE) scans were collected for
anatomical localization and cross-subject alignment, followed by functional EPI
scans on the first experimental day for each participant.
MRI Data Acquisition. Neuroimaging data were acquired on a 3.0 Tesla Philips
scanner with a 32-channel sensitivity encoding (SENSE) head coil at the Center
for Cybernics Research at the University of Tsukuba. Functional images were
collected using a high-speed T2*-weighted EPI sequence with an acquisition
matrix size of 64 ×64, repetition time of 2,000 ms, echo time of 35 ms, flip
angle of 70°, field of view (FOV) of 96 ×96 mm, SENSE parallel reduction
factor of 2, and in-plane resolution of 1.5 ×1.5 ×1.5 mm. Each volume
comprised 19 oblique 1.5-mm-thick axial slices with no gap parallel to the
principal axis of the hippocampus and covered the medial temporal lobe
bilaterally. Each run comprised 144 trials, and each trial was presented for
2,000 ms with a 500-ms interstimulus interval. Four initial “dummy”volumes
were acquired to ensure MR signal stabilization. Each subject completed four
functional runs. We also collected a high-resolution structural scan using an
MPRAGE T1-weighted sequence with an FOV of 384 ×384 mm, repetition
time of 12 ms, echo time of 5.9 ms, and flip angle of 9°, comprising 250
oblique slices with 0.65-mm isotropic resolution after functional runs of the
EX or CTL session. All images for each subject are uploaded in XNAT CENTRAL
(https://www.re3data.org/repository/r3d100010874; Acute Mild Exercise).
fMRI Data Analysis: General Linear Model Regression. Only test data are in-
cluded in the analyses. Behavioral vectors based on the trial type (i.e., target
hits and misses, lure CRs and FAs) were used to model the data using a
deconvolution approach based on multiple linear regression (3dDeconvolve).
Deconvolution of the hemodynamic response was achieved using tent
functions covering stimulus onset to 12 s after onset with six estimator
functions distributed across this time window. In addition to modeling trials
of interest, motion parameters were entered into the model as explicit repressors
to reduce the effect of motion on task-related parameter estimates. Additionally,
global signals from white matter and ventricles were regressed from the modeled
signal in the gray matter using ANATICOR (44), conforming to the rigorous data
scrubbing procedures recommended by Power et al. (45). These scripts, in
addition to those for preprocessing as outlined in MRI Data Acquisition,are
available at https://github.com/yassalab/afni_proc_py_pipeline.
The statistical fit coefficients resulting from the regression analysis represent
the difference in activity between trial types and the baseline (novel foil trials) for
a given time-point in a voxel. The sum of the fit coefficients over the expected
hemodynamic response (3–12 s after trial onset) was taken as the model’ses-
timate of the relative response to each trial type. Group analyses were per-
formed using a two-way analysis of variance (ANOVA) with trial type and
condition (EX vs. CTL) as fixed factors, and participant as a random factor, nested
within condition. Each participant’s overall F-statistic (i.e., activity that was
modulated by any aspect of the task) was thresholded at P<0.05 with a cluster-
corrected threshold of 19 voxels to create a mask of “task-active”voxels, which
was then combined with anatomical ROIs to create new hybrid functional/
structural ROIs. Importantly, use of the overall F-statistic eliminates concerns
about circularity because the voxels were not selected based on the contrast of
interest (17, 46). Voxels in these ROIs were collapsed and the mean activity in
each ROI was extracted to conduct second-level analyses. This approach reduces
voxel-selection biases and enhances the signal-to-noise ratio. This yielded eight
bilateral ROIs in the hippocampus (DG/CA3, CA1, and subiculum), cortical re-
gions (temporopolar cortex, PRC, EC, and PHC), and amygdala. A contrast of
activity during lure CRs vs. lure FAs was calculated. Subsequent testing was
conducted using a two-way repeated measures ANOVA with condition (EX and
CTL) and region (DG/CA3, CA1, subiculum, temporopolar cortex, PRC, EC, PHC,
amygdala). We kept all hippocampal subfield ROIs as bilateral ROIs and did not
split them by hemisphere to reduce the number of comparisons and because we
had no a priori reason to separate right from left in this particular task. When a
significant main effect or interaction was detected by the ANOVA, we adjusted
the significance threshold using the Holm-Bonferroni method to parse the ef-
fect with post hoc comparisons.
fMRI Data Analysis: Interregional Correlations and Interactions. We performed
a generalized PPI analysis, also termed context-dependent correlation
analysis (47), with the test data. Details of these analysis steps in Analysis of
Suwabe et al. PNAS Latest Articles
Functional NeuroImages can be found here: (https://afni.nimh.nih.gov/CD-
CorrAna). Briefly, a positive correlation indicates a positive relationship be-
tween significant voxels and a seed region in a given condition, whereas a
negative correlation indicates a negative relationship.
In the generalized PPI analysis, we individually modeled correlations
during target hits and misses, as well as lure CRs and FAs (i.e., the “psy-
chological variables”). In these data, we were particularly interested in lure
discrimination, so we focused our analyses on lure trials. Given that pattern
separation performed by the DG/CA3 is involved in lure discrimination, we
generated a seed time series in the bilateral DG/CA3 (3dmaskave; i.e., the
“physiological variable”) and detrended the time series (3dDetrend; this
seed time series features the same data scrubbing steps described previously).
After transposing the detrended time series as a column vector (1dtranspose),
we generated a canonical hemodynamic response function (waver) and used
this to extract the expected contributions of blood-oxygen-level−dependent
signaling to the time series and generate an up-sampled “neural time series”
(3dTfitter). We next multiplied the resulting neural time series with stimulus
timing files (timing_tool.py and 1deval), which were then convolved with the
canonical hemodynamic response function to create the interaction regressor
(waver). This regressor was entered into a general linear model (3dDe-
convolve), where the ensuing beta weights reflect the degree of context-
dependent correlations with the seeded region. To limit the effect of voxels
on the edge of the functional acquisitions whose susceptibility to motion and
partial volumes can induce spurious correlations, we removed the outermost-
edge voxels from the correlation maps before the regression analysis
(3dZeropad). To test whether any resultant significant correlations were
specific to DG/CA3, we also repeated these steps using each bilateral whole-
hippocampus, CA1, and subiculum seed.
To visualize correlational structures across the brain, PPI analyses were
voxel-based rather than ROI-based. In these cases, we applied the appropriate
statistical corrections. Individual subject maps were analyzed at the group
level using ttests (3dttest++). Voxels in the group analysis were considered
significant at P<0.05 corrected for family-wise error rate (parameters are as
reported in the ttest over the general linear model analysis described in
fMRI Data Analysis: General Linear Model Regression). Illustrative voxels in
the statistical maps were thresholded as described in fMRI Data Analysis:
General Linear Model Regression.
ACKNOWLEDGMENTS. We thank members of the Laboratory of Exercise
Biochemistry and Neuroendocrinology for their assistance with data collection.
This work was supported, in part, by the Special Funds for Education and
Research of the Ministry of Education, Culture, Sports, Science, and Technology
1111501004 (to H.S.); the Japan Society for the Promotion of Science Grants
HFH27016 (to H.S.), 16H06405 [“Creation and promotion of WILLDYNAMICS”
(to H.S.)], 18H04081 (to H.S.), and 16K20930 (to K.B.); the US National Insti-
tutes of Health Grants R01MH102392, R21AG049220, R01AG053555, and
P50AG16573 (to M.A.Y.); and the Center for Exercise Medicine and Sport
Sciences at the University of California, Irvine.
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