A Healthy Brain in a Healthy Body: Brain Network
Correlates of Physical and Mental Fitness
Linda Douw1,2*, Dagmar Nieboer3, Bob W. van Dijk4, Cornelis J. Stam4, Jos W. R. Twisk3
Abstract
A healthy lifestyle is an important focus in today’s society. The physical benefits of regular exercise are abundantly clear, but
physical fitness is also associated with better cognitive performance. How these two factors together relate to
characteristics of the brain is still incompletely understood. By applying mathematical concepts from ‘network theory’,
insights in the organization and dynamics of brain functioning can be obtained. We test the hypothesis that neural network
organization mediates the association between cardio respiratory fitness (i.e. VO2 max) and cognitive functioning. A healthy
cohort was studied (n = 219, 113 women, age range 41–44 years). Subjects underwent resting-state eyes-closed magneto-
encephalography (MEG). Five artifact-free epochs were analyzed and averaged in six frequency bands (delta-gamma). The
phase lag index (PLI) was used as a measure of functional connectivity between all sensors. Modularity analysis was
performed, and both within and between-module connectivity of each sensor was calculated. Subjects underwent a
maximum oxygen uptake (VO2 max) measurement as an indicator of cardio respiratory fitness. All subjects were tested with
a commonly used Dutch intelligence test. Intelligence quotient (IQ) was related to VO2 max. In addition, VO2 max was
negatively associated with upper alpha and beta band modularity. Particularly increased intermodular connectivity in the
beta band was associated with higher VO2 max and IQ, further indicating a benefit of more global network integration as
opposed to local connections. Within-module connectivity showed a spatially varied pattern of correlation, while average
connectivity did not show significant results. Mediation analysis was not significant. The occurrence of less modularity in the
resting-state is associated with better cardio respiratory fitness, while having increased intermodular connectivity, as
opposed to within-module connections, is related to better physical and mental fitness.
Citation: Douw L, Nieboer D, van Dijk BW, Stam CJ, Twisk JWR (2014) A Healthy Brain in a Healthy Body: Brain Network Correlates of Physical and Mental
Fitness. PLoS ONE 9(2): e88202. doi:10.1371/journal.pone.0088202
Editor: Renaud Lambiotte, University of Namur, Belgium
Received October 5, 2013; Accepted January 9, 2014; Published February 3, 2014
Copyright:  2014 Douw et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was funded by grants from the Dairy Foundation, the Netherlands Heart Foundation, the Dutch Prevention Fund, Heineken BV, the Ministry
of Public Health, Wellbeing and Sport (VWS), the Scientific Board of Smoking and Health, the VU University and the VU University Medical Center. The funders had
no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have the following interests: This study was funded in part by Heineken BV. Co-author Prof. CJ Stam is a PLOS ONE Editorial
Board member. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
* E-mail: linda.douw@gmail.com
Introduction
A healthy lifestyle is a major focus in today’s society. Regular
exercise and adequate physical fitness have proven to be important
for the immune system, metabolism, prevention of infectious
disease, skeletal functioning, and risk of cancer [1–6]. In addition
to these physical benefits, cardiorespiratory fitness is also related to
better cognitive functioning [7]. Several neural factors have been
reported to mediate the relationship between mental and physical
fitness, including increased neural vascularization [8], increased
production of brain derived neurotrophic factor (BDNF; [9]),
increased hippocampal volume [10], and higher levels of N-
acetylaspartate [11], although none of these mediators fully
explain the reported associations.
Another framework that has elucidated the neural correlates of
the association between cognition and physical fitness is resting-
state functional connectivity, as measured with functional mag-
netic resonance imaging (fMRI). The resting-state, during which
no task is present and alert relaxation is achieved, can be
characterized by several highly robust networks [12,13], of which
the default mode network (DMN) is the most stable and best
studied example [14,15]. This network seems to be the functional
backbone of the brain [16], and is implicated in almost all
neurological diseases. With respect to cardiorespiratory fitness,
higher connectivity within the DMN (as measured by seeding the
posterior cingulate cortex and examining its significantly correlat-
ed regions) is associated with better fitness level, and DMN
connectivity mediates the association between physical fitness and
cognitive functioning [17]. Moreover, a one-year aerobic training
intervention in older adults improves functional connectivity
within several resting-state networks, including the DMN and
the fronto-parietal network, which is thought to be important for
working memory [18]. Conversely, overweight adults show
increased DMN connectivity, which normalizes after a six month
exercise program [19]. The important role of functional connec-
tivity in the relationship between physical fitness and cognition was
confirmed in another study by Voss and colleagues, showing that
the association between exercise and connectivity is related to
BDNF, insulin-like growth factor type 1 (IGF-1), and vascular
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1 Department of Neurology, Vrije Universiteit Medical Center, Amsterdam, The Netherlands, 2 Department of Anatomy and Neurosciences, Vrije Universiteit Medical
Center, Amsterdam, The Netherlands, 3 Department of Epidemiology and Biostatistics, Vrije Universiteit Medical Center, Amsterdam, The Netherlands, 4 Department of
Clinical Neurophysiology and Magnetoencephalography Center, Vrije Universiteit Medical Center, Amsterdam, The Netherlands, 5 Department of Epidemiology and 
Biostatistics, EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands
endothelial
growth
factor
(VEGF),
which
are
markers
for
neuroplasticity [20].
However, fMRI is an indirect measure of neural functioning, as
it measures the slowly operating process of blood oxygenation.
Functional connectivity can also be determined frommagnetoen-
cephalography (MEG), which is a much more direct measure of
neural activity. Furthermore, functional connectivity in general
can be used as a starting point for more extensive, higher-order
analysis of the entire brain network using graph theory [21–23].
This type of analysis has shown that the brain network is very
comparable to many simpler biological and sociological systems
[24]. This elegantly theory-governed but still data-driven property
has made the application of network theory to the brain a very
interesting endeavor. For instance, the brain network is a ‘small-
world’, combining local segregation with global integration [24–
27]. Brain network topology is to a large extent genetically
determined [28–30] and is disturbed in several neurological
diseases [21,31].
The functional brain network also correlates with global
cognitive functioning and intelligence [32,33], indicating network
theory may add relevant information on neural correlates of
functioning above connectivity alone. Important information
about the integrity of the (brain) network can be extracted by
looking at modularity. Modules are clusters of nodes, or brain
areas, that are highly connected to each other, but much less to
nodes outside of their own module [34]. In the brain, five to seven
modules can be discerned, which correspond to major functional
systems [35]. Moreover, the role that specific brain areas play both
within their module and in connecting other modules has proven
relevant to brain functioning [36–38].
In this study, physical fitness, intelligence, and their neural
correlates in terms of network modularityare investigated. We
aimed to prove that VO2 max, a measure of cardio respiratory
fitness,is related to modular network topology based on MEG in a
large group of healthy subjects. Furthermore, we hypothesized that
intelligence is associated with physical fitness mediated through
brain network topology in terms of modular organization.
Materials and Methods
Ethics statement
This study was approved by the Medical Ethical Institutional
Review Board of the VU University Medical Center. All subjects
gave written informed consent before participation. This study was
carried out in accordance with the Declaration of Helsinki.
Subjects
All subjects participated in a prospective longitudinal study,
originally investigating natural development of growth, health and
lifestyle of adolescents, the Amsterdam Growth and Health
Longitudinal Study (AGHLS). This cohort study started in 1976
with four annual measurements and continued with an extensive
number of assessments with five to seven year time intervals[39].
All participants were born between 1961 and 1965 and were
residents of the Netherlands. First- and second-year pupils from
two equally large secondary schools were recruited. In 2006, MEG
recordings of the remaining 344 participants (who were all
between 41 and 44 years old) were obtained, in addition to the
health parameters that were investigated at each time-point of the
AGHLS [40]. These data are not publicly available at this point.
Physical fitness
Physical fitness was measured with a maximal running test on a
treadmill (Quinton 18–54; Quinton, Bothell, Wash), with a speed
of 8 km/h and increasing slope (every 2 minutes) and with direct
measurements of oxygen uptake (Ergoanalyzer; Jaeger, Bunnik,
the Netherlands). Maximum oxygen consumption (VO2 max) was
used as a measure of physical fitness (Kemper, 1995). This
measurement was performed approximately six years before MEG
recording.
Cognitive performance
Subjects underwent a cognitive test battery at the time of MEG
recording, to assess full-scale intelligence. The test battery
administered included the shortened Groninger Intelligence Test
(GIT [41]), which is a commonly used Dutch intelligence test.
Three subtests of the entire GIT were used, constituting the short
version of the test to assess intelligence [42]. Completion of the test
took approximately 45 minutes per subject.
Magnetoencephalography
Magnetic fields were recorded for five minutes while subjects
were seated inside a magnetically shielded room (Vacuumsch-
melze GmbH, Hanau, Germany), using a 151-channel whole-
head MEG system (CTF SystemsInc., Port Coquitlam, BC,
Canada). A third-order software gradient was used after online
band-pass filtering between 0.25 and 125 Hz. Sample frequency of
recording was 625 Hz. Fields were measured during a no-task,
eyes-closed condition of five minutes. At the beginning and ending
of each recording, the head position relative to the coordinate
system of the helmet was recorded by passing small alternating
currents through three head position coils attached to the left and
right pre-auricular points and the nasion on the subject’s head.
For each subject, the first five artifact-free epochs of 4096
samples (6.554 s) were selected by one of the authors [BWvD]. All
data analyses were performed using BrainWave [CJS, version
0.9.58, available from http://home.kpn.nl/stam7883/brainwave.
html]. Before calculating connectivity and network topology,
epochs were band-pass filtered into the commonly used frequency
bands delta (0.5–4 Hz), theta (4–8 Hz), lower alpha (8–10 Hz),
upper alpha (10–13 Hz), beta (13–30 Hz), and gamma (30–
45 Hz). All further analyses were performed for these bands
separately. The average relative power in the six abovementioned
frequency bands was calculated in each subject using a fast Fourier
transform as described in [43].
Phase Lag Index (PLI)
As a measure of functional connectivity, the phase lag index
(PLI) was used[44], which calculates the asymmetry of the
distribution of (instantaneous) phase differences between two
time-series. This asymmetry can be obtained from a time series of
phase differences DW (tk), k = 1… N samples:
PLI~ vsign sin D
tk
ð Þ
ð
Þ
½
w
j
j
The phase difference DQ is defined in the interval [2p, p] and
,. denotes the mean value. Volume conduction causes a zero
phase lag between two time-series, but the presence of a consistent,
non-zero,
phase
lag
between
two
time-series
reflects
true
interactions that are unaffected by volume conduction or common
sources.
Modularity, between and within module connectivity
First, to describe modularity in the whole-brain network we
used a version of previously described approaches[45], adapted for
weighted networks [37,46]:
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Qw
m ~
X
m
s~1
ls
L{
ds
2L

2
where m is the number of modules, ls is the sum of the weights of
all links in module s, L is the total sum of all weights in the
network, and ds is the sum of the strength of all nodes (i.e. the
summed weights per node) in module s. In short, the relation
between intra- and intermodular connections determines the
strength of each module. This measure describes modularity by
summing the relative strength of all the network modules, which
takes both within and between module connections into account.
A strongly modular network has modularity value close to 1, while
modularity is closer to 0 (but not absent) in a random network.
Finding the optimal modular organization in a network is a
computationally intensive problem. Simulated annealing can
overcome part of this issue, and was used in the current study
[45]. Initially, each of the N nodes was randomly assigned to one
of m possible clusters, where the initial m was taken as the square
root of N. At each step, one of the nodes was chosen at random,
and assigned a different randomly chosen module number from
the interval [1,N]. Modularity was calculated before and after this.
The cost C was defined as {Qw
m. The new partitioning was
preserved with probability p: if the final cost Cf was lower or equal
to the initial cost Ci (indicating no added cost when preserving the
partition), p was equal to 1. If Cf was higher than Ci, p was
calculated as follows:
p~ e{
cf { ci
T
The temperature T was 1 initially, and was lowered once every
100 steps as follows: Tnew = 0.995 Told. In total, the simulated
annealing algorithm was run for 106 steps. The partition with the
strongest modular organization (highest Q) was identified sepa-
rately for each epoch of every person for all the different frequency
bands, and subjected to further graph analysis.
Once the modular organization in a network has been
determined, the topological role of individual nodes can be
described in greater detail: nodes can be mainly involved in
communication with other nodes in the same module, but can also
preferably interact with other modules (see figure1). This aspect is
quantified by two properties: the within-module degree (Zi), and
the participation coefficient (PC) [45]. The within-module degree
measures the connectivity of the node within the module
compared to the other nodes in the same module, and thus
describes the relative importance within the module. It was
defined as follows:
zw
i ~ kw
i mi
ð
Þ{ k
w mi
ð
Þ
skw mi
ð
Þ
Here, mi is the module containing node i, kw
i mi
ð
Þ is the within
module strength of node i (the sum of all weights of the links
between i and all other nodes in mi), and k
w ( mi ) and skw mi
ð
Þ are
the respective mean and standard deviation of the within-module
mi degree distribution.
The participation coefficient expresses how strongly a node is
connected to other modules, and the weighted version is defined
as:
PCi ~1{
X
m[M
kw
i m
ð Þ
kw
i


M is the set of modules, and kw
i is the sum of all weights of the
links between i and all nodes in module m. The within module
degree and the participation coefficient determine the identity of a
node in the modular network structure.
Statistical analysis
Statistical analyses were performed using PASW Statistics
package (version 20.0) and Matlab version r2012b. Differences
between men and women regarding VO2max, IQ, and average
head surface were tested using Student’s t-tests. The association
between intelligence and VO2 max was analyzed using a linear
regression with IQ as the dependent variable and VO2 max as
independent variable, adjusted for sex. The association between
VO2 max and modular network topology was investigated with
regression analysis in which modularity indices per frequency band
were the dependent variables and VO2 max the dependent
variable, adjusted for sex, head surface size, and relative power per
frequency band. Similar analyses were used to explore average
between-module connectivity. Finally, the relationships between
VO2 max, network modularity, and intelligence were further
studied using mediation analyses. Mediation analysis investigates
whether a third parameter underlies an observed relationship
between two variables, meaning that the third variable governs the
Figure 1. Schematic representation of modularity and modular
connectivity. Note. In (a), two modules can be discerned. These
modules show high within-module connectivity, but low between-
module connectivity. (b) depicts two nodes in the network that are
characterized by high within-module connectivity, while (c) shows a
node with very high between-module connectivity.
doi:10.1371/journal.pone.0088202.g001
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association between the other two. In our study, we hypothesized
that the association between VO2 max and intelligence is
mediated by brain network modularity. This mediation model
was tested using the INDIRECT PASW statistics plug-in [47].
Direct and indirect effects between the dependent and indepen-
dent variables as well as the mediator were tested with regression
analyses (adjusted for significant abovementioned covariates), after
which 95% CIs were calculated for the total indirect effects using
bootstrapping (5,000 samples) as an unbiased means of testing
whether the mediation model was valid. The presence of a
mediation effect signifies that instead of having a direct causal
effect between the independent variable (VO2 max) and depen-
dent variable (IQ), the mediator (modular network topology) plays
an important role in the association between these two variables.
Results
Subject characteristics
At this time-point in the AGHLS study, 344 healthy subjects
participated. Our strict inspection of artifacts in the MEG
recordings caused exclusion of 79 subjects. Fourteen subjects were
excluded after examination of their intelligence scores, because
they performed well below average (,75). Of the remaining 251
subjects, VO2max measurements were performed in 219 subjects,
in whom all subsequent analyses were performed (see table1 for
subject characteristics). Subjects were on average 42 years old
(range 41–44). With respect to IQ, men and women did not differ
(t(217) = 1.005, p = 0.316). Men did have higher VO2 max
(t(217) = 14.006, p,0.001) and greater head surface in cm2
(t(217) = 8.979, p,0.001). To ascertain that network topology
results were not confounded by head surface size, this variable was
used as a covariate in all analyses. Four MEG sensors were
malfunctioning at the time of data collection, and these were
excluded from further analysis in all subjects.
The last VO2 max measurement took place six years before
MEG recording, when subjects were approximately 36 years old.
In order to investigate whether this gap could induce large changes
in physical fitness, we examined data from previous measurements
in the AGAHLS cohort. These measurements were performed at
13, 14, 15, 16, 21, 27, 29 and 32 years of age in subgroups of the
total cohort (with group sizes varying between 70 and 227
subjects). When looking at the consistency of VO2 max over these
time points, there is strong consistency within subjects over time
(see supplementary figure1), with an average correlation coefficient
R = 0.773 from one time point to the next. When comparing the
first adult measurement at 21 years old and the measurement used
in the remainder of this study at 36 years old (94 subjects
overlapping), the correlation coefficient is 0.791. Furthermore,
subjects who experienced major health burdens possibly influenc-
ing their lifestyle were excluded, which also ensures the stability
and consistency of the VO2 max measurements up to MEG and
IQ measurements six years later.
Physical fitness, intelligence and brain modularity
The previously reported association between physical fitness
and intelligence was confirmed: VO2 max was a significant
predictor of intelligence in a linear regression model (B = 0.322,
95% CI [0.049 0.594], p = 0.021). We then set out to investigate
our hypothesis concerning the association between physical fitness
and brain network topology. Lower modularity in the upper alpha
and beta bands was related to higher VO2 max (upper alpha band
B = 21.81, 95% CI [23.31 20.315], p = 0.018; beta band
B = 21.167, 95% CI [21.753 25.81], p = 0.017), adjusted for
sex, head surface, and relative power per frequency band (see
table2 for results of all frequency bands).
In order to confirm that these associations were indeed due to
network topology instead of global connectivity levels, we
performed an ANOVA with VO2 max as dependent variable
and both modularity and average connectivity in the upper alpha
and beta bands as independent variables. While the upper alpha
and beta connectivity indices did not yield significant results
(p = 0.529 and p = 0.869, resp.), modularity indices were signifi-
cantly related to VO2 max (p = 0.016 and p = 0.012, resp.). The
number of modules in the upper alpha and beta bands was not
associated with VO2 max, indicating that it was not the number of
modules that mattered, but the connectivity patterns within and
between those modules.
Modular connectivity
We then investigated the associations of between and within
module connectivity with VO2 max in these two frequency bands.
Results show that in the beta band, higher VO2max was related to
increased between-module connectivity (B = 0.674, 95% CI [0.101
1.246], p = 0.021), indicating indeed that physical fitness is
Table 1. Subject characteristics.
Total group (N = 219)
Men (N = 106)
Women (N = 113)
Mean age in years (SD)
42 (0.7)
42 (0.7)
42 (0.7)
IQ score (SD)
108 (13)
109 (13)
108 (13)
Head surface in cm2 (SD)
231 (19)**
242 (17)
222 (15)
VO2max (SD)
46 (8.6)**
52 (7.0)
40 (5.5)
Note ** = p,.01, significant gender difference.
doi:10.1371/journal.pone.0088202.t001
Table 2. Associations between band-specific modularity and
VO2 max.
B
95% CI (B)
p-value
Delta band modularity
0.435
[-.080 0.167]
0.487
Theta band modularity
20.390
[21.64 0.086]
0.540
Loweralphamodularity
0.509
[20.121 2.23]
0.561
Upperalphamodularity
21.81
[23.31 23.15]
0.018*
Betamodularity
21.17
[21.75 25.81]
0.017*
Gamma modularity
211.1
[280.9 58.6]
0.754
Note. * = p,0.05. Sex, relative power in each frequency band, and skull size
were entered as covariates in each regression.
doi:10.1371/journal.pone.0088202.t002
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important for better intermodular integration. Moreover, an
ANOVA with both between-module and average connectivity as
covariates shows significant results for between-module connec-
tivity only (p = 0.039 and p = 0.919, resp.), further underlining the
added
value
of modularity-based
connectivity
over
regular
connectivity alone.
Figure2a shows significant associations between VO2 max and
between-module connectivity per channel for both upper alpha
and beta bands, after correcting for the number of tests performed
with the false discovery rate (FDR, q,0.05 [48]). These maps
confirm the analysis of averaged between-module connectivity,
and show positive correlations between VO2 max and between-
module connectivity throughout the brain in the beta band, but
not the upper alpha band.
Due to the nature of the within-module calculation (i.e. within-
subject z-score is computed), no global average can be computed
for this measure. However, figure2b displays significant within-
module connectivity associations with VO2 max in the upper
alpha and beta bands, indicating that higher within-module
connectivity in the central areas is positively associated with VO2
max, while the within-module connectivity within lateral temporal
areas is negatively associated with physical fitness.
Modularity and between-module connectivity as VO2
max – IQ mediators
Finally, the associations between intelligence, VO2 max, and
brain modularity were analyzed using mediation analyses. Our
hypothesis was that better physical fitness leads to better cognitive
performance and thus higher IQ later, through the mediating
effect of brain network modularity (see figure3). This hypothesis
was not confirmed. Although separate regressions of the associ-
ations between both VO2 max and network characteristics and
intelligence were significant, the mediation effects, as evidenced by
significance levels and 95% confidence intervals through 5,000
bootstrapping samples, were not (see table3). This indicates that
although modularity, VO2 max and IQ are interrelated, the
association between VO2max and intelligence is not statistically
explained by modularity. Exploratory mediation analyses using
different dependent, independent, and mediating variables also did
not yield significant results.
Discussion
Physical fitness and cognitive functioning are related. We show
that this relation is also associated with topology of the functional
brain network during the resting-state. Decreased upper alpha and
beta band modularity were related to higher VO2 max, with
higher beta between-module connectivity being associated with
better physical fitness. Average functional connectivity did not
show this association with VO2 max. The association between
Figure 2. Significant sensor-specific associations between
modular connectivity and VO2 max. Note. (a) shows an FDR-
corrected t-map of significant associations between alpha band and
beta band (left and right panel, resp.) between-module connectivity
and VO2 max, while (b) shows the same for within-module connectivity.
Warm colors indicate positive associations, cool colors refer to negative
associations.
doi:10.1371/journal.pone.0088202.g002
Figure 3. Graphical representation of hypothesized mediation
effect. Note. A mediating effect of brain network topology on the
association between VO2 max (maximum oxygen uptake during an
effort test) and intelligence quotient (IQ) was hypothesized.
doi:10.1371/journal.pone.0088202.g003
Table 3. Mediation analyses of network topology on the
association between physical fitness and intelligence.
Upper alpha band modularity (total 95% CI
[20.042 0.055])
Beta
p
VO2max - upper alpha modularity
20.216
0.018*
VO2max - IQ total
0.187
0.046*
VO2max - IQ direct
0.185
0.052
Upper alpha band modularity mediation
20.010
0.885
Beta band modularity (total 95% CI [20.057
0.058])
Beta
p
VO2max - beta modularity
20.197
0.018*
VO2max - IQ total
0.187
0.046*
VO2max - IQ direct
0.188
0.049*
Beta band modularity mediation
0.004
0.959
Beta band PC (total 95% CI [20.032 0.074])
Beta
p
VO2max - beta PC
0.206
0.021*
VO2max - IQ total
0.187
0.046*
VO2max - IQ direct
0.184
0.053
Beta band PC mediation
0.013
0.854
Note. * p,0.05, CI = total confidence interval of indirect effects, based on 5,000
bootstrap samples.
Adjusted for sex, head surface, and relative band power. PC = participation
coefficient.
doi:10.1371/journal.pone.0088202.t003
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cardiorespiratory
fitness
and
intelligence
was
however
not
statistically mediated by network characteristics.
Modularity refers to the extent to which the brain can be
subdivided into coherent subsystems. Although such a modular
organization is generally beneficial for brain functioning [35],
having consistently tight connectivity within modules may be
detrimental. Our results show negative correlations between
modularity and both mental and physical functioning, indicating
that higher levels of within-module connectivity versus between-
module connectivity may be related to decreased functioning. An
MEG study comparing modularity during several conditions of a
working memory task reports decreasing modularity, i.e. increas-
ing intermodular communication, as effort increases [49]. A study
compiling a large number of task-related fMRI and PET studies
also shows the importance of the modular organization of the
brain for cognitive functioning [38]. However, how modularity
relates to healthy functioning during the resting-state has not been
reported.
Another resting-state study using modularity reports increased
delta and theta band modularity in Alzheimer’s patients when
compared to healthy controls, which was related to poorer
performance on a fluency task [37]. Furthermore, an fMRI study
during
task
performance
did
not
find
changes
in
overall
modularity over consecutive learning sessions, but does report
that the flexibility of particular nodes, i.e. the number of times that
each node in the network changes its belonging to specific
modules, was related to better performance [50]. That is, having a
highly dynamic modular structure, as opposed to a fixed modular
division, was related to better functioning. These task-based
findings
concerning
network
flexibility
have
recently
been
replicated, localizing these multi tasking nodes mainly in the
fronto-parietal network [51]. Our findings indicate that the
resting-state is characterized by lower modularity and increased
between-module, possibly long-range connections in brighter and
fitter individuals. It would be interesting to investigate the
transition from resting-state to any task, which may indicate that
the resting-state modular flexibility of the brain network is similar
to task-based dynamics.
The effects of modularity and between-module connectivity
were present in the upper alpha and beta bands. These frequency
bands havebeen studied extensively with respect to cognitive tasks,
albeit mostly with respect to power and not connectivity or
network properties. The (upper) alpha band has been related to
attention and working memory [52–55], while the beta band has
been implicated in learning, novelty detection, and reward
evaluation, indicating that this oscillation might be an important
mechanism for directing attention towards a novel stimulus [56–
59]. A previous study used electroencephalography (EEG) to
investigate connectivity and network efficiency during a task in
active versus sedentary subjects [60]. Results show that in the beta
band, active subjects show greater connectivity and network
efficiency than sedentary subjects. Similar results were obtained
when using coherence as a measure of connectivity, also in the
alpha and beta band [61]. None of these studies investigated
resting-state network topology.
With respect to the previously described study investigating
modularity during increasingly difficult cognitive conditions [49],
most effects of neural reconfiguration were found in the beta band,
which the authors ascribe to the need for higher long-range
synchronization, increased intermodular connectivity, and thus
loss of modularity in this frequency band during tasks. This
hypothesis, as well as our results, are corroborated by computa-
tional and animal work, showing that beta oscillations provide
excellent support for long-distance synchronization [59,62]. The
beta band may speculatively be at the heart of communication
between hub areas in the brain, which regulate higher-order
functioning
of
the
brain
network
and
therefore
relate
to
intelligence and cardio respiratory fitness, although more studies
are needed to confirm this hypothesis.
Previous studies have only reported associations between
resting-state functional connectivity, cognitive functioning, and
physical fitness. Particularly higher connectivity within the default
mode network (DMN) has been related to increased cardiorespi-
ratory
fitness,
while
DMN
connectivity
also
mediates
the
association between VO2 max and cognitive functioning [17].
After a 1-year exercise intervention in older adults, both the DMN
and the fronto-parietal network show higher connectivity than a
control group [18], further building on the causal relationships
that might exist between physical fitness and functional connec-
tivity. Our results partly corroborate these findings, and indicate
that there might be differential associations with particular types of
connectivity: in our investigation of a very large cohort of healthy
subjects with a direct measurement of neural activity, particularly
increased between-module connectivity was related to superior
cardiorespiratory fitness and intelligence. Also, our lack of findings
with respect to average functional connectivity indicate that
network analysis contributes valuable information to the associa-
tion between fitness and intelligence, and advocates for investiga-
tion of the brain network as a whole instead of only focusing on
connectivity between particular spatially determined areas.
Although circumstantial evidence is available, the definite
direction of the association between increased cardio respiratory
fitness, functional brain network organization, and cognition is still
uncertain, and our results in a large sample do not support the
hypothesis that better physical condition leads to better intelli-
gence through brain network topology. Several aerobic interven-
tion studies, which usually randomize between an exercise
program and a control intervention of for instance light stretching,
have reported increased cognitive functioning afterwards, but a
number of studies failed to find a cognitive effect of increased
cardio respiratory fitness [7,63]. Our study was not aimed at
addressing this issue, and mediation analyses were not significant.
Additionally, measurement of VO2 max took place approximately
six years prior to intelligence testing and MEG recording. Our
analysis of VO2 max at previous time points suggests that this
measurement is a relatively stable measure of physical fitness, and
all subjects with disease burden influencing their lifestyle were
excluded. Finally, the presence of associations between VO2 max
and intelligence six years later suggest that we are indeed looking
at a robust indication of physical fitness. However, we cannot
ascertain that this interval between measurements did not
influence our results. Future longitudinal studies are needed to
shed light on the causal relations between cardiorespiratory fitness,
intelligence, and network topology, while investigation of anatom-
ical brain connections may also yield further insights into this issue.
Increased physical fitness is associated with better functional
brain network topology. The step from exercise to functional brain
network may be difficult to understand. On a cellular level, better
physical fitness has often been associated with increases in BDNF
[64,65], and possibly with IGF-1 and VEGF [66]. A recent study
suggests that these exercise-induced cellular changes are indeed
related to functional connectivity, by comparing BDNF, IGF-1
and VEGF levels in two groups of participants undergoing either
an aerobic or non-aerobic intervention [20]. The link between
cellular biology and network functioning as measured with MEG
has recently also been addressed in a study of protein expression
and epilepsy in brain tumor patients [67]. We were able to show a
direct association between epilepsy-related protein expression and
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February 2014 | Volume 9 | Issue 2 | e88202
between-module connectivity of the tumor area, further indicating
that these network patterns may be the intermediate between
molecules and behavior. Future studies are needed to further
explore how cellular changes as a consequence of exercise lead to
changes in functional connectivity.
Several limitations of the current study should be recognized.
First of all, as previously mentioned, measurement of cardiorespi-
ratory fitness was performed several years before MEG recording
and intelligence testing took place. The influence of the lag
between measurements in the current study design on the reported
results is unknown. Secondly, this study was performed on the
sensor-level, since no anatomical MRI scans (which are necessary
to perform accurate source reconstruction in MEG data) were
available. This limits the spatial specificity of our results, and
prohibits further investigation of specific spatial network proper-
ties. Third, the spatial resolution of MEG is limited. Although
MEG is much less sensitive to volume conduction and disturbing
effects of the skull and scalp than EEG, common sources still pose
a serious problem for coupling analysis. However, the phase lag
index is a particularly strict measure of functioning connectivity,
because it excludes all non-zero lagged correlations [44,68].
In conclusion, we show that functional brain network organi-
zation may mediate the association between cardiorespiratory
fitness and intelligence. Less tightly connected, more intercon-
nected functional modular topology in the upper alpha and
particularly beta band may promote long-range connectivity in the
resting-state, which relates to both increased physical and mental
fitness.
Supporting Information
Figure S1
Temporal consistency of VO2 max measure-
ments
in
the
AGAHLS
cohort.
Note.
(a)
depicts
the
correlations between VO2 max measurements at each neighboring
time point in the AGAHLS study, the first two measurements
being performed at 13 and 14 years old (YO). The number of
overlapping subjects between time points is indicated in paren-
theses. In (b), the first adult VO2 max measurement at 21 years old
is correlated to the last measurement at 36 years old, which we
used in this study (correlation coefficient of 0.791, p,0.001).
(TIFF)
Acknowledgments
The authors would like to thank K. Plugge, P. Ris and N. Sijsma for their
help in recording MEG in all subjects.
Author Contributions
Conceived and designed the experiments: LD DN BWD CJS JWRT.
Performed the experiments: LD BWD JWRT. Analyzed the data: LD DN
BWD CJS JWRT. Contributed reagents/materials/analysis tools: BWD
CJS. Wrote the paper: LD DN BWD CJS JWRT.
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A healthy brain in a healthy body: brain network correlates of physical and mental fitness.
Douw, Linda,Nieboer, Dagmar,van Dijk, Bob W,Stam, Cornelis J,Twisk, Jos W R