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Research Article
The Exercising Brain: Changes in Functional
Connectivity Induced by an Integrated Multimodal Cognitive
and Whole-Body Coordination Training
Traute Demirakca, Vita Cardinale, Sven Dehn, Matthias Ruf, and Gabriele Ende
Department of Neuroimaging, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University,
J5,68159Mannheim,Germany
Correspondence should be addressed to Traute Demirakca; traute.demirakca@zi-mannheim.de
Received July ; Revised September ; Accepted September
Academic Editor: Feng Shi
Copyright © Traute Demirakca et al. is is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
is study investigated the impact of “life kinetik” training on brain plasticity in terms of an increased functional connectivity
during resting-state functional magnetic resonance imaging (rs-fMRI). e training is an integrated multimodal training that
combines motor and cognitive aspects and challenges the brain by introducing new and unfamiliar coordinative tasks. Twenty-
one subjects completed at least one-hour-per-week “life kinetik” training sessions in weeks as well as before and aer rs-
fMRI scans. Additionally, control subjects with rs-fMRI scans were included. e CONN toolbox was used to conduct several
seed-to-voxel analyses. We searched for functional connectivity increases between brain regions expected to be involved in the
exercises. Connections to brain regions representing parts of the default mode network, such as medial frontal cortex and posterior
cingulate cortex, did not change. Signicant connectivity alterations occurred between the visual cortex and parts of the superior
parietal area (BA). Premotor area and cingulate gyrus were also aected. We can conclude that the constant challenge of unfamiliar
combinations of coordination tasks, combined with visual perception and working memory demands, seems to induce brain
plasticity expressed in enhanced connectivity strength of brain regions due to coactivation.
1. Introduction
Already in Hebb proposed that simultaneous neuronal
ring stimulates synaptic plasticity []. Later several studies
found evidence for experience-dependent neurogenesis in
the hippocampi of adult mice (for a review see []). Today
there is accumulating evidence that also the human brain
continues to be shaped by experience throughout adulthood
[–]. ese adaptive changes have been shown to take place
on structural as well as functional level [–].
A practicable approach to study experience-dependent
plasticity in humans is to investigate longitudinal changes in
brain structure or function following exposure to training.
Recently, a number of studies have been published that
investigated the eect of training on the functional archi-
tecture of the brain by resting-state fMRI (rs-fMRI) (for a
review see [, ]). Resting-state functional connectivity is
commonly dened as temporal correlations of spontaneous,
low frequency uctuations of the BOLD signal between brain
areas during rest due to common history of coactivation. As
such, it allows a task-independent assessment of training-
related changes in brain function [–].
Training studies can roughly be subdivided into motor
and cognitive interventions. Motor training varied from
joystick tracking tasks [], chopstick handling [], nger
tapping [], and force-eld learning [] to whole-body
balancing [] and aerobic tness training []. Training
duration varied from minutes [] to several weeks or
months []. In the cognitive domain training comprised
working memory training [, ], multitasking [], and
logical reasoning [] and duration varied from weeks to
months.
Newer approaches used also video-gaming [] and fMRI
based neurofeedback [, ].
Hindawi Publishing Corporation
Neural Plasticity
Volume 2016, Article ID 8240894, 11 pages
http://dx.doi.org/10.1155/2016/8240894
Neural Plasticity
Within the motor domain, several research groups inves-
tigated dierent kinds of motor skills training with vary-
ing duration, intensity, and complexity. Overall, changes in
intrinsic functional connectivity were located in sensori-
motor and cerebellar areas. In these areas both intrinsic
functional connectivity increases [, –] and decreases
[–] have been found; decreases were rather associated
with cerebellar regions [, ]. In the studies conducted by
Taubert et al. [] and Ma et al. [] intrinsic functional
connectivity decreased back to baseline whereas decreases
werefoundbythegroupsofYooetal.[]andVahdatet
al. []; there, the intrinsic functional connectivity aer the
training was reduced compared to before the intervention.
In the cognitive domain, training rather aected intrinsic
functional connectivity between frontal and parietal areas
[, , ]. However, the precise location of training-related
change in intrinsic functional connectivity diers between
studies. Regarding the variety of changes found by dierent
research groups, the training eects seem to be rather specic
to the content of the training, the duration, the intensity,
and the timing of the resting-state quantication. However,
the studies mentioned before show that changes in intrinsic
functional connectivity can reliably be induced by training,
that is, experience, across a variety of domains.
e majority of published intervention studies inves-
tigated the eect of unimodal training. Within the eld
of healthy aging research the question rises if combined
interventions might be more successful than unimodal inter-
ventions (for a review see []). Also in the context of studies
investigating eects of physical exercise on neuroplasticity
and cognition it is suggested that adding cognitive training
might enhance the benecial eect of physical training (for
a review see []). Yet, there are only few studies that focus
on the eect of combined interventions. To our knowledge,
there are only two studies exploring the eect of a multimodal
training using neuroimaging techniques [, ]. In both
studies, physical and cognitivetraining were performed apart
from each other. In Li et al.’s study older adults took part in tai
chi exercises at one time and memory training and supportive
group counselling on another time. ey found increased
resting-state connectivity between the medial prefrontal cor-
tex and the medial temporal lobe. In Holzschneider et al.’s
study participants engaged in cycling sessions and additional
spatial memory training sessions. However, only task-based
fMRI changes were quantied. Aer combined training,
changes in brain activation and changes in cardiovascular
tness correlated positively in the medial frontal gyrus and
the cuneus.
Here, we investigated the eect of combined whole-
body motor coordination training with integrated cognitive
exercises in healthy adults. Lutz and colleagues (“life kinetik”:
http://www.lifekinetik.de/) developed a multimodal training
that combines coordinative, cognitive, and visual tasks in a
way that the physical exercise is performed while participants
arecognitivelychallengedatthesametime.etraining
consists of combinations of motor activity and cognitive
challenges and the training of visual perception, especially
the perception of the peripheral visual eld. Moving limbs
in dierent unusual combinations, catching, and throwing
objects, thus training the visual perception and limb-eye
coordination,isabasiccharacteristicofthetraining.More-
over, the training tasks are not practiced to perfection but
are modied aer a few minutes or whenever the perfor-
mance reaches about %. In addition to the avoidance of
boredom and frustration, this is supposed to stimulate the
brain to constantly adapt to new unfamiliar challenges. Our
motivation was to test a training concept that is exible
and interesting for the participants and includes cognitive
and motor elements. Although the “life kinetik” training
was originally designed to train the coordination of athletes
(soccer players, skiers) the diculty of the task can easily be
adapted to the capabilities of patient populations.
Based on the assumption that spontaneous activity
reects the history of coactivation within a local brain
network or between brain regions [, ] we expect increases
in resting-state connectivity of those brain regions probably
involved in the exercises and tasks.
e thalamus isasubcorticalbrainareaprocessingand
integrating neocortical inputs and outputs []. Its connec-
tionsseemtodecreasewithage[]anddiminishedinmild
cognitive impairment (MCI) and Alzheimer’s disease (AD)
[, ]. It serves as a “switchboard of information” or relay
station for sensory information. As the training includes
unusual pattern of motoric activity in combination with
cognitive task, we expect the connectivity of the thalamus to
increase.
All the exercises and tasks involve some motor action;
hence we expect changes in the primary motor area (BA,
M) and the premotor area (BA) because not only the
execution but also the constant alertness to perform an action
is involved in the task. In particular the connectivity to the
right motor areas may be increased because the exercises
include a considerable amount of movement of the le limbs,
which is challenging for the right handed participants.
e cerebellum is highly involved in motor activity and
learning and the functional connections reect the connec-
tions of the cortex [, ] so we can expect some changes in
its connectivity as well.
e frontal eye eld (FEF), a brain region responsible for
eye movement and gaze control, is known to be altered in the
course of learning to handle moving objects [–], which
is also part of the exercise, except that this is not trained to
perfection like in juggling.
e whole visual cortex is additionally challenged by the
attempt to train the peripheral vision and the manipulation
of dierent moving objects and due to the possibility of
assigning the requested action via a visual stimulus (specic
gesture of the trainer or colours). So the primary as well
as the secondary visual cortices (BA, BA, and BA) are
expected to change their connection to other brain regions.
Each exercise or task consists of chains of movements,
which alternate randomly. e prompt to change is frequently
given by a verbal command. Hence, we expect an increased
connectivity between auditory areas (primary and secondary
BA and BA) and other brain regions especially the motor
and premotor area and as a result of repeated coactivation.
e functional connectivity from and to the dorsolat-
eral prefrontal cortex (DLPFC) may be increased because
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the working memory is involved in linking the action or
movement to the assigned command or prompt. e anterior
cingulate cortex (ACC) is also expected to be involved as
a region needed for error detection and impulse control
and might accordingly change the connection to other brain
regions.
2. Methods
2.1. Participants. right handed subjects with no history
of psychiatric or neurological illness were included in the
study. individuals ( females, mean age (±) years)
participated in eleven or twelve of the “life kinetik” training
sessions ( hour per week). e other subjects ( females,
mean age (±) years) were interested in the training
but were not able to attend due to their time schedule but
completedtwoMRIscans.
e study was approved by the Ethics Committee of the
Medical Faculty Mannheim, University of Heidelberg, and
performed in accordance with the Declaration of Helsinki.
2.2. Training Description. “Life kinetik” training pursues the
goal to combine motor coordination exercises with cognitive
training with an emphasis on working memory. e motor
coordination exercises can involve multiple limbs at the same
time. Additionally, most of the time one or more pieces
of sports equipment (e.g., ball, racket, juggling balls, and
scarves) are used which have to be thrown, caught, bounced,
or similarly manipulated. e cognitive aspect comes into
play by assigning distinct motor tasks to dierent visual or
auditory cues (symbols/key words). For example, a red ag
might indicate bouncing a ball with the le hand while a
blue ag indicates throwing and catching a ball with the
right hand. e same movement-cue coupling can be done
with semantic categories, for example, city names, animals,
or trees. ese pairs of motor task and specic cue have to be
memorized during one training session. e randomization
of cues is self-evident. Within one training session ( hour per
week) approximately dierent types of exercises have been
performed either in groups, in pairs, or by oneself.
An essential aspect of this combined training is that the
exercises are not trained until automatized. As soon as partic-
ipant’s performance reaches about % correct trials the task
demands are changed and new combinations of symbols and
movements are introduced. e focus on novelty is supposed
to constantly challenge the participants. Moreover, cross talk
of the hemispheres is fostered by including movements where
limbs purposefully cross the sagittal midline (e.g., to catch a
ball arriving at the le side of the body with the right hand).
In total, there were training sessions of hour per week
of which our participants followed at least ; that is, the
minimal training duration was hours across a period of
weeks.
2.3. Data Acquisition. Functional and anatomical data were
acquired from each participant within weeks before the start
of the rst training session and within weeks aer the last
training session on a Tesla MRI Scanner (Magnetom Trio
with TIM technology, Siemens Medical Service, Erlangen,
Germany) equipped with a -channel head coil. rs-fMRI
images were acquired with gradient echo T2∗-weighted echo-
planar-imaging sequence (TE = ms, TR = . s, FOV =
mm × mm, matrix size = ×, and total duration
is min). A volume comprised slices in AC-PC orientation
with a thickness of mm and slice gap of mm. Participants’
heads were lightly restrained using so pads to prevent head
movement. Subjects were instructed to look at the xation
cross and keep their eyes open.
A T-weighted anatomical image was also recorded (TE =
. ms, TR = .s, slices and FOV = mm × mm,
matrix size is ×,andslicethicknessismm).
2.4. Data Preprocessing. Data were preprocessed and ana-
lyzed using SPM (e Wellcome Department of Cognitive
Neurology, London, UK, http://www.l.ion.ucl.ac.uk/spm/
soware/spm/). All functional images were slice-time cor-
rected and realigned to the rst volume using a six-parameter
rigid body transformation. reshold for exclusion due to
excessive motion was set to mm. e movement was not
morethan.mmineachsubject,sonoonehadtobe
removed.
e anatomical image and functional images were coreg-
istered for the corresponding time-point. Segmented gray
matter and white matter images of all participants were used
to construct a study specic template using DARTEL [].
e template was normalized to MNI space and all images,
anatomical and functional, were normalized to this template
using the according ow elds. e smoothing kernel for the
functional images was mm and mm for the anatomical
image.
2.5. Connectivity Analysis. Functional connectivity analyses
were carried out using the CONN-fMRI functional connec-
tivity toolbox v [] (http://www.nitrc.org/projects/conn).
e modest test-retest reliability of the rs-fMRI seems
attributable to remaining noise aer preprocessing, adding
nonneural correlation to the BOLD signal []. Removing
the noise is a possibility to increase the reliability of rs-fMRI
data. Several preprocessing steps have been proposed [] to
achieve this.
One major point is reducing the noise via the anatomical
CompCor approach. is method extracts principal compo-
nents ( each) from WM and CSF time series. WM and CSF
voxels are identied via a segmentation of the anatomical
images. ese components are added as confounds in the
denoising step of the CONN toolbox [, ]. e six head
motion parameters derived from spatial motion correction
were also added as confounds. We did not perform global
signal regression as the discussion about the impact is still
ongoing [, ] and it is not available on the CONN toolbox.
As recommended band-pass ltering was performed with
a frequency window of . to . Hz. is preprocessing step
was found to increase the retest reliability [].
Seed-to-voxel and ROI-to-ROI functional connectivity
maps were created for each participant. e ROI-to-ROI
analysis was used to identify possible dierences between
trainees and control subjects at pretraining and to verify that
brain networks of control subjects did not change over time.
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For this analysis we used all the provided Brodmann areas.
e mean BOLD time series was computed across all voxels
within each ROI. Bivariate-correlation analyses were used to
determine the linear association of the BOLD time series
between each pair of sources and a Fisher Z transformation
was applied.
Individual seed-to-voxel and ROI-to-ROI maps were
entered into a second-level analysis.
A within group ROI-to-ROI analysis for the control group
tested the stability of the connectivity over time. A between
groups ROI-to-ROI analysis veried the lack of dierences
between the two groups for the rst measurement.
Seed-to-voxel analyses were used for two purposes. First
we used the posterior cingulate and the medial prefrontal
cortex as seed region and veried the occurrence of the
default mode network in each group and to both time-points.
is seems necessary due to the dierent size of the two
groups.
e second seed-to-voxel analysis was used to examine
dierences in connectivity changes in a 2×2factorial analysis
with time by training interaction (group ∗time; contrast −
+ + −). Age and sex were entered as covariates of no interest
in the analysis []. e threshold for signicant changes was
set to 𝑝 = 0.05whole brain cluster level FWE corrected with a
cluster building threshold of 𝑝 = 0.001 uncorrected on voxel
level. As we expected an increase in training participants due
to coactivation and no change in control subjects we veried
the direction of changes with two post hoc paired sample
𝑡-tests for the trainee and the control group separately for
each signicant seed-to-voxel cluster. is step was chosen to
assure that the signicant results were not caused by between-
subject variance. e other reason for this approach was the
dierentsamplesizeofthetwogroups.Wereportsignicant
results due to three criteria: (a) signicant time by group
interaction, (b) signicant increase within the training group,
and (c) no signicant decrease in the control group.
For display purposes the cluster building threshold for the
result-gure was set to . uncorrected on voxel level.
2.6. Regions of Interest. As we could not investigate task
related activity for the exercises and it is somewhat arbitrary
and prone to bias to create a region of interest out of a single
coordinate and an according sphere, we used the provided
ROIs that are based on the Brodmann areas according
to the WFU PickAtlas (http://fmri.wfubmc.edu/soware/
PickAtlas). We used all existing areas as ROIs, in order to
get a complete picture of possible changes within the control
group. Some ROIs are not provided by the toolbox (e.g.,
thalamus, cerebellum, and FEF); here we created ROI using
the masks provided by WFU PickAtlas of the according brain
region.
3. Results
ROI-to-ROI analysis for the rs-fMRI at the rst time-point
showed no dierences between trainees and controls. e
default network could be shown with the medial prefrontal
cortex as seed in both groups and both time-points.
e impact of the training was analyzed by a ×
ANOVA (group and time) with age and sex as covariates
of no interest. All seed regions with signicant positive
connectivity changes in trainees and no signicant decreases
in controls are listed in Table .
e training involved a great amount of motor activity
and the motor region was one of the hypothesized regions
changing their connectivity strength. e increase occurred
only for the le motor region. e le primary motor area
(BA, M) showed increased connectivity to parts of the
visual cortex (Figure (a), red) and the somatosensory asso-
ciation area (BA, Figure (a), red). e right primary motor
cortex showed no changes to any other brain region. e
connectivity strength of the whole premotor areas (BA) as
seed to other cortical regions did not change.
e primary sensorimotor cortices (BA, BA, and BA)
as part of the sensorimotor network showed few changes
in connectivity strength. Only the spontaneous uctuations
of the le BA showed higher correlation to parts of the
associativevisualcortex(BA,Figure(a),cyan)andparts
of the parietal cortex (BA, Figure (a), cyan). Connectivity
from BA or BA did not change.
e functional coupling within the visual network
changed for the primary sensory areas (BA) of the right
hemisphere. is ROI increased in functional connectivity
to the ventral ACC (BA, Figure (b), violet) and parts
of the right premotor cortex (BA, Figure (c), violet). e
connection to the le premotor cortex (Figure (c), blue)
was increased for the right secondary visual cortices (BA).
e connection increase to the ventral ACC (midcingulate;
Figure (b), violet and blue) of the visual areas was overlap-
ping. Dierent areas of the visual cortex show changes in
functional coupling to the same premotor region and the
cingulate cortex.
e functional connectivity strength between the primary
auditory cortex (BA) as part of the auditory network and
therightcerebellum(areasVIIIandIX)increasedwiththe
training as well as the connection to the somatosensory
association cortex (BA, Figure (d), green and red). is
connectivity change was interhemispheric and overlapping.
e connections of the secondary auditory cortex (BA) to
the parietal cortex (BA, Figure (d), violet and blue) changed
as well, partly overlapping with the increased connectivity of
the primary auditory cortex. Connections from the auditory
to the visual cortex did also increase (Figures (d) and (e),
blue and green).
e le FEF but not the right FEF showed connectivity
changes to several clusters in the visual cortex (Figure (f),
blue) and the ventral ACC (Figure (f), blue).
e connectivity between the right dorsolateral prefrontal
cortex and the right supramarginal gyrus (Figure (f), red)
increased. e ACC (BA) showed no increase in connec-
tivity. A more posterior part of the cingulate gyrus showed
an increased functional connectivity to the right anterior
frontal cortex and partially of the dorsolateral prefrontal
cortex (BA and BA, Figure (f), violet).
e characteristic regions of the default mode network,
medial prefrontal cortex,lateral parietal cortex,posterior
cingulate,andsuperior frontal cortex, showed no change in
connectivity aer the training.
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T : Seed-to-voxel results of 2×2ANOVA (𝑡1 < 𝑡2; trainees >controls), age and sex as covariates, cluster building threshold 𝑝 = 0.001 uncorrected, cluster threshold 𝑝 = 0.05 FWE
corrected, post hoc test increase in trainees, and no decrease in controls.
Figure/colour Seed Seed
hemisphere Result region BA Result
hemisphere 𝑥𝑦 𝑧 𝑘 Cluster
𝑝-FWE
Cluster
𝑝-unc
Peak
𝑝-FWE
Peak
𝑝-unc
Motor area
(a)/red BA/M Le Somatosensory association
cortex BA Le −30 −48 52 83 0.04605 0.00103 0.373 0.000002
(a)/red BA/M Le Somatosensory association
cortex BA Right 26 −44 48 99 0.01981 0.00044 0.982 0.000035
(a)/red BA/M Le Associative visual cortex BA Right 38 −78 −4 258 0.00002 0 0.791 0.000011
Sensory-motor area
(a)/cyan BA Le Associative visual cortex BA Right 40 −82 8 137 0.00354 0.00008 0.869 0.000015
(a)/cyan BA Le Somatosensory association
cortex BA Right 30 −42 50 126 0.0059 0.00013 0.987 0.00004
Visual area
(b)/violet Primary visual cortex BA Right Ventral ACC BA Le −8 4 38 81 0.04406 0.00095 0.477 0.000003
(c)/violet Primary visual cortex BA Right Premotor cortex BA Right 22 −8 66 133 0.00294 6.2𝐸 − 05 0.844 0.000012
(b)/blue Secondary visual cortex BA Right Secondary visual cortex BA Le −14 −82 2 329 1𝐸 − 06 0 0.005 0
Secondary visual cortex BA Right Dorsal posterior cingulate cortex BA Right 14 −66 20 85 0.03536 0.00076 0.223 0.000001
(b)/blue Secondary visual cortex BA Right Ventral ACC BA Le −6 12 40 97 0.01841 0.00039 0.718 0.000008
(c)/blue Secondary visual cortex BA Right Premotor cortex BA Le −20 −2 60 96 0.01942 0.00041 0.923 0.000019
Auditory area
Primary auditory cortex BA Le Cerebellum Cerebellum
( & ) Right 20 −34 −46 163 0.00073 1.5𝐸 − 05 0.003 0
(e)/green Primary auditory cortex BA Right Somatosensory association
cortex & associative visual cortex BA & BA Ri ght 36 −78 12 874 0 0 0.007 0
(d)/green Primary auditory cortex BA Right Somatosensory association
cortex BA Le −26 −54 40 218 4.8𝐸 − 05 1𝐸 − 06 0.951 0.000022
(d)/red Primary auditory cortex BA Le Somatosensor y association
cortex BA Right 14 −66 52 200 0.00014 3𝐸 − 06 0.933 0.00002
(d)/red Primary auditory cortex BA Le Somatosensor y association
cortex BA Le −10 −70 52 131 0.00327 6.9𝐸 − 05 0.932 0.00002
(d)/violet Secondary auditory cortex BA Right Somatosensory association
cortex BA Right 22 −64 54 76 0.04641 0.00094 0.864 0.000012
(d)/violet Secondary auditory cortex BA Right Somatosensory association
cortex BA Le −20 −58 40 131 0.00225 4.4𝐸 − 05 0.583 0.000004
(d)/blue Secondary auditory cortex BA Le Somatosensory association
cortex BA Right 34 −50 48 130 0.00319 6.6𝐸 − 05 0.568 0.000004
(e)/blue Secondary auditory cortex BA Le Secondary visual cortex BA Midline 0 −92 0 140 0.00196 4.1𝐸 − 05 0.983 0.000033
Frontal cortex
(f)/red Dorsolateral prefrontal cortex
(BA) Right Supramarginal gyrus BA Right 64 −18 24 192 0.00026 6𝐸 − 06 0.737 0.000008
(f)/blue Frontal eye eld/superior frontal Le Ventral ACC BA Right 8 −12 38 110 0.00696 0.00014 0.563 0.000004
(f)/blue Frontal eye eld/superior frontal Le Secondary visual cortex BA Right 36 −90 6 397 0 0 0.577 0.000004
(f)/blue Frontal eye eld/superior frontal Le Secondary visual cortex BA Le −24 −96 14 193 0.00012 2𝐸 − 06 0.771 0.000009
(f)/violet Midcingulate gyrus Right Anterior prefrontal cortex &
dorsolateral prefrontal cortex BA & BA Right 6 66 18 100 0.01336 0.00027 0.934 0.000019
BA = Brodmann area, 𝑥, 𝑦,and𝑧= MNI coordinates, 𝑘= cluster size, and (a)–(f) = parts of Figure .
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(a) (b)
(c) (d)
(e) (f)
F : Greater connectivity increases in trainees compared to controls subjects; seed regions are represented in the small brain images;
seeds determine the colour of the result region: (a) BA le: red and BA le: cyan; (b) and (c) visual cortex: BA right: violet and BA right:
blue; (d) and (e) auditory cortex: BA right: green, BA le: red, BA right: violet, and BA le: blue; (f) dorsolateral prefrontal cortex:
right: red, FEF le: blue, and midcingulate cortex: violet.
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4. Discussion
is study is the rst to investigate the impact of an integrated
multimodal training on functional brain connectivity. e
training combines physical and cognitive exercises and does
not aim at automating but focuses on novelty. For this pur-
pose we compared the changes of the resting-state intrinsic
functional connectivity of a group of subjects attending the
rst standard training course of “life kinetik” with the rs-
fMRI changes of a control sample. e training oers a great
variety of exercises and can easily be adapted to clinical
populations. We found a considerable amount of changes in
resting-state functional connectivity in the training group.
e connections within the default mode network, the most
prominent resting-state network, did not change.
e changes in functional connectivity mirror the acti-
vation during the training and some increases in correlation
occur in regions that are known to be a key region in cognitive
decits, ageing or mental illness.
Connectivity increases of the motor region were assumed
to be the most probable ones. In particular the connections
from the right motor and premotor cortex, responsible for
the le part of the body, were expected to strengthen. e
training involved a great amount of motor activity and
all participants were right handed, thus being forced to
coordinate their le hand, arm, and leg. An increase of
the connectivity for the whole region was only visible for
the le primary motor area. e connection to parts of
the somatosensory association area changed as well as to
the visual cortex. e involvement of motor areas in brain
plasticity has been corroborated in several studies. Musicians,
for example, who have a long history of motoric training
showed increased resting-state connectivity in motor areas
and multisensory cortices compared to a control group [].
isisincontrasttootherstudiesthatfoundadecrease
in connectivity accompanied with cumulative performance
increase aer initial “beginners” increase of connectivity [].
As the “life kinetik” training focuses on the novelty of the
exercises a decrease of connectivity was not expected.
Various seed regions in the visual cortex showed an
increased connectivity with parts of the premotor cortex,
almostoverlapping(seeFigure(c)).Agreatnumberofthe
training tasks consisted of throwing and catching dierent
and, in some cases, relative small objects. e most compa-
rabletaskusedinotherstudieswasjugglingtrainingwhere
an impact of training intensity was found []. Low intensity
training resulted in increases in functional connectivity in the
motor network, whereas the high intensity juggling training
group showed decreased functional connectivity. e results
suggest that dierent training regimes are associated with
distinct patterns of brain change []. Our training on the
otherhandwasmuchlessintensivethanthelowintensity
juggling training which consisted of min per day and
furthermore, as already mentioned, “life kinetik” training is
not directed to perfection.
e cerebellum is mapped to the association areas of
the cerebrum [] so that we expected some changes in
connectivity, which we found but less pronounced than
expected. In contrast to the analysis of Buckner et al. [] who
found that the primary sensory cortices were not represented
in the cerebellum, we found a change in the correlation
of time courses in the primary auditory cortex and the
cerebellum. Maybe this is an eect of the verbal prompts
during the exercises, which indicated dierent movements.
e visual cortex is also diversied. Some subregions
responsible for the retention of visual-motion information
[]wereshowntochangetheirstructureduringmotor
training. Not structural but functional changes occur in our
sample of trainees in contrast to the control sample.
e brain region with the most prominent connectivity
changes was the secondary somatosensory association cortex
(BA). Several parts of BA exhibited connectivity increases
with other regions. Mainly the auditory cortices change their
relation to parts of BA (Figure (d)). ese clusters are all
overlapping irrespective of the according seed region. Also
distinct from these regions are the clusters changing the
connections to the motor cortex (Figure (b), red) and the
sensorimotor cortex. is result seems to corroborate the
ndings of the diversication of the parietal cortex [, ].
Grasping and visuospatial tasks activated dierent parts of
the parietal cortex [], overlapping with the regions that
showed changes during “life kinetik” training (Figures (a)
and (d)).
e functional connectivity between the le FEF as seed
region and clusters in the visual cortex and the ventral ACC
increased, but not with the dorsal attention network. e
FEF is responsible for eye movement and surely active during
throwingandcatchingofobjects.echangeofconnectivity
between FEF and visual cortex might be an indication for
a combined activation of these regions due to the increased
visual attention during the training.
Regions of the frontal cortex involved in working mem-
ory processes and error processing showed few connectivity
increases. e ACC showed, in contrast to our hypothesis, no
changesinconnectivitytootherbrainregions.eventral
part of the cingulate cortex showed increased connectivity to
the anterior prefrontal cortex, to the FEF, and to the visual
cortex. e le dorsolateral prefrontal cortex (BA) on the
other hand showed an increased connectivity to the right
supramarginal gyrus (BA).
Given the specic property of the training, the connec-
tivity changes seem reasonable. Prompts for the movements
and tasks are given by verbal or visual cues. e cue has to be
translated to an action, in most cases movements or manip-
ulation of objects. Parts of the premotor regions showed
increased connections to visual areas. ese regions were not
distinct but overlapping thus indicating the importance of
these regions for preparing the action and also for the object
manipulation [].
e question is why the auditory areas predominantly
showed increased functional connectivity to the somatosen-
sory areas (BA) but not to the premotor area. Attention is
one important aspect of the training in combination with
working memory. One major eort for the trainees is to
remember the according movement to the prompt. But this
did not result in the expected changes in connectivity of the
dorsolateral prefrontal cortex.
Neural Plasticity
e functional connection of the thalamus to the right
inferior frontal gyrus and insula increased in trainees but also
decreased in controls. is makes the interpretation dicult.
ethalamusisaregionwithmultipleconnections[,,
]. e strength of the thalamocortical connection has been
reported to predict the performance in motor learning [],
tochangewithage[],andtobediminishedinMCIandAD
[, ], and a disruption of the thalamus-cortex relation has
severe implications on mental health [–].
ProperoroptimalfunctionofBAseemstobeanindica-
tion for a cognitive reserve, preventing dementia symptoms
[]. Switching attention is an important part of the training;
regions that are activated in such a task are part of the parietal
cortex as well as premotor areas and the dorsolateral PFC
[] but the relation between these regions and especially the
change in relation have not yet been investigated.
With our study we could show that the applied “life
kinetik” training changes the connectivity strength between
several brain regions. ere is a lot of evidence for brain
plasticityevenintheadultandagingbrain.Basicresearch
has shown that dierent aspects of the brain can be shaped by
various types of training and tasks. Resting-state connectivity
seems to be relatively stable [], but disturbed in psychiatric
disorders [], changing with age [, ], and changeable
by activity []. Intrinsic connectivity is shown to be an
indicator for eciency [] and positively correlated with
cognitive performance [, ] and intelligence [].
e data on the direction of changes or alterations in
terms of increase or decrease are inconsistent. It is not gen-
erally known which direction is more benecial. is most
likely depends on many functional aspects the connectivity
is supporting. For example, patients with major depression
show an increased functional connectivity [] whereas
schizophrenia seems to be accompanied by decreased func-
tional connectivity [].
e functional connectivity of the motor cortex, for
example, is increasing with age but dierent relations to per-
formance were reported. One study interpreted the positive
relation of connectivity and performance as a protection
against decline []. e second study found the increase in
connectivity with age accompanied by poorer performance
[]. is contradicting consequence of connectivity increase
with regard to performance demonstrates that an interven-
tion leading to enhanced connectivity between brain areas
mightnotnecessarilyhelpattenuateagerelateddecline.
e tasks and types of training used in dierent studies
to investigate the change of brain networks are somewhat
arbitrary, varying from perception tasks according to robot-
hand movements to juggling or transcranial electrical or
magnetic stimulation. Most training concepts do not have the
potential to serve as a training method or therapy approach
for psychiatric patients or elderly individuals.
Exceptions are various types of motor training like
juggling [], video games [], aerobic training [], or
the quadrato-motor training [] which are all aimed at
perfectingthetaskwithoutvaryingthetask.
Our motivation was to look for a training that includes
motor and cognitive exercises and has the potential to be
stimulating for a patient population.
4.1. Limitations. Test-retest reliability is a not yet completely
resolved issue in fMRI studies [–]. Few studies addressed
this issue for rs-fMRI but retest reliability was found to
be robust [, ]. Improvement can be made via the
inclusion of several preprocessing steps []. is enhances
the intersession retest reliability to .. We addressed this
issue by including preprocessing steps that are known to
reduce noise [, , ]. We carefully screened the control
group for changes and reported only results with a signicant
post hoc test.
A second limitation for our results is the whole sample
size as well as the dierence in the size of the trainee and
control group. We tried to address this issue by verifying the
occurrence of the default mode network that did not change
in control subjects despite the small sample size.
e impact of the training intensity is unknown. e
actual program consisted of -hour training per week. Further
studies should investigate the eect of shorter but more
frequent training sessions.
e subject group participating in the training showed
an increase in resting-state functional connectivity but the
impact on task performance is unknown and could not be
monitoredduetothenatureofthetraining.Anextstepwould
betondsuitablemotorandcognitivetesttaskstoquantify
an improvement following “life kinetik” training.
Further investigations should include an active control
group (practicing either motor performance or cognitive
tasks or training a limited number of tasks to perfection) to
show the benet of the combined training compared to its
isolated parts. Furthermore, we will investigate the impact
of the training on cognitive performance and memory,
preferably in a group of impaired subjects.
Since it is not possible to measure the brain activity during
the training, we only could assume which brain regions are
activated. is study was planned as a pilot study to show that
the training is able to change brain connectivity. We assume
that our subjects show “normal” resting-state networks. In a
patient group the connectivity increases may depend on the
underlying alteration of the according network.
Disclosure
e results were presented at the OHBM meeting in
Honolulu.
Conflict of Interests
e author and the coauthors declare that there is no conict
of interests regarding the publication of this paper.
Acknowledgments
e authors thank Inanc Karaca and Laura Uhrig for provid-
ing the training sessions and Gunilla Oberth¨
ur and Julia van
Eijk for the support with the MR scan. is study was funded
by the DFG through Center Grant SFB, project Z.
Neural Plasticity
References
[] D. O. Hebb, e Organization of Behavior: A Neuropsychological
eory, Psychology Press, New York, NY, USA, .
[] G. Kempermann, H. G. Kuhn, and F. H. Gage, “More hippocam-
pal neurons in adult mice living in an enriched environment,”
Nature,vol.,no.,pp.–,.
[] C. S. Green and D. Bavelier, “Exercising your brain: a review of
human brain plasticity and training-induced learning,” Psychol-
ogy and Aging,vol.,no.,pp.–,.
[]D.Bavelier,D.M.Levi,R.W.Li,Y.Dan,andT.K.Hensch,
“Removing brakes on adult brain plasticity: from molecular to
behavioral interventions,” Journal of Neuroscience,vol.,no.
, pp. –, .
[] A. Gutchess, “Plasticity of the aging brain: new directions in
cognitive neuroscience,” Science,vol.,no.,pp.–,
.
[] R.J.Zatorre,R.D.Fields,andH.Johansen-Berg,“Plasticityin
gray and white: neuroimaging changes in brain structure during
learning,” Nature Neuroscience,vol.,no.,pp.–,.
[] A. May, “Experience-dependent structural plasticity in the adult
human brain,” Trends in Cognitive Sciences,vol.,no.,pp.
–, .
[] M.Spolidoro,A.Sale,N.Berardi,andL.Maei,“Plasticityinthe
adult brain:lessons from the visual system,” Experimental Brain
Research,vol.,no.,pp.–,.
[] B. Guerra-Carrillo, A. P. MacKey, and S. A. Bunge, “Resting-
state fMRI: a window into human brain plasticity,” Neuroscien-
tist,vol.,no.,pp.–,.
[] C. Kelly and F. X. Castellanos, “Strengthening connections:
functional connectivity and brain plasticity,” Neuropsychology
Review,vol.,no.,pp.–,.
[] B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde,
“Functional connectivity in the motor cortex of resting human
brain using echo-planar MRI,” Magnetic Resonance in Medicine,
vol. , no. , pp. –, .
[] M. J. Lowe, “e emergence of doing ‘nothing’ as a viable
paradigm design,” NeuroImage,vol.,no.,pp.–,.
[] A. Z. Snyder and M. E. Raichle, “A brief history of the resting
state: the Washington University perspective,” NeuroImage,vol.
, no. , pp. –, .
[] M. D. Fox and M. E. Raichle, “Spontaneous uctuations in brain
activity observed with functional magnetic resonance imaging,”
Nature Reviews Neuroscience,vol.,no.,pp.–,.
[] N. B. Albert, E. M. Robertson, and R. C. Miall, “e resting
human brain and motor learning,” Current Biology,vol.,no.
, pp. –, .
[] K. Yoo, W. S. Sohn, and Y. Jeong, “Tool-use practice induces
changes in intrinsic functional connectivity of parietal areas,”
Frontiers in Human Neuroscience,vol.,article,.
[] L. Ma, S. Narayana, D. A. Robin, P. T. Fox, and J. Xiong,
“Changes occur in resting state network of motor system during
weeks of motor skill learning,” NeuroImage,vol.,no.,pp.
–, .
[] S.Vahdat,M.Darainy,T.E.Milner,andD.J.Ostry,“Func-
tionally specic changes in resting-state sensorimotor networks
aer motor learning,” JournalofNeuroscience,vol.,no.,pp.
–, .
[] M. Taubert, G. Lohmann, D. S. Margulies, A. Villringer, and
P. Ragert, “Long-term eects of motor training on resting-state
networks and underlying brain structure,” NeuroImage,vol.,
no. , pp. –, .
[] M.W.Voss,R.S.Prakash,K.I.Ericksonetal.,“Plasticityof
brain networks in a randomized intervention trial of exercise
training in older adults,” Frontiers in Aging Neuroscience,vol.,
article , .
[] H. Takeuchi, Y. Taki, R. Nouchi et al., “Eects of working
memory training on functional connectivity and cerebral blood
ow during rest,” Cortex,vol.,no.,pp.–,.
[] D.D.Jolles,M.A.vanBuchem,E.A.Crone,andS.A.R.B.
Rombouts, “Functional brain connectivity at rest changes aer
working memory training,” Human Brain Mapping,vol.,no.
,pp.–,.
[] H. Takeuchi, Y. Taki, R. Nouchi et al., “Eects of multitasking-
training on gray matter structure and resting state neural
mechanisms,” Human Brain Mapping,vol.,no.,pp.–
, .
[] A. P. Mackey, A. T. M. Singley, and S. A. Bunge, “Intensive
reasoning training alters patterns of brain connectivity at rest,”
Journal of Neuroscience,vol.,no.,pp.–,.
[] K. Mart´
ınez, A. B. Solana, M. Burgaleta et al., “Changes in
resting-state functionally connected parietofrontal networks
aer videogame practice,” Human Brain Mapping,vol.,no.
, pp. –, .
[] T. Harmelech, S. Preminger, E. Wertman, and R. Malach,
“e day-aer eect: long term, hebbian-like restructuring of
resting-state fMRI patterns induced by a single epoch of cortical
activation,” Journal of Neuroscience,vol.,no.,pp.–
, .
[] F.Megumi,A.Yamashita,M.Kawato,andH.Imamizu,“Func-
tional MRI neurofeedback training on connectivity between
two regions induces long-lasting changes in intrinsic functional
network,” Frontiers in Human Neuroscience,vol.,article,
.
[] E. Kra, “Cognitive function, physical activity, and aging:
possible biological links and implications for multimodal inter-
ventions,” Aging, Neuropsychology,and Cognition,vol.,no.-,
pp. –, .
[] K. H¨
otting and B. R¨
oder, “Benecial eects of physical exercise
on neuroplasticity and cognition,” Neuroscience and Biobehav-
ioral Reviews,vol.,no.,pp.–,.
[] R.Li,X.Zhu,S.Yinetal.,“Multimodalinterventioninolder
adults improves resting-state functional connectivity between
the medial prefrontal cortex and medial temporal lobe,” Fron-
tiers in Aging Neuroscience,vol.,article,.
[] K. Holzschneider, T. Wolbers, B. R¨
oder, and K. H¨
otting,
“Cardiovascular tness modulates brain activation associated
with spatial learning,” NeuroImage,vol.,no.,pp.–,
.
[] C. M. Lewis, A. Baldassarre, G. Committeri, G. L. Romani, and
M. Corbetta, “Learning sculpts the spontaneous activity of the
resting human brain,” Proceedings of the National Academy of
Sciences of the United States of America,vol.,no.,pp.
–, .
[] T. E. J. Behrens, H. Johansen-Berg, M. W. Woolrich et al., “Non-
invasive mapping of connections between human thalamus and
cortex using diusion imaging,” Nature Neuroscience,vol.,no.
, pp. –, .
[] R. Fama and E. V. Sullivan, “alamic structures and associated
cognitive functions: relations with age and aging,” Neuroscience
& Biobehavioral Reviews, vol. , pp. –, .
[] B.Zhou,Y.Liu,Z.Zhangetal.,“Impairedfunctionalconnectiv-
ity of the thalamus in Alzheimer’ s disease and mild cognitive
Neural Plasticity
impairment: a resting-state FMRI study,” Current Alzheimer
Research, vol. , no. , pp. –, .
[] Z. Wang, X. Jia, P. Liang et al., “Changes in thalamus connec-
tivity in mild cognitive impairment: evidence from resting state
fMRI,” European Journal of Radiolog y,vol.,no.,pp.–,
.
[] R. L. Buckner, F. M. Krienen, A. Castellanos, J. C. Diaz, and
B. T. omas Yeo, “e organization of the human cerebellum
estimated by intrinsic functional connectivity,” Journal of Neu-
rophysiology,vol.,no.,pp.–,.
[] J. X. O’Reilly, C. F. Beckmann, V. Tomassini, N. Ramnani, andH.
Johansen-Berg, “Distinct and overlapping functional zones in
the cerebellum dened by resting state functional connectivity,”
Cerebral Cortex,vol.,no.,pp.–,.
[] J. Pa, S. Dutt, J. B. Mirsky et al., “e functional oculomotor
network and saccadic cognitive control in healthy elders,”
NeuroImage,vol.,pp.–,.
[] R.M.Hutchison,J.P.Gallivan,J.C.Culham,J.S.Gati,R.S.
Menon, and S. Everling, “Functional connectivity of the frontal
eye elds in humans and macaque monkeys investigated with
resting-state fMRI,” Journal of Neurophysiology,vol.,no.,
pp. –, .
[] E.J.Anderson,D.K.Jones,R.L.O’Gorman,A.Leemans,M.
Catani, and M. Husain, “Cortical network for gaze control in
humans revealed using multimodal MRI,” Cerebral Cortex,vol.
, no. , pp. –, .
[] J. Ashburner, “A fast dieomorphic image registration algo-
rithm,” NeuroImage,vol.,no.,pp.–,.
[] S. Whiteld-Gabrieli and A. Nieto-Castanon, “Conn: a func-
tional connectivity toolbox for correlated and anticorrelated
brain networks,” Brain Connectivity,vol.,no.,pp.–,
.
[] W.R.Shirer,H.Jiang,C.M.Price,B.Ng,andM.D.Greicius,
“Optimization of rs-fMRI pre-processing for enhanced signal-
noise separation, test-retest reliability, and group discrimina-
tion,” NeuroImage,vol.,pp.–,.
[] Y.Behzadi,K.Restom,J.Liau,andT.T.Liu,“Acomponentbased
noise correction method (CompCor) for BOLD and perfusion
based fMRI,” NeuroImage,vol.,no.,pp.–,.
[] Z.Qing,Z.Dong,S.Li,Y.Zang,andD.Liu,“Globalsignal
regression has complex eects on regional homogeneity of
resting state fMRI signal,” Magnetic Resonance Imaging,.
[] O.Agcaoglu,R.Miller,A.R.Mayer,K.Hugdahl,andV.D.Cal-
houn, “Lateralization of resting state networks and relationship
to age and gender,” NeuroImage,vol.,pp.–,.
[] C. Luo, Z.-W. Guo, Y.-X. Lai et al., “Musical training induces
functional plasticity in perceptual and motor networks: insights
from resting-state fMRI,” PLoS ONE,vol.,no.,ArticleID
e, .
[] C.Sampaio-Baptista,A.A.Khrapitchev,S.Foxleyetal.,“Motor
skill learning induces changes in white matter microstructure
and myelination,” Journal of Neuroscience,vol.,no.,pp.
–, .
[] C.Sampaio-Baptista,N.Filippini,C.J.Stagg,J.Near,J.Scholz,
and H. Johansen-Berg, “Changes in functional connectivity and
GABA levels with long-term motor learning,” NeuroImage,vol.
, pp. –, .
[] B. Draganski, C. Gaser, V. Busch, G. Schuierer, U. Bogdahn, and
A. May, “Neuroplasticity: changes in grey matter induced by
training,” Nature,vol.,no.,pp.–,.
[] J. C. Culham and N. G. Kanwisher, “Neuroimaging of cognitive
functions in human parietal cortex,” Current Opinion in Neuro-
biology, vol. , no. , pp. –, .
[] O. Simon, J.-F. Mangin, L. Cohen, D. Le Bihan, and S. Dehaene,
“Topographical layout of hand, eye, calculation, and language-
related areas in the human parietal lobe,” Neuron,vol.,no.,
pp. –, .
[] A. Bartels and S. Zeki, “Brain dynamics during natural viewing
conditions—a new guide for mapping connectivity in vivo,”
NeuroImage,vol.,no.,pp.–,.
[] M.-T. Herrero, C. Barcia, and J. M. Navarro, “Functional
anatomy of thalamus and basal ganglia,” Child’s Nervous System,
vol.,no.,pp.–,.
[] L. Hearne, L. Cocchi, A. Zalesky, and J. B. Mattingley, “Interac-
tions between default mode and control networks as a function
of increasing cognitive reasoning complexity,” Human Brain
Mapping,vol.,no.,pp.–,.
[] L. Bonzano, E. Palmaro, R. Teodorescu, L. Fleysher, M. Inglese,
and M. Bove, “Functional connectivity in the resting-state
motor networks inuences the kinematic processes during
motor sequence learning,” European Journal of Neuroscience,
vol. , no. , pp. –, .
[] N. C. Andreasen, “e role of the thalamus in schizophrenia,”
Canadian Journal of Psychiatry,vol.,no.,pp.–,.
[] J. D. Schmahmann and D. N. Pandya, “Disconnection syn-
dromes of basal ganglia, thalamus, and cerebrocerebellar sys-
tems,” Cortex,vol.,no.,pp.–,.
[]H.S.Wang,C.Rau,Y.Li,Y.Chen,andR.Yu,“Disrupted
thalamic resting-state functional networks in schizophrenia,”
FrontiersinBehavioralNeuroscience,vol.,article,.
[] C. Sol´
e-Padull´
es, D. Bartr´
es-Faz, C. Junqu´
eetal.,“Brain
structure and function related to cognitive reserve variables
in normal aging, mild cognitive impairment and Alzheimer’s
disease,” Neurobiolog y of Aging,vol.,no.,pp.–,.
[]C.-Y.C.Sylvester,T.D.Wager,S.C.Laceyetal.,“Switching
attention and resolving interference: fMRI measures of exec-
utive functions,” Neuropsychologia,vol.,no.,pp.–,
.
[] M. E. Raichle, “e restless brain: how intrinsic activity orga-
nizes brain function,” Philosophical Transactions of the Royal
SocietyofLondonSeriesB:BiologicalSciences,vol.,no.,
.
[] D. Zhang and M. E. Raichle, “Disease and the brain’s dark
energy,” Nature Reviews Neurology,vol.,no.,pp.–,.
[] E. Solesio-Jofre, L. Serbruyns, D. G. Woolley, D. Mantini, I.
A. M. Beets, and S. P. Swinnen, “Aging eects on the resting
state motor network and interlimb coordination,” Human Brain
Mapping,vol.,no.,pp.–,.
[] R. Seidler, B. Erdeniz, V. Koppelmans, S. Hirsiger, S. M´
erillat,
and L. J¨
ancke, “Associations between age, motor function,
and resting state sensorimotor network connectivity in healthy
older adults,” NeuroImage,vol.,pp.–,.
[] O. Ajilore, M. Lamar, and A. Kumar, “Association of brain
network eciency with aging, depression, and cognition,” e
American Journal of Geriatric Psychiatry,vol.,no.,pp.–
, .
[] Z. Zheng, X. Zhu, S. Yin et al., “Combined cognitive-psycho-
logical-physical intervention inducesreorganization of intrinsic
functional brain architecture in older adults,” Neural Plasticity,
vol.,ArticleID,pages,.
Neural Plasticity
[] A.Lampit,H.Hallock,C.Suo,S.L.Naismith,andM.Valen-
zuela, “Cognitive training-induced short-term functional and
long-term structural plastic change is related to gains in global
cognition in healthy older adults: a pilot study,” Frontiers in
Aging Neuroscience,vol.,article,.
[] M.W.Cole,T.Yarkoni,G.Repov
ˇ
s, A. Anticevic, and T. S. Braver,
“Global connectivity of prefrontal cortex predicts cognitive
control and intelligence,” eJournalofNeuroscience,vol.,
no. , pp. –, .
[] Y. I. Sheline, J. L. Price, Z. Yan, and M. A. Mintun, “Resting-state
functional MRI in depression unmasks increased connectivity
between networks via the dorsal nexus,” Proceedings of the
National Academy of Sciences of the United States of America,
vol. , no. , pp. –, .
[] M.W.Voss,K.I.Erickson,R.S.Prakashetal.,“Functional
connectivity: a source of variance in the association between
cardiorespiratory tness and cognition?” Neuropsychologia,vol.
,no.,pp.–,.
[] T. D. Ben-Soussan, A. Berkovich-Ohana, J. Glicksohn, and A.
Goldstein, “A suspended act: increased reectivity and gender-
dependent electrophysiological change following Quadrato
Motor Training,” Frontiers in Psychology,vol.,article,.
[] C. M. Bennett and M. B. Miller, “How reliable are the results
from functional magnetic resonance imaging?” Annals of the
New York Academy of Sciences,vol.,pp.–,.
[] K. J. Gorgolewski, A. J. Storkey, M. E. Bastin, I. Whittle, and C.
Pernet, “Single subject fMRI test-retest reliability metrics and
confounding factors,” NeuroImage,vol.,pp.–,.
[] R.Maitra,S.R.Roys,andR.P.Gullapalli,“Test-retestreliability
estimation of functional MRI data,” Magnetic Resonance in
Medicine,vol.,no.,pp.–,.
[] M.Raemaekers,S.duPlessis,N.F.Ramsey,J.M.H.Weusten,
and M. Vink, “Test-retest variability underlying fMRI measure-
ments,” NeuroImage,vol.,no.,pp.–,.
[] Z. Shehzad, A. M. C. Kelly, P. T. Reiss et al., “e resting brain:
unconstrained yet reliable,” Cerebral Cortex,vol.,no.,pp.
–, .
[] K. Murphy, R. M. Birn, and P. A. Bandettini, “Resting-state
fMRI confounds and cleanup,” NeuroImage,vol.,pp.–
, .
[] X.-N. Zuo and X.-X. Xing, “Test-retest reliabilities of resting-
state FMRI measurements in human brain functional connec-
tomics: a systems neuroscience perspective,” Neuroscience and
Biobehavioral Reviews C,vol.,pp.–,.
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