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The Exercising Brain: Changes in Functional Connectivity Induced by an Integrated Multimodal Cognitive and Whole-Body Coordination Training

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Neural Plasticity
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This 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). The training is an integrated multimodal training that combines motor and cognitive aspects and challenges the brain by introducing new and unfamiliar coordinative tasks. Twentyone subjects completed at least 11 one-hour-per-week “life kinetik” training sessions in 13 weeks as well as before and after rsfMRI scans. Additionally, 11 control subjects with 2 rs-fMRI scans were included. The 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 defaultmode network, such asmedial frontal cortex and posterior cingulate cortex, did not change. Significant connectivity alterations occurred between the visual cortex and parts of the superior parietal area (BA7). Premotor area and cingulate gyrus were also affected.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.
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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 aer 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. Signicant connectivity alterations occurred between the visual cortex and parts of the superior
parietal area (BA). Premotor area and cingulate gyrus were also aected. 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 eect 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 dened 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
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Within the motor domain, several research groups inves-
tigated dierent 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 aer the
training was reduced compared to before the intervention.
In the cognitive domain, training rather aected intrinsic
functional connectivity between frontal and parietal areas
[, , ]. However, the precise location of training-related
change in intrinsic functional connectivity diers between
studies. Regarding the variety of changes found by dierent
research groups, the training eects seem to be rather specic
to the content of the training, the duration, the intensity,
and the timing of the resting-state quantication. 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 eect 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 eects of physical exercise on neuroplasticity
and cognition it is suggested that adding cognitive training
might enhance the benecial eect of physical training (for
a review see []). Yet, there are only few studies that focus
on the eect of combined interventions. To our knowledge,
there are only two studies exploring the eect 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 quantied. Aer 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 eect 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 dierent 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 modied aer 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 diculty of the task can easily be
adapted to the capabilities of patient populations.
Based on the assumption that spontaneous activity
reects 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 reect 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 dierent moving objects and due to the possibility of
assigning the requested action via a visual stimulus (specic
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 dierent 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 specic cue have to be
memorized during one training session. e randomization
of cues is self-evident. Within one training session ( hour per
week) approximately  dierent 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 aer 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  ×,andslicethicknessismm).
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/
soware/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 specic 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 aer 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 identied 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 dierences 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 veried the lack of dierences
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 veried the occurrence of the
default mode network in each group and to both time-points.
is seems necessary due to the dierent size of the two
groups.
e second seed-to-voxel analysis was used to examine
dierences 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 signicant 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 veried
the direction of changes with two post hoc paired sample
𝑡-tests for the trainee and the control group separately for
each signicant seed-to-voxel cluster. is step was chosen to
assure that the signicant results were not caused by between-
subject variance. e other reason for this approach was the
dierentsamplesizeofthetwogroups.Wereportsignicant
results due to three criteria: (a) signicant time by group
interaction, (b) signicant increase within the training group,
and (c) no signicant 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/soware/
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 dierences 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 signicant positive
connectivity changes in trainees and no signicant 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. Dierent 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 aer 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 .
Neural Plasticity
(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.
Neural Plasticity
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 oers 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
decits, 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 aer 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 dierent
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 dierent 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 eect of the verbal prompts
during the exercises, which indicated dierent movements.
e visual cortex is also diversied. 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 diversication of the parietal cortex [, ].
Grasping and visuospatial tasks activated dierent 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 specic 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 eort 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 dicult.
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 [–].
ProperoroptimalfunctionofBAseemstobeanindica-
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 dierent 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 eciency [] 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 benecial. 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 dierent 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 dierent 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 signicant
post hoc test.
A second limitation for our results is the whole sample
size as well as the dierence 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 eect 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
betondsuitablemotorandcognitivetesttaskstoquantify
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 benet 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 conict
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
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... Results indicate a noticeable overlap between CNS structures addressed with these coordination exercises and those required for the conduction of spatial orientation or navigation tasks. These findings are supported by studies performing longer term CMT (e.g., 42,43). Particularly, parietal and occipital areas, the prefrontal cortex, premotor areas, and subcortical structures seem to be involved in spatial and coordinative activities (39,(41)(42)(43). ...
... These findings are supported by studies performing longer term CMT (e.g., 42,43). Particularly, parietal and occipital areas, the prefrontal cortex, premotor areas, and subcortical structures seem to be involved in spatial and coordinative activities (39,(41)(42)(43). When evaluating the effect of acute CMT on attention and concentration, Budde et al. (44) further suggest that CMT is associated with higher cerebellar and frontal lobe activation, structures also involved in spatial orientation. ...
... Initial evidence by Jansen and Richter (31) has shown beneficial effects of an acute motor training intervention on mental rotation (i.e., one aspect of spatial abilities) in healthy children. Moreover, previous research finds overlapping brain activation patterns regarding spatial orientation and CMT indicating that activation of these areas during CMT could also improve spatial functions by e.g., aiding functional connectivity, increasing cerebral blood flow or neurochemical adaptations (40)(41)(42)(43)(44)47). Based on these findings and aiming to contribute to this still relatively limited field of research, we hypothesized that participation in a single session (i.e., 30 min) of CMT would result in superior spatial ability performance compared to a resting control group (i.e., no training at all) as measured by five established spatial ability tests. ...
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Background In recent years, studies have found small-to-medium positive effects of physical activity on academic achievement. Already acute bouts of exercise appear to improve certain cognitive functions. Spatial abilities are one aspect of cognition that is encountered frequently in daily life and that is closely related to success in science, technology, engineering, and mathematics (STEM)-subjects. However, little is known about the effects of an acute exercise session on spatial abilities. The aim of this study was therefore to evaluate the effect of a single session of coordinative motor training (CMT) on spatial ability performances in healthy children. Methods Forty-nine children were assigned to either a single session of CMT (i.e., obstacle course with motor coordinative and spatial elements) ( n = 25, 12 females, mean age: 10.7 ± 0.6 years) or a resting control group ( n = 24, 12 females, mean age ± SD: 11.4 ± 0.5 years). Spatial abilities were evaluated in both groups using the Paper Folding Test (PFT), Mental Rotation Test (MRT), Water Level Task (WLT), Corsi Block Test (CBT), and Numbered Cones Run (NCR). Results A statistical main effect for Test was observed for the majority of outcomes (i.e., all but the MRT). Test × Group interactions did not reach the level of significance. Conclusion The results indicate that a single session of CMT does not improve spatial ability performances of healthy children. Future research should evaluate whether repeated longer-term interventions might be more suitable to generate significant improvements in spatial abilities.
... A promising approach for learning complex motor movements in high-performance sports is known as movement-related cognitive training. Movementrelated cognitive training promotes brain plasticity by enhancing functional connectivity, thus enabling the faster acquisition of complex motor skills [25]. It was demonstrated by functional magnetic resonance imaging that this training increases the connectivity between the visual cortex, the superior parietal lobe, the premotor cortex and the cingulate cortex [25]. ...
... Movementrelated cognitive training promotes brain plasticity by enhancing functional connectivity, thus enabling the faster acquisition of complex motor skills [25]. It was demonstrated by functional magnetic resonance imaging that this training increases the connectivity between the visual cortex, the superior parietal lobe, the premotor cortex and the cingulate cortex [25]. These regions are responsible for visual motion and attention control, motion planning and execution, sensory-motor integration and the evaluation of emotional and motivational information during task solving. ...
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Purpose Skilful arthroscopy requires an aboveaverage level of manual dexterity. It is evident that particular motor skills can be learned and trained before arthroscopic training. The aim of this prospective cohort study was to investigate the impact of movement‐related cognitive training on the learning curve during arthroscopic basic training. Methods Fifty right‐handed participants without arthroscopic experience were matched to an intervention group ( n = 25) and a control group ( n = 25). Prior to basic arthroscopic skill training with a simulator, the intervention group underwent 12 weeks of movement‐related cognitive training. Cognitive and motor skills were assessed in both groups by using standardised tests (CogniFit test, angle reproduction test, two‐arm coordination test) as a pretest and, for the intervention group, again before arthroscopic training as a posttest. For arthroscopic simulator training, three tasks (‘Telescoping’, ‘Periscoping’, ‘Triangulation’) from the Fundamentals of Arthroscopic Surgery Training module were selected and practiced 10 times with the camera in the right and left hands. The learning progress was quantified by exercise time, camera path length and hook path length. Results No significant differences in sex distribution, age distribution or the results of the pretests between the intervention group ( n = 21) and the control group ( n = 25) were found (n.s.). The intervention group improved significantly from the pretest to the posttest in the CogniFit ( p = 0.003) and two‐arm coordination test in terms of time ( p < 0.001) and errors ( p = 0.002) but not in the angle reproduction test. No significant differences were found between the groups for the three arthroscopic tasks. Conclusion The hypothesis that movement‐related cognitive training shortens the learning curve for acquiring arthroscopic basic skills cannot be confirmed. Other factors influencing the learning curve such as talent, teaching method and motivation have a greater impact on the acquisition of complex motor skills. Level of Evidence Level II.
... Long-term learning of motor skills has the potential to induce structural and functional plasticity in the brain [1][2][3][4]. Motor skill training refines movements through repeated practice and interactions with the environment, ultimately leading to effortless execution [5]. Studies suggest that individuals who engage in regular and varied training show structural and functional alterations in brain regions, such as the prefrontal cortex and hippocampus, which are linked to the duration and intensity of training [6][7][8][9]. ...
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(1) Background: This study investigates the resting-state brain characteristics of skeleton athletes compared to healthy age-matched non-athletes, using resting-state fMRI to investigate long-term skeleton-training-related changes in the brain. (2) Methods: Eleven skeleton athletes and twenty-three matched novices with no prior experience with skeleton were recruited. Amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity analyses were explored to investigate resting-state functional magnetic resonance imaging (rs-fMRI) data, aiming to elucidate differences in resting-state brain function between the two groups. (3) Results: Compared to the control group, skeleton athletes exhibited significantly higher ALFF in the left fusiform, left inferior temporal gyrus, right inferior frontal gyrus, left middle temporal gyrus, left and right insula, left Rolandic operculum, left inferior frontal gyrus, and left superior temporal gyrus. Skeleton athletes exhibit stronger functional connectivity in brain regions associated with cognitive and motor control (superior frontal gyrus, insula), as well as those related to reward learning (putamen), visual processing (precuneus), spatial cognition (inferior parietal), and emotional processing (amygdala), during resting-state brain function. (4) Conclusions: The study contributes to understanding how motor training history shapes skeleton athletes’ brains, which have distinct neural characteristics compared to the control population, indicating potential adaptations in brain function related to their specialized training and expertise in the sport.
... Later, it was developed as Life Kinetic (LK) exercises in Germany under the leadership of Horst Lutz and then spread worldwide (Çimen, 2021a). The basis of Life Kinetic training is to apply various basic movements with different movement activities that activate and associate cortical regions that can increase athlete efficiency during the training process (Demirakca et al., 2016). ...
... Compared to task-based fMRI, rsFC patterns are relatively stable over time, which renders them useful for studying individual differences and the effects of long-term training on brain networks (Guo et al. 2012). Moreover, rsFC can be utilized to examine the plasticity changes in the brain due to training (Demirakca et al. 2016;Wang et al. 2016;Raichlen et al. 2016;Vahdat et al. 2011;Ma et al. 2011;Dayan and Cohen 2011;Taubert et al. 2011), learning (Lewis et al. 2009), or rehabilitation (Park et al. 2011). For instance, Vahdat demonstrated that motor learning can induce changes in the functional connectivity of resting-state networks related to the sensorimotor system. ...
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The relation between the action verb semantic processing and sensorimotor experience remains controversial. In this study, we examined whether plasticity changes in brain are specifically related to semantic processing of foot action verbs when long-term motor training is mainly aimed at the foot. To address this question, we acquired resting-state functional magnetic resonance imaging scans and behavioral data from a verb two-choice task from female expertise football players and football novices. We compared the resting-state functional connectivity (rsFC) differences between experts and novices using motor execution regions and general semantic regions (left anterior temporal lobe, lATL) as seed, and explored the neural correlates of behavioral performance. Here, the drift rate (v) parameter of the drift diffusion model (DDM) was used to capture the semantic processing capability. We found experts showed increased correlation between lATL subregions and important brain regions for motor processing, including supplementary motor area (SMA), bilateral paracentral lobule (PL), superior parietal lobule and inferior parietal lobule, in contrast to novices. Further predictive model analysis showed the FC found in rsFC analysis can significantly predict drift rate of foot action verb in both experts and novices, but not drift rate of hand action verb. Our findings therefore establish a connection between effector-related semantic processing and the plasticity changes in brain functional connectivity, attributable to long-term foot-related motor training. This provides evidence supporting the view that semantic processing is fundamentally rooted in the sensorimotor system.
... Moreover, the coordinative practices in particular could affect children's brain connectivity (Demirakca et al., 2016). Using event-related potentials, a study could show that the hemispheric laterality during a mental rotation task was significantly lateralized towards the left hemisphere in second graders, non-significantly in sixth graders, and completely bi-lateralized in adults (Jansen-Osmann & Heil, 2007). ...
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Mental rotation is a spatial cognitive ability malleable by training, e.g., physical education. The importance of children’s physical activity on their self-concept is also well proven. The present study examines whether a ten-week boxing training improves ten-year-old children’s mental rotation performance and self-concept. Forty-five children (26 boys and 19 girls, age: M = 9.62, SD = 0.71) completed a mental rotation test and filled out a questionnaire about their academic, physical, and social self-concept. Seventeen of the children participated in a ten-week boxing training. After the training, all children completed the same test and questionnaire. Results showed that children in the training group improved more than children in the control group in all three aspects of self-concept and mental rotation performance. All interaction effects between time and group were moderate to large. We conclude that a ten-week boxing training successfully improves children’s self-concept and spatial abilities.
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Successful performance of challenging cognitive tasks depends on a consistent functional segregation of activity within the default-mode network, on the one hand, and control networks encompassing frontoparietal and cingulo-opercular areas on the other. Recent work, however, has suggested that in some cognitive control contexts nodes within the default-mode and control networks may actually cooperate to achieve optimal task performance. Here, we used functional magnetic resonance imaging to examine whether the ability to relate variables while solving a cognitive reasoning problem involves transient increases in connectivity between default-mode and control regions. Participants performed a modified version of the classic Wason selection task, in which the number of variables to be related is systematically varied across trials. As expected, areas within the default-mode network showed a parametric deactivation with increases in relational complexity, compared with neural activity in null trials. Critically, some of these areas also showed enhanced connectivity with task-positive control regions. Specifically, task-based connectivity between the striatum and the angular gyri, and between the thalamus and right temporal pole, increased as a function of relational complexity. These findings challenge the notion that functional segregation between regions within default-mode and control networks invariably support cognitive task performance, and reveal previously unknown roles for the striatum and thalamus in managing network dynamics during cognitive reasoning. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
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Traditionally studies of brain function have focused on task-evoked responses. By their very nature such experiments tacitly encourage a reflexive view of brain function. While such an approach has been remarkably productive at all levels of neuroscience, it ignores the alternative possibility that brain functions are mainly intrinsic and ongoing, involving information processing for interpreting, responding to and predicting environmental demands. I suggest that the latter view best captures the essence of brain function, a position that accords well with the allocation of the brain's energy resources, its limited access to sensory information and a dynamic, intrinsic functional organization. The nature of this intrinsic activity, which exhibits a surprising level of organization with dimensions of both space and time, is revealed in the ongoing activity of the brain and its metabolism. As we look to the future, understanding the nature of this intrinsic activity will require integrating knowledge from cognitive and systems neuroscience with cellular and molecular neuroscience where ion channels, receptors, components of signal transduction and metabolic pathways are all in a constant state of flux. The reward for doing so will be a much better understanding of human behaviour in health and disease.
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