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ORIGINAL RESEARCH
published: 12 August 2016
doi: 10.3389/fnagi.2016.00193
Frontiers in Aging Neuroscience | www.frontiersin.org 1August 2016 | Volume 8 | Article 193
Edited by:
Milica S. Prostran,
University of Belgrade, Serbia
Reviewed by:
Matthieu P. Boisgontier,
Katholieke Universiteit (KU) Leuven,
Belgium
Ramesh Kandimalla,
Emory University, USA
*Correspondence:
Alexandre Kubicki
alexandre.kubicki@u-bourgogne.fr
Received: 14 June 2016
Accepted: 29 July 2016
Published: 12 August 2016
Citation:
Kubicki A, Fautrelle L, Bourrelier J,
Rouaud O and Mourey F (2016) The
Early Indicators of Functional
Decrease in Mild Cognitive
Impairment.
Front. Aging Neurosci. 8:193.
doi: 10.3389/fnagi.2016.00193
The Early Indicators of Functional
Decrease in Mild Cognitive
Impairment
Alexandre Kubicki 1, 2, 3*, Lilian Fautrelle 1, 4, 5, Julien Bourrelier 1, 2 , Olivier Rouaud1, 6 and
France Mourey 1, 7
1Unité 1093, Cognition, Action et Plasticité Sensorimotrice, Institut National de la Santé et de la Recherche Médicale, Dijon,
France, 2Université de Bourgogne Franche Comté, Unité de Formation et de Recherche (UFR) Sciences et Techniques des
Activités Physiques et Sportives (STAPS), Dijon, France, 3Institut de Formation aux Métiers de la Santé (IFMS), Nord
Franche-Comté, Hôpital Nord Franche-Comté, Montbéliard, France, 4Université Paris Ouest Nanterre La Défense, Unité de
Formation et de Recherche (UFR) Sciences et Techniques des Activités Physiques et Sportives (STAPS), Nanterre, France,
5Centre de Recherche sur le Sport et le Mouvement, CeRSM, Unité de Formation et de Recherche (UFR) Sciences et
Techniques des Activités Physiques et Sportives (STAPS), Nanterre, France, 6Centre Mémoire Ressources et Recherche,
Centres Hospitaliers Universitaires (CHU), Dijon-Bourgogne, Dijon, France, 7Unité de Formation et de Recherche (UFR),
Santé, Université de Bourgogne Franche Comté, Dijon, France
Objectives: Motor deficiency is associated with cognitive frailty in patients with Mild
Cognitive Impairments (MCI). In this study we aimed to test the integrity in muscle
synergies involved in an arm-pointing movement in functionally unimpaired MCI patients.
We hypothesized that early motor indicators exist in this population at a preclinical level.
Methods: Electromyographic signals were collected for 11 muscles in 3 groups: Young
Adults (YA), Older Adults (OA), and MCI patients. The OA and MCI groups presented
the same functional status. Each subject performed 20 arm-pointing movements from a
standing position.
Results: The main differences were (1) an earlier activation of the left Obliquus internus
in MCI compared with OA group, (2) an earlier activation for the MCI compared with both
OA and YA. The temporal differences in muscle synergies between MCI and OA groups
were linked with executive functions of MCI patients, assessed by the trail making test.
Moreover, the results show a delayed activation of the right Biceps Femoris and the right
Erector Spinae at l3 in MCI and OA compared with YA.
Interpretation: The motor program changes highlighted in our patient MCI group
suggest that discrete modifications of the motor command seem to exist even in the
absence of functional impairment. Instead of showing an indication of delayed muscle
activation in the MCI patients, our results highlight some early activation of several trunk
muscles.
Keywords: Mild Cognitive Impairments, muscle synergy, Anticipatory Postural Adjustments, motor control,
cognitive functions
BACKGROUND
Mild Cognitive Impairments (MCI) is an intermediate stage between typical age-related cognitive
changes and dementia. There is growing evidence in the literature about the association of
sensory-motor dysfunction with cognitive frailty in aging (Albers et al., 2015). Several research
teams focused on the broad correlation between motor and cognitive capacities in aging.
Kubicki et al. Discrete Motor Changes in MCI Patients
In patients with MCI, there is a solid body of evidence supporting
the motor-related changes. These studies have reported some
gait and balance deficiencies (Boyle et al., 2005; Aggarwal et al.,
2006; Verghese et al., 2008; Gras et al., 2015), which become even
more significant in dual-task conditions (Montero-Odasso et al.,
2014), and a greater fall rate in this population (Doi et al., 2014).
Interestingly, a value of the gait speed test that predicts evolution
toward cognitive frailty has been also highlighted (Buracchio
et al., 2010).
These studies assessed overall motor functions (i.e., functional
abilities) in MCI patients. Motor function involves the peripheral
musculoskeletal system (information capture and torque
production), the peripheral nervous system (information
conduction) and the central nervous system (CNS; motor
planning and programming) (Caffarra et al., 2010; Cisek and
Kalaska, 2010). As the symptoms of MCI are mainly cognitive,
and thus related to central modifications, it could be interesting
to identify potential impairments affecting this central part of
motor behavior in MCI patients.
To assess the integrity of this central part of motor control,
researchers have usually focused on the preparatory period,
which includes motor program processes, and well-known as
Anticipatory Postural Adjustments (APA; Belen’kii et al., 1967;
Massion, 1992; Desmurget and Grafton, 2000; Maloney and
Mamassian, 2009). Indeed, our CNS has the ability to coordinate
posture and movement efficiently, especially by means of feed-
forward processes that allow the anticipated recruitment of
several muscles before the beginning of any self-generated
perturbation of our balanced-system (Massion, 1992). These APA
are mainly involved in maintaining the integrity of the balance
function and can be challenged in several situations (Horak,
2006). Actually, anticipatory activations of trunk muscles, such as
transversus abdominis and multifidus, were found to be delayed
in chronic low-back-pain patients (Hodges and Richardson,
1996). In the same vein, acute and experimentally-induced
pain seems to shift the anticipation activations from a deep
(transversus abdominis and internal obliquus) to a superficial
muscular layer (external obliquus) (Moseley and Hodges, 2005).
Normal aging also challenges these APA (Man’kovskii et al.,
1980; Inglin and Woollacott, 1988; Rogers et al., 1992; Woollacott
and Manchester, 1993; Bleuse et al., 2006; Kanekar and Aruin,
2014), with greater severity in frail elderly adults (Kubicki et al.,
2012a). Neurological disorders such as stroke or Parkinson’s
disease also impair the ability of the CNS to produce efficient APA
(Latash et al., 1995; Garland et al., 1997; Elble and Leffler, 2000).
Nonetheless, there are very few studies about APA in
cognitively impaired patients in the literature. Elble and Leffler
reported that postural and focal reaction times were slower
in Alzheimer Disease (AD) patients, but without specific
impairment in the postural preparatory period, than in age-
matched controls without AD (Elble and Leffler, 2000). To our
knowledge, there is no information in the literature about APA
in MCI patients.
This study is warranted by the lack of data about this
important question, especially when considering the link between
APA and balance function (Robinovitch et al., 2013). Our aim
was to test APA integrity in MCI patients, without functional
impairments, during an arm raising task. Our hypothesis was
that MCI patients would have delayed anticipation activations
compared with age-matched controls, but that the global synergy
of muscle activations would be respected. To better understand
these potential changes, and to take into account the APA
impairment highlighted in normal aging, we included a group of
young adults (YA) in our experiment.
MATERIALS AND METHODS
Participants
Participants (n=42) were divided into 3 groups: MCI (Mild
Cognitive Impairment group); OA (Older Adults group), and YA
(Young Adults group). All the participants were right handed
as assessed by the Edinburgh Handedness Inventory (Oldfield,
1971).
MCI subjects (n=14) were included at the Memory,
Resources and Research Centre (CMRR) of Dijon University
Hospital, France, during their annual medical consultation.
Inclusion criteria were (1) a recent diagnosis of amnestic MCI
according to the criteria of NIA-OA (Albert et al., 2011), (2) an
absence of a diagnosis of dementia due to Alzheimer’s disease,
(3) to be aged 60 years or over, (4) no other neurological disease,
such as Parkinsonism syndrome or pyramidal deficiency, (5) no
musculoskeletal deficiency that caused pain, balance deficit or
restricted function. To be more precise, the Alzheimer’s disease
and the Parkinsonism syndrome were clinically diagnosed by an
experienced neurologist. The Alzheimer’s disease was diagnosed
following the “recommendations from the National Institute
on Aging-Alzheimer’s Association workgroups on diagnostic
guidelines for Alzheimer’s disease” (Albert et al., 2011), and
the Parkinsonism syndrome following the recommendations
from the “MDS clinical diagnostic criteria for Parkinson’s
disease” (Postuma et al., 2015). One more time, both were
retained as exclusion criteria. These subjects were recruited
during their annual medical consultation, during which
they provided their written consent to participate in the
study.
MCI was diagnosed by an experienced neurologist (OR) by
means of the criteria of NIA-OA (Albert et al., 2011). A few
patients underwent associated tests, such as MRI T2 flair to detect
hyper-signals (Wahlund et al., 2001). Their neuropsychological
assessment included the following tests for the executive and
attentional functions: the Trail Making Test (TMT A and TMT
B) (Reitan, 1958; Corrigan and Hinkeldey, 1987; Gaudino et al.,
1995; Lezak et al., 2004) and the Digit Span (Forward and
Backward). The Free Cued Selective Reminding Test (FCSRT)
and the Delayed Matching to Sample (DMS 48) were done to
assess their memory functions. At inclusion, the mean scores
were 4.85 (Digit Span Forward); 3.71 (Digit Span Backward);
46.81 s (TMT A); 128 s (TMT B); 18.6 (FCSRT RL, on 48); 34.5
(FCSRT RT, on 48); 92.8 % (DMS 48, Set 1); 90.7 (DMS 48, Set 2).
OA subjects (n=14) were volunteers who had no
previous experience as subjects in experimental research. These
individuals had no medical diagnosis of or self-assessed cognitive
deficiency, such as memory loss or executive problems, and met
the same (3-4-5) inclusion criteria as MCI patients (see above).
Frontiers in Aging Neuroscience | www.frontiersin.org 2August 2016 | Volume 8 | Article 193
Kubicki et al. Discrete Motor Changes in MCI Patients
YA subjects (n=14) were recruited at the Sport Sciences
Department of the University of Dijon (Burgundy University,
France). They were recruited with respect to the (4-5) inclusion
criteria, they had no medical diagnosis of or self-assessed
cognitive deficiency, and were aged between 20 and 35 years.
This work has been carried out in accordance with The Code
of Ethics of the World Medical Association (Declaration of
Helsinki) for experiments involving humans. The privacy rights
of human subjects have been observed at all times.
Experimental Device and Procedure
As a prerequisite, the gait speed test was done at the beginning
of the session to test the functional ability of each patient. All
participants performed a short warm-up of the shoulder muscles
of 1 min duration consisting of global circumduction movements
prior to the start of the experiment. They were instructed to copy
the same movements of the experimenter who performed these
movements in front of the participants.
The starting position was the following: Participants stood
upright on the floor (feet were oriented at 15◦on both sides of
the sagittal plane, with 15 cm between the two medial malleoli),
the left arm was positioned in alignment with the trunk and the
right index finger pointing toward the ground, with an angle of
35◦between the arm and trunk (Figure 1). The subjects were
required to keep their eyes fixed on a horizontal bar placed at
2 m from the floor and 2.5 m from the participants’ feet. Two
diodes, 120 cm apart, were fixed on this horizontal bar. The
central point between the two diodes was situated exactly in front
of the participants’ right shoulder. Participants were told to point
their index finger at the diode (left or right) which was switched
on intermittently.
Participants were unaware of the location (left or right) of
the visual stimuli. They were asked to raise their arm as fast as
possible and to start as quickly as possible after the appearance
of the visual stimuli. This complex reaction time task, with a
relative uncertainty about the target location (and thus the motor
program to initiate), made it possible to generate greater APA
than a simple reaction time task (Slijper et al., 2009; Kubicki et al.,
2012b). They were told to point to the diode, to keep their arm
raised for a few seconds, and then lower their arm and move their
index finger back to the initial starting position. The participants
performed 20 pointing trials. All the included participants were
able to understand these instructions, to complete the warm-up
sequence and the 20 reaching movements.
Participant Equipment
Two systems were used to accurately measure (i) the movements
performed and (ii) timing of muscle activations during these
movements.
(i) Right upper limb kinematics were recorded using the
Vicon R
system (Oxford metrics group, UK). Participants
wore 5 motion sensors on the following anatomical sites: nail
of the index finger; dorsal aspect of the scaphoid bone; lateral
aspect of the elbow (lateral epicondyle); anterior aspect of the
shoulder (acromion).
FIGURE 1 | Experimental arm raising task. View of the experimental
set-up for the arm raising task showing a participant in initial position and the
two possible targets. The central point of the bar between the two targets was
situated exactly in front of the participants’ right shoulder. Participants were
asked to point their index finger at the target (left or right) which was lit
intermittently.
(ii) Surface Electromyographic (EMG) activity of 11 muscles was
collected. According to the EMG literature concerning the
detection of APA (Ng et al., 1998; Moseley and Hodges,
2005), we focused our measurements on the lower limbs
and trunk muscles in reference to the main muscle involved
in arm-pointing: the Anterior Deltoid (Crenna and Frigo,
1991). See Figure 4C displaying the electrodes placement.
EMG was recorded on both sides of each subject (right =r,
left =l) for the rectus femoris (RF), biceps femoris (BF), obliquus
internus (OI), erector spinae at the third lumbar vertebra (ESl3),
erector spinae at the seventh dorsal vertebra (ESd7), and on
the right side for the anterior portion of the deltoidus (DA).
To determine the positioning of the surface electrodes, the
participants were instructed how to selectively activate each
recorded muscle individually (Hislop and Montgomery, 2009).
These electrodes were placed parallel to the muscle fibers with an
interelectrode distance of 2.4 cm.
Data Recording and Statistical Analysis
Gait speed (m.s−1) was collected on a 4.5 m distance according
to Fried et al. (2001). All EMG signals were preamplified at the
source (VICON R
, Oxford Metrics Group) and were recorded at
a frequency of 2000 Hz. Raw EMG signals were first bandpass
filtered between 5 and 400 Hz and then full-wave rectified and
filtered using a no-lag averaging moving-window algorithm
(window size: 10 ms). As previously defined by Fautrelle et al.
(2010), in this study, we determined the initiation time as the
Frontiers in Aging Neuroscience | www.frontiersin.org 3August 2016 | Volume 8 | Article 193
Kubicki et al. Discrete Motor Changes in MCI Patients
delay between illumination of the diode (the “go-signal”) and
the beginning of significant muscle activation (Fautrelle et al.,
2010). To do this for the 11 recorded muscles, the EMG values
after the illumination of the diode and the EMG baselines
were compared for each value using t-tests for each muscle in
each group. The EMG baselines were computed as the mean
integrated activity of each muscle from −2 to −1 s before the
first diode was lit and when the participants maintained the
initial position. The first instant at which the P-value was lower
than 0.05 for a minimum duration of 50 ms determined the
beginning of muscle activation necessary to perform the pointing
movements.
We chose to calculate the timing of each muscle activation
with reference to activation of the Anterior Deltoid and we
named this parameter the activation timing. Thus, the activation
timing of muscles involving in the APA had negative values
and positive values refer to post-deltoid contractions. This EMG
synergy allowed us to clearly identify the muscles involved
principally in APA in each group, without confounding factors
associated with the electro-mechanical delays (Zhou et al.,
1995). A last aim of this study was to investigate some
potential relationships between muscle activation timings (i.e.,
physiological parameters from motor control) and the results of
clinical tests in MCI group. In this way, the “absolute difference
score” (ADS) in milliseconds (ms) were calculated between
MCI and OA participants for the muscles showing significant
differences in their activation timings between MCI and OA
groups only. Finally, a cumulative score of these differences
(Total ADS) was calculated by summing the 2 ADS scores in
absolute value.
Statistical Analysis
Concerning activation timings, in order to determine potential
differences between groups, we first checked that each variable
was normally distributed (Shapiro–Wilk W-test, all the p-values
are >0.1) and had equivalent variances (Levene’s test, all the p-
values are >0.2). Outliers detected using extreme studentized
deviate tests (ESD tests; Rosner, 1983) were removed. For more
precision, these outliers were due to rare and furtive loss of
contact between EMG sensors and elderly frail skin (24/280 for
MCI, 8/280 for OA groups) or excessive sudation (30/280 for
YA group, with a maximum of 7/20 for one participant due to
excessive sudation). Then a one-way ANOVA was conducted in
each recording muscle while the experimental group (x3: YA,
OA, MCI) remained the categorical factor (i.e. the independent
variable).
Concerning muscular activation rates which were reported
in percentage for each participant, Shapiro–Wilk W-tests
showed non-normal (but log-normal) distributions for the 10
tested muscles. Consequently, transformations were performed
(Bartlett, 1947) before investigate the potential differences in the
muscular activation rates using 10 different one-way ANOVA,
similarly to the analyses of the activation timings.
Post-hoc analyses were done with Scheffe tests when necessary.
For all these statistical treatments, the significance level was set
at p<0.05. Moreover, according to Cohen (Bartlett, 1947), the
effect size was specified by the partial eta squared (ηp2), and a
value ≥0.14 was considered as a large effect, ≥0.6 as a medium
effect, and ≥0.1 as a small effect (Cohen, 1988; Sink and Stroh,
2006).
Concerning some potential relationships between muscle
activation timings and the results of well-known clinical tests
in MCI group, a multiple regression model was applied to
the Total ADS (as a dependent variable) with the following
explanatory (independent) variables: Mini-Mental State score
(MMS), Walhund score, Gait speed score, Part A and Part B of
the Trail Making Test scores (TMT A and TMT B). According
to Cohen (1988) and Sink and Stroh (2006), the effect size of
the amount of variance accounted for was specified by the R
squared (R2) score. A R2≥0.14 was considered as a large
effect.
RESULTS
Participants Characteristics
Participants’ characteristics are summarized in Table 1. Women
accounted for 35.71, 64.28, and 57.14% in the YA, OA, and MCI
groups, respectively. The MCI and OA groups were no different
for age (p=0.353) and maximal velocity of the index movement
(p=0.069). Consistent with previous data showing the slow-
down of movement in aging (Ketcham et al., 2002), there was a
significant difference between aged participants of both the MCI
and OA groups and the YA group concerning the index maximal
velocity [F(2,728) =165.91, p<0.001, ηp2=0.89]. Moreover, the
functional status of the MCI and OA were equivalent, as attested
by the comparable Gait Velocity (p=0.448).
Finally, The ANOVA showed a main Group effect for
AD (anterior deltoid muscle) reaction time [F(2,728) =9.367,
TABLE 1 | Patient characteristics (Mean ±Standard Deviation) for each group: Young Adults (YA); Older Adults(OA); Mild Cognitive Impairment (MCI).
Parameter YA group OA group MCI group p-value for HY/OA p-value for OA/MCI p-value for HY/MCI
Age (Years) 28.72 ±5.65 70.62 ±4.14 70.15 ±7.17 <0.001 0.353 <0.001
Height (cm) 177 ±3.67 166 ±4.82 165 ±4.23 0.112 0.487 0.214
Weight (Kg) 71 ±5.48 79 ±6.91 76 ±7.33 0.081 0.192 0.154
Gait Speed (m.s−1) 1.11 ±0.05 0.94 ±0.11 0.91 ±0.13 <0.001 0.448 <0.001
Index MV (m.s−1) 6.781 ±1.58 4.889 ±1.04 4.67 ±1.46 <0.001 0.069 <0.001
p-values are presented for between-group differences. LDS fisher-tests (post-hoc) were done on the Group effects showed by the ANOVA (see Material and Methods section for further
details). “Index MV” means Index Maximal Velocity.
Frontiers in Aging Neuroscience | www.frontiersin.org 4August 2016 | Volume 8 | Article 193
Kubicki et al. Discrete Motor Changes in MCI Patients
p<0.001, ηp2=0.91]. The post-hoc decomposition showed that
AD reaction time was significantly shorter in the YA group than
in both the MCI and OA groups (p<0.001) but there was no
difference between the OA and MCI groups (p=0.103).
Muscle Activation Timing between Groups
To clarify the results and highlight the potential differences
between groups, muscle synergies were studied, with data for YA
subjects, considered optimal, as the reference. Figure 2 shows the
raw EMG data obtained for 20 trials for a typical subject of each
group.
Figure 3 shows the scatter of the overall dataset of the timing
of muscle activations (all the trials of all participants in the three
experimental groups).
In the YA group, 3 muscles participated in the APA in the
following order: lESl3, lESd7, and rBF.
The ANOVA showed a main Group effect for several muscles:
lESl3 [F(2,693) =14.92, p<0.001, ηp2=0.15]; rBF [F(2,647) =
4.226, p=0.015, ηp2=0.14]; rRF [F(2,266) =3.7367, p=0.025,
ηp2=0.23]; rESl3 [F(2,383) =9.2164, p<0.001, ηp2=0.22]; lOI
[F(2,459) =6.8433, p=0.0012, ηp2=0.44].
Comparing the timing of muscle activation between YA and
OA groups, we found 1 significant difference: the activation
timing of the rBF (p=0.008), was delayed for the OA group
compared with the YA group.
Interestingly, the post-hoc analysis revealed earlier activations
of the lESl3 (p<0.001) and the lOI (p<0.05) in MCI compared
with OA group. This chronological difference was also significant
between MCI and YA groups for the lESl3 (p=0.019).
Logically, activation timings of the MCI group were
significantly delayed compared with the YA group for the rBF (p
=0.014), the lRF (p=0.027), and rESl3 (p=0.028).
All the average muscle activations, with the ANOVA main
effect and the post-hoc analysis between the three groups are
presented on the Figure 4A.
Muscle Activation Rates between Groups
To determine the robustness of muscle synergy objectively, the
activation rate for each muscle in each group was calculated. For
each muscle, this rate corresponded to the percentage of trials
showing significant muscle activation. Differences were found for
only two muscles. The rESl3 [F(2,9489) =13.51, p<0.0001, ηp2=
0.92] and the rESD7 [F(2,2422) =7.59, p=0.001, ηp2=0.89]
were less frequently activated in the MCI group compared with
the OA group (p<0.001 and p=0.002 respectively) and the YA
group (p<0.001 and p=0.001 respectively). These results are
shown in Figure 4B.
Muscle Activation Timing and Clinical Data
in the MCI Group
The ADS was calculated to highlight potential links between
muscle activation timings and clinical data in MCI patients, in
milliseconds (ms), between MCI and OA subjects, for each of
the 2 muscles showing significant differences in their activation
timing (see Section Muscle Activation Timing between Groups
and Figure 4A): the lESl3 and the lOI. Please note here that the
decision was made to exclude the lRF muscle from this analysis,
as its timing was also modified in the MCI group compared to the
OA group, because this muscle activation presented an important
variability (see on Figure 3 lRF’ graph, and Figure 4A) and a
poor activation rate (see on Figure 4B). A cumulative score of
these differences (Total ADS) was calculated by summing the
2 ADS scores in absolute value. A multiple regression model
was applied to this Total ADS (as a dependent variable) with
the following explanatory (independent) variables: Mini-Mental
State score (MMS), Walhund score, Gait speed score, Part A and
Part B of the Trail Making Test scores (TMT A and TMT B).
The result of this complementary analysis is that the variance
explained by the model was R2=0.819, with only one significant
explanatory variable: The TMT A (Beta =1.08; p=0.03). To
represent this relationship graphically, the Total ADS was plotted
with the TMT A scores for all patients of the MCI group (see
Figure 5).
DISCUSSION
The aim of this study was to measure differences in muscle
synergies used by MCI patients during an arm-raising task,
especially in the preparatory period of postural control (APA)
associated with arm movement. In young adults (YA group),
our results showed -EMG- APA sequences originating from
3 muscles (among the 10 postural muscles we assessed): two
contralateral trunk muscles (lESl3 and lESD7) and one ipsilateral
lower limb muscles (rBF), which was in accordance with the
main literature about APA organization associated with arm
movements (Cordo and Nashner, 1982; Friedli et al., 1984;
Ketcham et al., 2002; Bonnetblanc et al., 2004; Bouisset and Do,
2008).
Effects of Aging on Muscle Synergy in Arm
Raising
Our results clearly showed a delay of ipsilateral Biceps Femoris
(right) in the older adults compared with the YA. This significant
difference probably reflect the first impairment of APA in
these active aged adults. This impairment of motor prediction
mechanisms has been highlighted in the literature through
several studies using EMG signals (Man’kovskii et al., 1980;
Inglin and Woollacott, 1988; Rogers et al., 1992; Woollacott and
Manchester, 1993; Bleuse et al., 2006; Kanekar and Aruin, 2014).
In these studies, the authors showed several delays in the timing
of muscles that participate in APA: postural muscles closer to
the muscle directly involved in the movement were recruited.
The worst case was described by Woollacott and Manchester
(1993). In this study, postural muscle activation occurred after
the arm movement had begun. Indeed, the authors showed a clear
impairment of the APA produced by contralateral erector spinae
muscles in normal aged adults, and a decreased activation rate
for the ipsilateral quadriceps muscles. By contrast, in our study
concerning the same aged population (69.53 ±3.12 years), we
only found a delay for one muscles (rBF), and no impairment
in the recruitment rate of any muscle compared with our young
subjects. It is essential to point out that in our study and contrary
to the Woollacott study, the participants were instructed to point
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Kubicki et al. Discrete Motor Changes in MCI Patients
FIGURE 2 | EMG activities of the 11 recorded muscles (line) for one typical participant in each group (column), from left to right: Young Adults (YA);
Older Adults(OA); Mild Cognitive Impairment (MCI). Each graph represents the EMG signals of every trials from the illumination of the diode (t=0) until 1 s. The
EMG activities (V) were bandpass filtered between 5 and 400 Hz and then full-wave rectified. For the sake of clarity in this figure, EMG signals were normalized by the
maximum EMG value (V) for each muscle and for each participant and were represented in arbitrary units (AU).
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Kubicki et al. Discrete Motor Changes in MCI Patients
FIGURE 3 | Scatter plot of the activation timings raw data for all the trials of every participant in the three experimental groups. These activation timings
(ms) were computed for each tested muscle with reference to the anterior deltoid activation. The timing of muscles involving in the APA had negative values and
positive values refer to post-deltoid contractions. In every graph, the activation timings of the young adults (YA) were reported in the left column (diamond markers),
the older adults (OA) in the middle column (cross markers), and the mild cognitive impairment group (MCI) in the right column (circle markers).
to the target as fast as possible; consequently, the older adults
pointed more slowly than the YA, the APA differences were
thus not attributable to aging processes alone, but also to the
differences of inertial forces associated with these self-generated
perturbations.
Effects of MCI on Muscle Synergy in Arm
Raising
This study highlighted two main results about muscle synergy in
arm raising in MCI patients.
First, our results show clearly that the muscle recruitment
in MCI subjects is no more delayed than those of the subjects
belonging to the OA group. On the contrary, two muscle
activations are programmed even earlier: the lESl3, which is
the first muscle involved in the APA in the YA subjects, and
the lOI, which was one of the last muscles activated in both
YA and OA groups. Concerning the lESl3, it is interesting
to note that this muscle recruitment, corresponding to the
first muscle anticipation, occurred earlier in our patient group
compared with both the control subjects participating to this
study (YA and OA). The lOI muscle, mainly involved in trunk
stability, is also recruited early by the MCI patients, compared
with the OA subjects. We have to recall here that the only
difference between these two groups was the presence (MCI)
or the lack (OA) of cognitive dysfunction, mainly involving
memory loss (amnestic MCI). The maximal velocity of the
arm movement was no different between these two groups.
Consequently, these results indicate that cognitive symptoms of
MCI patients are accompanied by fine motor changes mainly
expressed by an earlier activation of two trunk muscles. We
interpreted these results as follows: superficial trunk muscles are
probably recruited earlier to increase the postural steadiness of
the trunk and therefore to be able to compensate for the balance
perturbation caused by the arm movement. This kind of trunk
compensation has already been highlighted in a study by Morris
and Allison or by Moseley and Hodges for low-back-pain patients
(Moseley and Hodges, 2005; Morris and Allison, 2006), and could
be interpreted as compensatory behavior of over-protection, as
an over-estimation of the self-generated perturbation that will
be induced by the movement. Our results are in accordance
with these studies. In the same manner, MCI patients seem to
adopt a more cautious mode of motor control, adapting their
feedforward control to better stabilize the locomotor system.
Secondly, it is important to note that MCI patients presented
the same activation rates as the other groups for most muscles
studied in our task (see Section Muscle Activation Rates
between Groups and Figure 4B), except for the rESl3 and
rESD7. Therefore, these two muscles activated after the deltoid
contraction, were also less frequently activated. Concerning the
rESl3, the OA and MCI subjects are in late compared with the YA
group. However, only the MCI subjects show a less robust use of
this muscle, maybe by adaptation of their early activation of lESl3.
Except for these two muscles, the robustness of muscle synergy
seemed to be preserved in MCI patients throughout all the trials.
These small modifications in the motor command robustness are
in accordance with the lack of motor function impairment in
this sample of patients, shown by identical gait speeds and index
velocities in the two groups of aged adults.
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Kubicki et al. Discrete Motor Changes in MCI Patients
FIGURE 4 | (A) Muscle synergy used in the pointing movement. From left to right: Young Adults (YA); Older Adults (OA); Mild Cognitive Impairment (MCI). On the
y-axis, muscles were represented from the bottom up in the chronological order of their activations in the YA group. On the x-axis, timing of muscle activations (ms)
were represented with reference to that of the Anterior Deltoid (Mean ±Standard Deviation). The ANOVA results were displayed on the left (ns =p>0.05; *p<0.05).
Post-hoc results were displayed (when necessary) from left to right for the YA/OA; the OA/MCI and the MCI/YA analysis (*p<0.05; **p<0.01; ***p<0.001). (B)
Muscle activation rates: The percentages of trials with significant EMG activation were represented with box plots including median and quartiles, in the same layout
than above. The ANOVA results were displayed on the left (ns =p>0.05; *p<0.05). Post-hoc results were displayed (when necessary) from left to right for the
YA/OA; the OA/MCI and the MCI/YA analysis (*p<0.05). (C) For the sake of clarity, the muscle abbreviations, names and locations were reported here.
Taken together, these results strongly suggest that overall
motor prediction ability is not impaired in MCI patients.
Rather, the anticipated motor command is modified
toward an early recruitment of trunk muscles, probably
aimed to increase its steadiness in a more cautious motor
behavior.
Interestingly, the multiple regression model applied to the
Total ADS (see Section Muscle Activation Timing and Clinical
Data in the MCI Group), representing the muscular timing
differences between OA and MCI patients, highlighted a
relationship with the Trail Making Test, part A (TMT A). In
other words, the higher the cumulative differences in muscle
recruitment in absolute value, the higher the TMT A score. An
increased TMT A score reflects poor executive functions, mainly
in the processing speed of information (Reitan, 1958; Corrigan
and Hinkeldey, 1987; Gaudino et al., 1995; Lezak et al., 2004), and
have to be assessed in MCI patients since an early impairment of
these functions have been highlighted in AD patients (Amieva
et al., 2008). Consequently, our results indicated that TMT A
score reflects both an aspect of the cognitive independency
(processing speed of information) and an aspect of the physical
independency: the ability to accurately coordinate posture and
movement during self-generated perturbations.
This work presents several limits. Indeed, the three groups did
not point with the same maximal velocity. Both the OA and the
MCI subjects pointed more slowly than YA subjects. However,
we have to consider two main aspects: First, this difference did
not rule out comparisons of APA sequences between OA and
MCI subjects. The YA subjects were mainly studied to validate
the optimal command organization in light of previous work.
Secondly, one may wonder whether or not it may have been
more interesting to collect different set pointing speeds from our
participants in order to avoid this velocity difference. However,
voluntarily slowing the reaching movement could also modify the
associated APA sequence (Krakauer and Shadmehr, 2007). With
the instruction used in this study (“Please point to the illuminated
Frontiers in Aging Neuroscience | www.frontiersin.org 8August 2016 | Volume 8 | Article 193
Kubicki et al. Discrete Motor Changes in MCI Patients
FIGURE 5 | Relationship between the cumulative Absolute Difference
Score (Total ADS, x-axis, ms) and the Trail Making Test score, part A
(TMT A score, y-axis, s) for each MCI patient. The Pearson and associated
p-value were displayed at the top of the graph. Black line represented the
regression line, and dotted lines represented the 95% confident interval.
diode as fast as possible”), the movement was probably more
ecological.
Moreover, it is important to note a potential confounding
factor associated with subject gender in our results. Indeed, the
male/female ratio was not the same in the three groups, with a
male preponderance in the YA group. However, if a gender bias
exists, it did not concern the comparison between the OA and
the MCI participants, but only the comparison between the YHA
group and the two groups of aged adults.
Given these various elements, we can speculate that the
changes in the motor program could precede the beginning of
the motor-function impairment highlighted in the literature for
the MCI population. These early motor indicators could be tested
as others biomarkers in order to better predict the evolution of
the dementia (Petersen et al., 2013). Further studies will be done
to explore this hypothesis. In this vein, the included MCI patients
will be followed longitudinally, in order to answer, at least in part,
this interesting question.
AUTHOR CONTRIBUTIONS
AK, Conception and design, Collection and assembly of data,
Analysis and interpretation of the data, Drafting of the article,
Final approval of the article, Critical revision of the article
for important intellectual content, Statistical expertise. LF,
Conception and design, Collection and assembly of data, Analysis
and interpretation of the data, Drafting of the article, Final
approval of the article, Critical revision of the article for
important intellectual content, Statistical expertise. JB, Collection
and assembly of data, Critical revision of the article for important
intellectual content, Final approval of the article. OR, Provision
of study materials or patients, Critical revision of the article for
important intellectual content, Final approval of the article. FM,
Conception and design, Analysis and interpretation of the data,
Critical revision of the article for important intellectual content,
Administrative, technical, or logistic support, Final approval of
the article.
FUNDING
This work was supported by the ANR MAAMI project (Maladie
d’Alzheimer et Apprentissage Moteur Implicite). Research
National Agency (ANR), France.
ACKNOWLEDGMENTS
The authors would like to thank Clare Doyle, Geoffroy
Petrement, Philip Bastable, and Sophie Garnier-Carronnier for
their precious help.
REFERENCES
Aggarwal, N. T., Wilson, R. S., Beck, T. L., Bienias, J. L., and Bennett,
D. A. (2006). Motor dysfunction in mild cognitive impairment and the
risk of incident Alzheimer disease. Arch. Neurol. 12, 1763–1769. doi:
10.1001/archneur.63.12.1763
Albers, M. W., Gilmore, G. C., Kaye, J., Murphy, C., Wingfield, A.,
Bennett, D. A., et al. (2015). At the interface of sensory and motor
dysfunctions and Alzheimer’s disease. Alzheimers. Dement. 1, 70–98. doi:
10.1016/j.jalz.2014.04.514
Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox,
N. C., et al. (2011). The diagnosis of mild cognitive impairment due to
Alzheimer’s disease: recommendations from the National Institute on Aging-
Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s
disease. Alzheimers Dement. 3, 270–279. doi: 10.1016/j.jalz.2011.03.008
Amieva, H., Le Goff, M., Millet, X., Orgogozo, J. M., Pérès, K., Barberger-Gateau,
P., et al. (2008). Prodromal Alzheimer’s disease: successive emergence of the
clinical symptoms. Ann. Neurol. 5, 492–498. doi: 10.1002/ana.21509
Bartlett, M. S. (1947). The use of transformations. Biometrics 3, 39–52. doi:
10.2307/3001536
Belen’kii, V. E., Gurfinkel,’ V. S., and Pal’tsev, E. I. (1967). Control elements of
voluntary movements. Biofizika 12, 135–141.
Bleuse, S., Cassim, F., Blatt, J. L., Derambure, P., Guieu, J. D., and Defebvre, L.
(2006). Effect of age on anticipatory postural adjustments in unilateral arm
movement. Gait Posture 24, 203–210. doi: 10.1016/j.gaitpost.2005.09.001
Bonnetblanc, F., Martin, O., and Teasdale, N. (2004). Pointing to a target
from an upright standing position: anticipatory postural adjustments are
modulated by the size of the target in humans. Neurosci. Lett. 3, 181–184. doi:
10.1016/j.neulet.2004.01.020
Bouisset, S., and Do, M. C. (2008). Posture, dynamic stability, and voluntary
movement. Neurophysiol. Clin. 6, 345–362. doi: 10.1016/j.neucli.2008.10.001
Boyle, P. A., Wilson, R. S., Aggarwal, N. T., Arvanitakis, Z., Kelly, J., Bienias,
J. L., et al. (2005). Parkinsonian signs in subjects with mild cognitive
impairment. Neurology 12, 1901–1906. doi: 10.1212/01.wnl.0000188878.
81385.73
Buracchio, T., Dodge, H. H., Howieson, D., Wasserman, D., and Kaye, J. (2010).
The trajectory of gait speed preceding mild cognitive impairment. Arch. Neurol.
8, 980–986. doi: 10.1001/archneurol.2010.159
Caffarra, P., Gardini, S., Vezzadini, G., Bromiley, A., and Venner, A. (2010).
The ideation of movement is supported by fronto-temporal cortical
Frontiers in Aging Neuroscience | www.frontiersin.org 9August 2016 | Volume 8 | Article 193
Kubicki et al. Discrete Motor Changes in MCI Patients
regions involved in the retrieval of semantic knowledge. Acta Biomed. 1,
21–29.
Cisek, P., and Kalaska, J. F. (2010). Neural mechanisms for interacting with
a world full of action choices. Annu. Rev. Neurosci. 33, 269–298. doi:
10.1146/annurev.neuro.051508.135409
Cohen, J. (1988). Statistical Power Analysis for the Behavior Sciences, 2nd Edn.,
Hillsdale, NJ: Lawrence Erlbaum Associates.
Cordo, P. J., and Nashner, L. M. (1982). Properties of postural adjustments
associated with rapid arm movements. J. Neurophysiol. 2, 287–302.
Corrigan, J. D., and Hinkeldey, M. S. (1987). Relationships between parts A and B
of the Trail Making Test. J. Clin. Psychol. 4, 402–409.
Crenna, P., and Frigo, C. (1991). A motor program for the initiation of
forward-oriented movements in humans. J. Physio. 437, 635–653. doi:
10.1113/jphysiol.1991.sp018616
Desmurget, M., and Grafton, S. (2000). Forward modeling allows feedback
control for fast reaching movements. Trends Cogn. Sci. 11, 423–431. doi:
10.1016/S1364-6613(00)01537-0
Doi, T., Shimada, H., Park, H., Makizako, H., Tsutsumimoto, K., Uemura, K., et al.
(2014). Cognitive function and falling among older adults with mild cognitive
impairment and slow gait. Geriatr. Gerontol. Int. 8, 1073–1078.
Elble, R. J., and Leffler, K. (2000). Pushing and pulling with the upper extremities
while standing: the effects of mild Alzheimer dementia and Parkinson’s disease.
Mov. Disord. 2, 255–268.
Fautrelle, L., Prablanc, C., Berret, B., Ballay, Y., and Bonnetblanc, F. (2010).
Pointing to double-step visual stimuli from a standing position: very
short latency (express) corrections are observed in upper and lower limbs
and may not require cortical involvement. Neuroscience 2, 697–705. doi:
10.1016/j.neuroscience.2010.05.014
Fried, L. P., Tangen, C. M., Walston, J., Newman, A. B., Hirsch, C., Gottdiener, J.,
et al. (2001). Frailty in older adults: evidence for a phenotype. J. Gerontol. A
Biol. Sci. Med. Sci. 3, 146–156. doi: 10.1093/gerona/56.3.M146
Friedli, W. G., Hallett, M., and Simon, S. R. (1984). Postural adjustments associated
with rapid voluntary arm movements 1. Electromyographic data. J. Neurol.
Neurosurg. Psychiatr. 6, 611–622. doi: 10.1136/jnnp.47.6.611
Garland, S. J., Stevenson, T. J., and Ivanova, T. (1997). Postural responses to
unilateral arm perturbation in young, elderly, and hemiplegic subjects. Arch.
Phys. Med. Rehabil. 10, 1072–1077. doi: 10.1016/S0003-9993(97)90130-1
Gaudino, E. A., Geisler, M. W., and Squires, N. K. (1995). Construct validity in
the Trail Making Test: what makes Part B harder? J. Clin. Exp. Neuropsychol. 4,
529–535. doi: 10.1080/01688639508405143
Gras, L. Z., Kanaan, S., McDowd, J., Colgrove, Y. M., Burns, J., and Pohl, P. S.
(2015). Balance and gait of adults with very Mild Alzheimer Disease. J. Geriatr.
Phys. Ther. 1, 1–7. doi: 10.1519/JPT.0000000000000020
Hislop, H., and Montgomery, J. (2009). Le Bilan Musculaire de Daniels et
Worthingham: Techniques de Testing Manuel, 9th Edn., Los Angeles, CA:
Masson.
Hodges, P. W., and Richardson, C. A. (1996). Inefficient muscular stabilization of
the lumbar spine associated with low back pain. A motor control evaluation
of transversus abdominis. Spine 22, 2640–2650. doi: 10.1097/00007632-
199611150-00014
Horak, F. B. (2006). Postural orientation and equilibrium: what do we need to
know about neural control of balance to prevent falls? Age Ageing 2, ii7–ii11.
doi: 10.1093/ageing/afl077
Inglin, B., and Woollacott, M. (1988). Age-related changes in anticipatory postural
adjustments associated with arm movements. J. Gerontol. 43, M105–M113. doi:
10.1093/geronj/43.4.m105
Kanekar, N., and Aruin, A. S. (2014). The effect of aging on anticipatory postural
control. Exp. Brain Res. 4, 1127–1136. doi: 10.1007/s00221-014-3822-3
Ketcham, C. J., Seidler, R. D., Van Gemmert, A. W., and Stelmach, G. E. (2002).
Age-related kinematic differences as influenced by task difficulty, target size,
and movementamplitude. J. Gerontol. B Psychol. Sci. Soc. Sci. 1, 54–64. doi:
10.1093/geronb/57.1.P54
Krakauer, J. W., and Shadmehr, R. (2007). Towards a computational
neuropsychology of action. Prog. Brain Res. 165, 383–394. doi:
10.1016/S0079-6123(06)65024-3
Kubicki, A., Bonnetblanc, F., Petrement, G., Ballay, Y., and Mourey, F.
(2012a). Delayed postural control during self-generated perturbations in
the frail older adults. Clin. Interv. Aging 7, 65–75. doi: 10.2147/CIA.S
28352
Kubicki, A., Petrement, G., Bonnetblanc, F., Ballay, Y., and Mourey, F. (2012b).
Practice-related improvements in postural control during rapid arm movement
in older adults: a preliminary study. J. Gerontol. A Biol. Sci. Med. Sci. 2, 196–203.
doi: 10.1093/gerona/glr148
Latash, M. L., Aruin, A. S., Neyman, I., Nicholas, J. J., and Shapiro, M. B. (1995).
Feedforward postural adjustments in a simple two-joint synergy in patients
with Parkinson’s disease. Electroencephalogr. Clin. Neurophysiol. 2, 77–89. doi:
10.1016/0924-980X(94)00272-9
Lezak, M. D., Howieson, D. B., and Loring, D. W. (2004). Neuropsychological
Assessment. 4th Edn., New York, NY: Oxford University Press.
Maloney, L. T., and Mamassian, P. (2009). Bayesian decision theory as a model of
human visual perception: testing Bayesian transfer. Vis. Neurosci. 1, 147–155.
doi: 10.1017/s0952523808080905
Man’kovskii, N. B., Mints, A. Y., and Lysenyuk, V. P. (1980). Regulation of the
preparatory period for complex voluntary movement in old and extreme old
age. Hum. Physiol. 6, 46–50.
Massion, J. (1992). Movement, posture and equilibrium: interaction and
coordination. Prog Neurobiol. 1, 35–56. doi: 10.1016/0301-0082(92)
90034-C
Montero-Odasso, M., Oteng-Amoako, A., Speechley, M., Gopaul, K., Beauchet, O.,
Annweiler, C., et al. (2014). The motor signature of mild cognitive impairment:
results from the gait and brain study. J. Gerontol. A Biol. Sci. Med. Sci. 11,
1415–1421. doi: 10.1093/gerona/glu155
Morris, S. L., and Allison, G. T. (2006). Effects of abdominal muscle fatigue on
anticipatory postural adjustments associated with arm raising. Gait Posture 3,
342–348. doi: 10.1016/j.gaitpost.2005.10.011
Moseley, G. L., and Hodges, P. W. (2005). Are the changes in postural control
associated with low back pain caused by pain interference? Clin. J. Pain 4,
323–329. doi: 10.1097/01.ajp.0000131414.84596.99
Ng, J. K., Kippers, V., and Richardson, C. A. (1998). Muscle fibre orientation
of abdominal muscles and suggested surface EMG electrode positions.
Electromyogr. Clin. Neurophysiol. 1, 51–58.
Oldfield, R. C. (1971). The assessment and analysis of handedness: the
Edinburgh inventory. Neuropsychologia 9, 97–113. doi: 10.1016/0028-3932(71)
90067-4
Petersen, R. C., Aisen, P., Boeve, B. F., Geda, Y. E., Ivnik, R. J., Knopman, D. S., et al.
(2013). Mild cognitive impairment due to Alzheimer disease in the community.
Ann. Neurol. 2, 199–208.
Postuma, R. B., Berg, D., Stern, M., Poewe, W., Olanow, C. W., Oertel, W., et al.
(2015). MDS clinical diagnostic criteria for Parkinson’s disease. Mov. Disord.
30, 1591–1601. doi: 10.1002/mds.26424
Reitan, R. M. (1958). Validity of the Trail Making test as an indicator of
organic brain damage. Percept. Mot. Skills 8, 271–276. doi: 10.2466/pms.1958.
8.3.271
Robinovitch, S. N., Feldman, F., Yang, Y., Schonnop, R., Leung, P. M., Sarraf,
T., et al. (2013). Video capture of the circumstances of falls in elderly people
residing in long-term care: an observational study. Lancet 9860, 47–54. doi:
10.1016/S0140-6736(12)61263-X
Rogers, M. W., Kukulka, C. G., and Soderberg, G. L. (1992). Age-related
changes in postural responses preceding rapid self-paced and reaction
time arm movements. J. Gerontol. 5, M159–M165. doi: 10.1093/geronj/
47.5.m159
Rosner, B. (1983). Percentage Points for a Generalized ESD Many-Outlier
Procedure, Technometrics. 25, 165–172. doi: 10.1080/00401706.1983.104
87848
Sink, C. A., and Stroh, H. R. (2006). Practical significance: the use of effect
sizes in school counseling research. Prof. Sch. Couns 9, 401–411. doi:
10.5330/prsc.9.4.283746k664204023
Slijper, H., Richter, J., Over, E., Smeets, J., and Frens, M. (2009). Statistics predict
kinematics of hand movements during everyday activity. J. Mot. Behav. 41, 3–9.
doi: 10.1080/00222895.2009.10125922
Verghese, J., Robbins, M., Holtzer, R., Zimmerman, M., Wang, C., Xue, X.,
et al. (2008). Gait dysfunction in mild cognitive impairment syndromes.
J. Am. Geriatr. Soc. 7, 1244–1251. doi: 10.1111/j.1532-5415.2008.
01758.x
Frontiers in Aging Neuroscience | www.frontiersin.org 10 August 2016 | Volume 8 | Article 193
Kubicki et al. Discrete Motor Changes in MCI Patients
Wahlund, L. O., Barkhof, F., Fazekas, F., Bronge, L., Augustin, M., Sjögren,
M., et al. (2001). European task force on age-related white matter changes.
European task force on age related white matter changes. A new rating scale
for age-related white matter changes applicable to MRI and CT. Stroke 6,
1318–1322. doi: 10.1161/01.STR.32.6.1318
Woollacott, M. H., and Manchester, D. L. (1993). Anticipatory postural
adjustments in older adults: are changes in response characteristics due to
changes in strategy? J. Gerontol. 2, M64–M70. doi: 10.1093/geronj/48.2.M64
Zhou, S., Lawson, D. L., Morrison, W. E., and Fairweather, I. (1995).
Electromechanical delay in isometric muscle contractions evoked by voluntary,
reflex and electrical stimulation. Eur. J. Appl. Physiol. Occup. Physiol. 2,
138–145. doi: 10.1007/BF00361541
Conflict of Interest Statement: The authors declare that the research
was conducted in the absence of any commercial or financial
relationships that could be construed as a potential conflict of
interest.
Copyright © 2016 Kubicki, Fautrelle, Bourrelier, Rouaud and Mourey. This is an
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