Changes in Cerebral Hemodynamics during Complex Motor Learning by Character Entry into Touch-Screen Terminals
Changes in Cerebral Hemodynamics during Complex Motor Learning by Character Entry into Touch-Screen Terminals
Akira Sagari 0 1
Naoki Iso 0 1
Takefumi Moriuchi 0 1
Kakuya Ogahara 0 1
Eiji Kitajima 0 1
Koji Tanaka 0 1
Takayuki Tabira 0 1
Toshio Higashi 0 1
0 1 Unit of Rehabilitation Sciences, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan, 2 Japanese Red Cross Society Nagasaki Genbaku Hospital , Nagasaki , Japan , 3 Medical Corporation Tojinkai Miharadai Hospital , Nagasaki , Japan , 4 Faculty of Health and Social Work, School of Rehabilitation, Kanagawa University of Human Services , Kanagawa, Japan, 5 Center for Industry , University and Government Cooperation, Nagasaki University , Nagasaki , Japan , 6 Unit of Physical and Occupational Therapy, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan, 7 Faculty of Rehabilitation Sciences, Nishikyushu University , Saga , Japan
1 Editor: Dezhong Yao, University of Electronic Science and Technology of China , CHINA
Abbreviations: NIRS, near-infrared spectroscopy;
PFC, prefrontal cortex; PFC, left dorsolateral
prefrontal cortex; preSMA, presupplementary motor
area; SMA, supplementary motor area; PMC, dorsal
premotor cortex; SMC, sensorimotor cortex.
Studies of cerebral hemodynamics during motor learning have mostly focused on
neurorehabilitation interventions and their effectiveness. However, only a few imaging studies of
motor learning and the underlying complex cognitive processes have been performed.
We measured cerebral hemodynamics using near-infrared spectroscopy (NIRS) in relation
to acquisition patterns of motor skills in healthy subjects using character entry into a
touchscreen terminal. Twenty healthy, right-handed subjects who had no previous experience
with character entry using a touch-screen terminal participated in this study. They were
asked to enter the characters of a randomly formed Japanese syllabary into the
touchscreen terminal. All subjects performed the task with their right thumb for 15 s alternating
with 25 s of rest for 30 repetitions. Performance was calculated by subtracting the number
of incorrect answers from the number of correct answers, and gains in motor skills were
evaluated according to the changes in performance across cycles. Behavioral and
oxygenated hemoglobin concentration changes across task cycles were analyzed using
SpearPerformance correlated positively with task cycle, thus confirming motor learning.
Hemodynamic activation over the left sensorimotor cortex (SMC) showed a positive correlation with
task cycle, whereas activations over the right prefrontal cortex (PFC) and supplementary
motor area (SMA) showed negative correlations.
We suggest that increases in finger momentum with motor learning are reflected in the
activity of the left SMC. We further speculate that the right PFC and SMA were activated
during the early phases of motor learning, and that this activity was attenuated with learning
Humans accomplish a wide variety of motor and cognitive tasks in everyday life. Furthermore,
we continue acquiring new complex motor skills to overcome various challenges. Therefore, it
is important to study the underlying changes in cerebral hemodynamics during
motor-cognitive adaptation. In recent years, various functional brain imaging techniques that enable
noninvasive visualization of brain activity have advanced greatly [1–8]. Cerebral dynamics during
motor-cognitive task adaptations have been investigated by employing various neuroimaging
techniques, such as positron emission tomography (PET) or functional magnetic resonance
imaging (fMRI), while the subject is in a supine position [9–14]. However, in daily life,
motorcognitive adaptation tasks are usually performed in upright positions such as sitting or
standing. To address the issue of limited ecological validity and enable task performance in a more
natural setting, we used near-infrared spectroscopy (NIRS) as a less-limiting imaging method
that enables measurement of cortical activation during activities of daily life.
Although NIRS is characterized by marked limitations in temporal and spatial resolution,
its safety, non-restrictiveness, and portability enable broader and more flexible use than other
brain imaging methods . A number of studies have examined cerebral hemodynamics
using NIRS during a wide variety of motor activities such as a walking or running [16–20],
cycling [21–23], apple peeling , and finger tapping [25–28]. Additionally, researchers have
examined cerebral hemodynamics during cognitive tasks such as trail making [29–32], the
rock-paper-scissors game [33,34], maze navigation , and sequential finger touching
To our knowledge, NIRS studies that examine motor skill learning with ongoing adaptation
processes are rare. Two NIRS studies have reported changes of hemodynamics over time
related to motor skill learning using eye-hand coordination in pursuit rotor  or target
reaching tasks . These NIRS studies evaluated specific sensorimotor tasks. However, these
studies did not address changes in hemodynamic activity during motor learning of complex
skills with concurrent cognitive processing such as working memory and executive function.
Gentili et al. reported changes in cerebral hemodynamics as measured by NIRS during
performance of a motor-cognitive adaptation task and demonstrated activation only in the prefrontal
cortex (PFC) . However, the NIRS probe in their study only covered the forehead.
Therefore, no studies are available that examine the hemodynamic responses of other cortical regions
during performance of motor-cognitive adaptation tasks using a NIRS system that also
includes the posterior half-head.
Information technology has substantially affected modern society, and researchers
concerned with neurorehabilitation should examine interventions relevant to the lifestyle of
modern people. In recent years, the number of touch-screen terminal users has increased
remarkably and smartphones have spread all over the world. eMarketer, a U.S. research
company, speculated that the number of smartphone users will total 1.75 billion in 2014 and further
increase to 2.5 billion in 2017 . The operation of a smartphone requires flick inputs to
create an email sentence in Japanese, and the entry of the required characters is difficult and
will need to be considered in the field of neurorehabilitation in the future. However, there are
no studies measuring cerebral hemodynamics related to motor skill learning that address the
underlying complex cognitive processing in tasks such as character entry into a touch-screen
terminal. Therefore, in this study, we examined cerebral hemodynamics using NIRS associated
with acquisition patterns of motor-cognitive skills in healthy subjects using character entry
into a touch-screen terminal. Based on previous studies, we hypothesized that the behavioral
improvements resulting from adaptation would be accompanied by distinct patterns of
activation over the various cortical regions.
Twenty healthy subjects (9 men and 11 women; mean age, 27.5±5.5 years) participated in this
study. All subjects were self-reported as right-handed (S1 Dataset). Exclusionary criteria
included any medical illness affecting central nervous system function, psychiatric or
neurological disorders, history of head trauma, or current substance abuse. None of the subjects had
previous experience with character entry using a touch-screen terminal. Written informed
consent was obtained from each subject. The study was approved by the local ethics committee of
Nagasaki University Graduate School of Biomedical and Health Sciences. All of the
experimental procedures were conducted in accordance with the Declaration of Helsinki.
The subjects sat on a chair 80 cm away from a PC monitor (Epson LD1957S, Japan, 19-inch,
resolution: 1024 × 768 pixels) (Fig 1A). All subjects performed the task with their right thumb
for 15 s alternating with 25 s of rest for 30 repetitions (cycles 1 to 30) (Fig 1B). Gains in motor
skills were evaluated according to the number of characters entered into the touch-screen
terminal (Apple iPod Touch 4, Japan, 3.5-inch, resolution: 960×640 pixels) (Fig 1C). All subjects
were asked to enter the characters of a randomly formed Japanese syllabary presented on a PC
monitor, starting with the character from the upper left, into the touch-screen terminal as
quickly as possible (Fig 1D). The character string in the monitor was constructed in five lines
and nine rows, resulting in 45 characters. The characters of the randomly formed Japanese
syllabary were changed every cycle (S1 Supporting Information). All subjects were asked to fixate
on a single point at the center of the screen during rest and to stay relaxed. In English, the letter
combinations “P Q R S” and “W X Y Z” involve a three-way operation in the upper, right, and
left direction, whereas the input otherwise involves operation in two ways. In Japanese, the
combinations “ya yu yo” and “wa wo n” are two-way operations in the right and left directions,
whereas the input otherwise operates in four ways in the upper, lower, right, and left directions.
Therefore, operation of these devices using Japanese input is more complicated than that using
English input. The task of the present study was not simply a matter of entering as many
characters as possible; the only characters that were meant to be entered were those displayed.
There was thus a trade-off between the number of characters and the number of incorrect
entries. Thus, the task performance of each subject was calculated by subtracting the number
of errors from the number of correct answers in each cycle.
NIRS measurements were performed using a continuous wave system (ETG-4000, Hitachi
Medical Corp., Tokyo, Japan) equipped with 4 × 4 optode probe sets (8 incident light and 8
detector fibers), resulting in a total of 24 channels with an inter-optode distance of 3.0 cm. The
continuous-wave NIRS system utilizes two different wavelengths (~625 and 830 nm). Relative
changes in the absorption of near-infrared light were sampled at 10 Hz, and these measures
Fig 1. Experimental setup of touch-screen terminal task. (A) Experimental setup of character entry into
touch-screen terminal showing a subject with the custom-made cap with the NIRS device. (B) Display of task
and rest conditions. The display showing the task condition changed with each cycle. (C) The iPod Touch 4.
The character entry into screen mode was used to measure the number of character entries. All subjects
performed the task with their right thumb for 15 s interleaved with rest periods of 25 s for 30 repetitions. (D)
Methods of character entry into touch-screen terminal. All subjects were asked to enter the characters of a
randomly formed Japanese syllabary into the touch-screen terminal.
were converted into related concentration changes for oxygenated hemoglobin (oxy-Hb) and
deoxygenated hemoglobin (deoxy-Hb), based on the modified Beer-Lambert approach .
The moving average method (window: 5 s) was used to exclude short-term motion artifacts in
the analyzed data. The obtained data were analyzed in the integral mode, which calculates
average waveform. Pre-task baseline was defined as the 5-s period immediately prior to task onset.
In this study, we used changes in oxy-Hb concentration as an indicator of changes in regional
cerebral blood volume, as an earlier NIRS signal study using a perfused rat brain model
proposed that oxy-Hb, rather than deoxy-Hb, is the more sensitive parameter for the study of
activation . The NIRS channels were placed according to the international 10–20 system .
Regarding the positions of the optodes, we followed previous NIRS studies of motor-related
areas [37,45]. The optodes were positioned using a custom-made cap that covered the right
and left PFC, presupplementary motor area (preSMA), supplementary motor area (SMA),
dorsal premotor cortex (PMC), and sensorimotor cortex (SMC). The areas and optodes were as
follows: left SMC, channels 18 and 22; right SMC, channels 21 and 24; motor area, channels 19,
20, and 23; SMA, channels 9, 12, 13, and 16; preSMA, channels 2, 5, and 6; left PMC, channels
8, 11, and 15; right PMC, channels, 10, 14, and 17; left PFC, channels 1 and 4; and right PFC,
channels 3 and 7. The Cz position in the international 10–20 system was used as a marker for
ensuring replicable placement of the optodes (Fig 2).
Changes in performance across task cycles were analyzed by calculating Spearman’s rank
correlation coefficients; serial changes (1–30 cycles) in the level of oxy-Hb associated with cycle
repetition in the various regions were also evaluated using Spearman’s rank correlation
coefficients. These correlation coefficients tested for associations between the number of task cycles
and changes in oxy-Hb level for each region.
Fig 2. Location of the optodes and brain areas. Sixteen optodes, comprising 8 light source fibers (red) and
8 detectors (blue) that enabled 24-channel measurements, were arranged on the scalp. The channels
covering SMC, SMA, preSMA, PMC, and PFC are shown. See text for details.
Fig 3 shows the average performance (i.e., number of correct entries minus incorrect entries
into the touch-screen terminal) during the touch-screen task over 30 cycles. There was a highly
significant positive correlation between task cycle and performance (ρ = 0.924, p < 0.001) (S2
Dataset). Fig 4 shows the mean changes in oxy-Hb concentration for three cortical regions
over the 30 cycles of the touch-screen task. A significant positive correlation between task cycle
and oxy-Hb concentration for the channels covering the area of the left SMC (ρ = 0.387,
p < 0.05) was observed (S3 Dataset). In contrast, significant negative correlations between task
cycle and oxy-Hb concentration for the channels covering the SMA (ρ = -0.513, p < 0.01) (S4
Dataset) and right PFC (ρ = -0.364, p < 0.05) (S5 Dataset) were obtained. There were no
significant correlations between task cycle and oxy-Hb changes for the channels covering the other
areas (left PFC, preSMA, bilateral PMC, and right SMC) (S6 Dataset).
Flick input is one of the entry methods for Japanese characters in a touch-screen terminal.
However, in general, flick input operation is difficult. The user of the touch-screen terminal
has to learn to input a letter quickly to effectively convey information. Because the subjects of
this study were inexperienced in touch-screen terminal operation, this task was equivalent to
motor learning combined with complex cognitive processing. In recent years, activities
involving complex cognitive processing during operation of touch-screen terminals have become
abundant in everyday life. However, the cerebral blood flow dynamics during motor learning
with complex cognitive processing in humans are not well understood. Therefore, we used
NIRS to examine the cerebral blood flow dynamics of various cortical regions during motor
learning of flick input operation of a touch-screen terminal.
Fig 3. Correlation between performance (number of correctly entered characters minus incorrectly
entered characters) and number of task cycles. Performance significantly increased with cycle repetitions.
Role of SMC in motor learning
The left SMC was activated with an increasing number of task cycles, whereas the right SMC
did not show such an activation pattern. The left SMC is equivalent to a region including
primary motor and primary sensory areas primarily controlling operation of the hand . PET
and NIRS studies have reported that the cerebral blood flow volume of the contralateral
primary motor area of the operating hand increased with the frequency of finger tapping
[26,47,48]. In addition, the primary motor and primary sensory areas contralateral to the
operating hand became activated during motor learning of the finger. In the present study, the
number of character entries significantly increased with the task cycle. During the course of
one experiment, motor learning of the finger occurred in all subjects, as confirmed by the
improvement of the flick input operation. Therefore, we speculate that increases in the
momentum of the finger reflect motor learning.
Fig 4. Correlations between oxy-Hb changes over three regions and number of task cycles. Vertical
axis represents mean oxy-Hb concentration changes (in mM*mm). Left SMC activation significantly
increased, whereas SMA and right PFC activation significantly decreased with cycle repetition.
Role of SMA in motor learning
The SMA extracts motor programs that depend on memory information and the initiation of
spontaneous movements. Thus, the SMA plays an important role in situations where a
compound movement is controlled [49–53]. The examination of SMA activity during motor
learning can be accomplished by various neuroimaging techniques. However, there is little
consensus on the activity of the SMA during motor learning [54–60]. In a previous NIRS
study, SMA activation gradually increased with motor learning . That study used a pursuit
rotor task requiring simple motor learning and therefore differs from the motor learning task
of the current study that involved complex cognitive processing. Although the SMA showed
greatly increased activity immediately after experiment initiation, its activity was gradually
attenuated with increasing task cycles.
We suggest that these changes in cerebral blood flow were caused by motor inhibition. All
subjects of this study owned a feature phone and inputted characters using ten keys that
operated in a toggle manner. During the experiment, all subjects were required to input letters
using a method that differed from the operation they had mastered previously. We speculate
that the two divergent character input methods competed, leading to motor inhibition that
occurred in all subjects during this task. Preliminary research has suggested that the SMA is
activated by motor inhibition [61,62]. We further suggest that the SMA is activated initially by
motor inhibition; as learning progresses, SMA activity is attenuated. Although our findings
demonstrate activation of SMA by motor inhibition, we could not find similar reports that
examined changes of cerebral blood flow showing motor inhibition with cognitive learning
over time. Therefore, this study may be the first to report cerebral blood flow dynamics in
SMA with cognitive learning involving motor inhibition. However, the task required complex
cognitive processes including visuomotor adaptation and working memory. Therefore, the
current task was not specifically designed to test motor inhibition. In future experiments, we thus
need to examine the dynamics of cerebral blood flow over the SMA during more specific tasks
requiring motor inhibition.
This may be the first study to report the activity of the SMA during a motor learning task
with complex cognitive processing using character entry into a touch-screen terminal over
time. Therefore, further research is needed to examine the role of SMA during motor learning
using other complex tasks (e.g., character input on a personal computer, trail making, serial
reaction time, or other visuomotor adaptation and motor sequence learning tasks that involve
complex cognitive processing).
Role of the PFC in motor learning
The PFC is an important neural substrate for visual working memory . As the task of this
study consisted of entering a letter presented on a PC screen into a hand-held touch-screen
terminal, working memory was required to successfully accomplish this task. Memory encoding
has been associated with lateral PFC activation across a variety of experimental paradigms in
functional neuroimaging [64,65]. In the current study, the right PFC activation was gradually
attenuated with increasing task cycle. However, activation of the left PFC continued without
attenuation as the task cycles progressed. Activity of PFC regions has been reported to
gradually decrease with learning [66–71]. Therefore, acquisition of the cognitive skill associated with
working memory in our subjects may have attenuated the activity of the right PFC. As all
subjects in our study were right-handed, we assume that their left hemisphere was
language-dominant. Thus, the left PFC was likely involved in linguistic information manipulation [63,72,73]
and continued to be recruited during all task cycles, thereby maintaining the activity of the left
PFC. Previous studies have reported PFC asymmetry for memory encoding of verbal and
nonverbal code [74,75]. The right PFC is activated during a nonverbal task, whereas the left
PFC is activated during a verbal task, supporting the validity of our hypothesis.
Role of other regions
The PMC is involved in choice and control of movements that depend on sensory information
[76,77]. During the 1–30 cycles, the PMC continued to be activated bilaterally. As the task of
this study consisted of a visuomotor problem, we speculate that the activity of PMC was
maintained because the subjects depended on visual information throughout all task cycles. The
preSMA controls the aspect of the task that uses the rich entry from the premotor area, including
order decision of the complex movements during preparation and movement choice; this also
includes motor learning of changes in movement style and timing of the movement initiation.
Furthermore, the preSMA is activated by cognitive control in the absence of movement control
[78–82]. The activity of the preSMA was previously reported to be attenuated with motor
learning . However, in this study, the activity of preSMA continued during all task cycles.
The subjects performed a movement control problem with complex cognitive information
processing by translating the letter presented on a monitor to the movement direction of their
finger. We suggest that this step represents early stages of cognitive-motor learning. In the
current task, sustained attentional engagement from the subjects was required during cognitive
processing, and we speculate that this led to the continued activity of preSMA without
Limitations of this study
First, as our measurements included only the initial learning period of 30 cycles, it was not
possible to determine how cerebral blood flow dynamics might have changed after completion of
the 30-cycle learning stage. Therefore, further studies are needed to investigate cerebral blood
flow dynamics after task training.
Second, we used the number of characters entered minus the number of incorrect entries as
the performance index in this study, and investigated its relationship with Oxy-Hb
concentration changes in each region in this study,. However, it is also possible that the Oxy-Hb
concentration changes for each region would show a different course if the speed of entering the
characters and reaction time (i.e. the time to enter a character appearing on a monitor upon
confirmation) were investigated separately as a performance index. Further studies should be
performed to address this issue.
Third, activation in deeper structures such as the basal ganglia, which are closely linked
with the frontal cortices, cannot be detected because of the technical limitations of NIRS. We
thus could not address functional connectivity between brain areas, but it would be interesting
to investigate functional connectivity between the right PFC and SMA during complex motor
learning tasks. In future research, it will be necessary to consider other neuroimaging
techniques that measure cerebral blood flow dynamics in humans during motor learning of flick
input operation of a touch-screen terminal.
We examined cerebral blood flow dynamics during motor learning of flick input into a
touchscreen terminal using NIRS. The number of character inputs significantly increased with
repetition of task cycles. These results show that motor learning occurred in all subjects during the
course of one experiment. In the left SMC, SMA, and right PFC, there was a significant change
of cerebral blood flow dynamics as the task cycles progressed, indicating motor learning over
time. Changes in activity over the left SMC, SMA, and right PFC likely reflect distinct aspects
of acquisition of the motor task such as increase in finger momentum, motor inhibition, and
visual working memory, respectively.
S1 Dataset. Subjects information.
S2 Dataset. Correlations between task performance and task cycles.
S3 Dataset. Correlations between left SMC Oxy-Hb concentration changes and number of
S4 Dataset. Correlations between SMA Oxy-Hb concentration changes and number of task
S5 Dataset. Correlations between right PFC Oxy-Hb concentration changes and number of
S6 Dataset. Correlations between other regions Oxy-Hb concentration changes and
S1 Supporting Information. Presentation for the task.
The authors would like to thank all the participants in this study. Takehito Yonezawa, Kengo
Fujiwara, and Akira Nakashima contributed significantly with their flexible support of the
participants during data acquisition. We thank Hitachi Medical Corp., Japan for their skilled
technical support whenever needed. Moreover, we would particularly like to thank Takumi
Inakazu for his continuous support during the data acquisition and processing phase. We
would like to thank Editage (www.editage.jp) for English language editing.
Conceived and designed the experiments: AS TT KO TH. Performed the experiments: AS NI
TM TH EK. Analyzed the data: AS NI KT. Wrote the paper: AS TH TT.
5. Norris DG. Principles of magnetic resonance assessment of brain function. J Magn Reson Imaging.
2006; 23: 794–807. PMID: 16649206
1. Hillebrand A , Singh KD , Holliday IE , Furlong PL , Barnes GR. A new approach to neuroimaging with magnetoencephalography . Hum Brain Mapp . 2005 ; 25 : 199 - 211 . PMID: 15846771
2. Lauritzen M. Reading vascular changes in brain imaging: is dendritic calcium the key ? Nat Rev Neurosci . 2005 ; 6 : 77 - 85 . PMID: 15611729
3. Costafreda SG , Fu CHY , Lee L , Everitt B , Brammer MJ , David AS. A systematic review and quantitative appraisal of fMRI studies of verbal fluency: role of the left inferior frontal gyrus . Hum Brain Mapp . 2006 ; 27 : 799 - 810 . PMID: 16511886
4. Debener S , Ullsperger M , Siegel M , Engel AK . Single-trial EEG-fMRI reveals the dynamics of cognitive function . Trends Cogn Sci . 2006 ; 10 : 558 - 63 . PMID: 17074530
6. Otte A , Halsband U. Brain imaging tools in neurosciences . J Physiol Paris. 2006 ; 99 : 281 - 92 . PMID: 16713203
7. Stern JM . Simultaneous electroencephalography and functional magnetic resonance imaging applied to epilepsy . Epilepsy Behav . 2006 ; 8 : 683 - 92 . PMID: 16630747
8. Van Eimeren T , Siebner HR . An update on functional neuroimaging of parkinsonism and dystonia . Curr Opin Neurol . 2006 ; 19 : 412 - 9 . PMID: 16914982
9. Seidler RD , Noll DC . Neuroanatomical correlates of motor acquisition and motor transfer . J Neurophysiol . 2008 ; 99 : 1836 - 45 . doi: 10.1152/jn.01187. 2007 PMID: 18272874
10. Anguera JA , Reuter-lorenz PA , Willingham DT , Seidler RD . Contributions of Spatial Working Memory to Visuomotor Learning . J Cogn Neurosci . 2009 ; 22 : 1917 - 1930 .
11. King BR , Fogel SM , Albouy G , Doyon J. Neural correlates of the age-related changes in motor sequence learning and motor adaptation in older adults . Front Hum Neurosci . 2013 ; 7 : 142. doi: 10. 3389/fnhum.2013.00142 PMID: 23616757
12. Bédard P , Sanes JN . Brain representations for acquiring and recalling visual-motor adaptations . Neuroimage. Elsevier B.V . 2014 ; 101 : 225 - 35 . doi: 10.1016/j.neuroimage. 2014 . 07.009 PMID: 25019676
13. Deluca C , Golzar A , Santandrea E , Lo Gerfo E , Eštočinová J , Moretto G , et al. The cerebellum and visual perceptual learning: evidence from a motion extrapolation task . Cortex . 2014 ; 58 : 52 - 71 . doi: 10. 1016/j.cortex. 2014 . 04.017 PMID: 24959702
14. Stewart JC , Tran X , Cramer SC . Age-related variability in performance of a motor action selection task is related to differences in brain function and structure among older adults . Neuroimage. Elsevier Inc .; 2014 ; 86 : 326 - 34 . doi: 10.1016/j.neuroimage. 2013 . 10.016 PMID: 24125791
15. Okamoto M , Dan I. Functional near-infrared spectroscopy for human brain mapping of taste-related cognitive functions . J Biosci Bioeng . 2007 ; 103 : 207 - 15 . PMID: 17434422
16. Miyai I , Tanabe HC , Sase I , Eda H , Oda I , Konishi I , et al. Cortical mapping of gait in humans: a nearinfrared spectroscopic topography study . Neuroimage . 2001 ; 14 : 1186 - 92 . PMID: 11697950
17. Miyai I , Yagura H , Oda I , Konishi I , Eda H , Suzuki T , et al. Premotor cortex is involved in restoration of gait in stroke . Ann Neurol . 2002 ; 52 : 188 - 94 . PMID: 12210789
18. Hiroyuki H , Hamaoka T , Sako T , Nishio S , Kime R , Murakami M , et al. Oxygenation in vastus lateralis and lateral head of gastrocnemius during treadmill walking and running in humans . Eur J Appl Physiol . 2002 ; 87 : 343 - 9 . PMID: 12172872
19. Suzuki M , Miyai I , Ono T , Oda I , Konishi I , Kochiyama T , et al. Prefrontal and premotor cortices are involved in adapting walking and running speed on the treadmill: an optical imaging study . Neuroimage . 2004 ; 23 : 1020 - 6 . PMID: 15528102
20. Harada T , Miyai I , Suzuki M , Kubota K. Gait capacity affects cortical activation patterns related to speed control in the elderly . Exp brain Res . 2009 ; 193 : 445 - 54 . doi: 10.1007/s00221- 008 - 1643 -y PMID : 19030850
21. Subudhi AW , Dimmen AC , Roach RC . Effects of acute hypoxia on cerebral and muscle oxygenation during incremental exercise . J Appl Physiol . 2007 ; 103 : 177 - 83 . PMID: 17431082
22. Subudhi AW , Miramon BR , Granger ME , Roach RC . Frontal and motor cortex oxygenation during maximal exercise in normoxia and hypoxia . J Appl Physiol . 2009 ; 106 : 1153 - 8 . doi: 10.1152/japplphysiol. 91475. 2008 PMID: 19150853
23. Ide K , Horn A , Secher NH , Trangmar SJ , Chiesa ST , Stock CG , et al. Cerebral metabolic response to submaximal exercise maximal exercise in trained humans maximal exercise in trained humans Cerebral metabolic response to submaximal exercise . 2014 ; 1604 - 1608 .
24. Okamoto M , Dan H , Shimizu K , Takeo K , Amita T , Oda I , et al. Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI . Neuroimage. 2004 ; 21 : 1275 - 88 . PMID: 15050555
25. Kuboyama N , Nabetani T , Shibuya K-I , Machida K , Ogaki T. The effect of maximal finger tapping on cerebral activation . J Physiol Anthropol Appl Human Sci . 2004 ; 23 : 105 - 10 . PMID: 15314267
26. Kuboyama N , Nabetani T , Shibuya K , Machida K , Ogaki T. Relationship between Cerebral Activity and Movement Frequency of Maximal Finger Tapping . J Physiol Anthropol Appl Human Sci . 2005 ; 24 : 201 - 208 PMID: 15930807
27. Ito M , Fukuda M , Suto T , Uehara T , Mikuni M. Increased and decreased cortical reactivities in novelty seeking and persistence: a multichannel near-infrared spectroscopy study in healthy subjects . Neuropsychobiology . 2005 ; 52 : 45 - 54 . PMID: 15942263
28. Holper L , Biallas M , Wolf M. Task complexity relates to activation of cortical motor areas during uni- and bimanual performance: a functional NIRS study . Neuroimage. Elsevier Inc .; 2009 ; 46 : 1105 - 13 . doi: 10.1016/j.neuroimage. 2009 . 03.027 PMID: 19306929
29. Shibuya K , Kuboyama N. Human motor cortex oxygenation during exhaustive pinching task . Brain Res . 2007 ; 1156 : 120 - 4 . PMID: 17543291
30. Kubo M , Shoshi C , Kitawaki T , Takemoto R , Kinugasa K , Yoshida H , et al. Increase in prefrontal cortex blood flow during the computer version trail making test . Neuropsychobiology . 2008 ; 58 : 200 - 10 . doi: 10.1159/000201717 PMID: 19212135
31. Nakahachi T , Ishii R , Iwase M , Canuet L , Takahashi H , Kurimoto R , et al. Frontal cortex activation associated with speeded processing of visuospatial working memory revealed by multichannel near-infrared spectroscopy during Advanced Trail Making Test performance . Behav Brain Res. Elsevier B.V.; 2010 ; 215 : 21 - 7 . doi: 10.1016/j.bbr. 2010 . 06.016 PMID: 20600348
32. Ohsugi H , Ohgi S , Shigemori K , Schneider EB . Differences in dual-task performance and prefrontal cortex activation between younger and older adults . BMC Neurosci. BMC Neuroscience ; 2013 ; 14 : 10. doi: 10.1186/ 1471 - 2202 - 14 -10 PMID: 23327197
33. Kikuchi S , Iwata K , Onishi Y , Kubota F , Nisijima K , Tamai H , et al. Prefrontal cerebral activity during a simple “rock, paper, scissors” task measured by the noninvasive near-infrared spectroscopy method . Psychiatry Res . 2007 ; 156 : 199 - 208 . PMID: 17976959
34. Yamauchi Y , Kikuchi S , Miwakeichi F , Matsumoto K , Nishida M , Ishiguro M , et al. Relation between parametric change of the workload and prefrontal cortex activity during a modified version of the “rock, paper, scissors” task . Neuropsychobiology . 2013 ; 68 : 24 - 33 . doi: 10.1159/000350948 PMID: 23774939
35. Miyata H , Watanabe S , Minagawa-Kawai Y. Two successive neurocognitive processes captured by near-infrared spectroscopy: prefrontal activation during a computerized plus-shaped maze task . Brain Res. Elsevier B.V.; 2011 ; 1374 : 90 - 9 . doi: 10.1016/j.brainres. 2010 . 12.047 PMID: 21172310
36. Suto T , Fukuda M , Ito M , Uehara T , Mikuni M. Multichannel near-infrared spectroscopy in depression and schizophrenia: cognitive brain activation study . Biol Psychiatry . 2004 ; 55 : 501 - 11 . PMID: 15023578
37. Amemiya K , Ishizu T , Ayabe T , Kojima S. Effects of motor imagery on intermanual transfer: a near-infrared spectroscopy and behavioural study . Brain Res. Elsevier B.V.; 2010 ; 1343 : 93 - 103 . doi: 10.1016/j. brainres. 2010 . 04.048 PMID: 20423702
38. Hatakenaka M , Miyai I , Mihara M , Sakoda S , Kubota K. Frontal regions involved in learning of motor skill-A functional NIRS study . Neuroimage . 2007 ; 34 : 109 - 16 . PMID: 17067821
39. Ikegami T , Taga G . Decrease in cortical activation during learning of a multi-joint discrete motor task . Exp brain Res . 2008 ; 191 : 221 - 36 . doi: 10.1007/s00221- 008 - 1518 - 2 PMID: 18679662
40. Gentili RJ , Shewokis P a , Ayaz H , Contreras-Vidal JL . Functional near-infrared spectroscopy-based correlates of prefrontal cortical dynamics during a cognitive-
41. eMarketer. eMarketer. Smartphone Users Worldwide Will Total 1.75 Billion in 2014 . January 16 , 2014 . Available: http://www.emarketer. com/Article/Smartphone-Users-Worldwide-Will-Total-175-Billion2014/1010536. Accessed 6 December 2014
42. Obrig H , Villringer A. Beyond the visible-imaging the human brain with light . J Cereb Blood Flow Metab . 2003 ; 23 : 1 - 18 . PMID: 12500086
43. Hoshi Y , Kobayashi N , Tamura M. Interpretation of near-infrared spectroscopy signals: a study with a newly developed perfused rat brain model . J Appl Physiol . 2001 ; 90 : 1657 - 62 . PMID: 11299252
44. Okamoto M , Dan H , Sakamoto K , Takeo K , Shimizu K , Kohno S , et al. Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping . Neuroimage . 2004 ; 21 : 99 - 111 PMID: 14741647
45. Miyai I , Yagura H , Hatakenaka M , Oda I , Konishi I , Kubota K. Longitudinal optical imaging study for locomotor recovery after stroke . Stroke . 2003 ; 34 : 2866 - 70 . PMID: 14615624
46. Kakei S , Hoffman DS , Strick PL . Muscle and movement representations in the primary motor cortex . Science . 1999 ; 285 : 2136 - 9 . PMID: 10497133
47. Grafton ST , Hazeltine E , Ivry RB. Motor sequence learning with the nondominant left hand. A PET functional imaging study . Exp brain Res . 2002 ; 146 : 369 - 78 . PMID: 12232693
48. Kuboyama N , Nabetani T , Shibuya K-I , Machida K , Ogaki T. The effect of maximal finger tapping on cerebral activation . [Internet]. Journal of physiological anthropology and applied human science. 2004 . pp. 105 - 10 .
49. Roland PE , Larsen B , Lassen N a , Skinhøj E. Supplementary motor area and other cortical areas in organization of voluntary movements in man . J Neurophysiol . 1980 ; 43 : 118 - 36 . PMID: 7351547
50. Tanji J. New concepts of the supplementary motor area . Curr Opin Neurobiol . 1996 ; 6 : 782 - 7 . PMID: 9000016
51. Picard N , Strick P. Motor areas of the medial wall: a review of their location and functional activation . Cereb cortex . 1996 ; 6 : 342 - 53 . PMID: 8670662
52. Nachev P , Kennard C , Husain M. Functional role of the supplementary and pre-supplementary motor areas . Nat Rev Neurosci . 2008 ; 9 : 856 - 69 . doi: 10.1038/nrn2478 PMID: 18843271
53. Mita A , Mushiake H , Shima K , Matsuzaka Y , Tanji J. Interval time coding by neurons in the presupplementary and supplementary motor areas . Nat Neurosci . 2009 ; 12 : 502 - 7 . doi: 10.1038/nn.2272 PMID: 19252498
54. Grafton ST , Mazziotta JC , Presty S , Friston KJ , Frackowick RS , Phelps ME . Functional Anatomy of Human Procedural Learning Determined with Regional Cerebral Blood Flow and PET . J Neurosci . 1992 ; 12 : 2542 - 48 . PMID: 1613546
55. Toni I , Krams M , Turner R , Passingham RE . The time course of changes during motor sequence learning: a whole-brain fMRI study . Neuroimage . 1998 ; 8 : 50 - 61 . PMID: 9698575
56. Hund-Georgiadis M , von Cramon D. Motor-learning-related changes in piano players and non-musicians revealed by functional magnetic-resonance signals . Exp brain Res . 1999 ; 125 : 417 - 25 . PMID: 10323287
57. Krings T , Töpper R , Foltys H , Erberich S , Sparing R , Willmes K , et al. Cortical activation patterns during complex motor tasks in piano players and control subjects. A functional magnetic resonance imaging study . Neurosci Lett . 2000 ; 278 : 189 - 193 . PMID: 10653025
58. Hatta A , Nishihira Y , Higashiura T , Kim SR , Kaneda T. Long-term motor practice induces practicedependent modulation of movement-related cortical potentials (MRCP) preceding a self-paced nondominant handgrip movement in kendo players . Neurosci Lett . 2009 ; 459 : 105 - 8 . doi: 10.1016/j.neulet. 2009 . 05.005 PMID: 19427364
59. Lefebvre S , Dricot L , Gradkowski W , Laloux P , Vandermeeren Y. Brain activations underlying different patterns of performance improvement during early motor skill learning . Neuroimage. Elsevier Inc .; 2012 ; 62 : 290 - 9 . doi: 10.1016/j.neuroimage. 2012 . 04.052 PMID: 22569545
60. Wright DJ , Holmes PS , Di Russo F , Loporto M , Smith D. Differences in cortical activity related to motor planning between experienced guitarists and non-musicians during guitar playing . Hum Mov Sci. Elsevier B.V.; 2012 ; 31 : 567 - 77 . doi: 10.1016/j.humov. 2011 . 07.001 PMID: 21899906
61. Whitmer AJ , Banich MT . Brain activity related to the ability to inhibit previous task sets: an fMRI study . Cogn Affect Behav Neurosci . 2012 ; 12 : 661 - 70 . doi: 10.3758/s13415- 012 - 0118 - 6 PMID: 22956332
62. Nakata H , Sakamoto K , Honda Y , Kakigi R. Somato-motor inhibitory processing in humans: evidence from neurophysiology and neuroimaging . J Physiol Sci . 2014 ; 64 : 233 - 52 . doi: 10.1007/s12576- 014 - 0320 - 0 PMID: 24859317
63. Goldman-Rakic PS . Regional and cellular fractionation of working memory . Proc Natl Acad Sci U S A . 1996 ; 93 : 13473 - 80 . PMID: 8942959
64. Shallice T. Brain regions associated with acquisition and retrieval of verbal episodic memory . Nature . 1994 ; 368 : 633 - 635 . PMID: 8145849
65. Fletcher PC , Frith CD , Rugg MD . The functional neuroanatomy of episodic memory . Trends Neurosci . 1997 ; 20 : 213 - 8 . PMID: 9141197
66. Jenkins I , Brooks D , Nixon P , Frachowick S , Passingham R. Motor sequence learning: A Study with Positron Emission Tomography . J Neurosci . 1994 ; 14 : 3775 - 90 . PMID: 8207487
67. Deiber MP , Wise SP , Honda M , Catalan MJ , Grafman J , Hallett M. Frontal and parietal networks for conditional motor learning: a positron emission tomography study . J Neurophysiol . 1997 ; 78 : 977 - 91 . PMID: 9307128
68. Debaere F , Wenderoth N , Sunaert S , Van Hecke P , Swinnen SP . Changes in brain activation during the acquisition of a new bimanual coodination task . Neuropsychologia . 2004 ; 42 : 855 - 67 . PMID: 14998701
69. Floyer-Lea a, Matthews PM. Distinguishable brain activation networks for short- and long-term motor skill learning . J Neurophysiol . 2005 ; 94 : 512 - 8 . PMID: 15716371
70. Leff DR , Elwell CE , Orihuela-Espina F , Atallah L , Delpy DT , Darzi AW , et al. Changes in prefrontal cortical behaviour depend upon familiarity on a bimanual co-ordination task: an fNIRS study . Neuroimage . 2008 ; 39 : 805 - 13 . PMID: 17964187
71. James DRC , Leff DR , Orihuela-Espina F , Kwok K-W , Mylonas GP , Athanasiou T , et al. Enhanced frontoparietal network architectures following “gaze-contingent” versus “free-hand” motor learning . Neuroimage. Elsevier Inc .; 2013 ; 64 : 267 - 76 . doi: 10.1016/j.neuroimage. 2012 . 08.056 PMID: 22960153
72. Awh E , Jonides J , Smith EE , Schumacher EH , Koeppe RA , Katz S. Dissociation of Storage and Rehearsal in Verbal Working Memory: Evidence From Positron Emission Tomography . 1996 . pp. 25 - 31 .
73. Herbster AN , Mintun MA , Nebes RD , Becker JT . Regional Cerebral Blood Flow During Word and Nonword Reading. Hum Brain Mapp . 1997 ; 92 : 84 - 92 .
74. Opitz B , Mecklinger A , Friederici A. Functional asymmetry of human prefrontal cortex: encoding and retrieval of verbally and nonverbally coded information . Learn Mem . 2000 ; 7 : 85 - 96 . PMID: 10753975
75. Floel A , Poeppel D , Buffalo EA , Braun A , Wu CW-H , Seo H , et al. Prefrontal Cortex Asymmetry for Memory Encoding of Words and Abstract Shapes . Cereb Cortex . 2004 ; 14 : 404 - 9 . PMID: 15028644
76. Mushiake H , Inase M , Tanji J. Neuronal activity in the primate premotor, supplementary, and precentral motor cortex during visually guided and internally determined sequential movements Neuronal Activity in the Primate Premotor, Supplementary, and Precentral Motor Cortex During Vi . J Neurophysiol . 1991 ; 66 : 705 - 18 . PMID: 1753282
77. Murray E a , Bussey TJ , Wise SP . Role of prefrontal cortex in a network for arbitrary visuomotor mapping . Exp brain Res . 2000 ; 133 : 114 - 29 . PMID: 10933216
78. Hikosaka O , Sakai K , Miyauchi S , Takino R , Sasaki Y , Putz B. Activation of human presupplementary motor area in learning of sequential procedures: a functional MRI study . J Neurophysiol . 1996 ; 76 : 617 - 21 . PMID: 8836248
79. Hanakawa T , Honda M , Sawamoto N , Okada T , Yonekura Y , Fukuyama H , et al. The Role of Rostral Brodmann Area 6 in Mental-operation Tasks: an Integrative Neuroimaging Approach . Cereb Cortex. 2002 ; 12 : 1157 - 70 . PMID: 12379604
80. Leek EC , Johnston SJ . Functional specialization in the supplementary motor complex . Nat Rev Neurosci . 2009 ; 10 : 78; author reply 78 . doi: 10.1038/nrn2478-c1 PMID: 19096371
81. Hardwick RM , Rottschy C , Miall RC , Eickhoff SB. A quantitative meta-analysis and review of motor learning in the human brain . Neuroimage. Elsevier Inc .; 2013 ; 67 : 283 - 97 . doi: 10.1016/j.neuroimage. 2012 . 11.020 PMID: 23194819
82. Wilson TW , Kurz MJ , Arpin DJ . Functional specialization within the supplementary motor area: a fNIRS study of bimanual coordination . Neuroimage. Elsevier Inc .; 2014 ; 85 Pt 1: 445 - 50 . doi: 10.1016/j. neuroimage. 2013 . 04.112 PMID: 23664948