Clinical Neurophysiology 131 (2020) 951–957

Contents lists available at ScienceDirect

Clinical Neurophysiology
journal homepage: www.elsevier.com/locate/clinph

Visualization of electrophysiological activity at the carpal tunnel area
using magnetoneurography
Toru Sasaki a, Shigenori Kawabata b,⇑, Yuko Hoshino b, Kensuke Sekihara b, Yoshiaki Adachi c,
Miho Akaza d, Isamu Ozaki e, Koji Fujita a, Akimoto Nimura f, Toshitaka Yoshii a, Yuki Miyano g,
Yuki Mitani g, Taishi Watanabe a,g, Shinji Sato g, Sukchan Kim g, Atsushi Okawa aaDepartment of Orthopedic Surgery, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Department of Advanced Technology in Medicine, Graduate School of Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
Applied Electronics Laboratory, Kanazawa Institute of Technology, Kanazawa-shi, Ishikawa 920-1331, Japand
Respiratory and Nervous System Science, Biomedical Laboratory Science, Graduate School of Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo
113-8510, Japane
Faculty of Health Sciences, Aomori University of Health and Welfare, 58-1 Mase, Hamadate, Aomori-shi, Aomori 030-8505, Japanf
Department of Functional Joint Anatomy, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8510, Japang
Ricoh Institute of Future Technology, RICOH Company, Ltd., 2-3-10 Kandasurugadai Chiyoda-ku, Tokyo 101-0062, Japanbc

See Editorial, pages 938–939

a r t i c l e

i n f oArticle history:
Accepted 14 November 2019
Available online 10 December 2019
Keywords:
Magnetoneurography
Evoked magnetic field
Carpal tunnel
Median nerve
Digital nerve
Conduction velocity

h i g h l i g h t s
 Neural activity in the carpal tunnel area can be visualized by magnetoneurography.
 Conductions due to stimulation of the index and middle digital nerves can be differentiated.
 Intra-axonal currents and currents flowing across the membrane can be visualized.

a b s t r a c t
Objective: To establish a noninvasive method to measure the neuromagnetic fields of the median nerve at
the carpal tunnel after electrical digital nerve stimulation and evaluate peripheral nerve function.
Methods: Using a vector-type biomagnetometer system with a superconducting quantum interference
device, neuromagnetic fields at the carpal tunnel were recorded after electrical stimulation of the index
or middle digital nerve in five healthy volunteers. A novel technique for removing stimulus-induced artifacts was applied, and current distributions were calculated using a spatial filter algorithm and superimposed on X-ray.
Results: A neuromagnetic field propagating from the palm to the carpal tunnel was observed in all participants. Current distributions estimated from the magnetic fields had five components: leading andtrailing components parallel to the conduction pathway, outward current preceding the leading component, inward currents between the leading and trailing components, and outward current following thetrailing component. The conduction velocity and peak latency of the inward current agreed well withthose of sensory nerve action potentials.

Abbreviations: CSP, common-mode subspace projection; DIP, distal interphalangeal; MNG, magnetoneurography; MRI, magnetic resonance imaging; SNAP, sensory nerve
action potential; SQUID, superconducting quantum interference device; UGRENS, unit gain constraint recursively applied null-steering spatial filtering.
⇑ Corresponding author at: Department of Advanced Technology in Medicine, Graduate School of Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo
113-8510, Japan.
E-mail addresses: t-sasaki.orth@tmd.ac.jp (T. Sasaki), kawabata.orth@tmd.ac.jp (S. Kawabata), hoshino.atm@tmd.ac.jp (Y. Hoshino), k-sekihara@nifty.com (K. Sekihara),adachi@ael.kanazawa-it.ac.jp (Y. Adachi), mihonuro@tmd.ac.jp (M. Akaza), isamu@auhw.ac.jp (I. Ozaki), fujiorth@tmd.ac.jp (K. Fujita), nimura.orj@tmd.ac.jp (A. Nimura),
yoshii.orth@tmd.ac.jp (T. Yoshii), yuki.yh.hasegawa@jp.ricoh.com (Y. Miyano), yuuki.ym.mitani@jp.ricoh.com (Y. Mitani), taishi.watanabe@jp.ricoh.com (T. Watanabe), shinji.
s1.satoh@jp.ricoh.com (S. Sato), sukchan.kim@jp.ricoh.com (S. Kim), okawa.orth@tmd.ac.jp (A. Okawa).
https://doi.org/10.1016/j.clinph.2019.11.030
1388-2457/Ó 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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T. Sasaki et al. / Clinical Neurophysiology 131 (2020) 951–957

Conclusion: Removing stimulus-induced artifacts enabled magnetoneurography to noninvasively visualize with high spatial resolution the electrophysiological neural activity from the palm to the carpal tunnel.
Significance: This is the first report of using magnetoneurography to visualize electrophysiological nerve
activity at the palm and carpal tunnel.
Ó 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open
access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction
1.1. Electrophysiological examination of peripheral neuropathy
Carpal tunnel syndrome (CTS) is caused by entrapment of themedian nerve at the wrist and is the most common nerve entrapment syndrome. Although CTS is diagnosed mainly from clinicalfindings, morphological evaluation by magnetic resonance imaging(MRI) (Razek et al., 2017) and ultrasound (Beekman and Visser,
2003) and functional evaluation by electrophysiological examination are performed as supplementary diagnostic tests. Despiteadvances in morphological evaluation technology with the development of MRI and ultrasound techniques, morphological evaluation alone is not sufficient for neurological functional evaluation.
Electrophysiological examinations are useful to evaluate functionality and determine indications for surgical treatment, and there
have been many reports of electrophysiological assessments forfunctional evaluation of CTS (Buchthal et al., 1974; Stevens,
1987; Jablecki et al., 1993). However, the conventional electrophysiological tests used to diagnose CTS, such as distal motor
latency of the abductor pollicis brevis and digit-to-wrist antidromic sensory nerve conduction velocity, are associated with a highfalse-negative rate (Thomas et al., 1967; Sedal et al., 1973;
Buchthal et al., 1974). This is because, when the conduction delayof a nerve lesion is short, it is difficult to detect an abnormality bymeasuring the conduction time of a much longer distance, as in theconventional method.
Inching methods are useful to diagnose focal conduction
abnormalities at nerve entrapment sites. However, these methods have some disadvantages and are thus not used as a routine
examination. Orthodromic inching methods require multiple
recording surface electrodes and are time consuming (Seror,1998), and the electric potential recording from the body surface
is influenced by the presence of a thick skin or a thick transverse
ligament (Werner and Andary, 2011). Antidromic inching methods sometimes struggle to determine the exact point of nerve
activation due to the spread of the stimulating current(Kimura, 1979). The development is thus necessary of a nontechnically demanding method that allows the detailed andrapid determination of the location and severity of peripheral
nerve dysfunction.
1.2. Measurement of neuromagnetic fields
In contrast to surface electric potential measurement, the magnetic field is not affected by the surrounding tissue and has theoretically high spatial resolution (Trahms et al., 1989). Magnetic
fields have been measured from the peripheral nerve of the upper
extremities of humans (Trahms et al., 1989; Wikswo, 1990;
Hoshiyama et al., 1999; Mackert, 2004; Nakanishi et al., 2004).
However, there are no reports in the literature on magnetic fieldmeasurement of the palmar area from the body surface.
Our group has developed a technique to visualize the spatial
distribution of the neural current of the peripheral nerve and spinal
cord through neuromagnetic field measurement (Sato et al., 2009;
Ishii et al., 2012; Sumiya et al., 2017; Ushio et al., 2019). With tech-nical improvements, we have become able to visualize in a noninvasive manner and with high spatial resolution the distribution ofneural currents, such as intra-axonal currents and volume currents
including inward currents at depolarization sites. Although we had
tried to measure the neuromagnetic field at the carpal tunnel after
digital nerve stimulation, detection of the neuromagnetic field had
been impossible due to the major artifact of the electrical stimulation applied to the finger, due to its close proximity to the recording site.
In this study, we combined a newly developed artifact removal
method with magnetoneurography (MNG) from the body surface
and report for the first time visualization of electrophysiologicalactivity at the carpal tunnel area in response to digital nerve
stimulation.
2. Methods
2.1. MNG system
All recordings were performed in a magnetically shielded room
using a vector-type superconducting quantum interference device(SQUID) biomagnetometer system developed by Kanazawa Instituteof Technology and RICOH Company, Ltd. (Adachi et al., 2017). The
sensor array is composed of 44 SQUID magnetic flux sensors
arranged at 20–25-mm intervals (Fig. 1a and b) along a cylindricalsurface with a curvature radius of 200 mm. Each sensor is equippedwith three pickup coils oriented orthogonally to one another tosimultaneously detect magnetic field components both tangential
and radial to the body surface. The SQUID sensor array is placed ina protrusion along the upper surface of the system. Seated subjects
place their palm on the sensor area (Fig. 1c). The curved surface ofthe sensor area fits well to the shape of the human wrist joint andis more suitable for measuring the carpal tunnel magnetic field thanthe conventional flat sensor surface (Fig. 2b).
2.2. MNG measurement of the median nerve at the carpal tunnel area
All procedures in this study were approved by the Ethics Committee of Tokyo Medical and Dental University and were performedin accordance with the Declaration of Helsinki. We obtained written
informed consent from all participants, who comprised five healthy
volunteers aged 26–34 years (mean ± SD, 29.9 ± 3.0 years; one
woman) without nervous system disease. Participants sat on a chairin a relaxed state and placed their palm on the sensor surface areawith the forearm pronated. Lateral and frontal radiographs weretaken to obtain positional information on the subject and the magnetic sensor. The index and middle finger were electrically stimulated at the distal interphalangeal (DIP) joint (square wave pulse;
3 Hz; 0.2 ms in duration). The index and middle finger were alternately stimulated at over 2–3 times the sensory threshold (intensity,
4.2–6.0 mA), and evoked magnetic fields were recorded at the surface of the palmar area using a 40-kHz sampling rate and 100–
5000 Hz band-pass filter (Fig. 2a and b). For each finger, 2000 recordings of evoked magnetic fields were averaged.
To remove stimulus-induced artifacts from the acquired MNG
signals, we also recorded the magnetic signal of the stimulus-

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Fig. 1. Appearance and structure of the magnetoneurography (MNG) system. (a) The sensor array used to measure neuromagnetic fields. The array is composed of a 44-sensor
vector-type SQUID biomagnetometer. The sensor surface is upward and slightly curved to fit the alignment of the limb joint. (b) The sensor arrangement. The 44 sensors arearranged in an area of 180 mm  130 mm and spaced at 20–25-mm intervals. (c) MNG system and measurement position. The participant was seated on a chair in a relaxedstate and placed their palm on the sensor area of the cryostat. Ó 2017 IEEE. Reprinted with permission from DOI:10.1109/TASC.2016.2631422.

Fig. 2. Measurement of the neuromagnetic fields of the median nerve at the carpal tunnel area. (a) The subject put their carpal tunnel area on the center of the sensor area
(dotted rectangle). We measured neuromagnetic fields in response to electrical stimulation of the digital nerve at the distal interphalangeal joint of the index or middle finger.
(b) Side view of the measurement position. The sensor surface is slightly curved and fits the wrist joint. (c) Measurement of magnetic fields of stimulus-induced artifacts. A
30-mm urethane plate separates the nerve from the sensors and reduces the magnetic signal from neural activity. The position of the finger to be stimulated was the same. (d)Recording surface electrodes were placed at four locations—20 mm proximal and at 0, 20, and 40 mm distal to the wrist crease—and sensory nerve action potentials (SNAPs)
were recorded in response to the index finger stimulation.

induced artifacts alone. A 30-mm urethane plate was placed
between the palm and the sensor surface without moving the fingers to be stimulated, and the nerve magnetic field was measured
under the same conditions (Fig. 2c). In this method, because thedistance between the sensor and the nerve increases, the magnetic
signals derived from the nerve are greatly reduced, allowing signals derived from the stimulus-induced artifacts alone to beacquired.

finger (Fig. 2d). The digital nerve was stimulated at the DIP joint of
the index finger with orthodromic recording from the four points
at the palm area (intensity, over twice the sensory threshold; frequency, 3 Hz). Recording surface electrodes were placed at four
locations: 20 mm proximal and 0, 20, and 40 mm distal to the wrist
crease.

2.3. Measurement of sensory nerve action potentials

First, we removed stimulus-induced artifacts from the acquired
MNG signals by applying the common-mode subspace projection(CSP) algorithm (Watanabe et al., 2013). The CSP algorithm is used
to calculate magnetic signals derived from neural activity alone
using the entire magnetic signals (neural signal + artifact) andAs a comparison with conventional electric potential recording,sensory nerve action potentials (SNAPs) were measured in all participants with orthodromic recording after stimulation of the index

2.4. Signal processing

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set the virtual electrodes along the digital nerve and the median
nerve at 10-mm intervals.
3. Results
3.1. Neuromagnetic field

Fig. 3. Formulation of the depth between the nerve and the surface of the sensors.
(a) These distances were chosen from the distance between the bone and nerve
calculated from the MR images of five healthy volunteers. We measured distances
at four points: the shaft of the radial bone, the distal radius, the base of the third
metacarpal, and the shaft of the third metacarpal bone. The average distances were
9.2 mm, 5.6 mm, 7.4 mm, and 14.8 mm, respectively. (b) Lateral radiographs weretaken to obtain positional information on the subject and the magnetic sensor. The
distances from the MR images and the information from the X-ray images wereused to define the region of interest (ROI).

the magnetic signal of the stimulus-induced artifacts alone. The
CSP algorithm is effective when artifact sources are located in thevicinity of the signal source space.
A spatial filter algorithm, unit gain constraint recursively
applied null-steering spatial filtering (UGRENS), was applied tothe artifact-removed magnetic signal, and the temporal and spatial
distributions of the currents were calculated at a region of interest(ROI) (Sekihara and Nagarajan, 2015). The ROI was at a depth of
9.2 mm from the palmar side of the shaft of the radial bone,
5.6 mm from the palmar side of the distal end of the radial bone,
7.4 mm from the palmar side of the base of the third metacarpal
bone, and 14.8 mm from the palmar side of the shaft of the third
metacarpal bone. These distances are the average distances
between the bones and nerves measured from MRI of the five volunteers (Fig. 3). The calculated currents were superimposed on an
X-ray image to visualize their distributions and intensities. Student’s t-test was used to compare differences between the centerof the second metacarpal bone and the calculated nerve route forthe index finger stimulation and the middle finger stimulation. In
this method, the current waveforms at arbitrary points in ROI
can be calculated as if virtual electrodes were placed there. We(a)

The entire magnetic signal, comprising the neural signal andartifacts, extends from the palmar area to the carpal tunnel area
in response to the digital nerve stimulation. The noise artifacts
were too large to permit the identification of neural magnetic signals (Fig. 4a). After application of the CSP algorithm to the entire
magnetic signal, small neural signals buried in the stimulus artifact
were revealed in all volunteers (Fig. 4b and c).
Fig. 5 shows the evoked magnetic fields of a representative
recording from the X-, Y-, and Z-directed coils (X, red; Y, green;Z, black; (b) index finger stimulation; (c) middle finger stimulation). The magnetic fields from the X- and Z-directed coils had
biphasic waveforms and the magnetic fields from the Y-directedcoil had triphasic waveforms, and these waveforms propagatedin the distal to proximal direction. The averages of the maximum
peak-to-peak amplitudes of the magnetic fields in each direction
sensor were 78.4 ± 25.6 fT, 84.2 ± 28.2 fT, and 121.0 ± 21.3 fT (X,Y, and Z, respectively) in response to index finger stimulation
and 86.0 ± 26.9 fT, 94.6 ± 27.0 fT, and 154.4 ± 32.3 fT (X, Y, andZ, respectively) in response to middle finger stimulation.
3.2. Current distribution
The current distributions calculated from the artifact-removed
data using UGRENS were superimposed on an X-ray image of thehand as pseudocolor maps. We show a representative case in
Fig. 6. At 2.1 ms after the index finger stimulation, the leading component, which comprises the currents running parallel to the nerve
and was considered the intra-axonal current in our previous work
(Fukuoka et al., 2002, 2004), appeared at the hook of hamate leveland propagated from the carpal tunnel area in the proximal direction. At 3.3 ms after the stimulation, the trailing component
appeared at the hook of hamate level. In addition, inward currents
flowed perpendicularly between the leading and trailing components on both sides of the nerve. Moreover, outward currents wereobserved on both sides proximal to the intra-axonal leading component and distal to the trailing component. These inward and outward currents also propagated proximally along with the intraaxonal currents (Fig. 6a). There was a similar current pattern after
middle finger stimulation (Fig. 6b). The calculated currents in
response to the middle finger stimulation propagated along the(b)Time (ms)(c)

Time (ms)Time (ms)Fig. 4. Artifact removal by the common-mode subspace projection (CSP) algorithm. (a) The magnetic signals in response to index finger stimulation. We superimposed all
magnetic fields measured at each sensor. (b) Artifact-removed signals after application of the common-mode subspace projection algorithm. Stimulus-induced artifacts were
removed from the original signals. (c) The scale of the magnetic signal amplitude of Fig. 4b was expanded. Neural magnetic signals were clear.

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Fig. 5. Waveforms of the neural magnetic field at the palmar area in response to digital nerve stimulation. (a) An X-ray image of the hand superimposed on the positions of thesensors. (b) The three-directional magnetic signals recorded by each sensor after index finger stimulation. Viewed from the dorsal side of the hand, the sensor array was under thepalmar side of the hand. Red traces are magnetic fields in the left–right direction (right is upward). Green traces are magnetic fields parallel to the nerve (proximal is upward).
Black traces are magnetic fields in the volar–dorsal direction (volar is upward). (c) The magnetic signals after stimulation of the middle finger. The magnetic fields are illustrated
as in (b). Red and black waveforms show biphasic waveforms and propagate in the distal to proximal direction. Green waveform shows triphasic waveforms.

Fig. 6. Time course of the spatial distribution of calculated currents in a representative case. (a) Index finger stimulation. (b) Middle finger stimulation. Currents at the palmar
area were calculated by UGRENS and superimposed on an X-ray image. The small white arrows indicate the direction of current at each position. Current intensity is shownby a color scale (red is higher). The leading component of the currents (downward-pointing red arrow) propagated proximally along the nerve, and the trailing component(upward-pointing red arrow) propagated later. Another component appeared on either side of the nerve and flowed perpendicularly between the leading and trailingcomponents (inward-pointing blue arrows). In addition, two other outward components were observed prior to the leading component and posterior to the trailingcomponent (outward-pointing blue arrows).

third metacarpal bone, which was ulnar (11.14 ± 5.7 mm ulnar at
the mid-level of the metacarpal bone, P = 0.012) from the index finger stimulation currents, until the hook of hamate level. Proximal
to the hook of hamate level, currents in response to the middle finger stimulation propagated along the same pathway as the index
finger stimulation currents.
3.3. Waveforms of the calculated currents at the virtual electrodes
We set the virtual electrodes along the nerve pathway and calculated the waveforms of the currents parallel to the conduction

pathway (Fig. 7b). In addition, the waveforms of the currents perpendicular to the conduction pathway were calculated at each virtual electrode, which was set 15 mm ulnar from the conduction
pathway (Fig. 7a). The upward peak in the waveform of the currents parallel to the conduction pathway is equivalent to the leading component in Fig. 6a, and the downward peak is equivalent to
the trailing component (Fig. 7b). The waveform of the currents perpendicular to the conduction pathway had three phasic waveforms(Fig. 7a). The first downward peak, the second upward peak, andthe third downward peak correspond to the outward current preceding the intra-axonal leading component, the inward currents

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Fig. 7. Waveforms of calculated currents at the ‘‘virtual electrodes” and sensory nerve action potentials (SNAPs) in response to the index finger stimulation. (a) Currents at
virtual electrodes (X mark) 15 mm lateral from the nerve pathway (upward is toward the nerve pathway). (b) Currents at virtual electrodes (white circle) along the nerve
pathway (the distal to proximal direction is upward). (c) Sensory nerve action potentials with orthodromic recording.

3.4. Comparison of the calculated currents and SNAPs
We compared the calculated current waveform with the SNAP
waveform in response to the index finger stimulation. In all fivecases, there was almost a perfect match between the peak latenciesof the inward current flowing perpendicularly to the conduction
pathway of the virtual electrode set at the same level as the recording electrode of SNAPs (Fig. 7a) and the negative peak latencies of
SNAP (Fig. 7c). The average conduction velocity calculated from thepeak latency of the SNAPs was 52.7 ± 3.5 m/s.
4. Discussion

Fig. 8. Waveforms of calculated currents at the ‘‘virtual electrodes” in response to
the middle finger stimulation. Currents are illustrated in a similar manner to Fig. 7.
(a) Currents at virtual electrodes (X mark) 15 mm lateral from the nerve pathway(upward is toward the nerve pathway). (b) Currents at virtual electrodes (whitecircle) along the nerve pathway (the distal to proximal direction is upward).

between the leading and trailing components, and the outward
current following the intra-axonal trailing component in Fig. 6a,respectively. The calculated waveform after the middle finger stimulation showed the same pattern as the index finger stimulation(Fig. 8). The currents parallel and perpendicular to the conduction
pathway were propagated in the distal to proximal direction inboth the index and middle finger stimulations.
From the currents flowing perpendicularly to the conduction
pathway, we calculated the maximum current value at the centerof the carpal tunnel area (the midline between the hook of hamatebone and the pisiform bone). The averages of the maximum current values of the five hands were 0.34 ± 0.08 nAm in responseto the index finger stimulation and 0.55 ± 0.43 nAm in responseto the middle finger stimulation. The average conduction velocities
calculated from the peak latency of the inward currents were 52.
6 ± 4.6 m/s (index finger) and 52.7 ± 3.5 m/s (middle finger).

In this study, the CSP algorithm removed stimulus-induced artifacts and enabled very early neuronal signals buried in stimulusinduced artifacts to be visualized. The current calculated fromthe artifact-removed signal comprised five current components.
The leading components and trailing components parallel to the
conduction pathway, the outward current preceding the leading
component, the inward currents between the leading and trailingcomponents, and the outward current following the trailing component are considered to respectively indicate the intra-axonal
leading current, the intra-axonal trailing current, the outward current generated from the membrane capacity current, the inward
sodium current resulting from depolarization, and the outward
current generated from the repolarization phase (Noble, 1966).
These currents showed characteristics of axonal activity, and thecurrents superimposed on the X-ray image were conducted along
the nerve pathway from the digital nerve to the median nerve.
The conduction pathway was displaced to the radial side at theforearm due to its pronation. Compared with SNAPs, the peaklatency of the inward current and the negative latency of SNAPs
matched, and the conduction velocity was almost the same.
From these results, we consider that we were able, for the firsttime, to detect neural magnetic signals by using the CSP method
and to visualize the current distribution derived from the axonal
activity around the carpal tunnel area. We found that MNG has sufficiently high spatial resolution to distinguish the digital nerve
activities of the middle and index fingers.
Fukuoka et al. (2004); (Tomori et al., 2010) reported that MNG
could identify the conduction block site with high spatial resolu-

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tion in animal experiments. MNG may be able to reduce the falsenegative rate of electrophysiological testing in carpal tunnel syndrome through diagnosis of the local conduction block, which isdifficult to detect by the conventional method. From the viewpointof a surgeon, superimposition of the conduction block site andmorphological information, such as that provided by a plain Xray or MR image, might be useful for surgical planning.
In addition, MNG might be able to visualize not only the currents
flowing across the membrane (outward current generated from themembrane capacity current, inward sodium current resulting fromdepolarization, and outward current generated from the repolarization phase), but also leading and trailing intra-axonal currents,
which cannot be evaluated by the conventional electrical potential
measurement. Thus far, it is unclear how the intra-axonal currentschange in pathological situations such as entrapment neuropathy,
but we plan to prove the clinical significance of the intra-axonal
currents by accumulating MNG data on patients in the future.

5. Conclusion
We succeeded in using MNG to noninvasively visualize electrophysiological activity at the palmar area, the carpal tunnel area, andthe wrist in response to digital nerve stimulation. Technicalimprovements comprising the removal of stimulus-induced artifacts and source analysis allowed us to calculate the current waveform at an arbitrary point and superimpose the nerve action
currents on morphological information such as X-ray images withhigh spatial resolution. For the axonal activity, MNG was able tovisualize the intra-axonal currents and the currents flowing acrossthe membrane. This system will become a novel method for evaluating peripheral nerve function and contribute to the diagnosis ofvarious peripheral neuropathies, as well as carpal tunnel syndrome.

Funding
This research was supported by the RICOH Company Grand
Number 1250000125012A210. The funding body played no rolein study design, data collection and analysis, preparation of themanuscript, or the decision to publish.

CRediT authorship contribution statement
Toru Sasaki: Data curation, Formal anlysis, Investigation,
Methodology, Resources, Writing - original draft. Shigenori
Kawabata: Funding acquisition, Methodology, Project administration, Writing - review & editing. Yuko Hoshino: Formal anlysis,
Methodology. Kensuke Sekihara: Formal anlysis, Visualization.
Yoshiaki Adachi: Validation, Writing - original draft. Miho Akaza:
Data curation, Formal analysis, Investigation, Methodology. Isamu
Ozaki: Methodology, Supervision, Validation, Writing - originaldraft. Koji Fujita: Conceptualization, Methodology. Akimoto
Nimura: Conceptualization, Methodology. Toshitaka Yoshii:
Conceptualization, Methodology. Yuki Miyano: Data curation,
Formal analysis, Investigation, Software. Yuki Mitani: Data curation, Formal analysis, Investigation, Software. Taishi Watanabe:
Data curation, Formal analysis, Investigation, Software. ShinjiSato: Data curation, Formal analysis, Investigation, Software. Sukchan Kim: Data curation, Formal analysis, Investigation, Software.
Atsushi Okawa: Project administration, Supervision.

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Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared
to influence the work reported in this paper.
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