Using Variations of Somatosensory Evoked Potentials to Quantify Spinal Cord Injury Level

doi:10.4236/eng.2013.510B020

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Engineering, 2013, 5, 99-102

http://dx.doi.org/10.4236/eng.2013.510B020 Published Online October 2013 (http://www.scirp.org/journal/eng)

Using Variations of Somatosensory Evoked Potentials to

Quantify Spinal Cord Injury Level

Hasan Mir1, Hasan Al-Nashash1, Douglas Kerr2, Angelo All2, Nitish Thakor2

1Department of E lectrical Engineering, American University of Sharjah, Sharjah, UAE

2Department of Neurology, Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, USA

Email: [email protected], [email protected], [email protected], [email protected], [email protected]

Received December 2012

ABSTRACT

Existing work indicates that the degree of variation of somatosensory evoked potential (SEP) signals between a healthy

spinal pathway and spinal pathway affected by spinal cord injury (SCI) can be used to evaluate the integrity of the spin-

al pathway. This paper develops a metric that exploits the time-do main features of SEP signals (relative amplitude, time

scaling, and time duration) in order to quantify the level of SCI. The proposed method is tested on actual SEP signals

collected from rodents afflicted with focal demyelination SCI. Results indicate that the proposed method provides a

robust assessment of the different degrees of demyelination in the spinal cord.

Keywords: Somatosensory Evoked Potential (SEP); Spinal Cord Injury (SC I); Signal Morphology

1. Introduction

The spinal cord provides a transmission pathway for

motor and sensory signals between the central and peri-

pheral nervous systems [1]. Thus, any spinal cord injury

(SCI) impairs signal transmission, resulting in sensory

and/or motor function loss. Many patients around the

world suffer from SCI. In the United States alone, it is

estimated that there are more than 250,000 SCI patients

[2]. Given the large number of SCI patients worldwide,

there is a clear need to develop methods for evaluating

the level of SCI. Such methods are important not only for

evaluating the effectiveness of therapeutic mechanisms,

but also in providing timely surg ica l in tervention. Indeed,

immediate treatment of even a small number of spared

fibers after incomplete SCI can greatly improve the pa-

tient’s quality of life.

SCI detection is commonly performed using electro-

physiological [3,4] and imaging [5,6] techniques. Imag-

ing based approaches such as MRI only reveal informa-

tion concerning the injury location and the anatomical

damage, but do not provide information about the func-

tional integrity of the spinal cord. Amongst electrophysi-

ological techniques used in SCI studies, the evoked po-

tential provides an assessment of the electrophysiological

response of the neural system to an external stimulus. In

particular, somatosensory evoked potentials (SEP),

which are cortical signals recorded in response to sensory

stimulation, are obtained by electrical stimulation of the

median nerve at the wrist or the posterior tibial nerve at

the ankle [7]. This technique can be used to monitor the

ongoing neurophysiological changes during the recovery

period after SCI. In [8], it was shown that the similarity

between a reference SEP signal taken from a healthy

spinal pathway and an SEP signal from an injured trans-

mission pathway is a strong indicator of the level of in-

jury. Thus, it provides a complement to qualitative, be-

haviorial based assessments such as the Basso, Beattie,

and Bresnahan (BBB) test [9].

Previous work [10-12] has been done to develop me-

trics for quantifying the level of SCI by comparing the

variation between SEP signals. While such methods

show promise, they do not explicitly account for the

temporal relationships that exist between the SEP signals

under comparison. In this paper, parameters relating the

time -domain variation between SEP signals collected on

rodents are used to evaluate the level of SCI. These pa-

rameters reflect differences in amplitude, duration, and

delay between the SEP signals, and can combined into a

single index to quantify the level of SCI.

The rest of this paper is organized as follows: Section

2 describes the protocol for collecting the SEP signals

from rodents. Section 3 describes metrics for quantifying

the level of SCI. Section 4 presents the results of apply-

ing the methods in Section 3 to the data descr ib ed in S ec-

tion 2.

2. Protocol and Data Collection

In accordance with the Rodent Survival Surgery Manual,

and with approval from the Institutional Animal Care and

H. MIR ET AL.

100

Use Committee at the Johns Hopk ins Un iversity, the SE P

data used in this paper was obtained by inducing focal

demyelination lesion in dorsal pathways of rodents’s

spinal cord. Recombinant myelin oligodendrocyte glyco-

protein (MOG) corresponding to the N-terminal se-

quence of rodent MOG amino acids 1 to 125 (MOG1-

125) was emulsified in incomplete Freund’s adjuvant

(S igma-Aldrich) as 1:1 mixture (Imject IFA; Pierce). 100

μl = 50 μg of this emulsified MOG1-125 (co urtesy o f Dr.

Sha Mi, Biogen-Idec, Cambridge, MA) was injected sub-

cutaneously near the base of the tail of each rodent at 2

contralateral sites (50 μl per area to minimize irritation to

the skin). The rodents were subjected to T9 laminectomy

and either (2 × 2 μl) cytokines (250 ng of TNF-α, 150U

of INF-γ and 40 ng of IL-6) or (1 µg) ethidium bromide

or a combination of the two was injected into dorsal

white matter using Hamilton needle (31 G). This causes

inflammatory demyelinating lesions, similar to the active

demyelinating plaques that characterize multiple sclero-

sis (MS). Every seven days thereafter, SEP recordings

were performed. The signal-to-noise ratio of the SEP

signals was improved via averaging.

3. Quantification Metrics

Consider two signals: a reference signal

)(

and a test

signal

)(

, both defined over some time interval

ttt ≤≤

, as shown in F igure 1. It may be observed that

compared to the reference signal

)(

, the test signal

)(

2ts

exhibits the following characteristics:

1) The amplitude scale has changed;

2) The time scale has changed;

3) The relative time positions have shifted.

Various methods have been proposed in the literature

to quantify the variation between two signals. Amongst

the simplest methods, the Euclidean (

) distance pro-

vides a simple measure of signal v ariation. However, it is

not appropriate for cases when the two signals are not

temporally aligned, as in Figure 1. A more sophisticated

measure is provided by the Pearson correlation coeffi-

cient (PCC), defined as:

( )( )

( )

( )( )

212

12 22

,= dd

s tstt

stst sttstt

∫

∫∫

(1)

The PCC is a measure of linear dependence, and is

commonly used as a metric to quantify the similarity

between two signals. A key weakness of the PCC, how-

ever, is its insensitivity to amplitud e differences.

In this paper, a more robust means to characterize the

variation between two signals is proposed. Towards this

end, consider again the effects shown in Figure 1. Let

the transformation parameters

),,(

τβα

denote the

change in the amplitude scale, time scale, and relative

Figure 1. An illustration of signal variations between a re-

ference and test signal.

time position, respectively. As such, an approximation

)(

of the reference signal

)(

may be generated by

transforming the test signal

)(

in the following

manner:

( )











−

sts 21 =

(2)

In order to determine the degree to which trans-

formation parameters approximate the reference signal

)(

, define the error signal

( )( )

tstste

=)( −

(3)

The energy of the error signal will be used to define

the cost function

=( )d

J ett

∫

(4)

A suitable solution for the value of the transformation

parameters

),,(

τβα

as such may be found through

solving the optimization pr oblem

( )

stsJ

,,,,

argmin

argmin 











−

−

∫

τβατβα

(5)

The solution to (5) can be found using standard

optimization algorithms such as the Nelder-Mead method

[13].

To quantify the degree of SCI, a measure is proposed

that combines the deviation of the transformation para-

meters from their ideal values. In the ideal case of no

signal warping, the amplitude and time scale parameters

and

have a value of one, while the time shift

parameter τ has a value of zero. As such, a suitable

metric to quantify the overall degree of signal variation

(and hence the level of SCI) is:

( )( )()

τβαλ

+−+− 11=,

tsts

(6)

H. MIR ET AL.

101

4. Experimental Results

Adult female Fischer rodents were injected with rMOG,

after which SEP signals from the forelimb (

)(

) and

hindlimb (

)(

) were collected at various stages. The

forelimb SEP signal is used to provide a reference

(healthy) signal, since injury is inflicted below the

forelimb and thus does not impair the spinal pathway. In

view of the level of SCI, the injury severity is classified

into three grades which are termed moderate, severe, and

very severe. For the results in this section, the value of

the PCC

( )()()

tsts

and the proposed quantification

metric

( )()()

tsts

are computed for SEP signals

collected on a representative rodent sample and discussed

below.

Figures 2-4 show SEP signals for various levels of

SCI. In addition to showing the forelimb and hindlimb

SEP signals

)(

and

)(

, the transformed hindlimb

SEP signal











−

is also shown, where the para-

Figure 2. SEP signals for moderate SCI.

Figure 3. SEP signals for severe SCI.

Figure 4. SEP signals for very severe SCI.

meters

),,(

τβα

were computed as the solution to (5)

using the implementation of the Nelder-Mead method

provided by the MATLAB function f minsearch. It can be

seen that the forelimb SEP signal is fairly stable across

all injury levels, lending credence to its use as a reference

SEP signal. Conversely, because SCI is inflicted above

the hindlimb, the corresponding SEP signal exhibits a

large variation across injury levels. These variations can

be exploited to quantify the level of SCI.

It may also be noted in Figures 2-4 that that the

optimization in (5) produces a solution that naturally

matches the position and width the characteristic peak

that is present in all SEP signals. Even though the

amplitude scale parameter α is perhaps the most direct

indicator of injury level, the time scale and time shift

parameters also contribute to quantifying the severity of

SCI. Table 1 provides a comparison of the PCC

( )()()

tsts

and the proposed quantification metric

( )()()

tsts

for the various level of SCI. It is apparent

that the PCC yields non-monotonic and similar values

across injury levels, r esu lting from the insens itiv ity of the

PCC to amplitude differences. In comparison, the pro-

posed quantification metric yields values that are re-

presentative of the degree of time-domain variation be-

tween the signals. This gives the proposed metric an

advantage in clinical settings, since the values it yields

are directly related to the level of SCI, and the relatively

large spread of values aids in performing a quick and

simple injury assessment.

5. Conclusions

The level of SCI is indicated by the degree of time-

domain variations between the SEP signals from a

healthy spinal pathway and an injured spinal pathway.

Conventional metrics for quantifying the variation be-

tween two signals, such as the PCC, do not account for

important signal characteristics and can yield erroneous

results. The quantification method developed in this

paper uses the differences in amplitude, duration, and

delay between SEP signals, to quantify the level of SCI.

Results for SEP signals collected on rodents show that

the proposed quantification metric yields values that

reflect the degree of variation between forelimb and

hindlimb signals, and can thus provide a robust eva-

luation of the level of SCI.

Table 1. Comparison of SCI Levels and metric.

Metric Moderate Severe Very Severe

( )( )()

tsts 21 ,

0.62 0.77 0.69

( )( )()

tsts 21 ,

0.64 1.80 6.88

46810 12 14

-50

100

150

200

Time (ms)

Amplitude (

Forelimb SEP

Hindlimb SEP

Transformed Hindli m b SEP

468 10 12 14

-50

100

150

200

Time (ms)

Amplitude (

Forelimb SEP

Hindlimb SEP

Transformed Hindlim b SEP

468 10 12 14

-50

100

150

200

Time (ms)

Amplitude (

µ V)

Forelimb SEP

Hindlimb SEP

Transformed Hindlim b SEP

H. MIR ET AL.

102

6. Acknowledgements

This work was supported b y the Maryland Ste m Cell Re-

search Fund 2007-MSCRFII-0159-00 .

REFERENCES

[1] E. Niedermeyer, “Electroencephalography, Basic Principles,

Clinical Applications, and Related Fields,” 4th Edition,

Lippincott, Williams and Wilkins, Phil, 1999.

[2] R. Spears and A. Holtz, “Spinal Cord Injury,” Oxford

University Press, Oxford.

[3] W. Truccolo, “Trial-to-Trial Variabil ity of Cortical Evok-

ed Responses: Implications for the Analysis of Functional

Connectivity,” Clinical Neurophysiology, Vol. 113, 2002,

pp. 206-226.

http://dx.doi.org/10.1016/S1388-2457(01)00739-8

[4] M. Nuwer, “Spinal c ord monitoring,” Muscle Nerve, Vol.

22, 1999, pp. 1620-1630.

http://dx.doi.org/10.1002/(SICI)1097-4598(199912)22:12

<1620::AID-MUS2>3.0.CO;2-1

[5] M. Shepherd and M. Bracken, “Magnetic Resonance

Imaging and Neurological Recovery in Acute Spinal Cord

Injury: Observations from the National Acute Spinal Cord

Injury Study 3,” Spinal Cord, Vol. 37, 1999, pp. 833-837.

http://dx.doi.org/10.1038/sj.sc.3100927

[6] O. A. Glenn and J. Barkovich, “Magnetic Resonance Ima-

ging of the Fetal Brain and Spine: An Increasingly Im-

portant Tool in Prenatal Diagnosis: Part 2,” American

Journal of Neuroradiology, Vol. 27, 2006, pp. 1807-

1814.

[7] M. Nuwer, “Fundamentals of Evoked Potentials and

Common Clinical Applications Today,” Electroencepha-

lography and Clinical Neurophysiology, Vol. 106, 1998,

pp. 142-148.

http://dx.doi.org/10.1016/S0013-4694(97)00117-X

[8] H. Al-Nashash, et al., “Spinal Cord Injury Detection and

Monitoring Using Spectral Coherence,” IEEE Transac-

tions on Biomedical Engineering, Vol. 56, No. 8, 2009,

pp. 1971-1979.

http://dx.doi.org/10.1109/TBME.2009.2018296

[9] D. Basso, M. Beattie and J. Bresnahan, “A Sensitive and

Reliable Locomotor Rating Scale for Open Field Testing

in Rats,” Journal of Neurotrauma, Vol. 12, 1995, pp. 1-

21. http://dx.doi.org/10.1089/neu.1995.12.1

[10] H. Mir, H. Al-Nashash, D. Kerr, N. Thakor and A. All,

“Spinal Cord Injury Evaluation Using Morphological

Difference of Somatosensory Evoked Potentials,” Pro-

ceedings of the 2011 International Conference on Bioin-

formatics and Biomedical Engineering, 2011, pp. 1-4.

[11] H. Mir, H. Al-Nashash, D. Kerr, N. Thakor and A. All,

“Histogram Based Quantification of Spinal Cord Injury

Level Using Somatosensory Evoked Potentials,” Pro-

ceedings of 2010 International Conference of the IEEE

Engineering in Medicine and Biology Society, 2010, pp.

4942-4945.

[12] H. Mir, H. Al-Nasha sh, A. All and N. Thakor, “Quantifi-

cation of Spinal Cord Injury Level Using Somatosensory

Evoked Potentials,” Proceedings of the 2010 Internation-

al Conference on Bioinformatics and Biomedical Engi-

neering, 2010, pp. 1-5.

http://dx.doi.org/10.1109/ICBBE.2010.5515579

[13] M. Powell, “On Search Directions for Minimization Al-

gorithms,” Mathematical Programming, Vol . 4, 1973, pp.

4193-4201. http://dx.doi.org/10.1007/BF01584660