Component analysis of somatosensory evoked potentials for identifying spinal cord injury location

Identifying Intraoperative Spinal Cord Injury Location from Somatosensory Evoked Potentials' Time-Frequency Components

Excessive distraction in corrective spine surgery can lead to iatrogenic distraction spinal cord injury. Diagnosis of the location of the spinal cord injury helps in early removal of the injury source. The time-frequency components of the somatosensory evoked potential have been reported to provide information on the location of spinal cord injury, but most studies have focused on contusion injuries of the cervical spine. In this study, we established 19 rat models of distraction spinal cord injury at different levels and collected the somatosensory evoked potentials of the hindlimb and extracted their time-frequency components. Subsequently, we used k-medoid clustering and naive Bayes to classify spinal cord injury at the C5 and C6 level, as well as spinal cord injury at the cervical, thoracic, and lumbar spine, respectively. The results showed that there was a significant delay in the latency of the time-frequency components distributed between 15 and 30 ms and 50 and 150 Hz in all spinal cord injury groups. The overall classification accuracy was 88.28% and 84.87%. The results demonstrate that the k-medoid clustering and naive Bayes methods are capable of extracting the time-frequency component information depending on the spinal cord injury location and suggest that the somatosensory evoked potential has the potential to diagnose the location of a spinal cord injury.

A Prediction Model for Normal Variation of Somatosensory Evoked Potential During Scoliosis Surgery

Somatosensory evoked potential (SEP) has been commonly used as intraoperative monitoring to detect the presence of neurological deficits during scoliosis surgery. However, SEP usually presents an enormous variation in response to patient-specific factors such as physiological parameters leading to the false warning. This study proposes a prediction model to quantify SEP amplitude variation due to noninjury-related physiological changes of the patient undergoing scoliosis surgery. Based on a hybrid network of attention-based long-short-term memory (LSTM) and convolutional neural networks (CNNs), we develop a deep learning-based framework for predicting the SEP value in response to variation of physiological variables. The training and selection of model parameters were based on a 5-fold cross-validation scheme using mean square error (MSE) as evaluation metrics. The proposed model obtained MSE of 0.027[Formula: see text][Formula: see text] on left cortical SEP, MSE of 0.024[Formula: see text][Formula: see text] on left subcortical SEP, MSE of 0.031[Formula: see text][Formula: see text] on right cortical SEP, and MSE of 0.025[Formula: see text][Formula: see text] on right subcortical SEP based on the test set. The proposed model could quantify the affection from physiological parameters to the SEP amplitude in response to normal variation of physiology during scoliosis surgery. The prediction of SEP amplitude provides a potential varying reference for intraoperative SEP monitoring.

Application of electrophysiological measures in degenerative cervical myelopathy

Degenerative cervical myelopathy (DCM) is one of the leading causes of progressive spinal cord dysfunction in the elderly. Early diagnosis and treatment of DCM are essential to avoid permanent disability. The pathophysiology of DCM includes chronic ischemia, destruction of the blood–spinal cord barrier, demyelination, and neuronal apoptosis. Electrophysiological studies including electromyography (EMG), nerve conduction study (NCS), motor evoked potentials (MEPs) and somatosensory evoked potentials (SEPs) are useful in detecting the presymptomatic pathological changes of the spinal cord, and thus supplementing the early clinical and radiographic examinations in the management of DCM. Preoperatively, they are helpful in detecting DCM and ruling out other diseases, assessing the spinal cord compression level and severity, predicting short- and long-term prognosis, and thus deciding the treatment methods. Intra- and postoperatively, they are also useful in monitoring neurological function change during surgeries and disease progression during follow-up rehabilitation. Here, we reviewed articles from 1979 to 2021, and tried to provide a comprehensive, evidence-based review of electrophysiological examinations in DCM. With this review, we aim to equip spinal surgeons with the basic knowledge to diagnosis and treat DCM using ancillary electrophysiological tests.

Different Time-frequency Distribution Patterns of Somatosensory Evoked Potentials in Dual- and Single-level Spinal Cord Compression

Among patients with cervical myelopathy, the most common level of stenosis at spinal cord of all ages was reported to be between cervical levels C5-6. Previous studies found that time-frequency components (TFCs) of somatosensory evoked potentials (SEPs) possess location information of spinal cord injury (SCI) in single-level deficits in the spinal cord. However, the clinical reality is that there are multiple compressions at multiple spinal cord segments. This study proposed a new algorithm to differentiate distribution patterns of SEP TFCs between the dual-level compression and the corresponding single-level compression, which is potentials in providing precise diagnosis of cervical myelopathy. In the present animal study, a group of rats with dual-level compressive (C5+6) injury to cervical spinal cord was investigated. SEPs were collected at 2 weeks after surgery, while SEP TFCs were calculated. The SEP TFCs under dual-level compression were compared to an existent dataset with one sham control group and three single level compression groups at C4, C5, C6. Behavioral evaluation showed very similar scale of injury severity between individual rats, while histology evaluation confirmed the precise location of injury. According to time-frequency distribution patterns, it showed that the middle-energy components of dual-level showed similar patterns as that of each single-level group. In addition, the low-energy components of the dual-level C5+6 group had the highest correlation with C5 (R=0.3423, p<0.01) and C6 (R=0.4000, p<0.01) groups, but much lower with C4 group (R=0.1071, p=0.012). These results indicated that SEP TFCs components possess information regarding the location of neurological lesion after spinal cord compression. It preliminarily demonstrated that SEP TFCs are likely a useful measure to provide location information of neurological lesions after compression SCI.

Time-Frequency Representation of Motor Evoked Potentials in Brain Tumor Patients

Background: The integrity of the motor system can be examined by applying navigated transcranial magnetic stimulation (nTMS) to the cortex. The corresponding motor-evoked potentials (MEPs) in the target muscles are mirroring the status of the human motor system, far beyond corticospinal integrity. Commonly used time domain features of MEPs (e.g., peak-to-peak amplitudes and onset latencies) exert a high inter-subject and intra-subject variability. Frequency domain analysis might help to resolve or quantify disease-related MEP changes, e.g., in brain tumor patients. The aim of the present study was to describe the time-frequency representation of MEPs in brain tumor patients, its relation to clinical and imaging findings, and the differences to healthy subject.

Methods: This prospective study compared 12 healthy subjects with 12 consecutive brain tumor patients (with and without a paresis) applying nTMS mapping. Resulting MEPs were evaluated in the time series domain (i.e., amplitudes and latencies). After transformation into the frequency domain using a Morlet wavelet approach, event-related spectral perturbation (ERSP), and inter-trial coherence (ITC) were calculated and compared to diffusion tensor imaging (DTI) results.

Results: There were no significant differences in the time series characteristics between groups. MEPs were projecting to a frequency band between 30 and 300 Hz with a local maximum around 100 Hz for both healthy subjects and patients. However, there was ERSP reduction for higher frequencies (>100 Hz) in patients in contrast to healthy subjects. This deceleration was mirrored in an increase of the inter-peak MEP latencies. Patients with a paresis showed an additional disturbance in ITC in these frequencies. There was no correlation between the CST integrity (as measured by DTI) and the MEP parameters.

Conclusion: Time-frequency analysis may provide additional information above and beyond classical MEP time domain features and the status of the corticospinal system in brain tumor patients. This first evaluation indicates that brain tumors might affect cortical physiology and the responsiveness of the cortex to TMS resulting in a temporal dispersion of the corticospinal transmission.

A translational study of somatosensory evoked potential time-frequency components in rats, goats, and humans

Somatosensory evoked potentials (SEPs) have been widely used to assess neurological function in clinical practice. A good understanding of the association between SEP signals and neurological function is helpful for precise diagnosis of impairment location. Previous studies on SEPs have been reported in animal models. However, few studies have reported the relationships between SEP waveforms in animals and those in humans. In this study, we collected normal SEP waveforms and decomposed them into specific time-frequency components (TFCs). Our results showed three stable TFC distribution regions in intact goats and rats and in humans. After we induced spinal cord injury in the animal models, a greater number of small TFC distribution regions were observed in the injured goat and rat groups than in the normal group. Moreover, there were significant correlations (P < 0.05) and linear relationships between the main SEP TFCs of the human group and those of the goat and rat groups. A stable TFC distribution of SEP components was observed in the human, goat and rat groups, and the TFC distribution modes were similar between the three groups. Results in various animal models in this study could be translated to future clinical studies based on SEP TFC analysis. Human studies were approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster (approval No. UM 05-312 T/975) on December 5, 2005. Rat experiments were approved by the Committee on the Use of Live Animals in Teaching and Research of Li Ka Shing Faculty of Medicine of the University of Hong Kong (approval No. CULART 2912-12) on January 28, 2013. Goat experiments were approved by the Animal Ethics Committee of Affiliated Hospital of Guangdong Medical University (approval No. GDY2002132) on March 5, 2018.

Progression Prediction of Mild Cervical Spondylotic Myelopathy by Somatosensory-evoked Potentials

STUDY DESIGN

Retrospective study to correlate classification of somatosensory-evoked potentials (SEPs) with symptomatic progress of patients with mild cervical spondylotic myelopathy (CSM).

OBJECTIVE

The aim of this study was to evaluate the usefulness of SEPs for predicting symptomatic progress of mild CSM.

SUMMARY OF BACKGROUND DATA

SEPs have been used for clinical diagnosis and intraoperative neuromonitoring in patients with CSM. However, the prognostic value of SEPs in predicting the progression of CSM remains unclear.

METHODS

A total of 200 patients with a clinical diagnosis of mild CSM were enrolled between September 2014 and February 2018. All patients received clinical assessment with the modified Japanese Orthopedic Association scale (mJOA), magnetic resonance imaging, and SEP tests in the first clinical visit and at 1-year follow-up. A classification of upper and lower limbs SEP was developed. At 1-year follow-up, patients with symptom decline >2 points in mJOA were considered progressive myelopathy cases. The relationship of progressive myelopathy and classifications of SEP was investigated.

RESULTS

Fifty-four of 200 cases presented with progressive myelopathy. The incidence of progressive myelopathy was 2.6%, 27.7%, 23.8%, 86.7%, and 100% in Class I, II, III, IV, and V of upper SEPs, respectively, and 18.8%, 39.4%, 42.3%, and 62.5% in Class I, II, III, and IV of lower SEPs, respectively. For the combination classification of upper and lower SEPs, the incidence of progressive myelopathy was 0%, 13.7%, 24.3%, 91.1%, and 100% in Class I, II, III, IV, and V, respectively. There was a significant correlation of the incidence of progressive myelopathy with SEP classification for the upper SEPs (r = 0.94, P < 0.01) and the combination SEPs (r = 0.95, P < 0.01).

CONCLUSION

The incidence of progressive degenerative myelopathy increased with the upper and combination SEP classifications. Thus, classification of SEPs could predict the clinical decline in mJOA in CSM, reflecting the probability of worsening of myelopathy.

LEVEL OF EVIDENCE

4.