A novel method to simultaneously record spinal cord electrophysiology and electroencephalography signals.

The brain and the spinal cord together make up the central nervous system (CNS). The functions of the human brain have been the focus of neuroscience research for a long time. However, the spinal cord is largely ignored, and the functional interaction of these two parts of the CNS is only partly understood. This study developed a novel method to simultaneously record spinal cord electrophysiology (SCE) and electroencephalography (EEG) signals and validated its performance using a classical resting-state study design with two experimental conditions: eyes-closed (EC) and eyes-open (EO). We recruited nine postherpetic neuralgia patients implanted with a spinal cord stimulator, which was modified to record SCE signals simultaneously with EEG signals. For both EEG and SCE, similar differences were found in delta- and alpha-band oscillations between the EC and EO conditions, and the spectral power of these frequency bands was able to predict EC/EO behaviors. Moreover, causal connectivity analysis suggested a top-down regulation in delta-band oscillations from the brain to the spinal cord. Altogether, this study demonstrates the validity of simultaneous SCE-EEG recording and shows that the novel method is a valuable tool to investigate the brain-spinal interaction. With this method, we can better unite knowledge about the brain and the spinal cord for a deeper understanding of the functions of the whole CNS.

Training sparse least squares support vector machines by the QR decomposition.

The solution of an LS-SVM has suffered from the problem of non-sparseness. The paper proposed to apply the KMP algorithm, with the number of support vectors as the regularization parameter, to tackle the non-sparseness problem of LS-SVMs. The idea of the kernel matching pursuit (KMP) algorithm was first revisited from the perspective of the QR decomposition of the kernel matrix on the training set. Strategies are further developed to select those support vectors which minimize the leave-one-out cross validation error of the resultant sparse LS-SVM model. It is demonstrated that the LOOCV of the sparse LS-SVM can be computed accurately and efficiently. Experimental results on benchmark datasets showed that, compared to the SVM and variants sparse LS-SVM models, the proposed sparse LS-SVM models developed upon KMP algorithms maintained comparable performance in terms of both accuracy and sparsity.

Brain oscillations reflecting pain-related behavior in freely moving rats.

Recording oscillatory brain activity holds great promise in pain research. However, experimental results are variable and often difficult to reconcile. Some of these inconsistencies arise from the use of hypothesis-driven analysis approaches that (1) do not assess the consistency of the observed responses within and across individuals, and (2) do not fully exploit information sampled across the entire cortex. Here, we address these issues by recording the electrocorticogram directly from the brain surface of 12 freely moving rats. Using a hypothesis-free approach, we isolated brain oscillations induced by graded nociceptive stimuli and characterized their relation to pain-related behavior. We isolated 4 responses, one phase-locked event-related potential, 2 non-phase-locked event-related synchronizations, and one non-phase-locked event-related desynchronization (ERD), in different frequency bands (δ/θ-ERD, θ/α-event-related synchronization, and gamma-band event-related synchronization). All responses except the δ/θ-ERD correlated with pain-related behavior at within-subject level. Notably, the gamma-band event-related synchronization was the only response that reliably correlated with pain-related behavior between subjects. These results comprehensively characterize the physiological properties of the brain oscillations elicited by nociceptive stimuli in freely moving rodents and provide a foundational work to improve the translation of experimental animal findings to human physiology and pathophysiology.

[Mechanisms and applications of transcutaneous electrical nerve stimulation in analgesia].

Transcutaneous electrical nerve stimulation (TENS), as a non-pharmacological and non-invasive analgesic therapy with low-cost, has been widely used to relieve pain in various clinical applications, by delivering current pulses to the skin area to activate the peripheral nerve fibers. Nevertheless, analgesia induced by TENS varied in the clinical practice, which could be caused by the fact that TENS with different stimulus parameters has different biological mechanisms in relieving pain. Therefore, to advance our understanding of TENS in various basic and clinical studies, we discussed (1) neurophysiological and biochemical mechanisms of TENS-induced analgesia; (2) relevant factors that may influence analgesic effects of TENS from the perspectives of stimulus parameters, including stimulated position, pulse parameters (current intensity, frequency, and pulse width), stimulus duration and used times in each day; and (3) applications of TENS in relieving clinical pain, including post-operative pain, chronic low back pain and labor pain. Finally, we propose that TENS may involve multiple and complex psychological neurophysiological mechanisms, and suggest that different analgesic effects of TENS with different stimulus parameters should be taken into consideration in clinical applications. In addition, to optimize analgesic effect, we recommend that individual-based TENS stimulation parameters should be designed by considering individual differences among patients, e.g., adaptively adjusting the stimulation parameters based on the dynamic ratings of patients' pain.

Learning Differentially Expressed Gene Pairs in Microarray Data.

To identify differentially expressed genes (DEGs) in analysis of microarray data, a majority of existing filter methods rank gene individually. Such a paradigm could overlook the genes with trivial individual discriminant powers but significant powers of discrimination in their combinations. This paper proposed an impurity metric in which the number of split intervals for each feature is considered as a parameter to be optimized for gaining maximal discrimination. The proposed method was first evaluated by applying to a synthesized noisy rectangular grid dataset, in which the significant feature pair which forms a rectangular grid pattern was successfully recognized. Furthermore, applying to the identification of DEGs on colon microarray data, the proposed method demonstrated that it could become an alternative to Fisher's test for the prescreening of genes which led to better performance of the SVM-RFE method.

Functional Reorganization of the Primary Somatosensory Cortex of a Phantom Limb Pain Patient.

Functional reorganization of the somatosensory system was widely observed in phantom limb pain patients. Whereas some studies demonstrated that the primary somatosensory cortex (S1) of the amputated limb was engaged with the regions around it, others showed that phantom limb pain was associated with preserved structure and functional organization in the former brain region. However, according to the law of use and disuse, the sensitivity of S1 of the amputated limb to pain-related context should be enhanced due to the adaptation to the long-lasting phantom limb pain experience. Here, we collected neurophysiological data from a patient with 21-year phantom limb pain using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) techniques. EEG data showed that both laser-evoked potentials (LEPs) and tactile-evoked potentials (TEPs) were clearly presented only when radiant-heat laser pulses and electrical pulses were delivered to the shoulder of the healthy limb, but not of the amputated limb. This observation suggested the functional deficit of somatosensory pathways at the amputated side. FMRI data showed that significant larger brain activations by painful rather than non-painful stimuli in video clips were observed not only at visual-related brain areas and anterior/mid-cingulate cortex, but also at S1 contralateral to the amputated limb. This observation suggested the increased sensitivity of S1 of the amputated limb to the pain-related context. In addition, such increase of sensitivity was significantly larger if the context was associated with the amputated limb of the patient. In summary, our findings provided novel evidence for a possible neuroplasticity of S1 of the amputated limb: in an amputee with long-lasting phantom limb pain, the sensitivity of S1 to pain-related and amputated-limb-related context was greatly enhanced.

Was it a pain or a sound? Across-species variability in sensory sensitivity.

Natural selection has shaped the physiological properties of sensory systems across species, yielding large variations in their sensitivity. Here, we used laser stimulation of skin nociceptors, a widely used technique to investigate pain in rats and humans, to provide a vivid example of how ignoring these variations can lead to serious misconceptions in sensory neuroscience. In 6 experiments, we characterized and compared the physiological properties of the electrocortical responses elicited by laser stimulation in rats and humans. We recorded the electroencephalogram from the surface of the brain in freely moving rats and from the scalp in healthy humans. Laser stimuli elicited 2 temporally distinct responses, traditionally interpreted as reflecting the concomitant activation of different populations of nociceptors with different conduction velocities: small-myelinated Aδ-fibres and unmyelinated C-fibres. Our results show that this interpretation is valid in humans, but not in rats. Indeed, the early response recorded in rats does not reflect the activation of the somatosensory system, but of the auditory system by laser-generated ultrasounds. These results have wide implications: retrospectively, as they prompt for a reconsideration of a large number of previous interpretations of electrocortical rat recordings in basic, preclinical, and pharmacological research, and prospectively, as they will allow recording truly pain-related cortical responses in rats.

Analyzing kernel matrices for the identification of differentially expressed genes.

One of the most important applications of microarray data is the class prediction of biological samples. For this purpose, statistical tests have often been applied to identify the differentially expressed genes (DEGs), followed by the employment of the state-of-the-art learning machines including the Support Vector Machines (SVM) in particular. The SVM is a typical sample-based classifier whose performance comes down to how discriminant samples are. However, DEGs identified by statistical tests are not guaranteed to result in a training dataset composed of discriminant samples. To tackle this problem, a novel gene ranking method namely the Kernel Matrix Gene Selection (KMGS) is proposed. The rationale of the method, which roots in the fundamental ideas of the SVM algorithm, is described. The notion of ''the separability of a sample'' which is estimated by performing [Formula: see text]-like statistics on each column of the kernel matrix, is first introduced. The separability of a classification problem is then measured, from which the significance of a specific gene is deduced. Also described is a method of Kernel Matrix Sequential Forward Selection (KMSFS) which shares the KMGS method's essential ideas but proceeds in a greedy manner. On three public microarray datasets, our proposed algorithms achieved noticeably competitive performance in terms of the B.632+ error rate.

Feasibility of a clinical chemical analysis approach to predict misuse of growth promoting hormones in cattle.

A study was performed to determine if targeted metabolic profiling of cattle sera could be used to establish a predictive tool for identifying hormone misuse in cattle. Metabolites were assayed in heifers (n = 5) treated with nortestosterone decanoate (0.85 mg/kg body weight), untreated heifers (n = 5), steers (n = 5) treated with oestradiol benzoate (0.15 mg/kg body weight) and untreated steers (n = 5). Treatments were administered on days 0, 14, and 28 throughout a 42 day study period. Two support vector machines (SVMs) were trained, respectively, from heifer and steer data to identify hormone-treated animals. Performance of both SVM classifiers were evaluated by sensitivity and specificity of treatment prediction. The SVM trained on steer data achieved 97.33% sensitivity and 93.85% specificity while the one on heifer data achieved 94.67% sensitivity and 87.69% specificity. Solutions of SVM classifiers were further exploited to determine those days when classification accuracy of the SVM was most reliable. For heifers and steers, days 17-35 were determined to be the most selective. In summary, bioinformatics applied to targeted metabolic profiles generated from standard clinical chemistry analyses, has yielded an accurate, inexpensive, high-throughput test for predicting steroid abuse in cattle.