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Objective: This study aimed to develop a robust and data driven automatic motion artifacts (MA) removal technique from electrodermal activity (EDA) signal. Methods: we proposed a deep convolutional autoencoder (DCAE) approach for automatic MA removal in EDA signals. Our model was trained using several publicly available datasets that were collected using a wide variety of stimuli to cause EDA reactions; the sample size was large ($\mathbf{N}\ = \ 385\ \text{subjects}$). We trained and validated our DCAE network using both Gaussian white noise (GWN) and realistic MA data records collected using a novel circuitry in our lab. We further evaluated and compared the performance of our DCAE model with the existing methods on two independent and unseen datasets called Chon lab motion artifact dataset II (CMAD II) and central nervous system oxygen toxicity dataset (CNS-OT). Results: Our DCAE model showed significantly higher signal-to-noise-power-ratio improvement ($\mathbf{SN}{\mathbf{R}_{\mathbf{imp}}}$) and lower mean squared error ($\mathbf{MSE}$) when compared with that of the three previous methods (averaged $\boldsymbol{S\!N\!R}_\text{imp} = 35.25\,{\text{dB}}$ , and ${\boldsymbol{M\!S\!E\ }} = 0.028$ on the MA-corrupted data). Moreover, the reconstructed EDAs from the CMAD II dataset had a mean correlation value of 0.78 (statistically significantly higher when compared with other methods) with the reference clean data from the motionless hand, whereas the raw MA-corrupted data had a correlation value of only 0.68. Conclusion: The results presented in the paper indicates that our DCAE can remove MAs with higher intensity where the existing methods fails. Significance: Proposed DCAE model can be used to recover a significant amount of otherwise discarded EDA data. [ABSTRACT FROM AUTHOR]

Electroencephalogram(EEG) signals are generated from central nervous system which are difficult to disguise, leading to its popularity in emotion recognition. Recently,semi-supervisedlearning exhibits promisingemotion recognition performance by involving unlabeled EEG data into model training. However, if we first build a graph to characterize the sample similarities and then perform label propagation on this graph, these two steps cannotwell collaborate with each other. In this paper, we propose an OptimalGraph coupledSemi-Supervised Learning (OGSSL) model for EEG emotion recognition by unifying the adaptive graph learning and emotion recognition into a single objective. Besides, we improve the label indicator matrix of unlabeledsamples in order to directly obtain theiremotional states. Moreover, the key EEG frequency bands and brain regions in emotion expression are automatically recognized by the projectionmatrix of OGSSL. Experimental results on the SEED-IV data set demonstrate that 1) OGSSL achieves excellent average accuracies of 76.51%, 77.08% and 81.29% in three cross-sessionemotion recognition tasks, 2) OGSSL is competent for discriminative EEG feature selection in emotion recognition, and 3) the Gamma frequency band, the left/righttemporal, prefrontal,and (central) parietal lobes are identified to be more correlated with the occurrence of emotions. [ABSTRACT FROM AUTHOR]

Adult hippocampal neural stem/progenitor cells (AHPCs), which are self-renewing multipotent progenitors that can differentiate into neurons, astrocytes, and oligodendrocytes, are suitable as a central nervous system (CNS) molecular model, and the formation of 3D AHPC neurospheroids are potentially suitable as an in vitro brain model. In this paper we report a new microfluidic chip to culture AHPC neurospheroids (NSs-AHPC) inside culture-chambers on chip. After cell fixation and immunostaining were conducted, the fluorescence images of NSs-AHPC were analyzed. It has been found that the AHPCs comprising neurospheroids remained highly viable. Cell proliferation and neuronal differentiation have also been observed, indicating the feasibility of NSs-AHPC as the in vitro brain model on chip. Given its simple-to-use, low-cost, and orderly arranged culture-chambers, this type of chip is particularly suitable for culturing and analyzing multiple in vitro brain models in an efficient manner. [2021-0199] [ABSTRACT FROM AUTHOR]

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that is used for treating various neurological disorders such as major depressive disorder. TMS has been gaining popularity in the field of neurostimulation of the cerebellum, since the cerebellum is a complex structure connected with almost the entire central nervous system and TMS has promise for non-invasively probing cerebellar function. Recent studies have discovered that the cerebellum plays an important role not only in motor planning and behavior but also in the cognitive domain. However, few studies have explored how different coil designs and anatomical variations affect the effectiveness of cerebellar TMS. Therefore, in this paper, we investigated the effects of cerebellar TMS with different coil designs positioning on several locations. Finite-element modeling was conducted with coil and D-B80 coil. Each coil was positioned in the center, 1 and 3 cm to the left of center of the cerebellum, and all the locations were tangential to the scalp at a distance of 5 mm. Furthermore, 50 MRI derived head models were used in the computer modeling to examine how anatomical variations affect the distribution and intensity of electric field in cerebellar TMS. [ABSTRACT FROM AUTHOR]

The muscle synergy theory has been widely used to assess the modular organization of the central nervous system (CNS) during human locomotion. The pre-processing approach applied to the surface electromyographic (sEMG) signals influences the extraction of muscle synergies. The aim of this contribution is to assess the improvements in muscle synergy extraction obtained by using an innovative pre-processing approach. We evaluate the improvement in terms of the possible variation in the number of muscle synergies, of the intra-subject consistency, of the robustness, and of the interpretability of the results. The pre-processing approach presented in this paper is based on the extraction of the muscle principal activations (muscle activations strictly necessary to accomplish a specific biomechanical task) from the original sEMG signals, to then obtain muscle synergies using principal activations only. The results herein presented show that the application of this novel approach for the extraction of the muscle synergies provides a more robust and easily interpretable description of the modular organization of the CNS with respect to the standard pre-processing approach. [ABSTRACT FROM AUTHOR]

Parkinson’s disease (PD) is a neurodegenerative disorder of the central nervous system and one of its key symptoms is rest tremor. Deep brain stimulation (DBS) effectively suppresses rest tremor in Parkinson’s disease. Despite being a successful treatment option, its underlying principle and the mechanism by which it attenuates tremors is not yet fully understood. Since existing methods for tuning DBS parameters are largely trial and error, understanding how DBS works can help to reduce time and costs, and could also ultimately lead to better treatment strategies for PD. In this paper, we set out to analyze how a high-frequency stimulation applied through DBS can help reduce the low-frequency rest tremors observed in PD patients. We identify key elements in the sensorimotor loop (the feedback loop consisting of sensory feedbacks and motor responses) that play a role in the interaction of high-frequency DBS signal and the low-frequency tremor. Based on the analysis of these elements, we draw insights about the working of DBS and the role of frequency and the nature of stimulation. We verify these observations with numerical examples and a bench top experimental example. [ABSTRACT FROM PUBLISHER]

Serving as peers in the central nervous system, neurons make use of two communication paradigms, electrochemical, and molecular. Owing to their effective coordination of all the voluntary and involuntary actions of the body, an intriguing neuronal communication nominates as a potential paradigm for nano-networking. In this paper, we propose an alternative representation of the neuron-to-neuron communication process, which should offer a complementary insight into the electrochemical signals propagation. To this end, we apply communication-engineering tools and abstractions, represent information about chemical and ionic behavior with signals, and observe biological systems as input–output systems characterized by a frequency response. In particular, we inspect the neuron-to-neuron communication through the concepts of electrochemical communication, which we refer to as the intra-neuronal communication due to the pulse transmission within the cell, and molecular synaptic transmission, which we refer to as the inter-neuronal communication due to particle transmission between the cells. The inter-neuronal communication is explored by means of the transmitter, the channel, and the receiver, aiming to characterize the spiking propagation between neurons. Reported numerical results illustrate the contribution of each stage along the neuronal communication pathway, and should be useful for the design of a new communication technique for nano-networks and intrabody communications. [ABSTRACT FROM AUTHOR]

Objective: Neuronal networks are fundamental structures for information processing in the central nervous system. This processing function is severely impaired by abnormal axonal conduction from changes in functional ion channel expression. The evaluation of axonal conduction properties can be effective in the early diagnosis of information-processing abnormalities. However, little is known about functional ion channel expression in axons owing to lack of an appropriate method. In this study, we developed a device to measure changes in axonal conduction properties by selective pharmacological stimulation for the functional evaluation of Na channels expressed in axons. Methods: Axons of rat cortical neurons were guided across a pair of electrodes through microtunnel structures by employing surface patterning. Results: The developed device detected more than 50 axons while recording for 10 min. The conduction delay along the axons decreased by 22.5% with neuron maturation. Tetrodotoxin and lidocaine (Na channel blockers) increased the conduction delay in a concentration-dependent manner depending on their working concentrations, indicating the effectiveness of the device. Finally, selective Na channel blockers for various Na channel subtypes were used. Phrixotoxin, a Nav1.2 blocker, markedly increased the conduction delay, suggesting that Nav1.2 is functionally expressed in the unmyelinated axons of the cerebral cortex. Conclusion: These results show that our device is feasible for the high-throughput functional evaluation of Na channel subtypes in axons. Significance: The results obtained can contribute to the understanding of the pathogenic mechanisms of neurological diseases that involve changes in the functional expression states of ion channels in axons. [ABSTRACT FROM AUTHOR]

Objective: The quantification of functional brain–heart interplay through the dynamics of the central and autonomic nervous systems may provide effective biomarkers for cognitive, emotional, and autonomic state changes. Despite several computational models were proposed to this end, none provides a directional estimation of such interplay in a time-resolved and probabilistic fashion. Methods: In this study, a multivariate inhomogeneous point-process model for heartbeat dynamics is employed to derive subject-specific, time-resolved, functional estimates of the directional interplay occurring from the brain to the heart, whose activity is represented by electroencephalography and R-peaks intervals series. An inverse-Gaussian probability density function is used to predict heartbeat events as a function of neural dynamics, which is modeled as an exogenous input to the autoregressive cardiac dynamics. Results: The performance is evaluated using heart rate variability and electroencephalography series gathered from 24 healthy volunteers undergoing a cold-pressor test, and the modeling goodness-of-fit is assessed through the time-rescaling theorem. The results suggest that cortical dynamics drives heartbeat series with specific time delays in the range of 30s to 60s and 90s to 120s from the peripheral thermal stress onset. Conclusion: The proposed framework provides novel insights in human neurophysiology, exploiting a fully probabilistic definition of the continuous functional brain–heart interplay. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS. [ABSTRACT FROM AUTHOR]

Advances in magnetic resonance (MR) imaging techniques enable the accurate measurements of cerebrospinal fluid (CSF) flow in the human brain. In addition, image reconstruction tools facilitate the collection of patient-specific brain geometry data such as the exact dimensions of the ventricular and subarachnoidal spaces (SAS) as well as the computer-aided reconstruction of the CSF-filled spaces. The solution of the conservation of CSF mass and momentum balances over a finite computational mesh obtained from the MR images predict the patients' CSF flow and pressure field. Advanced image reconstruction tools used in conjunction with first principles of fluid mechanics allow an accurate verification of the CSF flow patters for individual patients. This paper presents a detailed analysis of pulsatile CSF flow and pressure dynamics in a normal and hydrocephalic patient. Experimental CSF flow measurements and computational results of flow and pressure fields in the ventricular system, the SAS and brain parenchyma are presented. The pulsating CSF motion is explored in normal and pathological conditions of communicating hydrocephalus. This paper predicts small transrnantle pressure differences between lateral ventricles and SASs (~ 10 Pa). The transmantle pressure between ventricles and SAS remains small even in the hydrocephalic patient (~ 30 Pa), but the ICP pulsatility increases by a factor of four. The computational fluid dynamics (CFD) results of the predicted CSF flow velocities are in good agreement with Cine MRI measurements. Differences between the predicted and observed CSF flow velocities in the prepontine area point towards complex brain-CSF interactions. The paper presents the complete computational model to predict the pulsatile CSF flow in the cranial cavity. [ABSTRACT FROM AUTHOR]

This paper reports a novel method to track brain shift using a laser-range scanner (LRS) and nonrigid registration techniques. The LRS used in this paper is capable of generating textured point-clouds describing the surface geometry/intensity pattern of the brain as presented during cranial surgery. Using serial LRS acquisitions of the brain's surface and two-dimensional (2-D) nonrigid image registration, we developed a method to track surface motion during neurosurgical procedures. A series of experiments devised to evaluate the performance of the developed shift-tracking protocol are reported. In a controlled, quantitative phantom experiment, the results demonstrate that the surface shift-tracking protocol is capable of resolving shift to an accuracy of approximately 1.6 mm given initial shifts on the order of 15 mm. Furthermore, in a preliminary in vivo case using the tracked LRS and an independent optical measurement system, the automatic protocol was able to reconstruct 50% of the brain shift with an accuracy of 3.7 mm while the manual measurement was able to reconstruct 77% with an accuracy of 2.1 mm. The results suggest that a LRS is an effective tool for tracking brain surface shift during neurosurgery. [ABSTRACT FROM AUTHOR]

Objective: Lower limb amputees are at high risk of falling as current prosthetic legs provide only limited functionality for recovering balance after unexpected disturbances. For instance, the most established control method used on powered leg prostheses tracks local joint impedance functions without taking the global function of the leg in balance recovery into account. Here, we explore an alternative control policy for powered transfemoral prostheses that considers the global leg function and is based on a neuromuscular model of human locomotion. Methods: We adapt this model to describe and simulate an amputee walking with a powered prosthesis using the proposed control, and evaluate the gait robustness when confronted with rough ground and swing leg disturbances. We then implement and partially evaluate the resulting controller on a leg prosthesis prototype worn by a nonamputee user. Results: In simulation, the proposed prosthesis control leads to gaits that are more robust than those obtained by the impedance control method. The initial hardware experiments with the prosthesis prototype show that the proposed control reproduces normal walking patterns qualitatively and effectively responds to disturbances in early and late swing. However, the response to midswing disturbances neither replicates human responses nor averts falls. Conclusions: The neuromuscular model control is a promising alternative to existing prosthesis controls, although further research will need to improve on the initial implementation and determine how well these results transfer to amputee gait. Significance: This paper provides a potential avenue for future development of control policies that help to improve amputee balance recovery. [ABSTRACT FROM AUTHOR]

Objective: We investigated the nature of interactions between the central nervous system (CNS) and the cardiorespiratory system during sleep. Methods: Overnight polysomnography recordings were obtained from 33 healthy individuals. The relative spectral powers of five frequency bands, three ECG morphological features and respiratory rate were obtained from six EEG channels, ECG, and oronasal airflow, respectively. The synchronous feature series were interpolated to 1 Hz to retain the high time-resolution required to detect rapid physiological variations. CNS-cardiorespiratory interaction networks were built for each EEG channel and a directionality analysis was conducted using multivariate transfer entropy. Finally, the difference in interaction between Deep, Light, and REM sleep (DS, LS, and REM) was studied. Results: Bidirectional interactions existed in central-cardiorespiratory networks, and the dominant direction was from the cardiorespiratory system to the brain during all sleep stages. Sleep stages had evident influence on these interactions, with the strength of information transfer from heart rate and respiration rate to the brain gradually increasing with the sequence of REM, LS, and DS. Furthermore, the occipital lobe appeared to receive the most input from the cardiorespiratory system during LS. Finally, different ECG morphological features were found to be involved with various central-cardiac and cardiac-respiratory interactions. Conclusion: These findings reveal detailed information regarding CNS-cardiorespiratory interactions during sleep and provide new insights into understanding of sleep control mechanisms. Significance: Our approach may facilitate the investigation of the pathological cardiorespiratory complications of sleep disorders. [ABSTRACT FROM AUTHOR]

Communication between neurons occurs via transmission of neural spike trains through junctional structures, either electrical or chemical synapses, providing connections among nerve terminals. Since neural communication is achieved at synapses, the process of neurotransmission is called synaptic communication. Learning and memory processes are based on the changes in strength and connectivity of neural networks which usually contain multiple synaptic connections. In this paper, we investigate multiple-access neuro-spike communication channel, in which the neural signal, i.e., the action potential, is transmitted through multiple synaptic paths directed to a common postsynaptic neuron terminal. Synaptic transmission is initiated with random vesicle release process from presynaptic neurons to synaptic paths. Each synaptic channel is characterized by its impulse response and the number of available postsynaptic receptors. Here, we model the multiple-access synaptic communication channel, and investigate the information rate per spike at the postsynaptic neuron, and how postsynaptic rate is enhanced compared to single terminal synaptic communication channel. Furthermore, we analyze the synaptic transmission performance by incorporating the role of correlation among presynaptic terminals, and point out the postsynaptic rate improvement. [ABSTRACT FROM PUBLISHER]

Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches. [ABSTRACT FROM PUBLISHER]

Motion capture provides the best interface to the understanding of trajectory planning and formations in the central nervous system, which has enormously benefited the research in interactive game and learning, animation, film special effects, health care, and navigation. The existing human motion capture techniques, which use structured multiple high-resolution cameras in a dedicated studio, are complicated and expensive. With the rapid development of microsensors on chip, a ubiquitous real-time low-cost human motion capture system that uses wearable microsensors becomes possible. Because of the agility in movement, upper limb motion estimation has been regarded as the most difficult problem in human motion capture. In this paper, we take the upper limb as our research subject and present a novel upper limb motion estimation algorithm by hierarchical fusion of sensor data and human skeleton constraints. Our method represents the orientations of upper limb segments in quaternion, which is computationally effective and can avoid the singularity problem. To address the nonlinear human body segment motion, a particle filter is proposed to fuse inertial and magnetic sensor data. To compensate for the drift, which is the most challenging issue in motion estimation using inertial sensors, we present a novel solution by modeling the geometrical constraint in elbow joint and fuse the constraint in the framework of particle filter to revise the sensor fusion results and improve the estimation accuracy. The experimental results have shown that the proposed algorithm can provide accurate results compared to the ground truth. [ABSTRACT FROM AUTHOR]

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing- learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies. [ABSTRACT FROM AUTHOR]

Mechanical impedance, which changes with posture and muscle activations, characterizes how the central nervous system regulates the interaction with the environment. Traditional approaches to impedance estimation, based on averaging of movement kinetics, requires a large number of trials and may introduce bias to the estimation due to the high variability in a repeated or periodic movement. Here, we introduce a data-driven modeling technique to estimate joint impedance considering the large gait variability. The proposed method can be used to estimate impedance in both the stance and swing phases of walking. A 2-pass clustering approach is used to extract groups of unperturbed gait data and estimate candidate baselines. Then patterns of perturbed data are matched with the most similar unperturbed baseline. The kinematic and torque deviations from the baselines are regressed locally to compute joint impedance at different gait phases. Simulations using the trajectory data of a subject’s gait at different speeds demonstrate a more accurate estimation of ankle stiffness and damping with the proposed clustering-based method when compared with two methods: i) using average unperturbed baselines, and ii) matching shifted and scaled average unperturbed velocity baselines. Furthermore, the proposed method requires fewer trials than methods based on average unperturbed baselines. The experimental results on human hip impedance estimation show the feasibility of clustering-based technique and verifies that it reduces the estimation variability. [ABSTRACT FROM AUTHOR]

Objective: Proprioceptive senses play an important role in human body reflex and movement, so far implementing physiological mathematical models of proprioceptors in the musculoskeletal model and investigating their effects have not been sufficiently investigated. The purpose of the present study was to establish a compact framework for a lower limb musculoskeletal model by considering both ascending signals from central nervous system and descending feedback neural signal from physiologically realistic proprioceptors and evaluate it with progressive experimental data as well as investigating the effects of the proprioceptive feedback on the human movement. Methods: The simulation framework was established by combining a lower limb musculoskeletal model, the forward dynamic tool from OpenSim codes, and an executive program based on Python codes. The physiological mathematical models of the muscle spindle and Golgi tendon organs were included in the feedback control loop for the model. The model was evaluated through both previous literature data and currently implemented volunteer reflex experiments from the neural organ level to the monosynaptic reflex loop, and finally the complicated movement, such as the firing rate of the proprioceptors, the knee-jerk reflex, and the normal gait. Simultaneously, the effects of the proprioceptors on human normal gait were initially investigated. Results: The reliability of the framework was properly evaluated by comparing the experimental data of neural firing rate, electromyography signals, and joint kinematics. The gait analysis indicated that the introduction of the proprioceptors in the motor control loop can substantially resist the external disturbance. Conclusion: The established framework has been evaluated at different levels, and it can be extended and applied to different musculoskeletal models for human movement analysis and evaluate the effects of the proprioceptors on them. [ABSTRACT FROM AUTHOR]

Objective: It is commonly believed that in intrathecal (IT) drug delivery, agent distribution is confined to a narrow region close to the injection site, thereby undermining the efficacy of the method. Methods: To test the claim, multimodal in vivo imaging was used to experimentally observe the effects of IT infusion in cynomolgus monkey, looking at cerebrospinal fluid flow, anatomy, and dispersion of a radiolabeled tracer. Results: At high infusion rates, the tracer reached the cervical region after only 2 h, demonstrating rapid and wide distribution. The same in vivo nonhuman primate imaging data also provided evidence in support of a computational fluid dynamic model for the prediction of drug distribution following IT injection. Tracer dispersion was predicted in two specimens matching the distribution acquired with positron emission tomography (PET). For the third specimen, tracer dispersion simulations were conducted as a blind study: predictions were made before in vivo biodistribution data was known. In all cases, the computational fluid dynamics (CFD) predictions of drug dispersion after IT administration showed close spatio-temporal agreement with tracer biodistribution in vivo. Conclusion: Validation by in vivo nonhuman primate data confirms our ability to predict the biodistribution of intrathecally administered agents in subject-specific models of the central nervous system from first principles. Significance: The experiments reinstate IT delivery as a viable administration method when targeting molecules to the whole spine or the brain. The proposed computational methodology enables rational design of novel therapies for neurological diseases that require reliable, efficient, and safe delivery of therapeutic agents to specific target sites in the central nervous system. [ABSTRACT FROM AUTHOR]

Fan-beam collimators are designed to improve the system sensitivity and resolution for imaging small objects such as the human brain and breasts in single photon emission computed tomography (SPECT). Many reconstruction algorithms have been studied and applied to this geometry to deal with every kind of degradation factor. This paper presents a new reconstruction approach for SPECT with circular orbit, which demonstrated good performance in terms of both accuracy and efficiency. The new approach compensates for degradation factors including noise, scatter, attenuation, and spatially variant detector response. Its uniform attenuation approximation strategy avoids the additional transmission scan for the brain attenuation map, hence reducing the patient radiation dose and furthermore simplifying the imaging procedure. We evaluate and compare this new approach with the well-established ordered-subset expectation-maximization iterative method, using Monte Carlo simulations. We perform quantitative analysis with regional bias-variance, receiver operating characteristics, and Hotelling trace studies for both methods. The results demonstrate that our reconstruction strategy has comparable performance with a significant reduction of computing time. [ABSTRACT FROM AUTHOR]

The lateral bending test is routinely used by clinicians for the preoperative assessment of spinal mobility. The evaluation of bending motion is usually based on the qualitative analysis of a two-dimensional (2-D) antero-posterior X-ray image. The aim of this paper is to introduce a novel three-dimensional (3-D) reconstruction technique that is a prerequisite for the quantitative 3-D analysis of lateral bending motion. An algorithm was developed for the 3-D reconstruction of the spine from a single X-ray image. The X-ray is calibrate using a small calibration object and an explicit calibration algorithm. The information contained in the single X-ray is completed by registering a priori 3LD geometric models of individual vertebrae. Part of the error yielded by the 3-D12-D registration is corrected by a vertebral alignment constraint that aims to minimize intervertebral dislocations. Three-di- mensional models of 15 different scoliosis patients, obtained from a standard stereo-radiographic 3-D reconstruction, were used in simulation and validation experiments. Experimental results show that the new method is robust and accurate. With pessimistic levels of simulated noise, the average root mean square reconstruction error is 2.89 mm, which is appropriate for common clinical applications. [ABSTRACT FROM AUTHOR]

The central nervous system (CNS) is the most difficult target for drug delivery therapies. Despite datasets available describing physiological, biochemical, cellular, and metabolic properties of the CNS, the development of infusion therapies still faces major delivery challenges. There is a need for the integration of data obtained from different experimental modalities to design molecular therapies. In this paper, we propose a novel mathematical method for the integration of datasets to generate useful dosing criteria for infusion therapies. A case study is used to demonstrate the design of gene silencing therapies to down regulate NMDA receptors in the spinal cord for chronic pain management. Based on experimentally derived kinetics for short interfering RNA (siRNA) and magnetic resonance images, the biodistribution and pharmacokinetics of siRNAs were predicted for different infusion modes. This adaptable, multiscale computational platform enables the prediction of dose–response on an organ-wide level. The quantitative integration of valuable datasets with engineering precision is expected to accelerate the clinical implementation of novel therapeutics. [ABSTRACT FROM PUBLISHER]

During the past 20 years, scientists have focused their efforts in the quest of real solutions in which the neural signals produced inside the human brain could be connected with computers or artificial prostheses that in a near future could be used to restore the mobility and communication abilities of patients with some damage in the central nervous system. In this paper, the procedure to control an artificial device with the thought is explained; the techniques used to extract the neural activity from the brain are classified and compared to establish their advantages, drawbacks, and future development. In addition, the main breakthroughs so far in brain–machine interfaces are described. [ABSTRACT FROM PUBLISHER]

Electromyographical (EMG) signals have been frequently used to estimate human muscular torques. In the field of human-assistive robotics, these methods provide valuable information to provide effectively support to the user. However, their usability is strongly limited by the necessity of complex user-dependent and session-dependent calibration procedures, which confine their use to the laboratory environment. Nonetheless, an accurate estimate of muscle torque could be unnecessary to provide effective movement assistance to users. The natural ability of human central nervous system of adapting to external disturbances could compensate for a lower accuracy of the torque provided by the robot and maintain the movement accuracy unaltered, while the effort is reduced. In order to explore this possibility, in this paper we study the reaction of ten healthy subjects to the assistance provided through a proportional EMG control applied by an elbow powered exoskeleton. This system gives only a rough estimate of the user muscular torque but does not require any specific calibration. Experimental results clearly show that subjects adapt almost instantaneously to the assistance provided by the robot and can reduce their effort while keeping full control of the movement under different dynamic conditions (i.e., no alterations of movement accuracy are observed). [ABSTRACT FROM PUBLISHER]

—“Knock, knock—who's there?” Here, we do not address this question but rather the underlying mechanism behind the perception of knocking impacts. When one knocks on a surface, her hand makes a forward-backward motion and, at the point of reversal, the knuckles collide with the rigid surface. How does one perceive the unity, or simultaneity, of the sensory events associated with this impact? Does this binding derive from a temporal estimate of simultaneity, or does the brain use some other mechanism? In this paper, we ask whether the tap and the reversal of the hand are perceived as happening together, since both took place at the same time or at a particular state of motion. The aim of this research is to find out whether a tactile event and the flow of proprioceptive information regarding the state of the arm are matched within the central nervous system according to time or state. We tested this experimentally with subjects who actively moved one arm, as well as subjects who were servoed by a robotic device. Our results suggest that time is the mechanism used for judging the unity of the modalities for both active and passive movements. Taken together, these results provide a useful cue for neuroscientists as to the structure and function of the perceptual and motor systems and essential engineering knowledge for the development of effective and realistic augmented reality systems with haptics for the telerobotics, telesurgery, and telepresence applications of the future. [ABSTRACT FROM PUBLISHER]

Mechanical damage to axons is a proximal cause of deficits following traumatic brain injury and spinal cord injury. Axons are injured predominantly by tensile strain, and identifying the strain experienced by axons is a critical step toward injury prevention. White matter demonstrates complex nonlinear mechanical behavior at the continuum level that evolves from even more complex, dynamic, and composite behavior between axons and the “glial matrix” at the microlevel. In situ, axons maintain an undulated state that depends on the location of the white matter and the stage of neurodevelopment. When exposed to tissue strain, axons do not demonstrate pure affine or non-affine behavior, but instead transition from non-affine-dominated kinematics at low stretch levels to affine kinematics at high stretch levels. This transitional and predominant kinematic behavior has been linked to the natural coupling of axons to each other via the glial matrix. In this paper, a transitional kinematic model is applied to a micromechanics finite element model to simulate the axonal behavior within a white matter tissue subjected to uniaxial tensile stretch. The effects of the transition parameters and the volume fraction of axons on axonal behavior is evaluated and compared to previous experimental data and numerical simulations. [ABSTRACT FROM AUTHOR]

Dendritic spines form postsynaptic contact sites in the central nervous system. The rapid and spontaneous morphology changes of spines have been widely observed by neurobiologists. Determining the relationship between dendritic spine morphology change and its functional properties such as memory learning is a fundamental yet challenging problem in neurobiology research. In this paper, we propose a novel algorithm to track the morphology change of multiple spines simultaneously in time-lapse neuronal images based on nonrigid registration and integer programming. We also propose a robust scheme to link disappearing-and-reappearing spines. Performance comparisons with other state-of-the-art cell and spine tracking algorithms, and the ground truth show that our approach is more accurate and robust, and it is capable of tracking a large number of neuronal spines in time-lapse confocal microscopy images. [ABSTRACT FROM AUTHOR]

Gait rehabilitation robots are of increasing importance in neurorehabilitation. Conventional devices are often criticized because they are limited to reproducing predefined movement patterns. Research on patient-cooperative control strategies aims at improving robotic behavior. Robots should support patients only as much as needed and stimulate them to produce maximal voluntary efforts. This paper presents a patient-cooperative strategy that allows patients to influence the timing of their leg movements along a physiologically meaningful path. In this "path control" strategy, compliant virtual walls keep the patient's legs within a "tunnel" around the desired spatial path. Additional supportive torques enable patients to move along the path with reduced effort. Graphical feedback provides visual training instructions. The path control strategy was evaluated with 10 healthy subjects and 15 subjects with incomplete spinal cord injury. The spatio-temporal characteristics of recorded kinematic data showed that subjects walked with larger temporal variability with the new strategy. Electromyographic data indicated that subjects were training more actively. A majority of iSCI subjects was able to actively control their gait timing. Thus, the strategy allows patients to train walking while being helped rather than controlled by the robot. [ABSTRACT FROM AUTHOR]

Background and Objective: New technology enables constant boost to the powers of mobile devices, which in the previous years have transformed from simple mobile phones to smart phones. Computational powers of these electronics enable actions that previously were possible only for computers. By the use of special applications, we may benefit from sensors and multimedia capabilities of operating systems. Therefore, a new era for devoted implementations opens, in which a smart application can take a role of computing system to estimate the symptoms of diseases by evaluating signals coming from a human body. Methods: We propose a model of an application implemented for mobile android systems, which can be used for examination of central nervous system motor disorders occurring in patients suffering from Huntington (HD), Alzheimer, or Parkinson diseases. In particular, the model tracks tremors (involuntary movements), and cognitive (memory loss or dementia) impairments using touch and visual stimulus modalities. The proposed model interprets the symptoms from human bodies that indicate one of the diseases of the nervous system. Pre-processing of collected data for feature extraction is executed on a mobile device by using core functionality and methods provided in android's application programming interface. The information is evaluated by a back-propagation neural network classifier and the result is presented to the end user. The system is able to contact medical supervision and provide an assistance from the clinic. Results: The system uses a collected dataset of 1928 records, taken from 11 HD patients and 11 healthy persons in Lithuania, to gather statistics about examinations and presents the results as medical evaluation with prediction on the state of health. The accuracy of recognition of early, prodromal symptoms for central nervous system motor disorders is 86.4% (F-measure 0.859). The app (available on Google Play) is easy to use and is efficient tool for decision support in medical examinations. Conclusions: The use of intelligent apps which can help to evaluate neurodegenerative disorders is an important enhancement to medical diagnosis. The developed smartphone app supports the doctor with additional results that are easy to compare with other examinations. This kind of examination is a nice change from classic stereotypes, especially for younger age patients, who are used to various aspects of information technology. [ABSTRACT FROM AUTHOR]

According to William E. Rees, "[a] parasite is an organism that gains its utility by sapping the vitality of its host [2]." A vivid illustration of this can be seen in WIRED science writer, Matt Simon's latest book, Plight of the Living Dead: What Real-Life Zombies Reveal about Our World - and Ourselves. Simon fastens on the startling machinations of "real" parasites and how they turn their hosts into agents of their own propagation. In his opening chapter, Simon zeroes in on Ampulex compressa, the jewel wasp, its "hypnotizing beauty — with big eyes and a precious green sheen to its body — [belying] its belligerence [3]." The unlucky target of her design is a roach. In her first move, the wasp injects her venom loaded stinger between the roach's front legs. Included in the venom's more than two hundred compound brew is the weaponized central nervous system inhibiting neurotransmitter, gamma-aminobutyric acid (GABA). For five minutes, GABA paralyzes the roach's ability to block the wasp's next move with its front legs. The now unobstructed wasp removes her stinger and redeploys it to penetrate the now defenseless victim's neck and from there moves into its brain. The wasp, for now at least, is not trying to kill her prey as she feels around for the right spot [actually two spots] in the brain to inject her mind-control potion [3]. These precise locations govern the roach's means of movement. After discharging her venom, the wasp again removes her stinger and lo and behold, in a few minutes the roach, instead of flooding with fear, begins grooming itself as though it hadn't a care in the world. [ABSTRACT FROM AUTHOR]

Functional corticomuscular coupling (FCMC) with different rhythmic oscillations plays different roles in neural communication and interaction between the central nervous system and the peripheral system. Larger methods, such as coherence and Granger causality (GC), have been used to describe the frequency band characteristics in the frequency domain, but they fail to account for the inherent complexity. Considering that the transfer entropy (TE) method as an information theory has advantages in complexity and direction, we extended it and proposed a novel method named transfer spectral entropy (TSE) to explore the local frequency band characteristics between two coupling signals. To verify this, we introduced a Henon model and a neural mass model to generate the simulation signals. We then applied the proposed method to explore the FCMC by analyzing the correlation between the EEG and EMG signals during steady-state force output. Simulation results showed that the TSE method, compared with the GC method, not only described the information interaction in the local frequency band but also restrained the “false coupling.” In addition, the results also revealed that the TSE method was sensitive to coupling strength but not to the data length. Further analysis of the experimental data showed that beta1 (15–25 Hz) and beta2 (25–35 Hz) bands were prominent in the FCMC for both EEG-to-EMG and EMG-to-EEG directions. In addition, the statistical analysis of the significant area indicated that the coupling in the EEG-to-EMG direction was higher at the beta1 and beta2 bands than that in the EMG-to-EEG direction, and the coupling in the EMG-to-EEG direction was higher at the gamma1 band (35–45 Hz) than that in the opposition. The FCMC results complementarily refined the previous studies that mainly focused on the beta band (15–35 Hz). The simulation and experimental data expound the effectiveness of the TSE model to describe the information interaction in the local frequency band between two time series, and this study extends the relative studies on FCMC. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases such as Alzheimer, Parkinson, motor neuron, and Chorea affect millions of people today. Their effect on the central nervous system causes the loss of brain functions as well as motor disturbances and sometimes cognitive deficits. In such a scenario, the monitoring and evaluation of early symptoms are mandatory for the improvement of the patient’s quality of life. Here, the authors describe the development, the laboratory calibration, and the “in-field validation” under the medical supervision of a movement tremors recorder for subjects affected by neurodegenerative diseases. The developed device is based on an array of four accelerometers connected to an embedded development board. This system is able to monitor tremor/movement, accidental falls, and, moreover, it can track the Alzheimer subjects’ geographical position. A remote supervisor can collect data from the system through Bluetooth, Wi-Fi, or GSM connections. A data compression algorithm was developed directly on board in order to increase the efficiency of data transmission and reduce power consumptions. [ABSTRACT FROM AUTHOR]

Reach-to-grasp tasks are composed of several actions that are more and more considered as simultaneously controlled by the central nervous system in a feedforward manner (at least for well-known activities). If this hypothesis is correct, during prehension tasks, the activity of proximal muscles (and not only of the distal ones used to control finger movements) is modulated according to the kind of object to be grasped and its position. This means that different objects could be identified by processing the electromyographic (EMG) signals recorded from proximal muscles. In this paper, specific experiments have been carried out to support this hypothesis in able-bodied subjects. The results achieved seem to confirm this possibility by showing that the activation of proximal muscles can be statistically different for different grip types. This finding supports the hypothesis that proximal and distal muscles are simultaneously controlled during reaching and grasping. Moreover, this kind of information could allow the development of an EMG-based control strategy based on the natural muscular activities selected by the central nervous system. [ABSTRACT FROM AUTHOR]

This paper investigates dynamic source imaging of the spinal cord electrophysiological activity from its evoked magnetic field by applying the spatial filter version of standardized low-resolution brain electromagnetic tomography (sLORETA). Our computer simulation shows that the sLORETA-based spatial filter can reconstruct the four current sources typically associated with the elicitation of the spinal cord evoked magnetic field (SCEF). The results from animal experiments show that significant changes in the latency and intensity of the reconstructed volume current arise near the location of the artificial incomplete conduction block. The results from the human SCEF show that the SCEF source imaging can visualize the dynamics of the volume currents and other nerve electrical activity propagating along the human spinal cord. These experimental results demonstrate the potential of SCEF source imaging as a future clinical tool for diagnosing cervical spinal cord disorders. [ABSTRACT FROM AUTHOR]

It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, which must be somehow combined into a single final segmentation. Majority voting is the generally used rule to fuse the segmentations, but more sophisticated methods have also been proposed. In this paper, we show that the use of global weights to ponderate candidate segmentations has a major limitation. As a means to improve segmentation accuracy, we propose the generalized local weighting voting method. Namely, the fusion weights adapt voxel-by-voxel according to a local estimation of segmentation performance. Using digital phantoms and MR images of the human brain, we demonstrate that the performance of each combination technique depends on the gray level contrast characteristics of the segmented region, and that no fusion method yields better results than the others for all the regions. In particular, we show that local combination strategies outperform global methods in segmenting high-contrast structures, while global techniques are less sensitive to noise when contrast between neighboring structures is low. We conclude that, in order to achieve the highest overall segmentation accuracy, the best combination method for each particular structure must be selected. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient method to acquire sources of tremor in patients with movement disorders based on blind source separation of convolutive mixtures. The extracted sources indicated neural activities that might be generated in the central nervous system. Four patients with essential tremor were tested in a set of movement tasks. Subjects wore a data glove that measured finger movements of the hand. The experimental data were then fed to a convolutive-mixture model, which revealed sources that imbibed in them the tremor frequency components of 2-8 Hz. Time-frequency analysis of these sources might. be of potential help to clinicians to devise tasks that can manifest visible tremor from patients. [ABSTRACT FROM AUTHOR]

Several treatment modalities for neurodegenerative diseases or tumors of the central nervous system involve invasive delivery of large molecular weight drugs to the brain. Despite the ample record of experimental studies, accurate drug targeting for the human brain remains a challenge. This paper proposes a systematic design method of administering drugs to specific locations in the human brain based on first principles transport in porous media. The proposed mathematical framework predicts achievable treatment volumes in target regions as a function of brain anatomy and infusion catheter position. A systematic procedure to deter- mine the optimal infusion and catheter design parameters that maximize the penetration depth and volumes of distribution will be discussed. The computer simulations are validated with agarose gel phantom experiments and rat data. The rigorous computational approach will allow physicians and scientists to better plan the administration of therapeutic drugs to the central nervous system. [ABSTRACT FROM AUTHOR]

This paper discusses some of the advances and limitations of the field of functional electrical stimulation (FES) and why the field has difficulty in achieving its promise whenever many wires are used throughout the body. We take the stand that the great time for FES will be when the stimulators and sensors are injectable and wireless and thus make it feasible to have many stimulation/sensing locations in the body. It has taken a long time to get there, but we are getting close to having a coordinated wireless system. The only commercially successful FES devices that exist today are the cardiac pacemakers, the spinal cord stimulators, and, to a lesser extent, the cochlear implants, none of which have more than one or two cables placed in the body. [ABSTRACT FROM AUTHOR]

Individuals with C5/C6 spinal cord injury (SCI) have a number of paralyzed muscles in their upper extremities that can be electrically activated in a coordinated manner to restore function. The selection of a practical subset of paralyzed muscles for stimulation depends on the specific condition of the individual, the functions targeted for restoration, and surgical considerations. This paper presents a musculoskeletal model-based approach for optimizing the muscle set used for functional electrical stimulation (FES) of the shoulder and elbow in this population. Experimentally recorded kinematics from able-bodied subjects served as inputs to a musculoskeletal model of the shoulder and elbow, which was modified to reflect the reduced muscle force capacities of an individual with C5 SCI but also the potential of using FES to activate paralyzed muscles. A large number of inverse dynamic simulations mimicking typical activities of daily living were performed that included 1) muscles with retained voluntary control and 2) many different combinations of stimulated paralyzed muscles. These results indicate that a muscle set consisting of the serratus anterior, infraspinatus and triceps would enable the greatest range of relevant movements. This set will become the initial target in a C5 SCI neuroprosthesis to restore shoulder and elbow function. [ABSTRACT FROM AUTHOR]

An important problem of the analysis of functional magnetic resonance imaging (fMRI) experiments is to achieve some noise reduction of the data without blurring the shape of the activation areas. As a novel solution to this problem, recently the propagation-separation (PS) approach has been proposed. PS is a structure adaptive smoothing method that adapts to different shapes of activation areas. In this paper, we demonstrate how this method results in a more accurate localization of brain activity. First, it is shown in numerical simulations that PS is superior over Gaussian smoothing with respect to the accurate description of the shape of activation clusters and results in less false detections. Second, in a study of 37 presurgical planning cases we found that PS and Gaussian smoothing often yield different results, and we present examples showing aspects of the superiority of PS as applied to presurgical planning. [ABSTRACT FROM AUTHOR]

A 64-site wireless current microstimulator chip (Interestim-2B) and a prototype implant based on the same chip have been developed for neural prosthetic applications. Modular standalone architecture allows up to 32 chips to be individually addressed and operated in parallel to drive up to 2048 stimulating sites. The only off-chip components are a receiver inductive-capacitive (LC) tank, a capacitive low-pass filter for ripple rejection, and arrays of microelectrodes for interfacing with the neural tissue. The implant receives inductive power up to 50 mW and data at 2.5 Mb/s from a frequency shift keyed (FSK) 5/10 MHZ carrier to generate up to 65800 stimulus pulses/s. Each Interestim-2B chip contains 16 current drivers with 270 μA full-scale current, 5-bit (32-steps) digital-to-analog converter (DAC) resolution, 100 MΩ output impedance, and a voltage compliance that extends within 150 and 250 mV of the 5 V supply and ground rails, respectively. It can generate any arbitrary current waveform and supports a variety of monopolar and bipolar stimulation protocols. A common analog line provides access to each site potential, and exhausts residual stimulus charges for charge balancing. The chip has site potential measurement and in situ site impedance measurement capabilities, which help its users indicate defective sites or characteristic shifts in chronic stimulations. Interestim-2B chip is fabricated in the AM! 1.5 μm standard complementary metal-oxide-semiconductor (CMOS) process and measures 4.6 × 4.6 × 0.5 min. The prototype implant size including test connectors is 19 × 14 × 6 min, which can be shrunk down to <0.5 CC. This paper also summarizes some of the in vitro and in vivo experiments performed using the Interestim-2B prototype implant. [ABSTRACT FROM AUTHOR]

This paper reports the development and in-vivo testing of a compact multitransducer microsystem intended for neuroethology experiments, including studies of gait dynamics in free-running insects. The system incorporates a combination of custom and off-the-shelf components. Its suite of measurement devices comprises leg-mounted strain gauges, electromyogram (EMG) and extracellular electrodes for the central nervous system, and a two-axis accelerometer. For signal conditioning and selection, the microsystem implements off-the-shelf electronics in a custom chip-on-board configuration. The microsystem measures 16 mm × 19 mm, supports 40 components and 56 I/O leads, and is assembled on a four-layer printed-circuit board. The entire system occupies only 0.65 cm³ and weighs less than 5 g. It has been successfully used to monitor leg-strain and EMG signals on walking cockroaches and for stimulation in the insect central nervous and muscular systems. [ABSTRACT FROM AUTHOR]

This paper examines how electrical stimulation of the spinal cord can modulate the output of the central pattern generator (CPG) for locomotion. Application of discrete current pulses to a single spinal segment was shown to affect multiple parameters of an ongoing locomotor pattern in an in vitro spinal cord. For any given stimulus, the effects on frequency, duration, and symmetry of locomotor output were strongly dependent on the phase at which stimulation was applied within the CPG cycle. Additionally, most stimuli had an immediate impact and evinced no effects on subsequent cycles. The most dramatic changes were seen when stimulation was applied during motor bursting: stimuli applied to the ipsilateral spinal hemicord increased the burst length, while stimuli applied to the contralateral spinal hemicord decreased the burst length. Smaller changes were observed when stimulating during delays between motor bursts. Thus, phasic stimulation was shown to influence the behavior of the CPG and spinal locomotion circuits on a cycle-by-cycle basis. This work represents the first step toward our ultimate goal of developing a neuroprosthetic device to restore locomotion after a severe spinal cord injury. [ABSTRACT FROM AUTHOR]

A study of motor nerve stimulation by flat interface nerve electrodes (FINE) using a new cuff electrode design based on finite element method, genetic algorithm and computational neuroscience method is presented. The purpose of this study is to obtain optimized parameters of electrode model for higher motor nerve stimulation efficiency. In this paper, the application for the focus stimulation on target fascicle is presented. [ABSTRACT FROM AUTHOR]

This paper reports on a novel method for quantifying the cortical activity of a patient during general anesthesia as a surrogate measure of the patient's level of consciousness. The proposed technique is based on the analysis of a single-channel (frontal) electroencephalogram (EEG) signal using stationary wavelet transform (SWT). The wavelet coefficients calculated from the EEG are pooled into a statistical representation, which is then compared to two well-defined states: the awake state with normal EEG activity, and the isoelectric state with maximal cortical depression. The resulting index, referred to as the wavelet-based anesthetic value for central nervous system monitoring (WAVCNS), quantifies the depth of consciousness between these two extremes. To validate the proposed technique, we present a clinical study which explores the advantages of the WAVCNS in comparison with the BIS monitor (Aspect Medical Systems, MA), currently a reference in consciousness monitoring. Results show that the WAVCNS and BIS are well correlated (r = 0.969) during periods of steady-state despite fundamental algorithmic differences. However, in terms of dynamic behavior, the WAVCNS offers faster tracking of transitory changes at induction and emergence, with an average lead of 15–30 s. Furthermore, and conversely to the BIS, the WAVCNS regains its preinduction baseline value when patients are responding to verbal command after emergence from anesthesia. We conclude that the proposed analysis technique is an attractive alternative to BIS monitoring. In addition, we show that the WAVCNS dynamics can be modeled as a linear time invariant transfer function. This index is, therefore, well suited for use as a feedback sensor in advisory systems, closed-loop control schemes, and for the identification of the pharmacodynamic models of anesthetic drugs. [ABSTRACT FROM AUTHOR]