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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

The effects of spinal cord injuries are likely to be ameliorated with the help of functional electrical stimulation of the spinal cord, a technique that may benefit from a new style of electrode: the cylindrical multielectrode. This paper describes the specifications for, fabrication techniques for, and in vitro evaluation of cylindrical multielectrodes. Four tip shapes were tested to determine which shape required the lowest peak force and would, therefore, be expected to minimize dimpling during implantation. The impedance of the electrode interface was monitored for changes due to insertion as well as repetitive delivery of current pulses. The charge delivery capacity was determined by testing with safe (≤ 0.6 mC/cm²) and damaging levels (≥ 0.8 mC/cm²) of charge density. The results of these tests suggest that this electrode design could be used to stimulate neurons in the ventral horn of the spinal cord. [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]

Estimation of intracranial electric activity from the scalp electroencephalogram (EEG) requires a solution to the EEG inverse problem, which is known as an ill-conditioned problem. In order to yield a unique solution, weighted minimum norm least square (MNLS) inverse methods are generally used. This paper proposes a recursive algorithm, termed Shrinking LORETA-FOCUSS, which combines and expands upon the central features of two well-known weighted MNLS methods: LORETA and FOCUSS. This recursive algorithm makes iterative adjustments to the solution space as well as the weighting matrix, thereby dramatically reducing the computation load, and increasing local source resolution. Simulations are conducted on a 3-shell spherical head model registered to the Talairach human brain atlas. A comparative study of four different inverse methods, standard Weighted Minimum Norm, L1-norm, LORETA-FOCUSS and Shrinking LORETA-FOCUSS are presented. The results demonstrate that Shrinking LORETA-FOCUSS is able to reconstruct a three-dimensional source distribution with smaller localization and energy errors compared to the other methods. [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]