Artificial feedback for invasive brain–computer interfaces

During the last two decades, considerable progress has been made in the studies of brain–computer interfaces (BCIs)—devices in which motor signals from the brain are registered by multi-electrode arrays and transformed into commands for artificial actuators such as cursors and robotic devices. This review is focused on one problem. Voluntary motor control is based on neurophysiological processes, which strongly depend on the afferent innervation of skin, muscles, and joints. Thus, invasive BCI has to be based on a bidirectional system in which motor control signals are registered by multichannel microelectrodes implanted in motor areas, whereas tactile, proprioceptive, and other useful signals are transported back to the brain through spatiotemporal patterns of intracortical microstimulation (ICMS) delivered to sensory areas. In general, the studies of invasive BCIs have advanced in several directions. The progress of BCIs with artificial sensory feedback will not only help patients, but will also expand base knowledge in the field of human cortical functions.     

Neurophysiological foundations and practical realizations of the brain–machine interfaces in the technology in neurological rehabilitation

The brain–computer interface (BCI) technology, based on the registration and interpretation of EEG, has recently become one of the most popular developments in neuroscience and psychophysiology. This is due not only to the intended future use of these technologies in many areas of practical human activity, but also to the fact that BCI is a completely new paradigm in psychophysiology, which allows testing hypotheses about the possibilities of the human brain to the development of skills of interaction with the outside world without the mediation of the motor system, i.e., only with the help of voluntary modulation of EEG generators. This paper examines the theoretical and experimental basis, the current state, and the prospects of development of training, communicational, and assisting complexes based on BCI to control them without muscular effort on the basis of decoding mental commands detected in the EEG of patients with severely impaired speech and motor system.     

Habit learning and brain–machine interfaces (BMI): a tribute to Valentino Braitenberg’s “Vehicles”

Brain–Machine Interfaces (BMI) allow manipulation of external devices and computers directly with brain activity without involvement of overt motor actions. The neurophysiological principles of such robotic brain devices and BMIs follow Hebbian learning rules as described and realized by Valentino Braitenberg in his book “Vehicles,” in the concept of a “thought pump” residing in subcortical basal ganglia structures. We describe here the application of BMIs for brain communication in totally locked-in patients and argue that the thought pump may extinguish—at least partially—in those people because of extinction of instrumentally learned cognitive responses and brain responses. We show that Pavlovian semantic conditioning may allow brain communication even in the completely paralyzed who does not show response-effect contingencies. Principles of skill learning and habit acquisition as formulated by Braitenberg are the building blocks of BMIs and neuroprostheses.     

A reductionist approach to the analysis of learning in brain–computer interfaces

The complexity and scale of brain–computer interface (BCI) studies limit our ability to investigate how humans learn to use BCI systems. It also limits our capacity to develop adaptive algorithms needed to assist users with their control. Adaptive algorithm development is forced offline and typically uses static data sets. But this is a poor substitute for the online, dynamic environment where algorithms are ultimately deployed and interact with an adapting user. This work evaluates a paradigm that simulates the control problem faced by human subjects when controlling a BCI, but which avoids the many complications associated with full-scale BCI studies. Biological learners can be studied in a reductionist way as they solve BCI-like control problems, and machine learning algorithms can be developed and tested in closed loop with the subjects before being translated to full BCIs. The method is to map 19 joint angles of the hand (representing neural signals) to the position of a 2D cursor which must be piloted to displayed targets (a typical BCI task). An investigation is presented on how closely the joint angle method emulates BCI systems; a novel learning algorithm is evaluated, and a performance difference between genders is discussed.     

Motor imaginary-based brain–machine interface design using programmable logic controllers for the disabled

Aiming at the implementation of brain–machine interfaces (BMI) for the aid of disabled people, this paper presents a system design for real-time communication between the BMI and programmable logic controllers (PLCs) to control an electrical actuator that could be used in devices to help the disabled. Motor imaginary signals extracted from the brain's motor cortex using an electroencephalogram (EEG) were used as a control signal. The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. A real-time test system was designed using MATLAB for signal processing, KEP-Ware V4 OLE for process control (OPC), a wireless local area network router, an Omron Sysmac CPM1 PLC and a 5 V/0.3 A motor. This paper explains the signal processing techniques, the PLC's hardware configuration, OPC configuration and real-time data exchange between MATLAB and PLC using the MATLAB OPC toolbox. The test results indicate that the function of exchanging real-time data can be attained between the BMI and PLC through OPC server and proves that it is an effective and feasible method to be applied to devices such as wheelchairs or electronic equipment.     

212 Shifting interfaces: changes in protein–protein and protein–DNA interfaces probed via molecular dynamics

Over the past decade, there has been a growing interest in studying the binding of DNA to the MutSalpha protein complex. This heterodimeric protein complex, the Msh2/Msh6 complex in humans, is the initial complex that binds mismatched DNA and other DNA defects that occur during replication. This complex has also been shown to bind at least some types of damaged DNA, such as the cross-linked adducts due to the chemotherapeutics cisplatin and carboplatin, or the incorporation of the chemotherapeutic, FdU. As a result of this interest, multiple studies have contrasted the interactions of MutSalpha with its normal mismatched substrate and with the interactions of MutsSalpha with the DNA damaged by chemotherapeutic cisplatin. To complement these studies, we examine the interaction between MutSalpha and the DNA damaged by carboplatin via all-atom molecular dynamics simulations. These simulations provide evidence for subtle changes in the protein–DNA and protein–protein interfaces. The interfaces shifts found are broadly similar to those found in binding with adduct from cis-platin, but have distinct differences. These subtle differences may play a role in the way of the different damages and mismatched DNA are signaled by MutSalpha, and suggest a signaling mechanism for DNA damage that chiefly involves shifts in protein–protein interactions as opposed to changes in protein conformation.     

Structure-based method for analyzing protein–protein interfaces

Hydrogen bond, hydrophobic and vdW interactions are the three major non-covalent interactions at protein–protein interfaces. We have developed a method that uses only these properties to describe interactions between proteins, which can qualitatively estimate the individual contribution of each interfacial residue to the binding and gives the results in a graphic display way. This method has been applied to analyze alanine mutation data at protein–protein interfaces. A dataset containing 13 protein–protein complexes with 250 alanine mutations of interfacial residues has been tested. For the 75 hot-spot residues (G1.5 kcal mol^-1), 66 can be predicted correctly with a success rate of 88%. In order to test the tolerance of this method to conformational changes upon binding, we utilize a set of 26 complexes with one or both of their components available in the unbound form. The difference of key residues exported by the program is 11% between the results using complexed proteins and those from unbound ones. As this method gives the characteristics of the binding partner for a particular protein, in-depth studies on protein–protein recognition can be carried out. Furthermore, this method can be used to compare the difference between protein–protein interactions and look for correlated mutation. Figure Key interaction grids at the interface between barnase and barstar. Key interaction grid for barnase and barstar are presented in one figure according to their coordinates. In order to distinguish the two proteins, different icons were assigned. Crosses represent key grids for barstar and dots represent key grids for barnase. The four residues in ball and stick are Asp40 in barstar and Arg83, Arg87, His102 in barnase.     

Implications of disease-related mutations at protein–protein interfaces

Protein–protein interfaces have been attracting great attention owing to their critical roles in protein–protein interactions and the fact that human disease-related mutations are generally enriched in them. Recently, substantial research progress has been made in this field, which has significantly promoted the understanding and treatment of various human diseases. For example, many studies have discovered the properties of disease-related mutations. Besides, as more large-scale experimental data become available, various computational approaches have been proposed to advance our understanding of disease mutations from the data. Here, we overview recent advances in characteristics of disease-related mutations at protein–protein interfaces, mutation effects on protein interactions, and investigation of mutations on specific diseases.     

Structural features of peptoid–peptide hybrids in lipid–water interfaces

The inclusion of peptoid monomers into antimicrobial peptides (AMPs) increases their proteolytic resistance, but introduces conformational flexibility (reduced hydrogen bonding ability and cis/trans isomerism). We here use NMR spectroscopy to answer how the insertion of a peptoid monomer influences the structure of a regular α-helical AMP upon interaction with a dodecyl phosphocholine (DPC) micelle. Insertion of [(2-methylpropyl)amino]acetic acid in maculatin-G15 shows that the structural change and conformational flexibility depends on the site of insertion. This is governed by the micelle interaction of the amphipathic helices flanking the peptoid monomer and the side chain properties of the peptoid and its preceding residue.     

Blood–brain barrier peptide shuttles

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Interspecies interaction extends bacterial survival at solid–air interfaces

Despite the ubiquity of biofilms in natural and man-made environments, research on surface-associated cells has focused primarily on solid–liquid interfaces. This study evaluated the extent to which bacterial cells persist on inanimate solid–air interfaces. The desiccation tolerance of bacterial strains isolated from indoor air, as well as of a test strain (Pseudomonas aeruginosa), was determined at different levels of relative humidity (RH) using the large droplet inoculation method in an aerosol chamber. The cells survived longer at lower (25 and 42%) than at high RH (95%). Four of the seven indoor strains selected for further study showed extended period of survival following deposition as 0.05–0.1?ml of washed culture followed by desiccation, each with different effects on the survival of the test strain, P. aeruginosa. A strain closely related to Arthrobacter species afforded the highest level of protection to the test strain. Even though the desiccation-tolerant strains survived when they were deposited as bioaerosols, the protective role towards the test strain was not observed when the latter was deposited as a bioaerosol. These, which are often-unculturable, bacteria may go undetected during routine monitoring of biofouling, thereby allowing them to act as reservoirs and extending the habitat range of undesired microorganisms.     

Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control

Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain–computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.     

Microfabricated nerve–electrode interfaces in neural prosthetics and neural engineering

Neural interfaces and implants are finding more clinical applications and there are rapid technological advances for more efficient and safe design, fabrication and materials to establish high-fidelity neural interfaces. In this review paper, we highlight new developments of the microfabricated electrodes and substrates with regard to the design, materials, fabrication and their clinical applications. There is a noticeable trend towards integration of microfluidic modules on a single neural platform. In addition to the microelectrodes for neural recording and stimulation, microfluidic channels are integrated into a nerve–electrode interface to explore the rich neurochemistry present at the neural interface and exploit it for enhanced electrochemical stimulation and recording of the central and peripheral nervous system.