Neural signals of “Intensity” but not “Wanting” or “Liking” of rewards may be trait markers for depression

We have shown previously that particpants “at risk” of depression have decreased neural processing of reward suggesting this might be a neural biomarker for depression. However, how the neural signal related to subjective experiences of reward (wanting, liking, intensity) might differ as trait markers for depression, is as yet unknown. Using SPM8 parametric modulation analysis the neural signal related to the subjective report of wanting, liking and intensity was compared between 25 young people with a biological parent with depression (FH) and 25 age/gender matched controls. In a second study the neural signal related to the subjective report of wanting, liking and intensity was compared between 13 unmedicated recovered depressed (RD) patients and 14 healthy age/gender matched controls. The analysis revealed differences in the neural signal for wanting, liking and intensity ratings in the ventral striatum, dmPFC and caudate respectively in the RD group compared to controls . Despite no differences in the FH groups neural signal for wanting and liking there was a difference in the neural signal for intensity ratings in the dACC and anterior insula compared to controls. These results suggest that the neural substrates tracking the intensity but not the wanting or liking for rewards and punishers might be a trait marker for depression.

Bi-directional human machine interface via direct neural connection

This paper presents an application study into the use of a bi-directional link with the human nervous system by means of an implant, positioned through neurosurgery. Various applications are described including the interaction of neural signals with an articulated hand, a group of cooperative autonomous robots and to control the movement of a mobile platform. The microelectrode array implant itself is described in detail. Consideration is given to a wider range of possible robot mechanisms, which could interact with the human nervous system through the same technique.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.

Frontoparietal cortical networks for directing attention and the eye to visual locations: Identical, independent, or overlapping neural systems?

Functional anatomical and single-unit recording studies indicate that a set of neural signals in parietal and frontal cortex mediates the covert allocation of attention to visual locations, as originally proposed by psychological studies. This frontoparietal network is the source of a location bias that interacts with extrastriate regions of the ventral visual system during object analysis to enhance visual processing. The frontoparietal network is not exclusively related to visual attention, but may coincide or overlap with regions involved in oculomotor processing. The relationship between attention and eye movement processes is discussed at the psychological, functional anatomical, and cellular level of analysis.

The resource consumption principle: attention and memory in volumes of neural tissue.

In the cerebral cortex, the small volume of the extracellular space in relation to the volume enclosed by synapses suggests an important functional role for this relationship. It is well known that there are atoms and molecules in the extracellular space that are absolutely necessary for synapses to function (e.g., calcium). I propose here the hypothesis that the rapid shift of these atoms and molecules from extracellular to intrasynaptic compartments represents the consumption of a shared, limited resource available to local volumes of neural tissue. Such consumption results in a dramatic competition among synapses for resources necessary for their function. In this paper, I explore a theory in which this resource consumption plays a critical role in the way local volumes of neural tissue operate. On short time scales, this principle of resource consumption permits a tissue volume to choose those synapses that function in a particular context and thereby helps to integrate the many neural signals that impinge on a tissue volume at any given moment. On longer time scales, the same principle aids in the stable storage and recall of information. The theory provides one framework for understanding how cerebral cortical tissue volumes integrate, attend to, store, and recall information. In this account, the capacity of neural tissue to attend to stimuli is intimately tied to the way tissue volumes are organized at fine spatial scales.

Exploração vocacional: recorrendo a biossensores cognitivos

Na sociedade actual é imperativo assegurar um emprego competente para que o Ser Humano possa atingir a sua subsistência, e consequentemente a sua própria felicidade. No entanto, devido à pressão da sociedade em que vivemos, promover a sustentabilidade do “emprego” hoje em dia, principalmente nas camadas mais jovens, traduz-se em algo extremamente difícil. Estes deparam-se com uma situação árdua quando escolhem um caminho para o seu futuro. Assim como eu, também os jovens da actualidade prevêem que no futuro essas dúvidas se irão manter, ainda de forma mais agravada devido à complexidade das profissões existentes no mundo do trabalho. O meu objectivo neste momento é facilitar a vida dos jovens que se vêem numa encruzilhada, com pouco tempo de resposta para a tomada de decisão do seu próprio futuro. Pretendo fomentar a descoberta e construção da sua própria vocação, dando uso aos novos avanços tecnológicos, juntamente com técnicas já utilizadas na área da Psicologia. Recorrendo a biossensores cognitivos, através do EPOC da Emotiv, que realiza a leitura de sinais neuronais e de uma aplicação informática criada para o efeito. Apesar de não resolver na totalidade, desejo reduzir substancialmente a dúvida dos Jovens na escolha da sua profissão futura.

Combining Human and Machine Brains - Practical systems in information & control

In this paper a look is taken at how the use of implant technology can be used to either increase the range of the abilities of a human and/or diminish the effects of a neural illness, such as Parkinson's Disease. The key element is the need for a clear interface linking the human brain directly with a computer. The area of interest here is the use of implant technology, particularly where a connection is made between technology and the human brain and/or nervous system. Pilot tests and experimentation are invariably carried out apriori to investigate the eventual possibilities before human subjects are themselves involved. Some of the more pertinent animal studies are discussed here. The paper goes on to describe human experimentation, in particular that carried out by the author himself, which led to him receiving a neural implant which linked his nervous system bi-directionally with the internet. With this in place neural signals were transmitted to various technological devices to directly control them. In particular, feedback to the brain was obtained from the fingertips of a robot hand and ultrasonic (extra) sensory input. A view is taken as to the prospects for the future, both in the near term as a therapeutic device and in the long term as a form of enhancement.

Linking human and machine brains

A look is taken here at how the use of implant technology is rapidly diminishing the effects of certain neural illnesses and distinctly increasing the range of abilities of those affected. An indication is given of a number of problem areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking the human brain directly with a computer. In order to assess the possible opportunities, both human and animal studies are reported on. The main thrust of the paper is however a discussion of neural implant experimentation linking the human nervous system bi-directionally with the internet. With this in place neural signals were transmitted to various technological devices to directly control them, in some cases via the internet, and feedback to the brain was obtained from such as the fingertips of a robot hand, ultrasonic (extra) sensory input and neural signals directly from another human's nervous system. Consideration is given to the prospects for neural implant technology in the future, both in the short term as a therapeutic device and in the long term as a form of enhancement, including the realistic potential for thought communication potentially opening up commercial opportunities. Clearly though, an individual whose brain is part human - part machine can have abilities that far surpass those with a human brain alone. Will such an individual exhibit different moral and ethical values to those of a human.? If so, what effects might this have on society?

On linking human and machine brains

A look is taken here at how the use of implant technology is rapidly diminishing the effects of certain neural illnesses and distinctly increasing the range of abilities of those affected. An indication is given of a number of problem areas in which such technology has already had a profound effect, a key element being the need for a clear interface linking the human brain directly with a computer. In order to assess the possible opportunities, both human and animal studies are reported on. The main thrust of the paper is, however, a discussion of neural implant experimentation linking the human nervous system bi-directionally with the internet. With this in place, neural signals were transmitted to various technological devices to directly control them, in some cases via the internet, and feedback to the brain was obtained from, for example, the fingertips of a robot hand, and ultrasonic (extra) sensory input and neural signals directly from another human's nervous system. Consideration is given to the prospects for neural implant technology in the future, both in the short term as a therapeutic device and in the long term as a form of enhancement, including the realistic potential for thought communication-potentially opening up commercial opportunities. Clearly though, an individual whose brain is part human-part machine can have abilities that far surpass those with a human brain alone. Will such an individual exhibit different moral and ethical values from those of a human? If so, what effects might this have on society? (C) 2008 Elsevier B.V. All rights reserved.

A novel scheme for human-friendly and time-delays robust neuropredictive teleoperation

A novel Neuropredictive Teleoperation (NPT) Scheme is presented. The design results from two key ideas: the exploitation of the measured or estimated neural input to the human arm or its electromyograph (EMG) as the system input and the employment of a predictor of the arm movement, based on this neural signal and an arm model, to compensate for time delays in the system. Although a multitude of such models, as well as measuring devices for the neural signals and the EMG, have been proposed, current telemanipulator research has only been considering highly simplified arm models. In the present design, the bilateral constraint that the master and slave are simultaneously compliant to each other's state (equal positions and forces) is abandoned, thus obtaining a simple to analyzesuccession of only locally controlled modules, and a robustness to time delays of up to 500 ms. The proposed designs were inspired by well established physiological evidence that the brain, rather than controlling the movement on-line, programs the arm with an action plan of a complete movement, which is then executed largely in open loop, regulated only by local reflex loops. As a model of the human arm the well-established Stark model is employed, whose mathematical representation is modified to make it suitable for an engineering application. The proposed scheme is however valid for any arm model. BIBO-stability and passivity results for a variety of local control laws are reported. Simulation results and comparisons with traditional designs also highlight the advantages of the proposed design.

Upgrading humans via implants - why not?

In his forum paper, Prof. Kevin Warwick considers four different examples of how the use of implant technology is opening up the possibility of upgrading human abilities, particularly in terms of mental cognition. The main thrust is an overview of Prof. Warwick's own research, which led to him receiving a neural implant linking his nervous system bi-directionally with the internet. With this implant in place, neural signals were transmitted to various technological devices to directly control them, in some cases via the internet, and feedback to the brain was obtained from such stimuli as the fingertips of a robot hand, ultrasonic (extra-) sensory input and neural signals directly from another human's nervous system. A view is taken as to the prospects for the future, both in the short-term as a therapeutic device and in the long-term as a form of enhancement, including the realistic potential, in the near future, for thought communication – thereby opening up tremendous commercial potential. The therapy/enhancement dichotomy is considered here, as well as military and medical issues. Clearly though, an individual whose brain is part human/part machine can have abilities that far surpass those who remain with a human brain alone. Will such an individual exhibit different moral and ethical values to those of a human? If so, what effects might this have on society?

Imperceptible electrical noise attenuates isometric plantar flexion force fluctuations with correlated reductions in postural sway

Optimal levels of noise stimulation have been shown to enhance the detection and transmission of neural signals thereby improving the performance of sensory and motor systems. The first series of experiments in the present study aimed to investigate whether subsensory electrical noise stimulation applied over the triceps surae (TS) in seated subjects decreases torque variability during a force-matching task of isometric plantar flexion and whether the same electrical noise stimulation decreases postural sway during quiet stance. Correlation tests were applied to investigate whether the noise-induced postural sway decrease is linearly predicted by the noise-induced torque variability decrease. A second series of experiments was conducted to investigate whether there are differences in torque variability between conditions in which the subsensory electrical noise is applied only to the TS, only to the tibialis anterior (TA) and to both TS and TA, during the force-matching task with seated subjects. Noise stimulation applied over the TS muscles caused a significant reduction in force variability during the maintained isometric force paradigm and also decreased postural oscillations during quiet stance. Moreover, there was a significant correlation between the reduction in force fluctuation and the decrease in postural sway with the electrical noise stimulation. This last result indicates that changes in plantar flexion force variability in response to a given subsensory random stimulation of the TS may provide an estimate of the variations in postural sway caused by the same subsensory stimulation of the TS. We suggest that the decreases in force variability and postural sway found here are due to stochastic resonance that causes an improved transmission of proprioceptive information. In the second series of experiments, the reduction in force variability found when noise was applied to the TA muscle alone did not reach statistical significance, suggesting that TS proprioception gives a better feedback to reduce force fluctuation in isometric plantar flexion conditions.

Complexity analysis of spontaneous brain activity: effects of depression and antidepressant treatment

Magnetoencephalography (MEG) allows the real-time recording of neural activity and oscillatory activity in distributed neural networks. We applied a non-linear complexity analysis to resting-state neural activity as measured using whole-head MEG. Recordings were obtained from 20 unmedicated patients with major depressive disorder and 19 matched healthy controls. Subsequently, after 6 months of pharmacological treatment with the antidepressant mirtazapine 30 mg/day, patients received a second MEG scan. A measure of the complexity of neural signals, the Lempel–Ziv Complexity (LZC), was derived from the MEG time series. We found that depressed patients showed higher pre-treatment complexity values compared with controls, and that complexity values decreased after 6 months of effective pharmacological treatment, although this effect was statistically significant only in younger patients. The main treatment effect was to recover the tendency observed in controls of a positive correlation between age and complexity values. Importantly, the reduction of complexity with treatment correlated with the degree of clinical symptom remission. We suggest that LZC, a formal measure of neural activity complexity, is sensitive to the dynamic physiological changes observed in depression and may potentially offer an objective marker of depression and its remission after treatment.

Formation of mnemonic neuronal responses to visual paired associates in inferotemporal cortex is impaired by perirhinal and entorhinal lesions.

Functional roles of the cortical backward signal in long-term memory formation were studied in monkeys performing a visual pair-association task. Before the monkeys learned the task, the anterior commissure was transected, disconnecting the anterior temporal cortex of each hemisphere. After training with 12 pairs of pictures, single units were recorded from the inferotemporal cortex of the monkeys as the control. By injecting a grid of ibotenic acid, we unilaterally lesioned the entorhinal and perirhinal cortex, which provides massive direct and indirect backward projections ipsilaterally to the inferotemporal cortex. After the lesion, the monkeys fixated the cue stimulus normally, relearned the preoperatively learned set (set A), and learned a new set (set B) of paired associates. Then, single units were recorded from the same area as for the prelesion control. We found that (i) in spite of the lesion, the sampled neurons responded strongly and selectively to both the set A and set B patterns and (ii) the paired associates elicited significantly correlated responses in the control neurons before the lesion but not in the cells tested after the lesion, either for set A or set B stimuli. We conclude that the ability of inferotemporal neurons to represent association between picture pairs was lost after the lesion of entorhinal and perirhinal cortex, most likely through disruption of backward neural signals to the inferotemporal neurons, while the ability of the neurons to respond to a particular visual stimulus was left intact.