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Journal articleDe Guio F, Jouvent E, Biessels GJ, et al., 2016,
Reproducibility and variability of quantitative magnetic resonance imaging markers in cerebral small vessel disease.
, Journal of Cerebral Blood Flow & Metabolism, Vol: 36, Pages: 1319-1337, ISSN: 0271-678XBrain imaging is essential for the diagnosis and characterization of cerebral small vessel disease. Several magnetic resonance imaging markers have therefore emerged, providing new information on the diagnosis, progression, and mechanisms of small vessel disease. Yet, the reproducibility of these small vessel disease markers has received little attention despite being widely used in cross-sectional and longitudinal studies. This review focuses on the main small vessel disease-related markers on magnetic resonance imaging including: white matter hyperintensities, lacunes, dilated perivascular spaces, microbleeds, and brain volume. The aim is to summarize, for each marker, what is currently known about: (1) its reproducibility in studies with a scan-rescan procedure either in single or multicenter settings; (2) the acquisition-related sources of variability; and, (3) the techniques used to minimize this variability. Based on the results, we discuss technical and other challenges that need to be overcome in order for these markers to be reliably used as outcome measures in future clinical trials. We also highlight the key points that need to be considered when designing multicenter magnetic resonance imaging studies of small vessel disease.
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Journal articleKim Y, Warren SC, Stone JM, et al., 2016,
Adaptive Multiphoton Endomicroscope Incorporating a Polarization-Maintaining Multicore Optical Fibre
, IEEE Journal of Selected Topics in Quantum Electronics, Vol: 22, ISSN: 1558-4542We present a laser scanning multiphoton endomicroscopewith no distal optics or mechanical components that incorporatesa polarization-maintaining (PM) multicore optical fibre todeliver, focus, and scan ultrashort pulsed radiation for two-photonexcited fluorescence imaging. We show theoretically that the use ofa PM multicore fibre in our experimental configuration enhancesthe fluorescence excitation intensity achieved in the focal spot comparedto a non-PM optical fibre with the same geometry and con-firm this by computer simulations based on numerical wavefrontpropagation. In our experimental system, a spatial light modulator(SLM) is utilised to program the phase of the light input to each ofthe cores of the endoscope fibre such that the radiation emergingfrom the distal end of the fibre interferes to provide the focusedscanning excitation beam. We demonstrate that the SLM can enabledynamic phase correction of path-length variations across themulticore optical fibre whilst the fibre is perturbed with an updaterate of 100 Hz.
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Journal articleReinkensmeyer DJ, Burdet E, Casadio M, et al., 2016,
Computational neurorehabilitation: modeling plasticity and learning to predict recovery
, Journal of Neuroengineering and Rehabilitation, Vol: 13, ISSN: 1743-0003Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.
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Journal articleReichenbach CS, Braiman C, Schiff ND, et al., 2016,
The auditory-brainstem response to continuous, non repetitive speech is modulated by the speech envelope and reflects speech processing
, Frontiers in Computational Neuroscience, Vol: 10, ISSN: 1662-5188The auditory-brainstem response (ABR) to short and simple acoustical signals is an important clinical tool used to diagnose the integrity of the brainstem. The ABR is also employed to investigate the auditory brainstem in a multitude of tasks related to hearing, such as processing speech or selectively focusing on one speaker in a noisy environment. Such research measures the response of the brainstem to short speech signals such as vowels or words. Because the voltage signal of the ABR has a tiny amplitude, several hundred to a thousand repetitions of the acoustic signal are needed to obtain a reliable response. The large number of repetitions poses a challenge to assessing cognitive functions due to neural adaptation. Here we show that continuous, non-repetitive speech, lasting several minutes, may be employed to measure the ABR. Because the speech is not repeated during the experiment, the precise temporal form of the ABR cannot be determined. We show, however, that important structural features of the ABR can nevertheless be inferred. In particular, the brainstem responds at the fundamental frequency of the speech signal, and this response is modulated by the envelope of the voiced parts of speech. We accordingly introduce a novel measure that assesses the ABR as modulated by the speech envelope, at the fundamental frequency of speech and at the characteristic latency of the response. This measure has a high signal-to-noise ratio and can hence be employed effectively to measure the ABR to continuous speech. We use this novel measure to show that the auditory brainstem response is weaker to intelligible speech than to unintelligible, time-reversed speech. The methods presented here can be employed for further research on speech processing in the auditory brainstem and can lead to the development of future clinical diagnosis of brainstem function.
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BookMerletti R, Farina D, 2016,
Surface Electromyography: Physiology, Engineering and Applications
Reflects on developments in noninvasive electromyography, and includes advances and applications in signal detection, processing and interpretation. Addresses EMG imaging technology together with the issue of decomposition of surface EMG. Includes advanced single and multi-channel techniques for information extraction from surface EMG signals. Presents the analysis and information extraction of surface EMG at various scales, from motor units to the concept of muscle synergies.
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Journal articleBraga RM, Fu RZ, Seemungal BM, et al., 2016,
Eye movements during auditory attention predict individual differences in dorsal attention network activity
, Frontiers in Human Neuroscience, Vol: 10, ISSN: 1662-5161The neural mechanisms supporting auditory attention are not fully understood. A dorsal frontoparietal network of brain regions is thought to mediate the spatial orienting of attention across all sensory modalities. Key parts of the this network, the frontal eye fields (FEF) and the superior parietal lobes (SPL), contain retinotopic maps and elicit saccades when stimulated. This suggests that their recruitment during auditory attention might reflect crossmodal oculomotor processes; however this has not been confirmed experimentally. Here we investigate whether task-evoked eye movements during an auditory task can predict the magnitude of activity within the dorsal frontoparietal network. A spatial and non-spatial listening task was used with on-line eye-tracking and functional magnetic resonance imaging. No visual stimuli or cues were used. The auditory task elicited systematic eye movements, with saccade rate and gaze position predicting attentional engagement and the cued sound location, respectively. Activity associated with these separate aspects of evoked eye-movements dissociated between the SPL and FEF. However these observed eye movements could not account for all the activation in the frontoparietal network. Our results suggest that the recruitment of the SPL and FEF during attentive listening reflects, at least partly, overt crossmodal oculomotor processes during non-visual attention. Further work is needed to establish whether the network’s remaining contribution to auditory attention is through covert crossmodal processes, or is directly involved in the manipulation of auditory information.
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Journal articleNegro F, Muceli S, Castronovo AM, et al., 2016,
Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation
, JOURNAL OF NEURAL ENGINEERING, Vol: 13, ISSN: 1741-2560 -
Journal articleHahne JM, Farina D, Jiang N, et al., 2016,
A novel percutaneous electrode implant for improving robustness in advanced myoelectric control
, Frontiers in Neuroscience, Vol: 10, ISSN: 1662-4548Despite several decades of research, electrically powered hand and arm prostheses are still controlled with very simple algorithms that process the surface electromyogram (EMG) of remnant muscles to achieve control of one prosthetic function at a time. More advanced machine learning methods have shown promising results under laboratory conditions. However, limited robustness has largely prevented the transfer of these laboratory advances to clinical applications. In this paper, we introduce a novel percutaneous EMG electrode to be implanted chronically with the aim of improving the reliability of EMG detection in myoelectric control. The proposed electrode requires a minimally invasive procedure for its implantation, similar to a cosmetic micro-dermal implant. Moreover, being percutaneous, it does not require power and data telemetry modules. Four of these electrodes were chronically implanted in the forearm of an able-bodied human volunteer for testing their characteristics. The implants showed significantly lower impedance and greater robustness against mechanical interference than traditional surface EMG electrodes used for myoelectric control. Moreover, the EMG signals detected by the proposed systems allowed more stable control performance across sessions in different days than that achieved with classic EMG electrodes. In conclusion, the proposed implants may be a promising interface for clinically available prostheses.
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Journal articleSartori M, Llyod DG, Farina D, 2016,
Neural data-driven musculoskeletal modeling for personalized neurorehabilitation technologies
, IEEE Transactions on Biomedical Engineering, Vol: 63, Pages: 879-893, ISSN: 0018-9294Objectives: The development of neurorehabilitation technologies requires the profound understanding of the mechanisms underlying an individual's motor ability and impairment. A major factor limiting this understanding is the difficulty of bridging between events taking place at the neurophysiologic level (i.e., motor neuron firings) with those emerging at the musculoskeletal level (i.e. joint actuation), in vivo in the intact moving human. This review presents emerging model-based methodologies for filling this gap that are promising for developing clinically viable technologies. Methods: We provide a design overview of musculoskeletal modeling formulations driven by recordings of neuromuscular activity with a critical comparison to alternative model-free approaches in the context of neurorehabilitation technologies. We present advanced electromyography-based techniques for interfacing with the human nervous system and model-based techniques for translating the extracted neural information into estimates of motor function. Results: We introduce representative application areas where modeling is relevant for accessing neuromuscular variables that could not be measured experimentally. We then show how these variables are used for designing personalized rehabilitation interventions, biologically inspired limbs, and human-machine interfaces. Conclusion: The ability of using electrophysiological recordings to inform biomechanical models enables accessing a broader range of neuromechanical variables than analyzing electrophysiological data or movement data individually. This enables understanding the neuromechanical interplay underlying in vivo movement function, pathology, and robot-assisted motor recovery. Significance: Filling the gap between our understandings of movement neural and mechanical functions is central for addressing one of the major challenges in neurorehabilitation: personalizing current technologies and interventions to an individual's anatomy and impai
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Journal articleMatthews PM, Roncaroli F, Waldman A, et al., 2016,
A practical review of the neuropathology and neuroimaging of multiple sclerosis
, Practical Neurology, Vol: 16, Pages: 279-287, ISSN: 1474-7766The variability in the severity and clinical course of multiple sclerosis (MS) has as its basis an extreme heterogeneity in the location, nature and extent of pathology in the brain and spinal cord. Understanding the underlying neuropathology and associated pathogenetic mechanisms of the disease helps to communicate the rationale for treatment and disease monitoring to patients. Neuroimaging is an important tool for this: it allows clinicians to relate neuropathological changes to clinical presentations and to monitor the course of their disease. Here, we review MS neuropathology and its imaging correlates to provide a practical guide for using MRI to assess disease severity and treatment responses. This provides a foundation for optimal management of patients based on the principle that they show 'no evidence of disease activity'.
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Conference paperTroiani F, Nikolic K, Constandinou TG, 2016,
Optical Coherence Tomography for detection of compound action potential in Xenopus Laevis sciatic nerve
, SPIE Photonics West (BIOS)Due to optical coherence tomography (OCT) high spatial and temporal resolution, this technique could be used to observe the quick changes in the refractive index that accompany action potential. In this study we explorethe use of time domain Optical Coherence Tomography (TD-OCT) for real time action potential detection in ex vivo Xenopus Laevis sciatic nerve. TD-OCT is the easiest and less expensive OCT technique and, if successful indetecting real time action potential, it could be used for low cost monitoring devices. A theoretical investigation into the order of magnitude of the signals detected by a TD-OCT setup is provided by this work. A lineardependence between the refractive index and the intensity changes is observed and the minimum SNR for which the setup could work is found to be SNR = 2 x10⁴.
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Journal articleRossant C, Kadir SN, Goodman DF, et al., 2016,
Spike sorting for large, dense electrode arrays
, Nature Neuroscience, Vol: 19, Pages: 634-641, ISSN: 1546-1726Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely spaced recording sites, and electrodes with thousands of sites are under development. These probes in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons from the raw data captured from the probes. Here we present a set of tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from the cortex, hippocampus and thalamus of rat, mouse, macaque and marmoset, demonstrating error rates as low as 5%.
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Journal articleGuven O, Eftekhar A, Kindt W, et al., 2016,
Computationally-efficient realtime interpolation algorithm for non-uniform sampled biosignals
, Healthcare Technology Letters, Vol: 3, Pages: 105-110, ISSN: 2053-3713This letter presents a novel, computationally-efficient interpolation method that has been optimised for use in ECG baseline drift removal. In our previous work 3 isoelectric baseline points per heart beat are detected, and here utilised as interpolation points. As an extension from linear interpolation, our algorithm segments the interpolation interval and utilises different piecewise linear equations. Thus the algorithm produces a linear curvature that is computationally efficient while avoiding overshoots on nonuniform samples. The proposed algorithm is tested using sinusoids with different fundamental frequencies from 0.05Hz to 0.7Hz and also validated with real baseline wander data acquired from the MIT-BIH Noise Stress Database. The synthetic data results show an RMS error of 0.9μV (mean), 0.63μV (median) and 0.6μV (std. dev.) per heart beat on a 1mVp-p 0.1Hz sinusoid. On real data we obtain an RMS error of 10.9μV (mean), 8.5μV (median) and 9.0μV (std. dev.) per heart beat. Cubic spline interpolation and linear interpolation on the other hand shows 10.7μV, 11.6μV (mean), 7.8μV, 8.9μV(median) and 9.8μV, 9.3μV (std. dev.) per heart beat respectively.
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Journal articleHemakom A, Goverdovsky V, Looney D, et al., 2016,
Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain-computer interface applications
, Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences, Vol: 374, ISSN: 1364-503XAn extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative brain–computer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate.
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Journal articleLin C, Wang B-H, Jiang N, et al., 2016,
Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials.
, IEEE Trans Neural Syst Rehabil Eng, Vol: 24, Pages: 921-927The detection of voluntary motor intention from EEG has been applied to closed-loop brain-computer interfacing (BCI). The movement-related cortical potential (MRCP) is a low frequency component of the EEG signal, which represents movement intention, preparation, and execution. In this study, we aim at detecting MRCPs from single-trial EEG traces. For this purpose, we propose a detector based on a discriminant manifold learning method, called locality sensitive discriminant analysis (LSDA), and we test it in both online and offline experiments with executed and imagined movements. The online and offline experimental results demonstrated that the proposed LSDA approach for MRCP detection outperformed the Locality Preserving Projection (LPP) approach, which was previously shown to be the most accurate algorithm so far tested for MRCP detection. For example, in the online tests, the performance of LSDA was superior than LPP in terms of a significant reduction in false positives (FP) (passive FP: 1.6 ±0.9/min versus 2.9 ±1.0/min, p = 0.002, active FP: 2.2 ±0.8/min versus 2.7 ±0.6/min , p = 0.03 ), for a similar rate of true positives. In conclusion, the proposed LSDA based MRCP detection method is superior to previous approaches and is promising for developing patient-driven BCI systems for motor function rehabilitation as well as for neuroscience research.
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Journal articleScott GPT, Ramlackhansingh A, Edison P, et al., 2016,
Amyloid pathology and axonal injury after brain trauma
, Neurology, Vol: 86, Pages: 821-828, ISSN: 0028-3878Objective: To image amyloid-β (Aβ) plaque burden in long-term survivors of traumatic brain injury (TBI), test whether traumatic axonal injury and Aβ are correlated, and compare the spatial distribution of Aβ to Alzheimer’s disease.Methods: Patients 11 months to 17 years after moderate-severe TBI had 11C-Pittsburgh compound-B (PIB) PET, structural and diffusion MRI and neuropsychological examination. Healthy aged controls and AD patients had PET and structural MRI. Binding potential (BPND) images of 11C-PIB, which index Aβ plaque density, were computed using an automatic reference region extraction procedure. Voxelwise and regional differences in BPND were assessed. In TBI, a measure of white matter integrity, fractional anisotropy (FA), was estimated and correlated with 11C-PIB BPND.Results: 28 participants (9 TBI, 9 controls, 10 AD) were assessed. Increased 11C-PIB BPND was found in TBI versus controls in the posterior cingulate cortex (PCC) and cerebellum. Binding in the PCC increased with decreasing FA of associated white matter tracts, and increased with time since injury. Compared to AD, binding after TBI was lower in neocortical regions, but increased in the cerebellum. Conclusions: Increased Aβ burden was observed in TBI. The distribution overlaps with, but is distinct from, that of AD. This suggests a mechanistic link between TBI and the development of neuropathological features of dementia, which may relate to axonal damage produced by the injury.
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Journal articleMrachacz-Kersting N, Jiang N, Stevenson AJT, et al., 2016,
Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface.
, J Neurophysiol, Vol: 115, Pages: 1410-1421Brain-computer interfaces (BCIs) have the potential to improve functionality in chronic stoke patients when applied over a large number of sessions. Here we evaluated the effect and the underlying mechanisms of three BCI training sessions in a double-blind sham-controlled design. The applied BCI is based on Hebbian principles of associativity that hypothesize that neural assemblies activated in a correlated manner will strengthen synaptic connections. Twenty-two chronic stroke patients were divided into two training groups. Movement-related cortical potentials (MRCPs) were detected by electroencephalography during repetitions of foot dorsiflexion. Detection triggered a single electrical stimulation of the common peroneal nerve timed so that the resulting afferent volley arrived at the peak negative phase of the MRCP (BCIassociative group) or randomly (BCInonassociative group). Fugl-Meyer motor assessment (FM), 10-m walking speed, foot and hand tapping frequency, diffusion tensor imaging (DTI) data, and the excitability of the corticospinal tract to the target muscle [tibialis anterior (TA)] were quantified. The TA motor evoked potential (MEP) increased significantly after the BCIassociative intervention, but not for the BCInonassociative group. FM scores (0.8 ± 0.46 point difference, P = 0.01), foot (but not finger) tapping frequency, and 10-m walking speed improved significantly for the BCIassociative group, indicating clinically relevant improvements. Corticospinal tract integrity on DTI did not correlate with clinical or physiological changes. For the BCI as applied here, the precise coupling between the brain command and the afferent signal was imperative for the behavioral, clinical, and neurophysiological changes reported. This association may become the driving principle for the design of BCI rehabilitation in the future. Indeed, no available BCIs can match this degree of functional improvement with such a short intervention.
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Journal articleMa ZB, Yang Y, Liu YX, et al., 2016,
Recurrently decomposable 2-D convolvers for FPGA-based digital image processing
, IEEE Transactions on Circuits and Systems, Vol: 63, Pages: 979-983, ISSN: 1549-7747Two-dimensional (2-D) convolution is a widely used operation in image processing and computer vision, characterized by intensive computation and frequent memory accesses. Previous efforts to improve the performance of field-programmable gate array (FPGA) convolvers focused on the design of buffering schemes and on minimizing the use of multipliers. A recently proposed recurrently decomposable (RD) filter design method can reduce the computational complexity of 2-D convolutions by splitting the convolution between an image and a large mask into a sequence of convolutions using several smaller masks. This brief explores how to efficiently implement RD based 2-D convolvers using FPGA. Three FPGA architectures are proposed based on RD filters, each with a different buffering scheme. The conclusion is that RD based architectures achieve higher area efficiency than other previously reported state-of-the-art methods, especially for larger convolution masks. An area efficiency metric is also suggested, which allows the most appropriate architecture to be selected.
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Journal articleYousif N, Bhatt H, Bain P, et al., 2016,
The effect of Pedunculopontine nucleus deep brain stimulation on postural sway and vestibular perception
, European Journal of Neurology, Vol: 23, Pages: 668-670, ISSN: 1468-1331Background and purposeDeep brain stimulation (DBS) of the pedunculopontine nucleus (PPN) reduces the number of falls in patients with Parkinson's disease (PD). It was hypothesized that enhanced sensory processing contributes to this PPN-mediated gait improvement.MethodsFour PD patients (and eight matched controls) with implanted bilateral PPN and subthalamic nucleus DBS electrodes were assessed on postural (with/without vision) and vestibular perceptual threshold tasks.ResultsPedunculopontine nucleus ON stimulation (compared to OFF) lowered vestibular perceptual thresholds but there was a disproportionate increase in the normal sway increase on going from light to dark.ConclusionsThe disproportionate increased sway with PPN stimulation in the dark may paradoxically improve balance function since mechanoreceptor signals rapidly adapt to continuous pressure stimulation from postural akinesia. Additionally, the PPN-mediated vestibular signal enhancement also improves the monitoring of postural sway. Overall, PPN stimulation may improve sensory feedback and hence balance performance.
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Journal articleEvans B, Jarvis S, Schultz S, et al., 2016,
PyRhO: A Multiscale Optogenetics Simulation Platform
, Frontiers in Neuroinformatics, Vol: 10, ISSN: 1662-5196Optogenetics has become a key tool for understanding the function of neural circuits and controlling their behavior. An array of directly light driven opsins have been genetically isolated from several families of organisms, with a wide range of temporal and spectral properties. In order to characterize, understand and apply these opsins, we present an integrated suite of open-source, multi-scale computational tools called PyRhO. The purpose of developing PyRhO is three-fold: (i) to characterize new (and existing) opsins by automatically fitting a minimal set of experimental data to three-, four-, or six-state kinetic models, (ii) to simulate these models at the channel, neuron and network levels, and (iii) provide functional insights through model selection and virtual experiments in silico. The module is written in Python with an additional IPython/Jupyter notebook based GUI, allowing models to be fit, simulations to be run and results to be shared through simply interacting with a webpage. The seamless integration of model fitting algorithms with simulation environments (including NEURON and Brian2) for these virtual opsins will enable neuroscientists to gain a comprehensive understanding of their behavior and rapidly identify the most suitable variant for application in a particular biological system. This process may thereby guide not only experimental design and opsin choice but also alterations of the opsin genetic code in a neuro-engineering feed-back loop. In this way, we expect PyRhO will help to significantly advance optogenetics as a tool for transforming biological sciences.
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Journal articleKozlov A, Gentner T, 2016,
Central auditory neurons have composite receptive fields
, Proceedings of the National Academy of Sciences of the United States of America, Vol: 113, Pages: 1441-1446, ISSN: 0027-8424High-level neurons processing complex, behaviorally relevant signals are sensitive to conjunctions of features. Characterizing the receptive fields of such neurons is difficult with standard statistical tools, however, and the principles governing their organization remain poorly understood. Here, we demonstrate multiple distinct receptive-field features in individual high-level auditory neurons in a songbird, European starling, in response to natural vocal signals (songs). We then show that receptive fields with similar characteristics can be reproduced by an unsupervised neural network trained to represent starling songs with a single learning rule that enforces sparseness and divisive normalization. We conclude that central auditory neurons have composite receptive fields that can arise through a combination of sparseness and normalization in neural circuits. Our results, along with descriptions of random, discontinuous receptive fields in the central olfactory neurons in mammals and insects, suggest general principles of neural computation across sensory systems and animal classes.
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Journal articleRiillo F, Bagnato C, Allievi AG, et al., 2016,
A Simple fMRI Compatible Robotic Stimulator to Study the Neural Mechanisms of Touch and Pain.
, Annals of Biomedical Engineering, Vol: 44, Pages: 2431-2441, ISSN: 1573-9686This paper presents a simple device for the investigation of the human somatosensory system with functional magnetic imaging (fMRI). PC-controlled pneumatic actuation is employed to produce innocuous or noxious mechanical stimulation of the skin. Stimulation patterns are synchronized with fMRI and other relevant physiological measurements like electroencephalographic activity and vital physiological parameters. The system allows adjustable regulation of stimulation parameters and provides consistent patterns of stimulation. A validation experiment demonstrates that the system safely and reliably identifies clusters of functional activity in brain regions involved in the processing of pain. This new device is inexpensive, portable, easy-to-assemble and customizable to suit different experimental requirements. It provides robust and consistent somatosensory stimulation, which is of crucial importance to investigating the mechanisms of pain and its strong connection with the sense of touch.
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Journal articleXu R, Jiang N, Dosen S, et al., 2016,
Endogenous Sensory Discrimination and Selection by a Fast Brain Switch for a High Transfer Rate Brain-Computer Interface.
, IEEE Trans Neural Syst Rehabil Eng, Vol: 24, Pages: 901-910In this study, we present a novel multi-class brain-computer interface (BCI) for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input. The user discriminated these choices by his/her endogenous sensory ability and selected the desired choice with an intuitive motor task. This selection was detected by a fast brain switch based on real-time detection of movement-related cortical potentials from scalp EEG. We demonstrated the feasibility of such a system with a four-class BCI, yielding a true positive rate of ∼ 80% and ∼ 70%, and an information transfer rate of ∼ 7 bits/min and ∼ 5 bits/min, for the movement and imagination selection command, respectively. Furthermore, when the system was extended to eight classes, the throughput of the system was improved, demonstrating the capability of accommodating a large number of classes. Combining the endogenous sensory discrimination with the fast brain switch, the proposed system could be an effective, multi-class, gaze-independent BCI system for communication and control applications.
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Journal articleLorenz R, Monti RP, Ribeiro Violante I, et al., 2016,
The Automatic Neuroscientist: A framework for optimizing experimentaldesign with closed-loop real-time fMRI
, Neuroimage, Vol: 129, Pages: 320-334, ISSN: 1095-9572Functional neuroimaging typically explores how a particular task activates a set of brain regions. Importantly though, the same neural system can be activated by inherently different tasks. To date, there is no approach available that systematically explores whether and how distinct tasks probe the same neural system. Here, we propose and validate an alternative framework, the Automatic Neuroscientist, which turns the standard fMRI approach on its head. We use real-time fMRI in combination with modern machine-learning techniques to automatically design the optimal experiment to evoke a desired target brain state. In this work, we present two proof-of-principle studies involving perceptual stimuli. In both studies optimization algorithms of varying complexity were employed; the first involved a stochastic approximation method while the second incorporated a more sophisticated Bayesian optimization technique. In the first study, we achieved convergence for the hypothesized optimum in 11 out of 14 runs in less than 10 min. Results of the second study showed how our closed-loop framework accurately and with high efficiency estimated the underlying relationship between stimuli and neural responses for each subject in one to two runs: with each run lasting 6.3 min. Moreover, we demonstrate that using only the first run produced a reliable solution at a group-level. Supporting simulation analyses provided evidence on the robustness of the Bayesian optimization approach for scenarios with low contrast-to-noise ratio. This framework is generalizable to numerous applications, ranging from optimizing stimuli in neuroimaging pilot studies to tailoring clinical rehabilitation therapy to patients and can be used with multiple imaging modalities in humans and animals.
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Journal articleCheung K, Schultz SR, Luk W, 2016,
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors
, Frontiers in Neuroscience, Vol: 9, ISSN: 1662-4548NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation.
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Journal articleTang J, Ardila Jimenez S, Chakraborty S, et al., 2016,
Visual receptive field properties of neurons in the mouse lateral geniculate nucleus
, PLOS One, Vol: 11, ISSN: 1932-6203The lateral geniculate nucleus (LGN) is increasingly regarded as a “smart-gating” operator for processing visual information. Therefore, characterizing the response properties of LGN neurons will enable us to better understand how neurons encode and transfer visual signals. Efforts have been devoted to study its anatomical and functional features, and recent advances have highlighted the existence in rodents of complex features such as direction/orientation selectivity. However, unlike well-researched higher-order mammals such as primates, the full array of response characteristics vis-à-vis its morphological features have remained relatively unexplored in the mouse LGN. To address the issue, we recorded from mouse LGN neurons using multisite-electrode-arrays (MEAs) and analysed their discharge patterns in relation to their location under a series of visual stimulation paradigms. Several response properties paralleled results from earlier studies in the field and these include centre-surround organization, size of receptive field, spontaneous firing rate and linearity of spatial summation. However, our results also revealed “high-pass” and “low-pass” features in the temporal frequency tuning of some cells, and greater average contrast gain than reported by earlier studies. In addition, a small proportion of cells had direction/orientation selectivity. Both “high-pass” and “low-pass” cells, as well as direction and orientation selective cells, were found only in small numbers, supporting the notion that these properties emerge in the cortex. ON- and OFF-cells showed distinct contrast sensitivity and temporal frequency tuning properties, suggesting parallel projections from the retina. Incorporating a novel histological technique, we created a 3-D LGN volume model explicitly capturing the morphological features of mouse LGN and localising individual cells into anterior/middle/posterior LGN. Based on th
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Book chapterLourenço PR, Abbott WW, Faisal AA, 2016,
Supervised EEG ocular artefact correction through eye-tracking
, Advances in Neurotechnology, Electronics and Informatics, Editors: Londral, Encarnação, Publisher: Springer International Publishing, Pages: 99-113, ISBN: 978-3-319-26240-6Electroencephalography (EEG) is a widely used brain signal recording technique with many uses. The information conveyed in these recordings is a useful tool in the diagnosis of some diseases and disturbances, basic science, as well as in the development of non-invasive Brain-Machine Interfaces (BMI). However, the electrical recording setup comes with two major downsides, a. poor signal-to-noise ratio and b. the vulnerability to any external and internal noise sources. One of the main sources of artefacts is eye movements due to the electric dipole between the cornea and the retina. We have previously proposed that monitoring eye-movements provides a complementary signal for BMIs. Here we propose a novel technique to remove eye-related artefacts from the EEG recordings. We coupled Eye Tracking with EEG allowing us to independently measure when ocular artefact events occur through the eye tracker and thus clean them up in a targeted “supervised” manner instead of using a “blind” artefact clean up correction technique. Three standard methods of artefact correction were applied in an event-driven, supervised manner: 1. Independent Components Analysis (ICA), 2. Wiener Filter and 3. Wavelet Decomposition and compared to “blind” unsupervised ICA clean up. These are standard artefact correction approaches implemented in many toolboxes and experimental EEG systems and could easily be applied by their users in an event-driven manner. Already the qualitative inspection of the clean up traces shows that the simple targeted artefact event-driven clean up outperforms the traditional “blind” clean up approaches. We conclude that this justifies the small extra effort of performing simultaneous eye tracking with any EEG recording to enable simple, but targeted, automatic artefact removal that preserves more of the original signal.
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Book chapterFaisal A, Krebs HI, Pedotti A, 2016,
Hands on neurotechnology
, Pages: VII-VIII -
Conference paperHassan S, Nightingale AM, Leong CL, et al., 2016,
Wearable droplet-based microfluidic sensor device for continuous sampling and real-time analysis
, Pages: 43-44This paper reports a wearable droplet-microfluidic based continuous chemical sensing device. The device combines microdialysis and droplet microfluidic techniques, can continuously sample from interstitial or other body fluids into nanolitre droplets and perform biochemical assays and measurements in situ and in real-time.
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Book chapterRodríguez M, Sylaidi A, Faisal AA, 2016,
An fMRI-Compatible System for 3DOF Motion Tracking of Objects in Haptic Motor Control Studies
, Advances in Neurotechnology, Electronics and Informatics, Editors: Londral, Encarnação, Publisher: Springer International Publishing, Pages: 115-123, ISBN: 978-3-319-26240-6
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