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  • Journal article
    Ghoreishizadeh S, Haci D, Liu Y, Donaldson N, Constandinou TGet al., 2017,

    Four-Wire Interface ASIC for a Multi-Implant Link

    , IEEE Transactions on Circuits and Systems I: Regular Papers, Vol: 64, Pages: 3056-3067, ISSN: 1549-8328

    This paper describes an on-chip interface for recovering power and providing full-duplex communication over an AC-coupled 4-wire lead between active implantable devices. The target application requires two modules to be implanted in the brain (cortex) and upper chest; connected via a subcutaneous lead. The brain implant consists of multiple identical ‘optrodes’ that facilitate a bidirectional neural interface (electrical recording, optical stimulation), and chest implant contains the power source (battery) and processor module. The proposed interface is integrated within each optrode ASIC allowing full-duplex and fully-differential communication based on Manchester encoding. The system features a head-to-chest uplink data rate(up to 1.6 Mbps) that is higher than that of the chest-to-head downlink (100 kbps) which is superimposed on a power carrier. On-chip power management provides an unregulated 5V DC supply with up to 2.5mA output current for stimulation, and two regulated voltages (3.3V and 3V) with 60 dB PSRR for recording and logic circuits. The 4-wire ASIC has been implemented in a 0.35 um CMOS technology, occupying 1.5mm2 silicon area,and consumes a quiescent current of 91.2u A. The system allows power transmission with measured efficiency of up to 66% from the chest to the brain implant. The downlink and uplink communication are successfully tested in a system with two optrodes and through a 4-wire implantable lead.

  • Conference paper
    Noronha B, Dziemian S, Zito GA, Konnaris C, Faisal AAet al., 2017,

    "Wink to grasp" – comparing eye, voice & EMG gesture control of grasp with soft-robotic gloves

    , IEEE Conference on Rehabilitation Robotics (ICORR 2017), Publisher: IEEE, Pages: 1043-1048

    The ability of robotic rehabilitation devices to support paralysed end-users is ultimately limited by the degree to which human-machine-interaction is designed to be effective and efficient in translating user intention into robotic action. Specifically, we evaluate the novel possibility of binocular eye-tracking technology to detect voluntary winks from involuntary blink commands, to establish winks as a novel low-latency control signal to trigger robotic action. By wearing binocular eye-tracking glasses we enable users to directly observe their environment or the actuator and trigger movement actions, without having to interact with a visual display unit or user interface. We compare our novel approach to two conventional approaches for controlling robotic devices based on electromyo-graphy (EMG) and speech-based human-computer interaction technology. We present an integrated software framework based on ROS that allows transparent integration of these multiple modalities with a robotic system. We use a soft-robotic SEM glove (Bioservo Technologies AB, Sweden) to evaluate how the 3 modalities support the performance and subjective experience of the end-user when movement assisted. All 3 modalities are evaluated in streaming, closed-loop control operation for grasping physical objects. We find that wink control shows the lowest error rate mean with lowest standard deviation of (0.23 ± 0.07, mean ± SEM) followed by speech control (0.35 ± 0. 13) and EMG gesture control (using the Myo armband by Thalamic Labs), with the highest mean and standard deviation (0.46 ± 0.16). We conclude that with our novel own developed eye-tracking based approach to control assistive technologies is a well suited alternative to conventional approaches, especially when combined with 3D eye-tracking based robotic end-point control.

  • Conference paper
    Etard, Reichenbach J,

    EEG-measured correlates of comprehension in speech-in-noise listening

    , Basic Auditory Science 2017
  • Conference paper
    Troiani F, Nikolic K, Constandinou TG, 2017,

    Optical coherence tomography for compound action potential detection: a computational study

    , SPIE/OSA European Conferences on Biomedical Optics (ECBO), Publisher: Optical Society of America / SPIE, Pages: 1-3

    The feasibility of using time domain optical coherence tomography (TD-OCT) to detect compound action potential in a peripheral nerve and the setup characteristics, are studied through the use of finite-difference time-domain (FDTD) technique.

  • Journal article
    Huntley JD, Hampshire A, Bor D, Owen AM, Howard RJet al., 2017,

    The importance of sustained attention in early Alzheimer's disease.

    , Int J Geriatr Psychiatry, Vol: 32, Pages: 860-867

    INTRODUCTION: There is conflicting evidence regarding impairment of sustained attention in early Alzheimer's disease (AD). We examine whether sustained attention is impaired and predicts deficits in other cognitive domains in early AD. METHODS: Fifty-one patients with early AD (MMSE > 18) and 15 healthy elderly controls were recruited. The sustained attention to response task (SART) was used to assess sustained attention. A subset of 25 patients also performed tasks assessing general cognitive function (ADAS-Cog), episodic memory (Logical memory scale, Paired Associates Learning), executive function (verbal fluency, grammatical reasoning) and working memory (digit and spatial span). RESULTS: AD patients were significantly impaired on the SART compared to healthy controls (total error β = 19.75, p = 0.027). SART errors significantly correlated with MMSE score (Spearman's rho = -0.338, p = 0.015) and significantly predicted deficits in ADAS-Cog (β = 0.14, p = 0.004). DISCUSSIONS: Patients with early AD have significant deficits in sustained attention, as measured using the SART. This may impair performance on general cognitive testing, and therefore should be taken into account during clinical assessment, and everyday management of individuals with early AD. Copyright © 2016 John Wiley & Sons, Ltd.

  • Journal article
    Sidiras C, Iliadou V, Nimatoudis I, Reichenbach T, Bamiou D-Eet al., 2017,

    Spoken word recognition enhancement due to preceding synchronized beats compared to unsynchronized or unrhythmic beats

    , Frontiers in Neuroscience, Vol: 11, ISSN: 1662-4548

    The relation between rhythm and language has been investigated over the last decades, with evidence that these share overlapping perceptual mechanisms emerging from several different strands of research. The dynamic Attention Theory posits that neural entrainment to musical rhythm results in synchronized oscillations in attention, enhancing perception of other events occurring at the same rate. In this study, this prediction was tested in 10 year-old children by means of a psychoacoustic speech recognition in babble paradigm. It was hypothesized that rhythm effects evoked via a short isochronous sequence of beats would provide optimal word recognition in babble when beats and word are in sync. We compared speech recognition in babble performance in the presence of isochronous and in sync vs. non-isochronous or out of sync sequence of beats. Results showed that (a) word recognition was the best when rhythm and word were in sync, and (b) the effect was not uniform across syllables and gender of subjects. Our results suggest that pure tone beats affect speech recognition at early levels of sensory or phonemic processing.

  • Journal article
    Leene L, Constandinou TG, 2017,

    Time Domain Processing Techniques Using Ring Oscillator-Based Filter Structures

    , IEEE Transactions on Circuits and Systems I: Regular Papers, Vol: 64, Pages: 3003-3012, ISSN: 1549-8328

    The ability to process time-encoded signals with high fidelity is becoming increasingly important for the time domain (TD) circuit techniques that are used at the advanced nanometer technology nodes. This paper proposes a compact oscillator-based subsystem that performs precise filtering of asynchronous pulse-width modulation encoded signals and makes extensive use of digital logic, enabling low-voltage operation. First- and second-order primitives are introduced that can be used as TD memory or to enable analogue filtering of TD signals. These structures can be modeled precisely to realize more advanced linear or nonlinear functionality using an ensemble of units. This paper presents the measured results of a prototype fabricated using a 65-nm CMOS technology to realize a fourth- order low-pass Butterworth filter. The system utilizes a 0.5-V supply voltage with asynchronous digital control for closed-loop operation to achieve a 73-nW power budget. The implemented filter achieves a maximum signal to noise and distortion ratio of 53 dB with a narrow 5-kHz bandwidth resulting in an figure- of-merit of 8.2 fJ/pole. With this circuit occupying a compact 0.004-mm2 silicon footprint, this technique promises a substantial reduction in size over conventional Gm-C filters, whilst addition- ally offering direct integration with digital systems.

  • Journal article
    Burridge JH, Lee ACW, Turk R, Stokes M, Whitall J, Vaidyanathan R, Clatworthy P, Hughes A-M, Meagher C, Franco E, Yardley Let al., 2017,

    Telehealth, Wearable Sensors, and the Internet: Will They Improve Stroke Outcomes Through Increased Intensity of Therapy, Motivation, and Adherence to Rehabilitation Programs?

    , JOURNAL OF NEUROLOGIC PHYSICAL THERAPY, Vol: 41, Pages: S32-S38, ISSN: 1557-0576
  • Conference paper
    Ghoreishizadeh S, Haci D, Liu Y, Constandinou Tet al., 2017,

    A 4-wire interface SoC for shared multi-implant power transfer and full-duplex communication

    , IEEE Latin American symposium on Circuits and Systems (LASCAS), Publisher: IEEE, Pages: 49-52, ISSN: 2473-4667

    This paper describes a novel system for recovering power and providing full-duplex communication over an AC-coupled 4-wire lead between active implantable devices. The target application requires a single Chest Device be connected to a Brain Implant consisting of multiple identical optrodes that record neural activity and provide closed loop optical stimulation. The interface is integrated within each optrode SoC allowing full-duplex and fully-differential communication based on Manchester encoding. The system features a head-to-chest uplink data rate (1.6 Mbps) that is higher than that of the chest-to-head downlink (100kbps) superimposed on a power carrier. On-chip power management provides an unregulated 5 V DC supply with up to 2.5 mA output current for stimulation, and a regulated 3.3 V with 60 dB PSRR for recording and logic circuits. The circuit has been implemented in a 0.35 μm CMOS technology, occupying 1.4 mm 2 silicon area, and requiring a 62.2 μA average current consumption.

  • Journal article
    Ciganovic N, Wolde-Kidan A, Reichenbach JDT, 2017,

    Hair bundles of cochlear outer hair cells are shaped to minimize their fluid-dynamic resistance

    , Scientific Reports, Vol: 7, ISSN: 2045-2322

    The mammalian sense of hearing relies on two types of sensory cells: inner hair cells transmit the auditory stimulus to the brain, while outer hair cells mechanically modulate the stimulus through active feedback. Stimulation of a hair cell is mediated by displacements of its mechanosensitive hair bundle which protrudes from the apical surface of the cell into a narrow fluid-filled space between reticular lamina and tectorial membrane. While hair bundles of inner hair cells are of linear shape, those of outer hair cells exhibit a distinctive V-shape. The biophysical rationale behind this morphology, however, remains unknown. Here we use analytical and computational methods to study the fluid flow across rows of differently shaped hair bundles. We find that rows of V-shaped hair bundles have a considerably reduced resistance to crossflow, and that the biologically observed shapes of hair bundles of outer hair cells are near-optimal in this regard. This observation accords with the function of outer hair cells and lends support to the recent hypothesis that inner hair cells are stimulated by a net flow, in addition to the well-established shear flow that arises from shearing between the reticular lamina and the tectorial membrane.

  • Conference paper
    Angeles P, Tai Y, Pavese N, Vaidyanathan Ret al., 2017,

    Assessing Parkinson's disease motor symptoms using supervised learning algorithms

    , 21st International Congress of Parkinson's Disease and Movement Disorders, Publisher: WILEY, ISSN: 0885-3185
  • Conference paper
    Maimon-Mor RO, Fernandez-Quesada J, Zito GA, Konnaris C, Dziemian S, Faisal AAet al.,

    Towards free 3D end-point control for robotic-assisted human reaching using binocular eye tracking

    , 15th IEEE Conference on Rehabilitation Robotics (ICORR 2017), Publisher: IEEE

    Eye-movements are the only directly observablebehavioural signals that are highly correlated with actions atthe task level, and proactive of body movements and thus reflectaction intentions. Moreover, eye movements are preserved inmany movement disorders leading to paralysis (or amputees)from stroke, spinal cord injury, Parkinson’s disease, multiplesclerosis, and muscular dystrophy among others. Despite thisbenefit, eye tracking is not widely used as control interface forrobotic interfaces in movement impaired patients due to poorhuman-robot interfaces. We demonstrate here how combining3D gaze tracking using our GT3D binocular eye tracker withcustom designed 3D head tracking system and calibrationmethod enables continuous 3D end-point control of a roboticarm support system. The users can move their own hand to anylocation of the workspace by simple looking at the target andwinking once. This purely eye tracking based system enablesthe end-user to retain free head movement and yet achieves highspatial end point accuracy in the order of 6 cm RMSE error ineach dimension and standard deviation of 4 cm. 3D calibrationis achieved by moving the robot along a 3 dimensional spacefilling Peano curve while the user is tracking it with theireyes. This results in a fully automated calibration procedurethat yields several thousand calibration points versus standardapproaches using a dozen points, resulting in beyond state-of-the-art 3D accuracy and precision.

  • Conference paper
    Angeles P, Tai Y, Pavese N, Wilson S, vaidyanathan Ret al.,

    Automated assessment of symptom severity changes during deep brain stimulation (DBS) therapy for Parkinson's disease.

    , Publisher: Institute of Electrical and Electronics Engineers Inc., ISSN: 1945-7901
  • Conference paper
    Luan S, Williams I, De-Carvalho F, Grand L, Jackson A, Quian Quiroga R, Constandinou TGet al., 2017,

    Standalone headstage for neural recording with real-time spike sorting and data logging

    , BNA Festival of Neuroscience, Publisher: The British Neuroscience Association Ltd
  • Journal article
    Quicke P, Barnes SJ, Knöpfel T, 2017,

    Imaging of Brain Slices with a Genetically Encoded Voltage Indicator.

    , Methods Mol Biol, Vol: 1563, Pages: 73-84

    Functional fluorescence microscopy of brain slices using voltage sensitive fluorescent proteins (VSFPs) allows large scale electrophysiological monitoring of neuronal excitation and inhibition. We describe the equipment and techniques needed to successfully record functional responses optical voltage signals from cells expressing a voltage indicator such as VSFP Butterfly 1.2. We also discuss the advantages of voltage imaging and the challenges it presents.

  • Conference paper
    Forte AE, Etard O, Reichenbach J, 2017,

    Complex Auditory-brainstem Response to the Fundamental Frequency of Continuous Natural Speech

    , ARO 2017
  • Journal article
    Kirkpatrick J, Pascanu R, Rabinowitz N, Veness J, Desjardins G, Rusu AA, Milan K, Quan J, Ramalho T, Grabska-Barwinska A, Hassabis D, Clopath C, Kumaran D, Hadsell Ret al.,

    Overcoming catastrophic forgetting in neural networks

    , Proceedings of the National Academy of Sciences of the United States of America, ISSN: 1091-6490

    The ability to learn tasks in a sequential fashion is crucial to the developmentof artificial intelligence. Until now neural networks havenot been capable of this and it has been widely thought that catastrophicforgetting is an inevitable feature of connectionist models.We show that it is possible to overcome this limitation and train networksthat can maintain expertise on tasks which they have not experiencedfor a long time. Our approach remembers old tasks by selectivelyslowing down learning on the weights important for thosetasks. We demonstrate our approach is scalable and effective bysolving a set of classification tasks based on the MNIST hand writtendigit dataset and by learning several Atari 2600 games sequentially.

  • Conference paper
    Luan S, Liu Y, Williams I, Constandinou TGet al., 2017,

    An Event-Driven SoC for Neural Recording

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 404-407

    This paper presents a novel 64-channel ultra-low power/low noise neural recording System-on-Chip (SoC) featuring a highly reconfigurable Analogue Front-End (AFE) and block-selectable data-driven output. This allows a tunable bandwidth/sampling rate for extracting Local Field Potentials (LFPs)and/or Extracellular Action Potentials (EAPs). Realtime spike detection utilises a dual polarity simple threshold to enable an event driven output for neural spikes (16-sample window). The 64-channels are organised into 16 sets of 4-channel recording blocks, with each block having a dedicated 10-bit SAR ADC that is time division multiplexed among the 4 channels. Eachchannel can be individually powered down and configured for bandwidth, gain and detection threshold. The output can thus combine continuous-streaming and event-driven data packets with the system configured as SPI slave. The SoC is implemented in a commercially-available 0.35u m CMOS technology occupying a silicon area of 19.1mm^2 (0.3mm^2 gross per channel) and requiring 32uW/channel power consumption (AFE only).

  • Conference paper
    Frehlick Z, Williams I, Constandinou TG, 2017,

    Improving Neural Spike Sorting Performance Using Template Enhancement

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 524-527

    This paper presents a novel method for improving the performance of template matching in neural spike sorting for similar shaped spikes, without increasing computational complexity. Mean templates for similar shaped spikes are enhanced to emphasise distinguishing features. Template optimisation is based on the variance of sample distributions. Improved spike sorting performance is demonstrated on simulated neural recordings with two and three neuron spike shapes. The method is designed for implementation on a Next Generation Neural Interface (NGNI) device at Imperial College London.

  • Conference paper
    Williams I, Rapeaux A, Liu Y, Luan S, Constandinou TGet al., 2017,

    A 32-channel bidirectional neural/EMG interface with on-chip spike detection for sensorimotor feedback

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 528-531

    This paper presents a novel 32-channel bidirectional neural interface, capable of high voltage stimulation and low power, low-noise neural recording. Current-controlled biphasic pulses are output with a voltage compliance of 9.25V, user configurable amplitude (max. 315 uA) & phase duration (max. 2 ms). The low-voltage recording amplifiers consume 23 uW per channel with programmable gain between 225 - 4725. Signals are10-bit sampled at 16 kHz. Data rates are reduced by granular control of active recording channels, spike detection and event-driven communication, and repeatable multi-pulse stimulation configurations.

  • Conference paper
    Leene L, Constandinou TG, 2017,

    A 2.7uW/Mips, 0.88GOPS/mm^2 Distributed Processor for Implantable Brain Machine Interfaces

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 360-363

    This paper presents a scalable architecture in 0.18u m CMOS for implantable brain machine interfaces (BMI) that enables micro controller flexibility for data analysis at the sensor interface. By introducing more generic computational capabilities the system is capable of high level adaptive function to potentially improve the long term efficacy of invasive implants. This topology features a compact ultra low power distributedprocessor that supports 64-channel neural recording system on chip (SOC) with a computational efficiency of 2.7uW/MIPS with a total chip area of 1.37mm2. This configuration executes 1024 instructions on each core at 20MHz to consolidate full spectrum high precision recordings from 4 analogue channels for filtering, spike detection, and feature extraction in the digital domain.

  • Conference paper
    Lauteslager T, Tommer M, Kjelgard KG, Lande TS, Constandinou TGet al., 2017,

    Intracranial Heart Rate Detection Using UWB Radar

    , IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 119-122

    Microwave imaging is a promising technique for noninvasive imaging of brain activity. A multistatic array of body coupled antennas and single chip pulsed ultra-wideband radars should be capable of detecting local changes in cerebral blood volume, a known indicator for neural activity. As an initialverification that small changes in the cerebrovascular system can indeed be measured inside the skull, we recorded the heart rate intracranially using a single radar module and two body coupled antennas. The obtained heart rate was found to correspond to ECG measurements. To confirm that the measured signal was indeed from within the skull, we performed simulations to predict the time-of-flight of radar pulses passing through differentanatomical structures of the head. Simulated time-of-flight through the brain corresponded to the measured delay of heart rate modulation in the radar signal. The detection of intracranial heart rate using microwave techniques has not previously been reported, and serves as a first proof that functional neuroimaging using radar could lie within reach.

  • Journal article
    Pedrosa V, Clopath C, 2017,

    The role of neuromodulators in cortical lasticity. A computational perspective

    , Frontiers in Synaptic Neuroscience, Vol: 8, ISSN: 1663-3563

    Neuromodulators play a ubiquitous role across the brain in regulating plasticity. With recent advances in experimental techniques, it is possible to study the effects of diverse neuromodulatory states in specific brain regions. Neuromodulators are thought to impact plasticity predominantly through two mechanisms: the gating of plasticity and the upregulation of neuronal activity. However, the consequences of these mechanisms are poorly understood and there is a need for both experimental and theoretical exploration. Here we illustrate how neuromodulatory state affects cortical plasticity through these two mechanisms. First, we explore the ability of neuromodulators to gate plasticity by reshaping the learning window for spike-timing-dependent plasticity. Using a simple computational model, we implement four different learning rules and demonstrate their effects on receptive field plasticity. We then compare the neuromodulatory effects of upregulating learning rate versus the effects of upregulating neuronal activity. We find that these seemingly similar mechanisms do not yield the same outcome: upregulating neuronal activity can lead to either a broadening or a sharpening of receptive field tuning, whereas upregulating learning rate only intensifies the sharpening of receptive field tuning. This simple model demonstrates the need for further exploration of the rich landscape of neuromodulator-mediated plasticity. Future experiments, coupled with biologically detailed computational models, will elucidate the diversity of mechanisms by which neuromodulatory state regulates cortical plasticity.

  • Conference paper
    Huang H-Y, Farkhatdinov I, Arami A, Burdet Eet al., 2017,

    Modelling Neuromuscular Function of SCI Patients in Balancing

    , 3rd International Conference on NeuroRehabilitation (ICNR), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 355-359, ISSN: 2195-3562
  • Journal article
    Rogers ML, Leong CL, Gowers SAN, Samper IC, Jewell SL, Khan A, McCarthy L, Pahl C, Tolias CM, Walsh DC, Strong AJ, Boutelle MGet al., 2017,

    Simultaneous monitoring of potassium, glucose and lactate during spreading depolarisation in the injured human brain - proof of principle of a novel real-time neurochemical analysis system, continuous online microdialysis (coMD)

    , Journal of Cerebral Blood Flow and Metabolism, Vol: 37, Pages: 1883-1895, ISSN: 1559-7016

    Spreading Depolarisations (SDs) occur spontaneously and frequently in injured human brain. They propagate slowly through injured tissue often cycling around a local area of damage. Tissue recovery after an SD requires greatly augmented energy utilisation to normalise ionic gradients from a virtually complete loss of membrane potential. In the injured brain this is difficult because local blood flow is often low and unreactive. In this study we use a new variant of microdialysis, continuous on-line microdialysis (coMD), to observe the effects of SDs on brain metabolism. The neurochemical changes are dynamic and take place on the timescale of the passage of an SD past the microdialysis probe. Dialysate potassium levels provide an ionic correlate of cellular depolarisation and show a clear transient increase. Dialysate glucose levels reflect a balance between local tissue glucose supply and utilization. These show a clear transient decrease of variable magnitude and duration. Dialysate lactate levels indicate non-oxidative metabolism of glucose and show a transient increase. Preliminary data suggest that the transient changes recover more slowly after the passage of a sequence of multiple SD’s giving rise to a decrease in basal dialysate glucose and an increase in basal dialysate potassium and lactate levels.

  • Journal article
    Monti RP, Lorenz R, Braga RM, Anagnostopoulos C, Leech R, Montana Get al., 2017,

    Real-time estimation of dynamic functional connectivity networks

    , Human Brain Mapping, Vol: 38, Pages: 202-220, ISSN: 1097-0193

    Two novel and exciting avenues of neuroscientific research involve the study of task-driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real-time. While the former is a well-established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real-time fMRI studies have focused on a single brain region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real-time. In this work, we propose a novel methodology with which to accurately track changes in time-varying functional connectivity networks in real-time. The proposed method is shown to perform competitively when compared to state-of-the-art offline algorithms using both synthetic as well as real-time fMRI data. The proposed method is applied to motor task data from the Human Connectome Project as well as to data obtained from a visuospatial attention task. We demonstrate that the algorithm is able to accurately estimate task-related changes in network structure in real-time. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

  • Journal article
    Ghajari M, Hellyer P, Sharp D, 2016,

    Computational modelling of traumatic brain injury predicts the location of chronic traumatic encephalopathy pathology

    , Brain, Vol: 140, Pages: 333-343, ISSN: 0006-8950

    Traumatic brain injury can lead to the neurodegenerative disease chronic traumatic encephalopathy. This condition has a clear neuropathological definition but the relationship between the initial head impact and the pattern of progressive brain pathology is poorly understood. We test the hypothesis that mechanical strain and strain rate are greatest in sulci, where neuropathology is prominently seen in chronic traumatic encephalopathy, and whether human neuroimaging observations converge with computational predictions. Three distinct types of injury were simulated. Chronic traumatic encephalopathy can occur after sporting injuries, so we studied a helmet-to-helmet impact in an American football game. In addition, we investigated an occipital head impact due to a fall from ground level and a helmeted head impact in a road traffic accident involving a motorcycle and a car. A high fidelity 3D computational model of brain injury biomechanics was developed and the contours of strain and strain rate at the grey matter–white matter boundary were mapped. Diffusion tensor imaging abnormalities in a cohort of 97 traumatic brain injury patients were also mapped at the grey matter–white matter boundary. Fifty-one healthy subjects served as controls. The computational models predicted large strain most prominent at the depths of sulci. The volume fraction of sulcal regions exceeding brain injury thresholds were significantly larger than that of gyral regions. Strain and strain rates were highest for the road traffic accident and sporting injury. Strain was greater in the sulci for all injury types, but strain rate was greater only in the road traffic and sporting injuries. Diffusion tensor imaging showed converging imaging abnormalities within sulcal regions with a significant decrease in fractional anisotropy in the patient group compared to controls within the sulci. Our results show that brain tissue deformation induced by head impact loading is greatest in sulcal

  • Book chapter
    Faisal AA, Neishabouri A, 2016,

    Fundamental Constraints on the Evolution of Neurons

    , The Wiley-Blackwell Handbook of Evolutionary Neuroscience, Pages: 153-172, ISBN: 9781119994695

    © 2017 John Wiley & Sons, Ltd. All rights reserved. This chapter focuses on two fundamental constraints that apply to any form of information processing system, be it a cell, a brain or a computer: Noise (random variability) and Energy (metabolic demand). It shows how these two constraints are fundamentally limited by the basic biophysical properties of the brain's building blocks (protein, fats, and salty water) and link nervous system structure to function. The understanding of the interdependence of information and energy has profoundly influenced the development of efficient telecommunication systems and computers. Noise diminishes the capacity to receive, process, and direct information, the key tasks of the brain. Investing in the brain's design can reduce the effects of noise, but this investment often increases energetic requirements, which is likely to be evolutionary unfavourable. The stochasticity of the system becomes critical when its inherent randomness makes it operationally infeasible, that is, when random action potential (APs) become as common as evoked APs.

  • Journal article
    Roberts RE, Arshad Q, Patel M, Dima D, Leech R, Seemungal BM, Sharp DS, Bronstein AMet al., 2016,

    Functional neuroimaging of visuo-vestibular interaction

    , Brain Structure & Function, Vol: 222, Pages: 2329-2343, ISSN: 1863-2661

    The brain combines visual, vestibular and proprioceptive information to distinguish between self-and world-motion. Often these signals are complementary and indicate that the individual is moving or stationary with respect to the surroundings. However, conflicting visual motion and vestibular cues can lead to ambiguous or false sensations of motion. In this study, we used functional magnetic resonance imaging to explore human brain activation when visual and vestibular cues were either complementary or in conflict. We combined a horizontally moving optokinetic stimulus with caloric irrigation of the right ear to produce conditions where the vestibular activation and visual motion indicatedthe same (congruent) or opposite directions of self-motion (incongruent). Visuo-vestibular conflict was associated with increased activation in a network of brain regions including posterior insular and transverse temporal areas, cerebellar tonsil, cingulate and medial frontal gyri. In the congruent condition there was increased activation in primary and secondary visual cortex. These findings suggest that when sensory information regarding self-motion is contradictory, there is preferential activation of multisensoryvestibular areas to resolve this ambiguity. When cues are congruent there is a bias towards visual cortical activation. The data support the view thata network of brain areas including the posterior insular cortex may play animportant role in integrating and disambiguating visual and vestibular cues.

  • Journal article
    Jager P, Ye Z, Yu X, Zagoraiou L, Prekop H-T, Partanen J, Jessell TM, Wisden W, Brickley SG, Delogu Aet al., 2016,

    Tectal-derived interneurons contribute to phasic and tonic inhibition in the visual thalamus

    , Nature Communications, Vol: 7, ISSN: 2041-1723

    The release of GABA from local interneurons in the dorsal lateral geniculate nucleus (dLGN-INs) provides inhibitory control during visual processing within the thalamus. It is commonly assumed that this important class of interneurons originates from within the thalamic complex, but we now show that during early postnatal development Sox14/Otx2-expressing precursor cells migrate from the dorsal midbrain to generate dLGN-INs. The unexpected extra-diencephalic origin of dLGN-INs sets them apart from GABAergic neurons of the reticular thalamic nucleus. Using optogenetics we show that at increased firing rates tectal-derived dLGN-INs generate a powerful form of tonic inhibition that regulates the gain of thalamic relay neurons through recruitment of extrasynaptic high-affinity GABAA receptors. Therefore, by revising the conventional view of thalamic interneuron ontogeny we demonstrate how a previously unappreciated mesencephalic population controls thalamic relay neuron excitability.

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