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Conference paperBaker CE, Montemiglio A, Li R, et al., 2022,
Assessing the influence of parameter variation on kinematic head injury metric uncertainty in multibody reconstructions of real-world pedestrian vehicle and ground impacts, 2022 IRCOBI Conference, Pages: 393-394, ISSN: 2235-3151
Journal articleYeung D, Guerra IM, Barner-Rasmussen I, et al., 2022,
Co-Adaptive Control of Bionic Limbs via Unsupervised Adaptation of Muscle Synergies., IEEE Trans Biomed Eng, Vol: 69, Pages: 2581-2592
OBJECTIVE: In this work, we present a myoelectric interface that extracts natural motor synergies from multi-muscle signals and adapts in real-time with new user inputs. With this unsupervised adaptive myocontrol (UAM) system, optimal synergies for control are continuously co-adapted with changes in user motor control, or as a function of perturbed conditions via online non-negative matrix factorization guided by physiologically informed sparseness constraints in lieu of explicit data labelling. METHODS: UAM was tested in a set of virtual target reaching tasks completed by able-bodied and amputee subjects. Tests were conducted under normative and electrode perturbed conditions to gauge control robustness with comparisons to non-adaptive and supervised adaptive myocontrol schemes. Furthermore, UAM was used to interface an amputee with a multi-functional powered hand prosthesis during standardized Clothespin Relocation Tests, also conducted in normative and perturbed conditions. RESULTS: In virtual tests, UAM effectively mitigated performance degradation caused by electrode displacement, affording greater resilience over an existing supervised adaptive system for amputee subjects. Induced electrode shifts also had negligible effect on the real world control performance of UAM with consistent completion times (23.91 ±1.33 s) achieved across Clothespin Relocation Tests in the normative and electrode perturbed conditions. CONCLUSION: UAM affords comparable robustness improvements to existing supervised adaptive myocontrol interfaces whilst providing additional practical advantages for clinical deployment. SIGNIFICANCE: The proposed system uniquely incorporates neuromuscular control principles with unsupervised online learning methods and presents a working example of a freely co-adaptive bionic interface.
Journal articleTan E, Kim J, Stewart K, et al., 2022,
The role of long-alkyl-group spacers in glycolated copolymers for high performance organic electrochemical transistors, Advanced Materials, Vol: 34, ISSN: 0935-9648
Semiconducting polymers with oligoethylene glycol sidechains have attracted strong research interest for organic electrochemical transistor (OECT) applications. However, key molecular design rules for high-performance OECTs via efficient mixed electronic/ionic charge transport are still unclear. Herein, we synthesize and characterize new glycolated copolymers (gDPP-TTT and gDPP-TTVTT) with diketopyrrolopyrrole (DPP) acceptor and thiophene-based (TTT or TTVTT) donor units for accumulation mode OECTs, where a long-alkyl-group (C12 ) attached to DPP unit acts as a spacer distancing the oligoethylene glycol from the polymer backbone. gDPP-TTVTT shows the highest OECT transconductance (61.9 S cm-1 ) and high operational stability, compared to gDPP-TTT and their alkylated counterparts. Surprisingly, gDPP-TTVTT also shows high electronic charge mobility in field-effect transistor, suggesting efficient ion injection/diffusion without hindering its efficient electronic charge transport. The elongated donor unit (TTVTT) facilitates the hole polaron formation more localized to the donor unit, leading to faster and easier polaron formation with less impact on polymer structure during OECT operation, as opposed to the TTT unit. This is supported by molecular dynamics (MD) simulation. We conclude that these simultaneously high electronic and ionic charge transport properties are achieved due to the long-alkyl-group spacer in amphipathic sidechains, providing an important molecular design rule for glycolated copolymers. This article is protected by copyright. All rights reserved.
Journal articleMendez Guerra I, Barsakcioglu DY, Vujaklija I, et al., 2022,
Far-field electric potentials provide access to the output from the spinal cord from wrist-mounted sensors, Journal of Neural Engineering, Vol: 19, ISSN: 1741-2552
OBJECTIVE: Neural interfaces need to become more unobtrusive and socially acceptable to appeal to general consumers outside rehabilitation settings. APPROACH: We developed a non-invasive neural interface that provides access to spinal motor neuron activities from the wrist, which is the preferred location for a wearable. The interface decodes far-field potentials present at the tendon endings of the forearm muscles using blind source separation. First, we evaluated the reliability of the interface to detect motor neuron firings based on far-field potentials, and thereafter we used the decoded motor neuron activity for the prediction of finger contractions in offline and real-time conditions. MAIN RESULTS: The results showed that motor neuron activity decoded from the far-field potentials at the wrist accurately predicted individual and combined finger commands and therefore allowed for highly accurate real-time task classification. SIGNIFICANCE: These findings demonstrate the feasibility of a non-invasive, neural interface at the wrist for precise real-time control based on the output of the spinal cord.
Journal articleHowe C, Song P, Verinaz Jadan HI, et al., 2022,
Comparing synthetic refocusing to deconvolution for the extraction of neuronal calcium transients from light fields, Neurophotonics, Vol: 9, Pages: 1-17, ISSN: 2329-4248
Significance: Light-field microscopy (LFM) enables fast, light-efficient, volumetric imaging of neuronal activity with calcium indicators. Calcium transients differ in temporal signal-to-noise ratio (tSNR) and spatial confinement when extracted from volumes reconstructed by different algorithms.Aim: We evaluated the capabilities and limitations of two light-field reconstruction algorithms for calcium fluorescence imaging.Approach: We acquired light-field image series from neurons either bulk-labeled or filled intracellularly with the red-emitting calcium dye CaSiR-1 in acute mouse brain slices. We compared the tSNR and spatial onfinement of calcium signals extracted from volumes reconstructed with synthetic refocusing and Richardson-Lucy 3D deconvolution with and without total variation regularization.Results: Both synthetic refocusing and Richardson-Lucy deconvolution resolved calcium signals from single cells and neuronal dendrites in three dimensions. Increasing deconvolution iteration number improved spatial confinement but reduced tSNR compared to synthetic refocusing. Volumetric light-field imaging did not decrease calcium signal tSNR compared to interleaved, widefield image series acquired in matched planes.Conclusions: LFM enables high-volume rate, volumetric imaging of calcium transients in single cells (bulk-labeled), somata and dendrites (intracellular loaded). The trade-offs identified for tSNR, spatial confinement, and computational cost indicate which of synthetic refocusing or deconvolution can better realize the scientific requirements of future LFM calcium imaging applications.
Journal articlePosirisuk P, Baker C, Ghajari M, 2022,
Computational prediction of head-ground impact kinematics in e-scooter falls, Accident Analysis and Prevention, Vol: 167, Pages: 1-11, ISSN: 0001-4575
E-scooters are the fastest growing mode of micro-mobility with important environmental benefits. However, there are serious concerns about injuries caused by e-scooter accidents. Falls due to poor road surface conditions are a common cause of injury in e-scooter riders, and head injuries are one of the most common and concerning injuries in e-scooter falls. However, the head-ground impact biomechanics in e-scooter falls and its relationship with e-scooter speed and design, road surface conditions and wearing helmets remain poorly understood. To address some of these key questions, we predicted the head-ground impact force and velocity of e-scooter riders in different falls caused by potholes. We used multi-body dynamics approach to model a commercially available e-scooter and simulate 180 falls using human body models. We modelled different pothole sizes to test whether the pothole width and depth influences the onset of falls and head-ground impact speed and force. We also tested whether the e-scooter travelling speed has an influence on the head-ground impact force and velocity. The simulations were carried out with three human body models to ensure that the results of the study are inclusive of a wide range of rider sizes. For our 10inch diameter e-scooter wheels, we found a sudden increase in the occurrence of falls when the pothole depth was increased from 3cm (no falls) to 6cm (41 falls out of 60 cases). When the falls occurred, we found a head-ground impact force of 13.23.4kN, which is larger than skull fracture thresholds. The head-ground impact speed was 6.31.4m/s, which is nearly the same as the impact speed prescribed in bicycle helmet standards. All e-scooter falls resulted in oblique head impacts, with an impact angle of 6510 (measured from the ground). Decreasing the e-scooter speed reduced the head impact speed. For instance, reducing the e-scooter speed from 30km/h to 20km/h led to a 14% reduction in the mean impact speed and 12% reduction in th
Journal articleBaker C, Martin P, Wilson M, et al., 2022,
The relationship between road traffic collision dynamics and traumatic brain injury pathology, Brain Communications, Vol: 4, ISSN: 2632-1297
Road traffic collisions are a major cause of traumatic brain injury. However, the relationship between road traffic collision dynamics and traumatic brain injury risk for different road users is unknown. We investigated 2,065 collisions from Great Britain’s Road Accident In-depth Studies collision database involving 5,374 subjects (2013-20). 595 subjects sustained a traumatic brain injury (20.2% of 2,940 casualties), including 315 moderate-severe and 133 mild-probable. Key pathologies included skull fracture (179, 31.9%), subarachnoid haemorrhage (171, 30.5%), focal brain injury (168, 29.9%) and subdural haematoma (96, 17.1%). These results were extended nationally using >1,000,000 police-reported collision casualties. Extrapolating from the in-depth data we estimate that there are ~20,000 traumatic brain injury casualties (~5,000 moderate-severe) annually on Great Britain’s roads, accounting for severity differences. Detailed collision investigation allows vehicle collision dynamics to be understood and the change-in-velocity (known as delta-V) to be estimated for a subset of in-depth collision data. Higher delta-V increased the risk of moderate-severe brain injury for all road users. The four key pathologies were not observed below 8km/h delta-V for pedestrians/cyclists and 19km/h delta-V for car occupants (higher delta-V threshold for focal injury in both groups). Traumatic brain injury risk depended on road user type, delta-V and impact direction. Accounting for delta-V, pedestrians/cyclists had a 6-times higher likelihood of moderate-severe brain injury than car occupants. Wearing a cycle helmet was protective against overall and mild-to-moderate-severe brain injury, particularly skull fracture and subdural haematoma. Cycle helmet protection was not due to travel or impact speed differences between helmeted and non-helmeted cyclist groups. We additionally examined the influence of delta-V direction. Car occupants exposed to a higher latera
Conference paperScheidwasser-Clow N, Kegler M, Beckmann P, et al., 2022,
SERAB: A MULTI-LINGUAL BENCHMARK FOR SPEECH EMOTION RECOGNITION, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 7697-7701, ISSN: 1520-6149
Conference paperBeckmann P, Kegler M, Cernak M, 2021,
Word-level embeddings for cross-task transfer learning in speech processing, 2021 29th European Signal Processing Conference (EUSIPCO), Publisher: IEEE
Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech representation were developed to foster automatic speech recognition. Up to date, most of these approaches are task-specific and designed for within-task transfer learning between different datasets or setups of a particular task. In turn, learning task-independent representation of speech and cross-task applications of transfer learning remain less common. Here, we introduce an encoder capturing word-level representations of speech for cross-task transfer learning. We demonstrate the application of the pre-trained encoder in four distinct speech and audio processing tasks: (i) speech enhancement, (ii) language identification, (iii) speech, noise, and music classification, and (iv) speaker identification. In each task, we compare the performance of our cross-task transfer learning approach to task-specific baselines. Our results show that the speech representation captured by the encoder through the pre-training is transferable across distinct speech processing tasks and datasets. Notably, even simple applications of our pre-trained encoder outperformed task-specific methods, or were comparable, depending on the task.
Journal articleShafti SA, Haar Millo S, Mio Zaldivar R, et al., 2021,
Playing the piano with a robotic third thumb: Assessing constraints of human augmentation, Scientific Reports, Vol: 11, Pages: 1-14, ISSN: 2045-2322
Contemporary robotics gives us mechatronic capabilities for augmenting human bodies with extra limbs. However, how our motor control capabilities pose limits on such augmentation is an open question. We developed a Supernumerary Robotic 3rd Thumbs (SR3T) with two degrees-of-freedom controlled by the user’s body to endow them with an extra contralateral thumb on the hand. We demonstrate that a pianist can learn to play the piano with 11 fingers within an hour. We then evaluate 6 naïve and 6 experienced piano players in their prior motor coordination and their capability in piano playing with the robotic augmentation. We show that individuals’ augmented performance with the SR3T could be explained by our new custom motor coordination assessment, the Human Augmentation Motor Coordination Assessment (HAMCA) performed pre-augmentation. Our work demonstrates how supernumerary robotics can augment humans in skilled tasks and that individual differences in their augmentation capability are explainable by their individual motor coordination abilities.
Journal articleKulkarni A, Kegler M, Reichenbach T, 2021,
Effect of visual input on syllable parsing in a computational model of a neural microcircuit for speech processing., Journal of Neural Engineering, Vol: 5, Pages: 1-14, ISSN: 1741-2552
Seeing a person talking can help to understand them, in particular in a noisy environment. However, how the brain integrates the visual information with the auditory signal to enhance speech comprehension remains poorly understood. Here we address this question in a computational model of a cortical microcircuit for speech processing. The model consists of an excitatory and an inhibitory neural population that together create oscillations in the theta frequency range. When simulated with speech, the theta rhythm becomes entrained to the onsets of syllables, such that the onsets can be inferred from the network activity. We investigate how well the obtained syllable parsing performs when different types of visual stimuli are added. In particular, we consider currents related to the rate of syllables as well as currents related to the mouth-opening area of the talking faces. We find that currents that target the excitatory neuronal population can influence speech comprehension, both boosting it or impeding it, depending on the temporal delay and on whether the currents are excitatory or inhibitory. In contrast, currents that act on the inhibitory neurons do not impact speech comprehension significantly. Our results suggest neural mechanisms for the integration of visual information with the acoustic information in speech and make experimentally-testable predictions.
Journal articleTyrrell JE, Petkos K, Drakakis EM, et al., 2021,
Organic electrochemical transistor common‐source amplifier for electrophysiological measurements, Advanced Functional Materials, Vol: 31, Pages: 1-13, ISSN: 1616-301X
The portability of physiological monitoring has necessitated the biocompatibility of components used in circuitry local to biological environments. A key component in processing circuitry is the linear amplifier. Amplifier circuit topologies utilize transistors, and recent advances in bioelectronics have focused on organic electrochemical transistors (OECTs). OECTs have shown the capability to transduce physiological signals at high signal-to-noise ratios. In this study high-performance interdigitated electrode OECTs are implemented in a common source linear amplifier topology. Under the constraints of OECT operation, stable circuit component parameters are found, and OECT geometries are varied to determine the best amplifier performance. An equation is formulated which approximates transistor behavior in the linear, nonlinear, and saturation regimes. This equation is used to simulate the amplifier response of the circuits with the best performing OECT geometries. The amplifier figures of merit, including distortion characterizations, are then calculated using physical and simulation measurements. Based on the figures of merit, prerecorded electrophysiological signals from spreading depolarizations, electrocorticography, and electromyography fasciculations are inputted into an OECT linear amplifier. Using frequency filtering, the primary features of events in the bioelectric signals are resolved and amplified, demonstrating the capability of OECT amplifiers in bioelectronics.
Journal articleSoreq E, Violante IR, Daws R, et al., 2021,
Neuroimaging evidence for a network sampling theory of individual differences in human intelligence, Nature Communications, Vol: 12, ISSN: 2041-1723
Despite a century of research, it remains unclear whether human intelligence should be studied as one dominant, several major, or many distinct abilities, and how such abilities relate to the functional organisation of the brain. Here, we combine psychometric and machine learning methods to examine in a data-driven manner how factor structure and individual variability in cognitive-task performance relate to dynamic-network connectomics. We report that 12 sub-tasks from an established intelligence test can be accurately multi-way classified (74%, chance 8.3%) based on the network states that they evoke. The proximities of the tasks in behavioural-psychometric space correlate with the similarities of their network states. Furthermore, the network states were more accurately classified for higher relative to lower performing individuals. These results suggest that the human brain uses a high-dimensional network-sampling mechanism to flexibly code for diverse cognitive tasks. Population variability in intelligence test performance relates to the fidelity of expression of these task-optimised network states.
Journal articleGava GP, McHugh SB, Lefèvre L, et al., 2021,
Integrating new memories into the hippocampal network activity space, Nature Neuroscience, Vol: 24, Pages: 326-330, ISSN: 1097-6256
By investigating the topology of neuronal co-activity, we found that mnemonic information spans multiple operational axes in the mouse hippocampus network. High-activity principal cells form the core of each memory along a first axis, segregating spatial contexts and novelty. Low-activity cells join co-activity motifs across behavioral events and enable their crosstalk along two other axes. This reveals an organizational principle for continuous integration and interaction of hippocampal memories.
Book chapterQuicke P, Howe CL, Foust A, 2021,
Balancing the fluorescence imaging budget for all-optical neurophysiology experiments, All-optical methods to study neuronal function, Editors: Papagiakoumou, Publisher: Humana Press
The goal of this chapter is to establish a framework to evaluate imaging methodologies for all-optical neurophysiology experiments. This is not an exhaustive review of fluorescent indicators and imaging modalities but rather aims to distill the functional imaging principles driving the choice of both. Scientific priorities determine whether the imaging strategy is based on an “optimal fluorescent indicator” or “optimal imaging modality.” The choice of the first constrains the choice of the second due to each’s contributions to the fluorescence budget and signal-to-noise ratio of time-varying fluorescence changes.
Journal articleGo MA, Rogers J, Gava G, et al., 2021,
Place cells in head-fixed mice navigating a floating real-world environment, Frontiers in Cellular Neuroscience, Vol: 15, ISSN: 1662-5102
The hippocampal place cell system in rodents has provided a major paradigm for the scientific investigation of memory function and dysfunction. Place cells have been observed in area CA1 of the hippocampus of both freely moving animals, and of head-fixed animals navigating in virtual reality environments. However, spatial coding in virtual reality preparations has been observed to be impaired. Here we show that the use of a real-world environment system for head-fixed mice, consisting of an air-floating track with proximal cues, provides some advantages over virtual reality systems for the study of spatial memory. We imaged the hippocampus of head-fixed mice injected with the genetically encoded calcium indicator GCaMP6s while they navigated circularly constrained or open environments on the floating platform. We observed consistent place tuning in a substantial fraction of cells despite the absence of distal visual cues. Place fields remapped when animals entered a different environment. When animals re-entered the same environment, place fields typically remapped over a time period of multiple days, faster than in freely moving preparations, but comparable with virtual reality. Spatial information rates were within the range observed in freely moving mice. Manifold analysis indicated that spatial information could be extracted from a low-dimensional subspace of the neural population dynamics. This is the first demonstration of place cells in head-fixed mice navigating on an air-lifted real-world platform, validating its use for the study of brain circuits involved in memory and affected by neurodegenerative disorders.
Journal articleTyrrell J, Boutelle M, Campbell A, 2021,
Measurement of electrophysiological signals in vitro using high-performance organic electrochemical transistors, Advanced Functional Materials, Vol: 31, Pages: 1-12, ISSN: 1616-301X
Biological environments use ions in charge transport for information transmission. The properties of mixed electronic and ionic conductivity in organic materials make them ideal candidates to transduce physiological information into electronically processable signals. A device proven to be highly successful in measuring such information is the organic electrochemical transistor (OECT). Previous electrophysiological measurements performed using OECTs show superior signal‐to‐noise ratios than electrodes at low frequencies. Subsequent development has significantly improved critical performance parameters such as transconductance and response time. Here, interdigitated‐electrode OECTs are fabricated on flexible substrates, with one such state‐of‐the‐art device achieving a peak transconductance of 139 mS with a 138 µs response time. The devices are implemented into an array with interconnects suitable for micro‐electrocorticographic application and eight architecture variations are compared. The two best‐performing arrays are subject to the full electrophysiological spectrum using prerecorded signals. With frequency filtering, kHz‐scale frequencies with 10 µV‐scale voltages are resolved. This is supported by a novel quantification of the noise, which compares the gate voltage input and drain current output. These results demonstrate that high‐performance OECTs can resolve the full electrophysiological spectrum and suggest that superior signal‐to‐noise ratios could be achieved in high frequency measurements of multiunit activity.
Conference paperVerinaz-Jadan H, Song P, Howe CL, et al., 2021,
DEEP LEARNING FOR LIGHT FIELD MICROSCOPY USING PHYSICS-BASED MODELS, 18th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 1091-1094, ISSN: 1945-7928
- Author Web Link
- Citations: 2
Journal articleMancero Castillo C, Wilson S, Vaidyanathan R, et al., 2021,
Wearable MMG-plus-one armband: evaluation of normal force on mechanomyography (MMG) to enhance human-machine interfacing, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 29, Pages: 196-205, ISSN: 1534-4320
In this paper, we introduce a new mode of mechanomyography (MMG) signal capture for enhancing the performance of human-machine interfaces (HMIs) through modulation of normal pressure at the sensor location. Utilizing this novel approach, increased MMG signal resolution is enabled by a tunable degree of freedom normal to the sensor-skin contact area. We detail the mechatronic design, experimental validation, and user study of an armband with embedded acoustic sensors demonstrating this capacity. The design is motivated by the nonlinear viscoelasticity of the tissue, which increases with the normal surface pressure. This, in theory, results in higher conductivity of mechanical waves and hypothetically allows to interface with deeper muscle; thus, enhancing the discriminative information context of the signal space. Ten subjects (seven able-bodied and three trans-radial amputees) participated in a study consisting of the classification of hand gestures through MMG while increasing levels of contact force were administered. Four MMG channels were positioned around the forearm and placed over the flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris muscles. A total of 852 spectrotemporal features were extracted (213 features per each channel) and passed through a Neighborhood Component Analysis (NCA) technique to select the most informative neurophysiological subspace of the features for classification. A linear support vector machine (SVM) then classified the intended motion of the user. The results indicate that increasing the normal force level between the MMG sensor and the skin can improve the discriminative power of the classifier, and the corresponding pattern can be user-specific. These results have significant implications enabling embedding MMG sensors in sockets for prosthetic limb control and HMI.
Journal articleKegler M, Reichenbach J, 2021,
Modelling the effects of transcranial alternating current stimulation on the neural encoding of speech in noise, NeuroImage, Vol: 224, ISSN: 1053-8119
Transcranial alternating current stimulation (tACS) can non-invasively modulate neuronal activity in the cerebral cortex, in particular at the frequency of the applied stimulation. Such modulation can matter for speech processing, since the latter involves the tracking of slow amplitude fluctuations in speech by cortical activity. tACS with a current signal that follows the envelope of a speech stimulus has indeed been found to influence the cortical tracking and to modulate the comprehension of the speech in background noise. However, how exactly tACS influences the speech-related cortical activity, and how it causes the observed effects on speech comprehension, remains poorly understood. A computational model for cortical speech processing in a biophysically plausible spiking neural network has recently been proposed. Here we extended the model to investigate the effects of different types of stimulation waveforms, similar to those previously applied in experimental studies, on the processing of speech in noise. We assessed in particular how well speech could be decoded from the neural network activity when paired with the exogenous stimulation. We found that, in the absence of current stimulation, the speech-in-noise decoding accuracy was comparable to the comprehension of speech in background noise of human listeners. We further found that current stimulation could alter the speech decoding accuracy by a few percent, comparable to the effects of tACS on speech-in-noise comprehension. Our simulations further allowed us to identify the parameters for the stimulation waveforms that yielded the largest enhancement of speech-in-noise encoding. Our model thereby provides insight into the potential neural mechanisms by which weak alternating current stimulation may influence speech comprehension and allows to screen a large range of stimulation waveforms for their effect on speech processing.
Conference paperBaker CE, Martin PS, Wilson M, et al., 2021,
Traumatic brain injury findings from Great Britain's in-depth RAIDS database relating to delta-V, Pages: 726-727, ISSN: 2235-3151
Journal articleDavies HJ, Morse SV, Copping MJ, et al., 2021,
Imaging with therapeutic acoustic wavelets–short pulses enable acoustic localization when time of arrival is combined with delay and sum, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, Vol: 68, Pages: 178-190, ISSN: 0885-3010
—Passive acoustic mapping (PAM) is an algorithm that reconstructs the location of acoustic sourcesusing an array of receivers. This technique can monitor therapeutic ultrasound procedures to confirm the spatial distribution and amount of microbubble activity induced. CurrentPAM algorithms have an excellentlateral resolution but havea poor axial resolution, making it difficult to distinguishacoustic sources within the ultrasound beams. With recentstudies demonstrating that short-length and low-pressurepulses—acoustic wavelets—have the therapeutic function,we hypothesizedthat the axial resolution could be improvedwith a quasi-pulse-echo approach and that the resolutionimprovement would depend on the wavelet’s pulse length.This article describes an algorithm that resolves acousticsources axially using time of flight and laterally using delayand-sum beamforming, which we named axial temporalposition PAM (ATP-PAM). The algorithm accommodates arapid short pulse (RaSP) sequence that can safely deliverdrugs across the blood–brain barrier. We developed ouralgorithm with simulations (k-wave) and in vitro experiments for one-, two-, and five-cycle pulses, comparingour resolution against that of two current PAM algorithms.We then tested ATP-PAM in vivo and evaluated whether thereconstructed acoustic sources mapped to drug delivery
Journal articleChan TG, O'Neill E, Habjan C, et al., 2020,
Combination Strategies to Improve Targeted Radionuclide Therapy, JOURNAL OF NUCLEAR MEDICINE, Vol: 61, Pages: 1544-1552, ISSN: 0161-5505
- Author Web Link
- Citations: 25
Journal articleGardner M, Mancero Castillo C, Wilson S, et al., 2020,
A multimodal intention detection sensor suite for shared autonomy of upper-limb robotic prostheses, Sensors, Vol: 20, ISSN: 1424-8220
Neurorobotic augmentation (e.g., robotic assist) is now in regular use to support individuals suffering from impaired motor functions. A major unresolved challenge, however, is the excessive cognitive load necessary for the human–machine interface (HMI). Grasp control remains one of the most challenging HMI tasks, demanding simultaneous, agile, and precise control of multiple degrees-of-freedom (DoFs) while following a specific timing pattern in the joint and human–robot task spaces. Most commercially available systems use either an indirect mode-switching configuration or a limited sequential control strategy, limiting activation to one DoF at a time. To address this challenge, we introduce a shared autonomy framework centred around a low-cost multi-modal sensor suite fusing: (a) mechanomyography (MMG) to estimate the intended muscle activation, (b) camera-based visual information for integrated autonomous object recognition, and (c) inertial measurement to enhance intention prediction based on the grasping trajectory. The complete system predicts user intent for grasp based on measured dynamical features during natural motions. A total of 84 motion features were extracted from the sensor suite, and tests were conducted on 10 able-bodied and 1 amputee participants for grasping common household objects with a robotic hand. Real-time grasp classification accuracy using visual and motion features obtained 100%, 82.5%, and 88.9% across all participants for detecting and executing grasping actions for a bottle, lid, and box, respectively. The proposed multimodal sensor suite is a novel approach for predicting different grasp strategies and automating task performance using a commercial upper-limb prosthetic device. The system also shows potential to improve the usability of modern neurorobotic systems due to the intuitive control design.
Journal articleMorse SV, Boltersdorf T, Chan TG, et al., 2020,
In vivo delivery of a fluorescent FPR2/ALX-targeted probe using focused ultrasound and microbubbles to image activated microglia, RSC Chemical Biology, Vol: 1, Pages: 385-389, ISSN: 2633-0679
To image activated microglia, a small-molecule FPR2/ALX-targeted fluorescent probe was locally delivered into the brain using focused ultrasound and microbubbles. The probe did not co-localise with neurons or astrocytes but accumulated in activated microglia, making this a potential imaging tool for future drug discovery programs focused on neurological disorders.
Journal articleQuicke P, Howe CL, Song P, et al., 2020,
Subcellular resolution three-dimensional light-field imaging with genetically encoded voltage indicators, Neurophotonics, Vol: 7, ISSN: 2329-4248
Significance: Light-field microscopy (LFM) enables high signal-to-noise ratio (SNR) and light efficient volume imaging at fast frame rates. Voltage imaging with genetically encoded voltage indicators (GEVIs) stands to particularly benefit from LFM's volumetric imaging capability due to high required sampling rates and limited probe brightness and functional sensitivity. Aim: We demonstrate subcellular resolution GEVI light-field imaging in acute mouse brain slices resolving dendritic voltage signals in three spatial dimensions. Approach: We imaged action potential-induced fluorescence transients in mouse brain slices sparsely expressing the GEVI VSFP-Butterfly 1.2 in wide-field microscopy (WFM) and LFM modes. We compared functional signal SNR and localization between different LFM reconstruction approaches and between LFM and WFM. Results: LFM enabled three-dimensional (3-D) localization of action potential-induced fluorescence transients in neuronal somata and dendrites. Nonregularized deconvolution decreased SNR with increased iteration number compared to synthetic refocusing but increased axial and lateral signal localization. SNR was unaffected for LFM compared to WFM. Conclusions: LFM enables 3-D localization of fluorescence transients, therefore eliminating the need for structures to lie in a single focal plane. These results demonstrate LFM's potential for studying dendritic integration and action potential propagation in three spatial dimensions.
Journal articleQuicke P, Howe CL, Song P, et al., 2020,
Subcellular resolution 3D light field imaging with genetically encoded voltage indicators, Neurophotonics, Vol: 7, ISSN: 2329-4248
Significance: Light-field microscopy (LFM) enables high signal-to-noise ratio (SNR) and light efficient volume imaging at fast frame rates. Voltage imaging with genetically encoded voltage indicators (GEVIs) stands to particularly benefit from LFM’s volumetric imaging capability due to high required sampling rates and limited probe brightness and functional sensitivity.Aim: We demonstrate subcellular resolution GEVI light-field imaging in acute mouse brain slices resolving dendritic voltage signals in three spatial dimensions.Approach: We imaged action potential-induced fluorescence transients in mouse brain slices sparsely expressing the GEVI VSFP-Butterfly 1.2 in wide-field microscopy (WFM) and LFM modes. We compared functional signal SNR and localization between different LFM reconstruction approaches and between LFM and WFM.Results: LFM enabled three-dimensional (3-D) localization of action potential-induced fluorescence transients in neuronal somata and dendrites. Nonregularized deconvolution decreased SNR with increased iteration number compared to synthetic refocusing but increased axial and lateral signal localization. SNR was unaffected for LFM compared to WFM.Conclusions: LFM enables 3-D localization of fluorescence transients, therefore eliminating the need for structures to lie in a single focal plane. These results demonstrate LFM’s potential for studying dendritic integration and action potential propagation in three spatial dimensions.
Journal articleDaws RE, Soreq E, Li Y, et al., 2020,
Contrasting hierarchical and multiple-demand accounts of frontal lobe functional organisation during task-switching
<jats:title>Abstract</jats:title><jats:p>There is an unresolved discrepancy between popular hierarchical and multiple-demand perspectives on the functional organisation of the human frontal lobes. Here, we tested alternative predictions of these perspectives with a novel fMRI switching paradigm. Each trial involved switching attention between stimuli, but at different levels of difficulty and abstraction. As expected, increasing response times were evident when comparing low-level perceptual switching to more abstract dimension, rule and task-switching. However, there was no evidence of an abstraction hierarchy within the prefrontal cortex (PFC). Nor was there recruitment of additional anterior PFC regions under increased switching demand. Instead, switching activated a widespread network of frontoparietal and cerebellar regions. Critically, the activity within PFC sub-regions uniformly increased with behavioural switch costs. We propose that both perspectives have some validity, but neither is complete. Too many studies have reported dissociations within MD for this volume to be functionally uniform, and the recruitment of more anterior regions with increased general difficulty cannot explain those results. Conversely, whilst reproducible evidence for a hierarchical functional organisation has been reported, this cannot be explained in terms of abstraction of representation or reconfiguration <jats:italic>per se</jats:italic>, because those interpretations generalise poorly to other task contexts.</jats:p>
Journal articleVilar R, Torres Huerta A, Chan TG, et al., 2020,
Molecular recognition of bisphosphonate-based drugs by di-zinc receptors in aqueous solution and on gold nanoparticles, Dalton Transactions, Vol: 49, Pages: 5939-5948, ISSN: 1477-9226
Metal-based anion receptors have several important applications in sensing, separation and transport of negatively charged species. Amongst these receptors, di-zinc(II) complexes are of particular interest for the recognition of oxoanions, in particular phosphate derivatives. Herein we report the synthesis of a di-zinc(II) receptor and show that it has high affinity and selectivity for bisphosphonates such as alendronate and etidronate – which are used to treat a number of skeletal disorders as well as showing interesting anticancer properties. The binding mode of the di-zinc(II) receptor with alendronate and etidronate has been unambiguously established by single crystal X-ray crystallography. In addition, by modifying the backbone of the receptor, we show that the drug-loaded receptor can be attached onto gold nanoparticles as potential drug-delivery vehicles.
Journal articleKeshavarzi M, Kegler M, Kadir S, et al., 2020,
Transcranial alternating current stimulation in the theta band but not in the delta band modulates the comprehension of naturalistic speech in noise, NeuroImage, Vol: 210, ISSN: 1053-8119
Auditory cortical activity entrains to speech rhythms and has been proposed as a mechanism for online speech processing. In particular, neural activity in the theta frequency band (4–8 Hz) tracks the onset of syllables which may aid the parsing of a speech stream. Similarly, cortical activity in the delta band (1–4 Hz) entrains to the onset of words in natural speech and has been found to encode both syntactic as well as semantic information. Such neural entrainment to speech rhythms is not merely an epiphenomenon of other neural processes, but plays a functional role in speech processing: modulating the neural entrainment through transcranial alternating current stimulation influences the speech-related neural activity and modulates the comprehension of degraded speech. However, the distinct functional contributions of the delta- and of the theta-band entrainment to the modulation of speech comprehension have not yet been investigated. Here we use transcranial alternating current stimulation with waveforms derived from the speech envelope and filtered in the delta and theta frequency bands to alter cortical entrainment in both bands separately. We find that transcranial alternating current stimulation in the theta band but not in the delta band impacts speech comprehension. Moreover, we find that transcranial alternating current stimulation with the theta-band portion of the speech envelope can improve speech-in-noise comprehension beyond sham stimulation. Our results show a distinct contribution of the theta- but not of the delta-band stimulation to the modulation of speech comprehension. In addition, our findings open up a potential avenue of enhancing the comprehension of speech in noise.
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