NeuroDots: From Single-Target to Brain-Network Modulation: Why and What Is Needed?

OBJECTIVES: Current techniques in brain stimulation are still largely based on a phrenologic approach that a single brain target can treat a brain disorder. Nevertheless, meta-analyses of brain implants indicate an overall success rate of 50% improvement in 50% of patients, irrespective of the brain-related disorder. Thus, there is still a large margin for improvement. The goal of this manuscript is to 1) develop a general theoretical framework of brain functioning that is amenable to surgical neuromodulation, and 2) describe the engineering requirements of the next generation of implantable brain stimulators that follow from this theoretic model. MATERIALS AND METHODS: A neuroscience and engineering literature review was performed to develop a universal theoretical model of brain functioning and dysfunctioning amenable to surgical neuromodulation. RESULTS: Even though a single target can modulate an entire network, research in network science reveals that many brain disorders are the consequence of maladaptive interactions among multiple networks rather than a single network. Consequently, targeting the main connector hubs of those multiple interacting networks involved in a brain disorder is theoretically more beneficial. We, thus, envision next-generation network implants that will rely on distributed, multisite neuromodulation targeting correlated and anticorrelated interacting brain networks, juxtaposing alternative implant configurations, and finally providing solid recommendations for the realization of such implants. In doing so, this study pinpoints the potential shortcomings of other similar efforts in the field, which somehow fall short of the requirements. CONCLUSION: The concept of network stimulation holds great promise as a universal approach for treating neurologic and psychiatric disorders.

ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations.

Introduction: In-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2) flexibility, and (3) ease of use for non-technical users. However, most existing platforms and libraries do not meet all three criteria, particularly for complex models such as the Hodgkin-Huxley (HH) model or for complex neuron-connectivity modeling such as gap junctions. Methods: This work introduces ExaFlexHH, an exascale-ready, flexible library for simulating HH models on multi-FPGA platforms. Utilizing FPGA-based Data-Flow Engines (DFEs) and the dataflow programming paradigm, ExaFlexHH addresses all three requirements. The library is also parameterizable and compliant with NeuroML, a prominent brain-description language in computational neuroscience. We demonstrate the performance scalability of the platform by implementing a highly demanding extended-Hodgkin-Huxley (eHH) model of the Inferior Olive using ExaFlexHH. Results: Model simulation results show linear scalability for unconnected networks and near-linear scalability for networks with complex synaptic plasticity, with a 1.99 × performance increase using two FPGAs compared to a single FPGA simulation, and 7.96 × when using eight FPGAs in a scalable ring topology. Notably, our results also reveal consistent performance efficiency in GFLOPS per watt, further facilitating exascale-ready computing speeds and pushing the boundaries of future brain-simulation platforms. Discussion: The ExaFlexHH library shows superior resource efficiency, quantified in FLOPS per hardware resources, benchmarked against other competitive FPGA-based brain simulation implementations.

Four-dimensional computational ultrasound imaging of brain hemodynamics.

Four-dimensional ultrasound imaging of complex biological systems such as the brain is technically challenging because of the spatiotemporal sampling requirements. We present computational ultrasound imaging (cUSi), an imaging method that uses complex ultrasound fields that can be generated with simple hardware and a physical wave prediction model to alleviate the sampling constraints. cUSi allows for high-resolution four-dimensional imaging of brain hemodynamics in awake and anesthetized mice.

Severity-Based Hierarchical ECG Classification Using Neural Networks.

Timely detection of cardiac arrhythmia characterized by abnormal heartbeats can help in the early diagnosis and treatment of cardiovascular diseases. Wearable healthcare devices typically use neural networks to provide the most convenient way of continuously monitoring heart activity for arrhythmia detection. However, it is challenging to achieve high accuracy and energy efficiency in these smart wearable healthcare devices. In this work, we provide architecture-level solutions to deploy neural networks for cardiac arrhythmia classification. We have created a hierarchical structure after analyzing various neural network topologies where only required network components are activated to improve energy efficiency while maintaining high accuracy. In our proposed architecture, we introduce a severity-based classification approach to directly help the users of the wearable healthcare device as well as the medical professionals. Additionally, we have employed computation-in-memory based hardware to improve energy efficiency and area consumption by leveraging in-situ data processing and scalability of emerging memory technologies such as resistive random access memory (RRAM). Simulation experiments conducted using the MIT-BIH arrhythmia dataset show that the proposed architecture provides high accuracy while consuming average energy of 0.11 µJ per heartbeat classification and 0.11 mm2 area, thereby achieving 25× improvement in average energy consumption and 12× improvement in area compared to the state-of-the-art.

Purkinje Cell Activity Resonation Generates Rhythmic Behaviors at the Preferred Frequency of 8 Hz.

Neural activity exhibits oscillations, bursts, and resonance, enhancing responsiveness at preferential frequencies. For example, theta-frequency bursting and resonance in granule cells facilitate synaptic transmission and plasticity mechanisms at the input stage of the cerebellar cortex. However, whether theta-frequency bursting of Purkinje cells is involved in generating rhythmic behavior has remained neglected. We recorded and optogenetically modulated the simple and complex spike activity of Purkinje cells while monitoring whisker movements with a high-speed camera of awake, head-fixed mice. During spontaneous whisking, both simple spike activity and whisker movement exhibit peaks within the theta band. Eliciting either simple or complex spikes at frequencies ranging from 0.5 to 28 Hz, we found that 8 Hz is the preferred frequency around which the largest movement is induced. Interestingly, oscillatory whisker movements at 8 Hz were also generated when simple spike bursting was induced at 2 and 4 Hz, but never via climbing fiber stimulation. These results indicate that 8 Hz is the resonant frequency at which the cerebellar-whisker circuitry produces rhythmic whisking.

EDEN: A High-Performance, General-Purpose, NeuroML-Based Neural Simulator.

Modern neuroscience employs in silico experimentation on ever-increasing and more detailed neural networks. The high modeling detail goes hand in hand with the need for high model reproducibility, reusability and transparency. Besides, the size of the models and the long timescales under study mandate the use of a simulation system with high computational performance, so as to provide an acceptable time to result. In this work, we present EDEN (Extensible Dynamics Engine for Networks), a new general-purpose, NeuroML-based neural simulator that achieves both high model flexibility and high computational performance, through an innovative model-analysis and code-generation technique. The simulator runs NeuroML-v2 models directly, eliminating the need for users to learn yet another simulator-specific, model-specification language. EDEN's functional correctness and computational performance were assessed through NeuroML models available on the NeuroML-DB and Open Source Brain model repositories. In qualitative experiments, the results produced by EDEN were verified against the established NEURON simulator, for a wide range of models. At the same time, computational-performance benchmarks reveal that EDEN runs from one to nearly two orders-of-magnitude faster than NEURON on a typical desktop computer, and does so without additional effort from the user. Finally, and without added user effort, EDEN has been built from scratch to scale seamlessly over multiple CPUs and across computer clusters, when available.

WhiskEras: A New Algorithm for Accurate Whisker Tracking.

Rodents engage in active touch using their facial whiskers: they explore their environment by making rapid back-and-forth movements. The fast nature of whisker movements, during which whiskers often cross each other, makes it notoriously difficult to track individual whiskers of the intact whisker field. We present here a novel algorithm, WhiskEras, for tracking of whisker movements in high-speed videos of untrimmed mice, without requiring labeled data. WhiskEras consists of a pipeline of image-processing steps: first, the points that form the whisker centerlines are detected with sub-pixel accuracy. Then, these points are clustered in order to distinguish individual whiskers. Subsequently, the whiskers are parameterized so that a single whisker can be described by four parameters. The last step consists of tracking individual whiskers over time. We describe that WhiskEras performs better than other whisker-tracking algorithms on several metrics. On our four video segments, WhiskEras detected more whiskers per frame than the Biotact Whisker Tracking Tool. The signal-to-noise ratio of the output of WhiskEras was higher than that of Janelia Whisk. As a result, the correlation between reflexive whisker movements and cerebellar Purkinje cell activity appeared to be stronger than previously found using other tracking algorithms. We conclude that WhiskEras facilitates the study of sensorimotor integration by markedly improving the accuracy of whisker tracking in untrimmed mice.

Functional Convergence of Autonomic and Sensorimotor Processing in the Lateral Cerebellum.

The cerebellum is involved in the control of voluntary and autonomic rhythmic behaviors, yet it is unclear to what extent it coordinates these in concert. We studied Purkinje cell activity during unperturbed and perturbed respiration in lobules simplex, crus 1, and crus 2. During unperturbed (eupneic) respiration, complex spike and simple spike activity encode the phase of ongoing sensorimotor processing. In contrast, when the respiratory cycle is perturbed by whisker stimulation, mice concomitantly protract their whiskers and advance their inspiration in a phase-dependent manner, preceded by increased simple spike activity. This phase advancement of respiration in response to whisker stimulation can be mimicked by optogenetic stimulation of Purkinje cells and prevented by cell-specific genetic modification of their AMPA receptors, hampering increased simple spike firing. Thus, the impact of Purkinje cell activity on respiratory control is context and phase dependent, highlighting a coordinating role for the cerebellar hemispheres in aligning autonomic and sensorimotor behaviors.

Functional Ultrasound (fUS) During Awake Brain Surgery: The Clinical Potential of Intra-Operative Functional and Vascular Brain Mapping.

BACKGROUND AND PURPOSE: Oncological neurosurgery relies heavily on making continuous, intra-operative tumor-brain delineations based on image-guidance. Limitations of currently available imaging techniques call for the development of real-time image-guided resection tools, which allow for reliable functional and anatomical information in an intra-operative setting. Functional ultrasound (fUS), is a new mobile neuro-imaging tool with unprecedented spatiotemporal resolution, which allows for the detection of small changes in blood dynamics that reflect changes in metabolic activity of activated neurons through neurovascular coupling. We have applied fUS during conventional awake brain surgery to determine its clinical potential for both intra-operative functional and vascular brain mapping, with the ultimate aim of achieving maximum safe tumor resection. METHODS: During awake brain surgery, fUS was used to image tumor vasculature and task-evoked brain activation with electrocortical stimulation mapping (ESM) as a gold standard. For functional imaging, patients were presented with motor, language or visual tasks, while the probe was placed over (ESM-defined) functional brain areas. For tumor vascular imaging, tumor tissue (pre-resection) and tumor resection cavity (post-resection) were imaged by moving the hand-held probe along a continuous trajectory over the regions of interest. RESULTS: A total of 10 patients were included, with predominantly intra-parenchymal frontal and temporal lobe tumors of both low and higher histopathological grades. fUS was able to detect (ESM-defined) functional areas deep inside the brain for a range of functional tasks including language processing. Brain tissue could be imaged at a spatial and temporal resolution of 300 µm and 1.5-2.0 ms respectively, revealing real-time tumor-specific, and healthy vascular characteristics. CONCLUSION: The current study presents the potential of applying fUS during awake brain surgery. We illustrate the relevance of fUS for awake brain surgery based on its ability to capture both task-evoked functional cortical responses as well as differences in vascular characteristics between tumor and healthy tissue. As current neurosurgical practice is still pre-dominantly leaning on inherently limited pre-operative imaging techniques for tumor resection-guidance, fUS enters the scene as a promising alternative that is both anatomically and physiologically informative.

A Real-Time Reconfigurable Multichip Architecture for Large-Scale Biophysically Accurate Neuron Simulation.

Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experiments. State-of-the-art neuron simulators are, however, capable of simulating at most few tens/hundreds of biophysically accurate neurons in real-time due to the exponential growth in the interneuron communication costs with the number of simulated neurons. In this paper, we propose a real-time, reconfigurable, multichip system architecture based on localized communication, which effectively reduces the communication cost to a linear growth. All parts of the system are generated automatically, based on the neuron connectivity scheme. Experimental results indicate that the proposed system architecture allows the capacity of over 3000 to 19 200 (depending on the connectivity scheme) biophysically accurate neurons over multiple chips.

Attacks on Heartbeat-Based Security Using Remote Photoplethysmography.

The time interval between consecutive heartbeats (interpulse interval, IPI) has previously been suggested for securing mobile-health solutions. This time interval is known to contain a degree of randomness, permitting the generation of a time- and person-specific identifier. It is commonly assumed that only devices trusted by a person can make physical contact with him/her, and that this physical contact allows each device to generate a similar identifier based on its own cardiac recordings. Under these conditions, the identifiers generated by different trusted devices can facilitate secure authentication. Recently, a wide range of techniques have been proposed for measuring heartbeats remotely, a prominent example of which is remote photoplethysmography (rPPG). These techniques may pose a significant threat to heartbeat-based security, as an adversary may pretend to be a trusted device by generating a similar identifier without physical contact, thus bypassing one of the core security conditions. In this paper, we assess the feasibility of such remote attacks using state-of-the-art rPPG methods. Our evaluation shows that rPPG has similar accuracy as contact PPG and, thus, forms a substantial threat to heartbeat-based-security systems that permit trusted devices to obtain their identifiers from contact PPG recordings. Conversely, rPPG cannot obtain an accurate representation of an identifier generated from electrical cardiac signals, making the latter invulnerable to state-of-the-art remote attacks.

BrainFrame: a node-level heterogeneous accelerator platform for neuron simulations.

OBJECTIVE: The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. APPROACH: In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU, a NVidia GP-GPU and a Maxeler Dataflow Engine. The PyNN software framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different experiment instances of a state-of-the-art neuron model, representing the inferior-olivary nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal-network dimensions but also different network-connectivity densities, which can drastically affect the workload's performance characteristics. MAIN RESULTS: The combined use of different HPC technologies demonstrates that BrainFrame is better able to cope with the modeling diversity encountered in realistic experiments while at the same time running on significantly lower energy budgets. Our performance analysis clearly shows that the model directly affects performance and all three technologies are required to cope with all the model use cases. SIGNIFICANCE: The BrainFrame framework is designed to transparently configure and select the appropriate back-end accelerator technology for use per simulation run. The PyNN integration provides a familiar bridge to the vast number of models already available. Additionally, it gives a clear roadmap for extending the platform support beyond the proof of concept, with improved usability and directly useful features to the computational-neuroscience community, paving the way for wider adoption.

Enhancing Heart-Beat-Based Security for mHealth Applications.

In heart-beat-based security, a security key is derived from the time difference between consecutive heart beats (the inter-pulse interval, IPI), which may, subsequently, be used to enable secure communication. While heart-beat-based security holds promise in mobile health (mHealth) applications, there currently exists no work that provides a detailed characterization of the delivered security in a real system. In this paper, we evaluate the strength of IPI-based security keys in the context of entity authentication. We investigate several aspects that should be considered in practice, including subjects with reduced heart-rate variability (HRV), different sensor-sampling frequencies, intersensor variability (i.e., how accurate each entity may measure heart beats) as well as average and worst-case-authentication time. Contrary to the current state of the art, our evaluation demonstrates that authentication using multiple, less-entropic keys may actually increase the key strength by reducing the effects of intersensor variability. Moreover, we find that the maximal key strength of a 60-bit key varies between 29.2 bits and only 5.7 bits, depending on the subject's HRV. To improve security, we introduce the inter-multi-pulse interval (ImPI), a novel method of extracting entropy from the heart by considering the time difference between nonconsecutive heart beats. Given the same authentication time, using the ImPI for key generation increases key strength by up to 3.4 × (+19.2 bits) for subjects with limited HRV, at the cost of an extended key-generation time of 4.8 × (+45 s).

Balancing accuracy, delay and battery autonomy for pervasive seizure detection.

A promising alternative for treating absence seizures has emerged through closed-loop neurostimulation, which utilizes a wearable or implantable device to detect and subsequently suppress epileptic seizures. Such devices should detect seizures fast and with high accuracy, while respecting the strict energy budget on which they operate. Previous work has overlooked one or more of these requirements, resulting in solutions which are not suitable for continuous closed-loop stimulation. In this paper, we perform an in-depth design space exploration of a novel seizure-detection algorithm, which uses a complex Morlet wavelet filter and a static thresholding mechanism to detect absence seizures. We consider both the accuracy and speed of our detection algorithm, as well as various trade-offs with device autonomy when executed on a low-power processor. For example, we demonstrate that a minimal decrease in average detection rate of only 1.83% (from 92.72% to 90.89%) allows for a substantial increase in device autonomy (of 3.7x) while also facilitating faster detection (from 710 ms to 540 ms).

Cerebellar output controls generalized spike-and-wave discharge occurrence.

OBJECTIVE: Disrupting thalamocortical activity patterns has proven to be a promising approach to stop generalized spike-and-wave discharges (GSWDs) characteristic of absence seizures. Here, we investigated to what extent modulation of neuronal firing in cerebellar nuclei (CN), which are anatomically in an advantageous position to disrupt cortical oscillations through their innervation of a wide variety of thalamic nuclei, is effective in controlling absence seizures. METHODS: Two unrelated mouse models of generalized absence seizures were used: the natural mutant tottering, which is characterized by a missense mutation in Cacna1a, and inbred C3H/HeOuJ. While simultaneously recording single CN neuron activity and electrocorticogram in awake animals, we investigated to what extent pharmacologically increased or decreased CN neuron activity could modulate GSWD occurrence as well as short-lasting, on-demand CN stimulation could disrupt epileptic seizures. RESULTS: We found that a subset of CN neurons show phase-locked oscillatory firing during GSWDs and that manipulating this activity modulates GSWD occurrence. Inhibiting CN neuron action potential firing by local application of the γ-aminobutyric acid type A (GABA-A) agonist muscimol increased GSWD occurrence up to 37-fold, whereas increasing the frequency and regularity of CN neuron firing with the use of GABA-A antagonist gabazine decimated its occurrence. A single short-lasting (30-300 milliseconds) optogenetic stimulation of CN neuron activity abruptly stopped GSWDs, even when applied unilaterally. Using a closed-loop system, GSWDs were detected and stopped within 500 milliseconds. INTERPRETATION: CN neurons are potent modulators of pathological oscillations in thalamocortical network activity during absence seizures, and their potential therapeutic benefit for controlling other types of generalized epilepsies should be evaluated.

Cerebellar control of gait and interlimb coordination.

Synaptic and intrinsic processing in Purkinje cells, interneurons and granule cells of the cerebellar cortex have been shown to underlie various relatively simple, single-joint, reflex types of motor learning, including eyeblink conditioning and adaptation of the vestibulo-ocular reflex. However, to what extent these processes contribute to more complex, multi-joint motor behaviors, such as locomotion performance and adaptation during obstacle crossing, is not well understood. Here, we investigated these functions using the Erasmus Ladder in cell-specific mouse mutant lines that suffer from impaired Purkinje cell output (Pcd), Purkinje cell potentiation (L7-Pp2b), molecular layer interneuron output (L7-Δγ2), and granule cell output (α6-Cacna1a). We found that locomotion performance was severely impaired with small steps and long step times in Pcd and L7-Pp2b mice, whereas it was mildly altered in L7-Δγ2 and not significantly affected in α6-Cacna1a mice. Locomotion adaptation triggered by pairing obstacle appearances with preceding tones at fixed time intervals was impaired in all four mouse lines, in that they all showed inaccurate and inconsistent adaptive walking patterns. Furthermore, all mutants exhibited altered front-hind and left-right interlimb coordination during both performance and adaptation, and inconsistent walking stepping patterns while crossing obstacles. Instead, motivation and avoidance behavior were not compromised in any of the mutants during the Erasmus Ladder task. Our findings indicate that cell type-specific abnormalities in cerebellar microcircuitry can translate into pronounced impairments in locomotion performance and adaptation as well as interlimb coordination, highlighting the general role of the cerebellar cortex in spatiotemporal control of complex multi-joint movements.

Peak misdetection in heart-beat-based security: Characterization and tolerance.

The Inter-Pulse-Interval (IPI) of heart beats has previously been suggested for security in mobile health (mHealth) applications. In IPI-based security, secure communication is facilitated through a security key derived from the time difference between heart beats. However, there currently exists no work which considers the effect on security of imperfect heart-beat (peak) detection. This is a crucial aspect of IPI-based security and likely to happen in a real system. In this paper, we evaluate the effects of peak misdetection on the security performance of IPI-based security. It is shown that even with a high peak detection rate between 99.9% and 99.0%, a significant drop in security performance may be observed (between -70% and -303%) compared to having perfect peak detection. We show that authenticating using smaller keys yields both stronger keys as well as potentially faster authentication in case of imperfect heart beat detection. Finally, we present an algorithm which tolerates the effect of a single misdetected peak and increases the security performance by up to 155%.

Does enriched acoustic environment in humans abolish chronic tinnitus clinically and electrophysiologically? A double blind placebo controlled study.

Animal research has shown that loss of normal acoustic stimulation can increase spontaneous firing in the central auditory system and induce cortical map plasticity. Enriched acoustic environment after noise trauma prevents map plasticity and abolishes neural signs of tinnitus. In humans, the tinnitus spectrum overlaps with the area of hearing loss. Based on these findings it can be hypothesized that stimulating the auditory system by presenting music compensating specifically for the hearing loss might also suppress chronic tinnitus. To verify this hypothesis, a study was conducted in three groups of tinnitus patients. One group listened just to unmodified music (i.e. active control group), one group listened to music spectrally tailored to compensate for their hearing loss, and a third group received music tailored to overcompensate for their hearing loss, associated with one (in presbycusis) or two notches (in audiometric dip) at the edge of hearing loss. Our data indicate that applying overcompensation to the hearing loss worsens the patients' tinnitus loudness, the tinnitus annoyance and their depressive feelings. No significant effects were obtained for the control group or for the compensation group. These clinical findings were associated with an increase in current density within the left dorsal anterior cingulate cortex in the alpha2 frequency band and within the left pregenual anterior cingulate cortex in beta1 and beta2 frequency band. In addition, a region of interest analysis also demonstrated an associated increase in gamma band activity in the auditory cortex after overcompensation in comparison to baseline measurements. This was, however, not the case for the control or the compensation groups. In conclusion, music therapy compensating for hearing loss is not beneficial in suppressing tinnitus, and overcompensating hearing loss actually worsens tinnitus, both clinically and electrophysiologically.

The case for a generic implant processor.

A more structured and streamlined design of implants is nowadays possible. In this paper we focus on implant processors located in the heart of implantable systems. We present a real and representative biomedical-application scenario where such a new processor can be employed. Based on a suitably selected processor simulator, various operational aspects of the application are being monitored. Findings on performance, cache behavior, branch prediction, power consumption, energy expenditure and instruction mixes are presented and analyzed. The suitability of such an implant processor and directions for future work are given.