Power Envelope Research Articles

Propofol-induced Unresponsiveness Is Associated with a Brain Network Phase Transition.

Background:The wakeful brain can easily access and coordinate a large repertoire of different states—dynamics suggestive of “criticality.” Anesthesia causes loss of criticality at the level of electroencephalogram waveforms, but the criticality of brain network connectivity is less well studied. The authors hypothesized that propofol anesthesia is associated with abrupt and divergent changes in brain network connectivity for different frequencies and time scales—characteristic of a phase transition, a signature of loss of criticality.Methods:As part of a previously reported study, 16 volunteers were given propofol in slowly increasing brain concentrations, and their behavioral responsiveness was assessed. The network dynamics from 31-channel electroencephalogram data were calculated from 1 to 20 Hz using four phase and envelope amplitude–based functional connectivity metrics that covered a wide range of time scales from milliseconds to minutes. The authors calculated network global efficiency, clustering coefficient, and statistical complexity (using the Jensen–Shannon divergence) for each functional connectivity metric and compared their findings with those from an in silico Kuramoto network model.Results:The transition to anesthesia was associated with critical slowing and then abrupt profound decreases in global network efficiency of 2 Hz power envelope metrics (from mean ± SD of 0.64 ± 0.15 to 0.29 ± 0.28 absolute value, P < 0.001, for medium; and from 0.47 ± 0.13 to 0.24 ± 0.21, P < 0.001, for long time scales) but with an increase in global network efficiency for 10 Hz weighted phase lag index (from 0.30 ± 0.20 to 0.72 ± 0.06, P < 0.001). Network complexity decreased for both the 10 Hz hypersynchronous (0.44 ± 0.13 to 0.23 ± 0.08, P < 0.001), and the 2 Hz asynchronous (0.73 ± 0.08 to 0.40 ± 0.13, P < 0.001) network states. These patterns of network coupling were consistent with those of the Kuramoto model of an order–disorder phase transition.Conclusions:Around loss of behavioral responsiveness, a small increase in propofol concentrations caused a collapse of long time scale power envelope connectivity and an increase in 10 Hz phase-based connectivity—suggestive of a brain network phase transition.

Disruption of large-scale electrophysiological networks in stroke patients with visuospatial neglect.

Stroke frequently produces attentional dysfunctions including symptoms of hemispatial neglect, which is characterized by a breakdown of awareness for the contralesional hemispace. Recent studies with functional MRI (fMRI) suggest that hemineglect patients display abnormal intra- and interhemispheric functional connectivity. However, since stroke is a vascular disorder and fMRI signals remain sensitive to nonneuronal (i.e., vascular) coupling, more direct demonstrations of neural network dysfunction in hemispatial neglect are warranted. Here, we utilize electroencephalogram (EEG) source imaging to uncover differences in resting-state network organization between patients with right hemispheric stroke (N = 15) and age-matched, healthy controls (N = 27), and determine the relationship between hemineglect symptoms and brain network organization. We estimated intra- and interregional differences in cortical communication by calculating the spectral power and amplitude envelope correlations of narrow-band EEG oscillations. We first observed focal frequency-slowing within the right posterior cortical regions, reflected in relative delta/theta power increases and alpha/beta/gamma decreases. Secondly, nodes within the right temporal and parietal cortex consistently displayed anomalous intra- and interhemispheric coupling, stronger in delta and gamma bands, and weaker in theta, alpha, and beta bands. Finally, a significant association was observed between the severity of left-hemispace search deficits (e.g., cancellation test omissions) and reduced functional connectivity within the alpha and beta bands. In sum, our novel results validate the hypothesis of large-scale cortical network disruption following stroke and reinforce the proposal that abnormal brain oscillations may be intimately involved in the pathophysiology of visuospatial neglect.

Demonstrating Analog Inference on the BrainScaleS-2 Mobile System

We present the BrainScaleS-2 mobile system as a compact analog inference engine based on the BrainScaleS-2 ASIC and demonstrate its capabilities at classifying a medical electrocardiogram dataset. The analog network core of the ASIC is utilized to perform the multiply-accumulate operations of a convolutional deep neural network. At a system power consumption of 5.6W, we measure a total energy consumption of 192uJ for the ASIC and achieve a classification time of 276us per electrocardiographic patient sample. Patients with atrial fibrillation are correctly identified with a detection rate of (93.7${\pm}$0.7)% at (14.0${\pm}$1.0)% false positives. The system is directly applicable to edge inference applications due to its small size, power envelope, and flexible I/O capabilities. It has enabled the BrainScaleS-2 ASIC to be operated reliably outside a specialized lab setting. In future applications, the system allows for a combination of conventional machine learning layers with online learning in spiking neural networks on a single neuromorphic platform.

Integrated Silicon Microfluidic Cooling of a High-Power Overclocked CPU for Efficient Thermal Management

The stagnation of Dennard scaling along with the move towards heterogeneous 2.5D and 3D ICs is increasing the thermal design power (TDP) envelopes of general-purpose CPUs. With conventional cooling approaches such as air-cooling and cold plates, it is challenging to meet these increased requirements. Moreover, these increasing cooling requirements necessitate larger thermal management overheads in datacenter settings. All these trends point towards the need for a more aggressive and efficient cooling solutions. Monolithic microfluidic cooling, where silicon micropin-fins or microchannels are etched directly into the backside of the functional silicon, has shown great potential in this regard. In this work, we use micropin-fins etched directly on the back of an Intel Core i7-8700K CPU and overclocked it to dissipate up to 215W of power while being cooled by room temperature de-ionized (DI) water. We demonstrate up to 44.4% reduction in junction-to-inlet thermal resistance while using only 0.3&#x00D7; of volumetric coolant flow per Watt of power dissipated in the CPU compared to a conventional cold-plate. Furthermore, we demonstrate higher sustained core frequencies even when being cooled with elevated inlet temperatures, showing the potential for more efficient datacenter operations without the need for expensive and energy intensive refrigeration loops. The scalability towards heterogeneous 2.5D and 3D devices is also discussed. These results point towards a more scalable, efficient cooling solution, which can unlock higher power, more efficient compute systems, while minimizing the environmental impacts by reducing the thermal management overheads.

Robust Classification of Contraband Substances using Longwave Hyperspectral Imaging and Full Precision and Neuromorphic Convolutional Neural Networks

Several agencies such as the US Department of Homeland Security (DHS) seek to improve the detection of illegal threats and materials passing through Ports of Entry (POE). A combined hardware/software solution that is portable, non-ionizing, handheld, low cost, and fast would represent a significant contribution towards that goal as existing systems do not fulfil many or all of these requirements. To design such a system, Quantum Ventura partnered with Bodkin Design and Engineering to combine long-wave infrared (LWIR) hyperspectral imaging (HSI) with convolutional neural networks (CNNs), implemented on full precision GPUs and neuromorphic computing modules. Our capability study showed that our system can accurately detect and classify contraband in a variety of situations, including varied backgrounds, temperatures, and purities. With a small size, weight, power and cost (SWaP-C) envelope, neuromorphic computing implementations of CNNs showed promising results, though not as well as full precision results.

Time Domain Analog Neuromorphic Engine Based on High-Density Non-Volatile Memory in Single-Poly CMOS

Increasing the energy efficiency of deep learning systems is critical for improving the cognitive capability of edge devices, often battery operated, as well as for data centers, constrained by the total power envelope. Specialized architectures accelerated by analog vector-matrix multipliers (VMMs) can reduce by orders of magnitude the energy per operation, since the reduced precision of analog computation does not undermine the classification accuracy of the neural network. We show an analog vector-matrix multiplier fabricated with industry-standard 0.18 μm CMOS process, exploiting a single-transistor non-volatile analog memory cell and dedicated technology circuit co-design. The design is focused on implementation in neural networks performing offline training. The VMM performs the analog multiplication of a vector of inputs, encoded in the duration of time pulses, times a matrix of weights, encoded in the programmable currents of the memory cells. A 1.72 μm2 memory cell is realized with a single transistor with floating gate, which can be operated as a two-terminal analog memristive device with more than 64 programmable current levels and high Ihigh/Ilow ratio (> 103), tuned by the charge injected in the floating gate. A small-area charge amplifier is used to convert the multiply and accumulate operation result into a voltage. System-level projections based on our measurements and simulations provide a throughput of 333.17 GOps/s and an energy efficiency of 122.3 TOps/J, higher than comparable-precision VMMs reported in the literature, and an equivalent area per cell down to 2.15 μm2, lower than any similar state-of-the-art solution. Of critical importance in view of translation to industry, our proposal uses in a new way an industry-standard low-cost single-poly CMOS process flow.

Design and performance test of discharge chamber and grid for multi-mode ion thrusters

In view of the application requirements of electric propulsion system for China’s asteroid deep space exploration mission, the overall scheme is designed, an ion thruster prototype model is established by using a four-ring-cusp field discharge chamber, 30-cm beam current extraction diameter three-grid ion optics system. Reasonableness and compatibility of discharge chamber and grid design are verified experimentally and theoretically . The test results are shown below. The ion thruster can operate steadily over an input power envelope of 277–3120 W, thrust increases linearly from 9.9 to 117.2 mN, specific impulse rises from 1269 to 3492 s, the beam divergence angle drops from 30.7° to 26.8° and stabilizes above a certain power value, the thrust vector angle is less than 1.5° and beam flatness parameter is greater than 0.75 at different operating points. The maximum percentage reduction in grid gap aberration is 90% with the strain relief molybdenum mounting ring thermal design. This research provides a reference for multi-mode ion thruster design and in-orbit engineering applications.

Atypical resting-state functional brain connectivity in children with developmental coordination disorder

Children with developmental coordination disorder (DCD) present lower abilities to acquire and execute coordinated motor skills. DCD is frequently associated with visual perceptual (with or without motor component) impairments. This magnetoencephalography (MEG) study compares the brain resting-state functional connectivity (rsFC) and spectral power of children with and without DCD.29 children with DCD and 28 typically developing (TD) peers underwent 2 × 5 min of resting-state MEG. Band-limited power envelope correlation and spectral power were compared between groups using a functional connectome of 59 nodes from eight resting-state networks. Correlation coefficients were calculated between fine and gross motor activity, visual perceptual and visuomotor abilities measures on the one hand, and brain rsFC and spectral power on the other hand. Nonparametric statistics were used.Significantly higher rsFC between nodes of the visual, attentional, frontoparietal, default-mode and cerebellar networks was observed in the alpha (maximum statistics, p = .0012) and the low beta (p = .0002) bands in children with DCD compared to TD peers. Lower visuomotor performance (copying figures) was associated with stronger interhemispheric rsFC within sensorimotor areas and power in the cerebellum (right lobule VIII).Children with DCD showed increased rsFC mainly in the dorsal extrastriate visual brain system and the cerebellum. However, this increase was not associated with their coordinated motor/visual perceptual abilities. This enhanced functional brain connectivity could thus reflect a characteristic brain trait of children with DCD compared to their TD peers. Moreover, an interhemispheric compensatory process might be at play to perform visuomotor task within the normative range.

Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales

State modeling of whole-brain electroencephalography (EEG) or magnetoencephalography (MEG) allows to investigate transient, recurring neurodynamical events. Two widely-used techniques are the microstate analysis of EEG signals and hidden Markov modeling (HMM) of MEG power envelopes. Both reportedly lead to similar state lifetimes on the 100 ms timescale, suggesting a common neural basis. To investigate whether microstates and power envelope HMM states describe the same neural dynamics, we used simultaneous MEG/EEG recordings at rest and compared the spatial signature and temporal activation dynamics of microstates and power envelope HMM states obtained separately from EEG and MEG. Results showed that microstates and power envelope HMM states differ both spatially and temporally. Microstates reflect sharp events of neural synchronization, whereas power envelope HMM states disclose network-level activity with 100–200 ms lifetimes. Further, MEG microstates do not correspond to the canonical EEG microstates but are better interpreted as split HMM states. On the other hand, both MEG and EEG HMM states involve the (de)activation of similar functional networks. Microstate analysis and power envelope HMM thus appear sensitive to neural events occurring over different spatial and temporal scales. As such, they represent complementary approaches to explore the fast, sub-second scale bursting electrophysiological dynamics in spontaneous human brain activity.

Parallel SVD Algorithm for a Three-Diagonal Matrix on a Video Card Using the Nvidia CUDA Architecture

SVD (Singular Value Decomposition) algorithm is used in recommendation systems, machine learning, image processing, and in various algorithms for working with matrices which can be very large and Big Data, so, given the peculiarities of this algorithm, it can be performed on a large number of computing threads that have only video cards.CUDA is a parallel computing platform and application programming interface model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit for general purpose processing – an approach termed GPGPU (general-purpose computing on graphics processing units). The GPU provides much higher instruction throughput and memory bandwidth than the CPU within a similar price and power envelope. Many applications leverage these higher capabilities to run faster on the GPU than on the CPU. Other computing devices, like FPGAs, are also very energy efficient, but they offer much less programming flexibility than GPUs.The developed modification uses the CUDA architecture, which is intended for a large number of simultaneous calculations, which allows to quickly process matrices of very large sizes. The algorithm of parallel SVD for a three-diagonal matrix based on the Givents rotation provides a high accuracy of calculations. Also the algorithm has a number of optimizations to work with memory and multiplication algorithms that can significantly reduce the computation time discarding empty iterations.This article proposes an approach that will reduce the computation time and, consequently, resources and costs. The developed algorithm can be used with the help of a simple and convenient API in C ++ and Java, as well as will be improved by using dynamic parallelism or parallelization of multiplication operations. Also the obtained results can be used by other developers for comparison, as all conditions of the research are described in detail, and the code is in free access.

Optimal drivetrain design methodology for enhancing dynamic and energy performances of dual-motor electric vehicles

Electric vehicles (EVs) are a dominant transportation trend towards a low-carbon future. However, there is a lack of systematic studies for full-electric off-road vehicles in terms of drivetrain design and energy management issues. This paper proposes a novel methodology to develop a drivetrain system for a four-wheel-drive (4WD) dual-motor off-road EV. The obtained design delivers high performance in both driving performance and energy efficiency of a desired vehicle using the two electric motors with their multi-speed gearbox. The proposed approach is to minimize the power envelope (force-speed characteristic) difference between a targeted model and the system under study. As a result, the objective function finds an optimal solution for a proper set of gear ratios and constant power speed ratio (CPSR) through a two-layer hierarchical scheme. The upper layer sets the targeted power envelope based on the expected driving measures. The lower layer minimizes the gap of the power envelope characteristics between the targeted vehicle and the dual-motor vehicle. In parallel, two optimization loops are implemented at the lower layer. The outer loop determines the proper value of CPSR, meanwhile, the gear ratios corresponding to the selected CPSR are optimized by a particle swarm optimization (PSO) algorithm in the inner loop. The results verify that the dynamic and energy performances of the studied dual-motor vehicle are enhanced over critical testing scenarios. In a comparison with the single-motor single-speed gearbox configuration, the optimized design shows that the top speed is 55% higher; the acceleration time to 45 km/h is reduced by 55.83%; and the gradeability is improved by 162.5%. Moreover, the test under a typical off-road cycle reveals a 1.9% overall efficiency increase compared to the dual-motor drivetrain using the same non-optimized gear ratio. The proposed comprehensive design approach has the potential to be applied to a wide range of vehicle areas.

Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions

BackgroundElectrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric. New methodsWe undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity. ResultsPower envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn’t strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals. ConclusionsThe competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.

Electroencephalographic connectivity predicts clinical response to repetitive transcranial magnetic stimulation in patients with insomnia disorder

BackgroundAccumulating evidence suggests that low frequency repetitive transcranial magnetic stimulation (rTMS), which generally decreases cortical excitability and remodels plastic connectivity, improves sleep quality in patients with insomnia disorder. However, the effects of rTMS vary substantially across individuals and treatment is sometimes unsatisfactory, calling for biomarkers for predicting clinical outcomes. ObjectiveThis study aimed to investigate whether functional connectivity of the target network in electroencephalography is associated with the clinical response to low frequency rTMS in patients with insomnia disorder. MethodsTwenty-five patients with insomnia disorder were subjected to 10 sessions of treatment with 1 Hz rTMS over the right dorsolateral prefrontal cortex. Resting-state electroencephalography was collected before rTMS. Pittsburgh Sleep Quality Index, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, and Mini-Mental State Exam were performed before and after rTMS treatment, with a follow-up after one month. Electroencephalographic connectivity was measured by the power envelope connectivity at the source level. Partial least squares regression identified models of connectivity that maximally accounted for the rTMS response. ResultsScores of Pittsburgh Sleep Quality Index, Hamilton Depression Rating Scale, and Hamilton Anxiety Rating Scale were decreased after rTMS and one-month later. Baseline weaker connectivity of a network in the beta and alpha bands between a brain region approximating the stimulated right dorsolateral prefrontal cortex and areas located in the frontal, insular, and limbic cortices was associated with a greater change in Pittsburgh Sleep Quality Index and Hamilton Depression Rating Scale following rTMS. ConclusionsLow frequency rTMS could improve sleep quality and depressive moods in patients with insomnia disorder. Moreover, electroencephalographic functional connectivity would potentially be a robust biomarker for predicting the therapeutic effects.

PMSM Torque-Speed-Efficiency Map Evaluation from Parameter Estimation Based on the Stand Still Test

During the last decades, a wide variety of methods to estimate permanent magnet synchronous motor (PMSM) performance have been developed. These methodologies have several advantages over conventional procedures, saving time and economic costs. This paper presents a new methodology to estimate the PMSM torque-speed-efficiency map based on the blocked rotor test using a single-phase voltage source. The methodology identifies the stator flux linkage depending on the current magnitude and angle while providing a detailed estimation of the iron losses. The torque-speed-efficiency map provides detailed information of the motor efficiency along its operating region, including the nominal conditions and the maximum power envelope. The proposed methodology does not require knowing the geometry of the machine to perform any load test, and it also avoids using expensive measurement devices and a complex experimental setup. Moreover, the proposed method allows the PMSM performance to be reproduced by applying different control strategies, which is useful when testing different drives. The method does not require the application of any optimization algorithm, thus simplifying and speeding up the process to determine the performance. Experimental validation is carried out by comparing motor performances obtained through the proposed method with those obtained by means of a conventional experimental method and against finite element analysis (FEA).

QAM-GFDM of Dual-Mode VCSEL Mixed 28-GHz MMW Carrier for Fiber-Wireless Link

An optical heterodyned 28-GHz carrier generation is demonstrated for synthesizer-free fifth-generation (5G) millimeter wave over fiber (MMWoF) link by using orthogonally polarized dual-mode vertical-cavity surface-emitting laser (VCSEL) as the frontend transmitter, and the power envelope detection is employed for self-heterodyne down-conversion of the generalized frequency division multiplexing (GFDM) data stream. The demonstrated MMWoF link exhibits superior immunity to the residual frequency and phase noises as well as the inter-carrier interference induced by the free-running dual-mode carrier. For back-to-back optical downstream wireline transmission, the receiving GFDM data optimizes its bit error rate (BER) to 2.2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> by adjusting the bias current of the dual-mode VCSEL to 8 mA (3 I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sub> ), and by grouping the GFDM data matrix with total symbols of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N = K×M</i> with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> = 2 subcarriers and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i> = 24 timeslots. After a long-reach delivering the optical data stream over 50-km dispersion-managed single-mode fiber (DM-SMF), the qualified receiving power sensitivity is −12 dBm, whereas the decoding GFDM data reveals improved receiving sensitivity with an extremely low power penalty of only 0.3 dB. For the wireless transmission after optical downstream receiving at the remote node, the self-heterodyned MMW carrier exhibits peak power of −62.3 dBm and carrier-to-noise (CNR) of 26.8 dB over long-reach 50-km DM-SMF downstream optical link. In comparison with the mutual heterodyned from two incoherent optical carriers, the self-heterodyned frequency fluctuation can be suppressed from >500 to <330 MHz with significantly suppressed phase noise. After wireless delivering over 2 m in free space, the self-heterodyne down-conversion by power envelope detection guarantees the down-converted quadrature amplitude modulation GFDM (QAM-GFDM) data stream with an average power of -48.4 dBm after optimizing the impedance matching by selecting the appropriate frequency response of the power envelope detector. As a result, the wireless MMW transmission of 8-Gbit/s 4-QAM GFDM data stream is qualified with error vector magnitude (EVM) of 16.9%, signal-to-noise ratio (SNR) of 8.5 dB, and BER of 3.8×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . By combining the free-running dual-mode source with two advanced techniques including mode-power equalization and self-heterodyned power envelope detection, the proposed synthesizer-free MMWoF network has demonstrated excellent immunity to the phase/frequency noise, inter-carrier interference which is particularly suitable for building up the compact and simplified optical transmitter and microwave receiver modules with cost-effective MMW self-heterodyne scheme.

Prediction of Dynamic Responses of Flow-Induced Vibration Using Deep Learning

Flow-induced vibration (FIV) is a phenomenon in which the flow passing through a structure exerts periodic forces on the structure. Most studies on FIVs focus on suppressing this phenomenon. However, the Marine Renewable Energy Laboratory (MRELab) at the University of Michigan, USA, has developed a technology called the vortex-induced vibration for aquatic clean energy (VIVACE) converters that reinforces FIV and converts the energy in tidal currents to electrical energy. This study introduces the experimental data of the VIVACE converter and the associated method using deep neural networks (DNNs) to predict the dynamic responses of the converter. The DNN was trained and verified with experimental data from the MRELab, and the findings show that the amplitudes and frequencies of a single cylinder in the FIV predicted by the DNN under various test conditions were in good agreement with the experimental data. Finally, based on both the predicted and experimental data, the optimal power envelope of the VIVACE converter was generated as a function of the flow speed. The predictions using DNNs are expected to be more accurate as they can be trained with more experimental data in the future and will help to substantially reduce the number of experiments on FIVs.

Green power aware approaches for scheduling independent tasks on a multi-core machine

The energy consumption of large Information and Communications Technology structures such as data and computation centers along with the corresponding carbon footprint are on the rise. Green computing has become an indispensable solution to face the resulting economical and environmental challenges. Powering these centers with renewable energy sources is however a challenge since these sources cannot guarantee a constant power supply due to their fluctuating power production. We here tackle the problem of scheduling independent tasks on a multi-core machine within a predicted renewable power envelope that varies over time. We evaluate the complexity of different instances of the problem from a theoretical point of view. We propose several heuristics, including genetic algorithms, and we conduct experiments to assess their performance. For some particular cases we compare the performance of these heuristics to optimal solutions.

Repetitive Transcranial Magnetic Stimulation of the Dorsolateral Prefrontal Cortex Modulates Electroencephalographic Functional Connectivity in Alzheimer's Disease.

Background: Increasing evidence demonstrates that repetitive transcranial magnetic stimulation (rTMS) treatment of the dorsolateral prefrontal cortex is beneficial for improving cognitive function in patients with Alzheimer’s disease (AD); however, the underlying mechanism of its therapeutic effect remains unclear.Objectives/Hypothesis: The aim of this study was to investigate the impact of rTMS to the dorsolateral prefrontal cortex on functional connectivity along with treatment response in AD patients with different severity of cognitive impairment.Methods: We conducted a 2-week treatment course of 10-Hz rTMS over the left dorsolateral prefrontal cortex in 23 patients with AD who were split into the mild or moderate cognitive impairment subgroup. Resting state electroencephalography and general cognition was assessed before and after rTMS. Power envelope connectivity was used to calculate functional connectivity at the source level. The functional connectivity of AD patients and 11 cognitively normal individuals was compared.Results: Power envelope connectivity was higher in the delta and theta bands but lower in the beta band in the moderate cognitive impairment group, compared to the cognitively normal controls, at baseline (p < 0.05). The mild cognitive impairment group had no significant abnormities. Montreal Cognitive Assessment scores improved after rTMS in the moderate and mild cognitive impairment groups. Power envelope connectivity in the beta band post-rTMS was increased in the moderate group (p < 0.05) but not in the mild group. No significant changes in the delta and theta band were found after rTMS in both the moderate and mild group.Conclusion: High-frequency rTMS to the dorsolateral prefrontal cortex modulates electroencephalographic functional connectivity while improving cognitive function in patients with AD. Increased beta connectivity may have an important mechanistic role in rTMS therapeutic effects.

Resting-state functional brain connectivity is related to subsequent procedural learning skills in school-aged children

This magnetoencephalography (MEG) study investigates how procedural sequence learning performance is related to prior brain resting-state functional connectivity (rsFC), and to what extent sequence learning induces rapid changes in brain rsFC in school-aged children.Procedural learning was assessed in 30 typically developing children (mean age ± SD: 9.99 years ± 1.35) using a serial reaction time task (SRTT). During SRTT, participants touched as quickly and accurately as possible a stimulus sequentially or randomly appearing in one of the quadrants of a touchscreen. Band-limited power envelope correlation (brain rsFC) was applied to MEG data acquired at rest pre- and post-learning. Correlation analyses were performed between brain rsFC and sequence-specific learning or response time indices.Stronger pre-learning interhemispheric rsFC between inferior parietal and primary somatosensory/motor areas correlated with better subsequent sequence learning performance and faster visuomotor response time. Faster response time was associated with post-learning decreased rsFC within the dorsal extra-striate visual stream and increased rsFC between temporo-cerebellar regions.In school-aged children, variations in functional brain architecture at rest within the sensorimotor network account for interindividual differences in sequence learning and visuomotor performance. After learning, rapid adjustments in functional brain architecture are associated with visuomotor performance but not sequence learning skills.

Investigation of Microwave Ablation Process in Sweet Potatoes as Substitute Liver.

The microwave ablation technique to destroy cancer tissues in liver is practiced clinically and is the subject of ongoing research, e.g., ablation monitoring. For studies, liver tissue from cattle or pigs is often used as a substitute material. In this work, sweet potato is presented as an alternative material for microwave ablation experiments in liver due to similar material properties. Sweet potatoes as a substitute for liver have the advantages of better handling, easy procurement and stable material properties over time for microwave ablation experiments. The dielectric constant and electrical conductivity of sweet potato are characterized for temperature variation with the help of high-temperature dielectric probe. Furthermore, a test setup is presented for microwave ablation experiments in which a bowtie slot antenna matched to sweet potato is placed on its surface to directly receive the microwave power from a self-developed microwave applicator inserted into a sweet potato 4 cm below the surface antenna. A high-power source was used to excite the microwave powers up to 80 W and a spectrum analyzer was used to measure the signal received by the surface antenna. The experiments were performed in an anechoic chamber for safety reasons. Power at 50 W and 80 W was stimulated for a maximum of 600 s at the 2.45 GHz ISM band in different sweet potato experiments. A correlation is found between the power received by the surface antenna and rise of temperature inside sweet potato; relative received power drops from 1 at 76 C to 0.6 at 88 C (max. temperature) represents a 40% relative change in a 50 W microwave ablation experiment. The received power envelope at the surface antenna is between 10 mW and 32 mW during 50 W microwave ablation. Other important results for 10 min, 80 W microwave ablation include: a maximum ablation zone short axis diameter of 4.5 cm and a maximum ablation temperature reached at 99 C, 3 mm away from the applicator’s slot. The results are compared with the state of the art in microwave ablation in animal liver. The dielectric constant and electrical conductivity evolution of sweet potato with rising temperature is comparable to animal liver in 50–60 C range. The reflection loss of self-developed applicator in sweet potato is below 15 dB which is equal to reflection loss in liver experiments for 600 s. The temperature rise for the first 90 s in sweet potato is 76 C as compared to 73 C in liver with 50 W microwave ablation. Similarly, with 80–75 W microwave ablation, for the first 60 s, the temperature is 98 C in sweet potato as compared to 100 C in liver. The ablation zone short-axis diameter after 600 s is 3.3 cm for 50 W microwave ablation in sweet potato as compared to 3.5 cm for 30 W microwave ablation in liver. The reasons for difference in microwave ablation results in sweet potato and animal liver are discussed. This is the first study to directly receive a signal from microwave applicator during a microwave ablation process with the help of a surface antenna. The work can be extended to multiple array antennas for microwave ablation monitoring.