Browsing by Author "Peterchev, AV"

Now showing 1 - 9 of 9
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    A model of variability in brain stimulation evoked responses.
    (Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2012) Goetz, SM; Peterchev, AV
    The input-output (IO) curve of cortical neuron populations is a key measure of neural excitability and is related to other response measures including the motor threshold which is widely used for individualization of neurostimulation techniques, such as transcranial magnetic stimulation (TMS). The IO curve parameters provide biomarkers for changes in the state of the target neural population that could result from neurostimulation, pharmacological interventions, or neurological and psychiatric conditions. Conventional analyses of IO data assume a sigmoidal shape with additive Gaussian scattering that allows simple regression modeling. However, careful study of the IO curve characteristics reveals that simple additive noise does not account for the observed IO variability. We propose a consistent model that adds a second source of intrinsic variability on the input side of the IO response. We develop an appropriate mathematical method for calibrating this new nonlinear model. Finally, the modeling framework is applied to a representative IO data set. With this modeling approach, previously inexplicable stochastic behavior becomes obvious. This work could lead to improved algorithms for estimation of various excitability parameters including established measures such as the motor threshold and the IO slope, as well as novel measures relating to the variability characteristics of the IO response that could provide additional insight into the state of the targeted neural population.
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    A Modular Multilevel Series/Parallel Converter for a Wide Frequency Range Operation
    (IEEE Transactions on Power Electronics, 2019-10-01) Li, Z; Ricardo Lizana, F; Yu, Z; Sha, S; Peterchev, AV; Goetz, SM
    When providing ac output, modular multilevel converters (MMCs) experience power fluctuation in the phase arms. The power fluctuation causes voltage ripple on the module capacitors, which grows with the output power and inversely to the output frequency. Thus, low-frequency operations of MMCs, e.g., for motor drives, require injecting common-mode voltages and circulating currents, and strict dc voltage output relative to ground is impossible. To address this problem, this paper introduces a novel module topology that allows parallel module connectivity in addition to the series and bypass states. The parallel state directly transfers power across the modules and arms to cancel the power fluctuations and hence suppresses the capacitor voltage ripple. The proposed series/parallel converter can operate at a wide frequency range down to dc without common-mode voltages or circulating currents; it also allows sensorless operation and full utilization of the components at higher output frequencies. We present detailed simulation and experiment results to characterize the advantages and limitations of the proposed solution.
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    A Reduced Series/Parallel Module for Cascade Multilevel Static Compensators Supporting Sensorless Balancing
    (IEEE Transactions on Industrial Electronics, 2020) Li, Z; Motwani, JK; Zeng, Z; Lukic, SM; Peterchev, AV; Goetz, SM
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    Complementary topology of maintenance and manipulation brain networks in working memory.
    (Scientific reports, 2018-12-13) Davis, SW; Crowell, CA; Beynel, L; Deng, L; Lakhlani, D; Hilbig, SA; Lim, W; Nguyen, D; Peterchev, AV; Luber, BM; Lisanby, SH; Appelbaum, LG; Cabeza, R
    Working memory (WM) is assumed to consist of a process that sustains memory representations in an active state (maintenance) and a process that operates on these activated representations (manipulation). We examined evidence for two distinct, concurrent cognitive functions supporting maintenance and manipulation abilities by testing brain activity as participants performed a WM alphabetization task. Maintenance was investigated by varying the number of letters held in WM and manipulation by varying the number of moves required to sort the list alphabetically. We found that both maintenance and manipulation demand had significant effects on behavior that were associated with different cortical regions: maintenance was associated with bilateral prefrontal and left parietal cortex, and manipulation with right parietal activity, a link that is consistent with the role of parietal cortex in symbolic computations. Both structural and functional architecture of these systems suggested that these cognitive functions are supported by two dissociable brain networks. Critically, maintenance and manipulation functional networks became increasingly segregated with increasing demand, an effect that was positively associated with individual WM ability. These results provide evidence that network segregation may act as a protective mechanism to enable successful performance under increasing WM demand.
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    Modular Multilevel Series/Parallel Converter for Bipolar DC Distribution and Transmission
    (IEEE Journal of Emerging and Selected Topics in Power Electronics, 2020) Lizana F., R; Rivera, S; Li, Z; Dekka, A; Rosenthal, L; Bahamonde I., H; Peterchev, AV; Goetz, SM
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    Modulation and Control of Series/Parallel Module for Ripple-Current Reduction in Star-Configured Split-Battery Applications
    (IEEE Transactions on Power Electronics, 2020) Li, Z; Lizana, R; Yu, Z; Sha, S; Peterchev, AV; Goetz, SM
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    Noninvasive Detection of Motor-Evoked Potentials in Response to Brain Stimulation Below the Noise Floor-How Weak Can a Stimulus Be and Still Stimulate.
    (Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2018-07) Goetz, SM; Li, Z; Peterchev, AV
    Motor-evoked potentials (MEP) are one of the most important responses to brain stimulation, such as supra-threshold transcranial magnetic stimulation (TMS) and electrical stimulation. The understanding of the neurophysiology and the determination of the lowest stimulation strength that evokes responses requires the detection of even smallest responses, e.g., from single motor units, but available detection and quantization methods are rather simple and suffer from a large noise floor. The paper introduces a more sophisticated matched-filter detection method that increases the detection sensitivity and shows that activation occurs well below the conventional detection level. In consequence, also conventional threshold definitions, e.g., as 50 μV median response amplitude, turn out to be substantially higher than the point at which first detectable responses occur. The presented method uses a matched-filter approach for improved sensitivity and generates the filter through iterative learning from the presented data. In contrast to conventional peak-to-peak measures, the presented method has a higher signal-to-noise ratio (≥14 dB). For responses that are reliably detected by conventional detection, the new approach is fully compatible and provides the same results but extends the dynamic range below the conventional noise floor. The underlying method is applicable to a wide range of well-timed biosignals and evoked potentials, such as in electroencephalography.
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    Older adults benefit from more widespread brain network integration during working memory.
    (NeuroImage, 2020-05-19) Crowell, CA; Davis, SW; Beynel, L; Deng, L; Lakhlani, D; Hilbig, SA; Palmer, H; Brito, A; Peterchev, AV; Luber, B; Lisanby, SH; Appelbaum, LG; Cabeza, R
    Neuroimaging evidence suggests that the aging brain relies on a more distributed set of cortical regions than younger adults in order to maintain successful levels of performance during demanding cognitive tasks. However, it remains unclear how task demands give rise to this age-related expansion in cortical networks. To investigate this issue, functional magnetic resonance imaging was used to measure univariate activity, network connectivity, and cognitive performance in younger and older adults during a working memory (WM) task. Here, individuals performed a WM task in which they held letters online while reordering them alphabetically. WM load was titrated to obtain four individualized difficulty levels with different set sizes. Network integration-defined as the ratio of within- versus between-network connectivity-was linked to individual differences in WM capacity. The study yielded three main findings. First, as task difficulty increased, network integration decreased in younger adults, whereas it increased in older adults. Second, age-related increases in network integration were driven by increases in right hemisphere connectivity to both left and right cortical regions, a finding that helps to reconcile existing theories of compensatory recruitment in aging. Lastly, older adults with higher WM capacity demonstrated higher levels of network integration in the most difficult task condition. These results shed light on the mechanisms of age-related network reorganization by demonstrating that changes in network connectivity may act as an adaptive form of compensation, with older adults recruiting a more distributed cortical network as task demands increase.
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    Online repetitive transcranial magnetic stimulation during working memory in younger and older adults: A randomized within-subject comparison.
    (PloS one, 2019-01) Beynel, L; Davis, SW; Crowell, CA; Hilbig, SA; Lim, W; Nguyen, D; Palmer, H; Brito, A; Peterchev, AV; Luber, B; Lisanby, SH; Cabeza, R; Appelbaum, LG
    Working memory is the ability to perform mental operations on information that is stored in a flexible, limited capacity buffer. The ability to manipulate information in working memory is central to many aspects of human cognition, but also declines with healthy aging. Given the profound importance of such working memory manipulation abilities, there is a concerted effort towards developing approaches to improve them. The current study tested the capacity to enhance working memory manipulation with online repetitive transcranial magnetic stimulation in healthy young and older adults. Online high frequency (5Hz) repetitive transcranial magnetic stimulation was applied over the left dorsolateral prefrontal cortex to test the hypothesis that active repetitive transcranial magnetic stimulation would lead to significant improvements in memory recall accuracy compared to sham stimulation, and that these effects would be most pronounced in working memory manipulation conditions with the highest cognitive demand in both young and older adults. Repetitive transcranial magnetic stimulation was applied while participants were performing a delayed response alphabetization task with three individually-titrated levels of difficulty. The left dorsolateral prefrontal cortex was identified by combining electric field modeling to individualized functional magnetic resonance imaging activation maps and was targeted during the experiment using stereotactic neuronavigation with real-time robotic guidance, allowing optimal coil placement during the stimulation. As no accuracy differences were found between young and older adults, the results from both groups were collapsed. Subsequent analyses revealed that active stimulation significantly increased accuracy relative to sham stimulation, but only for the hardest condition. These results point towards further investigation of repetitive transcranial magnetic stimulation for memory enhancement focusing on high difficulty conditions as those most likely to exhibit benefits.