Input-output Relationship Research Articles

Changes in neuronal excitability in the rat hippocampus in a prolonged febrile seizures model

Febrile seizures (FSs) are a prevalent neurological disorder among children aged 3 months to 5 years, with the highest incidence observed in the second year of life [1]. Considering that neuronal and glial cell development and synaptic contact formation are ongoing during this period [2, 3], FSs can potentially influence these processes. However, the present data on the effect of FSs on brain development remain inconsistent. This study aims to examine the impact of extended seizures on the attributes of hippocampal pyramidal neurons in rats of varying ages. Wistar rats were used in this study. FSs were induced on postnatal day (P)10 through placing pups onto the bottom of a glass chamber for 30 minutes and exposing them to a controlled stream of heated air, causing their body temperature to rise to 39 °C and trigger the occurrence of FSs. Only animals that underwent FSs that persisted for at least 15 minutes were included in the study. Littermates were employed as controls and were placed away from the nest for the same duration but kept at room temperature. At postnatal days 12, 21–23, and 51–55, the rats were decapitated, and their brains were extracted. Horizontal brain slices (400 micrometers) were sliced. The study examined the biophysical properties of CA1 pyramidal neurons using the whole-cell patch-clamp method. 1.5-second current pulses were injected, and subthreshold membrane properties such as resting membrane potential, input resistance, membrane time constant, and intrinsic firing properties, were assessed. Extracellular field excitatory postsynaptic potentials (fEPSPs) were recorded from the CA1 stratum radiatum of the hippocampus to assess the efficacy of synaptic neurotransmission at CA3-CA1 pyramidal neuron synapses. The amplitude and fiber volley (FV) amplitude of each fEPSP were measured by applying high amplitude currents to each slice. A sigmoidal Gompertz function was used to evaluate the efficacy of neurotransmission. Paired-pulse stimulation was used to examine potential alterations in short-term synaptic plasticity. Paired pulses were administered at intervals spanning from 10 to 500 ms, and the paired-pulse ratio (PPR) was assessed as the proportion of the amplitude of the second to the first fEPSP for each interval. Maximum electroshock seizure threshold (MEST) was determined two months after FS to assess the animals’ susceptibility to seizures. The lowest current at which tonic hind limb extension was exhibited was determined for every animal. No significant changes in subthreshold firing properties were observed in P12 or P21 rats following FSs. However, we did observe several changes in intrinsic firing properties. For example, the maximum firing frequency decreased by 23% in P12 rats exposed to FSs, relative to age-matched control rats. Additionally, we noticed that firing frequency adaptation was significantly less pronounced in P12 rats that underwent FSs, compared to control rats of the same age. Seizure-induced alterations in firing characteristics were absent in P21 rats. No significant variations in fEPSP or FV amplitudes were found among the different groups of P12 and P55 rats at different current intensities. However, a significant increase in FV amplitudes and decrease in neuronal input-output (I/O) relationships between fEPSP and FV amplitudes were observed in P21 rats following FSs. At P12, short-term synaptic plasticity was disrupted, evidenced by a significant increase in PPR in rats two days post-FS. However, at P21 and P55, experimental and control groups were not significantly different. MEST test displayed a significant rise in the hind limb extension threshold in rats two months following FS as compared to control animals. Overall, these findings suggest alterations in neuronal excitability after prolonged FS. Two days after FS, neuronal excitability transiently decreased while PPR increased, indicating a decrease in the probability of presynaptic glutamate release in hippocampal neurons. The efficiency of synaptic neurotransmission in CA3-CA1 was reduced in three-week-old animals. Additionally, the MEST test demonstrated that rats, two months following FS, exhibited a higher hind limb extension threshold in comparison to control animals.

Bayesian synergistic metamodeling (BSM) for physical information infused data-driven metamodeling

Bayesian synergistic metamodeling (BSM), a novel technique for physical information infused data-driven metamodeling, is proposed. A core challenge for modeling is to construct an explainable and reliable model that represents the input-output relationship of concern. To tackle this challenge, the proposed BSM fuses the information of the physical mechanism and the implicit features extracted from the captured data to develop the most suitable representation. The model discrepancy of physical information infused modeling is compensated via data-driven modeling. The resultant representation achieves the optimal balance between data fitting and model complexity. In addition, the uncertainties of all estimates are quantified to reflect the quality of the estimation and prediction. To demonstrate the efficacy and applicability of the proposed BSM, we present two simulated examples under various modeling conditions and a case study for seismic attenuation modeling utilizing the in-situ seismic records of a strong earthquake.

An RBF-based artificial neural network for prediction of dynamic viscosity of MgO/SAE 5W30 oil hybrid nano-lubricant to obtain the best performance of energy systems

Technological progress and complications in microfluidics usage have led researchers to use nanomaterials in different scientific fields. The properties and characteristics of hybrid Nanofluids are more enhanced compared to nanofluids based on single nanoparticles and conventional liquid. Recently, modeling methods have replaced most common statistical methods. Due to the high accuracy of the response and generalizability in various conditions, artificial neural networks (ANNs) to estimate nanofluids’ viscosity and thermal conductivity have become common. Dynamic viscosity (μ) (estimation analyzes one of the key factors in determining the hydrodynamic behavior of nanofluids. In this manuscript, an RBF-ANN is used to simulate the input-output relation of dynamic viscosity of the MgO- SAE 5W30 Oil hybrid nanofluid versus three important parameters, including volume fraction of nanoparticles, temperature, and shear rate. The results show that for this nanofluid, by increasing temperature and shear rate, the dynamic viscosity is decreased. In contrast, the volume fraction of nanoparticles directly affects the output, although this consequence can be neglected. By increasing the temperature from 5° to 55°C, the dynamic viscosity would decrease. Also, changing the shear rate from 50 to 1000 rpm decreases the dynamic viscosity from 400 cP to 25 cP. It is worth mentioning that the obtained trends and deviation of dynamic viscosity for MgO-SAE 5W30 Oil hybrid nanofluid versus temperature, the volume fraction of nanoparticles, and shear rate can be used by the academic community as well as an industrial section to obtain the best performance of energy systems based on this nanofluid.

Design and Analysis of an Adaptive Cable-Driven Manipulator Capable of Actively Transitioning between Two-Point Clamping and Envelope Grasping

Actively transitioning between clamping and grasping is a challenging problem for most manipulators with limited degrees of freedom. To overcome this problem, a cable-driven rigid–flexible combined manipulator capable of actively transitioning between clamping and grasping is proposed in this paper, which has a certain adaptability and compliance to achieve adaptive operation. First, the cable-driven unit and compliant unit of the cable-driven rigid–flexible combined manipulator are designed. Then, the sensitivity of the mechanism parameters is analyzed using the Monte Carlo method, and then the structure of the cable-driven rigid–flexible combined manipulator is optimized. After that, the force on the finger in two-point clamping mode is modelled using Newton’s second law. Furthermore, the input–output relationship modelling of the finger in envelope grasping mode is deeply analyzed using the principle of energy conservation. Finally, the stable grasping performance of the cable-driven rigid–flexible combined manipulator is verified using numerical simulation and physical prototype tests. The results show that the cable-driven rigid–flexible combined manipulator has good adaptability and compliance, which verifies the effectiveness and rationality of the design and modelling.

Energy-driven genome regulation by ATP-dependent chromatin remodellers.

The packaging of DNA into chromatin in eukaryotes regulates gene transcription, DNA replication and DNA repair. ATP-dependent chromatin remodelling enzymes (re)arrange nucleosomes at the first level of chromatin organization. Their Snf2-type motor ATPases alter histone-DNA interactions through a common DNA translocation mechanism.Whether remodeller activities mainly catalyse nucleosome dynamics or accurately co-determine nucleosome organization remained unclear. In this Review, we discuss the emerging mechanisms of chromatin remodelling: dynamic remodeller architectures and their interactions, the inner workings of the ATPase cycle, allosteric regulation and pathological dysregulation. Recent mechanistic insights argue for a decisive role of remodellers in the energy-driven self-organization of chromatin, which enables both stability and plasticity of genome regulation-for example, during development and stress. Different remodellers, such asmembers of the SWI/SNF, ISWI, CHD and INO80 families, process (epi)genetic information through specific mechanisms into distinct functional outputs. Combinatorial assembly of remodellers and their interplay with histone modifications, histone variants, DNA sequence or DNA-bound transcription factors regulate nucleosome mobilization or eviction or histone exchange. Such input-output relationships determine specific nucleosome positions and compositions with distinct DNA accessibilities and mediate differential genome regulation. Finally, remodeller genes are often mutated in diseases characterized by genome dysregulation, notably in cancer, and we discuss their physiological relevance.

A time series modeling approach for damage monitoring of concrete dam under seismic effects

In order to improve the applicability of time series models for the diagnosis of concrete dam damage under seismic loading, this paper proposes a diagnostic method for structural damage by combining linear time series modeling and non-linear modeling of model residuals. Starting from the linear structural dynamic response transmissibility function, the expressions of the generalized Auto-Regressive with eXtra inputs (GARX) model for the dynamic response under stationary excitation conditions were derived. The GARX model with dynamic coefficients was extended to model the dynamic response under seismic loading and the generalized Auto-Regressive Conditional Heteroscedasticity (GARCH) model was used to nonlinearly model the model residuals. To track the coefficients of the GARX model, an adaptive least squares recursion method based on fuzzy forgetting factor was studied. The damage indicator of Minkowski distance based on the GARCH model coefficients was proposed. Finally, the effectiveness of the diagnostic methods of the GARX and GARCH models was verified by a numerical calculation of the non-linear damage to a dam. The results showed that GARX can effectively describe the input-output relationship of the dam in the absence of damage. After damage occurs, the GARCH model and the improved Minkowski distance index for the model residuals of GARX can effectively characterize the non-linear damage evolution of the dam.

Analysis of Monosynaptic Inputs to Thalamic Paraventricular Nucleus Neurons Innervating the Shell of the Nucleus Accumbens and Central Extended Amygdala

The paraventricular nucleus of the thalamus (PVT) sends dense projections to the shell of the nucleus accumbens (NAcSh), dorsolateral region of the bed nucleus of the stria terminalis (BSTDL) and the lateral region of central nucleus of the amygdala (CeL). Projection specific modulation of these pathways has been shown to regulate appetitive and aversive behavioral responses. The present investigation applied an intersectional monosynaptic rabies tracing approach to quantify the brain-wide sources of afferent input to PVT neurons that primarily project to the NAcSh, BSTDL and CeL. The results demonstrate that these projection neurons receive monosynaptic input from similar brain regions. The prefrontal cortex and the ventral subiculum of the hippocampus were major sources of input to the PVT projection neurons. In addition, the lateral septal nucleus, thalamic reticular nucleus and the hypothalamic medial preoptic area, dorsomedial, ventromedial, and arcuate nuclei were sources of input. The subfornical organ, parasubthalamic nucleus, periaqueductal gray matter, lateral parabrachial nucleus, and nucleus of the solitary tract were consistent but lesser sources of input. This input–output relationship is consistent with recent observations that PVT neurons have axons that bifurcate extensively to divergently innervate the NAcSh, BSTDL and CeL.

On the Doppler Squint Effect in OTFS Systems Over Doubly-Dispersive Channels: Modeling and Evaluation

Extensive work has demonstrated the excellent performance of orthogonal time frequency space (OTFS) modulation in high-mobility scenarios. Time-variant wideband channel estimation serves as one of the key compositions of OTFS receivers since the data detection requires accurate channel state information (CSI). In practical wideband OTFS systems, the Doppler shift brought by the high mobility is frequency-dependent, which is referred to as the Doppler Squint Effect (DSE). Unfortunately, DSE was ignored in overall prior estimation schemes employed in OTFS systems, which leads to severe performance loss in channel estimation and the consequent data detection. In this paper, we investigate DSE of wideband time-variant channel in delay-Doppler domain and concentrate on the characterization of OTFS channel coefficients considering DSE. The formulation and evaluation of OTFS input-output relationship are provided for both ideal and rectangular waveforms considering DSE. The channel estimation is therefore formulated as a sparse signal recovery problem and an orthogonal matching pursuit (OMP)-based scheme is adopted to solve it. Simulation results confirm the significance of DSE and the performance superiority compared with traditional channel estimation approaches ignoring DSE.

A Framework to Define a Measurement-Based Model of MEC Nodes in the 5G System

Multi-access edge computing (MEC) represents an emerging solution to address the issues related to reliability, availability, context awareness and low latency in modern and future mobile networks. Despite the great interest and effort in developing MEC solutions, only a few works are available in the literature to model the existing trade-offs between available resources and the related performance of MEC nodes. This paper aims to fill this gap by proposing a measurement-based approach to indirectly derive an effective model for any type of MEC node, capable of capturing the existing trade-offs among different performance parameters. Basically, the idea is to characterize the input-output relationship between offered computational and communication load and achievable performance. A network emulator is used to generate the data to define the model. The results provide proof-of-concept of idea by analyzing the different trade-offs and achievable performance of a sample MEC node under CPU and networking limitations.

Evaluation of energy saving and emission reduction effect of distributed energy system

The conventional evaluation methods of energy conservation and emission reduction lack the discussion of quantification index of energy conservation and emission reduction potential, and the evaluation effect is poor. Therefore, an evaluation method for energy conservation and emission reduction of distributed energy systems is proposed, which considers system dynamics and demand response. According to the requirements of system dynamics analysis, the relational variables in the system dynamics model are determined. The input-output relationship of energy saving, and emission reduction effect evaluation is described by system dynamics equation, and the input-output relationship of energy saving, and emission reduction effect evaluation is determined. Based on the maximum profit objective function and the minimum cost objective function of the user, the continuum equation of the medium transmission process in the distributed energy system is obtained. The evaluation indexes of energy saving, and emission reduction effect are selected from the three aspects of power consumption side, power generation side and the system itself to realize the evaluation of energy saving and emission reduction effect. The experimental results show that the evaluation curve obtained by this method is basically consistent with the actual curve, and the evaluation accuracy is high.

Effect of pulsed current cathodic protection on pipeline steel API 5L X65 corrosion mitigation: An investigation and machine learning-assisted modeling

Pulsed current cathodic protection (PCCP) could be more effective than direct current cathodic protection (DCCP) for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters (frequency and duty cycle) and system outputs (corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency (2–10 kHz) and duty cycle (25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network (ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.

Towards resilience effectiveness: Assessing its patterns and determinants to identify optimal geographic zones

Resilience has a critical role to promote human well-being in turbulent environments. However, extensive interventions and stimulus measures not only achieve resilience goals with numerous unacceptable consequences, but also highlight the narrow definition of resilience. The study proposes the concept of resilience effectiveness to constrain the unsustainability of resilience in concept and practice. The resilience effectiveness of Chinese cities in the early stages of the pandemic is first implemented for evaluation based on input-output relationship, and then patterns and determinants are investigated using the GIS methods and the geographically optimal zones-based heterogeneity (GOZH) model. The study finds that the average resilience effectiveness is not high, indicating that China's strong resilience is the loss of safety, greenness, inclusiveness, and efficiency in the early stage of the pandemic. Resilience effectiveness has significant spatial heterogeneity and is dominated by innovation ability. The optimal geographic zones of Chinese cities for resilience effectiveness are identified, revealing that it is a complex and multifactorial process. This paper broadens resilience research and provides implications for city planning in the post-pandemic era.

Affine Frequency Division Multiplexing for Next Generation Wireless Communications

Affine Frequency Division Multiplexing (AFDM), a new chirp-based multicarrier waveform for high mobility communications, is introduced here. AFDM is based on discrete affine Fourier transform (DAFT), a generalization of discrete Fourier transform, which is characterized by two parameters that can be adapted based on the Doppler spread of doubly dispersive channels. First, we derive the explicit input-output relation in the DAFT domain showing the effect of AFDM parameters in the input-output relation. Second, we show how the DAFT parameters underlying AFDM have to be set so that the resulting DAFT domain impulse response conveys a full delay-Doppler representation of the channel. Then, we show analytically that AFDM can achieve the optimal diversity order in doubly dispersive channels, where optimal diversity order refers to the number of multipath components separable in either the delay or the Doppler domain, due to its full delay-Doppler representation. Furthermore, we present a low complexity detection method taking advantage of zero-padding. We also propose an embedded pilot-aided channel estimation scheme for AFDM, in which both channel estimation and data detection are performed within the same AFDM frame. Finally, simulations corroborate the validity of our analytical results and show the significant performance gains of AFDM over state-of-the-art multicarrier schemes in high mobility scenarios.

Spatial aggregation effects on the performance of machine learning metamodels for predicting transit time to baseflow

Water transit time is the duration between the entry and exit of a parcel of water across a hydrologic system. It is a fundamental characteristic that links hydrologic transport, biogeochemical processing, and water quality, and it has broad implications for resource vulnerability and sustainability. Physically based models can accurately describe transit time distributions but require significant computational resources when applied to large regions at high resolutions. In this study, we evaluate the potential of machine learning metamodels to emulate physically based models for computationally efficient prediction of key metrics from transit time distributions. Transit times are computed from a continental scale, integrated hydrologic model coupled with particle tracking. The metamodeling approach is illustrated in the 280,000-sq km Upper Colorado River Basin, USA, a principal headwater basin that is under multiple stresses, including resource overallocation, water quality threats, and climate change impacts. We evaluate the effects of using different types of spatial aggregation in the metamodels, including regular grids, hydrologic units, and upstream watersheds. We found that metamodels using upstream watershed aggregation exhibited the best overall performance across our target predictions. Errors were more pronounced in metamodels that employed smaller spatial aggregation units compared to larger units, suggesting that additional predictors that capture the heterogeneity of topographic, climatic, and geologic properties are needed at these scales. We also found that predictor importance and input-output relations were remarkably consistent across spatial aggregation type and agree with previous findings documented from physically based models and tracer-based studies. Our results show the feasibility of developing machine learning metamodels for predicting transit times and demonstrate the necessity of multiscale analyses to probe the robustness of the findings.

Torrefaction of Bambara Groundnut Shell: experimental optimization and prediction of the energy conversion efficiency using statistical and machine learning approaches

Optimising input variables is a necessity for efficient waste-to-energy conversion. Using a tubular furnace as the torrefaction medium, this study investigated the effects of four process parameters (viz., temperature, retention time, moisture contents and particle size) on the Higher Heating Value (HHV) and Energy Yield (EY) of Bambara Groundnut Shell (BBGS). An optimal HHV of 21.78 MJ/kg was obtained at a particle size of 1 mm, temperature of 260°C, retention time of 23 min and moisture content of 10%. The input–output relationships of the BBGS torrefaction process were modelled and validated using Response Surface Methodology (RSM) and Bayesian information criterion (BIC)-pruned stepwise regression (SR), resulting in a regression model that balances interpretability and strong prediction performance.

How to promote the sustainable development of strategic emerging industries: Evidence from Guangzhou

The strategic emerging industry (SEI) can effectively cope with high energy consumption, high pollution and low efficiency in traditional production mode, which attracts high attention. However, few studies quantitatively analyze the input-output relationships and the sustainable development level of SEIs. Moreover, existing method frameworks fail to consider both the current situation and development potential of SEIs. To deal with the above research gaps, a novel static-dynamic intuitionistic fuzzy evaluation framework integrating output-oriented data envelopment analysis model, improved Malmquist model, triangular intuitionistic fuzzy number, clustering method and Tobit regression model is first proposed. 120 SEIs from Guangzhou are selected for empirical study. Results reveal that all SEIs have almost no expected movement in operational cost but obvious redundancies in employee expenditure with a standard deviation of 1.771. It's urgent to optimize the employment, assessment and salary systems for staff. Moreover, SEI sustainable development scores vary from 0.473 to 1. It's necessary to strengthen cultivation of excellent SEIs and pay attention to supervision of the weak. Research findings can not only guide local government and industry associations to make scientific evaluation, but also help SEIs carry out self-inspections, which can further promote sustainable development of the entire industry.

Optimizing the Mechanical Performance of Green Composite Materials Using Muti-Integrated Optimization Solvers

Natural fiber composites are potential alternatives for synthetic materials due to environmental issues. The overall performance of the fiber composites depends on the reinforcement conditions. Thus, this work aimed to optimize the reinforcement conditions of the natural fiber composites to improve their mechanical performance via applying an integrated scheme of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and differential evolution (DE) methods considering various reinforcement conditions including fiber length, fiber loading, and treatment time for optimal characteristics of the composite mechanical performance. The B-Spline approximation function was adopted to predict the experimental performance of green composites. The B-Spline approximation function demonstrated incomparable accuracy compared to linear or quadratic regressions. The function is then optimized using an integrated optimization method. Results have demonstrated that optimal reinforcement conditions for the maximized desired mechanical performance of the composite were achieved with high accuracy. The robustness of the proposed approach was approved using various surface plots of the considered input-output parameter relations. Pareto front or the non-dominated solutions of the desired output mechanical properties were also obtained to demonstrate the interaction between the desired properties to facilitate finding the optimal reinforcement conditions of the composite materials.

Trust and specialization in complexity: Evidence from U.S. states

Industries with more complex input-output relationships are comparatively larger in U.S. states with higher levels of trust. This specialization pattern is not driven by differences in the states' and industries' other observable characteristics. Given the importance of complexity in economic growth and development, our results underscore the need to focus more on the role of social norms, particularly trust, in shaping patterns of economic specialization.

Quantised data-driven iterative learning bipartite consensus control for unknown heterogeneous linear MASs with varying trial lengths

This paper aims to realise the robust output bipartite consensus for unknown heterogeneous linear time-varying multiagent systems (MASs) subject to varying trial lengths, unknown measurement disturbances and data quantisation. To this end, inspired by the idea of quantised control, a quantised data-driven adaptive iterative learning bipartite consensus (AILBC) method is proposed. Specifically, to address the problem of varying trial lengths, a distributed auxiliary output prediction system is constructed based on the agents' input-output (I/O) dynamic relationship. An adaptive update protocol is developed to estimate the measurement disturbances and unknown parameters of I/O dynamic relationship. Subsequently, a quantised distributed data-driven iterative learning control (ILC) approach based on the quantised output information is proposed for MASs to achieve robust bipartite consensus tracking, with an attempt to relax the need of explicit model information. The bipartite consensus tracking errors are ultimately bounded through rigorous analysis, and this result is further extended to switching topologies. Finally, numerical simulations are conducted to verify the validity of the AILBC method.

Input-output relation of midbrain connectomics in a rodent model of depression

BackgroundThe symptoms associated with depression are believed to arise from disruptions in information processing across brain networks. The ventral tegmental area (VTA) influences reward-based behavior, motivation, addiction, and psychiatric disorders, including depression. Deep brain stimulation (DBS) of the medial forebrain bundle (MFB), is an emerging therapy for treatment-resistant depression. Understanding the depression associated anatomical networks crucial for comprehending its antidepressant effects. MethodsFlinders Sensitive Line (FSL), a rodent model of depression and Sprague-Dawley rats (n = 10 each) were used in this study. We used monosynaptic tracing to map inputs of VTA efferent neurons: VTA-to-NAc nucleus accumbens (NAc) (both core and shell) and VTA-to-prefrontal cortex (PFC). Quantitative analysis explored afferent diversity and strengths. ResultsVTA efferent neurons receive a variety of afferents with varying input weights and predominant neuromodulatory representation. Notably, NAc-core projecting VTA neurons showed stronger afferents from dorsal raphe, while NAc shell-projecting VTA neurons displayed lower input strengths from cortex, thalamus, zona incerta and pretectal area in FSL rats. NAc shell-projecting VTA neurons showed the most difference in connectivity across the experimental groups. LimitationsLack of functional properties of the anatomical connections is the major limitation of this study. Incomplete labeling and the cytotoxicity of the rabies virus should be made aware of. ConclusionsThese findings provide the first characterization of inputs to different VTA ascending projection neurons, shedding light on critical differences in the connectome of the midbrain-forebrain system. Moreover, these differences support potential network effects of these circuits in the context of MFB DBS neuromodulation for depression.