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Michelle Case,1 Sina Shirinpour,1 Huishi Zhang,1 Yvonne H Datta,2 Stephen C Nelson,3 Karim T Sadak,4 Kalpna Gupta,2 Bin He1,5 1Department of Biomedical Engineering, 2Department of Medicine, University of Minnesota, 3Pediatric Hematology-Oncology, Children’s Hospitals and Clinics of Minnesota, 4Pediatric Hematology-Oncology, University of Minnesota Masonic Children’s Hospital, 5Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, USA Objective: Pain is a major issue in the care of patients with sickle cell disease (SCD). The mechanisms behind pain and the best way to treat it are not well understood. We studied how electroencephalography (EEG) is altered in SCD patients. Methods: We recruited 20 SCD patients and compared their resting state EEG to that of 14 healthy controls. EEG power was found across frequency bands using Welch’s method. Electrophysiological source imaging was assessed for each frequency band using the eLORETA algorithm. Results: SCD patients had increased theta power and decreased beta2 power compared to controls. Source localization revealed that areas of greater theta band activity were in areas related to pain processing. Imaging parameters were significantly correlated to emergency department visits, which indicate disease severity and chronic pain intensity. Conclusion: The present results support the pain mechanism referred to as thalamocortical dysrhythmia. This mechanism causes increased theta power in patients. Significance: Our findings show that EEG can be used to quantitatively evaluate differences between controls and SCD patients. Our results show the potential of EEG to differentiate between different levels of pain in an unbiased setting, where specific frequency bands could be used as biomarkers for chronic pain. Keywords: sickle cell disease, electroencephalography, chronic pain, electrophysiological source imaging, thalamocortical dysrhythmia

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation method that modulates brain activity by inducing electric fields in the brain. Real-time, state-dependent stimulation with TMS has shown that neural oscillation phase modulates corticospinal excitability. However, such motor evoked potentials (MEPs) only indirectly reflect motor cortex activation and are unavailable at other brain regions of interest. The direct and secondary cortical effects of phase-dependent brain stimulation remain an open question. In this study, we recorded the cortical responses during single-pulse TMS using electroencephalography (EEG) concurrently with the MEP measurements in 20 healthy human volunteers (11 female). TMS was delivered at peak, rising, trough, and falling phases of mu (8-13 Hz) and beta (14-30 Hz) oscillations in the motor cortex. The cortical responses were quantified through TMS evoked potential components N15, P50, and N100 as peak-to-peak amplitudes (P50-N15 and P50-N100). We further analyzed whether the prestimulus frequency band power was predictive of the cortical responses. We demonstrated that phase-specific targeting modulates cortical responses. The phase relationship between cortical responses was different for early and late responses. In addition, pre-TMS mu oscillatory power and phase significantly predicted both early and late cortical EEG responses in mu-specific targeting, indicating the independent causal effects of phase and power. However, only pre-TMS beta power significantly predicted the early and late TEP components during beta-specific targeting. Further analyses indicated distinct roles of mu and beta power on cortical responses. These findings provide insight to mechanistic understanding of neural oscillation states in cortical and corticospinal activation in humans., Competing Interests: The authors declare no competing financial interests., (Copyright © 2024 Perera et al.)

Transcranial magnetic stimulation (TMS) modulates neuronal activity, but the efficacy of an open-loop approach is limited due to the brain state's dynamic nature. Real-time integration with electroencephalography (EEG) increases experimental reliability and offers personalized neuromodulation therapy by using immediate brain states as biomarkers. Here, we review brain state-controlled TMS-EEG studies since the first publication several years ago. A summary of experiments on the sensorimotor mu rhythm (8-13 Hz) shows increased cortical excitability due to TMS pulse at the trough and decreased excitability at the peak of the oscillation. Pre-TMS pulse mu power also affects excitability. Further, there is emerging evidence that the oscillation phase in theta and beta frequency bands modulates neural excitability. Here, we provide a guide for real-time TMS-EEG application and discuss experimental and technical considerations. We consider the effects of hardware choice, signal quality, spatial and temporal filtering, and neural characteristics of the targeted brain oscillation. Finally, we speculate on how closed-loop TMS-EEG potentially could improve the treatment of neurological and mental disorders such as depression, Alzheimer's, Parkinson's, schizophrenia, and stroke., (Creative Commons Attribution license.)

The mammalian neocortex, organized into six cellular layers or laminae, forms a cortical network within layers. Layer specific computations are crucial for sensory processing of visual stimuli within primary visual cortex. Laminar recordings of local field potentials (LFPs) are a powerful tool to study neural activity within cortical layers. Electric brain stimulation is widely used in basic neuroscience and in a large range of clinical applications. However, the layer-specific effects of electric stimulation on LFPs remain unclear. To address this gap, we conducted laminar LFP recordings of the primary visual cortex in monkeys while presenting a flash visual stimulus. Simultaneously, we applied a low frequency sinusoidal current to the occipital lobe with offset frequency to the flash stimulus repetition rate. We analyzed the modulation of visual-evoked potentials with respect to the applied phase of the electric stimulation. Our results reveal that only the deeper layers, but not the superficial layers, show phase-dependent changes in LFP components with respect to the applied current. Employing a cortical column model, we demonstrate that these in vivo observations can be explained by phase-dependent changes in the driving force within neurons of deeper layers. Our findings offer crucial insight into the selective modulation of cortical layers through electric stimulation, thus advancing approaches for more targeted neuromodulation.

Objective . Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity and thereby cause changes in local neural oscillatory power. Despite its increased use in cognitive and clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood. Approach . We developed a computational neuronal network model of two-compartment pyramidal neurons (PY) and inhibitory interneurons, which mimic the local cortical circuits. We modeled tACS with electric field strengths that are achievable in human applications. We then simulated intrinsic network activity and measured neural entrainment to investigate how tACS modulates ongoing endogenous oscillations. Main results . The intensity-specific effects of tACS are non-linear. At low intensities (<0.3 mV mm -1 ), tACS desynchronizes neural firing relative to the endogenous oscillations. At higher intensities (>0.3 mV mm -1 ), neurons are entrained to the exogenous electric field. We then further explore the stimulation parameter space and find that the entrainment of ongoing cortical oscillations also depends on stimulation frequency by following an Arnold tongue. Moreover, neuronal networks can amplify the tACS-induced entrainment via synaptic coupling and network effects. Our model shows that PY are directly entrained by the exogenous electric field and drive the inhibitory neurons. Significance . The results presented in this study provide a mechanistic framework for understanding the intensity- and frequency-specific effects of oscillating electric fields on neuronal networks. This is crucial for rational parameter selection for tACS in cognitive studies and clinical applications., (Creative Commons Attribution license.)

The gradual shifting of preferred neural spiking relative to local field potentials (LFPs), known as phase precession, plays a prominent role in neural coding. Correlations between the phase precession and behavior have been observed throughout various brain regions. As such, phase precession is suggested to be a global neural mechanism that promotes local neuroplasticity. However, causal evidence and neuroplastic mechanisms of phase precession are lacking so far. Here we show a causal link between LFP dynamics and phase precession. In three experiments, we modulated LFPs in humans, a non-human primate, and computational models using alternating current stimulation. We show that continuous stimulation of motor cortex oscillations in humans lead to a gradual phase shift of maximal corticospinal excitability by ~90°. Further, exogenous alternating current stimulation induced phase precession in a subset of entrained neurons (~30%) in the non-human primate. Multiscale modeling of realistic neural circuits suggests that alternating current stimulation-induced phase precession is driven by NMDA-mediated synaptic plasticity. Altogether, the three experiments provide mechanistic and causal evidence for phase precession as a global neocortical process. Alternating current-induced phase precession and consequently synaptic plasticity is crucial for the development of novel therapeutic neuromodulation methods., (© 2024. The Author(s).)

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation method that is rapidly growing in popularity for studying causal brain-behavior relationships. However, its dose-dependent centrally induced neural mechanisms and peripherally induced sensory costimulation effects remain debated. Understanding how TMS stimulation parameters affect brain responses is vital for the rational design of TMS protocols. Studying these mechanisms in humans is challenging because of the limited spatiotemporal resolution of available noninvasive neuroimaging methods. Here, we leverage invasive recordings of local field potentials in a male and a female nonhuman primate (rhesus macaque) to study TMS mesoscale responses. We demonstrate that early TMS-evoked potentials show a sigmoidal dose-response curve with stimulation intensity. We further show that stimulation responses are spatially specific. We use several control conditions to dissociate centrally induced neural responses from auditory and somatosensory coactivation. These results provide crucial evidence regarding TMS neural effects at the brain circuit level. Our findings are highly relevant for interpreting human TMS studies and biomarker developments for TMS target engagement in clinical applications. SIGNIFICANCE STATEMENT Transcranial magnetic stimulation (TMS) is a widely used noninvasive brain stimulation method to stimulate the human brain. To advance its utility for clinical applications, a clear understanding of its underlying physiological mechanisms is crucial. Here, we perform invasive electrophysiological recordings in the nonhuman primate brain during TMS, achieving a spatiotemporal precision not available in human EEG experiments. We find that evoked potentials are dose dependent and spatially specific, and can be separated from peripheral stimulation effects. This means that TMS-evoked responses can indicate a direct physiological stimulation response. Our work has important implications for the interpretation of human TMS-EEG recordings and biomarker development., (Copyright © 2023 the authors.)

Background: Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation (NIBS) technique to affect neural activity. TACS experiments have been coupled with computational simulations to predict the electromagnetic fields within the brain. However, existing simulations are focused on the magnitude of the field. As the possibility of inducing the phase gradient in the brain using multiple tACS electrodes arises, a simulation framework is necessary to investigate and predict the phase gradient of electric fields during multi-channel tACS., Objective: Here, we develop such a framework for phasor simulation using phasor algebra and evaluate its accuracy using in vivo recordings in monkeys., Methods: We extract the phase and amplitude of electric fields from intracranial recordings in two monkeys during multi-channel tACS and compare them to those calculated by phasor analysis using finite element models., Results: Our findings demonstrate that simulated phases correspond well to measured phases (r = 0.9). Further, we systematically evaluated the impact of accurate electrode placement on modeling and data agreement. Finally, our framework can predict the amplitude distribution in measurements given calibrated tissues' conductivity., Conclusions: Our validated general framework for simulating multi-phase, multi-electrode tACS provides a streamlined tool for principled planning of multi-channel tACS experiments., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The gradual shifting of preferred neural spiking relative to local field potentials (LFPs), known as phase precession, plays a prominent role in neural coding. Correlations between the phase precession and behavior have been observed throughout various brain regions. As such, phase precession is suggested to be a global neural mechanism that promotes local neuroplasticity. However, causal evidence and neuroplastic mechanisms of phase precession are lacking so far. Here we show a causal link between LFP dynamics and phase precession. In three experiments, we modulated LFPs in humans, a non-human primate, and computational models using alternating current stimulation. We show that continuous stimulation of motor cortex oscillations in humans lead to a gradual phase shift of maximal corticospinal excitability by ~90°. Further, exogenous alternating current stimulation induced phase precession in a subset of entrained neurons (~30%) in the non-human primate. Multiscale modeling of realistic neural circuits suggests that alternating current stimulation-induced phase precession is driven by NMDA-mediated synaptic plasticity. Altogether, the three experiments provide mechanistic and causal evidence for phase precession as a global neocortical process. Alternating current-induced phase precession and consequently synaptic plasticity is crucial for the development of novel therapeutic neuromodulation methods.

Non-human primates (NHPs) have become key for translational research in noninvasive brain stimulation (NIBS). However, in order to create comparable stimulation conditions for humans it is vital to study the accuracy of current modeling practices across species. Numerical models to simulate electric fields are an important tool for experimental planning in NHPs and translation to human studies. It is thus essential whether and to what extent the anatomical details of NHP models agree with current modeling practices when calculating NIBS electric fields. Here, we create highly accurate head models of two non-human primates (NHP) MR data. We evaluate how muscle tissue and head field of view (depending on MRI parameters) affect simulation results in transcranial electric and magnetic stimulation (TES and TMS). Our findings indicate that the inclusion of anisotropic muscle can affect TES electric field strength up to 22% while TMS is largely unaffected. Additionally, comparing a full head model to a cropped head model illustrates the impact of head field of view on electric fields for both TES and TMS. We find opposing effects between TES and TMS with an increase up to 24.8% for TES and a decrease up to 24.6% for TMS for the cropped head model compared to the full head model. Our results provide important insights into the level of anatomical detail needed for NHP head models and can inform future translational efforts for NIBS studies., Competing Interests: Declaration of Competing Interest Declarations of interest: none, (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Transcranial alternating current stimulation (tACS) can be used to non-invasively entrain neural activity, and thereby cause changes in local neural oscillatory power. Despite an increased use in cognitive and clinical neuroscience, the fundamental mechanisms of tACS are still not fully understood. Here, we develop a computational neuronal network model of two-compartment pyramidal neurons and inhibitory interneurons which mimic the local cortical circuits. We model tACS with electric field strengths that are achievable in human applications. We then simulate intrinsic network activity and measure neural entrainment to investigate how tACS modulates ongoing endogenous oscillations. First, we show that intensity-specific effects of tACS are non-linear. At low intensities (<0.3 mV/mm), tACS desynchronizes neural firing relative to the endogenous oscillations. At higher intensities (>0.3 mV/mm), neurons are entrained to the exogenous electric field. We then further explore the stimulation parameter space and find that entrainment of ongoing cortical oscillations also depends on frequency by following an Arnold tongue. Moreover, neuronal networks can amplify the tACS induced entrainment via excitation-inhibition balance. Our model shows that pyramidal neurons are directly entrained by the exogenous electric field and drive the inhibitory neurons. Our findings can thus provide a mechanistic framework for understanding the intensity- and frequency- specific effects of oscillating electric fields on neuronal networks. This is crucial for rational parameters selection for tACS in cognitive studies and clinical applications.

Transcranial alternating current stimulation (tACS) is a widely used noninvasive brain stimulation (NIBS) technique to affect neural activity. Neural oscillations exhibit phase-dependent associations with cognitive functions, and tools to manipulate local oscillatory phases can affect communication across remote brain regions. A recent study demonstrated that multi-channel tACS can generate electric fields with a phase gradient or traveling waves in the brain. Computational simulations using phasor algebra can predict the phase distribution inside the brain and aid in informing parameters in tACS experiments. However, experimental validation of computational models for multi-phase tACS is still lacking. Here, we develop such a framework for phasor simulation and evaluate its accuracy using in vivo recordings in nonhuman primates. We extract the phase and amplitude of electric fields from intracranial recordings in two monkeys during multi-channel tACS and compare them to those calculated by phasor analysis using finite element models. Our findings demonstrate that simulated phases correspond well to measured phases (r = 0.9). Further, we systematically evaluated the impact of accurate electrode placement on modeling and data agreement. Finally, our framework can predict the amplitude distribution in measurements given calibrated tissues’ conductivity. Our validated general framework for simulating multi-phase, multi-electrode tACS provides a streamlined tool for principled planning of multi-channel tACS experiments.

Background: Neural oscillations in the primary motor cortex (M1) shape corticospinal excitability. Power and phase of ongoing mu (8-13 Hz) and beta (14-30 Hz) activity may mediate motor cortical output. However, the functional dynamics of both mu and beta phase and power relationships and their interaction, are largely unknown., Objective: Here, we employ recently developed real-time targeting of the mu and beta rhythm, to apply phase-specific brain stimulation and probe motor corticospinal excitability non-invasively. For this, we used instantaneous read-out and analysis of ongoing oscillations, targeting four different phases (0°, 90°, 180°, and 270°) of mu and beta rhythms with suprathreshold single-pulse transcranial magnetic stimulation (TMS) to M1. Ensuing motor evoked potentials (MEPs) in the right first dorsal interossei muscle were recorded. Twenty healthy adults took part in this double-blind randomized crossover study., Results: Mixed model regression analyses showed significant phase-dependent modulation of corticospinal output by both mu and beta rhythm. Strikingly, these modulations exhibit a double dissociation. MEPs are larger at the mu trough and rising phase and smaller at the peak and falling phase. For the beta rhythm we found the opposite behavior. Also, mu power, but not beta power, was positively correlated with corticospinal output. Power and phase effects did not interact for either rhythm, suggesting independence between these aspects of oscillations., Conclusion: Our results provide insights into real-time motor cortical oscillation dynamics, which offers the opportunity to improve the effectiveness of TMS by specifically targeting different frequency bands., Competing Interests: Declaration of competing interest Authors declare no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Transcranial magnetic stimulation (TMS) can depolarize cortical neurons through the intact skin and skull. The characteristics of the induced electric field (E-field) have a major impact on specific outcomes of TMS. Using multi-scale computational modeling, we explored whether the stimulation parameters derived from the primary motor cortex (M1) induce comparable macroscopic E-field strengths and subcellular/cellular responses in the dorsolateral prefrontal cortex (DLPFC). To this aim, we calculated the TMS-induced E-field in 16 anatomically realistic head models and simulated the changes in membrane voltage and intracellular calcium levels of morphologically and biophysically realistic human pyramidal cells in the M1 and DLPFC. We found that the conventional intensity selection methods (i.e., motor threshold and fixed intensities) produce variable macroscopic E-fields. Consequently, it was challenging to produce comparable subcellular/cellular responses across cortical regions with distinct folding characteristics. Prospectively, personalized stimulation intensity selection could standardize the E-fields and the subcellular/cellular responses to repetitive TMS across cortical regions and individuals. The suggested computational approach points to the shortcomings of the conventional intensity selection methods used in clinical settings. We propose that multi-scale modeling has the potential to overcome some of these limitations and broaden our understanding of the neuronal mechanisms for TMS., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Turi, Hananeia, Shirinpour, Opitz, Jedlicka and Vlachos.)

Neural oscillations are a key mechanism for information transfer in brain circuits. Rhythmic fluctuations of local field potentials control spike timing through cyclic membrane de- and hyperpolarization. Transcranial alternating current stimulation (tACS) is a non-invasive neuromodulation method which can directly interact with brain oscillatory activity by imposing an oscillating electric field on neurons. Despite its increasing use, the basic mechanisms of tACS are still not fully understood. Here, we investigate in a computational study the effects of tACS on morphologically realistic neurons with ongoing spiking activity. We characterize the membrane polarization as a function of electric field strength and subsequent effects on spiking activity in a set of 25 neurons from different neocortical layers. We find that tACS does not affect the firing rate of investigated neurons for electric field strengths applicable to human studies. However, we find that the applied electric fields entrain the spiking activity of large pyramidal neurons and large basket neurons at < 1 mV/mm field strengths. Our model results are in line with recent experimental studies and can provide a mechanistic framework to understand the effects of oscillating electric fields on single neuron activity. They highlight the importance of neuron morphology and biophysics in responsiveness to electrical stimulation., Competing Interests: Declaration of Competing Interest The authors declare no competing financial interests., (Copyright © 2022. Published by Elsevier Inc.)

Background: Transcranial Magnetic Stimulation (TMS) is a widely used non-invasive brain stimulation method. However, its mechanism of action and the neural response to TMS are still poorly understood. Multi-scale modeling can complement experimental research to study the subcellular neural effects of TMS. At the macroscopic level, sophisticated numerical models exist to estimate the induced electric fields. However, multi-scale computational modeling approaches to predict TMS cellular and subcellular responses, crucial to understanding TMS plasticity inducing protocols, are not available so far., Objective: We develop an open-source multi-scale toolbox Neuron Modeling for TMS (NeMo-TMS) to address this problem., Methods: NeMo-TMS generates accurate neuron models from morphological reconstructions, couples them to the external electric fields induced by TMS, and simulates the cellular and subcellular responses of single-pulse and repetitive TMS., Results: We provide examples showing some of the capabilities of the toolbox., Conclusion: NeMo-TMS toolbox allows researchers a previously not available level of detail and precision in realistically modeling the physical and physiological effects of TMS., Competing Interests: Declaration of competing interest There are no conflicts of interest to report., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Transcranial magnetic stimulation (TMS) is an increasingly popular tool for stroke rehabilitation. Consequently, researchers have started to explore the use of TMS in pediatric stroke. However, the application of TMS in a developing brain with pathologies comes with a unique set of challenges. The effect of TMS-induced electric fields has not been explored in children with stroke lesions. Here, we used finite element method (FEM) modeling to study how the electric field strength is affected by the presence of a lesion. We created individual realistic head models from MRIs (n = 6) of children with unilateral cerebral palsy due to perinatal stroke. We conducted TMS electric field simulations for coil locations over lesioned and non-lesioned hemispheres. We found that the presence of a lesion can strongly affect the electric field distribution. On the group level, the mean electric field strength did not differ between lesioned and non-lesioned hemispheres but exhibited a greater variability in the lesioned hemisphere. Other factors such as coil-to-cortex distance have a strong influence on the TMS electric field even in the presence of lesions. Our study has important implications for the delivery of TMS in children with brain lesions with respect to TMS dosing and coil placement., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objective: Real-time approaches for transcranial magnetic stimulation (TMS) based on a specific EEG phase are a promising avenue for more precise neuromodulation interventions. However, optimal approaches to reliably extract the EEG phase in a frequency band of interest to inform TMS are still to be identified. Here, we implement a new real-time phase detection method for closed-loop EEG-TMS for robust phase extraction. We compare this algorithm with state-of-the-art methods and evaluate its performance both in silico and experimentally., Approach: We propose a new robust algorithm (Educated Temporal Prediction) for delivering real-time EEG phase-specific stimulation based on short prerecorded EEG training data. This method estimates the interpeak period from a training period and applies a bias correction to predict future peaks. We compare the accuracy and computation speed of the ETP algorithm with two existing methods (Fourier based, Autoregressive Prediction) using prerecorded resting EEG data and real-time experiments., Main Results: We found that Educated Temporal Prediction performs with higher accuracy than Fourier-based or Autoregressive methods both in silico and in vivo while being computationally more efficient. Further, we document the dependency of the EEG signal-to-noise ratio (SNR) on algorithm accuracy across all algorithms., Significance: Our results give important insights for real-time EEG-TMS technical development as well as experimental design. Due to its robustness and computational efficiency, our method can find broad use in experimental research or clinical applications. Through open sharing of code for all three methods, we enable broad access of TMS-EEG real-time algorithms to the community.

Transcranial magnetic stimulation (TMS) and transcranial electric stimulation (TES) are increasingly popular methods to noninvasively affect brain activity. However, their mechanism of action and dose-response characteristics remain under active investigation. Translational studies in animals play a pivotal role in these efforts due to a larger neuroscientific toolset enabled by invasive recordings. In order to translate knowledge gained in animal studies to humans, it is crucial to generate comparable stimulation conditions with respect to the induced electric field in the brain. Here, we conduct a finite element method (FEM) modeling study of TMS and TES electric fields in a mouse, capuchin and macaque monkeys, and a human model. We systematically evaluate the induced electric fields and analyze their relationship to head and brain anatomy. We find that with increasing head size, TMS-induced electric field strength first increases and then decreases according to a two-term exponential function. TES-induced electric field strength strongly decreases from smaller to larger specimen with up to 100x fold differences across species. Our results can serve as a basis to compare and match stimulation parameters across studies in animals and humans., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Sickle cell disease (SCD) is a hereditary blood disorder associated with many life-threatening comorbidities including cerebral stroke and chronic pain. The long-term effects of this disease may therefore affect the global brain network which is not clearly understood. We performed graph theory analysis of functional networks using non-invasive fMRI and high resolution EEG on thirty-one SCD patients and sixteen healthy controls. Resting state data were analyzed to determine differences between controls and patients with less severe and more severe sickle cell related pain. fMRI results showed that patients with higher pain severity had lower clustering coefficients and local efficiency. The neural network of the more severe patient group behaved like a random network when performing a targeted attack network analysis. EEG results showed the beta1 band had similar results to fMRI resting state data. Our data show that SCD affects the brain on a global level and that graph theory analysis can differentiate between patients with different levels of pain severity., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: Effective pain assessment and management strategies are needed to better manage pain. In addition to self-report, an objective pain assessment system can provide a more complete picture of the neurophysiological basis for pain. In this study, a robust and accurate machine learning approach is developed to quantify tonic thermal pain across healthy subjects into a maximum of ten distinct classes., Methods: A random forest model was trained to predict pain scores using time-frequency wavelet representations of independent components obtained from electroencephalography (EEG) data, and the relative importance of each frequency band to pain quantification is assessed., Results: The mean classification accuracy for predicting pain on an independent test subject for a range of 1-10 is 89.45%, highest among existing state of the art quantification algorithms for EEG. The gamma band is the most important to both intersubject and intrasubject classification accuracy., Conclusion: The robustness and generalizability of the classifier are demonstrated., Significance: Our results demonstrate the potential of this tool to be used clinically to help us to improve chronic pain treatment and establish spectral biomarkers for future pain-related studies using EEG.

The essential oil from seeds and aerial parts of Ferulago angulata (schlecht.) Boiss growing in Nevakoh and Shahoo mountains, West of Iran was studied by gas chromatography and gas chromatography-mass spectrometry constituents were. The major components of seeds gathered in Nevakoh and shahoo were, respectively 24 and 26 constituents and found to be cis-ocimene (64.8 and 76.11%), alpha-pinene (15.4 and 7.29%), gamma-terpinene (5.9 and 2.88%), rho-cymene (4.1 and 1.4%), myrcene (1.9 and 1.05%) and bornyl acetate (0.9 and 1.69%). The major components of aerial parts gathered in Nevakoh and shahoo were respectively, 34 and 30 constituents and found to be alpha-pinene (27.1 and, 25.7%), cis-ocimene (22.6 and, 27.9%), bornyl acetate (8.5 and 3.9%), germacrene D (6.5 and 22.3%), trans-verbenol (5.8 and 0%), myrcene (5.2 and 2%), beta-bourbonene (2.7 and 0%), p-mentah-1, 5-dien-8-ol (1.9 and 0%), sabinene (1.5 and 2.1%), linalool (1.5 and 0%), metyl eugenol (1.3 and 0%), bicyclogermacrene (1.3 and 0%) and alpha-terpineol (1.2 and 0%), 3-carene (0 and 1.9%), camphene (0 and 1.6%), gamma-element (0 and 1.1%) and beta-cubenone (0 and 1%).The major components which were common in both seeds and leaves and found to be, bornyl acetate, myrcene and trans-ocimene. The major components which were only found in aerial parts were germacreneD, beta-bourbonene, camphene, sabinene and linalool. The major components which only were found in seeds: gamma-terpinene and rho-cymene.