Publications by authors named "Babak Mahmoudi"

38 Publications

Amygdala Stimulation Leads to Functional Network Connectivity State Transitions in the Hippocampus.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:3625-3628

Several studies have shown that direct brain stimulation can enhance memory in humans and animal models. Investigating the neurophysiological changes induced by brain stimulation is an important step towards understanding the neural processes underlying memory function. Furthermore, it paves the way for developing more efficient neuromodulation approaches for memory enhancement. In this study, we utilized a combination of unsupervised and supervised machine learning approaches to investigate how amygdala stimulation modulated hippocampal network activities during the encoding phase. Using a sliding window in time, we estimated the hippocampal dynamic functional network connectivity (dFNC) after stimulation and during sham trials, based on the covariance of local field potential recordings in 4 subregions of the hippocampus. We extracted different network states by combining the dFNC samples from 5 subjects and applying k-means clustering. Next, we used the between-state transition numbers as the latent features to classify between amygdala stimulation and sham trials across all subjects. By training a logistic regression model, we could differentiate stimulated from sham trials with 67% accuracy across all subjects. Using elastic net regularization as a feature selection method, we identified specific patterns of hippocampal network state transition in response to amygdala stimulation. These results offer a new approach to better understanding of the causal relationship between hippocampal network dynamics and memory-enhancing amygdala stimulation.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176742DOI Listing
July 2020

Modeling and optimizing parameters affecting hexavalent chromium adsorption from aqueous solutions using Ti-XAD7 nanocomposite: RSM-CCD approach, kinetic, and isotherm studies.

J Environ Health Sci Eng 2019 Dec 11;17(2):873-888. Epub 2019 Dec 11.

7Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran.

Background: Due to the high toxicity of chromium, particularly as Hexavalent chromium Cr (VI), it is removed from industrial effluents before their discharge into the environment by a variety of methods, including loading catalysts onto the polymeric supports. This study focused on the removal of Cr(VI) from aqueous solutions using Amberlite XAD7 resin loaded titanium dioxide (Ti-XAD7).

Methods: Ti-XAD7 was synthesized using Amberlite XAD-7 impregnated with titanium tetraethoxide. The prepared Ti-XAD7 was characterized by using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and X-ray diffraction (XRD). Isotherms and kinetic studies were carried out to describe the adsorption behavior of adsorbent for the removal of Cr(VI) ions. Quadratic models considering independent variables, i.e. the initial Cr(VI) concentration, adsorbent dosage, time, and pH, were evaluated and optimized to describe the behavior of Cr(VI) adsorption onto the Ti-XAD7 using RSM based on a Five-level-four-factor CCD approach.

Results: The accuracy and the fitting of the model were evaluated by ANOVA with R > 0.725 and value = 5.221 × 10. The optimum conditions for the adsorption process were an initial Cr(VI) concentration 2750 ppb, contact time of 51.53 min, pH of 8.7, and Ti-XAD7 dosage of 5.05 g/L. The results revealed that the Langmuir and Sips isotherm models with R = 0.998 and 0.999 were the The adsorption capacity of Ti-XAD7 and R constant were 2.73 mg/g and 0.063-0.076 based on the Langmuir isotherm, respectively. Kinetic studies also indicated that the adsorption behavior of Cr(VI) was acceptably explained by the Elovich kinetic model with a good fitting (R = 0.97).

Conclusions: Comparison of the Ti-XAD7 and XAD7 yield in chromium adsorption showed that modified XAD7 had higher removal efficiency (about 98%) compared to XAD7 alone.
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http://dx.doi.org/10.1007/s40201-019-00405-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6985374PMC
December 2019

Towards automated patient-specific optimization of deep brain stimulation for movement disorders.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:6159-6162

In this paper we present a simulation framework for automated optimization of deep brain stimulation (DBS) parameters based on the hand kinematics signal as the feedback signal, in patients with essential tremor. We used Gaussian Process regression (GPR) models to develop patient-specific models for predicting the effect of DBS on the hand kinematics using the clinical data that was recorded during DBS programming. In this framework, we characterized the performance of a Bayesian Optimization method to identify the optimal DBS parameters that maximized the clinical efficacy. Our results demonstrate the feasibility of using black-box optimization methods for automated identification of optimal DBS parameters in clinical settings.
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http://dx.doi.org/10.1109/EMBC.2019.8857736DOI Listing
July 2019

A Cloud-based Framework for Implementing Portable Machine Learning Pipelines for Neural Data Analysis.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:4466-4469

Cloud-based computing has created new avenues for innovative research. In recent years, numerous cloud-based, data analysis projects within the biomedical domain have been implemented. As this field is likely to grow, there is a need for a unified platform for the developing and testing of advanced analytic and modeling tools that enables those tools to be easily reused for biomedical data analysis by a broad set of users with diverse technical skills. A cloud-based platform of this nature could greatly assist future research endeavors. In this paper, we take the first step towards building such a platform. We define an approach by which containerized analytic pipelines can be distributed for use on cloud-based or on-premise computing platforms. We demonstrate our approach by implementing a portable biomarker identification pipeline using a logistic regression model with elastic net regularization (LR-ENR) and running it on Google Cloud. We used this pipeline for the diagnosis of Parkinson's disease based on a combination of clinical, demographic, and MRI-based features and for the identification of the most predictive biomarkers.
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http://dx.doi.org/10.1109/EMBC.2019.8856929DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390749PMC
July 2019

Photocatalytic degradation of ketoconazole by Z-scheme AgPO/graphene oxide: response surface modeling and optimization.

Environ Sci Pollut Res Int 2020 Jan 30;27(1):250-263. Epub 2019 Nov 30.

Biomaterials Group, The Institute of Pharmaceutical Sciences (TIPS), Tehran University of Medical Sciences, Tehran, Iran.

Ketoconazole is an imidazole fungicide which is commonly used as pharmaceutical and healthcare products. Residual amount of this compound can cause adverse ecological health problems. The present study investigated ketoconazole photocatalytic degradation using AgPO/graphene oxide (GO). AgPO/GO and AgPO as visible light-driven photocatalysts was synthesized using the in situ growth method. Degradation of ketoconazole at the concentration of 1-20 mg/L in aqueous solutions was optimized in the presence of AgPO/GO nanocomposite with the dosage of 0.5-2 g/L, contact time of 15-20 min, and pH of 5-9 using response surface methodology. A second-order model was selected as the best fitted model with R value and lack of fit as 0.935 and 0.06, respectively. Under the optimized conditions, the AgPO/GO catalyst achieved a photocatalytic efficiency of 96.53% after 93.34 min. The photocatalytic activity, reaction kinetics, and stability were also investigated. The results indicated that the AgPO/GO nanocomposite exhibited higher photocatalytic activity for ketoconazole degradation, which was 2.4 times that of pure AgPO. Finally, a direct Z-scheme mechanism was found to be responsible for enhanced photocatalytic activity in the AgPO/GO nanocomposite. The high photocatalytic activity, acceptable reusability, and good aqueous stability make the AgPO/GO nanocomposite a promising nanophotocatalyst for photocatalytic degradation of azoles contaminants.
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http://dx.doi.org/10.1007/s11356-019-06812-5DOI Listing
January 2020

A Software-Defined Radio Receiver for Wireless Recording From Freely Behaving Animals.

IEEE Trans Biomed Circuits Syst 2019 12 24;13(6):1645-1654. Epub 2019 Oct 24.

To eliminate tethering effects on the small animals' behavior during electrophysiology experiments, such as neural interfacing, a robust and wideband wireless data link is needed for communicating with the implanted sensing elements without blind spots. We present a software-defined radio (SDR) based scalable data acquisition system, which can be programmed to provide coverage over standard-sized or customized experimental arenas. The incoming RF signal with the highest power among SDRs is selected in real-time to prevent data loss in the presence of spatial and angular misalignments between the transmitter (Tx) and receiver (Rx) antennas. A 32-channel wireless neural recording system-on-a-chip (SoC), known as WINeRS-8, is embedded in a headstage and transmits digitalized raw neural signals, which are sampled at 25 kHz/ch, at 9 Mbps via on-off keying (OOK) of a 434 MHz RF carrier. Measurement results show that the dual-SDR Rx system reduces the packet loss down to 0.12%, on average, by eliminating the blind spots caused by the moving Tx directionality. The system operation is verified in vivo on a freely behaving rat and compared with a commercial hardwired system.
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http://dx.doi.org/10.1109/TBCAS.2019.2949233DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6990704PMC
December 2019

A Machine Learning Approach to Characterize the Modulation of the Hippocampal Rhythms Via Optogenetic Stimulation of the Medial Septum.

Int J Neural Syst 2019 Dec 27;29(10):1950020. Epub 2019 Jun 27.

Department of Neurosurgery, Emory University, Atlanta, GA 30322, USA.

The medial septum (MS) is a potential target for modulating hippocampal activity. However, given the multiple cell types involved, the changes in hippocampal neural activity induced by MS stimulation have not yet been fully characterized. We combined MS optogenetic stimulation with local field potential (LFP) recordings from the hippocampus and leveraged machine learning techniques to explore how activating or inhibiting multiple MS neuronal subpopulations using different optical stimulation parameters affects hippocampal LFP biomarkers. First, of the seven different optogenetic viral vectors used for modulating different neuronal subpopulations, only two induced a substantial change in hippocampal LFP. Second, we found hippocampal low-gamma band to be most effectively modulated by the stimulation. Third, the hippocampal biomarkers were sensitive to the optogenetic virus type and the stimulation frequency, establishing those parameters as the critical ones for the regulation of hippocampal biomarker activity. Last, we built a Gaussian process regression model to describe the relationship between stimulation parameters and activity of the biomarker as well as to identify the optimal parameters for biomarker modulation. This new machine learning approach can further our understanding of the effects of neural stimulation and guide the selection of optimal parameters for neural control.
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http://dx.doi.org/10.1142/S0129065719500205DOI Listing
December 2019

Comparative health risk assessment of in-vehicle exposure to formaldehyde and acetaldehyde for taxi drivers and passengers: Effects of zone, fuel, refueling, vehicle's age and model.

Environ Pollut 2019 Nov 30;254(Pt A):112943. Epub 2019 Jul 30.

Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

This study aimed to assess the carcinogenic and non-carcinogenic risks of in-vehicle exposure in Tehran, Iran to formaldehyde and acetaldehyde for different models of taxis, and to explore the effects of city zone, taxi vehicle type, the taxi's age (<1, 1-5, 5-10), fuel type (gasoline, CNG, and LPG), and refueling activities on the estimated health risks based on previously measured concentrations. The overall and age-specific carcinogenic and non-carcinogenic risks of these compounds for taxi drivers and passengers were estimated separately using Monte Carlo simulations. Three scenarios of exposure frequency were defined for taxis commuting in different zones of city: Restricted Traffic Zone (RTZ) and Odd-Even Zone (OEZ) as two plans to reduce air pollution, and no-restriction zone (NRZ). The carcinogenic risks for drivers and passengers, the average risks of formaldehyde and acetaldehyde for most cases were above the 1 × 10. The health risks were greater in Restricted Traffic Zone (RTZ) and Odd-Even Zone (OEZ) in comparison to no-restriction zone (NRZ). The carcinogenic risk from formaldehyde exposures were higher than those for acetaldehyde in all cases. Taxis fueled with LPG showed lower cancer risks for both acetaldehyde and formaldehyde. Refueling increased the carcinogenic risk from both compounds. For non-carcinogenic risks from acetaldehyde, the average hazard ratios for both drivers and passengers were >1, indicating a non-negligible risk. Cancer and non-cancer risks for the taxi drivers were greater than the passengers given the higher time of occupancy. The present study showed that transportation in taxis can impose significant long-term health risks to both passengers and drivers. Development and investment in cleaner choices for public transportations are required.
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http://dx.doi.org/10.1016/j.envpol.2019.07.111DOI Listing
November 2019

Bioaccessibility analysis of toxic metals in consumed rice through an in vitro human digestion model - Comparison of calculated human health risk from raw, cooked and digested rice.

Food Chem 2019 Nov 3;299:125126. Epub 2019 Jul 3.

Department of Environmental Health, School of Public Health, Tehran University of Medical Science, Tehran, Iran; Center for Research Methodology and Data Analysis (CRMDA), Institute for Environmental Research (IER), Tehran University of Medical Sciences, Tehran, Iran. Electronic address:

The health risk assessment of exposure to toxic metals through the consumption food crops is very important. The present study was aimed to investigate the bioaccessibility of toxic metals (including arsenic, lead and cadmium) in rice through an in vitro gastrointestinal digestion model, and assess health risks associated with these metals in raw, cooked and digested rice. Total and bioaccessible concentration of metals were measured by introducing the prepared samples into the inductively coupled plasma-optical emission spectroscopy. Based on the results, the bioaccessible toxic metals in gastric phase were significantly higher than that in both oral and small intestinal phases. The estimated concentrations of these metals in the raw and cooked rice are very far from the actual exposure state. Therefore, to assess the extent of health risks associated with the subjected toxic metals through the rice consumption, the actual exposure value of the metals (bioaccessible value) should be considered.
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http://dx.doi.org/10.1016/j.foodchem.2019.125126DOI Listing
November 2019

Physiochemical characteristics and oxidative potential of ambient air particulate matter (PM) during dust and non-dust storm events: a case study in Tehran, Iran.

J Environ Health Sci Eng 2018 Dec 29;16(2):147-158. Epub 2018 Jun 29.

7Health and Environment Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.

In the present study, we investigated the characteristics of metal(loid)s, polycyclic aromatic hydrocarbons (PAHs) and oxidative potential (OP) in PM during dust and non-dust days in a rural and an urban area in Tehran. Water-soluble ions, metal(loid)s, PAHs, and OP were measured using ion chromatography (IC), inductively coupled plasma optical emission spectrometer (ICP-OES) and gas chromatography/mass spectrometry (GC-MS), and dithiothreitol (DTT) assay respectively. The results showed that the average concentrations of ambient PM were 284 ± 90.4 and 123 ± 31.4 μg m on dusty and regular days in urban areas respectively, and were 258 ± 48.3 and 124 ± 41.4 μg m on dusty and regular days in rural areas, respectively; these values were 95% above the World Health Organization (WHO) guideline level. The crustal elements Na, Mg, Ca, Al, Si, Fe and Ti were the dominant for PM on dusty days, and NO and SO were dominant for PM on regular days. The average ± SD concentrations of total PAHs were 34.3 ± 22.5 and 55.1 ± 28.3 ng m on dusty and regular days, respectively, with the maximum value occurring on inversion days. The average OP was 8.90 ± 7.15 and 1.41 ± 0.35 and was 11.4 ± 3.97 and 19.9 ± 8.67 (nmol min μg PM) for water and methanol extracts on dusty and regular days, respectively, with the lowest value occurring on dusty days. The OP was highly associated with Cu and Mn. Briefly; the results of this study demonstrate that OP is mass independent and consequence a promising proxy for PM mass.
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http://dx.doi.org/10.1007/s40201-018-0303-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277329PMC
December 2018

An Inductively-Powered Wireless Neural Recording and Stimulation System for Freely-Behaving Animals.

IEEE Trans Biomed Circuits Syst 2019 04 7;13(2):413-424. Epub 2019 Jan 7.

An inductively-powered wireless integrated neural recording and stimulation (WINeRS-8) system-on-a-chip (SoC) that is compatible with the EnerCage-HC2 for wireless/battery-less operation has been presented for neuroscience experiments on freely behaving animals. WINeRS-8 includes a 32-ch recording analog front end, a 4-ch current-controlled stimulator, and a 434 MHz on - off keying data link to an external software- defined radio wideband receiver (Rx). The headstage also has a bluetooth low energy link for controlling the SoC. WINeRS-8/EnerCage-HC2 systems form a bidirectional wireless and battery-less neural interface within a standard homecage, which can support longitudinal experiments in an enriched environment. Both systems were verified in vivo on rat animal model, and the recorded signals were compared with hardwired and battery-powered recording results. Realtime stimulation and recording verified the system's potential for bidirectional neural interfacing within the homecage, while continuously delivering 35 mW to the hybrid WINeRS-8 headstage over an unlimited period.
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http://dx.doi.org/10.1109/TBCAS.2019.2891303DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6510586PMC
April 2019

A Spark-based Analytic Pipeline for Seizure Detection in EEG Big Data Streams.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:4003-4006

Around 1% of the people in the world suffer from epilepsy, which is the second most neurological disorder in the human after stroke. The spontaneous recurrence of seizures is the main clinical manifestation of the epilepsy. Real time detecting the seizure in the Electroencephalogram (EEG) signal is a clinical way in the diagnosis and treatment of epilepsy. The unpredicted nature of the epileptic seizures, necessitates continuous monitoring and recording of the brain activities using high-throughput neurophysiological data acquisition systems over extended periods of time. The sheer volume and the velocity of the data generated from continuous monitoring the brain activities make real-time seizure detection a big data analytic problem. In this paper, we present a Spark-based machine-learning approach to the seizure detection problem using linear dimensionality reduction and classification. Using this approach, we achieved an average accuracy, sensitivity, specificity across all patients $(\mathrm{N=24)$ of 99.32%, 99.41%, and 95.25%, respectively. Also, the average Iatency of the Spark-based seizure detection framework is about 0.38 ms.
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http://dx.doi.org/10.1109/EMBC.2018.8513385DOI Listing
July 2018

Seizure Reduction using Model Predictive Control.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:3152-3155

This study presents a model predictive control approach for seizure reduction in a computational model of epilepsy. The differential dynamic programming (DDP) algorithm is implemented in a model predictive fashion to optimize a controller for suppressing seizures in a chaotic oscillator model. The chaotic oscillator model uses proportional-integral (PI) controller to represent the internal control mechanism that maintains stable neural activity in a healthy brain. In the pathological case, the gains of this PI controller are reduced, preventing sufficient feedback to suppress correlation increase between normal and pathological brain dynamics. This increase in correlation leads to synchronization of oscillator dynamics leading to the destabilization of neural activity and epileptic behavior. The pathological case of the chaotic oscillator model is formulated as an optimal control problem, which we solve using the dynamic programming principle. We propose using model predictive control with differential dynamic programming optimization as a possible method for controlling epileptic seizures in known models of epilepsy.
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http://dx.doi.org/10.1109/EMBC.2018.8512911DOI Listing
July 2018

Bayesian Optimization of Asynchronous Distributed Microelectrode Theta Stimulation and Spatial Memory.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:2683-2686

There is a great need for an electrical stimulation therapy to treat medication-resistant, surgically ineligible epileptic patients that successfully reduces seizure incidence with minimal side effects. Critical to advancing such therapies will be identifying the trade-offs between therapeutic efficacy and side effects. One novel treatment developed in the tetanus toxin rat model of mesial temporal lobe epilepsy, asynchronous distributed microelectrode stimulation (ADMETS) in the hippocampus has been shown to significantly reduce seizure frequency. However, our results have demonstrated that ADMETS has a negative effect on spatial memory that scales with the amplitude of stimulation. Given the high dimensional space of possible stimulation parameters, it is difficult to construct a mapping from variations in stimulation to behavioral effect. In this project, we present a novel, principled approach using closed-loop Bayesian optimization to tune stimulation that successfully maximize a desired objective - performance on a spatial memory assay.
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http://dx.doi.org/10.1109/EMBC.2018.8512801DOI Listing
July 2018

A machine learning approach to characterizing the effect of asynchronous distributed electrical stimulation on hippocampal neural dynamics in vivo.

Annu Int Conf IEEE Eng Med Biol Soc 2017 Jul;2017:2122-2125

Asynchronous distributed microelectrode theta stimulation (ADMETS) of the hippocampus has been shown to reduce seizure frequency in the tetanus toxin rat model of mesial temporal lobe epilepsy suggesting a hypothesis that ADMETS induces a seizure resistant state. Here we present a machine learning approach to characterize the nature of neural state changes induced by distributed stimulation. We applied the stimulation to two animals under sham and ADMETS conditions and used a combination of machine learning techniques on intra-hippocampal recordings of Local Field Potentials (LFPs) to characterize the difference in the neural state between sham and ADMETS. By iteratively fitting a logistic regression with data from the inter-stimulation interval under sham and ADMETS condition we found that the classification performance improves for both animals until 90s post stimulation before leveling out at AUC of 0.64 ± 0.2 and 0.67 ± 0.02 when all inter-stimulation data is included. The models for each animal were re-fit using elastic net regularization to force many of the model coefficients to 0, identifying those that do not optimally contribute to the classifier performance. We found that there is significant variation in the non-zero coefficients between animals (p <; 0.01), suggesting that the ADMETS induced state is represented differently between subject. These findings lay the foundation for using machine learning to robustly and quantitatively characterize neural state.
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http://dx.doi.org/10.1109/EMBC.2017.8037273DOI Listing
July 2017

Predicting the stimulation effectiveness using pre-stimulation neural states via optogenetic activation of the medial septum glutamatergic neurons modulating the hippocampal neural activity.

Annu Int Conf IEEE Eng Med Biol Soc 2017 Jul;2017:2105-2108

In this study, we explored the role of pre-stimulation neural states on the effectiveness of optogenetic stimulation. Optogenetic stimulation was applied to the medial septum glutamatergic neurons to modulate the hippocampal neural activity in a rat tetanus toxin seizure model. The hippocampal local field potential was recorded using a multi electrode array in an awake and behaving rat. Optical stimulation with a 465nm light source was applied at 35Hz in a 20 seconds off / 20 seconds on pattern with simultaneous recording from the hippocampus. Both the baseline and the stimulation period recordings were divided into 2 second segments and used for the further analysis. In the first experiment, a support vector machine (SVM) model classified the neural states by using spectral features between 0 and 50Hz. 447 out of 545 segments (82.02%) were correctly labeled as `Baseline' while only 326 out of 544 (59.93%) segments from the stimulation period were correctly labeled as `Stimulation.' As the ratio of mislabels is significantly higher for the stimulation period (chi-squared, p<;0.01), we concluded that the stimulation was not always effective. In the second experiment, an SVM model predicted the stimulation effectiveness using the spectral features of the pre-stimulation segments. The classification result shows that 63.7% of the pre-stimulation segments correctly predicted the stimulation effectiveness. These findings suggest that the prediction of the stimulation effectiveness may improve the stimulation efficacy by implementing a state-based stimulation protocol.
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http://dx.doi.org/10.1109/EMBC.2017.8037269DOI Listing
July 2017

Cross-entropy optimization for neuromodulation.

Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:6357-6360

This study presents a reinforcement learning approach for the optimization of the proportional-integral gains of the feedback controller represented in a computational model of epilepsy. The chaotic oscillator model provides a feedback control systems view of the dynamics of an epileptic brain with an internal feedback controller representative of the natural seizure suppression mechanism within the brain circuitry. Normal and pathological brain activity is simulated in this model by adjusting the feedback gain values of the internal controller. With insufficient gains, the internal controller cannot provide enough feedback to the brain dynamics causing an increase in correlation between different brain sites. This increase in synchronization results in the destabilization of the brain dynamics, which is representative of an epileptic seizure. To provide compensation for an insufficient internal controller an external controller is designed using proportional-integral feedback control strategy. A cross-entropy optimization algorithm is applied to the chaotic oscillator network model to learn the optimal feedback gains for the external controller instead of hand-tuning the gains to provide sufficient control to the pathological brain and prevent seizure generation. The correlation between the dynamics of neural activity within different brain sites is calculated for experimental data to show similar dynamics of epileptic neural activity as simulated by the network of chaotic oscillators.
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http://dx.doi.org/10.1109/EMBC.2016.7592182DOI Listing
August 2016

An Inductively-Powered Wireless Neural Recording System with a Charge Sampling Analog Front-End.

IEEE Sens J 2016 Jan 28;16(2):475-484. Epub 2015 Sep 28.

GT-Bionics lab, School of Electrical and Computer Engineering at the Georgia Institute of Technology, Atlanta, GA 30308, USA.

An inductively-powered wireless integrated neural recording system (WINeR-7) is presented for wireless and battery less neural recording from freely-behaving animal subjects inside a wirelessly-powered standard homecage. The WINeR-7 system employs a novel wide-swing dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which performs amplification, filtering, sampling, and analog-to-time conversion (ATC) with minimal interference and small amount of power. The output of the DSCS-AFE produces a pseudo-digital pulse width modulated (PWM) signal. A circular shift register (CSR) time division multiplexes (TDM) the PWM pulses to create a TDM-PWM signal, which is fed into an on-chip 915 MHz transmitter (Tx). The AFE and Tx are supplied at 1.8 V and 4.2 V, respectively, by a power management block, which includes a high efficiency active rectifier and automatic resonance tuning (ART), operating at 13.56 MHz. The 8-ch system-on-a-chip (SoC) was fabricated in a 0.35-μm CMOS process, occupying 5.0 × 2.5 mm and consumed 51.4 mW. For each channel, the sampling rate is 21.48 kHz and the power consumption is 19.3 μW. experiments were conducted on freely behaving rats in an energized homecage by continuously delivering 51.4 mW to the WINeR-7 system in a closed-loop fashion and recording local field potentials (LFP).
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http://dx.doi.org/10.1109/JSEN.2015.2483747DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4826074PMC
January 2016

The influence of the pre-stimulation neural state on the post-stimulation neural dynamics via distributed microstimulation of the hippocampus.

Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:1810-1813

In this study we investigated how the neural state influences how the brain responds to electrical stimulation using a 16-channel microelectrode array with 8 stimulation and recording channels implanted in the rat hippocampus. In two experiments we identified the stimulation threshold at which the brain changes to an afterdischarge state. In one experiment a range of suprathreshold stimulations were applied, and in another the stimulation was not changed. The neural state was measured by the power spectral density prior to stimulation. In the first experiment, these measures and the stimulation parameters were used as features, either together or separately, for training a Support Vector Machine (SVM) classifier to predict whether the stimulation would produce an afterdischarge. In the second experiment, recursive feature elimination was used to iteratively remove the neural state features from the recording channels that had the least impact on the overall accuracy. In the first experiment 43 stimulations elicited 26 afterdischarges. In predicting the post-stimulation state-change (afterdischarge vs. no afterdischarge) the feature space of only neural state had a higher accuracy (67.4%) than when combined with the stimulation parameters (65.1%) or the stimulation parameters alone (58.1%). The overall classification results from both feature spaces containing the neural state were non-independent (chi-squared p <; 0.01). In the second experiment, the channels that were the least predictive were those on the more distal ends of the recording electrode, and the most predictive were clustered in the center of the electrode. Additionally, the accuracy increased when 4 channels were removed. The findings from these experiments suggest that both the pre-stimulation state and the spatial properties from where it is measured can play a role in how neural stimulation can induce functional changes in the hippocampal networks.
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http://dx.doi.org/10.1109/EMBC.2016.7591070DOI Listing
August 2016

Less is more: novel less-invasive surgical techniques for mesial temporal lobe epilepsy that minimize cognitive impairment.

Curr Opin Neurol 2015 Apr;28(2):182-91

aDepartment of Neurosurgery bDepartment of Neurology cCoulter Department of Biomedical Engineering, Emory University School of Medicine, Atlanta, Georgia, USA.

Purpose Of Review: New minimally invasive techniques are becoming available to treat focal-onset epilepsy. The open surgical treatment of mesial temporal lobe epilepsy (MTLE), although associated with high rates of seizure freedom, is confounded by adverse impacts on neurocognitive function. This review covers new techniques being explored for surgical treatment of MTLE that in early studies have been achieving high seizure-free rates with preservation of memory and other functions referable to the mesial and lateral temporal regions.

Recent Findings: Multiple subpial transections of the hippocampus, and stereotactic approaches including radiofrequency ablation and laser interstitial thermal therapy have achieved rates of seizure freedom comparable to open resection but with fewer neurocognitive adverse effects. Electrical neuromodulation approaches, including responsive neurostimulation, direct hippocampal stimulation, and thalamic deep brain stimulation preserve cognitive function and achieve significant seizure suppression, but have not yet achieved high seizure-free rates.

Summary: With the recent success in minimally invasive approaches with respect to seizure freedom and preservation of neurocognitive functions, it is predicted that fewer patients will be receiving 'classic' open resections for MTLE such as temporal lobectomy. These new approaches also promise to decrease discomfort, time away from work, and healthcare utilization.
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http://dx.doi.org/10.1097/WCO.0000000000000176DOI Listing
April 2015

Effects of storage time and temperature on the antimony and some trace element release from polyethylene terephthalate (PET) into the bottled drinking water.

J Environ Health Sci Eng 2014 13;12(1):133. Epub 2014 Nov 13.

Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Background And Objectives: Heavy metals are considered as one of the major contaminants that can enter into the bottled waters. Antimony (Sb) is a contaminant, which may leach from the polyethylene terephthalate (PET) bottles into the water. The aim of this study was to investigate the content of antimony and other trace elements in bottled waters which was kept in varied storage conditions and temperatures.

Materials And Methods: Five time-temperature treatments were carried out on five different brands of commercially available bottled waters. Heavy metal measurement was performed by Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) method. Analysis of the collected data was processed by SPSS software.

Results: Antimony concentration was the main concern in our study. The concentrations increased in each of the sample during storage period at all temperatures. The results for different conditions were as follow: at 40°C, in outdoor and at room temperature the Sb concentrations were below the MCLs, i e. 6 ppb. However, at 65°C and 80°C for longer storage times Sb concentration exceeded the MCLs, and variations between the samples were significant (p ≤ 0.05). Storage time and temperature effects on the content of some other trace elements such as Al, Fe were also significant (p ≤ 0.05).

Conclusion: By increasing the duration of storage time and temperatures, antimony leaching from the PET bottles into the bottled water increased. The concentration of Al demonstrated an increase in higher temperatures and storage duration, whereas the content of Fe demonstrated no significant differences.
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http://dx.doi.org/10.1186/s40201-014-0133-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4245802PMC
November 2014

Real-time in vivo optogenetic neuromodulation and multielectrode electrophysiologic recording with NeuroRighter.

Front Neuroeng 2014 29;7:40. Epub 2014 Oct 29.

Translational Neuroengineering Group, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine Atlanta, GA, USA ; Department of Neurosurgery, Emory University School of Medicine Atlanta, GA, USA ; Department of Neurology, Emory University School of Medicine Atlanta, GA, USA.

Optogenetic channels have greatly expanded neuroscience's experimental capabilities, enabling precise genetic targeting and manipulation of neuron subpopulations in awake and behaving animals. However, many barriers to entry remain for this technology - including low-cost and effective hardware for combined optical stimulation and electrophysiologic recording. To address this, we adapted the open-source NeuroRighter multichannel electrophysiology platform for use in awake and behaving rodents in both open and closed-loop stimulation experiments. Here, we present these cost-effective adaptations, including commercially available LED light sources; custom-made optical ferrules; 3D printed ferrule hardware and software to calibrate and standardize output intensity; and modifications to commercially available electrode arrays enabling stimulation proximally and distally to the recording target. We then demonstrate the capabilities and versatility of these adaptations in several open and closed-loop experiments, demonstrate spectrographic methods of analyzing the results, as well as discuss artifacts of stimulation.
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http://dx.doi.org/10.3389/fneng.2014.00040DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4217045PMC
November 2014

Using reinforcement learning to provide stable brain-machine interface control despite neural input reorganization.

PLoS One 2014 30;9(1):e87253. Epub 2014 Jan 30.

Department of Biomedical Engineering, University of Miami, Coral Gables, Florida, United States of America ; Department of Neuroscience, University of Miami, Miami, Florida, United States of America ; Miami Project to Cure Paralysis, University of Miami, Miami, Florida, United States of America.

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder's neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087253PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907465PMC
September 2014

Feature extraction and unsupervised classification of neural population reward signals for reinforcement based BMI.

Annu Int Conf IEEE Eng Med Biol Soc 2013 ;2013:5250-3

New reinforcement based paradigms for building adaptive decoders for Brain-Machine Interfaces involve using feedback directly from the brain. In this work, we investigated neuromodulation in the Nucleus Accumbens (reward center) during a multi-target reaching task and investigated how to extract a reinforcing or non-reinforcing signal that could be used to adapt a BMI decoder. One of the challenges in brain-driven adaptation is how to translate biological neuromodulation into a single binary signal from the distributed representation of the neural population, which may encode many aspects of reward. To extract these signals, feature analysis and clustering were used to identify timing and coding properties of a user's neuromodulation related to reward perception. First, Principal Component Analysis (PCA) of reward related neural signals was used to extract variance in the firing and the optimum time correlation between the neural signal and the reward phase of the task. Next, k-means clustering was used to separate data into two classes.
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http://dx.doi.org/10.1109/EMBC.2013.6610733DOI Listing
August 2015

Towards autonomous neuroprosthetic control using Hebbian reinforcement learning.

J Neural Eng 2013 Dec 8;10(6):066005. Epub 2013 Oct 8.

Department of Neurosurgery, Emory University, Atlanta, GA, USA.

Objective: Our goal was to design an adaptive neuroprosthetic controller that could learn the mapping from neural states to prosthetic actions and automatically adjust adaptation using only a binary evaluative feedback as a measure of desirability/undesirability of performance.

Approach: Hebbian reinforcement learning (HRL) in a connectionist network was used for the design of the adaptive controller. The method combines the efficiency of supervised learning with the generality of reinforcement learning. The convergence properties of this approach were studied using both closed-loop control simulations and open-loop simulations that used primate neural data from robot-assisted reaching tasks.

Main Results: The HRL controller was able to perform classification and regression tasks using its episodic and sequential learning modes, respectively. In our experiments, the HRL controller quickly achieved convergence to an effective control policy, followed by robust performance. The controller also automatically stopped adapting the parameters after converging to a satisfactory control policy. Additionally, when the input neural vector was reorganized, the controller resumed adaptation to maintain performance.

Significance: By estimating an evaluative feedback directly from the user, the HRL control algorithm may provide an efficient method for autonomous adaptation of neuroprosthetic systems. This method may enable the user to teach the controller the desired behavior using only a simple feedback signal.
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http://dx.doi.org/10.1088/1741-2560/10/6/066005DOI Listing
December 2013

Brain-Machine Interface control of a robot arm using actor-critic rainforcement learning.

Annu Int Conf IEEE Eng Med Biol Soc 2012 ;2012:4108-11

Department of Biomedical Engineering, Miami University, Coral Gables, Fl 33146, USA.

Here we demonstrate how a marmoset monkey can use a reinforcement learning (RL) Brain-Machine Interface (BMI) to effectively control the movements of a robot arm for a reaching task. In this work, an actor-critic RL algorithm used neural ensemble activity in the monkey's motor cortext to control the robot movements during a two-target decision task. This novel approach to decoding offers unique advantages for BMI control applications. Compared to supervised learning decoding methods, the actor-critic RL algorithm does not require an explicit set of training data to create a static control model, but rather it incrementally adapts the model parameters according to its current performance, in this case requiring only a very basic feedback signal. We show how this algorithm achieved high performance when mapping the monkey's neural states (94%) to robot actions, and only needed to experience a few trials before obtaining accurate real-time control of the robot arm. Since RL methods responsively adapt and adjust their parameters, they can provide a method to create BMIs that are robust against perturbations caused by changes in either the neural input space or the output actions they generate under different task requirements or goals.
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http://dx.doi.org/10.1109/EMBC.2012.6346870DOI Listing
August 2013

A symbiotic brain-machine interface through value-based decision making.

PLoS One 2011 Mar 14;6(3):e14760. Epub 2011 Mar 14.

Department of Biomedical Engineering, University of Miami, Coral Gables, Florida, United States of America.

Background: In the development of Brain Machine Interfaces (BMIs), there is a great need to enable users to interact with changing environments during the activities of daily life. It is expected that the number and scope of the learning tasks encountered during interaction with the environment as well as the pattern of brain activity will vary over time. These conditions, in addition to neural reorganization, pose a challenge to decoding neural commands for BMIs. We have developed a new BMI framework in which a computational agent symbiotically decoded users' intended actions by utilizing both motor commands and goal information directly from the brain through a continuous Perception-Action-Reward Cycle (PARC).

Methodology: The control architecture designed was based on Actor-Critic learning, which is a PARC-based reinforcement learning method. Our neurophysiology studies in rat models suggested that Nucleus Accumbens (NAcc) contained a rich representation of goal information in terms of predicting the probability of earning reward and it could be translated into an evaluative feedback for adaptation of the decoder with high precision. Simulated neural control experiments showed that the system was able to maintain high performance in decoding neural motor commands during novel tasks or in the presence of reorganization in the neural input. We then implanted a dual micro-wire array in the primary motor cortex (M1) and the NAcc of rat brain and implemented a full closed-loop system in which robot actions were decoded from the single unit activity in M1 based on an evaluative feedback that was estimated from NAcc.

Conclusions: Our results suggest that adapting the BMI decoder with an evaluative feedback that is directly extracted from the brain is a possible solution to the problem of operating BMIs in changing environments with dynamic neural signals. During closed-loop control, the agent was able to solve a reaching task by capturing the action and reward interdependency in the brain.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0014760PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3056711PMC
March 2011

Spatio-temporal clustering of firing rates for neural state estimation.

Annu Int Conf IEEE Eng Med Biol Soc 2010 ;2010:6023-6

Department of Electrical and Computer Engineering, University of Florida, P.O. Box 116130 NEB 486, Bldg #33, Gainesville, FL 32611, USA.

Characterizing the dynamics of neural data by a discrete state variable is desirable in experimental analysis and brain-machine interfaces. Previous successes have used dynamical modeling including Hidden Markov Models, but the methods do not always produce meaningful results without being carefully trained or initialized. We propose unsupervised clustering in the spatio-temporal space of neural data using time embedding and a corresponding distance measure. By defining performance measures, the method parameters are investigated for a set of neural and simulated data with promising results. Our investigations demonstrate a different view of how to extract information to maximize the utility of state estimation.
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http://dx.doi.org/10.1109/IEMBS.2010.5627600DOI Listing
March 2011

Extracting an evaluative feedback from the brain for adaptation of motor neuroprosthetic decoders.

Annu Int Conf IEEE Eng Med Biol Soc 2010 ;2010:1682-5

Department of Biomedical Engineering, University of Florida, 130 BME Building, Gainesville, FL 32611, USA.

The design of Brain-Machine Interface (BMI) neural decoders that have robust performance in changing environments encountered in daily life activity is a challenging problem. One solution to this problem is the design of neural decoders that are able to assist and adapt to the user by participating in their perception-action-reward cycle (PARC). Using inspiration both from artificial intelligence and neurobiology reinforcement learning theories, we have designed a novel decoding architecture that enables a symbiotic relationship between the user and an Intelligent Assistant (IA). By tapping into the motor and reward centers in the brain, the IA adapts the process of decoding neural motor commands into prosthetic actions based on the user's goals. The focus of this paper is on extraction of goal information directly from the brain and making it accessible to the IA as an evaluative feedback for adaptation. We have recorded the neural activity of the Nucleus Accumbens in behaving rats during a reaching task. The peri-event time histograms demonstrate a rich representation of the reward prediction in this subcortical structure that can be modeled on a single trial basis as a scalar evaluative feedback with high precision.
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http://dx.doi.org/10.1109/IEMBS.2010.5626827DOI Listing
April 2011