Current Source Density Estimates Research Articles

KCSD-python, reliable current source density estimation with quality control.

Interpretation of extracellular recordings can be challenging due to the long range of electric field. This challenge can be mitigated by estimating the current source density (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method and related tools to facilitate CSD analysis of experimental data and the interpretation of results. We show how to counter the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for an arbitrary distribution of electrodes in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in a single cell, and includes a range of diagnostic aids. We demonstrate its features in a Jupyter Notebook tutorial which illustrates a typical analytical workflow and main functionalities useful in validating analysis results.

Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review.

Antiseizure medication (ASM) is the primary treatment for epilepsy. In clinical practice, methods to assess ASM efficacy (predict seizure freedom or seizure reduction), during any phase of the drug treatment lifecycle, are limited. This scoping review identifies and appraises prognostic electroencephalographic (EEG) biomarkers and prognostic models that use EEG features, which are associated with seizure outcomes following ASM initiation, dose adjustment, or withdrawal. We also aim to summarize the population and context in which these biomarkers and models were identified and described, to understand how they could be used in clinical practice. Between January 2021 and October 2022, four databases, references, and citations were systematically searched for ASM studies investigating changes to interictal EEG or prognostic models using EEG features and seizure outcomes. Study bias was appraised using modified Quality in Prognosis Studies criteria. Results were synthesized into a qualitative review. Of 875 studies identified, 93 were included. Biomarkers identified were classed as qualitative (visually identified by wave morphology) or quantitative. Qualitative biomarkers include identifying hypsarrhythmia, centrotemporal spikes, interictal epileptiform discharges (IED), classifying the EEG as normal/abnormal/epileptiform, and photoparoxysmal response. Quantitative biomarkers were statistics applied to IED, high-frequency activity, frequency band power, current source density estimates, pairwise statistical interdependence between EEG channels, and measures of complexity. Prognostic models using EEG features were Cox proportional hazards models and machine learning models. There is promise that some quantitative EEG biomarkers could be used to assess ASM efficacy, but further research is required. There is insufficient evidence to conclude any specific biomarker can be used for a particular population or context to prognosticate ASM efficacy. We identified a potential battery of prognostic EEG biomarkers, which could be combined with prognostic models to assess ASM efficacy. However, many confounders need to be addressed for translation into clinical practice.

Current source density (sLORETA) in patients undergoing cognitive rehabilitation using dual task in the early postoperative period of coronary artery bypass grafting

Highlights. For the first time, the method of standardized low resolution electromagnetic tomography – sLORETA was used to assess the neurophysiological correlates of the success of cognitive rehabilitation using dual task to recover the brain functions affected by ischemia during cardiac surgery.The patients who were successfully rehabilitated have demonstrated lower postoperative values of resting state theta activity within the right hemisphere, indicating the transfer effect, which is an important component of successful cognitive rehabilitation.Aim. Visualization and monitoring of brain ischemia is important for the diagnosis of cerebrovascular diseases. The aim of the study was to evaluate the possibilities and applicability of the method of standardized low resolution electromagnetic brain tomography – sLORETA for analyzing brain electrical activity in patients undergoing cognitive rehabilitation using dual tasks to recover impaired brain functions during ischemia associated with cardiac surgery.Methods. The study included 16 male patients (45–75 years old) who were admitted for planned coronary artery bypass grafting (CABG) to the Clinic at the Research Institute for Complex Issues of Cardiovascular Diseases. Clinical and neurophysiological examinations were performed 2–3 days before CABG and 1 week after surgery. All patients underwent cognitive rehabilitation that started at postoperative day 3 or 4 and lasted until discharge, the outcome was assessed as well. Monopolar EEG (62 channels) was recorded at rest in a sitting position with eyes closed in a light- and noise-insulated room using a Neuvo SynAmps2 Amplifier. Data processing of EEG background activity for sLORETA analysis was performed using the EEGLAB in MATLAB software (The MathWorks, Natick, MA, USA). The sLORETA algorithms were used to calculate dynamic cross spectrum and current source density within the analyzed frequency range (4–6 Hz). Statistical analysis of current source density indicators was carried out by using the method of statistical non-parametric mapping in the sLORETA software package, and the difference between the current source density in the pre- and postoperative period (after cognitive rehabilitation) was calculated as well.Results. Cognitive rehabilitation was successful in 44% (7 patients) of cases. The differences have been revealed between the groups with successful and unsuccessful cognitive rehabilitation by using sLORETA. The group with unsuccessful cognitive rehabilitation demonstrated higher current source density estimates in theta rhythm compared with the successful rehabilitation group, it was most pronounced (t>- 8.42; p<0.004) in the right hemisphere – Brodmann area 22, temporal lobe and superior temporal gyrus.Conclusion. The sLORETA method demonstrated the positive effect of cognitive training on changes in the spatial patterns of brain activity in patients undergoing onpump CABG. The successful cognitive rehabilitation was associated with lower postoperative resting state theta activity within the right hemisphere, indicating the transfer effect, which is an important component of successful cognitive rehabilitation.

Elevated and Slowed EEG Oscillations in Patients with Post-Concussive Syndrome and Chronic Pain Following a Motor Vehicle Collision.

(1) Background: Mild traumatic brain injury produces significant changes in neurotransmission including brain oscillations. We investigated potential quantitative electroencephalography biomarkers in 57 patients with post-concussive syndrome and chronic pain following motor vehicle collision, and 54 healthy nearly age- and sex-matched controls. (2) Methods: Electroencephalography processing was completed in MATLAB, statistical modeling in SPSS, and machine learning modeling in Rapid Miner. Group differences were calculated using current-source density estimation, yielding whole-brain topographical distributions of absolute power, relative power and phase-locking functional connectivity. Groups were compared using independent sample Mann–Whitney U tests. Effect sizes and Pearson correlations were also computed. Machine learning analysis leveraged a post hoc supervised learning support vector non-probabilistic binary linear kernel classification to generate predictive models from the derived EEG signatures. (3) Results: Patients displayed significantly elevated and slowed power compared to controls: delta (p = 0.000000, r = 0.6) and theta power (p < 0.0001, r = 0.4), and relative delta power (p < 0.00001) and decreased relative alpha power (p < 0.001). Absolute delta and theta power together yielded the strongest machine learning classification accuracy (87.6%). Changes in absolute power were moderately correlated with duration and persistence of symptoms in the slow wave frequency spectrum (<15 Hz). (4) Conclusions: Distributed increases in slow wave oscillatory power are concurrent with post-concussive syndrome and chronic pain.

Effects on Mood and EEG States After Meditation in Augmented Reality With and Without Adjunctive Neurofeedback

Research and design of virtual reality technologies with mental-health focused applications has increased dramatically in recent years. However, the applications and psychological outcomes of augmented reality (AR) technologies still remain to be widely explored and evaluated. This is particularly true for the use of AR for the self-management of stress, anxiety, and mood. In the current study, we examined the impact of a brief open heart meditation AR experience on participants with moderate levels of anxiety and/or depression. Using a randomized between-group design subjects participated in the AR experience or the AR experience plus frontal gamma asymmetry neurofeedback integrated into the experience. Self-reported mood state and resting-state EEG were recorded before and after the AR intervention for both groups. Participants also reported on engagement and perceived use of the experience as a stress and coping tool. EEG activity was analyzed as a function of the frontal, midline, and parietal scalp regions, and with sLORETA current source density estimates of anterior cingulate and insular cortical regions of interest. Results demonstrated that both versions of the AR meditation significantly reduced negative mood and increased positive mood. The changes in resting state EEG were also comparable between groups, with some trending differences observed, in line with existing research on open heart and other loving-kindness and compassion-based meditations. Engagement was favorable for both versions of the AR experience, with higher levels of engagement reported with the addition of neurofeedback. These results provide early support for the therapeutic potential of AR-integrated meditations as a tool for the self-regulation of mood and emotion, and sets the stage for more research and development into health and wellness-promoting AR applications.

Variability and effect sizes of intracranial current source density estimations during pain: Systematic review, experimental findings, and future perspectives.

Pain arises from the integration of sensory and cognitive processes in the brain, resulting in specific patterns of neural oscillations that can be characterized by measuring electrical brain activity. Current source density (CSD) estimation from low‐resolution brain electromagnetic tomography (LORETA) and its standardized (sLORETA) and exact (eLORETA) variants, is a common approach to identify the spatiotemporal dynamics of the brain sources in physiological and pathological pain‐related conditions. However, there is no consensus on the magnitude and variability of clinically or experimentally relevant effects for CSD estimations. Here, we systematically examined reports of sample size calculations and effect size estimations in all studies that included the keywords pain, and LORETA, sLORETA, or eLORETA in Scopus and PubMed. We also assessed the reliability of LORETA CSD estimations during non‐painful and painful conditions to estimate hypothetical sample sizes for future experiments using CSD estimations. We found that none of the studies included in the systematic review reported sample size calculations, and less than 20% reported measures of central tendency and dispersion, which are necessary to estimate effect sizes. Based on these data and our experimental results, we determined that sample sizes commonly used in pain studies using CSD estimations are suitable to detect medium and large effect sizes in crossover designs and only large effects in parallel designs. These results provide a comprehensive summary of the effect sizes observed using LORETA in pain research, and this information can be used by clinicians and researchers to improve settings and designs of future pain studies.

Early Appearance and Spread of Fast Ripples in the Hippocampus in a Model of Cortical Traumatic Brain Injury.

Fast ripples (FRs; activity of >250 Hz) have been considered as a biomarker of epileptic activity in the hippocampus and entorhinal cortex; it is thought that they signal the focus of seizure generation. Similar high-frequency network activity has been produced in vitro by changing extracellular medium composition, by using pro-epileptic substances, or by electrical stimulation. Here we study the propagation of these events between different subregions of the male rat hippocampus in a recently introduced experimental model of FRs in entorhinal cortex-hippocampal slices in vitro By using a matrix of 4096 microelectrodes, the sites of initiation, propagation pathways, and spatiotemporal characteristics of activity patterns could be studied with unprecedented high resolution. To this end, we developed an analytic tool based on bidimensional current source density estimation, which delimits sinks and sources with a high precision and evaluates their trajectories using the concept of center of mass. With this methodology, we found that FRs can arise almost simultaneously at noncontiguous sites in the CA3-to-CA1 direction, underlying the spatial heterogeneity of epileptogenic foci, while continuous somatodendritic waves of activity develop. An unexpected, yet important propagation route is the propagation of activity from CA3 into the hilus and dentate gyrus. This pathway may cause reverberating activation of both regions, supporting sustained pathological network events and altered information processing in hippocampal networks.SIGNIFICANCE STATEMENT Fast ripples (FRs) have been considered as a biomarker of epileptic activity and may signal the focus of seizure generation. In a model of traumatic brain injury in the rat, FRs appear in the hippocampus within a couple of hours after an extrahippocampal, cortical lesion. We analyzed the origin and dynamics of the FRs in the hippocampus using massive electrophysiological recordings, allowing an unprecedented high spatiotemporal resolution. We show that FRs originate in distinct and noncontiguous locations within the CA3 region and uncover, with high precision, the extent and dynamics of their current density. This activity propagates toward CA1 but also backpropagates to the hilus and the dentate gyrus, suggesting activation of defined microcircuits that can sustain recurrent excitation.

Virtual Reality for Anxiety Reduction Demonstrated by Quantitative EEG: A Pilot Study.

While previous research has established that virtual reality (VR) can be successfully used in the treatment of anxiety disorders, including phobias and PTSD, no research has examined changes in brain patterns associated with the use of VR for generalized anxiety management. In the current study, we compared a brief nature-based mindfulness VR experience to a resting control condition on anxious participants. Self-reported anxiety symptoms and resting-state EEG were recorded across intervals containing quiet rest or the VR intervention. EEG activity was analyzed as a function of global power shifts in Alpha and Beta activity, and with sLORETA current source density estimates of cingulate cortex regions of interest. Results demonstrated that both a quiet rest control condition and the VR meditation significantly reduced subjective reports of anxiety and increased Alpha power. However, the VR intervention uniquely resulted in shifting proportional power from higher Beta frequencies into lower Beta frequencies, and significantly reduced broadband Beta activity in the anterior cingulate cortex. These effects are consistent with a physiological reduction of anxiety. This pilot study provides preliminary evidence supporting the therapeutic potential of VR for anxiety management and stress reduction programs.

Current source density estimates improve the discriminability of scalp-level brain connectivity features related to motor-imagery tasks.

Recent progress in the number of studies involving brain connectivity analysis of motor imagery (MI) tasks for brain-computer interface (BCI) systems has warranted the need for pre-processing methods. The objective of this study is to evaluate the impact of current source density (CSD) estimation from raw electroencephalogram (EEG) signals on the classification performance of scalp level brain connectivity feature based MI-BCI. In particular, time-domain partial Granger causality (PGC) method was implemented on the raw EEG signals and CSD signals of a publicly available dataset for the estimation of brain connectivity features. Moreover, pairwise binary classifications of four different MI tasks were performed in inter-session and intra-session conditions using a support vector machine classifier. The results showed that CSD provided a statistically significant increase of the AUC: 20.28% in the inter-session condition; 12.54% and 13.92% with session 01 and session 02, respectively, in the intra-session condition. These results show that pre-processing of EEG signals is crucial for single-trial connectivity features based MI-BCI systems and CSD can enhance their overall performance.

PB 11 Phase-coupling optimization (PCO) – a new algorithm for the analysis and localization of phase-coupling in multivariate data

Introduction Phase-coupling between neuronal oscillations in the brain has been hypothesized as a mechanism for interactions between brain areas; moreover, phases of neuronal oscillations were found related to performance in memory and perception tasks. However, in multivariate data, such as multi-channel EEG or MEG, the analysis of phase-coupling remains challenging. Sensor-space analysis is suboptimal regarding the signal-to-noise ratio (SNR) and might fail to localize the sources of phase-coupling appropriately. Here, we introduce phase-coupling optimization (PCO), an algorithm seeking spatial filters that maximize the coupling of oscillatory phases to an independent variable (e.g., detected vs. undetected events in a perception task). The resulting spatial filters/patterns can then be used for inverse source modeling. Methods Due to its simplicity, the “mean vector length” measure was chosen to quantify the degree of phase-coupling and is being maximized using the quasi-Newton Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm. To avoid local minima, the optimization is restarted several times from low-discrepancy pseudo-random initial starting points. As regularization, spatio-spectral decomposition (SSD) reduces the search-space while maximizing the SNR in the selected frequency band. The performance of the algorithm was verified using realistic forward-model EEG simulations. Results Simulations demonstrated that PCO, together with SSD as pre-processing, can reliably localize the sources of phase-coupling in multi-channel EEG and characterize the coupling relation down to a SNR of −10 dB (i.e., a power ratio of 0.1) using 500 simulated task repetitions. Additionally, PCO is superior to a multiple regression approach and by a large margin outperforms sensor-space analysis based on a current source density estimation. Discussion The analysis of phase-coupling in sensor space is suboptimal due to the SNR of the signal and might even lead to erroneous source localization. Techniques such as beamforming on the other hand, require a well-founded a priori assumption on the spatial origin of phase-coupling and complex inverse problem solvers. PCO seeks the optimal spatial projection for the analysis of phase-coupling and demonstrated its reliability even for very low SNR. Hence, we regard it as a promising tool for the analysis of neuronal oscillations. Significance PCO increases the sensitivity and reliability of phase-coupling analyses. The obtained spatial patterns can be used for inverse source modeling.

From Maxwell's equations to the theory of current-source density analysis.

Despite the widespread use of current‐source density (CSD) analysis of extracellular potential recordings in the brain, the physical mechanisms responsible for the generation of the signal are still debated. While the extracellular potential is thought to be exclusively generated by the transmembrane currents, recent studies suggest that extracellular diffusive, advective and displacement currents—traditionally neglected—may also contribute considerably toward extracellular potential recordings. Here, we first justify the application of the electro‐quasistatic approximation of Maxwell's equations to describe the electromagnetic field of physiological origin. Subsequently, we perform spatial averaging of currents in neural tissue to arrive at the notion of the CSD and derive an equation relating it to the extracellular potential. We show that, in general, the extracellular potential is determined by the CSD of membrane currents as well as the gradients of the putative extracellular diffusion current. The diffusion current can contribute significantly to the extracellular potential at frequencies less than a few Hertz; in which case it must be subtracted to obtain correct CSD estimates. We also show that the advective and displacement currents in the extracellular space are negligible for physiological frequencies while, within cellular membrane, displacement current contributes toward the CSD as a capacitive current. Taken together, these findings elucidate the relationship between electric currents and the extracellular potential in brain tissue and form the necessary foundation for the analysis of extracellular recordings.

Interactive effect of 5-HTTLPR genotype and age on sources of cortical rhythms in healthy women

This study was aimed to localize the effects of 5-HTTLPR (serotonin-transporter-linked polymorphic region) on the age differences of spontaneous EEG activity in women using neuroimaging analysis sLORETA (Standardized Low Resolution brain Electromagnetic Tomography). DNA samples extracted from cheek swabs and resting-state EEG recorded at 60 standard leads were collected from young (YW, N=86, 18–35years) and older (OW, N=45; 55–80years) healthy women. We have shown that advanced age was associated with increased posterior EEG desynchronization in S′/S′. S′ (LG allele was grouped with S alleles owing to its functional equivalence and this group was labeled as S′) genotype carriers denoted by decrease of delta – beta1 current source density, and to a lesser extent in L/L homozygotes denoted by decrease in delta activity. In heterozygotes OW, as compared with heterozygotes YW, higher source density estimates of beta1 in frontal and temporal cortex were observed. Age differences were more pronounced in the right hemisphere in S′/S′ and L/L carriers and in the left hemisphere in heterozygotes. We also found that in OW, current source density estimates of theta, alpha1, alpha2, alpha3 and beta1 sources in the right occipital lobe were higher in S′/L than in S′/S′ carriers. These results may have implications for understanding 5-HTT-dependent variation in the effect of aging on brain activity.

1D Current Source Density (CSD) Estimation in Inverse Theory: A Unified Framework for Higher-Order Spectral Regularization of Quadrature and Expansion-Type CSD Methods.

Estimation of current source density (CSD) from the low-frequency part of extracellular electric potential recordings is an unstable linear inverse problem. To make the estimation possible in an experimental setting where recordings are contaminated with noise, it is necessary to stabilize the inversion. Here we present a unified framework for zero- and higher-order singular-value-decomposition (SVD)-based spectral regularization of 1D (linear) CSD estimation from local field potentials. The framework is based on two general approaches commonly employed for solving inverse problems: quadrature and basis function expansion. We first show that both inverse CSD (iCSD) and kernel CSD (kCSD) fall into the category of basis function expansion methods. We then use these general categories to introduce two new estimation methods, quadrature CSD (qCSD), based on discretizing the CSD integral equation with a chosen quadrature rule, and representer CSD (rCSD), an even-determined basis function expansion method that uses the problem's data kernels (representers) as basis functions. To determine the best candidate methods to use in the analysis of experimental data, we compared the different methods on simulations under three regularization schemes (Tikhonov, tSVD, and dSVD), three regularization parameter selection methods (NCP, L-curve, and GCV), and seven different a priori spatial smoothness constraints on the CSD distribution. This resulted in a comparison of 531 estimation schemes. We evaluated the estimation schemes according to their source reconstruction accuracy by testing them using different simulated noise levels, lateral source diameters, and CSD depth profiles. We found that ranking schemes according to the average error over all tested conditions results in a reproducible ranking, where the top schemes are found to perform well in the majority of tested conditions. However, there is no single best estimation scheme that outperforms all others under all tested conditions. The unified framework we propose expands the set of available estimation methods, provides increased flexibility for 1D CSD estimation in noisy experimental conditions, and allows for a meaningful comparison between estimation schemes.

Gender differences in association between serotonin transporter gene polymorphism and resting-state EEG activity

Human brain oscillations represent important features of information processing and are highly heritable. Gender has been observed to affect association between the 5-HTTLPR (serotonin-transporter-linked polymorphic region) polymorphism and various endophenotypes. This study aimed to investigate the effects of 5-HTTLPR on the spontaneous electroencephalography (EEG) activity in healthy male and female subjects. DNA samples extracted from buccal swabs and resting EEG recorded at 60 standard leads were collected from 210 (101 men and 109 women) volunteers. Spectral EEG power estimates and cortical sources of EEG activity were investigated. It was shown that effects of 5-HTTLPR polymorphism on electrical activity of the brain vary as a function of gender. Women with the S/L genotype had greater global EEG power compared to men with the same genotype. In men, current source density was markedly different among genotype groups in only alpha 2 and alpha 3 frequency ranges: S/S allele carriers had higher current source density estimates in the left inferior parietal lobule in comparison with the L/L group. In women, genotype difference in global power asymmetry was found in the central-temporal region. Contrasting L/L and S/L genotype carriers also yielded significant effects in the right hemisphere inferior parietal lobule and the right postcentral gyrus with L/L genotype carriers showing lower current source density estimates than S/L genotype carriers in all but gamma bands. So, in women, the effects of 5-HTTLPR polymorphism were associated with modulation of the EEG activity in a wide range of EEG frequencies. The significance of the results lies in the demonstration of gene by sex interaction with resting EEG that has implications for understanding sex-related differences in affective states, emotion and cognition.

Current Source Density Estimation for Single Neurons

Event Abstract Back to Event Current Source Density Estimation for Single Neurons Dorottya Cserpán1, 2*, Helena Głąbska3, Zoltán Somogyvári1 and Daniel K. Wójcik3 1 Wigner Research Center for Physics of the Hungarian Academy of Sciences, Department of Theory, Hungary 2 Budapest University of Technology and Economics, Department of Measurement and Information Systems, Faculty of Electrical Engineering and Informatics, Hungary 3 Nencki Institute of Experimental Biology Polish Academy of Sciences, Department of Neurophysiology, Poland Recent developments of multielectrode technology made it possible to measure the extracellular potential generated in the neural tissue with spatial precision on the order of tens of micrometers and on submillisecond time scale. Combining such measurements with imaging of single neurons within the studied tissue opens up new experimental possibilities for estimating distribution of current sources along a dendritic tree. In this work we show that if we are able to relate part of the recording of extracellular potential to a specific cell of known morphology we can estimate the spatiotemporal distribution of transmembrane currents along it. We present here an extension of the kernel CSD method (Potworowski et al., 2012) applicable in such case. We test it on several model neurons of progressively complicated morphologies from ball-and-stick to realistic, up to analysis of simulated neuron activity embedded in a substantial working network (Traub et al, 2005). We discuss the caveats and possibilities of this new approach. Acknowledgements Network simulations were in part performed in the Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, under grants G35-7 and POWIEW. Research supported by the grants from the Polish Ministry of Regional Development, POIG.02.03.00-00-003/09, POIG.02.03.00-00-018/08, and from the Polish Ministry of Science and Higher Education: 5428/B/P01/2010/39 and IP2011~030971. References Potworowski J, Jakuczun W, Leski S, Wójcik DK (2012) Kernel current source density method. Neural Comput 24: 541□575. Traub RD, Contreras D, Cunningham MO, Murray H, LeBeau FEN, et al. (2005) Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts. J Neurophysiol 93: 2194□2232. Keywords: CSD (current-source density), LFP analysis, morphology, kernel CSD method, imaging techniques Conference: 4th NAMASEN Training Workshop - Dendrites 2014, Heraklion, Greece, 1 Jul - 4 Jul, 2014. Presentation Type: Poster presentation Topic: morphological and functional characterizations Citation: Cserpán D, Głąbska H, Somogyvári Z and Wójcik DK (2014). Current Source Density Estimation for Single Neurons. Front. Syst. Neurosci. Conference Abstract: 4th NAMASEN Training Workshop - Dendrites 2014. doi: 10.3389/conf.fnsys.2014.05.00031 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 Apr 2014; Published Online: 12 Jun 2014. * Correspondence: Miss. Dorottya Cserpán, Wigner Research Center for Physics of the Hungarian Academy of Sciences, Department of Theory, Budapest, H-1121, Hungary, cserpan@mit.bme.hu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Dorottya Cserpán Helena Głąbska Zoltán Somogyvári Daniel K Wójcik Google Dorottya Cserpán Helena Głąbska Zoltán Somogyvári Daniel K Wójcik Google Scholar Dorottya Cserpán Helena Głąbska Zoltán Somogyvári Daniel K Wójcik PubMed Dorottya Cserpán Helena Głąbska Zoltán Somogyvári Daniel K Wójcik Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

1A1-N11 BMIを応用した運動麻痺患者に対するリハビリテーション装置の開発 : その意図と予備的実験結果について(リハビリテーションロボティクス・メカトロニクス(1))

A prototype pedaled wheelchair/ergometer system has been designed and developed for leg paralysis patients as part of the development of a rehabilitation system based on a brain-machine interface (BMI) for patient with motor paralysis. The wheelchair/ergometer system consists of a customized pedal driven wheelchair/ergometer and a BMI algorithm that detects the patient's intention. The patient's desire to move his (or her) legs is recognized from electroencephalography using current source density estimation and a support vector machine classifier. These recognition results are used for autonomous driving of the wheelchair/ergometer. The patient's feet are fixed to the pedals so that they move with the rotation of the wheels (pedals). Preliminary testing using this prototype system with an unimpaired subject showed that the discrimination accuracy between 'pedaling state' and 'resting state' was over 80% even in real-time operation.

Layer specific neuronal activities in response to visual object images in the inferior temporal cortex revealed by current source density analysis

Layer specific neuronal activities in response to visual object images in the inferior temporal cortex revealed by current source density analysis

Modeling the LFP footprint of unitary thalamic inputs to sensory cortex

The depth-resolved synaptic local-field potential (LFP) footprint in sensory cortex following firing in individual thalamic projection neurons can be accurately measured by averaging cortical multielectrode (ME) LFP signals over thousands of spontaneous thalamic firing events [1,2]. This spike-triggered LFP method offers a unique window into the thalamocortical connection. However, the interpretation of the detailed spatiotemporal profile of this LFP footprint is not trivial as the LFP signal reflects a weighted sum over contributions from all dendritic transmembrane currents located in the vicinity of the recording electrode [3]. We here present results from a biophysically detailed computational study of this LFP footprint, focusing on the thalamocortical LFP response in layer 4 of rodent barrel cortex [1]. As illustrated in Fig. ​Fig.1,1, the model considers large populations of synaptically activated RS (regular spiking cells) and/or FS (fast-spiking cells) (Fig. 1AB). The computational model, implemented in Python with NEURON, is constrained to predict plausible intracellular EPSCs [4] (Fig. ​(Fig.1C)1C) and EPSPs (Fig. ​(Fig.1D).1D). The model not only predicts the LFP (Fig. ​(Fig.1E),1E), but also the ground-truth CSD (current source-density) (Fig. ​(Fig.1F)1F) that can be used to test CSD estimation methods [5]. Candidate models mimicking experimental findings [1,2] will be presented. Figure 1 Model overview. A. Schematic of recording of unitary thalamic projection pattern to layer 4 with thalamic single-unit electrode (el.e), cortical clamp electrodes (el.i) and cortical multielectrode (ME) [1,2,4]. B. Example model population of reconstructed ...

Decreased cortical connectivity and information flow in type 1 diabetes

Objective To investigate the effect of type 1 diabetes on EEG connectivity and information flow and study the relationship between these parameters and electrophysiological, neuropsychological and clinical variables. Methods Connectivity was assessed using several measures (phase coherence, phase lag index, synchronization likelihood and phase slope index) on 119 patients and 61 healthy controls over several frequency bands (between 0.5 and 45 Hz). Data was further correlated to EEG power, event related potentials, neuropsychological function and demographic variables. Results Multivariate test on the connectivity data showed a difference between patients and controls both with mastoid reference ( p < 0.01) and current source density estimates ( p < 0.04). Connectivity and information flow correlated with EEG power but not with event related potentials or neuropsychological function. Conclusions Connectivity and information flow are decreased in diabetes. These variables assess other functions of the brain than captured by the present cognitive tests. Several tests need to be performed in order to monitor the effect of diabetes on brain function. Significance The decrease in connectivity and cortical information flow are EEG abnormalities that add to the previously described EEG and ERP abnormalities described for type 1 diabetes.

Micro-Electric Imaging: Inverse Solution for Localization of Single Neuron Currents Based on Extracellular Potential Measurements

We have introduced the first inverse current source density estimation method directly designed to extracellular action potentials. Based on a priori knowledge of the anatomical and biophysical properties of the cell, a parametric model of the current sources was constructed which is simple enough to estimate the corresponding parameters, but still provides accurate and general description of the system.