Prefrontal hemodynamic changes produced by anodal direct current stimulation.

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that has been investigated for the treatment of many neurological or neuropsychiatric disorders. Its main effect is to modulate the cortical excitability depending on the polarity of the current applied. However, understanding the mechanisms by which these modulations are induced and persist is still an open question. A possible marker indicating a change in cortical activity is the subsequent variation in regional blood flow and metabolism. These variations can be effectively monitored using functional near-infrared spectroscopy (fNIRS), which offers a noninvasive and portable measure of regional blood oxygenation state in cortical tissue. We studied healthy volunteers at rest and evaluated the changes in cortical oxygenation related to tDCS using fNIRS. Subjects were tested after active stimulation (12 subjects) and sham stimulation (10 subjects). Electrodes were applied at two prefrontal locations; stimulation lasted 10 min and fNIRS data were then collected for 20 min. The anodal stimulation induced a significant increase in oxyhemoglobin (HbO(2)) concentration compared to sham stimulation. Additionally, the effect of active 10-min tDCS was localized in time and lasted up to 8-10 min after the end of the stimulation. The cathodal stimulation manifested instead a negligible effect. The changes induced by tDCS on HbO(2), as captured by fNIRS, agreed with the results of previous studies. Taken together, these results help clarify the mechanisms underlying the regional alterations induced by tDCS and validate the use of fNIRS as a possible noninvasive method to monitor the neuromodulation effect of tDCS.

State transitions in physiologic systems: a complexity model for loss of consciousness.

Complex physiologic systems in which the emergent global (observable) behavior results from the interplay among local processes cannot be studied effectively by conventional mathematical models. In contrast to traditional computational methods which provide linear or nonlinear input-output data mapping without regard to the internal workings of the system, complexity theory offers scientifically and computationally tractable models which take into account microscopic mechanisms and interactions responsible for the overall input-output behavior. This article offers a brief introduction to some of the tenets of complexity theory and outlines the process involved in the development and testing of a model that duplicates the global dynamics of the induction of loss of consciousness (LOC) in humans due to cerebral ischemia. Under the broad definition of complexity, we view the brain of humans as a complex system. Successful development of a model for this complex system requires careful combination of basic knowledge of the physiological system both at the local (microscopic) and global (macroscopic) levels with experimental data and the appropriate mathematical tools. It represents an attempt to develop a model that can both replicate human data and provide insights about possible underlying mechanisms.

Model comparison for automatic characterization and classification of average ERPs using visual oddball paradigm.

OBJECTIVE: To determine whether automated classifiers can be used for correctly identifying target categorization responses from averaged event-related potentials (ERPs) along with identifying appropriate features and classification models for computer-assisted investigation of attentional processes. METHODS: ERPs were recorded during a target categorization task. Automated classification of average target ERPs versus average non-target ERPs was performed by extracting different combinations of features from the P300 and N200 components, which were used to train six classifiers: Euclidean classifier (EC), Mahalanobis discriminant (MD), quadratic classifier (QC), Fisher linear discriminant (FLD), multi-layer perceptron neural network (MLP) and support vector machine (SVM). RESULTS: The best classification performance (accuracy: 91-92%; sensitivity: 85-86%; specificity: 95-99%) was provided by QC, MLP, SVM on feature vectors extracted from P300 recorded at multiple sites. In general, non-linear and non-parametric classifiers (QC, MLP, SVM) performed better than linear classifiers (EC, MD, FLD). The N200 did not explain variance beyond that of P300 recorded at multiple sites. CONCLUSIONS: The results suggest that automatic characterization and classification of average target and non-target ERPs is feasible. Features of P300 recorded at multiple sites used to train non-linear classifiers are recommended for optimal classification performance. SIGNIFICANCE: Automatic characterization of target ERPs can provide an objective approach for detecting and diagnosing abnormalities and evaluating interventions for clinical populations, paving the way for future real-time monitoring of attentional processes.

The application of delta modulation to EEG waveforms for database reduction and real-time signal processing.

The large volume of digital data and the demanding processing task involved in electroencephalogram (EEG) analysis place stringent requirements on computer resources in terms of data transfer, computation speed, and temporary or permanent storage. The reduction of the database to a manageable size is therefore necessary for economical use of transmission channels and the storage media. The two criteria, waveform reproducibility and processing applications, must be analyzed and optimized in terms of signal-to-noise ratio (SNR) using the various factors affecting the coding. This analysis and optimization can become cumbersome, and a specialized workstation has been developed specifically for analysis of digital coding. Our interest in data compression stems from the study of the feasibility of predicting pilots' acceleration (Gz) tolerance during flight by processing both their uncoded and coded EEG.

Fractal dynamics of polarized bioelectrodes.

This study is concerned with mathematical modelling of the fundamental relationship which exists between the current density and the overpotential across the metal-solution interface in the linear range using methods of system theory enhanced by 'fractal' concepts. A primer for both 1/f-type scaling and 'anomalous' relaxation/dispersion concepts is provided followed by a brief review of the research history pertinent to the metal electrode polarization dynamics. Next, the 'fractal relaxation systems' approach is introduced to characterize systems which attenuate with a fractional power-low dependence on frequency through a 'scaling exponent'. The 'singularity structure' which is a scaling rational system function is proposed to expand fractal systems in terms of basic subsystems individually representing elementary exponential relaxations and collectively exhibiting scaling properties. We stres that the 'singularity structure' carries scaling information identical to the conventional 'distribution of relaxation times' function. 'Structure scale' and 'view scale' concepts are presented in the due course to streamline the analysis of scaling phenomena in general and the polarization impedance in particular. System theory-wise, the notable result is that the fractional power function attenuation, or equivalently, the logarithmic nature of the distribution function translates into the 'self-similar' pattern replication of the system singularities in the s-plane. The singularity arrangement is governed by a recursive rule solely based on the knowledge of the fractional power factor or the scaling exponent.

Effects of modulated RF energy on the EEG of mammalian brains. Effects of acute and chronic irradiations.

The effects of modulated radio frequency fields on mammalian EEGs were investigated using acute and chronic irradiations at non-thermal level. The EEG signals were computer processed to obtain power spectra. Rabbits were exposed to the field for 2 h a day for 6 weeks at 1-10 MHz (15 Hz modulation) at the level of 0.5-1 kV/M. Silver electrodes placed on the skull surface were used for recording of the EEG. Usually they were removed immediately after initial recordings of the EEG and reinserted before the final and intermediate EEG recordings. With this arrangement, modulated RF fields produced a change in EEG patterns by enhancing the low frequency components and decreasing high frequency activities. On the other hand, acute irradiations did not produce noticeable changes in the EEG at the level of 0.5-1 kV/M (1-30 MHz, 60 Hz modulation) as long as the use of intracranial electrodes was avoided.

Analysis of polarization dynamics by singularity decomposition method.

The driving point immittance (impedance or admittance) function is commonly used in electrical characterization of polarized materials and interfaces. The immittance function typically attenuates following a power function dependence on frequency. This fact has been recognized as a macroscopic dynamical property manifested by strongly interacting dielectric, viscoelastic and magnetic materials and interfaces between different conducting substances. Linear interfacial polarization processes which occur at metal electrode-electrolyte interfaces have been represented by the Fractional Power Pole [FPP] function in single or multiple stages. The FPP function is referred to as the Davidson-Cole function in the dielectrics literature. A related function widely used in mathematical modeling of dielectric and viscoelastic polarization dynamics is the Cole-Cole function. The fractional power factor which parametrizes the FPP or the Davidson-Cole function has been shown earlier to equal the logarithmic ratio of the locations of the pole-zero singularities. In this paper we first review a modified form of the singularity decomposition of the FPP function accomplished within a prescribed error range. The distribution spectrum and the corresponding simulation by a cascade R-C network, as opposed to the synthesis by a ladder R-C network, are readily obtained as the next step in the simulation. The method is then applied to decompose the Cole-Cole function; the pole-zero placement of the singularity function is determined and the equivalent cascade R-C network is synthesized.

Visual evoked potential processing under acceleration stress.

A study of the use of visual evoked potentials to detect acceleration induced blackout is presented. Decisions concerning the presence or absence of the visual evoked potential within the measured EEG were made using detection and estimation-based approaches. The performance of each technique is evaluated in two ways: (a) via a performance index (PI) which is introduced in this work, and (b) by the accuracy of pure detection. Variations of the main processing techniques such as output averaging and decision thresholding were also investigated.