A double-blind investigation of the relationship between seizure activity and the sleep EEG following EEG biofeedback training

The sleep EEGs of eight medically refractory epileptic patients were examined as part of a double-blind, ABA crossover study designed to determine the effectiveness of EEG biofeedback for the control of seizures. The patients were initially reinforced for one of three EEG criteria recorded from electrodes placed over sensorimotor cortex: (a) suppression of 3- to 7-Hz activity, (b) enhancement of 12- to 15-Hz activity, or (c) simultaneous suppression of 3- to 7-Hz and enhancement of 11- to 19-Hz activity. Reinforcement contingencies were reversed during the second or B phase, and then reinstated in their original form during the final A′ phase. All-night polysomnographic recordings were obtained at the end of each conditioning phase and were subjected to both visual and computer-based power spectral analyses. Four of the patients showed changes in their nocturnal paroxysmal activity that were either partially or totally consistent with the ABA′ contingencies of the study. The spectral data proved difficult to interpret, though two trends emerged from the analyses. Decreases in nocturnal 4- to 7-Hz activity were correlated with decreases in seizure activity, and increases in 8- to 11-Hz activity were correlated with decreases in seizure activity. These findings were shown to strengthen the hypothesis that EEG biofeedback may produce changes in the sleep EEG that are related to seizure incidence.     

Nonlinear EEG analysis based on a neural mass model

The well-known neural mass model described by Lopes da Silva et al. (1976) and Zetterberg et al. (1978) is fitted to actual EEG data. This is achieved by reformulating the original set of integral equations as a continuous-discrete state space model. The local linearization approach is then used to discretize the state equation and to construct a nonlinear Kalman filter. On this basis, a maximum likelihood procedure is used for estimating the model parameters for several EEG recordings. The analysis of the noise-free differential equations of the estimated models suggests that there are two different types of alpha rhythms: those with a point attractor and others with a limit cycle attractor. These attractors are also found by means of a nonlinear time series analysis of the EEG recordings. We conclude that the Hopf bifurcation described by Zetterberg et al. (1978) is present in actual brain dynamics. Received: 11 August 1997 / Accepted in revised form: 20 April 1999     

A unifying theory on the relationship between spike trains,EEG, and ERP based on the noise shaping/predictive neural coding hypothesis

Cracking the neural code has long been a central issue in neuroscience. However, it has been proved difficult because there logically exist an infinite number of other models and interpretations that could account for the same data and phenomena (i.e. the problem of underdetermination). Therefore, I suggest that applying biologically realistic multiple constraints from ion-channel level to system level (e.g. cognitive neuroscience and human brain disorders) can only solve the problem of underdetermination. Here I have explored whether the noise shaping/predictive neural coding hypothesis can provide a unified view on following realistic multiple constraints: (1) cortical gain control mechanisms in vivo; (2) the relationships between acetylcholine, nicotine, dopamine, calcium-activated potassium ion-channel, and cognitive functions; (3) oscillations and synchrony; (4) why should spontaneous activity be irregular; (5) whether the cortical neurons in vivo are coincidence detectors or integrators; and (6) the causal relationship between theta oscillation, gamma band fluctuation, and P3 (or P300) ERP responses. Finally, recent experimental results supporting the unified view shall be discussed.     

Quantitative analysis of the relationship between structure and antioxidant activity of tripeptides

Peptides from enzymatic hydrolysates of food proteins exhibit significant antioxidant activity. Several studies have attempted to determine the factors contributing to the antioxidant activity of peptides; however, the physicochemical properties and factors essential for the antioxidant activity of peptides are still unclear. In this study, in order to clarify the factors important for peptide antioxidant activity based on the properties of component amino acids, 55 tripeptides were synthesized from 20 natural amino acids and their antioxidant activity was measured using the Trolox equivalent antioxidant capacity (TEAC) assay system. The tripeptides were divided into two data sets: a training set comprising 50 compounds and a validated set comprising five compounds. The structure‐activity relationship of the training set was then analyzed using classical quantitative structure‐activity relationship (QSAR) analysis. The study findings demonstrate that the presence of a cysteine residue at any position, an aromatic amino acid at the C‐terminus, higher hydrophobicity of the N‐terminal residue, and smaller HOMO‐LUMO energy gap of the middle residue can significantly enhance the antioxidant activity. The activities of the five validated compounds were predicted using the constructed QSAR model, and a good correlation between measured and predicted activities was observed. The information obtained from the QSAR model could be useful for effective production of antioxidant peptides from food proteins such as egg white proteins.     

Quantitative analysis of the relationship between nucleotide sequence and functional activity.

Matrices can be used to evaluate sequences for functional activity. Multiple regression can solve for the matrix that gives the best fit between sequence evaluations and quantitative activities. This analysis shows that the best model for context effects on suppression by su2 involves primarily the two nucleotides 3' to the amber codon, and that their contributions are independent and additive. Context effects on 2AP mutagenesis also involve the two nucleotides 3' to the 2AP insertion, but their effects are not independent. In a construct for producing beta-galactosidase, the effects on translational yields of the tri-nucleotide 5' to the initiation codon are dependent on the entire triplet. Models based on these quantitative results are presented for each of the examples.     

Real-time analysis of hippocampal neural activity in the intervals between interictal spikes

Previously the electroencephalogram (EEG) was modeled as consisting of faster, smaller waves superimposed on larger, slower waves. The intent of this study is to modify the program to sample neural activity over specific intervals of time following detection of a distinct wave pattern. The use of conditional sampling is illustrated by considering wave detection following epileptic interictal spikes in the rabbit hippocampus. To create an epileptic focus, small pellets of sodium penicillin suspended in agar were placed on the rabbit hippocampus. This produced regularly recurring, spontaneous, large amplitude discharges, or interictal spikes, at the site of application. Following detection of an interictal spike, the program delayed the onset of a sample period for either 1.0 s or 6.0 s. The neural activity was then sampled for 5.1 s, and fast and slow waves were detected over the sample period. The frequency distribution of waves in four of these 5.1 s intervals was calculated. Comparison of the frequency distributions following the 1.0 s and 6.0 s delays showed no discernible differences. The data illustrate that not all types of neural activity are markedly modified by interictal spikes and suggest that hippocampal cellular populations generate similar waves 1.0 s and 6.0 s after such a spike. Moreover, this experiment illustrates adaptation of the program to sample activity over a limited period of time following detection of a specific cortical waveform.     

On the relationship between two models of neural entrainment

A comparison is established between the two models of neural entrainment proposed by Nagumo and Sato (1972) and by Torras (1986). Both are formulated in terms of a transition equation whose solutions of period m correspond to the different (m:r)-entrainment patterns. The existence of a transformation relating both transition equations implies the equivalence between stable entrainment for the former model and unstable entrainment for the latter one. However, there is no counterpart for the stable entrainment arising in Torras' model.     

The relationship between short-term memory capacity and EEG power spectral density

Multiplying memory span by mental speed, we obtain the information entropy of short-term memory capacity, which is rate-limiting for cognitive functions and corresponds with EEG power spectral density. The number of EEG harmonics (n = 1, 2,, 9) is identical with memory span, and the eigenvalues of the EEG impulse response are represented by the zero-crossings up to the convolved fundamental, the P300. In analogy to quantum mechanics the brain seems to be an ideal detector simply measuring the energy of wave forms. No matter what the stimulus is and how the brain behaves, the metric of signal and memory can always be understood as a superposition of n states of different energy and their eigenvalues.     

Theoretical relationship between the optimal models of the vascular tree

Two models of optimal branching structure of the vascular tree are compared. Murray’s minimum work model derived from minimum energy loss due to flow and volume in the duct system is proved to be included as a mathematical group in the authors’ model defined by the minimum volume under determinant pressure, flow and position at the terminals. The problem about heterotypical trees which are identical at the terminal conditions but different in the topological order of branch combinations are discussed, applying the results of analyses on the equivalent duct of uniform terminal pressure trees. It is proved that the minimum work tree has the least energy loss compared with its heterotypical minimum volume trees and is a better model of branching structure of the vascular tree.     

Adaptive kernels and transfer entropy for neural connectivity analysis in EEG signals

This paper aims at better understanding causal relationships between parts of the brain during epileptic seizures. Our objective is to detect effective connectivity, i.e. to discover whether neural activity in a given substructure influences activity in another part of the brain. Recent efforts have been devoted to develop nonlinear and nonparametric approaches, such as transfer entropy (TE), to overcome linear methods limitations. However, building efficient TE estimators still asks open questions. In this study, we propose a new strategy to improve TE estimation by introducing different nonparametric adaptive kernel density estimators. Among all techniques under study, the Gaussian adaptive kernel density estimator based approach presents the best behavior whatever the tested model (autoregressive or physiological model).     

The relationship between peptide structure and antibacterial activity

Cationic antimicrobial peptides are a class of small, positively charged peptides known for their broad-spectrum antimicrobial activity. These peptides have also been shown to possess anti-viral and anti-cancer activity and, most recently, the ability to modulate the innate immune response. To date, a large number of antimicrobial peptides have been chemically characterized, however, few high-resolution structures are available. Structure-activity studies of these peptides reveal two main requirements for antimicrobial activity, (1) a cationic charge and (2) an induced amphipathic conformation. In addition to peptide conformation, the role of membrane lipid composition, specifically non-bilayer lipids, on peptide activity will also be discussed.