State-related electroencephalographic deviances in attention deficit hyperactivity disorder

The dynamic security region (DSR) of bulk power system has been accepted more and more in recent years for providing plenty of security information and good prospect in online application. This paper compares three linear approximations for the dynamic security region of network-reduction power systems. The three linear approximations are the Q-linear approximation based on the quadratic approximation of stability region, the L-linear approximation based on the linear approximation of stability region and the L0-linear approximation based on the invariant assumption of the normal vector for the boundary of the stability region corresponding to different control variable. The three linear approximations are all obtained with a same critical point lying just on the boundary of dynamic security region. The critical point is searched with numerical simulation. The accuracy of the three linear approximations is compared, using the linear approximation obtained with the curve fitting approach or the actual boundary of DSR searched as the benchmark. Simulation results in IEEE 3-machine 9-bus system and 10-machine 39-bus New England system show that all the three linear approximations display fairly accurate estimation. Furthermore, from the computational viewpoint, the L-linear and the L0-linear method are two alternative choices to approximate the dynamic security region.link_to_subscribed_fulltex

State-related electroencephalographic deviances in attention deficit hyperactivity disorder

Objective: This study aimed to provide more insight into the functional significance of electroencephalographic (EEG) deviances in attention-deficit/hyperactivity disorder (ADHD) by evaluating quantitative EEG during performance on a task with a low activation level and a task tapping top-down executive control. Method: Behavioral performance and EEG activity were compared between 24 adults with ADHD and 20 typically developing adults during a simple slow-paced 2-choice reaction time (2-CRT) task and a moderately paced flanker task. Results: During the slow 2-CRT task, adults with ADHD responded slower, more variably, and tended to make more errors of commission. Although being slower, adults with ADHD showed equally large congruency effects during flanker task performance, indicating intact interference inhibition. In the slow 2-CRT task, (midline) theta and beta power were higher in the ADHD group than in the control group, whereas no significant EEG group differences were observed in the flanker task. A moderate positive correlation between theta power and errors of commission was found in the 2-CRT task for adults with ADHD. Conclusions: Adults with ADHD performed worse on a task inducing a low activation level but showed intact interference inhibition. The EEG findings are in accord with this, showing an abnormal EEG pattern in ADHD only when a low activation level was induced, not when top-down executive control load was high. Time-on-task effects could not explain the group deviances. The findings indicate that EEG deviances in ADHD are task-dependent and may be related to a suboptimal energetic state, rather than impaired top-down executive control

Bioelectric signal analysis and measurement

Nonstationary time series techniques are used to analyze EEG signals for the estimation of alertness. A time varying order is extracted in sequential time series measurement of these data and strategies are devised for obtaining optimal representation of the EEG signal

Complexity Analysis of Spontaneous Brain Activity in Attention-Deficit/Hyperactivity Disorder: Diagnostic Implications

Background: Attention-deficit/hyperactivity disorder (ADHD) is defined as the most common neurobehavioral disorder of childhood, but an objective diagnostic test is not available yet to date. Neurophychological, neuroimaging, and neurophysiological research offer ample evidence of brain and behavioral dysfunctions in ADHD, but these findings have not been useful as a diagnostic test. Methods: Whole-head magnetoencephalographic recordings were obtained from 14 diagnosed ADHD patients and 14 healthy children during resting conditions. Lempel-Ziv complexity (LZC) values were obtained for each channel and child and averaged in five sensor groups: anterior, central, left lateral, right lateral, and posterior. Results: Lempel-Ziv complexity scores were significantly higher in control subjects, with the maximum value in anterior region. Combining age and anterior complexity values allowed the correct classification of ADHD patients and control subjects with a 93% sensitivity and 79% specificity. Control subjects showed an age-related monotonic increase of LZC scores in all sensor groups, while children with ADHD exhibited a nonsignificant tendency toward decreased LZC scores. The age-related divergence resulted in a 100% specificity in children older than 9 years. Conclusions: Results support the role of a frontal hypoactivity in the diagnosis of ADHD. Moreover, the age-related divergence of complexity scores between ADHD patients and control subjects might reflect distinctive developmental trajectories. This interpretation of our results is in agreement with recent investigations reporting a delay of cortical maturation in the prefrontal corte

Motivational context for response inhibition influences proactive involvement of attention

Motoric inhibition is ingrained in human cognition and implicated in pervasive neurological diseases and disorders. The present electroencephalographic (EEG) study investigated proactive motivational adjustments in attention during response inhibition. We compared go-trial data from a stop-signal task, in which infrequently presented stop-signals required response cancellation without extrinsic incentives ("standard-stop"), to data where a monetary reward was posted on some stop-signals ("rewarded-stop"). A novel EEG analysis was used to directly model the covariation between response time and the attention-related N1 component. A positive relationship between response time and N1 amplitudes was found in the standard-stop context, but not in the rewarded-stop context. Simultaneously, average go-trial N1 amplitudes were larger in the rewarded-stop context. This suggests that down-regulation of go-signal-directed attention is dynamically adjusted in the standard-stop trials, but is overridden by a more generalized increase in attention in reward-motivated trials. Further, a diffusion process model indicated that behavior between contexts was the result of partially opposing evidence accumulation processes. Together these analyses suggest that response inhibition relies on dynamic and flexible proactive adjustments of low-level processes and that contextual changes can alter their interplay. This could prove to have ramifications for clinical disorders involving deficient response inhibition and impulsivity

Electroencephalographic field influence on calcium momentum waves

Macroscopic EEG fields can be an explicit top-down neocortical mechanism that directly drives bottom-up processes that describe memory, attention, and other neuronal processes. The top-down mechanism considered are macrocolumnar EEG firings in neocortex, as described by a statistical mechanics of neocortical interactions (SMNI), developed as a magnetic vector potential $\mathbf{A}$. The bottom-up process considered are $\mathrm{Ca}^{2+}$ waves prominent in synaptic and extracellular processes that are considered to greatly influence neuronal firings. Here, the complimentary effects are considered, i.e., the influence of $\mathbf{A}$ on $\mathrm{Ca}^{2+}$ momentum, $\mathbf{p}$. The canonical momentum of a charged particle in an electromagnetic field, $\mathbf{\Pi} = \mathbf{p} + q \mathbf{A}$ (SI units), is calculated, where the charge of $\mathrm{Ca}^{2+}$ is $q = - 2 e$, $e$ is the magnitude of the charge of an electron. Calculations demonstrate that macroscopic EEG $\mathbf{A}$ can be quite influential on the momentum $\mathbf{p}$ of $\mathrm{Ca}^{2+}$ ions, in both classical and quantum mechanics. Molecular scales of $\mathrm{Ca}^{2+}$ wave dynamics are coupled with $\mathbf{A}$ fields developed at macroscopic regional scales measured by coherent neuronal firing activity measured by scalp EEG. The project has three main aspects: fitting $\mathbf{A}$ models to EEG data as reported here, building tripartite models to develop $\mathbf{A}$ models, and studying long coherence times of $\mathrm{Ca}^{2+}$ waves in the presence of $\mathbf{A}$ due to coherent neuronal firings measured by scalp EEG. The SMNI model supports a mechanism wherein the $\mathbf{p} + q \mathbf{A}$ interaction at tripartite synapses, via a dynamic centering mechanism (DCM) to control background synaptic activity, acts to maintain short-term memory (STM) during states of selective attention.Comment: Final draft. http://ingber.com/smni14_eeg_ca.pdf may be updated more frequentl

Protocol for electrophysiological monitoring of carotid endarterectomies.

Near zero stroke rates can be achieved in carotid endarterectomy (CEA) surgery with selective shunting and electrophysiological neuromonitoring. though false negative rates as high as 40% have been reported. We sought to determine if improved training for interpretation of the monitoring signals can advance the efficacy of selective shunting with electrophysiological monitoring across multiple centers, and determine if other factors could contribute to the differences in reports. Processed and raw beta band (12.5-30 Hz) electroencephalogram (EEG) and median and tibial nerve somatosensory evoked potentials (SSEP) were monitored in 668 CEA cases at six surgical centers. A decrease in amplitude of 50% or more in any EEG or SSEP channel was the criteria for shunting or initiating a neuroprotective protocol. A reduction of 50% or greater in the beta band of the EEG or amplitude of the SSEP was observed in 150 cases. No patient showed signs of a cerebral infarct after surgery. Selective shunting based on EEG and SSEP monitoring can reduce CEA intraoperative stroke rate to a near zero level if trained personnel adopted standardized protocols. We also found that the rapid administration of a protective stroke protocol by attending anesthesiologists was an important aspect of this success rate

Delay Parameter Selection in Permutation Entropy Using Topological Data Analysis

Permutation Entropy (PE) is a powerful tool for quantifying the predictability of a sequence which includes measuring the regularity of a time series. Despite its successful application in a variety of scientific domains, PE requires a judicious choice of the delay parameter $\tau$. While another parameter of interest in PE is the motif dimension $n$, Typically $n$ is selected between $4$ and $8$ with $5$ or $6$ giving optimal results for the majority of systems. Therefore, in this work we focus solely on choosing the delay parameter. Selecting $\tau$ is often accomplished using trial and error guided by the expertise of domain scientists. However, in this paper, we show that persistent homology, the flag ship tool from Topological Data Analysis (TDA) toolset, provides an approach for the automatic selection of $\tau$. We evaluate the successful identification of a suitable $\tau$ from our TDA-based approach by comparing our results to a variety of examples in published literature