Curvature-induced spin-orbit coupling and spin relaxation in a chemically clean single-layer graphene

The study of spin-related phenomena in materials requires knowledge on the precise form of effective spin-orbit coupling of conducting carriers in the solid-states systems. We demonstrate theoretically that curvature induced by corrugations or periodic ripples in single-layer graphenes generates two types of effective spin-orbit coupling. In addition to the spin-orbit coupling reported previously that couples with sublattice pseudospin and corresponds to the Rashba-type spin-orbit coupling in a corrugated single-layer graphene, there is an additional spin-orbit coupling that does not couple with the pseudospin, which can not be obtained from the extension of the curvature-induced spin-orbit coupling of carbon nanotubes. Via numerical calculation we show that both types of the curvature-induced spin-orbit coupling make the same order of contribution to spin relaxation in chemically clean single-layer graphene with nanoscale corrugation. The spin relaxation dependence on the corrugation roughness is also studied.Comment: 8 pages, 4 figure

Parent Prevention Communication Profiles and Adolescent Substance Use: A Latent Profile Analysis and Growth Curve Model

This current study identifies distinct parent prevention communication profiles and examines whether youth with different parental communication profiles have varying substance use trajectories over time. Eleven schools in two rural school districts in the Midwestern United States were selected, and 784 students were surveyed at three time points from the beginning of 7th grade to the end of 8th grade. A series of latent profile analyses were performed to identify discrete profiles/subgroups of substance-specific prevention communication (SSPC). The results revealed a 4-profile model of SSPC: Active-Open, Passive-Open, Active-Silent, and Passive-Silent. A growth curve model revealed different rates of lifetime substance use depending on the youth’s SSPC profile. These findings have implications for parenting interventions and tailoring messages for parents to fit specific SSPC profiles

Subgraph "backbone" analysis of dynamic brain networks during consciousness and anesthesia.

General anesthesia significantly alters brain network connectivity. Graph-theoretical analysis has been used extensively to study static brain networks but may be limited in the study of rapidly changing brain connectivity during induction of or recovery from general anesthesia. Here we introduce a novel method to study the temporal evolution of network modules in the brain. We recorded multichannel electroencephalograms (EEG) from 18 surgical patients who underwent general anesthesia with either propofol (n = 9) or sevoflurane (n = 9). Time series data were used to reconstruct networks; each electroencephalographic channel was defined as a node and correlated activity between the channels was defined as a link. We analyzed the frequency of subgraphs in the network with a defined number of links; subgraphs with a high probability of occurrence were deemed network "backbones." We analyzed the behavior of network backbones across consciousness, anesthetic induction, anesthetic maintenance, and two points of recovery. Constitutive, variable and state-specific backbones were identified across anesthetic state transitions. Brain networks derived from neurophysiologic data can be deconstructed into network backbones that change rapidly across states of consciousness. This technique enabled a granular description of network evolution over time. The concept of network backbones may facilitate graph-theoretical analysis of dynamically changing networks

Subgraph Backbone Analysis of Dynamic Brain Networks during Consciousness and Anesthesia

General anesthesia significantly alters brain network connectivity. Graph-theoretical analysis has been used extensively to study static brain networks but may be limited in the study of rapidly changing brain connectivity during induction of or recovery from general anesthesia. Here we introduce a novel method to study the temporal evolution of network modules in the brain. We recorded multichannel electroencephalograms (EEG) from 18 surgical patients who underwent general anesthesia with either propofol (n = 9) or sevoflurane (n = 9). Time series data were used to reconstruct networks; each electroencephalographic channel was defined as a node and correlated activity between the channels was defined as a link. We analyzed the frequency of subgraphs in the network with a defined number of links; subgraphs with a high probability of occurrence were deemed network "backbones." We analyzed the behavior of network backbones across consciousness, anesthetic induction, anesthetic maintenance, and two points of recovery. Constitutive, variable and state-specific backbones were identified across anesthetic state transitions. Brain networks derived from neurophysiologic data can be deconstructed into network backbones that change rapidly across states of consciousness. This technique enabled a granular description of network evolution over time. The concept of network backbones may facilitate graph-theoretical analysis of dynamically changing networks.open1112sciescopu

Brain networks and dynamic network backbones for nine subjects and each anesthetic (cross-correlation network).

<p>A unique network is a network with a distinctive connection structure in the given network time series. Network ensemble size is the number of unique network structures appearing in the data. The number of common backbones is the number of commonly found backbones across most subjects (>77%, 7/9 subjects in this study). <i>maxP</i> denotes the maximum occupation probability that the network backbones can have for the pooled data. <i>minP</i> denotes the minimum occupation probability that the network backbones can have. For example, <i>minP</i>(12) means the minimum occupation probability of the top 12 backbones. The <i>minP(#)</i> demonstrates how slowly the occupation probability was reduced as the number of network backbones considered increases. Note that in the anesthetized state the number of unique networks was reduced from baseline, indicating more frequent repetition of the same network and network backbone.</p

The inter-subject similarity of network backbone configurations for different states and anesthetic groups.

<p>a) The similarities of network backbones profiles among subjects were measured by cosine similarity. The network backbone profile for a subject was constructed with 400 backbones and was compared with others in five states of each anesthetic group. To test the significance of the similarity of network backbones, the 400 surrogate data sets for each subject were generated and the similarity of network backbones was calculated (Green bars). Errorbar indicates standard deviation of the similarities for all pairs of subjects. b) The number of unique network backbones for each state (from top 1600 backbones). Errorbar indicates standard error of subject variability. Unique backbones are the backbones that appear only in a certain state for each anesthetic group. The higher number of unique backbones in the baseline state of the sevoflurane group indicates that the other states share fewer backbones with baseline state compared to the propofol group.</p

Bandwidth-specific backbone-state rank diagram for the propofol group.

<p>The network time series are constructed (a) by Pearson correlation coefficient with zero lag (all bands: 0.5∼35 Hz, delta: 0.1∼4.0 Hz, theta: 4.0∼8.0 Hz, alpha: 8.0∼12.0 Hz, beta:12.0∼30.0 Hz) and (b) by mean phase coherence (band-specific) between EEG channels. 4-link backbones are extracted. The top 60 dynamic network backbones are shown in these figures. The network backbones were sorted from constitutive to variable and state-specific backbones in descending order. The darker color means a higher occupation probability of the backbone in a network time series. The network backbones were categorized into groups based on the connection, and presented with colors: prefrontal-frontal connections (green), (pre)frontal-parietal connections (blue), intra-parietal connections (gray), inter-hemispheric connections (purple) and temporal-parietal connections (orange).</p

Demonstration of backbone-state rank diagram for each anesthetic group.

<p>a) Propofol patient group and b) Sevoflurane patient group. The network time series are constructed by Pearson correlation coefficient with zero lag (0.5∼35 Hz) between EEG channels. Only the top 24 dynamic network backbones are shown in these figures (for the sake of readability). The network backbones were sorted from constitutive to variable and state-specific backbones in descending order, clockwise from top center. The wider patch represents a higher occupation probability of the backbone in a network time series. The network backbones were categorized into groups based on the connection, and presented with colors: prefrontal-frontal connections (green), (pre)frontal-parietal connections (blue), intra-parietal connections (gray), inter-hemispheric connections (purple) and temporal-parietal connections (orange).</p

The robustness of network backbones in response to noise contamination.

<p>The noise effect was introduced into 450 simulated network time series in two ways: increasing the random rewiring ratio (from 1% to 20%) for each network and increasing the period of random rewiring (from 0.025% to 75%) for the sequential networks. Dotted lines indicate the standard deviation of tests. Each series has 4,000 sequential networks. Each network has 6 nodes and about 9.5 links on average. About 70% of the network backbones were preserved with the random rewiring ratio of up to 20%. However, the period of random rewiring more significantly affected the original configuration of network backbones. Each color corresponds to the period of noise contamination. The error bar indicates the standard deviation over the 450 simulated network time series.</p

Pseudo-code of dynamic network backbones detection algorithm.

<p>Pseudo-code of dynamic network backbones detection algorithm.</p