Superconductivity of Rb 3 C 60 : breakdown of the Migdal-Eliashberg theory

In this paper, through an exhaustive analysis within the Migdal-Eliashberg theory, we show the incompatibility of experimental data of Rb₃C₆₀ with the basic assumptions of the standard theory of superconductivity. For different models of the electron-phonon spectral function α ² F (Ω) we solve numerically the Eliashberg equations to find which values of the electron-phonon coupling λ, of the logarithmic phonon frequency and of the Coulomb pseudopotential μ ^* reproduce the experimental data of Rb₃C₆₀. We find that the solutions are essentially independent of the particular shape of α ² F (Ω) and that, to explain the experimental data of Rb₃C₆₀, one has to resort to extremely large couplings: λ = 3.0±0.8. This results differs from the usual partial analyses reported up to now and we claim that this value exceeds the maximum allowed λ compatible with the crystal lattice stability. Moreover, we show quantitatively that the obtained values of λ and strongly violate Migdal's theorem and consequently are incompatible with the Migdal-Eliashberg theory. One has therefore to consider the generalization of the theory of superconductivity in the nonadiabatic regime to account for the experimental properties of fullerides. Received 30 March 2001     

Adhesive interfaces of enamel and dentin prepared by air-abrasion at different distances

The purpose of this study was to analyse, by scanning electron microscopy (SEM), the morphology of enamel and dentin/adhesive interfaces in cavities prepared by air-abrasion at different working distances. Thirty sound third human molars were selected and, on both their buccal and lingual surfaces, class V cavities were prepared by air-abrasion, at 2-, 4-, 6-, 8- and 10-mm working distances, or high-speed bur (control group). After preparation, all cavities were etched with 35% phosphoric acid gel and restored with Single Bond/Filtek Z-250. Buccal and lingual surfaces were separated and restorations sectioned in a buccolingual direction, providing two sections of each cavity, which were analysed by scanning electron microscopy. It was observed that the distances of 6 and 8 mm promoted more homogeneous dentin/adhesive interfaces, with tags formation, and more uniform for enamel, which were similar to the control group. It may be concluded that the air-abrasion working distance can influence the morphology of enamel and dentin/adhesive interfaces, and the intermediate distances provided better adhesive interfaces.     

Validating the performance of one-time decomposition for fMRI analysis using ICA with automatic target generation process

Independent component analysis (ICA) has been proven to be effective for functional magnetic resonance imaging (fMRI) data analysis. However, ICA decomposition requires to optimize the unmixing matrix iteratively whose initial values are generated randomly. Thus the randomness of the initialization leads to different ICA decomposition results. Therefore, just one-time decomposition for fMRI data analysis is not usually reliable. Under this circumstance, several methods about repeated decompositions with ICA (RDICA) were proposed to reveal the stability of ICA decomposition. Although utilizing RDICA has achieved satisfying results in validating the performance of ICA decomposition, RDICA cost much computing time. To mitigate the problem, in this paper, we propose a method, named ATGP-ICA, to do the fMRI data analysis. This method generates fixed initial values with automatic target generation process (ATGP) instead of being produced randomly. We performed experimental tests on both hybrid data and fMRI data to indicate the effectiveness of the new method and made a performance comparison of the traditional one-time decomposition with ICA (ODICA), RDICA and ATGP-ICA. The proposed method demonstrated that it not only could eliminate the randomness of ICA decomposition, but also could save much computing time compared to RDICA. Furthermore, the ROC (Receiver Operating Characteristic) power analysis also denoted the better signal reconstruction performance of ATGP-ICA than that of RDICA.     

Regional homogeneity of resting-state fMRI contributes to both neurovascular and task activation variations

The task induced blood oxygenation level dependent signal changes observed using functional magnetic resonance imaging (fMRI) are critically dependent on the relationship between neuronal activity and hemodynamic response. Therefore, understanding the nature of neurovascular coupling is important when interpreting fMRI signal changes evoked via task. In this study, we used regional homogeneity (ReHo), a measure of local synchronization of the BOLD time series, to investigate whether the similarities of one voxel with the surrounding voxels are a property of neurovascular coupling. FMRI scans were obtained from fourteen subjects during bilateral finger tapping (FTAP), digit–symbol substitution (DSST) and periodic breath holding (BH) paradigm. A resting-state scan was also obtained for each of the subjects for 4 min using identical imaging parameters. Inter-voxel correlation analyses were conducted between the resting-state ReHo, resting-state amplitude of low frequency fluctuations (ALFF), BH responses and task activations within the masks related to task activations. There was a reliable mean voxel-wise spatial correlation between ReHo and other neurovascular variables (BH responses and ALFF). We observed a moderate correlation between ReHo and task activations (FTAP: r = 0.32; DSST: r = 0.22) within the task positive network and a small yet reliable correlation within the default mode network (DSST: r = − 0.08). Subsequently, a linear regression was used to estimate the contribution of ReHo, ALFF and BH responses to the task activated voxels. The unique contribution of ReHo was minimal. The results suggest that regional synchrony of the BOLD activity is a property that can explain the variance of neurovascular coupling and task activations; but its contribution to task activations can be accounted for by other neurovascular factors such as the ALFF.     

Separating 4D multi-task fMRI data of multiple subjects by independent component analysis with projection

Independent component analysis (ICA) is a widely accepted method to extract brain networks underlying cognitive processes from functional magnetic resonance imaging (fMRI) data. However, the application of ICA to multi-task fMRI data is limited due to the potential non-independency between task-related components. The ICA with projection (ICAp) method proposed by our group (Hum Brain Mapp 2009;30:417–31) is demonstrated to be able to solve the interactions among task-related components for single subject fMRI data. However, it still must be determined if ICAp is capable of processing multi-task fMRI data over a group of subjects. Moreover, it is unclear whether ICAp can be reliably applied to event-related (ER) fMRI data. In this study, we combined the projection method with the temporal concatenation method reported by Calhoun (Hum Brain Mapp 2008;29:828–38), referred to as group ICAp, to perform the group analysis of multi-task fMRI data. Both a human fMRI rest data-based simulation and real fMRI experiments, of block design and ER design, verified the feasibility and reliability of group ICAp, as well as demonstrated that ICAp had the strength to separate 4D multi-task fMRI data into multiple brain networks engaged in each cognitive task and to adequately find the commonalities and differences among multiple tasks.     

Spatially aggregated multiclass pattern classification in functional MRI using optimally selected functional brain areas

In previous works, boosting aggregation of classifier outputs from discrete brain areas has been demonstrated to reduce dimensionality and improve the robustness and accuracy of functional magnetic resonance imaging (fMRI) classification. However, dimensionality reduction and classification of mixed activation patterns of multiple classes remain challenging. In the present study, the goals were (a) to reduce dimensionality by combining feature reduction at the voxel level and backward elimination of optimally aggregated classifiers at the region level, (b) to compare region selection for spatially aggregated classification using boosting and partial least squares regression methods and (c) to resolve mixed activation patterns using probabilistic prediction of individual tasks. Brain activation maps from interleaved visual, motor, auditory and cognitive tasks were segmented into 144 functional regions. Feature selection reduced the number of feature voxels by more than 50%, leaving 95 regions. The two aggregation approaches further reduced the number of regions to 30, resulting in more than 75% reduction of classification time and misclassification rates of less than 3%. Boosting and partial least squares (PLS) were compared to select the most discriminative and the most task correlated regions, respectively. Successful task prediction in mixed activation patterns was feasible within the first block of task activation in real-time fMRI experiments. This methodology is suitable for sparsifying activation patterns in real-time fMRI and for neurofeedback from distributed networks of brain activation.     

联立薛定谔系统新精确解及其所描述的孤子脉冲和时间孤子

利用拓展的Riccati映射法，讨论了联立薛定谔系统，得到了其新的精确解，并根据所得到的解模拟出孤子脉冲、飞秒孤子和时间孤子，以及时间孤子间的弹性相互作用. 关键词： 联立薛定谔方程 拓展Riccati映射 孤子脉冲 时间孤子     

Electron dynamics in Na and Pt clusters from time-dependent Hartree-Fock theory

We present a method for the numerical investigation of the electron dynamics in small metallic clusters in intense laser fields. We obtain information about collective excitations and relaxation processes in the Na ₉ ⁺ and Pt₃ clusters analyzing the power spectrum of the dipole moment within a mean-field approach. The power spectrum is computed for various laser pulse parameters as well as for the limit of an infinitely short laser pulse. Due to the basis set expansion of the wave function our method is capable to follow the dynamics not only of the whole electron cloud, but of any particular molecular orbital. Received 28 March 2002 / Received in final form 31 May 2002 Published online 24 September 2002 RID="a" ID="a"e-mail: pavlyukh@mpi-halle.de     

静息状态下脑功能连接的磁共振成像研究

静息状态下脑功能连接的磁共振成像研究近年来取得了迅猛发展. 通过对fMRI信号低频涨落成分的同步性分析,可以得到大脑静息态任意脑区的功能连接和多套网络系统,其中“默认网络”的发现可能为人脑固有网络的研究提供新的思路. 而静息态网络与解剖连接之间可能存在的对应,以及在神经精神疾病患者脑中性质和连接的异常改变,使其具有重要的研究和临床应用价值. 该文总结了静息状态功能磁共振成像的主要研究成果,对静息状态脑功能网络的发现和发展、研究方法、各网络及其特点以及在临床方面的应用进行简单的介绍和分析.     

Neural and vascular variability and the fMRI-BOLD response in normal aging

Neural, vascular and structural variables contributing to the blood oxygen level-dependent (BOLD) signal response variability were investigated in younger and older humans. Twelve younger healthy human subjects (six male and six female; mean age: 24 years; range: 19–27 years) and 12 older healthy subjects (five male and seven female; mean age: 58 years; range: 55–71 years) with no history of head trauma and neurological disease were scanned. Functional magnetic resonance imaging measurements using the BOLD contrast were made when participants performed a motor, cognitive or a breath hold (BH) task. Activation volume and the BOLD response amplitude were estimated for the younger and older at both group and subject levels. Mean activation volume was reduced by 45%, 40% and 38% in the elderly group during the motor, cognitive and BH tasks, respectively, compared to the younger. Reduction in activation volume was substantially higher compared to the reduction in the gray matter volume of 14% in the older compared to the younger. A significantly larger variability in the intersubject BOLD signal change occurred during the motor task, compared to the cognitive task. BH-induced BOLD signal change between subjects was significantly less-variable in the motor task-activated areas in the younger compared to older whereas such a difference between age groups was not observed during the cognitive task. Hemodynamic scaling using the BH signal substantially reduced the BOLD signal variability during the motor task compared to the cognitive task. The results indicate that the origin of the BOLD signal variability between subjects was predominantly vascular during the motor task while being principally a consequence of neural variability during the cognitive task. Thus, in addition to gray matter differences, the type of task performed can have different vascular variability weighting that can influence age-related differences in brain functional response.