Idiopathic generalized epilepsy (IGE) has been linked with disrupted intra-network connectivity of multiple resting-state networks (RSNs); however, whether impairment is present in inter-network interactions between RSNs, remains largely unclear. Here, 50 patients with IGE characterized by generalized tonic-clonic seizures (GTCS) and 50 demographically matched healthy controls underwent resting-state fMRI scans. A dynamic method was implemented to investigate functional network connectivity (FNC) in patients with IGE-GTCS. Specifically, independent component analysis was first carried out to extract RSNs, and then sliding window correlation approach was employed to obtain dynamic FNC patterns. Finally, k-mean clustering was performed to characterize six discrete functional connectivity states, and state analysis was conducted to explore the potential alterations in FNC and other dynamic metrics. Our results revealed that state-specific FNC disruptions were observed in IGE-GTCS and the majority of aberrant functional connectivity manifested itself in default mode network. In addition, temporal metrics derived from state transition vectors were altered in patients including the total number of transitions across states and the mean dwell time, the fraction of time spent and the number of subjects in specific FNC state. Furthermore, the alterations were significantly correlated with disease duration and seizure frequency. It was also found that dynamic FNC could distinguish patients with IGE-GTCS from controls with an accuracy of 77.91% (P < 0.001). Taken together, this study not only provided novel insights into the pathophysiological mechanisms of IGE-GTCS but also suggested that the dynamic FNC analysis was a promising avenue to deepen our understanding of this disease. Hum Brain Mapp 38:957-973, 2017. © 2016 Wiley Periodicals, Inc.

Recent research has shown that social anxiety disorder (SAD) is accompanied by abnormalities in brain functional connections. However, these findings are based on group comparisons, and, therefore, little is known about whether functional connections could be used in the diagnosis of an individual patient with SAD. Here, we explored the potential of the functional connectivity to be used for SAD diagnosis. Twenty patients with SAD and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. The whole brain was divided into 116 regions based on automated anatomical labeling atlas. The functional connectivity between each pair of regions was computed using Pearson's correlation coefficient and used as classification feature. Multivariate pattern analysis was then used to classify patients from healthy controls. The pattern classifier was designed using linear support vector machine. Experimental results showed a correct classification rate of 82.5 % (p < 0.001) with sensitivity of 85.0 % and specificity of 80.0 %, using a leave-one-out cross-validation method. It was found that the consensus connections used to distinguish SAD were largely located within or across the default mode network, visual network, sensory-motor network, affective network, and cerebellar regions. Specifically, the right orbitofrontal region exhibited the highest weight in classification. The current study demonstrated that functional connectivity had good diagnostic potential for SAD, thus providing evidence for the possible use of whole brain functional connectivity as a complementary tool in clinical diagnosis. In addition, this study confirmed previous work and described novel pathophysiological mechanisms of SAD.

Previous studies have demonstrated that the use of integrated information from multi-modalities could significantly improve diagnosis of Alzheimer's Disease (AD). However, feature selection, which is one of the most important steps in classification, is typically performed separately for each modality, which ignores the potential strong inter-modality relationship within each subject. Recent emergence of multi-task learning approach makes the joint feature selection from different modalities possible. However, joint feature selection may unfortunately overlook different yet complementary information conveyed by different modalities. We propose a novel multi-task feature selection method to preserve the complementary inter-modality information. Specifically, we treat feature selection from each modality as a separate task and further impose a constraint for preserving the inter-modality relationship, besides separately enforcing the sparseness of the selected features from each modality. After feature selection, a multi-kernel Support Vector Machine (SVM) is further used to integrate the selected features from each modality for classification. Our method is evaluated using the baseline PET and MRI images of subjects obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our method achieves a good performance, with an accuracy of 94.37% and an Area Under the ROC Curve (AUC) of 0.9724 for AD identification, and also an accuracy of 78.80% and an AUC of 0.8284 for Mild Cognitive Impairment (MCI) identification. Moreover, the proposed method achieves an accuracy of 67.83% and an AUC of 0.6957 for separating between MCI converters and MCI nonconverters (to AD). These performances demonstrate the superiority of the proposed method over the state-of-the-art classification methods.

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These findings suggest that the neurovascular decoupling in the brain may be a possible neuropathological mechanism of schizophrenia.

Whether a metallic ground state exists in a two-dimensional system beyond Anderson localization remains an unresolved question. We studied how quantum phase coherence evolves across superconductor–metal–insulator transitions through magnetoconductance quantum oscillations in nanopatterned high-temperature superconducting films. We tuned the degree of phase coherence by varying the etching time of our films. Between the superconducting and insulating regimes, we detected a robust intervening anomalous metallic state characterized by saturating resistance and oscillation amplitude at low temperatures. Our measurements suggest that the anomalous metallic state is bosonic and that the saturation of phase coherence plays a prominent role in its formation.

Superconductivity is a fascinating quantum phenomenon characterized by zero electrical resistance and the Meissner effect. To date, several distinct families of superconductors (SCs) have been discovered. These include three-dimensional (3D) bulk SCs in both inorganic and organic materials as well as two-dimensional (2D) thin film SCs but only in inorganic materials. Here we predict superconductivity in 2D and 3D organic metal-organic frameworks by using first-principles calculations. We show that the highly conductive and recently synthesized Cu-benzenehexathial (BHT) is a Bardeen-Cooper-Schrieffer SC. Remarkably, the monolayer Cu-BHT has a critical temperature (T) of 4.43 K, while T of bulk Cu-BHT is 1.58 K. Different from the enhanced T in 2D inorganic SCs which is induced by interfacial effects, the T enhancement in this 2D organic SC is revealed to be the out-of-plane soft-mode vibrations, analogous to surface mode enhancement originally proposed by Ginzburg. Our findings not only shed new light on better understanding 2D superconductivity but also open a new direction to search for SCs by interface engineering with organic materials.

Neural oscillations are the intrinsic characteristics of brain activities. Traditional electrophysiological techniques (e.g., the steady-state evoked potential, SSEP) have provided important insights into the mechanisms of neural oscillations in the high frequency ranges (>1 Hz). However, the neural oscillations within the low frequency ranges (<1 Hz) and deep brain areas are rarely examined. Based on the advantages of the low frequency blood oxygen level dependent (BOLD) fluctuations, we expected that the steady-state BOLD responses (SSBRs) would be elicited and modulate low frequency neural oscillations. Twenty six participants completed a simple reaction time task with the constant stimuli frequencies of 0.0625 Hz and 0.125 Hz. Power analysis and hemodynamic response function deconvolution method were used to extract SSBRs and recover neural level signals. The SSEP-like waveforms were observed at the whole brain level and at several task-related brain regions. Specifically, the harmonic phenomenon of SSBR was task-related and independent of the neurovascular coupling. These findings suggested that the SSBRs represent non-linear neural oscillations but not brain activations. In comparison with the conventional general linear model, the SSBRs provide us novel insights into the non-linear brain activities, low frequency neural oscillations, and neuroplasticity of brain training and cognitive activities.