Search

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components. Furthermore, channel-wise spectral filtering via weighting the sub-band components are implemented jointly with spatial filtering to improve the discriminability of EEG signals, with an l 2-norm regularization term embedded in the objective function to address the underlying over-fitting issue. Finally, sparse Bayesian learning with Gaussian prior is applied to the extracted power features, yielding an RVM classifier. The classification performance of SEOWADE is significantly better than those of several competing algorithms (CSP, FBCSP, CSSP, CSSSP, and shallow ConvNet). Moreover, scalp weight maps of the spatial filters optimized by SEOWADE are more neurophysiologically meaningful. In summary, these results demonstrate the effectiveness of SEOWADE in extracting relevant spatio-temporal information for single-trial EEG classification.

Using elaboration of likelihood model, this study examines how syntactic structure (structural and lexical simplicity) and nonverbal cues (smiling and professional attire) interact to influence crowdfunding performance. By studying 209 crowdfunding projects, we show that both structural simplicity and lexical simplicity are positively related to crowdfunding performance. Further, professional attire strengthens the simplicity and performance relationship. However, smiling shows mixed effects. [ABSTRACT FROM AUTHOR]

Objective To assess the efficacy of long-lasting repeated high-definition transcranial direct current stimulation (HD-tDCS) over the precuneus on consciousness recovery in patients with minimally conscious state (MCS). Methods A prospective case-control study was carried out on 40 MCS patients admitted in the Fifth Affiliated Hospital of Zhengzhou University from June 2018 to January 2021. They were randomly divided into HD-tDCS group (n=20) and control group (n=20). The patients in the control group received routine treatment, while those in the HD-tDCS group were treated with HD-tDCS protocol over the precuneus (at 2 mA, for 20 min per time, twice per day for 14 d, with totally 28 sessions) on the basis of routine treatment. Gender, age, duration of consciousness, Coma Recovery Scale-Revised (CRS-R) scores before (T0), and at a single session (T1), 7 d (T2), and 14 d (T3) were recorded and analyzed. Results The CRS-R score at T3 was significantly higher in the HD-tDCS group than the control group (P < 0.05). In the control group, 2 MCS patients demonstrated a new sign of consciousness at T3, and the CRS-R scores at T1, T2 and T3 were not significantly changed than those at T0. While in the HD-tDCS group, 17 patients showed a new sign of consciousness, and the CRS-R scores at T1, T2, and T3 were obviously increased when compared with those at T0 (P < 0.05). And the scores of visual, auditory and motor subscales at T3 were also statistically elevated than the scores at baseline (P < 0.01). Conclusion Long-lasting repeated HD-tDCS over the precuneus improves the recovery of consciousness in MCS patients at the 14th day of neuromodulation, especially in the visual, auditory and motor aspects of conscious behaviors.

Against the backdrop of increasingly severe global climate change, the risk of rainstorm-induced waterlogging has become the primary threat to the safety of historic and cultural districts worldwide. This paper focuses on the historic and cultural districts of Beijing, China, and explores techniques and methods for identifying extreme rainstorm warnings in cultural heritage areas. Refined warning and forecasting have become important non-engineering measures to enhance these districts' waterlogging prevention control and emergency management capabilities. This paper constructs a rainstorm-induced waterlogging risk warning model tailored for Beijing's historical and cultural districts. This model system encompasses three sets of models: a building waterlogging early-warning model, a road waterlogging early-warning model, and a public evacuation early-warning model. During the construction of the model, the core concepts and determination methods of "1 h rainfall intensity water logging index" and "the waterlogging risk index in historical and cultural districts" were proposed. The construction and application of the three models take into full account the correlation between rainfall intensity and rainwater accumulation, while incorporating the characteristics of flood resilience in buildings, roads, and the society in districts. This allows for a precise grading of warning levels, leading to the formulation of corresponding warning response measures. Empirical tests have shown that the construction method proposed in this paper is reliable. The innovative results not only provide a new perspective and method for the early-warning of rainstorm-induced waterlogging, but also offer scientific support for emergency planning and response in historical and cultural districts. [ABSTRACT FROM AUTHOR]