A novel method for sparse dynamic functional connectivity analysis from resting-state fMRI.

BACKGROUND: There is growing interest in understanding the dynamic functional connectivity (DFC) between distributed brain regions. However, it remains challenging to reliably estimate the temporal dynamics from resting-state functional magnetic resonance imaging (rs-fMRI) due to the limitations of current methods. NEW METHODS: We propose a new model called HDP-HSMM-BPCA for sparse DFC analysis of high-dimensional rs-fMRI data, which is a temporal extension of probabilistic principal component analysis using Bayesian nonparametric hidden semi-Markov model (HSMM). Specifically, we utilize a hierarchical Dirichlet process (HDP) prior to remove the parametric assumption of the HMM framework, overcoming the limitations of the standard HMM. An attractive superiority is its ability to automatically infer the state-specific latent space dimensionality within the Bayesian formulation. RESULTS: The experiment results of synthetic data show that our model outperforms the competitive models with relatively higher estimation accuracy. In addition, the proposed framework is applied to real rs-fMRI data to explore sparse DFC patterns. The findings indicate that there is a time-varying underlying structure and sparse DFC patterns in high-dimensional rs-fMRI data. COMPARISON WITH EXISTING METHODS: Compared with the existing DFC approaches based on HMM, our method overcomes the limitations of standard HMM. The observation model of HDP-HSMM-BPCA can discover the underlying temporal structure of rs-fMRI data. Furthermore, the relevant sparse DFC construction algorithm provides a scheme for estimating sparse DFC. CONCLUSION: We describe a new computational framework for sparse DFC analysis to discover the underlying temporal structure of rs-fMRI data, which will facilitate the study of brain functional connectivity.

NHSMM-MAR-sdNC: A novel data-driven computational framework for state-dependent effective connectivity analysis.

The brain exhibits intrinsic dynamics characterized by spontaneous spatiotemporal reorganization of neural activity or metastability, which is associated closely with functional integration and segregation. Compared to dynamic functional connectivity, state-dependent effective connectivity (i.e., dynamic effective connectivity) is more suitable for exploring the metastability as its ability to infer causalities between brain regions. However, methods for state-dependent effective connectivity are scarce and urgently needed. In this study, a novel data-driven computational framework, named NHSMM-MAR-sdNC integrating nonparametric hidden semi-Markov model combined with multivariate autoregressive model and state-dependent new causality, is proposed to investigate the state-dependent effective connectivity. The framework is not constrained by any biological assumptions. Furthermore, state number can be inferred from the observed data directly and the state duration distributions will be estimated explicitly rather than restricted by geometric form, which overcomes limitations of hidden Markov model. Experimental results of synthetic data show that the framework can identify the state number adaptively and the state-dependent causality networks accurately. The dynamics of state-related causality networks are also revealed by the new method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for identifying state-dependent effective connectivity, which will facilitate the identification and assessment of metastability and itinerant dynamics of the brain.

Ladder-Type Redox-Active Polymer Achieves Ultra-stable and Fast Proton Storage in Aqueous Proton Batteries.

Affordable and safe aqueous proton batteries (APBs) with unique "Grotthuss mechanism," are very significant for advancing carbon neutrality initiatives. While organic polymers offer a robust and adaptable framework that is well-suited for APB electrodes, the limited proton-storage redox capacity has constrained their broader application. Herein, a ladder-type polymer (PNMZ) has been designed via a covalent cycloconjugation conformational strategy that exhibits optimized electronic structure and fast intra-chain charge transport within the high-aromaticity polymeric skeleton. As a result, the polymer exhibits exceptional proton-storage redox kinetics, which are evidenced by in-operando monitoring techniques and theoretical calculations. It achieves a remarkable proton-storage capacity of 189 mAh g-1 at 2 A g-1 and excellent long-term cycling stability, with approximately 97.8% capacity retention over 10,000 cycles. Finally, a high-performance all-polymer APB device has been successfully constructed with a desirable capacity retention of 99.7% after 6,000 cycles and high energy density of 56.3 Wh kg-1.