arXiv subjects

Lihua Zhong

Publications and source records attributed to Lihua Zhong.

3 recordsLinked to original sources

arXiv2026

Theory and Experiment of Chirality-induced Magnetic Nonreciprocity Manifested by Coupling Phase

Magnetic interactions have long served as the most robust and widely used approach for realizing nonreciprocity, with an externally applied magnetic field breaking time-reversal symmetry (TRS) and chiral photon-magnon interactions introducing spatial asymmetry. In this work, we investigate the chirality mechanisms essential for magnetic nonreciprocity from a unified experimental and theoretical perspective. We begin by examining conventional chiral interactions that generate chiral electromagnetic fields through specially designed structures, and then place particular emphasis on synthetic chirality enabled by nontrivial phase accumulation in traveling-wave-mediated coupling systems. We establish a microscopic theoretical framework that maps field polarization onto the phase of a complex coupling strength and validate it with systematic experiments, thereby providing a consistent formalism that describes both conventional and synthetic chirality. Notably, we highlight the symmetry properties and the unique features of synthetic chirality that distinguish it from conventional nonreciprocal mechanisms.

cond-mat.mes-hall↗

arXiv2025

Repetitive TMS-based Identification of Methamphetamine-Dependent Individuals Using EEG Spectra

The impact of repetitive transcranial magnetic stimulation (rTMS) on methamphetamine (METH) users' craving levels is often assessed using questionnaires. This study explores the feasibility of using neural signals to obtain more objective results. EEG signals recorded from 20 METH-addicted participants Before and After rTMS (MBT and MAT) and from 20 healthy participants (HC) are analyzed. In each EEG paradigm, participants are shown 15 METH-related and 15 neutral pictures randomly, and the relative band power (RBP) of each EEG sub-band frequency is derived. The average RBP across all 31 channels, as well as individual brain regions, is analyzed. Statistically, MAT's alpha, beta, and gamma RBPs are more like those of HC compared to MBT, as indicated by the power topographies. Utilizing a random forest (RF), the gamma RBP is identified as the optimal frequency band for distinguishing between MBT and HC with a 90% accuracy. The performance of classifying MAT versus HC is lower than that of MBT versus HC, suggesting that the efficacy of rTMS can be validated using RF with gamma RBP. Furthermore, the gamma RBP recorded by the TP10 and CP2 channels dominates the classification task of MBT versus HC when receiving METH-related image cues. The gamma RBP during exposure to METH-related cues can serve as a biomarker for distinguishing between MBT and HC and for evaluating the effectiveness of rTMS. Therefore, real-time monitoring of gamma RBP variations holds promise as a parameter for implementing a customized closed-loop neuromodulation system for treating METH addiction.

q-bio.NC↗

arXiv2025

NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis

Methamphetamine dependence poses a significant global health challenge, yet its assessment and the evaluation of treatments like repetitive transcranial magnetic stimulation (rTMS) frequently depend on subjective self-reports, which may introduce uncertainties. While objective neuroimaging modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) offer alternatives, their individual limitations and the reliance on conventional, often hand-crafted, feature extraction can compromise the reliability of derived biomarkers. To overcome these limitations, we propose NeuroCLIP, a novel deep learning framework integrating simultaneously recorded EEG and fNIRS data through a progressive learning strategy. This approach offers a robust and trustworthy biomarker for methamphetamine addiction. Validation experiments show that NeuroCLIP significantly improves discriminative capabilities among the methamphetamine-dependent individuals and healthy controls compared to models using either EEG or only fNIRS alone. Furthermore, the proposed framework facilitates objective, brain-based evaluation of rTMS treatment efficacy, demonstrating measurable shifts in neural patterns towards healthy control profiles after treatment. Critically, we establish the trustworthiness of the multimodal data-driven biomarker by showing its strong correlation with psychometrically validated craving scores. These findings suggest that biomarker derived from EEG-fNIRS data via NeuroCLIP offers enhanced robustness and reliability over single-modality approaches, providing a valuable tool for addiction neuroscience research and potentially improving clinical assessments.

eess.SP↗