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Xiaoli Li

9 papers in the library · 60 citations · publishing 2020-2026

Papers

Human high-order thalamic nuclei gate conscious perception through the thalamofrontal loop.

Science (New York, N.Y.) April 4, 2025 Zepeng Fang, Yuanyuan Dang, An'An Ping et al. 45 citations

The intralaminar and medial thalamic nuclei act as a gate to drive prefrontal cortex activity during the emergence of conscious perception. In patients with implanted electrodes performing a visual consciousness task, these nuclei showed earlier and stronger consciousness-related activity compared to ventral nuclei and prefrontal cortex. Transient thalamofrontal neural synchrony and cross-frequency coupling were driven by the θ phase of the intralaminar and medial nuclei during conscious perception.

State-related Electroencephalography Microstate Complexity during Propofol- and Esketamine-induced Unconsciousness.

Anesthesiology May 1, 2024 Zhenhu Liang, Bo Tang, Yu Chang et al. 13 citations

Two new measures of EEG microstate complexity—type I, quantifying randomness, and type II, quantifying fluctuation complexity—track anesthetic-induced unconsciousness independently of the drug used (propofol or esketamine). In 20 patients, type I complexity increased from wakefulness to unconsciousness and decreased upon recovery, while type II complexity showed the opposite pattern. Both measures changed significantly under both anesthetics, suggesting they reflect the state of consciousness rather than the specific drug. These complexity measures may serve as state-related neural correlates of consciousness during general anesthesia.

A practical measure of integrated information reveals alpha-band activity and the posterior cortex as neural correlates of arousal.

Neuroimage July 18, 2025 Xin Wen, Yu Chang, Sijie Li et al. 1 citation

A new measure called Φcopula, which uses a Gaussian copula approach to estimate integrated information, outperforms common estimators by maintaining the lowest bias and mean squared error even in non-Gaussian high-dimensional systems. Applied to electroencephalographic data across awake, propofol-induced unresponsive, and NREM sleep states, alpha-band Φcopula significantly decreased during both anesthesia and sleep. Φcopula-based classifiers distinguished arousal states more accurately than functional connectivity and network efficiency measures. The dorsal attention network and default mode network contributed most to Φcopula, with the cingulate and posterior cortices showing the greatest contributions. The posterior cortex, especially the posterior cingulate cortex, appears critical for arousal-related information integration and consciousness.

Intracranial neural representation of phenomenal and access consciousness in the human brain

bioRxiv April 8, 2024 Zepeng Fang, Yuanyuan Dang, Xiaoli Li et al. 1 citation preprint

Consciousness-related neural activity in the human brain can be separated into two processes: phenomenal consciousness (early, brief awareness) and access consciousness (later, reportable awareness). Using electrodes implanted in epilepsy patients, researchers found that visual awareness-related brain signals appeared at two distinct latencies—short and long—that originate from different brain regions, except in the lateral prefrontal cortex, where both types mix. Early activity was confined to the side of the brain opposite the visual stimulus, while late activity appeared on both sides. Information flowed from early to late sites, supporting a two-stage model of conscious perception and providing the first direct evidence from intracranial recordings for this division.

Esketamine Preserves Network Connectivity and Promotes Recovery in Consciousness Disorders.

CNS Neurosci Ther May 1, 2026 Xuewei Qin, Xuanling Chen, Lan Yao et al.

In patients with disorders of consciousness, the anesthetic esketamine preserved brain electrical complexity and gamma-band functional connectivity better than propofol during spinal cord stimulator implantation. Esketamine was linked to faster recovery of spontaneous breathing (12 vs. 17 minutes on average) and a lower need for blood pressure support during surgery. At three months, esketamine was associated with significantly improved consciousness outcomes. The findings suggest esketamine may offer neuroprotective benefits in this population.

Distinct effects of global signal regression on brain activity during propofol and sevoflurane anesthesia.

Frontiers in Neuroscience January 1, 2025 Fa Lu, Lunxu Li, Juan Wang et al.

Global signal regression (GSR), a common preprocessing step in fMRI analysis, affects brain activity patterns differently depending on the anesthetic agent used. Using fMRI data from patients under general anesthesia, the work shows that GSR alters specific network connections under propofol but broadly reduces connectivity differences under sevoflurane. Network topology analyses reveal that GSR minimally affects propofol-induced changes in graph theoretical measures but significantly diminishes sevoflurane-related network alterations. These findings indicate that GSR's impact on functional brain organization is anesthetic-specific, with sevoflurane-induced changes being particularly sensitive to global signal removal. The results suggest that GSR should be applied cautiously when comparing different anesthetic agents.

Differential engagement of thalamic nuclei orchestrates consciousness states across anesthesia, sleep, and disorders of consciousness.

Communications Biology December 18, 2025 Fa Lu, Juan Wang, Xuewei Qin et al.

Altered consciousness—from anesthesia and sleep to disorders of consciousness—involves distinct changes in thalamic nuclei. Analyzing fMRI data across these states, the authors found that propofol anesthesia disrupted pulvinar-cortical connections, sleep transitions affected specific nuclei (VLp, medial geniculate, centromedian), and disorders of consciousness showed widespread disconnections. Five key nuclei showed state-specific alterations, with higher-order nuclei (pulvinar, centromedian, mediodorsal) more consistently involved. Decreased local brain signal complexity occurred in 4–6 nuclei during anesthesia and 4–5 in patients with disorders of consciousness. The coupling between local fluctuation and connectivity varied systematically with consciousness state, suggesting potential therapeutic targets.

Intracranial neural representation of phenomenal and access consciousness in the human brain.

Neuroimage August 15, 2024 Zepeng Fang, Yuanyuan Dang, Xiaoli Li et al.

Neural activity related to visual awareness shows a bimodal latency distribution, with early and late responses in distinct brain regions, except in the lateral prefrontal cortex (lPFC), which contains both. This suggests the lPFC links phenomenal and access consciousness, though the division is not as simple as previously thought. In four patients with bilateral prefrontal electrodes, early awareness-related activity was only on the opposite side of the brain, while late activity appeared on both sides. Information flowed from early to late sites. These intracranial recordings provide the first local field potential evidence for distinct neural correlates of phenomenal and access consciousness, clarifying their spatiotemporal dynamics.

Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine

IEEE transactions on neural systems and rehabilitation engineering January 8, 2020 Zhenhu Liang, Shuai Shao, Zhe Lv et al.

A framework using electroencephalography (EEG) and nonlinear analysis methods can differentiate four states of consciousness: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. Permutation entropy (PE) distinguished all four states. Altered contents of consciousness were best differentiated by sample entropy (SampEn) and permutation Lempel-Ziv complexity (PLZC), while levels of consciousness were best differentiated by relative power of Gamma and PE. A multi-dimensional index combining PE, PLZC, SampEn, and detrended fluctuation analysis (DFA) achieved 92.3% classification accuracy using a genetic algorithm-based support vector machine (GA-SVM), outperforming random forest and neural networks. A multivariable linear regression model constructed coordinate values for level and content dimensions.