visual attention, serial dependence, Bayesian inference
18
Scopus Publications
Scopus Publications
Biphasic adaptation of gBOLD-CSF coupling during sleep deprivation reflects compensatory enhancement and temporal disruption in glymphatic function Dai Zhang, Rong Wang, Liqin Zhou, Ke Zhou, Zhentao Zuo, Guochen Sun Neuroimage, 2026 Sleep deprivation (SD) significantly impacts brain function, particularly through disruption of the glymphatic system, an essential mechanism for cerebral metabolic waste clearance dependent on cerebrospinal fluid (CSF) dynamics. Recent advances link CSF flow to global brain activity, measurable via global blood-oxygenation-level-dependent (gBOLD) signals. However, how gBOLD-CSF coupling changes during prolonged wakefulness remains unclear. Using resting-state functional magnetic resonance imaging (rs-fMRI), we investigated how 36-hour sleep deprivation affects gBOLD-CSF coupling in healthy participants. We observed a significant transient increase in gBOLD-CSF coupling strength as sleep deprivation progressed, peaking after approximately 30 h of wakefulness. Importantly, changes in coupling strength correlated quantitatively with heightened subjective sleep pressure but not with vigilance performance. Furthermore, SD induced a temporary phase shift in CSF signal timing relative to gBOLD, indicating disrupted temporal coordination. These results suggest that SD triggers both a transient enhancement and a temporal instability in neuro-fluid coupling, reflecting a biphasic modulation of brain-CSF coupling linked to glymphatic-related dynamics. Our findings reveal novel compensatory adjustments within the glymphatic system during prolonged wakefulness, advancing our understanding of the physiological underpinnings linking sleep loss, metabolic clearance, and brain function, with potential implications for cognitive health and neurodegenerative disease risk.
Inhibitory Decay and Supercritical Brain Dynamics During Sleep Deprivation Dai Zhang, Liqin Zhou, Rong Wang, Yuehua Han, Zhentao Zuo, Yanghua Tian, Ke Zhou Advanced Science, 2026 Sleep deprivation (SD) changes brain‐wide dynamics, but the circuit‐level perturbation that can generate this systems‐level shift remains unclear. We scanned 26 participants at seven time points across 36 h of continuous wakefulness and assessed criticality from resting‐state functional Magnetic Resonance Imaging (rs‐fMRI) blood‐oxygen‐level‐dependent (BOLD) signals using neuronal avalanche metrics (branching ratio and mean avalanche size). The branching ratio increased from 0.98 at baseline to 1.08 after 36 h, indicating a progressive shift from near‐critical to supercritical propagation. Interestingly, the shift was heterogeneous. Visual and sensorimotor networks showed the largest deviations, whereas the limbic network remained close to criticality. Criticality changes tracked accumulated subjective sleep pressure but were largely dissociated from psychomotor vigilance lapses. SD also reshaped functional network organization, with the functional connectivity (FC) degree distribution shifting toward more high‐degree nodes. In a recurrent excitatory–inhibitory network model, gamma‐band power provided an interpretable proxy for effective gain and inhibitory control. Using this proxy, selectively reducing inhibitory efficacy was sufficient to capture the direction of the near‐critical‐to‐supercritical drift and a limbic‐like resilience pattern, supporting inhibitory decay as a plausible candidate circuit‐level mechanism linking SD to large‐scale propagation instability.
Shared and distinct neural signatures of feature and spatial attention Anmin Yang, Jinhua Tian, Wenbo Wang, Liqin Zhou, Ke Zhou Neuroimage, 2025 The debate on whether feature attention (FA) and spatial attention (SA) share a common neural mechanism remains unresolved. Previous neuroimaging studies have identified fronto-parietal-temporal attention-related regions that exhibited consistent activation during various visual attention tasks. However, these studies have been limited by small sample sizes and methodological constraints inherent in univariate analysis. Here, we utilized a between-subject whole-brain machine learning approach with a large sample size ( N = 235 ) to investigate the neural signatures of FA (FAS) and SA (SAS). Both FAS and SAS showed cross-task predictive capabilities, though inter-task prediction was weaker than intra-task prediction, suggesting both shared and distinct mechanisms. Specifically, the frontoparietal network exhibited the highest predictive performance for FA, while the visual network excelled in predicting SA, highlighting their respective prominence in the two attention processes. Moreover, both signatures demonstrated distributed representations across large-scale brain networks, as each cluster within the signatures was sufficient for predicting FA and SA, but none of them were deemed necessary for either FA or SA. Our study challenges traditional network-centric models of attention, emphasizing distributed brain functioning in attention, and provides comprehensive evidence for shared and distinct neural mechanisms underlying FA and SA. • We identified neural signatures for feature attention (FAS) and spatial attention (SAS) using a between-subject whole-brain machine learning approach with a large sample size of 235 participants. • FA and SA exhibited both shared and distinct neural components across whole-brain, network, cluster and voxel levels, revealing the intricate interactions within attentional networks. • The clusters associated with FAS and SAS were sufficient for predicting their respective attention types, but not were individually necessary, supporting the notion of a distributed neural representation for both forms of attention.
Precision-dependent modulation of social attention Wenhui Gao, Changbo Zhu, Bailu Si, Liqin Zhou, Ke Zhou Neuroimage, 2025 Social attention, guided by cues like gaze direction, is crucial for effective social interactions. However, how dynamic environmental context modulates this process remains unclear. Integrating a hierarchical Bayesian model with fMRI, this study investigated how individuals adjusted attention based on the predictions about cue validity (CV). Thirty-three participants performed a modified Posner location-cueing task with varying CV. Behaviorally, individuals' allocation of social attention was finely tuned to the precision (inverse variance) of CV predictions, with the predictions updated by precision-weighted prediction errors (PEs) about the occurrence of target locations. Neuroimaging results revealed that the interaction between allocation of social attention and CV influenced activity in regions involved in spatial attention and/or social perception. Precision-weighted PEs about target locations specifically modulated activity in the temporoparietal junction (TPJ), superior temporal sulcus (STS), and primary visual cortex (V1), underscoring their roles in refining attentional predictions. Dynamic causal modeling (DCM) further demonstrated that enhanced absolute precision-weighted PEs about target locations strengthened the effective connectivity from V1 and STS to TPJ, emphasizing their roles in conveying residual error signals upwards to high-level critical attention areas. These findings emphasized the pivotal role of precision in attentional modulation, enhancing our understanding of context-dependent social attention.
The distinct development of stimulus and response serial dependence Liqin Zhou, Yujie Liu, Yuhan Jiang, Wenbo Wang, Pengfei Xu, Ke Zhou Psychonomic Bulletin and Review, 2024 Serial dependence (SD) is a phenomenon wherein current perceptions are biased by the previous stimulus and response. This helps to attenuate perceptual noise and variability in sensory input and facilitates stable ongoing perceptions of the environment. However, little is known about the developmental trajectory of SD. This study investigates how the stimulus and response biases of the SD effect develop across three age groups. Conventional analyses, in which previous stimulus and response biases were assessed separately, revealed significant changes in the biases over time. Previous stimulus bias shifted from repulsion to attraction, while previous response bias evolved from attraction to greater attraction. However, there was a strong correlation between stimulus and response orientations. Therefore, a generalized linear mixed-effects (GLME) analysis that simultaneously considered both previous stimulus and response, outperformed separate analyses. This revealed that previous stimulus and response resulted in two distinct biases with different developmental trajectories. The repulsion bias of previous stimulus remained relatively stable across all age groups, whereas the attraction bias of previous response was significantly stronger in adults than in children and adolescents. These findings demonstrate that the repulsion bias towards preceding stimuli is established early in the developing brain (at least by around 10 years old), while the attraction bias towards responses is not fully developed until adulthood. Our findings provide new insights into the development of the SD phenomenon and how humans integrate two opposing mechanisms into their perceptual responses to external input during development.
Reviving Bistable Perception in Patients With Depression by Decreasing the Overestimation of Prior Precision Wenbo Wang, Changbo Zhu, Ting Jia, Meidan Zu, Yandong Tang, Liqin Zhou, Yanghua Tian, Bailu Si, Ke Zhou Cognitive Science, 2024 Slower perceptual alternations, a notable perceptual effect observed in psychiatric disorders, can be alleviated by antidepressant therapies that affect serotonin levels in the brain. While these phenomena have been well documented, the underlying neurocognitive mechanisms remain to be elucidated. Our study bridges this gap by employing a computational cognitive approach within a Bayesian predictive coding framework to explore these mechanisms in depression. We fitted a prediction error (PE) model to behavioral data from a binocular rivalry task, uncovering that significantly higher initial prior precision and lower PE led to a slower switch rate in patients with depression. Furthermore, serotonin‐targeting antidepressant treatments significantly decreased the prior precision and increased PE, both of which were predictive of improvements in the perceptual alternation rate of depression patients. These findings indicated that the substantially slower perception switch rate in patients with depression was caused by the greater reliance on top‐down priors and that serotonin treatment's efficacy was in its recalibration of these priors and enhancement of PE. Our study not only elucidates the cognitive underpinnings of depression, but also suggests computational modeling as a potent tool for integrating cognitive science with clinical psychology, advancing our understanding and treatment of cognitive impairments in depression.
The effect of task relevance on serial dependence in numerosity Yujie LIU, Chenmiao LIU, Liqin ZHOU, Ke ZHOU Acta Psychologica Sinica, 2024 摘要： 序列依赖效应反映了当前的知觉体验不仅取决于当下的刺激输入, 还受到近期历史的影响。这一效应对于我们在动态变化的环境中形成相对稳定的知觉至关重要。本研究使用点阵作为刺激材料, 在数量/面积(实验1)或数量/大小(实验2)两个维度上进行正交设计, 旨在通过估计任务探索任务相关性如何影响线性分布特征的序列依赖效应。结果显示无论特征是否与任务相关, 前一试次与当前试次同一特征总会对当前试次的知觉产生相反的影响。对于任务相关特征, 前一试次产生的序列依赖始终为排斥效应。而对于任务无关特征, 如果在当前试次中无关特征对被试的知觉反应有正向预测作用, 则前一试次无关特征产生排斥的序列依赖效应; 反之, 如果在当前试次中无关特征对被试的知觉反应有负向预测, 则前一试次无关特征产生吸引的序列依赖效应。任务相关性对序列依赖效应的影响主要体现在效应幅值的降低。这些发现揭示了线性分布特征的序列依赖效应受任务相关性以及特征本身特性的共同影响, 而无关特征的序列依赖效应则暗示在客体水平也可以产生序列依赖效应。
Leading basic modes of spontaneous activity drive individual functional connectivity organization in the resting human brain Xi Chen, Haoda Ren, Zhonghua Tang, Ke Zhou, Liqin Zhou, Zhentao Zuo, Xiaohua Cui, Xiaosong Chen, Zonghua Liu, Yong He, Xuhong Liao Communications Biology, 2023 Spontaneous activity of the human brain provides a window to explore intrinsic principles of functional organization. However, most studies have focused on interregional functional connectivity. The principles underlying rich repertoires of instantaneous activity remain largely unknown. We apply a recently proposed eigen-microstate analysis to three resting-state functional MRI datasets to identify basic modes that represent fundamental activity patterns that coexist over time. We identify five leading basic modes that dominate activity fluctuations. Each mode exhibits a distinct functional system-dependent coactivation pattern and corresponds to specific cognitive profiles. In particular, the spatial pattern of the first leading basis mode shows the separation of activity between the default-mode and primary and attention regions. Based on theoretical modelling, we further reconstruct individual functional connectivity as the weighted superposition of coactivation patterns corresponding to these leading basic modes. Moreover, these leading basic modes capture sleep deprivation-induced changes in brain activity and interregional connectivity, primarily involving the default-mode and task-positive regions. Our findings reveal a dominant set of basic modes of spontaneous activity that reflect multiplexed interregional coordination and drive conventional functional connectivity, furthering the understanding of the functional significance of spontaneous brain activity.
