Biology subjects

Zhang, L.-B.

Publications and source records attributed to Zhang, L.-B..

3 recordsLinked to original sources

bioRxiv2026

Intensity-dependent topographical expansion of sensory representations

Neuroimaging studies typically assume that sensory properties are encoded in response magnitude within fixed neural populations. However, this approach does not capture changes in the spatial extent of activation topography, despite growing evidence for its behavioral relevance. Stimulus intensity provides a powerful test case for the role of activation topography as a coding feature because it is a basic, parametrically varying property shared across sensory modalities. Using a Bayes factor-based approach and four functional magnetic resonance imaging datasets (three large-scale datasets [total N = 609] and one precision dataset [>2300 trials]), we tested whether higher-intensity stimulation is associated with expansion of activation topography. Participants received sensory stimuli of varying intensities in somatosensory (heat, laser, tactile), auditory, and visual modalities. High-versus low-intensity painful stimulation consistently produced topographical expansion in areas including the primary somatosensory, posterior midcingulate, primary visual cortices, and cerebellar lobules V and VI. This result replicated across two independent large-scale datasets and within individual participants in the precision dataset. Expansion was also observed for tactile, auditory, and visual stimulation, and its extent correlated with psychophysical discriminability. Topographical expansion involved both the enlargement of already-activated areas and the recruitment of novel regions. These findings establish topographical expansion as a replicable feature of intensity coding, challenging the prevailing assumption of a fixed neural topography.

neuroscience↗

bioRxiv2025

Neural variability reliably and selectively encodes pain discriminability

Neural activity varies dramatically across time. While such variability has been associated with cognition, its relationship with pain remains largely unexplored. Here, we systematically investigated the relationship between neural variability and pain, particularly pain discriminability, in five large electroencephalography (EEG) datasets (total N = 489), collected from healthy individuals (Datasets 1-4) and patients with postherpetic neuralgia (PHN; Dataset 5) who had received painful or nonpainful sensory stimuli. We found robust correlations between neural variability and interindividual pain discriminability. These correlations were (1) replicable in multiple datasets, (2) pain selective, as no significant correlations were observed in nonpain modalities, and (3) clinically relevant, as they were partly disrupted in patients with PHN. Importantly, variability and amplitude of EEG signals were mutually independent and had distinct temporal and oscillatory profiles in encoding pain discriminability. These findings demonstrate that neural variability is a replicable and selective indicator of pain discriminability above and beyond amplitude, thereby enhancing the understanding of neural encoding of pain discriminability and underscoring the value of neural variability in pain studies.

neuroscience↗

bioRxiv2024

A replicable and generalizable neuroimaging-based indicator of pain sensitivity across individuals

Developing neural indicators of pain sensitivity is crucial for revealing the neural basis of individual differences in pain and advancing individualized pain treatment. To identify reliable neural indicators of pain sensitivity, we leveraged six large and diverse functional magnetic resonance imaging (fMRI) datasets (total N=1046). We found replicable and generalizable correlations between nociceptive-evoked fMRI responses and pain sensitivity for laser heat, contact heat, and mechanical pains. These fMRI responses correlated more strongly with pain sensitivity than with tactile, auditory, and visual sensitivity. Moreover, we developed a machine learning model that accurately predicted not only pain sensitivity but also pain reduction from different interventions in healthy individuals. Notably, these findings were influenced considerably by sample sizes, requiring >200 for univariate correlation analysis and >150 for multivariate machine learning modelling. Altogether, we demonstrate the validity of decoding pain sensitivity from fMRI responses, thus facilitating interpretations of subjective pain reports and promoting more mechanistically informed investigation of pain physiology.

neuroscience↗