Biology subjects

Pan, W.-J.

Publications and source records attributed to Pan, W.-J..

8 recordsLinked to original sources

bioRxiv2026

Time-Varying Directed Interactions in Functional Brain Networks: Modeling and Validation

Brain function depends on time-resolved directional organization: influence is continuously redistributed across regions, shaping who drives whom, when, and for how long. Yet dynamic neuroimaging has largely emphasized undirected fluctuations in functional connectivity, limiting biological interpretation of asymmetric interactions. Here we show that directed influence can be resolved into two biologically distinct signatures: strength, reflecting the efficacy of influence, and duration, reflecting its temporal extent. To test this principle, we developed sliding-window prediction correlation (SWpC), a framework for estimating these signatures in fMRI and related signals. Across simultaneous rat local field potential (LFP)-fMRI and human motor-task fMRI, strength more closely tracks neural-BOLD coupling and task-evoked reconfiguration, whereas duration captures temporally extended BOLD patterns more susceptible to hemodynamic timing. In post-concussion vestibular dysfunction, altered strength-duration profiles define reproducible vestibular-multisensory states and improve patient-control discrimination. Together, these findings support a general principle of dynamic brain organization spanning neurophysiology, behavior, and disease.

bioengineering↗

bioRxiv2026

Connectomes across temporal scales with simultaneous wide-field optical imaging and resting-state functional MRI

Resting-state functional MRI (rs-fMRI) is a cornerstone of human brain research, yet its interpretation is complicated by its sensitivity to the slow hemodynamic response that obscures the organization of neural activity across faster time scales. Here we use simultaneous wide-field optical imaging (WOI) and rs-fMRI to directly examine the relationship between neural and hemodynamic functional connectomes across time scales. We show that much of the large-scale spatial structure is preserved across modalities, across time scales, and across frequencies. Although rs-fMRI robustly captures time-averaged neural activity, time-resolved rs-fMRI estimates of functional connectivity exhibit significantly greater variability, which partially reflects sensitivity limitations. Hemodynamic WOI signals maintain greater similarity to neural activity than rs-fMRI, although their fidelity is reduced at high frequencies. Together, our findings demonstrate that the time-averaged spatial structure of neural activity is faithfully represented in hemodynamics and rs-fMRI; provide insight into the reliability of time-resolved rs-fMRI across temporal scales; and establish a multimodal framework for validating features of dynamic brain activity. TeaserSpatial patterns of neural activity present across time scales are largely preserved in hemodynamics measured with optical imaging and rs-fMRI.

neuroscience↗

bioRxiv2026

An integrated platform for simultaneous wide-field voltage/calcium imaging and fMRI (EPI & ZTE) reveals neuronal infraslow dynamics underlying functional connectivity

Wide-field optical imaging acquired simultaneously with functional MRI (fMRI) has the ability to provide unprecedented insight into the neural origins of time-varying whole-brain activity. Simultaneously linking cellular-scale activity to whole-brain fMRI remains challenging due to optical access, RF coil placement, and transmission constraints in the MRI environment. We present an integrated platform that combines a long-distance tube-lens optical path (>98% transmission), a chronically stable optically-fused cranial window, and a subject-conformal RF surface coil compatible with both echo planar imaging (EPI) and zero-echo-time (ZTE) fMRI. The system supports concurrent wide-field imaging of genetically encoded voltage or calcium indicators concurrently with intrinsic hemoglobin signals. In individual mice, wide-field optical and fMRI measures yield concordant functional connectivity, and cross-modal timing analyses demonstrate that neuronal infraslow dynamics (<0.1 Hz) underlie the majority of fMRI connectivity, after removing hemodynamic crosstalk. The platform's sensitivity, chronic stability, and sequence flexibility broaden access to cellular-to-whole-brain investigations across basic and translational neuroimaging.

neuroscience↗

bioRxiv2026

Fidelity of Spatiotemporal Patterns of Brain Activity Across Sampling Rate, Scan Duration, and Frequency Content

Intrinsic brain activity is characterized by large-scale spatiotemporal patterns that underpin functional connectivity and cognition. Quasi-periodic patterns (QPPs) and complex principal component analysis (cPCA) have emerged as reproducible methods for capturing spatiotem-poral network interactions in resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods remain sensitive to methodological factors such as scan duration, repetition time (TR), and frequency band selection. This study systematically evaluates how these parameters influence the stability and reliability of QPP- and cPCA-derived functional connectivity patterns across multiple datasets. Using five independent rs-fMRI datasets, we evaluate the impact of scan length on pattern reliability, explore the effects of TR on spatiotemporal patterns, and compare the sensitivity of different frequency bands (Slow-5, Slow-4, infraslow) in capturing network dynamics. Our findings reveal that while both QPPs and cPCA detect intrinsic network activity, their reliability varies with acquisition parameters. QPPs exhibit greater stability in shorter scans, making them suitable for individual-level analyses, whereas cPCA provides a broader representation of phase-coherent fluctuations but shows greater between-subject variability and benefits more from longer, group-level acquisi-tions. Additionally, frequency band selection significantly influences the temporal structure of extracted patterns: in our analyses, Slow-5 (0.01-0.027 Hz) tended to emphasize more recurrent, synchronized network configurations, whereas Slow-4 (0.027-0.073 Hz) more often revealed transitions between connectivity states. These results provide critical insights into optimizing methodological choices for dynamic functional connectivity analysis, enhancing the interpretability of spatiotemporal patterns in both basic and clinical neuroimaging research.

neuroscience↗

bioRxiv2024

Variation in the Distribution of Large-scale Spatiotemporal Patterns of Activity Across Brain States

A few large-scale spatiotemporal patterns of brain activity (quasiperiodic patterns or QPPs) account for most of the spatial structure observed in resting state functional magnetic resonance imaging (rs-fMRI). The QPPs capture well-known features such as the evolution of the global signal and the alternating dominance of the default mode and task positive networks. These widespread patterns of activity have plausible ties to neuromodulatory input that mediates changes in nonlocalized processes, including arousal and attention. To determine whether QPPs exhibit variations across brain conditions, the relative magnitude and distribution of the three strongest QPPs were examined in two scenarios. First, in data from the Human Connectome Project, the relative incidence and magnitude of the QPPs was examined over the course of the scan, under the hypothesis that increasing drowsiness would shift the expression of the QPPs over time. Second, using rs-fMRI in rats obtained with a novel approach that minimizes noise, the relative incidence and magnitude of the QPPs was examined under three different anesthetic conditions expected to create distinct types of brain activity. The results indicate that both the distribution of QPPs and their magnitude changes with brain state, evidence of the sensitivity of these large-scale patterns to widespread changes linked to alterations in brain conditions.

neuroscience↗

bioRxiv2022

Spatial and Spectral Components of the BOLD Global Signal in Rat Resting-State Functional MRI

In resting-state fMRI (rs-fMRI), the global signal average captures widespread fluctuations related to unwanted sources of variance such as motion and respiration, and has long been used as a regressor to reduce noise during data preprocessing. However, coherent neural activity in spatially-extended functional networks can also contribute to the global signal. The relative contributions of neural and non-neural sources to the global signal remain poorly understood. This study sought to tackle this problem through the comparison of the blood oxygenation level dependent (BOLD) global signal to an adjacent non-brain tissue signal from the same scan in rs-fMRI obtained from anesthetized rats. In this dataset motion was minimal and ventilation was phase-locked to image acquisition to minimize respiratory fluctuations. In addition to contrasting the spatial and spectral components of these two signals, we also observed these differences across the use of three different anesthetics: isoflurane, dexmedetomidine, and a combination of dexmedetomidine and light isoflurane. Here, we report differences in the spectral composition of the two signals as evaluated by a power spectral density (PSD) estimate and a fractional amplitude of low-frequency fluctuations (fALFF) calculation. Additionally, we show spatial selectivity for specific brain structures that show an increased correlation to the global signal both statically and dynamically, through Pearsons correlation and co-activation pattern analysis, respectively. All of the observed differences between the BOLD global signal and the adjacent non-brain tissue signal were maintained across all three anesthetic conditions, showing that the global signal is distinct from the noise contained in the tissue signal. This study provides a unique perspective to the contents of the global signal and their origins.

bioengineering↗

bioRxiv2019

The Relationship between BOLD and Neural Activity Arises from Temporally Sparse Events

Resting state functional magnetic resonance (rs-fMRI) imaging offers insights into how different brain regions are connected into functional networks. It was recently shown that networks that are almost identical to the ones created from conventional correlation analysis can be obtained from a subset of high-amplitude data, suggesting that the functional networks may be driven by instantaneous co-activations of multiple brain regions rather than ongoing oscillatory processes. The rs-fMRI studies, however, rely on the blood oxygen level dependent (BOLD) signal, which is only indirectly sensitive to neural activity through neurovascular coupling. To provide more direct evidence that the neuronal co-activation events produce the time-varying network patterns seen in rs-fMRI studies, we examined the simultaneous rs-fMRI and local field potential (LFP) recordings in rats performed in our lab over the past several years. We developed complementary analysis methods that focus on either the temporal or spatial domain, and found evidence that the interaction between LFP and BOLD may be driven by instantaneous co-activation events as well. BOLD maps triggered on high-amplitude LFP events resemble co-activation patterns created from rs-fMRI data alone, though the co-activation time points are defined differently in the two cases. Moreover, only LFP events that fall into the highest or lowest thirds of the amplitude distribution result in a BOLD signal that can be distinguished from noise. These findings provide evidence of an electrophysiological basis for the time-varying co-activation patterns observed in previous studies.

neuroscience↗

bioRxiv2017

Instantaneous Brain Dynamics Mapped to a Continuous State Space

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brains dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel-level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet-ICA state vectors is a graph that may be embedded onto a lower-dimensional space to assist the interpretation of state-space dynamics. Applying this procedure to a large sample of resting and task data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus-dependent brain states. We also demonstrate that resting brain activity includes brain states that are very similar to those adopted during some tasks, as well as brain states that are distinct from experimentally-defined tasks. Back-projection of segmented brain states onto the brains surface reveals the patterns of brain activity that support each experimental state.

neuroscience↗