Biology subjects

Cui, E.

Publications and source records attributed to Cui, E..

5 recordsLinked to original sources

bioRxiv2026

Fast Generalized Longitudinal Functional Principal Components Analysis with Applications to Population-Level Neuronal Dynamics

Functional data are widely collected across many fields. One approach for modeling functional data collected across repeated measures is longitudinal functional PCA (LFPCA), which allows for the decomposition of variability at multiple levels. However, existing estimation approaches for LFPCA can be computationally demanding for large or high-dimensional datasets. Here, we propose fast generalized LFPCA, a novel estimation technique that bins data along the functional domain and fits a marginal mixed model at each bin. Simulation results show that the proposed method is orders of magnitude faster than existing techniques while achieving similar accuracy, making LFPCA scalable to datasets with more densely sampled observations and larger sample sizes. We apply the proposed technique to model neuronal firing rate data both on and off deep brain stimulation (DBS) to investigate how the intervention modulates population-level neuronal activity. Our results suggest that the vast majority of between-neuron variation can be explained by four principal components, which relate to individual differences in neuronal firing as well as the arm manipulation paradigm under study. We also find that DBS significantly increases the variability of firing activity, likely driven by increased firing from a subset of neurons in response to DBS, a novel insight not obtained in previous analyses. Code to implement the proposed method and run simulation studies is included in the supplement.

neuroscience↗

bioRxiv2025

Capturing instantaneous neural signal-behavior relationships with concurrent functional mixed models

We previously proposed an analysis framework for fiber photometry data based on functional linear mixed models (FLMMs). Functional LMMs allow modeling associations between photometry traces and trial-specific scalar values like behavioral summaries and session number, while also accounting for between-animal heterogeneity. Here, we extend the method to concurrent FLMMs (cFLMMs), a method that can fit the instantaneous relationship between functional outcomes and functional covariates. Concurrent FLMMs enable testing of how the photometry signal is associated with, for example, a behavioral variable that evolves across within-trial timepoints (e.g. animal speed). cFLMMs can also model the relationship between the photometry signal and covariates in experiments with variable trial lengths (e.g., in studies where trials end when an animal responds). We illustrate the application of cFLMMs on two published studies and show the method can identify signal-behavior associations in analyses not possible with FLMMs. We find that analyzing photometry-behavior associations based on behavioral summaries (e.g., latency-to-response, average lick rate) can lead to misleading conclusions. We published our method in the fastFMM package, available as an R package through GitHub (https://github.com/awqx/fastFMM).

neuroscience↗

bioRxiv2025

INTACT: A method for integration of longitudinal physical activity data from multiple sources

Wearable devices and digital phenotyping are increasingly used in observational and interventional studies to measure real-time biosignals such as physical activity. However, integrating and comparing data across studies and cohorts remains challenging due to variability in device types, acquisition protocols, and preprocessing methods. A key challenge is removing unwanted study- or device-specific effects while preserving meaningful biological signals. These difficulties are exacerbated by the longitudinal and within-day correlations inherent in high-resolution time-varying data collected from wearable sensors. To address this, we propose INTACT (INtegration of Time-varying data from weArable sensors for physiCal acTivity), a novel method for harmonizing time-varying physical activity intensity data from accelerometers. INTACT models shared information through common eigenvalues and eigenfunctions while allowing for source-specific scale and rotation adjustments. We apply the proposed method to two real-world applications: (i) integration of accelerometer data from two waves of the National Health and Nutrition Examination Survey (NHANES), measured using different devices and reported in different units; and (ii) integration of NHANES accelerometry data with accelerometer and gyroscope measures from commercial devices. Across both applications, INTACT outperforms existing approaches in mitigating source effects while preserving biological variation, enabling more reliable cross-study comparisons of physical activity patterns.

bioinformatics↗

bioRxiv2023

A Statistical Framework for Analysis of Trial-Level Temporal Dynamics in Fiber Photometry Experiments

Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials. We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. For example, our method identifies two dopamine components with distinct temporal dynamics in response to reward delivery. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package and analysis guide for applying our framework.

neuroscience↗

bioRxiv2023

Eye movements decrease during effortful speech listening

Pupillometry is the most used objective tool to assess listening effort but has several disadvantages. The current study explores a new, objective way to assess listening effort through eye movements. Building on cognitive and neurophysiological work, we examine the hypothesis that eye movements decrease when speech listening becomes challenging. In three experiments with human participants from both sexes, we demonstrate, consistent with this hypothesis, that fixation duration increases and spatial gaze dispersion decreases with increasing speech masking. Eye movements decreased during effortful speech listening for different visual scenes (free viewing; object tracking) and speech materials (simple sentences; naturalistic stories). In contrast, pupillometry was insensitive to speech masking during story listening, highlighting the challenges with pupillometric measures for the assessments of listening effort in naturalistic speech-listening paradigms. Our results reveal a critical link between eye movements and cognitive load, and provide the foundation for a novel measure of listening effort applicable in a wide range of contexts. Significance statementAssessment of listening effort is critical for early diagnosis of age-related hearing loss. Pupillometry is most used but has several disadvantages. The current study explores a new, objective way to assess listening effort through eye movements. We examine the hypothesis that eye movements decrease when speech listening becomes effortful. We demonstrate, consistent with this hypothesis, that fixation duration increases and gaze dispersion decreases with increasing speech masking. Eye movements decreased during effortful speech listening for different visual scenes (free viewing; object tracking) and speech materials (sentences; naturalistic stories). Our results reveal a critical link between eye movements and cognitive load, and provide the foundation for a novel measure of listening effort applicable in a wide range of contexts.

neuroscience↗