Biology subjects

Liu, D.-Q.

Publications and source records attributed to Liu, D.-Q..

4 recordsLinked to original sources

bioRxiv2024

A systematic evaluation of dynamic functional connectivity methods using simulation data

Numerous dynamic functional connectivity (dFC) methods have been proposed to study time-resolved network reorganization in rest and task fMRI. However, a comprehensive comparison of their performance is lacking. In this study, we compared the efficacy of seven dFC methods (and their enhanced versions) to track transient network reconfiguration using simulation data. The seven methods include flexible least squares (FLS), dynamic conditional correlation (DCC), general linear Kalman filter (GLKF), multiplication of temporal derivatives (MTD), sliding-window functional connectivity with L1-regularization (SWFC), hidden Markov models (HMM), and hidden semi-Markov models (HSMM). Multiple datasets of non-fMRI-BOLD and fMRI-BOLD signals with predefined covariance structures, signal-to-noise ratio levels, and sojourn time distributions were simulated. We adopted inter-subject analysis to eliminate the effects of signals of non-interest, resulting in enhanced methods: ISSWFC, ISMTD, ISDCC, ISFLS, ISKF, ISHMM, and ISHSMM. Efficacy was defined as the spatiotemporal association between simulated and estimated data. We found that all enhanced dFC methods outperformed their original versions. Efficacies depend on several factors, such as considering the neurovascular effect in simulated data, the covariance structure between two time series, state sojourn distribution, and signal-to-noise ratio levels. These results highlight the importance of selecting appropriate dFC methods in fMRI study.

neuroscience↗

bioRxiv2022

The domain-separation low-dimensional language network dynamics in the resting-state support the flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that the language and speech processing is organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct frame-wise time-varying language networks and investigate the temporal reoccurring patterns. We found that the time-varying language networks in the resting-state robustly clustered into four low-dimensional states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of primary auditory processing and lexical-phonological processing, motor and speech production processing, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and subserved as a baseline state. Temporally, the first three states appeared exclusively in limited time bins ([~]15%), and most of the time (> 55%), the language network kept inactive in state 4. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in the resting-state, which forms a dynamic "meta-networking" (network of networks) framework. HighlightsO_LIThe time-varying language network in the resting-state is robustly clustered into four low-dimensional states. C_LIO_LISpatially, the first three dFC states are cognitively meaningful, which highly resemble the neurobiology of primary auditory processing and lexical-phonological representation, speech production processing, and semantic processing, respectively. C_LIO_LITemporally, the first three states appeared exclusively in limited time bins ([~]15%), and most of the time (> 55%), the language network kept inactive in state 4. C_LIO_LIA dynamic "meta-networking" framework of language network in the resting-state is proposed. C_LI

neuroscience↗

bioRxiv2020

Hidden state dynamics reveals metastability across the adult lifespan

Adult lifespan is accompanied by functional reorganization of brain networks, but the dynamic patterns behind this reorganization remain largely unclear. This study focuses on modelling the dynamic process of spontaneous activity of large-scale networks using hidden Markov model (HMM), and investigates how it changes with age. The HMM with 12 hidden states was applied to temporally concatenated resting state fMRI data from two dataset of 176 / 170 subjects (aged 20-80 years), and each hidden state was characterized by distinct activation patterns of 17 brain networks. Results showed that (a) For both datasets, the elder tended to spend less time on and had less transitions to states showing antagonistic activity between various pairs of networks including default mode network, cognitive control and salience/ventral attention networks. (b) For both datasets, the elder were probable to spend more time on, have less transitions from and have more transitions to an baseline state with only moderate-level activation of all networks, the time spent on this state also showed an U-shaped lifespan trajectory. (c) For both datasets, HMM exhibited higher specificity and reproducibility in uncovering the age effects compared with temporal clustering method, especially for age effects in transition probability. (d) These results demonstrate the age-correlated decrease of the anti-correlation between various networks, and further validate the prediction of Naik et al. (2017) that the existence of a particular network state with lower transition probability and higher fractional occupancy in old cohort, which may reflect the shift of the dynamical working point across the adult lifespan.

neuroscience↗

bioRxiv2017

Percent amplitude of fluctuation: a simple measure for resting-state fMRI signal at single voxel level

The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxels ALFF by the global mean ALFF. However, this makes the individual voxels ALFF dependent on the global mean. Although Fractional ALFF (fALFF), proposed as a ratio of the ALFF to the total amplitude within the full frequency band, offers possible solution of the standardization, it actually mixes with the fluctuation power within the full frequency band and thus cannot reveal the true amplitude characteristics of a given frequency band. We proposed a new standardized, stand-alone, single-voxel metrics for RS-fMRI, namely percent amplitude of fluctuation (PerAF). PerAF is an analog to the percent signal change that has been widely used in the task fMRI communities, which allows it to be a straightforward measurement of BOLD signal fluctuations during resting state. We further conducted a test-retest reliability analysis comparing the relevant metrics, which indicated that PerAF was generally more reliable than the ALFF and fALFF. In a real RS-fMRI application, we further demonstrated that with and without standardization by global mean PerAF yielded prominently different results when comparing eyes open with eyes closed resting conditions, suggesting that future study should provide both with and without global mean standardization. The above results suggest that PerAF is a more reliable, straightforward and promising measurement for voxelwise brain activity-based RS-fMRI studies. For prompting future application of PerAF, we also implemented this method into a user-friendly toolbox REST-PerAF.

neuroscience↗