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Qingyang Li

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5 papers

2 author rows

YNICL Journal 2025 Journal Article

Early cortical alterations and neuropsychological mechanisms in amyotrophic lateral sclerosis

Qianqian Zhang
Yu Ding
Yu Zhang
Qingyang Li
Shiyu Shi
Yaxi Liu
Sijie Chen
Qian Wu

OBJECTIVE: This study investigates the characteristics of cortical structural and functional alterations in amyotrophic lateral sclerosis (ALS) patients and their modulation of emotional and cognitive functions, as well as to discuss their diagnostic value in early-stage ALS. METHODS: Fifty-nine ALS patients (28 in ALS 1 and 31 in ALS 2, categorized using King's College Staging) and 31 healthy controls were evaluated using multiparametric MRI, motor and neuropsychological assessments, and serum neurofilament light chain (NfL) levels. Mediation analyses were performed to examine how cortical alterations influence the relationship between emotional and cognitive functions. Support vector machine (SVM) classification models were constructed to assess the diagnostic utility of differential cortical parameters. RESULTS: ALS 1 patients exhibited increased cortical thickness (CT) and functional activity in the cingulate and frontotemporal regions, correlating with neuropsychological performance and NfL levels. Mediation analysis revealed that perigenual and frontotemporal functional activity significantly modulated the relationship between depressive symptoms and cognitive function. SVM classification showed that the combined altered regions with Amplitude of Low Frequency Fluctuations (ALFF) model achieved slightly better performance (AUC = 0.853, 95 %CI: 0.687-1.000, p < 0.001) compared to CT (AUC = 0.779, 95 %CI: 0.587-0.972, p < 0.001), although both models showed limited efficacy in differentiating between ALS 1 and ALS 2 groups. CONCLUSIONS: Cortical structural and functional alterations in ALS mediate the impact of depression on cognitive function, offering insights into the neuropsychological mechanisms of the disease and potential biomarkers for early-stage diagnosis.

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ICRA Conference 2021 Conference Paper

Autonomous Decentralized Shape-Based Navigation for Snake Robots in Dense Environments

Guillaume Sartoretti
Tianyu Wang 0010
Gabriel Chuang
Qingyang Li
Howie Choset

In this work, we focus on the autonomous navigation of snake robots in densely-cluttered environments, where collisions between the robot and obstacles are frequent, which could happen often in disaster scenarios, underground caves, or grassland/forest environments. This work takes the view that obstacles are not to be avoided, but rather exploited to support and direct the motion of the snake robot. We build upon a decentralized state-of-the-art compliant controller for serpenoid locomotion, and develop a bi-stable dynamical system that relies on inertial feedback to continuously steer the robot toward a desired direction. We experimentally show that this controller allows the robot to autonomously navigate dense environments by consistently locomoting along a given, global direction of travel in the world, which could be selected by a human operator or a higher level planner. We further equip the robot with an onboard vision system, allowing the robot to autonomously select its own direction of travel, based on the obstacle distribution ahead of its position (i. e. , enacting feedforward control). In those additional experiments on hardware, we show how such an exteroceptive sensor can allow the robot to steer before hitting obstacles and to preemptively avoid challenging regions where proprioception-only (i. e. , torque and inertial) feedback control would not suffice.

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NeurIPS Conference 2021 Conference Paper

Offline Model-based Adaptable Policy Learning

Xiong-Hui Chen
Yang Yu
Qingyang Li
Fan-Ming Luo
Zhiwei Qin
Wenjie Shang
Jieping Ye

In reinforcement learning, a promising direction to avoid online trial-and-error costs is learning from an offline dataset. Current offline reinforcement learning methods commonly learn in the policy space constrained to in-support regions by the offline dataset, in order to ensure the robustness of the outcome policies. Such constraints, however, also limit the potential of the outcome policies. In this paper, to release the potential of offline policy learning, we investigate the decision-making problems in out-of-support regions directly and propose offline Model-based Adaptable Policy LEarning (MAPLE). By this approach, instead of learning in in-support regions, we learn an adaptable policy that can adapt its behavior in out-of-support regions when deployed. We conduct experiments on MuJoCo controlling tasks with offline datasets. The results show that the proposed method can make robust decisions in out-of-support regions and achieve better performance than SOTA algorithms.

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YNIMG Journal 2013 Journal Article

A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics

Chao-Gan Yan
Brian Cheung
Clare Kelly
Stan Colcombe
R. Cameron Craddock
Adriana Di Martino
Qingyang Li
Xi-Nian Zuo

Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that “micro” head movements can introduce artifactual inter-individual and group-related differences in R-fMRI metrics have raised concerns. Here, we first build on prior demonstrations of regional variation in the magnitude of framewise displacements associated with a given head movement, by providing a comprehensive voxel-based examination of the impact of motion on the BOLD signal (i. e. , motion–BOLD relationships). Positive motion–BOLD relationships were detected in primary and supplementary motor areas, particularly in low motion datasets. Negative motion–BOLD relationships were most prominent in prefrontal regions, and expanded throughout the brain in high motion datasets (e. g. , children). Scrubbing of volumes with FD>0. 2 effectively removed negative but not positive correlations; these findings suggest that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact. We also examined the ability of motion correction strategies to eliminate artifactual differences related to motion among individuals and between groups for a broad array of voxel-wise R-fMRI metrics. Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level. Notably, global signal regression reduced relationships between motion and inter-individual differences in correlation-based R-fMRI metrics; Z-standardization (mean-centering and variance normalization) of subject-level maps for R-fMRI metrics prior to group-level analyses demonstrated similar advantages. Finally, our test–retest (TRT) analyses revealed significant motion effects on TRT reliability for R-fMRI metrics. Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics — particularly, amplitude of low frequency fluctuations (ALFF). The implications of our findings for decision-making regarding the assessment and correction of motion are discussed, as are insights into potential differences among volume-based metrics of motion.

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YNIMG Journal 2011 Journal Article

Emotional perception: Meta-analyses of face and natural scene processing

Dean Sabatinelli
Erica E. Fortune
Qingyang Li
Aisha Siddiqui
Cynthia Krafft
William T. Oliver
Stefanie Beck
Joshua Jeffries

Functional imaging studies of emotional processing typically contain neutral control conditions that serve to remove simple effects of visual perception, thus revealing the additional emotional process. Here we seek to identify similarities and differences across 100 studies of emotional face processing and 57 studies of emotional scene processing, using a coordinate-based meta-analysis technique. The overlay of significant meta-analyses resulted in extensive overlap in clusters, coupled with offset and unique clusters of reliable activity. The area of greatest overlap is the amygdala, followed by regions of medial prefrontal cortex, inferior frontal/orbitofrontal cortex, inferior temporal cortex, and extrastriate occipital cortex. Emotional face-specific clusters were identified in regions known to be involved in face processing, including anterior fusiform gyrus and middle temporal gyrus, and emotional scene studies were uniquely associated with lateral occipital cortex, as well as pulvinar and the medial dorsal nucleus of the thalamus. One global result of the meta-analysis reveals that a class of visual stimuli (faces vs. scenes) has a considerable impact on the resulting emotion effects, even after removing the basic visual perception effects through subtractive contrasts. Pure effects of emotion may thus be difficult to remove for the particular class of stimuli employed in an experimental paradigm. Whether a researcher chooses to tightly control the various elements of the emotional stimuli, as with posed face photographs, or allow variety and environmental realism into their evocative stimuli, as with natural scenes, will depend on the desired generalizability of their results.

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