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Lujie Chen

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4 papers

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YNICL Journal 2026 Journal Article

Fronto-limbic disconnection correlates with paroxysmal sympathetic hyperactivity following traumatic brain injury: An indirect disconnection-symptom mapping study

Eric W Moffet
Sancharee Hom Chowdhury
Ediel Almeida
Xiangxiang Kong
Lujie Chen
Jiachen Zhuo
Nicholas A Morris
Gunjan Y Parikh

Paroxysmal sympathetic hyperactivity (PSH) is a clinically important manifestation of dysautonomia following traumatic brain injury (TBI). While it is thought to arise from central autonomic network disconnection, supporting evidence is limited. Here, we integrate clinically obtained magnetic resonance imaging (MRI) lesion data with human connectome data to identify specific white matter tract disconnections and gray matter parcel damage associated with PSH. Our sample included 117 patients who underwent susceptibility weighted imaging and 3D T1 MRI sequences as part of clinical care while admitted at our institution between January 1, 2016 and July 1, 2018. Susceptibility lesion masks were manually created and registered to standard template space. High quality registrations were obtained in 96 patients (50% with PSH), who were included in the study. Using the Matlab Lesion Quantification Toolkit, we assessed white matter tract disconnection severity and gray matter parcel damage for each patient. We compared results according to a binary PSH clinical diagnosis using Wilcoxon rank sum tests and a standard ordinal PSH diagnostic likelihood score (with 0-11 range) using Pearson correlations, Bonferroni-corrected for multiple comparisons. PSH diagnosis was associated with greater disconnection severity in nine tracts, two of which also correlated with higher diagnosis likelihood: the right uncinate fasciculus and the anterior corpus callosum. Damaged parcels associated with PSH included left prefrontal regions of the default mode network and the ventral salience network. In summary, our work implicates disconnection of fronto-limbic components of the central autonomic network in the pathophysiology of TBI-related PSH.

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AAAI Conference 2020 Conference Paper

Discriminating Cognitive Disequilibrium and Flow in Problem Solving: A Semi-Supervised Approach Using Involuntary Dynamic Behavioral Signals

Mononito Goswami
Lujie Chen
Artur Dubrawski

Problem solving is one of the most important 21st century skills. However, effectively coaching young students in problem solving is challenging because teachers must continuously monitor their cognitive and affective states, and make real-time pedagogical interventions to maximize their learning outcomes. It is an even more challenging task in social environments with limited human coaching resources. To lessen the cognitive load on a teacher and enable affect-sensitive intelligent tutoring, many researchers have investigated automated cognitive and affective detection methods. However, most of the studies use culturally-sensitive indices of affect that are prone to social editing such as facial expressions, and only few studies have explored involuntary dynamic behavioral signals such as gross body movements. In addition, most current methods rely on expensive labelled data from trained annotators for supervised learning. In this paper, we explore a semi-supervised learning framework that can learn low-dimensional representations of involuntary dynamic behavioral signals (mainly gross-body movements) from a modest number of short time series segments. Experiments on a real-world dataset reveal a signiﬁcant advantage of these representations in discriminating cognitive disequilibrium and ﬂow, as compared to traditional complexity measures from dynamical systems literature, and demonstrate their potential in transferring learned models to previously unseen subjects.

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AAAI Conference 2020 Short Paper

Modeling Involuntary Dynamic Behaviors to Support Intelligent Tutoring (Student Abstract)

Mononito Goswami
Lujie Chen
Chufan Gao
Artur Dubrawski

Problem solving is one of the most important 21st century skills. However, effectively coaching young students in problem solving is challenging because teachers must continuously monitor their cognitive and affective states and make real-time pedagogical interventions to maximize students’ learning outcomes. It is an even more challenging task in social environments with limited human coaching resources. To lessen the cognitive load on a teacher and enable affectsensitive intelligent tutoring, many researchers have investigated automated cognitive and affective detection methods. However, most of the studies use culturally-sensitive indices of affect that are prone to social editing such as facial expressions, and only few studies have explored involuntary dynamic behavioral signals such as gross body movements. In addition, most current methods rely on expensive labelled data from trained annotators for supervised learning. In this paper, we explore a semi-supervised learning framework that can learn low-dimensional representations of involuntary dynamic behavioral signals (mainly gross-body movements) from a modest number of short time series segments. Experiments on a real-world dataset reveal a signiﬁcant utility of these representations in discriminating cognitive disequilibrium and ﬂow and demonstrate their potential in transferring learned models to previously unseen subjects.

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AAAI Conference 2020 Short Paper

Predicting Students’ Attention Level with Interpretable Facial and Head Dynamic Features in an Online Tutoring System (Student Abstract)

Shimeng Peng
Lujie Chen
Chufan Gao
Richard Jiarui Tong

Engaged learners are effective learners. Even though it is widely recognized that engagement plays a vital role in learning effectiveness, engagement remains to be an elusive psychological construct that is yet to ﬁnd a consensus deﬁnition and reliable measurement. In this study, we attempted to discover the plausible operational deﬁnitions of engagement within an online learning context. We achieved this goal by ﬁrst deriving a set of interpretable features on dynamics of eyes, head and mouth movement from facial landmarks extractions of video recording when students interacting with an online tutoring system. We then assessed their predicative value for engagement which was approximated by synchronized measurements from commercial EEG brainwave headset worn by students. Our preliminary results show that those features reduce root mean-squared error by 29% compared with default predictor and we found that the random forest model performs better than a linear regressor.

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