Jing's research work focuses on the cognitive and neural mechanisms of social communication. She received her Master degree at Beijing Normal University in 2013, where she mainly studied the unique neural underpinnings of face-to-face verbal communication using fNIRS-based hyperscanning. During her Ph.D. studies at Max Planck Institute for Human Cognitive and Brain Sciences and Humboldt-Universität zu Berlin in Germany from 2013 to 2017,she combined various techniques such as fMRI, MEG and eye tracking to study neural mechanisms of one important component in social interaction: eye contact.She joined the Etkin Lab in 2017 and has specifically focused on the causal neural circuitry of emotion processing in social context using TMS-fMRI.
Doctor of Philosophy, Humboldt-Universität zu Berlin & Max Planck Institute for Human Cognitive and Brain Sciences (2017)
Master of Science, Beijing Normal University (2013)
Bachelor of Science, Capital Normal University (2010)
An electroencephalographic signature predicts antidepressant response in major depression.
Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n=309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.
View details for DOI 10.1038/s41587-019-0397-3
View details for PubMedID 32042166
Global connectivity and local excitability changes underlie antidepressant effects of repetitive transcranial magnetic stimulation.
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology
Repetitive transcranial magnetic stimulation (rTMS) is a commonly used treatment for major depressive disorder (MDD). However, our understanding of the mechanism by which TMS exerts its antidepressant effect is minimal. Furthermore, we lack brain signals that can be used to predict and track clinical outcome. Such signals would allow for treatment stratification and optimization. Here, we performed a randomized, sham-controlled clinical trial and measured electrophysiological, neuroimaging, and clinical changes before and after rTMS. Patients (N = 36) were randomized to receive either active or sham rTMS to the left dorsolateral prefrontal cortex (dlPFC) for 20 consecutive weekdays. To capture the rTMS-driven changes in connectivity and causal excitability, resting fMRI and TMS/EEG were performed before and after the treatment. Baseline causal connectivity differences between depressed patients and healthy controls were also evaluated with concurrent TMS/fMRI. We found that active, but not sham rTMS elicited (1) an increase in dlPFC global connectivity, (2) induction of negative dlPFC-amygdala connectivity, and (3) local and distributed changes in TMS/EEG potentials. Global connectivity changes predicted clinical outcome, while both global connectivity and TMS/EEG changes tracked clinical outcome. In patients but not healthy participants, we observed a perturbed inhibitory effect of the dlPFC on the amygdala. Taken together, rTMS induced lasting connectivity and excitability changes from the site of stimulation, such that after active treatment, the dlPFC appeared better able to engage in top-down control of the amygdala. These measures of network functioning both predicted and tracked clinical outcome, potentially opening the door to treatment optimization.
View details for DOI 10.1038/s41386-020-0633-z
View details for PubMedID 32053828
Neural mechanisms of eye contact when listening to another person talking
SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE
2017; 12 (2): 319–28
Eye contact occurs frequently and voluntarily during face-to-face verbal communication. However, the neural mechanisms underlying eye contact when it is accompanied by spoken language remain unexplored to date. Here we used a novel approach, ﬁxation-based event-related functional magnetic resonance imaging (fMRI), to simulate the listener making eye contact with a speaker during verbal communication. Participants' eye movements and fMRI data were recorded simultaneously while they were freely viewing a pre-recorded speaker talking. The eye tracking data were then used to define events for the fMRI analyses. The results showed that eye contact in contrast to mouth fixation involved visual cortical areas (cuneus, calcarine sulcus), brain regions related to theory of mind/intentionality processing (temporoparietal junction, posterior superior temporal sulcus, medial prefrontal cortex) and the dorsolateral prefrontal cortex. In addition, increased effective connectivity was found between these regions for eye contact in contrast to mouth fixations. The results provide first evidence for neural mechanisms underlying eye contact when watching and listening to another person talking. The network we found might be well suited for processing the intentions of communication partners during eye contact in verbal communication.
View details for DOI 10.1093/scan/nsw127
View details for Web of Science ID 000397312200013
View details for PubMedID 27576745
View details for PubMedCentralID PMC5390711
Leader emergence through interpersonal neural synchronization
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2015; 112 (14): 4274–79
The neural mechanism of leader emergence is not well understood. This study investigated (i) whether interpersonal neural synchronization (INS) plays an important role in leader emergence, and (ii) whether INS and leader emergence are associated with the frequency or the quality of communications. Eleven three-member groups were asked to perform a leaderless group discussion (LGD) task, and their brain activities were recorded via functional near infrared spectroscopy (fNIRS)-based hyperscanning. Video recordings of the discussions were coded for leadership and communication. Results showed that the INS for the leader-follower (LF) pairs was higher than that for the follower-follower (FF) pairs in the left temporo-parietal junction (TPJ), an area important for social mentalizing. Although communication frequency was higher for the LF pairs than for the FF pairs, the frequency of leader-initiated and follower-initiated communication did not differ significantly. Moreover, INS for the LF pairs was significantly higher during leader-initiated communication than during follower-initiated communications. In addition, INS for the LF pairs during leader-initiated communication was significantly correlated with the leaders' communication skills and competence, but not their communication frequency. Finally, leadership could be successfully predicted based on INS as well as communication frequency early during the LGD (before half a minute into the task). In sum, this study found that leader emergence was characterized by high-level neural synchronization between the leader and followers and that the quality, rather than the frequency, of communications was associated with synchronization. These results suggest that leaders emerge because they are able to say the right things at the right time.
View details for DOI 10.1073/pnas.1422930112
View details for Web of Science ID 000352287800039
View details for PubMedID 25831535
View details for PubMedCentralID PMC4394311
Neural control of rising and falling tones in Mandarin speakers who stutter
BRAIN AND LANGUAGE
2012; 123 (3): 211–21
Neural control of rising and falling tones in Mandarin people who stutter (PWS) was examined by comparing with that which occurs in fluent speakers [Howell, Jiang, Peng, and Lu (2012). Neural control of fundamental frequency rise and fall in Mandarin tones. Brain and Language, 121(1), 35-46]. Nine PWS and nine controls were scanned. Functional connectivity analysis showed that the connections between the insula and LMC and between the LMC and the putamen differed significantly between PWS and fluent speakers during both rising and falling tones. The connection between the insula and the brainstem differed between PWS and fluent speakers only during the falling tone. These results indicated the neural control for the rising tone and the falling tone are affected in PWS. Moreover, whilst both rising and falling tones were affected in PWS, falling-tone control appeared to be affected more.
View details for DOI 10.1016/j.bandl.2012.09.010
View details for Web of Science ID 000312048800008
View details for PubMedID 23122701
Neural Synchronization during Face-to-Face Communication
JOURNAL OF NEUROSCIENCE
2012; 32 (45): 16064–69
Although the human brain may have evolutionarily adapted to face-to-face communication, other modes of communication, e.g., telephone and e-mail, increasingly dominate our modern daily life. This study examined the neural difference between face-to-face communication and other types of communication by simultaneously measuring two brains using a hyperscanning approach. The results showed a significant increase in the neural synchronization in the left inferior frontal cortex during a face-to-face dialog between partners but none during a back-to-back dialog, a face-to-face monologue, or a back-to-back monologue. Moreover, the neural synchronization between partners during the face-to-face dialog resulted primarily from the direct interactions between the partners, including multimodal sensory information integration and turn-taking behavior. The communicating behavior during the face-to-face dialog could be predicted accurately based on the neural synchronization level. These results suggest that face-to-face communication, particularly dialog, has special neural features that other types of communication do not have and that the neural synchronization between partners may underlie successful face-to-face communication.
View details for DOI 10.1523/JNEUROSCI.2926-12.2012
View details for Web of Science ID 000310842000038
View details for PubMedID 23136442
Classification of Types of Stuttering Symptoms Based on Brain Activity
2012; 7 (6): e39747
Among the non-fluencies seen in speech, some are more typical (MT) of stuttering speakers, whereas others are less typical (LT) and are common to both stuttering and fluent speakers. No neuroimaging work has evaluated the neural basis for grouping these symptom types. Another long-debated issue is which type (LT, MT) whole-word repetitions (WWR) should be placed in. In this study, a sentence completion task was performed by twenty stuttering patients who were scanned using an event-related design. This task elicited stuttering in these patients. Each stuttered trial from each patient was sorted into the MT or LT types with WWR put aside. Pattern classification was employed to train a patient-specific single trial model to automatically classify each trial as MT or LT using the corresponding fMRI data. This model was then validated by using test data that were independent of the training data. In a subsequent analysis, the classification model, just established, was used to determine which type the WWR should be placed in. The results showed that the LT and the MT could be separated with high accuracy based on their brain activity. The brain regions that made most contribution to the separation of the types were: the left inferior frontal cortex and bilateral precuneus, both of which showed higher activity in the MT than in the LT; and the left putamen and right cerebellum which showed the opposite activity pattern. The results also showed that the brain activity for WWR was more similar to that of the LT and fluent speech than to that of the MT. These findings provide a neurological basis for separating the MT and the LT types, and support the widely-used MT/LT symptom grouping scheme. In addition, WWR play a similar role as the LT, and thus should be placed in the LT type.
View details for DOI 10.1371/journal.pone.0039747
View details for Web of Science ID 000305781700062
View details for PubMedID 22761887
View details for PubMedCentralID PMC3382568
Neural control of fundamental frequency rise and fall in Mandarin tones
BRAIN AND LANGUAGE
2012; 121 (1): 35–46
The neural mechanisms used in tone rises and falls in Mandarin were investigated. Nine participants were scanned while they named one-character pictures that required rising or falling tone responses in Mandarin: the left insula and right putamen showed stronger activation between rising and falling tones; the left brainstem showed weaker activation between rising and falling tones. Connectivity analysis showed that the significant projection from the laryngeal motor cortex to the brainstem which was present in rising tones was absent in falling tones. Additionally, there was a significant difference between the connection from the insula to the laryngeal motor cortex which was negative in rising tones but positive in falling tones. These results suggest that the significant projection from the laryngeal motor cortex to the brainstem used in rising tones was not active in falling tones. The connection from the left insula to the laryngeal motor cortex that differs between rising and falling tones may control whether the rise mechanism is active or not.
View details for DOI 10.1016/j.bandl.2012.01.004
View details for Web of Science ID 000301887500004
View details for PubMedID 22341758