Honors & Awards
Bio-X Bowes Interdisciplinary Fellowship (mentorship: Amit Etkin & Fei-Fei Li), Stanford University (2019 - 2022)
Stanford Mind, Brain, Computation & Technology Trainee (mentorship: Amit Etkin & Daniel Yamins), Stanford University (2018 - 2021)
Stanford Graduate Fellowship in Science & Engineering (SGF), Stanford University (2016 - 2019)
Biosciences Office of Graduate Education Travel Grant, Stanford University (2019)
Stanford Center for Cognitive & Neurobiological Imaging (CNI) Innovation Grant, Stanford University (2018)
Bio-X Travel Award, Stanford University (2018)
Biosciences Office of Graduate Education Travel Grant, Stanford University (2018)
Biosciences Office of Graduate Education Travel Grant, Stanford University (2017)
Stanford Biosciences ADVANCE Fellowship, Stanford University (2016)
Education & Certifications
MS, Columbia University (2016)
BS, Yale University (2013)
Effect of Low-Frequency Repetitive Transcranial Magnetic Stimulation on Impulse Inhibition in Abstinent Patients With Methamphetamine Addiction: A Randomized Clinical Trial.
JAMA network open
2020; 3 (3): e200910
Importance: Impulsivity during periods of abstinence is a critical symptom of patients who use methamphetamine (MA).Objective: To evaluate changes in impulse inhibition elicited by repetitive transcranial magnetic stimulation (rTMS) in patients with MA addiction.Design, Setting, and Participants: This randomized clinical trial was conducted in Da Lian Shan Addiction Rehabilitation Center, Nanjing, China, from December 1, 2018, to April 20, 2019. Effects of the intervention were examined at 3 time points: after a single session (day 1), 24 hours after 10 repeated sessions (day 11), and at 3 weeks of follow-up (day 31). Men with MA addiction and healthy male control participants were recruited for this study. Data analysis was performed from March 2019 to October 2019.Interventions: Patients who use MA were randomized to undergo sham rTMS (36 patients) and or 1-Hz rTMS (37 patients) to the left prefrontal cortex, receiving daily TMS treatments for 10 consecutive days.Main Outcomes and Measures: The primary outcome was impulse inhibition, which is primarily embodied by accuracy reduction (ie, accuracy cost) from standard to deviant trials in a 2-choice oddball task (80% standard and 20% deviant trials).Result: The study included 73 men with MA addiction (mean [SD] age, 38.49 [7.69] years) and 33 male healthy control participants without MA addiction (mean [SD] age, 35.15 [9.68] years). The mean (SD) duration of abstinence for the men with MA addiction was 9.27 (4.61) months. Compared with the control group, patients with MA addiction exhibited greater impulsivity (accuracy cost, 3.3% vs 6.2%). The single session of 1-Hz rTMS over the left prefrontal cortex significantly increased accuracy from 91.4% to 95.7% (F1,36=9.58; P<.001) and reaction time delay from 50 milliseconds to 77 milliseconds (F1,36=22.66; P<.001) in deviant trials. These effects were seen consistently after 10 sessions of 1-Hz rTMS treatment (day 11 vs day 1, t26=1.59; P=.12), and the behavioral improvement was maintained at least for 3 weeks after treatment (day 31 vs day 1, t26=0.26; P=.80). These improvement effects of impulse inhibition were coupled with a reduction in addictive symptoms as measured by cue-induced craving. The pretest accuracy cost was positively correlated with the change in impulse inhibition (r=0.615; P<.001) and change in craving (r=0.334; P=.01), suggesting that these 2 behaviors may be modified simultaneously.Conclusions and Relevance: These findings suggest that repeated rTMS sessions have sustained effects on impulse inhibition in patients with MA addiction and provide novel data on impulsivity management strategies for addiction rehabilitation.Trial Registration: ChiCTR-ROC-16008541.
View details for DOI 10.1001/jamanetworkopen.2020.0910
View details for PubMedID 32167568
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
Classification of TMS evoked potentials using ERP time signatures and SVM versus deep learning.
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
2019; 2019: 3539–42
Modeling transcranial magnetic stimulation (TMS) evoked potentials (TEP) begins with classification of stereotypical single-pulse TMS responses in order to select validation targets for generative dynamical models. Several dimensionality reduction techniques are commonly in use to extract statistically independent features from experimental data for regression against model parameters. Here, we first designed a 3-dimensional feature space based on commonly described event-related potentials (ERP) from the literature. We then compared classification schemes which take as inputs either the 3D projection space or the original full rank input space. Their ability to discriminate TEP recorded from different brain regions given a stimulus site were evaluated. We show that a deep learning architecture, employing Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP), yields better accuracy than the 3D projection and raw TEP input combined with Support Vector Machines. Such supervised feature extraction models may therefore be useful for scoring neural circuit simulations based on their ability to reproduce the underlying dynamical processes responsible for differential TEP responses.
View details for DOI 10.1109/EMBC.2019.8857583
View details for PubMedID 31946642
- Cortical Plasticity in Heroin and Methamphetamine Addiction ELSEVIER SCIENCE INC. 2019: S296
Classification of TMS evoked potentials using ERP time signatures and SVM versus deep learning
IEEE. 2019: 3539–42
View details for Web of Science ID 000557295303221
Dissociating the Neural Correlates of Experiencing and Imagining Affective Touch
2015; 25 (9): 2623–30
This functional magnetic resonance imaging (fMRI) study examined experiencing and imagining gentle arm and palm touch to determine whether these processes activate overlapping or distinct brain regions. Although past research shows brain responses to experiencing and viewing touch, this study investigates neural processing of touch absent of visual stimulation. C-tactile (CT) nerves, present in hairy skin, respond specifically to caress-like touch. CT-targeted touch activates "social brain" regions including insula, right posterior superior temporal sulcus, amygdala, temporal poles, and orbitofrontal cortex ( McGlone et al. 2012). We addressed whether activations reflect sensory input-driven mechanisms, cognitive-based mechanisms, or both. We identified a functional dissociation between insula regions. Posterior insula responded during experienced touch. Anterior insula responded during both experienced and imagined touch. To isolate stimulus-independent mechanisms recruited during physical experience of CT-targeted touch, we identified regions active to experiencing and imagining such touch. These included amygdala and temporal pole. We posit that the dissociation of insula function suggests posterior and anterior insula involvement in distinct yet interacting processes: coding physical stimulation and affective interpretation of touch. Regions active during experiencing and imagining CT-targeted touch are associated with social processes indicating that imagining touch conjures affective aspects of experiencing such touch.
View details for DOI 10.1093/cercor/bhu061
View details for Web of Science ID 000361464000025
View details for PubMedID 24700583
View details for PubMedCentralID PMC4537425
The Neural Attunement Effects of Oxytocin in Children with Autism Disorders
ELSEVIER SCIENCE INC. 2014: 84S
View details for Web of Science ID 000334101800263
How to design PET experiments to study neurochemistry: application to alcoholism.
The Yale journal of biology and medicine
2014; 87 (1): 33–54
Positron Emission Tomography (PET) (and the related Single Photon Emission Computed Tomography) is a powerful imaging tool with a molecular specificity and sensitivity that are unique among imaging modalities. PET excels in the study of neurochemistry in three ways: 1) It can detect and quantify neuroreceptor molecules; 2) it can detect and quantify changes in neurotransmitters; and 3) it can detect and quantify exogenous drugs delivered to the brain. To carry out any of these applications, the user must harness the power of kinetic modeling. Further, the quality of the information gained is only as good as the soundness of the experimental design. This article reviews the concepts behind the three main uses of PET, the rationale behind kinetic modeling of PET data, and some of the key considerations when planning a PET experiment. Finally, some examples of PET imaging related to the study of alcoholism are discussed and critiqued.
View details for PubMedID 24600335
View details for PubMedCentralID PMC3941463
Oxytocin enhances brain function in children with autism
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2013; 110 (52): 20953–58
Following intranasal administration of oxytocin (OT), we measured, via functional MRI, changes in brain activity during judgments of socially (Eyes) and nonsocially (Vehicles) meaningful pictures in 17 children with high-functioning autism spectrum disorder (ASD). OT increased activity in the striatum, the middle frontal gyrus, the medial prefrontal cortex, the right orbitofrontal cortex, and the left superior temporal sulcus. In the striatum, nucleus accumbens, left posterior superior temporal sulcus, and left premotor cortex, OT increased activity during social judgments and decreased activity during nonsocial judgments. Changes in salivary OT concentrations from baseline to 30 min postadministration were positively associated with increased activity in the right amygdala and orbitofrontal cortex during social vs. nonsocial judgments. OT may thus selectively have an impact on salience and hedonic evaluations of socially meaningful stimuli in children with ASD, and thereby facilitate social attunement. These findings further the development of a neurophysiological systems-level understanding of mechanisms by which OT may enhance social functioning in children with ASD.
View details for DOI 10.1073/pnas.1312857110
View details for Web of Science ID 000328858800036
View details for PubMedID 24297883
View details for PubMedCentralID PMC3876263