Tzu-Yao Chiu. In real life, we move our eyes frequently to gather information in the environment, but each eye movement also disrupts the visual representation in the brain. This project seeks to understand how humans maintain stable object information across each eye movement, especially when making an eye movement towards the object.

Isaac Liao. Our brains contain specialized regions for recognizing faces, bodies, and objects, and research suggests that each region's function is shaped by which other brain areas it is connected to. This project asks whether those connected areas are not just associated with visual recognition, but actually necessary for it. We use fMRI to identify the key network partners of each category-selective region for individual participants, then apply TMS to those areas while participants complete a recognition task. If disrupting a face-network region selectively impairs face recognition but leaves body and object recognition intact (and likewise for the other categories), this would provide direct causal evidence that these brain networks are functionally specialized for their respective visual categories.

Jingyi Luo. The experience of affect is in part supported by interoception, or the brain’s representation of one’s internal bodily states. The proposed study adopts a network neuroscience approach to examine the function and organization of large-scale interoception-related brain systems, the allostatic-interoceptive system (AIS), during affect induction in an adolescent sample. Specifically, adolescent participants completed a behavioural task measuring their accuracy in perceiving heart rate and a naturalistic social affective video viewing task during an fMRI scan. The study looks at the association between behavioral cardiac interoceptive accuracy and the efficiency of information flow within and between brain networks during affect. 

Quinn Meyerson. This project will examine the preliminary effects of an online, asynchronous adaptation of a mindfulness-based stress reduction (iMBSR) program on neural, behavioral, self-report, and daily-life measures of mind-wandering. Using data from the Internet-based Mind-Body Training (IMBT) Stage 1 randomized controlled trial, this project will analyze how mindfulness training influences the brain networks implicated in mind-wandering and whether these neural changes manifest behaviorally in the lab and daily life. We hypothesize that, relative to our active control, iMBSR will demonstrate increased intra-network connectivity of the default mode network, reductions in self-reported mind-wandering in both lab-based tasks and daily life, as well as a reduction in behavioral mind-wandering during lab-based tasks.

Derrek Montalvo. My study investigates how the brain filters out distracting information while maintaining goal-relevant information. Previous work has shown that salient distractions not only capture attention but can cause irrelevant information to be unintentionally encoded into visual working memory (VWM). The goal of my study is to use TMS to disrupt activity in brain regions associated with attentional filtering and top-down control while participants complete visual search tasks in which distracting information is encountered. We aim to determine whether disrupting these regions alters how strongly attention is captured by salient distractors and how selectively irrelevant information is encoded into VWM. This research aims to advance our understanding of how attentional filtering sometimes fails in dynamic visual environments.
 

Xinnan Wang. My research focuses on understanding how the human brain develops before birth using fetal functional MRI (fMRI). This project investigates how hypertensive disorders of pregnancy (HDP), including preeclampsia, may alter the development of fetal brain functional networks. Using a unique fetal fMRI dataset collected at CCBBI, together with a publicly available cohort, I will identify and compare brain networks to better understand the earliest neural effects of adverse prenatal environments. Findings from this work may improve our understanding of how prenatal health influences later neurodevelopment and inform future strategies for early detection and intervention.

Dan Zhu. Rapid advances in artificial intelligence (AI) have enabled widespread human bonding with AI chatbots. The proposed study aims to characterize how people perceive and relate to AI companions. Participants will complete three weeks of daily self-disclosure chats with a Replika AI companion to build rapport and then undergo fMRI scans while playing rock-paper-scissors against a human partner, their AI companion, and a control agent. Their brain activation patterns during interaction will be analyzed together with behavioral data collected before the scan.