Cortical tracking, a method that examines how neural activity encodes the dynamic features of the incoming speech stimuli, allows for the study of naturally produced continuous speech. Successful encoding of acoustic features is fundamental for language processing and comprehension. Studies show that cortical tracking of at least some acoustic speech features is already robust in the first year of life. However, it is unclear whether bilingual infants exhibit enhanced cortical tracking of non-native languages compared to monolingual infants, consistent with the idea of having a "bilingual advantage" as suggested in prior research. To investigate this, we recorded neural responses from 11-month-old English learning monolinguals, English-Mandarin learning bilinguals, and two mature comparison groups of English monolingual and English-Mandarin bilingual adults, while they listen to naturally produced, continuous, infant directed speech using electroencephalography (EEG) in three conditions: English, Mandarin, and Vietnamese. Stimuli were presented at an overall level of 70 dB SPL in a sound-attenuated booth. Using a combination of machine learning and linear modeling (i.e., Multivariate Temporal Response Function approach), we analyze the EEG signals using a multivariate encoding model consisting of acoustic features including envelope, envelope derivative, word onset, and phoneme onset. We hypothesize that both bilingual adults and infants will exhibit enhanced encoding of acoustic features in Vietnamese compared to monolingual adults and infants, indicating bilingual advantage in processing a third language. Additionally, we anticipate the bilingual advantage to be more prominent in infants than adults. These findings will contribute to the understanding of how bilingualism influences neural encoding across different languages and provide neural evidence of bilingual advantage in processing and acquiring a third language. I participated in study design, recruitment, data acquisition and analysis.

Brains can somehow maintain functionality despite significant neuron loss. However, we do not yet fully understand what factors contribute to this robustness or under what conditions brains become fragile to neuron loss. Research in our lab has identified two types of neurons: those that, when removed, lead to large changes in the network’s expected activity patterns, and those that do not appear to be so critical. My research aims to address this gap. I study the network properties that confer robustness in an ideal system: the Drosophila fly, the most complex organism with a fully mapped brain at ~140,000 neurons. I am focusing on one particular brain region, the Antennal Motor and Mechanosensory Center (AMMC), because it is a primary sensory region that receives direct connections from the fly’s ear (the antennae) and contributes to auditory-driven behaviors such as courtship, which we can easily measure.  I have found that many anatomically distinct neurons share high level network properties. I hypothesize that the morphological and network properties of these neurons make them special. Here, I investigate the morphological features of several neurons, such as arborization patterns, neurotransmitter profile, and synaptic partners, and also search for genetic driver lines through a large database that will help us test the impact of these neurons in a living fly. Investigating the relationship between neural properties and robustness to removal of a neuron is crucial for developing a deeper understanding of how brain circuitry copes with injuries and the progression of neurodegenerative diseases.

Multitasking, such as walking and talking, is common for humans and other animals, yet we are limited in how many behaviors we can perform simultaneously. The neural circuit mechanisms that limit multitasking are not well understood. Uncovering these mechanisms will help us understand how brains combine some, but not all, behaviors during normal function, but also in the context of aging and neurological disorders such as Parkinson’s disease, where multitasking gets compromised. The fruit fly Drosophila melanogaster walks and “sings” by vibrating a wing during courtship, in a natural example of multitasking. These stereotyped behaviors are controlled by a relatively simple brain, which can be experimentally driven via artificial stimulation of key neurons, making the fly an amenable model to study multitasking. I therefore developed a platform to record and manipulate the interaction between locomotion and “singing”. I will activate sing-inducing neurons during two contexts, when flies are stationary (single-tasking) vs. moving (multitasking). I hypothesize that singing characteristics will change depending on context. For example, multitasking may decrease the likelihood of singing because the fly’s nervous system is “busy” controlling locomotion. Alternatively, locomotor context may make it easier to drive wing vibrations because of the higher activity levels in the circuits involved. My results will therefore help uncover how neural circuit interactions shape an animal’s ability to multitask.