Biology subjects

Liu, Y.-F.

Publications and source records attributed to Liu, Y.-F..

8 recordsLinked to original sources

bioRxiv2025

Rapid "recycling" of logical algorithm representations in fronto-parietal reasoning systems following computer programming instructions

Programming is a cornerstone of modern society, yet its cognitive and neural basis remains poorly understood. In this study, we test the hypothesis that programming "recycles" pre-existing neural mechanisms and representations in fronto-parietal reasoning networks. Using fMRI, we scanned programming-naive undergraduates (n=22) before (PRE) and after (POST) an introductory Python course. During the PRE scan, participants viewed pseudocode (plain English descriptions of algorithms), and during the POST scan, they read Python code. We found that a left-lateralized fronto-parietal network, previously implicated in programming experts, distinguished between "for" loops and "if" conditionals across both pseudocode and Python code. Representational similarity analysis revealed consistent representations of algorithms across formats (code/pseudocode) and learning stages. Furthermore, such representations encode abstract meanings rather than superficial features. Our findings demonstrate that programming not only recycles pre-existing neural resources evolved for logical reasoning, but the recycling takes place rapidly with only a single semester of training.

neuroscience↗

bioRxiv2024

High-Efficiency Capture and Proteomic Analysis of Plasma-Derived Extracellular Vesicles through Affinity Purification

Plasma-derived extracellular vesicles (EVs) are promising sources of biomarkers. It is still a challenge to isolate EVs from a small amount of human plasma for downstream proteomic analysis. The separation process is hindered by contamination with high-abundance blood proteins and lipoprotein particles, which adversely impact proteomic analyses. Moreover, although EVs immune-separation via magnetic beads often integrates with flow sorting and western blotting (WB), it lacks compatibility with nanoparticle tracking analysis (NTA) and proteomic analysis. To address these issues, we have developed a functional affinity magnetic bead, EVlent (Extracellular Vesicles isoLated Efficiently, Naturally, and Totally), enabling the rapid and efficient separation of EVs from plasma. By optimizing the quantities of magnetic beads and plasma used, we characterized the isolated EVs through WB, NTA, and transmission electron microscopy (TEM), showing a successfully separation of EVs from plasma. Proteomic analysis of these EVs identified over 2,000 proteins and 15,000 peptides from just 100 L of plasma, and nearly 1,000 proteins from trace samples as small as 5 L. Additionally, this isolation method significantly reduced contaminants, including plasma proteins and lipoproteins, compared to ultracentrifugation. Finally, we applied this strategy to plasma samples of healthy individuals and those with Parkinsons disease, identifying four potential biomarkers that provide a promising guidance for clinical diagnosis.

biochemistry↗

bioRxiv2022

Neural signatures of reading-related orthographic processing in braille

Blind readers use a tactile reading systems consisting of raised dot arrays: braille/. How does the human brain implement reading by touch? The current study looked for signatures of reading-specific orthographic processes in braille, separate from low-level somatosensory responses and semantic retrieval. Of specific interest were responses in posterior parietal cortices (PPC), because of their role in high-level tactile perception. Congenitally blind, proficient braille readers read real words and pseudowords by touch while undergoing fMRI. We leveraged the system of contractions in English braille, where one or more braille cells can represent combinations of English print letters (e.g., "ing" , "one" ), making it possible to separate physical and uncontracted letter-length. All words in the study consisted of 4 braille cells, but their corresponding Roman spellings varied from 4 to 7 letters (e.g., "con-c-er-t" . contracted: 4 cells; uncontracted: 7 letters). We found that the bilateral supramarginal gyrus (SMG) in the PPC increased its activity as the uncontracted word length increased. By contrast, in the hand region of primary somatosensory cortex (S1), activity increased as a function of a low-level somatosensory feature: dot-number per word. The PPC also showed greater response to pseudowords than real words and distinguished between real and pseudowords in multi-voxel-pattern analysis. Parieto-occipital, early visual and ventral occipito-temporal, as well as prefrontal cortices also showed sensitivity to the real-vs-pseudoword distinction. We conclude that PPC is involved in sublexical orthographic processing for braille, possibly due to brailles tactile modality. Significance statementBlind readers use tactile reading systems of raised dot arrays: braille. To identify signatures of orthographic processing for reading by touch, and dissociate it from tactile and linguistic process, we leveraged the system of contractions in English braille, where one or more braille characters represents combinations of English print letters. Blind proficient braille readers read real words and pseudowords during fMRI scans. While all words consisted of 4 braille characters, the uncontracted spelling ranged from 4-7 letters. Activity in bilateral-posterior-parietal cortices, just posterior to primary-somatosensory cortex, increased with uncontracted word length, independent of tactile complexity (number of raised dots per word). By contrast, primary-somatosensory activity increased with tactile complexity. The posterior-parietal cortices contribute to tactile reading.

neuroscience↗

bioRxiv2022

Structural Genomics of the Human Dopamine Receptor System

The dopamine system, including five dopamine receptors (D1R to D5R), plays essential roles in the central nervous system (CNS) and ligands that activate dopamine receptors have been used to treat many neuropsychiatric disorders, including Parkinsons Disease (PD) and schizophrenia. Here, we report five cryo-EM structures of all subtypes of human dopamine receptors in complex with G-protein and bound to the pan agonist, Rotigotine, which is used to treat PD and restless legs syndrome. The structures reveal the basis of Rotigotine binding modes to different dopamine receptors. Structural analysis together with functional assays illuminate determinants of ligand polypharmacology and selectivity. The structures also uncover the mechanisms of the dopamine receptor activation, unique structural features among the five receptor subtypes, and the basis of G-protein coupling specificity. Our works provide a comprehensive set of structural templates for the rational design of specific ligands to treat CNS diseases targeting the dopaminergic system.

biochemistry↗

bioRxiv2020

Narratives: fMRI data for evaluating models of naturalistic language comprehension

The "Narratives" collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.

neuroscience↗

bioRxiv2020

Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network

Despite the importance of programming to modern society, the cognitive and neural bases of code comprehension are largely unknown. Programming languages might recycle neurocognitive mechanisms originally used for natural languages. Alternatively, comprehension of code could depend on fronto-parietal networks shared with other culturally derived symbol systems, such as formal logic and math. Expert programmers (average 11 years of programming experience) performed code comprehension and memory control tasks while undergoing fMRI. The same participants also performed language, math, formal logic, and executive control localizer tasks. A left-lateralized fronto-parietal network was recruited for code comprehension. Patterns of activity within this network distinguish between "for" loops and "if" conditional code functions. Code comprehension overlapped extensively with neural basis of formal logic and to a lesser degree math. Overlap with simpler executive processes and language was low, but laterality of language and code covaried across individuals. Cultural symbol systems, including code, depend on a distinctive fronto-parietal cortical network.

neuroscience↗

bioRxiv2020

Think Like an Expert: Neural Alignment Predicts Understanding in Students Taking an Introduction to Computer Science Course

How do students understand and remember new information? Despite major advances in measuring human brain activity during and after educational experiences, it is unclear how learners internalize new content, especially in real-life and online settings. In this work, we introduce a neural measure for predicting and assessing learning outcomes. Our approach hinges on the idea that successful learning involves forming the "right" set of neural representations, which are captured in "canonical" activity patterns shared across individuals. Specifically, we hypothesized that understanding is mirrored in "neural alignment": the degree to which an individual learners neural representations match those of experts, as well as those of other learners. We tested this hypothesis in a longitudinal functional MRI study that regularly scanned college students enrolled in an introduction to computer science course. We additionally scanned graduate student "experts" in computer science. We found that alignment among students successfully predicted overall performance in a final exam. Furthermore, within individual students, concepts that evoked better alignment with the experts and with their fellow students were better understood, revealing neural patterns associated with understanding specific concepts. These results provide support for a novel neural measure of concept understanding that can be used to assess and predict learning outcomes in real-life contexts.

neuroscience↗

bioRxiv2019

Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space

Connectivity hyperalignment can be used to estimate a single shared response space across disjoint datasets. We develop a connectivity-based shared response model that factorizes aggregated fMRI datasets into a single reduced-dimension shared connectivity space and subject-specific topographic transformations. These transformations resolve idiosyncratic functional topographies and can be used to project response time series into shared space. We evaluate this algorithm on a large collection of heterogeneous, naturalistic fMRI datasets acquired while subjects listened to spoken stories. Projecting subject data into shared space dramatically improves between-subject story time-segment classification and increases the dimensionality of shared information across subjects. This improvement generalizes to subjects and stories excluded when estimating the shared space. We demonstrate that estimating a simple semantic encoding model in shared space improves between-subject forward encoding and inverted encoding model performance. The shared space estimated across all datasets is distinct from the shared space derived from any particular constituent dataset; the algorithm leverages shared connectivity to yield a consensus shared space conjoining diverse story stimuli.\n\nHighlightsO_LIConnectivity SRM estimates a single shared space across subjects and stimuli\nC_LIO_LITopographic transformations resolve idiosyncrasies across individuals\nC_LIO_LIShared connectivity space enhances spatiotemporal intersubject correlations\nC_LIO_LISemantic model-based encoding and decoding improves across subjects\nC_LIO_LITransformations project into a consensus space conjoining diverse stimuli\nC_LI

neuroscience↗