Biology subjects

Chang, Y.-N.

Publications and source records attributed to Chang, Y.-N..

5 recordsLinked to original sources

bioRxiv2022

Distance-dependent distribution thresholding in probabilistic tractography

Tractography is widely used in human studies of connectivity with respect to every brain region, function, and is explored developmentally, in adulthood, aging, and in disease. However, the core issue of how to systematically threshold, taking into account the inherent differences in connectivity values for different track lengths, and to do this in a comparable way across studies has not been solved. The study adopted Monte Carlo derived distance-dependent distributions (DDDs) to generate distance-dependent thresholds with various levels of alpha for connections of varying lengths. As a test case, we applied the DDD approach to generate a language connectome. The resulting connectome showed expected short- and long-distance structural connectivity in the close and distant regions within the language network. The finding demonstrates that the DDD approach can be used for both individual and group thresholding. Critically, it offers a standard method that can be applied to various probabilistic tracking datasets.

neuroscience↗

bioRxiv2021

A computational model of typical and impaired reading: the role of visual processing

Computational modelling has served as a powerful tool to advance our understanding of language processes by making theoretical ideas rigorously specified and testable (a form of " open science" for theory building). In reading research, one of the most influential computational modelling frameworks is the triangle model of reading that characterises the mappings between orthography, phonology and semantics. Currently, most instantiations of the triangle modelling framework start the processes from orthographic levels which abstract away visual processing. Moreover, without visual processing, most models do not provide an opportunity to investigate visual-related dyslexia. To bridge this crucial gap, the present study extended the existing triangle models by implementing an additional visual input. We trained the model to learn to read from visual input without pre-defined orthographic representations. The model was assessed by reading tasks in both intact and after damage (to mimic acquire alexias). The simulation results demonstrated that the model was able to name word and nonwords as well as make lexical decisions. Damage to the visual, phonological or semantic components of the model resulted in the expected reading impairments associated with pure alexia, phonological dyslexia, and surface dyslexia, respectively. The simulation results demonstrated for the first time that both typical and neurologically-impaired reading including both central and peripheral dyslexia could be addressed in this extended triangle model of reading. The findings are consistent with the primary systems account.

neuroscience↗

bioRxiv2020

A unified neurocomputational bilateral pathway model of spoken language production in healthy participants and recovery in post-stroke aphasia

Understanding the processes underlying normal, impaired and recovered language performance has been a long-standing goal for cognitive and clinical neuroscience. Many verbally-described hypotheses about language lateralisation and recovery have been generated. However, they have not been considered within a single, unified and implemented computational framework, and the literatures on healthy participants and patients are largely separated. These investigations also span different types of data, including behavioural results and fMRI brain activations, which augment the challenge for any unified theory. Consequently, many key issues, apparent contradictions and puzzles remain to be solved. We developed a neurocomputational, bilateral pathway model of spoken language production, designed to provide a unified framework to simulate different types of data from healthy participants and aphasic patients. The model encapsulates key computational principles (differential computational capacity, emergent division of labour across pathways, experience-dependent plasticity-related recovery) and provides an explanation for the bilateral yet asymmetric lateralisation of language in healthy participants, chronic aphasia after left rather than right hemisphere lesions, and the basis of partial recovery in patients. The model provides a formal basis for understanding the relationship between behavioural performance and brain activation. The unified model is consistent with the degeneracy and variable neuro-displacement theories of language recovery, and adds computational insights to these hypotheses regarding the neural machinery underlying language processing and plasticity-related recovery following damage. Significance StatementStudies of healthy and impaired language have generated many verbally-described hypotheses. Whilst these verbal descriptions have advanced our understanding of language processing, some explanations are mutually incompatible and it is unclear how they work mechanistically. We constructed a neurocomputational bilateral model of spoken language production to simulate a range of phenomena in healthy participants and patients with aphasia simultaneously, including language lateralisation, impaired performance after left but not right damage, and hemispheric involvement in plasticity-dependent recovery. The model demonstrates how seemly contradictory findings can be simulated within a single framework. To our knowledge, this provides the first coherent mechanistic account of language lateralisation and recovery from post-stroke aphasia.

neuroscience↗

bioRxiv2019

A Computational Model of Normal and Impaired Lexical Decision: Graded Semantic Effects

Lexical decision is an important paradigm in studies of visual word recognition yet the underlying mechanisms supporting the activity are not well understood. While most models of visual word recognition focus on orthographic processing as the primary locus of the lexical decision, a number of behavioural studies have suggested a flexible role for semantic processing regulated by the similarity of the nonword foil to real words. Here we developed a computational model that interactively combines visual-orthographic, phonological and semantic processing to perform lexical decisions. Importantly, the model was able to differentiate words from nonwords by dynamically integrating measures of polarity across the key processing layers. The model was more reliant on semantic information when nonword foils were pseudowords as opposed to consonant strings. Moreover, the model was able to capture a range of standard reading effects in lexical decision. Damage to the model also resulted in reading patterns observed in patients with pure alexia, phonological dyslexia, and semantic dementia, demonstrating for the first time that both normal and neurologically-impaired lexical decision can be addressed in a connectionist computational model of reading.

neuroscience↗

bioRxiv2019

Structural basis for functional interactions in dimers of SLC26 transporters

The SLC26 family of transporters maintains anion equilibria in all kingdoms of life. The family shares a 7 + 7 transmembrane segments inverted repeat architecture with the SLC4 and SLC23 families, but holds a regulatory STAS domain in addition. While the only experimental SLC26 structure is monomeric, SLC26 proteins form structural and functional dimers in the lipid membrane. Here we resolve the structure of an SLC26 dimer embedded in a lipid membrane and characterize its functional relevance by combining PELDOR distance measurements and biochemical studies with MD simulations and spin-label ensemble refinement. Our structural model reveals a unique interface different from the SLC4 and SLC23 families. The functionally relevant STAS domain exerts a stabilizing effect on regions central in this dimer. Characterization of heterodimers indicates that protomers in the dimer functionally interact. The combined structural and functional data define the framework for a mechanistic understanding of functional cooperativity in SLC26 dimers.

biochemistry↗