Abstract Phonological processing is widely considered central to reading development in hearing individuals, with the temporoparietal cortex (TPC) regarded as essential for encoding phonological information from print. Hearing people who struggle to read show altered activation and less precise phonological representations in this region. Notably, skilled deaf readers show this same profile, suggesting that precise phonological representations in the TPC are not necessary for proficient reading. This raises the question of what the TPC encodes when phonological access differs. Using fMRI, we compared skilled deaf and hearing readers viewing real words (RWs), pseudowords (PWs), and pseudohomophones (PHs)—letter strings that are orthographically novel but phonologically identical to real words (e.g., brane). Because PHs carry no lexical or semantic identity, they can isolate whole word phonological representations accessed through print. We examined BOLD signal differences within individually localized TPC ROIs. Deaf and hearing readers showed distinct response patterns. Hearing readers showed equivalent responses to PHs and RWs, both greater than PWs ((RW = PH) < PW), indicating that lexical-level phonological representations were accessed from print. Deaf readers showed the largest response to RWs and equivalent responses to PHs and PWs (RW < (PH = PW)), indicating that whole-word phonological forms were not reliably accessed from print. These findings suggest that in skilled deaf readers phonological information from print is encoded with reduced precision and/or less automatically, supporting the proposal that deaf readers achieve literacy through a potentially unique reading architecture where the direct orthographic-to-semantic pathway carries the primary functional load.
Abstract Real-world language processing unfolds in environments of volatility and uncertainty, requiring comprehenders to continuously adapt semantic predictions as environmental statistics change. However, the fundamental computational-neural mechanisms supporting such hierarchical adaptive prediction remain incompletely understood. Critically, our goal was to examine how comprehenders update their beliefs about high-level environmental reliability as evidence accumulates across trials, and how this update modulates low-level semantic prediction. Here, we introduce a paradigm that extends traditional filler-based designs and allows for the examination of trial-by-trial adaptation and corresponding neural responses. We further implemented a computational neural analytical framework based on the Hierarchical Gaussian Filter (HGF) to model the trial-by-trial hierarchical inference process. We showed that environmental statistical regularities systematically modulate multiple neural signatures, from the well-established N400 to later ERP components, such as late anterior positivity (LAP) and P600, as well as cortical oscillatory components in the theta and beta bands. Model-based analyses showed that distinct computational variables were reflected in dissociable neural signatures. At the ERP level, N400 activity was associated with both high-level belief states and precision-weighted prediction error (pwPE2), whereas LAP was more strongly associated with pwPE2. At the level of neural oscillations, theta-band activity was associated with high-level belief states, whereas beta-band activity selectively tracks prior belief2. Together, these findings indicate that distinct neural signatures are differentially associated with complementary components of hierarchical Bayesian inference, supporting the ongoing estimation of the environmental statistical structure. More broadly, our results provide model-based evidence that hierarchical predictive coding operates in advanced language comprehension under uncertainty.
Abstract Developmental dyslexia (DD) is a common neurodevelopmental disorder, and abnormalities in the functional brain network structure are suggested to be closely related to dysregulation of connections between brain regions. However, the applicability of different brain atlas segmentation strategies in DD brain network modeling has not been systematically evaluated. To this end, this study constructed brain functional networks using three brain atlases (AAL, Craddock, and Power) for comparative analysis and built a Graph Convolutional Network (GCN) classification framework that integrates traditional graph-theoretic features and higher-order global topological features extracted using Persistent Homology (PH). The results of the experiment show that the AAL template performs best in DD recognition, with a classification accuracy rate of 83.31%. Additionally, by converging cross-threshold topological feature stability analysis with Grad-CAM model attribution, we identified a set of brain regions that exhibited both robust intergroup differences across multiple sparsity levels and high importance in the GCN’s decision-making process, thereby jointly pinpointing the core neural substrates of DD. Our results provide a theoretical basis for brain atlas selection and functional connectivity modeling and offer a new path for optimizing DD-assisted recognition methods.
Abstract Understanding how the structure of a writing system shapes neural organization is critical to uncovering the biological and experiential bases of literacy and neurocognition. Here, we used machine learning and functional magnetic resonance imaging to identify language-distinct brain activity patterns in Chinese, English, Hebrew and Spanish readers. This model captured neural activity patterns common across readers of a given language, and those which discriminated readers of different writing systems. Accordingly, we observed strong differentiation of visually distinct languages (Chinese vs. Hebrew vs. English/Spanish) in ventral occipital regions, but weaker ability to differentiate English from Spanish, which use a common alphabetic code. In contrast, left posterior fusiform and occipital-temporal cortex provided accurate classification of English, Spanish, and Hebrew, but chance performance of Chinese. More idiosyncratic patterns were also observed, including a unique advantage toward Hebrew in left inferior parietal gyrus, perhaps reflecting its right-to-left writing direction. Finally, weak overall classification in temporal and frontal regions that form the classical speech network suggests these regions are less likely to be shaped by reading-specific experience. Overall, the finding that writing systems induce dissociable patterns of activity in only selective subregions of the reading network expands our understanding of how a universally constrained reading network can be shaped by the varying characteristics of the world's writing systems.
Abstract Congenital blindness provides a unique model for studying sensory deprivation and experience-dependent brain plasticity. While neural plasticity is pronounced during early sensitive periods, it remains unclear how the brain’s intrinsic functional architecture changes across development in congenital blindness and how changes relate to higher order cognitive functions, such as language processing. We integrated multimodal resting-state functional magnetic resonance imaging metrics with language behavioral components in 31 congenitally blind individuals (children and adults) and matched sighted controls. Two distinct age-related patterns emerged: (1) elevated metrics in ventral temporal and frontal regions during blind childhood that diminished in adulthood, and (2) reduced metrics in dorsal parietal regions in blind childhood that became enhanced in adulthood. Among resting-state metrics, regional homogeneity and degree centrality—reflecting local synchrony and network-level integration—were particularly sensitive to these age-related changes. Behaviorally, significant brain-language correlations were evident primarily in adults. In both blind and sighted adults (SA), the intrinsic functional architecture of the inferior frontal and middle temporal gyrus correlated with modality-independent language-related processing. In addition, experience specialization also emerged: the left hippocampus preferentially correlated with visual- and language-related processing in SA, whereas the left superior temporal gyrus preferentially correlated with language-related processing in blind adults. In contrast, blind children exhibited a broader network related to tactile processing. These findings suggest that the intrinsic functional neural plasticity in congenital blindness is a dynamic and multi-level process, and its formation might be shaped by multiple factors such as the inherent structure, experience, and age.
