Human language comprehension requires transforming linear word sequences into hierarchical structure. Using Mandarin as a case study, we investigated how the brain builds phrase-level structure from two sequentially presented words. Participants processed the same phrases in two tasks emphasizing either the semantic meaning of the phrase or the composition relation between the two words that form the phrase. We focused on three components of phrase building–the syntactic category of individual words, the syntactic category of the resulting constituent, and the composition relation between the two words–and examined them using representational similarity analysis, Bayesian hierarchical modeling, and variance decomposition with semantic controls. Word-category representations emerged during second-word processing and were observed mainly in left middle temporal and inferior frontal regions. Phrasal constituent category representations were found in left posterior temporal, left supramarginal, and right inferior frontal regions. Composition-relation representations were detected in left angular and left frontal regions and remained after controlling for constituent category, indicating that word-to-word relations and resulting constituent categories capture distinct aspects of composition. Variance decomposition showed that structural effects in several regions could not be fully reduced to embedding-derived semantic similarity. Task demands further modulated these representations differently: word category effects were largely task-independent, whereas constituent category effects showed frontal modulation and composition relation effects were strongest when the task explicitly required relation judgments. Together, these findings show that different aspects of phrase structure are represented in different brain regions and help explain how the brain combines words into phrases and builds more complex linguistic structures.
Fei-Zhen Cao, M. Xiang, Shu-Guang Yang et al.· Neurobiology of Language· 0 citations
Hepatocellular carcinoma (HCC) exhibits substantial interpatient heterogeneity, leading to markedly variable outcomes and survival even among patients with similar stages and imaging phenotypes. Mainstream staging systems remain suboptimal, whereas pathology-dependent factors and high-cost genomic assays are neither scalable nor timely for clinical decision-making. Existing algorithms provide coarse risk stratification or static binary predictions, failing to capture the time-varying risk of death. We developed and externally validated TEMPO-HCC, a multimodal deep survival model with hierarchical interpretability, to estimate individualized overall survival risk trajectories after curative-intent resection. We curated a six-center cohort of 1475 patients and integrated multiphasic MRI, postoperative H&E whole-slide images, and perioperative predictors. A discrete-time survival head generated probabilities at 1, 2, 3, and 5 years after surgery. TEMPO-HCC outperformed unimodal models and guideline-based staging systems, achieving C-index of 0.751 and time-dependent AUCs of 0.836, 0.781, 0.812, and 0.680 at 12, 24, 36, and 60 months, respectively, in the external validation cohort. TEMPO-HCC represents a paradigm shift from static binary classification toward clinically actionable temporal risk prediction. Its hierarchical interpretability provides an auditable evidence chain linking macro-scale radiologic phenotypes to micro-scale histopathologic patterns. Importantly, augmenting guideline staging with TEMPO-HCC improved discrimination, enabling personalized postoperative surveillance and risk-adapted clinical management.
Fan Li, Huancheng Yang, Ruishan Liu et al.· npj Digital Medicine· 0 citations
Surf_2_Volume is presented, a workflow that combines Connectome Workbench, FreeSurfer, AFNI, AFNI, neuromaps, and Python image processing to convert cortical and subcortical CIFTI parcellations into Neuroimaging Informatics Technology Initiative (NIfTI) volumes.
Shu-Guang Yang, Zi-Yi Wang, Yue-Ru Shen et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
