arXiv subjects

Suiping Wang

Publications and source records attributed to Suiping Wang.

3 recordsLinked to original sources

arXiv2026

Surf_2_Volume: a workflow for converting CIFTI parcellations to NIfTI volume space

Parcellations distributed in Connectivity Informatics Technology Initiative (CIFTI) format cannot be used directly in many analysis programs that require volume input. Existing conversion options may leave voxels in cortical gray matter unlabeled or assign labels outside gray matter, depending on the mapping parameters. We present Surf_2_Volume, a workflow that combines Connectome Workbench, FreeSurfer, AFNI, neuromaps, and Python image processing to convert cortical and subcortical CIFTI parcellations into Neuroimaging Informatics Technology Initiative (NIfTI) volumes. The workflow separates cortical and subcortical components, transfers cortical labels through fsaverage and a surface representation of the target MNI152 template, restricts voxel assignment using an adjustable probability threshold for gray matter, and recombines the components. Using the Cole-Anticevic Brain-wide Network Partition, Surf_2_Volume had an adjusted Dice score of 0.776, compared with a maximum of 0.637 among the evaluated Connectome Workbench settings. In a separate test using the Schaefer 2018 17-network volume atlas, the scores were 0.727 for Surf_2_Volume and 0.535 for the best Workbench setting. Across both atlas evaluations, Surf_2_Volume had higher adjusted Dice scores than the evaluated Workbench settings. The workflow provides a way to use surface parcellations in software that requires NIfTI input while allowing explicit control over gray matter coverage.

q-bio.QM↗

arXiv2026

Predictive and feedback signals differently shape the formation of group-level and individualized language representations

Adults vary greatly in how effectively they learn a new language, but the signals driving the learning processes and individual differences remain unclear. Over seven days, we tracked behavioral learning and collected fMRI data from 102 adults as they learned an artificial language with corrective feedback. We trained matched transformer models with prediction, feedback, or combined objectives and compared their internal representations to brain activity. Representations derived from the prediction-focused model accounted for the largest share of unique neural variance at the group level, despite the human task being feedback-based. Throughout model training, both objectives showed a shift in brain-model alignment from sensory to higher-order language and associative networks, indicating abstraction processing. Conversely, neural patterns related to the feedback model were most useful for predicting individual generalization outcomes on Day 7. These findings support a multi-signal model of adult language learning, in which prediction shapes a common neural learning architecture across learners, whereas feedback-related mechanisms better explain individual differences over time.

q-bio.NC↗

arXiv2025

Multi-network Topology Underlying Individual Language Learning Success

Adult language learning varies greatly among individuals. Traditionally associated with frontotemporal language regions, this variability is increasingly seen as stemming from distributed brain networks. However, the role of these networks and their topological organization in explaining these differences remains unclear. We hypothesize that graph-theory-based network analysis of intrinsic multimodal connectivities across multiple networks explains overall and component-specific variations in language learning. We tested this in 101 healthy adults who underwent resting-state fMRI, structural MRI, and diffusion tensor imaging before seven days of six artificial language training tasks. We identified one dominant general learning component shared across tasks and five task-specific ones. Cross-validated predictive models used multimodal multi-network graph-theoretic metrics to predict final learning outcomes (LO) and rates (LR). We significantly predicted the LO and LR of the general component, which were primarily contributed by dorsal attention and frontoparietal networks. Nodal local efficiency was the most consistent predictor, with additional contributions from node clustering coefficient and network centrality for LR, highlighting local robustness, mesoscale network segregation, and global influence in explaining individual differences. Only task-specific word learning LO was predictable, relying on default mode and frontoparietal hubs with high betweenness centrality and efficiency. These findings demonstrate that intrinsic network topologies underlie differences in language learning success, supporting a multiple-systems hypothesis in which attentional-control networks interact with default and subcortical systems to shape learning trajectories. This advances mechanistic understanding and paves the way for personalized language education.

q-bio.NC↗