Volume 1 Issue 1 (2026)

Autism spectrum disorder (ASD) is associated with atypical functional connectivity (FC), but its relationship with verbal ability remains unclear. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) data from 219 individuals with ASD and 322 healthy controls (HCs) with valid verbal intelligence quotient (VIQ) scores were obtained from 12 sites in the Autism Brain Imaging Data Exchange (ABIDE) database. Region of interest (ROI)-based FC and whole-brain network metrics were computed and correlated with VIQ. Three feature sets—raw connectivity and topology, group-difference metrics, and group-difference metrics combined with VIQ—were evaluated using support vector machine (SVM) and manifold learning. ASD showed widespread FC alterations with overall increases and localized decreases in specific networks. At the global level, ASD patients exhibited a significantly lower area under the receiver operating characteristic curve (AUC) specifically for the normalized clustering coefficient γ compared with HCs. Nodal analyses revealed increased degree centrality and efficiency in subcortical and frontal regions, but decreased values in limbic and temporal regions in ASD. Centrality of the anterior cingulate cortex showed a negative correlation with VIQ, while nodal efficiency and degree centrality in occipital regions showed positive correlations. Classification using the raw feature set achieved the best overall performance under the Principal Component Analysis-Uniform Manifold Approximation and Projection-Support Vector Machine (PCA-UMAP-SVM) pipeline, whereas group-difference features augmented with VIQ yielded the highest AUC among the standard SVM models. These results suggest that VIQ-anchored network features may help explain variability in verbal ability and brain organization in ASD.

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Language research has never been richer; spanning formal theory, documentation, neuroscience, psychology, education and AI. Yet it remains partitioned by disciplinary silos, methodological habits, and Western, Educated, Industrialized, Rich, and Democratic (WEIRD) sampling biases. We argue that sustained progress on core problems, i.e., how language is learned, processed, varies, breaks down and can be engineered, requires “retiring linguistics” as an isolated discipline and consolidating expertise within an integrated Language Science. This shift does not compromise rigor; rather, it situates formal modelling alongside quantitative evidence, field-based research, clinical and technological applications. Recent advances that bridge traditional boundaries such as neurosemantic mapping, speech neuroprosthetics, and computational approaches to sociolinguistic variation illustrate the potential of such integration. At the same time structural barriers including departmental incentives, fragmented training pathways, and inconsistent terminology continue to limit coordination across fields. This article combines a critical review of contemporary language research with a concrete proposal for institutional and epistemic reforms: transdisciplinary institutes and appointments, evaluation criteria that reward collaboration, curricula that braid theory, computation and field methods, funding and venues for cross-field work, ethical frameworks centred on partnership, and benefit-sharing with language communities. Unifying around problems rather than on departments can deliver more universally applicable research and greater societal benefit, from equitable language technologies and education, to improved clinical outcomes, by aligning explanations across levels from neurons to social networks.

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