Neurotransmitter circuits of the human language network and their disruption in post-stroke aphasia
Year: 2026
Type: preprint
Abstract: Language depends on distributed brain networks whose neurochemical organisation remains largely unknown. Here, we combine large-scale functional MRI, positron-emission tomography–derived receptor and transporter maps, and structural connectomics to characterise the neurotransmitter architecture of the human language network and its disruption in post-stroke aphasia. We show that language circuits are organised by connection class: cortico-cortical pathways are dominated by serotonergic and glutamatergic signalling, thalamo-cortical projections are predominantly cholinergic, and anterior temporal connections show GABAergic circuit enrichment. In 239 stroke patients, aphasia was associated with distinct neurochemical disruption profiles, with lobar lesions showing postsynaptic serotonergic injury and subcortical aphasia showing preferential presynaptic cholinergic or serotonergic disruption. These findings map the neurochemical architecture of the language network and establish a neurotransmitter circuit-level framework for post-stroke aphasia. The post-hoc analysis of four pharmacological trials revealed an association between treatment-to-neurotransmitter matching and aphasia improvement, suggesting that personalised pharmacological strategies may enhance behavioural intervention responsiveness.
Institutions: University of Lisbon, Hospital de Santa Maria, Unidade Local de Saúde Santa Maria, University of South Carolina, Instituto de Investigación Biomédica de Málaga, Universidad de Málaga, UCL Queen Square Institute of Neurology, University College London, Centre National de la Recherche Scientifique, Université de Bordeaux, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Institut des Maladies Neurodégénératives, Radboud University Nijmegen, Max Planck Institute for Psycholinguistics
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: European Social Fund
Award: G8715608323
OpenAlex ID: W7161300329
