How can graph theory inform the dual-stream model of speech processing? a resting-state fMRI study of post-stroke aphasia
Haoze Zhu, Megan C. FitzhughiD, Lynsey M. KeatoriD, Lisa JohnsoniD, Chris RordeniD, Leonardo BonilhaiD, Julius FridrikssoniD, Corianne RogalskyiD
Abstract
The dual-stream model of speech processing has been proposed to represent the cortical networks involved in speech comprehension and production. Although it is arguably the prominent neuroanatomical model of speech processing, it is not yet known if the dual-stream model represents actual intrinsic functional brain networks. Furthermore, it is unclear how disruptions after a stroke to the functional connectivity of the dual-stream models regions are related to specific types of speech production and comprehension impairments seen in aphasia. To address these questions, in the present study, we examined two independent resting-state fMRI datasets: (1) 28 neurotypical matched controls and (2) 28 chronic left-hemisphere stroke survivors with aphasia collected at another site. Structural MRI, as well as language and cognitive behavioral assessments, were collected. Using standard functional connectivity measures, we successfully identified an intrinsic resting-state network amongst the dual-stream models regions in the control group. We then used both standard functional connectivity analyses and graph theory approaches to determine how the functional connectivity of the dual-stream network differs in individuals with post-stroke aphasia, and how this connectivity may predict performance on clinical aphasia assessments. Our findings provide strong evidence that the dual-stream model is an intrinsic network as measured via resting-state MRI, and that weaker functional connectivity of the hub nodes of the dual-stream network defined by graph theory methods, but not overall average network connectivity, is weaker in the stroke group than in the control participants. Also, the functional connectivity of the hub nodes predicted specific types of impairments on clinical assessments. In particular, the relative strength of connectivity of the right hemispheres homologues of the left dorsal stream hubs to the left dorsal hubs versus right ventral stream hubs is a particularly strong predictor of post-stroke aphasia severity and symptomology.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 0
- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Individualized functional localization of the language and multiple demand network in chronic post-stroke aphasia
Pieter De Clercq, Alicia Ronnie Gonsalves, … Maaike Vandermosten·bioRxiv·Jan 12, 2024
- Language networks in aphasia and health: a 1000 participant Activation Likelihood Estimate analysis
James D. Stefaniak, Reem S. W. Alyahya, Matthew A. Lambon Ralph·NeuroImagePeer-reviewed·Mar 18, 2021
- A unified neurocomputational bilateral model of spoken language production in healthy participants and recovery in post-stroke aphasia
Ya‐Ning Chang, Matthew A. Lambon Ralph·Proceedings of the National Academy of SciencesPeer-reviewed·Dec 3, 2020
- Reorganisation of Functional Connectivity Gradients in Post-Stroke Aphasia
Ramya Balakrishnan, Tirso RJ Gonzalez Alam, … Elizabeth Jefferies·bioRxiv·Jun 10, 2026
- Connectivity of Implicit Statistical Learning Networks is Associated with Language Impairment Severity in Post-Stroke Aphasia
Amy E. Ramage, Beau Underwood, … Kirrie J. Ballard·bioRxiv·Nov 4, 2026
- Glass Half Full: Preserved Anatomical Bypasses Predict Variance in Language Functions After Stroke
Brian Albert Erickson, Brian Kim, … John Dominic Medaglia·bioRxiv·Aug 31, 2021