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Hemispheric Network Dynamics During Auditory Language Comprehension and Its Clinical Implications Regarding

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Functional connectivity (FC) patterns during language processing differ between resting-state (rs-fMRI) and task-based (tb-fMRI) scans. Dynamic FC networks show greater fluctuation during auditory tasks than at rest, particularly in the left hemisphere.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Medical Imaging

Background:

  • Growing interest in neurobehavioral modeling necessitates understanding functional connectivity (FC) in language processing.
  • Previous research suggested static rs-fMRI and tb-fMRI might be interchangeable for mapping language regions, leading some to question the necessity of tb-fMRI in clinical settings.
  • Language processing involves temporal dynamics crucial for computational models, yet static imaging may not capture these nuances.

Purpose of the Study:

  • To examine the stability and temporal dynamics of language neural networks during both resting-state functional MRI (rs-fMRI) and task-based fMRI (tb-fMRI) during auditory comprehension in healthy participants.
  • To compare dynamic FC patterns between rs-fMRI and tb-fMRI to determine if they are interchangeable for language network analysis.

Main Methods:

  • Twenty-three participants underwent rs-fMRI and 12 underwent tb-fMRI while performing an auditory description task.
  • Dynamic FC was estimated using sliding scale time series correlation.
  • Spearman correlation with Bonferroni correction was employed to identify statistically significant whole-brain dynamic networks.

Main Results:

  • Dynamic FC networks exhibited more significant temporal fluctuation during the auditory task (tb-fMRI) compared to resting-state conditions (rs-fMRI).
  • Distinct differences in dynamic FC were observed in specific common networks within the left hemisphere during tb-fMRI.
  • A total of 59,831 data points were analyzed, including 42,159 from rs-fMRI, 10,152 from tb-fMRI, and 7,520 in the overlap.

Conclusions:

  • The temporal dynamics of neural networks engaged in language processing are markedly different between rs-fMRI and tb-fMRI, especially in the left hemisphere.
  • These temporal differences represent a critical feature for developing advanced computational models.
  • Understanding these dynamic FC distinctions may enhance the predictive accuracy of models for clinical outcomes following central nervous system injury.