Schedule of Events | Symposia

Function of the auditory cortex characterized with its intrinsic dynamic coactivation patterns estimated in individuals

Poster Session F - Tuesday, April 1, 2025, 8:00 – 10:00 am EDT, Back Bay Ballroom/Republic Ballroom

Maria Hakonen^1,2 (mhakonen@mgh.harvard.edu), Kaisu Lankinen^1,2, Parker Kotlarz^1,2, Jonathan Polimeni^1,2,3, Tori Turpin¹, Jianxun Ren⁴, Danhong Wang^1,2, Hesheng Liu^4,5, Jyrki Ahveninen^1,2; ¹Athinoula A. Martinos Center for Biomedical Imaging, ²Massachusetts General Hospital / Harvard Medical School, ³Harvard-MIT Program in Health Sciences and Technology, ⁴Changping Laboratory, ⁵Biomedical Pioneering Innovation Center, Peking University

Local functional connectivity in auditory areas of the superior temporal cortex supports the hierarchical processing of sound. Although brain connectivity is now recognized as dynamic, most studies have overlooked smaller local networks, which can be obscured by their individual variability and the dominance of larger networks. Here, we investigated local dynamic functional connectivity, or coactivation patterns, of the networks in the auditory cortex (AC) determined from single frames of 7T fMRI data with a novel individualized network-based algorithm. The template network patterns were created by clustering and averaging the frames across participants. Thereafter, the frames of each participant were assigned to the template with the shortest spatial distance. The eight-cluster solution was selected for closer examination based on its high within-participant reproducibility values, which were 0.86 for the pattern occurrence rates and 0.79 for pattern spatial topographies. In contrast, between-participant correlations were lower, 0.66 for pattern occurrence rates and 0.58 for spatial topographies, indicating greater variability across individuals compared to within the same individual. Thus, dynamic AC patterns also captured interindividual variability. Each coactivation pattern had a corresponding inverse pattern in which the same network was deactivated. The coactivation patterns shared similarities between resting-state and auditory-task data, as indicated by the group-level similarity of 0.84 and individual-level similarity of 0.71 in the spatial topographies. Furthermore, the occurrence rates of AC patterns correlated with specific task contrast regressors. Our results suggest that the AC function can be characterized by recurring coactivation patterns that share similarities during resting state and auditory simulation.

Topic Area: PERCEPTION & ACTION: Audition