Auditory Narrative Processing: Cortical Gradients in Story Listening
Poster No:
1361
Submission Type:
Abstract Submission
Authors:
Sudesna Chakraborty1
Institutions:
1Aoyama Gakuin University, Sagamihara, Kanagawa
First Author:
Introduction:
Neuroscience research has long explored how cortical systems transform simple sensory inputs into complex cognitive experiences. Traditionally, research has focused on sensory-to-cognition hierarchies, such as visual pathway studies demonstrating how neurons integrate basic visual elements into complex object perception (Felleman & Van Essen, 1991; Hubel & Wiesel, 1962). These findings support the conceptualization of brain organization as a hierarchical information processing system, where cognitive experiences emerge from integrating modality-specific cortical area inputs. Recent functional connectivity (FC) research has introduced innovative approaches to understanding brain organization. Functional connectivity, measured through correlated brain region signal patterns, can be analyzed using dimensionality reduction techniques like diffusion map embedding. These methods revealed principal gradients spanning from unimodal to transmodal cortical regions, capturing the sensory-to-cognition organizational principle (Margulies et al., 2016). Previous gradient studies predominantly utilized resting-state functional magnetic resonance imaging (fMRI), leaving a critical gap in understanding active neural processing. Story listening offers a unique paradigm to investigate cortical information transformation, providing a pure auditory experience that can specifically target linguistic and cognitive processing mechanisms. Here, narrative fMRI data and diffusion embedding approach was used to capture gradients of connectivity differences between story listening and listening to a temporally scrambled version of it.
Methods:
The fMRI datasets used in this study are openly accessible. The primary dataset, "Pie Man" from Princeton Dataspace (https://dataspace.princeton.edu/jspui/handle/88435/dsp015d86p269k), comprised a total of 105 subjects with different conditions of story listening: intact condition had 36 subjects, paragraph scrambled had 18 subjects and word scrambled had 36 subjects. Additionally, the dataset included resting state data of 36 subjects. Further details of the dataset and the preprocessing can be found in Simony et al., 2016.To ensure reproducibility, a retest was conducted using "It's Not the Fall that Gets You". This dataset is part of the Narratives collection (Chang et al., 2022; Nastase et al., 2019) and included intact (20 subjects) and short-scrambled (20 subjects) data. The data with details of preprocessing is available at: https://openneuro.org/datasets/ds002345. To reduce computational complexity, parcel-wise correlation of signal fluctuations was computed using the Schaefer atlas (Schaefer et al., 2018) containing 400 parcels. Diffusion embedding, a nonlinear dimensionality reduction technique that identifies multiple axes of variation was employed to the group averaged functional connectomes to characterize connectivity differences between the parcels in different conditions. Finally, the gradient distributions were plotted against the Yeo 7 network (Yeo et al., 2011) to visualize connectivity patterns (Fig.1).
·Workflow
Results:
The variance explained for each dataset and conditions, as well as the gradient distribution within the Yeo networks for the top 5 gradients (G), is shown in Fig. 2A-D for "Pieman" and Fig. 2E-F for "It's Not the Fall that Gets You." In the "Pieman" dataset, G1 captures visual function across all conditions, while a similar pattern is observed in G2 for "It's Not the Fall that Gets You." The hierarchical organization is evident in G2 (Fig. 2A-D) and G1 (Fig. 2E-F). The sensorimotor network is represented in G3, while G4 and G5 exhibit different connectivity patterns depending on the story-listening conditions.
·Variance explained by each gradient component and gradient values within the Yeo 7 networks for top 5 gradients
Conclusions:
This study demonstrates how cortical systems transform simple auditory inputs into complex cognitive experiences, revealing connectivity gradients that capture sensory, hierarchical, and network-specific patterns during coherent and scrambled story listening.
Higher Cognitive Functions:
Higher Cognitive Functions Other
Language:
Language Comprehension and Semantics 2
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Keywords:
Cortex
Language
Modeling
MRI
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Provide references using APA citation style.
Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex (New York, N.Y.: 1991), 1(1), 1–47.
Hubel, D. H., & Wiesel, T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of Physiology, 160(1), 106–154.
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., Petrides, M., Jefferies, E., & Smallwood, J. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proceedings of the National Academy of Sciences of the United States of America, 113(44), 12574–12579.
Nastase, S. A., Gazzola, V., Hasson, U., & Keysers, C. (2019). Measuring shared responses across subjects using intersubject correlation. Social Cognitive and Affective Neuroscience, 14(6), 667–685.
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex , 28(9), 3095–3114.
Simony, E., Honey, C. J., Chen, J., Lositsky, O., Yeshurun, Y., Wiesel, A., & Hasson, U. (2016). Dynamic reconfiguration of the default mode network during narrative comprehension. Nature Communications, 7(May 2015), 12141.
Yeo, B. T. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., Fischl, B., Liu, H., & Buckner, R. L. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165