Poster No:
807
Submission Type:
Abstract Submission
Authors:
Atsuko Kurosu1, Ninet Sinaii2, Kristen Wingert1, Divesh Thaploo1, Adebiyi Sobitan1, Nadia Biassou2
Institutions:
1National Institutes on Deafness and Other Communication Disorders, Bethesda, MD, 2National Institutes of Health, Bethesda, MD
First Author:
Atsuko Kurosu, PhD
National Institutes on Deafness and Other Communication Disorders
Bethesda, MD
Co-Author(s):
Kristen Wingert, MS
National Institutes on Deafness and Other Communication Disorders
Bethesda, MD
Divesh Thaploo, PhD
National Institutes on Deafness and Other Communication Disorders
Bethesda, MD
Introduction:
Language processing is highly complex dynamic neuronal activity which requires a recruitment of large-scale networks including regions critical for language processing (e.g., left hemisphere frontal and temporal areas)(Blank & Fedorenko, 2017; Fedorenko et al., 2024; Mineroff et al., 2018; Williams et al., 2022; Wylie & Regner, 2014). Larger-scale language networks include language-specific networks and language-related cognitive networks, and some are overlapped (Fedorenko et al., 2024; Mineroff et al., 2018; Wylie & Regner, 2014). Language networks are not encapsulated but interconnected, and the networks constantly change their connections depending on task demand and complexity (Fedorenko, 2014; Fedorenko & Thompson-Schill, 2014; Jung & Lambon Ralph, 2023). Given the interconnected relationship between language specific and language-related cognitive networks, it is difficult to differentiate those two. Our previous studies indicated that there were distinct dynamic functional connectivity (FC) correlation variability pattern differences during resting-state fMRI (rs-fMRI) and language processing during task-based fMRI (tb-fMRI). Given this evidence, we speculated that examining the patterns of FC network dynamics may differentiate functional specialization of each FC network during language processing. We hypothesized that there should be language task-related temporal FC correlation variability on language-specific networks whereas networks that are processing general cognitive information (i.e., saliency, attention, and default mode network information) may follow resting-state dynamics. Thus, in the current study, we tested whether dynamic FC network patterns differentiate language-specific and language-related cognitive networks in real-time.
Methods:
3T fMRI data from 172 healthy participants (86 males, 27.6 ±3.8 years) from the Human Connectome Project were analyzed. Participants completed an auditory comprehension task and a math task as the baseline control during tb-fMRI while they lied down still during rs-fMRI. Sliding-scale time series correlation was used to analyze dynamic FC time series data with a sliding window of 42 and stride of 3. Network correlations over time were identified using Spearman correlation with Bonferroni correction. Linear mixed models with error and intercept random effects were used for statistical analyses.
Results:
During language processing, there were some networks that showed correlation variability over time, which were distinctly different from other cognitive patterns (p<0.009) nor dynamic rs-fMRI networks (p>0.11). Thus, these networks may be thought of as being language specific. Other networks, primarily in the right hemisphere and interhemispheric regions, demonstrated patterns that parallel resting-state networks with similar dynamic correlation variability patterns observed during rs-fMRI (p<0.002). These networks likely represent the language-related cognitive processing, such as generalized attentional, saliency, and default mode information processing that are subserving language processing.
Conclusions:
The results of the current study suggest that dynamic FC patterns may be useful to evaluate the types of information which networks are processing. Dynamic FC analyses of task-based fMRI may shed light on how language-specific networks perform differently from language-related networks and generalized cognitive networks in real-time. Examining tasked-based FC network dynamics during language processing may improve the evaluation of patient's neurolinguistic profile following central nervous system injury.
Language:
Language Comprehension and Semantics 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Language
NORMAL HUMAN
1|2Indicates the priority used for review
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.
I accept
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:
I do not want to participate in the reproducibility challenge.
Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Not applicable
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.
Not applicable
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.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
AFNI
Provide references using APA citation style.
Blank, I. A., & Fedorenko, E. (2017). Domain-general brain regions do not track linguistic input as closely as language-selective regions. Journal of Neuroscience, 37(41), 9999–10011. https://doi.org/10.1523/JNEUROSCI.3642-16.2017
Fedorenko, E. (2014). The role of domain-general cognitive control in language comprehension. Frontiers in Psychology, 5(APR). https://doi.org/10.3389/fpsyg.2014.00335
Fedorenko, E., Ivanova, A. A., & Regev, T. I. (2024). The language network as a natural kind within the broader landscape of the human brain. Nature Reviews Neuroscience, 25(5), 289–312. https://doi.org/10.1038/s41583-024-00802-4
Fedorenko, E., & Thompson-Schill, S. L. (2014). Reworking the language network. Trends in Cognitive Sciences, 18(3), 120–126. https://doi.org/10.1016/j.tics.2013.12.006
Jung, J., & Lambon Ralph, M. A. (2023). Distinct but cooperating brain networks supporting semantic cognition. Cerebral Cortex, 33(5), 2021–2036. https://doi.org/10.1093/cercor/bhac190
Mineroff, Z., Blank, I. A., Mahowald, K., & Fedorenko, E. (2018). A robust dissociation among the language, multiple demand, and default mode networks: Evidence from inter-region correlations in effect size. Neuropsychologia, 119, 501–511. https://doi.org/10.1016/j.neuropsychologia.2018.09.011
Williams, K. A., Numssen, O., & Hartwigsen, G. (2022). Task-specific network interactions across key cognitive domains. Cerebral Cortex, 32(22), 5050–5071. https://doi.org/10.1093/cercor/bhab531
Wylie, K. P., & Regner, M. F. (2014). Large-scale network involvement in language processing. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 34(47), 15505–15507. https://doi.org/10.1523/JNEUROSCI.3539-14.2014
No