Cerebellum Resting-State Functional Connectivity and Its Link to Language Task Performance
Poster No:
1661
Submission Type:
Abstract Submission
Authors:
Jose Francisco Delerin-Cortes1, Azalea Reyes-Aguilar1, Juan Silva-Pereyra1, Juan Fernandez-Ruiz1
Institutions:
1National Auntonomus University of Mexico, Mexico City, Mexico City
First Author:
Co-Author(s):
Introduction:
The dual-route model of language processing (Hickok & Poeppel, 2000) describes two main pathways: the dorsal pathway, lateralized to the left hemisphere, translates phonological input into motor articulations, while the ventral pathway, bilaterally organized, maps word meaning. Notably, semantic processing extends beyond classical pathways into non-classical regions like the cerebellum, forming an "extended pathway" in the brain (Dickens et al., 2019; Rauschecker & Scott, 2009; Reyes-Aguilar et al., 2023).
The cerebellum mirrors cortical semantic processes, shown by simultaneous activation of cerebellar and cortical language regions during semantic tasks in fMRI studies. These coactivations sugest intrinsic functional connectivity between the cerebellum and cortical language areas (Nakatani et al., 2023). Right Crus I and II are consistently associated with this function (LeBel & D'Mello, 2023), though recent evidence suggests comparable involvement of the left Crus (Metchenberg et al., 2024).
Based on these findings, we propose the following hypotheses regarding the cerebellum's intrinsic connectivity in semantic language processing:
1. Resting-state (rs) functional connectivity exists between bilateral Crus I and II and semantic language-related ROIs, spanning classical dorsal, ventral, and extended pathways.
2. These Intrinsic networks predict performance in verb fluency tasks.
The cerebellum mirrors cortical semantic processes, shown by simultaneous activation of cerebellar and cortical language regions during semantic tasks in fMRI studies. These coactivations sugest intrinsic functional connectivity between the cerebellum and cortical language areas (Nakatani et al., 2023). Right Crus I and II are consistently associated with this function (LeBel & D'Mello, 2023), though recent evidence suggests comparable involvement of the left Crus (Metchenberg et al., 2024).
Based on these findings, we propose the following hypotheses regarding the cerebellum's intrinsic connectivity in semantic language processing:
1. Resting-state (rs) functional connectivity exists between bilateral Crus I and II and semantic language-related ROIs, spanning classical dorsal, ventral, and extended pathways.
2. These Intrinsic networks predict performance in verb fluency tasks.
Methods:
The sample comprised 98 native Spanish-speaking participants (M = 27.46 years, SD = 3.77), aged 20-35, with a minimum of 15 years of education and no psychiatric disorders (SCL-90, M = 0.73, SD = 0.6). Resting-state fMRI data were acquired using a 3.0T GE MR750 scanner (General Electric, Waukesha, WI). During the 10-minute scan, participants kept their eyes close while avoiding specific thoughts, prayer, meditation, or sleep. Afterward, they completed an action fluency task. Informed consent was obtained, and the study was approved by the Ethics Committee of the Institute of Neurobiology (INB), UNAM.
Resting-state functional connectivity was analyzed using a ROI-to-ROI approach with the default pipeline of the CONN toolbox v22. Semantic-related ROIs were identified through a Neurosynth meta-analysis (1,031 studies, FDR = 0.01). Bilateral Crus I and II were segmented using the automated CERES tool from volBrain (Romero, Coupé & Manjón, 2017).
Resting-state functional connectivity was analyzed using a ROI-to-ROI approach with the default pipeline of the CONN toolbox v22. Semantic-related ROIs were identified through a Neurosynth meta-analysis (1,031 studies, FDR = 0.01). Bilateral Crus I and II were segmented using the automated CERES tool from volBrain (Romero, Coupé & Manjón, 2017).
Results:
Using "semantic" as a search term, 22 functional ROIs were identified: 13 in the right hemisphere and 9 in the left. Of these, 2 were linked to the ventral pathway, 3 to the dorsal pathway, and 17 to the extended pathway. Right Crus I appeared in 2 distinct functional coordinates.
Rs-fMRI revealed connectivity between the four bilateral Crus regions and semantic ROIs. The right Crus showed positive correlation with the dorsal pathway and anticorrelation with the ventral pathway, while the left Crus connected only to the ventral pathway (anticorrelation). However, no significant asymmetry in general intrinsic connectivity was found between bilateral Crus I and II and semantic ROIs across hemispheres (F(3,22) = 0.211, p = 0.888). Consistent networks also linked the bilateral Crus to posterior semantic ROIs in the extended pathway, including the left precuneal cortex, right lateral occipital cortex, and left occipital pole (Fig.1, Fig.2). Two distinct networks involving right Crus I predicted performance in the action fluency task (p ≤ 0.05): one with the dorsal pathway ROI (Coefficient = -5.01, R² = 0.01, adjusted R² = 0.01) and another with the ventral pathway ROI (Coefficient = -5.71, R² = 0.02, adjusted R² = 0.02).
Rs-fMRI revealed connectivity between the four bilateral Crus regions and semantic ROIs. The right Crus showed positive correlation with the dorsal pathway and anticorrelation with the ventral pathway, while the left Crus connected only to the ventral pathway (anticorrelation). However, no significant asymmetry in general intrinsic connectivity was found between bilateral Crus I and II and semantic ROIs across hemispheres (F(3,22) = 0.211, p = 0.888). Consistent networks also linked the bilateral Crus to posterior semantic ROIs in the extended pathway, including the left precuneal cortex, right lateral occipital cortex, and left occipital pole (Fig.1, Fig.2). Two distinct networks involving right Crus I predicted performance in the action fluency task (p ≤ 0.05): one with the dorsal pathway ROI (Coefficient = -5.01, R² = 0.01, adjusted R² = 0.01) and another with the ventral pathway ROI (Coefficient = -5.71, R² = 0.02, adjusted R² = 0.02).
·Resting-state networks between the right Crus I and semantic ROIs, showing positive correlations (warm colors) and anticorrelations (blue).
·Resting-state networks between the left Crus I and semantic ROIs, with positive correlations (warm colors), anticorrelations (blue), and no correlation with dorsal language pathway ROIs.
Conclusions:
Bilateral Crus I and II show functional connectivity with semantic ROIs. Moreover no general asymmetry exists between bilateral Crus I/II and semantic ROIs, but specific asymmetries appear in classical language pathways, indicating lateralized roles. Finally right Crus I connectivity with dorsal and ventral ROIs negatively predicts verbal fluency, reflecting these pathways' roles in verb processing.
Language:
Language Comprehension and Semantics 2
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 1
Keywords:
Language
Sub-Cortical
Other - Cerebellum, resting state functional connectivity, semantic processing
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:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Hickok, G., & Poeppel, D. (2000). Towards a functional neuroanatomy of speech perception. Trends in Cognitive Sciences, 4(4), 131-138. https://doi.org/10.1016/S1364-6613(00)01463-7.
LeBel, A., & D’Mello, A. M. (2023). A seat at the (language) table: Incorporating the cerebellum into frameworks for language processing. Current Opinion in Behavioral Sciences, 53, 101310.
Mechtenberg, H., Heffner, C. C., Myers, E. B., & Guediche, S. (2024). The cerebellum is sensitive to the lexical properties of words during spoken language comprehension. Neurobiology of Language, 5(3), 757–773. https://doi.org/10.1162/nol_a_00126.
Nakatani, H., Nakamura, Y., & Okanoya, K. (2023). Respective involvement of the right cerebellar Crus I and II in syntactic and semantic processing for comprehension of language. The Cerebellum, 22(4), 739-755. https://doi.org/10.1007/s12311-022-01451-y.
Rauschecker, J. P., & Scott, S. K. (2009). Maps and streams in the auditory cortex: Nonhuman primates illuminate human speech processing. Nature Neuroscience, 12(6), 718–724. https://doi.org/10.1038/nn.2331.
Reyes-Aguilar, A., Licea-Haquet, G., Arce, B. I., & Giordano, M. (2023). Contribution and functional connectivity between cerebrum and cerebellum on sub-lexical and lexical-semantic processing of verbs. PLOS One, 18(9), e0291558. https://doi.org/10.1371/journal.pone.0291558.
Romero, J. E., Coupé, P., Giraud, R., Ta, V. T., Fonov, V., Park, M. T. M., ... & Manjón, J. V. (2017). CERES: A new cerebellum lobule segmentation method. NeuroImage, 147, 916-924. https://doi.org/10.1016/j.neuroimage.2016.11.003.