The Organization of the Shared Neural Substrate of Autobiographical, Semantic, and Social Cognition
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M3 (Mezzanine Level)
Poster No:
632
Submission Type:
Abstract Submission
Authors:
Veronica Diveica1, Nathan Spreng2
Institutions:
1Montreal Neurological Institute, Montreal, Quebec, 2McGill University, Montreal, Quebec
First Author:
Co-Author:
Introduction:
Remembering personal past events (autobiographical memory), attributing meaning to memories and external stimuli (semantic cognition), and inferring the thoughts and feelings of others (mentalizing) share a common neural substrate, as demonstrated within participants performing each task (Balgova et al., 2022; Hughes et al., 2024; Tanguay et al., 2023) and in meta-analyses of independent task domains (Balgova et al., 2024; Spreng et al., 2009). However, the intrinsic functional organization of this shared neural substrate remains poorly understood. In this study, we use resting-state functional connectivity (rsFC) to examine the shared functional neuroanatomy of these cognitive domains.
Methods:
Step 1. Identifying brain regions involved in each cognitive domain via coordinate-based meta-analysis. The datasets of peak coordinates were retrieved from 28 autobiographical memory experiments (Fenerci et al., 2022; Spreng et al., 2009), 190 semantic experiments (Balgova et al., 2024) and 136 mentalizing experiments (Diveica et al., 2021). Meta-analytic contrasts were performed via activation likelihood estimation (Eickhoff et al., 2012) to identify regions more consistently activated by one domain compared to the other two (henceforth domain-specific regions).
Step 2. Assessing overlap in the rsFC patterns of domain-specific regions. Resting-state fMRI data were collected from 150 young adults across two runs (see Spreng et al., 2022 for dataset details). Key methodological strengths include 1) fMRI data acquisition using a multi-echo sequence, which ensures superior signal-to-noise ratio and whole-brain coverage; and 2) Parcellation of data using GPIP and the Schaefer cortical atlas (400 parcels), a method accounting for inter-subject variability in brain functional architecture while enabling direct group comparisons. Data from the first run were used to identify the whole-brain rsFC for each domain-specific region, which were then combined into a conjoint probability map.
Step 3. Exploring the intrinsic organization of the shared brain network. The network of brain regions with >80% probability of FC with at least one domain-specific region was analyzed using: 1) Graph network analyses (on reliable positive connections estimate via bootstrapping) to assess the relative centrality of each region, and 2) Ward's hierarchical clustering algorithm to identify finer-grained clusters of regions, both of which were applied to data from the second rsFC run.
Step 4. Functional characterization of clusters via: 1) Pairwise contrasts of whole-brain rsFC patterns and 2) Meta-analytic functional decoding using the NeuroQuery database (Dockès et al., 2020) to identify functional terms associated with activation within the cluster.
Step 2. Assessing overlap in the rsFC patterns of domain-specific regions. Resting-state fMRI data were collected from 150 young adults across two runs (see Spreng et al., 2022 for dataset details). Key methodological strengths include 1) fMRI data acquisition using a multi-echo sequence, which ensures superior signal-to-noise ratio and whole-brain coverage; and 2) Parcellation of data using GPIP and the Schaefer cortical atlas (400 parcels), a method accounting for inter-subject variability in brain functional architecture while enabling direct group comparisons. Data from the first run were used to identify the whole-brain rsFC for each domain-specific region, which were then combined into a conjoint probability map.
Step 3. Exploring the intrinsic organization of the shared brain network. The network of brain regions with >80% probability of FC with at least one domain-specific region was analyzed using: 1) Graph network analyses (on reliable positive connections estimate via bootstrapping) to assess the relative centrality of each region, and 2) Ward's hierarchical clustering algorithm to identify finer-grained clusters of regions, both of which were applied to data from the second rsFC run.
Step 4. Functional characterization of clusters via: 1) Pairwise contrasts of whole-brain rsFC patterns and 2) Meta-analytic functional decoding using the NeuroQuery database (Dockès et al., 2020) to identify functional terms associated with activation within the cluster.
Results:
Meta-analytic comparisons across the three cognitive domains revealed both overlapping and domain-specific task-based activations (Figure 1A). Not all pairs of domain-specific regions were functionally coupled at rest (Figure 1B). Despite this, and their definition based on preferential activation for specific domains, all domain-specific regions were functionally connected a rest with a shared network of brain regions, predominantly within the default and limbic networks (Figure 1C). Within this shared network, the lateral anterior temporal cortex and medial prefrontal cortex exhibited the highest node centralities (Figure 2A). The shared network divided into four clusters (Figure 2B), each with distinct functional profiles: sub-network 1 – sensorimotor processing; sub-network 2 – semantic processing & episodic simulation; sub-network 3 – self-referential and reward valuation; sub-network 4 – integrative and elaborative processing (Figure 2C).
Conclusions:
Our findings demonstrate that the brain is intrinsically wired to integrate autobiographical, semantic, and social information, and provide a detailed characterization of this intrinsic connectivity, underscoring the essential role of memory in shaping social cognition and behavior.
Emotion, Motivation and Social Neuroscience:
Social Cognition 1
Learning and Memory:
Long-Term Memory (Episodic and Semantic) 2
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis
Keywords:
ADULTS
Cognition
FUNCTIONAL MRI
Memory
Meta- Analysis
Social Interactions
1|2Indicates the priority used for review
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Provide references using APA citation style.
Balgova, E., Diveica, V., Walbrin, J., & Binney, R. J. (2022). The role of the ventrolateral anterior temporal lobes in social cognition. Human Brain Mapping, 43(15), 4589–4608.
Diveica, V., Koldewyn, K., & Binney, R. J. (2021). Establishing a role of the semantic control network in social cognitive processing: A meta-analysis of functional neuroimaging studies. NeuroImage, 245, 118702.
Dockès, J., Poldrack, R. A., Primet, R., Gözükan, H., Yarkoni, T., Suchanek, F., Thirion, B., & Varoquaux, G. (2020). Neuroquery, comprehensive meta-analysis of human brain mapping. ELife, 9. https://doi.org/10.7554/ELIFE.53385
Eickhoff, S. B., Bzdok, D., Laird, A. R., Kurth, F., & Fox, P. T. (2012). Activation likelihood estimation meta-analysis revisited. NeuroImage, 59(3), 2349–2361.
Fenerci, C., Gurguryan, L., Spreng, R. N., & Sheldon, S. (2022). Comparing neural activity during autobiographical memory retrieval between younger and older adults: An ALE meta-analysis. Neurobiology of Aging, 119, 8–21. https://doi.org/10.1016/j.neurobiolaging.2022.06.009
Hughes, C., Setton, R., Mwilambwe-Tshilobo, L., Baracchini, G., Turner, G. R., & Spreng, R. N. (2024). Precision Mapping of the Default Network Reveals Common and Distinct (Inter)activity for Autobiographical Memory and Theory of Mind. Journal of Neurophysiology.
Spreng, R. N., Mar, R. A., & Kim, A. S. N. (2009). The Common Neural Basis of Autobiographical Memory, Prospection, Navigation, Theory of Mind, and the Default Mode: A Quantitative Meta-analysis. Journal of Cognitive Neuroscience, 21(3), 489–510.
Spreng, R. N., Setton, R., Alter, U., Cassidy, B. N., Darboh, B., DuPre, E., Kantarovich, K., Lockrow, A. W., Mwilambwe-Tshilobo, L., Luh, W.-M., Kundu, P., & Turner, G. R. (2022). Neurocognitive aging data release with behavioral, structural and multi-echo functional MRI measures. Scientific Data, 9(1).
Tanguay, A. F., Palombo, D. J., Love, B., Glikstein, R., Davidson, P. S., & Renoult, L. (2023). The shared and unique neural correlates of personal semantic, general semantic, and episodic memory. ELife, 12, e83645.