The molecular and cellular underpinnings of human brain lateralization
Presented During:
Thursday, June 26, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M4 (Mezzanine Level)
Poster No:
810
Submission Type:
Abstract Submission
Authors:
Loïc Labache1, Sidhant Chopra2, Xihan Zhang3, Avram Holmes1
Institutions:
1Rutgers University, New Brunswick, NJ, 2Orygen, Preston, Victoria, 3Yale University, New Haven, CT
First Author:
Co-Author(s):
Introduction:
Hemispheric lateralization is a fundamental feature of human brain organization, guiding processes such as language and visuospatial attention (Tzourio-Mazoyer et al., 2020). While previous research has focused on neurotransmitter systems that support the organization of brain networks, the influence of molecular and cellular phenotypes on regional hemispheric lateralization remains poorly understood. Here, we demonstrate that neurotransmitter systems (acetylcholine-norepinephrine axis), mitochondrial distribution, microglia, and intratelencephalic-projecting neurons help shape these functional asymmetries. We further identify two molecular and cellular networks: a left-lateralized network associated with language and a right-lateralized network associated with visuospatial attention.
Methods:
Using population-average maps, we examined the molecular and cellular correlates of functional asymmetries (Fig.1a-d). We combined fMRI activation maps (3 language tasks, 1 attention task, n=125, (Mazoyer et al., 2016)), receptor densities (19 receptors, in vivo PET, n=1238, (Hansen et al., 2022)), 3 mitochondrial phenotypes (postmortem, n=1, (Mosharov et al., 2024)), and 24 cell-type abundances (ex vivo gene expression, n=6, (Zhang et al., 2024)). We used the homotopic AICHA atlas (340 regions, (Joliot et al., 2015)) to compute left-right asymmetry indices for each modality.
Canonical Correlation Analysis (CCA, (Winkler et al., 2020)) was used to identify receptor density patterns linked to lateralized task activations. Statistical significance was assessed using permutation testing that accounted for spatial autocorrelation (10,000 permutations, (Váša et al., 2018)). We then examined associations between the significant receptor canonical mode to mitochondrial phenotypes and cell types using Pearson correlations and multiple regression with stepwise selection. Finally, we applied hierarchical clustering to examine how canonical brain networks (Yan et al., 2023) segregate based on their lateralized molecular-cellular fingerprints (Fig.2a-b).
Canonical Correlation Analysis (CCA, (Winkler et al., 2020)) was used to identify receptor density patterns linked to lateralized task activations. Statistical significance was assessed using permutation testing that accounted for spatial autocorrelation (10,000 permutations, (Váša et al., 2018)). We then examined associations between the significant receptor canonical mode to mitochondrial phenotypes and cell types using Pearson correlations and multiple regression with stepwise selection. Finally, we applied hierarchical clustering to examine how canonical brain networks (Yan et al., 2023) segregate based on their lateralized molecular-cellular fingerprints (Fig.2a-b).
Results:
CCA revealed a key neurotransmitter axis along acetylcholine (M1) and norepinephrine (NET) correlated with task lateralization (r=0.39, p=0.01; Fig.1e-g). Language-related regions were associated with leftward asymmetry in NET receptors and rightward asymmetry in M1 receptors. Attention-related regions exhibited the opposite pattern. Mitochondrial phenotypes correlated significantly with this neurotransmitter axis, suggesting leftward mitochondrial asymmetry aligns with leftward M1 and rightward NET lateralization (Fig.1h).
Stepwise regression linked 4 cell types (L5 IT, Micro PVM, L6 IT, and L2/3 IT) to the neurotransmitter axis (Fig.1i). All contributed negatively, indicating that rightward asymmetry in these cell types corresponds to leftward M1 and rightward NET expression.
Hierarchical clustering of 7 canonical networks based on molecular-cellular fingerprints yielded two clusters (Fig.2c-d). The first (frontoparietal, dorsal attention, somatomotor) was predominantly right-lateralized and aligned with the attention network (Labache et al., 2024). The second (default, ventral attention, visual, limbic) was largely left-lateralized and aligned with the language network (Labache et al., 2024). This organization differed significantly (Barnard's unconditional test, p<10-2) in the distribution of asymmetry indices, indicating that attention networks are five times more likely to show rightward asymmetry than language networks.
Stepwise regression linked 4 cell types (L5 IT, Micro PVM, L6 IT, and L2/3 IT) to the neurotransmitter axis (Fig.1i). All contributed negatively, indicating that rightward asymmetry in these cell types corresponds to leftward M1 and rightward NET expression.
Hierarchical clustering of 7 canonical networks based on molecular-cellular fingerprints yielded two clusters (Fig.2c-d). The first (frontoparietal, dorsal attention, somatomotor) was predominantly right-lateralized and aligned with the attention network (Labache et al., 2024). The second (default, ventral attention, visual, limbic) was largely left-lateralized and aligned with the language network (Labache et al., 2024). This organization differed significantly (Barnard's unconditional test, p<10-2) in the distribution of asymmetry indices, indicating that attention networks are five times more likely to show rightward asymmetry than language networks.
·Figure 1. Overview of the multimodal relationship between cellular, molecular biomarkers, and function lateralization.
·Figure 2. Organization of molecular and cellular profiles of the seven canonical intrinsic networks.
Conclusions:
Hemispheric lateralization is closely tied to distinct neurotransmitter, mitochondrial, and cellular asymmetries. These biological underpinnings produce two major large-scale network clusters: one primarily left-lateralized and language-related and the other right-lateralized and attention-related. This integrated molecular-cellular framework advances our understanding of how the human brain achieves functional specialization.
Genetics:
Transcriptomics
Language:
Language Other 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Multivariate Approaches 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Systems
Keywords:
Acetylcholine
Cellular
Cognition
Cortex
FUNCTIONAL MRI
Hemispheric Specialization
Language
Neurotransmitter
Noradrenaline
Other - visuospatial attention
1|2Indicates the priority used for review
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Provide references using APA citation style.
Joliot, M. (2015). AICHA: An atlas of intrinsic connectivity of homotopic areas. Journal of Neuroscience Methods, 254, 46–59.
Labache, L. (2024). Atlas for the Lateralized Visuospatial Attention Networks (ALANs): Insights from fMRI and network analyses. Imaging Neuroscience, 2, 1–22.
Mazoyer, B. (2016). BIL&GIN: A neuroimaging, cognitive, behavioral, and genetic database for the study of human brain lateralization. NeuroImage, 124(Pt B), 1225–1231.
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Tzourio-Mazoyer, N. (2020). Development of handedness, anatomical and functional brain lateralization. Handbook of Clinical Neurology, 173, 99–105.
Váša, F. (2018). Adolescent Tuning of Association Cortex in Human Structural Brain Networks. Cerebral Cortex (New York, N.Y. : 1991), 28(1), 281–294.
Winkler, A. M. (2020). Permutation inference for canonical correlation analysis. NeuroImage, 220, 117065.
Yan, X. (2023). Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity. NeuroImage, 273, 120010.
Zhang, X.-H. (2024). The cell-type underpinnings of the human functional cortical connectome. Nature Neuroscience. https://doi.org/10.1038/s41593-024-01812-2