Functional and anatomical profiles of functionally selective and integrative zones in the insula
Poster No:
730
Submission Type:
Abstract Submission
Authors:
Mijin Kwon1, Lukas Van Oudenhove2, Philip Kragel3, Tor Wager1, Affective Neuroimaging Consortium1
Institutions:
1Dartmouth College, Hanover, NH, 2KU Leuven, Leuven, Belgium, 3Emory University, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
A central question in cognitive neuroscience is understanding how the brain constructs unified perceptual experiences by integrating interoceptive, emotional and exteroceptive signals across modalities. The insular cortex has been proposed as a critical hub, with the anterior insula hypothesized as an integrative zone synthesizing diverse functional modalities. However, the insula also contains specialized areas (e.g., the posterior insula for pain) demonstrating significant functional specificity. Reconciling functional specialization and integration has been hindered by limitations in spatial specificity and small samples in prior research.
Here, leveraging Bayes factor analysis of aggregated task fMRI data (N = 540 participant-level maps; 36 studies) across pain, appetitive processes, aversive processes, and cognitive control domains, we examine the balance of functional integration and modular specialization in the human insular cortex. We ask: 1) Are there discrete functionally selective zones alongside a domain-general hub? 2) What microscale and macroscale functional and anatomical features enable this complex organization?
Here, leveraging Bayes factor analysis of aggregated task fMRI data (N = 540 participant-level maps; 36 studies) across pain, appetitive processes, aversive processes, and cognitive control domains, we examine the balance of functional integration and modular specialization in the human insular cortex. We ask: 1) Are there discrete functionally selective zones alongside a domain-general hub? 2) What microscale and macroscale functional and anatomical features enable this complex organization?
Methods:
We first aggregated 540 participant-level contrast images from 36 task-based fMRI studies systematically sampling from each domain (each with 3 subdomains, 3 independent studies within each subdomain, and 15 participants equally sampled from each study) (Figure 1). The current study was conducted as part of a collaborative research effort, Affective Neuroimaging Consortium [1].
We then applied a Bayes factor framework testing voxelwise activation selectivity for each domain versus domain-general convergence by assessing evidence for and against activation. This identified voxels activated exclusively by one domain or across all domains. We characterized the functional and anatomical properties enabling selectivity and integration: 1) Neurosynth decoding of represented functions, 2) cytoarchitectonic mapping, 3) neurotransmitter receptor density profiling, and 4) whole-brain co-activation.
We then applied a Bayes factor framework testing voxelwise activation selectivity for each domain versus domain-general convergence by assessing evidence for and against activation. This identified voxels activated exclusively by one domain or across all domains. We characterized the functional and anatomical properties enabling selectivity and integration: 1) Neurosynth decoding of represented functions, 2) cytoarchitectonic mapping, 3) neurotransmitter receptor density profiling, and 4) whole-brain co-activation.
Results:
Bayes factor analysis revealed domain-selective zones exhibiting selectivity pain (mid-posterior insula), appetitive processes (mid-insula), aversive processes (ventral anterior insula), and cognitive control (dorsal anterior/mid-insula) alongside a distinct domain-general hub in the dorsal anterior insula, showing overall activations across all domains (Fig. 1).
Each domain-selective and domain-general zones showed distinct patterns in functional decoding, cytoarchitectonic mapping, neurotransmitter receptor density profile, and coactivation with other brain areas. For example, the pain-selective zone showed: 1) highest association with pain-related topics such as "pain", "somatosensory stimulation" (functional decoding); 2) highest overlap with the granular-dysgranular cytoarchitectonic cluster that is well positioned to receive main somatosensory inputs (cytoarchitectonic mapping); 3) relatively higher association with glutamate, GABA, and opioids (neurotransmitter receptor density profile); 4) connections with a pain processing network including somatosensory, cingulate, and medial prefrontal cortex (Fig. 2).
Each domain-selective and domain-general zones showed distinct patterns in functional decoding, cytoarchitectonic mapping, neurotransmitter receptor density profile, and coactivation with other brain areas. For example, the pain-selective zone showed: 1) highest association with pain-related topics such as "pain", "somatosensory stimulation" (functional decoding); 2) highest overlap with the granular-dysgranular cytoarchitectonic cluster that is well positioned to receive main somatosensory inputs (cytoarchitectonic mapping); 3) relatively higher association with glutamate, GABA, and opioids (neurotransmitter receptor density profile); 4) connections with a pain processing network including somatosensory, cingulate, and medial prefrontal cortex (Fig. 2).
·Domain-selective and domain-general zones with their activation profile
·Functional and anatomical profiles of pain-selective subregion
Conclusions:
In summary, leveraging a Bayesian framework, large-scale multi-study dataset, and multi-level functional and anatomical profiling, our study provides comprehensive mapping of insular functional topology. The results validate discrete domain-selective modules alongside a distinct domain-general hub in the insula. By situating these functional zones within specific microscale and macroscale anatomical contexts, we reveal neurobiological mechanisms enabling both domain-specific representation and multi-modal integration.
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other 1
Higher Cognitive Functions:
Higher Cognitive Functions Other
Modeling and Analysis Methods:
Classification and Predictive Modeling
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 2
Keywords:
Cognition
Emotions
Open Data
Pain
1|2Indicates the priority used for review
Provide references using author date format
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