Microstructural organization of human insula is linked to its macrofunctional circuitry and predicts cognitive control

  1. Vinod Menon  Is a corresponding author
  2. Guillermo Gallardo
  3. Mark A Pinsk
  4. Van-Dang Nguyen
  5. Jing-Rebecca Li
  6. Weidong Cai
  7. Demian Wassermann  Is a corresponding author
  1. Stanford University School of Medicine, United States
  2. Max Planck Institute for Human Cognitive and Brain Sciences, Germany
  3. Princeton University, United States
  4. Royal Institute of Technology in Stockholm, Sweden
  5. Inria Centre de Recherche Saclay Île-de-France, France

Abstract

The human insular cortex is a heterogeneous brain structure which plays an integrative role in guiding behavior. The cytoarchitectonic organization of the human insula has been investigated over the last century using postmortem brains but there has been little progress in noninvasive in vivo mapping of its microstructure and large-scale functional circuitry. Quantitative modeling of multi-shell diffusion MRI data from 413 participants revealed that human insula microstructure differs significantly across subdivisions that serve distinct cognitive and affective functions. Insular microstructural organization was mirrored in its functionally interconnected circuits with the anterior cingulate cortex that anchors the salience network, a system important for adaptive switching of cognitive control systems. Furthermore, insular microstructural features, confirmed in Macaca mulatta, were linked to behavior and predicted individual differences in cognitive control ability. Our findings open new possibilities for probing psychiatric and neurological disorders impacted by insular cortex dysfunction, including autism, schizophrenia, and fronto-temporal dementia.

Data availability

All data used in this study is available in open-source databases. The human data comes from the Human Connectome Project, the primate data is available at the INDI Primate Data Exchange, and the three-dimensional neuronal models are available from the NeuroMorpho website. All custom code is available on GitHub accesible through the Zenodo DOI: 10.5281/zenodo.3759708. All code was developed based on open-source, publicly available software packages.

The following data sets were generated

Article and author information

Author details

  1. Vinod Menon

    Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, United States
    For correspondence
    menon@stanford.edu
    Competing interests
    The authors declare that no competing interests exist.
  2. Guillermo Gallardo

    Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    Competing interests
    The authors declare that no competing interests exist.
  3. Mark A Pinsk

    Scully Center for the Neuroscience of Mind & Behavior Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Van-Dang Nguyen

    Computer Science, Royal Institute of Technology in Stockholm, Stockholm, Sweden
    Competing interests
    The authors declare that no competing interests exist.
  5. Jing-Rebecca Li

    Defi, Inria Centre de Recherche Saclay Île-de-France, Palaiseau, France
    Competing interests
    The authors declare that no competing interests exist.
  6. Weidong Cai

    Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Demian Wassermann

    Parietal, Inria Centre de Recherche Saclay Île-de-France, Palaiseau, France
    For correspondence
    demian.wassermann@inria.fr
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5194-6056

Funding

European Commission (NeuroLang -- 757672)

  • Demian Wassermann

National Institutes of Health (HD094623,HD059205,MH084164)

  • Vinod Menon

National Institutes of Health (MH105625)

  • Weidong Cai

Inria (LargeBrainNets)

  • Demian Wassermann

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: Animal data was obtained from the INDI-Prime primate data exchange database collection (http://fcon_1000.projects.nitrc.org/indi/indiPRIME.html) . All methods and procedures were approved by the Princeton University IACUC

Human subjects: Data was obtained from the HCP database. Informed consent for this study was not explicitly required. However, subjects signed a written informed consent when the database was constituted. IRB approval was obtained for the database construction with the following details: Mapping the Human Connectome: Structure, Function, and HeritabilityIRB # 201204036

Reviewing Editor

  1. Timothy E Behrens, University of Oxford, United Kingdom

Publication history

  1. Received: November 9, 2019
  2. Accepted: June 3, 2020
  3. Accepted Manuscript published: June 4, 2020 (version 1)
  4. Version of Record published: June 22, 2020 (version 2)

Copyright

© 2020, Menon et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Vinod Menon
  2. Guillermo Gallardo
  3. Mark A Pinsk
  4. Van-Dang Nguyen
  5. Jing-Rebecca Li
  6. Weidong Cai
  7. Demian Wassermann
(2020)
Microstructural organization of human insula is linked to its macrofunctional circuitry and predicts cognitive control
eLife 9:e53470.
https://doi.org/10.7554/eLife.53470

Further reading

    1. Developmental Biology
    2. Neuroscience
    Emily L Heckman, Chris Q Doe
    Research Advance

    The organization of neural circuits determines nervous system function. Variability can arise during neural circuit development (e.g. neurite morphology, axon/dendrite position). To ensure robust nervous system function, mechanisms must exist to accommodate variation in neurite positioning during circuit formation. Previously we developed a model system in the Drosophila ventral nerve cord to conditionally induce positional variability of a proprioceptive sensory axon terminal, and used this model to show that when we altered the presynaptic position of the sensory neuron, its major postsynaptic interneuron partner modified its dendritic arbor to match the presynaptic contact, resulting in functional synaptic input (Sales et al., 2019). Here we investigate the cellular mechanisms by which the interneuron dendrites detect and match variation in presynaptic partner location and input strength. We manipulate the presynaptic sensory neuron by (a) ablation; (b) silencing or activation; or (c) altering its location in the neuropil. From these experiments we conclude that there are two opposing mechanisms used to establish functional connectivity in the face of presynaptic variability: presynaptic contact stimulates dendrite outgrowth locally, whereas presynaptic activity inhibits postsynaptic dendrite outgrowth globally. These mechanisms are only active during an early larval critical period for structural plasticity. Collectively, our data provide new insights into dendrite development, identifying mechanisms that allow dendrites to flexibly respond to developmental variability in presynaptic location and input strength.

    1. Epidemiology and Global Health
    2. Neuroscience
    Lorenza Dall'Aglio, Hannah H Kim ... Henning Tiemeier
    Research Article Updated

    Background:

    Associations between attention-deficit/hyperactivity disorder (ADHD) and brain morphology have been reported, although with several inconsistencies. These may partly stem from confounding bias, which could distort associations and limit generalizability. We examined how associations between brain morphology and ADHD symptoms change with adjustments for potential confounders typically overlooked in the literature (aim 1), and for the intelligence quotient (IQ) and head motion, which are generally corrected for but play ambiguous roles (aim 2).

    Methods:

    Participants were 10-year-old children from the Adolescent Brain Cognitive Development (N = 7722) and Generation R (N = 2531) Studies. Cortical area, volume, and thickness were measured with MRI and ADHD symptoms with the Child Behavior Checklist. Surface-based cross-sectional analyses were run.

    Results:

    ADHD symptoms related to widespread cortical regions when solely adjusting for demographic factors. Additional adjustments for socioeconomic and maternal behavioral confounders (aim 1) generally attenuated associations, as cluster sizes halved and effect sizes substantially reduced. Cluster sizes further changed when including IQ and head motion (aim 2), however, we argue that adjustments might have introduced bias.

    Conclusions:

    Careful confounder selection and control can help identify more robust and specific regions of associations for ADHD symptoms, across two cohorts. We provided guidance to minimizing confounding bias in psychiatric neuroimaging.

    Funding:

    Authors are supported by an NWO-VICI grant (NWO-ZonMW: 016.VICI.170.200 to HT) for HT, LDA, SL, and the Sophia Foundation S18-20, and Erasmus University and Erasmus MC Fellowship for RLM.