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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. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, United States
  2. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, United States
  3. Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, United States
  4. Athena, Inria Sophia Antipolis, Université Côte d’Azur, France
  5. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Germany
  6. Princeton Neuroscience Institute, Princeton University, United States
  7. Department of Computational Science and Technology Royal Institute of Technology in Stockholm, Sweden
  8. Defi, Inria Saclay Île-de-France, École Polytechnique Université Paris Sud, France
  9. Parietal, Inria Saclay Île-de-France, CEA Université Paris Sud, France
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Cite this article as: eLife 2020;9:e53470 doi: 10.7554/eLife.53470

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.

Introduction

The human insular cortex plays a critical role in identifying salient sensory, affective, and cognitive cues for guiding attention and behavior (Nieuwenhuys, 2012; Menon and Uddin, 2010; Uddin, 2015; Craig, 2009). Critically, it is also one of the most widely activated brain regions in all of human neuroimaging research (Cai et al., 2014; Swick et al., 2011; Levy and Wagner, 2011). Dysfunction of the human insula and its interconnected regions are now thought to be core features of many psychiatric and neurological disorders (Goodkind et al., 2015; Namkung et al., 2017). However, little is known about the normative microstructural organization of the human insular cortex and its relation to behavior. Precise quantitative in vivo characterization of the microstructural organization of the human insular cortex and links to its functional circuitry are critical for understanding its function and role in development and psychopathology.

The insular cortex is a structurally heterogeneous brain region with a distinct cytoarchitectonic profile characterized by less differentiated cortical layers (Nieuwenhuys, 2012; Mesulam and Mufson, 1982a; Morel et al., 2013; Evrard, 2019). Investigations of the microstructural features of the human insular cortex have been based solely on postmortem brains with small samples and limited behavioral characterizations. Insular cytoarchitectonic organization has been investigated using histological techniques over the last century (Allman et al., 2010; Brodmann, 1909; Von Economo et al., 2008; Mesulam and Mufson, 1985; Augustine, 1996). Stereological analyses of the insula have identified a cellular cortical architecture which differs considerably from the 6-layer granular architecture seen in most cortical areas (Mesulam and Mufson, 1982a; Mesulam and Mufson, 1982b; Augustine, 1985). In a seminal study, Mesulam and Mufson (Mesulam and Mufson, 1982b) proposed the concept of ‘granularity’, based on the presence of an inner granular layer, as a key feature for identifying the anatomical subdivisions of the insular cortex. In the ensuing years, several histological studies have focused on demarcating the microstructural properties of insular subdivisions but no consensus has yet emerged about their precise boundaries (Morel et al., 2013; Allman et al., 2010; Kurth et al., 2010). Thus far, investigations of the distinct structural features of the human insular cortex have been based solely on postmortem brains and have not been amenable to characterization using non-invasive brain imaging techniques. Quantitative modeling of non-invasive in vivo brain imaging is therefore critically needed to address this major gap.

Despite the lack of consistency and precision across previous histological studies, some general patterns have emerged regarding the general cytoarchitectonic organization of the insula. The ventral anterior insula has an agranular structure characterized by undifferentiated layers II/III, distinct from the fully developed granular cortex with a canonical 6-layer architecture. The dorsal anterior insula and most central territories of the insula display an intermediate dysgranular profile (Mesulam and Mufson, 1982a; Morel et al., 2013; Mesulam and Mufson, 1985). In contrast, large sections of the posterior insula show a canonical granular structure (Kurth et al., 2010). Another unique aspect of the neuronal organization of the human insula is the presence of von Economo neurons in the anterior aspects of the insula (Namkung et al., 2017). These neurons differ from the typical pyramidal neurons by virtue of their large spindle shape and thick basal and apical dendrites which allow for speeded communication (Seeley et al., 2012; von Economo, 1926). The dysgranular organization of the human insula, together with the presence of specialized neurons, have been hypothesized to contribute to its crucial role in goal-directed behaviors and emotional regulation, through rapid processing of attentional, cognitive, interoceptive, emotional, and autonomic signals (Craig, 2009; Seeley et al., 2007; Critchley et al., 2004). However, the lack of tools for assessing morphological variations in the insula in vivo and its relation to behavior has limited our understanding of the microstructural organization of the insula in health and disease.

The insular cortex is also functionally heterogeneous and integrates signals across its cognitive and affective subdivisions to support adaptive behavior. The anterior aspects of the insula are important for detection of salient external stimuli and for mediating goal-directed cognitive control while the posterior aspects are important for integrating autonomic and interoceptive signals (Craig, 2009). This functional organization is supported by a distinct pattern of long-range connections: the dorsal aspects of the anterior insula (AI) are more strongly connected to brain areas important for cognitive control, most notably the dorsal anterior cingulate cortex (ACC), while the ventral anterior has stronger links with subcortical and limbic regions important for emotion, reward and homeostatic regulation, including the amygdala, ventral striatum and hypothalamus (Menon and Uddin, 2010; Craig, 2009; Dosenbach et al., 2007; Chikama et al., 1997). Consistent with these reports, meta-analysis of task-related coactivation patterns in human neuroimaging studies point to distinct functional networks associated with insular subdivisions (Chang et al., 2013). Critically, the anterior insula and the dorsal ACC anchor the salience network (SN), a tightly coupled network that is among the most widely co-activated set of brain regions in all of human neuroimaging research (Cai et al., 2014; Swick et al., 2011; Levy and Wagner, 2011). Remarkably, besides the insula, the only other brain region where von Economo neurons are known to be strongly expressed is the ACC (Allman et al., 2010; Nimchinsky et al., 1999). However, the link between the microstructural organization of the insula and its functional circuit properties is currently not known. Furthermore, it is unclear whether these regional functional circuit properties are mirrored in the long-range connectivity of the insula in the context of the large-scale organization of AI-ACC circuits that anchor the SN. To the best of our knowledge there have been no histological investigations of the correspondence between cytoarchitecture features of the AI and ACC.

Given the challenges inherent in obtaining histological data from postmortem brains, the bulk of current research on the human insula has focused on its functional organization both in relation to regional activation by specific cognitive and affective tasks and its distinct patterns of functional connectivity with other brain regions (Cai et al., 2014; Chang et al., 2013; Deen et al., 2011; Ryali et al., 2015; Jakab et al., 2012; Cauda et al., 2012; Kelly et al., 2012). Crucially, non-invasive voxel-wise quantitative mapping of functional brain connectivity has allowed researchers to identify distinct subdivisions within the insular cortex (Deen et al., 2011; Ryali et al., 2015; Faillenot et al., 2017). Based on their unique fingerprints of connectivity, previous research has identified three distinct functionally subdivisions in human insular cortex: the dorsal anterior (dAI), ventral anterior (vAI), and posterior insula (PI) (Namkung et al., 2017; Deen et al., 2011; Glasser et al., 2016). These subdivisions also show distinct patterns of intrinsic functional connectivity within the SN, with the dAI being more tightly linked to the ACC node of the SN (Deen et al., 2011). Whether functionally-defined regions of the insula and its functional circuits associated with the SN have distinct microstructural features is currently not known, and addressing this link has the potential to contribute to a deeper understanding of structure-function relations in the human brain.

Noninvasive in vivo investigation of tissue microstructure is a key application of diffusion MRI (dMRI), but few studies have tapped its potential due to limitations of most previous dMRI techniques (Calamante et al., 2018). A key discovery that now permits more precise microstructural analysis of human gray matter arose from the work by Latour et al. (1994) demonstrating the feasibility of analyzing cellular size in biological tissue with time-dependent diffusion MR. In subsequent studies, dMRI was used to determine sensitivity of dMRI signals in gray matter tissue (Basser and Pierpaoli, 2011; Thornton et al., 1997). More recently, dMRI has been used to characterize normal human brain maturation (Mukherjee et al., 2002) and cortical microstructure in preterm infants (Ball et al., 2013). Building on these studies, multi-shell models have recently been used to demonstrate regional variability in cortical microstructure (Calamante et al., 2018; Fukutomi et al., 2018), as well as sensitivity of multi-shell dMRI for probing fine-scale and region-specific microstructure using high angular and spatial resolution diffusion MRI (Aggarwal et al., 2015; McNab et al., 2013).

Here we leverage recent advances in multi-shell dMRI acquisition protocols and recent signal reconstruction techniques (Özarslan et al., 2013; Fick et al., 2016) to determine the microstructural features of insular cortex using the normalized Return to Origin Probability (RTOP) density index (Mitra et al., 1995; Schwartz et al., 1997). RTOP measures water diffusion in neural tissue (Mitra et al., 1995), with higher values reflecting more restricted diffusion. RTOP has been shown to be inversely correlated with the average volume of pores restricting diffusion in microbeads (Mitra et al., 1995; Schwartz et al., 1997). In neural tissue, spinal cord maturation has been shown to increase RTOP (Assaf et al., 2000). Recently, RTOP has been shown to be more sensitive to cellular organization than other more commonly used diffusion MRI measures, such as mean and radial diffusivity (Avram et al., 2016). Based on these observations, we hypothesized that RTOP in grey matter would be sensitive to the cytoarchitectonic and neuronal organization of the insular cortex.

The first aspect of our study involved leveraging a large (N = 413) cohort of adult participants from the Human Connectome Project (HCP) (Glasser et al., 2013). We evaluated RTOP across three major functional subdivisions of the human insula and demonstrate that they have microstructurally distinct profiles. Based on histological observations, we predicted smaller RTOP values in its dorsal and anterior subdivisions, consistent with their agranular and dysgranular organization, compared to the posterior insula and its granular organization. Next, because of the paucity of insula-wide histological data in humans, we used dMRI data from two macaques to examine whether RTOP measures are sensitive to known cytoarchitectural features of the primate insula (Brockhaus, 1940; Rose, 1928). We then examined whether the microstructural distinctions seen in the insular cortex are mirrored in the ACC node of the salience network, and demonstrate a strong link between functional circuits linking the insula and ACC and their microstructural organization in human brain. Crucially, we examine whether variation in microstructural features of the insula are related to behavior and individual differences in cognitive control ability. Stability, cross-validation and replication analyses are used to demonstrate the robustness of our findings. Figure 1 provides an overview of the data analysis pipeline.

Flow chart illustrating data analysis pipeline.

(A) Main components of human insula microstructure analysis using diffusion magnetic resonance imaging (dMRI) data from 413 Human Connectome Project (HCP) participants. Key steps include: (i) measurement of microstructure features based on Return to Origin Probability (RTOP), the ratio between the probability of molecules returning to their starting position in biological tissue versus free diffusion, (ii) demarcation of functional subdivisions in insula and its interconnected anterior cingulate cortex (ACC) subdivisions, which together anchor the salience network, (iii) computation of microstructure features of the insula within its functional subdivisions and its interconnected ACC subdivisions, (iv) computation of microstructural gradients along the anterior-posterior and dorsal-ventral axes of the insula, and (v) analysis of relation between insula microstructural organization and cognitive control abilities.

Results

Insula microstructure across its functionally defined subdivisions

We characterized insula microstructure across three major functional subdivisions of the insula (Deen et al., 2011; Glasser et al., 2016): dAI, vAI, and PI. RTOP (Mitra et al., 1995) measures in each subdivision were computed by averaging across all mesh-vertices in each subdivision (see Appendix 1 for details of the mathematical formulation and computations).

We first examined insula subdivisions using the functional parcellation derived by Deen and colleagues (Deen et al., 2011Figure 2A). To determine whether RTOP values differed among the three insular subdivisions, we conducted an ANOVA with factors subdivision (vAI, dAI and PI) and hemisphere (left vs. right). We found a significant interaction between hemisphere and subdivision (ANOVA, F(2,824) = 33.7, p<8.2E-15) and significant main effects of subdivision ANOVA, (F(2,824) = 1102, p<2E-16) and hemisphere (ANOVA, F(1,412) = 50.2, p<6.1E-12). Post-hoc t-tests further revealed a gradient of RTOP values vAI <dAI < PI in both right and left hemisphere (paired t-tests, all ps <2.0E-11, except p=0.037 in the case of right vAI <dAI), with vAI and PI having significantly smaller values in the right, compared to the left, hemisphere (paired t-tests, ps <1.6E-15) (Figure 2B).

Figure 2 with 1 supplement see all
Microstructural properties of functional insular cortex subdivisions.

(A) Functional subdivisions spanning the posterior insula (PI), dorsal anterior insula (dAI) and ventral anterior insula (vAI) (Deen et al., 2011). (B) RTOP differs significantly between PI, dAI and vAI in the left hemisphere and between PI and dAI or vAI in the right hemisphere (p<0.001, Bonferroni corrected). The right vAI has the smallest RTOP value among all the subdivisions (all ps < 0.001, except p=0.036 for right vAI < dAI; Bonferroni corrected). (C) Stability of findings as a function of sample size. A sample size of N = 25 was sufficient to achieve a stable differentiation (p<0.01) between PI and vAI in both hemispheres, while differentiating the vAI and dAI required a larger sample size. a PI: posterior insula; dAI: dorsal anterior insula; vAI: ventral anterior insula. (D–F) Replication with an independent multimodal parcellation using HCP data (Glasser et al., 2016).

Next, we examined the stability of these findings as a function of sample size. We found that a sample size of N = 25 was sufficient to achieve a stable differentiation (p<0.01) between PI and vAI in both hemispheres, while differentiating the vAI and dAI required a larger sample size of N = 100 (Figure 2C).

We then conducted a replication analysis using a multimodal parcellation of the insula from a large sample of HCP participants (Glasser et al., 2016Figure 2D). Here again, we found a significant interaction between hemisphere and subdivision (ANOVA, F(2,824) = 207.3, p<2E-16) and a significant main effect of subdivision (ANOVA, F(2,824) = 11265, p<2E-16) and hemisphere (ANOVA, F(1,412) = 10.9, p<0.001). Post-hoc t-tests further revealed a gradient of RTOP values vAI <dAI < PI in both right and left hemispheres (paired t-test, all ps <3.2E-06), with vAI and PI having significantly smaller values in the right hemisphere and vAI having significantly smaller values in the left hemisphere (paired t-test, ps <4E-13) (Figure 2E).

Stability analysis revealed that a sample size of N = 25 was sufficient to achieve a stable differentiation (p<0.01) between PI and vAI and between PI and dAI in both hemispheres and between dAI and vAI in the left hemisphere (Figure 2F).

Our findings provide robust and replicable evidence for distinct microstructural variations across these functional subdivisions of the insula, and suggest a close correspondence between the microstructural and functional organization of the insula.

Gradient analysis of insula microstructural features along the anterior-posterior and dorsal-ventral axes

To further delineate the microstructural organization of the human insula, we conducted a detailed profile analysis and used directional statistics to characterize gradients in RTOP along its anterior-posterior and ventral-dorsal axes (Figure 3). Figure 3A shows isolevels of RTOP values in the insula at a group level. We found a low dispersion of RTOP gradients in both the left (0.36π ±0.09 π, SEM = 0.005) and right (0.36π ±0.09 π, SEM = 0.005) hemispheres. We then assessed the significance of this finding using the Rayleigh test, against the null hypothesis of a uniform directional distribution (Mardia, 2000). We found strong evidence for both an anterior-to-posterior and ventral-to-dorsal gradient in the left insula (ci = 0.02π, Rayleigh statistic = 724, p<1.0e-10, N = 413, df = 3) and the right insula (ci = 0.04π, Rayleigh statistic = 270., p<1.0e-10, N = 413, df = 3) (Figure 3B–C, Supplementary file 1-Supplementary Table 1). The confidence interval showed a low dispersion, indicating a consistent pattern of gradients across participants (Supplementary file 1-Supplementary Table 1). The lowest values of RTOP were localized to the vAI, convergent with findings from the analysis of the three functional subdivisions described above. Finally, analysis of microstructure isolines further revealed gradient ‘fingers’ from the vAI extending along a ventral-dorsal axis (Figure 4). We repeated these analyses using the multi-modal parcellation. Here again we found low dispersion in RTOP gradients in both left (0.16π ±0.07π, SEM = 0.01) and right hemisphere (0.23 ± 0.09π, SEM = 0.01). We again found evidence for an anterior-posterior and ventral-dorsal gradients spanning from vAI extending along a ventral-dorsal axis through PI (Figures 3E–F and 4B, Supplementary file 1-Supplementary Table 2). These results point to a consistent and reliable pattern of gradients and microstructural organization of the human insula along its anterior-posterior and dorsal-ventral axes.

Insula microstructure gradients along its anterior-posterior and dorsal-ventral axes.

(A) RTOP averaged over all participants (N = 413) illustrates inhomogeneity in insula microstructure with a ventral anterior insula peak, and gradients along the anterior-posterior and dorsal-ventral gradients axes. Larger RTOP indicates smaller average compartments. There is a prominent gradient from the insular pole towards the posterior insular section. Note right hemisphere dominance. (B) Main gradient directions, computed using Rayleigh directional statistics in each functionally defined subdivision. The main directions show an anterior-to-posterior and inferior-to-superior RTOP organization in the left insular cortex and an anterior-to-posterior organization in the right insular cortex. The polar plots show the distribution of main gradient directions in each functional subdivision. (C) Gradient direction histograms. The mean direction is represented with solid lines on top of the distribution histogram; the shaded region represents the 95% confidence interval. For detailed statistics (Table S1). (D–F) Replication with an independent multimodal parcellation using HCP data (Glasser et al., 2016).

RTOP isocontours reflect insula cytoarchitectonic organization.

Isocontours of the population-average RTOP (left) are closely aligned to cytoarchitectonic organization of the insula and VEN expression from studies of post-mortem brains (right, based on Morel et al., 2013). (B) Replication with an independent.

RTOP captures known microstructural features of the insular cortex in macaques

We next used dMRI data from two macaques to examine whether RTOP measures are sensitive to known cytoarchitectural features of the non-human primate insula. Histological studies have demonstrated that the macaque insula can be demarcated into agranular, dysgranular and granular subdivisions based on their unique cytoarchitectural properties (Brockhaus, 1940; Rose, 1928; Evrard et al., 2012; Evrard et al., 2014; Nieuwenhuys, 2012). To investigate whether RTOP measures are sensitive to known cytoarchitectural features of the primate insula, we acquired dMRI data from two macaque monkeys using protocols similar to the HCP (Appendix 1), and examined RTOP values in the three known cytoarchitectonic subdivisions of the primate insula. We found that RTOP captures known microstructural features of the insular cortex in macaques (Appendix 2; Figure 2—figure supplement 1, Supplementary file 1-Supplementary Table 3), providing convergent validation of our in vivo human RTOP findings.

Microstructural features in the insula and ACC nodes of the salience network

The insular cortex and ACC are the two major cortical nodes of the SN (Menon and Uddin, 2010; Seeley et al., 2007). Individual functional subdivisions of the insula have preferential connections to different subdivisions of the ACC (Chang et al., 2013; Deen et al., 2011; Cauda et al., 2012; Taylor et al., 2009), but it is not known whether they share similar microstructural features. To address this gap, we first conducted a seed-based whole-brain functional connectivity analysis, where seeds are the three functional subdivisions of the insular cortex in each hemisphere (Deen et al., 2011).

We first examined the insular functional parcellation (Deen et al., 2011). Our analysis revealed that the three insular subdivisions had distinct functional connectivity patterns with the ACC (Figure 5A). Specifically, the vAI showed stronger connection with the most anterior and ventral ACC (denoted as ACC-vAI), the dAI showed stronger connection with the middle and dorsal ACC (ACC-dAI), and the PI showed stronger connections with the posterior ACC (ACC-PI) (Figure 6A).

Intrinsic functional connectivity of insula subdivisions dAI, vAI and PI.

(A) Whole-brain resting-state functional connectivity analysis revealed distinct functional connectivity patterns of the insula subdivisions (Deen et al., 2011), bilateral posterior insula (PI), dorsal anterior insula (dAI) and ventral anterior insula (vAI) (all ps < 0.001, FDR corrected). (B) Replication with an independent multimodal parcellation using HCP data (Glasser et al., 2016).

Functional connectivity between insula and ACC subdivisions.

(A) RTOP differs across ACC functional subdivisions differentially linked to the three insular subdivisions (Deen et al., 2011). (B) Replication with an independent multimodal parcellation using HCP data (Glasser et al., 2016).

Next, we created functional subdivisions within the ACC based on their differential connectivity patterns with the dAI, vAI and PI. First, we examined functional connectivity differences between each pair of seeds in each hemisphere (e.g. left PI >left dAI, thresholded, p<0.01, FDR corrected). We then applied a logical AND operation to identify voxels surviving the two binarized maps that indicated the voxel’s connectivity to one insula subdivision is significantly greater than the others (e.g. left PI >left dAI and left PI >left vAI). Finally, the resulting binarized maps were overlapped with an ACC mask (Figure 7A).

Microstructural properties of anterior cingulate cortex subdivisions mirror profiles in corresponding insula functional subdivisions.

(A) Illustration of ACC subdivisions. Each ACC subdivision preferentially connects to one of the three insular subdivisions defined using an independent functional parcellation (Deen et al., 2011). ACC subdivisions showed significantly greater functional connectivity with one insula subdivision over others (e.g. right PI > right dAI) and (right PI > right vAI) (all ps < 0.01, FDR corrected). (B) RTOP values were significantly different among the three ACC subdivisions in each hemisphere (p<0.001, Bonferroni corrected). The ACC subdivision differentially connected to vAI has smaller RTOP values than the other subdivisions (all ps < 0.001, Bonferroni corrected). (C) RTOP differences among three ACC subdivisions were robust and reliable at sample sizes of N = 25 or more. PI: posterior insula; dAI: dorsal anterior insula; vAI: ventral anterior insula. (D–E) Replication with an independent multimodal parcellation using HCP data (Glasser et al., 2016).

To determine whether RTOP differs across the ACC functional subdivisions linked to individual insular divisions we conducted an ANOVA with factors subdivision (ACC-vAI, ACC-dAI and ACC-PI) and hemisphere (left vs. right). We found a significant interaction between subdivision and hemisphere (ANOVA, F(2,824) = 1014, p=2E-16), and significant main effects of subdivision (ANOVA, F(2,824) = 1151, p=2E-16) and hemisphere (ANOVA, F(1,412) = 330, p=2E-16). Post-hoc paired t-tests revealed significant differences in RTOP: ACC-vAI < ACC-dAI < ACC-PI in both hemispheres (paired t-tests, all ps < 2E-16) (Figure 7B). Stability analysis demonstrated that these differences were highly reliable (paired t-tests, p<0.01) for samples sizes > N = 25 (Figure 7C).

We then conducted a replication analysis using a multimodal HCP insular parcellation (Glasser et al., 2016). Once again, the three insular subdivisions had distinct functional connectivity patterns with the ACC (Figure 5B). Specifically, the vAI showed stronger connection with the most anterior and ventral ACC (ACC-vAI), the dAI showed stronger connection with the middle and dorsal ACC (ACC-dAI), and the PI showed stronger connections with the posterior ACC (ACC-PI) (Figure 6B). We found a significant interaction between ACC subdivisions (Figure 7D) and hemisphere (ANOVA, F(2,824) = 245.7, p=2E-16), and significant main effects of subdivision (ANOVA, F(2,824) = 343, p=2E-16) and hemisphere (ANOVA, F(1,412) = 248.9, p=2E-16) (Figure 7E). Post-hoc paired t-tests revealed significant differences in RTOP: ACC-vAI < ACC-dAI < ACC-PI in both hemispheres (paired t-tests, all ps < 6E-11) (Figure 7E). Stability analysis demonstrated that the differences between PI and vAI and between dAI and vAI in both hemisphere and between PI and dAI in the right hemisphere were highly reliable (paired t-tests, p<0.01) for samples sizes > N = 50 (Figure 7F).

The three ACC subdivisions overlapped with distinct areas demarcated by the HCP multimodal atlas (Glasser et al., 2016): the ACC-vAI overlapped with ACC area p24, ACC-dAI overlapped with cingulate area ap24pr, and ACC-PI overlapped with ACC area p24pr, thus demonstrating distinct patterns of connectivity linking insula and ACC subdivisions.

These results demonstrate that the microstructural organization of the ACC mirrors the microstructural organization of the three insula subdivisions with which it is differentially connected.

Insula microstructure and relation to cognitive control ability

The human insula plays an important role in detection of salient external stimuli and in mediating goal-directed cognitive control (Craig, 2009). We investigated the relationship between microstructural properties of the insula and cognitive control ability, using canonical correlation analysis (CCA) with cross-validation and prediction analysis. Mean RTOP values from the six insular subdivisions, three in each hemisphere, were used to predict cognitive-behavioral measures associated with processing speed, working memory, response inhibition and cognitive flexibility.

We first used the insular functional parcellation (Deen et al., 2011). We found a significant relation between insula RTOP values and individuals cognitive control abilities (Canonical correlation, Pillai’s trace = 0.21, p<0.05). The canonical weights of the 1st latent variable in microstructural measures were significantly correlated with the canonical weights of the 1st latent variable in behavioral measures (Pearson’s correlation, r = 0.32, p<0.001, Cohen’s d = 0.67, Figure 8A; Supplementary file 1-Supplementary Table 4). A leave-one-out cross-validation procedure further revealed that, based on microstructural properties of the insula, our CCA model could predict cognitive control ability on unseen data (Pearson’s correlation, r = 0.19, p<0.001, Cohen’s d = 0.39, Figure 8B), with the strongest predictive weights in the right dAI.

Insula microstructural features predict cognitive control abilities.

(A) Canonical correlation analysis (CCA) revealed a significant relationship between mean RTOP in each insular subdivision (Deen et al., 2011) and cognitive control measures. CCA weights of RTOP measures were significantly correlated with CCA weights of cognitive control measures in CCA Axis 1. (B) Cross-validation analysis revealed that CCA-derived weights of RTOP predicted cognitive control measures on unseen data. (C–D) Replication with an independent multimodal parcellation using HCP data (Glasser et al., 2016).

Finally, we conducted a replication analysis using the multimodal HCP parcellation (Glasser et al., 2016). We found a marginally significant relationship between insula RTOP values and individuals’ cognitive control abilities (Canonical correlation, Pillai’s trace = 0.20, p=0.08). The canonical weights of the 1st latent variable in microstructural measures were significantly correlated with the canonical weights of the 1st latent variable in behavioral measures (Pearson’s correlation, r = 0.31, p<0.001, Cohen’s d = 0.65, Figure 8C; Supplementary file 1-Supplementary Table 5). A leave-one-out cross-validation procedure further revealed that, based on microstructural properties of the insula, our CCA model could predict cognitive control ability on unseen data (Pearson’s correlation, r = 0.17, p<0.001, Cohen’s d = 0.35, Figure 8D), with the strongest predictive weights in the right dAI.

Discussion

We leveraged recent advances in diffusion weighted imaging of gray matter and large high-quality samples from the HCP to investigate the microstructural properties of the insular cortex and its macrofunctional circuits associated with the salience network. Novel quantitative modeling of in vivo dMRI and fMRI signals allowed us to probe links between the functional subdivisions of the insula and its microstructure properties, overcoming limitations of postmortem studies. We found that the human insular cortex is characterized by a systematic profile of microstructural variation across its distinct functional subdivisions. Beyond the boundaries of the functionally defined subdivisions, our analysis revealed gradients along the anterior-posterior axis as well as dorsal-ventral axis that are consistent with known cytoarchitectonic differences in the insula derived from studies of post-mortem human brains (Morel et al., 2013; Ding et al., 2016). Notably, we observed significant hemispheric differences with the right hemisphere showing lower RTOP values than the left hemisphere. Critically, the microstructural organization of the insula was mirrored in the dorsal ACC, which together forms the backbone of the salience network, a system important for rapid orienting of attention and adaptive switching of cognitive control systems (Sridharan et al., 2008). Remarkably, microstructural properties of the insula were correlated with cognitive control abilities, and furthermore predicted these abilities in a left-out sample. Crucially, we replicated all the major findings using two independent insular parcellations. Our findings provide novel insights into human insular cortex architecture and its contribution to human cognition.

Insula microstructure differs across functionally defined subdivisions

We used multi-shell dMRI data from a large cohort of 413 participants to characterize the microstructural properties of the human insular cortex in relation to its major functional subdivisions. There currently are no known landmarks that can distinguish different subdivisions of the insula on the basis of anatomical MRI data. To overcome this problem and provide a better link with its underlying functional organization, we took advantage of recent advances in functional parcellation of the human insular cortex (Deen et al., 2011; Ryali et al., 2015; Faillenot et al., 2017). Resting-state fMRI studies have identified distinct functional subdivisions in the insular cortex based on differential patterns of intrinsic functional connectivity (Deen et al., 2011; Ryali et al., 2015; Faillenot et al., 2017). These subdivisions have been widely used to determine the cognitive and affective role of the insula, and therefore serve as a basis for linking microstructure features of the insula with its functional subdivisions. A widely used parcellation divides the insula into three functional clusters in each hemisphere: dAI, vAI, and PI (Figure 2). Our analysis revealed robust evidence for distinct microstructural variations across these functional subdivisions of the insula. Specifically, in both the right and left hemispheres, the lowest RTOP values were in the vAI, followed by the dAI, and the largest in the PI. This pattern is consistent with RTOP profiles in macaque insular cortex (Appendix 2; Figure 2—figure supplement 1, Supplementary file 1-Supplementary Table 3) and reports of higher density of von Economo neurons in the ventral and dorsal anterior insula, which together form the agranular insular cortex in humans (Namkung et al., 2017; Morel et al., 2013; Nimchinsky et al., 1999). Novel stability analysis revealed that this pattern was reproducible and reliable in samples of 50 or more.

To address the generality of our findings we examined a multimodal HCP surface-based whole-brain parcellation from Glasser and colleagues (Glasser et al., 2016), with boundaries similar to but not identical to the functional parcellation from Deen and colleagues (Deen et al., 2011). Crucially, all our findings were replicated using this parcellation. Replicability of our findings based on functional subdivisions obtained from two independent studies (Deen et al., 2011; Glasser et al., 2016), different data and methodological approaches, and stability with respect to sample size in both the primary and replication analyses, emphasizes a close correspondence between functional organization of the insula and its microstructural organization.

Anterior-posterior and dorsal-ventral gradients in insula microstructure

To further delineate the microstructural organization of the human insula, we conducted a detailed profile analysis to characterize gradients in RTOP along its anterior-posterior and ventral-dorsal axes. The lowest values of RTOP were localized to the vAI, convergent with findings from the analysis of the three functional subdivisions described above. We found strong evidence of an anterior-to-posterior and ventral-to-dorsal gradient in each subdivision of both left and right insula (Figure 3A–C). Analysis of microstructure isolines revealed gradient ‘fingers’ from the vAI extending along a ventral-dorsal axis. This pattern is strikingly consistent with findings from known cytoarchitectonic features in the insula derived from studies of post-mortem brains (Morel et al., 2013). The vAI regions where we observed the lowest RTOP is also consistent with histological studies that have reported higher expression of von Economo neurons in this region (Morel et al., 2013; Allman et al., 2010; Evrard et al., 2012Figure 4A). When using the multi-modal parcellation of Glasser and colleagues (Glasser et al., 2016), we also found a gradient spanning from vAI and extending along a ventral-dorsal axis (Figure 3D–F). Once more, obtaining a pattern consistent with cytoarchitectonic features of the insula (Figure 4B).

The anterior-posterior-ventral-dorsal gradients and convergent findings from the three distinct functional subdivisions provide new quantitative insights into the microstructural organization of the human insular cortex. The precise anatomical boundaries of the agranular, dysgranular, and granular architectonic areas within the human insula are not known, and differences in stereological methods and criteria have led to different segmentation schemes of the insular cortex in primates (Roberts and Hanaway, 1970; Paxinos et al., 2000). However, there is general consensus that the anterior and most of the mid insula areas are agranular or dysgranular while the posterior most aspects are granular (Namkung et al., 2017). The spatial gradient of RTOP in the insular cortex along the dorsocaudal-rostroventral axis provides new metrics for noninvasive in vivo analysis of the general cytoarchitectonic organization in the insular cortex in the human brain and is an advance over previous studies that have thus far been based on invasive histological and electrophysiological data of human and non-human primates (Morel et al., 2013; Brodmann, 1909; Mesulam and Mufson, 1985; Mesulam and Mufson, 1982b; Bonthius et al., 2005).

Clusters vs. gradients in relation to insula microstructural and functional organization

Our study focused on a tripartite functional organization of the human insular cortex consisting of dAI, vAI, and PI subdivisions, which is broadly consistent with prior histological reports (Mesulam and Mufson, 1982b; Chikama et al., 1997; Brockhaus, 1940; Rose, 1928). This approach assumes a clustered structure; however, depending on the parcellation techniques employed, resting-state fMRI-based studies have reported that the human insula can be divided into anywhere between 2 and 13 distinct subdivisions. Tian and Zalesky have noted that traditional clustering methods applied to insula parcellations based on resting-state fMRI data have several limitations which can be overcome by characterizing the diversity of its functional connectivity along a continuum (Tian and Zalesky, 2018; Tian et al., 2019). A similar observation has been made in the context of dMRI-derived white matter tracts of the insula (Cerliani et al., 2012). Interestingly, this is also the view that emerges from our microstructural characterization of the insula using RTOP which found gradients spanning the anterior-posterior and dorsal-ventral axes, as described in detail above. Taken together, these observations suggest that gradient-based approaches, rather than cluster-based ones, might better reflect the underlying cytoarchitecture of the insula and its connectivity. Further studies using multimodal gradient-based approaches are needed to more precisely probe the association between insula microstructure, connectivity, and function.

Hemispheric asymmetry in microstructural organization of the human insula

Our analysis of the left and right insula revealed a prominent hemispheric asymmetry in microstructure. RTOP values were significantly lower in the right hemisphere, with the lowest values among all six subdivisions (three in each hemisphere) being localized to the vAI in the right hemisphere. This is consistent with observations in human postmortem data (Allman et al., 2010). Interestingly, this asymmetry parallels the differential functional role and engagement of the right AI in cognitive and emotion control tasks such as the Go-Nogo, Stop signal and Emotional Stroop tasks (Cai et al., 2014; Cai et al., 2016; Ham et al., 2013; Cai et al., 2019). Consistent with these observations, the right AI has been shown to exert significant causal influences on multiple other brain areas in a wide range of cognitive control tasks (Cai et al., 2014; Cai et al., 2016; Ham et al., 2013; Chen et al., 2015) and lesions to this region are known to impair cognitive control (Ding et al., 2016). Hemispheric asymmetry in microstructural organization of the insula, and its putative links with differential expression of von Economo neurons, are consistent with reports of a left-right functional asymmetry in the insula (Craig, 2005). Specifically, it has been suggested that homeostatic afferent, including hot and cold pain, muscle and visceral pain, sensual touch and sexual arousal all produce strong right-lateralized activation in the right AI (Craig, 2002). Heartbeat-related evoked potentials and interoceptive awareness of heartbeat timing, arising from ‘sympathetic’ homeostatic afferent activity are also associated with AI activity (Critchley et al., 2004). We suggest that hemispheric asymmetry in microstructural organization of the insula may contribute to lateralization of function and in particular the differential role of the right insula in monitoring internal bodily states and subjective awareness across a wide range of cognitive and affective processing tasks (Craig, 2002).

Linked microstructural features in the insula and ACC nodes of the salience

The insular cortex together with the ACC are the two major cortical nodes that anchor the SN (Menon and Uddin, 2010; Seeley et al., 2007). Noninvasive brain imaging studies using both fMRI and DTI have shown that the insula and ACC are strongly connected functionally and structurally (Cai et al., 2014; Allman et al., 2010; Seeley et al., 2007; Sridharan et al., 2008; van den Heuvel et al., 2009; Smith et al., 2009; Cauda et al., 2011), together forming the backbone of the SN. Previous studies have shown that individual functional subdivisions of the insula have preferential connections to different subdivisions of the ACC (Chang et al., 2013; Deen et al., 2011; Cauda et al., 2012; Taylor et al., 2009). However, it is not known whether functional subdivisions within the ACC share the similar microstructural properties to the insular subdivisions they connect to. In a significant advance over previous research, we found a close correspondence between microstructural organization of the insula and its interconnected ACC subdivisions. First, using whole-brain functional connectivity analysis we discovered three dorsal ACC subdivisions each with a pattern of preferential functional connectivity with the three insula subdivisions - vAI, dAI and PI. Crucially, as with the three insula subdivisions, we found that these three functionally-defined dorsal ACC subdivisions themselves have distinct microstructural organization. Moreover, the underlying pattern is characterized by a one to one mapping between their respective RTOP values. The ACC subdivision with low RTOP was strongly connected to the insular subdivision with low RTOP, and the ACC subdivision with high RTOP is strongly connected to the insular subdivision with high RTOP. Our findings suggest a strong link between functional circuits linking the insula and ACC and their microstructural organization.

Our findings provide novel insights into the link between the functional organization of the SN and its microstructural features. Although there have been no direct histological investigations of the correspondence between cytoarchitecture features of the human AI and ACC, tract-tracing studies in macaques have shown that the ventral anterior insula receives input from pyramidal neurons in ipsilateral dorsal ACC (Mesulam and Mufson, 1982b). Our findings help link microstructural features of the insula with its macroscopic functional connectivity with the ACC for the first time. The correspondence between microstructural and large-scale functional connectivity profiles across the three insula and ACC subdivisions suggests that local and large-scale circuit features may together contribute to the integrity of the SN.

Insula microstructure predicts cognitive control ability

The human insula has been implicated in a broad range of cognitive functions (Menon and Uddin, 2010; Uddin, 2015; Cai et al., 2014; Seeley et al., 2012; Chen et al., 2016), but links between its microstructural features and individual differences in cognitive control abilities have not been previously examined. Our analysis revealed an association between the microstructural properties of the insula and individual differences in cognitive control ability. Remarkably, machine learning algorithms and cross-validation leveraging the large HCP sample size also revealed that cognitive control abilities could be predicted in a left-out sample with the strongest predictive weights in the right anterior insula. Our findings provide novel in vivo evidence that the microstructural integrity of the insula is crucial for implementing cognitive control. Gray matter lesions and insults to white matter pathways associated with the insular cortex and pathways linking it to the salience network have also been shown to impair cognitive control ability (Jilka et al., 2014; Clark et al., 2008; Gläscher et al., 2012). In particular, the AI, the key node in the salience network, is one of the most consistently activated brain regions during tasks involving cognitive control (Cai et al., 2014; Swick et al., 2011). Moreover, the strength of functional and anatomical connectivity between anterior insula and other cognitive control regions is modulated by cognitive demands (Cai et al., 2016; Ham et al., 2013; Chen et al., 2015; Chen et al., 2016; Cai et al., 2017; Taghia et al., 2018; Wen et al., 2013). Aberrant connectivity of the anterior insula has been also associated with cognitive deficits in psychiatric disorders (Cai et al., 2018; Uddin et al., 2013; Palaniyappan et al., 2013). Integrating these and other related findings, a prominent neurocognitive model has proposed that the salience network, especially the anterior insula, plays an important role in dynamically switching between other core brain networks to facilitate access to cognitive resources (Menon and Uddin, 2010). A unique feature of the human insular cortex that has been hypothesized to support its role in fast switching is the presence of von Economo neurons, whose large axons could provide a neuronal basis for rapid signal relay between AI and ACC and other brain networks. Our findings provide convergent support for this hypothesis and demonstrate a link between the unique microstructural features of the anterior insula and cognitive control function in humans, and they provide new quantitative metrics for investigating multiple psychiatric and neurological disorders known to impact the insula transdiagnostically (Goodkind et al., 2015).

Conclusion

Our novel quantitative analysis of multi-shell dMRI data provides reliable and replicable noninvasive in vivo measures of gray matter microstructure. We identified several unique microstructural features of the human insula and functional circuits associated with the salience network. Crucially, microstructural properties of the insular cortex are behaviorally relevant as they predicted human cognitive control abilities, in agreement with its crucial role in adaptive human behaviors. Our study provides a novel template for non-invasive investigations of microstructural heterogeneity of the human insula and the salience network, and how its distinct organization may impact human cognition, emotion and interoception. Our findings also open new possibilities for probing psychiatric and neurological disorders that are known to be impacted by insular and cingulate cortex dysfunction, including autism, schizophrenia, depression and fronto-temporal dementia (Bonthius et al., 2005; Allman et al., 2005; Brüne et al., 2010; Seeley et al., 2006; Santos et al., 2011).

Materials and methods

Key resources table
Reagent type (species)
or resource
DesignationSource or referenceIdentifiersAdditional
information
Other (Human)Human Connectome Project (HCP)Glasser et al., 2013 (doi: 10.1016/j.neuroimage.2013.04.127)Dataset: WU-Minn HCP Data - 1200
Other (Macaque)NKI PRIMatE Data Exchange database (PRIME)Milham et al., 2018 (doi: 10.1016/j.neuron.2018.08.039)Dataset: Princeton NA&P Lab
Software, algorithmDIPYDipy Contributors et al., 2014 (doi:10.3389/fninf.2014.00008)

Human datasets

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Data acquisition for the HCP was approved by the Institution Review Board of the Washington University in St. Louis (IRB #201204036), and all open access data were deidentified. Details of the data acquisition, preprocessing and analysis steps are described in the supplementary materials sections B, C, and D see also Figure 1.

Insula ROIs

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Our primary analysis focused on an independent tripartite insula parcellation provided by Deen and colleagues (Deen et al., 2011Figure 2A). To investigate the generality of our findings, we conducted a replication analysis using the a multimodal HCP-based atlas provided by Glasser and colleagues (Glasser et al., 2016). Since the multimodal atlas contains eight insular subdivisions, we reconstructed an equivalent tripartite organization by (i) combining regions AVI+MI+FOP3 into a dAI subdivision, (ii) using AAIC as the corresponding vAI subdivision, and (iii) combining Pol1+Pol2+Ig+FOP2 into a PI subdivision (see Supplementary Materials for details).

RTOP stability analysis

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To evaluate stability of our findings regarding RTOP differences between ROIs, we used subsampling procedures and determined minimum sample sizes that consistently reproduced findings.

RTOP gradient analysis

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To determine whether RTOP values followed an anterior-to-posterior and inferior-to-superior cytoarchitectonic organization of the insular cortex (Nieuwenhuys, 2012; Namkung et al., 2017; Morel et al., 2013), we computed a RTOP gradient field along the insular surface of each participant. To assess that the main gradient directions obtained for each participant were following a common direction, their mean and its confidence interval was computed. Furthermore, we used a Rayleigh test to reject the hypothesis that participants’ main gradient directions were uniformity distributed.

HCP resting-state fMRI data acquisition and processing

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Minimal preprocessed resting-state fMRI data (session: rfMRI_REST1_LR) was obtained from the HCP Q1-Q6 Data Release. The resting-state fMRI analysis included 347 subjects from the 433 subjects after head motion screening (total displacement <2 mm, frame-wise displacement <0.2 mm). During scanning, each participant had their eyes fixated on a projected crosshair on the screen. Spatial smoothing with a Gaussian kernel of 6 mm FWHM was first applied to the minimal preprocessed data to improve signal-to-noise ratio as well as anatomy correspondence between individuals. Bandpass temporal filtering (0.008 Hz <f < 0.1 Hz) was then applied.

fMRI connectivity analysis

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Seed-based functional connectivity analyses were conducted to examine whole-brain connectivity patterns of each insular subdivision. First, time series across all the voxels within each insular subdivision were extracted and averaged. The resulting averaged time series was then used as a covariate of interest in a linear regression of the whole-brain analysis. A global time series, computed across all brain voxels, along with six motion parameters were used as additional covariates to remove confounding effects of physiological noise and participant movement. Linear regression was conducted at the individual level. A group map was generated using one-sample t-tests (p<0.01, FDR corrected). Last paired t tests were applied at the group level to identify brain regions significantly more correlated with one subdivision than the other (p<0.01, FDR corrected).

FMRI connectivity analysis was used to identify functional subdivisions of the ACC that were differentially connected to each of the insular subdivisions. First, maps of paired comparisons between functional connectivity of different insular subdivisions were thresholded (p<0.01, FDR corrected) and binarized. Second, logical AND operation was applied to identify voxels surviving two paired comparisons for an insular subdivision versus the others (e.g. left PI >left dAI and left PI >left vAI). Last, the resulting mask was overlapped with an ACC mask from Harvard-Oxford Probabilistic Atlas of Human Cortical Area (http://www.cma.mgh.harvard.edu/fsl_atlas.html).

Relation between insula microstructure and behavior

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The relationship between brain and behavioral measures was examined using Canonical Correlation Analysis (CCA) and a cross-validation with prediction approach (Hotelling, 1992). Six participants were excluded in the analysis because of missing values in behavioral measures. Brain measures consisted of RTOP values for each subdivision of the insular cortex in each hemisphere (six variables in total). Behavioral measures consisted of in- and out-scanner variables that are highly relevant to cognitive control capacity. In-scanner behavioral task measures included n-back working memory task accuracy and reaction time (RT), relational task accuracy and RT, and gambling task percentage accuracy and RT for larger choice. Out-of-scanner behavioral measures consisted of performance on List sorting, Flanker, Card sorting, Picture sequence tasks from the NIH toolbox, as well as the processing speed. Together, there were 11 behavioral measures. Prediction analysis was performed using leave-one-out cross-validation. Pearson’s correlation was used to evaluate the correlation between the predicted brain and behavioral measures.

Appendix 1

Supplementary methods

Return to Origin Probability (RTOP) Density of Water Molecules as Measured by Diffusion MRI

In biological tissue, the dMRI signal (Latour et al., 1994) and Return-to-Origin probability (RTOP) density are known to be sensitive to volume, surface-to-volume ratio (Mitra et al., 1995; Schwartz et al., 1997), and mesoscopic organization of cells (Novikov et al., 2014). The diffusion MRI signal characterizes the probability that water molecules within a voxel experience, on average, a net displacement after a given diffusion time. This enables the measurement of the probability that, after a diffusion time t, the water particles in a voxel are in the vicinity of their starting position, namely RTOP density (Mitra et al., 1995). RTOP has been shown, theoretically and experimentally to be an index closely related, in cellular tissue, to cellular shape and organization (Mitra et al., 1995; Schwartz et al., 1997). Furthermore, an advantage of RTOP is that it is easy to estimate by integrating dMRI signals in a 3D volume.

The unnormalized RTOP measurement is obtained from the dMRI signal S as:

Pt=1S0,tR3Sq,tdq

In this expression, Pt is the unnormalized RTOP at a given diffusion time t, and q is the wavevector related to the usual diffusion b-value as q=b/t, and the diffusion time is t=Δ-δ/3 where Δ and δ are the gradient pulse separation and length respectively.

Theoretical results enable us to link unnormalized RTOP measurements to tissue microstructure (Mitra et al., 1995; Schwartz et al., 1997). Specifically, for free diffusion, as in the ventricles, it can be shown that (Mitra et al., 1995; Schwartz et al., 1997):

Pt=4πDventt-3/2

where Dvent is the ventricular diffusion constant which we estimate by computing the average mean diffusivity within the ventricle.

To render the unnormalized RTOP measurement, Pt, comparable across participants we use its normalized version (Mitra et al., 1995).

R(t)=(4πDventt)3/2P(t),

where Dvent is obtained from the ventricles in each participant, by computing the average ventricular mean diffusivity. RTOP, Rt, is now a dimensionless quantity reflecting the relative enhancement of the unnormalized RTOP density, Pt, with respect to free water diffusion (Mitra et al., 1995; Schwartz et al., 1997).

HCP diffusion MRI data acquisition and preprocessing

Minimal preprocessed diffusion MRI data (Glasser et al., 2013; Sotiropoulos et al., 2013) from 433 participants were obtained from the HCP Q1-Q6 Data Release. Twenty participants were excluded in the analysis because of outliers of RTOP values (defined as 1.5 interquartile ranges), leading to 413 subjects in the final sample (22–36 years old, 250 female/172 male). The acquisition protocol was optimized for high spatial resolution and signal-to-noise ratio. Two datasets with LR and RL polarities were acquired in each participant: TE/TR 89 ms/5.5 s; 1.25 mm isotropic voxels; b = 0 (6 images), and 271 directions equally distributed across 3 b-shells of 1, 2, and 3 ms µm -2; diffusion times ∆=43.1 ms and δ = 10.6 ms. We refer the interested reader to Sotiropoulos et al. (2013) for further detail of the acquisition and preprocessing pipeline.

Computing RTOP from diffusion MRI data

The dMRI signal characterizes the probability that water molecules within a voxel experience a net displacement after a given diffusion time. This enables the measurement of the probability that, after a diffusion time t, water in a voxel are in the vicinity of their starting position, namely the RTOP density (Mitra et al., 1995). We computed RTOP at the voxel level on the preprocessed diffusion MRI images provided by the HCP. At each voxel, we computed RTOP by evaluating a regularized representation of the signal based on the Mean Average Propagator formalism with Laplacian regularization (Fick et al., 2016) (MAPL) included in the dipy open-source software package (Dipy Contributors et al., 2014) (http://nipy.org/dipy). The regularization parameter was selected through generalized cross validation and RTOP was computed analytically from the fitted MAPL parameters. Finally, to render the Return-To-Origin probability comparable across subjects, we normalized it by the average ventricular Return-To-Origin probability of each subject’s cortico-spinal fluid in the ventricles. The normalized RTOP quantifies the enhancement ratio over free diffusion (Mitra et al., 1995; Schwartz et al., 1997) specific to each participant (Appendix 1). Furthermore, the normalized RTOP is sensitive to the volume and distribution of cell types across the insula, thereby allowing us to capture probe insular cortex cytoarchitectonic organization (Appendix 2.

Projecting RTOP values to the template cortical surface

To obtain the values of RTOP on a template cortical surface we started by sampling the voxel-level RTOP on to the participant-specific 32k cortical surfaces. We used the provided mid-thickness surfaces. This was done with the objective of projecting onto the surface the interpolated voxel RTOP value at the mid-point between the pial surface and the grey-white matter interface. Finally, to bring the surface-projected RTOP values to the common HCP template space we used the correspondence between the subject-specific surfaces and the template-registered MSMAll surfaces (Glasser et al., 2013).

Human insula and anterior cingulate cortex ROIs

We used functional parcellation of insular cortex from an independent study (Deen et al., 2011) to study RTOP variability between insular subregions. This parcellation of the insula includes ventral anterior insula (vAI), dorsal anterior insula (dAI) and posterior insula (PI).

To replicate our findings, we used the multimodal whole-brain atlas derived from a large number of HCP participants (Glasser et al., 2016). We constructed an equivalent tripartite organization we defined as a homologous set of dAI, vAI and PI subdivisions by (i) combining regions AVI+MI+FOP3 into a dAI subdivision, (ii) using AAIC as the corresponding vAI subdivision, and (iii) combining Pol1+Pol2+Ig+FOP2 into a PI subdivision. We projected the resulting regions to each participants’ cortical surface by leveraging the fact that surfaces are coregistered across subjects in the HCP dataset (Glasser et al., 2013).

Anterior cingulate cortex (ACC) ROIs were demarcated using the Harvard-Oxford atlas (Desikan et al., 2006). We also examined whether ACC subdivisions defined by their distinct functional connectivity with the insular subdivisions, matched distinct ACC areas p24, a24pr and p24pr in the HCP multimodal atlas (Glasser et al., 2016).

Macaque MRI dataset

We used data from two male rhesus macaques (Macaca mulatta). The Princeton University Animal Care and Use Committee approved all procedures, which conformed to the National Institutes of Health guidelines for the humane care and use of laboratory animals.

Macaque MRI data acquisition and processing

We scanned two male rhesus macaques (Macaca mulatta, ages = 4 years, body weights = 5.6/6.8 kg). For all scan sessions, animals were first sedated with ketamine (10 mg/kg IM) and maintained with isoflurane gas (2.5–3.0%) using an MR-compatible anesthesia workstation (Integra SP II, DRE Inc, Louisville KY). Data were collected to resemble the HCP dMRI protocol as close as possible. Specifically, we acquired the whole brain on a Siemens 3T MAGNETOM Prisma (80 mT/m @ 200 T/m/s gradient strength) using a surface coil (11 cm Loop Coil; Siemens AG, Erlangen Germany) secured above the head. Nine T1-weighted volumes were collected for averaging to obtain a high-quality structural volume and diffusion images were acquired using a double spin-echo EPI readout pulse sequence. Two datasets per macaque were acquired with opposite LR, RL polarity: resolution 1 mm isotropic; TE/TR 83 ms/8 s; 50 slices; FOV/Matrix 96 mm/96; 18 images at b = 0; and 271 directions equally distributed at b = 0.85, 1.65, and 2.5 ms µm−2 where the gradient directions were distributed optimally across the three spherical shells; diffusion times ∆=36.8 ms and δ = 14.1 ms. Distortion correction was applied using FSL’s Eddy tool. All macaque MRI data are publicly available on the NKI PRIMatE Data Exchange database (Milham et al., 2018). The T1-weighted images were registered to the D99 atlas through the NMT—NIMH Macaque Template allowing to subdivide the insula into granular, dysgranular, and agranular areas along with smaller subdivisions of each parcel. RTOP was computed using similar procedures as the human dMRI data.

Appendix 2

Supplementary results

RTOP captures known microstructural features of the insular cortex in macaques

Previous histological studies have demonstrated that the macaque insula can be clearly demarcated into agranular, dysgranular and granular subdivisions based on their unique cytoarchitectural properties (Evrard et al., 2012; Evrard et al., 2014Figure 2—figure supplement 1). To investigate whether RTOP measures are sensitive to known cytoarchitectural features of the primate insula, we acquired dMRI data from two macaque monkeys, X77 and X181, using protocols similar to the HCP, and examined RTOP values in the known cytoarchitectonic subdivisions of the primate insula. We found that in both macaques, the lateral agranular insula (Ial) was the region with the lowest RTOP values. Specifically, in animal X77, the RTOP values inside the right Ial were significantly lower than other insular regions (Figure 2—figure supplement 1B, Supplementary file 1-Supplementary Table 3). In X77’s left hemisphere, the Ial had significantly lower RTOP than all other regions (two-sided t-test) except Agranular Insula (Ia) and the posterior-medial agranular insula (Iapm) (Figure 2—figure supplement 1B, Supplementary file 1-Supplementary Table 3). In X181, the left Ial showed significantly lower RTOP than other insular regions (Figure 2—figure supplement 1B, Supplementary file 1-Supplementary Table 3). Finally, the right Ial had lower RTOP than other regions, except for the Ia (Figure 2—figure supplement 1B, Supplementary file 1-Supplementary Table 3). Crucially, the granular insular cortex (Ig) contained the highest RTOP values in both monkeys. Ig also has significantly higher RTOP values than other insular regions, with the exception of the left intermediate agranular insula (Iai) in X77 (Supplementary file 1-Supplementary Table 3). These results demonstrate that the agranular insula shows the lowest RTOP, demonstrating converging findings with known cytoarchitectonic studies of the insula in macaques.

RTOP and insula microstructure

Here we present simplified theoretical back-of-the-envelope calculations relating RTOP to insula microstructure. At long diffusion times RTOP can be simplified: if the compartment is restricted along d dimensions, the unnormalized RTOP converges asymptotically to

Ptl-dDintratd-3/2

where Dintra is the water diffusion constant within the compartment and l is the average characteristic length scale of the compartment. For a sphere-type restriction, which models a simplified cellular soma, there are no unrestricted dimensions, hence d=3 and l3V is the soma’s internal volume, hence, when 6Dintratl,Ptl-3V-1.

The specific properties of insular tissue are consistent with these approximations: first, the long diffusion time bound can be estimated by approximating the intra-cellular water diffusivity as Dintra~3µm2ms−1 and taking into account the HCP diffusion times of t=40ms, then 6Dintrat~27µm; second, the average external diameter for the largest neurons in the insula is of l24µm (Evrard et al., 2012), leading to a smaller internal diameter. Hence, the expression of the soma component of our normalized RTOP measure in the insula can be approximated as

RtV-1Dventt32

To determine the extent to which the Rt is dominated by the soma, as opposed to axonal and dendritic processes, we obtained three-dimensional reconstructions of Allman et al. (2010) insular neurons from NeuroMorpho.org. We then computed, on a per neuron basis, the ratio of volume between soma and axonal/dendritic processes obtaining an average ratio of 3. Using the biophysical model by Veraart et al. (2019), with intra-cellular water diffusivity of Dintra~3µm2ms−1 at a maximum b-value of 3 ms µm−2, we can estimate that less that 10% of Rt’s intracellular signal is modulated by axonal and dendritic processes.

Assuming that extra-cellular compartment volume remains constant, these analyses suggest that RTOP is sensitive to volume and distribution of cell types across the insula and that RTOP reflects key aspects of variation in insular cortex cytoarchitectonic organization. Detailed computer simulations that take into account known layer specific distributions of neurons and glia are needed to further validate these theoretical calculations.

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Decision letter

  1. Timothy E Behrens
    Senior and Reviewing Editor; University of Oxford, United Kingdom
  2. Matthew F Glasser
    Reviewer; Washington University in St. Louis, United States
  3. Leonardo Cerliani
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The paper demonstrates the efficacy of a new non-invasive method that is sensitive to histological features such as cell size. This is an important advance that opens up studies, for example, in other brain regions and clinical populations. It provides a new level of detail in our understanding of the microstructure of the human insula. The study is further unusual in providing comparison between human and nonhuman primate. This is important as much of our knowledge about the cytoarchitecture and connectivity in general, and in insula in particular, is derived from extrapolations from other primate species.

Decision letter after peer review:

Thank you for submitting your article "Microstructural organization of human insula is linked to its macrofunctional circuitry and predicts cognitive control" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by Timothy Behrens as the Senior and Reviewing Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Matthew F Glasser (Reviewer #1); Leonardo Cerliani (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

The authors provide an interesting analysis of the relationships between microstructure, functional connectivity and behavioural specialization for different regions of the human insular cortex. The most original finding relates to the distribution of a DWI-derived microstructural index denoted RTOP (Return to Origin Probability), which is sensitive to the volume and distribution of cell types in different cortical regions, and to the relationship of microstructural feature and functional specialization in insular and functionally connected cingulate regions, together representing the core of the Salience network. The major contributions of the work are a demonstration of sensitivity to histological features such as cell size in vivo with an innovative MR technique, a demonstration of how these features change in the human insular cortex, and a demonstration of the relationship between these features across distant, but connected, regions. The authors should also be commended for replicating the RTOP analysis in specimens of nonhuman primates, as most of our knowledge about insular cytoarchitecture and connectivity still relies on extrapolation from other primate species.

Essential revisions:

In the below, I have replicated comments from the original reviews, but I have previewed them with key elements of our discussion where we tried to clarify which comments should be addressed directly and which could be discussed.

Insular Parcellation:

Several concerns were raised about the insular cortex parcellation that you use. These concerns were about (i) the quality, and (2) the hemispheric asymmetry of the particular parcellation that you use, which could affect the results and (3) the group- rather than individual- nature of the parcellation, in the face of large reported individual differences. Reviewer comments are copied below. In the discussion, the reviewers agreed that it would be sufficient to reproduce key results using the HCP parcellation (described in first comment below), and to discuss the potential impact of individual variability in the discussion. It is hoped that the HCP parcellation (which does not contain the clear asymmetry present in the Deen parcellation) will improve some puzzles in the current results (eg pronounced asymmetry in Figure 2C). We hope that this will be straightforward as it simply requires running your existing pipeline on a new set of insular masks that are freely available.

Comment 1:

"The insular parcellation used by the authors appears of poor quality. For one thing, it is a volume-based parcellation generated from a small number of subjects (n=30) using a clustering algorithm (whose winner take all hard parcellation will be sensitive to noise when near the boundary) only on resting state data. The HCP has produced a much higher quality multi-modal parcellation of the whole cerebral cortex (Glasser et al., 2016 see Supplementary Neuroanatomical Results Figure 14), including the insula, from 210 HCP subjects and precisely replicated it in an additional 210 HCP subjects. The HCP recommends the use of this parcellation together with HCP data. Notably, this parcellation does not show the large hemispheric asymmetry found in the insular parcellation used by the authors in either the final parcellation or the multi-modal data it was defined from, despite the fact that such asymmetries could be found (and in fact were found) in some parts of the cortex in the HCP parcellation. Thus, any of the asymmetries found by the authors using the parcellation may result from idiosyncrasies of that parcellation (e.g. the failure to separate right dAI vs vAI, which I didn't see mentioned anywhere). That said, the three regions described by the authors might be reconstructed from the HCP's parcellation by grouping AAIC (vAI), AVI+MI(dAI), and Pol1+Pol2+Ig(PI) if desired. Depending on how the authors want to define the insular cortex, they might also include FOP3 and FOP2 in dAI and π respectively. Similarly, if the analysis were done with an HCP-Style approach (Glasser et al., 2016) the authors might find that their anterior cingulate regions match areas p24, a24pr, and p24pr (Glasser et al., 2016, see Supplementary Neuroanatomical Results Figure 19). Of note, the agranular to granular transition in the anterior cingulate cortex is much more pronounced in the inferior to superior direction than the anterior to posterior direction (Glasser et al., 2016 Supplementary Neuroanatomical Results section 19, Glasser and Van Essen, 2011, Journal of Neuroscience section on Cingulate Cortex)."

Comment 2: (Which should be addressed in discussion and not in new analyses.)

Interindividual variability and delimitation of insular regions.

The Authors state that "no consensus has yet emerged about their precise boundaries because of small sample sizes, limited insular divisions examined and high degree of variability across individuals". The first rationale of the statement – the small sample size of the histological samples – is not necessarily pertinent to accurately localize cytoarchitectonic transitions. For instance the transition between primary sensory and motor cortex is always very stablly localized, while other transitions – e.g. between BA44 and BA45 – are also clear at a microscopic examination, but present high intersubjective variability in terms of localization (cfr Amunts K et al., 1999 J Comp Neurology). In fact, the latter – rather than small sample sizes – could represent the main reason why also in the insula it is difficult to distinguish different architectural territories. Given this intersubjective variability – as mentioned by the Authors – it is peculiar that the present investigation relies on an insular parcellation which does not estimate such interindividual differences, as it was extracted from the mean connectivity matrix (cfr. Deen et al., 2010 p. 2).

In light of these considerations, I believe that the present analysis would have been much more compelling and data-driven if instead of using a predefined parcellation, the Authors would have carried out a tripartite parcellation of the insula in every individual using the rs-fMRI data at hand, even just with a simple k-means. Since the rs-fMRI data has already been preprocessed for the functional connectivity analysis, such procedure would have been quite easy to implement. I understand that this would probably be an excessive burden to carry out since the insular parcellation represents the very first step of the analysis, but I would like to ask the Authors to acknowledge the limitations the current choice.

Reference to prior work in insular parcellation:

There were several comments about your scholarship with reference to prior work in insular parcellation (some of this work is referenced in the comments above and others in the comments I have copied here)

"Further, there are multiple statements in the paper about lack of prior non-invasive mapping of the insula in the paper that must be toned down in the face of extensive prior literature."

"The Authors present the results by Deen, 2010 and Chang, 2012 as the reference for insular parcellation. They should mention that also other parcellations have been proposed based on similar data, e.g. Jakab et al., 2012; Cauda et al., 2010; Kelly et al., 2012. Importantly, most of these work do not report that a tripartite parcellation represents the best solution for the insula.

As a side note, reference work for human – vs other primates – insular parcellation are not cited. See e.g. the work of Rose and Brockhaus in the review by Nieuwenhuys."

"Correct the neuroanatomical statements in the paper, since they contain several claims which diverge from the reference literature.

"The dysgranular cortex has an intermediate profile that has been mainly observed in the dorsal anterior aspects of the insula". This is not entirely correct, and in this form it is misleading. The dysgranular architecture characterizes as well the central and ventral territory of the insula. Indeed, most of the insular cortex can be characterized as dysgranular. The results in the cited reference (Kurth et al., 2010) show that the ventral middle part of the insula has a dysgranular architecture, while they do not provide results regarding the anterior dorsal territory.

"The anterior insula is more strongly connected to brain areas important for cognitive control, most notably the dorsal anterior cingulate cortex (ACC) while the posterior insula has stronger links with subcortical and limbic regions important for emotion, including the amygdala and ventral striatum". This statement diverges from the reference literature: the amygdaloid complex and the ventral striatum are mostly connected with the ventral anterior insula (cfr. Amaral and Price, 1984 J Comp Neurol and Chikama et al., 1997, respectively), while these connections decrease along the antero-posterior axis."

Data processing:

The reviewers raised concerns that the data processing is not state of the art. This was a surprise as the HCP data that the authors have used is freely available in processed form with state of the art techniques. The relevant reviewer comment is copied below. In discussion, the reviewers agreed that we should strongly encourage the authors to redo analyses as described in the comment. We think this represents best practice. However, we realise that this means redoing the analyses from scratch and, although, we believe it would improve results and represent best practice, we do not think it would fundamentally change the key message of the paper. Hence, in line with eLife review policy, this is therefore a recommendation not a requirement for publication.

Reviewer comment:

"It has been replicated repeatedly that cross-subject cortical alignment and ability to localize signals to cortical areas is maximized by using surface-based intersubject registration instead of volume-based registration and, in particular, by avoiding 3D unconstrained volume-based smoothing (Reviewed Glasser et al., 2016 Nature Neuroscience; explicitly redemonstrated Coalson et al., 2018 PNAS). As such, the HCP has provided precisely aligned surface-based CIFTI resting state data (aligned with the MSMAll algorithm). Thus, it was puzzling to find that the authors choose to start from the less well aligned volume-based data and then smoothed the data with "a Gaussian kernel of 6mm FWHM was first applied to the minimal preprocessed data to improve signal-to noise ratio as well as anatomy correspondence between individuals." Importantly, the second statement is a common brain imaging misconception that has been decisively disproved by Coalson et al., 2018 PNAS. Further, the fMRI denoising approach chosen by the authors does not match HCP recommendations-rather they should use the data denoised by spatial ICA and the FIX classifier and do not need to remove movement regressors again. Temporal filtering is also discouraged by the HCP as it affects both signal and noise equally. Further, for the purposes of this study, the authors can easily make use of the publicly available group average MIGP resting state data, where the alignment, structured noise denoising, and significant unstructured noise denoising have already been taken care of-if they do not wish to worry about these methodological details. "

Reporting of methodological detail.

There were several concerns related to the reporting of methodological detail that need to be addressed.

The description of key imaging methods is inadequate. It is useful to restate the key aspects of the HCP diffusion acquisition and preprocessing for readers so they need not refer back to another paper. Additionally, the macaque data acquisition description, apparently new data acquired for the paper, is inadequate. For example, the resolution, TR/TE, b-shells, and preprocessing details are left unspecified. Of note the HCP data were acquired with a single spin echo sequence to enable minimizing the TE and maximizing the SNR. The preprocessing of the macaque diffusion data needs to be described. Further, no details are given as to how the surface-based maps of RTOP were created and any additional processing (e.g. spatial smoothing, cross-subject alignment) that was done on the HCP preprocessed diffusion data or derivatives of this. Depending on the preferred approach of the authors (ideally the one that worked the best) the RTOP data should have been computed in the individual subject's physical volume space and then mapped from between the individual's MSMAll aligned white matter and pial surface meshes onto the 32k MSMAll surface standard space without any spatial smoothing or after mapping the preprocessed diffusion data from between the individual's MSMAll aligned white matter and pial surface meshes onto the 32k MSMAll surface standard space without any spatial smoothing and then modeling RTOP. This data could then simply be averaged across subjects. With 449 subjects, smoothing is unlikely to be necessary for SNR purposes, but modest surface-based smoothing reduces precision much less than volume-based smoothing (Coalson et al., 2018 PNAS).

More information about the CCA analysis.

The association between RTOP and cognitive control measures represents an important part of the results. While the correlation between the left and right canonical vectors is significant and features a moderate effect size, I have some issues with the interpretation of the weights in the first component of the CCA, especially because I know little about this technique.

1) The right dorsal anterior insula has the highest weight, as expected and detailed in the Discussion. However it is a negative weight, while other weights, and notably the rvAI, are positive. What is the meaning of the sign and of the sign difference in the weights, in the interpretation of the microstructure-behavioural association?

2) The weights of the first behavioural canonical variate appear flat, and most of them are zero. What are the implications of this for the interpretations?

3) It would be useful to have a plot of the cumulative variance explained e.g. by the first N pairs of canonical variate, to assess whether a different combination of regional microstructur/behaviour adds a substatial amount of variance

4) Does the significance of the correlation between the first pair of canonical variates survive if permutation testing is used instead of parametric testing?

Introduce the RTOP measure more clearly to a non-specialised audience:

"Explain neuroanatomical correlates of RTOP, which is also useful to interpret the behavioural correlation with microstructure. Differences in insular microstructure are quantified using the RTOP DWI derivative. However I couldn't find an explanation of the microstructural properties that the RTOP targets in the Introduction or in the Results, with only one small mention at the end of the Supplementary Materials. Think e.g. about fractional anisotropy (FA): a neuroanatomist would like to know not only that there are localized differences in FA between samples of participants, but also how FA relates to the microstructure of axonal integrity and myelin sheet, in order to interpret those differences. Please include a brief description of the microstructural properties that RTOP allows to highlight, and the rationale that links these properties to the RTOP calculation. Preferably this should be present in the Introduction."

https://doi.org/10.7554/eLife.53470.sa1

Author response

[…] Comment 1:

"The insular parcellation used by the authors appears of poor quality. For one thing, it is a volume-based parcellation generated from a small number of subjects (n=30) using a clustering algorithm (whose winner take all hard parcellation will be sensitive to noise when near the boundary) only on resting state data. The HCP has produced a much higher quality multi-modal parcellation of the whole cerebral cortex (Glasser et al., 2016 see Supplementary Neuroanatomical Results Figure 14), including the insula, from 210 HCP subjects and precisely replicated it in an additional 210 HCP subjects. The HCP recommends the use of this parcellation together with HCP data. Notably, this parcellation does not show the large hemispheric asymmetry found in the insular parcellation used by the authors in either the final parcellation or the multi-modal data it was defined from, despite the fact that such asymmetries could be found (and in fact were found) in some parts of the cortex in the HCP parcellation. Thus, any of the asymmetries found by the authors using the parcellation may result from idiosyncrasies of that parcellation (e.g. the failure to separate right dAI vs vAI, which I didn't see mentioned anywhere). That said, the three regions described by the authors might be reconstructed from the HCP's parcellation by grouping AAIC (vAI), AVI+MI(dAI), and Pol1+Pol2+Ig(PI) if desired. Depending on how the authors want to define the insular cortex, they might also include FOP3 and FOP2 in dAI and π respectively. Similarly, if the analysis were done with an HCP-Style approach (Glasser et al., 2016) the authors might find that their anterior cingulate regions match areas p24, a24pr, and p24pr (Glasser et al., 2016, see Supplementary Neuroanatomical Results Figure 19). Of note, the agranular to granular transition in the anterior cingulate cortex is much more pronounced in the inferior to superior direction than the anterior to posterior direction (Glasser et al., 2016 Supplementary Neuroanatomical Results section 19, Glasser and Van Essen, 2011, Journal of Neuroscience section on Cingulate Cortex)."

Thank you for raising these important points. Following these suggestions, we have now conducted a replication analysis using the HCP multimodal parcellation35. These results are now presented in the manuscript. Crucially, all major findings were replicated with the HCP multimodal parcellation. Our approach avoids potential circularity of using multimodal HCP parcellations to probe insular organization, while also demonstrating replicability.

To examine reproducibility of the insula microstructure across functionally defined subdivisions, we conducted the same analysis using the independent insular parcellation from Glasser et al35 (Figure 2D). We found a significant interaction between hemisphere and subdivision (ANOVA, F(2,824) = 207.3, p < 2E-16) and significant main effects of subdivision (ANOVA, F(2,824) = 11265, p < 2E-16) and hemisphere (ANOVA, F(1,412) = 10.9, p < 0.001) (Figure 2E). Post-hoc t-tests further revealed a gradient of RTOP values vAI < dAI < π in both right and left hemisphere (all ps < 3.2E-06, with vAI and π having significantly smaller values in right hemisphere and vAI having significantly smaller values in the left hemisphere (paired t-test, all ps < 3.2E-06) (Figure 2E). Stability analysis revealed that a sample size of N=25 was sufficient to achieve a stable differentiation (p <0.01) between π and vAI and between π and dAI in both hemispheres and between dAI and vAI in the left hemisphere (Figure 2F).

To examine reproducibility of microstructural features in ACC nodes of the salience network, we extended the analysis using ACC subdivisions determined by their functional connectivity with the insula parcellation of Glasser et al35. We found a significant interaction between ACC subdivision and hemisphere (ANOVA, F(2,824) = 245.7, p = 2E-16), and significant main effects of subdivision (ANOVA, F(2,824) = 343, p = 2E-16) and hemisphere (ANOVA, F(1,412) = 248.9, p = 2E-16) (Figure 7E). Post-hoc paired t-tests revealed significant differences in RTOP: ACC-vAI < ACC-dAI < ACC-PI in both hemispheres (paired t-tests, all ps < 6E-11) (Figure 7E). Stability analysis demonstrated that the differences between π and vAI and between dAI and vAI in both hemispheres and between π and dAI in the right hemisphere were highly reliable (paired t-tests, p < 0.01) for samples sizes > N=50 (Figure 7F). The three ACC subdivisions overlapped with distinct areas demarcated by the HCP multimodal atlas35: the ACC-vAI overlapped with ACC area p24, ACC-dAI overlapped with cingulate area ap24pr, and ACC-PI overlapped with ACC area p24pr, thus demonstrating distinct patterns of connectivity linking insula and ACC subdivisions.

To examine reproducibility of insula microstructure in relation to cognitive control ability, we conducted the same analysis using the insular parcellation from Glasser et al35. We found a marginally significant relation between insula RTOP measures and individual cognitive control abilities (Canonical correlation, Pillai’s trace = 0.20, p = 0.08). The canonical weights of the 1st latent variable in microstructural measures were significantly correlated with the canonical weights of the 1st latent variable in behavioral measures (Pearson’s correlation, r = 0.31, p < 0.001, Cohen’s d = 0.65, Figure 8C; Supplementary file 1—supplementary table 5). A leave-one-out cross-validation procedure further revealed that, based on microstructural properties of the insula, our CCA model could predict cognitive control ability on unseen data (Pearson’s correlation, r = 0.17, p < 0.001, Cohen’s d = 0.35, Figure 8D ), with the strongest predictive weights in the right dAI.

In summary, these results demonstrate replication of our previous findings using the HCP parcellation.

Comment 2: (Which should be addressed in discussion and not in new analyses.)

Interindividual variability and delimitation of insular regions.

The Authors state that "no consensus has yet emerged about their precise boundaries because of small sample sizes, limited insular divisions examined and high degree of variability across individuals". The first rationale of the statement – the small sample size of the histological samples – is not necessarily pertinent to accurately localize cytoarchitectonic transitions. For instance the transition between primary sensory and motor cortex is always very stablly localized, while other transitions – e.g. between BA44 and BA45 – are also clear at a microscopic examination, but present high intersubjective variability in terms of localization (cfr Amunts K et al., 1999 J Comp Neurology). In fact, the latter – rather than small sample sizes – could represent the main reason why also in the insula it is difficult to distinguish different architectural territories. Given this intersubjective variability – as mentioned by the Authors – it is peculiar that the present investigation relies on an insular parcellation which does not estimate such interindividual differences, as it was extracted from the mean connectivity matrix (cfr. Deen et al., 2010 p. 2).

In light of these considerations, I believe that the present analysis would have been much more compelling and data-driven if instead of using a predefined parcellation, the Authors would have carried out a tripartite parcellation of the insula in every individual using the rs-fMRI data at hand, even just with a simple k-means. Since the rs-fMRI data has already been preprocessed for the functional connectivity analysis, such procedure would have been quite easy to implement. I understand that this would probably be an excessive burden to carry out since the insular parcellation represents the very first step of the analysis, but I would like to ask the Authors to acknowledge the limitations the current choice.

We thank the reviewers for this suggestion. We appreciate that there are several reasons why no consensus has yet emerged about insular subdivisions, and note that the goal of our study was not to resolve potential issues of variability in insular boundaries. Rather, our goal was to use generally accepted consensus parcellations to probe insular microstructure. We have now therefore revised the statement to read: “In the ensuing years, several histological studies have focused on demarcating the microstructural properties of insular subdivisions but no consensus has yet emerged about their precise boundaries”. We agree with the sentiment of the reviewer that individual subject parcellations may have the potential to provide additional insights into insular functional organization. It may be noted, however, that while the use of individual parcellations has its strengths, there are also limitations as different participants might have different numbers of optimal (k-means-derived) clusters, making it difficult to match parcellations across individuals. Please also see reply below regarding gradients vs. clusters.

Importantly, to address the generality of our findings we examined an independent surface-based parcellation35, albeit with boundaries similar but not identical to the functional parcellation of Deen and colleagues29. Crucially, all our findings were replicated using the multimodal parcellation. The replicability our findings based on functional subdivisions obtained from two independent studies29,35, based on different data and methodological approaches, and stability with respect to sample size in both the primary and replication analyses thus points to a close correspondence between functional organization of the insula and its microstructural organization.

Reference to prior work in insular parcellation:

There were several comments about your scholarship with reference to prior work in insular parcellation (some of this work is referenced in the comments above and others in the comments I have copied here)

"Further, there are multiple statements in the paper about lack of prior non-invasive mapping of the insula in the paper that must be toned down in the face of extensive prior literature."

"The Authors present the results by Deen et al., 2010 and Chang et al., 2012 as the reference for insular parcellation. They should mention that also other parcellations have been proposed based on similar data, e.g. Jakab et al., 2012; Cauda et al., 2010; Kelly et al., 2012. Importantly, most of these work do not report that a tripartite parcellation represents the best solution for the insula.

As a side note, reference work for human – vs other primates – insular parcellation are not cited. See e.g. the work of Rose and Brockhaus in the review by Nieuwenhuys."

We apologize for the confusion here. Please note that our statements about lack of prior non-invasive mapping of the insula did not refer to resting-state fMRI studies of which there are many. Rather, our statements referred to lack of non-invasive mapping studies on microstructural properties of the insula. We have gone over all such statements and ensured that this is indeed the case.

We apologize for the oversight and have now cited the important work of Jakab et al., Cauda et al. and Kelly et al. in the Introduction. Depending on the parcellation techniques employed, previous studies have reported that the human insula can be divided into anywhere between 2 and 13 distinct subdivisions. Most studies, however, point to distinct dorsal anterior insula, ventral anterior insula, and posterior insula segments, consistent with histological reports26 Ultimately, as noted by Tjan, Zalesky and colleagues64,65, clustering methods are not ideal for studying the insula, and may need to be replaced with gradient based analyses. Please see reply below for further details.

We have also now cited the important original work of Rose and Brockhaus, and thank the reviewer for pointing them out to us.

"Correct the neuroanatomical statements in the paper, since they contain several claims which diverge from the reference literature.

"The dysgranular cortex has an intermediate profile that has been mainly observed in the dorsal anterior aspects of the insula". This is not entirely correct, and in this form it is misleading. The dysgranular architecture characterizes as well the central and ventral territory of the insula. Indeed, most of the insular cortex can be characterized as dysgranular. The results in the cited reference (Kurth et al., 2010) show that the ventral middle part of the insula has a dysgranular architecture, while they do not provide results regarding the anterior dorsal territory.

We have corrected this statement to read “The dysgranular cortex has an intermediate profile that has been observed in the anterior aspects of the insula”. We thank the reviewer for this feedback.

"The anterior insula is more strongly connected to brain areas important for cognitive control, most notably the dorsal anterior cingulate cortex (ACC) while the posterior insula has stronger links with subcortical and limbic regions important for emotion, including the amygdala and ventral striatum". This statement diverges from the reference literature: the amygdaloid complex and the ventral striatum are mostly connected with the ventral anterior insula (cfr. Amaral and Price, 1984 J Comp Neurol and Chikama et al., 1997, respectively), while these connections decrease along the antero-posterior axis."

We have now clarified the distinction between the dorsal and ventral anterior insula and now state: “This functional organization is supported by a distinct pattern of long-range connections: the dorsal anterior insula is more strongly connected to brain areas important for cognitive control, most notably the dorsal anterior cingulate cortex (ACC) while the ventral anterior insula and posterior insula have stronger links with subcortical and limbic regions important for emotion, reward and homeostatic regulation, including the amygdala, ventral striatum and hypothalamus2,4,25,26. Consistent with these reports, meta-analysis of task-related coactivation patterns in human neuroimaging studies point to distinct functional networks associated with insular subdivisions27”.

Data processing:

The reviewers raised concerns that the data processing is not state of the art. This was a surprise as the HCP data that the authors have used is freely available in processed form with state of the art techniques. The relevant reviewer comment is copied below. In discussion, the reviewers agreed that we should strongly encourage the authors to redo analyses as described in the comment. We think this represents best practice. However, we realise that this means redoing the analyses from scratch and, although, we believe it would improve results and represent best practice, we do not think it would fundamentally change the key message of the paper. Hence, in line with eLife review policy, this is therefore a recommendation not a requirement for publication.

Reviewer comment:

"It has been replicated repeatedly that cross-subject cortical alignment and ability to localize signals to cortical areas is maximized by using surface-based intersubject registration instead of volume-based registration and, in particular, by avoiding 3D unconstrained volume-based smoothing (Reviewed Glasser et al., 2016 Nature Neuroscience; explicitly redemonstrated Coalson et al., 2018 PNAS). As such, the HCP has provided precisely aligned surface-based CIFTI resting state data (aligned with the MSMAll algorithm). Thus, it was puzzling to find that the authors choose to start from the less well aligned volume-based data and then smoothed the data with "a Gaussian kernel of 6mm FWHM was first applied to the minimal preprocessed data to improve signal-to noise ratio as well as anatomy correspondence between individuals." Importantly, the second statement is a common brain imaging misconception that has been decisively disproved by Coalson et al., 2018 PNAS. Further, the fMRI denoising approach chosen by the authors does not match HCP recommendations-rather they should use the data denoised by spatial ICA and the FIX classifier and do not need to remove movement regressors again. Temporal filtering is also discouraged by the HCP as it affects both signal and noise equally. Further, for the purposes of this study, the authors can easily make use of the publicly available group average MIGP resting state data, where the alignment, structured noise denoising, and significant unstructured noise denoising have already been taken care of-if they do not wish to worry about these methodological details."

We appreciate the recommendations of the reviewers and thank the editors for the opportunity to address this issue without having to redo all our analyses from scratch. When we started the project, the HCP pipeline data were not available for all participants and we therefore used volumetric pipelines that are standard in the field. The functional connectivity patterns between the AI and ACC obtained here are consistent with many previous neuroimaging studies. While the ICA and FIX based approaches have their strengths, they are not without limitations. Crucially, all our findings were replicated using the parcellation developed by Glasser and colleagues which is based on the multimodal surface-based pipeline described by the reviewer.

Reporting of methodological detail.

There were several concerns related to the reporting of methodological detail that need to be addressed.

The description of key imaging methods is inadequate. It is useful to restate the key aspects of the HCP diffusion acquisition and preprocessing for readers so they need not refer back to another paper. Additionally, the macaque data acquisition description, apparently new data acquired for the paper, is inadequate. For example, the resolution, TR/TE, b-shells, and preprocessing details are left unspecified. Of note the HCP data were acquired with a single spin echo sequence to enable minimizing the TE and maximizing the SNR. The preprocessing of the macaque diffusion data needs to be described. Further, no details are given as to how the surface-based maps of RTOP were created and any additional processing (e.g. spatial smoothing, cross-subject alignment) that was done on the HCP preprocessed diffusion data or derivatives of this. Depending on the preferred approach of the authors (ideally the one that worked the best) the RTOP data should have been computed in the individual subject's physical volume space and then mapped from between the individual's MSMAll aligned white matter and pial surface meshes onto the 32k MSMAll surface standard space without any spatial smoothing or after mapping the preprocessed diffusion data from between the individual's MSMAll aligned white matter and pial surface meshes onto the 32k MSMAll surface standard space without any spatial smoothing and then modeling RTOP. This data could then simply be averaged across subjects. With 449 subjects, smoothing is unlikely to be necessary for SNR purposes, but modest surface-based smoothing reduces precision much less than volume-based smoothing (Coalson et al., 2018 PNAS).

We thank the reviewers for this suggestion. We have addressed these concerns, by

1) Including details of the HCP dMRI acquisition protocol in the Supplementary Information Section I.B.

2) Including details of the RTOP computational procedures in the Supplementary Information Section I.C. These were done exactly as in accordance to the method envisioned by the reviewer performing a subject-space computation, projecting it to the 32k MSMAll-aligned surface mesh, specifically the mid thickness one, without any spatial smoothing and then taking advantage of the vertex correspondences across the 32k MSMAll provided surfaces to perform the whole-sample analyses.

3) Including details of the macaque dMRI acquisition and processing protocols in the Supplementary Information Section I.E. the details of the monkey dataset diffusion MRI acquisition and processing in supplementary information section I.E.

More information about the CCA analysis.

The association between RTOP and cognitive control measures represents an important part of the results. While the correlation between the left and right canonical vectors is significant and features a moderate effect size, I have some issues with the interpretation of the weights in the first component of the CCA, especially because I know little about this technique.

1) The right dorsal anterior insula has the highest weight, as expected and detailed in the Discussion. However it is a negative weight, while other weights, and notably the rvAI, are positive. What is the meaning of the sign and of the sign difference in the weights, in the interpretation of the microstructure-behavioural association?

2) The weights of the first behavioural canonical variate appear flat, and most of them are zero. What are the implications of this for the interpretations?

3) It would be useful to have a plot of the cumulative variance explained e.g. by the first N pairs of canonical variate, to assess whether a different combination of regional microstructur/behaviour adds a substatial amount of variance

4) Does the significance of the correlation between the first pair of canonical variates survive if permutation testing is used instead of parametric testing?

We thank the reviewers for the opportunity to clarify the canonical correlation analysis (CCA) and our brain-behavior relationship findings. CCA is a multivariate statistical method for investigating the relationship between two sets of variables. It identifies linear combinations in both sets of variables such that they bear strong statistical associations. CCA is well suited to uncover multivariate relationships between two sets of high-dimensional features in cognitive neuroscience as it goes beyond mapping one-to-one and one-to-many features relationships. Below we address each specific comment raised by the reviewer:

1) In this study, we used functional parcellations of insula from both Deen et al.29 and Glasser et al.35 to demonstrate the reproducibility of our findings. CCA showed that the right dorsal anterior insula has the highest, but negative, weight in both insular parcellations, suggesting the robustness of the result. It suggests that RTOP of the right dorsal anterior insula (rdAI) has the highest contribution to the latent linear relationship on the brain variable set side. The negative weight of the rdAI RTOP is aligned with the functional role of the rdAI in cognitive control. Lower RTOP value may be associated with higher expression of von Economo neurons in this region, which could provide a neuronal basis for rapid signal relay between rdAI and dACC. Interestingly, both the right ventral anterior insula (rvAI) and the right posterior insula (rPI) have positive CCA weights, suggesting they have opposite contributions to the brain-behavior relationship in comparison to the rdAI. This is also consistent with weak association between their areas and cognitive control. On the behavioral variable side, results from both insular parcellation atlas show positive canonical weight of the standard scores from the ListSort, Flanker and working memory tasks and negative weights of the standard scores from the ProcSpeed and PicSeq tasks, indicating the robustness of our findings.

2) We apologize for the confusion. The near zero weights in behavioral canonical variate is due to the scale of behavioral variables. We have rescaled all the variables, conducted the CCA analysis (mean = 0, standard deviation = 1), and updated the tables. Note that the scaling does not influence estimation of canonical correlations. The CCA models and canonical correlations remain the same before and after rescaling.

3) Author response image 1 shows cumulative R square from the first to the sixth latent variables in the CCA analysis. Both parcellations from Deen et al.29 and Glasser et al.35 show similar profiles. The first set of latent variables explain more variance than the other set of latent variables, and the second set of latent variables explain the second most variance in the data.

Author response image 1
Cumulative R square from the set of latent variables in CCA models.

Functional parcellation29 and Multimodal parcellation35 show similar profiles.

4) Yes. We tested the significance of the correlation between the first pair of canonical variates using permutation tests. In each permutation, we randomly shuffled the first canonical variate in behavior data but kept the first canonical variate in RTOP data in their original order. Then, we computed the correlation coefficient between permuted data. This step was repeated 1000 times to get a null distribution of brain-behavior correlation coefficient, from which the p value of the original correlation coefficient was computed. We found that the original correlation coefficient was significant using the insular parcellation atlas from both Deen et al29 and Glasser et al.35 (ps<0.001).

Introduce the RTOP measure more clearly to a non-specialised audience:

"Explain neuroanatomical correlates of RTOP, which is also useful to interpret the behavioural correlation with microstructure. Differences in insular microstructure are quantified using the RTOP DWI derivative. However I couldn't find an explanation of the microstructural properties that the RTOP targets in the Introduction or in the Results, with only one small mention at the end of the supplementary materials. Think e.g. about fractional anisotropy (FA): a neuroanatomist would like to know not only that there are localized differences in FA between samples of participants, but also how FA relates to the microstructure of axonal integrity and myelin sheet, in order to interpret those differences. Please include a brief description of the microstructural properties that RTOP allows to highlight, and the rationale that links these properties to the RTOP calculation. Preferably this should be present in the Introduction."

We agree with the reviewers that a better introduction to the RTOP measure enhances the quality of the manuscript. We have added, in the Introduction, an overview of the microstructural properties that RTOP captures:

“RTOP quantifies the restrictedness of water diffusion in the tissue47, with higher values reflecting more restricted diffusion. RTOP has been shown to be inversely correlated with the average volume of pores restricting diffusion in microbeads47,48. In neural tissue, spinal cord maturation has been shown to increase RTOP49. Recently, RTOP has been shown to be more sensitive to cellular organization than other more commonly used diffusion MRI measures, such as mean and radial diffusivity50. Based on these observations, we hypothesized that RTOP in grey matter would be sensitive to the cytoarchitectonic and neuronal organization of the insular cortex.”

https://doi.org/10.7554/eLife.53470.sa2

Article and author information

Author details

  1. Vinod Menon

    1. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, Stanford, United States
    2. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, Stanford, United States
    3. Stanford Neurosciences Institute, Stanford University School of Medicine, Stanford, Stanford, United States
    Contribution
    Conceptualization, Supervision, Investigation
    For correspondence
    menon@stanford.edu
    Competing interests
    No competing interests declared
  2. Guillermo Gallardo

    1. Athena, Inria Sophia Antipolis, Université Côte d’Azur, Sophia Antipolis, France
    2. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    Contribution
    Data curation, Software, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8271-2516
  3. Mark A Pinsk

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Contribution
    Resources, Data curation, Methodology
    Competing interests
    No competing interests declared
  4. Van-Dang Nguyen

    Department of Computational Science and Technology Royal Institute of Technology in Stockholm, Stockholm, Sweden
    Contribution
    Formal analysis
    Competing interests
    No competing interests declared
  5. Jing-Rebecca Li

    Defi, Inria Saclay Île-de-France, École Polytechnique Université Paris Sud, Palaiseau, France
    Contribution
    Formal analysis, Supervision, Investigation
    Competing interests
    No competing interests declared
  6. Weidong Cai

    Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, Stanford, United States
    Contribution
    Conceptualization, Formal analysis, Validation, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9581-7774
  7. Demian Wassermann

    Parietal, Inria Saclay Île-de-France, CEA Université Paris Sud, Palaiseau, France
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology
    For correspondence
    demian.wassermann@inria.fr
    Competing interests
    No competing interests declared
    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)

  • Vinod Menon

National Institutes of Health (HD059205)

  • Vinod Menon

National Institutes of Health (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.

Acknowledgements

We thank the editor and two anonymous reviewers for valuable feedback which helped improve the study.

Ethics

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 Heritability IRB # 201204036.

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.

Senior and Reviewing Editor

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

Reviewers

  1. Matthew F Glasser, Washington University in St. Louis, United States
  2. Leonardo Cerliani

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, which permits unrestricted use and redistribution provided that the original author and source are credited.

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