Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior

Alberto Llera; Thomas Wolfers; Peter Mulders; Christian F Beckmann

doi:10.7554/eLife.44443

. 2019 Jul 3;8:e44443. doi: 10.7554/eLife.44443

Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior

Alberto Llera ^1,^2,^3,^✉, Thomas Wolfers ^1,^2,³, Peter Mulders ^1,^2,^3,⁴, Christian F Beckmann ^1,^2,^3,^4,⁵

Editors: Moritz Helmstaedter⁶, Richard B Ivry⁷

PMCID: PMC6663467 PMID: 31268418

Abstract

We perform a comprehensive integrative analysis of multiple structural MR-based brain features and find for the first-time strong evidence relating inter-individual brain structural variations to a wide range of demographic and behavioral variates across a large cohort of young healthy human volunteers. Our analyses reveal that a robust ‘positive-negative’ spectrum of behavioral and demographic variates, recently associated to covariation in brain function, can already be identified using only structural features, highlighting the importance of careful integration of structural features in any analysis of inter-individual differences in functional connectivity and downstream associations with behavioral/demographic variates.

Research organism: Human

eLife digest

For years, scientists have tried to explain human behavior by measuring brain characteristics. During the first half of the 19^th century, craniometry, the science of taking measurements of the skull, was a popular field of research and cognitive abilities as well as many behaviors were associated with different skull sizes and shapes. Although craniometry has been broadly discredited as a science, the study of brain structure and function, and their correlation to human behavior, continues to this day.

Currently, one of the most powerful tools used in the study of the brain is magnetic resonance imaging (MRI), which relies on strong magnetic fields and radio waves to produce detailed imaging. These images can provide functional information, by measuring changes in blood flow to different parts of the brain, as well as structural information such as the amount of gray or white matter or the size of different brain regions. Many studies have shown correlations between functional MRI (fMRI) data and behavioral and demographic traits, such as years of education, lifestyle habits or stress. Another advance in the study of the relationship between behaviors and the brain has been the emergence of better statistical analysis tools thanks to increasing computing power. These tools have made it possible to integrate data from different sources and analyze many variables at the same time, allowing patterns to emerge that would have been previously missed.

Llera et al. have analyzed a large dataset from young healthy volunteers to show that changes in behavioral traits can be predicted by brain structure, and not just by brain function as previously shown. Different types of brain structural data, including what the surface of the brain looks like and relative volumes of gray and white matter, were integrated and analyzed, and correlations between changes in these variables and changes in the demographic and behavioral traits of the subjects were found. Previously, a robust relationship had been established between specific patterns of connections and activity in the brain and a group of characteristics such as life satisfaction, working memory, weight and strength, loneliness, family history of drugs and alcohol use, etc. Llera et al. show that this relationship also holds between the traits and structural brain data. As an example, there is a positive correlation between changes in the number of years of education and the income of the subjects and changes in a pattern of integrated structural data that include the amount of gray matter, white matter integrity and size of specific brain structures. Given these findings it becomes important to reconsider whether differences between individuals previously attributed to brain function could simply explained by the shape or size of the brain and its parts.

These findings show that physical brain characteristics, including its size or the shape of its surface, could predict information such as individuals’ lifestyle decisions or their income; also implying that these characteristics are not simply a product of brain function. The results also demonstrate the power of combining different types of brain data to predict patterns in behavior.

Introduction

Understanding individual human behavior has attracted the attention of scientists and philosophers since antiquity. The first quantitative approach intended to deepen such understanding dates to the first half of the 19-th century when skull measures were related to human behavior or cognitive abilities (Simpson, 2005; Fodor, 1983). Technical, intellectual and clinical advances in the last two centuries allow us to now accurately quantify brain structure and function (Lerch et al., 2017; Huettel et al., 2004; Friston et al., 2002; Woolrich et al., 2004; Rorden et al., 2007), and to summarize certain ‘aspects’ of human behavior by means of standardized tests. Such advances facilitate exploratory statistical learning analyses to uncover previously hidden relationships between brain features and human behavior, demographics or pathologies (Poldrack and Farah, 2015). These developments are expected to be pushed even further with the emergence of the big data magnetic resonance imaging (MRI) epidemiology phenomenon (Van Essen et al., 2013; Collins, 2012), and some examples of such expectations have already reported associations with blood-oxygen-level dependent (BOLD) brain function (Finn et al., 2015; Smith et al., 2015); for example, functional connectivity patterns can be used to identify individuals (Finn et al., 2015), predict fluid intelligence (Finn et al., 2015), or describe a mode of functional connectivity variation that relates to lifestyle, happiness and well-being (Smith et al., 2015).

Although the brain’s structural-functional relationships are not yet fully understood, linking structure to behavior is essential for either type of imaging modality to be fully interpretable as an imaging phenotype. Furthermore, given the long-term character of some demographic variables (e.g. overall happiness), we hypothesize that different brain structural features, such as regional variation in the density of gray matter or subject-dependent degree of cortical expansion, should also reflect these relationships. To test these hypotheses, in this work we make use of the large quantity of high quality behavioral and neuroimaging data collected by one of the big data initiatives, the Human Connectome Project (Van Essen et al., 2013) (HCP). The HCP sample includes detailed structural imaging, diffusion MRI, resting-state and several different functional MRI tasks for each subject. Furthermore, the availability of more than 300 behavioral and demographic measures (Van Essen et al., 2012) allows the post-hoc exploration of a wide range of associations (Groves et al., 2011). We further hypothesize that behavioral variations can be explained by more general brain structure variations than isolated single feature variations (e.g. cortical thickness variations); we consequently extract multiple structural features from the different MR modalities and perform a simultaneous analysis by linked independent component analysis (Linked ICA; Groves et al., 2011; Groves et al., 2012). Linked ICA is a Bayesian extension of Independent Component Analyses developed for multi-modal data integration, where multiple ICA factorizations are simultaneously performed and all of them share the same unique mixing matrix. Such analyses increase statistical power by evidence integration across different features (Wolfers et al., 2017; Doan et al., 2017) and have been shown to be powerful in identifying correlated patterns of structural and diffusion spatial variation that can then be studied in relation to individual behavioral and demographic measures (Doan et al., 2017; Douaud et al., 2014; Francx et al., 2016). Although similar analyses have been previously performed (Douaud et al., 2014), in this work we benefit from the unique characteristics of the data sample; we consider brain and behavioral data from close to 500 ‘healthy young adults’ which reduces common pathology- and age-related variance and increases the power to detect associations due to normal cross-sectional variability.

Our results support the hypothesis that structural brain features are strongly associated with demographic and behavioral variates. Interestingly, the most relevant mode of inter-individual variations across brain structural measures identified through the multi-modal data fusion approach maps on to recent findings obtained using functional MRI data from the same HCP cohort. In particular, our findings closely resemble the ‘positive-negative’ set of behavioral measures identified in Smith et al. (2015) on the basis of functional (co-)variations. Using post-hoc analysis of the functional and structural modes we show that inter-individual differences attributed to brain function need to be reconsidered taking into account variations in brain structure across the cohort.

Results

The multi-modal structural brain data analyses (Figure 1, operations A and B) resulted in a total of 100 collections of component maps, each of which can be represented by a collection of 7 spatial maps covering the gray-matter space (voxel-based morphometry feature (VBM)), diffusion skeleton space (Fractional Anisotropy (FA), Mean Diffusivity (MD) and Anisotropy Mode (MO) features), cortical vertex space (cortical thickness (CT) and pial area (PA) features) and a voxel-wise map of the Jacobian deformation (JD). In addition, each collection of maps is associated with a single vector of contributions that describe the degree to which a given collection is ‘driven’ by the different modalities (feature loadings). Finally, each collection is associated with a single vector that describes how each individual subject contributes to the component (subject loadings). Post-hoc linear correlation analyses of these subject contributions with behavioral measures identified, after FDR correction (Figure 1, operations C, D and E, FDR corrected q < 2.2 × 10^-4), a total of 155 significant brain-behavior correlations, summarized by 30 components reflecting at least one significant relationship to behavior. We provide the full results in Supplementary file 2 and a brief summary in the bottom left panel of Figure 1 where we color code the significant Pearson correlation values for the components showing at least one Bonferroni corrected (Bonferroni corrected q < 1.4 × 10^-6) significant correlation to a behavioral or demographic measure.

Only a single component (number 6) shows strong associations across a broad set of behavioral domains (48 measures) and across all structural modalities (i.e. is not dominated by one of the structural data types in that no single modality contributes >50% to the total variance of the component). The relative contributions from the different modalities are 22% for VBM, 6% JD, 15% FA, 23% MD, 20% MO, 7% CT and 4% PA (Appendix 1—figure 1), reflecting a dominance of gray matter densities and diffusion measures and a lower involvement of the purely morphometric and cortical measures. Relating the behavioral associations, in Figure 1 bottom right panel we provide a summary of the behavioral measures significantly correlating with component six as well as the corresponding Pearson correlation values. Note that in the cases where several measures are grouped together we report their mean correlation value - full results are given in Supplementary file 2. We observe that component number six relates to various behavioral scores including working memory, language function and general wellbeing (life satisfaction, social support). In Figure 2 we present the associated spatial maps: VBM measures are most heavily weighted in bilateral orbitofrontal cortex, temporal pole, lingual gyrus and the putamen (first row). Morphometric differences (JD features) load into temporal lobes, caudate and brainstem (second row), and white matter tracts do most heavily weigh onto the internal capsule, anterior thalamic radiation and the anterior corona radiata (3^rd, 4^th, and 5^th rows). Cortical effects (6^th and 7^th rows) are largely associations with multi-modal association cortex that show effects whereas primary sensory cortices are not implicated. Note that the involvement of areas such as the putamen and lingual gyrus are relevant to explain the behavioral relationships found with working memory and word processing. Furthermore, the involvement of structural connections between subcortical and prefrontal areas as well as the orbitofrontal cortex and temporal poles could explain the link to more complex functions such as emotional support or life satisfaction. Note that each component considers structural multi-modal characterizations of the brain where each modality contains unique information and together builds into a multi-modal multivariate component. Consequently, although these results are hard to interpret as being nested into the same space as (functional) canonical brain networks, the structural weighting in gray matter modalities in orbitofrontal and temporal cortex, in conjunction with the white matter tracts that connect those regions, is a clear indication of an underlying network structure relating to component six and consequently, to its behavioral associations.

Figure 2. — From top to bottom we visualize the VBM (Voxel Based Morphometry), JD (Jacobian Determinants), FA (Fractional Anisotropy), MD (Mean Diffusivity), MO (Mode of Anisotropy), PA (Pial Area), and CT (Cortical Thickness) spatial maps. For improved visualization, each modality has been thresholded at a z-value of 2. This mode of structural variation, component 6, that strongly reflects a ‘positive-negative’ behavioral spectrum, links to a wide range of brain regions across structural modalities and might reflect the structural multi-modal foundation of a functional brain network linked to these variations that has been earlier identified.

Several other components also reflect behavioral patterns worth recognizing. In Figure 3 we report spatial maps associated with the components showing at least one Bonferroni corrected significant relationship with any behavioral measure (p<1.4×10⁻⁶). For components 1, 2 and 89 we show spatial maps for all modalities contributing (Appendix 1—figure 1). In order to provide a clearer interpretation, for the other components we decided to show a selection of the relevant modalities and full NIfTI maps are separately available as supplementary material. Component one relates mainly to gender, physical strength and language and it is defined by significant changes in gray matter density (VBM measures) and cortical areal expansion (PA measure). Its associated spatial patterns appear to in fact reflect brain size and cortical area differences in both temporal lobes. Similarly, component two is driven by VBM maps and correlates with variations in gender, age, height, weight and strength. Its spatial extent includes the paracingulate gyrus and bilateral insular and opercular cortex. Components 7, 24, 25, 29 and 55 are driven by at least three feature modalities and they map into gender, weight, body mass and height. Component seven maps into gender and shows cingulate gyrus and insular cortex.

Component 24 maps into weight and body mass and is mapped into putamen, intracalcarine cortex and thalamus. Components 25 and 29 relate height with the inferior temporal gyrus and the cerebellum together with strong DWI weightings in the brainstem. Component 55 relates to weight and maps into the precentral gyrus and asymmetric differences in DWI measures. Number 89 maps VBM and JD into hematocrit and involves the lingual and occipital fusiform gyrus. Finally, component 20 maps JD and VBM in the posterior midline into age and relationship status.

Although another set of components show associations to behavior, these are limited to a single modality and/or a small set of behavioral variates (Supplementary file 2). Many of these components show simple relationships to overall size measures such as weight, body mass (BMI) or height, and the associations are weaker than those reported above; we consequently decided to not further discuss their spatial extent in this work and we provide full NIfTI images as supplementary material.

To validate the robustness of the presented results to the model order choice we performed analyses at different dimensionalities and observe that especially lower indexed components are highly reproducible. In particular, component number six is recovered at dimensionalities 90 and 110 with a subject mode correlation value of around r ~ 0.9 (details can be found in Appendix 1, section ‘Robustness: model order’). Regarding the influence of purely morphometric differences in the analyses, a comparative analysis excluding the JD revealed essentially unaltered brain-behavior associations. Analysis of the JD feature in isolation showed that no fully corrected significant association to the reported positive-negative structural mode is found when considering uniquely morphometric differences, even if considering several components together. However, uncorrected statistics suggests that information of the positive-negative mode could already be present at the morphometric level. These results are presented in Appendix 1, section ‘Robustness: analyses without Jacobians’, and ‘Robustness: Analyzing morphometric differences’.

Given the similar associations to behavior found between the presented structural mode (component 6) and the ‘positive-negative’ functional mode reported in Smith et al. (2015), and since both results are obtained using the HCP sample, we quantified the linear relation between them. With the analysis restricted to the 421 subjects common to both studies, we found that the structural and the functional subject modes are significantly correlated (r = 0.4643, r² = 0.21, permutation p<10⁻⁵). Post-hoc correlation analyses to behavior replicated the original functional positive-negative mode by identifying 60 functional-behavioral relationships (Smith et al., 2015); we found that 22 of these behavioral measures are also associated to the structural mode and that there is no significant difference in the correlation values provided by the functional or the structural analyses at these intersecting behavioral measures (details can be found in Appendix 1, section: ‘On the power of structural and functional associations to behavior’).

To identify the linear dependence between the behavioral/demographic modes obtained from functional and structural data we used a generalized linear model (GLM). We regressed the structural mode from the functional one and performed post-hoc linear correlation analysis of the residualised functional mode relative to behavioral variates as in Smith et al. (2015). Note that structural features – due to the necessary co-alignment within the functional pipelines – acts as a mediator and therefore could induce significant imaging-to-behavior associations (also see Bijsterbosch et al., 2018). Conversely, however, the structural features enter into the cross-subject analysis without any possible cross-talk from functional data, so that there is no possible interference from functional to structural features. The post-hoc correlation analysis of the residualised functional mode to behavior revealed a significant decrease in correlation (mean r decrease = 0.078, p<0.01) that result in the structural mode removing 73% of the 60 associations originally found using functional data. The remaining 16 significant relationships involve measures as handedness, education, tobacco use, list sorting, delay discount, and intelligence. As such, the two modes are significantly overlapping.

We also performed a structural-functional Linked-ICA analyses where we added partial correlation matrices obtained from resting state fMRI to the set of originally considered structural features. We selected functional fMRI features to match Smith et al. (2015); details on data availability and processing are provided in Appendix 1, section ‘Individual features pre-processing’. This structural-functional analysis recovered the positive-negative mode reported in the originally reported multi-modal structural analyses. More concretely, we found a component, number 16, significantly correlating (r = 0.89, p<10^−5) with the mainly reported structural mode (component 6). The contribution of each modality to this mode equals 20% for VBM, 15.6% for FA, 24.4% for MD, 23.9% for MO, 7% for CT, 3% for PA, 5% for JD and 0.0012% for the functional partial correlation feature. While all structural features reflect approximately the same contribution as in the original structural analyses, it is interesting that the functional data does marginally contribute to the found mode, suggesting that structure in its own can explain the positive-negative behavioral mode.

As a final step in our analysis we performed a causal analysis between the structural mode (component 6) and the functional mode reported in Smith et al. (2015), on the basis of calculating pairwise likelihood ratios (Hyvarinen and Smith, 2013) between the function-to-structure and structure-to-function model. This analysis estimated a likelihood ratio of ~0.04, that is a significant structure to function causation effect (p<0.0025, using permutation testing) (Hyvarinen and Smith, 2013) (for details see Appendix 1, section ‘Testing structure-function causal effects’). Replication of these causal results was achieved by performing an analogous multi-modal structural Linked ICA analyses considering this time the HCP1200 sample, and including only the subjects not used in the originally considered HCP500 sample. The analyses revealed a component that, restricted to the 331 intersecting subjects, showed once more a significant correlation with the positive-negative mode reported in Smith et al. (r = 0.1773, p<0.002). Furthermore, consecutive causal analyses from the structural mode to the functional mode estimated a likelihood ratio of ~0.09, that is a significant structure-to-function causation effect (p<0.005).

Discussion

We present a simultaneous analysis of brain structural measures that reveals how several types of behavior and demographics link to variations in such measures of brain structure. Several components detect simple associations between brain size (encoded in gray matter density and cortical area) being related to gender, strength, endurance or language function. More interestingly, we encounter a single pattern of gray and white matter covariation that is strongly associated with several measures relating to cognitive function including working memory and language function, while also being strongly related to several measures of wellbeing including life satisfaction or emotional support. Accordingly, the spatial organization of the component that relates to these measures predominantly includes regions and connections that are relevant to working memory and word processing such as the putamen and lingual gyrus (Mechelli et al., 2000; Arsalidou et al., 2013). Additionally, the inclusion of regions such as the orbitofrontal cortex and temporal poles, as well as structural connections from subcortical to prefrontal regions, could explain the link to more complex functions such as emotional support and life satisfaction. Furthermore, the mode of structural variation we report here relates to several recently reported results obtained using functional MRI. In particular, our results relate to the ones presented in Finn et al. (2015) since it identifies fluid intelligence measures and it also shares many behavioral measures also identified by the ‘positive-negative’ mode reported in Smith et al. (2015). Clearly, the functional analyses presented in Smith et al. (2015) and the one we present here, while using entirely different MRI measurements, are both able to get at the core of the same behavioral spectrum; in fact, the structural mode and the functional mode are strongly correlated subject measures (r = 0.46). Our analyses reliably augment the spectrum of behavioral variables reported by the functional analyses by extending it with many working memory, language, relational task, ASR and DSM measures (Figure 1 bottom right and Supplementary file 2). It is to note here that while the statistics reported in Smith et al. (2015) were obtained from a Canonical Correlation Analyses (CCA) between partial correlation matrices and all behavioral measures at once, the statistics we present here involve simple linear correlations. While the former type of analysis can benefit from the multi-variate type of analysis through the application of CCA, ensuing results can be hard to interpret. The straight-forward individual linear correlation analysis against the behavioral/demographic measures separately instead affords simple interpretation.

These findings directly look into the relationship between brain structure and function. In fact, the functional mode of variation is strongly associated with connectivity in brain areas approximately resembling the Default Mode Network (Smith et al., 2015) and, given the spatial extent and the strong weight of the DWI data in the structural mode we report, it seems reasonable to assume that these white matter structure variations could contribute to the functional connectivity changes reported in Smith et al. (2015). Further, we found no clear spatial overlap between the reported structural mode and the cortical functional extent of the ‘positive-negative’ mode, suggesting that integrated functional-structural analyses should increase the sensitivity of both functional and structural analyses. Further, these results might question whether group functional connectivity measures using fMRI provide direct measures of brain connectivity or are biased due to individual structural differences that may become ‘visible’ in the analysis of functional cross-subject. An analogous multimodal analysis excluding the JD feature provided equivalent results to those presented here (Supplementary file 3) and unimodal analysis of only the JD features (using simple ICA-based decomposition (Beckmann and Smith, 2004) of the single JD modality) did not provide significant correlation to the behavioral mode at the level of fully corrected statistics. These extra analyses confirm that the structural features relating to the behavioral mode are not uniquely driven by morphometric differences. The post-hoc correlation analysis of the residualised functional mode to behavior revealed a significant decrease in correlation (mean r decrease = 0.078, p<0.01) that result in the structural mode removing 73% of the 60 associations originally found using functional data. The remaining 16 significant relationships involve measures as handedness, education, tobacco use, list sorting, delay discount, or intelligence. As such, our results confirm that many associations previously attributed to functional connectivity are already present at the structural level. This could be interpreted in terms of a specialization of functional imaging towards a specific subset of behavioral measures for which it provides strong effects even after linear accounting for the structural findings, implying that not all previously identified associations can be explained through inter-individual differences in brain structure. As such, these two modes are significantly overlapping measures that are not fully reflected in the Jacobian deformation field. The presence of residual functional associations to behavior suggests that these associations - although possibly influenced by structural variation - cannot uniquely be attributed to simple morphometric differences. These results align with recent findings by Bijsterbosch et al. (2018) who show that individual spatial configurations extracted from functional MRI rather than the connectivity profiles between areas seem to stronger relate to the positive-negative mode.

While the presence of residual associations could be interpreted as evidence for functional-structural integration, care needs to be taken with regards to the interpretation of these associations and changes thereof. First, note that all of these methods interrogate the linear relationships between variates. It is entire possible that the association between imaging phenotypes and behavioral/demographic measures involve non-linear relationships that remain at best incompletely accounted for within these analytical frameworks. Second, the implicit symmetry of linear correlations implies that a corresponding residualised analysis (where we regress functional variations from the structural mode) similarly removes significant associations. Indeed, in such a case only 7 out of 48 associations remain significant (relating to weight, antisocial behavior (DSM), family structure problems, relational task or adult self-report (ASR) questions, see Appendix 1, section ‘On the power of structural and functional associations to behavior’). These results suggest a segregation of different structural and functional specializations towards different behavioral measures with for example, intelligence being, not only, but more related to brain function, and antisocial or relational task measures relating more strongly to brain structure.

Finally, a causal analysis revealed a significant structural to functional mode causation (Hyvarinen and Smith, 2013) where the likelihood of the structural mode causally influencing the functional mode (from Smith et al., 2015) is >20 times higher than the likelihood of the reverse causation. Although the causal model introduced in Hyvarinen and Smith (2013) considers the residuals after linear modeling of a pair of signals, care is advised when considering causal inference on two vectors of observations, as we cannot exclude the possibility that unobserved underlying processes simultaneously influence brain structure and function (‘hidden causation’). Nevertheless, these causal findings align with the fact that cross-subject analysis of functional data typically necessitates processing of structural data (e.g. through co-registration into a common space). As such, structural variations will enter as mediating factor in any functional analysis pipeline and need to be accounted for suitably. However, there is no reverse influence of functional variations in the analysis of structural measures. Such dependencies remain poorly modeled in current analysis procedures and future work will have to focus on robustifying functional MRI analysis with regard to cross-subject variations in brain structure, for example by more advanced alignment procedures and/or through derivation of functional measures that are invariant under variations in structure. This will have important implications for the interpretation of future finding across neuroimaging ‘big data’ studies and will help improve our understanding of the functional-structural integration and its relation to behavioral associations.

Materials and methods

In this work, we use data from the Human Connectome Project (HCP) N = 500 release which contains data from healthy young adults including twins and their non-twin siblings. In addition to performing more than 300 behavioral/demographic tests, each subject participated in structural, diffusion and several functional MRI recordings (Van Essen et al., 2012; Elam and Van Essen, 2013). A description of all MRI and behavioral/demographic measures included in our analysis can be found in van Van Essen et al. (2012) and a short description is available at https://www.humanconnectome.org/storage/app/media/documentation/q3/HCP_Q3_Release_Appendix_VII.pdf; we also provide a summary of the latter in the Appendix 1, Supplementary file 1. Due to structure-function integration we hypothesize that different biological features such as regional variation in the density of gray matter, white matter connectivity or subject dependent degree of cortical expansion should reflect similar associations with behavior as the ones reported at Finn et al. (2015) and Smith et al. (2015). To investigate such hypothesis, the structural MRI T1-weighted images were used to extract gray matter densities and cortical measures, using a Voxel Based Morphometry (VBM) (Ashburner and Friston, 2000; Ashburner and Friston, 2005) (http://www.fil.ion.ucl.ac.uk/spm) pipeline to extract cortical gray matter probability maps as well as maps of cortical thickness (CT) and pial area (PA)(Dale et al., 1999; Fischl et al., 1999) estimates by means of transforming all anatomical T1-weighted cortical surfaces through FreeSurfer v5.3 (http://surfer.nmr.mgh.harvard.edu). Further, the diffusion-weighted MRI data were used to extract several features, that is fractional anisotropy (FA), anisotropy mode (MO) and mean diffusivity (MD) (Smith et al., 2006; Jenkinson et al., 2012) (https://fsl.fmrib.ox.ac.uk/fsl/v5.0.9). In addition to these structural readouts and in order to also include purely local morphometric differences across subjects, we also consider the images containing the Jacobian determinants (JD) of the warp fields defining the transformations of each subject’s structural image onto a reference brain. These feature extraction operations are schematically summarized in Figure 1 operation A, and full details on the data processing performed to achieve each feature are provided in the Appendix 1 under the section ‘Individual features pre-processing’. From the initial N = 500 participants, several subjects were excluded on the basis of abnormalities in any of the features. In total, N = 448 subjects were entered into further analyses. We then use the Linked-ICA model (Groves et al., 2011) to simultaneously factorize the considered N = 448 subjects’ VBM, FA, MO, MD, CT, PA and JD features into independent sources (or components) of spatial variation. In brief, Linked-ICA is an extension of Bayesian ICA (Choudrey, 2002) to multiple input sets, where all individual ICA factorizations are linked through a shared common mixing matrix that reflect the subject-wise contribution to each component. This operation is represented in Figure 1 operation B where we can also appreciate that such factorization provides - per component - a set of spatial maps (one per feature modality), a vector of feature loadings that describe the degree to which the component is ‘driven’ by the different modalities, and a vector that describes how each individual subject contributes to a given component. Importantly, the subject-loadings define the cross-subject variation of the multi-modal effects and can subsequently be used to study relationships to other behavioral or demographic cross-subject variations by means of simple correlations. All mathematical derivations involved in the Linked ICA factorization can be found at the original paper describing the algorithm (Groves et al., 2011). Given our sample size and following (Groves et al., 2011; Groves et al., 2012) we report full results from a 100 dimensional factorization. Different model order decompositions were also performed to demonstrate the robustness to the choice of dimensionality. Further, we also performed two analogous multi-modal analyses, one including resting state fMRI data and another excluding the JD features, as well as an independent component analyses (Beckmann and Smith, 2004) of the JD features in isolation to evaluate the dependency of the results on purely morphometric differences- these are reported in Appendix 1.

Further details and code implementing each feature extraction procedure as well as the Linked ICA factorization are publicly available at Llera (2019) (copy archived at https://github.com/elifesciences-publications/Llera_elife_2019_1).

Statistical analysis

To uncover relationships between the behavioral/demographic measures and the components obtained from the Linked-ICA decomposition we perform a correlation analysis between each independent component subjects’ contribution and each available behavioral measure. This operation is schematically summarized in Figure 1 operation C. To take into account the family structure present in the HCP sample while assessing significance we use the Permutation Analysis of Linear Models (PALM) (Winkler et al., 2014; Winkler et al., 2015) and use 10⁶ permutations per tested correlation (Figure 1 operation D). We define significance at p<0.05 and address the multiple comparison by applying FDR correction (Benjamini and Hochberg, 1995) as well as full Bonferroni correction (Figure 1 operation E).

Acknowledgements

Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

We are grateful to Stephen M Smith for the helpful discussions and for sharing with us their results as presented in Smith et al. (2015). We would also like to thank V Kumar for help with the visualization of the DWI results and to Paula C Salamone for the help with the graphics. The research leading to these results has received funding through the developing Human Connectome Project (dHCP), a Synergy Grant by the European Research Council under the European Union’s Seventh Framework Programme (FP/2007–2013), ERC Grant Agreement no. 319456. We further gratefully acknowledge support from the Netherlands Organization for Scientific Research (NWO) through VIDI grant to CFB (864.12.003) and we also gratefully acknowledge funding from the Wellcome Trust UK Strategic Award (098369/Z/12/Z).

Appendix 1

In Supplementary file 1 we provide a summary of the behavioral and demographic measures present in the Human Connectome Project (HCP) sample. For easier interpretation we grouped them here by categories and a full detailed description can be found in van Essen et al. (Van Essen et al., 2012).

Individual features pre-processing:

The structural data was preprocessed using the computational analysis toolbox computational analysis toolbox (CAT)−12 (http://dbm.neuro.uni-jena.de/cat/) (Nenadic et al., 2015) which is an extension of Voxel Based Morphometry in statistical parametric mapping (SPM) (Ashburner and Friston, 2000). CAT-12 uses an internal interpolation to provide more reliable results than SPM12-VBM. It extends standard SPM processing by for instance including different denoising methods and a modified brain extraction procedure. Prior to gray matter volume estimation, all participants’ T1 images were affinely aligned. Subsequently, images were segmented, normalized, and bias-field-corrected (Elam and Van Essen, 2013; Ashburner and Friston, 2000), yielding images containing gray and white matter segments plus CSF. DARTEL (Ashburner, 2007) was then used to normalize all images to a standard gray matter template provided by CAT-12. Subsequently, all gray matter volumes were smoothed with a 9.4 mm FWHM Gaussian smoothing kernel (sigma = 4 mm).

Structural MRI images were processed with the FreeSurfer v5.3 software to extract measures for cortical thickness and areal expansion (Dale et al., 1999; Fischl et al., 1999) (http://surfer.nmr.mgh.harvard.edu). The standard FreeSurfer preprocessing pipeline (recon-all) was applied to these images, in which a reconstruction of the cortical sheet was estimated using intensity and continuity information. Cortical thickness was determined as the closest distance from the gray/white boundary to the gray/cerebrospinal fluid (CSF) boundary at each vertex (Fischl and Dale, 2000). Surface area in FreeSurfer is estimated as relative amount of expansion or compression at each vertex when registering each participant's surface to a common atlas. Surface maps were resampled and mapped to a common coordinate system (Fischl et al., 2008). During preprocessing, the data were registered onto the high-resolution average participant surface space (fsaverage), and a 10 mm FWHM surface-based smoothing kernel was applied.

Further, the Jacobian images for each subject are directly available from the HCP repository and the diffusion weighted data (DWI) was preprocessed using the DTIFIT routine from FSL (Jenkinson et al., 2012; Ashburner, 2007) (https://fsl.fmrib.ox.ac.uk/fsl) to create the FA, MO and MD images that were then feed into the TBSS pipeline (Smith et al., 2006).

Finally, for computational reasons (Groves et al., 2011; Groves et al., 2012), the VBM images were spatially down sampled to 4 mm isotropic and the DWI images to 2 mm isotropic voxels.

To perform structural-functional integration we used partial correlation matrices obtained from resting state fMRI. More concretely we considered a 200-dimensional group ICA decomposition followed by dual regression to extract individual spatial maps and time courses. These data as well as more detailed pre-processing details are publically available at https://db.humanconnectome.org/data/projects/HCP_1200. We then computed regularized partial correlation matrices (size 200 × 200), remove diagonal and redundant elements due to symmetry, and vectorize them for each subject. Finally, we built a functional MRI partial correlation feature matrix by putting each subject partial correlation vector on a column and use this matrix as input for the Linked ICA factorization together with all previously considered structural features.

Further details and code implementing all the operations described here can be found at Llera (2019).

Image Quality Assessment (QA)

All structural images were checked on the basis of their associated quality measures calculated by CAT-12 toolbox and eyeballed by experts on quality. Diffusion images were rejected from the analyses by expert’s visual inspection of the registration, brain extraction and results of the DWI pipeline (Jenkinson et al., 2012; Ashburner, 2007). Resting state fMRI data was corrected for motion using FSLFIX; for further details on the resting state preprocessing we refer the reader to https://db.humanconnectome.org/data/projects/HCP_1200.

Behavioral data processing

We used the restricted behavioral data as provided by the HCP consortium. For each subset of subjects considered at each analyses we removed all behavioral measures that were not available for more than a 5% of subjects. For the remaining measures, missing values were inputted by substituting the missing values for the mean all other subjects in the subset. Data was demeaned before further processing.

Main results

In Supplementary file 2 we summarize the significant results obtained (FDR corrected q < 2.2×10⁻⁴). From left to right columns we present the component number, behavioral/demographical measure, correlation value, and the permutation p-value (PALM).

Feature modalities relative contribution to components

In Appendix 1—figure 1 we color code the relative contribution of each feature modality to the components that show at least one Bonferroni corrected significant relationship to behavior or demographics measures (q < 1.4×10⁻⁶).

Robustness: model order

In this section we assess the robustness of the results with respect to the model order choice. To that end we perform a correlation analyses between the reported 100 dimensional factorization subjects-mode and that of a 90 and a 110 dimensional factorizations. In the top row of Appendix 1—figure 2 we present correlation matrices between a 100 dimensional factorization (y-axis) and a 90 and 110 dimensional factorizations (top left and right panels respectively). Only significant correlations after Bonferroni correction are reported (that is p-value smaller than 0.05 /(100 × 90) and 0.05/ (100 × 110) respectively). For each component of the 100 dimensional factorization we present in the bottom row of Appendix 1—figure 2 the absolute correlation value with each of the components of the different dimensionality factorization. We appreciate that most components are recovered with high accuracy (r close to 1) independently of the order of the factorization. The black dashed lines represent the most relevant of the reported components, independent component number 6.

Appendix 1—figure 2. — Bottom: sorted absolute correlations for each of the components of the reported factorization with the other model orders components. The black discontinuous line represents component number 6.

Robustness: analyses without the Jacobians

We present results summarizing the significant findings when performing an analogous analysis to the one reported in the main manuscript without the use of the JD feature. In Supplementary file 3 we present a comparison between the positive-negative mode as reported in the main text, and the set of behavioral measures significantly associated to component number nine on the new analysis.

Note that component number nine in this analysis (without the JD feature) corresponds to the component number six reported in the main text and it recovers the strongest behavioral associations. Further, this component presented other significant relationships not appearing significant in the main reported mode, for example personality related measures (NEOFAC-C).

Robustness: Analyzing morphometric differences

In this section we perform an independent component analyses (ICA) factorization (Beckmann and Smith, 2004) only of the Jacobian determinant matrices followed by post-hoc correlation analyses with the behavioral and demographic measures. We performed a 100-dimensional factorization and found a set of 9 components significantly correlating (Bonferroni corrected) with the component number six obtained from the multi-modal Linked ICA analyses. In Supplementary file 4 we present the correspondence between the positive-negative mode we found through the multi-modal Linked ICA analyses and these nine components. As before, for the multi-modal analyses we only report correlations to behavior significant after FDR correction and for the JD analyses we mark with double asterisk the significant relationships after FDR correction and with a single asterisk the nominal or uncorrected significant relations (p<0.01). We appreciate that although sub FDR significance threshold we observe some correspondence between the purely morphometric differences and the positive-negative mode, these relationships disappear after statistical correction for multiple comparisons.

On the power of structural and functional associations to behavior

The multi-modal structural analysis we presented here shares 22 behavioral associations with the functional analyses reported in Smith et al. (2015). Paired t-tests, using the absolute correlation values to behavior, revealed no significant difference in r values between the structural and the functional mode at these intersecting behavioral measures. Further, and as expected, the structural mode provides higher correlation values than the functional analyses at the 48 behavioral measures associated to the structural mode (mean r difference = 0.046, p<0.01); on the other side, the functional mode provides higher correlation values than the structural analyses at the 60 behavioral measures associated to the functional mode (mean r difference = 0.048, p<0.01).

To identify the linear dependence between the behavioral/demographic modes obtained from functional and structural data we used a generalized linear model (GLM). We regressed the structural mode from the functional one and performed post-hoc linear correlation analysis of the residualised functional mode relative to behavioral variates as in Smith et al. (2015). Note that structural features – due to the necessary co-alignment within the functional pipelines – acts as a mediator and therefore could induce significant imaging-to-behavior associations (also see Bijsterbosch et al., 2018). Conversely, however, the structural features are being analyzed without any possible cross-talk from functional data, so that there is no possible interference from functional to structural features. Post-hoc correlation analysis of the residualised functional mode to behavior revealed a significant decrease in correlation (mean r decrease = 0.078, p<0.01) that result in the structural mode removing 73% of the 60 associations originally found using functional data. The remaining 16 significant relationships involve measures as handedness, education, tobacco use, list sorting, delay discount, or intelligence. As such, the two modes are significantly overlapping.

Although functional data is not required for structural MRI analyses, we also use a GLM to remove the functional effect linearly from the structural mode and also found a significant decrease in r-values (mean r difference = 0.074, p<0.01); in this case remain seven significant behavioral measures that include measures of weight, antisocial behavior (DSM), family structural problems, relational task or adult self-report (ASR) questions.

Testing structure-function causal effects

We tested the causal effect between the structural mode (component 6) and the functional mode reported in Smith et al. (2015) using the model presented in Hyvarinen and Smith (2013) and found a functional dependence on structure with a likelihood-ratio measure of 0.0437. This implies a > 20 times higher likelihood of a model of structure-to-function causation relative to the reversed causal model. Note, however, that this only tests these two alternative models relative to each other and does not address the possibility of a third effect (of biological origin) having a causal effect both on brain structure and function (possibly simultaneously). To assess the significance of these findings we use permutation testing. To build a null distribution for this problem we need to break any causal dependence in the data, while keeping similar correlation to the original data (structural and functional modes correlation). To that end, at each permutation, we interchange the structural and functional mode values for a subset of 210 random subjects while keeping the remaining values fixed; note that 210 is approximately half of the subjects common to both functional and structural analyses. In this way correlation is approximately maintained while breaking any possible causal structure. Then we apply the model presented in Hyvarinen and Smith (2013) and obtain a likelihood-ratio measure. We repeated this strategy 10⁵ times to build a null distribution and found that 0.0437 is significant at a p-value=0.0023.

Replication of these causal results was achieved by performing an analogous multi-modal structural Linked ICA analyses considering this time the HCP1200 sample, and including only the subjects not used in the originally considered HCP500 sample. Considering all subjects for whom all structural measures where available, and after QC, this linked ICA factorization was performed using data from 547 subjects. Restricted to the 331 intersecting subjects with the analyses performed in Smith et al., the analyses showed once more a significant correlation between a Linked ICA mode (component 17) and the one reported in Smith et al. (r = 0.1773, p<0.002). Further, consecutive causal analyses from the structural mode to the functional mode estimated a likelihood ratio of ~0.09, that is a significant structure to function causation effect (p<0.005).

Funding Statement

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

Contributor Information

Alberto Llera, Email: a.llera@donders.ru.nl.

Moritz Helmstaedter, Max Planck Institute for Brain Research, Germany.

Richard B Ivry, University of California, Berkeley, United States.

Funding Information

This paper was supported by the following grants:

Wellcome Trust UK Strategic Award 098369/Z/12/Z to Christian F Beckmann.
Nederlandse Organisatie voor Wetenschappelijk Onderzoek 864.12.003 to Christian F Beckmann.
EU Seventh Framework Programme Synergy Grant ERC Grant Agreement no.319456 to Christian F Beckmann.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing.

Conceptualization, Resources, Software, Formal analysis, Writing—original draft.

Conceptualization, Validation, Investigation, Writing—original draft.

Conceptualization, Supervision, Funding acquisition.

Ethics

Human subjects: HCP data were acquired using protocols approved by the Washington University institutional review board. Informed consent was obtained from subjects. Anonymised data are publicly available from ConnectomeDB (db.humanconnectome.org; Hodge et al., 2016). Certain parts of the dataset used in this study, such as the age of the subjects, are available subject to restricted data usage terms, requiring researchers to ensure that the anonymity of subjects is protected (Van Essen et al., 2013). Informed consent and consent to publish was obtained from the Human Connectome Project according to the declaration of Helsinki. Research conducted at the Donders Center for Cognitive Neuroimage is covered by the protocol approved by the 'Commissie Mensgebonden Onderzoek (CMO) Regio Arnhem-Nijmegen' registered under CMO number 2014/288.

Additional files

Supplementary file 1. Summary of the behavioral/demographic measures present in the HCP sample.

elife-44443-supp1.xlsx^{(17.2KB, xlsx)}

DOI: 10.7554/eLife.44443.006

Supplementary file 2. Significant results.

First column presents the component number, second the behavioral or demographic measure it correlates with and third and fourth columns present the correlation value and the permutation p-value. Significance is defined at p<0.05 and we used FDR correction for multiple correction (q < 2.2×10⁻⁴).

elife-44443-supp2.xlsx^{(14KB, xlsx)}

DOI: 10.7554/eLife.44443.007

Supplementary file 3. Comparison between the positive-negative mode presented in the main text and the multi-modal analyses excluding the JD feature (right column).

elife-44443-supp3.xlsx^{(12.1KB, xlsx)}

DOI: 10.7554/eLife.44443.008

Supplementary file 4. Summary of the uni-modal analyses using the JD feature.

In the second row all relationships are significant after multiple comparison correction. For the uni-modal analysis (the third row), significant associations after multiple comparison correction are denoted with a double asterisk and nominal significant but not significant after multiple comparison correction are marked with a single asterisk.

elife-44443-supp4.xlsx^{(12.1KB, xlsx)}

DOI: 10.7554/eLife.44443.009

Supplementary file 5. Linked ICA spatial maps associated with the FA feature.

elife-44443-supp5.gz^{(34.5MB, gz)}

DOI: 10.7554/eLife.44443.010

Supplementary file 6. Linked ICA spatial maps associated with the MD feature.

elife-44443-supp6.gz^{(34.4MB, gz)}

DOI: 10.7554/eLife.44443.011

Supplementary file 7. Linked ICA spatial maps associated with the MO feature.

elife-44443-supp7.gz^{(34.4MB, gz)}

DOI: 10.7554/eLife.44443.012

Supplementary file 8. Linked ICA spatial maps associated with the VBM feature.

elife-44443-supp8.gz^{(75.4MB, gz)}

DOI: 10.7554/eLife.44443.013

Supplementary file 9. Linked ICA spatial maps associated with the JD feature.

elife-44443-supp9.gz^{(88.7MB, gz)}

DOI: 10.7554/eLife.44443.014

Supplementary file 10. Linked ICA spatial maps associated with the left hemisphere CT feature.

elife-44443-supp10.mgh^{(62.5MB, mgh)}

DOI: 10.7554/eLife.44443.015

Supplementary file 11. Linked ICA spatial maps associated with the right hemisphere CT feature.

elife-44443-supp11.mgh^{(62.5MB, mgh)}

DOI: 10.7554/eLife.44443.016

Supplementary file 12. Linked ICA spatial maps associated with the left hemisphere PA feature.

elife-44443-supp12.mgh^{(62.5MB, mgh)}

DOI: 10.7554/eLife.44443.017

Supplementary file 13. Linked ICA spatial maps associated with the right hemisphere PA feature.

elife-44443-supp13.mgh^{(62.5MB, mgh)}

DOI: 10.7554/eLife.44443.018

Transparent reporting form

elife-44443-transrepform.pdf^{(819.5KB, pdf)}

DOI: 10.7554/eLife.44443.019

Data availability

All data analysed during this study are anonymised and publicly available from ConnectomeDB (db.humanconnectome.org; Hodge et al., 2016). It can be freely downloaded after creation of an account at "https://db.humanconnectome.org/app/template/Login.vm". Certain parts of the dataset used in this study, such as the age of the subjects, are available subject to restricted data usage terms, requiring researchers to ensure that the anonymity of subjects is protected (Van Essen et al., 2013). Relevant data generated by the analyses we performed are included in the manuscript and supporting files. Further details can be found at https://github.com/allera/Llera_elife_2019_1 (copy archived at https://github.com/elifesciences-publications/Llera_elife_2019_1).

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eLife. doi: 10.7554/eLife.44443.024

Decision letter

Editor: Moritz Helmstaedter¹

Reviewed by: Franco Pestilli², Jason P Lerch³

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Inter-individual differences in human brain structure and morphometry link to variation in demographics and behavior" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen Moritz Helmstaedter as the Reviewing Editor and Richard Ivry as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Franco Pestilli (Reviewer #1) and Jason P Lerch (Reviewer #3).

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

The reviewers value the importance of the work and see significance in the finding that structural features, not just functional ones, can show high correlation to behavioral phenotypes in humans.

Essential revisions:

1) Additional analyses to solidify the (possibly causal) relation between structural and functional determinants are required as suggested by the reviewers:

a) Including all functional and structural modalities to begin with would at least provide a way of testing their relative contribution to behavioral prediction within the same analytic framework, and/or as suggested (reviewer 2)

b) To strengthen the findings some level of replication from an additional dataset would be helpful (e.g. UK BioBank for older subjects, etc.). (reviewer 3)

c) It would be valuable to try and validate the multivariate imaging components that are seen to predict behavior. For example, are the components of each signature more spatially nested within the canonical brain networks than one would expect by chance? (reviewer 2)

2) Careful revision of the manuscript to enhance clarity and methodological detail (see associated comments by all reviewers below). This especially applies to the figures, which were considered improvable. Please make use of the generous space offering at eLife, include more detailed methodological figures where possible (also the supplementary figure can be integrated into the main paper). An illustration of all relevant structural components could help, as well (beyond component number 6).

3) The requests for methodological explanations/clarifications are considered essential for a successful revision. In particular with respect to:

a) Different pre-processing compared to Smith et al., and linked-ICA, reviewer 1);

b) Publication of code on github/gitlab and usability of code (reviewer 1): code and methods need to be available for evaluation, replicability, and reuse;

c) Scale, image QA, motion effects, choice of anatomical features (reviewer 2);

d) Issues of causality and colinearity (reviewer 3, but also raised by the other reviewers).

We are providing the reviewers' full set of comments below for your consideration. However, in line with eLife’s consolidated approach, we emphasize that the above requests are the ones we deem essential and ask that you provide a letter detailing your response when you submit the revision. The comments below can be treated as recommendations and a point-by-point response is not required.

Reviewer #1:

This is a nice paper following up on previous work by Smith et al., 2015. The results replicate and extend the previously published ones. The manuscript is well written and succinct.

Care should be taken when revising the text as several sections seem unpolished. Yet, I think that several sections of the Materials and methods can improve with added details of the methods. Figure 1 especially should be improved (visually it does not seem to be ready for publication) but also it should be clarified how the dimensionality of the data for each step in the algorithm. It would have to accompany the figure with equations in the Materials and methods describing the mathematical operations and code implementing the model. See below.

A more thorough description of the Linked-ICA is necessary to let this article stand on its own feet. The Materials and methods should provide additional details on the ICA approach, brain and behavioral preprocessing. The detailed description of the preprocessing steps for extractive the behavioral variable should clearly state whether and how the data preprocessing different from Smith et al. If differences existed the authors should clarify why they were necessary and how their choice affected the final result.

In addition, I believe Smith preprocessed/standardized the behavioral and phenotypical data in a way that is not described in the current manuscript. is this correct? Why was the preprocessing performed differently?

Code implementing the analysis should be made readily available. The code should be well documented and allow the readers to reproduce that analyses. The code should help a reader perform the following:

Extract the features as used in the current article starting from each data modality in the HCP release.

Extract and preprocess the behavioral and phenotypical variables given the files that can be obtained by the HCP consortium.

Built the matrices of features as needed for the Linked-ICA modeling.

Perform the additional analysis given the model results.

Reproduce the major plots in the article.

This is especially important in this case as this dataset is public and readers are likely to attempt going beyond the current work. Ideally, the code should be deposited on a platform to allow version tracking and accompanied by a comprehensive readme file describing license and step by step how-to. Github.com seems to be a proper platform for this.

What were the criteria for exclusion for subjects? This is not clearly reported in the Materials and methods.

A few sentences link the following were vague and should be clarified:

"The straight-forward individual linear correlation analysis against the behavioral/demographic measures separately instead affords simple interpretation, albeit possibly being over-conservative given the chosen significance level."

The claim that the current structural mode and Smith functional mode are “strongly correlated” seems an overstatement (r value is only 0.46) I would say that is moderate. Still interesting.

The introductory sentences about phrenology seem out of context unless it is clarified how phrenology fits with the current work.

Reviewer #2:

There are several strengths to the work presented. The unbiased modeling of multiple anatomical variables as predictors of multidimensional measures of behavior is valuable, and represents an important complement to the traditional (but likely less biological valid) approach of mass univariate tests of one structural feature against one behavioral measure. A downside of the many-many multivariate analyses is that they can be hard to map back into a lower dimensional space that is easier for us to interpret – but the authors do an excellent job of "translating" the brain-behavior relationships that they find into text and figures that can be more concretely interpreted. The authors also conduct several useful sensitivity analyses which help readers better understand the conditions under which their core findings hold. The authors also quantitatively assess the interrelationship between structural component #6 in their work, and the previously reported multivariate functional imaging component reported in the HCP by Smith et al.

The main potential for novelty and impact of this manuscript (above and beyond the earlier functional imaging study by Smith et al.) rested very heavily on the questions of relative predictive capacity and directional interdependent between multivariate structural (this paper) and functional (Smith et al) predictors of behavior. However, these questions are fundamentally hard to address meaningfully in the absence of longitudinal multimodal data – which would be the ideal observational study design in humans. Appreciate that the authors used components from the Smith paper in causal analyses to conclude in favor of a structure-to-function model (vs. function-to-structure), but I am not confident that this analytic approach can carry the weight being placed upon it. A caveat here however is that I am not qualified to provide an expert statistical review of the Hyvarinen and Smith paper presenting the method used for directional inference. My point is rather a simpler one about limits around the certainty with which one can infer causal processes from cross-sectional data. I wondered if including all functional and structural modalities to begin with would at least provide a way of testing their relative contribution to behavioral prediction within the same analytic framework – even though this would only go some way to getting at the relative predictive utility of structural vs. functional metrics, while still leaving directionality untouched.

I also through the following issues would benefit from further consideration:

The behavioral variables include both raw and age adjusted version for many scales, but age is already a predictor itself. I think it would be good to provide further details around the rationale for selecting which types of scale go in to the multivariate behavior/demographic matrices to be predicted.

It would be good to include more details around image QA and exploration of motion as potential confound.

I appreciate that ratio of variables to observations is an issue, but these analyses would really benefit from a discovery-replication design.

The authors did drop JD to test if structural prediction still there when excluding information about "morphological variation" – but (i) I see this as a specific instance of the more general need to assess relative contribution of different anatomical metrics to different behavioral dimensions., and (ii) opening up the important question of which anatomical features one considers in the first place (for example, no folding or sulfa depth information despite this being provided by FreeSurfer).

It would be valuable to try and validate the multivariate imaging components that are seen to predict behavior. For example, are the components of each signature more spatially nested within the canonical brain networks than one would expect by chance?

Reviewer #3:

This is a very interesting article recovering brain-behaviour relations from brain structure in ways that map onto previous findings in brain function. These results are exciting, providing evidence of brain structure influencing (or being influenced by) function. My core reading of the paper leads me to two conclusions:

- I buy that brain structure can predict function – the authors provide solid evidence. To strengthen the findings I would like to see some level of replication from an additional dataset (e.g. UK BioBank for older subjects, etc.).

- I am less convinced of the mediation between structure and function, and especially the claimed link that methodological/misalignment issues might be the cause. That argument could be dropped without weakening the paper; if the authors feel strongly about this point then they need to make a better case.

More detailed comments:

- I don't understand footnotes 1 and 2. The authors chose an FDR threshold of q < 2.2x10e-4? That seems arbitrary? Or does a q < 0.05 correspond to an uncorrected p<2.2x10e4?

- It is curious that VBM would explain much more variation in component 6 than thickness or surface area. It is not easy to determine why from the figure, though part of the explanation could be that subcortical regions play a strong role.

- It is even more curious that JD explains so much less in comp 6 than VBM. What type of VBM was conducted – were the tissue densities modulated by the Jacobians?

- Components 1 and 2 have a strong gender contribution and show significant VBM contributions. How were variations in overall brain volume accounted for, if at all?

- The discussion in the fourth paragraph of the Results confuses me. The authors appear to imply that only the Jacobian determinant measures uniquely morphometric differences; how is it that thickness, surface area, and VBM do not reflect morphometric differences? And aren't these results more of an argument that non-linear registrations in this study were either not tuned very well or that alignment of ideosyncratic cortical features is a hard problem?

- The section on linking the structural and functional analyses is problematic. Given that they correlate using the linear model will obviously run into issues of colinearity. This can be seen by the bivariate nature of the results – covaring structure on function or function on structure gives a similar change in r and removes multiple findings. The secondary argument that the reason why covarying structure removes so many of the function findings – due to misalignment or similar methodological issues – is thus suspect and needs to be expanded on.

- I would like the authors to expand on the advantages of linear models over CCA or other multivariate analyses. In that vein, the Smith et al. paper often referred to in this manuscript also tested structural associations and found them much less relevant than rsfMRI – why the results are different should be discussed.

eLife. 2019 Jul 3;8:e44443. doi: 10.7554/eLife.44443.025

Author response

Essential revisions:

1) Additional analyses to solidify the (possibly causal) relation between structural and functional determinants are required as suggested by the reviewers:

a) Including all functional and structural modalities to begin with would at least provide a way of testing their relative contribution to behavioral prediction within the same analytic framework, and/or as suggested (reviewer 2)

The main point of our original submission is to demonstrate that the putative relationship between functional observations and behavioral/demographic variates is already detectable using exclusively structural data modalities. We therefore think that it is important to remain focused on this point by presenting the analysis of structural features without additional functional features first. Nevertheless we agree that the question raised by the reviewers is an important downstream question that naturally results from this primary finding. As suggested we therefore performed an extra analysis using the same Linked ICA methodology, but integrating an additional set of functional features along with the original set of structural ones. In order to fully map on to the Smith et al. paper these functional features are the partial correlation matrices obtained from the two-hundred dimensional group ICA (soft) parcellation performed in resting state fMRI data (publicly available at https://db.humanconnectome.org/data/projects/HCP_1200) and used for the main analysis in Smith et al.

This structural-functional Linked-ICA decomposition recovered the positive-negative mode reported in our original submission; we found a component significantly correlating (r=0.89, p<10^-5) with the mainly reported structural mode (component 6). The contribution of each modality to this mode equals 20% for VBM, 15.6% for FA, 24.4% for MD, 23.9% for MO, 7% for CT, 3% for PA, 5% for JD and 0.0012% for the functional partial correlation feature.

While all structural features provide approximately the same contribution as in the original analyses, it is interesting that the functional data does marginally contribute to this mode, suggesting that structure on its own can explain the positive-negative behavioral mode.

These results have been added to the ninth paragraph of the Results section.

b) To strengthen the findings some level of replication from an additional dataset would be helpful (e.g. UK BioBank for older subjects, etc.). (reviewer 3)

We agree that a further validation into another sample would be of high value. Validation of the reported results using the UK BioBank sample is not possible due to the absence of the right battery of behavioral measures, making it impossible to find the functional mode as the one reported in Smith et al., the reason being that such mode is learned using Canonical Correlation Analyses including both functional and behavioral data.

As an alternative to the UK BioBank proposal we considered the full HCP1200 sample. Our previous results were generated from the HCP-500 release of the project. Therefore we computed all structural features necessary and run an Linked ICA analyses analogous to the originally reported, selecting the subjects not used in our original submission. Considering only subjects for which all structural measures where available, and after QC, this additional analysis was performed using data from independent 547 subjects. From these 547 subjects, 331 where common to the analyses performed in Smith et al., and again we identified a single component significantly correlating with the functional positive-negative mode reported in Smith et al. (r = 0.1773, p < 0.002). Causal analyses from this structural Linked ICA mode to the functional estimated a likelihood ratio of ~0.09, i.e. confirming a significant structure to function causation effect (p<0.005, using permutation testing).

These results have been added to the last paragraph of the Results section.

c) It would be valuable to try and validate the multivariate imaging components that are seen to predict behavior. For example, are the components of each signature more spatially nested within the canonical brain networks than one would expect by chance? (reviewer 2)

In our analysis we consider structural multi-modal characterizations of the brain where each modality contains unique information and together builds into a multi-modal multivariate component. As such, these results cannot be nested into the same space as (functional) canonical brain networks e.g. all DWI-derived features will necessarily be focused on white-matter with little to no overlap with canonical gray-matter brain networks. However, the structural weighting in grey matter modalities in orbitofrontal and temporal cortex, in conjunction with the white matter tracts that connect those regions, is a clear indication of an underlying network structure. We have emphasized this information in the revised manuscript (Results, second paragraph) and made available the full NIfTI images for readers to download.

2) Careful revision of the manuscript to enhance clarity and methodological detail (see associated comments by all reviewers below). This especially applies to the figures, which were considered improvable. Please make use of the generous space offering at eLife, include more detailed methodological figures where possible (also the supplementary figure can be integrated into the main paper). An illustration of all relevant structural components could help, as well (beyond component number 6).

We revised the full manuscript to improve clarity and added more methodological details (see also response to the next point below). Further, all images quality have been improved and the main figure of the supplementary material has been included in the revised manuscript as Figure 3. The spatial extent of all involved components has also been integrated into the main text of the revised manuscript (Results, third paragraph). We would further like to point out that the improved version of Figure 1 is a clear summary of a very complex analyses pipeline and results. We have added in the revised manuscript more references to the original methods paper where different graphical representations and all formulae can be found, but we certainly believe that the presented Figure 1 summarizes perfectly the process performed.

3) The requests for methodological explanations/clarifications are considered essential for a successful revision. In particular with respect to:

a) Different pre-processing compared to Smith et al., and linked-ICA, reviewer 1);

Indeed, the data pre-processing in our original manuscript is different from Smith et al. Our previous submission is using structural rather than functional MR data. In our revised version we made sure that for the functional data we map our pre-processing to the once used in Smith et al. In the revised manuscript we clarified this. With respect to the different pre-processing of behavioral data with respect to Smith et al. we would like to clarify that Smith et al. needed to standardize the data before running the CCA to ensure that different scaled behavioral variables are not biasing the CCA analyses towards a subset of behavioral readouts. This is not the case in our case since the different behavioral variates enter our analyses separately through calculating simple linear correlations. As such, no harmonization across the different measures is required. We added a section in the SM where we detail how behavioral data were pre-process before entering the linear correlation.

In the revised manuscript we further expand on the description of the Linked-ICA factorization approach and clarify that all code and model derivations are already available as part of Groves et al., 2011. This is included in the revised manuscript (Introduction second paragraph and Materials and methods, first paragraph).

b) Publication of code on github/gitlab and usability of code (reviewer 1): code and methods need to be available for evaluation, replicability, and reuse;

All code has been made publicly available as a github repository (https://github.com/allera/Llera_eLife_2019_1) and detailed instructions have been provided. The code includes scripts to extract all features, construct all matrices, smooth the data, perform the Linked ICA factorization, perform post-hoc statistics and reproduce the main figures.

This has also been clearly stated in the revised manuscript (Materials and methods, last paragraph).

c) Scale, image QA, motion effects, choice of anatomical features (reviewer 2);

We have now added a section in the SM where we provide additional details. In summary:

Scale: The Linked ICA model is able to automatically estimate and deal with different scales or spatial degrees of freedom across modalities.

Image QA: The structural data was preprocessed with the computational analysis toolbox (http://dbm.neuro.uni-jena.de/cat/) [Nenadic et al., 2015], which is based on VBM analysis in SPM [Ashburner et al., 2000]. This pipeline generates a number of QC measures which were checked and each individual image was manually inspected for gross artefacts. All images that were of good quality were included in the analyses. All DWI images were also visually inspected for anomalies and only subjects with proper structural and DWI data were included in the Linked-ICA factorization.

Motion effects: Motion artefacts in the structural and DWI measures are addressed by directly removing the affected subjects after automatic and visual QC assessment. The motion effects in the resting state fMRI data were addressed using FSLFIX in precisely the same fashion as in Smith et al.

Choice of anatomical features: All anatomical features, with the exception of the Jacobians feature, were selected based on our extensive experience working with the Linked ICA model. Note that in previous publications [Douaud et al., 2014, Francks et al., 2016, Wolfers et al., 2017] we validated the use of the selected set of features to relate to behavioral measures. The feature set has not been further optimized, the first analyses we performed provided the reported results.

d) Issues of causality and colinearity (reviewer 3, but also raised by the other reviewers).

We agree with the reviewers that causal estimation in general, and on imaging data in particular, is not an easy problem. In fact, the causal analyses we included in the previous manuscript is a consequence of editorial requirements to allow the paper to be sent out for review. We believe we have been very careful in reporting such findings and warning on the interpretation by embedding them into a permutation test for significance assessment and stating clearly that “care is advised when considering causal inference on two vectors of observations, as we cannot exclude the possibility that unobserved underlying processes simultaneously influence brain structure and function (‘hidden causation’).”

With respect to the collinearity issue, we would like to clarify that the model we used for causal assessments (Hyvarinen and Smith, 2013) is based on high order statistics, and, to claim a causal relationship, considers the residuals after linear modelling the pair of signals. Consequently, the collinearity between the two variates is accounted for prior to further causal inference. This is now further clarified in the revised version (Discussion, last paragraph).

We are providing the reviewers' full set of comments below for your consideration. However, in line with eLife’s consolidated approach, we emphasize that the above requests are the ones we deem essential and ask that you provide a letter detailing your response when you submit the revision. The comments below can be treated as recommendations and a point-by-point response is not required.

Reviewer #1:

This is a nice paper following up on previous work by Smith et al., 2015. The results replicate and extend the previously published ones. The manuscript is well written and succinct.

Care should be taken when revising the text as several sections seem unpolished. Yet, I think that several sections of the Materials and methods can improve with added details of the methods. Figure 1 especially should be improved (visually it does not seem to be ready for publication) but also it should be clarified how the dimensionality of the data for each step in the algorithm. It would have to accompany the figure with equations in the Materials and methods describing the mathematical operations and code implementing the model. See below.

In the revised manuscript all figures have been improved. We decided to do not include further dimensionality details or mathematical derivations of the Linked-ICA model since these were already reported Groves et al., 2011. We clarified this in the revised manuscript (see 3 a).

A more thorough description of the Linked-ICA is necessary to let this article stand on its own feet. The Materials and methods should provide additional details on the ICA approach, brain and behavioral preprocessing. The detailed description of the preprocessing steps for extractive the behavioral variable should clearly state whether and how the data preprocessing different from Smith et al. If differences existed the authors should clarify why they were necessary and how their choice affected the final result.

In addition, I believe Smith preprocessed/standardized the behavioral and phenotypical data in a way that is not described in the current manuscript. is this correct? Why was the preprocessing performed differently?

Pre-processing of the behavioral data only differs in terms of the presence (Smith et al) or absence (here) of a cross-measurement normalization stage. In Smith et al. a CCA approach is used to assess associations, requiring harmonization across scales that natively can vary substantially in terms of their range. In our case we calculate correlations of the linked ICA subject mode vector against the interindividual variations of each behavioral measure separately. No cross-measure is therefore required and we can simplify analysis and interpretation.

Code implementing the analysis should be made readily available. The code should be well documented and allow the readers to reproduce that analyses. The code should help a reader perform the following:

Extract the features as used in the current article starting from each data modality in the HCP release.

Extract and preprocess the behavioral and phenotypical variables given the files that can be obtained by the HCP consortium.

Built the matrices of features as needed for the Linked-ICA modeling.

Perform the additional analysis given the model results.

Reproduce the major plots in the article.

This is especially important in this case as this dataset is public and readers are likely to attempt going beyond the current work. Ideally, the code should be deposited on a platform to allow version tracking and accompanied by a comprehensive readme file describing license and step by step how-to. Github.com seems to be a proper platform for this.

We created a GitHub repository containing code and a web-page with all the required instructions (see 3 b).

What were the criteria for exclusion for subjects? This is not clearly reported in the Materials and methods.

We did add a section to the SM of the revised manuscript where we detail the QC procedure.

A few sentences link the following were vague and should be clarified:

"The straight-forward individual linear correlation analysis against the behavioral/demographic measures separately instead affords simple interpretation, albeit possibly being over-conservative given the chosen significance level."

We agree that the phrasing could be confusing and opted by remove the end of the sentence. In the revised manuscript reads “The straight-forward individual linear correlation analysis against the behavioral/demographic measures separately instead affords simple interpretation.”

The claim that the current structural mode and Smith functional mode are “strongly correlated” seems an overstatement (r value is only 0.46) I would say that is moderate. Still interesting.

We have changed strongly by significantly.

The introductory sentences about phrenology seem out of context unless it is clarified how phrenology fits with the current work.

We have removed the implicit mention to the phrenology.

Reviewer #2:

There are several strengths to the work presented. The unbiased modeling of multiple anatomical variables as predictors of multidimensional measures of behavior is valuable, and represents an important complement to the traditional (but likely less biological valid) approach of mass univariate tests of one structural feature against one behavioral measure. A downside of the many-many multivariate analyses is that they can be hard to map back into a lower dimensional space that is easier for us to interpret – but the authors do an excellent job of "translating" the brain-behavior relationships that they find into text and figures that can be more concretely interpreted. The authors also conduct several useful sensitivity analyses which help readers better understand the conditions under which their core findings hold. The authors also quantitatively assess the interrelationship between structural component #6 in their work, and the previously reported multivariate functional imaging component reported in the HCP by Smith et al.

The main potential for novelty and impact of this manuscript (above and beyond the earlier functional imaging study by Smith et al.) rested very heavily on the questions of relative predictive capacity and directional interdependent between multivariate structural (this paper) and functional (Smith et al) predictors of behavior. However, these questions are fundamentally hard to address meaningfully in the absence of longitudinal multimodal data – which would be the ideal observational study design in humans. Appreciate that the authors used components from the Smith paper in causal analyses to conclude in favor of a structure-to-function model (vs. function-to-structure), but I am not confident that this analytic approach can carry the weight being placed upon it. A caveat here however is that I am not qualified to provide an expert statistical review of the Hyvarinen and Smith paper presenting the method used for directional inference. My point is rather a simpler one about limits around the certainty with which one can infer causal processes from cross-sectional data. I wondered if including all functional and structural modalities to begin with would at least provide a way of testing their relative contribution to behavioral prediction within the same analytic framework – even though this would only go some way to getting at the relative predictive utility of structural vs. functional metrics, while still leaving directionality untouched.

We thank the reviewer for the positive view of our work and for pointing out some valuable critical points for discussion. The causality and structural-functional points have been answered at 1a.

I also through the following issues would benefit from further consideration:

The behavioral variables include both raw and age adjusted version for many scales, but age is already a predictor itself. I think it would be good to provide further details around the rationale for selecting which types of scale go in to the multivariate behavior/demographic matrices to be predicted.

Please refer to answer to point 3 a.

It would be good to include more details around image QA and exploration of motion as potential confound.

More details have been added in the SM of the revised manuscript.

I appreciate that ratio of variables to observations is an issue, but these analyses would really benefit from a discovery-replication design.

Please refer to answer to point 1 b.

The authors did drop JD to test if structural prediction still there when excluding information about "morphological variation" – but (i) I see this as a specific instance of the more general need to assess relative contribution of different anatomical metrics to different behavioral dimensions., and (ii) opening up the important question of which anatomical features one considers in the first place (for example, no folding or sulfa depth information despite this being provided by FreeSurfer).

We refer the reviewer to the answer to point 3 c. As a brief resume, the set of selected features, except for the JD, is based in previous Linked ICA analyses we performed that showed these set of features as relevant to relate to behavioral measures publications [Douaud et al., 2014, Francks et al., 2016, Wolfers et al., 2017]. The inclusion/exclusion of the JD feature was intended to test if the deformation fields, that are used to map fMRI data to a spatial common space (MNI), could explain the positive-negative mode in their own. Further mention that the subset of features used have not been optimized.

It would be valuable to try and validate the multivariate imaging components that are seen to predict behavior. For example, are the components of each signature more spatially nested within the canonical brain networks than one would expect by chance?

We refer the reviewer to the answer to point 3 c.

Reviewer #3:

This is a very interesting article recovering brain-behaviour relations from brain structure in ways that map onto previous findings in brain function. These results are exciting, providing evidence of brain structure influencing (or being influenced by) function. My core reading of the paper leads me to two conclusions:

- I buy that brain structure can predict function – the authors provide solid evidence. To strengthen the findings I would like to see some level of replication from an additional dataset (e.g. UK BioBank for older subjects, etc.).

We address this point in 1 b.

- I am less convinced of the mediation between structure and function, and especially the claimed link that methodological/misalignment issues might be the cause. That argument could be dropped without weakening the paper; if the authors feel strongly about this point then they need to make a better case.

In the revised manuscript we have more rephrased that part and have removed direct references to the misalignment argument (Results, eighth paragraph and Discussion, second paragraph).

More detailed comments:

- I don't understand footnotes 1 and 2. The authors chose an FDR threshold of q < 2.2x10e-4? That seems arbitrary? Or does a q < 0.05 correspond to an uncorrected p<2.2x10e4?

Yes, we mean that p < 0.05 corresponds to q < 2.2x10e-4.

- It is curious that VBM would explain much more variation in component 6 than thickness or surface area. It is not easy to determine why from the figure, though part of the explanation could be that subcortical regions play a strong role.

We agree and have added this to the manuscript.

- It is even more curious that JD explains so much less in comp 6 than VBM. What type of VBM was conducted – were the tissue densities modulated by the Jacobians?

Yes the VBM is modulated with the Jacobians. Details on the VBM pipeline are improved in the revised manuscript.

- Components 1 and 2 have a strong gender contribution and show significant VBM contributions. How were variations in overall brain volume accounted for, if at all?

All analyses are performed in a common spatial space and we did not further account for brain volume differences.

- The discussion in the fourth paragraph of the Results confuses me. The authors appear to imply that only the Jacobian determinant measures uniquely morphometric differences; how is it that thickness, surface area, and VBM do not reflect morphometric differences?

We identified the JD feature as purely morphometric differences because they directly reflect structural disagreement with respect to a template. All other features reflect measures that do not relate directly to differences, but to each individual structural characteristics.

And aren't these results more of an argument that non-linear registrations in this study were either not tuned very well or that alignment of ideosyncratic cortical features is a hard problem?

We checked the registration and rejected subjects based on that criteria. We further agree that alignment of ideosyncratic cortical features is a hard problem, which is in fact a problem for most structural as well as functional analyses.

- The section on linking the structural and functional analyses is problematic. Given that they correlate using the linear model will obviously run into issues of colinearity. This can be seen by the bivariate nature of the results – covaring structure on function or function on structure gives a similar change in r and removes multiple findings. The secondary argument that the reason why covarying structure removes so many of the function findings – due to misalignment or similar methodological issues – is thus suspect and needs to be expanded on.

We refer the reviewer to 1 b. Further, in the revised manuscript we have removed direct references to the mis-alignment argument (Results, eighth paragraph and Discussion, second paragraph).

- I would like the authors to expand on the advantages of linear models over CCA or other multivariate analyses. In that vein, the Smith et al. paper often referred to in this manuscript also tested structural associations and found them much less relevant than rsfMRI – why the results are different should be discussed.

The reason why we find different results that Smith et al. is that we use a multi-modal integration approach in this sample. We did not find such strong associations with any unimodal structural analyses.

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file 1. Summary of the behavioral/demographic measures present in the HCP sample.

elife-44443-supp1.xlsx^{(17.2KB, xlsx)}

DOI: 10.7554/eLife.44443.006

Supplementary file 2. Significant results.

elife-44443-supp2.xlsx^{(14KB, xlsx)}

DOI: 10.7554/eLife.44443.007

Supplementary file 3. Comparison between the positive-negative mode presented in the main text and the multi-modal analyses excluding the JD feature (right column).

elife-44443-supp3.xlsx^{(12.1KB, xlsx)}

DOI: 10.7554/eLife.44443.008

Supplementary file 4. Summary of the uni-modal analyses using the JD feature.

elife-44443-supp4.xlsx^{(12.1KB, xlsx)}

DOI: 10.7554/eLife.44443.009

Supplementary file 5. Linked ICA spatial maps associated with the FA feature.

elife-44443-supp5.gz^{(34.5MB, gz)}

DOI: 10.7554/eLife.44443.010

Supplementary file 6. Linked ICA spatial maps associated with the MD feature.

elife-44443-supp6.gz^{(34.4MB, gz)}

DOI: 10.7554/eLife.44443.011

Supplementary file 7. Linked ICA spatial maps associated with the MO feature.

elife-44443-supp7.gz^{(34.4MB, gz)}

DOI: 10.7554/eLife.44443.012

Supplementary file 8. Linked ICA spatial maps associated with the VBM feature.

elife-44443-supp8.gz^{(75.4MB, gz)}

DOI: 10.7554/eLife.44443.013

Supplementary file 9. Linked ICA spatial maps associated with the JD feature.

elife-44443-supp9.gz^{(88.7MB, gz)}

DOI: 10.7554/eLife.44443.014

Supplementary file 10. Linked ICA spatial maps associated with the left hemisphere CT feature.

elife-44443-supp10.mgh^{(62.5MB, mgh)}

DOI: 10.7554/eLife.44443.015

Supplementary file 11. Linked ICA spatial maps associated with the right hemisphere CT feature.

elife-44443-supp11.mgh^{(62.5MB, mgh)}

DOI: 10.7554/eLife.44443.016

Supplementary file 12. Linked ICA spatial maps associated with the left hemisphere PA feature.

elife-44443-supp12.mgh^{(62.5MB, mgh)}

DOI: 10.7554/eLife.44443.017

Supplementary file 13. Linked ICA spatial maps associated with the right hemisphere PA feature.

elife-44443-supp13.mgh^{(62.5MB, mgh)}

DOI: 10.7554/eLife.44443.018

Transparent reporting form

elife-44443-transrepform.pdf^{(819.5KB, pdf)}

DOI: 10.7554/eLife.44443.019

Data Availability Statement

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PERMALINK

Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior

Alberto Llera

Thomas Wolfers

Peter Mulders

Christian F Beckmann

Roles

Abstract

eLife digest

Introduction

Results

Figure 1. Data processing pipeline and main results.

Figure 2. Component number six feature sources of variation.

Figure 3. Summary of relevant modalities spatial maps associated with the components indexed in the most left column.

Discussion

Materials and methods

Statistical analysis

Acknowledgements

Appendix 1

Individual features pre-processing:

Image Quality Assessment (QA)

Behavioral data processing

Main results

Feature modalities relative contribution to components

Appendix 1—figure 1. Relative contributions of each feature modality to the most relevant components.

Robustness: model order

Appendix 1—figure 2. Top: Significant correlations between the reported (100 dimensional) factorization and a 90 dimensional (left panel) and 110 dimensional (right panel).

Robustness: analyses without the Jacobians

Robustness: Analyzing morphometric differences

On the power of structural and functional associations to behavior

Testing structure-function causal effects

Funding Statement

Contributor Information

Funding Information

Additional information

Competing interests

Author contributions

Ethics

Additional files

Data availability

References

Decision letter

Roles

Author response

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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