BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Wael, Reinder Vos de; Benkarim, Oualid; Paquola, Casey; Larivière, Sara; Royer, Jessica; Tavakol, Shahin; Xu, Ting; Hong, Seok‐Jun; Langs, Georg; Valk, Sofie L.; Mišić, Bratislav; Milham, Michael P.; Margulies, Daniel S.; Smallwood, Jonathan; Bernhardt, Boris C.

doi:10.1038/s42003-020-0794-7

Cited by 359 publications

(431 citation statements)

References 56 publications

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“…There is growing appreciation in the neuroimaging field that innovative methods are required to account for the impact of SA on statistical analyses of large-scale brain maps and spatial gradients (Alexander-Bloch et al, 2018; 35 Burt et al, 2018;de Wael et al, 2019). Recent proposals have focused primarily on deriving corrected p-values for tests of spatial correspondence between pairs of brain maps.…”

Section: Introductionmentioning

confidence: 99%

Generative modeling of brain maps with spatial autocorrelation

Burt

Helmer

Shinn

et al. 2020

Preprint

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Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene ontology enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization.

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Section: Introductionmentioning

confidence: 99%

Generative modeling of brain maps with spatial autocorrelation

Burt

Helmer

Shinn

et al. 2020

Preprint

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“…Recognizing the high dimensionality of functional connectivity data, however, emerging efforts have highlighted the need for identifying summary metrics that can distill complex whole-brain connectivity data into more parsimonious sets of organizing principles. Toward this goal, a framework has been introduced to reduce such complexity into a set of dimensions describing the 'connectivity space' of the brain (Haak et al, 2018;Langs et al, 2016;Margulies et al, 2016;Mars et al, 2018aMars et al, , 2018bVos de Wael et al, 2020). Despite the value of these approaches, there is currently a lack of consensus on which method is the most applicable to develop an effective imaging biomarker.…”

Section: Introductionmentioning

confidence: 99%

Toward a Connectivity Gradient-Based Framework for Reproducible Biomarker Discovery

Hong

Nikolaidis

et al. 2020

Preprint

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Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent studies applying dimensionality reduction techniques to resting-state fMRI (R-fMRI) have unveiled neurocognitively meaningful connectivity gradients that are present in both human and primate brains, and appear to differ meaningfully among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery.Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209×2) and the Midnight scan club (n=9), we tested the following key biomarker traits -reliability, reproducibility and predictive validity -of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (R-fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment.

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“…Gradient mapping. Microstructural gradient in the insula were identified with the BrainSpace toolbox for Matlab (https://github.com/MICA-MNI/BrainSpace) (63). Group-averaged MPC matrices were thresholded row-wise to retain only the top 10% of values as in prior work (24,25,34,35) and subsequently transformed into normalized angle affinities (Figure 1B).…”

Section: Microstructural Profile Covariance (Mpc) and Gradient Mappingmentioning

confidence: 99%

“…Standardized residuals from a simple linear model of histological measures predicted by cortical morphological features were related to G1 using Spearman rank correlations. Statistical significance of reported correlations was assessed with Moran spectral randomization (41) implemented in the BrainSpace toolbox (63). This method computes a metric for spatial autocorrelation, Moran's I, and generates normally distributed data with similar auto-correlation.…”

Section: Cytoarchitectonic Markersmentioning

confidence: 99%

“…These matrices were mapped into a lower dimensional manifold with diffusion map embedding applying identical parameters to those used in the Discovery cohort (33). Resulting gradients were aligned to those obtained with T1w/T2w data of the corresponding hemisphere using Procrustes analysis (63). Only the first two gradients were retained and correlated with microstructural gradients generated in the HCP sample using Spearman correlations.…”

Section: Replication In Independent Samplementioning

confidence: 99%

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Myeloarchitecture gradients in the human insula serve as blueprints for its diverse connectivity and function

Royer

Paquola

Larivière

et al. 2019

Preprint

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Insular cortex is a core hub involved in multiple cognitive and socio-affective processes. Yet, the anatomical mechanisms that explain how it is involved in such a diverse array of functions remain incompletely understood. Here, we define a novel framework to test the hypothesis that changes in myeloarchitecture across the insular cortex explain how it can be involved in many different facets of cognitive function. Detailed intracortical profiling, performed across hundreds of insular locations on the basis of myelin-sensitive magnetic resonance imaging (MRI), was compressed into a lower-dimensional space uncovering principal axes of myeloarchitectonic variation. Leveraging two datasets with different high-resolution MRI contrasts, we obtained robust support for two principal dimensions of insular myeloarchitectonic differentiation in vivo, one running from ventral anterior to posterior banks and one radiating from dorsal anterior towards both ventral anterior and posterior subregions. Analyses of post mortem 3D histological data showed that the antero-posterior axis was mirrored in cytoarchitectural markers, even when controlling for anatomical landmarks and sulco-gyral folding. Resting-state functional connectomics in the same individuals and ad hoc meta-analyses showed that myelin gradients in the insula constrained affiliation to macroscale intrinsic functional systems, showing differential shifts in functional network embedding across each myelin-derived gradient. Collectively, our findings offer a novel approach to capture structure-function interactions of a key node of the limbic system, and suggest a multidimensional structural basis underlying the diverse functional roles of the insula. KEYWORDS: Insula | Multiscale | Myelin | Functional connectivity | HierarchyRoyer et al. | Myeloarchitecture blueprints in the insula

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BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets

Cited by 359 publications

References 56 publications

Generative modeling of brain maps with spatial autocorrelation

Generative modeling of brain maps with spatial autocorrelation

Toward a Connectivity Gradient-Based Framework for Reproducible Biomarker Discovery

Myeloarchitecture gradients in the human insula serve as blueprints for its diverse connectivity and function

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