A positive-negative mode of population covariation links brain connectivity, demographics and behavior

By Stephen M. Smith, Thomas E. Nichols, Diego Vidaurre, Anderson M. Winkler, Timothy Edward John Behrens, Matthew F. Glasser, Kamil Ugurbil, Deanna M. Barch, D. V. Van Essen, Karla L. Miller. Nature Neuroscience, 2015

887 citations recorded by the source corpus.

We investigated the relationship between individual subjects' functional connectomes and 280 behavioral and demographic measures in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation: subjects were predominantly spread along a single 'positive-negative' axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a0001afe82cd7f3b510f9bd91cac1d1be0778c55

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In Cognitive Neuroscience

In Experimental and Cognitive Psychology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The Neuroscience of Intelligence (2016, background): “intelligence variables were among the strongest related to overall connectivity among brain areas, such that greater connectivity was associated with higher test scores (Smith et al., 2015).”
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, background): “Second, our results suggest that individual-specific cortical network topography might serve as a fingerprint of human behavior, which might complement the usage of functional connectivity strength in the vast majority of previous literature (Hampson et al., 2006; Finn et al., 2015; Rosenberg et al.”
The relation between statistical power and inference in fMRI (2017, methodology): “Canonical Correlation Analyses (CCA) is another powerful multivariate tool to address the correspondence of two datasets, see for instance refs [83,84].”
Sex differences in the adult human brain: Evidence from 5,216 UK Biobank participants (2017, methodology): “Before analysis of sex differences, we multiplied the strength of each connection by the sign of its group-mean (Smith et al. 2015).”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, positive): “Particularly, significant associations between this measure and behavior/cognition have been repeatedly observed (Dubois and Adolphs, 2016; Liu et al., 2017; Smith et al., 2015).”
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI (2017, background): “Just as importantly, the parcellations might potentially be utilized as a dimensionality reduction tool in many rs-fMRI applications, where dealing with the fMRI data at the original voxel level resolution is difficult (Finn et al. 2015; Smith et al. 2015).”
Metric learning with spectral graph convolutions on brain connectivity networks (2018, background): “This is primarily due to the fact that associations between the topological organisation of these networks and brain development (Hagmann et al., 2010), function (Smith et al., 2015) as well as disease (Catani and ffytche, 2005; Fornito et al., 2015) have been established.”
Situating the default-mode network along a principal gradient of macroscale cortical organization (2016, background): “Along those lines, a “positive–negative” axis of brain– behavior covariation describes a similar connectivity spectrum, distinguishing the DMN from sensory/motor regions (63).”
The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds (2018, methodology): “This informationmay be used to evaluate the relationship between individual differences in these characteristics and brain network connectivity (e.g. (Finn et al., 2015; Smith et al., 2015),).”
A distributed brain network predicts general intelligence from resting-state human neuroimaging data (2018, background): “It is worth mentioning a related, entirely data-driven study that was recently conducted by Smith and colleagues on an earlier release of the same HCP dataset (N=461) [104].”
Building a Science of Individual Differences from fMRI. (2016, background): “in examining individual differences in relation to healthy ageing [4,5], personality [6], intelligence [7,8], mood [9] and genetic polymorphism [10].”
Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, positive): “…supervised learning settings, such as regression – e.g. for age prediction (Liem et al., 2017) – multi-output approaches as with Canonical Correlation Analysis popular in large-scale population imaging settings (Smith et al., 2015; Miller et al., 2016) for dimensional approaches to psychology.”
Cross‐validation failure: Small sample sizes lead to large error bars (2017, background): “…of cognition (Haynes and Rees, 2006), information mapping (Kriegeskorte et al., 2006), prediction of individual differences –behavioral or clinical– (Smith et al., 2015), rich encoding models (Nishimoto et al., 2011), principled reverse inferences (Poldrack et al., 2009), etc. Replacing in-sample…”
The Human Connectome Project’s Neuroimaging Approach (2016, background): “Smith et al. 93 identified a “positive–negative” phenotypic axis that covaries with differences in the strength of functional connectivity in a subset of brain regions that are generally implicated in higher cognitive functions.”
The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction (2017, methodology): “…of adult cortical organisation (Glasser and Van Essen, 2011), and improve understanding of how the functional connectome correlates with behaviour (Smith et al., 2015) A core tenet, underlying the success of the HCP approach, has been the advocation of surface-based processing and analysis of…”
Brain network dynamics are hierarchically organized in time (2017, methodology): “Next, to assess the individual significance of each variable (either FO or trait), we use canonical correlation analysis (24).”
Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology (2021, neutral): “…of one’s socioemotional, educational, and neighborhood environments (Alnæs et al., 2020; Leonard et al., 2019; Llera et al., 2019; Modabbernia et al., 2021; Smith et al., 2015; Tooley et al., 2020) is thought to affect association cortex maturation in middle and late periods of development.”