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A multivariate distance-based analytic framework for connectome-wide association studies
The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain–phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain–behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity–phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-DOPA pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain–behavior relationships in the connectome.
•Develop novel approach to connectome-wide association studies.•Identify voxels whose whole-brain connectivity maps are associated with a phenotype.•Discover associations with IQ in default, ventral attention, and visual networks.•Results robust to removal of global signal, mean connectivity, and motion.•Significant associations can guide seed-selection for seed correlation analysis.
Journal Article
Getting your head around the brain
\"This short and student-friendly introduction to the brain will help students to understand more about the links between the brain and behavior. Following the BPS accredited syllabus for biological psychology, its accessible structure, multiple examples and engaging tone make this book ideal introductory reading\"--Publisher information.
Book
A distributed brain network predicts general intelligence from resting-state human neuroimaging data
Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here, we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N = 884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.
This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.
Journal Article
A deep learning based approach identifies regions more relevant than resting‐state networks to the prediction of general intelligence from resting‐state fMRI
Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that specific domain, being agnostic to time‐distributed features and dynamics. We used an ensemble of recurrent neural networks to circumvent this limitation, bypassing feature extraction, to predict general intelligence from resting‐state functional magnetic resonance imaging regional signals of a large sample (n = 873) of Human Connectome Project adult subjects. Ablating common resting‐state networks (RSNs) and measuring degradation in performance, we show that model reliance can be mostly explained by network size. Using our approach based on the temporal variance of saliencies, that is, gradients of outputs with regards to inputs, we identify a candidate set of networks that more reliably affect performance in the prediction of general intelligence than similarly sized RSNs. Our approach allows us to further test the effect of local alterations on data and the expected changes in derived metrics such as functional connectivity and instantaneous innovations. We predict general intelligence from RS‐fMRI timeseries using a recurrent neural network ensemble in Human Connectome Project data. We propose the selection of networks based on the variance of saliencies per ROI. Resting‐state networks (RSNs) impact on prediction can be explained by their size while with our strategy we find salient networks whose importance exceed that of RSNs.
Journal Article
How the vertebrate brain regulates behavior : direct from the lab
Historically, neuroscientists often chose to work with the simplest non-mammalian species out of a fear that the mammalian brain would be too complex and would defy precise methodology. My lab's work has proven that by choosing problems and methods with care, it is possible to explain a mammalian behavior. The timing of this book reflects that it is now fifty years since I discovered hormone receptors in the brain. These hormone receptors led us to unravel the neural circuitry for a laboratory animal mating behavior and also permit us to use molecular biological techniques in the brain. The behavior explained is a social behavior, which makes it still more surprising that it has been susceptible of analysis. My lab's accomplishments typify, in one scientific story, what needs to happen as neuroscientists continue to explore mechanisms in the mammalian brain.-- Provided by publisher
Book
Altered Brain‐Behavior Association During Resting State is a Potential Psychosis Risk Marker
Alterations in cognitive and neuroimaging measures in psychosis may reflect altered brain‐behavior interactions patterns accompanying the symptomatic manifestation of the disease. Using graph connectivity‐based approaches, we tested the brain‐behavior association between cognitive functioning and functional connectivity at different stages of psychosis. We collected resting‐state fMRI of 204 neurotypical controls (NC) in two independent cohorts, 43 patients with chronic psychosis (PSY), and 22 subjects with subthreshold psychotic symptoms (STPS). In NC, we calculated graph connectivity metrics and tested their associations with neuropsychological scores. Replicable associations were tested in PSY and STPS and externally validated in three cohorts of 331, 371, and 232 individuals, respectively. NC showed a positive correlation between the degree centrality of a right prefrontal‐cingulum‐striatal circuit and total errors on Wisconsin Card Sorting Test. Conversely, PSY and STPS showed negative correlations. External replications confirmed both associations while highlighting the heterogeneity of STPS. Group differences in either centrality or cognition alone were not equally replicable. In four independent cohorts totaling 1,203 participants, we identified a replicable alteration of the brain‐behavior association in different stages of psychosis. These results highlight the high replicability of multimodal markers and suggest the opportunity for longitudinal investigations that may test this marker for early risk identification. The study detects a potential multimodal biomarker that can be promising for identifying early markers of psychosis. It shows a consistent brain‐behavior association between a circuit of interconnected regions and executive function in neurotypical controls and individuals at various stages of psychosis. These findings are supported by data from four independent cohorts, totaling 1,203 participants.
Journal Article
The chemistry of culture : brain-based strategies to create a culture of learning
\"Neuroscientists are discovering the Chemistry of Culture by revealing the neurological links between our brain and our relationships. This book brings that brain research out of the lab and into schools by connecting it to highly effective culture-building strategies\" -- Provided by publisher.
Book
The degenerate coding of psychometric profiles through functional connectivity archetypes
Degeneracy in the brain-behavior code refers to the brain's ability to utilize different neural configurations to support similar functions, reflecting its adaptability and robustness. This study aims to explore degeneracy by investigating the non-linear associations between psychometric profiles and resting-state functional connectivity (RSFC).
The study analyzed RSFC data from 500 subjects to uncover the underlying neural configurations associated with various psychometric outcomes. Self-organized maps (SOM), a type of unsupervised machine learning algorithm, were employed to cluster the RSFC data. And identify distinct archetypal connectivity profiles characterized by unique within- and between-network connectivity patterns.
The clustering analysis using SOM revealed several distinct archetypal connectivity profiles within the RSFC data. Each archetype exhibited unique connectivity patterns that correlated with various cognitive, physical, and socioemotional outcomes. Notably, the interaction between different SOM dimensions was significantly associated with specific psychometric profiles.
This study underscores the complexity of brain-behavior interactions and the brain's capacity for degeneracy, where different neural configurations can lead to similar behavioral outcomes. These findings highlight the existence of multiple brain architectures capable of producing similar behavioral outcomes, illustrating the concept of neural degeneracy, and advance our understanding of neural degeneracy and its implications for cognitive and emotional health.
Journal Article