Prediction of Individual Brain Maturity Using fMRI

By Nico U F Dosenbach, Binyam Nardos, Alexander Li Cohen, Damien A. Fair, Jonathan D. Power, Jessica A. Church, Steven M. Nelson, Gagan S. Wig, Alecia C. Vogel, Christina N. Lessov-Schlaggar and 8 more. Science, 2010

2,167 citations recorded by the source corpus.

Connectivity Map of the Brain The growing appreciation that clinically abnormal behaviors in children and adolescents may be influenced or perhaps even initiated by developmental miscues has stoked an interest in mapping normal human brain maturation. Several groups have documented changes in gray and white matter using structural and functional magnetic resonance imaging (fMRI) in cross-sectional and longitudinal studies. Dosenbach et al. (p. 1358) developed an index of resting-state functional connectivity (that is, how tightly neuronal activities in distinct brain regions are correlated while the subject is at rest or even asleep) from analyses of three independent data sets (each based on fMRI scans of 150 to 200 individuals from ages 6 to 35 years old). Long-range connections increased with age and short-range connections decreased, indicating that networks become sparser and sharper with brain maturation. Multivariate pattern analysis of 5-minute brain scans provides a measure of brain maturity. Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals’ brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain’s major functional networks.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e059a3c1fbeeeb94b6869ce17ec1352dde1f4919

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In Cognitive Neuroscience

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Citations in the corpus

A distributed brain network predicts general intelligence from resting-state human neuroimaging data (2018, background): “There are known effects of gender [78,79], age [80,81], in-scanner motion [82–84] and brain size [85] on the functional connectivity patterns measured in the resting-state with fMRI.”
MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. (2020, background): “The concept of estimating brain age from MRI scans has been previously explored (Dosenbach, 2010; Franke et al., 2010; Brown et al., 2012; Habes et al., 2016; Madan and Kensinger, 2018), showing that it is possible to accurately predict the chronological age of subjects from volumetric or voxelwise…”
Anticorrelations in resting state networks without global signal regression (2011, positive): “, 2010), and can thus provide useful information about brain organization differences across different clinical populations (Dosenbach et al., 2010; Seeley et al., 2009) and during development (Dosenbach et al.”
Why use a connectivity-based approach to study stroke and recovery of function? (2012, positive): “For instance a recent study showed that patterns of functional connectivity across multiple networks were able to predict the developmental age of normal children (Dosenbach et al., 2010).”
BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics (2013, background): “Notably, other atlases are generated from anatomical [66,69] or functional [26,67,68] parcellations.”
Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation (2018, background): “, 2015), change across the lifespan (Chan et al., 2014; Dosenbach et al., 2010; Satterthwaite et al., 2013a), and respond to different cognitive demands (Betti et al.”
Extraordinary neoteny of synaptic spines in the human prefrontal cortex (2011, result): “Taken together, the previous data and the present findings strongly indicate that anatomical (23, 24, 26–28, 31) and functional changes (15, 18, 19, 21, 22, 25, 35) in the prefrontal cortex observed in vivo during late adolescence and young adulthood reflect the dynamic reorganization of synaptic ci”
Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers. (2017, neutral): “Using structural or functional neuroimaging data, it is now possible to predict age [7,8].”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, positive): “Given these merits, a growing number of neuroimaging studies have been focusing on individualized prediction of behavior/cognition based on ML regression approaches (Dosenbach et al., 2010; Gabrieli et al., 2015; Siegel et al., 2016).”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “…had found that functional connectivity between anatomically proximate ROIs gradually decreased, whereas long-range functional connections, especially anterior-posterior links, gradually increased until adulthood (Dosenbach et al., 2010; Fair et al., 2009; Kelly et al., 2009; Supekar et al., 2009).”