Growing together and growing apart: Regional and sex differences in the lifespan developmental trajectories of functional homotopy

By Xi-Nian Zuo, Clare Kelly, A. di Martino, Maarten Mennes, Daniel S. Margulies, S. Bangaru, Rebecca L. Grzadzinski, Alan C. Evans, Yufeng Zang, F. X. Castellanos and 1 more. Journal of Neuroscience, 2010

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Functional homotopy, the high degree of synchrony in spontaneous activity between geometrically corresponding interhemispheric (i.e., homotopic) regions, is a fundamental characteristic of the intrinsic functional architecture of the brain. However, despite its prominence, the lifespan development of the homotopic resting-state functional connectivity (RSFC) of the human brain is rarely directly examined in functional magnetic resonance imaging studies. Here, we systematically investigated age-related changes in homotopic RSFC in 214 healthy individuals ranging in age from 7 to 85 years. We observed marked age-related changes in homotopic RSFC with regionally specific developmental trajectories of varying levels of complexity. Sensorimotor regions tended to show increasing homotopic RSFC, whereas higher-order processing regions showed decreasing connectivity (i.e., increasing segregation) with age. More complex maturational curves were also detected, with regions such as the insula and lingual gyrus exhibiting quadratic trajectories and the superior frontal gyrus and putamen exhibiting cubic trajectories. Sex-related differences in the developmental trajectory of functional homotopy were detected within dorsolateral prefrontal cortex (Brodmann areas 9 and 46) and amygdala. Evidence of robust developmental effects in homotopic RSFC across the lifespan should serve to motivate studies of the physiological mechanisms underlying functional homotopy in neurodegenerative and psychiatric disorders.

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Citations in the corpus

Predicting regional neurodegeneration from the healthy brain functional connectome. (2012, background): “Although the human connectome evolves with aging (Zuo et al., 2010), we chose healthy older subjects to capture the connectome upon which neurodegeneration is most often superimposed.”
Changes in structural and functional connectivity among resting-state networks across the human lifespan (2014, methodology): “While MLR models of this type have been widely used in the study of agerelated changes in brain networks, they have some inherent limitations (Cao et al., 2014; Zuo et al., 2010).”
Networks of anatomical covariance (2013, unknown): “…or fMRI (Achard and Bullmore, 2007, 2006; Bassett and Bullmore, 2006; Bellec et al., 2010; Biswal et al., 2010; Eguiluz et al., 2005; Greicius et al., 2003, 2004; He et al., 2009a; Honey et al., 2007, 2009; Salvador et al., 2005a, 2005b; Smith et al., 2009; Vértes et al., 2012; Zuo et al., 2010).”
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, methodology): “This is not surprising, as Z-standardization (i.e., within-subject mean centering and variance normalization) was applied in accord with common practice (e.g., Buckner et al., 2009; Zou et al., 2008; Zuo et al., 2010a, 2012, 2013).”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, background): “Keywords: data processing; quality control; resting-state fMRI; standardization; statistical analysis”
The Human Connectome Project: A data acquisition perspective (2012, unknown): “2225 MR hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2225 ing Initiative; CMRR, Center for Magnetic Resonance Research at UMinn; dMRI, diffusion imaging; DZ, dizygotic; EPI, echo-Minn Consortium; INDI, International ”
The Neuro Bureau ADHD-200 Preprocessed Repository (2016, background): “Note that, by selecting a symmetric template, it is possible to study functional connectivity between homotopic regions by simply ﬂipping the x axis in stereotaxic space, e.g. [39].”
The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry (2012, background): “…in pediatric medicine (Falkner, 1958; Nellhaus, www.frontiersin.org October 2012 | Volume 6 | Article 152 | 1 1968), such trajectories would facilitate the identification and characterization of pathophysiological processes contributing to the emergence of neuropsychiatric illness…”
Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data (2011, methodology): “The dataset has been used to examine TRT reliability of seed-based resting-state functional connectivity (RSFC) [27], independent component analysis and dual regression [29], amplitude of low-frequency fluctuations [30] and functional homotopy [31].”
Impact of In-Scanner Head Motion on Multiple Measures of Functional Connectivity: Relevance for Studies of Neurodevelopment in Youth (2012, methodology): “In contrast to seed-based or pairwise approaches, ALFF and fALFF provide a measure of low frequency oscillations that contribute to connectivity but are fundamentally different from between-region measures of connectivity (Zuo et al., 2010a).”
Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence (2017, background): “…with general intell..., Intelligence (2016), http://dx.doi.org/10.1016/j.intell.2016.11.001 to be relatively stable over time (Shehzad et al., 2009; Zuo et al., 2010), to relate closely to underlying anatomical connections (Greicius, Supekar, Menon, & Dougherty, 2009; Hagmann et al., 2008; Honey,…”
How Visual Is the Visual Cortex? Comparing Connectional and Functional Fingerprints between Congenitally Blind and Sighted Individuals (2015, methodology): “To rule out age effects on the RSFC patterns in the VOTC (Biswal et al., 2010; Zuo et al., 2010; Yan et al., 2011), we performed a control analysis using resting-state data of a new group of 7 sighted subjects who were age-matched with our congenitally blind subjects.”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, methodology): “This subject-wise ReHo normalization has been demonstrated to be able to improve both normality and reliability of amplitude measures across subjects in our previous work as described in Supplementary Materials from Zuo et al. (2010a).”