Decreased centrality of subcortical regions during the transition to adolescence: A functional connectivity study
Introduction
The transition from childhood to adolescence involves major changes in cognitive and emotional functions (Paus, 2005, Rubia, 2014). Evidence supports that such changes are a result of important refinements in complex neural dynamics and may ultimately reflect the organization of the human brain (Fair et al., 2009, Kelly et al., 2009, Stevens et al., 2009, Dosenbach et al., 2010, Blakemore, 2012).
The brain can be conceptualized as a set of partially segregated networks emerging from a complex relationship between a variety of nerve cells organized into circuits and functional areas (Power et al., 2010). Neurodevelopmental studies investigating the trajectories of these complex networks may shed light on the hierarchical structure of such circuitries during development. These types of studies are especially important in transitional periods, such as adolescence, as they can advance our understanding of this window of vulnerability for disruptions in typical development and increased incidence of mental disorders (Kim-Cohen et al., 2003, Ernst et al., 2006, Insel, 2009, Salum et al., 2010).
Several brain structural changes occur during this period. Although some regions exhibit linear changes in cortical thickness and volume, others appear to follow a maturational course, with an initial childhood increase until approximately 7–10 years of age and a subsequent adolescent decline (Giedd, 2004, Gogtay et al., 2004, Shaw et al., 2008). This decline in thickness during adolescence is followed by a period of slower decline, stabilizing in adulthood (Shaw et al., 2008, Sowell et al., 2003).
Intrinsic functional connectivity (Biswal et al., 1995, Buckner et al., 2013) based on fMRI is sensitive to coupling dynamics and has the ability to broadly survey the whole brain, providing information about relationships between networks (Buckner et al., 2013). The first studies investigating the development of brain networks (Fair et al., 2007, Fair et al., 2008, Fair et al., 2009, Dosenbach et al., 2010) focused on the organizing principles of such development and found evidence of functional segregation of nearby functional areas across time, which occur through the weakening of short-range functional connections. These studies also detected the integration of distant regions in a functional network, which occur by strengthening long-range functional connections. Subsequent studies more specifically investigated the meaning of such connections and addressed important questions, such as communication between cortical and subcortical areas that are thought to undergo dramatic changes over time, especially in adolescence (Paus, 2005, Lebel et al., 2008, Supekar et al., 2009, Blakemore, 2012, Rubia, 2014). Studies have suggested that children have stronger subcortical-cortical and weaker cortico-cortical connectivity compared to young adults (Supekar et al., 2009). Some research (Power et al., 2012, Satterthwaite et al., 2012, Van Dijk et al., 2012) has shown that earlier developmental studies could have been influenced by head movement artifacts. These may bias functional connectivity by decreasing long-distance correlations (of BOLD signal) and increasing short-distance correlations. Interestingly, many initial findings were replicated in a recent study, even after adjusting for movement (Satterthwaite et al., 2013a).
A method that is increasingly being used to investigate the relevance of brain network nodes comes from a branch of mathematics called graph theory. In graph theory, a network is a collection of items (nodes) that possesses pairwise relationships (edges). Graphs represent the collection of nodes and edges. There are a variety of methods to characterize how important or central a node is to the network, and each seems to capture independent aspects of brain networks (Bullmore and Sporns, 2009, Rubinov and Sporns, 2010, Sporns, 2011, Zuo et al., 2012). Investigating a variety of measures of centrality can reveal how brain regions communicate and characterize network properties that support inter-regional interactions (Hwang et al., 2013).
Nevertheless, few studies have investigated developmental changes of these brain networks in periods of high incidence (or onset) of many psychiatric disorders. Moreover, studies so far have included samples with potential selection bias, such as those composed of voluntary research subjects or specific populations (Uher and Rutter, 2012). Therefore, it is unclear if such developmental effects can be easily translated to population samples. In addition, most studies relied on relatively small sample sizes (fewer than one hundred) and concentrate solely on American and European populations (Satterthwaite et al., 2013b), which are limited on miscegenation (genetic admixture) and environmental factors (e.g., education and violence). Lastly, all available studies have been performed in high-income countries, and to the best of our knowledge, there are no neuroimaging development studies in low- and middle-income countries based on large samples.
This study examines a unique large community sample from a developing country (Brazil) with high levels of genetic admixture. It is important to highlight that, due to environmental factors (e.g., poverty, violence, poor education and medical assistance, etc.), children living in developing countries are more vulnerable to mental disorders and represent the majority of such cases worldwide (Kieling et al., 2009). Thus, the investigation of children and adolescents under these environmental and genetic factors is crucial to enhance the comprehension of the typical neurodevelopment.
Here, we performed a whole-brain graph analysis aiming to identify brain regions with age-effects on eigenvector centrality (EVC) measure. Given previous empirical evidence demonstrating a process of cortical maturation (Fair et al., 2007, Fair et al., 2008, Fair et al., 2009, Supekar et al., 2009, Shaw et al., 2008, Satterthwaite et al., 2013a), the existence of developmental changes in global network properties (Hwang et al., 2013) and the presence of hub regions in adults (Power et al., 2013), we hypothesize that age-related changes occur in the central regions of brain networks, with an increase in cortical integrative regions.
Section snippets
Participants
The participants of this study are a subsample (N = 447, healthy children) of the High Risk Cohort Study for Psychiatric Disorders in Childhood (HRC, N = 2512 children). For a detailed description of this cohort, see Salum et al., 2013, Salum et al., in press. The current investigation was based on healthy children from two different Brazilian centers in the cities of São Paulo and Porto Alegre. The study was approved by the local ethics committee (University of Sao Paulo, IORG0004884, 1138/08) and
Results
The results of the age effects analysis revealed a statistically significant (corrected p-value < 0.05, Bonferroni method considering 325 multiple comparisons, the number of ROIs) increase in the EVC of the angular gyrus. In addition, significant decreases in EVC measures were found in the caudate, thalamus; cerebellar tonsils and pyramis; and the fusiform, parahippocampal, parietal sub-gyral and semilunar lobule. The statistical information of significant age-dependent regions is highlighted in
Discussion
In this study, we investigated the neurodevelopmental changes in functional connectivity in a large sample of 447 healthy children and adolescents in an age range from 7 to 15 years old. The main motivation of characterizing typical development during this period is that most psychiatric disorders, which are deviations from the normal developmental trajectory, have an early onset in youth. Fifty percent of DSM-IV disorders have an onset before 14 years old, and 75% have an onset before the age of
Disclosure
Dr. Luis Augusto Rohde LAR has been on the speakers’ bureau/advisory board and/or acted as consultant for Eli-Lilly, Janssen-Cilag, Novartis and Shire in the last 3 years. The ADHD and Juvenile Bipolar Disorder Outpatient Programs chaired by him received unrestricted educational and research support from the following pharmaceutical companies in the last 3 years: Eli-Lilly, Janssen-Cilag, Novartis, and Shire. He receives authorship royalties from Oxford Press and ArtMed. Dr. Rodrigo Affonseca
Acknowledgments
The opinions, hypothesis, conclusions, and recommendations of this study are under the responsibility of the authors, not necessarily representative of the opinion of the funding agencies. The authors are grateful to Sao Paulo Research Foundation–FAPESP (J.R.S. grant nos. 2013/10498-6 and 2013/00506-1), CAPES, and CNPq, Brazil, for funding this research. This is a study from the National Institutes of Science and Technology for Developmental Psychiatry of Children and Adolescents (INPD)
References (61)
Imaging brain development: the adolescent brain
NeuroImage
(2012)AFNI: software for analysis and visualization of functional magnetic resonance neuroimages
Comput. Biomed. Res.
(1996)- et al.
FSL
NeuroImage
(2012) - et al.
A global perspective on the dissemination of mental health research
Lancet
(2009) - et al.
Microstructural maturation of the human brain from childhood to adulthood
NeuroImage
(2008) Mapping brain maturation and cognitive development during adolescence
Trends Cogn. Sci.
(2005)- et al.
The development of human functional brain networks
Neuron
(2010) - et al.
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
NeuroImage
(2012) - et al.
Evidence for hubs in human functional brain networks
Neuron
(2013) - et al.
Complex network measures of brain connectivity: uses and interpretations
NeuroImage
(2010)
Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth
NeuroImage
Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth
NeuroImage
Hemodynamic brain correlates of disgust and fear ratings
NeuroImage
The influence of head motion on intrinsic functional connectivity MRI
NeuroImage
Neural indices of improved attentional modulation over middle childhood
Dev. Cogn. Neurosci.
Bayesian analysis of neuroimaging data in FSL
NeuroImage
A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics
NeuroImage
Manual for the ASEBA School-Age Forms and Profiles
Brain network alterations in Alzheimer's disease measured by Eigenvector centrality in fMRI are related to cognition and CSF biomarkers
Hum. Brain Mapp.
Functional connectivity in the motor cortex of resting human brain using echo-planar MRI
Magn. Reson. Med.
Power and centrality: a family of measures
Am. J. Sociol.
Opportunities and limitations of intrinsic functional connectivity MRI
Nat. Neurosci.
Complex brain networks: graph theoretical analysis of structural and functional systems
Nat. Rev. Neurosci.
A whole brain fMRI atlas generated via spatially constrained spectral clustering
Hum. Brain Mapp.
Prediction of individual brain maturity using fMRI
Science
The neural bases of social pain: evidence for shared representations with physical pain
Psychosom. Med.
Triadic model of the neurobiology of motivated behavior in adolescence
Psychol. Med.
Development of distinct control networks through segregation and integration
Proc. Natl. Acad. Sci. U. S. A.
The maturing architecture of the brain's default network
Proc. Natl. Acad. Sci. U. S. A.
Functional brain networks develop from a local to distributed organization
PLoS Comput. Biol.
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