Maps of the Development of the Brain’s Functional Architecture: Could They Provide Growth Charts for Psychiatry?

Health care professionals working with young children are familiar with growth charts. For example, deviation from ageexpected values in head circumference triggers an evaluation for underlying disorders. Although it is an invaluable measure, head circumference is nonetheless imperfectly correlatedwithbrainvolumeand does not reflect brain function. Imagine a future in which a child’s neuropsychiatric status isassessed throughreference tocharts thatdirectlymeasurebrainmaturation.Thispossibility seems far-fetched;however, the study by Kessler and colleagues1 joins other recent work as tentative steps in this direction. Thestudy leveragesarichandaccessibledatasource (Philadelphia Neurodevelopment Cohort) that integrates state-ofthe-art neuroimaging with neuropsychological and clinical data. The starting point is a child’s personal connectome. The connectome is defined from the coordinatedpatterns of brain activity,measuredby functionalmagnetic resonance imaging, that spontaneously emerge when a child is free from explicit task demands. From these data on 519 youth, maps of functional connectivity (components) were extracted, with meticulous attention to data quality. Each component captures distinct patterns of connectivity within and across wellestablished, large-scale functional networks. The components also vary in their prominence or expression across children. As a component becomes more prominent, so does its signaturepatternof increases anddecreases in functional connectivity within and between brain networks. Many of these components are consonant with current models of howbrainnetworks interact. For example, the core of one component ismadeupof dense connectionswithin regions of the default mode network (a network readily detected in the task-free state). This component is also characterizedbydecreased functional connectivitybetween this core and networks that support performance on cognitive tasks, such as the attention networks. This counterbalance between a task-free (default mode) and task-positive networks might contribute to the ability to sustain attention.2 Theauthors1 nextmappedhowthese components change with age in the mainly healthy cohort. This mapping provides normative growth curves for these functional connectivitynetworks.Most components showan increase inexpression throughout childhood, which peaks in midadolescence and then declines. These maturational curves provide fascinating data. For example, others3 have found that the default mode network becomes increasingly isolated with age from task-positive networks. This isolationmay contribute to agerelated improvement in staying on task. The present study largely confirms this pattern, but also finds some age-related increases in connectivity between thedefaultmode and taskpositive networks. Further mapping of the “cross-talk” between developing brain networks could help to unravel the neurocognitive bases of overlapping psychiatric disorders. In modeling development, the authors1 considered only linear changewith age andmodels that incorporate phases of increase and decrease (quadratic). This approach is sensible when chartingdevelopmentusing cross-sectional data on approximately 500 children.However,we should not forget the snakelike shape of height and weight growth curves: brain growthwill be every bit as complex. Capturing such complexitywill likely requireneuroimagingofvery largecohortsof epidemiologically representative children.Growth is also aproperty of the individual child. Suchwithin-personprocesses are better defined by within-person, longitudinal observations than by purely cross-sectional data. Inferring developmental processes from cross-sectional data is often the only option, and this study1 exemplifies how elegantly this can be done. Nonetheless, the inherent limitations of cross-sectional data mean that definitive growth curves will require longitudinal observations.4 Health care professionals may bemost interested in how the authors1 used these data to determine a child’s cognitive andclinical features.First, theyquantified thedegree towhich each component characterizes or is expressed in a child’s personal connectome. Then, they calculated how far each component’s expression deviates from age-expected values: the “maturational deviation score.” The authors found that this score accounted for approximately 25%of thevariance in sustained attention skills. To place this in context, general intelligence explainedonly approximately 7%of the variance. The finding represents an impressivevalidationofhowpatternsof functional connectivitycanpertain toacorecognitiveskill.The questionnowarisesofwhether these functionalalterationsalso contribute to thedeficits in sustainedattention found inother childhood disorders—not just ADHD. Can thematurational deviation scores also inform the diagnosis of ADHD? The authors1 found that the scores indicated the diagnosis of ADHD with a diagnostic odds ratio of approximately 2. To provide a clinical context, the diagnostic Related article page 481 Opinion