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Original Investigation |

Growth Charting of Brain Connectivity Networks and the Identification of Attention Impairment in Youth

Daniel Kessler, BS1; Michael Angstadt, MAS1; Chandra Sripada, MD, PhD1
[+] Author Affiliations
1Department of Psychiatry, University of Michigan, Ann Arbor
JAMA Psychiatry. 2016;73(5):481-489. doi:10.1001/jamapsychiatry.2016.0088.
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Importance  Intrinsic connectivity networks (ICNs), important units of brain functional organization, demonstrate substantial maturation during youth. In addition, interrelationships between ICNs have been reliably implicated in attention performance. It is unknown whether alterations in ICN maturational profiles can reliably detect impaired attention functioning in youth.

Objective  To use a network growth charting approach to investigate the association between alterations in ICN maturation and attention performance.

Design, Setting, and Participants  Data were obtained from the publicly available Philadelphia Neurodevelopmental Cohort, a prospective, population-based sample of 9498 youths who underwent genomic testing, neurocognitive assessment, and neuroimaging. Data collection was conducted at an academic and children’s hospital health care network between November 1, 2009, and November 30, 2011, and data analysis was conducted between February 1, 2015, and January 15, 2016.

Main Outcomes and Measures  Statistical associations between deviations from normative network growth were assessed as well as 2 main outcome measures: accuracy during the Penn Continuous Performance Test and diagnosis with attention-deficit/hyperactivity disorder.

Results  Of the 9498 individuals identified, 1000 youths aged 8 to 22 years underwent brain imaging. A sample of 519 youths who met quality control criteria entered analysis, of whom 25 (4.8%) met criteria for attention-deficit/hyperactivity disorder. The mean (SD) age of the youth was 15.7 (3.1) years, and 223 (43.0%) were male. Participants’ patterns of deviations from normative maturational trajectories were indicative of sustained attention functioning (R2 = 24%; F6,512 = 26.89; P < 2.2 × 10−16). Moreover, these patterns were found to be a reliable biomarker of severe attention impairment (peak receiver operating characteristic curve measured by area under the curve, 79.3%). In particular, a down-shifted pattern of ICN maturation (shallow maturation), rather than a right-shifted pattern (lagged maturation), was implicated in reduced attention performance (Akaike information criterion relative likelihood, 3.22 × 1026). Finally, parallel associations between ICN dysmaturation and diagnosis of attention-deficit/hyperactivity disorder were identified.

Conclusions and Relevance  Growth charting methods are widely used to assess the development of physical or other biometric characteristics, such as weight and head circumference. To date, this is the first demonstration that this method can be extended to development of functional brain networks to identify clinically relevant conditions, such as dysfunction of sustained attention.

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Figure 1.
Schematic Diagram Illustrating Key Analysis Steps

Resting-state functional connectomes from 519 participants enter joint independent components analysis (ICA), which parses the connectomes into several cohesive components (2 illustrative components are shown in the boxes at middle). For each component, growth charts are then constructed that depict the normative change in component expression with age. Next, each participant is assigned a maturational deviation score for each component that reflects the degree to which that component is underexpressed or overexpressed relative to what is expected by age. These maturational deviation scores are then used as predictors of performance on a sustained attention task. DMN indicates default mode network; FPN, frontoparietal control network; VAN, ventral attention network.

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Figure 2.
Connectomic Maps for Components A, B, and C

Large circles represent individual intrinsic connectivity networks (based on the network parcellation by Yeo and colleagues50) and each dot within a large circle represents a region of interest (ROI) within that network. To further aid interpretability, groups of ROIs are assigned to anatomical regions within a network. Each line reflects a superthreshold connection; red lines reflect increased connectivity while blue lines reflect decreased connectivity. These highly maturing components consistently show prominent alterations within and between default mode network and task-positive networks (TPNs), including dorsal attention network (DAN), ventral attention network (VAN), and frontoparietal control network (FPN) (eMethods in the Supplement provides details on component display).

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Figure 3.
Maturation of Connectomic Components

Maturational profiles of components A, B, and C, 3 highly maturing components. The solid line is the quadratic fit, and gray shading represents 95% CIs for a quadratic model. Component expression is in arbitrary units (AU).

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Figure 4.
Performance of Logistic Regression Classifiers in Distinguishing Low vs Normal Performers in a Sustained Attention Task

For each connectomic component, we calculated maturational deviation scores that quantify the extent to which expression of the component deviates from age-typical levels. We entered maturational deviation scores for components A to F into a logistic regression model to classify participants as low and normal performers on a sustained attention task. The percentile used to define low performers ranged from a stringent 5th percentile to a liberal 50th percentile (with the remaining participants considered normal performers). The performance of the classifier in a leave-one-out cross-validation framework was measured by area under the curve (AUC) for receiver operating characteristic (ROC) curves. Classifier performance was significantly better than chance for all performance cut points tested and reached a high of 79.3% in identifying youth in the bottom 10% of performance vs the remaining participants. Gray shading indicates 95% CIs; the solid line represents classification performance as measured by AUC ROC.

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