Recent advances in magnetic resonance imaging (MRI) now allow the reliable mapping of functional and structural maturation in the human brain to derive analogs of height and weight growth charts. Such norms would allow early detection of pathologic process before clinically significant symptomatology onsets. Unfortunately, the datasets needed to develop comprehensive growth curves for brain function and structure do not yet exist, as technology has outpaced the collection of high quality longitudinal imaging data. Here, we propose to generate and share a large-scale, community-ascertained, structured multi-cohort, longitudinal sample from ages 6.0-20.5 yrs. This open access resource will allow the developmental trajectories of brain function and structure, as well as relationships with phenotypic measures to be delineated. We will generate at least 192 quality-controlled longitudinal datasets, evenly divided across 12 one-year age cohorts (6.0-17.9 yrs old at enrollment). Each dataset will contain 3 time-points separated by 15 months, with time-of-day and menstrual cycle phase controlled. State-of-the-art multiband imaging will be used to collect resting state functional MRI (R-fMRI) scans alternately optimized for temporal and spatial resolution. Multiband imaging will also enable the acquisition of 137-direction diffusion tensor imaging (DTI) in less than 7 minutes - thereby minimizing motion confounds. Arterial spin labeling perfusion measures will also provide additional quantitative information. Dimensional cognitive, behavioral, psychiatric, endocrine, immunologic, and metabolic phenotyping will be included. Genetic samples will be obtained and shared via the NIMH Genetics Repository. Given the paucity of data regarding the reliability of imaging measures in pediatric populations, we will also obtain retest scans for each participant within 2 weeks of their initial scan session. Careful determination of reliability is a prerequisite for determination of eventual clinical applicability, and especially pertinent in pediatric populations where risk of artifact and physiological variations are greatest. The proposed work builds on the Nathan Kline Institute-Rockland Sample (NKI-RS), a cross-sectional sample of brain development, maturation and aging spanning ages 6-85 years, currently funded to recruit and assess 1000 community-ascertained participants using multiband imaging-based R-fMRI, DTI, and deep phenoytyping. Building upon the NKI-RS effort will minimize startup costs, infrastructure and design needs, and maximize the focus on follow-up data collection. Consistent with the NKI-RS protocol, anonymized datasets will be shared weekly on a prospective (pre-publication) basis via The 1000 Functional Connectomes Project (FCP)/ International Neuroimaging Data-sharing Initiative (INDI) (www.nitrc.org). Additionally, any analysis methods and software developed in the course of the project will be made fully available on publication.

Public Health Relevance

Given that two-thirds of mental disorders begin prior to the age of 21, the brain imaging community has prioritized the discovery of markers of psychiatric illness in the developing brain. Central to this goal is the ascertainment of normative measures of brain function across the lifespan, like the growth curves used by pediatricians. We propose to generate a large set of imaging data, from which the percentiles of human brain function measures can be derived across childhood and adolescence. As soon as the data is collected, it will be anonymized and shared with the broader scientific community in order to facilitate hypothesis generation and testing in the field.

Agency
National Institute of Health (NIH)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01MH099059-02
Application #
8726487
Study Section
Child Psychopathology and Developmental Disabilities Study Section (CPDD)
Program Officer
Zehr, Julia L
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Nathan Kline Institute for Psychiatric Research
Department
Type
DUNS #
City
Orangeburg
State
NY
Country
United States
Zip Code
10962
Di Martino, Adriana; Fair, Damien A; Kelly, Clare et al. (2014) Unraveling the miswired connectome: a developmental perspective. Neuron 83:1335-53