A longitudinal resource for studying connectome development and its psychiatric associations during childhood

Abstract

Most psychiatric disorders are chronic, associated with high levels of disability and distress, and present during pediatric development. Scientific innovation increasingly allows researchers to probe brain-behavior relationships in the developing human. As a result, ambitions to (1) establish normative pediatric brain development trajectories akin to growth curves, (2) characterize reliable metrics for distinguishing illness, and (3) develop clinically useful tools to assist in the diagnosis and management of mental health and learning disorders have gained significant momentum. To this end, the NKI-Rockland Sample initiative was created to probe lifespan development as a large-scale multimodal dataset. The NKI-Rockland Sample Longitudinal Discovery of Brain Development Trajectories sub-study (N=369) is a 24- to 30-month multi-cohort longitudinal pediatric investigation (ages 6.0-17.0 at enrollment) carried out in a community-ascertained sample. Data include psychiatric diagnostic, medical, behavioral, and cognitive phenotyping, as well as multimodal brain imaging (resting fMRI, diffusion MRI, morphometric MRI, arterial spin labeling), genetics, and actigraphy. Herein, we present the rationale, design, and implementation of the Longitudinal Discovery of Brain Development Trajectories protocol.

An open, analysis-ready, and quality controlled resource for pediatric brain white-matter research

“…Because the XGB-q model most readily generalizes to other QSIPrep outputs, we package it as an independent QC service in the QSIQC software package ( Richie-Halford and Rokem, 2022b ), available both as a docker image at and as a Streamlit app at https://share.streamlit.io/richford/qsiqc/main/app.py. The decision to use a more interpretable but slightly less performant method of generating QC scores was also advocated by ( Tobe et al, 2021 ), who noted that the Euler number of T1-weighed images ( Rosen et al, 2018 ) in the NKI-Rockland dataset can reliably predict scores generated with Braindr , the community science application developed in our previous work ( Keshavan et al, 2019 ).…”

Cerebrovascular reactivity increases across development in multiple networks as revealed by a breath‐holding task: A longitudinal fMRI study

“…The acquisition time for the T1-weighted image was 4 min 18 s, with 176 slices, a TR of 1900 ms, and TE of 2.52 ms. The acquisition time for the BH fMRI data was 4 min 30 s, with 64 slices, 2 Â 2 Â 2 mm 3 resolution, a field of view (FOV) of 224 mm, % FOV phase at 100%, matrix size 112 Â 112, a flip angle of 65 , multiband acceleration factor of 4, TR of 1400 ms, and TE of 30 ms (Tobe et al, 2022).…”

“…rocklandsample.rfmh.org/). For this study, we focused on the NKI-RS Longitudinal Discovery of Brain Development Trajectories sub-study (N = 369) (Tobe et al, 2022), which implements a longitudinal design to study connectome development in children. The age at enrollment ranged from 6 to 17 years and the oldest age at completion of the studies is 20 years.…”

“…For those who had inconsistencies in the type of BH paradigm performed (either 15 or 18 s) from one session's scan to the next, only the last session's data were kept for that particular subject; therefore, that subject would only have one time point of data. The NKI study included participants based on the following criteria: (1) ages 6-20.5 years (ages 6-17.9 years at enrollment), (2) children who have the capacity to understand and provide informed consent, (3) children aged 6-17.9 years who can sign assent and parent/guardian who can sign informed consent, (4) fluent in English, and (5) proof of residency in Rockland County, Orange County, Westchester County, NY, or Bergen County, NJ (Tobe et al, 2022).…”

“…The goal of the current study was to investigate the BH response in children across different ages of development ranging from 6 to 20 years, using a longitudinal fMRI data set from the Nathan Kline Institute (NKI) (Tobe et al, 2022). This data set includes both longitudinal and cross-sectional data, which allows us to better understand within-subject and between-subject developmental trajectories, respectively.…”