# Temporal connectomics for infant brain: neurodevelopment modulated by pathology

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $571,164

## Abstract

Abstract
Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder affecting more than 1% of
children (and almost 2% of boys), with those born in a high risk (HR) family with a child diagnosed with ASD
having a ~20 fold greater risk than the general population. Although it can only be reliably diagnosed after the
second year of life, ASD originates from a neurodevelopmental mechanism, with symptoms emerging as early
as 6 months of age. Prospective large-scale longitudinal neuroimaging studies like the Infant Brain Imaging
Study (IBIS), of those born into a high-risk (HR) family, are extremely valuable as they facilitate discovery of
the earliest manifestations of ASD. The overarching goal of this proposal is a comprehensive comparative
longitudinal analysis of brain organization of high- and low-risk populations, by using IBIS diffusion MRI (dMRI)
data collected at 3 time points in the first 2 years of life, to elucidate the earliest manifestations of ASD and
disorder trajectory. In addition, we aim to create a biomarker of ASD risk from brain connectivity features and
their developmental trajectories, and to understand the brain bases of familial risk (independent of ASD). dMRI
provides an insight into the structural organization of the brain represented as a connectome. “Miswiring” of the
structural connectome can manifest as neuro-immaturity of WM and changes in network structures, that is,
subnetworks (collection of strongly inter-connected regions associated with a distinct communication pattern),
and the communication backbone (overall architecture of communication between subnetworks). Both
connectivity and WM quality can be compromised in ASD compared to controls, suggesting a “miswired”
connectome. Studying ASD related early developmental changes in connectivity requires design of novel
analysis methods based on “longitudinal” connectomic features that are 4D features that incorporate their
temporal evolution over development, culminating in the creation of novel imaging-based biomarkers. In Aim
1, we will create a new method of extracting longitudinal fiber tracts to identify and analyze differences
between the developmental tract trajectories in LR-, HR- and HR+ subjects using geometry and quality
features derived from the fiber bundles, to identify differences in neuro-immaturity. In aim 2, we will design
novel methods to extract longitudinal network structures like structurally-cohesive and functionally-defined
subnetworks and the global communication backbone, and investigate developmental differences modulated
by familial risk and gender. Finally, in Aim 3 we will use the tract and network features extracted from Aims 1
and 2 to develop imaging based markers that will assist with early ASD prediction, identifying siblings who
could gain from an early therapeutic intervention and provide an index of development in the form of brain
connectivity “maturational age” to help characterize developmental delays and b...

## Key facts

- **NIH application ID:** 9848443
- **Project number:** 5R01HD089390-04
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Ragini Verma
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $571,164
- **Award type:** 5
- **Project period:** 2017-02-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9848443

## Citation

> US National Institutes of Health, RePORTER application 9848443, Temporal connectomics for infant brain: neurodevelopment modulated by pathology (5R01HD089390-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9848443. Licensed CC0.

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