Biology subjects

Feczko, E. J.

Publications and source records attributed to Feczko, E. J..

7 recordsLinked to original sources

bioRxiv2026

A Scalable fMRI Estimate of Basal Ganglia Brain Tissue Iron for Use in Developmental and Translational Neuroscience

Dopaminergic (DA) function and basal ganglia neurobiology are central to reward learning, motivation, and cognitive control, and dysregulation of these systems contributes to neuropsychiatric conditions that emerge during development. Adolescence is marked by profound reorganization of DAergic basal ganglia circuitry, yet direct in vivo assessment of the DA system remains limited in youth. Brain tissue iron is a developmentally sensitive marker of DA-related neurobiology that can be measured non-invasively via magnetic resonance imaging (MRI). Iron is an essential co-factor for DA synthesis and a foundational metabolic resource that supports cellular metabolism, myelination, and energetic demands of the basal ganglia. T2*-weighted echo-planar imaging (EPI), collected during functional MRI (fMRI), is sensitive to magnetic susceptibility of non-heme brain iron. Leveraging this property, we demonstrate the validity and broad applicability of an iron-sensitive metric that can be derived from conventional single-echo fMRI: {Delta}R2*. In a longitudinal developmental dataset (N = 151; age range 12-31), {Delta}R2* showed high reliability, strong longitudinal stability, and validity via robust convergence with established quantitative relaxometry-based iron measures (R2* and R2). Critically, {Delta}R2* can be retrospectively estimated from extant fMRI data and derived in large-scale consortium data repositories, demonstrated here in the Adolescent Brain and Cognitive Development (ABCD) baseline cohort (N = 8,366; ages 9-11). We show that {Delta}R2* captures known age-related increases in basal ganglia iron, highlighting neurodevelopmental sensitivity at population-scale. Together, these findings establish {Delta}R2* as a reliable, widely accessible marker of basal ganglia iron, enabling scalable investigation of lifespan trajectories and neuropsychiatric risk in existing and future datasets.

neuroscience↗

bioRxiv2025

Examining the heritability of functional brain networks in adolescence

Discovering mechanisms underlying mental illness requires disentangling genetic and environmental factors influencing mental health. Researchers have started investigating the brains role as a potential intermediate biomarker linking genes and environment to mental health; however, understanding how much genetics shapes adolescent brain function remains elusive. Using data from the ABCD study (5,247 unrelated, 330 dizygotic, 248 monozygotic twin subjects), we estimated the heritability of functional connectivity and topography using both SNP and twin data. SNP-based heritability was calculated using genetic correlation and the recently developed AdjHE-RE estimator. We found low SNP heritability for brain functional connectivity (median = 2e-10% Gordon, 5.8e-7% probabilistic) and topography (max = 2%). Twin estimates using ACE models replicated prior findings from the literature (median = 8.9e-6% Gordon, 6% probabilistic, 27% topography). This suggest that additive genetic effects are minimally associated with functional brain features in adolescents highlighting the importance of considering both genetic and environmental factors when studying the development of functional brain networks and their relevance to mental health.

genomics↗

bioRxiv2025

Task-evoked functional connectivity exhibits novel and strengthened relationships with executive function relative to the resting state

Executive functioning in children has been linked to intrinsic brain network organization assessed during the resting state, as well as to brain network organization during the performance of cognitive tasks. Prior work has established that task-based brain networks are stronger predictors of behavior than resting state networks, yet it is unclear if tasks only strengthen relationships that exist weakly at rest or if tasks also evoke unique relationships. A lack of discernment regarding how tasks and the resting state commonly and uniquely support executive functions precludes a holistic understanding of the neurobiological basis of executive functions. This project investigated differences in brain network organization and relationships with executive function ability between the resting state and two executive function tasks, a stop signal task and an emotional n-back task, using the Adolescent Brain and Cognitive Development (ABCD) Study dataset. Both executive function tasks evoked a more integrated network organization than the resting state, and executive function ability was related to different aspects of brain network organization during the resting state and during the tasks. Further, task-related shifts in brain network organization evoked several new relationships with executive function that were not detectable during the resting state and strengthened a relationship with executive function that existed weakly during the resting state. Overall, this study establishes a distinction between common and unique features of intrinsic and task-evoked brain function that facilitate executive function in children. SignificanceExecutive functions, which encompass goal-directed behaviors critical for life success, emerge from interactions within and between networks of brain regions. Here, we tested how executive functions are linked to functional brain network interactions during the resting state, in which there are no external cognitive demands, and during executive function tasks in late childhood. We found that brain network organization during the performance of executive function tasks evoked a combination of new and strengthened relationships with executive function ability compared to the resting state. This suggests that the brain facilitates executive function performance in children through the recruitment of specific functional interactions during the enactment of executive function.

neuroscience↗

bioRxiv2024

Baby Open Brains: An Open-Source Repository of Infant Brain Segmentations

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable protocols and processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold standard brain segmentations. Addressing this core limitation, we constructed the Baby Open Brains (BOBs) Repository, an open source resource comprising manually curated and expert-reviewed infant brain segmentations. Markers and expert reviewers manually segmented anatomical MRI data from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Anatomical images showed dramatic differences in myelination and intensities across the 1 to 9 month age range, emphasizing the need for densely sampled gold standard manual segmentations in these ages. The BOBs repository is publicly available through the Masonic Institute for the Developing Brain (MIDB) Open Data Initiative, which links S3 storage, Datalad for version control, and BrainBox for visualization. This repository represents an open-source paradigm, where new additions and changes can be added, enabling a community-driven resource that will improve over time and extend into new ages and protocols. These manual segmentations and the ongoing repository provide a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this repository provides a vitally needed foundation for early-life large-scale studies such as HBCD.

neuroscience↗

bioRxiv2022

Motion Impact Score for Detecting Spurious Brain-Behavior Associations

Between-participant differences in head motion introduce systematic bias to resting state fMRI brain-wide association studies (BWAS) that is not completely removed by denoising algorithms. Researchers who study traits, or phenotypes associated with in-scanner head motion (e.g. psychiatric disorders) need to know if trait-functional connectivity (FC) effects are biased by residual motion artifact in order to avoid reporting false positive results. We devised an adaptable method, Split Half Analysis of Motion Associated Networks (SHAMAN), to assign a motion impact score to specific trait-FC effects. The SHAMAN approach distinguishes between motion artifact causing overestimation or underestimation of trait-FC effects. SHAMAN was > 95% specific at sample sizes of n = 100 and above. SHAMAN was powered to detect motion overestimation scores 80% of the time at sample sizes of n = 5,000 but could detect motion underestimation scores only 50% of the time at n = 5000, making it most useful for researchers seeking to avoid overestimating trait-FC effects in large BWAS. We computed motion impact scores for trait-FC effect with 45 demographic, biophysical, cognitive, and personality traits from n = 7,270 participants in the Adolescent Brain Cognitive Development (ABCD) Study. After standard denoising with ABCD-BIDS and without motion censoring, 42% (19/45) of traits had significant (p < 0.05) motion overestimation scores and 38% (17/45) of traits had significant motion underestimation scores. Censoring at framewise displacement (FD) < 0.2 mm reduced the proportion of traits with significant motion overestimation scores from 42% to 2% (1/45) but did not decrease the number of traits with significant motion underestimation scores.

neuroscience↗

bioRxiv2022

Personalized Functional Brain Network Topography Predicts Individual Differences in Youth Cognition

Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization during development reflects the brains functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive DevelopmentSM Study. Across matched discovery (n=3,525) and replication (n=3,447) samples, the total cortical representation of fronto-parietal PFNs positively correlated with general cognition. Cross-validated ridge regressions trained on PFN topography predicted cognition across domains, with prediction accuracy increasing along the cortexs sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.

neuroscience↗

bioRxiv2022

A Precision Functional Atlas of Network Probabilities and Individual-Specific Network Topography

The brain is organized into a broad set of functional neural networks. These networks and their various characteristics have been described and scrutinized through in vivo resting state functional magnetic resonance imaging (rs-fMRI). While the basic properties of networks are generally similar between healthy individuals, there is vast variability in the precise topography across the population. These individual differences are often lost in population studies due to population averaging which assumes topographical uniformity. We leveraged precision brain mapping methods to establish a new open-source, method-flexible set of precision functional network atlases: the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. Using participants from the Adolescent Brain Cognitive Development (ABCD) study, single subject precision network maps were generated with two supervised network-matching procedures (template matching and non-negative matrix factorization), an overlapping template matching method for identifying integration zones, as well as an unsupervised community detection algorithm (Infomap). From these individualized maps we also generated probabilistic network maps and integration zones for two demographically-matched groups of n[~]3000 each. We demonstrate high reproducibility between groups (Pearsons r >0.999) and between methods (r=0.96), revealing both regions of high invariance and high variability. Compared to using parcellations based on groups averages, the MIDB Precision Brain Atlas allowed us to derive a set of brain regions that are largely invariant in network topography across populations, which provides more reproducible statistical maps of executive function in brain-wide associations. We also explore an example use case for probabilistic maps, highlighting their potential for use in targeted neuromodulation. The MIDB Precision Brain Atlas is expandable to alternative datasets and methods and is provided open-source with an online web interface to encourage the scientific community to experiment with probabilistic atlases and individual-specific topographies to more precisely relate network phenomenon to functional organization of the human brain.

neuroscience↗