Biology subjects

Elison, J. T.

Publications and source records attributed to Elison, J. T..

16 recordsLinked to original sources

bioRxiv2026

Salience network expansion as a transdiagnostic neural marker for psychopathology risk in adolescence

Increased cortical surface area of the brain's salience network (SAL) has been observed in depression and appears to emerge during early adolescence. However, similar expansion has also been reported in bipolar disorder (BD), obsessive compulsive disorder (OCD), and other atypicalities of salience network function have been broadly implicated across multiple psychiatric conditions. Whether increased SAL surface area is specific to depression or related more generally with psychopathology remains unclear. Using data collected as part of the Adolescent Brain Cognitive Development (ABCD) Study (baseline: N = 7942; ages 9-10; Year 2: N = 5901; ages 11-12), we quantified individual SAL size and examined associations with dimensional symptoms from the Child Behavior Checklist. SAL surface area showed substantial inter-individual variability, with expansion occurring primarily through encroachment into the default mode (DMN), action-mode (AMN), and frontoparietal (FP) networks. Larger SAL size showed a modest positive association with depressive symptoms in the baseline sample, consistent with prior reports; however, this association did not replicate in the year 2 internal replication sample. Instead, associations with total problems were stronger and replicated across both timepoints, suggesting a subtle, dimensional relationship between SAL organization and general psychopathology burden. These findings suggest that individual differences in SAL organization may represent a broader transdiagnostic neural correlate of dimensional psychopathology rather than a disorder-specific neural signature.

neuroscience↗

bioRxiv2026

Microbes on the Mind: Multi-modal Neuroimaging Reveals Gut-Brain Axes in Infants

The gut microbiome develops from birth and is associated with cognition and mental health, but its relationship with early brain maturation remains unclear. We studied 223 typically developing infants aged 0-3 years using multimodal MRI and paired fecal shotgun metagenomics. Microbiome-wide association analysis linked microbial features to cortical morphology, white-matter microstructure, and functional networks. Regularized canonical correlation analysis identified structural-metabolic and functional-taxonomic covariance patterns. Sparse partial least-squares analysis recapitulated both full-sample CCA patterns and their defining structural and functional phenotypes, providing cross-method corroboration. Longitudinal canonical-score slopes did not significantly covary, whereas earlier neuroimaging CCA1 scores predicted microbiome CCA1 scores 1-6 months later in bidirectional autoregressive cross-lag models. These observational results identify coordinated developmental patterns and temporal ordering without establishing causality.

developmental biology↗

bioRxiv2026

Trajectories of hippocampal subregion development in the first years of life and their association with school-aged episodic memory outcomes

Brain networks that support episodic memory development in the first years of life remain poorly understood. Protracted growth of regions such as the hippocampus have been suggested as a causal role in episodic memory development, but development of these memory brain networks and their role in episodic memory development is not yet fully elucidated. In this study, subcortical memory network regions (hippocampus, thalamus, amygdala) were segmented from MRI images in 835 visits spanning 0-4 years of age across 322 participants in the Baby Connectome Project. Hippocampal segmentations were further subdivided into head, body, and tail subregions manually for 426 visits, which were used to train models that automatically segmented hippocampal subregions for the remaining visits. 58 participants returned for an early school-age follow-up, including two episodic memory tasks. Volumetric growth trajectories differed across regions and across subregions within the hippocampus, with the head of the hippocampus showing steep growth that plateaued months later than the body or tail of the hippocampus. In the right hemispheres hippocampal head, age- and sex- adjusted volumes positively predicted future early school-age episodic memory performance. After accounting for total brain volume, the right thalamus also predicted memory performance. Total sleep duration at the follow-up visit accounted for performance variance above and beyond brain volume correlations. Altogether, results suggest that trajectories of growth and relationships between volume and episodic memory performance are region and subregion specific, and provide evidence for the important role of sleep in associations between brain networks and early episodic memory development. SignificanceThe hippocampus is a critical structure in episodic memory, yet precise longitudinal developmental trajectories of this structure have yet to be elucidated. This study provides detailed, subregion specific hippocampal trajectories, and demonstrates that variation in these trajectories is associated with variation in later episodic memory performance. This insight fills a current gap in the literature delineating how brain development and episodic memory behaviors are related in the first five years of life. Considering this is the same age range during which adults begin to have long-term memories available from childhood, this gap represents an important opportunity to understand how changes in the brain support the development of basic episodic memory skills.

neuroscience↗

bioRxiv2025

Precision functional imaging in infants using multi-echo fMRI at 7T

Personalized functional brain developmental trajectories can be studied with Precision Functional Mapping (PFM). Our previous work has demonstrated that PFM can be achieved in infants despite rapid brain growth. However, even with extensive data collection (up to 1 hour of fMRI), the reliability and precision of these maps remain lower than those observed in youth and adults - particularly within subcortical structures. In this work we demonstrate the utility of high-field 7T MRI compared to 3T MRI for facilitating PFM in infants. We showcase data from multi-echo fMRI acquisitions in the same infants at both 7T and 3T and demonstrate that 7T imaging in infants is safe and feasible with our subject-specific safety workflow. Moreover, we demonstrate that the use of a higher magnetic field strength affords a spatial resolution more appropriately matched to infants smaller head and brain sizes, yielding notable improvements in data quality, especially for PFM. The increase in both spatial precision and reliability also suggests that 7T MRI can reduce the amount of data required for PFM. Last, we show how ultra-high field imaging can help us study the development of subcortical-to-cortical connectivity patterns, crucial for understanding brain development during this developmental window. 7T MRI is a promising new avenue for developmental cognitive neuroscience.

neuroscience↗

bioRxiv2025

Commonality and Variability in Functional Networks In Young Children Under 5 Years Old

Functional brain networks support human cognition, yet how individualized network architecture emerges in early childhood remains poorly understood. Averaging across participants can obscure age-specific organization and person-to-person differences, particularly in slowly developing association cortices. We developed an age-appropriate functional reference that captured common structure across toddlers without averaging away individual variability, enabling estimation of each childs networks from resting-state fMRI. Across cohorts of 8-60-month-old children, we found individualized network organization--including finer-scale subdivisions and emerging language lateralization--well before age five. Network layouts showed longitudinal stability, with greater consistency in sensory than association regions. Within-network connectivity was stronger and explained age-related variance when networks were defined using individualized rather than group-consensus topography. Left-lateralization of language networks tracked age-normalized verbal ability, linking early functional architecture to emerging cognition. These findings show that behaviorally relevant brain networks arise far earlier than previously recognized, providing a foundation for studying typical development and early biomarkers.

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

Precision Functional Neuroimaging Reveals Individually Specific Auditory Responses in Infants

Adaptively responding to salient stimuli in the environment is a fundamental feature of cognitive development in early life, which is enabled by the developing brain. Understanding individual variability in how the brain supports this fundamental process is essential for uncovering neurodevelopmental trajectories and potential neurodevelopmental risks. In the present study, we used a precision functional imaging approach to probe activation in response to salient auditory stimuli and its relation to brain functional networks in individual infants. A minimum of 60 minutes of fMRI BOLD data with an auditory oddball paradigm were collected in ten infants with a mean postmenstrual age of 48 weeks. Results demonstrate the feasibility of performing a precision functional imaging study to investigate individual specific responses to salient stimuli in infants. While responses to the auditory oddball were consistent between individuals in auditory processing areas, responses across the rest of the brain differed across individuals in their magnitude and shape. Individual specific response patterns appeared to be relatively stable and differed from other participants response patterns, despite fluctuations across runs. Commonalities and differences between individuals demonstrated in this sample contribute to our understanding of how the developing brain instantiates processing of salient stimuli. Our findings suggest that during early development, early unimodal processing is well conserved across individuals, however subsequent perceptual processing is still being personally defined. In this context, individual specific response patterns could be a promising target for biomarkers of normative brain and cognitive development.

neuroscience↗

bioRxiv2025

Less is More: Lower Levels of Task-Induced Hippocampal Activation Predict Better Performance on a Separate Verbal Memory Evaluation

Memories that differ in content or duration differ in the extent to which they depend on the hippocampus, and also the part of the hippocampus, posterior (pHPC) or anterior (aHPC), that they implicate. Inter-individual differences in learning-related activation in different hippocampal subregions have been found to predict specific differences in memory abilities. The complexity of these relationships creates a setting that is ripe for theoretically informative investigation, but that can also lead to reports of spurious relationships that do not reflect underlying neurobiological associations. Across-study replication is therefore a critical first step toward understanding how differences in hippocampal activity drive individual differences in memory ability. In the domain of verbal memory, Wig et al. (2008) identified a negative relationship between task-induced activation in pHPC and out-of-scanner verbal memory test performance. Replicating this result in an independent sample of 86 participants, we identified the same negative correlation between pHPC activation during a Lithuanian word learning task and out-of-scanner California Verbal Learning Test (CVLT-II) performance. This replication represents a critical step toward understanding how pHPC supports verbal memory by answering the basic question of whether the relationship can be reliably observed.

neuroscience↗

bioRxiv2025

fMRIPrep Lifespan: Extending A Robust Pipeline for Functional MRI Preprocessing to Developmental Neuroimaging

The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.

bioinformatics↗

bioRxiv2025

Deep-learning based Embedding of Functional Connectivity Profiles for Precision Functional Mapping

Spatial correlation of functional connectivity profiles across matching anatomical locations in individuals is often calculated to delineate individual differences in functional networks. Likewise, spatial correlation is assessed across average functional connectivity profiles of groups to evaluate the maturity of functional networks during development. Despite its widespread use, spatial correlation is limited to comparing two samples at a time. In this study, we employed a variational autoencoder to embed functional connectivity profiles from various anatomical locations, individuals, and group averages for simultaneous comparison. We demonstrate that our variational autoencoder, with pre-trained weights, can project new functional connectivity profiles from the vertex space to a latent space with as few as two dimensions, yet still retain meaningful global and local structures in the data. Functional connectivity profiles from various functional networks occupy distinct compartments of the latent space. Moreover, the variability of functional connectivity profiles from the same anatomical location is readily captured in the latent space. We believe that this approach could be useful for visualization and exploratory analyses in precision functional mapping.

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↗

bioRxiv2024

Early Life Neuroimaging: The Generalizability of Cortical Area Parcellations Across Development

The cerebral cortex consists of distinct areas that develop through intrinsic embryonic patterning and postnatal experiences. Accurate parcellation of these areas in neuroimaging studies improves statistical power and cross-study comparability. Given significant brain changes in volume, microstructure, and connectivity during early life, we hypothesized that cortical areas in 1- to 3-year-olds would differ markedly from neonates and increasingly resemble adult patterns as development progresses. Here, we parcellated the cerebral cortex into putative areas using local functional connectivity gradients in 92 toddlers at 2 years old. We demonstrate high reproducibility of these cortical regions across 1- to 3-year-olds in two independent datasets. The area boundaries in 1- to 3-year-olds were more similar to those in adults than those in neonates. While the age-specific group area parcellation better fit the underlying functional connectivity in individuals during the first 3 years, adult area parcellations might still have some utility in developmental studies, especially in children older than 6 years. Additionally, we provide connectivity-based community assignments of the parcels, showing fragmented anterior and posterior components based on the strongest connectivity, yet alignment with adult systems when weaker connectivity was included.

neuroscience↗

bioRxiv2024

A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development

The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized into functional networks. These networks show stronger internal and weaker external functional connectivity (FC), with FC profiles more similar within the same network. Previous studies have shown these networks develop from nascent forms before birth to their mature, adult-like structures in childhood. However, these analyses often rely on adult functional network definitions. This study assesses the potential misidentification of infant functional networks when using adult models and explores the consequences and possible solutions to this problem. Our findings suggest that although adult networks only marginally describe infant FC organization better than chance, misidentification is primarily driven by specific areas. Restricting functional networks to areas with adult-like network clustering revealed consistent within-network FC across scans and throughout development. These areas are also near locations with low network identity variability. Our results highlight the implications of using adult networks for infants and offer guidance for selecting and utilizing functional network models based on research questions and scenarios. HighlightsO_LIPrevious studies primarily investigated age-specific networks in infants, with limited focus on how well adult networks describe infant functional connectivity (FC). C_LIO_LIur analysis identified a subset of areas in infants showing adult-like network organization, where within-network FC shows less age-related variation and higher scan-to-scan reliability. C_LIO_LIThese areas are positioned near locations with low variability in functional network identity in adults, indicating a potential link between developmental sequence and interindividual variability in functional network organization. C_LI

neuroscience↗

bioRxiv2023

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans

ObjectivesBrain segmentation of infant magnetic resonance (MR) images is vitally important for studying typical and atypical brain development. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model for robust and generalizable brain segmentation leveraging data augmentation and a large sample size of manually annotated images. Experimental DesignIncluded in model training and testing were MR brain images from 90 participants with an age range of 0-8 months (median age 4.6 months). Using the BOBs repository of manually annotated real images along with synthetic segmentation images produced using SynthSeg, the model was trained using a 10-fold procedure. Model performance of segmentations was assessed by comparing BIBSNet, joint label fusion (JLF) inferred segmentation to ground truth segmentations using Dice Similarity Coefficient (DSC). Additionally, MR data along with the FreeSurfer compatible segmentations were processed with the DCAN labs infant-ABCD-BIDS processing pipeline from ground truth, JLF, and BIBSNet to further assess model performance on derivative data, including cortical thickness, resting state connectivity and brain region volumes. Principal ObservationsBIBSNet segmentations outperforms JLF across all regions based on DSC comparisons. Additionally, with processed derived metrics, BIBSNet segmentations outperforms JLF segmentations across nearly all metrics. ConclusionsBIBSNet segmentation shows marked improvement over JLF across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF, produces FreeSurfer-compatible segmentation labels, and can be easily included in other processing pipelines. BIBSNet provides a viable alternative for segmenting the brain in the earliest stages of development.

neuroscience↗

bioRxiv2022

Jointly analyzing the association of human milk nutrients with cognition and temperament traits during the first 6 months of life

Early dietary exposure via human milk (HM) components offers a window of opportunity to support cognitive and temperamental development. While several studies have focused on associations of few pre-selected HM components with cognition and temperament, it is highly plausible that HM components synergistically and jointly support cognitive and behavioral development in early life. We aimed to discern the combined associations of a wide array of HM nutrients with cognition and temperament during the first six months of life and explore if there were persistent effects up to 18 months old, when HM is the primary source of an infants nutrition. The Mullen Scales of Early Learning and Infant Behavior Questionnaires-Revised were used to assess cognition and temperament, respectively, of fifty-four exclusively/predominantly breastfed infants in the first 6 months of life, whose follow-ups were conducted at 6-9, 9-12 and 12-18 months old. HM samples were obtained from the mothers of the participants at less than 6 months of life and analyzed for fatty acids (total monounsaturated fatty acids, polyunsaturated fatty acid, total saturated fatty acid (TSFA), arachidonic acid (ARA), docosahexaenoic acid (DHA), ARA/DHA, omega-6/omega-3 polyunsaturated fatty acids ratio (n-6/n-3)), phospholipids (phosphatidylcholine, phosphatidylethanolamine (PE), phosphatidylinositol (PI), sphingomyelin) and choline (free choline, phosphocholine (PCho), glycerophosphocholine). Feature selection was performed to select nutrients associated with cognition and temperament, respectively. The combined effects of selected nutrients were analyzed using multiple regression. A positive association between the arachidonic acid (ARA) and surgency was observed (p = 0.024). Significant effect of DHA, n-6/n-3, PE and TSFA concentrations on receptive language (R2 = 0.39, p = 0.025), and the elevated ARA, PCho, and PI with increased surgency (R2 = 0.43, p = 0.003) was identified, suggesting that DHA and ARA may have distinct roles for temperament and language functions. Furthermore, the exploratory association analyses suggest that the effects of HM nutrients on R.L. and surgency may persist beyond the first 6 months of life, particularly surgency at 12-18 months (p = 0.002). Our studies highlighted that various HM nutrients work together to support the development of cognition and temperament traits during early infancy.

developmental biology↗

bioRxiv2021

Real-time motion monitoring improves functional MRI data quality in infants

Imaging the infant brain with MRI has improved our understanding of early stages of neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are commonly scanned while asleep, they commonly exhibit motion during scanning, causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Hence, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD [≤] 0.2mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.

neuroscience↗