Biology subjects

Grant, E. P.

Publications and source records attributed to Grant, E. P..

4 recordsLinked to original sources

bioRxiv2026

Metabolite Relaxation Times at 3T From Birth to 7 Years

PurposeWith growing interest in pediatric MRS to understand the neurochemistry of brain development, it is essential to establish pediatric-specific metabolite T1 and T2 relaxation times for reliable quantification of metabolites. Furthermore, characterization of age-related changes in relaxation times may provide insight into developmental differences in tissue microstructure and cellular composition. Therefore, the goals of this study were two-fold: (1) to estimate metabolite T1 and T2 relaxation times in a pediatric cohort; and (2) to assess linear relationships between age and relaxation times. MethodsTI- and TE-series data were acquired from the bilateral thalamus in 20 and 25 participants aged between 0 and 7 years, respectively. Data were processed and modelled in Osprey 2.5.0. Metabolite T1 and T2 relaxation times of total N-acetylaspartate at 2.0 ppm (tNAA2.0), total creatine at 3.0 ppm (tCr3.0), total creatine at 3.9 ppm (tCr3.9), total choline (tCho), myo-inositol (mI) and the combined glutamate and glutamine (Glx) were estimated by fitting the modelled signal amplitudes to TI-series inversion recovery signal and TE-series exponential decay equations, respectively. Linear relationships with age were tested for metabolite T1 and T2 relaxation times using Spearman correlations. ResultstNAA2.0 and Glx showed significant changes in T1 with age. tNAA2.0, tCr3.0, tCho and mI showed significant decrease in T2 with age. ConclusionSeveral metabolites showed significant changes in relaxation times with age in the thalamus region. Therefore, use of age-specific relaxation correction for pediatric studies using the linear equations provided is encouraged for reliable quantification of metabolites.

neuroscience↗

bioRxiv2026

Fetal MRI reveals altered prenatal cortical surface area in fetuses later diagnosed with autism spectrum disorder

Autism spectrum disorder (ASD) is increasingly conceptualized as a condition rooted in altered prenatal neurodevelopment, yet in vivo evidence from fetal brain imaging remains limited. Using retrospective fetal MRI and surface-based morphometry, we investigated cortical development in 15 fetuses later diagnosed with ASD (77%; mean gestational age [GA] = 26.7 weeks) without major structural brain abnormalities and compared them with 60 typically developing controls (57% male; mean GA = 28.4 weeks). Fetuses later diagnosed with ASD showed significantly reduced whole-brain inner cortical plate surface area compared with controls ({beta} = -0.08 {+/-} 0.02 SE, p = 0.002, partial {superscript 2} = 0.13), corresponding to an estimated [~]7.7% reduction (predicted at GA = 28.1 weeks). Lobar mixed-effects analyses demonstrated broadly distributed reductions across all cortical lobes (FDR-corrected p = <0.001-0.024; Cohens d = -0.06 to -0.10), with modest regional heterogeneity indicating relatively greater frontal and insular involvement (groupxlobe: F = 19.31, p = 0.002, {superscript 2} = 0.08). Surface area findings remained directionally stable across sensitivity analyses, including restriction to neurodevelopmentally confirmed controls and models accounting for image quality variability, although effect sizes were attenuated after quality adjustment. Normative modeling further demonstrated subtle negative deviations from typical prenatal cortical surface area trajectories in ASD (mean Z = -0.27, p = 0.018). These findings suggest that aspects of cortical morphogenesis may diverge prenatally in individuals later diagnosed with ASD and suggest the feasibility of fetal MRI-based surface morphometry for studying early neurodevelopmental variation associated with ASD risk.

neuroscience↗

bioRxiv2025

Charting the Normal Development of Structural Brain Connectivity in Utero using Diffusion MRI

Understanding the structural connectivity of the human brain during fetal life is critical for uncovering the early foundations of neural function and vulnerability to developmental disorders. Diffusion-weighted MRI (dMRI) enables non-invasive mapping of white matter pathways and construction of the brains structural connectome, but its application to the fetal brain has been limited by data scarcity and technical difficulties in analyzing fetal dMRI data. Here, we present the largest study to date of in utero brain connectivity, analyzing high-quality dMRI data from 198 fetuses between 22 and 37 gestational weeks from the Developing Human Connectome Project. We employed advanced fetal-specific tools for brain segmentation and parcellation, and used ensemble tractography to encourage more complete reconstruction of various white matter tracts. For connection weighting, we relied on the notion of fiber bundle capacity. We reconstructed individual structural connectomes and characterized the developmental trajectories. Graph-theoretical analysis revealed consistent increases in integration and segregation metrics over gestation, while bootstrapping confirmed the robustness of nodal and edgewise developmental patterns. Furthermore, we proposed a novel method for constructing age-specific connectome templates based on aggregation of individual subject connectomes. The new method follows an optimization-based approach to ensure that the connectome templates closely represent individual subject connectomes, are temporally consistent, and proportionally preserve short and long connections. Our analysis shows that this approach is superior to spatial alignment and averaging of the data in image space, with the resulting connectome templates supporting accurate prediction of the gestational age of individual fetuses (mean error= 0.90 {+/-} 0.81 weeks). In summary, this study provides robust normative benchmarks for fetal brain structural connectivity and demonstrates that connectome-based measures capture meaningful developmental signatures, offering a framework for future studies of early brain development.

neuroscience↗

bioRxiv2020

Infant functional connectivity fingerprints predict long-term language and pre-literacy outcomes

Functional brain networks undergo extensive development within the first few years of life. Previous studies have linked infant functional connectivity to cognitive abilities in toddlerhood. However, little is known regarding the long-term relevance of functional connections established in infancy for the protracted development of higher-order abilities of language and literacy. Employing a five-year longitudinal imaging project starting in infancy, this study utilizes resting-state functional MRI to demonstrate prospective associations between infant functional connectivity fingerprints and subsequent language and foundational literacy skills at a mean age of 6.5. These longitudinal associations are preserved when key environmental influences are controlled for and are independent of emergent language abilities in infancy, suggesting early development of functional network characteristics in supporting the acquisition of high-order language and pre-literacy skills. Altogether, the current results highlight the importance of functional organization established in infancy as a neural scaffold underlying the learning process of complex cognitive functions.

neuroscience↗