Biology subjects

Romero Garcia, R.

Publications and source records attributed to Romero Garcia, R..

4 recordsLinked to original sources

bioRxiv2024

ComBatLS: A location- and scale-preserving method for multi-site image harmonization

Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.

neuroscience↗

bioRxiv2022

MIND Networks: Robust Estimation of Structural Similarity from Brain MRI

Structural similarity networks are a central focus of magnetic resonance imaging (MRI) research into human brain connectomes in health and disease. We present Morphometric INverse Divergence (MIND), a robust method to estimate within-subject structural similarity between cortical areas based on the Kullback-Leibler divergence between the multivariate distributions of their structural features. Compared to the prior approach of morphometric similarity networks (MSNs) on N>10,000 data from the ABCD cohort, MIND networks were more consistent with known cortical symmetry, cytoarchitecture, and (in N=19 macaques) gold-standard tract-tracing connectivity, and were more invariant to cortical parcellation. Importantly, MIND networks were remarkably coupled with cortical gene co-expression, providing fresh evidence for the unified architecture of brain structure and transcription. Using kinship (N=1282) and genetic data (N=4085), we characterized the heritability of MIND phenotypes, identifying stronger genetic influence on the relationship between structurally divergent regions compared to structurally similar regions. Overall, MIND presents a biologically-validated lens for analyzing the structural organization of the cortex using readily-available MRI measurements.

neuroscience↗

bioRxiv2018

Waves of maturation and senescence in micro-structural MRI markers of human cortical myelination over the lifespan

Seminal human brain histology work has demonstrated developmental waves of myelination. Here, using a micro-structural magnetic resonance imaging (MRI) marker linked to myelin, we studied fine-grained age differences to deduce waves of growth, stability, and decline of cortical myelination over the life-cycle. In 484 participants, aged 8-85 years, we fitted smooth growth curves to T1- to T2-weighted ratio in each of 360 regions from one of 7 cytoarchitectonic classes. From the first derivatives of these generally inverted-U trajectories, we defined three milestones: the age at peak growth; the age at onset of a stable plateau; and the age at the onset of decline. Age at peak growth had a bimodal distribution comprising an early (pre-pubertal) wave of primary sensory and motor cortices and a later (post-pubertal) wave of association, insular and limbic cortices. Most regions reached stability in the 30s but there was a second wave reaching stability in the 50s. Age at onset of decline was also bimodal: in some right hemisphere regions, the curve declined from the 60s, but in other left hemisphere regions, there was no significant decline from the stable plateau. These results are consistent with regionally heterogeneous waves of intracortical myelinogenesis and age-related demyelination.

neuroscience↗

bioRxiv2017

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network (SCN) from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we use this to define, transcriptomic brain networks (TBN) by estimating gene co-expression between pairs of cortical regions. Finally, we explore the hypothesis that TBN and the SCN are coupled.\n\nTBN and SCN were correlated across connection weights and showed qualitatively similar complex topological properties. There were differences between networks in degree and distance distributions. However, cortical areas connected to each other within modules of the SCN network had significantly higher levels of whole genome co-expression than expected by chance.\n\nNodes connected in the SCN had significantly higher levels of expression and co-expression of a Human Supragranular Enriched (HSE) gene set that are known to be important for large-scale cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not completely related to the common constraint of physical distance on both networks.

neuroscience↗