Biology subjects

Aleman-Gomez, Y.

Publications and source records attributed to Aleman-Gomez, Y..

13 recordsLinked to original sources

bioRxiv2026

MRSIPrep: A Standardized Post-Quantification Framework for Preprocessing Whole-Brain Magnetic Resonance Spectroscopic Imaging

Magnetic resonance spectroscopic imaging (MRSI) enables non-invasive mapping of neu-rometabolites levels across the human brain. Although spectral fitting and metabolite quan-tification are increasingly supported by mature software tools, the downstream processing of quantified metabolite maps remains heterogeneous across laboratories. Here, we introduce MRSIPrep, an open-source, modular, and reproducible post-quantification framework for whole-brain MRSI. MRSIPrep standardizes quality control, tissue correction, spatial nor- malization, atlas projection, and derivative generation from quantified metabolite maps and associated quality metrics. The framework produces voxelwise, regional, and connectomics-ready outputs together with automated quality-control reports. We describe the architecture of MRSIPrep and demonstrate its utility for reproducible MRSI analysis across datasets,acquisition protocols, and downstream applications.

neuroscience↗

bioRxiv2026

Mapping sex-specific hormone-metabolite coupling in the adolescent brain: a longitudinal whole-brain spectroscopic imaging study

Adolescence is marked by coordinated endocrine and brain maturation, yet how blood circulating steroid hormones relate to neurochemical change in the brain remains largely unknown. We combined longitudinal data from a novel fast whole-brain, high-resolution three-dimensional proton magnetic resonance spectroscopic imaging technique with repeated measurements of sexual hormones and adrenal steroids in the serum of 42 healthy adolescents (13-15 years; 24 females) totalizing 100 scan-visits. Longitudinal voxel-wise models separated within-individual changes from stable between-individual differences and controlled the false discovery rate across whole-brain tests. In the whole sample, increasing age was associated with higher N-acetylaspartate plus N-acetylaspartylglutamate within individuals, whereas age was positively associated with higher glutamate plus glutamine between individuals. We then observed a hormone-metabolite coupling that differed by sex and steroids. In males, within-individual increases in testosterone tracked frontal increases in glutamate plus glutamine and disseminated increases in total N-acetylaspartate. In females, higher mean estradiol between individuals was associated with higher frontal choline-containing compounds. Within-individual changes in cortisone were associated with myo-inositol and choline-containing compounds in a widespread sex-interaction effect, with positive coupling in males and negative in females. The cortisone/cortisol ratio showed a similar sex-interaction for choline-containing compounds. These findings reveal spatially distributed, sex-dependent links between steroid maturation and adolescent brain neurochemical composition and underscore the importance of differentiating within- and between-individual associations. These observational data extend predominantly structural descriptions of pubertal brain development by identifying distinct coupling of gonadal hormones with neuronal-metabolic markers and glucocorticoid interconversion with glia-weighted metabolites.

neuroscience↗

bioRxiv2026

Sex differences in brain metabolism assessed with whole-brain magnetic resonance spectroscopic imaging

Sex differences in brain disorders span age at onset, symptom profiles, disease course and treatment response, and may partly reflect underlying differences in cellular metabolism. Indeed, in vivo evidence of sex-related neurometabolic variation remains sparse, with heterogenous and conflicting findings. Using fast high-resolution whole-brain three-dimensional magnetic resonance spectroscopic imaging, we mapped five brain metabolites in three independent cohorts of healthy participants (total n = 114). In a discovery sample of adolescents scanned at 3 Tesla (3T) (n = 61), males showed higher total N-acetylaspartate (tNAA) across widespread gray matter regions. Regional analyses further revealed opposing sex patterns with a complementary higher total creatine (tCr) observed in females, motivating examination of their ratio as an integrative metabolic index. The tNAA/tCr ratio was consistently higher in males in the discovery sample and this finding was replicated across two independent young-adult samples (3T, n = 26; 7T, n = 27), with a widespread gray and white matter distribution. This tNAA/tCr ratio may link neuronal mitochondrial metabolism with cellular energy buffering, positioning it as a potential index of bioenergetic balance relevant for conditions showing both sex differences and altered neurometabolism, notably multiple sclerosis, Alzheimer disease, and psychosis. Together, these findings reveal a reproducible, distributed metabolic sexual dimorphism in the human brain, and underscore the importance of accounting for sex-specific neurometabolic profiles in studies of brain health and disease.

neuroscience↗

bioRxiv2026

The microstructure-weighted human connectome: network properties and structure-function correlations across spatial scales

Conventional connectome edge weights, such as number of streamlines (NOS) or diffusion tensor imaging (DTI) metrics, lack specificity to microstructural details which may hold relevance for macroscale brain organisation. Since biophysical diffusion modelling offers greater specificity to microstructure, we investigated whether parameters from the Standard Model of white matter diffusion provide informative alternatives for connectome weights - namely the intra-axonal signal fraction (f) and perpendicular extra-axonal diffusivity (DePerp). Using diffusion MRI data from healthy adults, we constructed structural networks at four parcellation scales, weighted by f, DePerp, NOS, fractional anisotropy (FA) and radial diffusivity (RD). While all weights reproduced expected small-world properties and hub structure, only DePerp and normalised NOS captured non-random properties of local organisation at all spatial scales. We then correlated each weighted connectome with resting-state fMRI functional connectivity and intracranial measurements of conduction velocity. At both the whole-brain and regional level, NOS gave strong coupling with fMRI functional connectivity. Connectomes weighted by DePerp, FA and RD gave weak but significant coupling with fMRI functional connectivity and conduction velocity. Notably, DePerp and RD gave highest consistency in regional structure-function coupling across spatial scales. Thus, connectome weights derived from DePerp capture meaningful aspects of brain network organisation with functional relevance.

neuroscience↗

bioRxiv2026

Sulcal Widening in Schizophrenia Maps onto Sulcal Hubs and Energy-Synaptic Genes

Schizophrenia (SZ) is increasingly framed as a disorder of large-scale brain networks emerging from atypical early neurodevelopment, yet how network architecture relates to cortical folding abnormalities remains unclear. Here, we introduce a sulcal morphological-centred network framework that integrates normative modelling of sulcal width with diffusion-derived structural connectivity and transcriptomic data in a large multisite cohort (n = 5,392; 377 SZ). Individuals with SZ showed widespread sulcal widening, affecting 30 of 40 sulci and most pronounced in frontal, temporal and occipital regions. Critically, sulci with higher degree centrality, reflecting greater embedding within the structural connectome, exhibited disproportionately greater widening in SZ (pspin = 0.02), indicating that network hubs of cortical folding are preferentially affected. Transcriptomic integration using partial least squares regression identified a single component explaining 56.5% of SZ-related sulcal widening variance (pperm = 0.041), implicating genes enriched for synaptic signalling and energy metabolism with adult cortical expression bias and genetic enrichment for cross-disorder psychiatric risk. In contrast, oppositely weighted genes showed prenatal expression bias and enrichment for rare disruptive variants in autism spectrum disorder. Together, these findings link aberrant sulcal morphology in SZ to the brains network topology and molecular architecture, positioning cortical folding as a network-embedded phenotype in SZ.

neuroscience↗

bioRxiv2025

Non-invasive prediction of conduction velocities in the human brain from MRI-derived microstructure features at 7 Tesla

The conduction velocity of neuronal signals along axons is a key neurophysiological property that can be altered in various disease processes. While cortico-cortical evoked potentials (CCEPs) can be measured in presurgical assessment to provide information about conduction delay between a subset of brain regions, it is currently not possible to efficiently and systematically estimate conduction velocity in vivo across the whole brain. Given the established link between conduction velocity and axon morphology (most notably axon diameter but also myelination), mapping a reliable and quantitative metric linked to axon properties could fill the gap of inferring conduction velocity across the entire human brain. By integrating multiple MRI-derived microstructural measures - including axon radius, axonal water fraction, extra-axonal perpendicular diffusivity, and longitudinal relaxation time - and conduction velocity estimates obtained from a large database of CCEPs, we developed a whole-brain prediction model of conduction velocity. Our multivariate MRI-based model explained 29% of variance in neurophysiological conduction velocity, making it possible to partially predict whole-brain conduction velocity and delay matrices along connections for which no direct measurement is commonly available from epilepsy surgery investigations. This integrative MRI-based approach could provide a non-invasive framework for comprehensively characterising conduction delays in vivo across the human brain white matter.

neuroscience↗

bioRxiv2025

Large-scale comparative analysis reveals top graph signal processing features for subject identification

In magnetic resonance imaging, graph signal processing (GSP) is an analytical framework that enables to express regional functional activity time courses in terms of the underlying structural connectivity backbone. To this end, several parameters must be set during the processing of structural and functional data, and a variety of output features have been proposed. While emerging applications of the GSP framework have shown clear merits to reveal the neural underpinnings of brain disorders, behavioural facets or individuality, at present, the optimal parameter choices and feature types for an outcome of interest remain unknown. Here, we fill this gap by conducting a large-scale comparative analysis across parameter choices and candidate feature types. First, we show that all the studied factors of variation within the GSP pipeline significantly modulate feature vector patterns and feature coefficient values, evidencing the importance of an exhaustive characterization. Second, focusing on the ability to fingerprint individual subjects, we demonstrate that power spectral density and the structural decoupling index are the most all-around feature types, which harmoniously balance robustness to external sources of variation (head movement and acquisition settings), parsimony of the telling feature set, and generalization to altered parcellation specificities. Our results emphasize the importance, for future GSP studies, of carefully considering the undertaken structural connectivity and functional parameter choices as a function of the outcome measure of interest. More globally, they also highlight the relevance of large-scale comparative strategies in optimizing an analytical pipeline towards a specific goal. Our reported methodology can seamlessly be extended to other analytical approaches and outcome measures of interest, which we hope will be of use for future researchers in and outside the GSP subfield. Key pointsO_LIGraph signal processing (GSP) analysis involves many parameters to select and can yield diverse types of features. C_LIO_LIAll investigated parameters significantly modulated feature vector patterns and values. C_LIO_LIPower spectral density and structural decoupling index are recommended for fingerprinting overall. C_LI

neuroscience↗

bioRxiv2025

High-resolution whole-brain magnetic resonance spectroscopic imaging in youth at risk for psychosis

Advances in three-dimensional magnetic resonance spectroscopic imaging (3D-MRSI) allow for the high-resolution mapping of multiple neurometabolites throughout the entire brain in vivo and within clinically compatible time frames. Leveraging this capability, we created a voxel-based pipeline that corrects and spatially normalizes whole-brain maps of total N-acetylaspartate (tNAA), myo-inositol (Ins), choline compounds, glutamate + glutamine, and creatine + phosphocreatine. We examined 2 different 3D-MRSI dataset: first, a clinical sample of adolescents and young adults at risk for psychosis (n= 21) meeting DSM-5 criteria for Attenuated Psychosis Syndrome (APS) or Schizotypal Personality Disorder (SCZT), and age-/sex-matched healthy controls (n =13); and second, a non-clinical sample of adolescents (n = 61) scanned on a different site. The objective of the study was threefold: first, to assess the reproducibility of 3D-MRSI measures across datasets and scanning sites; second, to validate the feasibility of whole-brain, voxel-based analyses on 3D-MRSI data; and third, to test the sensitivity of this approach. Metabolite distributions showed reproducible regional variation in standard space between the two independent samples and scanning sites (r ranging from 0.82 to 0.99). Relative to controls, at-risk participants exhibited higher tNAA levels in frontal grey matter; the SCZT subgroup additionally displayed widespread cortical and subcortical elevations of Ins levels compared with both APS and controls. Voxel-based analyses of structural (i.e., gray and white matter volumes or densities) and diffusion (i.e., generalized fractional anisotropy) parameters yielded no significant differences between patients and controls. These preliminary findings suggest that high-resolution 3D-MRSI may be sensitive enough to detect subtle neurometabolic alterations at the group level in the early stages of psychotic disorders when structural or diffusion measures show no difference. High-resolution whole-brain metabolic mapping may have the potential to help with early identification of young people at risk for psychosis or other mental disorders.

neuroscience↗

bioRxiv2025

Constructing the Human Brain Metabolic Connectome Using MR Spectroscopic Imaging: Insights into Biochemical Organization, Cytoarchitectonic Similarity and Gene Co-expression Networks

Network science has revolutionized our understanding of brain organization by revealing self-organizing patterns underlying its structural and functional connectivity. However, capturing metabolic contrast remains a challenge, leaving a critical gap in connectomics. Using advanced 3D whole-brain proton MR spectroscopic imaging with high spatial resolution and shortened acquisition times, we constructed the first human brain metabolic connectome in 51 healthy subjects, validated on an independent cohort (N=12) scanned at a different site. Our pipeline generates consistent and reliable metabolic similarity matrices. Upon further analysis, metabolic similarity networks display distinct topological features, notably a smoothly varying gradient delineating functionally and spatially distinct yet integrated brain regions via connector hubs. Although metabolic hubs correlate with structural hubs, overall alignment with structural connectivity is poor. However, metabolic organization aligns more closely with cytoarchitectonic and genetic co-expression patterns, suggesting a neurodevelopmental origin. This work puts forward the metabolic similarity gradient as a hallmark of the brains overarching biochemical organization, and provides a foundation for incorporating metabolite imaging into the broader domain of connectomics and its potential applications in health and disease.

neuroscience↗

bioRxiv2024

Defacing biases visual quality assessments of structural MRI

A critical step before data-sharing of human neuroimaging is removing facial features to protect individuals privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. This introduces undesired variability into downstream analysis and interpretation. This registered report investigated the degree to which the so-called defacing altered the quality assessment of T1-weighted images of the human brain from the openly available "IXI dataset". The effect of defacing on manual quality assessment was investigated on a single-site subset of the dataset (N=185). By comparing two linear mixed-effects models, we determined that four trained human raters perception of quality was significantly influenced by defacing by modeling their ratings on the same set of images in two conditions: "nondefaced" (i.e., preserving facial features) and "defaced". In addition, we investigated these biases on automated quality assessments by applying repeated-measures, multivariate ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N=581; three acquisition sites). This study found that defacing altered the quality assessments by humans and showed that MRIQCs quality metrics were mostly insensitive to defacing.

bioinformatics↗

bioRxiv2022

Estimation of Vertex-wise Sulcal Width Maps on Cortical Surfaces

Sulcal width, defined as the physical distance between opposing sulcal banks, has shown promise as a biomarker. We present the first method to obtain a vertex-wise representation of this metric directly on the brains cortical surface. The algorithm samples the surface at different depths and estimates the distances between the opposing sulcal banks. The method is validated against a simulated sulcus and compared to other sulcal width tools based on regions of interest, showing solid correlations between the proposed algorithm and two widely-used reference methods. A vertex-wise assessment of the sulcal width is carried out by evaluating the correlation between sulcal width and age in a sample of an aging population, revealing clusters in the central, cingulate, and temporal sulcus regions associating wider sulci for older participants. The results confirm that the algorithm is reliable for obtaining sulcal width maps, allowing for vertex-wise analyses, and providing aggregated measures similar to the existing methods. The present algorithm is publicly available via https://github.com/HGGM-LIM/SWiM.

neuroscience↗

bioRxiv2022

ABLE: Automated Brain Lines Extraction Based on Laplacian Surface Collapse

The archetypical folded shape of the human cortex has been a long-standing topic for neuroscientific research. Nevertheless, the accurate neuroanatomical segmentation of sulci remains a challenge. Part of the problem is the uncertainty of where a sulcus transitions into a gyrus and vice versa. This problem can be avoided by focusing on sulcal fundi and gyral crowns which represent the topological opposites of cortical folding. We present Automated Brain Lines Extraction (ABLE), a method based on Laplacian surface collapse to segment sulcal fundi and gyral crown lines reliably. ABLE is built to work on standard FreeSurfer outputs, and eludes the delineation of anastomotic sulci while maintaining sulcal fundi lines that traverse the regions with the highest depth and curvature. First, it segments the cortex into gyral and sulcal surfaces; then, each surface is spatially filtered. A Laplacian-collapse-based algorithm is then applied to obtain a thinned representation of the surfaces. This surface is then used for careful detection of the endpoints of the lines. Finally, sulcal fundi and gyral crown lines are obtained by eroding the surfaces while preserving the connectivity between the endpoints. The method is validated by comparing ABLE with three other sulcal extraction methods using the Human Connectome Project (HCP) test-retest database to assess the reproducibility of the different tools. The results confirm ABLE as a reliable method to obtain sulcal lines with an accurate representation of the sulcal topology while ignoring anastomotic branches and the overestimation of the sulcal fundi lines. ABLE is publicly available via https://github.com/HGGM-LIM/ABLE.

neuroscience↗

bioRxiv2021

Through-plane super-resolution with autoencoders in diffusion magnetic resonance imaging of the developing human brain

Fetal brain diffusion magnetic resonance images are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network to enhance the through-plane resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and on the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize to fetal data with different levels of motion and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.

bioinformatics↗