Biology subjects

Conus, P.

Publications and source records attributed to Conus, P..

5 recordsLinked to original sources

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↗

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↗

bioRxiv2023

Brain network connectivity underlying remission in early psychosis: a whole brain model approach

BackgroundAlterations in brain connectivity occur early during psychosis and underlie the clinical manifestations of the illness as well as patient functioning and outcome. After a first episode of psychosis (FEP), different trajectories are possible and best described by the clinical-staging model that places the patient along a continuum of conditions: from non-remitting chronic symptoms to full-remission, often followed by relapses. However, little is known about the differences in brain connectivity that could underlie these differences in clinical outcome. MethodsIn this study, we included resting-state fMRI and DSI data from a cohort of 128 healthy controls (HC) and 88 patients with early psychosis (EP) stratified based on their ability to remit after the FEP. In particular we focused on differences between stage IIIb,c remitting-relapsing (EP3R) and stage IIIa non-remitting (EP3NR) patients. We investigated alterations in resting-state functional connectivity (FC), and combined information derived from fMRI and DSI into generative whole-brain models of each condition to explore the underlying mechanisms. ResultsOpposite alterations in FC could be found in patients as compared to HC, depending on their stage. In non-remitting patients (EP3NR), we observed a reduction of FC, aligned with the reduced structural connectivity found in previous studies, while remitting-relapsing patients (EP3R) showed increased FC, potentially indicating a relevant compensatory mechanism. By means of a whole-brain network model, we showed that in HC a subset of areas is characterized by increased stability to prevent an oversynchronisation of the network, while in EP3 patients such property is lost. This alteration was more relevant in the EP3R than in EP3NR patients, probably indicating a compensatory response to the reduced effective conductivity (global coupling) highlighted by the model in both EP3 conditions as compared to controls. ConclusionsThese findings highlight the significance of categorizing patients into subgroups based on the progression of their psychotic disorders, providing insights into the factors contributing to heterogeneity in functional alterations. They enhance our understanding of the interplay between structural and functional properties, shedding light on the mechanisms of psychosis emergence, remission and progression, with potential implications for future therapeutic advancements.

neuroscience↗

bioRxiv2019

Stable Biomarker Identification For Predicting Schizophrenia in the Human Connectome

Schizophrenia, as a psychiatric disorder, has recognized brain alterations both at the structural and at the functional magnetic resonance imaging level. The developing field of connec-tomics has attracted much attention as it allows researchers to take advantage of powerful tools of network analysis in order to study structural and functional connectivity abnormalities in schizophrenia. Many methods have been proposed to identify biomarkers in schizophrenia, focusing mainly on improving the classification performance or performing statistical comparisons between groups. However, the stability of biomarkers selection has been for long overlooked in the connectomics field. In this study, we follow a machine learning approach where the identification of biomarkers is addressed as a feature selection problem for a classification task. We perform a recursive feature elimination and support vector machines (RFE-SVM) approach to identify the most meaningful biomarkers from the structural, functional, and multi-modal connectomes of healthy controls and patients. Furthermore, the stability of the retrieved biomarkers is assessed across different subsamplings of the dataset, allowing us to identify the affected core of the pathology. Considering our technique altogether, it demonstrates a principled way to achieve both accurate and stable biomarkers while highlighting the importance of multi-modal approaches to brain pathology as they tend to reveal complementary information.

neuroscience↗