Patric Hagmann

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 from 20 healthy participants – 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 for log-transformed conduction velocity. Our multivariate MRI-based model explained 29% of variance in neurophysiological velocity estimates, 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.

Brain network alterations preceding first-episode psychosis often escape connectomic approaches based on conventional neuroimaging, such as diffusion or functional magnetic resonance imaging (MRI). Whole-brain magnetic resonance spectroscopic imaging (MRSI) offers a potentially more sensitive alternative: by mapping neurometabolites across the brain, it enables the construction of metabolic connectomes that may capture very early and subtle disruptions of brain network properties. We combined whole-brain proton MRSI, diffusion MRI, and resting-state functional MRI in a sample of 29 individuals meeting DSM-5 criteria for Attenuated Psychosis Syndrome (APS) or Schizotypal Personality Disorder (SCZT), pooled into a single High Risk for Psychosis (HR) group, and 30 healthy controls (HC) to construct metabolic, structural, and functional connectomes. Network-based statistics identified a significant metabolic connectivity subnetwork with higher connectivity in HR than in HC, whereas no corresponding structural or functional connectivity differences were detected. Nodes of this subnetwork had modestly higher structural strength but were not consistently enriched among structural hubs, and no significant regional abnormalities in individual metabolite concentrations were detected. Eigenmode analysis of metabolic connectivity revealed a low-dimensional organization spanning a posterior unimodal-to-transmodal axis and a transmodal-to-limbic axis. Path analysis revealed higher bilateral energy in HR, indicating reduced coherence along a metabolic gradient that parallels the neurodevelopmental sensorimotor- to-association axis. These findings identify whole-brain metabolic network organization derived from MRSI as a systems-level phenotype of psychosis risk and raise the possibility that it captures altered development of large-scale cortical organization.

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 (De⟂). Using diffusion MRI data from healthy adults, we constructed structural networks at four parcellation scales, weighted by f, De⟂, NOS, fractional anisotropy (FA) and radial diffusivity (RD). While all weights reproduced expected small-world properties and hub structure, only De⟂ 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 De⟂, FA and RD gave weak but significant coupling with fMRI functional connectivity and conduction velocity. Notably, De⟂ and RD gave highest consistency in regional structure-function coupling across spatial scales. Thus, connectome weights derived from De⟂ capture meaningful aspects of brain network organisation with functional relevance.

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.

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.

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.

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 (Cho), glutamate + glutamine (Glx) and creatine + phosphocreatine (tCr). 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.

Recent research highlights the critical role of white matter (WM) alterations in psychosis and schizophrenia (SZ), reporting volumetric and structural brain changes in affected individuals. In this study, we explored the role of astroglia in SZ, which is believed to play a role in white matter integrity. We investigated for the first time the associations between advanced diffusion Magnetic Resonance Imaging (dMRI) measures of WM microstructure and Magnetic Resonance Spectroscopy (MRS)-derived glial markers in 30 subjects with early psychosis (EP, mean age 24 ± 6) versus 49 healthy controls (HC, mean age 25 ± 6). We focused on two metabolites involved in glia: myo-Inositol (myo-Ins) and total Choline (tCho), measured in the medial prefrontal cortex (mPFC), relating them to quantitative dMRI metrics derived from Diffusion Kurtosis Imaging (DKI) and WM Tract Integrity-Watson (WMTI-W) biophysical model, including mean diffusivity and kurtosis, axonal water fraction and extra-axonal diffusivities in the whole white matter. Our findings reveal a difference between EP and HC in WM diffusivities, specifically in the extra-axonal parallel direction, but not in MRS metabolites. However, we found that the mPFC myo-Ins concentrations in EP are exclusively and strongly associated with proximal WM microstructure features, in the form of a positive correlation with axonal water fraction, a proxy for axonal density, and a negative correlation with extra-axonal parallel diffusivity, suggesting the white matter alterations could be linked to astrocytic changes in early psychosis.

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 diffusion in white matter provide informative alternatives for connectome weights – namely the intra-axonal signal fraction (f) and perpendicular extra-axonal diffusivity , as proxies of axonal density and myelination, respectively. Using diffusion MRI data from healthy adults, we constructed structural networks at four parcellation scales, weighted by f, , NOS, fractional anisotropy (FA) and radial diffusivity (RD). While all weights reproduced expected small-world properties, only and normalised NOS captured non-random properties of local organisation across all spatial scales. We then correlated each weighted connectome with resting-state fMRI functional connectivity and intracranial measurements of conduction velocity. At the whole-brain level, although NOS gave strongest coupling with fMRI functional connectivity, only exhibited significant structure-function coupling across all spatial scales and modalities. At the regional level, and RD gave highest consistency in structure-function coupling across spatial scales. Thus, connectome weights derived from capture meaningful aspects of brain network organisation with functional relevance.

Very preterm (VPT) children are prone to a variety of neurodevelopmental impairments, particularly regarding their attention and executive functions (i.e., inhibition, shifting, and working memory). Here, we aimed to investigate whether morphometric and connectivity characteristics from key brain regions associated with attention and executive functions may underlie their difficulties. Thirty-three VPT children (M _{gestational age} = 27.22 weeks, SD = 1.36) aged 8–10 years (M _age = 8.85, SD = 0.49, 17 girls) underwent a brain magnetic resonance imaging (MRI) session alongside neurodevelopmental testing. We performed a factor analysis to group the different behavioural variables measuring executive and attentional capacities. The analysis yielded a tripartite structure wherein the first factor was predominantly characterized by inhibitory abilities, the second by attentiveness, and the third by flexibility. To encompass brain regions involved in attention and executive processes, based on functional MRI meta-analyses, we selected the anterior cingulate (ACC) and the dorsolateral prefrontal cortices (DLPFC). From T1-weighted and diffusion MRI images we estimated their cortical thickness, fractional anisotropy, volume, cortical surface area, and betweenness centrality. Significant negative associations were observed for cortical thickness after multiple comparison corrections and adjustments for age and sex. Thinner cortex was related to higher inhibitory, flexibility, and attentional functioning. While these associations were independent of the hemispheres, the association with the inhibitory abilities was stronger in the DLPFC than in the ACC. No associations were found for the other brain measures. These findings provide new insights into brain structures underpinning executive and attentional abilities in VPT children at school age.