Maria Angelique Di Biase

Biological heterogeneity presents a formidable challenge in understanding psychiatric illness, yet it remains underexplored at scale or in a pan-disease setting. We characterized biochemical and brain heterogeneity in 445,374 individuals, considering 9 psychiatric disorders and 30 non-psychiatric medical conditions. Psychiatric and non-psychiatric illnesses were characterized by greater biological heterogeneity than predicted by healthy variation. Notably, heterogeneity in psychiatric disorders was comparable to non-psychiatric illnesses and often diverged from diagnostic classification. Circulating glucose and glycated hemoglobin levels were the most heterogeneous traits in 65% of diagnoses, suggesting that glucose dysregulation is pervasive yet variable across diseases. Diagnosis-specific patterns also surfaced—e.g., in schizophrenia, developmental brain markers ranked among the most heterogeneous traits. These characterizations of biochemical and brain heterogeneity lay the groundwork for biological focal points to aid precision psychiatry research. To support this effort, we provide an interactive online resource for exploring and downloading illness-specific heterogeneity measures.

Psychotic disorders lack treatment-informative biomarkers, especially during the earliest illness stages when interventions are most effective. Integrating etiological theories on hippocampal pathology and whole-brain neural dysconnectivity, we studied connectivity changes within the hippocampus as a neuroimaging marker of emerging symptomatic and functional trajectories in the psychosis risk state. We analyzed multicenter longitudinal clinical and functional neuroimaging data across an eight-month period from 434 participants (356 individuals at clinical high risk for psychosis and 78 healthy controls) using latent variable regressions. Decreases of intra-hippocampal connectivity over time tracked worsening negative symptoms, depressive symptoms, and psychosocial functioning in at-risk subjects. This finding was not observed for attenuated positive symptoms and cognition, was specific to high-risk individuals relative to healthy controls, and was not obtained for connectivity within other brain areas. Unveiling the temporal sequence of these associations, we found that an early decrease in connectivity within the hippocampus preceded a subsequent worsening of negative symptoms, but not vice versa. These findings position intra-hippocampal connectivity changes as a neuroimaging marker of early affective-motivational and functional trajectories in the psychosis risk state. They further indicate that changes of connectivity within the hippocampus hold prognostic value specifically for emerging negative symptoms. This informs future risk stratification approaches and neurostimulation therapies in the psychosis risk state: Intra-hippocampal connectivity decline could be a valuable predictive marker to improve risk stratification. Ameliorating connectivity reduction within the hippocampus may represent a promising neurostimulation target to prevent unfavorable clinical trajectories. One Sentence Summary: Decreasing connectivity within the hippocampus is a neural prognostic marker of worsening negative symptoms in the psychosis risk state

Fixel-based analysis (FBA) has gained substantial interest for its ability to probe fibre-specific changes in the brain’s white matter from diffusion-weighted imaging data. However, the reproducibility and reliability of fixel-based measures across different scanners remains largely unknown. In this work, we present TRAMFIX (TRavelling Across Melbourne for FIXel-based analysis): a multisite dataset of traveling participants (n = 10 healthy adults) scanned across four 3T MRI scanners using a harmonised multi-shell diffusion-weighted imaging (DWI) protocol. DWI data were processed using two pipelines that can be adopted when performing multi-site FBA studies (site-specific vs. pooled processing). We extracted fixel-based measures of fibre density (FD), fibre cross-section (FC), and fibre density and cross-section (FDC) from the harmonized protocol. While the primary goal was to assess reproducibility and reliability of FBA metrics, we additionally computed diffusion tensor imaging (DTI)-based fractional anisotropy (FA) and mean diffusivity (MD) for the purposes of comparison with previous studies. Within-subject coefficients of variation (CVws) and intraclass correlation coefficients (ICC) of FBA and DTI measures were computed at multiple resolutions of computation and analysis: (i) the whole-brain averaged level, (ii) tract-level, and (iii) fixel- or voxel-level. Fixel-based metrics demonstrated high reproducibility and reliability at the whole-brain level (CVws ranging between 0.51% to 1.57% and ICC between 0.782 and 0.994). While reproducibility and reliability remained high for tract-averaged FBA measures (particularly the FC and FDC metrics), some tracts exhibited lower ICC values < 0.8 for the FD measure. When examining fixel-level reliability and reproducibility, clear spatial patterns emerged, with lower ICC across subcortical and cerebellar regions, and higher CVws at the cortical boundaries. FBA metrics demonstrated comparable, if not slightly better, reliability than tensor-based metrics derived from a subset of the same data. Our findings provide support for the reproducibility and reliability of fixel-based measures, highlighting their potential for use in multi-site FBA studies. Future work examining protocol-related differences, as well as appropriate harmonization strategies when pooling data across sites and scanners, will be valuable. To facilitate this, we provide the TRAMFIX dataset as a resource for investigating reproducibility, reliability, and harmonization of fixel-based analysis measures.

Depression often co-occurs with physical health conditions, including heart disease, diabetes and obesity. While dysregulation of the immunometabolic system is posited to underpin several of these comorbidities, the course of immunometabolic dysregulation in depression and its impact on structural brain changes linked to the disorder remain poorly understood. Using brain imaging and metabolomics data from the UK Biobank, we comprehensively evaluated cross-sectional and longitudinal immunometabolic profiles in depression, including inflammatory markers, lipoprotein lipids, fatty acids, amino acids, glycolysis metabolites and various low-molecular-weight metabolites. We found that depression is characterized by a relatively persistent pattern over time of elevated systemic inflammation, upregulated very-low-density lipoprotein and lipids and downregulated high-density lipoprotein with small-to-modest effect sizes (|Cohen’s d| = 0.01-0.16), and predates illness onset (mean prodromal period: 7 years). We mapped network-level systemic changes in metabolites, implicating the core role of glycolysis in depression-related metabolic dysregulation. We also showed that peripheral immunometabolic dysfunction, particularly elevated inflammation, is associated with lower brain gray matter volume in depression. We concluded that altered lipids and inflammatory markers predate the onset of depression, remain altered throughout the illness course and associate with lower gray matter volume. By comprehensively profiling immunometabolic dysfunction in depression and related brain changes, our work highlights the importance of monitoring and managing chronic low-grade inflammation and altered lipid and glucose metabolism in the disorder.

Long-term imaging formats are ideal for capturing dynamic neuronal network formation in vitro, yet fluorescent techniques are often constrained by the impact of phototoxicity on cell survival. Here we present a live-imaging protocol that was optimised via quantitative analysis of 3 target culturing conditions on neuromorphological health: extracellular matrix (human- versus murine-derived laminin), culture media (Neurobasal™ versus Brainphys™ Imaging media), and seeding density (1×10 ⁵ versus 2×10 ⁵ cells/cm ² ). A cortical neuron reporter line was differentiated from human embryonic stem cells by transduction of Neurogenin-2 and green fluorescent protein, then fluorescently imaged in 8 different microenvironments daily for 33 days. Alongside viability analysis by PrestoBlue assay and gene quantification by digital polymerase chain reaction, an automated image analysis pipeline was developed to characterise network morphology and organisation over time. Brainphys™ Imaging medium was observed to support neuron viability, outgrowth, and self-organisation to a greater extent than Neurobasal™ medium with either laminin type, while the combination of Neurobasal™ medium and human laminin reduced cell survival. Further, a higher seeding density fostered somata clustering, but did not significantly extend viability compared to low density. These findings suggest a synergistic relationship between species-specific laminin and culture media in phototoxic environments, which is positively mediated by light-protective compounds found in Brainphys™ Imaging medium.

Fixel-based analysis (FBA) has gained substantial interest for its ability to probe fibre-specific changes in the brain’s white matter from diffusion-weighted imaging data. However, the reproducibility and reliability of fixel-based measures across different scanners remains largely unknown. In this work, we present TRAMFIX: a multisite dataset of travelling participants (n=10 healthy adults) scanned across four 3T MRI scanners using a harmonised multi-shell DWI protocol. DWI data were processed using two pipelines that can adopted when performing multi-site FBA studies (site-specific versus pooled processing). We extracted fixel-based measures of fibre density (FD), fibre cross-section (FC), and fibre density and cross-section (FDC), as well as diffusion tensor imaging (DTI)-based fractional anisotropy (FA) and mean diffusivity (MD) from the harmonised protocol. Within-subject coefficients of variation (CV _ws ) and intraclass correlation coefficients (ICC) of FBA and DTI measures were computed at multiple resolutions of computation and analysis: (i) the whole-brain averaged level, (ii) tract-level, and (iii) fixel- or voxel-level. Fixel-based metrics demonstrated high reproducibility and reliability at the whole-brain level (CV _ws ranging between 0.51% to 1.57% and ICC between 0.93 and 0.996). While reproducibility and reliability remained high for tract-averaged FBA measures (particularly the FC and FDC metrics), some tracts exhibited lower ICC values < 0.8 for the FD measure. When examining fixel-level reliability and reproducibility clear spatial patterns emerged, with lower ICC across subcortical and cerebellar regions, and higher CV _ws at the cortical boundaries. Across all levels, tensor-based metrics demonstrated slightly lower reproducibility and reliability metrics than FBA measures, suggesting that FBA metrics are just as, if not more, reproducible than DTI metrics across scanners when using identical protocols. Our findings provide support for the reproducibility and reliability of fixel-based measures, highlighting their potential for use in multi-site FBA studies. Future work examining protocol-related differences, as well as appropriate harmonisation strategies when pooling data across sites and scanners, will be valuable.

Normative modeling in neuroscience aims to characterize interindividual variation in brain phenotypes and establish reference ranges, or brain charts, against which individuals can be compared. Normative models are typically limited to coarse spatial scales due to computational constraints, limiting their spatial specificity. Furthermore, dependence on fixed parcellation atlases limits their adaptability to alternative parcellation schemes. To overcome these key limitations, we propose spectral normative modeling (SNM), which leverages brain eigenmodes to efficiently generate normative ranges for arbitrarily defined regions of interest. Training SNM on over 78,000 healthy brain scans, we generate accurate lifespan thickness growth charts across different spatial scales, from millimeters to the whole brain. These charts reveal three principal thickness growth gradients, aligning neurotypical cortical change with established anatomical, genetic, and functional hierarchies. We further demonstrate SNM’s utility by elucidating high-resolution individual cortical atrophy patterns that characterize the heterogeneous expression of neurodegeneration in Alzheimer’s disease. SNM lays the groundwork for a new generation of spatially precise brain charts, offering substantial potential to drive advances in individualized precision medicine.

The current view of neurodevelopment after preterm birth presents a strong paradox: diverse neurocognitive outcomes suggest heterogeneous neurodevelopment, yet numerous brain imaging studies focusing on average dysmaturation imply largely uniform aberrations across individuals ¹ . Here we show both, spatially heterogeneous individual brain abnormality patterns (IBAPs) but with consistent underlying biological mechanisms of injury and plasticity. Using cross-sectional structural magnetic resonance imaging data from preterm neonates and longitudinal data from preterm children and adults in a normative reference framework ² , we demonstrate that brain development after preterm birth is highly heterogeneous in both severity and patterns of deviations. Individual brain abnormalities were also consistent for their extent and location along the life course, associated with glial cell underpinnings, and plastic for influences of the early social environment. Thus, IBAPs of preterm birth are spatially heterogenous, temporally consistent for extent, spatial location, and cellular underpinnings, and plastic for social-environmental impacts. Our findings extend conventional views of preterm neurodevelopment, revealing a nuanced landscape of individual variation, with consistent commonalities between subjects. This integrated perspective of preterm neurodevelopment implies more targeted theranostic intervention strategies, specifically integrating brain charts ² and imaging at birth, as well as social interventions during early development ³ .

Connectome generative models, otherwise known as generative network models, provide insight into the wiring principles underpinning brain network organization. While these models can approximate numerous statistical properties of empirical networks, they typically fail to explicitly characterize an important contributor to brain organization – axonal growth. Emulating the chemoaffinity guided axonal growth, we provide a novel generative model in which axons dynamically steer the direction of propagation based on distance-dependent chemoattractive forces acting on their growth cones. This simple dynamic growth mechanism, despite being solely geometry-dependent, is shown to generate axonal fiber bundles with brain-like geometry and features of complex network architecture consistent with the human brain, including lognormally distributed connectivity weights, scale-free nodal degrees, small-worldness, and modularity. We demonstrate that our model parameters can be fitted to individual connectomes, enabling connectome dimensionality reduction and comparison of parameters between groups. Our work offers an opportunity to bridge studies of axon guidance and connectome development, providing new avenues for understanding neural development from a computational perspective. Author Summary Generative models of the human connectome provide insight into principles driving brain network development. However, current models do not capture axonal outgrowth, which is crucial to the formation of neural circuits. We develop a novel generative connectome model featuring dynamic axonal outgrowth, revealing the contribution of microscopic axonal guidance to the network topology and axonal geometry of macroscopic connectomes. Simple axonal outgrowth rules representing continuous chemoaffinity gradients are shown to generate complex, brain-like topologies and realistic axonal fascicle architectures. Our model is sufficiently sensitive to capture subtle interindividual differences in axonal outgrowth between healthy adults. Our results are significant because they reveal core principles that may give rise to both complex brain networks and brain-like axonal bundles, unifying neurogenesis across scales.