Edward Thomas Bullmore

Brain network formation unfolds on a nonuniform developmental timetable, with different cortical regions generating connections at different developmental phases. Generative network models (GNMs) aim to uncover the principles underpinning the organization of connectomes by creating synthetic networks according to simple computational rules. These models capture the connectome’s topology, operationalized here as the cumulative distributions of network metrics (e.g., modularity, small-worldness, rich-club structure). However, GNMs typically ignore the differential timing of connectivity formation, and by omitting this temporal programme they often misplace topological features in physical space. Here, we add a heterochronous growth term to GNMs and use a model fitness function that weighs topology and topography equally. Topography refers to the spatial embedding of the network—the actual anatomical positions of the axonal tracts. With these advances, we can generate synthetic networks that more faithfully reproduce the spatial layout of diffusion-MRI connectomes from two independent adult cohorts. Compared with classical, temporally agnostic models, heterochronous simulations improve model fit, accurately locate cortical hubs and modules across individuals, and converge on a single caudal-to-rostral gradient of brain maturation. Overall, integrating heterochronicity makes GNMs more faithful to brain development, and allows for better identification of individual differences in network formation.

Neurodevelopmental and psychiatric conditions have long been hypothesised to emerge from changes in the structure and function of the brain. However, the molecular and cellular mechanisms linking brain structure to these conditions remain unknown. Here, using genetic and structural/diffusion neuroimaging data from 59,283 individuals spanning adults, children and neonates, we identify widespread genetic associations between ADHD, anxiety and depression and multiple phenotypes indexing cortical size and subcortical volume. Multivariate genetic analyses demonstrate that these associations are largely driven by a common cortical size genetic factor and ADHD. Mendelian randomisation indicates that genetically predicted reduced cortical size is causal for ADHD and not vice versa . Using single-nucleus RNA sequencing of the developing cortex, we further demonstrate that although cortical size and ADHD are enriched in different cell types - proliferating progenitors and postmitotic neurons, respectively, they converge on neurogenic genetic programmes active during the transition between the two cell types. This programme peaks at mid-gestation and is localised to the outer subventricular and intermediate zones, the regions where progenitor amplification and early neuronal migration drive human cortical expansion, and is enriched for genes that are differentially expressed in human and macaque developing cortex. Separately, the negative genetic correlation between ADHD and cortical size measures are strongest in cortical regions that have expanded most in humans relative to macaques. Together, these findings identify a novel, spatiotemporally restricted molecular mechanism underlying neurogenesis that is enriched for polygenic effects of both cortical size and ADHD.

Brains are often described as cost-efficient communication networks that optimally balance long-connection costs against fast communication. Inspired by the “use it or lose it” principle, we present a game-theoretic model of self-organizing neural units showing the brain is suboptimal in both regards. Regional competition for connectivity under propagative dynamics yields networks resembling the human cortex yet more efficient and economical. In addition, using a reservoir computing framework, we find comparable information processing capacity, but synthetic optimal communication networks show lower computational reliability. Last, virtual lesions reveal why these networks are fragile: To optimize communication, they funnel information through a spatially clustered “oligarchy” of transmodal hubs. The human brain instead uses a distributed “rich-club” backbone that better resists targeted attacks, despite higher wiring costs and less efficient communication. Cortical networks thus trade both cost and efficiency for reliable computation, highlighting computational reliability as an overlooked and perhaps even more prominent driver of brain connectivity than wiring cost or communication efficiency.

Dysconnectivity in schizophrenia is a pervasive concept across various levels of systems biology. To better understand disrupted patterns of molecular connectivity in schizophrenia, we apply innovative approaches to gene co-expression networks starting from multiple regions of postmortem brain (bulk RNA-Seq from dorso-lateral prefrontal cortex- (DLPFC) (N_donors=297; sex: M/F = 212/85), hippocampus (N_donors=250; sex: M/F = 181/69) and caudate (N_donors=349; sex: M/F = 242/107). Here we identify differentially connected genes (DCG) in schizophrenia networks that deviate from architectural relationships characteristic of neurotypical gene networks based on three network metrics- total connectivity (kTot), clustering coefficient (C), and intra-module degree (kIn). We find multiple DCG consistent across all brain regions, most of which we then independently confirm in brain single nuclei (snRNAseq) data and in four independent human iPSC-derived brain organoids. DCG specific for each network parameter shows enrichment in schizophrenia genetic signal, in pathways prominently related to neurons and oligodendroglia functionality, and in cell-type specific co-expression patterns that differ between neurotypical and schizophrenia, implicating neuronal-oligodendroglia incoordination.

Distinguishing causal biology from confounding or downstream consequences of psychiatric disorders remains a key barrier for drug development in psychiatry. We performed a large-scale proteogenomic investigation using a sequential triangulation framework integrating plasma proteomics, Mendelian randomisation, genetic colocalisation, transcriptomics, rare-variant analyses, and clinical phenotyping to identify causal proteins and prioritise therapeutic targets for depression, anxiety, bipolar disorder, and psychotic disorders. Using 2,920 plasma proteins measured in 52,615 UK Biobank participants, we identified 830 protein-disorder associations involving 574 proteins. Mendelian randomisation and colocalisation prioritised 26 proteins with putative causal effects, of which 17 are potentially druggable. Integrating multi-omic and phenotypic evidence ultimately resulted in five high-confidence causal candidates: DDR1 and LTB for depression, DDR1 for anxiety, DSG3 and PBXIP1 for bipolar disorder, and PDIA3 for psychosis. These findings provide convergent evidence implicating neuroimmune and neurodevelopmental pathways in psychiatric disorder biology, while also identifying potentially tractable targets for therapeutic development.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis involves dynamic changes in glutamatergic signalling. Magnetic resonance spectroscopy can monitor these changes but lacks temporal resolution and cell-type specificity. We investigated whether urinary astrocyte-derived extracellular vesicles (ADEVs) could serve as a non-invasive proxy for brain receptor dynamics. We prospectively collected longitudinal urine and cerebrospinal fluid (CSF) samples from a 30–35-year-old female patient during 34 days of treatment. We isolated ADEVs using a specific protocol and measured GluN1 protein levels. A 30–35-year-old healthy female provided control samples. Wavelet transform analysis of the patient’s GluN1 time series revealed two distinct patterns. First, a low-frequency trend showed declining GluN1 levels over the treatment period, which mirrored the reduction in CSF GluN1 concentrations. Second, a high-frequency oscillation appeared to be coupled with methotrexate infusions, with GluN1 peaks occurring approximately 48 hours after each dose. This secondary increase may reflect drug-induced p53 activation, which promotes the exosomal release of internalised receptors. These findings suggest that urinary ADEVs provide a feasible and informative method to monitor real-time molecular fluxes in the brain.

The unique and intricate pattern of human cortical folding is rooted in fetal neurodevelopmental processes and can now be comprehensively quantified by new neuroimaging-derived measures of sulcal complexity. Here, we provide the first genetic maps of human sulcal complexity. Beginning with large effects of rare variants, we survey nine different neurogenetic syndromes ( n =615), detecting visible changes in sulcal complexity on a shared axis of sulcal change coupled to the prenatal timing of sulcation. Turning to common genetic variants, we use genome-wide association studies of complexity scores for 40 sulci in the UK Biobank ( n ∼29,000) to (i) resolve variable heritability across sulci, (ii) reveal both local and remote shared genetic effects with cortical morphology, and (iii) identify complexity-associated genes and their embedding in brain maps of prenatal gene expression. These reference genetic maps uncover multiple new mechanistic pathways for cortical morphogenesis in health and disease.

The clinical heterogeneity of depression has defied biological classification, limiting personalized treatment. Previous neuroimaging- or symptom-based subtyping of depression failed to clarify the underlying pathoetiology, while plasma proteins which integrates signals from multiple organ systems, offers a promising way to define biologically grounded subtypes. Using plasma proteomics from 2,127 incident depression cases in a cohort of 53000 individuals, we identified three biologically distinct subtypes differing in inflammation, aging, and metabolic profiles. The most prevalent subtype (‘inflammation/ageing’) was characterized by aging-related inflammation, poorest prognosis with hippocampal atrophy and highest suicide risk, mediated by age-related amygdala atrophy; this subtype had highest anhedonia burden. A distinct ‘inflammation/energy dysregulation’ group had metabolic pathway enrichment with high inflammation and lifestyle risk factors (smoking) but no ageing trend, predominantly physical/psychomotor symptoms and decreased thalamic volume. In contrast, the ‘inflammation-resilient’ group had the lowest inflammatory proteomic loading, lowest depression severity, more resilient lifestyle and increased hippocampal volume. These proteomic signatures, detectable years before symptom onset, enable risk stratification and suggest subtype-specific targeted physical and lifestyle interventions.

It is well recognized that many pandemic viruses are associated with neurologic complications, most recently with COVID-19. After the outbreak of the COVID-19 pandemic, neurologic surveillance platforms were implemented to characterize the complications of COVID-19. Surveillance platforms are invaluable in providing timely data, informing clinical practice, and directing future research. Lessons learned from recent neurologic surveillance networks include the importance of global and cross-specialty collaboration. It is critical for future surveillance systems to consider these aspects, as it will also serve to improve representation of low and middle-income countries (LMICs) and communities. Trainees played a critical role in the success of neurologic surveillance networks; as frontline health care workers, they were able to provide timely data collection, and their fresh insights are important for future pandemic surveillance system development. In this article, we review the methods of recent neurologic surveillance networks and discuss their strengths and limitations. We explore the outlook for pandemic surveillance platforms and the crucial role global collaboration plays in ensuring that LMICs are represented. We review the role of trainees in pandemic surveillance networks and discuss how it is vital to encourage their continued involvement to ensure that, as future health care leaders, they are prepared to manage future pandemics effectively.