The core insight behind TFdisc is that wild-type single-cell transcriptomes already encode a surprising amount of information about how cells would respond if a regulatory gene were lost. The simulator works by first reconstructing gene regulatory networks from reference single-cell RNA sequencing data, mapping which transcription factors are likely to control which target genes based on co-expression patterns, regulatory motifs and expression dynamics across cell states. When a user specifies a transcription factor to perturb, TFdisc propagates the simulated disruption through this network, adjusting the expression of downstream targets and then recomputing the resulting cell states. The output is a predicted post-perturbation single-cell dataset: a virtual census of how each cell type would respond, which genes would shift their expression, and whether cells would drift toward or away from their normal identities and differentiation trajectories.

Validation was a central concern for the team, because a simulator is only useful if its predictions match reality. The researchers benchmarked TFdisc against multiple experimental perturbation datasets in which transcription factors had genuinely been knocked out or knocked down in the laboratory. Across these datasets, the model proved accurate in three demanding tasks: reconstructing the underlying gene regulatory networks, identifying the differentially expressed genes that change after perturbation, and predicting shifts in cell identity and lineage differentiation. The authors also compared TFdisc against existing perturbation-prediction tools, including approaches that require experimental knockout data for training, and found that their simulator, which can operate on wild-type data alone, performed competitively. Supplementary analyses covered imputation methods for sparse single-cell data, robustness of network construction, and sensitivity analyses designed to confirm that the findings were not artifacts of normalization or dataset size.

With the simulator validated, the team turned it toward one of the most stubborn problems in neuroscience: the genetic architecture of brain disorders. Conditions such as schizophrenia and autism spectrum disorder are influenced by large numbers of genetic risk variants, most of which individually contribute only a small amount of risk. Genome-wide association studies and sequencing efforts have catalogued hundreds of candidate risk genes, but converting those lists into mechanistic understanding has proven extraordinarily difficult. TFdisc offered a way to ask a question that would otherwise require decades of animal work: what happens to the developing and adult human brain, cell by cell, when each of these risk transcription factors is perturbed, alone or in combination?

The simulations revealed that different brain disorders are built on strikingly different genetic blueprints. When the researchers simulated the simultaneous perturbation of multiple schizophrenia risk transcription factors, the individual effects appeared scattered and heterogeneous, yet when combined they converged on a coherent set of shared molecular pathways. The team described this architecture as a jigsaw mechanism: each risk factor contributes one piece, and the disorder emerges only when many pieces are assembled together, with no single gene sufficient to produce the disease-relevant disruption. Autism spectrum disorder showed the opposite pattern. Perturbing its risk transcription factors produced overlapping, redundant effects in which individual factors each engaged a common core of pathways, an architecture the authors termed a monolithic mechanism. In this picture, many different genetic insults funnel into a similar biological outcome, which may help explain why autism can arise from such a wide variety of genetic lesions yet present as a recognizable clinical syndrome.

The simulator also traced how disease-relevant perturbation effects unfold across development. By applying TFdisc to single-cell reference data spanning prenatal development through adulthood, the researchers could identify the cell types and developmental stages in which simulated perturbations of risk factors produced their strongest molecular signatures. The supplementary materials document cell-type-stage-specific effects from prenatal development to adulthood, and independent validation of the predicted developmental regulatory programs using an external dataset confirmed that the simulator’s developmental predictions held up against real data. Shared pathways between schizophrenia and autism were detectable across developmental stages, but the timing and cellular context of peak pathway activity differed between the two disorders, reinforcing the conclusion that they follow distinct trajectories despite overlapping genetic risk.

Technically, the pipeline behind these findings involved several layers of quality control. The team benchmarked four imputation methods for handling the dropout and sparsity that plague single-cell RNA sequencing, evaluated gene regulatory network construction through robustness and sensitivity analyses, and assessed performance on simulated single-cell datasets where ground truth was known. Curated lists of transcription factors and risk genes associated with ten diseases were assembled from public databases and the literature, and pathway enrichment results were de-redundant to avoid inflating apparent biological signal with overlapping gene ontology terms. Clustering analyses of the risk transcription factors themselves helped organize the jigsaw and monolithic patterns, and phenotypic analyses connected the simulated perturbation clusters to known disease characteristics. This methodological scaffolding is important because perturbation prediction is a young field in which evaluation standards are still being established, and the Fudan team’s benchmarks provide a template for how future simulators should be assessed.

The implications for drug discovery and experimental design are considerable. A validated in silico perturbation simulator allows researchers to prioritize which transcription factors, cell types and developmental windows deserve the most intensive laboratory attention, dramatically narrowing a search space that would otherwise be prohibitive. For schizophrenia, the jigsaw architecture suggests that therapeutic strategies aimed at a single risk gene may be doomed to fail, and that interventions targeting the convergent downstream pathways, or combinations of factors, may be more promising. For autism, the monolithic architecture implies that a therapy correcting the shared core pathways could potentially benefit patients whose conditions arise from very different genetic causes. More broadly, the approach demonstrates that computational simulation can serve as a first-pass screen for perturbation biology, generating testable hypotheses about gene function at a scale unattainable in the wet laboratory.

The work, supported by funding from China’s National Key Research and Development Project, the National Science and Technology Innovation 2030 Major Program, the National Natural Science Foundation of China and the Shanghai Science and Technology Commission, arrives as single-cell biology and artificial intelligence converge on the problem of complex disease. The authors caution that their simulations are predictions, not proof, and that experimental validation of specific perturbation effects remains essential. Yet the study offers a compelling demonstration that the regulatory code written into ordinary single-cell data can be decoded to reveal how genetic risk is transformed into molecular dysfunction. As perturbation datasets accumulate and simulators like TFdisc are refined, the prospect of systematically mapping the causal paths from risk variant to altered cell state, and ultimately to disorder, moves from aspiration toward routine practice, promising a more mechanistic era for psychiatric genetics.

Subject of Research: An in silico transcription factor perturbation simulator that models gene regulatory responses in brain disorders using single-cell RNA sequencing data.

Article Title: An in silico transcription factor perturbation simulator uncovers diverse genetic architectures of brain disorders

Article References: Wang, H., Li, Q., & Zhu, Y. (2026). An in silico transcription factor perturbation simulator uncovers diverse genetic architectures of brain disorders. Genome Medicine. https://doi.org/10.1186/s13073-026-01752-5

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01752-5

Keywords: transcription factors, single-cell RNA sequencing, gene regulatory networks, schizophrenia, autism spectrum disorder, brain disorders, in silico perturbation, TFdisc, genetic architecture, computational biology, Genome Medicine, Fudan University

The research team, led by Lars Jacob Stovner of the Norwegian University of Science and Technology together with Ann Kristin Skrindo Knudsen, Lars Lien, Henrik Peersen, and Nils Erik Gilhus, extracted and summed GBD 2023 estimates for more than two dozen conditions, from Alzheimer’s disease and stroke to anxiety, depression, migraine, epilepsy, and addiction. Because the GBD framework has no single “brain disorders” category, the authors combined the individual diagnoses themselves, drawing on data from the Norwegian Cause of Death Registry, patient registries, and epidemiological surveys processed through GBD’s Bayesian meta-regression tool, DisMod-AT. The scale of the data effort is considerable: GBD 2023 drew on more than 100,000 data sources worldwide to generate internally consistent estimates of prevalence, incidence, and mortality for every age group, sex, and location.

The headline numbers reveal a critical asymmetry. Brain disorders were responsible for 30.77 percent of all years lived with disability (YLDs) but only 22.31 percent of all years of life lost (YLLs). In other words, these are primarily conditions people live with rather than die from, which makes them especially consequential from a socioeconomic standpoint. Mental disorders were the single largest contributor to disability, accounting for 17.56 percent of all YLDs, with anxiety disorders alone responsible for 7.35 percent. Headache disorders, dominated by migraine, contributed another 4.91 percent of all YLDs, affecting half the population in any given year. When it comes to mortality, neurological disorders dominate, chiefly because of Alzheimer’s disease and dementia, which alone accounted for 5.47 percent of all years of life lost.

The study is also the first to track how the total burden of brain disorders has shifted over time in a single country. Comparing age-standardized rates from 1990 to 2023, the researchers found a tale of two very different trajectories. On the positive side, the burden of stroke fell dramatically, with DALY rates down 65 percent over the 34-year period. This decline reflects a genuine reduction in incidence of 52 percent, alongside a 71 percent drop in mortality, driven by falling smoking rates, better control of blood pressure and cholesterol, improved diabetes management, faster acute stroke treatment, and more targeted rehabilitation. Alcohol use disorder prevalence fell 43 percent, and meningitis, once a feared killer, saw its DALY rate collapse by 89 percent thanks to vaccination and antimicrobial therapy. There were even modest, though statistically uncertain, reductions in the prevalence and burden of Alzheimer’s disease and dementia, a trend that mirrors observations elsewhere in the world and is plausibly linked to improving cerebrovascular health across generations.

Against these successes stand several deeply worrying counter-trends. The most dramatic is Parkinson’s disease, for which age-standardized prevalence rose 271 percent and incidence rose 115 percent over the study period, producing a 43 percent increase in DALY rates and an extraordinary 263 percent increase in years lived with disability. The authors note that better survival and improved diagnosis may explain part of this rise, since mortality increased far less than prevalence, but they cannot fully account for the steep increase in incidence itself. Possible contributors under investigation include environmental toxicants and even the decline of smoking in industrialized countries, a protective factor in Parkinson’s. Identifying the preventable causes of this rise, the researchers argue, should be a high scientific priority.

Mental disorders show no such improvement and, in aggregate, grew substantially. Overall mental disorder prevalence rose 29 percent, driven mainly by anxiety disorders (up 57 percent), depressive disorders (up 26 percent), and eating disorders (up 18 percent). Anxiety disorders alone saw DALY rates climb 58 percent, making them the single largest level-3 brain disorder contributor to overall disease burden in Norway at 3.79 percent of all DALYs. None of the major mental disorders showed improvement over three decades, and the authors point to shifting diagnostic practices, increased awareness, and the documented rise in mental health problems among young girls as likely contributing factors. Similar trends have been reported in the United Kingdom, suggesting the pattern is not unique to Norway. Compounding the concern, drug use disorders went in the opposite direction to alcohol, with prevalence up 23 percent and mortality from drug use soaring 97 percent, a trend the authors link partly to the increasing availability of potent synthetic opioids and poly-substance abuse.

The methodology behind these estimates deserves attention, both for its power and its limitations. GBD quantifies health loss by multiplying disease prevalence by disability weights derived from community surveys of tens of thousands of people across many countries, scaled from zero to one. Years of life lost are calculated against the highest observed life expectancy globally, ensuring that no population is penalized for biology alone. Crucially, the GBD framework corrects for comorbidity in a multiplicative rather than additive fashion, so summing individual conditions to estimate the total brain disorder burden does not inflate the result. Yet the authors are candid that many Norwegian figures rest on extrapolations from neighboring and epidemiologically similar countries, and that residual bias from changing diagnostic definitions cannot be excluded. Some categories, notably autism spectrum disorder and central nervous system cancers, carry uncertainty intervals so wide that no firm trend conclusions can be drawn, and neurotrauma had to be excluded from the total burden entirely because GBD reports injuries by cause rather than anatomical site, meaning the reported figures are minimum estimates.

The socioeconomic implications extend well beyond healthcare budgets. Many of the most common brain disorders, including ADHD, migraine, epilepsy, multiple sclerosis, schizophrenia, and depression, strike in childhood, adolescence, or early adulthood, precisely the years when people build careers, relationships, and families. Early-onset conditions reduce workforce participation, generating costs through disability pensions, absenteeism, and reduced productivity at work, while their cumulative effects ripple through later life outcomes including marriage, careers, and child-rearing. Conditions that appear later in life, such as dementia, Parkinson’s disease, and stroke, impose a different but equally heavy load: intensive caregiving demands on relatives and mounting pressure on nursing home capacity. With Norway’s population continuing to age, the total number of stroke and dementia patients will likely remain high even as age-specific rates decline.

The message for policymakers is clear and double-edged. Where prevention and treatment have been effective, the data show measurable, decades-long improvements, as stroke and meningitis demonstrate. But for anxiety, depression, and headache disorders, three of the most burdensome conditions in the entire analysis, thirty years have delivered essentially no progress, and for Parkinson’s disease and drug use disorders the situation is actively deteriorating. The GBD study identifies few modifiable risk factors for mental disorders, underscoring how much basic research remains to be done. The authors urge continued monitoring of brain health through future GBD iterations, arguing that only systematic measurement can evaluate the real-world impact of new and often very costly treatments, pinpoint emerging threats before they become crises, and direct scarce research and healthcare resources to where they will save the most healthy years of life. As Norway’s experience shows, the brain is both the greatest single source of health loss in a modern welfare state and, until now, one of its most fragmented and neglected policy priorities.

Subject of Research: The national burden of neurological, mental, and substance use disorders in Norway and their trends from 1990 to 2023 using Global Burden of Disease Study 2023 data.

Article Title: The burden of brain disorders in Norway: an analysis of data from the global burden of disease study 2023

Article References: Stovner, L. J., Skrindo Knudsen, A. K., Lien, L., Peersen, H., & Gilhus, N. E. (2026). The burden of brain disorders in Norway: an analysis of data from the global burden of disease study 2023. The Lancet Regional Health – Europe, 70, Article 101857. https://doi.org/10.1016/j.lanepe.2026.101857

Image Credits: AI Generated

DOI: 10.1016/j.lanepe.2026.101857

Keywords: brain disorders, disease burden, DALYs, Norway, Global Burden of Disease, mental health, neurological disorders, Parkinson's disease, stroke, anxiety disorders, substance use, disability