Tag: Biomarker

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AI Model Offers Map of How Genes Work Together in Different Cellular Contexts

Scientists at the Icahn School of Medicine at Mount Sinai have created a new artificial intelligence (AI) model that helps reveal how genes function together inside human cells, offering a powerful new way to understand biology and disease. Their study, headed by Avi Ma’ayan, PhD, professor of pharmacological sciences and director of the Mount Sinai Center for Bioinformatics at the Icahn School of Medicine at Mount Sinai, introduces a gene set foundation model (GSFM) designed to learn patterns in how genes are grouped and function across thousands of biological contexts.

The work draws inspiration from advances in large language models (LLMs) such as ChatGPT, which learn how words gain meaning depending on their context. In a similar way, a GSFM learns how genes behave differently depending on their cellular “context.”

The model provides a new way to understand the structural and functional organization of genes and their products inside human cells. This improved understanding could eventually support the development of better diagnostics, biomarkers, and therapies. By mapping how genes relate to one another across many biological situations, the GSFM creates a reference framework that can help scientists interpret complex multiomics datasets more effectively, say the investigators. “The organization of genes within cells remains one of the major unsolved questions in biology,” Ma’ayan noted. “The GSFM helps address this by learning from millions of gene groupings derived from published research and gene expression datasets.”

Ma’ayan is senior corresponding author of the team’s published paper in Patterns, titled “GSFM: A gene set foundation model pre-trained on a massive collection of diverse gene sets.”

In their paper the authors explained, “Genes are a bit like words, and gene sets are a bit like sentences, because words are reused in different contexts to express unique meanings, and cells reuse genes to carry out different biological functions.”

“Genes rarely act alone,” Ma’ayan further noted. “Instead, they participate in multiple biological processes, forming different molecular groupings depending on where and when they are active in the cell. A single gene can play different roles in different settings, much like a word can have different meanings in different sentences. Just as modern language models learn the meaning of words from context, we asked whether AI could learn the ‘meaning’ of genes in the same way. Our GSFM was designed to do exactly that.”

To build the model, the researchers compiled millions of gene sets from published scientific studies and gene expression datasets. In total, the system learned from hundreds of thousands of independent research efforts.

The AI model was trained in a way similar to solving a puzzle: it was given part of a gene set and asked to predict the missing pieces. Over time, it learned underlying patterns that describe how genes are grouped and interact.

The AI model was then benchmarked against other approaches and demonstrated strong performance, including the ability to identify gene-gene and gene-function relationships before they were confirmed experimentally. To evaluate this, the model was trained using gene sets from publications up to a defined cutoff date, and then tested on whether it could predict discoveries reported in studies published after that cutoff date.

“Unlike previous biological AI models that primarily rely on gene expression data, our GSFM is uniquely trained on gene sets, a different and largely underused type of biological information,” Ma’ayan stated. “This approach allows the model to integrate diverse data from many diseases, experimental methods, and research conditions, creating a unified representation of gene relationships across biology.”

The team’s studies showed that the new model can help identify the function of poorly understood genes without immediate laboratory experiments, highlight genes involved in disease processes, and suggest potential new drug targets and biomarkers. The model offers a reusable knowledge system for many types of biomedical research data analysis tasks—for example, improved gene set enrichment analysis. In essence, the researchers suggested, GSFM offers a new “map” of how genes work together in different contexts. “Unlike prior methods that are mainly based on similarity of all genes to annotated genes, GSFM’s architecture can capture the more complex non-linear and multi-modal relationships between genes and the gene modules these genes constitute,” the investigators wrote. “GSFM’s ability to predict genes held out from known gene sets can be useful for many applications in computational systems biology.”

GSFMs could enhance existing bioinformatics tools and improve the interpretation of data collected with omics technologies. One immediate application is in gene set enrichment analysis, a widely used method in molecular biology research. By improving how scientists interpret gene groupings, the model may help uncover new biological insights from both existing and future datasets.

“Like the way LLMs predict the next word in a sentence, GSFM guesses the next missing gene when presented with a gene set,” the scientists stated. “With this power, GSFM can be used to reliably assign the most likely functions to understudied genes, and make gene set enrichment analysis more precise, ranking the most relevant enriched terms when presented with any query gene set.”

The research team plans to expand the system by combining GSFM with other AI foundation models. One goal is to integrate it with language-based models to generate natural-language explanations of gene functions. Another future direction is combining GSFM with drug-focused AI models, with the long-term aim of predicting how drugs interact with cells and supporting the design of new therapeutics.

“In summary, GSFM’s ability to distil knowledge from large amounts of unlabeled gene sets automatically, and to do so successfully across multiple sources of knowledge, can be translated into many ‘‘low-hanging fruit’’ hypotheses that could be tested in wet lab experiments to rapidly advance knowledge in biomedical research,” the investigators concluded.

The gene pages and the GSFM model are accessible at https://gsfm.maayanlab.cloud and https://github.com/MaayanLab/gsfm.

The post AI Model Offers Map of How Genes Work Together in Different Cellular Contexts appeared first on GEN – Genetic Engineering and Biotechnology News.

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Oncology’s Next AI Battleground: Instant Clinical and Commercial Insight

Across the oncology pharmaceutical industry, the bar for precision is constantly being raised. Cancer drug development has become increasingly biomarker-driven, trial populations are narrowing, and the cost of identifying eligible patients for studies continues to rise. At the same time, life sciences organizations are under growing pressure to generate real-world evidence (RWE) faster for commercialization strategies as well as regulators and payers.

The race to operationalize artificial intelligence (AI) across oncology research has entered a new phase. After years of building massive catalogs of real-world data (RWD) from electronic health records (EHRs), molecular testing, and longitudinal patient outcomes, healthcare technology companies are now competing to transform those datasets into interactive intelligence systems capable of answering complex clinical and commercial questions in real time.

That convergence has fueled a wave of oncology AI platform development from companies including SOPHiA GENETICS, Ontada, COTA Healthcare, and now Flatiron Health. “As oncology becomes more complex, the ability to quickly identify the right patients and answer critical research questions is no longer a nice-to-have, it’s essential,” Kate Estep, chief product officer at Flatiron Health, told Inside Precision Medicine.

The move by Flatiron Health supports the continuing trend of data companies in oncology and across healthcare positioning themselves beyond simply aggregating clinical datasets toward creating AI-native research environments where clinicians, commercial strategists, and researchers can interact directly with data using natural language.

The need for speed

Historically, RWE generation has been labor-intensive. Pharmaceutical teams often rely on analysts or biostatistics groups to construct cohorts, validate inclusion criteria, and generate feasibility assessments—a process that can take days or weeks before a research question even begins to take shape. That workflow is increasingly incompatible with modern oncology development, where therapies are often targeted toward highly specific molecular subpopulations.

Cancer research may be uniquely suited for AI-native evidence generation systems. Compared with many therapeutic areas, oncology already produces unusually data-dense patient journeys involving pathology reports, genomic sequencing, imaging, biomarker testing, treatment lines, progression tracking, and survival endpoints. Oncology drug development is also increasingly dependent on identifying narrow molecular populations quickly and accurately. That complexity creates ideal conditions for conversational AI systems capable of navigating structured and unstructured clinical data simultaneously.

Flatiron Telescope attempts to address that bottleneck by giving users a conversational interface layered on top of Flatiron’s oncology-specific datasets. Researchers can describe inclusion and exclusion criteria in natural language, dynamically refine cohorts, and immediately view patient counts, attrition curves, treatment patterns, and survival analyses without writing code. “We were chatting with one of our early access partners last week, and this person was remarking, ‘I could answer my question in 30 minutes, and that would have taken me two days before waiting for my data team to come back to me,’” Estep said during a media briefing ahead of launch.

That acceleration may ultimately become the defining metric in the AI healthcare infrastructure market: not simply the size of a company’s dataset, but how quickly actionable insight can be extracted from it.

From data vendors to research platforms

But the challenge is not merely access to information. Trust and scientific validity remain central concerns. “One of the things our head of data science was sharing is that off-the-shelf models are roughly 60% accurate,” Estep said. “When built and trained with the clinical and scientific best practices that we have applied to model context because we have been asking cohort questions of our data for 15 years, that’s 90% plus accuracy.”

Those comments point toward an increasingly important divide in healthcare AI: the distinction between general-purpose AI models and clinically fine-tuned systems trained on domain-specific workflows. For companies like Flatiron, the competitive moat may ultimately come less from the underlying language models themselves and more from proprietary clinical context, curation methodologies, and validated evidence-generation pipelines.

The emergence of platforms like Telescope also reflects a broader transformation occurring across healthcare AI. The first generation of healthcare data companies focused primarily on aggregation, assembling electronic health record (EHR) data, claims data, genomic profiles, and imaging repositories into structured datasets. The second generation is now focused on orchestration: enabling users to interrogate those datasets continuously through AI-driven systems.

Flatiron is betting that domain specificity will matter more than generic AI capability. “Most people in the space either give you data, they give you analytics, or they give you a platform,” Estep said. “Very few cut across all three buckets.”

That positioning distinguishes Flatiron somewhat from competitors. Tempus AI has built a broad precision medicine tech ecosystem for both providers and life sciences companies. SOPHiA GENETICS has emphasized multimodal analytics and genomic interpretation. Ontada, backed by McKesson, combines oncology data assets with point-of-care tools and network analytics.

Flatiron, by contrast, is leaning heavily into its reputation for longitudinal oncology RWE and EHR-derived clinical depth. The company says Telescope is powered by more than 15 years of oncology-specific data infrastructure spanning over 4,700 providers and 1,600 clinical sites in the United States, representing approximately 40% of U.S. community oncology practices. Globally, the company now manages data from more than five million patient journeys across the U.S., U.K., Germany, and Japan.

That scale matters because oncology AI systems depend heavily on context-rich longitudinal data. Large language models alone are insufficient if the underlying clinical infrastructure lacks standardized outcomes, biomarker histories, treatment sequences, or progression events. “Flatiron has spent the last decade and a half building high-quality, oncology-specific real-world datasets,” Estep said. “Telescope really sits at the epicenter of that.”

Global oncology intelligence

Another major shift underway in oncology AI involves international interoperability. Historically, most RWE systems were fragmented geographically, with datasets built independently for different markets. But as pharmaceutical companies globalize clinical development programs, pressure is increasing to harmonize datasets across countries.

Flatiron says it is now building globally interoperable oncology datasets across the U.S., U.K., Germany, and Japan, beginning with prostate cancer data expected later this year. “We are ensuring that our datasets are interoperable from a global perspective,” Estep said. “Conclusions drawn on definitions of variables and data models in one market can easily be applied or explored in another.”

The long-term implications could be substantial. Globally harmonized oncology datasets would allow researchers to study treatment variation, biomarker prevalence, and outcomes across healthcare systems at a scale previously difficult to achieve. It may also help address longstanding concerns around representativeness in RWE generation. “Representativeness of a RWD is probably one of the single biggest requirements for us as we think about whether this dataset is considered reliable,” Estep said.

Perhaps the most important industry trend underlying Telescope’s launch is the democratization of advanced analytics. Historically, sophisticated oncology data analysis required teams of data scientists, epidemiologists, or biostatisticians. AI interfaces are beginning to collapse those barriers, enabling clinical operations leaders, medical affairs teams, and commercial strategists to interact directly with research-grade datasets. “There’s no coding required,” Estep said. “Any team member can use it, not just your data analysts.”

That shift could fundamentally change how oncology organizations make decisions, reducing delays between hypothesis generation and evidence generation while broadening access to sophisticated analytical capabilities across enterprise teams.

Whether Telescope ultimately becomes a dominant platform remains to be seen. But its launch reflects a broader reality now reshaping healthcare: in oncology, the future competitive advantage may belong not simply to companies with the most data but to those capable of turning clinical complexity into usable intelligence fastest.

The post Oncology’s Next AI Battleground: Instant Clinical and Commercial Insight appeared first on Inside Precision Medicine.

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Associations between serum tumor necrosis factor-alpha, hippocampal-prefrontal-ventricular volumes, and clinical profiles in schizophrenia: a biomarker and neuroimaging study in North Sumatera, Indonesia

IntroductionSchizophrenia is a neurodevelopmental disorder with systemic inflammation that is thought to play a role in its pathophysiology. The pro-inflammatory cytokine tumor necrosis factor-alpha (TNF-α) has been reported to be dysregulated in individuals with schizophrenia, but its relationship with brain volume changes remains inconsistent. This study aimed to analyze differences in TNF-α levels and brain volume (hippocampus, prefrontal cortex, and lateral ventricles) and their correlations in schizophrenia patients compared to healthy controls.MethodsThis analytical cross-sectional investigation was conducted on 50 schizophrenia patients and 50 healthy Batak controls at the Prof. M. Ildrem Provincial Mental Hospital, Medan. Serum TNF-α levels were measured by ELISA, while brain structure volume was measured by 1.5 T MRI and analyzed using AnalyzePro software.ResultsTNF-α levels in the schizophrenia group were significantly lower (3.35 pg/dl) than in the control group (16.90 pg/dl). No significant differences in hippocampal and prefrontal cortex volume were found between the groups. However, lateral ventricle volume was significantly larger in schizophrenia. Correlation analysis showed a weak negative relationship between TNF-α and prefrontal cortex volume only in the schizophrenia group.DiscussionThe finding of low TNF-α in schizophrenia supports the complexity of immune dysfunction in schizophrenia, which may be influenced by medication or disease phase. Consistent ventricular enlargement reinforces previous neuroanatomical findings. The association of TNF-α with the prefrontal cortex only within the schizophrenia cohort indicates a specific interaction between inflammation and brain morphology in this patient population.ConclusionsThe findings support TNF-α dysregulation in schizophrenia, albeit at lower levels. Lateral ventricular enlargement was consistently found, while changes in hippocampal and prefrontal cortex volume may be more subtle in this population. The negative correlation between TNF-α and the prefrontal cortex in schizophrenia suggests a potential role for inflammation in the pathology of this brain region.
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Precision diagnosis model for treatment-resistant depression integrating serum metabolomics and clinical risk factors

ObjectiveThis study aimed to construct a high-efficiency dual-modal diagnostic model for treatment-resistant depression (TRD) by integrating serum metabolomics and clinical risk factors, and explore its metabolic pathological mechanisms.MethodsA total of 93 major depressive disorder (MDD) patients (53 TRD, 40 non-TRD) were enrolled for a single-center retrospective study. Serum untargeted metabolomics and clinical baseline data were collected, with differential metabolites and clinical risk factors screened by statistical analysis and multi-step machine learning to identify core features. Five machine learning algorithms were compared to build unimodal and random forest-based dual-modal diagnostic models, and KEGG pathway enrichment analysis was performed.Results3 core clinical risk factors (medical history, HDL, FBG) and 8 core metabolic biomarkers were identified. The dual-modal model achieved AUC 0.996 (training) and 0.911 (validation), outperforming unimodal models. Differential metabolites were mainly enriched in lipid (44.8%) and amino acid (23.9%) metabolism. Fibrinopeptide A516, 12-HETE and the three clinical factors were core driving features.ConclusionThe dual-modal model has high diagnostic efficiency for TRD. TRD is associated with endocannabinoid system hypofunction and metabolic imbalance, which provides an objective diagnostic tool and new insights for TRD mechanism research and therapy development.
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Molecular Imaging Could Detect Early Kidney Fibrosis

Scientists have developed a new biomarker test that can noninvasively detect kidney fibrosis from a urine sample. A study published today in Science Translational Medicine reports the test could accurately differentiate between mild and severe disease, something that is not yet possible with the diagnostic methods available today. 

“Noninvasive and accurate diagnosis of kidney fibrosis remains unavailable in clinics, limiting effective patient management,” write the authors of the study, led by Zhou Jiaguo, PhD, professor of pharmacology at Sun Yat-sen University in China. “Although biopsy is considered the gold standard for assessing fibrotic progression, this invasive approach is subject to sampling error and interobserver variability, which may bias the diagnosis and is improper for repeated monitoring.”

Chronic kidney disease affects approximately 12% of the global population, with rates steadily rising as the world’s population ages. Characterized by the excessive formation of scar tissue, kidney fibrosis is a hallmark of chronic kidney disease that causes the proggressive impairment of kidney function, eventually leading to end-stage renal disease that requires dialysis or transplantation. 

Diagnosing kidney fibrosis at early stages can be critical for preventing irreversible damage to the kidneys. However, current diagnostic methods present some major limitations. While biopsies are invasive and difficult to accurately interpret, blood biomarker tests cannot distinguish kidney fibrosis from other kidney conditions or from fibrosis in other organs.

To circumvent these limitations, Jiaguo’s team identified two biomarkers that are specific to kidney fibrosis and can accurately differentiate this diagnosis from other related conditions. These are the transglutaminase 2 (TG2) enzyme and its substrate, lysyl oxidase–derived allysine (LysAld), which are both upregulated during the formation of fibrotic kidney tissue. 

The researchers developed a fluorescent reporter that binds to LysAld in the kidney and is then cleaved by TG2, turning on its fluorescence. Higher biomarker levels result in a stronger signal, providing an indication of how advanced fibrosis is in the patient’s kidneys. 

In a small cohort of 35 participants, the molecular imaging test could distinguish patients with kidney fibrosis from healthy controls with 84% sensitivity and 94% specificity. In addition, the test could differentiate mild and severe cases of fibrosis, with results confirmed with histological imaging. In contrast, traditional clinical measurements such as glomerular filtration rate, serum creatinine, and blood urea nitrogen were not able to classify patients according to the severity of the condition. 

Large-scale studies will be needed to establish this novel approach as a diagnostic and prognostic. Going forward, Jiaguo and colleagues are also interested in exploring using this test to monitor disease progression over time as a potential tool to evaluate treatment response and help clinicians make critical decisions to improve patient outcomes. 

“This noninvasive activatable reporter holds translational potential for early identification of renal fibrosis and patient stratification of [chronic kidney disease] to improve clinical management and patient outcome,” write the researchers. “We do not envision the probe as a standalone replacement for current standards; synergistic combinations with other biomarkers could further enhance its clinical utility.”

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ctDNA and Immune Signals Reveal Who Benefits Most from Metastasis-Directed Radiation

Analysis of the Phase II EXTEND clinical trial suggests that adding metastasis-directed therapy (MDT)—primarily radiation therapy—to standard systemic treatment can significantly improve progression-free survival in patients with oligometastatic cancer, particularly prostate and pancreatic cancer. The treatment can also trigger measurable systemic immune responses and changes in circulating tumor DNA (ctDNA) that may help refine patient selection and treatment monitoring.

The findings come from the multicenter External Beam Radiation to Eliminate Nominal Metastatic Disease (EXTEND) trial (NCT03599765), one of the largest randomized studies to date examining MDT across multiple tumor types. Investigators reported that patients receiving MDT plus standard of care (SOC) experienced significantly longer progression-free survival compared with patients receiving standard systemic therapy alone. The study also identified histology-specific differences in benefit and uncovered translational biomarkers that could help define which patients are most likely to respond. The results were published in the Journal of Clinical Oncology.

The EXTEND trial enrolled patients with one to five metastatic lesions across six disease-specific “baskets,” including breast, pancreatic, kidney, prostate, and other tumor types. Patients were randomized to receive either standard systemic therapy alone or systemic therapy plus MDT, which consisted overwhelmingly of radiation therapy.

“This is probably the largest trial published to date in which we randomized patients to radiation versus not,” said lead investigator Chad Tang, MD, of MD Anderson Cancer Center. “We found that overall, if you add all the baskets together, there was a progression-free survival benefit with metastasis-directed therapy.”

Of 350 randomized patients, 334 were included and randomized to either standard of care SOC or SOC/MDT. Radiotherapy was used for 98% of treated metastases. After a median follow-up of 53 months, investigators found that MDT plus standard therapy reduced the risk of progression or death by 46% compared with standard therapy alone.

Not all tumor types responded equally, however.

“Some baskets, like the prostate baskets or the pancreas basket, had a big benefit for progression-free survival with metastasis-directed therapy,” Tang said. “Other baskets, like breast and kidney, there was no benefit.”

The investigators say the variability highlights the biological complexity of oligometastatic disease and underscores the need for more precise biomarkers beyond conventional imaging.

To improve disease characterization, the researchers incorporated ctDNA analyses using a methylation-based assay designed to detect molecular residual disease (MRD). The team found that detectable ctDNA at baseline was associated with significantly shorter progression-free and overall survival across tumor types. Conversely, patients whose ctDNA cleared within three months after enrollment experienced improved survival outcomes.

“If you were to clear your ctDNA—you go from MRD positive to negative—you also did much better for overall survival,” Tang said. “That may be an early endpoint showing that something positive happened for these patients.”

The findings suggest ctDNA could eventually help physicians determine which patients are appropriate candidates for MDT. “If your ctDNA is really high, maybe you shouldn’t do metastasis-directed therapy,” Tang said. “Maybe you should be changing your drug therapy.”

The study also revealed substantial differences in ctDNA shedding between tumor types. Pancreatic cancers showed the highest detection rates, while prostate cancers demonstrated relatively low ctDNA shedding, likely reflecting differences in tumor biology and treatment context.

Beyond ctDNA, the investigators conducted extensive immune profiling to better understand how radiation-based MDT might produce systemic effects. The team analyzed cytokines, flow cytometry markers, and T cell receptor (TCR) sequencing data collected before and after treatment.

One longstanding hypothesis in radiation oncology is that localized radiation can stimulate broader antitumor immune responses—sometimes referred to as the “abscopal effect.” Previous attempts to harness this phenomenon, particularly in combination with immunotherapy, have produced inconsistent clinical results. Tang believes the EXTEND findings may point to a different and potentially more clinically relevant mechanism.

“Here we’re radiating all the sites, and there’s only microscopic disease left,” he said. “The thinking is that we’re trying to turn the immune system on to control microscopic disease, which may not yet have developed a strongly immunosuppressive environment.”

Patients receiving MDT demonstrated substantially greater immune activation than those receiving systemic therapy alone. One of the strongest signals involved TCR remodeling—expansion and contraction of specific T cell clones following treatment.

“That means the T cells are getting channeled toward some antigen,” Tang explained. “Patients who had those T cell receptor modifications seemed to do better for overall survival.”

Notably, the most pronounced TCR changes occurred in pancreatic and prostate cancer baskets—the same groups that demonstrated progression-free survival benefits. By contrast, breast and kidney cancer cohorts showed minimal TCR changes and did not demonstrate clear clinical benefit from MDT.

The researchers also identified another potentially important immune biomarker: induction of proliferating CD8-positive, PD-1-positive T cells following MDT. Patients exhibiting this immune signature experienced improved progression-free survival.

“These two phenomena—TCR modification and induction of this proliferating T-cell population—were associated with good outcomes,” Tang said.

The findings may help inform future combination strategies involving radiation and immunotherapy, as well as the development of biomarkers to guide treatment intensification.

Tang said the broader message of the study is that oligometastatic disease likely represents a biologically heterogeneous state rather than a simple lesion count.

“The effect of metastasis-directed therapy seems to be very variable,” he said. “ctDNA may help us understand whether we’re actually treating the right patients, and the immune data suggest there may be ways to further enhance these responses.”

 

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Antibody-drug conjugates in breast cancer: Progress and future directions

Newman et al. review the evolving landscape of antibody-drug conjugates (ADCs) in breast cancer, including approved agents, resistance mechanisms, and combinatorial strategies. The authors highlight novel agents, antigen targets, and next-generation platforms, underscoring the need for predictive biomarkers and optimized sequencing strategies to improve patient selection and efficacy.

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ADHD Brain Marker Value in Doubt

Longstanding links between attention-related problems and changes in the brain’s cortex during childhood and adolescence could simply be due to developmental differences between the sexes, a study suggests.

The findings, in PNAS, showed that links between attention problems and slowed rates of cortical thinning were no longer evident after taking sex into account.

The results call into question proposals to use brain maturation patterns as biomarkers for attention deficit/hyperactivity disorder (ADHD) and related conditions.

More generally, they reveal the importance of accounting for sex differences during scientific investigations.

“For nearly two decades, delayed age-related cortical thinning has been widely viewed as a neurodevelopmental marker of attention-related psychopathology and ADHD,” reported Shannon O’Connor, PhD, from the University of Vermont, and co-workers.

“However, our findings suggest that this association may be largely confounded by sex differences in cortical development.”

Delayed thinning of the brain’s cortex—its outermost layer of grey matter—has long been proposed as a biomarker for ADHD-related conditions.

To investigate its value further, O’Connor and team studied 26,496 MRI scans from 11,025 adolescents.

The team initially found there was a link between attention problems and reduced rates of age-related cortical thinning across predominantly frontoparietal regions. Reduced rates of age-related thinning were associated with higher scores relating to attention problems.

However, this link dramatically reduced after accounting for sex differences in cortical thickness trajectories.

There were no significant age and attention problem interactions on cortical thickness when the sexes were studied separately. Furthermore, the genetic risk of ADHD was also not associated with slowed thinning in this region of the brain.

“Taken together, these findings suggest that previously reported associations between attention problems and delayed cortical thinning are largely attributable to unaccounted-for sex differences in neurodevelopment,” the authors concluded.

“Our results call into question an influential framework in developmental neuroscience and psychiatry that has shaped clinical understanding of ADHD for nearly 20 years, underscoring that cortical maturation patterns should not be interpreted as biomarkers for attention-related psychopathology without rigorous accounting for sex-related variation in brain development.”

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