Neurodevelopmental Trajectories

We finally have the first "Brain Atlas"!!! The first comprehensive atlas showing how the human brain’s functional organization evolves from birth to over 100 years old. Published in Nature Magazine in March 2026, this groundbreaking study analyzed functional MRI scans from 3,556 healthy individuals, ranging from newborns just 16 days old to centenarians. Researchers mapped functional connectivity gradients, patterns that reveal how different brain regions communicate and coordinate with each other, rather than just looking at physical structure. They mapped distinct developmental trajectories for how brain networks mature and specialize over a lifetime, found clear milestones in functional hierarchy and integration, and explained how coordinated activity changes across infancy, childhood, adolescence, adulthood, and advanced aging. This atlas provides a powerful new reference for understanding normal brain development, aging, and what goes wrong in neurological and psychiatric conditions. It bridges a major gap in neuroscience by offering a continuous, lifespan-wide view of brain function. As one of the study’s authors noted, this work could eventually help identify when and how interventions might be most effective for brain health across all stages of life. #Neuroscience #BrainAtlas #BrainDevelopment #FunctionalConnectivity #LifespanNeuroscience #BrainHealth #Neuroimaging #Aging

🧠𝗔𝗱𝗼𝗹𝗲𝘀𝗰𝗲𝗻𝗰𝗲 𝗟𝗮𝘀𝘁𝘀 𝟮𝟯 𝗬𝗲𝗮𝗿𝘀? 𝗡𝗲𝘄 𝗦𝘁𝘂𝗱𝘆 𝗠𝗮𝗽𝘀 𝘁𝗵𝗲 𝗕𝗿𝗮𝗶𝗻’𝘀 𝗛𝗶𝗱𝗱𝗲𝗻 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝗣𝗼𝗶𝗻𝘁𝘀 𝗔𝗰𝗿𝗼𝘀𝘀 𝟵𝟬 𝗬𝗲𝗮𝗿𝘀 A new study published this week in Nature Communications, led by neuroscientists at the University of Cambridge, takes a lifespan view of how the brain’s structural networks evolve from birth to age 90. Using diffusion MRI from 4,200+ people, the researchers applied manifold learning (UMAP) to reveal the big-picture patterns in how the brain’s wiring reorganizes over time. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵𝗲𝗿𝘀 𝗳𝗼𝘂𝗻𝗱: The brain doesn’t mature or age in a smooth line. Instead, it moves through five distinct epochs, marked by major turning points around ages 9, 32, 66, and 83. These shifts reflect system-wide reorganizations, not simple increases or decreases in single metrics. They reflect wholesale changes in how networks integrate, specialize, and communicate. 𝗘𝗮𝗰𝗵 𝗲𝗽𝗼𝗰𝗵 𝗵𝗮𝘀 𝗶𝘁𝘀 𝗼𝘄𝗻 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝘀𝗶𝗴𝗻𝗮𝘁𝘂𝗿𝗲: 𝟬-𝟵: Increasing local clustering as early circuits consolidate.  𝟵-𝟯𝟮: A 23-year stretch of rising efficiency and specialization, extending adolescence well beyond where we usually assume it ends.  𝟯𝟮-𝟲𝟲: A shift toward greater segregation and reduced integration, matching what we know about white matter slowing and cognitive stability.  𝟲𝟲-𝟴𝟯: A new trajectory marked by rising modularity and increasing importance of certain "hub" regions.  𝟴𝟯-𝟵𝟬: A notable weakening of the relationship between age and brain topology, suggesting late-life variability as age becomes less tied to network organization. 🌟 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 By analyzing 11 network measures together across nine datasets, the study shows that brain development is: ✔ non-linear, ✔ unfolds in epochs, and ✔ guided by different organizational principles at different ages This reframes how we think about brain “maturity,” “aging,” and transitions like adolescence or midlife. For example, the idea that a major organizational turning point occurs around age 32 challenges cultural assumptions about when adulthood (cognitively and neurologically) truly begins. 𝗧𝗟𝗗𝗥: Human brain development is a series of topological eras, not a single curve. This work opens the door to richer lifespan neuroscience, better age-tailored interventions, and perhaps a more nuanced understanding of what “typical development” looks like across the lifespan. 🔗 Link to article in comments

A new article [in the comments] leverages computational methods to integrate high-dimensional genomic and neuroimaging data to uncover the developmental role of regional gene expression differences in the human cortex and their association with neurodevelopmental disorders like autism spectrum disorder (ASD) and schizophrenia (SCZ). The study explores how cortical gene expression dynamics during different developmental stages correlate with the structural and functional organization of the human brain, and how these patterns might deviate in neurodevelopmental disorders. Using a computational framework, the study analyzes gene expression data from the Allen Human Brain Atlas in conjunction with neuroimaging data and other genomic datasets like PsychENCODE. Data analytics and dimension reduction methods (e.g., PCA, DME) are employed to identify robust patterns. Findings: [1] The analysis highlights three major transcriptional components (C1, C2, C3) that correspond to different aspects of cerebral function and linkage to disorders. C1 is associated with neuron-specific patterns, C2 with metabolic processes, and C3 with synaptic planning and immune responses. [2] These components show distinct temporal patterns across fetal to adolescent brain development, with implications for understanding the evolution of cortical functions. [3] C1 and C2 show a strong correlation with ASD across multiple data modalities, whereas C3 is more closely associated with SCZ. This highlights how different developmental trajectories and gene expression disruptions can relate to specific clinical outcomes. Implications for Computational Psychiatry: [1] The research demonstrates the utility of integrating genomic, transcriptomic, and neuroimaging data in a computational framework to study complex brain disorders, providing a more comprehensive understanding of the underpinnings of these conditions. [2] The identified gene expression components could further be utilized to develop predictive models for identifying individuals at high risk for these disorders based on their cortical gene expression patterns. [3] Understanding specific gene-environment interactions that lead to disorder-specific deviations from normal cortical development might open up new avenues for targeted therapeutic interventions. Conclusion: The study effectively uses computational tools to link high-dimensional biological data with brain organization and disorder phenotypes and makes a significant contribution by providing insights into the molecular mechanisms contributing to neurodevelopmental disorders. This computational approach not only uncovers the intricate gene expression dynamics that shape the human cortex but also illustrates how deviations from these normative patterns are associated with clinical conditions, thus offering new pathways for diagnosis and treatment.

PUBERTY BRAIN SHIFT MAY EXPLAIN AUTISM IN GENETIC DISORDER Researchers have identified changes in brain connectivity before and after puberty that may explain why some children with chromosome 22q11.2 deletion syndrome are more susceptible to autism and schizophrenia. Using brain imaging in both mice and humans, the study found that brain regions involved in social behavior were hyperconnected in childhood but under-connected after puberty. These shifts appear linked to changes in synaptic structure, particularly a sharp loss of dendritic spines after puberty. Inhibiting a key protein, GSK3-beta, partially restored connectivity in mice, highlighting a potential therapeutic target for neurodevelopmental disorders. 3 Key Facts: 1. Connectivity Flip: Brain regions in 22q deletion were overconnected before puberty, under-connected after. 2. Synaptic Link: Post-pubertal drop in dendritic spines correlated with disrupted social behaviors. 3. Targetable Pathway: Inhibiting GSK3-beta restored connectivity and spine density in mice. Source: https://lnkd.in/gyMmyXjq

Aging does not affect the brain uniformly. A new large scale study has mapped how different brain cell types age at the molecular level, revealing that the biology of aging is far more selective than previously thought. Using single-cell transcriptomics across multiple regions of the human brain, researchers analyzed gene expression patterns in millions of individual cells. What emerged was a striking pattern: each cell type follows its own aging trajectory. Some of the most pronounced changes appeared in excitatory neurons, where shifts in gene expression were linked to pathways involved in synaptic signaling and cognitive decline. Microglia, the brain’s immune cells, showed increased activation of inflammatory genes, suggesting a progressive shift toward neuroinflammation. Meanwhile, oligodendrocytes, which maintain myelin insulation around neurons, displayed disruptions in genes associated with myelin maintenance and cellular metabolism. The key insight: brain aging is not a single process affecting all cells equally. It is a cell-type-specific biological program unfolding differently across neural circuits. Understanding these trajectories may help explain why different neurodegenerative diseases target specific brain regions and cell populations. Source: Nature, 2026 — “Single-cell transcriptomic atlas reveals cell-type-specific aging trajectories in the human brain.” #Neuroscience #BrainAging #SingleCellBiology #Neurodegeneration #BrainResearch #Genomics #LifeSciences #BiomedicalResearch #Innovation

I'm delighted to share our newest preprint on detailed mapping of early neural trajectories in an in vitro microfluidic model of rostro-caudal and dorso-ventral human neural tube specification. We show that cells already in the late epiblast stage - several days before onset of neuralisation - acquire fate-determined rostral and caudal fates, determining whether the cells will become forebrain or hindbrain upon neuralisation. This means that not only the spinal cord, but also the anterior part of the CNS becomes regionalised before it becomes neuralised. This data underscores a hypothesis we've had for some time: that there likely is no such thing as a general universal neural stem cell. The gradient model was also used to dissect the temporal and transcriptional events underlying the early specification of the ventral forebrain and of the midbrain-hindbrain boundary. The single cell data from our model is open for browsing through this link: https://lnkd.in/ddFryvVf We hope you will find it to be a useful resource for monitoring the expression patterns of your favourite genes and transcription factors temporally across both the rostro-caudal and dorso-ventral axes of the early human neural tube. We use this dataset to become better at interpreting gene expression patterns in the context of temporal development. When looking for expression across all regions and time points, you may often find that your favourite markers are perhaps not as specific as you may have hoped for. Many amazing co-authors have contributed to this work: Gaurav Singh Rathore, Tune Pers, Pedro Rifes, Erno Hänninen, Barbara Treutlein, Gray Camp, Fátima Sanchís Calleja, Matias Ankjær, Zehra Abay-Nørgaard, Charlotte Rusimbi, Janko Kajtez, Amalie Holm Nygaard, Louise Savioe Piilgaard, Ugnė Dubonytė, Jens Bager Christensen, Kristoffer Lihme Egerod Please access the updated version of this preprint here: https://lnkd.in/dV5fhMQt

Five Lives of Your Brain You’re 30 — and your brain is still a teenager. Seriously. Neurobiologists from Cambridge found that the adolescent phase of brain development doesn’t fully end until around the age of 32. And that’s just one of the discoveries from a large-scale study. Scientists analyzed MRI scans of 3,802 people — from newborns to 90-year-olds. They used diffusion imaging, which tracks the movement of water molecules through brain tissue and allows researchers to map neural connections. It turns out that the brain doesn’t change smoothly over a lifetime. It goes through four sharp turning points — roughly at ages 9, 32, 66, and 83. Between these turning points lie five relatively stable epochs, each with its own architecture. ⸻ 1. Childhood — until age 9 This is the phase of “network consolidation.” A baby is born with an excess of synapses, and the brain gradually prunes the redundant ones, preserving only the most active. Grey and white matter grow rapidly, and the cortex reaches its peak thickness. 2. Adolescence — from 9 to 32 The brain works on efficiency: it shortens neural pathways and optimizes connections between regions. This is the only period in which the brain’s efficiency increases. 3. Major turning point — age 32 This is the most critical shift. “We observe the most directed changes in wiring and the biggest trajectory shift compared to all other turning points,” explains Dr. Alexa Mousley, who led the study. 4. Adulthood — the longest and calmest phase About 30 years without major structural upheavals. The brain remains stable, although segregation gradually begins — regions become more specialized and isolated. 5. Early aging — after age 66 White matter slowly degrades, and connectivity drops. By age 83, the brain switches from global information processing to local — relying on specific regions instead of the whole organ. Why this matters Understanding these turning points can help identify periods when the brain is most vulnerable. Learning difficulties in childhood, mental disorders in adolescence, dementia in old age — all of this is linked to how the brain restructures its connections over time. https://lnkd.in/egjgBbQR

Autism has never behaved like a single disorder, and that’s because it isn’t one. It has no single mechanism, no single cause, and no single developmental trajectory. What we call “autism” is a heterogeneous construct—a clustering of sensory, cognitive, and regulatory patterns that reflect how different nervous systems adapt under different early conditions. Once we understand this, we stop looking for a defect and start recognizing the neurotype underneath. Every child begins life with a bio-neurotype: a characteristic way of sensing, predicting, and regulating the world. When development unfolds under stable, predictable conditions, these neurotypes stay flexible. But when a child experiences high stress, sensory overwhelm, medical instability, or repeated unpredictability, their developmental patterns can become constrained. In complexity science, these patterns form attractor basins—deep grooves in the regulatory landscape that are harder to climb out of. This is one of the clearest ways to understand severe and profound autism. These children are not “more autistic”—their systems have been pushed into narrow, energetically expensive basins shaped by chronic overload. Their brains work extraordinarily hard just to maintain safety, regulation, and orientation. What looks like “profound impairment” is often a system conserving energy, reducing complexity, and narrowing behavior to stay afloat. This is not pathology—it is impacted development under heavy stress load. And because the difficulty lies in the depth of the basin, not in a broken brain, change is possible. When we reduce sensory threat, stabilize routines, support immune and metabolic health, and improve sleep and predictability, we give the child more energetic capacity. The basin widens. Flexibility increases. Communication becomes more possible. Many parents have seen this intuitively, even without scientific language: when stress comes down, connection goes up. This is why we need a new and more nuanced conversation—one that moves beyond blame, beyond “vaccines cause autism,” and beyond “autism is simply a genetic disorder.” Vaccines don’t cause autism because autism is not a single disorder to cause. But some children do have sensitive systems that respond strongly to stress, immune activation, or unpredictability. The real question is not “What causes autism?” but “How do different bio-neurotypes respond to stress, and how can we support those systems before they break under load?” When we shift the frame from pathology to entropy, from causation to development, and from defect to neurotype, everything should start to make more sense.

New research: 🌍 🏥 Environmental Modifiers in 🧬Genetic Epilepsy For decades, we’ve treated genetic neurodevelopmental disorders (NDDs) with epilepsy as conditions whose trajectories are largely “built in” at birth. But families and clinicians know this isn’t the whole story — children with the same pathogenic variant can have profoundly different developmental paths. Today, I’m excited to share our preprint in which Christian Bosselmann, MD investigates this space across many genetic #epilepsies at an unprecedented scale: 📊 We analyzed 970 individuals across 93 genetic NDDs and delivered the first quantitative, cross-etiology assessment of how external exposures — socioeconomic, treatment-related, pregnancy-related — shape real-world function and quality of life. 🔍 What we found Across this large and genetically diverse cohort, we discovered that environmental and treatment-related factors explain substantially more variance in developmental outcomes than genetic diagnosis alone — an additional 19.6%, on average. Some examples that stood out: ✅ Hospitalizations matter — and their effects are dose-dependent. ✅ Treatment burden is a powerful modifier. ✅ Social determinants of health directly shape quality of life. ✅ Gene-specific susceptibility exists. Environmental effects explained >30% of outcome variance in some etiologies but only ~15% in others — highlighting distinct levels of vulnerability to external factors. 🎯 Why it matters This work advances a new paradigm: genetic epilepsies are genome-informed and exposure-sensitive. Families want to know what they can influence — and our data point to clear priorities: - reduce avoidable hospitalizations - minimize treatment burden - support early seizure control - address socioeconomic inequities - build natural history studies that incorporate the exposome For clinical trials, these effect sizes refine non-seizure endpoints, caregiver-reported outcomes, stratification, and trajectory modeling. And the substantial unexplained variance (38–58%) highlights the path forward: integrating genomics, exposomics, polygenic background, and digital longitudinal data into truly multidimensional models of development. 🌍 A step forward for the field This study was only possible because of the families contributing to research registries. We thank Simons Searchlight for data access, the amazing first author Christian Bosselmann, MD and huge thanks to many collaborators including Natasha N. Ludwig, Ph.D., Calliope Holingue, Andres Jimenez-Gomez MD, Andrea Ganna, M. Scott Perry and @Ana Arenivas. 🔗 Preprint: https://lnkd.in/gdpksKUW #GeneticEpilepsy #Neurodevelopment #Exposome #PrecisionMedicine #SimonsSearchlight #NDD #DEEs #EpilepsyResearch #QualityOfLife #RareDisease #ChildNeurology #EpilepsyCare #ClinicalTrials #AES2025, #Epilepsies

🧠 New Research Insight on Creatine Transporter Deficiency (CTD) The Vigilan observational study (NCT02931682) provides the most comprehensive prospective view to date of CTD’s developmental course.Key findings: • 📉 Significant and persistent intellectual disabilities • 🧩 Skill development occurs, but at a slower-than-average pace • 🚶 All participants learned to walk • 🗣️ 78% developed some verbal speech; 34% used phrases/sentences • ⚡ High prevalence of febrile/non-febrile seizures • 🩺 Frequent GI symptoms & growth challenges • 📊 Absolute neurodevelopmental scores show slow but present developmental gains • 👥 Cohort effects highlight differences across agesThis work underscores the importance of using absolute metrics (e.g., person ability scores) to capture meaningful progress often missed by standardized scores.🔗 Read more: Longitudinal Characterization of Males With X-Linked Creatine Transporter Deficiency: Final Results of a Multiyear Observational Study - ScienceDirect