Pub Date : 2026-01-07DOI: 10.1038/s41593-025-02193-w
Luis A. Mejia
{"title":"Neuropixels go ultra","authors":"Luis A. Mejia","doi":"10.1038/s41593-025-02193-w","DOIUrl":"10.1038/s41593-025-02193-w","url":null,"abstract":"","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"29 1","pages":"1-1"},"PeriodicalIF":20.0,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145909374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1038/s41593-025-02165-0
Alana Amelan, Stephan C. Collins, Nadirah S. Damseh, Nanako Hamada, Ahd Salim, Elad Dvir, Galya Monderer-Rothkoff, Tamar Harel, Koh-ichi Nagata, Binnaz Yalcin, Sagiv Shifman
{"title":"CRISPR knockout screens reveal genes and pathways essential for neuronal differentiation and implicate PEDS1 in neurodevelopment","authors":"Alana Amelan, Stephan C. Collins, Nadirah S. Damseh, Nanako Hamada, Ahd Salim, Elad Dvir, Galya Monderer-Rothkoff, Tamar Harel, Koh-ichi Nagata, Binnaz Yalcin, Sagiv Shifman","doi":"10.1038/s41593-025-02165-0","DOIUrl":"https://doi.org/10.1038/s41593-025-02165-0","url":null,"abstract":"","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"83 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145902701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-02DOI: 10.1038/s41593-025-02161-4
Keying Zhu, Yun Liu, Jin-Hong Min, Vijay Joshua, Jianing Lin, Yue Li, Judith C Kreutzmann, Yuxi Guo, Wenlong Xia, Elyas Mohammadi, Melanie Pieber, Valerie Suerth, Yiming Xia, Zaneta Andrusivova, Jean-Philippe Hugnot, Shigeaki Kanatani, Per Uhlén, Joakim Lundeberg, Xiaofei Li, Stephen P J Fancy, Heela Sarlus, Robert A Harris, Harald Lund
Microglia survey and regulate central nervous system myelination during embryonic development and adult homeostasis. However, whether microglia-myelin interactions are spatiotemporally regulated remains unexplored. Here, by examining spinal cord white matter tracts in mice, we determined that myelin degeneration was particularly prominent in the dorsal column (DC) during normal aging. This was accompanied by molecular and functional changes in DC microglia as well as an upregulation of transforming growth factor beta (TGF)β signaling. Disrupting TGFβ signaling in microglia led to unrestrained microglial responses and myelin loss in the DC, accompanied by neurological deficits exacerbated with aging. Single-nucleus RNA-sequencing analyses revealed the emergence of a TGFβ signaling-sensitive microglial subset and a disease-associated oligodendrocyte subset, both of which were spatially restricted to the DC. We further discovered that microglia rely on a TGFβ autocrine mechanism to prevent damage of myelin in the DC. These findings demonstrate that TGFβ signaling is crucial for maintaining microglial resilience to myelin degeneration in the DC during aging. This highlights a previously unresolved checkpoint mechanism of TGFβ signaling with regional specificity and spatially restricted microglia-oligodendrocyte interactions.
{"title":"TGFβ signaling mediates microglial resilience to spatiotemporally restricted myelin degeneration.","authors":"Keying Zhu, Yun Liu, Jin-Hong Min, Vijay Joshua, Jianing Lin, Yue Li, Judith C Kreutzmann, Yuxi Guo, Wenlong Xia, Elyas Mohammadi, Melanie Pieber, Valerie Suerth, Yiming Xia, Zaneta Andrusivova, Jean-Philippe Hugnot, Shigeaki Kanatani, Per Uhlén, Joakim Lundeberg, Xiaofei Li, Stephen P J Fancy, Heela Sarlus, Robert A Harris, Harald Lund","doi":"10.1038/s41593-025-02161-4","DOIUrl":"https://doi.org/10.1038/s41593-025-02161-4","url":null,"abstract":"<p><p>Microglia survey and regulate central nervous system myelination during embryonic development and adult homeostasis. However, whether microglia-myelin interactions are spatiotemporally regulated remains unexplored. Here, by examining spinal cord white matter tracts in mice, we determined that myelin degeneration was particularly prominent in the dorsal column (DC) during normal aging. This was accompanied by molecular and functional changes in DC microglia as well as an upregulation of transforming growth factor beta (TGF)β signaling. Disrupting TGFβ signaling in microglia led to unrestrained microglial responses and myelin loss in the DC, accompanied by neurological deficits exacerbated with aging. Single-nucleus RNA-sequencing analyses revealed the emergence of a TGFβ signaling-sensitive microglial subset and a disease-associated oligodendrocyte subset, both of which were spatially restricted to the DC. We further discovered that microglia rely on a TGFβ autocrine mechanism to prevent damage of myelin in the DC. These findings demonstrate that TGFβ signaling is crucial for maintaining microglial resilience to myelin degeneration in the DC during aging. This highlights a previously unresolved checkpoint mechanism of TGFβ signaling with regional specificity and spatially restricted microglia-oligodendrocyte interactions.</p>","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":" ","pages":""},"PeriodicalIF":20.0,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145892830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-31DOI: 10.1038/s41593-025-02142-7
Sergi Roig-Puiggros, Maëlle Guyoton, Dmitrii Suchkov, Aurélien Fortoul, Giulio Matteucci, Sabine Fièvre, Alessandra Panzeri, Nikolaos Molochidis, Francesca Barcellini, Emma Maino, Charlie G. Foucher, Daniel Fuciec, Awais Javed, Esther Klingler, Fiona Francis, Valerio Zerbi, Camilla Bellone, Marat Minlebaev, Sami El-Boustani, Françoise Watrin, Jean-Bernard Manent, Denis Jabaudon
Brain architectures vary widely across species, yet how neuronal positioning constrains the type of circuits that can be made, and their function, remains poorly understood. Here we examine how neuronal position affects molecular identity, connectivity and function by studying Eml1 knockout mice, which exhibit abnormally located (heterotopic) neurons beneath the cortex. Heterotopic neurons maintained their molecular signatures, formed appropriate long-range connections and exhibited coherent electrophysiological properties. They organized into functional sensory-processing centers that mirrored their cortical counterparts, with preserved somatotopic mapping and responsiveness to sensory stimuli. Remarkably, cortical silencing did not impair sensory discrimination, revealing that heterotopic neurons were the main drivers of this function. Hence, equivalent circuits can emerge in different spatial configurations, allowing diverse brain architectures to converge on similar functional outcomes. Even when neocortical neurons form in abnormal locations, they retain their identity and function, revealing that brain circuit formation can be guided by intrinsic developmental programs rather than physical position.
{"title":"Position-independent emergence of neocortical neuron molecular identity, connectivity and function","authors":"Sergi Roig-Puiggros, Maëlle Guyoton, Dmitrii Suchkov, Aurélien Fortoul, Giulio Matteucci, Sabine Fièvre, Alessandra Panzeri, Nikolaos Molochidis, Francesca Barcellini, Emma Maino, Charlie G. Foucher, Daniel Fuciec, Awais Javed, Esther Klingler, Fiona Francis, Valerio Zerbi, Camilla Bellone, Marat Minlebaev, Sami El-Boustani, Françoise Watrin, Jean-Bernard Manent, Denis Jabaudon","doi":"10.1038/s41593-025-02142-7","DOIUrl":"10.1038/s41593-025-02142-7","url":null,"abstract":"Brain architectures vary widely across species, yet how neuronal positioning constrains the type of circuits that can be made, and their function, remains poorly understood. Here we examine how neuronal position affects molecular identity, connectivity and function by studying Eml1 knockout mice, which exhibit abnormally located (heterotopic) neurons beneath the cortex. Heterotopic neurons maintained their molecular signatures, formed appropriate long-range connections and exhibited coherent electrophysiological properties. They organized into functional sensory-processing centers that mirrored their cortical counterparts, with preserved somatotopic mapping and responsiveness to sensory stimuli. Remarkably, cortical silencing did not impair sensory discrimination, revealing that heterotopic neurons were the main drivers of this function. Hence, equivalent circuits can emerge in different spatial configurations, allowing diverse brain architectures to converge on similar functional outcomes. Even when neocortical neurons form in abnormal locations, they retain their identity and function, revealing that brain circuit formation can be guided by intrinsic developmental programs rather than physical position.","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"29 2","pages":"315-324"},"PeriodicalIF":20.0,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145878693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-30DOI: 10.1038/s41593-025-02130-x
Sebastian A. Bruijns, International Brain Laboratory, Kcénia Bougrova, Inês C. Laranjeira, Petrina Y. P. Lau, Guido T. Meijer, Nathaniel J. Miska, Jean-Paul Noel, Alejandro Pan-Vazquez, Noam Roth, Karolina Z. Socha, Anne E. Urai, Peter Dayan
Learning the contingencies of a task is difficult. Individuals learn in an idiosyncratic manner, revising their approach multiple times as they explore and adapt. Quantitative characterization of these learning curves requires a model that can capture both new behaviors and slow changes in existing ones. Here we suggest a dynamic infinite hidden semi-Markov model, whose latent states are associated with specific components of behavior. This model can describe new behaviors by introducing new states and capture more modest adaptations through dynamics in existing states. We tested the model by fitting it to behavioral data of >100 mice learning a contrast-detection task. Although animals showed large interindividual differences while learning this task, most mice progressed through three stages of task understanding, new behavior often arose at session onset, and early response biases did not predict later ones. We thus provide a new tool for comprehensively capturing behavior during learning. Bruijns et al. present a modeling tool that enables the tracking of learning dynamics across subjects to reveal how behaviors emerge and adapt. Applying the tool to a decision-making task in mice uncovers similarities and differences across individuals.
{"title":"Infinite hidden Markov models can dissect the complexities of learning","authors":"Sebastian A. Bruijns, International Brain Laboratory, Kcénia Bougrova, Inês C. Laranjeira, Petrina Y. P. Lau, Guido T. Meijer, Nathaniel J. Miska, Jean-Paul Noel, Alejandro Pan-Vazquez, Noam Roth, Karolina Z. Socha, Anne E. Urai, Peter Dayan","doi":"10.1038/s41593-025-02130-x","DOIUrl":"10.1038/s41593-025-02130-x","url":null,"abstract":"Learning the contingencies of a task is difficult. Individuals learn in an idiosyncratic manner, revising their approach multiple times as they explore and adapt. Quantitative characterization of these learning curves requires a model that can capture both new behaviors and slow changes in existing ones. Here we suggest a dynamic infinite hidden semi-Markov model, whose latent states are associated with specific components of behavior. This model can describe new behaviors by introducing new states and capture more modest adaptations through dynamics in existing states. We tested the model by fitting it to behavioral data of >100 mice learning a contrast-detection task. Although animals showed large interindividual differences while learning this task, most mice progressed through three stages of task understanding, new behavior often arose at session onset, and early response biases did not predict later ones. We thus provide a new tool for comprehensively capturing behavior during learning. Bruijns et al. present a modeling tool that enables the tracking of learning dynamics across subjects to reveal how behaviors emerge and adapt. Applying the tool to a decision-making task in mice uncovers similarities and differences across individuals.","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"29 1","pages":"186-194"},"PeriodicalIF":20.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41593-025-02130-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145864100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-30DOI: 10.1038/s41593-025-02146-3
Micha Hacohen, Adam Levy, Hadas Kaiser, LeeAnne Green Snyder, Alpha Amatya, Brigitta B. Gundersen, John E. Spiro, Ilan Dinstein
The Simons Sleep Project (SSP) is an open-science resource designed to accelerate digital health research into sleep and daily behaviors of autistic children. The SSP contains data from Dreem3 EEG headbands, multi-sensor EmbracePlus smartwatches and Withings’ sleep mats, as well as parent questionnaires and daily sleep diaries. It contains data from >3,600 days and nights collected from 102 children (aged 10–17 years) with idiopathic autism and 98 of their nonautistic siblings, and enables access to whole-exome sequencing for all participants. Here we present the breadth of available harmonized data and show that digital devices have higher accuracy and reliability compared to parent reports. The data show that autistic children have longer sleep-onset latencies than their siblings and longer latencies are associated with behavioral difficulties in all participants, regardless of diagnosis. The results highlight the advantages of using digital devices and demonstrate the opportunities afforded by the SSP to study autism and develop broad digital phenotyping techniques. This paper describes the Simons Sleep Project, an open resource designed to accelerate research into the sleep and daily behaviors of autistic children using synchronized recordings from multiple wearable and nearable devices for >3,600 days and nights.
{"title":"An open science resource for accelerating scalable digital health research in autism and other neurodevelopmental conditions","authors":"Micha Hacohen, Adam Levy, Hadas Kaiser, LeeAnne Green Snyder, Alpha Amatya, Brigitta B. Gundersen, John E. Spiro, Ilan Dinstein","doi":"10.1038/s41593-025-02146-3","DOIUrl":"10.1038/s41593-025-02146-3","url":null,"abstract":"The Simons Sleep Project (SSP) is an open-science resource designed to accelerate digital health research into sleep and daily behaviors of autistic children. The SSP contains data from Dreem3 EEG headbands, multi-sensor EmbracePlus smartwatches and Withings’ sleep mats, as well as parent questionnaires and daily sleep diaries. It contains data from >3,600 days and nights collected from 102 children (aged 10–17 years) with idiopathic autism and 98 of their nonautistic siblings, and enables access to whole-exome sequencing for all participants. Here we present the breadth of available harmonized data and show that digital devices have higher accuracy and reliability compared to parent reports. The data show that autistic children have longer sleep-onset latencies than their siblings and longer latencies are associated with behavioral difficulties in all participants, regardless of diagnosis. The results highlight the advantages of using digital devices and demonstrate the opportunities afforded by the SSP to study autism and develop broad digital phenotyping techniques. This paper describes the Simons Sleep Project, an open resource designed to accelerate research into the sleep and daily behaviors of autistic children using synchronized recordings from multiple wearable and nearable devices for >3,600 days and nights.","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"29 2","pages":"467-478"},"PeriodicalIF":20.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s41593-025-02146-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145863993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-30DOI: 10.1038/s41593-025-02169-w
Mackenzie Weygandt Mathis
Biological intelligence is inherently adaptive—animals continually adjust their actions in response to environmental feedback. However, creating adaptive artificial intelligence (AI) remains a major challenge. The next frontier is to go beyond traditional AI to develop ‘adaptive intelligence’, defined here as harnessing insights from biological intelligence to build agents that can learn online, generalize and rapidly adapt to changes in their environment. Recent advances in neuroscience offer inspiration through studies that increasingly focus on how animals naturally learn and adapt their models of the world. This Perspective reviews the behavioral and neural foundations of adaptive biological intelligence, examines parallel progress in AI, and explores brain-inspired approaches for building more adaptive algorithms. Adaptive intelligence envisions AI that, like animals, learns online, generalizes and adapts quickly. This Perspective reviews biological foundations, progress in AI and brain-inspired strategies for building flexible and adaptive AI algorithms.
{"title":"Leveraging insights from neuroscience to build adaptive artificial intelligence","authors":"Mackenzie Weygandt Mathis","doi":"10.1038/s41593-025-02169-w","DOIUrl":"10.1038/s41593-025-02169-w","url":null,"abstract":"Biological intelligence is inherently adaptive—animals continually adjust their actions in response to environmental feedback. However, creating adaptive artificial intelligence (AI) remains a major challenge. The next frontier is to go beyond traditional AI to develop ‘adaptive intelligence’, defined here as harnessing insights from biological intelligence to build agents that can learn online, generalize and rapidly adapt to changes in their environment. Recent advances in neuroscience offer inspiration through studies that increasingly focus on how animals naturally learn and adapt their models of the world. This Perspective reviews the behavioral and neural foundations of adaptive biological intelligence, examines parallel progress in AI, and explores brain-inspired approaches for building more adaptive algorithms. Adaptive intelligence envisions AI that, like animals, learns online, generalizes and adapts quickly. This Perspective reviews biological foundations, progress in AI and brain-inspired strategies for building flexible and adaptive AI algorithms.","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"29 1","pages":"13-24"},"PeriodicalIF":20.0,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145864093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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