Pub Date : 2026-02-06DOI: 10.1038/s41593-026-02209-z
Ioana A. Marin
{"title":"Satellite glia fuel up neurons","authors":"Ioana A. Marin","doi":"10.1038/s41593-026-02209-z","DOIUrl":"10.1038/s41593-026-02209-z","url":null,"abstract":"","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"29 2","pages":"249-249"},"PeriodicalIF":20.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122861","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 : 2026-02-06DOI: 10.1038/s41593-026-02210-6
William P. Olson
{"title":"Calcium spikes can flip signals","authors":"William P. Olson","doi":"10.1038/s41593-026-02210-6","DOIUrl":"10.1038/s41593-026-02210-6","url":null,"abstract":"","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"29 2","pages":"249-249"},"PeriodicalIF":20.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122862","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 : 2026-02-06DOI: 10.1038/s41593-026-02212-4
Leonie Welberg
{"title":"Drifting off to repair DNA","authors":"Leonie Welberg","doi":"10.1038/s41593-026-02212-4","DOIUrl":"10.1038/s41593-026-02212-4","url":null,"abstract":"","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"29 2","pages":"249-249"},"PeriodicalIF":20.0,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122863","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 : 2026-02-05DOI: 10.1038/s41593-026-02222-2
Siling Du, Khai M Nguyen, Alina Ulezko Antonova, Jose L Fachi, Patrick Fernandes Rodrigues, Alice Verdiani, Martina Molgora, Igor Smirnov, Jasmin Herz, Tornike Mamuladze, Jennifer Ponce, Amanda Swain, Mattia Bugatti, Susan Gilfillan, Marina Cella, William Vermi, Jonathan Kipnis, Marco Colonna, Simone Brioschi
{"title":"Publisher Correction: Diversity and immune dynamics of choroid plexus macrophages are shaped by distinct developmental origins.","authors":"Siling Du, Khai M Nguyen, Alina Ulezko Antonova, Jose L Fachi, Patrick Fernandes Rodrigues, Alice Verdiani, Martina Molgora, Igor Smirnov, Jasmin Herz, Tornike Mamuladze, Jennifer Ponce, Amanda Swain, Mattia Bugatti, Susan Gilfillan, Marina Cella, William Vermi, Jonathan Kipnis, Marco Colonna, Simone Brioschi","doi":"10.1038/s41593-026-02222-2","DOIUrl":"10.1038/s41593-026-02222-2","url":null,"abstract":"","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":" ","pages":""},"PeriodicalIF":20.0,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146125913","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 : 2026-02-05DOI: 10.1038/s41593-026-02202-6
Divyanshu Tak, Biniam A Garomsa, Anna Zapaishchykova, Tafadzwa L Chaunzwa, Juan Carlos Climent Pardo, Zezhong Ye, John Zielke, Yashwanth Ravipati, Suraj Pai, Sri Vajapeyam, Maryam Mahootiha, Mitchell Parker, Luke R G Pike, Ceilidh Smith, Ariana M Familiar, Kevin X Liu, Sanjay Prabhu, Omar Arnaout, Pratiti Bandopadhayay, Ali Nabavizadeh, Sabine Mueller, Hugo Jwl Aerts, Raymond Y Huang, Tina Y Poussaint, Benjamin H Kann
Artificial intelligence applied to brain magnetic resonance imaging (MRI) holds potential to advance diagnosis, prognosis and treatment planning for neurological diseases. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. By leveraging self-supervised learning, pretraining and targeted adaptation, foundation models present a promising paradigm to overcome these limitations. Here we present Brain Imaging Adaptive Core (BrainIAC)-a foundation model designed to learn generalized representations from unlabeled brain MRI data and serve as a core basis for diverse downstream application adaptation. Trained and validated on 48,965 brain MRIs across a broad spectrum of tasks, we demonstrate that BrainIAC outperforms localized supervised training and other pretrained models, particularly in low-data, few-shot, settings and in high-difficulty prediction tasks, allowing for application in scenarios otherwise infeasible. BrainIAC can be integrated into imaging pipelines and multimodal frameworks and may lead to improved biomarker discovery and artificial intelligence clinical translation.
{"title":"A generalizable foundation model for analysis of human brain MRI.","authors":"Divyanshu Tak, Biniam A Garomsa, Anna Zapaishchykova, Tafadzwa L Chaunzwa, Juan Carlos Climent Pardo, Zezhong Ye, John Zielke, Yashwanth Ravipati, Suraj Pai, Sri Vajapeyam, Maryam Mahootiha, Mitchell Parker, Luke R G Pike, Ceilidh Smith, Ariana M Familiar, Kevin X Liu, Sanjay Prabhu, Omar Arnaout, Pratiti Bandopadhayay, Ali Nabavizadeh, Sabine Mueller, Hugo Jwl Aerts, Raymond Y Huang, Tina Y Poussaint, Benjamin H Kann","doi":"10.1038/s41593-026-02202-6","DOIUrl":"10.1038/s41593-026-02202-6","url":null,"abstract":"<p><p>Artificial intelligence applied to brain magnetic resonance imaging (MRI) holds potential to advance diagnosis, prognosis and treatment planning for neurological diseases. The field has been constrained, thus far, by limited training data and task-specific models that do not generalize well across patient populations and medical tasks. By leveraging self-supervised learning, pretraining and targeted adaptation, foundation models present a promising paradigm to overcome these limitations. Here we present Brain Imaging Adaptive Core (BrainIAC)-a foundation model designed to learn generalized representations from unlabeled brain MRI data and serve as a core basis for diverse downstream application adaptation. Trained and validated on 48,965 brain MRIs across a broad spectrum of tasks, we demonstrate that BrainIAC outperforms localized supervised training and other pretrained models, particularly in low-data, few-shot, settings and in high-difficulty prediction tasks, allowing for application in scenarios otherwise infeasible. BrainIAC can be integrated into imaging pipelines and multimodal frameworks and may lead to improved biomarker discovery and artificial intelligence clinical translation.</p>","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":" ","pages":""},"PeriodicalIF":20.0,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146125893","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 : 2026-02-04DOI: 10.1038/s41593-025-02183-y
Albert J. Wakhloo, Will Slatton, SueYeon Chung
Animals can recognize latent structures in their environment and apply this information to efficiently navigate the world. Several works argue that the brain supports these abilities by forming neural representations from which behaviorally relevant variables can be read out across contexts and tasks. However, it is unclear which features of neural activity facilitate downstream readout. Here we analytically determine the geometric properties of neural activity that govern linear readout generalization on a set of tasks sharing a common latent structure. We show that four statistics summarizing the dimensionality, factorization and correlation structures of neural activity determine generalization. Early in learning, optimal neural representations are lower dimensional and exhibit higher correlations between single units and task variables than late in learning. We support these predictions through biological and artificial neural data analysis. Our results tie the linearly decodable information in neural population activity to its geometry.
{"title":"Neural population geometry and optimal coding of tasks with shared latent structure","authors":"Albert J. Wakhloo, Will Slatton, SueYeon Chung","doi":"10.1038/s41593-025-02183-y","DOIUrl":"https://doi.org/10.1038/s41593-025-02183-y","url":null,"abstract":"Animals can recognize latent structures in their environment and apply this information to efficiently navigate the world. Several works argue that the brain supports these abilities by forming neural representations from which behaviorally relevant variables can be read out across contexts and tasks. However, it is unclear which features of neural activity facilitate downstream readout. Here we analytically determine the geometric properties of neural activity that govern linear readout generalization on a set of tasks sharing a common latent structure. We show that four statistics summarizing the dimensionality, factorization and correlation structures of neural activity determine generalization. Early in learning, optimal neural representations are lower dimensional and exhibit higher correlations between single units and task variables than late in learning. We support these predictions through biological and artificial neural data analysis. Our results tie the linearly decodable information in neural population activity to its geometry.","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"23 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116263","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 : 2026-02-02DOI: 10.1038/s41593-025-02198-5
{"title":"Studying infant vision in the scanner and in silico reveals the richness of early brain function.","authors":"","doi":"10.1038/s41593-025-02198-5","DOIUrl":"https://doi.org/10.1038/s41593-025-02198-5","url":null,"abstract":"","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":" ","pages":""},"PeriodicalIF":20.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146106342","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 : 2026-02-02DOI: 10.1038/s41593-025-02187-8
Cliona O’Doherty, Áine T. Dineen, Anna Truzzi, Graham King, Lorijn Zaadnoordijk, Keelin Harrison, Enna-Louise D’Arcy, Jessica White, Chiara Caldinelli, Tamrin Holloway, Anna Kravchenko, Jörn Diedrichsen, Ailbhe Tarrant, Angela T. Byrne, Adrienne Foran, Eleanor J. Molloy, Rhodri Cusack
What are the foundations of visual categories in the human brain? Although infant looking behavior characterizes the development of overt categorization, it cannot measure neural representation or distinguish the underlying mechanism. For this, we need rich neuroimaging from young infants and the capacity to apply advanced computational models of vision. In this study, we conducted an awake functional magnetic resonance imaging (fMRI) study of more than 100 2-month-old infants, with follow-ups at 9 months, finding that categorical structure is present in high-level visual cortex from 2 months of age. This precedes its emergence in lateral visual cortex, suggesting non-hierarchical development of category representations. A deep neural network model aligned with infants’ representational geometry, indicating that the features comprising infants’ category template span a range of complexities and can be learned from the statistics of visual input. Our results reveal the existence of complex function in ventral visual cortex at 2 months of age and describe the early development of category perception.
{"title":"Infants have rich visual categories in ventrotemporal cortex at 2 months of age","authors":"Cliona O’Doherty, Áine T. Dineen, Anna Truzzi, Graham King, Lorijn Zaadnoordijk, Keelin Harrison, Enna-Louise D’Arcy, Jessica White, Chiara Caldinelli, Tamrin Holloway, Anna Kravchenko, Jörn Diedrichsen, Ailbhe Tarrant, Angela T. Byrne, Adrienne Foran, Eleanor J. Molloy, Rhodri Cusack","doi":"10.1038/s41593-025-02187-8","DOIUrl":"https://doi.org/10.1038/s41593-025-02187-8","url":null,"abstract":"What are the foundations of visual categories in the human brain? Although infant looking behavior characterizes the development of overt categorization, it cannot measure neural representation or distinguish the underlying mechanism. For this, we need rich neuroimaging from young infants and the capacity to apply advanced computational models of vision. In this study, we conducted an awake functional magnetic resonance imaging (fMRI) study of more than 100 2-month-old infants, with follow-ups at 9 months, finding that categorical structure is present in high-level visual cortex from 2 months of age. This precedes its emergence in lateral visual cortex, suggesting non-hierarchical development of category representations. A deep neural network model aligned with infants’ representational geometry, indicating that the features comprising infants’ category template span a range of complexities and can be learned from the statistics of visual input. Our results reveal the existence of complex function in ventral visual cortex at 2 months of age and describe the early development of category perception.","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":"30 1","pages":""},"PeriodicalIF":25.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102124","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 : 2026-01-29DOI: 10.1038/s41593-025-02195-8
Yi Li, Xu An, Patrick J Mulcahey, Yongjun Qian, X Hermione Xu, Shengli Zhao, Hemanth Mohan, Shreyas M Suryanarayana, Ludovica Bachschmid-Romano, Nicolas Brunel, Ian Q Whishaw, Z Josh Huang
The coordination of forelimb and orofacial movements to compose an ethological reach-to-consume behavior likely involves neural communication across brain regions. Leveraging wide-field imaging and photoinhibition to survey across the cortex, we identified a cortical network and a high-order motor area (the central region of the secondary motor cortex (MOs-c)), which coordinate action progression in a mouse reach-and-withdraw-to-drink (RWD) behavior. Electrophysiology and photoinhibition across multiple projection neuron types within the MOs-c revealed differential contributions of pyramidal tract and corticothalamic (CTMOs) output channels to action progression and hand-mouth coordination. Notably, CTMOs display sustained firing throughout RWD actions and selectively enhance RWD-relevant activity in postsynaptic thalamus neurons, which also contribute to action coordination. CTMOs receive converging monosynaptic inputs from forelimb and orofacial sensorimotor areas and are reciprocally connected to thalamic neurons, which project back to the cortical network. Therefore, the motor cortex CT channel may selectively amplify the thalamic integration of cortical and subcortical sensorimotor streams to coordinate a skilled motor behavior.
{"title":"Corticothalamic communication for action coordination in a skilled motor behavior.","authors":"Yi Li, Xu An, Patrick J Mulcahey, Yongjun Qian, X Hermione Xu, Shengli Zhao, Hemanth Mohan, Shreyas M Suryanarayana, Ludovica Bachschmid-Romano, Nicolas Brunel, Ian Q Whishaw, Z Josh Huang","doi":"10.1038/s41593-025-02195-8","DOIUrl":"10.1038/s41593-025-02195-8","url":null,"abstract":"<p><p>The coordination of forelimb and orofacial movements to compose an ethological reach-to-consume behavior likely involves neural communication across brain regions. Leveraging wide-field imaging and photoinhibition to survey across the cortex, we identified a cortical network and a high-order motor area (the central region of the secondary motor cortex (MOs-c)), which coordinate action progression in a mouse reach-and-withdraw-to-drink (RWD) behavior. Electrophysiology and photoinhibition across multiple projection neuron types within the MOs-c revealed differential contributions of pyramidal tract and corticothalamic (CT<sup>MOs</sup>) output channels to action progression and hand-mouth coordination. Notably, CT<sup>MOs</sup> display sustained firing throughout RWD actions and selectively enhance RWD-relevant activity in postsynaptic thalamus neurons, which also contribute to action coordination. CT<sup>MOs</sup> receive converging monosynaptic inputs from forelimb and orofacial sensorimotor areas and are reciprocally connected to thalamic neurons, which project back to the cortical network. Therefore, the motor cortex CT channel may selectively amplify the thalamic integration of cortical and subcortical sensorimotor streams to coordinate a skilled motor behavior.</p>","PeriodicalId":19076,"journal":{"name":"Nature neuroscience","volume":" ","pages":""},"PeriodicalIF":20.0,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146086668","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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