<p>Age-related cognitive decline in learning and decision-making may arise from increased variability of neural responses. Here, we investigated how ageing affects behavioural and neural variability by recording &gt;18,000 neurons across 16 brain regions (including cortex, hippocampus, thalamus, midbrain, and basal ganglia) in younger and older mice performing a visual decision-making task. Older mice showed more variable response times, reproducing a common finding in human ageing studies. Ageing globally increased firing rates&#xa0;and post-stimulus neural variability (quantified using the Fano factor), and decreased variability quenching–the reduction in neural variability upon stimulus presentation. Older animals showed higher overall firing rates across areas of visual and motor cortex, striatum, midbrain, and hippocampus, but lower firing rates in thalamic areas. Age-related attenuation in stimulus-induced variability quenching was most prominent in visual and motor cortex, striatum, and thalamic area. These findings show how large-scale neural recordings can help uncover regional specificity of ageing effects in single neurons, ultimately improving our understanding of the neural basis of age-related cognitive decline.</p>

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Age-related changes in behavioural and neural variability in a decision-making task

  • Fenying Zang,
  • Anup Khanal,
  • Sonja Förster,
  • Larry Abbott,
  • Luigi Acerbi,
  • Valeria Aguillon-Rodriguez,
  • Mandana Ahmadi,
  • Jaweria Amjad,
  • Dora Angelaki,
  • Jaime Arlandis,
  • Zoe C. Ashwood,
  • Kush Banga,
  • Hailey Barrell,
  • Hannah M. Bayer,
  • Brandon Benson,
  • Julius Benson,
  • Jai Bhagat,
  • Dan Birman,
  • Niccolò Bonacchi,
  • Kcenia Bougrova,
  • Julien Boussard,
  • Sebastian A. Bruijns,
  • E. Kelly Buchanan,
  • Robert Campbell,
  • Matteo Carandini,
  • Joana A. Catarino,
  • Fanny Cazettes,
  • Gaelle A. Chapuis,
  • Davide Crombie,
  • Yang Dan,
  • Felicia Davatolhagh,
  • Peter Dayan,
  • Sophie Denève,
  • Eric EJ DeWitt,
  • Tatiana Engel,
  • Michele Fabbri,
  • Mayo Faulkner,
  • Robert Fetcho,
  • Ila Fiete,
  • Charles Findling,
  • Laura Freitas-Silva,
  • Surya Ganguli,
  • Berk Gercek,
  • Naureen Ghani,
  • Ivan Gordeliy,
  • Laura M. Haetzel,
  • Kenneth D. Harris,
  • Michael Hausser,
  • Naoki Hiratani,
  • Sonja Hofer,
  • Fei Hu,
  • Felix Huber,
  • Julia M. Huntenburg,
  • Cole Hurwitz,
  • Christopher S. Krasniak,
  • Sanjukta Krishnagopal,
  • Michael Krumin,
  • Debottam Kundu,
  • Agnès Landemard,
  • Christopher Langdon,
  • Christopher Langfield,
  • Inês C. Laranjeira,
  • Peter Latham,
  • Petrina Lau,
  • Hyun Dong Lee,
  • Ari Liu,
  • Zachary F. Mainen,
  • Amalia Makri-Cottington,
  • Hernando Martinez-Vergara,
  • Brenna McMannon,
  • Isaiah McRoberts,
  • Guido T. Meijer,
  • Maxwell Melin,
  • Leenoy Meshulam,
  • Kim Miller,
  • Nathaniel J. Miska,
  • Catalin Mitelut,
  • Zeinab Mohammadi,
  • Thomas Mrsic-Flogel,
  • Masayoshi Murakami,
  • Jean-Paul Noel,
  • Kai Nylund,
  • Farideh Oloomi,
  • Alejandro Pan Vazquez,
  • Liam Paninski,
  • Sabrina Perrenoud,
  • Alberto Pezzotta,
  • Samuel Picard,
  • Jonathan W. Pillow,
  • Alexandre Pouget,
  • Carolina Quadrado,
  • Pranav Rai,
  • Georg Raiser,
  • Florian Rau,
  • Cyrille Rossant,
  • Noam Roth,
  • Nicholas A. Roy,
  • Kamron Saniee,
  • Rylan Schaeffer,
  • Michael M. Schartner,
  • Yanliang Shi,
  • Karolina Z. Socha,
  • Cristian Soitu,
  • Nicholas A. Steinmetz,
  • Karel Svoboda,
  • Marsa Taheri,
  • Charline Tessereau,
  • Matthew Tucker,
  • Erdem Varol,
  • Shuqi Wang,
  • Miles J. Wells,
  • Steven J. West,
  • Matthew R. Whiteway,
  • Charles Windolf,
  • Olivier Winter,
  • Ilana Witten,
  • Lauren E. Wool,
  • Zekai Xu,
  • Kenneth Yang,
  • Yaxuan Yang,
  • Han Yu,
  • Anthony M. Zador,
  • Yizi Zhang,
  • Anne K. Churchland,
  • Anne E. Urai

摘要

Age-related cognitive decline in learning and decision-making may arise from increased variability of neural responses. Here, we investigated how ageing affects behavioural and neural variability by recording >18,000 neurons across 16 brain regions (including cortex, hippocampus, thalamus, midbrain, and basal ganglia) in younger and older mice performing a visual decision-making task. Older mice showed more variable response times, reproducing a common finding in human ageing studies. Ageing globally increased firing rates and post-stimulus neural variability (quantified using the Fano factor), and decreased variability quenching–the reduction in neural variability upon stimulus presentation. Older animals showed higher overall firing rates across areas of visual and motor cortex, striatum, midbrain, and hippocampus, but lower firing rates in thalamic areas. Age-related attenuation in stimulus-induced variability quenching was most prominent in visual and motor cortex, striatum, and thalamic area. These findings show how large-scale neural recordings can help uncover regional specificity of ageing effects in single neurons, ultimately improving our understanding of the neural basis of age-related cognitive decline.