Background: Information processing in the brain requires large amounts of metabolic energy, the spatial distribution of which is highly heterogeneous reflecting complex activity patterns in the mammalian brain.
Results: Here, it is found based on empirical data that, despite this heterogeneity, the volume-specific cerebral glucose metabolic rate of many different brain structures scales with brain volume with almost the same exponent around -0.15. The exception is white matter, the metabolism of which seems to scale with a standard specific exponent -1/4. The scaling exponents for the total oxygen and glucose consumptions in the brain in relation to its volume are identical and equal to $0.86\pm 0.03$, which is significantly larger than the exponents 3/4 and 2/3 suggested for whole body basal metabolism on body mass.
Conclusions: These findings show explicitly that in mammals (i) volume-specific scaling exponents of the cerebral energy expenditure in different brain parts are approximately constant (except brain stem structures), and (ii) the total cerebral metabolic exponent against brain volume is greater than the much-cited Kleiber's 3/4 exponent. The neurophysiological factors that might account for the regional uniformity of the exponents and for the excessive scaling of the total brain metabolism are discussed, along with the relationship between brain metabolic scaling and computation.
Allometric scaling of brain activity explained by avalanche criticality
University of Campania “Luigi Vanvitelli”（坎帕尼亚路易吉范维特利大学）；Universidade Federal do Ceará（塞阿拉联邦大学）；Center for Complexity and Biosystems, Department of Physics, University of Milan（米兰大学复杂系统与生物系统中心）；CNR - Consiglio Nazionale delle Ricerche, Istituto di Chimica della Materia Condensata e di Tecnologie per l’Energia（国家研究委员会凝聚态化学与能源技术研究所）
SurfAge-Net: A Hierarchical Surface-Based Network for Interpretable Fine-Grained Brain Age Prediction
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University（甘肃省可穿戴计算重点实验室，信息科学与工程学院，兰州大学）；Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital（医学影像中心，甘肃省妇幼保健院）；Department of Magnetic Resonance, Lanzhou University Second Hospital（磁共振科，兰州大学第二医院）；Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（计算机科学与技术系，哈尔滨工业大学（深圳））；Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University（教育部生物医学工程重点实验室，生物医学工程学院，浙江大学）
Enhanced brain structure-function tethering in transmodal cortex revealed by high-frequency eigenmodes
The brain's structural connectome supports signal propagation between neuronal elements, shaping diverse coactivation patterns that can be captured as functional connectivity . While the link between structure and function remains an ongoing challenge, the prevailing hypothesis is that the structure-function relationship may itself be gradually decoupled along a macroscale functional gradient spanning unimodal to transmodal regions. However, this hypothesis is strongly constrained by the underlying models which may neglect requisite signaling mechanisms. Here, we transform the structural conne…
Functional Whole-Brain Models: A New Framework for Unifying Brain Structure and Cognitive Function
Maastricht University（马斯特里赫特大学）；Institute of Biomedical Research August Pi i Sunyer (IDIBAPS)（奥古斯特·皮伊·苏内尔生物医学研究所）；Masaryk University（马萨里克大学）；University of Amsterdam（阿姆斯特丹大学）；The Czech Academy of Sciences（捷克科学院）
Multiplex Nodal Modularity: A novel network metric for the regional analysis of amnestic mild cognitive impairment during a working memory binding task
Association of Timing and Duration of Moderate-to-Vigorous Physical Activity with Cognitive Function and Brain Aging: A Population-Based Study Using the UK Biobank
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida（朱·克雷顿·普瑞特生物医学工程系，佛罗里达大学）；Department of Health Services Research, Management and Policy, University of Florida（健康服务研究、管理与政策系，佛罗里达大学）；Department of Radiation Oncology, Stanford University（放射肿瘤学系，斯坦福大学）；Norman Fixel Institute for Neurological Diseases, University of Florida（诺曼·菲克斯神经疾病研究所，佛罗里达大学）；Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida（认知衰老与记忆中心，麦金尼脑研究所，佛罗里达大学）；Department of Electrical and Computer Engineering, University of Florida（电气与计算机工程系，佛罗里达大学）
Brain-Semantoks: Learning Semantic Tokens of Brain Dynamics with a Self-Distilled Foundation Model
Hertie Institute for AI in Brain Health, University of Tübingen（图宾根大学脑健康人工智能研究所）；Tübingen AI Center, University of Tübingen（图宾根大学图宾根人工智能中心）；Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy（柏林夏里特医学院心理治疗系）
