Ibanez Agustin, Kringelbach Morten L, Deco Gustavo
Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile; Global Brain Health Institute (GBHI), University California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Department of Psychiatry, University of Oxford, Oxford, UK.
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
Trends Cogn Sci. 2024 Apr;28(4):319-338. doi: 10.1016/j.tics.2023.12.006. Epub 2024 Jan 20.
Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.
尽管我们对脑部疾病的理解有了显著进步,但仍存在许多障碍。认知神经科学面临四大主要挑战:复杂的结构-功能关联;疾病表型异质性;缺乏跨诊断模型;以及局限于实验室的过于简化的认知方法。在此,我们提出一个协同学框架,它有助于对大脑、身体和环境之间复杂相互作用进行必要的降维处理。关键解决方案包括用于脑结构关联的低维时空层次结构、处理表型多样性的全脑建模、共享跨诊断病理生理途径的模型整合,以及平衡实验控制和生态效度的自然主义框架。创建具有简化流形并结合生态测量的全脑模型,可以增进我们对脑部疾病的理解,并有助于识别新的干预措施。协同学为临床和认知神经科学的未来进展提供了一个综合框架,将脑部健康和疾病的边界推向更成熟、自然主义的方法。