Cortical Microstructure & Aging (CM-Age)

我们生活在一个老龄化的社会：德国人的平均年龄为46岁，比国际平均年龄高出15岁，而德国公民目前的预期寿命约为80岁。然而，与年龄相关的运动、感觉和认知能力下降开始得很早，大约在40岁左右，而且往往进展迅速。这种结合导致依赖外部医疗保健的人数急剧增加(目前德国有234万人)，这构成了当前医疗保健系统的主要负担，并显著降低了个人的生活质量。由于人口结构的变化，受影响的老年人数量预计每20年翻一番，保护老年人的感觉运动和认知能力是当前人类神经科学研究的主要挑战之一。动物实验表明，大多数受年龄影响的神经元机制表现在皮质微结构水平，并在不同的层次水平上影响皮质功能，如皮质宏观结构和行为表型。然而，目前对人类衰老进行的神经科学研究往往缺乏微观层次的解释，这是因为缺乏技术，和/或缺乏描述亚毫米尺度(即1 mm-100微米，通常也称为中尺度)神经行为变化所需的方法学专业知识。这导致目前缺乏从动物研究中获得的洞察力到人类年龄相关变化的机械性知识转移，以及相关的行为表型。CM-Age将针对这个问题。CM-Age将应用新的成像技术(即7特斯拉MRI)，将基础神经科学研究与感觉运动、认知和现象学水平的行为表型相结合，以构建与年龄相关的神经行为变化的连贯、机械框架。我在这里所遵循的“机械性”的定义期望初级感觉皮层在微观和中观尺度上的基本神经科学调节机制向在运动、感觉、认知和现象学水平上更复杂的行为表型转移。CM-Age的最终目标是利用所获得的洞察力来开发机械基础的训练范例，以阻止或扭转与年龄相关的功能丧失。

We live in an aging society: The German mean age is 46 years – 15 years above the international average – and a German citizen can currently expect to live for around 80 years. Age-related motor, sensory, and cognitive decline, however, start early, around the age of 40, and often progress rapidly. This combination leads to dramatically increasing numbers of people being dependent on external health care (currently 2.34 million people in Germany), which constitutes a major burden for current health care systems, and significantly reduces an individual’s quality-of-life. Because the number of affected elderly people is expected to double every 20 years due to demographic change, preserving sensorimotor and cognitive abilities in the elderly is one of the major challenges for current human neuroscience research. It is known from animal work that most neuronal mechanisms that are affected by age manifest at the level of cortical microstructure, and influence cortical functions at different hierarchical levels, such as cortical macrostructure, and behavioral phenotypes. Current neuroscience research that is conducted on human aging, however, often lacks a microstructural level of explanation due to missing technology, and/or missing methodological expertise needed to describe neuro-behavioral changes at a sub-millimeter scale (i.e., 1mm-100µm, also often referred to as mesoscale). This leads to a current lack of mechanistic knowledge transfer from insights gained in animal research to age-related changes in humans, and associated behavioral phenotypes. CM-Age will target this problem. CM-Age will apply novel imaging technology (i.e., 7 Tesla MRI) to integrate basic neuroscience research with behavioral phenotypes at the sensorimotor, cognitive, and phenomenological level to build coherent, mechanistic frameworks on age-related neuro-behavioral change. The definition of ‘mechanistic’ I follow here expects a transfer of basic neuroscientific regulatory mechanisms in primary sensory cortex at the micro- and mesoscale to more complex behavioral phenotypes at the motor, sensory, cognitive, and phenomenological level. CM-Age has the final goal to use gained insights to develop mechanistically grounded training paradigms to stop or reverse age-related loss-of-function.