Neth Bryan J, Graff-Radford Jonathan, Mielke Michelle M, Przybelski Scott A, Lesnick Timothy G, Schwarz Christopher G, Reid Robert I, Senjem Matthew L, Lowe Val J, Machulda Mary M, Petersen Ronald C, Jack Clifford R, Knopman David S, Vemuri Prashanthi
Department of Neurology, Mayo Clinic, Rochester, MN, United States.
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
Front Aging Neurosci. 2020 Jan 9;11:355. doi: 10.3389/fnagi.2019.00355. eCollection 2019.
Brain reserve can be defined as the individual variation in the brain structural characteristics that later in life are likely to modulate cognitive performance. Late midlife represents a point in aging where some structural brain imaging changes have become manifest but the effects of cognitive aging are minimal, and thus may represent an ideal opportunity to determine the relationship between risk factors and brain imaging biomarkers of reserve.
We aimed to assess neuroimaging measures from multiple modalities to broaden our understanding of brain reserve, and the late midlife risk factors that may make the brain vulnerable to age related cognitive disorders.
We examined multimodal [structural and diffusion Magnetic Resonance Imaging (MRI), FDG PET] neuroimaging measures in 50-65 year olds to examine the associations between risk factors (Intellectual/Physical Activity: education-occupation composite, physical, and cognitive-based activity engagement; General Health Factors: presence of cardiovascular and metabolic conditions (CMC), body mass index, hemoglobin A1c, smoking status (ever/never), CAGE Alcohol Questionnaire (>2, yes/no), Beck Depression Inventory score), brain reserve measures [Dynamic: genu corpus callosum fractional anisotropy (FA), posterior cingulate cortex FDG uptake, superior parietal cortex thickness, AD signature cortical thickness; Static: intracranial volume], and cognition (global, memory, attention, language, visuospatial) from a population-based sample. We quantified dynamic proxies of brain reserve (cortical thickness, glucose metabolism, microstructural integrity) and investigated various protective/risk factors.
Education-occupation was associated with cognition and total intracranial volume (static measure of brain reserve), but was not associated with any of the dynamic neuroimaging biomarkers. In contrast, many general health factors were associated with the dynamic neuroimaging proxies of brain reserve, while most were not associated with cognition in this late middle aged group.
Brain reserve, as exemplified by the four dynamic neuroimaging features studied here, is itself at least partly influenced by general health status in midlife, but may be largely independent of education and occupation.
脑储备可定义为大脑结构特征的个体差异,这种差异在生命后期可能会调节认知表现。中年晚期是衰老过程中的一个阶段,此时大脑的一些结构影像学变化已经显现,但认知衰老的影响最小,因此可能是确定储备风险因素与脑成像生物标志物之间关系的理想时机。
我们旨在评估多种模式的神经影像学测量方法,以加深我们对脑储备以及可能使大脑易患与年龄相关认知障碍的中年晚期风险因素的理解。
我们对50至65岁的人群进行了多模式[结构和扩散磁共振成像(MRI)、氟代脱氧葡萄糖正电子发射断层显像(FDG PET)]神经影像学测量,以研究风险因素(智力/身体活动:教育-职业综合指标、身体活动和基于认知的活动参与度;一般健康因素:心血管和代谢疾病(CMC)的存在、体重指数、糖化血红蛋白、吸烟状况(曾经/从不)、CAGE酒精问卷(>2,是/否)、贝克抑郁量表评分)、脑储备测量指标[动态指标:胼胝体膝部各向异性分数(FA)、后扣带回皮质FDG摄取、顶上叶皮质厚度、阿尔茨海默病特征性皮质厚度;静态指标:颅内体积]与认知(整体、记忆、注意力、语言、视觉空间)之间的关联。我们对脑储备的动态指标(皮质厚度、葡萄糖代谢、微观结构完整性)进行了量化,并研究了各种保护/风险因素。
教育-职业与认知和总颅内体积(脑储备的静态测量指标)相关,但与任何动态神经影像学生物标志物均无关联。相比之下,许多一般健康因素与脑储备的动态神经影像学指标相关,而在这个中年晚期组中,大多数因素与认知无关。
以这里研究的四个动态神经影像学特征为例,脑储备本身至少部分受中年时期一般健康状况的影响,但可能在很大程度上独立于教育和职业。