Single time point high-dimensional morphometry in Alzheimer's disease: group statistics on longitudinally acquired data

Radiological-Pathological Correlation in Alzheimer’s Disease: Systematic Review of Antemortem Magnetic Resonance Imaging Findings

“…Furthermore, the capacity of MRI-derived hippocampal volume to differentiate between AD and vascular dementia has been touted, as neuronal loss in CA1 is more consistently found in AD subjects than in those with vascular dementia [52]. Additional studies using atrophy maps specific to each neurodegenerative dementia [80] or more developed techniques such as image appearance [81] or grading [82], especially focused on medial temporal areas, are needed to confirm these findings, since other authors have suggested that cumulative ischemia without amyloid pathology ! 21!…”

Predicting cognitive decline in a low-dimensional representation of brain morphology

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High-to-low dimensionality transformations have been carried out via a number of approaches in the past. In some instances, dimensionality reduction or machine learning algorithms were applied directly to the raw voxel data [13][14][15][16][17] . Other approaches first use tools (e.g.

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Brainsmatics—bridging the brain science and brain-inspired artificial intelligence

“…析和理解的方法, 包括统计信号处理 [43] 、高维数据 分析 [44] 、大数据处理 [45] 等, 而对所理解的知识进行建 模的方法, 则包括统计建模 [46] 、机器学习 [47] 、深度学 习 [48] 所未有的巨大挑战 [49] . 大数据的计算离不开高性能计 算领域的参与, 欧盟"人类脑计划"在巨型计算机上对 人脑建模 [50] , 通过超级计算机描绘和模拟大脑神经元 活动的海量数据, 对大脑的功能和活动进行模拟.…”