Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Neuroscience Research Building 225E, 635 Charles Young Drive, Los Angeles, CA 90095-1769, USA.
Neuroimage. 2010 May 15;51(1):63-75. doi: 10.1016/j.neuroimage.2010.01.104. Epub 2010 Feb 6.
Neuroimaging centers and pharmaceutical companies are working together to evaluate treatments that might slow the progression of Alzheimer's disease (AD), a common but devastating late-life neuropathology. Recently, automated brain mapping methods, such as tensor-based morphometry (TBM) of structural MRI, have outperformed cognitive measures in their precision and power to track disease progression, greatly reducing sample size estimates for drug trials. In the largest TBM study to date, we studied how sample size estimates for tracking structural brain changes depend on the time interval between the scans (6-24 months). We analyzed 1309 brain scans from 91 probable AD patients (age at baseline: 75.4+/-7.5 years) and 189 individuals with mild cognitive impairment (MCI; 74.6+/-7.1 years), scanned at baseline, 6, 12, 18, and 24 months. Statistical maps revealed 3D patterns of brain atrophy at each follow-up scan relative to the baseline; numerical summaries were used to quantify temporal lobe atrophy within a statistically-defined region-of-interest. Power analyses revealed superior sample size estimates over traditional clinical measures. Only 80, 46, and 39 AD patients were required for a hypothetical clinical trial, at 6, 12, and 24 months respectively, to detect a 25% reduction in average change using a two-sided test (alpha=0.05, power=80%). Correspondingly, 106, 79, and 67 subjects were needed for an equivalent MCI trial aiming for earlier intervention. A 24-month trial provides most power, except when patient attrition exceeds 15-16%/year, in which case a 12-month trial is optimal. These statistics may facilitate clinical trial design using voxel-based brain mapping methods such as TBM.
神经影像学中心和制药公司正在合作评估可能减缓阿尔茨海默病（AD）进展的治疗方法，AD 是一种常见但具有破坏性的晚年神经病理学。最近，自动化脑图谱方法，如结构 MRI 的张量基形态测量（TBM），在精确性和跟踪疾病进展的能力方面优于认知测量，大大降低了药物试验的样本量估计。在迄今为止最大的 TBM 研究中，我们研究了跟踪结构脑变化的样本量估计如何取决于扫描之间的时间间隔（6-24 个月）。我们分析了 91 名可能的 AD 患者（基线时年龄：75.4+/-7.5 岁）和 189 名轻度认知障碍（MCI；74.6+/-7.1 岁）的 1309 个脑扫描，这些患者在基线、6、12、18 和 24 个月时进行了扫描。统计图谱显示了相对于基线的每次随访扫描的大脑萎缩的 3D 模式；数值摘要用于量化在统计学定义的感兴趣区域内的颞叶萎缩。功效分析显示，与传统临床指标相比，样本量估计具有优势。仅需要 80、46 和 39 名 AD 患者，分别在 6、12 和 24 个月的假设临床试验中，使用双侧检验（alpha=0.05，功效=80%）检测平均变化减少 25%。相应地，需要 106、79 和 67 名 MCI 患者进行类似的试验，以实现早期干预。除了每年患者流失超过 15-16%的情况外，24 个月的试验提供最大的功效，在这种情况下，12 个月的试验是最佳的。这些统计数据可能有助于使用基于体素的脑图谱方法（如 TBM）进行临床试验设计。
