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Mapping Alzheimer's disease progression in 1309 MRI scans: power estimates for different inter-scan intervals.

Abstract：

: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.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Hua X,Lee S,Hibar DP,Yanovsky I,Leow AD,Toga AW,Jack CR Jr,Bernstein MA,Reiman EM,Harvey DJ,Kornak J,Schuff N,Alexander GE,Weiner MW,Thompson PM,Alzheimer's Disease Neuroimaging Initiative.

doi

10.1016/j.neuroimage.2010.01.104

subject

Has Abstract

pub_date

2010-05-15 00:00:00

pages

63-75

issue

1

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(10)00143-6

journal_volume

51

pub_type

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