Fractional anisotropy derived from the diffusion tensor distribution function boosts power to detect Alzheimer's disease deficits

for the Alzheimer's Diseaase Neuroimaginng Initiative (ADNI)

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Purpose: In diffusion MRI (dMRI), fractional anisotropy derived from the single-tensor model (FADTI) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors. Methods: We compared the ability of standard FADTI and TDF-derived FA (FATDF), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume. Results: Across angular resolutions and statistical tests, FATDF showed larger effect sizes than FADTI, particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies. Conclusion: The TDF “corrected” form of FA may be a more sensitive and accurate alternative to the commonly used FADTI, even in clinical quality dMRI data. Magn Reson Med 78:2322–2333, 2017.

Original languageEnglish (US)
Pages (from-to)2322-2333
Number of pages12
JournalMagnetic Resonance in Medicine
Issue number6
StatePublished - Dec 2017


  • Alzheimer's disease
  • diffusion-weighted imaging
  • fractional anisotropy
  • tensor distribution function
  • white matter

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


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