Mentor: Carlo Pierpaoli, MD.Ph.D. | carlo.pierpaoli@nih.gov
Lab: Laboratory on Quantitative Medical Imaging (QMI)
NIBIB
Mentor: Rakibul Hafiz, Ph.D. |
Lab: Laboratory on Quantitative Medical Imaging (QMI)
NIBIB

Nature or Nurture? Differences in brain white matter structure and architecture in twins using a novel diffusion MRI technique to assess brain morphometry

The Laboratory on Quantitative Medical Imaging develops methods to derive biomarkers from data acquired by non-invasive imaging techniques (such as Magnetic Resonance Imaging, MRI) that are informative about anatomy and physiology and that provide new, accurate and reliable tools for assessment of normal brain development and aging, as well as various medical conditions.

Diffusion tensor-based brain morphometry (DTBM) is a sensitive technique to quantify volume measurement of white matter structures based on diffusion tensors. Previous literature has used T1W morphometry measurements (T1-TBM) to assess differences or similarities amongst the twin pairs included in the human connectome project (HCP) study. In the proposed technical project, using DTBM and T1-TBM, we will analyze the similarities and the differences in volume of brain structures within a twin pair included in the HCP study.

The incoming BESIP intern will participate in this study and will:

  1. Get familiarized with brain anatomy.
  2. Learn the fundamentals of Magnetic Resonance Imaging (MRI) and Diffusion MRI
  3. Learn about diffusion tensor imaging and DTBM/T1W -TBM processing.
  4. Gain experience working with data from large public databases such as the HCP.
  5. Assist in literature search pertaining to twin and heritability analysis.
  6. Perform DTBM and T1W–TBM measurements.
  7. Present the preliminary findings of the technical evaluation at the annual summer poster day (https://www.training.nih.gov/me/spd/).

Throughout the project, mentors will guide the intern about data processing and its analysis. The intern will also participate in weekly lab meetings and gain a perspective about quantitative neuroimaging research.