Brain Imaging and Behavior

, Volume 9, Issue 4, pp 765–775 | Cite as

Fiber bundle length and cognition: a length-based tractography MRI study

  • Ashley M. Behrman-LayEmail author
  • Christina Usher
  • Thomas E. Conturo
  • Stephen Correia
  • David H. Laidlaw
  • Elizabeth M. Lane
  • Jacob Bolzenius
  • Jodi M. Heaps
  • Lauren E. Salminen
  • Laurie M. Baker
  • Ryan Cabeen
  • Erbil Akbudak
  • Xi Luo
  • Peisi Yan
  • Robert H. Paul
Original Research


Executive function (EF) and cognitive processing speed (CPS) are two cognitive performance domains that decline with advanced age. Reduced EF and CPS are known to correlate with age-related frontal-lobe volume loss. However, it remains unclear whether white matter microstructure in these regions is associated with age-related decline in EF and/or CPS. We utilized quantitative tractography metrics derived from diffusion-tensor MRI to investigate the relationship between the mean fiber bundle lengths (FBLs) projecting to different lobes, and EF/CPS performance in 73 healthy aging adults. We measured aspects of EF and CPS with the Trail Making Test (TMT), Color-Word Interference Test, Letter-Number Sequencing (L-N Seq), and Symbol Coding. Results revealed that parietal and occipital FBLs explained a significant portion of variance in EF. Frontal, temporal, and occipital FBLs explained a significant portion of variance in CPS. Shorter occipital FBLs were associated with poorer performance on the EF tests TMT-B and CWIT 3. Shorter frontal, parietal, and occipital FBLs were associated with poorer performance on L-N Seq and Symbol Coding. Shorter frontal and temporal FBLs were associated with lower performance on CPS tests TMT-A and CWIT 1. Shorter FBLs were also associated with increased age. Results suggest an age-related FBL shortening in specific brain regions related to poorer EF and CPS performance among older adults. Overall, results support both the frontal aging hypothesis and processing speed theory, suggesting that each mechanism is contributing to age-related cognitive decline.


Fiber bundle lengths DTI White matter Cognitive processing speed Executive function Aging 



This study was supported by the following grants: NIH/NINDS grant numbers R01 NS052470 and R01 NS039538, NIH/NIMH grant number R21 MH090494. Recruitment database searches were supported in part by NIH/NCRR grant UL1 TR000448.

Conflict of interest

Ashley M. Behrman-Lay, Christina Usher, Thomas E. Conturo, Stephan Correia, David H. Laidlaw, Elizabeth M. Lane, Jacob Bolzenius, Jodi M. Heaps, Lauren E. Salminen, Laurie M. Baker, Ryan Cabeen, Erbil Akbudak, Xi Luo, Peisi Yan, and Robert H. Paul declare that they have no actual or potential conflicts of interest on this manuscript.

Supplementary material

11682_2014_9334_MOESM1_ESM.docx (109 kb)
ESM 1 (DOCX 109 kb)
11682_2014_9334_MOESM2_ESM.docx (33 kb)
ESM 2 (DOCX 33 kb)


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Ashley M. Behrman-Lay
    • 1
    Email author
  • Christina Usher
    • 1
  • Thomas E. Conturo
    • 2
  • Stephen Correia
    • 3
  • David H. Laidlaw
    • 4
  • Elizabeth M. Lane
    • 5
  • Jacob Bolzenius
    • 1
  • Jodi M. Heaps
    • 1
  • Lauren E. Salminen
    • 1
  • Laurie M. Baker
    • 1
  • Ryan Cabeen
    • 4
  • Erbil Akbudak
    • 2
  • Xi Luo
    • 6
  • Peisi Yan
    • 7
  • Robert H. Paul
    • 1
  1. 1.Department of PsychologyUniversity of Missouri – Saint LouisSaint LouisUSA
  2. 2.Mallinckrodt Institute of RadiologyWashington University School of MedicineSt. LouisUSA
  3. 3.Department of Psychiatry & Human BehaviorAlpert Medical School, Brown UniversityProvidenceUSA
  4. 4.Computer Science DepartmentBrown UniversityProvidenceUSA
  5. 5.Vanderbilt University Medical CenterNashvilleUSA
  6. 6.Department of Biostatistics and Center for Statistical SciencesBrown UniversityProvidenceUSA
  7. 7.Center for Computation and VisualizationBrown UniversityProvidenceUSA

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