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Cited by 26Advanced Neuroimaging Techniques and ApplicationsCognitive Abilities and TestingNeural and Behavioral Psychology Studies

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

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Behrman-Lay, Ashley M., Usher, Christina, Conturo, Thomas E., Correia, Stephen, Laidlaw, David H., Lane, Elizabeth M., Bolzenius, Jacob, Heaps, Jodi M. +7 more·2015·Brain Imaging and Behavior
DOI 10.1007/s11682-014-9334-8PMC PMC4424188
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Abstract

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.

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  • The volume and length of a fiber tract can be computed directly from tractography and have been used as quantitative measures.

    “Other quantitative measures can be computed directly from tractography, such as the volume and the length of a fiber tract (Bajada et al., 2019; Behrman-Lay et al., 2015; Catani et al., 2007; Chandio et al., 2020a; Colby et al., 2012a; Grinberg et al., 2018; Rheault et al., 2020a; Song et al., 2015; Yeatman et al., 2012), have also been used.”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Behrman-Lay et al., 2015

    Methods §2.5 says the authors computed mean track length from tractography-derived arc lengths and used it as a quantitative measure. It does not report computing fiber-tract volume; the volume mentioned there is intracranial volume, used to normalize length. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Authors
Behrman-Lay, A. M., Usher, C., Conturo, T. E., Correia, S., Laidlaw, D. H., Lane, E. M., Bolzenius, J., Heaps, J. M., Salminen, L. E., Baker, L. M., Cabeen, R., Akbudak, E., Luo, X., Yan, P., & Paul, R. H.
Affiliations
  • University of Missouri–St. Louis (US)
  • Brown University (US)
  • Vanderbilt University Medical Center (US)
  • Washington University in St. Louis (US)
Corresponding author
Behrman-Lay, Ashley M.
ORCID
Venue
Brain Imaging and Behavior
Publisher
Springer Science and Business Media LLC
Published
2014-11-07
Volume
9
Issue
4
Pages
765-775
ISSN
  • 1931-7557 (ISSN-L)
  • 1931-7565
DOI
10.1007/s11682-014-9334-8
PMC
PMC4424188
OpenAlex
W2047255721
Cited by
26
References
66
Citations by year
2026: 2 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 3 · 2021: 2 · 2019: 3 · 2018: 1 · 2017: 3 · 2016: 6 · 2015: 1
FWCI
2.61
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Brain Imaging and Behavior (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (UL1TR000005, UL1TR000448)
  • National Institute of Mental Health (R21MH090494, R21MH105822)
  • National Institute of General Medical Sciences (P20GM103645)
  • National Institute of Neurological Disorders and Stroke (R01NS039538, R01NS052470)
  • National Center for Advancing Translational Sciences
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Keywords
white matter microstructure, executive function, processing speed, cognitive aging, age-related cognitive decline, diffusion tractography, healthy older adults, frontal lobe, parietal lobe, occipital lobe, Trail Making Test, Color-Word Interference Test, letter-number sequencing, symbol coding
MeSH terms
Aged, Aged, 80 and over, Aging, Brain, Cognition, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Neural Pathways, Neuropsychological Tests, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Executive Function

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