Evidence map›Paper›PMID 32304267›Full record

ArticleHuman brain mapping2020

Changes in white matter fiber density and morphology across the adult lifespan: A cross-sectional fixel-based analysis.

Shao Wei Choy, Epifanio Bagarinao, Hirohisa Watanabe, Eric Tatt Wei Ho, Satoshi Maesawa, Daisuke Mori, Kazuhiro Hara, Kazuya Kawabata, Noritaka Yoneyama, Reiko Ohdake and 15 more

Abstract read
In one paragraph

Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Shao Wei ChoyCenter for Intelligent Signal and Imaging Research, Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia.ORCID 0000-0001-6855-3670
Epifanio BagarinaoBrain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.ORCID 0000-0001-5000-6943
Hirohisa WatanabeBrain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
Eric Tatt Wei HoCenter for Intelligent Signal and Imaging Research, Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia.
Satoshi MaesawaBrain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
Daisuke MoriBrain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
Kazuhiro HaraDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Kazuya KawabataDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Noritaka YoneyamaDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Reiko OhdakeDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Kazunori ImaiDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Michihito MasudaDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Takamasa YokoiDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Aya OguraDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Toshiaki TaokaDepartment of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Shuji KoyamaBrain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
Hiroki C TanabeDepartment of Cognitive and Psychological Sciences, Graduate School of Informatics, Nagoya University, Nagoya, Aichi, Japan.
Masahisa KatsunoDepartment of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Toshihiko WakabayashiDepartment of Neurosurgery, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Masafumi KuzuyaDepartment of Community Healthcare and Geriatrics, Nagoya University Graduate School of Medicine and Institute of Innovation for Future Society, Nagoya University, Nagoya, Aichi, Japan.
Minoru HoshiyamaBrain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
Haruo IsodaBrain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
Shinji NaganawaDepartment of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan.
Norio OzakiBrain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.
Gen SobueBrain and Mind Research Center, Nagoya University, Nagoya, Aichi, Japan.ORCID 0000-0003-4769-5922

Funding

Ministry of Education, Culture, Sports, Science and Technology 26117002Ministry of Education, Culture, Sports, Science and Technology 80569781Ministry of Health, Labour and WelfareMinistry of Higher Education Malaysia HI-CoE Program
6 · The paper itself

Abstract

White matter (WM) fiber bundles change dynamically with age. These changes could be driven by alterations in axonal diameter, axonal density, and myelin content. In this study, we applied a novel fixel-based analysis (FBA) framework to examine these changes throughout the adult lifespan. Using diffusion-weighted images from a cohort of 293 healthy volunteers (89 males/204 females) from ages 21 to 86 years old, we performed FBA to analyze age-related changes in microscopic fiber density (FD) and macroscopic fiber morphology (fiber cross section [FC]). Our results showed significant and widespread age-related alterations in FD and FC across the whole brain. Interestingly, some fiber bundles such as the anterior thalamic radiation, corpus callosum, and superior longitudinal fasciculus only showed significant negative relationship with age in FD values, but not in FC. On the other hand, some segments of the cerebello-thalamo-cortical pathway only showed significant negative relationship with age in FC, but not in FD. Analysis at the tract-level also showed that major fiber tract groups predominantly distributed in the frontal lobe (cingulum, forceps minor) exhibited greater vulnerability to the aging process than the others. Differences in FC and the combined measure of FD and cross section values observed between sexes were mostly driven by differences in brain sizes although male participants tended to exhibit steeper negative linear relationship with age in FD as compared to female participants. Overall, these findings provide further insights into the structural changes the brain's WM undergoes due to the aging process.

Indexed as

Diffusion Magnetic Resonance ImagingAdultAgedAged, 80 and overAgingCross-Sectional StudiesFemaleHuman DevelopmentHumansMaleMiddle AgedNerve Fibers, MyelinatedNeural PathwaysSex FactorsWhite MatterYoung Adultadult lifespanagingfiber cross sectionfiber densityfixel-based analysiswhite matter

Identifiers

PMID32304267
PMCPMC7375080

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.