PLoS ONE (Jan 2022)

Relationship between finger movement characteristics and brain voxel-based morphometry.

  • Junpei Sugioka,
  • Shota Suzumura,
  • Katsumi Kuno,
  • Shiori Kizuka,
  • Hiroaki Sakurai,
  • Yoshikiyo Kanada,
  • Tomohiko Mizuguchi,
  • Izumi Kondo

DOI
https://doi.org/10.1371/journal.pone.0269351
Journal volume & issue
Vol. 17, no. 10
p. e0269351

Abstract

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BackgroundAging is the most significant risk factor for dementia. Alzheimer's disease (AD) accounts for approximately 60-80% of all dementia cases in older adults. This study aimed to examine the relationship between finger movements and brain volume in AD patients using a voxel-based reginal analysis system for Alzheimer's disease (VSRAD) software.MethodsPatients diagnosed with AD at the Center for Comprehensive Care and Research on Memory Disorders were included. The diagnostic criteria were based on the National Institute on Aging-Alzheimer's Association. A finger-tapping device was used for all measurements. Participants performed the tasks in the following order: with their non-dominant hand, dominant hand, both hands simultaneously, and alternate hands. Movements were measured for 15 s each. The relationship between distance and output was measured. Magnetic resonance imaging measurements were performed, and VSRAD was conducted using sagittal section 3D T1-weighted images. The Z-score was used to calculate the severity of medial temporal lobe atrophy. Pearson's product-moment correlation coefficient analyzed the relationship between the severity of medial temporal lobe atrophy and mean values of the parameters in the finger-tapping movements. The statistical significance level was set at ResultsSixty-two patients were included in the study. Comparison between VSRAD and MoCA-J scores corrected for p-values showed a significant negative correlation with the extent of gray matter atrophy (r = -0. 52; pConclusionsThe SD of distance rate of velocity peak in extending movements extracted from finger taps may be a useful parameter for the early detection of AD and diagnosis of its severity.