[P4–142]: QUANTITATIVE MEASUREMENTS OF MOTOR FUNCTION IN ALZHEIMER's DISEASE, FRONTOTEMPORAL DEMENTIA, AND LEWY BODY DEMENTIA: A PROOF‐OF‐CONCEPT STUDY

Kragh, Frederikke Jeppesen; Hasselbalch, Steen Gregers; Nielsen, Jørgen E.; Bruun, Marie; Reilmann, Ralf; Schubert, Robin

doi:10.1016/j.jalz.2017.06.2008

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“…Finger tapping is also found to be affected in MCI and AD, with studies generally showing that compared to healthy controls these groups have longer response times (AD [18]), slower tapping speed [19][20][21][22] (although one study reported no significant differences in tapping speed between controls and AD subjects [23]), more variable tapping speed [20-22, 24, 25], longer tap intervals [18,20,21], longer tap duration [18,21,22], weaker tapping force [22], slower finger flexion [22], and less steady hands during tapping [19]. Although some of these results were less pronounced in MCI than in AD [18], index finger tapping speed was not different between individuals with MCI and unspecified dementia in another study [26], which could indicate that tapping is affected early in the disease process, but does not further deteriorate over later stages of AD.…”

Section: Introductionmentioning

confidence: 99%

Delayed and More Variable Unimanual and Bimanual Finger Tapping in Alzheimer’s Disease: Associations with Biomarkers and Applications for Classification

Koppelmans,

Ruitenberg,

Schaefer

et al. 2023

JAD

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Background: Despite reports of gross motor problems in mild cognitive impairment (MCI) and Alzheimer’s disease (AD), fine motor function has been relatively understudied. Objective: We examined if finger tapping is affected in AD, related to AD biomarkers, and able to classify MCI or AD. Methods: Forty-seven cognitively normal, 27 amnestic MCI, and 26 AD subjects completed unimanual and bimanual computerized tapping tests. We tested 1) group differences in tapping with permutation models; 2) associations between tapping and biomarkers (PET amyloid-β, hippocampal volume, and APOE ɛ4 alleles) with linear regression; and 3) the predictive value of tapping for group classification using machine learning. Results: AD subjects had slower reaction time and larger speed variability than controls during all tapping conditions, except for dual tapping. MCI subjects performed worse than controls on reaction time and speed variability for dual and non-dominant hand tapping. Tapping speed and variability were related to hippocampal volume, but not to amyloid-β deposition or APOE ɛ4 alleles. Random forest classification (overall accuracy = 70%) discriminated control and AD subjects, but poorly discriminated MCI from controls or AD. Conclusions: MCI and AD are linked to more variable finger tapping with slower reaction time. Associations between finger tapping and hippocampal volume, but not amyloidosis, suggest that tapping deficits are related to neuropathology that presents later during the disease. Considering that tapping performance is able to differentiate between control and AD subjects, it can offer a cost-efficient tool for augmenting existing AD biomarkers.

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