Evidence map›Paper›PMID 36352038›Full record

ArticleScientific reports2022

Differentiation of patients with mild cognitive impairment and healthy controls based on computer assisted hand movement analysis: a proof-of-concept study.

Andras Attila Horvath, Dalida Borbala Berente, Balazs Vertes, David Farkas, Gabor Csukly, Tom Werber, Janos Andras Zsuffa, Mate Kiss, Anita Kamondi

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 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

9 authors.

Andras Attila HorvathDepartment of Anatomy Histology and Embryology, Semmelweis University, Budapest, Hungary. horvath.andras1@med.semmelweis-univ.hu.
Dalida Borbala BerenteNeurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, 57 Amerikai út, 1145, Budapest, Hungary.
Balazs VertesPrecognize Ltd, Budapest, Hungary.
David FarkasPrecognize Ltd, Budapest, Hungary.
Gabor CsuklyNeurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, 57 Amerikai út, 1145, Budapest, Hungary.
Tom WerberNeurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, 57 Amerikai út, 1145, Budapest, Hungary.
Janos Andras ZsuffaNeurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, 57 Amerikai út, 1145, Budapest, Hungary.
Mate KissSiemens Healthcare, Budapest, Hungary.
Anita KamondiNeurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, 57 Amerikai út, 1145, Budapest, Hungary.

Funding

EU Joint Programme- Neurodegenerative Disease Research (JPND) 2019-2.1.7-ERA-NET-2020-00006National Brain Research Program II 2017-1.2.1-NKP-2017-00002
6 · The paper itself

Abstract

Mild cognitive impairment (MCI) is the prodromal phase of dementia, and it is highly underdiagnosed in the community. We aimed to develop an automated, rapid (< 5 min), electronic screening tool for the recognition of MCI based on hand movement analysis. Sixty-eight individuals participated in our study, 46 healthy controls and 22 patients with clinically defined MCI. All participants underwent a detailed medical assessment including neuropsychology and brain MRI. Significant differences were found between controls and MCI groups in mouse movement characteristics. Patients showed higher level of entropy for both the left (F = 5.24; p = 0.001) and the right hand (F = 8.46; p < 0.001). Longer time was required in MCI to perform the fine motor task (p < 0.005). Furthermore, we also found significant correlations between mouse movement parameters and neuropsychological test scores. Correlation was the strongest between motor parameters and Clinical Dementia Rating scale (CDR) score (average r: - 0.36, all p's < 0.001). Importantly, motor parameters were not influenced by age, gender, or anxiety effect (all p's > 0.05). Our study draws attention to the utility of hand movement analysis, especially to the estimation of entropy in the early recognition of MCI. It also suggests that our system might provide a promising tool for the cognitive screening of large populations.

Indexed as

Cognitive DysfunctionAnimalsComputersMagnetic Resonance ImagingMiceNeuroimagingNeuropsychological TestsProof of Concept Study

Identifiers

PMID36352038
PMCPMC9646851

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