PhD Scientific Days 2024

Poster Session I - Neurosciences 2.

Fine Movement Analysis for the Early Recognition of Cognitive Decline

Előadó neve

Dr. Berente, Dalida Borbála

Neptun code

IFUNSD

Előadó munkahelye

Nyírő Gyula National Institute of Psychiatry, and Addictology, Neurocognitive Research Center, Budapest, Hungary

Előadó telefonszáma

+36209874926

Előadó e-mail címe

dalida.berente@gmail.com

Az előadás címe

Fine Movement Analysis for the Early Recognition of Cognitive Decline

Szerző(k) neve és munkahelye

Dalida Borbala Berente¹, Gergely Hanczar², Bence Borbely³, Agnes Chripko³, Anett Novak¹, Dora Korosi¹, Gabor Benyei¹, Andras Attila Horvath¹, Anita Kamondi¹

1: Nyírő Gyula National Institute of Psychiatry, and Addictology, Neurocognitive Research Center, Budapest, Hungary
2: Cursor Insight, Head Of Research & Development, London, United Kingdom
3: Cursor Insight, Research, London, United Kingdom

Bemutatás módja

Poszter

Szekció

Poster Session I - Neurosciences 2.

Language of the presentation

English

Preferred session

Neurosciences

Összefoglaló szövege

Introduction: Widely available screening methods for large patient population with Alzheimer's disease (AD) and its prodromal phase, mild cognitive impairment (MCI), are not currently available. However, AD is the most common cause of major neurocognitive disorders (NCDs) in the elderly; thus, the development of early screening methods is crucial.
Aims: In this study, we aimed to develop a fine movement analysis system for MCI screening, that is objective, easy to use, sensitive and specific to early-stage cognitive decline, and widely available in clinical settings.
Methods: 86 subjects (mean age=67.2±8.8 years) participated in this study, 78 healthy controls, 5 patients with clinically defined MCI, and 3 patients with AD. They underwent comprehensive neurological and neuropsychological evaluation and MRI acquisition. The participants completed a fine movement analysis paradigm consisting of a digitalized version of the Archimedes spiral, the Trail-Making Test (TMT), and the Benson Complex Figure, among others. The predictive power of fine movement parameters in distinguishing patients with different types and stages of cognitive decline was analysed.
Results: Preliminary results showed that Benson Complex Figure had the highest sensitivity (80.0±20.1) and specificity (82.7±13.3), followed by TMT (sensitivity=81.9±21.9, specificity=61.1±24.2) and Archimedes spiral (sensitivity=79.1±22.9, specificity= 64.8±20.6) tasks, in distinguishing between different patient groups with cognitive decline.
Conclusion: Based on our results, fine movement analysis might serve a screening purpose, as our paradigm shows promising potential for the early recognition of cognitive decline. Our findings are promising for the future development of an automatic, digital screening method for MCI. However, one should refrain from drawing firm conclusions because of the limited number of patients involved, thus, we need to increase the number of patients involved in the study.
Funding: Supported by the ÚNKP-23-3-II-SE-98 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund; Development of scientific workshops in medical, health sciences and pharmacy training (EFOP-3.6.3-VEKOP-16-2017-00009); SE 250+ Excellence Scholarship; National Brain Research Program III (NAP2022-I-9/2022)

University

Semmelweis University

Supervisor

András Attila Horváth MD, PhD

Publication of my abstract

I give consent to the publication of my abstract on the website of the congress.

Kind

Szabad

Status

elfogadva

Accepted presentation method

poszter

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

6880

Start

15:25

End

15:28