Technology and Dementia Preconference

Area of Science:

• Neuroscience
• Digital Health
• Cognitive Science

Background:

• Mild Cognitive Impairment (MCI) presents challenges in early detection using traditional neuropsychological tests.
• Computerized assessments offer potential for more sensitive detection of cognitive deficits.
• Integrating eye and hand tracking provides fine-grained movement data for enhanced analysis.

Purpose of the Study:

• To investigate fine-grained motor and oculomotor differences in individuals with MCI compared to neurotypical controls.
• To explore the utility of a computerized Trail Making Test (c-TMT) with integrated eye and hand tracking as a digital biomarker for MCI.
• To assess the potential of movement dynamics in identifying subtle cognitive impairments.

Main Methods:

• Twenty-nine MCI patients and 28 controls completed a c-TMT with integrated eye (500 Hz) and mouse tracking.
• Participants alternated between TMT-A and TMT-B trials, with stimuli presented on a screen.
• Linear Mixed Models (LMM) analyzed performance metrics (completion percentage, trial time) and eye-tracking data (scanpath length, fixation duration).

Main Results:

• Significant differences were found between MCI patients and controls in completion percentage and trial completion time.
• Scanpath length (number of fixations) showed significant differences based on both trial type and subject group (MCI vs. control).
• Fixation duration and eye-hand coordination metrics did not reveal significant group differences.

Conclusions:

• The developed c-TMT effectively identified significant differences in scanpath length between MCI patients and controls.
• Analysis of combined hand and eye movements, particularly fixation patterns, can reveal subtle alterations in MCI.
• This approach holds promise as a tool in Digital Neuropsychology for more precise cognitive assessment.