Mild Cognitive Impairment

Country:

Washington, USA

Target group:

Patients with mild cognitive impairment

Use case:

Before dementia, early stages such as mild cognitive impairment (MCI) provide a crucial window for intervention but are challenging to detect. This study monitored 51 individuals over a year, using the SGMA Index on a weekly basis to track changes and identify early markers of cognitive decline.

 

MCI Clinical Diagnosis

MCI is commonly described as an intermediate stage between normal cognitive aging and dementia. In this study, MCI was defined as a decline in cognitive abilities exceeding what is typical for a person’s age and educational background (1–1.5 standard deviations below normative expectations) but not severe enough to meet the criteria for a dementia diagnosis (as recommended in Winblad et al., 2004). Diagnosis of MCI was based on a combination of clinical evaluation, cognitive testing, and medical history. Clinical evaluations were conducted by a geriatric psychiatrist or neurologist who assessed participants’ cognitive and functional abilities using standardized tools. Healthy controls were screened for cognitive impairment using the same methods as those applied to MCI participants.

Highlights

MI AUC

In this study, we introduced a dependable, reliable, and repeatable model-based system for the online assessment of clinical memory impairment. Our findings demonstrated that this system can efficiently detect memory impairments using only 8 minutes of data collected online, marking a significant improvement over traditional assessments that typically require 3 hours in a clinical setting. These findings open up the possibility of inexpensive population-level monitoring of memory function.

 

Figure (left): Diagnosis probability. The sigmoid curve shows the relationship between SGMA Index values and the likelihood of an MCI diagnosis, with point size representing the number of observations contributing to each data point.

Figure (right): Performance curve. The curve illustrates how well the model distinguishes between MCI and normal cognition across various SGMA Index thresholds. The area under the curve (AUC) reflects the model’s accuracy and is compared to the Montreal Cognitive Assessment (MoCA), one of the most commonly used diagnostic tools for MCI.

Looking for support?

Connect with our dedicated research team—we’re ready to assist you!

Researcher at MemoryLab

Thomas
Wilschut

Researcher & Partnership Coordinator

Looking for support?

Connect with our dedicated research team—we’re ready to assist you!

anais-capik

Anais Capik

Project Coordinator

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Researcher at MemoryLab

Thomas Wilschut

Researcher & Partnership Coordinator

anais-capik

Anais Capik

Project Coordinator

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