Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity

Area of Science:

• Neuroscience
• Medical Imaging
• Endocrinology

Background:

• Type 2 diabetes mellitus (T2DM) is linked to cognitive impairments, including dementia.
• The neural mechanisms underlying T2DM-related cognitive decline are not fully understood.
• Resting state functional connectivity (RSFC) offers insights into brain network function.

Purpose of the Study:

• To investigate the relationship between whole-brain RSFC and cognitive status in T2DM patients.
• To identify specific RSFC patterns associated with cognitive impairment.
• To evaluate the potential of RSFC as a biomarker for cognitive status in T2DM.

Main Methods:

• Utilized an elastic net model to estimate Montreal Cognitive Assessment (MoCA) scores and select relevant RSFCs in 95 T2DM patients.
• Employed machine learning to assess the discriminative power of identified RSFC patterns.
• Analyzed functional connectivity within and between the default mode network (DMN) and task-positive networks.

Main Results:

• A significant correlation (R=0.81, MAE=1.20) was found between estimated and chronological MoCA scores.
• The RSFC pattern accurately identified cognitive impairment in T2DM patients (90.54% accuracy, 0.9737 AUC).
• The predictive connectivity pattern involved networks within the DMN, task-positive networks, and their interconnections.

Conclusions:

• RSFC patterns are significantly associated with cognitive status in T2DM.
• Brain connectivity analysis can effectively identify cognitive impairments in T2DM patients.
• RSFC patterns show promise as a non-invasive biomarker for assessing cognitive health in T2DM.