A neuroimaging functional connectivity signature of emotional conflict monitoring predicting cognitive decline in type 2 diabetes

Type 2 diabetes (T2D) is associated with cognitive decline and neurodegenerative disorders. Changes in the connections between brain regions responsible for emotions and memory might play a role in the reduced cognitive function observed in individuals with diabetes. Utilizing machine learning approaches on neuroimaging data shows potential for exploring these intricate associations. A research study was carried out with 40 individuals diagnosed with T2D and 30 control participants, all of whom were middle-aged and right-handed. The participants underwent neuropsychological assessments and fMRI scans while engaging in an emotional Stroop task. Our analysis concentrated on the functional connectivity of specific brain regions associated with cognitive control. We utilized a fully connected network (FCN)-based machine learning approach to predict cognitive decline using neural connectivity patterns. The FCN accurately forecasted Montreal Cognitive Assessment scores in patients with Type 2 Diabetes, showing a robust relationship between anticipated and observed scores in both the training and testing sets. It revealed important patterns of connectivity in the anterior cingulate cortex and other regions responsible for cognitive control, which played a vital role in predicting cognitive deterioration. Our results indicate that machine learning approach, when using functional connectivity information, have the capability to forecast cognitive deterioration in T2D patients. This method may aid in early identification and intervention plans, potentially reducing the effects of cognitive deficits in this group. Further studies should confirm these results with larger and more varied samples to improve their applicability and relevance for clinical practice.