Disrupted topological organization of the default mode network in mild cognitive impairment with subsyndromal
Yang Du1,2, Jing Nie1,2, Jian-Ye Zhang3
1Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Aims:
Subsyndromal depression (SSD) is common in mild cognitive impairment (MCI). However, the neural mechanisms underlying MCI with SSD (MCID) are unclear. The default mode network (DMN) is associated with cognitive processes and depressive symptoms. Therefore, we aimed to explore the topological organization of the DMN in patients with MCID.
Methods:
Forty-two MCID patients, 34 MCI patients without SSD (MCIND), and 36 matched healthy controls (HCs) were enrolled. The resting-state functional connectivity of the DMN of the participants was analyzed using a graph theoretical approach. Correlation analyses of network topological metrics, depressive symptoms, and cognitive function were conducted. Moreover, support vector machine (SVM) models were constructed based on topological metrics to distinguish MCID from MCIND. Finally, we used 10 repeats of 5-fold cross-validation for performance verification.
Results:
We found that the global efficiency and nodal efficiency of the left anterior medial prefrontal cortex (aMPFC) of the MCID group were significantly lower than the MCIND group. Moreover, small-worldness and global efficiency were negatively correlated with depressive symptoms in MCID, and the nodal efficiency of the left lateral temporal cortex and left aMPFC was positively correlated with cognitive function in MCID. In cross-validation, the SVM model had an accuracy of 0.83 [95% CI 0.79-0.87], a sensitivity of 0.88 [95% CI 0.86-0.90], a specificity of 0.75 [95% CI 0.72-0.78] and an area under the curve of 0.88 [95% CI 0.85-0.91].
Conclusions:
The coexistence of MCI and SSD was associated with the greatest disrupted topological organization of the DMN. The network topological metrics could identify MCID and serve as biomarkers of different clinical phenotypic presentations of MCI.
Insights
Subsyndromal depression in mild cognitive impairment disrupts the default mode network
Area of Science:
- Neuroscience
- Cognitive Science
- Psychiatry
Background:
- Subsyndromal depression (SSD) is prevalent in mild cognitive impairment (MCI).
- The neural underpinnings of MCI with SSD (MCID) remain poorly understood.
- The default mode network (DMN) is implicated in cognition and depression.
Purpose of the Study:
- To investigate the topological organization of the DMN in patients with MCID.
- To explore the relationship between DMN topology, depressive symptoms, and cognitive function in MCID.
- To assess the potential of DMN topological metrics as biomarkers for MCID.
Main Methods:
- Utilized graph theory to analyze resting-state functional connectivity of the DMN in 42 MCID patients, 34 MCI without SSD (MCIND) patients, and 36 healthy controls (HCs).
- Performed correlation analyses between DMN network metrics, depressive symptoms, and cognitive scores.
- Developed and validated Support Vector Machine (SVM) models using DMN topological metrics to differentiate MCID from MCIND.
Main Results:
- MCID patients exhibited significantly reduced global and nodal efficiency in the left anterior medial prefrontal cortex (aMPFC) compared to MCIND patients.
- In MCID, DMN small-worldness and global efficiency negatively correlated with depressive symptom severity.
- Nodal efficiency in the left lateral temporal cortex and left aMPFC positively correlated with cognitive function in MCID.
- The SVM model achieved high accuracy (0.83) in distinguishing MCID from MCIND.
Conclusions:
- The co-occurrence of MCI and SSD is associated with significant disruptions in DMN topological organization.
- DMN topological metrics can effectively differentiate MCID from MCIND.
- These network metrics show promise as biomarkers for distinct clinical presentations of MCI.
More Related Videos
12:09Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
Published on: August 5, 2014
08:43Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
