Imagine your brain as a bustling city, with different neighborhoods responsible for different functions. One important neighborhood, called the default mode network (DMN), plays a crucial role in cognitive processes. In a recent study, scientists investigated how the DMN is connected to regions involved in visual processing and its potential as a biomarker for delayed neurocognitive recovery (DNR). They used resting-state functional magnetic resonance imaging (fMRI) to study 74 patients before surgery and found that DNR patients had significantly decreased connectivity between the DMN and visual processing regions. This suggests that disrupted communication between these brain areas could be a sign of impaired cognitive recovery. To further explore this, the researchers employed powerful machine-learning algorithms to predict DNR based on the functional connectivity features. Their best-performing model, using the random forest algorithm, achieved an impressive accuracy of 84.0% and provided valuable insights into the neural mechanisms of DNR. The study opens up new avenues for understanding how visual processing regions influence cognitive recovery and may help identify individuals at risk for delayed neurocognitive recovery. For more details, dive into the full research article!

ObjectivesThe abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms.MethodsResting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test.ResultsWe found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus.ConclusionThe decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR.Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR-DCD-15006096.

Read Full Article (External Site)