Altered voxel-wise degree centrality associated with patients with comitant exotropia

From convergence insufficiency to functional reorganization: A longitudinal randomized controlled trial of treatment-induced connectivity plasticity

INTRODUCTION

Convergence Insufficiency (CI) is the most prevalent oculomotor dysfunction of binocular vision that negatively impacts quality of life when performing visual near tasks. Decreased resting-state functional connectivity (RSFC) is reported in the CI participants compared to binocularly normal control participants. Studies report that therapeutic interventions such as office-based vergence and accommodative therapy (OBVAT) can improve CI participants' clinical signs, visual symptoms, and task-related functional activity. However, longitudinal studies investigating the RSFC changes after such treatments in participants with CI have not been conducted. This study aimed to investigate the neural basis of OBVAT using RSFC in CI participants compared to the placebo treatment to understand how OBVAT improves visual function and symptoms.

METHODS

A total of 51 CI participants between 18 and 35 years of age were included in the study and randomly allocated to receive either 12 one-hour sessions of OBVAT or placebo treatment for 6 to 8 weeks (1 to 2 sessions per week). Resting-state functional magnetic resonance imaging and clinical assessments were evaluated at baseline and outcome for each treatment group. Region of interest (ROI) analysis was conducted in nine ROIs of the oculomotor vergence network, including the following: cerebellar vermis (CV), frontal eye fields (FEF), supplementary eye fields (SEF), parietal eye fields (PEF), and primary visual cortices (V1). Paired t-tests assessed RSFC changes in each group. A linear regression analysis was conducted for significant ROI pairs in the group-level analysis for correlations with clinical measures.

RESULTS

Paired t-test results showed increased RSFC in 10 ROI pairs after the OBVAT but not placebo treatment (p < 0.05, false discovery rate corrected). These ROI pairs included the following: Left (L)-SEF-Right (R)-V1, L-SEF-CV, R-SEF-R-PEF, R-SEF-L-V1, R-SEF-R-V1, R-SEF-CV, R-PEF-CV, L-V1-CV, R-V1-CV, and L-V1-R-V1. Significant correlations were observed between the RSFC strength of the R-SEF-R-PEF ROI pair and the following clinical visual function parameters: positive fusional vergence and near point of convergence (p < 0.05).

CONCLUSION

OBVAT, but not placebo treatment, increased the RSFC in the ROIs of the oculomotor vergence network, which was correlated with the improvements in the clinical measures of the CI participants.

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

Objectives

The 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.

Methods

Resting-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.

Results

We 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.

Conclusion

The 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.