Resting-State Neural Networks at Complex Visual Hallucinations in Charles Bonnet Syndrome

Background: Charles Bonnet syndrome (CBS) is a prototype phenomenon for investigating complex visual hallucination. Our research focuses on resting-state neural networks features of CBS patients with a comparison of patients with equally matched visual loss and healthy subjects in order to investigate the mechanism behind complex visual hallucinations. Methods: Four CBS patients CBS(+), 3 patients with visual loss but no visual hallucinations CBS(-) and 15 healthy controls (HC) undergo resting-state fMRI recordings and their resting-state data is analyzed. Cognitive functions of the participants were also evaluated through MMSE and um-PDHQ Results: Although we found no difference in DMN between CBS(-) and CBS(+), and between CBS(-) and HC groups, we detected decreased connectivity in CBS(+) compared to the HC group especially in visual hetero-modal association centers (bilateral lateral occipital and lingual gyrus, occipital pole, right medial temporal and temporo-occipital cortex) when left angular gyrus was selected as ROI. Similarly, we detected decreased connectivity in CBS(+) compared to HC in right medial frontal, posterior cingulate, supramarginal, left inferior temporal, and angular gyrus when selected right superior frontal gyrus as ROI. In contrast, increased connectivity was detected in CBS+ compared to HC, in bilateral occipital poles, occipital fusiform gyrus, intra-calcarine cortex, right lingual gyrus and precuneus regions when left medial temporal gyrus was selected as ROI. Conclusion: Our findings suggest a combined mechanism in CBS related to increased internal created images caused by decreased visual external input causing visual hallucinations along with impaired frontotemporal resource tracking system that together impairs cognitive processing.