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.