Alterations in resting-state functional connectivity in Charles Bonnet Syndrome
Bibliographic record
Abstract
Charles Bonnet Syndrome (CBS) is a debilitating phenomenon where individuals experience complex visual hallucinations secondary to vision loss, e.g., age-related macular degeneration, and glaucoma. Altered resting-state networks may contribute to the visual hallucinatory manifestations of CBS. Additionally, CBS symptoms may arise from increased glutamatergic and decreased 𝛾-aminobutyric acid (GABA) receptor activity in areas surrounding deafferented cells in visual cortex. The current study examined functional connectivity between resting-state networks and visual cortex GABA+ and glutamate (Glx) concentrations in CBS. A CBS participant was compared to healthy age-matched controls. A multi-band multi-echo resting-state fMRI sequence and seed-to-target analysis of network connectivity was performed. A MEscher-GArwood Point RESolved Spectroscopy (MEGA-PRESS) sequence was performed with a 25 mm3 voxel placed medially in the visual cortex to quantify GABA+ and Glx concentrations. The CBS participant showed changes in connectivity (both increases and decreases) within the salience network (SN), default mode network (DMN) and visual network (VN). For example, there were decreases in connectivity of the VN with the medial pre-frontal cortex in the DMN; decreases in connectivity with the precuneus in the VN; and decreases in connectivity with the superior temporal gyrus and an increase with the fusiform gyrus within the SN. There was no change in GABA+ or Glx concentrations in V1 between the participant with CBS and controls. Our findings of functional cortical changes but no neurometabolite changes in the CBS participant suggest network level alterations in CBS which could account for the experience of their visual hallucinations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".