Disrupted Coupling Between Cerebral Glucose Metabolism and Intrinsic Functional Connectivity: A Hybrid <scp>PET</scp> / <scp>fMRI</scp> Study on Frontotemporal Dementia
Bibliographic record
Abstract
It is increasingly established that the organization of the brain into functional resting-state networks allows efficient integration and processing of information. Functional hubs anchoring such networks are characterized by a high degree of communication, which relies on efficient utilization of glucose. Alzheimer's disease (AD) disrupts the balance between glucose metabolism and intrinsic functional connectivity (FC). We hypothesized that this critical coupling would also be weakened in frontotemporal dementia (FTD), particularly within the salience network, given its association with the disease. Towards this goal, behavioral variant FTD (bvFTD) patients (n = 21) and healthy participants (n = 18) underwent simultaneous FDG-PET and functional MRI imaging in a hybrid PET/MR system, with an additional cohort completing the MRI component only. PET images were converted into standardized uptake value ratios (SUVr), and local FC was quantified using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF), two metrics that have been demonstrated to be related to FDG-PET uptake. The interplay between FC and glucose metabolism was investigated within the salience and default mode networks. The bvFTD group showed network-level functional breakdown and significantly weakened metabolism/FC coupling, especially in the dorsal anterior insula and posterior cingulate cortex. Importantly, reduced coupling in the posterior cingulate cortex was associated with cognitive and behavioral symptoms in patients. Though significant, the reduction in whole-brain metabolic/FC coupling in bvFTD was not as strong as reported previously for AD. These results highlight the vulnerability of functional hubs to neurodegenerative disease. Aberrant regional disruptions in the coupling between metabolism and neuronal activity may drive network-level dysfunction and contribute to functional impairments characteristic of the disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".