Cerebellum Hyperactivity and its cortical connectivity in Alzheimer’s Disease (AD) utilizing <i>FDG PET</i> and <i>Resting State Functional Magnetic Resonance</i>.
Bibliographic record
Abstract
Abstract Background The role of the cerebellum in Alzheimer’s disease (AD) is not fully understood. There are a few studies that have demonstrated impaired cortical – cerebellar connectivity in AD. Additionally, transgenic mouse studies clearly show amyloid‐β (Aβ) deposition in the cerebellum affecting synaptic transmission and plasticity, sometimes before plaque formation. To characterize metabolic activity in the cerebellum, we analyzed 18F‐fluorodeoxyglucose (FDG) positron emission tomography (PET) and resting‐state functional MRI (rs‐fMRI) data in AD, mild‐cognitive impaired (MCI), and age‐matched cognitively normal (CN) subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Our focus was on the intrinsic functional connectivity of the cerebellum with three brain networks: the default mode network (DMN), dorsal attention network (DAN), and the primary olfactory cortex (POC) in the medial temporal lobe. We hypothesized that the resting state functional connectivity of cerebellar regions (that showed differential FDG metabolic activity) would exhibit impaired brain‐wide network connectivity. Method A group of 272 ADNI subjects (CN = 80, MCI = 149, AD = 43) with FDG‐PET scans were analyzed in this study. All 272 subjects did not have consistent rs‐fMRI data therefore, we analyzed 160 subjects (CN = 80, MCI = 84, AD = 29) that had consistent rs‐fMRI data acquisition parameters. The PET preprocessing was performed in SPM12. Partial volume correction was performed using the Van‐Cittert deconvolution technique. The rs‐fMRI data was preprocessed in DPABI; PET and rs‐fMRI data were normalized to the Montreal Neurological Institute template. DMN and DAN masks were downloaded from neurovault.org. The Statistical analyses were performed in DPABI. Result The SPM group analysis of PET data identified crus II, right cerebellum 4,5 and vermis lobule 6 as hyperactive (increased glucose metabolism) in AD and MCI compared to CN (Figure 1). Subsequent analyses identified brain regions within the DMN, DAN, and POC that are correlated with hyperactive cerebellar regions. Similarity analyses detected impaired resting state functional connectivity of hyperactive cerebellar regions with the three brain networks (Figure 2). Conclusion The hyper‐metabolism in the cerebellum may reflect disruption of local and brain‐wide network connectivity due to neurodegeneration. Observed hyper‐metabolism may be related to inhibitory dynamics of the cerebellum — providing a hypothetical mechanism to explain the susceptibility of brain networks in AD.
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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) | 0.001 | 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".