Reduced neuronal activity in Alzheimer’s disease and mild cognitive impairment measured by resting state fMRI
Bibliographic record
Abstract
Abstract Background Early pathological changes in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) gradually decrease neuronal metabolism and function measured by PET and functional MRI (fMRI). These changes are often associated with cognitive decline and can help in the diagnosis of AD. However more sensitive indicators of the earliest stages of disease must be developed to detect disease prior to cognitive impairment. Recently, a novel neuronal activity (NA) metric was introduced based on resting‐state fMRI (rs‐fMRI) signal oscillations, which showed dramatically lower NA in AD. Here we introduced a more sophisticated and potentially more sensitive NA metric to identify subtle NA differences between three groups: normal elderly controls (NEC), MCI, and AD. Method The rs‐fMRI scans (TR=∼2400, ∼10 minute acquisition) were acquired at 3T from 8 MRI scanners in the Ontario Neurodegenerative Disease Research Initiative (AD group, N=40, aged 71.8 ± 8.1, 42% female; and MCI groups, N=80, aged 70.3 ± 8.3, 46% female), and the Gait and Brain Study (NEC group, N=30, aged 71.3 ± 6.0, 23.33% female). The rs‐fMRI signal was pre‐processed using FMRIB Software Library (FSL) and decomposed into independent components (ICs) using IC analysis. The ICs were classified into neuronal and non‐neuronal using a support vector machine classifier. The proposed voxel‐wise NA metric was defined based on similarity (cross covariance) between rs‐fMRI signal and neuronal ICs. This metric was utilized to create resting‐state NA map in each subject, which were compared between groups. Result Single subject and group average NA maps are provided in Fig. 1. The % difference maps between each group are shown in Fig. 2. The average voxelwise % difference in NA between groups was 38% between NEC and MCI, 38% between NEC and AD, and 10% between MCI and AD. Average NA in whole gray matter, hippocampus, posterior cingulate cortex, and precuneus, in each group are also provided in Fig. 3. Conclusion Lower NA was clearly detected in AD and MCI groups compared to NEC. The large decrease in NA detected in MCI subjects compared to NEC suggests this metric is highly sensitive to early changes in neuronal function.
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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".