Default mode network connectivity in stable vs progressive mild cognitive impairment
Bibliographic record
Abstract
OBJECTIVE: Dysfunction of the default mode network (DMN) has been identified in prior cross-sectional fMRI studies of Alzheimer disease (AD) and mild cognitive impairment (MCI); however, no studies have examined its utility in predicting future cognitive decline. METHODS: fMRI scans during a face-name memory task were acquired from a cohort of 68 subjects (25 normal control, 31 MCI, and 12 AD). Subjects with MCI were followed for 2.4 years (±0.8) to determine progression to AD. Maps of DMN connectivity were compared with a template DMN map constructed from elderly normal controls to obtain goodness-of-fit (GOF) indices of DMN expression. Indices were compared between groups and correlated with cognitive decline. RESULTS: GOF indices were highest in normal controls, intermediate in MCI, and lowest in AD (p < 0.0001). In a predictive model (that included baseline GOF indices, age, education, Mini-Mental State Examination score, and an index of DMN gray matter volume), the effect of GOF index on progression from MCI to dementia was significant. In MCI, baseline GOF indices were correlated with change from baseline in functional status (Clinical Dementia Rating-sum of boxes) (r = -0.40, p < 0.04). However, there was no additional predictive value for DMN connectivity when baseline delayed recall was included in the models. CONCLUSIONS: fMRI connectivity indices distinguish patients with MCI who undergo cognitive decline and conversion to AD from those who remain stable over a 2- to 3-year follow-up period. Our data support the notion of different functional brain connectivity endophenotypes for "early" vs "late" MCI, which are associated with different baseline memory scores and different rates of progression and conversion.
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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 | 0.001 | 0.002 |
| 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".