Impaired Memory Awareness and Loss Integration in Self-Referential Network Across the Progression of Alzheimer’s Disease Spectrum
Bibliographic record
Abstract
BACKGROUND: Anosognosia, or unawareness of memory deficits, is a common manifestation of Alzheimer's disease (AD), but greatly variable in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) subjects. Self-referential network (SRN) is responsible for self-referential processing and considered to be related to AD progression. OBJECTIVE: Our aim is to explore connectivity changes of SRN and its interaction with memory-related network and primary sensorimotor network (SMN) in the AD spectrum. METHODS: About 444 Alzheimer's Disease Neuroimaging Initiative subjects (86 cognitively normal [CN]; 156 SCD; 146 aMCI; 56 AD) were enrolled in our study. The independent component analysis (ICA) method was used to extract the SRN, SMN, and memory-related network from all subjects. The alteration of functional connectivity (FC) within SRN and its connectivity with memory-related network/SMN were compared among four groups and further correlation analysis between altered FC and memory awareness index as well as episodic memory score were performed. RESULTS: Compared with CN group, individuals with SCD exhibited hyperconnectivity within SRN, while aMCI and AD patients showed hypoconnectivity. Furthermore, aMCI patients and AD patients both showed the interruption of the FC between the SRN and memory-related network compared to CN group. Pearson correlation analysis showed that disruptive FC within SRN and its interaction with memory-related network were related to memory awareness index and episodic memory scores. CONCLUSION: In conclusion, impaired memory awareness and episodic memory in the AD spectrum are correlated to the disconnection within SRN and its interaction with memory-related network.
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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".