Hippocampal Network Alterations in Alzheimer's Disease and Down Syndrome: From Structure to Therapy
Bibliographic record
Abstract
Hippocampal structural and functional alterations in Alzheimer's disease (AD), detected by advanced imaging methods, have been linked to significant abnormalities in multiple internal and external networks in this critical brain region. Uncovering the temporal and anatomical pattern of these network alterations would provide important clues into understanding the pathophysiology of AD and suggest new therapeutic strategies for this multi-system and prevalent disorder. Over the last decade, we have focused on studying brain structures that provide major projections to the hippocampus (HC) and the pattern of de-afferentation of this area in mouse models of AD and a related neurodegenerative disorder, i.e. Down syndrome (DS). Our studies have revealed that major inputs into the hippocampal structure undergo significant age-dependent alterations. Studying locus coeruleus (LC), the sole source of noradrenergic terminals for the HC, it has been shown that these neurons show significant age-dependent degeneration in both mouse models of DS and AD. Furthermore, increasing noradrenergic signaling was able to restore cognitive function by improving synaptic plasticity, and possibly promoting microglia recruitment, and amyloid β (Aβ) clearance in transgenic (tg) mouse models of AD. Here, we re-examine the effects of alterations in major inputs to the hippocampal region and their structural and functional consequences in mouse models of neurodegenerative disorders. We will conclude that improving the function of major hippocampal inputs could lead to a significant improvement in cognitive function in both AD and DS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".