Virtual spatial memory improvement program: impact on resting state functional connectivity in the default-mode network
Bibliographic record
Abstract
Healthy aging is known to affect the structure and function of the hippocampus In order to stimulate this region in healthy older adults, we developed an automated computerized spatial memory intervention program called NeuroNautilus because spatial memory is a function critical to the hippocampus. NeuroNautilus is designed to promote the use of spatial strategies, taking particular attention to avoid the use of stimulus-response strategies that are dependent on the caudate nucleus, a region of the brain which competes against the hippocampus. In this study, we aimed to characterize the effects of the NeuroNautilus training program on the functional topology of the default-mode network (DMN), the first intrinsic functional network to show degradations during aging. We also investigated the resting differences in the topology of the DMN between individuals who employ hippocampus-based spatial strategies and individuals who employ caudate nucleus based stimulus-response strategies. Healthy older adults were assigned to take part in the NeuroNautilus training or an active placebo control condition. Consistent with our hypotheses, we found that the NeuroNautilus training program significantly increased within network connectivity in the precuneus, a structure involved in higher order executive processes highly vulnerable to aging. Furthermore, we found that the placebo control group showed greater increase in connectivity within the dorsal medial prefrontal cortex, a finding consistent with other aging literature. No significant differences were found in the topology of the DMN between spatial and stimulus-response strategy users, although trends highlight the importance of larger samples in such studies. These results highlight the potential for spatial memory training in restoring activity to the brain’s intrinsic functional networks.
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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".