Neural Basis of Long-term Musical Memory in Cognitively Impaired Older Persons
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine whether exposure to long-known music would evoke more extensive activation of brain regions minimally affected by Alzheimer disease (AD) pathology and outside traditional memory networks using a functional magnetic resonance imaging paradigm involving listening to long-known and recently-learned music in older adults with cognitive impairment to provide insight into mechanisms of long-term musical memory preservation in cognitively impaired older persons. METHODS: Seventeen subjects with a diagnosis of mild AD or mild cognitive impairment were recruited for this study. Subjects were scanned using functional magnetic resonance imaging while they performed a music listening task, which included short clips of personally selected music from the patient's past and newly-composed music heard for the first time 60 minutes before scanning. From this task, we obtained group-level maps comparing brain areas associated with long-known and recently-heard music in all subjects. RESULTS: Exposure to long-known music preferentially activated brain regions including the medial prefrontal cortex, precuneus, anterior insula, basal ganglia, hippocampus, amygdala, and cerebellum relative to recently-heard music. These areas are involved in autobiographical memory and associated emotional responses. In addition, they are minimally affected by early stage AD pathology, thus providing a neural basis for long-known musical memory survival. CONCLUSIONS: Long-known music activates a bilateral network of prefrontal, emotional, motor, auditory, and subcortical regions (cerebellum, putamen, limbic structures). This extensive activation, relative to recently-heard music, may offer structural and functional clues as to why long-term musical memory appears to be relatively preserved among cognitively impaired older persons.
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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".