A Professional Musician with Sight‐Reading Difficulties: A Case Study
Bibliographic record
Abstract
Abstract Background There have been few reports of musical alexia in neurodegeneration. In this case report, we describe a professional orchestral musician who presented with a chief complaint of decline in performance accuracy. While he had previously been exceptional in sight‐reading, he was now making frequent mistakes and falling behind. He stated that performing a familiar piece was, “like reading it for the first time.” He later developed difficulties with visuospatial abilities and language. Methods We aimed to characterize his musical and cognitive symptoms using a battery that consisted of the Montreal Battery of Evaluation of Amusia, novel tasks to assess accuracy of music reading and music production, standard neuropsychological testing, and review of clinical imaging and CSF. Results The patient completed the Montreal Battery of Evaluation of Amusia (MBAE) with no errors. His pitch, interval, auditory memory, rhythm perception, as well as basic note names and values were unaffected. (Fig.1). We had designed two novel measures to assess his ability to play unfamiliar music using a key signature task and a performance piece. (Fig 2,3,4). Results indicated that he was able to sight read familiar excerpts but not unfamiliar excerpts. On unfamiliar pieces, his sight reading, comprehension, and production showed marked impairment. He was unable to accurately reproduce note sequences in musical passages. On standard neuropsychological measures, he demonstrated impairment on tasks requiring mental rotation and figure discrimination as well as language measures including reading comprehension (BDAE), and confrontation naming (MINT). MRI (Fig 5) showed atrophy in the patient’s parietal lobes and CSF fluid analysis was positive for abnormal Aβ‐42 and normal tau (Aβ‐42:521.55, ptau:25.55). Conclusion Our novel tasks revealed specific deficits in music sight reading, echoed in impairment in visuospatial abilities and language on neuropsychological measures. The patient’s imaging, positive CSF amyloid testing and cognitive testing suggest that amusia was an early stage of a probable parietal variant of Alzheimer’s disease (i.e., PCA). His case highlights the importance of considering a neurodegenerative process in patients presenting with isolated deficits in music reading. Future studies will be needed for normative and validation studies of our novel tasks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".