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Record W2795199094 · doi:10.1093/schbul/sby017.710

F179. NEURAL CORRELATES IN MUSICAL DEFICITS IN SCHIZOPHRENIA

2018· article· en· W2795199094 on OpenAlexaffabout
Ken Sawada, Sanae Hatada, Ryoshuke Fujito, William G. Honer

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsArcuate fasciculusSchizophrenia (object-oriented programming)PsychologyFractional anisotropyWhite matterCorpus callosumAudiologyNeuroscienceTractographySuperior longitudinal fasciculusDiffusion MRICardiologyPsychiatryMedicineMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Several studies have shown that patients with schizophrenia have low musical ability that correlates with poor cognitive functions and severe negative symptoms. Recently, using surface-based analysis, we reported that thinner cortical thickness in the left temporal, parietal, and inferior frontal regions is associated with lower musical ability in schizophrenia. Interestingly, thicker cortical thickness in the left supramarginal region correlates with lower musical ability in controls. Musical deficits such as congenital amusia was shown to contribute to white and grey matter pathophysiology in schizophrenia. We, therefore, sought to investigate diffusion tensor images (DTI) of patients with schizophrenia using an automated probabilistic tractography algorithm. Twenty-two right-handed patients with schizophrenia (12 males and 10 females, mean age = 45.9 years) and 20 right-handed healthy control subjects (13 males and 7 females, mean age = 42.8 years) consented to participate in this study. We measured musical ability, cognitive functions, and clinical assessments using the Montreal Battery for Evaluation of Amusia (MBEA), Brief Assessment of Cognition in Schizophrenia (BACS), and Positive and Negative Syndrome Scale (PANSS), respectively. We employed automatic probabilistic tractography DTI analysis using TRActs Constrained by UnderLying Anatomy (TRACULA) available in the Freesurfer software for the reconstruction of major tract bundles. Whole-tract diffusion characteristics in patients with schizophrenia and controls were significantly different. Fractional anisotropy (FA) was lower for patients with schizophrenia compared to controls in the left superior longitudinal fasciculus - parietal endings (slfp) (p < 0.001), left cingulum - angular bundle (cab) (p < 0.001), and corpus callosum - forceps minor (fminor) (p < 0.001). We found significant correlation between musical abilities and FA alterations in slfp in both controls and patients with schizophrenia. While lower musical ability corresponds to lower FA in slfp of controls (r = -0.572, p = 0.013), it is associated with higher FA in the slfp of patients with schizophrenia (r = 0.515, p = 0.021). This study shows that TRACULA can be used for the detection of decrements in several DTI tracts including the left slfp, left cab, and fminor in patients with schizophrenia. It revealed that while lower musical ability correlates with lower FA values in the left slfp in controls, it is associated with higher FA values in the same region in patients with schizophrenia. This contradictory finding in controls and patients with schizophrenia with regard to white matter pathology may reflect left supramarginal region malfunction resulting in cortical pathology in patients with schizophrenia. The data suggest that patients with schizophrenia may be more susceptible to changes in cortical thickness in the supramarginal region, and white matter alteration in the left slfp. Further study is needed to confirm the results. The characteristics of grey and white matter in the left parietal region which are relevant to musical ability may provide insight into pathological progression in patients with schizophrenia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.025
GPT teacher head0.263
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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