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Record W1534202620 · doi:10.5772/26511

Music and Steroids – Music Facilitates Steroid–Induced Synaptic Plasticity

2011· book-chapter· en· W1534202620 on OpenAlexaboutno aff
Hajime Fukui, Kumiko Toyoshim

Bibliographic record

VenueInTech eBooks · 2011
Typebook-chapter
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsnot available
Fundersnot available
KeywordsMusic therapyPsychologyMusicologyMOZARTDementiaPsychotherapistNeurosciencePsychoanalysisCognitive psychologyMedicineArtLiteratureDisease

Abstract

fetched live from OpenAlex

Music and medicine have always been closely related. This remains true even in huntergatherer cultures that are thought to reflect primitive human forms, as clarified by cultural anthropological and ethno-musicological studies (Lee & Daly, 2005; Merriam & Merriam, 1964). Interestingly, music has also been used for the treatment of neuropsychiatric disorders in hunter-gatherer cultures (Lee & Daly, 2005; Merriam & Merriam, 1964). However, in westernized societies, no established music therapy exists for neuropsychiatric disorders such as stress disorders, mood disorders (depression), and dementia. Experience has shown that music has certain therapeutic effects on neuropsychiatric disorders (both functional and organic disorders), and music therapy is currently being used in the United States and Europe in clinical and welfare settings. However, the mechanisms of action underlying music therapy remain unknown. Various studies have examined the effects of listening to music on the brain (Bermudez & Zatorre, 2005; Nan et al., 2008). The study by Rauscher et al. on the “Mozart effect” is one of the most famous studies and has had both positive and negative impacts on music therapy (Rauscher et al., 1993). However, many subsequent studies have questioned the reliability of those results, and Chabris et al. published a study disproving the Mozart effect (Chabris, 1999). However, the fact that music affects the human body and mind was not disproved. In fact, more scientific studies on music have been conducted in recent years, mainly in the field of neuroscience, and the level of interest among researchers is increasing (Zatorre, 2003; Zatorre & McGill, 2005). Results of past studies have clarified that music influences and affects cerebral nerves in humans from fetuses to adults (Abbott, 2002). The most significant finding has been that music enhances synaptic changes in the brain. In other words, studies comparing musicians and non-musicians and music learners and nonlearners have clarified that music brings about cerebral plasticity. Music affects neuronal learning and readjustment (response of brain cells to sound and music stimuli, and changes in cell counts), and this effect lasts for a long period (Abbott, 2002). For example, even when neurodegenerative diseases such as Alzheimer’s disease cause memory loss, patients can still remember music from the past, and listening to music can facilitate the recovery of other memories. This type of memory recovery is accompanied by the reconfiguration of existing neuron networks, which may allow access to long-term memory. However, most studies have been based on brain imaging modalities such as positron emission tomography (PET) or functional magnetic resonance imaging (fMRI). The effects of music at a cellular

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.106
GPT teacher head0.262
Teacher spread0.156 · 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; a candidate call from one teacher head, not a consensus.

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

Citations3
Published2011
Admission routes1
Has abstractyes

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