{"title":"Do Neurochemicals Reflect Psychophysiological Dimensions in Behaviors? A Transdisciplinary Perspective Based on Analogy with Maslow's Needs Pyramid.","authors":"Sandrine Parrot","doi":"10.1021/acschemneuro.4c00566","DOIUrl":null,"url":null,"abstract":"<p><p>All behaviors, including motivated behaviors, result from integration of information in the brain via nerve impulses, with two main means of communication: electrical gap-junctions and chemical signaling. The latter enables information transfer between brain cells through release of biochemical messengers, such as neurotransmitters. Neurochemical studies generate plentiful biochemical data, with many variables per individual, since there are many methods to quantify neurotransmitters, precursors and metabolites. The number of variables can be far higher using other concomitant techniques to monitor behavioral parameters on the same subject of study. Surprisingly, while many quantitative variables are obtained, data analysis and discussion focus on just a few or only on the neurotransmitter known to be involved in the behavior, and the other biochemical data are, at best, regarded as less important for scientific interpretation. The present article aims to provide novel transdisciplinary arguments that all neurochemical data can be regarded as items of psychophysiological dimensions, just as questionnaire items identify modified behaviors or disorders using latent classes. A first proof of concept on nonmotivated and motivated behaviors using a multivariate data-mining approach is presented.</p>","PeriodicalId":13,"journal":{"name":"ACS Chemical Neuroscience","volume":" ","pages":""},"PeriodicalIF":4.1000,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Chemical Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1021/acschemneuro.4c00566","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
All behaviors, including motivated behaviors, result from integration of information in the brain via nerve impulses, with two main means of communication: electrical gap-junctions and chemical signaling. The latter enables information transfer between brain cells through release of biochemical messengers, such as neurotransmitters. Neurochemical studies generate plentiful biochemical data, with many variables per individual, since there are many methods to quantify neurotransmitters, precursors and metabolites. The number of variables can be far higher using other concomitant techniques to monitor behavioral parameters on the same subject of study. Surprisingly, while many quantitative variables are obtained, data analysis and discussion focus on just a few or only on the neurotransmitter known to be involved in the behavior, and the other biochemical data are, at best, regarded as less important for scientific interpretation. The present article aims to provide novel transdisciplinary arguments that all neurochemical data can be regarded as items of psychophysiological dimensions, just as questionnaire items identify modified behaviors or disorders using latent classes. A first proof of concept on nonmotivated and motivated behaviors using a multivariate data-mining approach is presented.
期刊介绍:
ACS Chemical Neuroscience publishes high-quality research articles and reviews that showcase chemical, quantitative biological, biophysical and bioengineering approaches to the understanding of the nervous system and to the development of new treatments for neurological disorders. Research in the journal focuses on aspects of chemical neurobiology and bio-neurochemistry such as the following:
Neurotransmitters and receptors
Neuropharmaceuticals and therapeutics
Neural development—Plasticity, and degeneration
Chemical, physical, and computational methods in neuroscience
Neuronal diseases—basis, detection, and treatment
Mechanism of aging, learning, memory and behavior
Pain and sensory processing
Neurotoxins
Neuroscience-inspired bioengineering
Development of methods in chemical neurobiology
Neuroimaging agents and technologies
Animal models for central nervous system diseases
Behavioral research