Relevant bibliographies by topics / Brain-Behavior

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Brain-Behavior'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Brain-Behavior"

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Crews, David. "Epigenetics, brain, behavior, and the environment." HORMONES 9, no. 1 (January 15, 2010): 41–50. http://dx.doi.org/10.14310/horm.2002.1251.

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Bigler, Erin D. "Brain and Behavior." Contemporary Psychology: A Journal of Reviews 38, no. 4 (April 1993): 356–57. http://dx.doi.org/10.1037/033205.

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KAHN, DAVID A. "Brain and Behavior." Journal of Psychiatric Practice 24, no. 3 (May 2018): 206–8. http://dx.doi.org/10.1097/pra.0000000000000302.

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Yakimovskii, Аndrey F. "Zinc, brain, behavior." Reviews on Clinical Pharmacology and Drug Therapy 19, no. 1 (May 21, 2021): 23–35. http://dx.doi.org/10.17816/rcf19123-35.

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The purpose of the review was to analyze current notions about role of essential trace element zinc in brain activity and therefore in behavior. At the beginning of the review the basic data about zinc metabolism was described. The facts of zinc involvement into neurologic disorders and human cognition were represented. The results of the own investigation, devoted zinc peroral treatment and intrabrain microinjections influence on rats normal and pathological motor behavior were described. In particular, it is shown that zinc, depending on the dose and its mode of entry into the organism, can weaken and prevent the development of picrotoxin-induced neostriatal hyperkinesis (human Huntington horea analog), but it may aggravate hyperkinesis symptoms and even independently cause the motor stereotypy. On the basis of their own data and literary, it was suggested that neurons membranes structures are different sensitive to a certain zinc concentration and what does the specific way of behavior realization is ultimately depend.
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Isherwood, Anna. "The brain and behavior." Journal of Mental Health 20, no. 5 (September 30, 2011): 496–97. http://dx.doi.org/10.3109/09638237.2011.593595.

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Fan, X., and Y. Agid. "Behavior, brain and astrocytes." Revue Neurologique 173, no. 10 (December 2017): 619–22. http://dx.doi.org/10.1016/j.neurol.2017.05.017.

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Scott, Thomas R. "Brain and feeding behavior." Appetite 17, no. 1 (August 1991): 78–79. http://dx.doi.org/10.1016/0195-6663(91)90092-7.

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Kolb, Bryan, and Ian Q. Whishaw. "BRAIN PLASTICITY AND BEHAVIOR." Annual Review of Psychology 49, no. 1 (February 1998): 43–64. http://dx.doi.org/10.1146/annurev.psych.49.1.43.

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Dishman, R. K., F. Chauoloff, S. Bailey, M. S. Sothmann, G. M. Kastello, and G. R. Vogel. "EXERCISE, BRAIN, AND BEHAVIOR." Medicine & Science in Sports & Exercise 27, Supplement (May 1995): S37. http://dx.doi.org/10.1249/00005768-199505001-00208.

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Monti, Martin M., and Adrian M. Owen. "Behavior in the Brain." Journal of Psychophysiology 24, no. 2 (January 2010): 76–82. http://dx.doi.org/10.1027/0269-8803/a000016.

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Recent evidence has suggested that functional neuroimaging may play a crucial role in assessing residual cognition and awareness in brain injury survivors. In particular, brain insults that compromise the patient’s ability to produce motor output may render standard clinical testing ineffective. Indeed, if patients were aware but unable to signal so via motor behavior, they would be impossible to distinguish, at the bedside, from vegetative patients. Considering the alarming rate with which minimally conscious patients are misdiagnosed as vegetative, and the severe medical, legal, and ethical implications of such decisions, novel tools are urgently required to complement current clinical-assessment protocols. Functional neuroimaging may be particularly suited to this aim by providing a window on brain function without requiring patients to produce any motor output. Specifically, the possibility of detecting signs of willful behavior by directly observing brain activity (i.e., “brain behavior”), rather than motoric output, allows this approach to reach beyond what is observable at the bedside with standard clinical assessments. In addition, several neuroimaging studies have already highlighted neuroimaging protocols that can distinguish automatic brain responses from willful brain activity, making it possible to employ willful brain activations as an index of awareness. Certainly, neuroimaging in patient populations faces some theoretical and experimental difficulties, but willful, task-dependent, brain activation may be the only way to discriminate the conscious, but immobile, patient from the unconscious one.
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Dissertations / Theses on the topic "Brain-Behavior"

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Walton, James C. "Photoperiod, Brain Plasticity, and Behavior." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1364994837.

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Olsson, Joakim. "Narcissism - Brain and Behavior : Self-Views and Empathy in the Narcissistic Brain." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-9590.

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This thesis reviews both psychological and neural research in the fields of self-evaluation, self-views and self-enhancement bias. The research has made associations to grandiosity and need for admiration, which are two of the defining characteristics of narcissistic personality disorder. Neural correlates associated with this research are the medial prefrontal cortex, orbitofrontal cortex, posteromedial cortex and anterior insula. Narcissists have been seen to have a decreased form of emotional empathy even though they rate themselves to have higher emotional empathy than they actually have, which is linked to self-enhancement bias and grandiosity. Alexithymia has not gained much attention in relation to narcissism, but research presented suggests that this might need to change. Neural correlates that are associated with lack of emotional empathy and alexithymia are the anterior insula, frontoparalimbic areas and the medial prefrontal cortex. Narcissistic personality disorder is in the DSM-5 specified to be defined by a grandiose sense of self, a need for admiration, and a lack of empathy in either fantasy or behavior. However according to researchers in the field this only covers a part of the spectrum of narcissism. Deficits in the DSM-5 will he highlighted, as well as suggestions on what to do in order to help clarify the definition in DSM-5 and the concept in general.
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Rice, Judy A. "Brain Behavior: The Role of Nursing in the Care of Brain Injured Individuals." Digital Commons @ East Tennessee State University, 2014. https://dc.etsu.edu/etsu-works/7623.

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Pyter, Leah M. "Seasonal plasticity of physiological systems, brain, and behavior." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1141319505.

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Fernandes, José Joaquim Fonseca Ribas. "Hierarchical Reinforcement Learning in Behavior and the Brain." Doctoral thesis, Universidade Nova de Lisboa. Instituto de Tecnologia química e Biológica, 2013. http://hdl.handle.net/10362/11971.

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Dissertation presented to obtain the Ph.D degree in Biology, Neuroscience
Reinforcement learning (RL) has provided key insights to the neurobiology of learning and decision making. The pivotal nding is that the phasic activity of dopaminergic cells in the ventral tegmental area during learning conforms to a reward prediction error (RPE), as speci ed in the temporal-di erence learning algorithm (TD). This has provided insights to conditioning, the distinction between habitual and goal-directed behavior, working memory, cognitive control and error monitoring. It has also advanced the understanding of cognitive de cits in Parkinson's disease, depression, ADHD and of personality traits such as impulsivity.(...)
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Sokoliuk, Rodika. "From alpha to perception : investigating behavior and brain activity." Toulouse 3, 2014. http://thesesups.ups-tlse.fr/2353/.

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Il a été proposé que la perception visuelle soit organisée de façon discrète, reposant sur l'activité cérébrale oscillatoire d'environ 10Hz. Bien qu'en condition normale notre perception visuelle nous paraisse continue, nous montrons dans une première expérience que cette continuité peut être interrompue lors de la perception d'un stimulus spécifique pourtant statique, révélant les cycles d'oscillations alpha (~10Hz) à la base de la perception visuelle. La 'Flickering Wheel Illusion' ('L'illusion de la roue qui clignote') génère un clignotement illusoire régulier, réapparaissant toutes les 100ms ce qui correspond à une phase du cycle alpha. Des études précédentes ont montré que certaines phases des oscillations alpha occipitales (tout comme des oscillations theta fronto-centrales) portent des propriétés 'inhibitrices' en vue du traitement visuel tandis que leurs phases opposées tendent à augmenter la probabilité de détecter des stimuli visuels. Dans une deuxième étude, nous avons analysé comment ce rapport phase-détection temporel est représenté au niveau spatial dans le cortex visuel. Pour cela, nous avons entraîné des oscillations alpha via le clignotement d'un disque à une certaine position spatiale du champ visuel, pendant que les participants avaient pour tâche de détecter des stimuli visuels de basse luminance pouvant apparaître à de multiples positions spatiales. Les oscillations entraînées modulaient la performance de détection à toutes les positions auxquelles des cibles pourraient apparaître montrant des maxima à différentes phases de l'oscillation. De façon intéressante, nous avons pu montrer que la phase des oscillations entraînées se propageait dans l'espace comme une onde progressive, une propriété qui pourrait impliquer des rôles fonctionnels spécifiques pour le traitement sensoriel. En plus de son influence sur les processus cognitives comme la perception visuelle, la phase des oscillations lentes peut aussi moduler l'amplitude d'une oscillation rapide ('phase-amplitude cross-frequency coupling' ; 'couplage phase-amplitude inter-fréquentiel' ;'PAC'). Dans une troisième expérience, nous avons révélé l'existence d'un PAC entre des oscillations alpha et gamma pendant l'état de repos (en l'absence de tâche cognitive spécifique) ce que nous avons assigné à de 'l'inhibition pulsative', un mécanisme de contrôle du traitement sensoriel porté par le rythme alpha. Selon cette théorie, les oscillations alpha mettraient donc à disposition des moments ou bien des phases spécifiques qui mènent à une augmentation d'amplitude d'oscillations gamma entrainant une augmentation du traitement sensoriel, tandis que des phases opposées inhiberaient le traitement sensoriel. En outre, nous avons observé un PAC robuste entre des oscillations theta et beta pendant l'état de repos ainsi que pendant une tâche de détection. Ce PAC pourrait aider à maintenir un état d'activité cérébrale de base. Finalement, nous avons pu révéler un PAC entre des oscillations theta et gamma principalement dans des régions fronto-centrales, influençant la perception visuelle avec emploi d'attention visuelle. Ce résultat est en continuité et complète des résultats précédents de notre groupe qui montraient l'influence de la phase des oscillations theta sur la perception visuelle. Le travail présenté dans cette thèse contribue aux connaissances du rôle important des oscillations spontanées pour la perception visuelle en utilisant plusieurs approches expérimentales et révèlent de nouvelles questions de ce champ de recherche
It has been suggested that visual perception is organized in discrete snapshots relying on an oscillatory brain rhythm of about 10Hz. Even though, in normal conditions, our visual percept seems continuous to us, we show that a specific static stimulus pattern can lead to disruption of this continuity and uncover cycles of alpha oscillations (~10Hz) underlying visual perception. This 'Flickering Wheel Illusion' produces a regular illusory flicker recurring every ~100ms thus at one phase of the alpha cycle. According to previous work, specific phases of occipital alpha (and also fronto-central theta) oscillations are designated as 'inhibitory' for visual processing whereas opposite phases increase probability to detect visual stimuli. In a psychophysical experiment, we analyzed how this temporal phase-detection relationship is organized spatially in the visual cortex. We therefore entrained alpha oscillations with a specific spatial origin and probed visual detection of low-threshold targets at different spatial positions. Detection performance was modulated by the entrained oscillation at all target locations, showing maxima at different phases of the oscillation. We could show that the phase of the entrained alpha oscillations propagated over space like a traveling wave that could implicate important functional roles for sensory processing. Besides its influence on cognitive processes like visual perception, the phase of low frequency oscillations can also modulate the amplitude of fast oscillations (phase-amplitude cross-frequency coupling; PAC). In another experiment, we could reveal PAC between alpha and gamma oscillations during the resting state, what we referred to as pulsed inhibition, illustrating the alpha rhythm as a control mechanism of sensory processing. Alpha oscillations would thus provide specific moments, or phases that enhance gamma amplitude and thus sensory processing, whereas opposite phases would inhibit sensory processing. Moreover, we found a robust PAC between theta and beta oscillations which was also present during a visual detection task. This PAC could help maintaining a specific activity state of the brain. Finally, we could reveal PAC between theta and gamma oscillations in mainly fronto-central regions, influencing visual perception in the detection task but only within the focus of attention. This result is consistent with and complements previous findings of our group showing the influence of the phase of theta oscillations on visual perception. This work contributed to the findings of the important role of ongoing oscillations in visual perception, using multiple experimental approaches; yet our promising results uncovered new questions in this large field of research
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Sreekumar, Vishnu. "Context in the wild: Environment, behavior, and the brain." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1447539252.

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Stewart, Lee S. "Emergent behaviors following multifocal brain injury, a novel approach to the study of brain-behavior relationships." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0009/MQ61302.pdf.

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Fine, Eric Michael. "Representing facial affect representations in the brain and in behavior /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3244172.

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Zhu, Shun-Wei. "Brain neurotrophin levels and mouse behavior : relationship to environmental influences /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-843-6/.

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Books on the topic "Brain-Behavior"

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Wyrwicka, Wanda. Brain and feeding behavior. Springfield, Ill: Charles C. Thomas, 1988.

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Psychology: Brain, behavior, & culture. 3rd ed. New York: John Wiley, 2001.

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Psychology: Brain, behavior & culture. New York: John Wiley, 2003.

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A, Nelson Charles, Lazerson Arlyne, and Annenberg/CPB Project, eds. Brain, mind, and behavior. 3rd ed. [New York]: Worth Publishers, 2001.

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Salamone, J. D. (John D.), ed. Drugs, brain, and behavior. 6th ed. Boston, MA: Pearson Education, 2012.

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Arlyne, Lazerson, ed. Brain, mind, and behavior. 2nd ed. New York: Freeman, 1988.

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Westen, Drew. Psychology: Brain, behavior, & culture. 3rd ed. New York: John Wiley, 2001.

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Arlyne, Lazerson, and Hofstadter Laura, eds. Brain, mind, and behavior. New York: W.H. Freeman, 1985.

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Petronis, Arturas, and Jonathan Mill, eds. Brain, Behavior and Epigenetics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17426-1.

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Sassone Corsi, Paolo, and Yves Christen, eds. Epigenetics, Brain and Behavior. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27913-3.

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Book chapters on the topic "Brain-Behavior"

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Wideman, Timothy H., Michael J. L. Sullivan, Shuji Inada, David McIntyre, Masayoshi Kumagai, Naoya Yahagi, J. Rick Turner, et al. "Brain-Behavior Relationships." In Encyclopedia of Behavioral Medicine, 263. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_100213.

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Damasceno, Benito. "Brain-Behavior Correlations." In Research on Cognition Disorders, 113–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57267-9_12.

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Stellar, Eliot. "Brain and Behavior." In Neurobiology of Food and Fluid Intake, 3–22. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4613-0577-4_1.

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Onaolapo, Adejoke, and Olakunle Onaolapo. "Melatonin, Brain, and Behavior." In Serotonin and Melatonin, 347–58. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315369334-24.

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Numan, Michael. "Maternal Behavior, Brain Control." In States of Brain and Mind, 51–52. Boston, MA: Birkhäuser Boston, 1988. http://dx.doi.org/10.1007/978-1-4899-6771-8_20.

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Richmond, Gail, and Lynwood Clemens. "Sexual Behavior, Brain Control." In States of Brain and Mind, 104–5. Boston, MA: Birkhäuser Boston, 1988. http://dx.doi.org/10.1007/978-1-4899-6771-8_42.

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Davis, Michelle R., Vincent P. Culotta, Eric A. Levine, and Elisabeth Hess Rice. "The Brain and Behavior." In School Success for Kids With Emotional and Behavioral Disorders, 207–30. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003237853-13.

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Swanson, Larry W. "Brain and Behavior." In Brain Architecture, 115–34. Oxford University Press, 2011. http://dx.doi.org/10.1093/med/9780195378580.003.1062.

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"Brain and Behavior." In Encyclopedia of Sciences and Religions, 280. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_100129.

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"Brain and Behavior." In Brain, Mind, and the Narrative Imagination. Bloomsbury Academic, 2021. http://dx.doi.org/10.5040/9781350127838.ch-003.

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Conference papers on the topic "Brain-Behavior"

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Shimazu, Takuya, Keiko Kasakawa, and Satoshi Suzuki. "Impression analysis for robot behavior using brain monitoring." In 2013 6th International Conference on Human System Interactions (HSI). IEEE, 2013. http://dx.doi.org/10.1109/hsi.2013.6577873.

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Zhang, Mingming, Keya Ding, Huibin Jia, and Dongchuan Yu. "Brain-to-brain synchronization of the expectation of cooperation behavior: A fNIRS hyperscanning study." In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018. http://dx.doi.org/10.1109/embc.2018.8512315.

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Albetyarova, Karina. "SIMULATION OF THE BRAIN NEURONS BEHAVIOR IN EPILEPTIC ACTIVITY." In XVI International interdisciplinary congress "Neuroscience for Medicine and Psychology". LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m911.sudak.ns2020-16/61-62.

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Bisch-Knaden, Sonja. "Linking brain activity to odor-guided behavior inManduca sexta." In 2016 International Congress of Entomology. Entomological Society of America, 2016. http://dx.doi.org/10.1603/ice.2016.107311.

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Elkin, B. S., M. A. Shaik, and B. Morrison. "Chondroitin sulfate proteoglycans contribute to brain tissue swelling behavior." In 2010 36th Annual Northeast Bioengineering Conference. IEEE, 2010. http://dx.doi.org/10.1109/nebc.2010.5458138.

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Yuqiong Ding, Leslie Ying, Na Zhang, and Dong Liang. "Noise behavior of MR brain reconstructions using compressed sensing." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610709.

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Schrum, Jacob, Joel Lehman, and Sebastian Risi. "Automatic Evolution of Multimodal Behavior with Multi-Brain HyperNEAT." In GECCO '16: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2908961.2908965.

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Volkow, Nora. ""Imaging the Addicted Human Brain: from Molecules to Behavior"." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.259770.

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Cao, Yinan, Hame Park, Bruno L. Giordano, Christoph Kayser, Charles Spence, and Christopher Summerfield. "Unfolding of multisensory inference in the brain and behavior." In 2019 Conference on Cognitive Computational Neuroscience. Brentwood, Tennessee, USA: Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1219-0.

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Prévost, Thibault, Asha Balakrishnan, and Simona Socrate. "Large Strain Behavior of Brain Tissue: Mechanical Testing and Preliminary Modeling." In ASME 2009 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2009. http://dx.doi.org/10.1115/sbc2009-206485.

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Understanding the mechanical response of brain tissue to dynamic loading conditions is critically needed for the development of realistic brain injury models. The characterization of the tissue behavior via mechanical testing and numerical modeling remains, however, challenging because of the strongly nonlinear time- and strain-dependencies inherent in the tissue response. While several studies [1–4] have uncovered some essential features of this response, the integration of all these features — nonlinearities, hysteresis, volumetric behavior — into one single constitutive framework remains an area of active research [5].
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Reports on the topic "Brain-Behavior"

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Drew, Donald A. Brain Behavior Evolution during Learning: Emergence of Hierarchical Temporal Memory. Fort Belvoir, VA: Defense Technical Information Center, August 2013. http://dx.doi.org/10.21236/ada608125.

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Vasold, Kerri, and Madeline Eller. Brain Health and Nutrition Behavior in Hispanic Adults Age 40-Plus: Fact Sheet. AARP Research, June 2019. http://dx.doi.org/10.26419/res.00187.002.

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Vasold, Kerri, and Madeline Eller. Brain Health and Nutrition Behavior in African American/Black Adults Age 40-Plus: Fact Sheet. AARP Research, June 2019. http://dx.doi.org/10.26419/res.00187.003.

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Ceremuga, Thomas E. Effects of Herbal Supplements on PTSD-Induced Changes in Rat Behavior & Brain Gene Expression. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada608252.

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Cutting, Laurie E. Magnetic Resonance Spectroscopy Imaging and Function Magnetic Resonance Imaging of Neurofibromatosis Type I: In vivo Pathophysiology, Brain-Behavior Relationships and Reading Disabilities. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada436879.

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Cutting, Laurie E. Magnetic Resonance Spectroscopy Imaging and Functional Magnetic Resonance Imaging of Neurofibromatosis Type I: In Vivo Pathophysiology Brain-Behavior Relationships and Reading Disabilities. Fort Belvoir, VA: Defense Technical Information Center, October 2003. http://dx.doi.org/10.21236/ada420953.

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Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.

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We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.
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