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Статті в журналах з теми "Functional integration in neuroscience"

Автор: Grafiati

Опубліковано: 14 грудня 2024

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1

Shibkova, Dariya Zakharovna, and Pavel Azifovich Baiguzhin. "NEUROSCIENCE: INTERDISCIPLINARY INTEGRATION OR EXPANSION?" Психология. Психофизиология 13, no. 3 (October 21, 2020): 111–21. http://dx.doi.org/10.14529/jpps200312.

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Aim. The paper aims to study the differentiation and integration of scientific disciplines in the natural sciences and humanities research areas of neuroscience based on a review of Russian scientific works and to propose a structural and functional model of neuroscience as an interdisciplinary system of knowledge about brain features that ensure human activity in various professional spheres. Methods. A theoretical analysis of scientific publications on the topic over the last ten years has been used along with such methods as comparison, generalization, and modelling. Results. The paper presents various points of view on the subject field of separate disciplines within neuroscience, as well as on the relations between them. The interdisciplinarity of neuroscience is considered by a number of authors (philosophers) as a form of disciplinary colonization, epistemic expansion or intervention. Another group of authors considers neuroscience as a systemic level of science that unites multidisciplinary research activities related to the study of the brain. The third position is represented by authors who consider neuroscience as an extension of the problem field of neurobiology or as its synonym. A number of authors pay special attention to the popularity of neuroscience among politicians, military structures, pharmacological companies and other professionals with their disciplinary totality: neurophilosophy, neuropsychology, neuroinformatics, neurogenetics, neurobiology, neurosociology, neuropedagogy, etc. The paper demonstrates that there is no unified point of view on psychophysiology as a part of neuroscience, which also has interdisciplinary connections with many sciences that study individual psychological characteristics and behavior. Conclusion. Based on the analysis of the discussion, the authors emphasize the need to logically build the structural and functional relationships of individual disciplines within a unified neuroscience and determine its subject field on the basis of a systemic evolutionary approach.

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2

Ramsey, Richard. "Neural Integration in Body Perception." Journal of Cognitive Neuroscience 30, no. 10 (October 2018): 1442–51. http://dx.doi.org/10.1162/jocn_a_01299.

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The perception of other people is instrumental in guiding social interactions. For example, the appearance of the human body cues a wide range of inferences regarding sex, age, health, and personality, as well as emotional state and intentions, which influence social behavior. To date, most neuroscience research on body perception has aimed to characterize the functional contribution of segregated patches of cortex in the ventral visual stream. In light of the growing prominence of network architectures in neuroscience, the current article reviews neuroimaging studies that measure functional integration between different brain regions during body perception. The review demonstrates that body perception is not restricted to processing in the ventral visual stream but instead reflects a functional alliance between the ventral visual stream and extended neural systems associated with action perception, executive functions, and theory of mind. Overall, these findings demonstrate how body percepts are constructed through interactions in distributed brain networks and underscore that functional segregation and integration should be considered together when formulating neurocognitive theories of body perception. Insight from such an updated model of body perception generalizes to inform the organizational structure of social perception and cognition more generally and also informs disorders of body image, such as anorexia nervosa, which may rely on atypical integration of body-related information.

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3

Povich, Mark. "Model-based cognitive neuroscience: Multifield mechanistic integration in practice." Theory & Psychology 29, no. 5 (July 21, 2019): 640–56. http://dx.doi.org/10.1177/0959354319863880.

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Autonomist accounts of cognitive science suggest that cognitive model building and theory construction (can or should) proceed independently of findings in neuroscience. Common functionalist justifications of autonomy rely on there being relatively few constraints between neural structure and cognitive function. In contrast, an integrative mechanistic perspective stresses the mutual constraining of structure and function. In this article, I show how Model-Based Cognitive Neuroscience (MBCN) epitomizes the integrative mechanistic perspective and concentrates the most revolutionary elements of the cognitive neuroscience revolution. I also show how the prominent subset account of functional realization supports the integrative mechanistic perspective I take on MBCN and use it to clarify the intralevel and interlevel components of integration.

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4

Wrobel, Andrzej. "The need of neuroinformatic approach in functional neurophysiology." Acta Neurobiologiae Experimentalis 65, no. 4 (December 31, 2005): 421–23. http://dx.doi.org/10.55782/ane-2005-1570.

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Despite the enormous growth in the number of facts and regularities observed in neuroscience, the current state of the field does not allow their integration in coherent theoretical frameworks. Neuroinformatics is a new research field devoted to development of neuroscience data bases together with computational models and analytical tools for sharing, integration and analysis of experimental data and the advancement of theories of nervous system function. From 1996 the OECD countries promote fostering neuroinformatics by eliminating the barriers that prevent cooperation and by providing incentives to potential participants. This activity was formulated in the Report on Neuroinformatics from The Global Science Forum Neuroinformatics Working Group of the OECD (2002) as well as in several printed publications by the Neuroinformatics group (Amari et al. 2002, Eckersley et al. 2003). This article summarizes the ideas presented in the Report and stresses the importance of analytical and modeling approach to functional neurophysiology

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5

Castro, L. C. "Affective Neuroscience: A Crucial Role in Psychiatry." European Psychiatry 24, S1 (January 2009): 1. http://dx.doi.org/10.1016/s0924-9338(09)71130-7.

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Background:Neuroscience has been a growing revolutionary field of scientific knowledge. The increasing recognition of the importance of emotional processes and subjective experience in several aspects of human behaviour parallel the growing amount of research in the field of affective neuroscience. Affective neuroscience studies the brain mechanisms subjacent to emotional behaviour.Aim:To discuss the relevance of affective neuroscience research in social and biological sciences, namely within psychiatric and psychological researches.Methods:Review of the literature. MEDLINE and PubMed databases searches for peer-reviewed studies, published between 1994 and 2008, using combinations of the Medline Subject Heading terms affective neuroscience, emotions, affective sciences and psychiatry, psychology, biological sciences, social sciences.Results:Several studies addresses brain functions and how emotions relate to genetics, learning, primary motivations, stress response and human behaviour. Some actual areas of research within affective neuroscience include: emotional learning, affective behaviour, emotional empathy, psychosomatic medicine, functional and structural biomarkers, emotional disorders and stress response, among others.Discussion:In Psychiatry, affective neurosciences find application in understanding the neurobiology of mood disorders, the neural control of interpersonal and social behaviour and the emotional systems that underlie psychopathology. Affective neuroscience reflects the integration of knowledge across disciplines allowing a broader understanding of human functioning. The field of affective neuroscience is an exciting field of future psychiatric research and it provides an investigational framework for studying psychiatric morbidity.

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6

Friston, Karl. "Functional integration and inference in the brain." Progress in Neurobiology 68, no. 2 (October 2002): 113–43. http://dx.doi.org/10.1016/s0301-0082(02)00076-x.

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7

Krebs, H. I., T. Brashers-Krug, S. L. Rauch, C. R. Savage, N. Hogan, R. H. Rubin, A. J. Fischman, and N. M. Alpert. "Integration of robotic technology with functional imaging." NeuroImage 3, no. 3 (June 1996): S394. http://dx.doi.org/10.1016/s1053-8119(96)80396-x.

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8

Arachchige, Arosh S. Perera Molligoda. "Transitioning from PET/MR to trimodal neuroimaging: why not cover the temporal dimension with EEG?" AIMS Neuroscience 10, no. 1 (2023): 1–4. http://dx.doi.org/10.3934/neuroscience.2023001.

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<abstract> <p>The possibility of multimodality imaging with PET/MR and the availability of ultra-high field MRI has allowed to investigate novel aspects of neuropsychiatric conditions. One of the major barriers in current studies is the lack of an instrument that allows to accurately cover the temporal aspect under the same physiological conditions. The aim of this commentary is to provide our perspective on how the integration of EEG-PET-MR could be a solution to the current challenge in molecular imaging and seems to hold great promise in future pharmacological challenging-based studies, understanding different functional states of the brain, and could furthermore aid in the diagnostic and prognostic evaluations of neurocognitive disorders.</p> </abstract>

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9

Parlatini, Valeria, Joaquim Radua, Flavio Dell’Acqua, Anoushka Leslie, Andy Simmons, Declan G. Murphy, Marco Catani, and Michel Thiebaut de Schotten. "Functional segregation and integration within fronto-parietal networks." NeuroImage 146 (February 2017): 367–75. http://dx.doi.org/10.1016/j.neuroimage.2016.08.031.

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10

Aydarkin, Evgeny K., and Marina A. Pavlovskaya. "Functional hemisphere asymmetry and sensorimotor integration." International Journal of Psychophysiology 77, no. 3 (September 2010): 327. http://dx.doi.org/10.1016/j.ijpsycho.2010.06.254.

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11

Rohr, Christiane S., Anish Arora, Ivy Y. K. Cho, Prayash Katlariwala, Dennis Dimond, Deborah Dewey, and Signe Bray. "Functional network integration and attention skills in young children." Developmental Cognitive Neuroscience 30 (April 2018): 200–211. http://dx.doi.org/10.1016/j.dcn.2018.03.007.

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12

Laxane, Rahul. "Neuro-Robotics: Bridging Neuroscience and Robotics." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 5, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30166.

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The field of neurorobotics represents the combination of neuroscience and robotics, aiming to elucidate neural functional principles and use them to create intelligent robots. This article considers the symbiotic relationship between the two fields and explores how insights from neuroscience can inform the design and control of robots; Robotic platforms offer a unique opportunity to learn and validate insights from neuroscience. For example, this article focuses on the core concepts of neuroscience and robotics and highlights key advances that support the integration of these fields, including brain-computer interfaces, neurorobotic simulations, and bionic design. It examines how discoveries in neuroscience, such as the understanding of sensorimotor control, learning processes, and cognitive processes, are supporting the creation of biomimetic robots that can address behavioural challenges and interact with their environments.

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13

Vilcaes, A. Alejandro, Natali L. Chanaday, and Ege T. Kavalali. "Interneuronal exchange and functional integration of synaptobrevin via extracellular vesicles." Neuron 109, no. 6 (March 2021): 971–83. http://dx.doi.org/10.1016/j.neuron.2021.01.007.

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14

Jakubs, K., S. Bonde, R. E. Iosif, C. T. Ekdahl, Z. Kokaia, M. Kokaia, and O. Lindvall. "Inflammation Regulates Functional Integration of Neurons Born in Adult Brain." Journal of Neuroscience 28, no. 47 (November 19, 2008): 12477–88. http://dx.doi.org/10.1523/jneurosci.3240-08.2008.

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15

Kundu, Prantik, Brenda E. Benson, Dana Rosen, Sophia Frangou, Ellen Leibenluft, Wen-Ming Luh, Peter A. Bandettini, Daniel S. Pine, and Monique Ernst. "The Integration of Functional Brain Activity from Adolescence to Adulthood." Journal of Neuroscience 38, no. 14 (February 27, 2018): 3559–70. http://dx.doi.org/10.1523/jneurosci.1864-17.2018.

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16

Sepulcre, Jorge. "Functional Streams and Cortical Integration in the Human Brain." Neuroscientist 20, no. 5 (April 15, 2014): 499–508. http://dx.doi.org/10.1177/1073858414531657.

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17

Garrett, Douglas D., Samira M. Epp, Alistair Perry, and Ulman Lindenberger. "Local temporal variability reflects functional integration in the human brain." NeuroImage 183 (December 2018): 776–87. http://dx.doi.org/10.1016/j.neuroimage.2018.08.019.

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18

Lavigne, Katie M., Paul D. Metzak, and Todd S. Woodward. "Functional brain networks underlying detection and integration of disconfirmatory evidence." NeuroImage 112 (May 2015): 138–51. http://dx.doi.org/10.1016/j.neuroimage.2015.02.043.

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19

Piccinini, Gualtiero, and Carl Craver. "Integrating psychology and neuroscience: functional analyses as mechanism sketches." Synthese 183, no. 3 (March 11, 2011): 283–311. http://dx.doi.org/10.1007/s11229-011-9898-4.

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20

Macé, Émilie, Gabriel Montaldo, Stuart Trenholm, Cameron Cowan, Alexandra Brignall, Alan Urban, and Botond Roska. "Whole-Brain Functional Ultrasound Imaging Reveals Brain Modules for Visuomotor Integration." Neuron 100, no. 5 (December 2018): 1241–51. http://dx.doi.org/10.1016/j.neuron.2018.11.031.

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21

Triarhou, Lazaros C. "The cerebellar model of neural grafting: Structural integration and functional recovery." Brain Research Bulletin 39, no. 3 (January 1996): 127–38. http://dx.doi.org/10.1016/0361-9230(95)02090-x.

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22

Song, H. "New Neurons in the Adult Mammalian Brain: Synaptogenesis and Functional Integration." Journal of Neuroscience 25, no. 45 (November 9, 2005): 10366–68. http://dx.doi.org/10.1523/jneurosci.3452-05.2005.

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23

van den Heuvel, M. P., and O. Sporns. "An Anatomical Substrate for Integration among Functional Networks in Human Cortex." Journal of Neuroscience 33, no. 36 (September 4, 2013): 14489–500. http://dx.doi.org/10.1523/jneurosci.2128-13.2013.

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24

Fan, J., P. R. Hof, K. G. Guise, J. A. Fossella, and M. I. Posner. "The Functional Integration of the Anterior Cingulate Cortex during Conflict Processing." Cerebral Cortex 18, no. 4 (July 25, 2007): 796–805. http://dx.doi.org/10.1093/cercor/bhm125.

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25

Benedetti, Bruno, Dominik Dannehl, Richard König, Simona Coviello, Christina Kreutzer, Pia Zaunmair, Dominika Jakubecova, et al. "Functional Integration of Neuronal Precursors in the Adult Murine Piriform Cortex." Cerebral Cortex 30, no. 3 (October 22, 2019): 1499–515. http://dx.doi.org/10.1093/cercor/bhz181.

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Abstract The extent of functional maturation and integration of nonproliferative neuronal precursors, becoming neurons in the adult murine piriform cortex, is largely unexplored. We thus questioned whether precursors eventually become equivalent to neighboring principal neurons or whether they represent a novel functional network element. Adult brain neuronal precursors and immature neurons (complex cells) were labeled in transgenic mice (DCX-DsRed and DCX-CreERT2 /flox-EGFP), and their cell fate was characterized with patch clamp experiments and morphometric analysis of axon initial segments. Young (DCX+) complex cells in the piriform cortex of 2- to 4-month-old mice received sparse synaptic input and fired action potentials at low maximal frequency, resembling neonatal principal neurons. Following maturation, the synaptic input detected on older (DCX−) complex cells was larger, but predominantly GABAergic, despite evidence of glutamatergic synaptic contacts. Furthermore, the rheobase current of old complex cells was larger and the maximal firing frequency was lower than those measured in neighboring age-matched principal neurons. The striking differences between principal neurons and complex cells suggest that the latter are a novel type of neuron and new coding element in the adult brain rather than simple addition or replacement for preexisting network components.

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26

Zhang, Wei, and Qian Hong. "Modeling Brain Functional Networks Using Graph Neural Networks: A Review and Clinical Application." IECE Transactions on Intelligent Systematics 1, no. 2 (September 23, 2024): 58–68. http://dx.doi.org/10.62762/tis.2024.680959.

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The integration of graph neural networks (GNNs) with brain functional network analysis is an emerging field that combines neuroscience and machine learning to enhance our understanding of complex brain dynamics. We first briefly introduce the fundamentals of brain functional networks, followed by an overview of Graph Neural Network principles and architectures. The review then focuses on the applications of these networks and address current challenges in the field, such as the need for interpretable models and effective integration of multi-modal neuroimaging data. We also highlight the potential of GNNs in clinical perimenopausal areas such as perimenopausal depression research, demonstrating the broad applicability of this approach. The review concludes by outlining future research directions, including the development of more sophisticated architectures for large-scale, heterogeneous brain graphs, and the exploration of causal inference in brain networks. By synthesizing recent advances and identifying key research directions, this review aims to summarize the focal points of brain functional network analysis and GNNs, explore the potential of their integration, and provide a reference for advancing this interdisciplinary field.

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27

Fauzi, Muhammad Ilfan. "Pemanfaan Neurosains dalam Desain Pengembangan Kurikulum Bahasa Arab." Arabiyatuna : Jurnal Bahasa Arab 4, no. 1 (May 8, 2020): 1. http://dx.doi.org/10.29240/jba.v4i1.1095.

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This paper discusses the contribution of neuroscience in the development of the Arabic language curriculum of the modern era with the development of a very strategic and practical reconstruction. One of them is the emergence of integration between nation, brain based learning, and character building using a neuroscience approach. The basic point of this study is the right brain functional statement as the basic for developing the Arabic language curriculum. The purpose of this paper is to provide an understanding that the learning process of Arabic can be integrated with the neuroscience approach. This type of research used library research with content analysis in the form of descriptive analysis using primary sources in the form of bibliographies and scientific journal articles. The results showed that the application of neuroscience can be implemented in the formulation of Arabic curriculum development with a series; 1) optimization of the right and left brain nerve system; 2) integrating the role of the brain into the educational and psychological basis of cognitive aspects (Arabic substance), psychomotor (mastery of Arabic language skills), and affective (attitudes, values, and characters) into Arabic learning; 3) interconnecting these three aspecs into the nature of the objectives, methods, strategies of learning Arabic. On this basis, neuroscience can be implicated in the standard development of Arabic learning curriculum in the modern era.

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28

Bédard, Mélanie, Line Laplante, and Julien Mercier. "Development of Reading Remediation for Dyslexic Individuals." Zeitschrift für Psychologie 224, no. 4 (October 2016): 240–46. http://dx.doi.org/10.1027/2151-2604/a000259.

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Abstract. Dyslexia is a phenomenon for which the brain correlates have been studied since the beginning of the 20th century. Simultaneously, the field of education has also been studying dyslexia and its remediation, mainly through behavioral data. The last two decades have seen a growing interest in integrating neuroscience and education. This article provides a quick overview of pertinent scientific literature involving neurophysiological data on functional brain differences in dyslexia and discusses their very limited influence on the development of reading remediation for dyslexic individuals. Nevertheless, it appears that if certain conditions are met – related to the key elements of educational neuroscience and to the nature of the research questions – conceivable benefits can be expected from the integration of neurophysiological data with educational research. When neurophysiological data can be employed to overcome the limits of using behavioral data alone, researchers can both unravel phenomenon otherwise impossible to document and raise new questions.

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29

Ray, Dipanjan, Nilambari Hajare, Dipanjan Roy, and Arpan Banerjee. "Large-scale Functional Integration, Rather than Functional Dissociation along Dorsal and Ventral Streams, Underlies Visual Perception and Action." Journal of Cognitive Neuroscience 32, no. 5 (May 2020): 847–61. http://dx.doi.org/10.1162/jocn_a_01527.

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Visual dual-stream theory posits that two distinct neural pathways of specific functional significance originate from primary visual areas and reach the inferior temporal (ventral) and posterior parietal areas (dorsal). However, there are several unresolved questions concerning the fundamental aspects of this theory. For example, is the functional dissociation between ventral and dorsal stream driven by features in input stimuli or is it driven by categorical differences between visuoperceptual and visuomotor functions? Is the dual stream rigid or flexible? What is the nature of the interactions between the two streams? We addressed these questions using fMRI recordings on healthy human volunteers and employing stimuli and tasks that can tease out the divergence between visuoperceptual and visuomotor variants of dual-stream theory. fMRI scans were repeated after seven practice sessions that were conducted in a non-MRI environment to investigate the effects of neuroplasticity. Brain activation analysis supports an input-based functional dissociation and existence of context-dependent neuroplasticity in dual-stream areas. Intriguingly, premotor cortex activation was observed in the position perception task and distributed deactivated regions were observed in all perception tasks, thus warranting a network-level analysis. Dynamic causal modeling analysis incorporating activated and deactivated brain areas during perception tasks indicates that the brain dynamics during visual perception and actions could be interpreted within the framework of predictive coding. Effectively, the network-level findings point toward the existence of more intricate context-driven functional networks selective of “what” and “where” information rather than segregated streams of processing along ventral and dorsal brain regions.

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30

Hogan, Patrick Colm. "Continuity and change in narrative study." Narrative Inquiry 16, no. 1 (August 29, 2006): 66–74. http://dx.doi.org/10.1075/ni.16.1.10hog.

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Since its ancient origins, narrative theory has involved two broad types of analysis — componential and functional. Componential analysis seeks to isolate the elements and operations that make up narrative. Functional analysis explores the purposes of narrative. Commonly, writers isolate two functions — one emotive, the other ethical and/or political. The broad framework of narrative theory has remained largely the same since its inception. Changes have primarily been a matter of expanding the scope or detail of componential or functional analyses. In the twentieth century, there was a particular expansion of the ethico-political part of functional analysis. One distinctive feature of very recent narrative theory is its use of cognitive neuroscience to expand our componential analyses treating narrative causality and plot organization and our functional analyses treating emotion. Unfortunately, the work on emotional functional analysis has not been integrated with its ethico-political counterpart. This lack of integration may be due to the political economy in which cognitive literary study arose. Moreover, the future of such integration may be less a matter of the analyses themselves and more a matter of the political economy in which these analyses are embedded.

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31

Cooper, C. Garret, and Benjamin M. Ramsden. "Clustered cortical organization and the enhanced probability of intra-areal functional integration." Network: Computation in Neural Systems 21, no. 1-2 (March 2010): 1–34. http://dx.doi.org/10.3109/0954898x.2010.484475.

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32

Gohel, Suril R., and Bharat B. Biswal. "Functional Integration Between Brain Regions at Rest Occurs in Multiple-Frequency Bands." Brain Connectivity 5, no. 1 (February 2015): 23–34. http://dx.doi.org/10.1089/brain.2013.0210.

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33

Macaluso, Emiliano, and Jon Driver. "Multisensory spatial interactions: a window onto functional integration in the human brain." Trends in Neurosciences 28, no. 5 (May 2005): 264–71. http://dx.doi.org/10.1016/j.tins.2005.03.008.

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34

Syed, N. I., R. L. Ridgway, K. Lukowiak, and A. G. M. Bulloch. "Transplantation and functional integration of an identified respiratory interneuron in lymnaea stagnalis." Neuron 8, no. 4 (April 1992): 767–74. http://dx.doi.org/10.1016/0896-6273(92)90097-w.

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35

Spencer, G. E., N. I. Syed, E. van Kesteren, K. Lukowiak, W. P. M. Geraerts, and J. van Minnen. "Synthesis and functional integration of a neurotransmitter receptor in isolated invertebrate axons." Journal of Neurobiology 44, no. 1 (2000): 72–81. http://dx.doi.org/10.1002/1097-4695(200007)44:1<72::aid-neu7>3.0.co;2-#.

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36

Feklicheva, Inna, Ilya Zakharov, Nadezda Chipeeva, Ekaterina Maslennikova, Svetlana Korobova, Timofey Adamovich, Victoria Ismatullina, and Sergey Malykh. "Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity." Brain Sciences 11, no. 1 (January 13, 2021): 94. http://dx.doi.org/10.3390/brainsci11010094.

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Анотація:

The present study investigates the relationship between individual differences in verbal and non-verbal cognitive abilities and resting-state EEG network characteristics. We used a network neuroscience approach to analyze both large-scale topological characteristics of the whole brain as well as local brain network characteristics. The characteristic path length, modularity, and cluster coefficient for different EEG frequency bands (alpha, high and low; beta1 and beta2, and theta) were calculated to estimate large-scale topological integration and segregation properties of the brain networks. Betweenness centrality, nodal clustering coefficient, and local connectivity strength were calculated as local network characteristics. We showed that global network integration measures in the alpha band were positively correlated with non-verbal intelligence, especially with the more difficult part of the test (Raven’s total scores and E series), and the ability to operate with verbal information (the “Conclusions” verbal subtest). At the same time, individual differences in non-verbal intelligence (Raven’s total score and C series), and vocabulary subtest of the verbal intelligence tests, were negatively correlated with the network segregation measures. Our results show that resting-state EEG functional connectivity can reveal the functional architecture associated with an individual difference in cognitive performance.

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37

De Pascalis, Vilfredo. "Brain Functional Correlates of Resting Hypnosis and Hypnotizability: A Review." Brain Sciences 14, no. 2 (January 24, 2024): 115. http://dx.doi.org/10.3390/brainsci14020115.

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This comprehensive review delves into the cognitive neuroscience of hypnosis and variations in hypnotizability by examining research employing functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG) methods. Key focus areas include functional brain imaging correlations in hypnosis, EEG band oscillations as indicators of hypnotic states, alterations in EEG functional connectivity during hypnosis and wakefulness, drawing critical conclusions, and suggesting future research directions. The reviewed functional connectivity findings support the notion that disruptions in the available integration between different components of the executive control network during hypnosis may correspond to altered subjective appraisals of the agency during the hypnotic response, as per dissociated and cold control theories of hypnosis. A promising exploration avenue involves investigating how frontal lobes’ neurochemical and aperiodic components of the EEG activity at waking-rest are linked to individual differences in hypnotizability. Future studies investigating the effects of hypnosis on brain function should prioritize examining distinctive activation patterns across various neural networks.

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Schlösser, R., K. Koch, G. Wagner, C. Schultz, M. Röbel, C. Schachtzabel, J. R. Reichenbach, and H. Sauer. "Intensive practice of a cognitive task is associated with enhanced functional integration in schizophrenia." Psychological Medicine 39, no. 11 (April 20, 2009): 1809–19. http://dx.doi.org/10.1017/s0033291709005820.

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BackgroundThere is increasing evidence that the frequently reported working memory impairments in schizophrenia might be partly due to an alteration in the functional connectivity between task-relevant areas. However, little is known about the functional connectivity patterns in schizophrenia patients during learning processes. In a previous study, Koch et al. [Neuroscience (2007) 146, 1474–1483] have demonstrated stronger exponential activation decreases in schizophrenia patients during overlearning of short-term memory material. The question arises whether these differential temporal patterns of activation in schizophrenia patients and controls are going along with changes in task-related functional connectivity.MethodTherefore, in the current study, 13 patients with schizophrenia and 13 controls were studied while performing a short-term memory task associated with increasing overlearning of verbal stimulus material. Functional connectivity was investigated by analyses of psychophysiological interactions (PPI).ResultsResults revealed significant task-related modulation of functional connectivity between the left dorsolateral prefrontal cortex (DLPFC) and a network including the right DLPFC, left ventrolateral prefrontal cortex, premotor cortex, right inferior parietal cortex, left and right cerebellum as well as the left occipital lobe in patients during the course of overlearning and practice. No significant PPI results were detectable in controls.ConclusionsActivation changes with practice were associated with high functional connectivity between task-relevant areas in schizophrenia patients. This could be interpreted as a compensatory resource allocation and network integration in the context of cortical inefficiency and may be a specific neurophysiological signature underlying the pathophysiology of schizophrenia.

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39

Montemayor, Carlos, and Marc Wittmann. "The Varieties of Presence: Hierarchical Levels of Temporal Integration." Timing & Time Perception 2, no. 3 (2014): 325–38. http://dx.doi.org/10.1163/22134468-00002030.

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We propose a hierarchical, three-level analysis of the present, in terms of simultaneity of events, experienced presence, and an extended mental presence containing the narrative self. The literature on the philosophy, psychology and neuroscience of time consciousness does not precisely distinguish these varieties of presence: first, a functional moment of perception in the range of milliseconds defines what is simultaneous and successive. Below a certain threshold events are processed as co-temporal. Secondly, the experienced moment of two to three seconds is related to a temporal-processing mechanism enabling conscious experience of the present moment. Thirdly, the continuity of experience is formed by working memory in the range of multiple seconds leading to the sense of mental presence over time, generating a temporal platform for the narrative self. These varieties of presence help solve puzzles pertaining to duration and simultaneity.

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40

Hu, Yi, and Dihao Wang. "Integrative approaches in cognitive neuroscience: Computational tools for analyzing EEG and fMRI data." Applied and Computational Engineering 82, no. 1 (November 8, 2024): 148–53. http://dx.doi.org/10.54254/2755-2721/82/20240963.

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Abstract. This paper explores the integrative approaches in cognitive neuroscience that utilize computational tools to analyze electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) data. These methodologies provide comprehensive insights into the complex mechanisms underlying cognitive functions. By leveraging advanced computational models, researchers can decode brain activity patterns and understand the neural correlates of cognitive processes. This paper discusses technological advancements, such as machine learning algorithms, statistical models, and signal processing techniques, and their applications in studying memory, learning, attention, perception, and decision-making. The challenges of data integration, model interpretability, and computational resources are also examined. Detailed case studies and quantitative analyses demonstrate the effectiveness of these methods in cognitive neuroscience research. The future prospects and potential improvements in this field are highlighted, emphasizing the role of interdisciplinary collaboration and technological innovation.

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41

Karakas, H. M. "Epilogue: Integration structural and functional MRI data with EEG." International Journal of Psychophysiology 69, no. 3 (September 2008): 161. http://dx.doi.org/10.1016/j.ijpsycho.2008.05.411.

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42

Babiloni, Fabio, Filippo Carducci, Cosimo Del Gratta, Febo Cincotti, Mauro Palomba, Giuseppe Ferrante, Gian Luca Romani, Paolo Maria Rossini, and Claudio Babiloni. "On the integration of high resolution EEG and event-related functional MR." NeuroImage 11, no. 5 (May 2000): S631. http://dx.doi.org/10.1016/s1053-8119(00)91561-1.

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43

Wei, Jing, Xiaoyue Wang, Xiaohong Cui, Bin Wang, Jiayue Xue, Yan Niu, Qianshan Wang, Arezo Osmani, and Jie Xiang. "Functional Integration and Segregation in a Multilayer Network Model of Patients with Schizophrenia." Brain Sciences 12, no. 3 (March 10, 2022): 368. http://dx.doi.org/10.3390/brainsci12030368.

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Research has shown that abnormal brain networks in patients with schizophrenia appear at different frequencies, but the relationship between these different frequencies is unclear. Therefore, it is necessary to use a multilayer network model to evaluate the integration of information from different frequency bands. To explore the mechanism of integration and separation in the multilayer network of schizophrenia, we constructed multilayer frequency brain network models in 50 patients with schizophrenia and 69 healthy subjects, and the entropy of the multiplex degree (EMD) and multilayer clustering coefficient (MCC) were calculated. The results showed that the ability to integrate and separate information in the multilayer network of patients was significantly higher than that of normal people. This difference was mainly reflected in the default mode network, sensorimotor network, subcortical network, and visual network. Among them, the subcortical network was different in both MCC and EMD outcomes. Furthermore, differences were found in the posterior cingulate gyrus, hippocampus, amygdala, putamen, pallidum, and thalamus. The thalamus and posterior cingulate gyrus were associated with the patient’s symptom scores. Our results showed that the cross-frequency interaction ability of patients with schizophrenia was significantly enhanced, among which the subcortical network was the most active. This interaction may serve as a compensation mechanism for intralayer dysfunction.

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44

Làdavas, Elisabetta, and Francesco Pavani. "Neuropsychological evidence of the functional integration of visual, auditory and proprioceptive spatial maps." NeuroReport 9, no. 6 (April 1998): 1195–200. http://dx.doi.org/10.1097/00001756-199804200-00043.

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45

Sørensen, Andreas Toft, Lachlan Thompson, Deniz Kirik, Anders Björklund, Olle Lindvall, and Merab Kokaia. "Functional properties and synaptic integration of genetically labelled dopaminergic neurons in intrastriatal grafts." European Journal of Neuroscience 21, no. 10 (May 2005): 2793–99. http://dx.doi.org/10.1111/j.1460-9568.2005.04116.x.

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46

Pardillo-Díaz, Ricardo, Patricia Perez-Garcia, Livia Carrascal, María Ortego-Domínguez, Samuel Dominguez-Garcia, Ricardo Gomez-Oliva, Carmen Castro, and Pedro Nunez-Abades. "NEWLY GENERATED NEURONS IN MECHANICAL CORTICAL BRAIN INJURIES: A STUDY OF FUNCTIONAL INTEGRATION." IBRO Neuroscience Reports 15 (October 2023): S194—S195. http://dx.doi.org/10.1016/j.ibneur.2023.08.299.

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47

Fulcher, Ben D. "Discovering Conserved Properties of Brain Organization Through Multimodal Integration and Interspecies Comparison." Journal of Experimental Neuroscience 13 (January 2019): 117906951986204. http://dx.doi.org/10.1177/1179069519862047.

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The primate cerebral cortex is broadly organized along hierarchical processing streams underpinned by corresponding variation in the brain’s microstructure and interareal connectivity patterns. Fulcher et al. recently demonstrated that a similar organization exists in the mouse cortex by combining independent datasets of cytoarchitecture, gene expression, cell densities, and long-range axonal connectivity. Using the T1w:T2w magnetic resonance imaging map as a common spatial reference for data-driven comparison of cortical gradients between mouse and human, we highlighted a common hierarchical expression pattern of numerous brain-related genes, providing new understanding of how systematic structural variation shapes functional specialization in mammalian brains. Reflecting on these findings, here we discuss how open neuroscience datasets, combined with advanced neuroinformatics approaches, will be crucial in the ongoing search for organization principles of brain structure. We explore the promises and challenges of integrative studies and argue that a tighter collaboration between experimental, statistical, and theoretical neuroscientists is needed to drive progress further.

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48

Fein, Deborah. "Thoughtful People Thinking About People Thinking About Thinking People." Journal of the International Neuropsychological Society 12, no. 5 (September 2006): 759–60. http://dx.doi.org/10.1017/s1355617706220939.

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Social Neuroscience: People Thinking About Thinking People. John T. Cacioppo, Penny S. Visser, and Cynthia L. Pickett (Eds.). 2006. Cambridge, MA: The MIT Press, 328 pp., $45.00 (HB)Perhaps not since the flowering of clinical neuropsychology thirty years ago have we seen this sense of exhilaration about the emergence of a new field through the integration of existing disciplines. Clearly, these authors and thinkers feel the same excitement that could be felt when new collaborations were being forged among neurologists, psychiatrists, psychologists, and communication specialists in earlier decades. For the emerging field of social neuroscience, the parent fields include social psychology (see, for example, chapters on Race and Emotion, The Social Neuroscience of Stereotyping and Prejudice, Social and Physical Pain, and Animal Models of Human Attitudes), clinical neuropsychology (Neurological Substrates of Emotional and Social Intelligence: Evidence from Patients with Focal Brain Lesions), social cognition (Neural Substrates of Self Awareness, and three chapters bearing directly on Theory of Mind) and, of course, cognitive and basic neuroscience. Each chapter includes theoretical perspectives from multiple fields and reviews studies that use diverse techniques (including functional imaging, ERP, behavioral scales, lesion studies, developmental studies, and animal studies), although the book is very heavy on functional imaging data. As the editors acknowledge, animal and patient data are not represented in a thorough way.

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49

Cinciute, Sigita. "Translating the hemodynamic response: why focused interdisciplinary integration should matter for the future of functional neuroimaging." PeerJ 7 (March 25, 2019): e6621. http://dx.doi.org/10.7717/peerj.6621.

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The amount of information acquired with functional neuroimaging techniques, particularly fNIRS and fMRI, is rapidly growing and has enormous potential for studying human brain functioning. Therefore, many scientists focus on solving computational neuroimaging and Big Data issues to advance the discipline. However, the main obstacle—the accurate translation of the hemodynamic response (HR) by the investigation of a physiological phenomenon called neurovascular coupling—is still not fully overcome and, more importantly, often overlooked in this context. This article provides a brief and critical overview of significant findings from cellular biology and in vivo brain physiology with a focus on advancing existing HR modelling paradigms. A brief historical timeline of these disciplines of neuroscience is presented for readers to grasp the concept better, and some possible solutions for further scientific discussion are provided.

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50

Calabro, Finnegan J., Vishnu P. Murty, Maria Jalbrzikowski, Brenden Tervo-Clemmens, and Beatriz Luna. "Development of Hippocampal–Prefrontal Cortex Interactions through Adolescence." Cerebral Cortex 30, no. 3 (October 16, 2019): 1548–58. http://dx.doi.org/10.1093/cercor/bhz186.

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Abstract Significant improvements in cognitive control occur from childhood through adolescence, supported by the maturation of prefrontal systems. However, less is known about the neural basis of refinements in cognitive control proceeding from adolescence to adulthood. Accumulating evidence indicates that integration between hippocampus (HPC) and prefrontal cortex (PFC) supports flexible cognition and has a protracted neural maturation. Using a longitudinal design (487 scans), we characterized developmental changes from 8 to 32 years of age in HPC-PFC functional connectivity at rest and its associations with cognitive development. Results indicated significant increases in functional connectivity between HPC and ventromedial PFC (vmPFC), but not dorsolateral PFC. Importantly, HPC-vmPFC connectivity exclusively predicted performance on the Stockings of Cambridge task, which probes problem solving and future planning. These data provide evidence that maturation of high-level cognition into adulthood is supported by increased functional integration across the HPC and vmPFC through adolescence.

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