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1

Stern, Peter. "Brain structure and function mature together." Science 355, no. 6320 (2017): 35.4–37. http://dx.doi.org/10.1126/science.355.6320.35-d.

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Yoo, Seung‐Wan, Mary G. Motari, Keiichiro Susuki, et al. "Sialylation regulates brain structure and function." FASEB Journal 29, no. 7 (2015): 3040–53. http://dx.doi.org/10.1096/fj.15-270983.

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3

LEHTO, V., V. WASENIUS, and S. ESKELINEN. "Structure-function relationships of brain spectrin." Cell Biology International Reports 14 (September 1990): 23. http://dx.doi.org/10.1016/0309-1651(90)90202-a.

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4

SILVESTRI, L., A. L. ALLEGRA MASCARO, J. LOTTI, L. SACCONI, and F. S. PAVONE. "ADVANCED OPTICAL TECHNIQUES TO EXPLORE BRAIN STRUCTURE AND FUNCTION." Journal of Innovative Optical Health Sciences 06, no. 01 (2013): 1230002. http://dx.doi.org/10.1142/s1793545812300029.

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Understanding brain structure and function, and the complex relationships between them, is one of the grand challenges of contemporary sciences. Thanks to their flexibility, optical techniques could be the key to explore this complex network. In this manuscript, we briefly review recent advancements in optical methods applied to three main issues: anatomy, plasticity and functionality. We describe novel implementations of light-sheet microscopy to resolve neuronal anatomy in whole fixed brains with cellular resolution. Moving to living samples, we show how real-time dynamics of brain rewiring can be visualized through two-photon microscopy with the spatial resolution of single synaptic contacts. The plasticity of the injured brain can also be dissected through cutting-edge optical methods that specifically ablate single neuronal processes. Finally, we report how nonlinear microscopy in combination with novel voltage sensitive dyes allow optical registrations of action potential across a population of neurons opening promising prospective in understanding brain functionality. The knowledge acquired from these complementary optical methods may provide a deeper comprehension of the brain and of its unique features.
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Haas, L. "DISCOVERIES IN THE HUMAN BRAIN. NEUROSCIENCE PREHISTORY, BRAIN STRUCTURE, AND FUNCTION." Brain 122, no. 4 (1999): 785–86. http://dx.doi.org/10.1093/brain/122.4.785.

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6

Goksan, Sezgi, Froso Argyri, Jonathan D. Clayden, Frederique Liegeois, and Li Wei. "Early childhood bilingualism: effects on brain structure and function." F1000Research 9 (May 15, 2020): 370. http://dx.doi.org/10.12688/f1000research.23216.1.

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Growing up in a bilingual environment is becoming increasingly common. Yet, we know little about how this enriched language environment influences the connectivity of children’s brains. Behavioural research in children and adults has shown that bilingualism experience may boost executive control (EC) skills, such as inhibitory control and attention. Moreover, increased structural and functional (resting-state) connectivity in language-related and EC-related brain networks is associated with increased executive control in bilingual adults. However, how bilingualism factors alter brain connectivity early in brain development remains poorly understood. We will combine standardised tests of attention with structural and resting-state functional magnetic resonance imaging (MRI) in bilingual children. This study will allow us to address an important field of inquiry within linguistics and developmental cognitive neuroscience by examining the following questions: Does bilingual experience modulate connectivity in language-related and EC-related networks in children? Do differences in resting-state brain connectivity correlate with differences in EC skills (specifically attention skills)? How do bilingualism-related factors, such as age of exposure to two languages, language usage and proficiency, modulate brain connectivity? We will collect structural and functional MRI, and quantitative measures of EC and language skills from two groups of English-Greek bilingual children - 20 simultaneous bilinguals (exposure to both languages from birth) and 20 successive bilinguals (exposure to English between the ages of 3 and 5 years) - and 20 English monolingual children, 8-10 years old. We will compare connectivity measures and attention skills between monolinguals and bilinguals to examine the effects of bilingual exposure. We will also examine to what extent bilingualism factors predict brain connectivity in EC and language networks. Overall, we hypothesize that connectivity and EC will be enhanced in bilingual children compared to monolingual children, and each outcome will be modulated by age of exposure to two languages and by bilingual language usage.
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Goksan, Sezgi, Froso Argyri, Jonathan D. Clayden, Frederique Liegeois, and Li Wei. "Early childhood bilingualism: effects on brain structure and function." F1000Research 9 (November 4, 2020): 370. http://dx.doi.org/10.12688/f1000research.23216.2.

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Growing up in a bilingual environment is becoming increasingly common. Yet, we know little about how this enriched language environment influences the connectivity of children’s brains. Behavioural research in children and adults has shown that bilingualism experience may boost executive control (EC) skills, such as inhibitory control and attention. Moreover, increased structural and functional (resting-state) connectivity in language-related and EC-related brain networks is associated with increased executive control in bilingual adults. However, how bilingualism factors alter brain connectivity early in brain development remains poorly understood. We will combine standardised tests of attention with structural and resting-state functional magnetic resonance imaging (MRI) in bilingual children. This study will allow us to address an important field of inquiry within linguistics and developmental cognitive neuroscience by examining the following questions: Does bilingual experience modulate connectivity in language-related and EC-related networks in children? Do differences in resting-state brain connectivity correlate with differences in EC skills (specifically attention skills)? How do bilingualism-related factors, such as age of exposure to two languages, language usage and proficiency, modulate brain connectivity? We will collect structural and functional MRI, and quantitative measures of EC and language skills from two groups of English-Greek bilingual children - 20 simultaneous bilinguals (exposure to both languages from birth) and 20 successive bilinguals (exposure to English between the ages of 3 and 5 years) - and 20 English monolingual children, 8-10 years old. We will compare connectivity measures and attention skills between monolinguals and bilinguals to examine the effects of bilingual exposure. We will also examine to what extent bilingualism factors predict brain connectivity in EC and language networks. Overall, we hypothesize that connectivity and EC will be enhanced in bilingual children compared to monolingual children, and each outcome will be modulated by age of exposure to two languages and by bilingual language usage.
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8

Jursky, F., S. Tamura, A. Tamura, S. Mandiyan, H. Nelson, and N. Nelson. "Structure, function and brain localization of neurotransmitter transporters." Journal of Experimental Biology 196, no. 1 (1994): 283–95. http://dx.doi.org/10.1242/jeb.196.1.283.

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We studied four different cDNAs encoding GABA transporters and three different cDNAs encoding glycine transporters in mouse and rat brains. A genomic clone of two of the glycine transporters (GLYT1a and GLYT1b) revealed that they derive from differential splicing of a single gene. The third glycine transporter (GLYT2) is encoded by a separate gene. Antibodies were raised against seven of these neurotransmitter transporters and their cytochemical localization in the mouse brain was studied. In general, we observed a deviation from the classical separation of neuronal and glial transporters. It seems that each of the neurotransmitter transporters is present in specific places in the brain and is expressed in a different way in very specific areas. For example, the GABA transporter GAT4, which also transports beta-alanine, was localized to neurons. However, GAT1, which is specific for GABA, was localized not only to neurons but also to glial cells. The recently discovered glycine transporter GLYT2 was of particular interest because of its deviation from the general structure by a very extended N terminus containing multiple potential phosphorylation sites. Western analysis and immunocytochemistry in frozen sections of mouse brain demonstrated a clear caudal-rostral gradient of GLYT2 distribution, with massive accumulation in the spinal cord and brainstem and less in the cerebellum. Its distribution is typically neuronal and it is present in processes with varicosities. A correlation as observed between the pattern we obtained and that observed previously from strychnine binding studies. The results indicate that GLYT2 is involved in the termination of glycine neurotransmission at the classical inhibitory system in the hindbrain. The availability of four different GABA transporters made it possible to look for specific binding sites upon the neurotransmitter transporters. An extensive program of site-directed mutagenesis led us to identify a potential neurotransmitter binding site on the GABA transporters.
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9

Granger, Richard. "The evolution of computation in brain circuitry." Behavioral and Brain Sciences 29, no. 1 (2006): 17–18. http://dx.doi.org/10.1017/s0140525x06279019.

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The attempt to derive mental function from brain structure is highly constrained by study of the allometric changes among brain components with evolution. In particular, even if homologous structures in different species produce similar computations, they may be constituents of larger systems (e.g., cortical-subcortical loops) that exhibit different composite operations as a function of relative size and connectivity in different-sized brains. The resulting evolutionary constraints set useful and specific conditions on candidate hypotheses of brain circuit computation.
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10

Vila-Pueyo, Marta, Jan Hoffmann, Marcela Romero-Reyes, and Simon Akerman. "Brain structure and function related to headache: Brainstem structure and function in headache." Cephalalgia 39, no. 13 (2018): 1635–60. http://dx.doi.org/10.1177/0333102418784698.

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Objective To review and discuss the literature relevant to the role of brainstem structure and function in headache. Background Primary headache disorders, such as migraine and cluster headache, are considered disorders of the brain. As well as head-related pain, these headache disorders are also associated with other neurological symptoms, such as those related to sensory, homeostatic, autonomic, cognitive and affective processing that can all occur before, during or even after headache has ceased. Many imaging studies demonstrate activation in brainstem areas that appear specifically associated with headache disorders, especially migraine, which may be related to the mechanisms of many of these symptoms. This is further supported by preclinical studies, which demonstrate that modulation of specific brainstem nuclei alters sensory processing relevant to these symptoms, including headache, cranial autonomic responses and homeostatic mechanisms. Review focus This review will specifically focus on the role of brainstem structures relevant to primary headaches, including medullary, pontine, and midbrain, and describe their functional role and how they relate to mechanisms of primary headaches, especially migraine.
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11

Als, H., F. H. Duffy, G. B. McAnulty, et al. "Early Experience Alters Brain Function and Structure." PEDIATRICS 113, no. 4 (2004): 846–57. http://dx.doi.org/10.1542/peds.113.4.846.

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12

Rogers, Lesley. "Brain Asymmetry of Structure and/or Function." Symmetry 11, no. 2 (2019): 214. http://dx.doi.org/10.3390/sym11020214.

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13

Yasui, Masato. "Regulation, structure and function of brain aquaporin." Rinsho Shinkeigaku 49, no. 11 (2009): 786–88. http://dx.doi.org/10.5692/clinicalneurol.49.786.

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14

Tregellas, Jason. "Connecting Brain Structure and Function in Schizophrenia." American Journal of Psychiatry 166, no. 2 (2009): 134–36. http://dx.doi.org/10.1176/appi.ajp.2008.08111685.

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15

Vidal, Jean-Sébastien, Sigurdur Sigurdsson, Maria K. Jonsdottir, et al. "Coronary Artery Calcium, Brain Function and Structure." Stroke 41, no. 5 (2010): 891–97. http://dx.doi.org/10.1161/strokeaha.110.579581.

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16

Zeisel, Steven H., and Mihai D. Niculescu. "Perinatal Choline Influences Brain Structure and Function." Nutrition Reviews 64, no. 4 (2006): 197–203. http://dx.doi.org/10.1111/j.1753-4887.2006.tb00202.x.

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17

Smith, J. H. "The Migraine Brain: Imaging Structure and Function." Neurology 80, no. 17 (2013): 1623. http://dx.doi.org/10.1212/wnl.0b013e31828f196a.

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18

Hoffmann, Jan, and Philip R. Holland. "Brain structure and function related to headache." Cephalalgia 39, no. 13 (2019): 1603–5. http://dx.doi.org/10.1177/0333102419884007.

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19

Amunts, Katrin, and Gereon R. Fink. "The convergence of brain structure and function." Anatomy and Embryology 210, no. 5-6 (2005): 335. http://dx.doi.org/10.1007/s00429-005-0023-7.

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20

Hynie, S. "Hormonally active brain peptides; Structure and function." Neuropharmacology 25, no. 1 (1986): 110. http://dx.doi.org/10.1016/0028-3908(86)90068-7.

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21

Yund, Brianna, Alia Ahmed, Kathleen Delaney, et al. "Brain Structure and Function in MPS II." Molecular Genetics and Metabolism 105, no. 2 (2012): S67. http://dx.doi.org/10.1016/j.ymgme.2011.11.182.

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22

Crofts, J. J., M. Forrester, and R. D. O'Dea. "Structure-function clustering in multiplex brain networks." EPL (Europhysics Letters) 116, no. 1 (2016): 18003. http://dx.doi.org/10.1209/0295-5075/116/18003.

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23

Koch, Christof. "A Brain Structure Looking for a Function." Scientific American Mind 25, no. 6 (2014): 24–27. http://dx.doi.org/10.1038/scientificamericanmind1114-24.

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24

Young, Malcolm P., and Jack W. Scannell. "Brain structure–function relationships: advances from neuroinformatics." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 355, no. 1393 (2000): 3–6. http://dx.doi.org/10.1098/rstb.2000.0545.

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25

Zaidi, Habib, Marie-Louise Montandon, and Frédéric Assal. "Structure-Function–Based Quantitative Brain Image Analysis." PET Clinics 5, no. 2 (2010): 155–68. http://dx.doi.org/10.1016/j.cpet.2010.02.003.

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26

Snyder, Abraham Z., and Adam Q. Bauer. "Mapping Structure-Function Relationships in the Brain." Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 4, no. 6 (2019): 510–21. http://dx.doi.org/10.1016/j.bpsc.2018.10.005.

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27

Joel, Daphna, and Anne Fausto-Sterling. "Beyond sex differences: new approaches for thinking about variation in brain structure and function." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1688 (2016): 20150451. http://dx.doi.org/10.1098/rstb.2015.0451.

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In the study of variation in brain structure and function that might relate to sex and gender, language matters because it frames our research questions and methods. In this article, we offer an approach to thinking about variation in brain structure and function that pulls us outside the sex differences formulation. We argue that the existence of differences between the brains of males and females does not unravel the relations between sex and the brain nor is it sufficient to characterize a population of brains. Such characterization is necessary for studying sex effects on the brain as well as for studying brain structure and function in general. Animal studies show that sex interacts with environmental, developmental and genetic factors to affect the brain. Studies of humans further suggest that human brains are better described as belonging to a single heterogeneous population rather than two distinct populations. We discuss the implications of these observations for studies of brain and behaviour in humans and in laboratory animals. We believe that studying sex effects in context and developing or adopting analytical methods that take into account the heterogeneity of the brain are crucial for the advancement of human health and well-being.
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Vázquez-Rodríguez, Bertha, Laura E. Suárez, Ross D. Markello, et al. "Gradients of structure–function tethering across neocortex." Proceedings of the National Academy of Sciences 116, no. 42 (2019): 21219–27. http://dx.doi.org/10.1073/pnas.1903403116.

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The white matter architecture of the brain imparts a distinct signature on neuronal coactivation patterns. Interregional projections promote synchrony among distant neuronal populations, giving rise to richly patterned functional networks. A variety of statistical, communication, and biophysical models have been proposed to study the relationship between brain structure and function, but the link is not yet known. In the present report we seek to relate the structural and functional connection profiles of individual brain areas. We apply a simple multilinear model that incorporates information about spatial proximity, routing, and diffusion between brain regions to predict their functional connectivity. We find that structure–function relationships vary markedly across the neocortex. Structure and function correspond closely in unimodal, primary sensory, and motor regions, but diverge in transmodal cortex, particularly the default mode and salience networks. The divergence between structure and function systematically follows functional and cytoarchitectonic hierarchies. Altogether, the present results demonstrate that structural and functional networks do not align uniformly across the brain, but gradually uncouple in higher-order polysensory areas.
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Grgurevic, Neza, and Gregor Majdic. "Sex differences in the brain–an interplay of sex steroid hormones and sex chromosomes." Clinical Science 130, no. 17 (2016): 1481–97. http://dx.doi.org/10.1042/cs20160299.

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Although considerable progress has been made in our understanding of brain function, many questions remain unanswered. The ultimate goal of studying the brain is to understand the connection between brain structure and function and behavioural outcomes. Since sex differences in brain morphology were first observed, subsequent studies suggest different functional organization of the male and female brains in humans. Sex and gender have been identified as being a significant factor in understanding human physiology, health and disease, and the biological differences between the sexes is not limited to the gonads and secondary sexual characteristics, but also affects the structure and, more crucially, the function of the brain and other organs. Significant variability in brain structures between individuals, in addition to between the sexes, is factor that complicates the study of sex differences in the brain. In this review, we explore the current understanding of sex differences in the brain, mostly focusing on preclinical animal studies.
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Di, Xin, Senhua Zhu, Hua Jin, et al. "Altered Resting Brain Function and Structure in Professional Badminton Players." Brain Connectivity 2, no. 4 (2012): 225–33. http://dx.doi.org/10.1089/brain.2011.0050.

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31

Arbib, Michael A., and Péter Érdi. "Précis of Neural organization: Structure, function, and dynamics." Behavioral and Brain Sciences 23, no. 4 (2000): 513–33. http://dx.doi.org/10.1017/s0140525x00003368.

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Neural organization: Structure, function, and dynamics shows how theory and experiment can supplement each other in an integrated, evolving account of the brain's structure, function, and dynamics. (1) Structure: Studies of brain function and dynamics build on and contribute to an understanding of many brain regions, the neural circuits that constitute them, and their spatial relations. We emphasize Szentágothai's modular architectonics principle, but also stress the importance of the microcomplexes of cerebellar circuitry and the lamellae of hippocampus. (2) Function: Control of eye movements, reaching and grasping, cognitive maps, and the roles of vision receive a functional decomposition in terms of schemas. Hypotheses as to how each schema is implemented through the interaction of specific brain regions provide the basis for modeling the overall function by neural networks constrained by neural data. Synthetic PET integrates modeling of primate circuitry with data from human brain imaging. (3) Dynamics: Dynamic system theory analyzes spatiotemporal neural phenomena, such as oscillatory and chaotic activity in both single neurons and (often synchronized) neural networks, the self-organizing development and plasticity of ordered neural structures, and learning and memory phenomena associated with synaptic modification. Rhythm generation involves multiple levels of analysis, from intrinsic cellular processes to loops involving multiple brain regions. A variety of rhythms are related to memory functions. The Précis presents a multifaceted case study of the hippocampus. We conclude with the claim that language and other cognitive processes can be fruitfully studied within the framework of neural organization that the authors have charted with John Szentágothai.
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Young, Malcolm P., Claus–C Hilgetag, and Jack W. Scannell. "On imputing function to structure from the behavioural effects of brain lesions." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 355, no. 1393 (2000): 147–61. http://dx.doi.org/10.1098/rstb.2000.0555.

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What is the link, if any, between the patterns of connections in the brain and the behavioural effects of localized brain lesions? We explored this question in four related ways. First, we investigated the distribution of activity decrements that followed simulated damage to elements of the thalamocortical network, using integrative mechanisms that have recently been used to successfully relate connection data to information on the spread of activation, and to account simultaneously for a variety of lesion effects. Second, we examined the consequences of the patterns of decrement seen in the simulation for each type of inference that has been employed to impute function to structure on the basis of the effects of brain lesions. Every variety of conventional inference, including double dissociation, readily misattributed function to structure. Third, we tried to derive a more reliable framework of inference for imputing function to structure, by clarifying concepts of function, and exploring a more formal framework, in which knowledge of connectivity is necessary but insufficient, based on concepts capable of mathematical specification. Fourth, we applied this framework to inferences about function relating to a simple network that reproduces intact, lesioned and paradoxically restored orientating behaviour. Lesion effects could be used to recover detailed and reliable information on which structures contributed to particular functions in this simple network. Finally, we explored how the effects of brain lesions and this formal approach could be used in conjunction with information from multiple neuroscience methodologies to develop a practical and reliable approach to inferring the functional roles of brain structures. of brain lesions.
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Graham, Daniel, and Daniel Rockmore. "The Packet Switching Brain." Journal of Cognitive Neuroscience 23, no. 2 (2011): 267–76. http://dx.doi.org/10.1162/jocn.2010.21477.

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The computer metaphor has served brain science well as a tool for comprehending neural systems. Nevertheless, we propose here that this metaphor be replaced or supplemented by a new metaphor, the “Internet metaphor,” to reflect dramatic new network theoretic understandings of brain structure and function. We offer a “weak” form and a “strong” form of this metaphor: The former suggests that structures and processes unique to Internet-like architectures (e.g., domains and protocols) can profitably guide our thinking about brains, whereas the latter suggests that one particular feature of the Internet—packet switching—may be instantiated in the structure of certain brain networks, particularly mammalian neocortex.
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34

De Vries, Geert J. "Minireview: Sex Differences in Adult and Developing Brains: Compensation, Compensation, Compensation." Endocrinology 145, no. 3 (2004): 1063–68. http://dx.doi.org/10.1210/en.2003-1504.

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Abstract Despite decades of research, we do not know the functional significance of most sex differences in the brain. We are heavily invested in the idea that sex differences in brain structure cause sex differences in behavior. We rarely consider the possibility that sex differences in brain structure may also prevent sex differences in overt functions and behavior, by compensating for sex differences in physiological conditions, e.g. gonadal hormone levels that may generate undesirable sex differences if left unchecked. Such a dual function for sex differences is unlikely to be restricted to adult brains. This review will entertain the possibility that transient sex differences in gene expression in developing brains may cause permanent differences in brain structure but prevent them as well, by compensating for potentially differentiating effects of sex differences in gonadal hormone levels and sex chromosomal gene expression. Consistent application of this dual-function hypothesis will make the search for the functional significance of sex differences more productive.
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35

Goetz, C. G. "Discoveries in the Human Brain: Neuroscience Prehistory, Brain Structure, and Function." Neurology 51, no. 3 (1998): 929–30. http://dx.doi.org/10.1212/wnl.51.3.929-b.

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36

Mayer, Richard F. "Discoveries in the Human Brain: Neuroscience Prehistory, Brain Structure, and Function." Journal of Nervous & Mental Disease 187, no. 10 (1999): 647–48. http://dx.doi.org/10.1097/00005053-199910000-00012.

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37

Jacyna, L. S. "Discoveries in the Human Brain: Neuroscience Prehistory, Brain Structure, and Function." Bulletin of the History of Medicine 73, no. 2 (1999): 325–26. http://dx.doi.org/10.1353/bhm.1999.0066.

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38

Evans, R. W. "Discoveries in the Human Brain: Neuroscience Prehistory, Brain Structure, and Function." JAMA: The Journal of the American Medical Association 279, no. 22 (1998): 1837—a—1838. http://dx.doi.org/10.1001/jama.279.22.1837-a.

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39

Ventegodt, Søren, Tyge Dahl Hermansen, Isack Kandel, and Joav Merrick. "Human Development XII: A Theory for the Structure and Function of the Human Brain." Scientific World JOURNAL 8 (2008): 621–42. http://dx.doi.org/10.1100/tsw.2008.7.

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The human brain is probably the most complicated single structure in the biological universe. The cerebral cortex that is traditionally connected with consciousness is extremely complex. The brain contains approximately 1,000,000 km of nerve fibers, indicating its enormous complexity and which makes it difficult for scientists to reveal the function of the brain. In this paper, we propose a new model for brain functions, i.e., information-guided self-organization of neural patterns, where information is provided from the abstract wholeness of the biophysical system of an organism (often called the true self, or the “soul””). We present a number of arguments in favor of this model that provide self-conscious control over the thought process or cognition. Our arguments arise from analyzing experimental data from different research fields: histology, anatomy, electroencephalography (EEG), cerebral blood flow, neuropsychology, evolutionary studies, and mathematics. We criticize the popular network theories as the consequence of a simplistic, mechanical interpretation of reality (philosophical materialism) applied to the brain. We demonstrate how viewing brain functions as information-guided self-organization of neural patterns can explain the structure of conscious mentation; we seem to have a dual hierarchical representation in the cerebral cortex: one for sensation-perception and one for will-action. The model explains many of our unique mental abilities to think, memorize, associate, discriminate, and make abstractions. The presented model of the conscious brain also seems to be able to explain the function of the simpler brains, such as those of insects and hydra.
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40

Kornelsen, Jennifer, Kelcie Witges, Jennifer Labus, Emeran A. Mayer, and Charles N. Bernstein. "Brain structure and function changes in ulcerative colitis." Neuroimage: Reports 1, no. 4 (2021): 100064. http://dx.doi.org/10.1016/j.ynirp.2021.100064.

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41

Koziol, Leonard F., Lauren A. Barker, Arthur W. Joyce, and Skip Hrin. "Structure and Function of Large-Scale Brain Systems." Applied Neuropsychology: Child 3, no. 4 (2014): 236–44. http://dx.doi.org/10.1080/21622965.2014.946797.

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42

Alex, Raichel M., Nazaneen D. Mousavi, Rong Zhang, Robert J. Gatchel, and Khosrow Behbehani. "Obstructive sleep apnea: Brain hemodynamics, structure, and function." Journal of Applied Biobehavioral Research 22, no. 4 (2017): e12101. http://dx.doi.org/10.1111/jabr.12101.

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43

Brinton, Roberta Diaz. "Oestrogen therapy affects brain structure but not function." Nature Reviews Neurology 12, no. 10 (2016): 561–62. http://dx.doi.org/10.1038/nrneurol.2016.147.

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44

Mansury, Yuri, and Thomas S. Deisboeck. "Simulating ‘structure–function’ patterns of malignant brain tumors." Physica A: Statistical Mechanics and its Applications 331, no. 1-2 (2004): 219–32. http://dx.doi.org/10.1016/j.physa.2003.09.013.

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45

Abbott, N. Joan, Adjanie A. K. Patabendige, Diana E. M. Dolman, Siti R. Yusof, and David J. Begley. "Structure and function of the blood–brain barrier." Neurobiology of Disease 37, no. 1 (2010): 13–25. http://dx.doi.org/10.1016/j.nbd.2009.07.030.

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Solowij, N., S. Broyd, H. Van Hell, et al. "Cannabinoid effects on brain structure, function and neurochemistry." European Neuropsychopharmacology 26 (October 2016): S116—S117. http://dx.doi.org/10.1016/s0924-977x(16)30879-3.

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Suárez, Laura E., Ross D. Markello, Richard F. Betzel, and Bratislav Misic. "Linking Structure and Function in Macroscale Brain Networks." Trends in Cognitive Sciences 24, no. 4 (2020): 302–15. http://dx.doi.org/10.1016/j.tics.2020.01.008.

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Honey, Christopher J., Jean-Philippe Thivierge, and Olaf Sporns. "Can structure predict function in the human brain?" NeuroImage 52, no. 3 (2010): 766–76. http://dx.doi.org/10.1016/j.neuroimage.2010.01.071.

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O’Neill, Aisling, and Thomas Frodl. "Brain structure and function in borderline personality disorder." Brain Structure and Function 217, no. 4 (2012): 767–82. http://dx.doi.org/10.1007/s00429-012-0379-4.

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Toga, Arthur W., and Bradley A. Payne. "Animating the 3D structure and function of brain." Computerized Medical Imaging and Graphics 15, no. 5 (1991): 285–91. http://dx.doi.org/10.1016/0895-6111(91)90135-i.

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