Journal articles: 'Brain variability'

1

Kaas, Jon H., and Christine E. Collins. "Variability in the sizes of brain parts." Behavioral and Brain Sciences 24, no. 2 (April 2001): 288–90. http://dx.doi.org/10.1017/s0140525x01333952.

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Rogachov, Anton, Joshua C. Cheng, Nathalie Erpelding, Kasey S. Hemington, Adrian P. Crawley, and Karen D. Davis. "Regional brain signal variability." PAIN 157, no. 11 (November 2016): 2483–92. http://dx.doi.org/10.1097/j.pain.0000000000000665.

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Croxson, Paula L., Stephanie J. Forkel, Leonardo Cerliani, and Michel Thiebaut de Schotten. "Structural Variability Across the Primate Brain: A Cross-Species Comparison." Cerebral Cortex 28, no. 11 (October 13, 2017): 3829–41. http://dx.doi.org/10.1093/cercor/bhx244.

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Abstract A large amount of variability exists across human brains; revealed initially on a small scale by postmortem studies and, more recently, on a larger scale with the advent of neuroimaging. Here we compared structural variability between human and macaque monkey brains using grey and white matter magnetic resonance imaging measures. The monkey brain was overall structurally as variable as the human brain, but variability had a distinct distribution pattern, with some key areas showing high variability. We also report the first evidence of a relationship between anatomical variability and evolutionary expansion in the primate brain. This suggests a relationship between variability and stability, where areas of low variability may have evolved less recently and have more stability, while areas of high variability may have evolved more recently and be less similar across individuals. We showed specific differences between the species in key areas, including the amount of hemispheric asymmetry in variability, which was left-lateralized in the human brain across several phylogenetically recent regions. This suggests that cerebral variability may be another useful measure for comparison between species and may add another dimension to our understanding of evolutionary mechanisms.

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Garrett, D. D., N. Kovacevic, A. R. McIntosh, and C. L. Grady. "The Importance of Brain Variability." NeuroImage 47 (July 2009): S81. http://dx.doi.org/10.1016/s1053-8119(09)70579-8.

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Shemie, Sam D. "Variability of Brain Death Practices." Critical Care Medicine 32, no. 12 (December 2004): 2564–65. http://dx.doi.org/10.1097/01.ccm.0000153903.72797.0b.

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Oliver, Isaura, Jaroslav Hlinka, Jakub Kopal, and Jörn Davidsen. "Quantifying the Variability in Resting-State Networks." Entropy 21, no. 9 (September 11, 2019): 882. http://dx.doi.org/10.3390/e21090882.

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Recent precision functional mapping of individual human brains has shown that individual brain organization is qualitatively different from group average estimates and that individuals exhibit distinct brain network topologies. How this variability affects the connectivity within individual resting-state networks remains an open question. This is particularly important since certain resting-state networks such as the default mode network (DMN) and the fronto-parietal network (FPN) play an important role in the early detection of neurophysiological diseases like Alzheimer’s, Parkinson’s, and attention deficit hyperactivity disorder. Using different types of similarity measures including conditional mutual information, we show here that the backbone of the functional connectivity and the direct connectivity within both the DMN and the FPN does not vary significantly between healthy individuals for the AAL brain atlas. Weaker connections do vary however, having a particularly pronounced effect on the cross-connections between DMN and FPN. Our findings suggest that the link topology of single resting-state networks is quite robust if a fixed brain atlas is used and the recordings are sufficiently long—even if the whole brain network topology between different individuals is variable.

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SHERIDAN, M. R., and K. A. FLOWERS. "MOVEMENT VARIABILITY AND BRADYKINESIA IN PARKINSON'S DISEASE." Brain 113, no. 4 (1990): 1149–61. http://dx.doi.org/10.1093/brain/113.4.1149.

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Nolano, M., V. Provitera, and L. Santoro. "Internodal length variability of dermal myelinated fibres." Brain 133, no. 6 (February 15, 2010): e142-e142. http://dx.doi.org/10.1093/brain/awq004.

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Wang, Yifeng, Yujia Ao, Qi Yang, Yang Liu, Yujie Ouyang, Xiujuan Jing, Yajing Pang, Qian Cui, and Huafu Chen. "Spatial variability of low frequency brain signal differentiates brain states." PLOS ONE 15, no. 11 (November 12, 2020): e0242330. http://dx.doi.org/10.1371/journal.pone.0242330.

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Temporal variability of the neural signal has been demonstrated to be closely related to healthy brain function. Meanwhile, the evolving brain functions are supported by dynamic relationships among brain regions. We hypothesized that the spatial variability of brain signal might provide important information about brain function. Here we used the spatial sample entropy (SSE) to investigate the spatial variability of neuroimaging signal during a steady-state presented face detection task. Lower SSE was found during task state than during resting state, associating with more repetitive functional interactions between brain regions. The standard deviation (SD) of SSE during the task was negatively related to the SD of reaction time, suggesting that the spatial pattern of neural activity is reorganized according to particular cognitive function and supporting the previous theory that greater variability is associated with better task performance. These results were replicated with reordered data, implying the reliability of SSE in measuring the spatial organization of neural activity. Overall, the present study extends the research scope of brain signal variability from the temporal dimension to the spatial dimension, improving our understanding of the spatiotemporal characteristics of brain activities and the theory of brain signal variability.

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McIntosh, Anthony Randal, Natasa Kovacevic, and Roxane J. Itier. "Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development." PLoS Computational Biology 4, no. 7 (July 4, 2008): e1000106. http://dx.doi.org/10.1371/journal.pcbi.1000106.

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Biswas, Abhik K., and John F. Summerauer. "Heart Rate Variability and Brain Death." Journal of Neurosurgical Anesthesiology 16, no. 1 (January 2004): 62. http://dx.doi.org/10.1097/00008506-200401000-00011.

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Garrett, Douglas D., Anthony R. McIntosh, and Cheryl L. Grady. "Brain Signal Variability is Parametrically Modifiable." Cerebral Cortex 24, no. 11 (June 7, 2013): 2931–40. http://dx.doi.org/10.1093/cercor/bht150.

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Gilles, F., R. Davis, L. Rorke, and A. Leviton. "INTRAOBSERVER VARIABILITY IN BRAIN TUMOR DIAGNOSIS." Journal of Neuropathology and Experimental Neurology 45, no. 3 (May 1986): 317. http://dx.doi.org/10.1097/00005072-198605000-00015.

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Freitas, J., J. Puig, A. P. Rocha, P. Lago, J. Teixeira, M. J. Carvalho, O. Costa, and A. Falcão de Freitas. "Heart rate variability in brain death." Clinical Autonomic Research 6, no. 3 (June 1996): 141–46. http://dx.doi.org/10.1007/bf02281900.

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15

Bookheimer, Susan. "Variability in Autism: Brain and Behavior." Biological Psychiatry 89, no. 9 (May 2021): S3. http://dx.doi.org/10.1016/j.biopsych.2021.02.029.

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Saporta, M. A., I. Katona, R. A. Lewis, S. Masse, M. E. Shy, and J. Li. "Reply: Internodal length variability of dermal myelinated fibres." Brain 133, no. 6 (February 15, 2010): e143-e143. http://dx.doi.org/10.1093/brain/awq005.

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Nishida, Satoshi, Yusuke Nakano, Antoine Blanc, Naoya Maeda, Masataka Kado, and Shinji Nishimoto. "Brain-Mediated Transfer Learning of Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5281–88. http://dx.doi.org/10.1609/aaai.v34i04.5974.

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The human brain can effectively learn a new task from a small number of samples, which indicates that the brain can transfer its prior knowledge to solve tasks in different domains. This function is analogous to transfer learning (TL) in the field of machine learning. TL uses a well-trained feature space in a specific task domain to improve performance in new tasks with insufficient training data. TL with rich feature representations, such as features of convolutional neural networks (CNNs), shows high generalization ability across different task domains. However, such TL is still insufficient in making machine learning attain generalization ability comparable to that of the human brain. To examine if the internal representation of the brain could be used to achieve more efficient TL, we introduce a method for TL mediated by human brains. Our method transforms feature representations of audiovisual inputs in CNNs into those in activation patterns of individual brains via their association learned ahead using measured brain responses. Then, to estimate labels reflecting human cognition and behavior induced by the audiovisual inputs, the transformed representations are used for TL. We demonstrate that our brain-mediated TL (BTL) shows higher performance in the label estimation than the standard TL. In addition, we illustrate that the estimations mediated by different brains vary from brain to brain, and the variability reflects the individual variability in perception. Thus, our BTL provides a framework to improve the generalization ability of machine-learning feature representations and enable machine learning to estimate human-like cognition and behavior, including individual variability.

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Sharda, Megha, Nicholas E. V. Foster, and Krista L. Hyde. "Imaging Brain Development: Benefiting from Individual Variability." Journal of Experimental Neuroscience 9s1 (January 2015): JEN.S32734. http://dx.doi.org/10.4137/jen.s32734.

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Human brain development is a complex process that evolves from early childhood to young adulthood. Major advances in brain imaging are increasingly being used to characterize the developing brain. These advances have further helped to elucidate the dynamic maturational processes that lead to the emergence of complex cognitive abilities in both typical and atypical development. However, conventional approaches involve categorical group comparison models and tend to disregard the role of widespread interindividual variability in brain development. This review highlights how this variability can inform our understanding of developmental processes. The latest studies in the field of brain development are reviewed, with a particular focus on the role of individual variability and the consequent heterogeneity in brain structural and functional development. This review also highlights how such heterogeneity might be utilized to inform our understanding of complex neuropsychiatric disorders and recommends the use of more dimensional approaches to study brain development.

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Xu, Yuehua, Miao Cao, Xuhong Liao, Mingrui Xia, Xindi Wang, Tina Jeon, Minhui Ouyang, et al. "Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain." Cerebral Cortex 29, no. 10 (December 7, 2018): 4208–22. http://dx.doi.org/10.1093/cercor/bhy302.

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Abstract Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31–42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.

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Stoecklein, Sophia, Anne Hilgendorff, Meiling Li, Kai Förster, Andreas W. Flemmer, Franziska Galiè, Stephan Wunderlich, et al. "Variable functional connectivity architecture of the preterm human brain: Impact of developmental cortical expansion and maturation." Proceedings of the National Academy of Sciences 117, no. 2 (December 30, 2019): 1201–6. http://dx.doi.org/10.1073/pnas.1907892117.

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Functional connectivity (FC) is known to be individually unique and to reflect cognitive variability. Although FC can serve as a valuable correlate and potential predictor of (patho-) physiological nervous function in high-risk constellations, such as preterm birth, templates for individualized FC analysis are lacking, and knowledge about the capacity of the premature brain to develop FC variability is limited. In a cohort of prospectively recruited, preterm-born infants undergoing magnetic resonance imaging close to term-equivalent age, we show that the overall pattern could be reliably detected with a broad range of interindividual FC variability in regions of higher-order cognitive functions (e.g., association cortices) and less interindividual variability in unimodal regions (e.g., visual and motor cortices). However, when comparing the preterm and adult brains, some brain regions showed a marked shift in variability toward adulthood. This shift toward greater variability was strongest in cognitive networks like the attention and frontoparietal networks and could be partially predicted by developmental cortical expansion. Furthermore, FC variability was reflected by brain tissue characteristics indicating cortical maturation. Brain regions with high functional variability (e.g., the inferior frontal gyrus and temporoparietal junction) displayed lower cortical maturation at birth compared with somatosensory cortices. In conclusion, the overall pattern of interindividual variability in FC is already present preterm; however, some brain regions show increased variability toward adulthood, identifying characteristic patterns, such as in cognitive networks. These changes are related to postnatal cortical expansion and maturation, allowing for environmental and developmental factors to translate into marked individual differences in FC.

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21

Maloney, Shane K., Duncan Mitchell, and Dominique Blache. "The contribution of carotid rete variability to brain temperature variability in sheep in a thermoneutral environment." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 292, no. 3 (March 2007): R1298—R1305. http://dx.doi.org/10.1152/ajpregu.00275.2006.

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The degree of variability in the temperature difference between the brain and carotid arterial blood is greater than expected from the presumed tight coupling between brain heat production and brain blood flow. In animals with a carotid rete, some of that variability arises in the rete. Using thermometric data loggers in five sheep, we have measured the temperature of arterial blood before it enters the carotid rete and after it has perfused the carotid rete, as well as hypothalamic temperature, every 2 min for between 6 and 12 days. The sheep were conscious, unrestrained, and maintained at an ambient temperature of 20–22°C. On average, carotid arterial blood and brain temperatures were the same, with a decrease in blood temperature of 0.35°C across the rete and then an increase in temperature of the same magnitude between blood leaving the rete and the brain. Rete cooling of arterial blood took place at temperatures below the threshold for selective brain cooling. All of the variability in the temperature difference between carotid artery and brain was attributable statistically to variability in the temperature difference across the rete. The temperature difference between arterial blood leaving the rete and the brain varied from −0.1 to 0.9°C. Some of this variability was related to a thermal inertia of the brain, but the majority we attribute to instability in the relationship between brain blood flow and brain heat production.

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Vaidya, Chandan J., and Evan M. Gordon. "Phenotypic Variability in Resting-State Functional Connectivity: Current Status." Brain Connectivity 3, no. 2 (April 2013): 99–120. http://dx.doi.org/10.1089/brain.2012.0110.

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Riganello, Francesco, Sergio Garbarino, and Walter G. Sannita. "Heart Rate Variability, Homeostasis, and Brain Function." Journal of Psychophysiology 26, no. 4 (January 1, 2012): 178–203. http://dx.doi.org/10.1027/0269-8803/a000080.

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Measures of heart rate variability (HRV) are major indices of the sympathovagal balance in cardiovascular research. These measures are thought to reflect complex patterns of brain activation as well and HRV is now emerging as a descriptor thought to provide information on the nervous system organization of homeostatic responses in accordance with the situational requirements. Current models of integration equate HRV to the affective states as parallel outputs of the central autonomic network, with HRV reflecting its organization of affective, physiological, “cognitive,” and behavioral elements into a homeostatic response. Clinical application is in the study of patients with psychiatric disorders, traumatic brain injury, impaired emotion-specific processing, personality, and communication disorders. HRV responses to highly emotional sensory inputs have been identified in subjects in vegetative state and in healthy or brain injured subjects processing complex sensory stimuli. In this respect, HRV measurements can provide additional information on the brain functional setup in the severely brain damaged and would provide researchers with a suitable approach in the absence of conscious behavior or whenever complex experimental conditions and data collection are impracticable, as it is the case, for example, in intensive care units.

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Ayroles, Julien F., Sean M. Buchanan, Chelsea O’Leary, Kyobi Skutt-Kakaria, Jennifer K. Grenier, Andrew G. Clark, Daniel L. Hartl, and Benjamin L. de Bivort. "Behavioral idiosyncrasy reveals genetic control of phenotypic variability." Proceedings of the National Academy of Sciences 112, no. 21 (May 7, 2015): 6706–11. http://dx.doi.org/10.1073/pnas.1503830112.

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Quantitative genetics has primarily focused on describing genetic effects on trait means and largely ignored the effect of alternative alleles on trait variability, potentially missing an important axis of genetic variation contributing to phenotypic differences among individuals. To study the genetic effects on individual-to-individual phenotypic variability (or intragenotypic variability), we used Drosophila inbred lines and measured the spontaneous locomotor behavior of flies walking individually in Y-shaped mazes, focusing on variability in locomotor handedness, an assay optimized to measure variability. We discovered that some lines had consistently high levels of intragenotypic variability among individuals, whereas lines with low variability behaved as although they tossed a coin at each left/right turn decision. We demonstrate that the degree of variability is itself heritable. Using a genome-wide association study (GWAS) for the degree of intragenotypic variability as the phenotype across lines, we identified several genes expressed in the brain that affect variability in handedness without affecting the mean. One of these genes, Ten-a, implicates a neuropil in the central complex of the fly brain as influencing the magnitude of behavioral variability, a brain region involved in sensory integration and locomotor coordination. We validated these results using genetic deficiencies, null alleles, and inducible RNAi transgenes. Our study reveals the constellation of phenotypes that can arise from a single genotype and shows that different genetic backgrounds differ dramatically in their propensity for phenotypic variabililty. Because traditional mean-focused GWASs ignore the contribution of variability to overall phenotypic variation, current methods may miss important links between genotype and phenotype.

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Bartley, A. "Genetic variability of human brain size and cortical gyral patterns." Brain 120, no. 2 (February 1, 1997): 257–69. http://dx.doi.org/10.1093/brain/120.2.257.

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Semplicini, Claudio, Elisabeth Ollagnon-Roman, Sarah Leonard-Louis, Guenaelle Piguet-Lacroix, Manon Silvestre, Philippe Latour, and Tanya Stojkovic. "High intra-familiar clinical variability in MORC2 mutated CMT2 patients." Brain 140, no. 4 (February 23, 2017): e21-e21. http://dx.doi.org/10.1093/brain/awx019.

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27

Taylor, Paul A., Suril Gohel, Xin Di, Martin Walter, and Bharat B. Biswal. "Functional Covariance Networks: Obtaining Resting-State Networks from Intersubject Variability." Brain Connectivity 2, no. 4 (August 2012): 203–17. http://dx.doi.org/10.1089/brain.2012.0095.

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Piguet, Olivier. "Neither white nor black: embracing clinical variability in dementia diagnosis." Brain 143, no. 5 (May 1, 2020): 1291–93. http://dx.doi.org/10.1093/brain/awaa119.

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Sheng, Jinhua, Qingqiang Liu, Bocheng Wang, Luyun Wang, Meiling Shao, and Yu Xin. "Characteristics and variability of functional brain networks." Neuroscience Letters 729 (June 2020): 134954. http://dx.doi.org/10.1016/j.neulet.2020.134954.

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Heisz, Jennifer J., Judith M. Shedden, and Anthony R. McIntosh. "Relating brain signal variability to knowledge representation." NeuroImage 63, no. 3 (November 2012): 1384–92. http://dx.doi.org/10.1016/j.neuroimage.2012.08.018.

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31

Alnaes, Dag, Tobias Kaufmann, Aldo Córdova Palomera, Emanuel Schwarz, Dennis van der Meer, Torgeir Moberget, Jarek Rokicki, et al. "S193. Brain Variability in Schizophrenia Spectrum Disorder." Biological Psychiatry 83, no. 9 (May 2018): S422—S423. http://dx.doi.org/10.1016/j.biopsych.2018.02.1085.

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32

Ma, Liying, Lixia Tian, Tianyu Hu, Tianzi Jiang, and Nianming Zuo. "Development of Individual Variability in Brain Functional Connectivity and Capability across the Adult Lifespan." Cerebral Cortex 31, no. 8 (April 5, 2021): 3925–38. http://dx.doi.org/10.1093/cercor/bhab059.

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Abstract Individual variability exists in both brain function and behavioral performance. However, changes in individual variability in brain functional connectivity and capability across adult development and aging have not yet been clearly examined. Based on resting-state functional magnetic resonance imaging data from a large cohort of participants (543 adults, aged 18–88 years), brain functional connectivity was analyzed to characterize the spatial distribution and differences in individual variability across the adult lifespan. Results showed high individual variability in the association cortex over the adult lifespan, whereas individual variability in the primary cortex was comparably lower in the initial stage but increased with age. Individual variability was also negatively correlated with the strength/number of short-, medium-, and long-range functional connections in the brain, with long-range connections playing a more critical role in increasing global individual variability in the aging brain. More importantly, in regard to specific brain regions, individual variability in the motor cortex was significantly correlated with differences in motor capability. Overall, we identified specific patterns of individual variability in brain functional structure during the adult lifespan and demonstrated that functional variability in the brain can reflect behavioral performance. These findings advance our understanding of the underlying principles of the aging brain across the adult lifespan and suggest how to characterize degenerating behavioral capability using imaging biomarkers.

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33

Ossenkoppele, Rik, Daniel R. Schonhaut, Michael Schöll, Samuel N. Lockhart, Nagehan Ayakta, Suzanne L. Baker, James P. O’Neil, et al. "Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease." Brain 139, no. 5 (March 8, 2016): 1551–67. http://dx.doi.org/10.1093/brain/aww027.

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Stuss, D. T. "Staying on the job: the frontal lobes control individual performance variability." Brain 126, no. 11 (November 1, 2003): 2363–80. http://dx.doi.org/10.1093/brain/awg237.

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Taebi, Arezoo, Hannah Kiesow, Kai Vogeley, Leonhard Schilbach, Boris C. Bernhardt, and Danilo Bzdok. "Population variability in social brain morphology for social support, household size and friendship satisfaction." Social Cognitive and Affective Neuroscience 15, no. 6 (June 2020): 635–47. http://dx.doi.org/10.1093/scan/nsaa075.

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Abstract The social brain hypothesis proposes that the complexity of human brains has coevolved with increasing complexity of social interactions in primate societies. The present study explored the possible relationships between brain morphology and the richness of more intimate ‘inner’ and wider ‘outer’ social circles by integrating Bayesian hierarchical modeling with a large cohort sample from the UK Biobank resource (n = 10 000). In this way, we examined population volume effects in 36 regions of the ‘social brain’, ranging from lower sensory to higher associative cortices. We observed strong volume effects in the visual sensory network for the group of individuals with satisfying friendships. Further, the limbic network displayed several brain regions with substantial volume variations in individuals with a lack of social support. Our population neuroscience approach thus showed that distinct networks of the social brain show different patterns of volume variations linked to the examined social indices.

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Brouwers, N., K. Sleegers, S. Engelborghs, V. Bogaerts, S. Serneels, K. Kamali, E. Corsmit, et al. "Genetic risk and transcriptional variability of amyloid precursor protein in Alzheimer's disease." Brain 129, no. 11 (September 29, 2006): 2984–91. http://dx.doi.org/10.1093/brain/awl212.

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Annweiler, Cedric, Manuel Montero-Odasso, Robert Bartha, John Drozd, Vladimir Hachinski, and Olivier Beauchet. "Association between gait variability and brain ventricle attributes: a brain mapping study." Experimental Gerontology 57 (September 2014): 256–63. http://dx.doi.org/10.1016/j.exger.2014.06.015.

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Suh, Minah, Rachel A. Kolster, Sumit Niogi, Bruce D. McCandliss, Richard B. Ivry, Henning U. Voss, Ranjeeta Sarkar, Jamshid Ghajar, and the Cognitive. "Degree of Brain Connectivity Predicts Eye-Tracking Variability." Journal of the Korean Physical Society 53, no. 6 (December 15, 2008): 3468–73. http://dx.doi.org/10.3938/jkps.53.3468.

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Kelly, A. M. Clare, Lucina Q. Uddin, Bharat B. Biswal, F. Xavier Castellanos, and Michael P. Milham. "Competition between functional brain networks mediates behavioral variability." NeuroImage 39, no. 1 (January 2008): 527–37. http://dx.doi.org/10.1016/j.neuroimage.2007.08.008.

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Seghier, Mohamed L., and Cathy J. Price. "Interpreting and Utilising Intersubject Variability in Brain Function." Trends in Cognitive Sciences 22, no. 6 (June 2018): 517–30. http://dx.doi.org/10.1016/j.tics.2018.03.003.

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M. L. KING, S. W. LICHTMAN, G. SELI. "Heart-rate variability in chronic traumatic brain injury." Brain Injury 11, no. 6 (January 1997): 445–53. http://dx.doi.org/10.1080/026990597123421.

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Mejia, RE, and MM Pollack. "Variability in brain death determination practices in children." International Journal of Trauma Nursing 2, no. 2 (April 1996): 60. http://dx.doi.org/10.1016/s1075-4210(96)80012-5.

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Mejia, R. E. "Variability in brain death determination practices in children." JAMA: The Journal of the American Medical Association 274, no. 7 (August 16, 1995): 550–53. http://dx.doi.org/10.1001/jama.274.7.550.

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Mejia, Rodrigo E. "Variability in Brain Death Determination Practices in Children." JAMA: The Journal of the American Medical Association 274, no. 7 (August 16, 1995): 550. http://dx.doi.org/10.1001/jama.1995.03530070048028.

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Booth, Tom, Dominika Dykiert, Janie Corley, Alan J. Gow, Zoe Morris, Susana Muñoz Maniega, Natalie A. Royle, et al. "Reaction time variability and brain white matter integrity." Neuropsychology 33, no. 5 (July 2019): 642–57. http://dx.doi.org/10.1037/neu0000483.

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KNAUS, T., A. BOLLICH, D. COREY, L. LEMEN, and A. FOUNDAS. "Variability in perisylvian brain anatomy in healthy adults." Brain and Language 97, no. 2 (May 2006): 219–32. http://dx.doi.org/10.1016/j.bandl.2005.10.008.

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Hordacre, Brenton, Michael C. Ridding, and Mitchell R. Goldsworthy. "Response variability to non-invasive brain stimulation protocols." Clinical Neurophysiology 126, no. 12 (December 2015): 2249–50. http://dx.doi.org/10.1016/j.clinph.2015.04.052.

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Mori, S., N. H. Sternberger, M. M. Herman, and L. A. Sternberger. "Variability of laminin immunoreactivity in human autopsy brain." Histochemistry 97, no. 3 (March 1992): 237–41. http://dx.doi.org/10.1007/bf00267633.

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Schwarz, G., G. Pfurtscheller, and W. List. "Heart rate variability in brain death and coma." Electroencephalography and Clinical Neurophysiology 61, no. 3 (September 1985): S201. http://dx.doi.org/10.1016/0013-4694(85)90768-0.

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Myers, N. E., M. G. Stokes, L. Walther, and A. C. Nobre. "Oscillatory Brain State Predicts Variability in Working Memory." Journal of Neuroscience 34, no. 23 (June 4, 2014): 7735–43. http://dx.doi.org/10.1523/jneurosci.4741-13.2014.

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Journal articles on the topic 'Brain variability'

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