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Artículos de revistas sobre el tema "Whole brain mapping"

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Publicado: 3 de mayo de 2025

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1

Hardy, T. L., L. R. D. Brynildson, J. G. Gray y D. Spurlock. "Three-Dimensional Whole-Brain Mapping". Stereotactic and Functional Neurosurgery 58, n.º 1-4 (1992): 141–43. http://dx.doi.org/10.1159/000098987.

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2

Randlett, Owen, Caroline L. Wee, Eva A. Naumann, Onyeka Nnaemeka, David Schoppik, James E. Fitzgerald, Ruben Portugues et al. "Whole-brain activity mapping onto a zebrafish brain atlas". Nature Methods 12, n.º 11 (14 de septiembre de 2015): 1039–46. http://dx.doi.org/10.1038/nmeth.3581.

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3

Sempeles, Susan. "Whole-Brain Mapping Enhanced by Automated Imaging". Journal of Clinical Engineering 37, n.º 2 (2012): 36–37. http://dx.doi.org/10.1097/jce.0b013e31824d8e8d.

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4

Shibata, Shinsuke, Yuji Komaki, Fumiko Seki, Michiko O. Inouye, Toshihiro Nagai y Hideyuki Okano. "Connectomics: comprehensive approaches for whole-brain mapping". Microscopy 64, n.º 1 (18 de diciembre de 2014): 57–67. http://dx.doi.org/10.1093/jmicro/dfu103.

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5

Wu, Bing, Wei Li, Arnaud Guidon y Chunlei Liu. "Whole brain susceptibility mapping using compressed sensing". Magnetic Resonance in Medicine 67, n.º 1 (10 de junio de 2011): 137–47. http://dx.doi.org/10.1002/mrm.23000.

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6

Shimono, Masanori y Kazuhisa Niki. "Global Mapping of the Whole-Brain Network Underlining Binocular Rivalry". Brain Connectivity 3, n.º 2 (abril de 2013): 212–21. http://dx.doi.org/10.1089/brain.2012.0129.

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7

Mattay, V. S., J. A. Frank, A. K. Santha, J. J. Pekar, J. H. Duyn, A. C. McLaughlin y D. R. Weinberger. "Whole-brain functional mapping with isotropic MR imaging." Radiology 201, n.º 2 (noviembre de 1996): 399–404. http://dx.doi.org/10.1148/radiology.201.2.8888231.

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8

Kasai, Atsushi, Kaoru Seiriki y Hitoshi Hashimoto. "Whole-brain activity mapping at single-cell resolution". Folia Pharmacologica Japonica 153, n.º 6 (2019): 278–83. http://dx.doi.org/10.1254/fpj.153.278.

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9

Bao, Chenglong, Jae Kyu Choi y Bin Dong. "Whole Brain Susceptibility Mapping Using Harmonic Incompatibility Removal". SIAM Journal on Imaging Sciences 12, n.º 1 (enero de 2019): 492–520. http://dx.doi.org/10.1137/18m1191452.

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10

Banerjee, Tirtha Das, Linwan Zhang y Antónia Monteiro. "Mapping Gene Expression in Whole Larval Brains of Bicyclus anynana Butterflies". Methods and Protocols 8, n.º 2 (13 de marzo de 2025): 31. https://doi.org/10.3390/mps8020031.

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Butterfly larvae display intricate cognitive capacities and behaviors, but relatively little is known about how those behaviors alter their brains at the molecular level. Here, we optimized a hybridization chain reaction 3.0 (HCR v3.0) protocol to visualize the expression of multiple RNA molecules in fixed larval brains of the African butterfly Bicyclus anynana. We optimized the polyacrylamide gel mounting, fixation, and sample permeabilization steps, and mapped the expression domains of ten genes in whole larval brain tissue at single-cell resolution. The genes included optomotor blind (omb), yellow-like, zinc finger protein SNAI2-like (SNAI2), weary (wry), extradenticle (exd), Synapsin, Distal-less (Dll), bric-à-brac 1 (bab1), dachshund (dac), and acetyl coenzyme A acetyltransferase B (AcatB). This method can be used alongside single-cell sequencing to visualize the spatial location of brain cells that change in gene expression or splicing patterns in response to specific behaviors or cognitive experiences.
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11

Mitra, Partha P., Marcello G. P. Rosa y Harvey J. Karten. "Panoptic Neuroanatomy: Digital Microscopy of Whole Brains and Brain-Wide Circuit Mapping". Brain, Behavior and Evolution 81, n.º 4 (2013): 203–5. http://dx.doi.org/10.1159/000350241.

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12

Kim, Woonhee y Chihye Chung. "Whole-brain cellular mapping of stress exposure in male and female brains". IBRO Reports 6 (septiembre de 2019): S271. http://dx.doi.org/10.1016/j.ibror.2019.07.842.

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13

Niemeyer, James E. "Mapping whole brain seizure network recruitment with optogenetic kindling". Journal of Neurophysiology 127, n.º 2 (1 de febrero de 2022): 393–96. http://dx.doi.org/10.1152/jn.00525.2021.

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Epilepsy is often labeled as a network disorder, though a common view of seizures holds that they initiate in a singular onset zone before expanding contiguously outward. A recent report by Choy et al. (Choy M, Dadgar-Kiani E, Cron GO, Duffy BA, Schmid F, Edelman BJ, Asaad M, Chan RW, Vahdat S, Lee JH. Neuron 2021 Oct 23: S0896-6273(21)00778-9.) leverages new tools to study whole brain dynamics during epileptic seizures originating in the hippocampus. Cell-type-specific kindling and functional imaging revealed how various brain regions were recruited to seizures and uncovered a novel form of migrating seizure core.
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14

HAYWORTH, KENNETH J. "ELECTRON IMAGING TECHNOLOGY FOR WHOLE BRAIN NEURAL CIRCUIT MAPPING". International Journal of Machine Consciousness 04, n.º 01 (junio de 2012): 87–108. http://dx.doi.org/10.1142/s1793843012400057.

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15

Hagmann, Patric, Maciej Kurant, Xavier Gigandet, Patrick Thiran, Van J. Wedeen, Reto Meuli y Jean-Philippe Thiran. "Mapping Human Whole-Brain Structural Networks with Diffusion MRI". PLoS ONE 2, n.º 7 (4 de julio de 2007): e597. http://dx.doi.org/10.1371/journal.pone.0000597.

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16

Xie, Ziyan, Tasuku Kayama, Nahoko Kuga, Musashi Yamakawa y Takuya Sasaki. "Whole-brain mapping of neuronal activation by peripheral inflammation". Proceedings for Annual Meeting of The Japanese Pharmacological Society 97 (2023): 1—B—P—084. http://dx.doi.org/10.1254/jpssuppl.97.0_1-b-p-084.

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17

Tyson, Adam L., Charly V. Rousseau, Christian J. Niedworok, Sepiedeh Keshavarzi, Chryssanthi Tsitoura, Lee Cossell, Molly Strom y Troy W. Margrie. "A deep learning algorithm for 3D cell detection in whole mouse brain image datasets". PLOS Computational Biology 17, n.º 5 (28 de mayo de 2021): e1009074. http://dx.doi.org/10.1371/journal.pcbi.1009074.

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Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. However, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an open-source algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.
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18

Rugg-Gunn, F. J., P. A. Boulby, M. R. Symms, G. J. Barker y J. S. Duncan. "Whole-brain T2 mapping demonstrates occult abnormalities in focal epilepsy". Neurology 64, n.º 2 (24 de enero de 2005): 318–25. http://dx.doi.org/10.1212/01.wnl.0000149642.93493.f4.

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19

Deng, Ke, Lu Yang, Jing Xie, He Tang, Gui-Sheng Wu y Huai-Rong Luo. "Whole-brain mapping of projection from mouse lateral septal nucleus". Biology Open 8, n.º 7 (17 de junio de 2019): bio043554. http://dx.doi.org/10.1242/bio.043554.

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20

Watabe-Uchida, Mitsuko, Lisa Zhu, Sachie K. Ogawa, Archana Vamanrao y Naoshige Uchida. "Whole-Brain Mapping of Direct Inputs to Midbrain Dopamine Neurons". Neuron 74, n.º 5 (junio de 2012): 858–73. http://dx.doi.org/10.1016/j.neuron.2012.03.017.

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21

Vousden, Dulcie A., Jonathan Epp, Hiroyuki Okuno, Brian J. Nieman, Matthijs van Eede, Jun Dazai, Timothy Ragan et al. "Whole-brain mapping of behaviourally induced neural activation in mice". Brain Structure and Function 220, n.º 4 (24 de abril de 2014): 2043–57. http://dx.doi.org/10.1007/s00429-014-0774-0.

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22

Bhave, Sampada, Sajan Goud Lingala, Casey P. Johnson, Vincent A. Magnotta y Mathews Jacob. "Accelerated whole‐brain multi‐parameter mapping using blind compressed sensing". Magnetic Resonance in Medicine 75, n.º 3 (marzo de 2016): 1175–86. http://dx.doi.org/10.1002/mrm.25722.

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23

Burke, Matthew J., Juho Joutsa, Alexander L. Cohen, Louis Soussand, Danielle Cooke, Rami Burstein y Michael D. Fox. "Mapping migraine to a common brain network". Brain 143, n.º 2 (9 de enero de 2020): 541–53. http://dx.doi.org/10.1093/brain/awz405.

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Abstract Inconsistent findings from migraine neuroimaging studies have limited attempts to localize migraine symptomatology. Novel brain network mapping techniques offer a new approach for linking neuroimaging findings to a common neuroanatomical substrate and localizing therapeutic targets. In this study, we attempted to determine whether neuroanatomically heterogeneous neuroimaging findings of migraine localize to a common brain network. We used meta-analytic coordinates of decreased grey matter volume in migraineurs as seed regions to generate resting state functional connectivity network maps from a normative connectome (n = 1000). Network maps were overlapped to identify common regions of connectivity across all coordinates. Specificity of our findings was evaluated using a whole-brain Bayesian spatial generalized linear mixed model and a region of interest analysis with comparison groups of chronic pain and a neurologic control (Alzheimer’s disease). We found that all migraine coordinates (11/11, 100%) were negatively connected (t ≥ ±7, P < 10−6 family-wise error corrected for multiple comparisons) to a single location in left extrastriate visual cortex overlying dorsal V3 and V3A subregions. More than 90% of coordinates (10/11) were also positively connected with bilateral insula and negatively connected with the hypothalamus. Bayesian spatial generalized linear mixed model whole-brain analysis identified left V3/V3A as the area with the most specific connectivity to migraine coordinates compared to control coordinates (voxel-wise probability of ≥90%). Post hoc region of interest analyses further supported the specificity of this finding (ANOVA P = 0.02; pairwise t-tests P = 0.03 and P = 0.003, respectively). In conclusion, using coordinate-based network mapping, we show that regions of grey matter volume loss in migraineurs localize to a common brain network defined by connectivity to visual cortex V3/V3A, a region previously implicated in mechanisms of cortical spreading depression in migraine. Our findings help unify migraine neuroimaging literature and offer a migraine-specific target for neuromodulatory treatment.
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24

Kirilina, Evgeniya, Saskia Helbling, Markus Morawski, Kerrin Pine, Katja Reimann, Steffen Jankuhn, Juliane Dinse et al. "Superficial white matter imaging: Contrast mechanisms and whole-brain in vivo mapping". Science Advances 6, n.º 41 (octubre de 2020): eaaz9281. http://dx.doi.org/10.1126/sciadv.aaz9281.

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Superficial white matter (SWM) contains the most cortico-cortical white matter connections in the human brain encompassing the short U-shaped association fibers. Despite its importance for brain connectivity, very little is known about SWM in humans, mainly due to the lack of noninvasive imaging methods. Here, we lay the groundwork for systematic in vivo SWM mapping using ultrahigh resolution 7 T magnetic resonance imaging. Using biophysical modeling informed by quantitative ion beam microscopy on postmortem brain tissue, we demonstrate that MR contrast in SWM is driven by iron and can be linked to the microscopic iron distribution. Higher SWM iron concentrations were observed in U-fiber–rich frontal, temporal, and parietal areas, potentially reflecting high fiber density or late myelination in these areas. Our SWM mapping approach provides the foundation for systematic studies of interindividual differences, plasticity, and pathologies of this crucial structure for cortico-cortical connectivity in humans.
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25

Lee, Hyunyeol y Felix W. Wehrli. "Whole-brain 3D mapping of oxygen metabolism using constrained quantitative BOLD". NeuroImage 250 (abril de 2022): 118952. http://dx.doi.org/10.1016/j.neuroimage.2022.118952.

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26

Niu, Misaki, Atsushi Kasai, Kaoru Seiriki y Hitoshi Hashimoto. "Whole-brain mapping of activated neurons and circuits in brains after exposure to acute stressors". Proceedings for Annual Meeting of The Japanese Pharmacological Society 94 (2021): 1—S08–2. http://dx.doi.org/10.1254/jpssuppl.94.0_1-s08-2.

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27

Zhang, Meng, Xingjie Pan, Won Jung, Aaron R. Halpern, Stephen W. Eichhorn, Zhiyun Lei, Limor Cohen et al. "Molecularly defined and spatially resolved cell atlas of the whole mouse brain". Nature 624, n.º 7991 (13 de diciembre de 2023): 343–54. http://dx.doi.org/10.1038/s41586-023-06808-9.

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AbstractIn mammalian brains, millions to billions of cells form complex interaction networks to enable a wide range of functions. The enormous diversity and intricate organization of cells have impeded our understanding of the molecular and cellular basis of brain function. Recent advances in spatially resolved single-cell transcriptomics have enabled systematic mapping of the spatial organization of molecularly defined cell types in complex tissues1–3, including several brain regions (for example, refs. 1–11). However, a comprehensive cell atlas of the whole brain is still missing. Here we imaged a panel of more than 1,100 genes in approximately 10 million cells across the entire adult mouse brains using multiplexed error-robust fluorescence in situ hybridization12 and performed spatially resolved, single-cell expression profiling at the whole-transcriptome scale by integrating multiplexed error-robust fluorescence in situ hybridization and single-cell RNA sequencing data. Using this approach, we generated a comprehensive cell atlas of more than 5,000 transcriptionally distinct cell clusters, belonging to more than 300 major cell types, in the whole mouse brain with high molecular and spatial resolution. Registration of this atlas to the mouse brain common coordinate framework allowed systematic quantifications of the cell-type composition and organization in individual brain regions. We further identified spatial modules characterized by distinct cell-type compositions and spatial gradients featuring gradual changes of cells. Finally, this high-resolution spatial map of cells, each with a transcriptome-wide expression profile, allowed us to infer cell-type-specific interactions between hundreds of cell-type pairs and predict molecular (ligand–receptor) basis and functional implications of these cell–cell interactions. These results provide rich insights into the molecular and cellular architecture of the brain and a foundation for functional investigations of neural circuits and their dysfunction in health and disease.
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28

Jaggard, James B., Evan Lloyd, Anders Yuiska, Adam Patch, Yaouen Fily, Johanna E. Kowalko, Lior Appelbaum, Erik R. Duboue y Alex C. Keene. "Cavefish brain atlases reveal functional and anatomical convergence across independently evolved populations". Science Advances 6, n.º 38 (septiembre de 2020): eaba3126. http://dx.doi.org/10.1126/sciadv.aba3126.

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Environmental perturbation can drive behavioral evolution and associated changes in brain structure and function. The Mexican fish species, Astyanax mexicanus, includes eyed river-dwelling surface populations and multiple independently evolved populations of blind cavefish. We used whole-brain imaging and neuronal mapping of 684 larval fish to generate neuroanatomical atlases of surface fish and three different cave populations. Analyses of brain region volume and neural circuits associated with cavefish behavior identified evolutionary convergence in hindbrain and hypothalamic expansion, and changes in neurotransmitter systems, including increased numbers of catecholamine and hypocretin/orexin neurons. To define evolutionary changes in brain function, we performed whole-brain activity mapping associated with behavior. Hunting behavior evoked activity in sensory processing centers, while sleep-associated activity differed in the rostral zone of the hypothalamus and tegmentum. These atlases represent a comparative brain-wide study of intraspecies variation in vertebrates and provide a resource for studying the neural basis of behavioral evolution.
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29

Zhu, Zhibo, R. Marc Lebel, Yannick Bliesener, Jay Acharya, Richard Frayne y Krishna S. Nayak. "Sparse precontrast T 1 mapping for high‐resolution whole‐brain DCE‐MRI". Magnetic Resonance in Medicine 86, n.º 4 (25 de mayo de 2021): 2234–49. http://dx.doi.org/10.1002/mrm.28849.

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30

Yarnykh, Vasily L., James D. Bowen, Alexey Samsonov, Pavle Repovic, Angeli Mayadev, Peiqing Qian, Beena Gangadharan, Bart P. Keogh, Kenneth R. Maravilla y Lily K. Jung Henson. "Fast Whole-Brain Three-dimensional Macromolecular Proton Fraction Mapping in Multiple Sclerosis". Radiology 274, n.º 1 (enero de 2015): 210–20. http://dx.doi.org/10.1148/radiol.14140528.

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31

Wei, Hongjiang, Luke Xie, Russell Dibb, Wei Li, Kyle Decker, Yuyao Zhang, G. Allan Johnson y Chunlei Liu. "Imaging whole-brain cytoarchitecture of mouse with MRI-based quantitative susceptibility mapping". NeuroImage 137 (agosto de 2016): 107–15. http://dx.doi.org/10.1016/j.neuroimage.2016.05.033.

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32

Hagberg, GE, J. Bause, T. Ethofer, P. Ehses, T. Dresler, C. Herbert, R. Pohmann et al. "Whole brain MP2RAGE-based mapping of the longitudinal relaxation time at 9.4T". NeuroImage 144 (enero de 2017): 203–16. http://dx.doi.org/10.1016/j.neuroimage.2016.09.047.

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33

Guipponi, Olivier, Justine Cléry, Soline Odouard, Claire Wardak y Suliann Ben Hamed. "Whole brain mapping of visual and tactile convergence in the macaque monkey". NeuroImage 117 (agosto de 2015): 93–102. http://dx.doi.org/10.1016/j.neuroimage.2015.05.022.

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34

Igarashi, Hisato, Atsushi Kasai, Misaki Niu, Kaoru Seiriki, Takahiro Kuwaki, Masato Tanuma, Shun Yamaguchi, Takanobu Nakazawa y Hitoshi Hashimoto. "Whole-brain mapping of neuronal activity in mice after social defeat stress". Proceedings for Annual Meeting of The Japanese Pharmacological Society WCP2018 (2018): PO3–1–36. http://dx.doi.org/10.1254/jpssuppl.wcp2018.0_po3-1-36.

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35

Lecocq, A., Y. Le Fur, A. Amadon, A. Vignaud, M. Bernard, M. Guye y J. P. Ranjeva. "Fast whole brain quantitative proton density mapping to quantify metabolites in tumors". Physica Medica 29 (junio de 2013): e11-e12. http://dx.doi.org/10.1016/j.ejmp.2013.08.040.

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36

Yarnykh, Vasily L. "Time-efficient, high-resolution, whole brain three-dimensional macromolecular proton fraction mapping". Magnetic Resonance in Medicine 75, n.º 5 (22 de junio de 2015): 2100–2106. http://dx.doi.org/10.1002/mrm.25811.

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37

Xu, Guoping, Yogesh Rathi, Joan A. Camprodon, Hanqiang Cao y Lipeng Ning. "Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning". PLOS ONE 16, n.º 7 (30 de julio de 2021): e0254588. http://dx.doi.org/10.1371/journal.pone.0254588.

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Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification of subject-specific brain regions for TMS is important to improve the treatment efficacy and understand the mechanism of treatment response. Numerical computations have been used to estimate the stimulated electric field (E-field) by TMS in brain tissue. But the relative long computation time limits the application of this approach. In this paper, we propose a deep-neural-network based approach to expedite the estimation of whole-brain E-field by using a neural network architecture, named 3D-MSResUnet and multimodal imaging data. The 3D-MSResUnet network integrates the 3D U-net architecture, residual modules and a mechanism to combine multi-scale feature maps. It is trained using a large dataset with finite element method (FEM) based E-field and diffusion magnetic resonance imaging (MRI) based anisotropic volume conductivity or anatomical images. The performance of 3D-MSResUnet is evaluated using several evaluation metrics and different combinations of imaging modalities and coils. The experimental results show that the output E-field of 3D-MSResUnet provides reliable estimation of the E-field estimated by the state-of-the-art FEM method with significant reduction in prediction time to about 0.24 second. Thus, this study demonstrates that neural networks are potentially useful tools to accelerate the prediction of E-field for TMS targeting.
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38

Liu, Jing, Angela Jakary, Javier E. Villanueva-Meyer, Nicholas A. Butowski, David Saloner, Jennifer L. Clarke, Jennie W. Taylor et al. "Automatic Brain Tissue and Lesion Segmentation and Multi-Parametric Mapping of Contrast-Enhancing Gliomas without the Injection of Contrast Agents: A Preliminary Study". Cancers 16, n.º 8 (17 de abril de 2024): 1524. http://dx.doi.org/10.3390/cancers16081524.

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This study aimed to develop a rapid, 1 mm3 isotropic resolution, whole-brain MRI technique for automatic lesion segmentation and multi-parametric mapping without using contrast by continuously applying balanced steady-state free precession with inversion pulses throughout incomplete inversion recovery in a single 6 min scan. Modified k-means clustering was performed for automatic brain tissue and lesion segmentation using distinct signal evolutions that contained mixed T1/T2/magnetization transfer properties. Multi-compartment modeling was used to derive quantitative multi-parametric maps for tissue characterization. Fourteen patients with contrast-enhancing gliomas were scanned with this sequence prior to the injection of a contrast agent, and their segmented lesions were compared to conventionally defined manual segmentations of T2-hyperintense and contrast-enhancing lesions. Simultaneous T1, T2, and macromolecular proton fraction maps were generated and compared to conventional 2D T1 and T2 mapping and myelination water fraction mapping acquired with MAGiC. The lesion volumes defined with the new method were comparable to the manual segmentations (r = 0.70, p < 0.01; t-test p > 0.05). The T1, T2, and macromolecular proton fraction mapping values of the whole brain were comparable to the reference values and could distinguish different brain tissues and lesion types (p < 0.05), including infiltrating tumor regions within the T2-lesion. Highly efficient, whole-brain, multi-contrast imaging facilitated automatic lesion segmentation and quantitative multi-parametric mapping without contrast, highlighting its potential value in the clinic when gadolinium is contraindicated.
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39

Chen, Zhiye, Wei Dai, Xiaoyan Chen, Mengqi Liu, Lin Ma y Shengyuan Yu. "Voxel-based quantitative susceptibility mapping revealed increased cerebral iron over the whole brain in chronic migraine". Molecular Pain 17 (enero de 2021): 174480692110208. http://dx.doi.org/10.1177/17448069211020894.

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Background The previous documents demonstrated that iron deposition was identified in brain deep nuclei and periaqueductal gray matter region in chronic migraine (CM), and less is known about the cerebral iron deposition in CM. The aim of this study is to investigate the cerebral iron deposition in CM using an advanced voxel-based quantitative susceptibility mapping. Methods A multi-echo gradient echo MR sequence was obtained from 14 CM patients and 28 normal controls (NC), and quantitative susceptibility mapping images were reconstructed and voxel-based analysis was performed over the whole cerebrum. The susceptibility value of all the positive brain regions was extracted and correlation was calculated between the susceptibility value and the clinical variables. Results The brain regions with increased susceptibility value in CM patients located in right precuneus, insula, supramarginal gyrus, dorsolateral superior frontal gyrus, postcentral gyrus, cuneus and left postcentral gyrus compared with NC. The correlation analysis demonstrated that a positive correlation was identified between susceptibility value of all the positive brain regions and VAS score. Conclusion The current study demonstrated increased cerebral iron deposition presented in chronic patients, which suggested that increased cerebral iron deposition might play a role in the migraine chronicization.
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40

Kaboodvand, Neda, Martijn P. van den Heuvel y Peter Fransson. "Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates". Network Neuroscience 3, n.º 4 (enero de 2019): 1094–120. http://dx.doi.org/10.1162/netn_a_00104.

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Whole-brain computational modeling based on structural connectivity has shown great promise in successfully simulating fMRI BOLD signals with temporal coactivation patterns that are highly similar to empirical functional connectivity patterns during resting state. Importantly, previous studies have shown that spontaneous fluctuations in coactivation patterns of distributed brain regions have an inherent dynamic nature with regard to the frequency spectrum of intrinsic brain oscillations. In this modeling study, we introduced frequency dynamics into a system of coupled oscillators, where each oscillator represents the local mean-field model of a brain region. We first showed that the collective behavior of interacting oscillators reproduces previously shown features of brain dynamics. Second, we examined the effect of simulated lesions in gray matter by applying an in silico perturbation protocol to the brain model. We present a new approach to map the effects of vulnerability in brain networks and introduce a measure of regional hazardousness based on mapping of the degree of divergence in a feature space.
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41

Li, Juchen, Mengchao Pei, Binshi Bo, Xinxin Zhao, Jing Cang, Fang Fang y Zhifeng Liang. "Whole‐brain mapping of mouse CSF flow via HEAP‐METRIC phase‐contrast MRI". Magnetic Resonance in Medicine 87, n.º 6 (2 de febrero de 2022): 2851–61. http://dx.doi.org/10.1002/mrm.29179.

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42

Garin, Clément M., Nachiket A. Nadkarni, Jérémy Pépin, Julien Flament y Marc Dhenain. "Whole brain mapping of glutamate distribution in adult and old primates at 11.7T". NeuroImage 251 (mayo de 2022): 118984. http://dx.doi.org/10.1016/j.neuroimage.2022.118984.

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43

Ridler, Khanum, John Suckling, Nicholas Higgins, Patrick Bolton y Edward Bullmore. "Standardized Whole Brain Mapping of Tubers and Subependymal Nodules in Tuberous Sclerosis Complex". Journal of Child Neurology 19, n.º 9 (septiembre de 2004): 658–65. http://dx.doi.org/10.1177/08830738040190090501.

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44

Lin, Wenkai, Lingyu Xu, Yanrong Zheng, Sile An, Mengting Zhao, Weiwei Hu, Mengyao Li et al. "Whole-brain mapping of histaminergic projections in mouse brain". Proceedings of the National Academy of Sciences 120, n.º 14 (28 de marzo de 2023). http://dx.doi.org/10.1073/pnas.2216231120.

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Histamine is a conserved neuromodulator in mammalian brains and critically involved in many physiological functions. Understanding the precise structure of the histaminergic network is the cornerstone in elucidating its function. Herein, using histidine decarboxylase (HDC)-CreERT2 mice and genetic labeling strategies, we reconstructed a whole-brain three dimensional (3D) structure of histaminergic neurons and their outputs at 0.32 × 0.32 × 2 μm 3 pixel resolution with a cutting-edge fluorescence microoptical sectioning tomography system. We quantified the fluorescence density of all brain areas and found that histaminergic fiber density varied significantly among brain regions. The density of histaminergic fiber was positively correlated with the amount of histamine release induced by optogenetic stimulation or physiological aversive stimulation. Lastly, we reconstructed a fine morphological structure of 60 histaminergic neurons via sparse labeling and uncovered the largely heterogeneous projection pattern of individual histaminergic neurons. Collectively, this study reveals an unprecedented whole-brain quantitative analysis of histaminergic projections at the mesoscopic level, providing a foundation for future functional histaminergic study.
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45

Wang, Lijie, Jinping Xu, Chao Wang y Jiaojian Wang. "Whole Brain Functional Connectivity Pattern Homogeneity Mapping". Frontiers in Human Neuroscience 12 (24 de abril de 2018). http://dx.doi.org/10.3389/fnhum.2018.00164.

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46

de Smit, F. y H. Hoogduin. "Fast whole brain T1 mapping at 3 Tesla". RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 178, n.º 01 (20 de marzo de 2006). http://dx.doi.org/10.1055/s-2006-931846.

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47

de Smit, F. y H. Hoogduin. "Fast whole brain T1 mapping at 3 Tesla". RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 177, n.º 01 (10 de julio de 2006). http://dx.doi.org/10.1055/s-2005-931815.

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48

Albanese, Alexandre y Kwanghun Chung. "Whole-brain imaging reaches new heights (and lengths)". eLife 5 (20 de enero de 2016). http://dx.doi.org/10.7554/elife.13367.

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49

Zheng, Weijie, Huawei Mu, Zhiyi Chen, Jiajun Liu, Debin Xia, Yuxiao Cheng, Qi Jing et al. "NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping". National Science Review, 26 de marzo de 2024. http://dx.doi.org/10.1093/nsr/nwae109.

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Abstract Quantitative analysis of activated neurons in mice brains by a specific stimulation is usually a primary step to locate the responsive neurons throughout the brain. However, it’s challenging to comprehensively and consistently analyze the neuronal activity trace in whole brains of large cohort of mice from many Terabytes of volumetric imaging data. Here, we introduce NEATmap, a deep-learning based high-efficiency, high-precision, and user-friendly software for whole brain NEuronal Activity Trace mapping by automated segmentation and quantitative analysis of immunofluorescence labeled c-Fos+ neurons. We applied NEATmap to study the brain-wide differentiated neuronal activation in response to physical and psychological stressors in cohorts of mice.
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50

Han, Xiaofeng, Shuxia Guo, Nan Ji, Tian Li, Jian Liu, Xiangqiao Ye, Yi Wang et al. "Whole human-brain mapping of single cortical neurons for profiling morphological diversity and stereotypy". Science Advances 9, n.º 41 (13 de octubre de 2023). http://dx.doi.org/10.1126/sciadv.adf3771.

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Quantifying neuron morphology and distribution at the whole-brain scale is essential to understand the structure and diversity of cell types. It is exceedingly challenging to reuse recent technologies of single-cell labeling and whole-brain imaging to study human brains. We propose adaptive cell tomography (ACTomography), a low-cost, high-throughput, and high-efficacy tomography approach, based on adaptive targeting of individual cells. We established a platform to inject dyes into cortical neurons in surgical tissues of 18 patients with brain tumors or other conditions and one donated fresh postmortem brain. We collected three-dimensional images of 1746 cortical neurons, of which 852 neurons were reconstructed to quantify local dendritic morphology, and mapped to standard atlases. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor field of neuron morphology to characterize anatomical modularity of a human brain.
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