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Articoli di riviste sul tema "Whole brain mapping"

Autore: Grafiati

Pubblicato: 3 maggio 2025

Ultima modifica: 31 luglio 2025

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1

Hardy, T. L., L. R. D. Brynildson, J. G. Gray, and D. Spurlock. "Three-Dimensional Whole-Brain Mapping." Stereotactic and Functional Neurosurgery 58, no. 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, et al. "Whole-brain activity mapping onto a zebrafish brain atlas." Nature Methods 12, no. 11 (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, no. 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, and Hideyuki Okano. "Connectomics: comprehensive approaches for whole-brain mapping." Microscopy 64, no. 1 (2014): 57–67. http://dx.doi.org/10.1093/jmicro/dfu103.

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5

Wu, Bing, Wei Li, Arnaud Guidon, and Chunlei Liu. "Whole brain susceptibility mapping using compressed sensing." Magnetic Resonance in Medicine 67, no. 1 (2011): 137–47. http://dx.doi.org/10.1002/mrm.23000.

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6

Shimono, Masanori, and Kazuhisa Niki. "Global Mapping of the Whole-Brain Network Underlining Binocular Rivalry." Brain Connectivity 3, no. 2 (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, et al. "Whole-brain functional mapping with isotropic MR imaging." Radiology 201, no. 2 (1996): 399–404. http://dx.doi.org/10.1148/radiology.201.2.8888231.

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8

Kasai, Atsushi, Kaoru Seiriki, and Hitoshi Hashimoto. "Whole-brain activity mapping at single-cell resolution." Folia Pharmacologica Japonica 153, no. 6 (2019): 278–83. http://dx.doi.org/10.1254/fpj.153.278.

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9

Bao, Chenglong, Jae Kyu Choi, and Bin Dong. "Whole Brain Susceptibility Mapping Using Harmonic Incompatibility Removal." SIAM Journal on Imaging Sciences 12, no. 1 (2019): 492–520. http://dx.doi.org/10.1137/18m1191452.

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10

Banerjee, Tirtha Das, Linwan Zhang, and Antónia Monteiro. "Mapping Gene Expression in Whole Larval Brains of Bicyclus anynana Butterflies." Methods and Protocols 8, no. 2 (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),

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11

Mitra, Partha P., Marcello G. P. Rosa, and Harvey J. Karten. "Panoptic Neuroanatomy: Digital Microscopy of Whole Brains and Brain-Wide Circuit Mapping." Brain, Behavior and Evolution 81, no. 4 (2013): 203–5. http://dx.doi.org/10.1159/000350241.

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12

Kim, Woonhee, and Chihye Chung. "Whole-brain cellular mapping of stress exposure in male and female brains." IBRO Reports 6 (September 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, no. 2 (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 m

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14

HAYWORTH, KENNETH J. "ELECTRON IMAGING TECHNOLOGY FOR WHOLE BRAIN NEURAL CIRCUIT MAPPING." International Journal of Machine Consciousness 04, no. 01 (2012): 87–108. http://dx.doi.org/10.1142/s1793843012400057.

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15

Hagmann, Patric, Maciej Kurant, Xavier Gigandet, et al. "Mapping Human Whole-Brain Structural Networks with Diffusion MRI." PLoS ONE 2, no. 7 (2007): e597. http://dx.doi.org/10.1371/journal.pone.0000597.

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16

Xie, Ziyan, Tasuku Kayama, Nahoko Kuga, Musashi Yamakawa, and 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, et al. "A deep learning algorithm for 3D cell detection in whole mouse brain image datasets." PLOS Computational Biology 17, no. 5 (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 a

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18

Rugg-Gunn, F. J., P. A. Boulby, M. R. Symms, G. J. Barker, and J. S. Duncan. "Whole-brain T2 mapping demonstrates occult abnormalities in focal epilepsy." Neurology 64, no. 2 (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, and Huai-Rong Luo. "Whole-brain mapping of projection from mouse lateral septal nucleus." Biology Open 8, no. 7 (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, and Naoshige Uchida. "Whole-Brain Mapping of Direct Inputs to Midbrain Dopamine Neurons." Neuron 74, no. 5 (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, et al. "Whole-brain mapping of behaviourally induced neural activation in mice." Brain Structure and Function 220, no. 4 (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, and Mathews Jacob. "Accelerated whole‐brain multi‐parameter mapping using blind compressed sensing." Magnetic Resonance in Medicine 75, no. 3 (2016): 1175–86. http://dx.doi.org/10.1002/mrm.25722.

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23

Burke, Matthew J., Juho Joutsa, Alexander L. Cohen, et al. "Mapping migraine to a common brain network." Brain 143, no. 2 (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 m

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24

Kirilina, Evgeniya, Saskia Helbling, Markus Morawski, et al. "Superficial white matter imaging: Contrast mechanisms and whole-brain in vivo mapping." Science Advances 6, no. 41 (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 link

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25

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

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26

Niu, Misaki, Atsushi Kasai, Kaoru Seiriki, and 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, et al. "Molecularly defined and spatially resolved cell atlas of the whole mouse brain." Nature 624, no. 7991 (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 im

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28

Jaggard, James B., Evan Lloyd, Anders Yuiska, et al. "Cavefish brain atlases reveal functional and anatomical convergence across independently evolved populations." Science Advances 6, no. 38 (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

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29

Liu, Jing, Angela Jakary, Javier E. Villanueva-Meyer, 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, no. 8 (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

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30

Xu, Guoping, Yogesh Rathi, Joan A. Camprodon, Hanqiang Cao, and Lipeng Ning. "Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning." PLOS ONE 16, no. 7 (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 limit

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31

Zhu, Zhibo, R. Marc Lebel, Yannick Bliesener, Jay Acharya, Richard Frayne, and Krishna S. Nayak. "Sparse precontrast T 1 mapping for high‐resolution whole‐brain DCE‐MRI." Magnetic Resonance in Medicine 86, no. 4 (2021): 2234–49. http://dx.doi.org/10.1002/mrm.28849.

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32

Yarnykh, Vasily L., James D. Bowen, Alexey Samsonov, et al. "Fast Whole-Brain Three-dimensional Macromolecular Proton Fraction Mapping in Multiple Sclerosis." Radiology 274, no. 1 (2015): 210–20. http://dx.doi.org/10.1148/radiol.14140528.

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33

Wei, Hongjiang, Luke Xie, Russell Dibb, et al. "Imaging whole-brain cytoarchitecture of mouse with MRI-based quantitative susceptibility mapping." NeuroImage 137 (August 2016): 107–15. http://dx.doi.org/10.1016/j.neuroimage.2016.05.033.

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34

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

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35

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

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36

Igarashi, Hisato, Atsushi Kasai, Misaki Niu, et al. "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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37

Lecocq, A., Y. Le Fur, A. Amadon, et al. "Fast whole brain quantitative proton density mapping to quantify metabolites in tumors." Physica Medica 29 (June 2013): e11-e12. http://dx.doi.org/10.1016/j.ejmp.2013.08.040.

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38

Yarnykh, Vasily L. "Time-efficient, high-resolution, whole brain three-dimensional macromolecular proton fraction mapping." Magnetic Resonance in Medicine 75, no. 5 (2015): 2100–2106. http://dx.doi.org/10.1002/mrm.25811.

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39

Chiaruttini, Nicolas, Carlo Castoldi, Linda Maria Requie, et al. "ABBA+BraiAn, an integrated suite for whole-brain mapping, reveals brain-wide differences in immediate-early genes induction upon learning." Cell Reports 44, no. 7 (2025): 115876. https://doi.org/10.1016/j.celrep.2025.115876.

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40

Chen, Zhiye, Wei Dai, Xiaoyan Chen, Mengqi Liu, Lin Ma, and Shengyuan Yu. "Voxel-based quantitative susceptibility mapping revealed increased cerebral iron over the whole brain in chronic migraine." Molecular Pain 17 (January 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 c

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41

Kaboodvand, Neda, Martijn P. van den Heuvel, and Peter Fransson. "Adaptive frequency-based modeling of whole-brain oscillations: Predicting regional vulnerability and hazardousness rates." Network Neuroscience 3, no. 4 (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 o

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42

Wang, Hao, Qingyuan Zhu, Lufeng Ding, et al. "Scalable volumetric imaging for ultrahigh-speed brain mapping at synaptic resolution." National Science Review 6, no. 5 (2019): 982–92. http://dx.doi.org/10.1093/nsr/nwz053.

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Abstract The speed of high-resolution optical imaging has been a rate-limiting factor for meso-scale mapping of brain structures and functional circuits, which is of fundamental importance for neuroscience research. Here, we describe a new microscopy method of Volumetric Imaging with Synchronized on-the-fly-scan and Readout (VISoR) for high-throughput, high-quality brain mapping. Combining synchronized scanning beam illumination and oblique imaging over cleared tissue sections in smooth motion, the VISoR system effectively eliminates motion blur to obtain undistorted images. By continuously im

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43

Lin, Wenkai, Lingyu Xu, Yanrong Zheng, et al. "Whole-brain mapping of histaminergic projections in mouse brain." Proceedings of the National Academy of Sciences 120, no. 14 (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 area

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44

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

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45

de Smit, F., and H. Hoogduin. "Fast whole brain T1 mapping at 3 Tesla." RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 178, no. 01 (2006). http://dx.doi.org/10.1055/s-2006-931846.

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de Smit, F., and H. Hoogduin. "Fast whole brain T1 mapping at 3 Tesla." RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren 177, no. 01 (2006). http://dx.doi.org/10.1055/s-2005-931815.

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47

Zheng, Weijie, Huawei Mu, Zhiyi Chen, et al. "NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping." National Science Review, March 26, 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

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48

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

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Han, Xiaofeng, Shuxia Guo, Nan Ji, et al. "Whole human-brain mapping of single cortical neurons for profiling morphological diversity and stereotypy." Science Advances 9, no. 41 (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 p

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50

Niesen, Svenja, Marten Veldmann, Philipp Ehses, and Tony Stöcker. "Spiral 3DREAM sequence for fast whole‐brain B1 mapping." Magnetic Resonance in Medicine, September 2024. http://dx.doi.org/10.1002/mrm.30282.

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AbstractPurposeThis work demonstrates a new variant of the 3DREAM sequence for whole‐brain mapping employing a three‐dimensional (3D) stack‐of‐spirals readout. The spiral readout reduces the echo train length after the STEAM preparation in order to overcome the significant blurring in STE* images due to the decreasing STE* signal with each excitation pulse.MethodsThe 3DREAM sequence rapidly acquires two contrasts to calculate whole‐brain flip angle maps. In the proposed spiral 3DREAM sequence, the Cartesian readout scheme is replaced by an accelerated 3D stack‐of‐spirals readout with a CAIPIRI

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