Technologies for imaging neural activity in large volumes

被引:208
作者
Ji, Na [1 ]
Freeman, Jeremy [1 ]
Smith, Spencer L. [2 ,3 ,4 ]
机构
[1] Howard Hughes Med Inst, Janelia Res Campus, Ashburn, VA 20147 USA
[2] Univ N Carolina, Sch Med, Dept Cell Biol & Physiol, Chapel Hill, NC 27599 USA
[3] Univ N Carolina, Sch Med, Neurosci Ctr, Chapel Hill, NC 27599 USA
[4] Univ N Carolina, Sch Med, Carolina Inst Dev Disabil, Chapel Hill, NC 27599 USA
基金
美国国家卫生研究院; 美国国家科学基金会;
关键词
MOUSE VISUAL-CORTEX; IN-VIVO; HIGH-SPEED; CELLULAR-RESOLUTION; FLUORESCENCE MICROSCOPY; MULTIPHOTON MICROSCOPY; NONLINEAR MICROSCOPY; FUNCTIONAL SPECIALIZATION; SCANNING MICROSCOPY; 2-PHOTON MICROSCOPE;
D O I
10.1038/nn.4358
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Conventional microscopy collects data from individual planes and cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point-spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for processing and analyzing volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics and helping elucidate how brain regions work in concert to support behavior.
引用
收藏
页码:1154 / 1164
页数:11
相关论文
共 145 条
[1]   Inferring input nonlinearities in neural encoding models [J].
Ahrens, Misha B. ;
Paninski, Liam ;
Sahani, Maneesh .
NETWORK-COMPUTATION IN NEURAL SYSTEMS, 2008, 19 (01) :35-67
[2]  
Ahrens MB, 2013, NAT METHODS, V10, P413, DOI [10.1038/NMETH.2434, 10.1038/nmeth.2434]
[3]   Fast spatial beam shaping by acousto-optic diffraction for 3D non-linear microscopy [J].
Akemann, Walther ;
Leger, Jean-Francois ;
Ventalon, Cathie ;
Mathieu, Benjamin ;
Dieudonne, Stephane ;
Bourdieu, Laurent .
OPTICS EXPRESS, 2015, 23 (22) :28191-28205
[4]  
Amat F, 2014, NAT METHODS, V11, P951, DOI [10.1038/NMETH.3036, 10.1038/nmeth.3036]
[5]   Simultaneous imaging of multiple focal planes using a two-photon scanning microscope [J].
Amir, W. ;
Carriles, R. ;
Hoover, E. E. ;
Planchon, T. A. ;
Durfee, C. G. ;
Squier, J. A. .
OPTICS LETTERS, 2007, 32 (12) :1731-1733
[6]   Chronic Cellular Imaging of Entire Cortical Columns in Awake Mice Using Microprisms [J].
Andermann, Mark L. ;
Gilfoy, Nathan B. ;
Goldey, Glenn J. ;
Sachdev, Robert N. S. ;
Woelfel, Markus ;
McCormick, David A. ;
Reid, R. Clay ;
Levene, Michael J. .
NEURON, 2013, 80 (04) :900-913
[7]   Functional Specialization of Mouse Higher Visual Cortical Areas [J].
Andermann, Mark L. ;
Kerlin, Aaron M. ;
Roumis, Demetris K. ;
Glickfeld, Lindsey L. ;
Reid, R. Clay .
NEURON, 2011, 72 (06) :1025-1039
[8]   Chronic cellular imaging of mouse visual cortex during operant behavior and passive viewing [J].
Andermann, Mark L. ;
Kerlin, A. M. ;
Reid, R. C. .
FRONTIERS IN CELLULAR NEUROSCIENCE, 2010, 4
[9]   Neural correlations, population coding and computation [J].
Averbeck, BB ;
Latham, PE ;
Pouget, A .
NATURE REVIEWS NEUROSCIENCE, 2006, 7 (05) :358-366
[10]   Time-lapse imaging of disease progression in deep brain areas using fluorescence microendoscopy [J].
Barretto, Robert P. J. ;
Ko, Tony H. ;
Jung, Juergen C. ;
Wang, Tammy J. ;
Capps, George ;
Waters, Allison C. ;
Ziv, Yaniv ;
Attardo, Alessio ;
Recht, Lawrence ;
Schnitzer, Mark J. .
NATURE MEDICINE, 2011, 17 (02) :223-U120