Abstract

Widefield optical imaging of neuronal populations over large portions of the cerebral cortex in awake behaving animals provides a unique opportunity for investigating the relationship between brain function and behavior. In this paper, we demonstrate that the temporal characteristics of calcium dynamics obtained through widefield imaging can be utilized to infer the corresponding behavior. Cortical activity in transgenic calcium reporter mice (n=6) expressing GCaMP6f in neocortical pyramidal neurons is recorded during active whisking (AW) and no whisking (NW). To extract features related to the temporal characteristics of calcium recordings, a method based on visibility graph (VG) is introduced. An extensive study considering different choices of features and classifiers is conducted to find the best model capable of predicting AW and NW from calcium recordings. Our experimental results show that temporal characteristics of calcium recordings identified by the proposed method carry discriminatory information that are powerful enough for decoding behavior.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (1)

D. Shimaoka, K. D. Harris, and M. Carandini, “Effects of Arousal on Mouse Sensory Cortex Depend on Modality,” Cell Rep. 22, 3160–3167 (2018).
[Crossref] [PubMed]

2017 (8)

D. J. O’shea, E. Trautmann, C. Chandrasekaran, S. Stavisky, J. C. Kao, M. Sahani, S. Ryu, K. Deisseroth, and K. V. Shenoy, “The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces,” Exp. Neurol. 287, 437–451 (2017).
[Crossref]

D. Ringuette, M. A. Jeffrey, S. Dufour, P. L. Carlen, and O. Levi, “Continuous multi-modality brain imaging reveals modified neurovascular seizure response after intervention,” Biomed. Opt. Express 8, 873–889 (2017).
[Crossref] [PubMed]

D. Xiao, M. P. Vanni, C. C. Mitelut, A. W. Chan, J. M. LeDue, Y. Xie, A. C. Chen, N. V. Swindale, and T. H. Murphy, “Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons,” Elife 6, e19976 (2017).
[Crossref] [PubMed]

W. E. Allen, I. V. Kauvar, M. Z. Chen, E. B. Richman, S. J. Yang, K. Chan, V. Gradinaru, B. E. Deverman, L. Luo, and K. Deisseroth, “Global Representations of Goal-Directed Behavior in Distinct Cell Types of Mouse Neocortex,” Neuron 94, 891–907 (2017).
[Crossref] [PubMed]

L. Lacasa, S. Sannino, S. Stramaglia, and D. Marinazzo, “Visibility graphs for fMRI data: multiplex temporal graphs and their modulations across resting state networks,” Network Neuroscience 3208–221 (2017).

I. Carcea, M. N. Insanally, and R. C. Froemke, “Dynamics of auditory cortical activity during behavioural engagement and auditory perception,” Nat. Commun. 8, 14412 (2017).
[Crossref] [PubMed]

A. Kyriakatos, V. Sadashivaiah, Y. Zhang, A. Motta, M. Auffret, and C. C. Petersen, “Voltage-sensitive dye imaging of mouse neocortex during a whisker detection task,” Neurophotonics 4, 031204 (2017).
[Crossref]

J. Friedrich, P. Zhou, and L. Paninski, “Fast online deconvolution of calcium imaging data,” PLoS Comput Biol 13, e1005423 (2017).
[Crossref] [PubMed]

2016 (15)

S. J. Kayser, S. W. McNair, and C. Kayser, “Prestimulus influences on auditory perception from sensory representations and decision processes,” Proc. Natl. Acad. Sci. 113, 4842–4847 (2016).
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J. Reimer, M. J. McGinley, Y. Liu, C. Rodenkirch, Q. Wang, D. A. McCormick, and A. S. Tolias, “Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex,” Nat. Commun. 7, 13289 (2016).
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Y. Ma, M. A. Shaik, S. H. Kim, M. G. Kozberg, D. N. Thibodeaux, H. T. Zhao, H. Yu, and E. M. Hillman, “Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches,” Phil. Trans. R. Soc. B 371, 20150360 (2016).
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E. A. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel, D. Pfau, T. Reardon, Y. Mu, C. Lacefield, W. Yang, M. Ahrens, R. Bruno, T. Jessell, D. Peterka, R. Yuste, and L. Paninsk, “Simultaneous denoising, deconvolution, and demixing of calcium imaging data,” Neuron 89, 285–299 (2016).
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L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. R. Rosón, T. Baden, T. Euler, A. S. Tolias, and M. Bethge, “Benchmarking spike rate inference in population calcium imaging,” Neuron 90, 471–482 (2016).
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V. Sreenivasan, V. Esmaeili, T. Kiritani, K. Galan, S. Crochet, and C. C. Petersen, “Movement initiation signals in mouse whisker motor cortex,” Neuron 92, 1368–1382 (2016).
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J. Zhang, X. Li, S. T. Foldes, W. Wang, J. L. Collinger, D. J. Weber, and A. Bagić, “Decoding brain states based on magnetoencephalography from prespecified cortical regions,” IEEE Trans. on Biomed. Eng. 63, 30–42 (2016).
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2015 (12)

L. Madisen, A. R. Garner, D. Shimaoka, A. S. Chuong, N. C. Klapoetke, L. Li, A. van der Bourg, Y. Niino, L. Egolf, C. Monetti, H. Gu, M. Mills, A. Cheng, B. Tasic, T. N. Nguyen, S. M. Sunkin, A. Benucci, A. Nagy, A. Miyawaki, F. Helmchen, R. M. Empson, T. Knopfel, E. S. Boyden, R. C. Reid, M. Carandini, and H. Zeng, “Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance,” Neuron 85, 942–958 (2015).
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T. Murakami, T. Yoshida, T. Matsui, and K. Ohki, “Wide-field Ca2+ imaging reveals visually evoked activity in the retrosplenial area,” Front. Mol. Neurosci. 8, 20 (2015).
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D. A. McCormick, M. J. McGinley, and D. B. Salkoff, “Brain state dependent activity in the cortex and thalamus,” Curr. Opin. Neurobiol. 31, 133–140 (2015).
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M. J. McGinley, M. Vinck, J. Reimer, R. Batista-Brito, E. Zagha, C. R. Cadwell, A. S. Tolias, J. A. Cardin, and D. A. McCormick, “Waking state: rapid variations modulate neural and behavioral responses,” Neuron 87, 1143–1161 (2015).
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J. L. Chen, D. J. Margolis, A. Stankov, L. T. Sumanovski, B. L. Schneider, and F. Helmchen, “Pathway-specific reorganization of projection neurons in somatosensory cortex during learning,” Nat. Neurosci. 18, 1101–1108 (2015).
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M. J. McGinley, S. V. David, and D. A. McCormick, “Cortical membrane potential signature of optimal states for sensory signal detection,” Neuron 87, 179–192 (2015).
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M. Vinck, R. Batista-Brito, U. Knoblich, and J. A. Cardin, “Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding,” Neuron 86, 740–754 (2015).
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L. Lacasa, V. Nicosia, and V. Latora, “Network structure of multivariate time series,” Sci. Rep. 5, 15508 (2015).
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M. Stephen, C. Gu, and H. Yang, “Visibility graph based time series analysis,” PloS one 10, e0143015 (2015).
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C. Donos, M. Dümpelmann, and A. Schulze-Bonhage, “Early seizure detection algorithm based on intracranial EEG and random forest classification,” Int. J. of Neur. Syst. 25, 1550023 (2015).
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A. Page, C. Sagedy, E. Smith, N. Attaran, T. Oates, and T. Mohsenin, “A flexible multichannel EEG feature extractor and classifier for seizure detection,” IEEE Trans. on Cir. and Syst. II: Express Briefs 62, 109–113 (2015).

T. P. Patel, K. Man, B. L. Firestein, and D. F. Meaney, “Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging,” J. Neurosci. Methods 243, 26–38 (2015).
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2014 (8)

H. Dana, T.-W. Chen, A. Hu, B. C. Shields, C. Guo, L. L. Looger, D. S. Kim, and K. Svoboda, “Thy1-GCaMP6 transgenic mice for neuronal population imaging in vivo,” PloS one 9, e108697 (2014).
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G. Zhu, Y. Li, P. P. Wen, and S. Wang, “Analysis of alcoholic EEG signals based on horizontal visibility graph entropy,” Brain Inform 1, 19–25 (2014).
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J. Reimer, E. Froudarakis, C. R. Cadwell, D. Yatsenko, G. H. Denfield, and A. S. Tolias, “Pupil fluctuations track fast switching of cortical states during quiet wakefulness,” Neuron 84, 355–362 (2014).
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S. J. Bensmaia and L. E. Miller, “Restoring sensorimotor function through intracortical interfaces: progress and looming challenges,” Nat. Rev. Neurosci. 15, 313–325 (2014).
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F. Hutzler, “Reverse inference is not a fallacy per se: Cognitive processes can be inferred from functional imaging data,” NeuroImage 84, 1061–1069 (2014).
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E. Eggermann, Y. Kremer, S. Crochet, and C. C. Petersen, “Cholinergic signals in mouse barrel cortex during active whisker sensing,” Cell Rep. 9, 1654–1660 (2014).
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M. P. Vanni and T. H. Murphy, “Mesoscale transcranial spontaneous activity mapping in GCaMP3 transgenic mice reveals extensive reciprocal connections between areas of somatomotor cortex,” J. Neurosci. 34, 15931–15946 (2014).
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X.-W. Wang, D. Nie, and B.-L. Lu, “Emotional state classification from eeg data using machine learning approach,” Neurocomputing 129, 94–106 (2014).
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2013 (6)

T. W. Chen, T. J. Wardill, Y. Sun, S. R. Pulver, S. L. Renninger, A. Baohan, E. R. Schreiter, R. A. Kerr, M. B. Orger, V. Jayaraman, L. L. Looger, K. Svoboda, and D. S. Kim, “Ultrasensitive fluorescent proteins for imaging neuronal activity,” Nature 499, 295–300 (2013).
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N. Naseer and K.-S. Hong, “Classification of functional near-infrared spectroscopy signals corresponding to the right-and left-wrist motor imagery for development of a brain–computer interface,” Neurosci. Lett. 553, 84–89 (2013).
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K. R. Gray, P. Aljabar, R. A. Heckemann, A. Hammers, D. Rueckert, and I. Alzheimer’s Disease Neuroimaging, “Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease,” NeuroImage 65, 167–175 (2013).
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J. Oñativia, S. R. Schultz, and P. L. Dragotti, “A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging,” J. Neural. Eng. 10, 046017 (2013).
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I. J. Park, Y. V. Bobkov, B. W. Ache, and J. C. Principe, “Quantifying bursting neuron activity from calcium signals using blind deconvolution,” J. Neurosci. Methods 218, 196–205 (2013).
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S. Sachidhanandam, V. Sreenivasan, A. Kyriakatos, Y. Kremer, and C. C. Petersen, “Membrane potential correlates of sensory perception in mouse barrel cortex,” Nat. Neurosci. 16, 1671–1677 (2013).
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2012 (4)

R. V. Donner and J. F. Donges, “Visibility graph analysis of geophysical time series: Potentials and possible pitfalls,” Acta Geophysica 60, 589–623 (2012).
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M. Minderer, W. R. Liu, L. T. Sumanovski, S. Kugler, F. Helmchen, and D. J. Margolis, “Chronic imaging of cortical sensory map dynamics using a genetically encoded calcium indicator,” J. Physiol.-London 590, 99–107 (2012).
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2011 (2)

J. Richiardi, H. Eryilmaz, S. Schwartz, P. Vuilleumier, and D. Van De Ville, “Decoding brain states from fMRI connectivity graphs,” NeuroImage 56, 616–626 (2011).
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K. D. Harris and A. Thiele, “Cortical state and attention,” Nat. Rev. Neurosci. 12, 509–523 (2011).
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2010 (5)

B. Blankertz, C. Sannelli, S. Halder, E. M. Hammer, A. Kübler, K.-R. Müller, G. Curio, and T. Dickhaus, “Neurophysiological predictor of SMR-based BCI performance,” NeuroImage 51, 1303–1309 (2010).
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N. Kanwisher, “Functional specificity in the human brain: a window into the functional architecture of the mind,” Proc. Natl. Acad. Sci. 107, 11163–11170 (2010).
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M. L. Andermann, A. M. Kerlin, and R. Reid, “Chronic cellular imaging of mouse visual cortex during operant behavior and passive viewing,” Front. Cell Neurosci. 4, 3 (2010). doi:.
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M. Rubinov and O. Sporns, “Complex network measures of brain connectivity: uses and interpretations,” NeuroImage 52, 1059–1069 (2010).
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J. T. Vogelstein, A. M. Packer, T. A. Machado, T. Sippy, B. Babadi, R. Yuste, and L. Paninski, “Fast nonnegative deconvolution for spike train inference from population calcium imaging,” J. Physiol. 104, 3691–3704 (2010).

2009 (5)

H. B. He and E. A. Garcia, “Learning from imbalanced data,” IEEE Trans. on Knowledge and Data Eng. 21, 1263–1284 (2009).
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F. Pereira, T. Mitchell, and M. Botvinick, “Machine learning classifiers and fMRI: a tutorial overview,” NeuroImage 45, 199–209 (2009).
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B. A. Wilt, L. D. Burns, E. T. Wei Ho, K. K. Ghosh, E. A. Mukamel, and M. J. Schnitzer, “Advances in light microscopy for neuroscience,” Annu. Rev. Neurosci. 32, 435–506 (2009).
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E. A. Mukamel, A. Nimmerjahn, and M. J. Schnitzer, “Automated analysis of cellular signals from large-scale calcium imaging data,” Neuron 63, 747–760 (2009).
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E. Bullmore and O. Sporns, “Complex brain networks: graph theoretical analysis of structural and functional systems,” Nat. Rev. Neurosci. 10, 186–198 (2009).
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2008 (2)

J. F. Poulet and C. C. Petersen, “Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice,” Nature 454, 881–885 (2008).
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L. Lacasa, B. Luque, F. Ballesteros, J. Luque, and J. C. Nuno, “From time series to complex networks: the visibility graph,” Proc. Natl. Acad. Sci. 105, 4972–4975 (2008).
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2007 (2)

W. A. Chaovalitwongse and R. C. Sachdeo, “On the time series K-nearest neighbor classification of abnormal brain activity,” IEEE Trans. on Syst. Man and Cyber. Part a-Systems and Humans 37, 1005–1016 (2007).
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N. D. Schiff, J. T. Giacino, K. Kalmar, J. D. Victor, K. Baker, M. Gerber, B. Fritz, B. Eisenberg, T. Biondi, J. O’Connor, E. J. Kobylarz, S. Farris, A. Machado, C. McCagg, F. Plum, J. J. Fins, and A. R. Rezai, “Behavioural improvements with thalamic stimulation after severe traumatic brain injury,” Nature 448, 600–603 (2007).
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2006 (3)

T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett. 27, 861–874 (2006).
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I. Ferezou, S. Bolea, and C. C. Petersen, “Visualizing the cortical representation of whisker touch: voltage-sensitive dye imaging in freely moving mice,” Neuron 50, 617–629 (2006).
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R. A. Poldrack, “Can cognitive processes be inferred from neuroimaging data?” Trends Cogn. Sci. 10, 59–63 (2006).
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2005 (3)

F. Helmchen and W. Denk, “Deep tissue two-photon microscopy,” Nat. Methods 2, 932–940 (2005).
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A. Subasi and E. Ercelebi, “Classification of eeg signals using neural network and logistic regression,” Computer Methods and Programs in Biomedicine 78, 87–99 (2005).
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P. M. Knutsen, D. Derdikman, and E. Ahissar, “Tracking whisker and head movements in unrestrained behaving rodents,” J. Physiol. 93, 2294–2301 (2005).

2003 (1)

C. Mehring, J. Rickert, E. Vaadia, S. C. de Oliveira, A. Aertsen, and S. Rotter, “Inference of hand movements from local field potentials in monkey motor cortex,” Nat. Neurosci. 6, 1253–1254 (2003).
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1997 (1)

W. Denk and K. Svoboda, “Photon upmanship: why multiphoton imaging is more than a gimmick,” Neuron 18, 351–357 (1997).
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I. J. Park, Y. V. Bobkov, B. W. Ache, and J. C. Principe, “Quantifying bursting neuron activity from calcium signals using blind deconvolution,” J. Neurosci. Methods 218, 196–205 (2013).
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Aertsen, A.

C. Mehring, J. Rickert, E. Vaadia, S. C. de Oliveira, A. Aertsen, and S. Rotter, “Inference of hand movements from local field potentials in monkey motor cortex,” Nat. Neurosci. 6, 1253–1254 (2003).
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Ahissar, E.

P. M. Knutsen, D. Derdikman, and E. Ahissar, “Tracking whisker and head movements in unrestrained behaving rodents,” J. Physiol. 93, 2294–2301 (2005).

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E. A. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel, D. Pfau, T. Reardon, Y. Mu, C. Lacefield, W. Yang, M. Ahrens, R. Bruno, T. Jessell, D. Peterka, R. Yuste, and L. Paninsk, “Simultaneous denoising, deconvolution, and demixing of calcium imaging data,” Neuron 89, 285–299 (2016).
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Aljabar, P.

K. R. Gray, P. Aljabar, R. A. Heckemann, A. Hammers, D. Rueckert, and I. Alzheimer’s Disease Neuroimaging, “Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease,” NeuroImage 65, 167–175 (2013).
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W. E. Allen, I. V. Kauvar, M. Z. Chen, E. B. Richman, S. J. Yang, K. Chan, V. Gradinaru, B. E. Deverman, L. Luo, and K. Deisseroth, “Global Representations of Goal-Directed Behavior in Distinct Cell Types of Mouse Neocortex,” Neuron 94, 891–907 (2017).
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Amin, H. U.

H. U. Amin, A. S. Malik, N. Kamel, and M. Hussain, “A novel approach based on data redundancy for feature extraction of EEG signals,” Brain Topogr. 29, 207–217 (2016).
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Andermann, M. L.

M. L. Andermann, A. M. Kerlin, and R. Reid, “Chronic cellular imaging of mouse visual cortex during operant behavior and passive viewing,” Front. Cell Neurosci. 4, 3 (2010). doi:.
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Attaran, N.

A. Page, C. Sagedy, E. Smith, N. Attaran, T. Oates, and T. Mohsenin, “A flexible multichannel EEG feature extractor and classifier for seizure detection,” IEEE Trans. on Cir. and Syst. II: Express Briefs 62, 109–113 (2015).

Auffret, M.

A. Kyriakatos, V. Sadashivaiah, Y. Zhang, A. Motta, M. Auffret, and C. C. Petersen, “Voltage-sensitive dye imaging of mouse neocortex during a whisker detection task,” Neurophotonics 4, 031204 (2017).
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J. T. Vogelstein, A. M. Packer, T. A. Machado, T. Sippy, B. Babadi, R. Yuste, and L. Paninski, “Fast nonnegative deconvolution for spike train inference from population calcium imaging,” J. Physiol. 104, 3691–3704 (2010).

Baden, T.

L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. R. Rosón, T. Baden, T. Euler, A. S. Tolias, and M. Bethge, “Benchmarking spike rate inference in population calcium imaging,” Neuron 90, 471–482 (2016).
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J. Zhang, X. Li, S. T. Foldes, W. Wang, J. L. Collinger, D. J. Weber, and A. Bagić, “Decoding brain states based on magnetoencephalography from prespecified cortical regions,” IEEE Trans. on Biomed. Eng. 63, 30–42 (2016).
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N. D. Schiff, J. T. Giacino, K. Kalmar, J. D. Victor, K. Baker, M. Gerber, B. Fritz, B. Eisenberg, T. Biondi, J. O’Connor, E. J. Kobylarz, S. Farris, A. Machado, C. McCagg, F. Plum, J. J. Fins, and A. R. Rezai, “Behavioural improvements with thalamic stimulation after severe traumatic brain injury,” Nature 448, 600–603 (2007).
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L. Lacasa, B. Luque, F. Ballesteros, J. Luque, and J. C. Nuno, “From time series to complex networks: the visibility graph,” Proc. Natl. Acad. Sci. 105, 4972–4975 (2008).
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B. Luque, L. Lacasa, F. J. Ballesteros, and A. Robledo, “Analytical properties of horizontal visibility graphs in the Feigenbaum scenario,” Chaos 22, 013109 (2012).
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T. W. Chen, T. J. Wardill, Y. Sun, S. R. Pulver, S. L. Renninger, A. Baohan, E. R. Schreiter, R. A. Kerr, M. B. Orger, V. Jayaraman, L. L. Looger, K. Svoboda, and D. S. Kim, “Ultrasensitive fluorescent proteins for imaging neuronal activity,” Nature 499, 295–300 (2013).
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M. J. McGinley, M. Vinck, J. Reimer, R. Batista-Brito, E. Zagha, C. R. Cadwell, A. S. Tolias, J. A. Cardin, and D. A. McCormick, “Waking state: rapid variations modulate neural and behavioral responses,” Neuron 87, 1143–1161 (2015).
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M. Vinck, R. Batista-Brito, U. Knoblich, and J. A. Cardin, “Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding,” Neuron 86, 740–754 (2015).
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S. J. Bensmaia and L. E. Miller, “Restoring sensorimotor function through intracortical interfaces: progress and looming challenges,” Nat. Rev. Neurosci. 15, 313–325 (2014).
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L. Madisen, A. R. Garner, D. Shimaoka, A. S. Chuong, N. C. Klapoetke, L. Li, A. van der Bourg, Y. Niino, L. Egolf, C. Monetti, H. Gu, M. Mills, A. Cheng, B. Tasic, T. N. Nguyen, S. M. Sunkin, A. Benucci, A. Nagy, A. Miyawaki, F. Helmchen, R. M. Empson, T. Knopfel, E. S. Boyden, R. C. Reid, M. Carandini, and H. Zeng, “Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance,” Neuron 85, 942–958 (2015).
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L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. R. Rosón, T. Baden, T. Euler, A. S. Tolias, and M. Bethge, “Benchmarking spike rate inference in population calcium imaging,” Neuron 90, 471–482 (2016).
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Bethge, M.

L. Theis, P. Berens, E. Froudarakis, J. Reimer, M. R. Rosón, T. Baden, T. Euler, A. S. Tolias, and M. Bethge, “Benchmarking spike rate inference in population calcium imaging,” Neuron 90, 471–482 (2016).
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N. D. Schiff, J. T. Giacino, K. Kalmar, J. D. Victor, K. Baker, M. Gerber, B. Fritz, B. Eisenberg, T. Biondi, J. O’Connor, E. J. Kobylarz, S. Farris, A. Machado, C. McCagg, F. Plum, J. J. Fins, and A. R. Rezai, “Behavioural improvements with thalamic stimulation after severe traumatic brain injury,” Nature 448, 600–603 (2007).
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Blankertz, B.

B. Blankertz, C. Sannelli, S. Halder, E. M. Hammer, A. Kübler, K.-R. Müller, G. Curio, and T. Dickhaus, “Neurophysiological predictor of SMR-based BCI performance,” NeuroImage 51, 1303–1309 (2010).
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Bobkov, Y. V.

I. J. Park, Y. V. Bobkov, B. W. Ache, and J. C. Principe, “Quantifying bursting neuron activity from calcium signals using blind deconvolution,” J. Neurosci. Methods 218, 196–205 (2013).
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Bolea, S.

I. Ferezou, S. Bolea, and C. C. Petersen, “Visualizing the cortical representation of whisker touch: voltage-sensitive dye imaging in freely moving mice,” Neuron 50, 617–629 (2006).
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Boly, M.

C. Koch, M. Massimini, M. Boly, and G. Tononi, “Neural correlates of consciousness: progress and problems,” Nat. Rev. Neurosci. 17, 307–321 (2016).
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F. Pereira, T. Mitchell, and M. Botvinick, “Machine learning classifiers and fMRI: a tutorial overview,” NeuroImage 45, 199–209 (2009).
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L. Madisen, A. R. Garner, D. Shimaoka, A. S. Chuong, N. C. Klapoetke, L. Li, A. van der Bourg, Y. Niino, L. Egolf, C. Monetti, H. Gu, M. Mills, A. Cheng, B. Tasic, T. N. Nguyen, S. M. Sunkin, A. Benucci, A. Nagy, A. Miyawaki, F. Helmchen, R. M. Empson, T. Knopfel, E. S. Boyden, R. C. Reid, M. Carandini, and H. Zeng, “Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance,” Neuron 85, 942–958 (2015).
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Bruno, R.

E. A. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel, D. Pfau, T. Reardon, Y. Mu, C. Lacefield, W. Yang, M. Ahrens, R. Bruno, T. Jessell, D. Peterka, R. Yuste, and L. Paninsk, “Simultaneous denoising, deconvolution, and demixing of calcium imaging data,” Neuron 89, 285–299 (2016).
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Supplementary Material (2)

NameDescription
» Visualization 1       The video shows cortical activity recorded through widefield calcium imaging. Frames corresponding to ``AW' are identified by ``W', shown on the top left of frames.
» Visualization 1       The video shows cortical activity recorded through widefield calcium imaging. Frames corresponding to ``AW' are identified by ``W', shown on the top left of frames.

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Figures (9)

Fig. 1
Fig. 1 Summary of the proposed analysis procedure.
Fig. 2
Fig. 2 a) Left: Illustration of the experimental setup used for widefield imaging of cortical activity of mice expressing GCaMP6f and simultaneous recording of whisker movement. Right, top: raw image of neocortical surface through transparent skull preparation. M1, S1, and V1 are schematically labeled. Asterisk indicates position of Bregma. Right, bottom: ROIs are superimposed on a map based on the Allen Institute common coordinate framework v3 of mouse cortex (brain-map.org; adapted from [43]). ROI: 1, Retrosplenial area, lateral agranular part (RSPagl); 2, Retrosplenial area, dorsal (RSPd); 3, 4, 9, Secondary motor area (MOs); 5, 7, 8, 10, Primary motor area (MOp); 6, Primary somatosensory area, mouth (SSp-m) / upper limb (SSp-ul); 11, 16, Primary somatosensory area, lower limb (SSp-ll); 12, SS-ul; 13, Primary somatosensory area, nose (SSp-n); 14, 20, Primary somatosensory area, barrel field (SSp-bfd); 15, SSp-bfd / Primary somatosensory area, unassigned (SSp-un); 17, Retrosplenial area, lateral agranular part (RSPagl); 18, Anterior visual area (VISa) / Primary somatosensory area, trunk (SSp-tr); 19, VISa / SSp-tr / SSp-bfd; 21, Supplementary somatosensory area (SSs); 22, Auditory area (AUD); 23, Temporal association areas (TEa); 24, SSp-bfd / Rostrolateral visual area (VISrl); 25, 29, 30, Primary visual area (VISp); 26, Anteromedial visual area (VISam); 27, RSPagl / RSPd; 28, Posteromedial visual area (VISpm). b) A sample 20 s movie obtained during a block. Frames corresponding to “AW” are identified by “W”, shown on the top left of frames (see Visualization 1).
Fig. 3
Fig. 3 Experimental protocol that was followed for each subject. Each subject participated in two sessions per day. In each session, spontaneous activity was acquired for sixteen 20.47 s blocks, with 20 s of rest between blocks.
Fig. 4
Fig. 4 Sample images and time series recorded from block #1 of subject #1. (a)–(b) baseline-corrected images, (c) time series corresponding to ROI-6 and ROI-27, (d) measured angle corresponding to whisker movement signal recorded from the same block, and (e) standard deviation-based time series of the signal, (d) where the threshold level used for labeling AW and NW conditions is shown as a red line.
Fig. 5
Fig. 5 Preprocessed calcium signals of recording block #1 from subject #1 from ROI-6 (a), ROI-8 (f), ROI-30 (k) and ROI-19 (p). For each case, 2 s segments of signals corresponding to AW (shown in red in (b), (g), (l) and (q)) and NW (shown in blue in (c), (h), (m) and (r)) conditions as determined from whisker movement recordings. For each ROI, the adjacency matrices for 2 s AW are shown in (d), (i), (n), and (s), and for 2 s NW are shown in (e), (j), (o), and (t). Measures extracted from VG of 2 s duration of AW time series (shown in red) and from VG of 2 s NW time series (shown in blue) are also shown in (u) for each ROI.
Fig. 6
Fig. 6 Color-coded graph measures for all ROIs as a function of time during a recording block. (a) Edge density (D), (b) Averaged clustering coefficient (C), and (c) Characteristic pathlength (L). (d) Whisker movement recording obtained simultaneously in the same block.
Fig. 7
Fig. 7 Classification results when using kNN as classifier.
Fig. 8
Fig. 8 Classification results when using regularized logistic regression (LR) as classifier.
Fig. 9
Fig. 9 Classification results when using random forest (RF) as classifier.

Tables (4)

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Table 1 Number of blocks and number of AW/NW segments for each subject, when the window length of 2 s with a step size of 0.5 s is used.

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Table 2 Classification results for best sensitivity obtained for each subject when using kNN, regularized logistic regression (LR), and random forest (RF) as classifier. Features, window lengths (w), and related parameters from which the optimum results have been obtained are also listed (SS is short for subsample). Note that “+” in the “Feature” rows represent using multiparametric approach for performing the classification.

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Table 3 Classification performance using unified parameters across subjects and classifiers. D + C is used as the feature, and w = 200 points is used as the window length for extracting features in all cases.

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Table 4 Performance comparison of classification experiments based on i) VG-based feature extraction from all ROIs, ii), Spike-based feature extraction from all ROIs, iii) Variance-based feature extraction from all ROIs, iv) VG-based feature extraction only from ROI-20, and v) VG-based feature extraction from ROIs 25–30.

Equations (5)

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x ( p ) < x ( j ) + [ x ( i ) x ( j ) ] [ t ( j ) t ( p ) t ( j ) t ( i ) ] ,
D = 1 N ( N 1 ) i , j a i , j .
C = 1 N i = 1 N C i = 1 N i , j , l a i j a i l a j l K i ( K i 1 ) ,
L = 1 N ( N 1 ) i , j l i j ,
f : VG measures ( t 0 , t 0 + w ) { AW , NW } ,

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