Skip to main content
Top
Published in:

Open Access 01-04-2023 | Research

Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition

Authors: Aybüke Erol, Chagajeg Soloukey, Bastian Generowicz, Nikki van Dorp, Sebastiaan Koekkoek, Pieter Kruizinga, Borbála Hunyadi

Published in: Neuroinformatics | Issue 2/2023

Login to get access

Abstract

Functional ultrasound (fUS) indirectly measures brain activity by detecting changes in cerebral blood volume following neural activation. Conventional approaches model such functional neuroimaging data as the convolution between an impulse response, known as the hemodynamic response function (HRF), and a binarized representation of the input signal based on the stimulus onsets, the so-called experimental paradigm (EP). However, the EP may not characterize the whole complexity of the activity-inducing signals that evoke the hemodynamic changes. Furthermore, the HRF is known to vary across brain areas and stimuli. To achieve an adaptable framework that can capture such dynamics of the brain function, we model the multivariate fUS time-series as convolutive mixtures and apply block-term decomposition on a set of lagged fUS autocorrelation matrices, revealing both the region-specific HRFs and the source signals that induce the hemodynamic responses. We test our approach on two mouse-based fUS experiments. In the first experiment, we present a single type of visual stimulus to the mouse, and deconvolve the fUS signal measured within the mouse brain’s lateral geniculate nucleus, superior colliculus and visual cortex. We show that the proposed method is able to recover back the time instants at which the stimulus was displayed, and we validate the estimated region-specific HRFs based on prior studies. In the second experiment, we alter the location of the visual stimulus displayed to the mouse, and aim at differentiating the various stimulus locations over time by identifying them as separate sources.
Literature
go back to reference Aguirre, G. K., Zarahn, E., & D’esposito, M. (1998). The variability of human, BOLD hemodynamic responses. NeuroImage, 8(4), 360–369.PubMedCrossRef Aguirre, G. K., Zarahn, E., & D’esposito, M. (1998). The variability of human, BOLD hemodynamic responses. NeuroImage, 8(4), 360–369.PubMedCrossRef
go back to reference Ahmadlou, M., Zweifel, L. S., & Heimel, J. A. (2018). Functional modulation of primary visual cortex by the superior colliculus in the mouse. Nature Communications, 9, 3895.PubMedPubMedCentralCrossRef Ahmadlou, M., Zweifel, L. S., & Heimel, J. A. (2018). Functional modulation of primary visual cortex by the superior colliculus in the mouse. Nature Communications, 9, 3895.PubMedPubMedCentralCrossRef
go back to reference Asemani, D., Morsheddost, H., & Shalchy, M. A. (2017). 06. Effects of ageing and Alzheimer disease on haemodynamic response function: a challenge for event-related fMRI. Healthcare Technology Letters, 4, 109–114.PubMedPubMedCentralCrossRef Asemani, D., Morsheddost, H., & Shalchy, M. A. (2017). 06. Effects of ageing and Alzheimer disease on haemodynamic response function: a challenge for event-related fMRI. Healthcare Technology Letters, 4, 109–114.PubMedPubMedCentralCrossRef
go back to reference Aydin, A.K., W.Haselden, Y.Houssen, C.Pouzat, R.Rungta, C.Demené, M.Tanter, P.J. Drew, S.Charpak, and D.Boido. 2020, 06. Transfer functions linking neural calcium to single voxel functional ultrasound signal. Nature Communications,11, 2954. Aydin, A.K., W.Haselden, Y.Houssen, C.Pouzat, R.Rungta, C.Demené, M.Tanter, P.J. Drew, S.Charpak, and D.Boido. 2020, 06. Transfer functions linking neural calcium to single voxel functional ultrasound signal. Nature Communications,11, 2954.
go back to reference Aydin, A. K., Haselden, W. D., Dang, J., Drew, P. J., Charpak, S., & Boido, D. (2021). Iliski, a software for robust calculation of transfer functions. PLoS Computational Biology, 17(6), e1008614.PubMedPubMedCentralCrossRef Aydin, A. K., Haselden, W. D., Dang, J., Drew, P. J., Charpak, S., & Boido, D. (2021). Iliski, a software for robust calculation of transfer functions. PLoS Computational Biology, 17(6), e1008614.PubMedPubMedCentralCrossRef
go back to reference Bai, X., & He, B. (2006). Estimation of number of independent brain electric sources from the scalp eegs. IEEE Transactions on Biomedical Engineering, 53(10), 1883–1892.PubMedCrossRef Bai, X., & He, B. (2006). Estimation of number of independent brain electric sources from the scalp eegs. IEEE Transactions on Biomedical Engineering, 53(10), 1883–1892.PubMedCrossRef
go back to reference Baranger, J., Demené, C., Frerot, A., Faure, F., Delanoë, C., Serroune, H., Houdouin, A., Mairesse, J., Biran, V., Baud, O., & Tanter, M. (2021). Bedside functional monitoring of the dynamic brain connectivity in human neonates. Nature Communications, 12(1080). Baranger, J., Demené, C., Frerot, A., Faure, F., Delanoë, C., Serroune, H., Houdouin, A., Mairesse, J., Biran, V., Baud, O., & Tanter, M. (2021). Bedside functional monitoring of the dynamic brain connectivity in human neonates. Nature Communications, 12(1080).
go back to reference Blaize, K., Arcizet, F., Gesnik, M., & Picaud, S. (2020). Functional ultrasound imaging of deep visual cortex in awake nonhuman primates. Proceedings of the National Academy of Sciences, 117(25), 14453–63.CrossRef Blaize, K., Arcizet, F., Gesnik, M., & Picaud, S. (2020). Functional ultrasound imaging of deep visual cortex in awake nonhuman primates. Proceedings of the National Academy of Sciences, 117(25), 14453–63.CrossRef
go back to reference Bolshakova, N., & Azuaje, F. (2003). Cluster validation techniques for genome expression data. Signal Processing, 83(4), 825–833.CrossRef Bolshakova, N., & Azuaje, F. (2003). Cluster validation techniques for genome expression data. Signal Processing, 83(4), 825–833.CrossRef
go back to reference Bousbia-Salah, H., Belouchrani, A., & Abed-Meriam, K. (2001). Jacobi-like algorithm for blind signal separation of convolutive mixtures. Electronics Letters, 37, 1049–1050.CrossRef Bousbia-Salah, H., Belouchrani, A., & Abed-Meriam, K. (2001). Jacobi-like algorithm for blind signal separation of convolutive mixtures. Electronics Letters, 37, 1049–1050.CrossRef
go back to reference Brunner, C., Grillet, M., Urban, A., Roska, B., Montaldo, G., & Macé, E. (2021). Whole-brain functional ultrasound imaging in awake head-fixed mice. Nature Protocols, 16, 3547–3571.PubMedCrossRef Brunner, C., Grillet, M., Urban, A., Roska, B., Montaldo, G., & Macé, E. (2021). Whole-brain functional ultrasound imaging in awake head-fixed mice. Nature Protocols, 16, 3547–3571.PubMedCrossRef
go back to reference Bunzeck, N., Wuestenberg, T., Lutz, K., Heinze, H., & Jancke, L. (2005). Scanning silence: mental imagery of complex sounds. NeuroImage, 26(4), 1119–1127.PubMedCrossRef Bunzeck, N., Wuestenberg, T., Lutz, K., Heinze, H., & Jancke, L. (2005). Scanning silence: mental imagery of complex sounds. NeuroImage, 26(4), 1119–1127.PubMedCrossRef
go back to reference Caballero, C., Petridou, N., Dryden, I., Bai, L., Francis, S., & Gowland, P. (2011). Detection and characterization of single-trial fMRI BOLD responses: Paradigm free mapping. Human Brain Mapping, 32, 1400–1428.CrossRef Caballero, C., Petridou, N., Dryden, I., Bai, L., Francis, S., & Gowland, P. (2011). Detection and characterization of single-trial fMRI BOLD responses: Paradigm free mapping. Human Brain Mapping, 32, 1400–1428.CrossRef
go back to reference Chen, J. E., Glover, G. H., Fultz, N. E., Rosen, B. R., Polimeni, J. R., & Lewis, L. D. (2021). Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics. NeuroImage, 245, 118658.PubMedCrossRef Chen, J. E., Glover, G. H., Fultz, N. E., Rosen, B. R., Polimeni, J. R., & Lewis, L. D. (2021). Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics. NeuroImage, 245, 118658.PubMedCrossRef
go back to reference Cherkaoui, H., Moreau, T., Halimi, A., & Ciuciu, P. (2019). Sparsity-based blind deconvolution of neural activation signal in fmri. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1323–132. Cherkaoui, H., Moreau, T., Halimi, A., & Ciuciu, P. (2019). Sparsity-based blind deconvolution of neural activation signal in fmri. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1323–132.
go back to reference Cherkaoui, H., Moreau, T., Halimi, A., Leroy, C., & Ciuciu, P. (2021). Multivariate semi-blind deconvolution of fMRI time series. NeuroImage, 241,PubMedCrossRef Cherkaoui, H., Moreau, T., Halimi, A., Leroy, C., & Ciuciu, P. (2021). Multivariate semi-blind deconvolution of fMRI time series. NeuroImage, 241,PubMedCrossRef
go back to reference Correa, N., T.Adali, Yi-Ou Li, and V.D. Calhoun 2005. Comparison of blind source separation algorithms for fMRI using a new matlab toolbox: Gift. In Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., Volume5, pp. v/401–v/404. Correa, N., T.Adali, Yi-Ou Li, and V.D. Calhoun 2005. Comparison of blind source separation algorithms for fMRI using a new matlab toolbox: Gift. In Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., Volume5, pp. v/401–v/404.
go back to reference Deffieux, T., Demené, C., & Tanter, M. (2021). Functional Ultrasound Imaging: A New Imaging Modality for Neuroscience. Neuroscience, 474, 110–121.PubMedCrossRef Deffieux, T., Demené, C., & Tanter, M. (2021). Functional Ultrasound Imaging: A New Imaging Modality for Neuroscience. Neuroscience, 474, 110–121.PubMedCrossRef
go back to reference Demené, C., Deffieux, T., Pernot, M., Osmanski, B. F., Biran, V., Gennisson, J. L., Sieu, L. A., Bergel, A., Franqui, S., Correas, J. M., Cohen, I., Baud, O., & Tanter, M. (2015). Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity. IEEE Transactions on Medical Imaging, 34(11), 2271–2285.PubMedCrossRef Demené, C., Deffieux, T., Pernot, M., Osmanski, B. F., Biran, V., Gennisson, J. L., Sieu, L. A., Bergel, A., Franqui, S., Correas, J. M., Cohen, I., Baud, O., & Tanter, M. (2015). Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity.  IEEE Transactions on Medical Imaging, 34(11), 2271–2285.PubMedCrossRef
go back to reference Demené, C., Deffieux, T., Pernot, M., Osmanski, B. F., Biran, V., Gennisson, J. L., Sieu, L. A., Bergel, A., Franqui, S., Correas, J. M., Cohen, I., Baud, O., & Tanter, M. (2015). Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity. IEEE Transactions on Medical Imaging, 34(11), 2271–2285.PubMedCrossRef Demené, C., Deffieux, T., Pernot, M., Osmanski, B. F., Biran, V., Gennisson, J. L., Sieu, L. A., Bergel, A., Franqui, S., Correas, J. M., Cohen, I., Baud, O., & Tanter, M. (2015). Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity. IEEE Transactions on Medical Imaging, 34(11), 2271–2285.PubMedCrossRef
go back to reference Demmel, J. W. (1997). Applied numerical linear algebra. SIAM. Demmel, J. W. (1997). Applied numerical linear algebra. SIAM.
go back to reference Elbau, I. G., Brücklmeier, B., Uhr, M., Arloth, J., Czamara, D., Spoormaker, V. I., Czisch, M., Stephan, K. E., Binder, E. B., & Sämann, P. G. (2018). 10. The brain’s hemodynamic response function rapidly changes under acute psychosocial stress in association with genetic and endocrine stress response markers. Proceedings of the National academy of Sciences of the United States of America, 115, E10206–E10215.PubMedPubMedCentral Elbau, I. G., Brücklmeier, B., Uhr, M., Arloth, J., Czamara, D., Spoormaker, V. I., Czisch, M., Stephan, K. E., Binder, E. B., & Sämann, P. G. (2018). 10. The brain’s hemodynamic response function rapidly changes under acute psychosocial stress in association with genetic and endocrine stress response markers. Proceedings of the National academy of Sciences of the United States of America, 115, E10206–E10215.PubMedPubMedCentral
go back to reference Erol, A., S.VanEyndhoven, S.Koekkoek, P.Kruizinga, and B.Hunyadi. 2020. Joint estimation of hemodynamic response and stimulus function in functional ultrasound using convolutive mixtures. Proceedings of the Asilomar Conference on Signals, Systems, and Computers, pp 246–250. Erol, A., S.VanEyndhoven, S.Koekkoek, P.Kruizinga, and B.Hunyadi. 2020. Joint estimation of hemodynamic response and stimulus function in functional ultrasound using convolutive mixtures. Proceedings of the Asilomar Conference on Signals, Systems, and Computers, pp 246–250.
go back to reference Franklin, K. B. J., & Paxinos, G. (2001). The mouse brain in stereotaxic coordinates (Second ed.). Academic Press. Franklin, K. B. J., & Paxinos, G. (2001). The mouse brain in stereotaxic coordinates (Second ed.). Academic Press.
go back to reference Fransson, P., Krüger, G., Merboldt, K. D., & Frahm, J. (1999). Temporal and spatial MRI responses to subsecond visual activation. Magnetic Resonance Imaging, 17(1), 1–7.PubMedCrossRef Fransson, P., Krüger, G., Merboldt, K. D., & Frahm, J. (1999). Temporal and spatial MRI responses to subsecond visual activation. Magnetic Resonance Imaging, 17(1), 1–7.PubMedCrossRef
go back to reference Friston, K. J. (2002). Functional integration and inference in the brain. Progress in Neurobiology, 68(2), 113–143.PubMedCrossRef Friston, K. J. (2002). Functional integration and inference in the brain. Progress in Neurobiology, 68(2), 113–143.PubMedCrossRef
go back to reference Friston, K. J., Fletcher, P., Josephs, O., Holmes, A., & Turner, R. (1998). Event-related fMRI: Characterizing differential responses. NeuroImage, 7, 30–40.PubMedCrossRef Friston, K. J., Fletcher, P., Josephs, O., Holmes, A., & Turner, R. (1998). Event-related fMRI: Characterizing differential responses. NeuroImage, 7, 30–40.PubMedCrossRef
go back to reference Friston, K. J., Harrison, L., & Pennya, W. (2003). Dynamic causal modelling. NeuroImage, 19, 1273–1302.PubMedCrossRef Friston, K. J., Harrison, L., & Pennya, W. (2003). Dynamic causal modelling. NeuroImage, 19, 1273–1302.PubMedCrossRef
go back to reference Friston, K. J., Mechelli, A., Turner, R., & Price, C. J. (2000). Nonlinear responses in fMRI: The balloon model, volterra kernels, and other hemodynamics. NeuroImage, 12, 466–477.PubMedCrossRef Friston, K. J., Mechelli, A., Turner, R., & Price, C. J. (2000). Nonlinear responses in fMRI: The balloon model, volterra kernels, and other hemodynamics. NeuroImage, 12, 466–477.PubMedCrossRef
go back to reference Gale, S., & Murphy, G. (2016). Active dendritic properties and local inhibitory input enable selectivity for object motion in mouse superior colliculus neurons. Journal of Neuroscience, 36, 9111–9123.PubMedCrossRef Gale, S., & Murphy, G. (2016). Active dendritic properties and local inhibitory input enable selectivity for object motion in mouse superior colliculus neurons. Journal of Neuroscience, 36, 9111–9123.PubMedCrossRef
go back to reference Ganis, G., Thopson, W. L., & Kosslyn, S. M. (2004). Brain areas underlying visual mental imagery and visual perception: an fmri study. Cognitive Brain Research, 20(2), 226–241.PubMedCrossRef Ganis, G., Thopson, W. L., & Kosslyn, S. M. (2004). Brain areas underlying visual mental imagery and visual perception: an fmri study. Cognitive Brain Research, 20(2), 226–241.PubMedCrossRef
go back to reference Gesnik, M., Blaize, K., Deffieux, T., Gennisson, J., Sahel, J., Fink, M., Picaud, S., & Tanter, M. (2017). 3D functional ultrasound imaging of the cerebral visual system in rodents. NeuroImage, 149, 267–274.PubMedCrossRef Gesnik, M., Blaize, K., Deffieux, T., Gennisson, J., Sahel, J., Fink, M., Picaud, S., & Tanter, M. (2017). 3D functional ultrasound imaging of the cerebral visual system in rodents. NeuroImage, 149, 267–274.PubMedCrossRef
go back to reference Glover, G. H. (1999). Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage, 9, 416–429.PubMedCrossRef Glover, G. H. (1999). Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage, 9, 416–429.PubMedCrossRef
go back to reference Gur, M., & Snodderly, D. M. (1997). Visual receptive fields of neurons in primary visual cortex (V1) move in space with the eye movements of fixation. Vision Research, 37, 257–265.PubMedCrossRef Gur, M., & Snodderly, D. M. (1997). Visual receptive fields of neurons in primary visual cortex (V1) move in space with the eye movements of fixation. Vision Research, 37, 257–265.PubMedCrossRef
go back to reference Handwerker, D., Ollinger, J., & D’Esposito, M. (2004). Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. NeuroImage, 21, 1639–1651.PubMedCrossRef Handwerker, D., Ollinger, J., & D’Esposito, M. (2004). Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. NeuroImage, 21, 1639–1651.PubMedCrossRef
go back to reference Himberg, J., Hyvärinen, A., & Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage, 22(3), 1214–1222.PubMedCrossRef Himberg, J., Hyvärinen, A., & Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage, 22(3), 1214–1222.PubMedCrossRef
go back to reference Hütel, M., Antonelli, M., Melbourne, A., & Ourselin, S. (2021). Hemodynamic matrix factorization for functional magnetic resonance imaging. NeuroImage, 231, 117814.PubMedCrossRef Hütel, M., Antonelli, M., Melbourne, A., & Ourselin, S. (2021). Hemodynamic matrix factorization for functional magnetic resonance imaging. NeuroImage, 231, 117814.PubMedCrossRef
go back to reference Huang, C., Song, P., Gong, P., Trzasko, J. D., Manduca, A., & Chen, S. (2019). Debiasing-Based Noise Suppression for Ultrafast Ultrasound Microvessel Imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 66, 1281–1291.PubMedPubMedCentralCrossRef Huang, C., Song, P., Gong, P., Trzasko, J. D., Manduca, A., & Chen, S. (2019). Debiasing-Based Noise Suppression for Ultrafast Ultrasound Microvessel Imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 66, 1281–1291.PubMedPubMedCentralCrossRef
go back to reference Imbault, M., Chauvet, D., Gennisson, J. L., Capelle, L., & Tanter, M, (2017). Intraoperative Functional Ultrasound Imaging of Human Brain Activity. Scientific Reports, 7(7304). Imbault, M., Chauvet, D., Gennisson, J. L., Capelle, L., & Tanter, M, (2017). Intraoperative Functional Ultrasound Imaging of Human Brain Activity. Scientific Reports, 7(7304).
go back to reference Inayat, S., Barchini, J., Chen, H., Feng, L., Liu, X., & Cang, J. (2015). Neurons in the most superficial lamina of the mouse superior colliculus are highly selective for stimulus direction. The Journal of Neuroscience, 35, 7992–8003.PubMedPubMedCentralCrossRef Inayat, S., Barchini, J., Chen, H., Feng, L., Liu, X., & Cang, J. (2015). Neurons in the most superficial lamina of the mouse superior colliculus are highly selective for stimulus direction. The Journal of Neuroscience, 35, 7992–8003.PubMedPubMedCentralCrossRef
go back to reference Ito, S., Feldheim, D. A., & Litke, A. M. (2017). Segregation of visual response properties in the mouse superior colliculus and their modulation during locomotion. The Journal of Neuroscience, 37, 8428–8443.PubMedPubMedCentralCrossRef Ito, S., Feldheim, D. A., & Litke, A. M. (2017). Segregation of visual response properties in the mouse superior colliculus and their modulation during locomotion. The Journal of Neuroscience, 37, 8428–8443.PubMedPubMedCentralCrossRef
go back to reference Ito, S., Si, Y., Litke, A. M., & Feldheim, D. A. (2021). Nonlinear visuoauditory integration in the mouse superior colliculus. PLoS Computational Biology, 17(11), e1009181.PubMedPubMedCentralCrossRef Ito, S., Si, Y., Litke, A. M., & Feldheim, D. A. (2021). Nonlinear visuoauditory integration in the mouse superior colliculus. PLoS Computational Biology, 17(11), e1009181.PubMedPubMedCentralCrossRef
go back to reference Kara, P., Reinagel, P., & Reid, R. C. (2000). Low response variability in simultaneously recorded retinal, thalamic, and cortical neurons. Neuron, 27(3), 635–646.PubMedCrossRef Kara, P., Reinagel, P., & Reid, R. C. (2000). Low response variability in simultaneously recorded retinal, thalamic, and cortical neurons. Neuron, 27(3), 635–646.PubMedCrossRef
go back to reference Karahanoglu, F. I., Caballero, C., Lazeyras, F., & Van De Ville, D. (2013). Total activation: fMRI deconvolution through spatio-temporal regularization. NeuroImage, 73, 121–134.PubMedCrossRef Karahanoglu, F. I., Caballero, C., Lazeyras, F., & Van De Ville, D. (2013). Total activation: fMRI deconvolution through spatio-temporal regularization. NeuroImage, 73, 121–134.PubMedCrossRef
go back to reference Karahanoglu, F. I., & Van De Ville, D. (2015). Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks. Nature Communications, 6, 7751.PubMedCrossRef Karahanoglu, F. I., & Van De Ville, D. (2015). Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks. Nature Communications, 6, 7751.PubMedCrossRef
go back to reference Koekkoek, S. K. E., Soloukey, S., Generowicz, B. S., van Hoogstraten, W. S., DeOude, N. L., Boele, H. J., Strydis, C., Leus, G., Bosch, J. G., Van DerSteen, A. F. W., DeZeeuw, C. I., & Kruizinga, P. (2018). High frequency functional ultrasound in mice. In IEEE International Ultrasonics Symposium, pp. 1–4. Koekkoek, S. K. E., Soloukey, S., Generowicz, B. S., van Hoogstraten, W. S., DeOude, N. L., Boele, H. J., Strydis, C., Leus, G., Bosch, J. G., Van DerSteen, A. F. W., DeZeeuw, C. I., & Kruizinga, P. (2018). High frequency functional ultrasound in mice. In IEEE International Ultrasonics Symposium, pp. 1–4.
go back to reference Lau, C., Zhou, I. Y., Cheung, M. M., Chan, K. C., & Wu, E. X. (2011). BOLD temporal dynamics of rat superior colliculus and lateral geniculate nucleus following short duration visual stimulation. PLoS One, 6(4), e18914.PubMedPubMedCentralCrossRef Lau, C., Zhou, I. Y., Cheung, M. M., Chan, K. C., & Wu, E. X. (2011). BOLD temporal dynamics of rat superior colliculus and lateral geniculate nucleus following short duration visual stimulation. PLoS One, 6(4), e18914.PubMedPubMedCentralCrossRef
go back to reference Lewis, L. D., Setsompop, K., Rosen, B. R., & Polimeni, J. R. (2018). Stimulus-dependent hemodynamic response timing across the human subcortical-cortical visual pathway identified through high spatiotemporal resolution 7T fMRI. NeuroImage, 181, 279–291.PubMedCrossRef Lewis, L. D., Setsompop, K., Rosen, B. R., & Polimeni, J. R. (2018). Stimulus-dependent hemodynamic response timing across the human subcortical-cortical visual pathway identified through high spatiotemporal resolution 7T fMRI. NeuroImage, 181, 279–291.PubMedCrossRef
go back to reference Lindquist, M., Loh, J. M., Atlas, L., & Wager, T. (2009). Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling. NeuroImage, 45, S187–S198.PubMedCrossRef Lindquist, M., Loh, J. M., Atlas, L., & Wager, T. (2009). Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling. NeuroImage, 45, S187–S198.PubMedCrossRef
go back to reference Luo, D., Huang, H., & Ding, C. (2011). 02. Are Tensor Decomposition Solutions Unique? On the Global Convergence HOSVD and ParaFac Algorithms. Proceeding of the PAKDD: 148–159 . Luo, D., Huang, H., & Ding, C. (2011). 02. Are Tensor Decomposition Solutions Unique? On the Global Convergence HOSVD and ParaFac Algorithms. Proceeding of the PAKDD: 148–159 .
go back to reference Macé, E., Montaldo, G., Trenholm, S., Cowan, C., Brignall, A., Urban, A., & Roska, B. (2018). Whole-brain functional ultrasound imaging reveals brain modules for visuomotor integration. Neuron, 100, 1241–1251.PubMedPubMedCentralCrossRef Macé, E., Montaldo, G., Trenholm, S., Cowan, C., Brignall, A., Urban, A., & Roska, B. (2018). Whole-brain functional ultrasound imaging reveals brain modules for visuomotor integration. Neuron, 100, 1241–1251.PubMedPubMedCentralCrossRef
go back to reference Macé, E., Montaldo, G., Cohen, I., Baulac, M., Fink, M., & Tanter, M. (2011). Functional ultrasound imaging of the brain. Nature Methods, 8(8), 662–664.PubMedCrossRef Macé, E., Montaldo, G., Cohen, I., Baulac, M., Fink, M., & Tanter, M. (2011). Functional ultrasound imaging of the brain. Nature Methods, 8(8), 662–664.PubMedCrossRef
go back to reference Marrelec, G., Benali, H., Ciuciu, P., Pélégrini-Issac, M., & Poline, J. (2003). Robust bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information. Human Brain Mapping, 19, 1–17.PubMedPubMedCentralCrossRef Marrelec, G., Benali, H., Ciuciu, P., Pélégrini-Issac, M., & Poline, J. (2003). Robust bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information. Human Brain Mapping, 19, 1–17.PubMedPubMedCentralCrossRef
go back to reference Marshel, J. H., Garrett, M. E., Nauhaus, I., & Callaway, E. M. (2011). Functional specialization of seven mouse visual cortical areas. Neuron, 72(6), 1040–1054.PubMedPubMedCentralCrossRef Marshel, J. H., Garrett, M. E., Nauhaus, I., & Callaway, E. M. (2011). Functional specialization of seven mouse visual cortical areas. Neuron, 72(6), 1040–1054.PubMedPubMedCentralCrossRef
go back to reference Mayer, A. R., Toulouse, T., Klimaj, S., Ling, J. M., Pena, A., & Bellgowan, P. S. F. (2014). 01. Investigating the properties of the hemodynamic response function after mild traumatic brain injury. Journal of Neurotrauma, 31, 189–197.PubMedPubMedCentralCrossRef Mayer, A. R., Toulouse, T., Klimaj, S., Ling, J. M., Pena, A., & Bellgowan, P. S. F. (2014). 01. Investigating the properties of the hemodynamic response function after mild traumatic brain injury. Journal of Neurotrauma, 31, 189–197.PubMedPubMedCentralCrossRef
go back to reference Mitianoudis, N., & Davies, M. E. (2003). Audio source separation of convolutive mixtures. IEEE Transactions on Audio, Speech and Language Processing, 11, 489–497.CrossRef Mitianoudis, N., & Davies, M. E. (2003). Audio source separation of convolutive mixtures. IEEE Transactions on Audio, Speech and Language Processing, 11, 489–497.CrossRef
go back to reference Naik, G. R., & Wang, W. (2014). Blind source separation. Berlin: Springer, 10. Naik, G. R., & Wang, W. (2014). Blind source separation. Berlin: Springer, 10.
go back to reference Niranjan, A., Christie, I. N., Solomon, S. G., Wells, J. A., & Lythgoe, M. F. (2016). fMRI mapping of the visual system in the mouse brain with interleaved snapshot GE-EPI. NeuroImage, 139, 337–345.PubMedCrossRef Niranjan, A., Christie, I. N., Solomon, S. G., Wells, J. A., & Lythgoe, M. F. (2016). fMRI mapping of the visual system in the mouse brain with interleaved snapshot GE-EPI. NeuroImage, 139, 337–345.PubMedCrossRef
go back to reference Norman, S. L., Maresca, D., Christopoulos, V. N., Griggs, W. S., Demene, C., Tanter, M., Shapiro, M. G., & Andersen, R. A. (2021). Single-trial decoding of movement intentions using functional ultrasound neuroimaging. Neuron, 109(9), 1554-1566.e4.PubMedPubMedCentralCrossRef Norman, S. L., Maresca, D., Christopoulos, V. N., Griggs, W. S., Demene, C., Tanter, M., Shapiro, M. G., & Andersen, R. A. (2021). Single-trial decoding of movement intentions using functional ultrasound neuroimaging. Neuron, 109(9), 1554-1566.e4.PubMedPubMedCentralCrossRef
go back to reference Nunez-Elizalde, A., Krumin, M., Reddy, C., Montaldo, G., Urban, A., Harris, K., & Carandini, M. (2022). 03. Neural correlates of blood flow measured by ultrasound. Neuron, 110, 1–10.CrossRef Nunez-Elizalde, A., Krumin, M., Reddy, C., Montaldo, G., Urban, A., Harris, K., & Carandini, M. (2022). 03. Neural correlates of blood flow measured by ultrasound. Neuron, 110, 1–10.CrossRef
go back to reference Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.CrossRef Otsu, N. (1979). A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.CrossRef
go back to reference Rahnev, D. A., Bahdo, L., de Lange, F. P., & Lau, H. (2012). Prestimulus hemodynamic activity in dorsal attention network is negatively associated with decision confidence in visual perception. Journal of Neurophysiology, 108(5), 1529–1536.PubMedCrossRef Rahnev, D. A., Bahdo, L., de Lange, F. P., & Lau, H. (2012). Prestimulus hemodynamic activity in dorsal attention network is negatively associated with decision confidence in visual perception. Journal of Neurophysiology, 108(5), 1529–1536.PubMedCrossRef
go back to reference Rangaprakash, D., Tadayonnejad, R., Deshpande, G., O’Neill, J., & Feusner, J. D. (2021). 06. FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response. Brain Imaging and Behavior, 15, 1622–1640.PubMedPubMedCentralCrossRef Rangaprakash, D., Tadayonnejad, R., Deshpande, G., O’Neill, J., & Feusner, J. D. (2021). 06. FMRI hemodynamic response function (HRF) as a novel marker of brain function: applications for understanding obsessive-compulsive disorder pathology and treatment response. Brain Imaging and Behavior, 15, 1622–1640.PubMedPubMedCentralCrossRef
go back to reference Rau, R., Kruizinga, P., Mastik, F., Belau, M., De Jong, N., Bosch, J. G., Scheffer, W., & Maret, G. (2018). 3D functional ultrasound imaging of pigeons. NeuroImage, 183, 469–477.PubMedCrossRef Rau, R., Kruizinga, P., Mastik, F., Belau, M., De Jong, N., Bosch, J. G., Scheffer, W., & Maret, G. (2018). 3D functional ultrasound imaging of pigeons. NeuroImage, 183, 469–477.PubMedCrossRef
go back to reference Roels, S. P., Moerkerke, B., & Loeys, T. (2015). Bootstrapping fMRI data: dealing with misspecification. Neuroinformatics, 13(3), 337–352.PubMedCrossRef Roels, S. P., Moerkerke, B., & Loeys, T. (2015). Bootstrapping fMRI data: dealing with misspecification. Neuroinformatics, 13(3), 337–352.PubMedCrossRef
go back to reference Sala-Llonch, R., Smith, S. M., Woolrich, M., & Duff, E. P. (2019). Spatial Parcellations, Spectral Filtering, and Connectivity Measures in fMRI: Optimizing for Discrimination. Human Brain Mapping, 40(2), 407–419.PubMedCrossRef Sala-Llonch, R., Smith, S. M., Woolrich, M., & Duff, E. P. (2019). Spatial Parcellations, Spectral Filtering, and Connectivity Measures in fMRI: Optimizing for Discrimination. Human Brain Mapping, 40(2), 407–419.PubMedCrossRef
go back to reference Sano, A. (1993). Optimally regularized inverse of singular value decomposition and application to signal extrapolation. Signal Processing, 30(2), 163–176.CrossRef Sano, A. (1993). Optimally regularized inverse of singular value decomposition and application to signal extrapolation. Signal Processing, 30(2), 163–176.CrossRef
go back to reference Schrouff, J., Rosa, M. J., Rondina, J. M., Marquand, A. F., Chu, C., Ashburner, J., Phillips, C., Richiardi, J., & Mourao-Miranda, J. (2013). PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics, 11(3), 319–337.PubMedPubMedCentralCrossRef Schrouff, J., Rosa, M. J., Rondina, J. M., Marquand, A. F., Chu, C., Ashburner, J., Phillips, C., Richiardi, J., & Mourao-Miranda, J. (2013). PRoNTo: pattern recognition for neuroimaging toolbox. Neuroinformatics, 11(3), 319–337.PubMedPubMedCentralCrossRef
go back to reference Seghouane, A. K., & Shah, A. (2012). HRF estimation in fMRI data with an unknown drift matrix by iterative minimization of the Kullback-Leibler divergence. IEEE Transactions on Medical Imaging, 31(2), 192–206.PubMedCrossRef Seghouane, A. K., & Shah, A. (2012). HRF estimation in fMRI data with an unknown drift matrix by iterative minimization of the Kullback-Leibler divergence. IEEE Transactions on Medical Imaging, 31(2), 192–206.PubMedCrossRef
go back to reference Shah, A., & Seghouane, A. K. (2014). 06. An integrated framework for joint HRF and drift estimation and HbO/HbR signal improvement in fNIRS data. IEEE Transactions on Medical Imaging, 33, 2086–2097.PubMedCrossRef Shah, A., & Seghouane, A. K. (2014). 06. An integrated framework for joint HRF and drift estimation and HbO/HbR signal improvement in fNIRS data. IEEE Transactions on Medical Imaging, 33, 2086–2097.PubMedCrossRef
go back to reference Soloukey, S., Vincent, A. J. P. E., Satoer, D. D., Mastik, F., Smits, M., Dirven, C. M. F., Strydis, C., Bosch, J. G., Van Der Steen, A. F. W., De Zeeuw, C. I., Koekkoek, S. K. E., & Kruizinga, P. (2020). Functional Ultrasound (fUS) During Awake Brain Surgery: The Clinical Potential of Intra-Operative Functional and Vascular Brain Mapping. Frontiers in Neuroscience, 13, 1384.PubMedPubMedCentralCrossRef Soloukey, S., Vincent, A. J. P. E., Satoer, D. D., Mastik, F., Smits, M., Dirven, C. M. F., Strydis, C., Bosch, J. G., Van Der Steen, A. F. W., De Zeeuw, C. I., Koekkoek, S. K. E., & Kruizinga, P. (2020). Functional Ultrasound (fUS) During Awake Brain Surgery: The Clinical Potential of Intra-Operative Functional and Vascular Brain Mapping. Frontiers in Neuroscience, 13, 1384.PubMedPubMedCentralCrossRef
go back to reference Song, P., Manduca, A., Trzasko, J. D., & Chen, S. (2017). Ultrasound small vessel imaging with block-wise adaptive local clutter filtering. IEEE Transactions on Medical Imaging, 36(1), 251–262.PubMedCrossRef Song, P., Manduca, A., Trzasko, J. D., & Chen, S. (2017). Ultrasound small vessel imaging with block-wise adaptive local clutter filtering. IEEE Transactions on Medical Imaging, 36(1), 251–262.PubMedCrossRef
go back to reference Sorber, L., Van Barel, M., & De Lathauwer, L. (2013a). Optimization-Based Algorithms for Tensor Decompositions: Canonical Polyadic Decomposition, Decomposition in Rank-(Lr, Lr, 1) Terms, and a New Generalization. SIAM Journal on Optimization, 23(2), 695–720. Sorber, L., Van Barel, M., & De Lathauwer, L. (2013a). Optimization-Based Algorithms for Tensor Decompositions: Canonical Polyadic Decomposition, Decomposition in Rank-(Lr, Lr, 1) Terms, and a New Generalization. SIAM Journal on Optimization, 23(2), 695–720.
go back to reference Sorber, L., Van Barel, M., & De Lathauwer, L. (2013b). Optimization-based algorithms for tensor decompositions: Canonical polyadic decomposition, decomposition in rank-$(l_r, l_r, 1)$ terms, and a new generalization. SIAM Journal on Optimization, 23(2), 695–720. Sorber, L., Van Barel, M., & De Lathauwer, L. (2013b). Optimization-based algorithms for tensor decompositions: Canonical polyadic decomposition, decomposition in rank-$(l_r, l_r, 1)$ terms, and a new generalization. SIAM Journal on Optimization, 23(2), 695–720.
go back to reference Sorber, L., Van Barel, M., & De Lathauwer, L. (2015). Structured data fusion. IEEE Journal of Selected Topics in Signal Processing, 9, 586–600.CrossRef Sorber, L., Van Barel, M., & De Lathauwer, L. (2015). Structured data fusion. IEEE Journal of Selected Topics in Signal Processing, 9, 586–600.CrossRef
go back to reference Sreenivasan, K. R., Havlicek, M., & Deshpande, G. (2015). Nonparametric hemodynamic deconvolution of fMRI using homomorphic filtering. IEEE Transactions on Medical Imaging, 34(5), 1155–1163.PubMedCrossRef Sreenivasan, K. R., Havlicek, M., & Deshpande, G. (2015). Nonparametric hemodynamic deconvolution of fMRI using homomorphic filtering. IEEE Transactions on Medical Imaging, 34(5), 1155–1163.PubMedCrossRef
go back to reference Uruñuela, E., Bolton, T. A. W., Ville, D. V. D., & Caballero-Gaudes, C. (2021). Hemodynamic deconvolution demystified: Sparsity-driven regularization at work. Uruñuela, E., Bolton, T. A. W., Ville, D. V. D., & Caballero-Gaudes, C. (2021). Hemodynamic deconvolution demystified: Sparsity-driven regularization at work.
go back to reference Van Eeghem, F., & De Lathauwer, L. (2017). Second-order tensor-based convolutive ICA: Deconvolution versus tensorization. Proceeding of the International Conf. Conference on Acoustics, Speech and Signal Processing, pp. 2252–2256. Van Eeghem, F., & De Lathauwer, L. (2017). Second-order tensor-based convolutive ICA: Deconvolution versus tensorization. Proceeding of the International Conf. Conference on Acoustics, Speech and Signal Processing, pp. 2252–2256.
go back to reference Van Eyndhoven, S., Dupont, P., Tousseyn, S., Vervliet, N., Van Paesschen, W., Van Huffel, S., & Hunyadi, B. (2021). Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data. NeuroImage, 228,PubMedCrossRef Van Eyndhoven, S., Dupont, P., Tousseyn, S., Vervliet, N., Van Paesschen, W., Van Huffel, S., & Hunyadi, B. (2021). Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data. NeuroImage, 228,PubMedCrossRef
go back to reference Van Eyndhoven, S., Vervliet, N., De Lathauwer, L., & Van Huffel, S. (2019). Identifying stable components of matrix /tensor factorizations via low-rank approximation of inter-factorization similarity. In 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1–5. Van Eyndhoven, S., Vervliet, N., De Lathauwer, L., & Van Huffel, S. (2019). Identifying stable components of matrix /tensor factorizations via low-rank approximation of inter-factorization similarity. In 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1–5.
go back to reference Wang, J., Sarnaik, R., Rangarajan, K., Lui, X., & Cang, J. (2010). Visual receptive field properties of neurons in the superficial superior colliculus of the mouse. The Journal of Neuroscience, 30, 16573–16584.PubMedPubMedCentralCrossRef Wang, J., Sarnaik, R., Rangarajan, K., Lui, X., & Cang, J. (2010). Visual receptive field properties of neurons in the superficial superior colliculus of the mouse. The Journal of Neuroscience, 30, 16573–16584.PubMedPubMedCentralCrossRef
go back to reference West, K. L., Zuppichini, M. D., Turner, M. P., Sivakolundu, D. K., Zhao, Y., Abdelkarim, D., Spence, J. S., & Rypma, B. (2019). BOLD hemodynamic response function changes significantly with healthy aging. NeuroImage, 188, 199–207.CrossRef West, K. L., Zuppichini, M. D., Turner, M. P., Sivakolundu, D. K., Zhao, Y., Abdelkarim, D., Spence, J. S., & Rypma, B. (2019). BOLD hemodynamic response function changes significantly with healthy aging. NeuroImage, 188, 199–207.CrossRef
go back to reference Winder, A. T., Echagarruga, C., Zhang, Q., & Drew, P. J. (2017). Weak correlations between hemodynamic signals and ongoing neural activity during the resting state. Nature Neuroscience, 20(12), 1761–1760.PubMedPubMedCentralCrossRef Winder, A. T., Echagarruga, C., Zhang, Q., & Drew, P. J. (2017). Weak correlations between hemodynamic signals and ongoing neural activity during the resting state. Nature Neuroscience, 20(12), 1761–1760.PubMedPubMedCentralCrossRef
go back to reference Wu, G. R., Liao, W., Stramaglia, S., Ding, J., Chen, H., & Marinazzo, D. (2013). A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Medical Image Analysis, 17, 365–374.PubMedCrossRef Wu, G. R., Liao, W., Stramaglia, S., Ding, J., Chen, H., & Marinazzo, D. (2013). A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data. Medical Image Analysis, 17, 365–374.PubMedCrossRef
Metadata
Title
Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition
Authors
Aybüke Erol
Chagajeg Soloukey
Bastian Generowicz
Nikki van Dorp
Sebastiaan Koekkoek
Pieter Kruizinga
Borbála Hunyadi
Publication date
01-04-2023
Publisher
Springer US
Published in
Neuroinformatics / Issue 2/2023
Print ISSN: 1539-2791
Electronic ISSN: 1559-0089
DOI
https://doi.org/10.1007/s12021-022-09613-3

Other articles of this Issue 2/2023

Neuroinformatics 2/2023 Go to the issue

Advances in Alzheimer's

Alzheimer's research and care is changing rapidly. Keep up with the latest developments from key international conferences, together with expert insights on how to integrate these advances into practice.

This content is intended for healthcare professionals outside of the UK.

Supported by:
  • Lilly
Developed by: Springer Healthcare IME
Learn more