Thalamocortical interactions shape hierarchical neural variability during stimulus perception.

[en] The brain is organized hierarchically to process sensory signals. But, how do functional connections within and across areas contribute to this hierarchical order? We addressed this problem in the thalamocortical network, while monkeys detected vibrotactile stimulus. During this task, we quantified neural variability and directed functional connectivity in simultaneously recorded neurons sharing the cutaneous receptive field within and across VPL and areas 3b and 1. Before stimulus onset, VPL and area 3b exhibited similar fast dynamics while area 1 showed slower timescales. During the stimulus presence, inter-trial neural variability increased along the network VPL-3b-1 while VPL established two main feedforward pathways with areas 3b and 1 to process the stimulus. This lower variability of VPL and area 3b was found to regulate feedforward thalamocortical pathways. Instead, intra-cortical interactions were only anticipated by higher intrinsic timescales in area 1. Overall, our results provide evidence of hierarchical functional roles along the thalamocortical network.

Disciplines :

Neurosciences & behavior
Life sciences: Multidisciplinary, general & others

Author, co-author :

Tauste Campo, Adrià ; Computational Biology and Complex Systems group, Department of Physics, Universitat Politècnica de Catalunya, Avinguda Dr. Marañón, 44-50, 08028 Barcelona, Catalonia, Spain

Zainos, Antonio ; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico

Vázquez, Yuriria ; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico

Adell Segarra, Raul ; Computational Biology and Complex Systems group, Department of Physics, Universitat Politècnica de Catalunya, Avinguda Dr. Marañón, 44-50, 08028 Barcelona, Catalonia, Spain

Álvarez, Manuel ; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico

Deco, Gustavo ; Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias I Fargas 25-27, 08005 Barcelona, Catalonia, Spain ; Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, 08010 Barcelona, Catalonia, Spain

Díaz, Héctor ; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico

Parra Sánchez, Sergio ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Brain-Inspired Computing

Romo, Ranulfo ; El Colegio Nacional, Mexico City 06020, Mexico

Rossi-Pool, Román ; Instituto de Fisiología Celular-Neurociencias, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico ; Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Mexico City, Mexico

Language :

English

Title :

Thalamocortical interactions shape hierarchical neural variability during stimulus perception.

Publication date :

19 July 2024

Journal title :

iScience

eISSN :

2589-0042

Publisher :

Elsevier Inc., United States

Volume :

Issue :

Pages :

110065

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S2589004224012902?httpAccept=text/xml

Funders :

UNAM - Universidad Nacional Autónoma de México
Bial Foundation
MICINN - Ministerio de Ciencia, Innovación y Universidades
CONACYT - Consejo Nacional de Ciencia y Tecnología
IBRO - International Brain Research Organization

Funding text :

A.T.C. was supported by the Spanish national research project (ref. PID2020-119072RA-I00/AEI/10.13039/501100011033 ) funded by the Spanish Ministry of Science, Innovation, and Universities ( MCIU ) and the Bial Foundation grant 106/18 . G.D. was supported by the Spanish national research project (ref. PID2019-105772GB-I00/AEI/10.13039/501100011033 ) funded by the Spanish Ministry of Science, Innovation, and Universities ( MCIU ). This work was supported by grants PAPIITIN205022 from the Direcci\u00F3n de Asuntos del Personal Acad\u00E9mico de la Universidad Nacional Aut\u00F3noma de M\u00E9xico (to R.R.-P.); CONAHCYT-319347 (to R.R.P.) from Consejo Nacional de Ciencia y Tecnolog\u00EDa ; and IBRO Early Career Award 2022 (to R.R.-P.) from International Brain Research Association.A.T.C. was supported by the Spanish national research project (ref. PID2020-119072RA-I00/AEI/10.13039/501100011033) funded by the Spanish Ministry of Science, Innovation, and Universities (MCIU) and the Bial Foundation grant 106/18. G.D. was supported by the Spanish national research project (ref. PID2019-105772GB-I00/AEI/10.13039/501100011033) funded by the Spanish Ministry of Science, Innovation, and Universities (MCIU). This work was supported by grants PAPIITIN205022 from the Direcci\u00F3n de Asuntos del Personal Acad\u00E9mico de la Universidad Nacional Aut\u00F3noma de M\u00E9xico (to R.R.-P.); CONAHCYT-319347 (to R.R.P.) from Consejo Nacional de Ciencia y Tecnolog\u00EDa; and IBRO Early Career Award 2022 (to R.R.-P.) from International Brain Research Association. R.R. directed the experiments; A.Z. Y.V. M.A. H.D. and R.R. were responsible for the neural recordings and animal training; R.R. R.R.-P. A.T.C. and G.D. designed study and provided financial support; A.T.C. R.A.S. R.R.-P. and S.P. were responsible for the data analysis and wrote manuscript. All authors have no relevant financial or non-financial interests to disclose.

Available on ORBi :

since 29 January 2025

Statistics

Number of views

2 (2 by ULiège)

Number of downloads

0 (0 by ULiège)

More statistics

Scopus citations^®

Scopus citations^®
without self-citations

OpenCitations

OpenAlex citations

Bibliography

Wang, X.-J., Macroscopic gradients of synaptic excitation and inhibition in the neocortex. Nat. Rev. Neurosci. 21 (2020), 169–178.
Harris, J.A., Mihalas, S., Hirokawa, K.E., Whitesell, J.D., Choi, H., Bernard, A., Bohn, P., Caldejon, S., Casal, L., Cho, A., et al. Hierarchical organization of cortical and thalamic connectivity. Nature 575 (2019), 195–202.
Romo, R., Rossi-Pool, R., Turning Touch into Perception. Neuron 105 (2020), 16–33.
Sherman, S.M., Guillery, R.W., The role of the thalamus in the flow of information to the cortex. Philos. Trans. R. Soc. Lond. B Biol. Sci. 357 (2002), 1695–1708.
Halassa, M.M., Sherman, S.M., Thalamocortical Circuit Motifs: A General Framework. Neuron 103 (2019), 762–770.
Shepherd, G.M.G., Yamawaki, N., Untangling the cortico-thalamo-cortical loop: cellular pieces of a knotty circuit puzzle. Nat. Rev. Neurosci. 22 (2021), 389–406.
O'Reilly, C., Iavarone, E., Yi, J., Hill, S.L., Rodent somatosensory thalamocortical circuitry: Neurons, synapses, and connectivity. Neurosci. Biobehav. Rev. 126 (2021), 213–235.
Alitto, H.J., Usrey, W.M., Corticothalamic feedback and sensory processing. Curr. Opin. Neurobiol. 13 (2003), 440–445.
Crandall, S.R., Cruikshank, S.J., Connors, B.W., A Corticothalamic Switch: Controlling the Thalamus with Dynamic Synapses. Neuron 86 (2015), 768–782.
Briggs, F., Usrey, W.M., Corticogeniculate feedback and visual processing in the primate. J. Physiol. 589 (2011), 33–40.
Sherman, S.M., Thalamus plays a central role in ongoing cortical functioning. Nat. Neurosci. 19 (2016), 533–541.
Sherman, S.M., The thalamus is more than just a relay. Curr. Opin. Neurobiol. 17 (2007), 417–422.
Reinhold, K., Lien, A.D., Scanziani, M., Distinct recurrent versus afferent dynamics in cortical visual processing. Nat. Neurosci. 18 (2015), 1789–1797.
Yu, J., Gutnisky, D.A., Hires, S.A., Svoboda, K., Layer 4 fast-spiking interneurons filter thalamocortical signals during active somatosensation. Nat. Neurosci. 19 (2016), 1647–1657.
Whitmire, C.J., Liew, Y.J., Stanley, G.B., Thalamic state influences timing precision in the thalamocortical circuit. J. Neurophysiol. 125 (2021), 1833–1850.
Wright, N.C., Borden, P.Y., Liew, Y.J., Bolus, M.F., Stoy, W.M., Forest, C.R., Stanley, G.B., Rapid Cortical Adaptation and the Role of Thalamic Synchrony during Wakefulness. J. Neurosci. 41 (2021), 5421–5439.
Bruno, R.M., Sakmann, B., Cortex is driven by weak but synchronously active thalamocortical synapses. Science 312 (2006), 1622–1627.
Haegens, S., Vázquez, Y., Zainos, A., Alvarez, M., Jensen, O., Romo, R., Thalamocortical rhythms during a vibrotactile detection task. Proc. Natl. Acad. Sci. USA 111 (2014), E1797–E1805.
Bastos, A.M., Briggs, F., Alitto, H.J., Mangun, G.R., Usrey, W.M., Simultaneous recordings from the primary visual cortex and lateral geniculate nucleus reveal rhythmic interactions and a cortical source for γ-band oscillations. J. Neurosci. 34 (2014), 7639–7644.
Tauste Campo, A., Vázquez, Y., Álvarez, M., Zainos, A., Rossi-Pool, R., Deco, G., Romo, R., Feed-forward information and zero-lag synchronization in the sensory thalamocortical circuit are modulated during stimulus perception. Proc. Natl. Acad. Sci. USA 116 (2019), 7513–7522.
Chaudhuri, R., Knoblauch, K., Gariel, M.-A., Kennedy, H., Wang, X.-J., A Large-Scale Circuit Mechanism for Hierarchical Dynamical Processing in the Primate Cortex. Neuron 88 (2015), 419–431.
Ashaber, M., Pálfi, E., Friedman, R.M., Palmer, C., Jákli, B., Chen, L.M., Kántor, O., Roe, A.W., Négyessy, L., Connectivity of somatosensory cortical area 1 forms an anatomical substrate for the emergence of multifinger receptive fields and complex feature selectivity in the squirrel monkey (Saimiri sciureus). J. Comp. Neurol. 522 (2014), 1769–1785.
Saadon-Grosman, N., Arzy, S., Loewenstein, Y., Hierarchical cortical gradients in somatosensory processing. Neuroimage, 222, 2020, 117257.
Song, Y., Su, Q., Yang, Q., Zhao, R., Yin, G., Qin, W., Iannetti, G.D., Yu, C., Liang, M., Feedforward and feedback pathways of nociceptive and tactile processing in human somatosensory system: A study of dynamic causal modeling of fMRI data. Neuroimage, 234, 2021, 117957.
Vázquez, Y., Salinas, E., Romo, R., Transformation of the neural code for tactile detection from thalamus to cortex. Proc. Natl. Acad. Sci. USA 110 (2013), E2635–E2644.
Song, W., Semework, M., Tactile representation in somatosensory thalamus (VPL) and cortex (S1) of awake primate and the plasticity induced by VPL neuroprosthetic stimulation. Brain Res. 1625 (2015), 301–313.
Kaas, J.H., Nelson, R.J., Sur, M., Lin, C.-S., Merzenich, M.M., Multiple Representations of the Body Within the Primary Somatosensory Cortex of Primates. Science 204 (1979), 521–523.
Sur, M., Wall, J.T., Kaas, J.H., Modular distribution of neurons with slowly adapting and rapidly adapting responses in area 3b of somatosensory cortex in monkeys. J. Neurophysiol. 51 (1984), 724–744.
Simoncelli, E.P., Paninski, L., Pillow, J., Schwartz, O., Characterization of neural responses with stochastic stimuli. Cognit. Neurosci., 3, 2004, 1.
Goris, R.L.T., Movshon, J.A., Simoncelli, E.P., Partitioning neuronal variability. Nat. Neurosci. 17 (2014), 858–865.
Rajdl, K., Lansky, P., Kostal, L., Fano Factor: A Potentially Useful Information. Front. Comput. Neurosci., 14, 2020, 569049.
Murray, J.D., Bernacchia, A., Freedman, D.J., Romo, R., Wallis, J.D., Cai, X., Padoa-Schioppa, C., Pasternak, T., Seo, H., Lee, D., Wang, X.J., A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 17 (2014), 1661–1663.
Rossi-Pool, R., Zainos, A., Alvarez, M., Parra, S., Zizumbo, J., Romo, R., Invariant timescale hierarchy across the cortical somatosensory network. Proc. Natl. Acad. Sci. USA, 118, 2021, e2021843118.
Siegle, J.H., Jia, X., Durand, S., Gale, S., Bennett, C., Graddis, N., Heller, G., Ramirez, T.K., Choi, H., Luviano, J.A., et al. Survey of spiking in the mouse visual system reveals functional hierarchy. Nature 592 (2021), 86–92.
de Lafuente, V., Romo, R., Neuronal correlates of subjective sensory experience. Nat. Neurosci. 8 (2005), 1698–1703.
Churchland, M.M., Yu, B.M., Cunningham, J.P., Sugrue, L.P., Cohen, M.R., Corrado, G.S., Newsome, W.T., Clark, A.M., Hosseini, P., Scott, B.B., et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat. Neurosci. 13 (2010), 369–378.
Charles, A.S., Park, M., Weller, J.P., Horwitz, G.D., Pillow, J.W., Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability. Neural Comput. 30 (2018), 1012–1045.
Kara, P., Reinagel, P., Reid, R.C., Low response variability in simultaneously recorded retinal, thalamic, and cortical neurons. Neuron 27 (2000), 635–646.
Ecker, A.S., Berens, P., Cotton, R.J., Subramaniyan, M., Denfield, G.H., Cadwell, C.R., Smirnakis, S.M., Bethge, M., Tolias, A.S., State Dependence of Noise Correlations in Macaque Primary Visual Cortex. Neuron 82 (2014), 235–248.
Bayones, L., Zainos, A., Manuel Alvarez, J., Romo, R., Franci, A., Rossi-Pool, R., Orthogonality of sensory and contextual categorical dynamics embedded in a continuum of responses from the second somatosensory cortex. Preprint at bioRxiv, 2023, 10.1101/2023.09.22.559070.
Gardner, R.J., Hermansen, E., Pachitariu, M., Burak, Y., Baas, N.A., Dunn, B.A., Moser, M.-B., Moser, E.I., Toroidal topology of population activity in grid cells. Nature 602 (2022), 123–128.
Parra, S., Díaz, H., Zainos, A., Alvarez, M., Zizumbo, J., Rivera-Yoshida, N., Pujalte, S., Bayones, L., Romo, R., Rossi-Pool, R., Hierarchical unimodal processing within the primary somatosensory cortex during a bimodal detection task. Proc. Natl. Acad. Sci. USA, 119, 2022, e2213847119, 10.1073/pnas.2213847119.
Tauste Campo, A., Martinez-Garcia, M., Nácher, V., Luna, R., Romo, R., Deco, G., Task-driven intra- and interarea communications in primate cerebral cortex. Proc. Natl. Acad. Sci. USA 112 (2015), 4761–4766.
Carnevale, F., de Lafuente, V., Romo, R., Barak, O., Parga, N., Dynamic Control of Response Criterion in Premotor Cortex during Perceptual Detection under Temporal Uncertainty. Neuron 86 (2015), 1067–1077.
Joglekar, M.R., Mejias, J.F., Yang, G.R., Wang, X.-J., Inter-areal Balanced Amplification Enhances Signal Propagation in a Large-Scale Circuit Model of the Primate Cortex. Neuron 98 (2018), 222–234.e8.
Luna, R., Hernández, A., Brody, C.D., Romo, R., Neural codes for perceptual discrimination in primary somatosensory cortex. Nat. Neurosci. 8 (2005), 1210–1219.
Mountcastle, V.B., Steinmetz, M.A., Romo, R., Frequency discrimination in the sense of flutter: psychophysical measurements correlated with postcentral events in behaving monkeys. J. Neurosci. 10 (1990), 3032–3044.
de Lafuente, V., Romo, R., Neural correlate of subjective sensory experience gradually builds up across cortical areas. Proc. Natl. Acad. Sci. USA 103 (2006), 14266–14271.
Salinas, E., Hernandez, A., Zainos, A., Romo, R., Periodicity and Firing Rate As Candidate Neural Codes for the Frequency of Vibrotactile Stimuli. J. Neurosci. 20 (2000), 5503–5515.
Mir, Y., Zalányi, L., Pálfi, E., Ashaber, M., Roe, A.W., Friedman, R.M., Négyessy, L., Modular Organization of Signal Transmission in Primate Somatosensory Cortex. Front. Neuroanat., 16, 2022, 915238.
Mainen, Z.F., Sejnowski, T.J., Reliability of spike timing in neocortical neurons. Science 268 (1995), 1503–1506.
Gilson, M., Tauste Campo, A., Chen, X., Thiele, A., Deco, G., Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data. Netw. Neurosci. 1 (2018), 357–380.
Tauste Campo, A., Inferring neural information flow from spiking data. Comput. Struct. Biotechnol. J. 18 (2020), 2699–2708.
Zandvakili, A., Kohn, A., Coordinated Neuronal Activity Enhances Corticocortical Communication. Neuron 87 (2015), 827–839.
Cohen, M.R., Kohn, A., Measuring and interpreting neuronal correlations. Nat. Neurosci. 14 (2011), 811–819.
Semedo, J.D., Zandvakili, A., Machens, C.K., Yu, B.M., Kohn, A., Cortical Areas Interact through a Communication Subspace. Neuron 102 (2019), 249–259.e4.
Semedo, J.D., Jasper, A.I., Zandvakili, A., Krishna, A., Aschner, A., Machens, C.K., Kohn, A., Yu, B.M., Feedforward and feedback interactions between visual cortical areas use different population activity patterns. Nat. Commun., 13, 2022, 1099.
Ferro, D., van Kempen, J., Boyd, M., Panzeri, S., Thiele, A., Directed information exchange between cortical layers in macaque V1 and V4 and its modulation by selective attention. Proc. Natl. Acad. Sci. USA, 118, 2021, e2022097118.
Yates, J.L., Park, I.M., Katz, L.N., Pillow, J.W., Huk, A.C., Functional dissection of signal and noise in MT and LIP during decision-making. Nat. Neurosci. 20 (2017), 1285–1292.
Keeley, S.L., Zoltowski, D.M., Aoi, M.C., Pillow, J.W., Modeling statistical dependencies in multi-region spike train data. Curr. Opin. Neurobiol. 65 (2020), 194–202.
Yang, G.R., Wang, X.-J., Artificial Neural Networks for Neuroscientists: A Primer. Neuron 107 (2020), 1048–1070.
Yamins, D.L.K., DiCarlo, J.J., Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19 (2016), 356–365.
Steinmetz, N.A., Aydin, C., Lebedeva, A., Okun, M., Pachitariu, M., Bauza, M., Beau, M., Bhagat, J., Böhm, C., Broux, M., et al. Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings. Science, 372, 2021, eabf4588.
Vázquez, Y., Zainos, A., Alvarez, M., Salinas, E., Romo, R., Neural coding and perceptual detection in the primate somatosensory thalamus. Proc. Natl. Acad. Sci. USA 109 (2012), 15006–15011.
Good, P.I., Permutation, Parametric and Bootstrap Tests of Hypotheses. 2005, Springer Science & Business Media.
Shadlen, M.N., Newsome, W.T., The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding. J. Neurosci. 18 (1998), 3870–3896.
AdTau. AdTau/DI-Inference v1.0. Zenodo https://doi.org/10.5281/ZENODO.4059445, 2020.
Winkler, A.M., Ridgway, G.R., Webster, M.A., Smith, S.M., Nichols, T.E., Permutation inference for the general linear model. Neuroimage 92 (2014), 381–397.
Cohen, J., Statistical Power Analysis for the Behavioral Sciences. 2013, Academic press.
McInnes, L., Healy, J., Saul, N., Großberger, L., UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw., 3, 2018, 861.