Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex
Tóm tắt
Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also identifying synergistic connectivity—that is, connectivity that gives rise to information not contained in either site or neuron alone. Here, we measured redundant and synergistic functional connectivity between neurons in the mouse primary auditory cortex during a sound discrimination task. Specifically, we measured directed functional connectivity between neurons simultaneously recorded with calcium imaging. We used Granger Causality as a functional connectivity measure. We then used Partial Information Decomposition to quantify the amount of redundant and synergistic information about the presented sound that is carried by functionally connected or functionally unconnected pairs of neurons. We found that functionally connected pairs present proportionally more redundant information and proportionally less synergistic information about sound than unconnected pairs, suggesting that their functional connectivity is primarily redundant. Further, synergy and redundancy coexisted both when mice made correct or incorrect perceptual discriminations. However, redundancy was much higher (both in absolute terms and in proportion to the total information available in neuron pairs) in correct behavioural choices compared to incorrect ones, whereas synergy was higher in absolute terms but lower in relative terms in correct than in incorrect behavioural choices. Moreover, the proportion of redundancy reliably predicted perceptual discriminations, with the proportion of synergy adding no extra predictive power. These results suggest a crucial contribution of redundancy to correct perceptual discriminations, possibly due to the advantage it offers for information propagation, and also suggest a role of synergy in enhancing information level during correct discriminations.
Tài liệu tham khảo
Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541. https://doi.org/10.1002/mrm.1910340409
Greicius MD, Krasnow B, Reiss AL, Menon V (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA 100(1):253–258. https://doi.org/10.1073/pnas.0135058100
Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA 102(27):9673–9678. https://doi.org/10.1073/pnas.0504136102
Panzeri S, Moroni M, Safaai H, Harvey CD (2022) The structures and functions of correlations in neural population codes. Nat Rev Neurosci 23(9):551–567. https://doi.org/10.1038/s41583-022-00606-4
Engel AK, Gerloff C, Hilgetag CC, Nolte G (2013) Intrinsic coupling modes: multiscale interactions in ongoing brain activity. Neuron 80(4):867–886. https://doi.org/10.1016/j.neuron.2013.09.038
Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, Della Penna S, Duyn JH, Glover GH, Gonzalez-Castillo J, Handwerker DA, Keilholz S, Kiviniemi V, Leopold DA, de Pasquale F, Sporns O, Walter M, Chang C (2013) Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80:360–378. https://doi.org/10.1016/j.neuroimage.2013.05.079
Vincent JL, Patel GH, Fox MD, Snyder AZ, Baker JT, Van Essen DC, Zempel JM, Snyder LH, Corbetta M, Raichle ME (2007) Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447(7140):83–86. https://doi.org/10.1038/nature05758
Gozzi A, Schwarz AJ (2016) Large-scale functional connectivity networks in the rodent brain. Neuroimage 127:496–509. https://doi.org/10.1016/j.neuroimage.2015.12.017
Fox MD, Greicius M (2010) Clinical applications of resting state functional connectivity. Front Syst Neurosci 4:19. https://doi.org/10.3389/fnsys.2010.00019
Bertero A, Liska A, Pagani M, Parolisi R, Masferrer ME, Gritti M, Pedrazzoli M, Galbusera A, Sarica A, Cerasa A, Buffelli M, Tonini R, Buffo A, Gross C, Pasqualetti M, Gozzi A (2018) Autism-associated 16p112 microdeletion impairs prefrontal functional connectivity in mouse and human. Brain 141(7):2055–2065. https://doi.org/10.1093/brain/awy111
Mediano PAM, Rosas FE, Luppi AI, Jensen HJ, Seth AK, Barrett AB, Carhart-Harris RL, Bor D (2022) Greater than the parts: a review of the information decomposition approach to causal emergence. Phil Trans Roy Soc A 380(2227):20210246. https://doi.org/10.1098/rsta.2021.0246
Newman EL, Varley TF, Parakkattu VK, Sherrill SP, Beggs JM (2022) Revealing the dynamics of neural information processing with multivariate information decomposition. Entropy 24(7):930. https://doi.org/10.3390/e24070930
Varley TF, Pope M, Faskowitz J, Sporns O (2023) Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex. Commun Biol 6:451. https://doi.org/10.1038/s42003-023-04843-w
Luppi AI, Mediano PAM, Rosas FE, Holland N, Fryer TD, O’Brien JT, Rowe JB, Menon DK, Bor D, Stamatakis EA (2022) A synergistic core for human brain evolution and cognition. Nat Neurosci 25(6):771–782. https://doi.org/10.1038/s41593-022-01070-0
Varley TF, Sporns O, Schaffelhofer S, Scherberger H, Dann B (2023) Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior. Proc Natl Acad Sci USA 120(2):e2207677120. https://doi.org/10.1073/pnas.2207677120
Sporns O (2022) The complex brain: connectivity, dynamics, information. Trends Cogn Sci 26(12):1066–1067. https://doi.org/10.1016/j.tics.2022.08.002
Valente M, Pica G, Bondanelli G, Moroni M, Runyan CA, Morcos AS, Harvey CD, Panzeri S (2021) Correlations enhance the behavioral readout of neural population activity in association cortex. Nat Neurosci 24(7):975–986. https://doi.org/10.1038/s41593-021-00845-1
Gatica M, Cofre R, Mediano PAM, Rosas FE, Orio P, Diez I, Swinnen SP, Cortes JM (2021) High-Order Interdependencies in the Aging Brain. Brain Connect 11(9):734–744. https://doi.org/10.1089/brain.2020.0982
van den Heuvel MP, Hulshoff Pol HE (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20(8):519–534. https://doi.org/10.1016/j.euroneuro.2010.03.008
Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta M (2013) Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. J Neurosci 33(27):11239–11252. https://doi.org/10.1523/JNEUROSCI.1091-13.2013
Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, Hagmann P (2009) Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci USA 106(6):2035–2040. https://doi.org/10.1073/pnas.0811168106
Lachaux JP, Rodriguez E, Martinerie J, Varela FJ (1999) Measuring phase synchrony in brain signals. Hum Brain Mapp 8(4):194–208. https://doi.org/10.1002/(sici)1097-0193(1999)8:4%3c194::aid-hbm4%3e3.0.co;2-c
Nolte G, Bai O, Wheaton L, Mari Z, Vorbach S, Hallett M (2004) Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin Neurophysiol 115(10):2292–2307. https://doi.org/10.1016/j.clinph.2004.04.029
Sherrill SP, Timme NM, Beggs JM, Newman EL (2021) Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures. PLoS Comput Biol 17(7):e1009196. https://doi.org/10.1371/journal.pcbi.1009196
Seth AK, Barrett AB, Barnett L (2015) Granger causality analysis in neuroscience and neuroimaging. J Neurosci 35(8):3293–3297. https://doi.org/10.1523/JNEUROSCI.4399-14.2015
Sheikhattar A, Miran S, Liu J, Fritz JB, Shamma SA, Kanold PO, Babadi B (2018) Extracting neuronal functional network dynamics via adaptive Granger causality analysis. Proc Natl Acad Sci USA 115(17):E3869–E3878. https://doi.org/10.1073/pnas.1718154115
Schreiber T (2000) Measuring information transfer. Phys Rev Lett 85(2):461–464. https://doi.org/10.1103/PhysRevLett.85.461
Celotto M, Bím J, Tlaie A, De Feo V, Lemke S, Chicharro D, Nili H, Bieler M, Hanganu-Opatz IL, Donner TH, Brovelli A, Panzeri S (2023) An information-theoretic quantification of the content of communication between brain regions. Adv Neural Inf Process Syst (NeurIPS) 37 (in press). https://neurips.cc/virtual/2023/poster/70605.
Besserve M, Lowe SC, Logothetis NK, Schölkopf B, Panzeri S (2015) Shifts of gamma phase across primary visual cortical sites reflect dynamic stimulus-modulated information transfer. PLOS Biol 13(9):e1002257. https://doi.org/10.1371/journal.pbio.1002257
Williams PL, Beer RD (2010) Nonnegative decomposition of multivariate information. https://doi.org/10.48550/arXiv.1004.2515
Wibral M, Priesemann V, Kay JW, Lizier JT, Phillips WA (2017) Partial information decomposition as a unified approach to the specification of neural goal functions. Brain Cogn 112:25–38. https://doi.org/10.1016/j.bandc.2015.09.004
Schneidman E, Bialek W, Berry MJ (2003) Synergy, redundancy, and independence in population codes. J Neurosci 23(37):11539–11553. https://doi.org/10.1523/JNEUROSCI.23-37-11539.2003
Nigam S, Pojoga S, Dragoi V (2019) Synergistic coding of visual information in columnar networks. Neuron 104(2):402–411. https://doi.org/10.1016/j.neuron.2019.07.006
Francis NA, Mukherjee S, Koçillari L, Panzeri S, Babadi B, Kanold PO (2022) Sequential transmission of task-relevant information in cortical neuronal networks. Cell Rep 39(9):110878. https://doi.org/10.1016/j.celrep.2022.110878
Koçillari L, Celotto M, Francis NA, Mukherjee S, Babadi B, Kanold PO, Panzeri S. (2023) Measuring Stimulus-Related Redundant and Synergistic Functional Connectivity with Single Cell Resolution in Auditory Cortex. In: Liu F, Zhang Y, Kuai H, Stephen EP and Wang H (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science. 13974. Springer, Cham., pp. 45–56 https://doi.org/10.1007/978-3-031-43075-6_5.
Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
Quian Quiroga R, Panzeri S (2009) Extracting information from neuronal populations: information theory and decoding approaches. Nat Rev Neurosci 10(3):173–185. https://doi.org/10.1038/nrn2578
Magri C, Whittingstall K, Singh V, Logothetis NK, Panzeri S (2009) A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings. BMC Neurosci 10:81. https://doi.org/10.1186/1471-2202-10-81
Panzeri S, Senatore R, Montemurro MA, Petersen RS (2007) Correcting for the sampling bias problem in spike train information measures. J Neurophysiol 98(3):1064–1072. https://doi.org/10.1152/jn.00559.2007
Pica G, Piasini E, Safaai H, Runyan CA, Diamond ME, Fellin T, Kayser C, Harvey CD, Panzeri S. (2017) Quantifying how much sensory information in a neural code is relevant for behavior. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S and Garnett R (eds) Adv Neural Inf Process Syst (NeurIPS). 30. Curran Associates, Inc, pp. 3689–3699.
Panzeri S, Harvey CD, Piasini E, Latham PE, Fellin T (2017) Cracking the neural code for sensory perception by combining statistics, intervention, and behavior. Neuron 93(3):491–507. https://doi.org/10.1016/j.neuron.2016.12.036
McGill WJ (1954) Multivariate information transmission. Psychometrika 19(2):97–116. https://doi.org/10.1007/BF02289159
Griffith V, Koch C. (2014) Quantifying Synergistic Mutual Information. In: Prokopenko M (eds) Guided self-organization: inception, emergence, complexity and computation. 9. Springer, Berlin, Heidelberg, pp. 159–190 https://doi.org/10.1007/978-3-642-53734-9_6.
Bertschinger N, Rauh J, Olbrich E, Jost J, Ay N (2014) Quantifying unique information. Entropy 16(4):2161–2183. https://doi.org/10.3390/e16042161
Makkeh A, Theis DO, Vicente R (2018) BROJA-2PID: a robust estimator for bivariate partial information decomposition. Entropy 20(4):271. https://doi.org/10.3390/e20040271
Barnett L, Barrett AB, Seth AK (2009) Granger causality and transfer entropy are equivalent for gaussian variables. Phys Rev Lett 103(23):238701. https://doi.org/10.1103/PhysRevLett.103.238701
Williams PL, Beer RD (2011) Generalized Measures of Information Transfer. https://doi.org/10.48550/arXiv.1102.1507.
Luna R, Hernández A, Brody CD, Romo R (2005) Neural codes for perceptual discrimination in primary somatosensory cortex. Nat Neurosci 8(9):1210–1219. https://doi.org/10.1038/nn1513
Rigotti M, Barak O, Warden MR, Wang X-J, Daw ND, Miller EK, Fusi S (2013) The importance of mixed selectivity in complex cognitive tasks. Nature 497:585–590. https://doi.org/10.1038/nature12160
Zuo Y, Safaai H, Notaro G, Mazzoni A, Panzeri S, Diamond ME (2015) Complementary contributions of spike timing and spike rate to perceptual decisions in rat S1 and S2 cortex. Curr Biol 25(3):357–363. https://doi.org/10.1016/j.cub.2014.11.065
Runyan CA, Piasini E, Panzeri S, Harvey CD (2017) Distinct timescales of population coding across cortex. Nature 548(7665):92–96. https://doi.org/10.1038/nature23020
Kira S, Safaai H, Morcos AS, Panzeri S, Harvey CD (2023) A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions. Nat Commun 14(1):2121. https://doi.org/10.1038/s41467-023-37804-2
Tononi G, Sporns O, Edelman GM (1994) A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc Natl Acad Sci USA 91(11):5033–5037. https://doi.org/10.1073/pnas.91.11.5033
Averbeck BB, Latham PE, Pouget A (2006) Neural correlations, population coding and computation. Nat Rev Neurosci 7(5):358–366. https://doi.org/10.1038/nrn1888
Celotto M, Lemke S, Panzeri S (2022) Inferring the temporal evolution of synaptic weights from dynamic functional connectivity. Brain Inf 9:28. https://doi.org/10.1186/s40708-022-00178-0
Rocchi F, Canella C, Noei S, Gutierrez-Barragan D, Coletta L, Galbusera A, Stuefer A, Vassanelli S, Pasqualetti M, Iurilli G, Panzeri S, Gozzi A (2022) Increased fMRI connectivity upon chemogenetic inhibition of the mouse prefrontal cortex. Nat Commun 13(1):1056. https://doi.org/10.1038/s41467-022-28591-3
Makkeh A, Gutknecht AJ, Wibral M (2021) Introducing a differentiable measure of pointwise shared information. Phys Rev E 103(3):032149. https://doi.org/10.1103/PhysRevE.103.032149
Finn C, Lizier JT (2018) Pointwise partial information decomposition using the specificity and ambiguity lattices. Entropy 20(4):297. https://doi.org/10.3390/e20040297
Kolchinsky A (2022) A novel approach to the partial information decomposition. Entropy 24(3):403. https://doi.org/10.3390/e24030403
Ince RAA (2017) Measuring multivariate redundant information with pointwise common change in surprisal. Entropy 19(7):318. https://doi.org/10.3390/e19070318
Panzeri S, Schultz SR, Treves A, Rolls ET (1999) Correlations and the encoding of information in the nervous system. Proc Biol Sci 266(1423):1001–1012. https://doi.org/10.1098/rspb.1999.0736
Pola G, Thiele A, Hoffmann KP, Panzeri S (2003) An exact method to quantify the information transmitted by different mechanisms of correlational coding. Network 14(1):35–60. https://doi.org/10.1088/0954-898x/14/1/303
Latham PE, Nirenberg S (2005) Synergy, redundancy, and independence in population codes, revisited. J Neurosci 25(21):5195–5206. https://doi.org/10.1523/JNEUROSCI.5319-04.2005