Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling

PLoS Computational Biology

Volumn 11, Issue 8, 2015, Pages

  Ponce Alvarez, Adrián ^a   He, Biyu J ^b   Hagmann, Patric ^c,d   Deco, Gustavo ^a  

^a UNIVERSITAT POMPEU FABRA   (Spain)

^b NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE   (United States)

^c LAUSANNE UNIVERSITY HOSPITAL   (Switzerland)

^d EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMICS; ENTROPY; FUNCTIONAL NEUROIMAGING; MAGNETIC RESONANCE IMAGING;

ACTIVITY SPACES; BRAIN DYNAMICS; BRAIN SIGNALS; CONDITION; CORTICAL ACTIVITY; EXPERIMENTAL CONDITIONS; EXTERNAL INPUT; FUNCTIONAL MAGNETIC RESONANCE IMAGING; TASK-DRIVEN; WHOLE BRAINS;

BRAIN;

ADULT; ARTICLE; BOLD SIGNAL; BRAIN CORTEX; BRAIN FUNCTION; BRAIN REGION; CONTROLLED STUDY; ENTROPY; EVOKED RESPONSE; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; HUMAN EXPERIMENT; MALE; MENTAL TASK; NERVE CELL NETWORK; SYNAPTIC TRANSMISSION; TASK PERFORMANCE; ADOLESCENT; BIOLOGICAL MODEL; BIOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES; REST; YOUNG ADULT;

ADOLESCENT; ADULT; CEREBRAL CORTEX; COMPUTATIONAL BIOLOGY; ENTROPY; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; MODELS, NEUROLOGICAL; REST; TASK PERFORMANCE AND ANALYSIS; YOUNG ADULT;

EID: 84940755087     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004445     Document Type: Article

References (50)

1. Monier C, Chavane F, Baudot P, Graham LJ, Fregnac Y, Orientation and direction selectivity of synaptic inputs in visual cortical neurons: a diversity of combinations produces spike tuning. Neuron. 2003; 37: 663–680. 12597863
2. Churchland MM, Yu BM, Cunningham JP, Sugrue LP, Cohen MR, Corrado GS, et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat Neurosci. 2010; 13(3): 369–378. doi: 10.1038/nn.2501 20173745
3. He BJ, Spontaneous and task-evoked brain activity negatively interact. J Neurosci. 2013; 33(11): 4672–4682. doi: 10.1523/JNEUROSCI.2922-12.2013 23486941
4. He BJ, Zempel JM, Average is Optimal: An Inverted-U Relationship between Trial-to-Trial Brain Activity and Behavioral Performance. PLoS Comput Biol. 2013; 9: e1003348. doi: 10.1371/journal.pcbi.1003348 24244146
5. Ponce-Alvarez A, Thiele A, Albright TD, Stoner GR, Deco G, Stimulus-dependent variability and noise correlations in cortical MT neurons. Proc Natl Acad Sci USA. 2013; 110: 13162–13167. doi: 10.1073/pnas.1300098110 23878209
6. He BJ, Scale-Free Properties of the Functional Magnetic Resonance Imaging Signal during Rest and Task. J Neurosci. 2011; 31: 13786–13795. doi: 10.1523/JNEUROSCI.2111-11.2011 21957241
7. He BJ, Zempel JM, Snyder AZ, Raichle ME, The temporal structures and functional significance of scale-free brain activity. Neuron. 2010; 66: 353–369. 20471349
8. Poulet JF, Petersen CC, Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice. Nature. 2008; 454: 881–885. doi: 10.1038/nature07150 18633351
9. Oram MV, Visual stimulation decorrelates neuronal activity. J Neurophysiol. 2011; 105: 942–957. doi: 10.1152/jn.00711.2009 20861439
10. Luczak A, Bartho P, Harris KD, Spontaneous events outline the realm of possible sensory responses in neocortical populations. Neuron. 2009; 62: 413–425. 19447096
11. Deco G, Ponce-Alvarez A, Hagmann P, Romani GL, Mantini D, Corbetta M, How local excitation—inhibition ratio impacts the whole brain dynamics. J Neurosci. 2014; 34(23): 7886–7898. doi: 10.1523/JNEUROSCI.5068-13.2014 24899711
12. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey C, Wedeen VJ, et al. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008; 6: e159. doi: 10.1371/journal.pbio.0060159 18597554
13. Wong K, Wang X, A recurrent network mechanism of time integration in perceptual decisions. J Neurosci. 2006; 26: 1314–1328. 16436619
14. Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A, Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001; 412: 150–157. 11449264
15. Thomsen K, Offenhauser N, Lauritzen M, Principal neuron spiking: neither necessary nor sufficient for cerebral blood flow in rat cerebellum. J Physiol. 2004; 560: 181–189. 15272036
16. Viswanathan A, Freeman RD, Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity. Nat Neurosci. 2007; 10: 1308–1312. 17828254
17. Rauch A, Rainer G, Logothetis NK, The effect of a serotonin-induced dissociation between spiking and perisynaptic activity on BOLD functional MRI. Proc Natl Acad Sci USA. 2008; 105: 6759–6764. doi: 10.1073/pnas.0800312105 18456837
18. Niedermeyer E, Lopes da Silva F, Electroencephalography: basic principles, clinical applications, and related fields. Philadelphia, PA: Lippincott Williams & Wilkins; 2005.
19. Friston K, Harrison L, Penny W, Dynamic causal modelling. Neuroimage. 2003; 19: 1273–1302. 12948688
20. Robinson PA, Drysdale PM, Van der Merwe H, Kyriakou E, Rigozzi MK, Germanoska B, et al. BOLD responses to stimuli: Dependence on frequency, stimulus form, amplitude, and repetition rate. Neuroimage. 2006; 31: 585–599. 16466935
21. Deco G, Ponce-Alvarez A, Mantini D, Romani GL, Hagmann P, Corbetta M, Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. J Neurosci. 2013; 33: 11239–11252. doi: 10.1523/JNEUROSCI.1091-13.2013 23825427
22. Garrett DD, Kovacevic N, McIntosh AR, Grady CL, Blood oxygen level dependent signal variability is more than just noise. J Neurosci. 2010; 30: 4914–4921. doi: 10.1523/JNEUROSCI.5166-09.2010 20371811
23. Garrett DD, Kovacevic N, McIntosh AR, Grady CL, The importance of being variable. J Neurosci. 2011; 31: 4496–4503. doi: 10.1523/JNEUROSCI.5641-10.2011 21430150
24. Yao Y, Lu WL, Xu B, Li CB, Lin CP, Waxman D, et al. The increase of the functional entropy of the human brain with age. Nat Sci Rep. 2013; 3: 2853.
25. Kenet T, Bibitchkov D, Tsodyks M, Grinvald A, Arieli A, Spontaneously emerging cortical representations of visual attributes. Nature. 2003; 425: 954–956. 14586468
26. Vincent J, Patel G, Fox M, Snyder A, Baker J, Van Essen D, et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 2007; 447: 83–86. 17476267
27. McIntosh AR, Kovacevic N, Lippe S, Garrett D, Grady C, Jirsa V, The development of a noisy brain. Arch Ital Biol. 2010; 148: 323–337. 21175017
28. Norman K, Polyn SM, Detre G, Haxby JV, Beyond mind-reading: multi-voxel pattern analysis of fmri data. Trends Cogn Sci. 2006; 10.
29. Haynes J, Rees G, Decoding mental states from brain activity in humans. Nat Rev Neurosci. 2006; 7: 523–534. 16791142
30. Pereira F, Mitchell T, Botvinick M, Machine learning classifiers and fMRI: a tutorial overview. Neuroimage. 2009; 45: S199–209. doi: 10.1016/j.neuroimage.2008.11.007 19070668
31. Honey C, Kötter R, Breakspear M, Sporns O, Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci USA. 2007; 104: 10240–10245. 17548818
32. Magri C, Schridde U, Murayama Y, Panzeri S, Logothetis NK, The amplitude and timing of the BOLD signal reflects the relationship between local field potential power at different frequencies. J Neurosci. 2012; 32: 1395–1407. doi: 10.1523/JNEUROSCI.3985-11.2012 22279224
33. Deco G, Hugues E, Neural network mechanisms underlying stimulus driven variability reduction. PLoS Comput Biol. 2012; 8: e1002395. doi: 10.1371/journal.pcbi.1002395 22479168
34. Litwin-Kumar A, Doiron B, Slow dynamics and high variability in balanced cortical networks with clustered connections. Nat Neurosci. 2012; 15: 1498–1505. doi: 10.1038/nn.3220 23001062
35. Goldberg JA, Rokni U, Sompolinsky H, Patterns of ongoing activity and the functional architecture of the primary visual cortex. Neuron. 2004; 42: 489–500. 15134644
36. Blumenfeld B, Bibitchkov D, Tsodyks M, Neural network model of the primary visual cortex: From fucntional architecture to lateral connectivity and back. J Comput Neurosci. 2006; 20: 219–241. 16699843
37. He BJ, Snyder AZ, Vincent JL, Epstein A, Shulman GL, Corbetta M, Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron. 2007; 53: 905–918. 17359924
38. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, et al. Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci USA. 2009; 106: 13040–13045. doi: 10.1073/pnas.0905267106 19620724
39. Betti V, Della Penna S, de Pasquale F, Mantini D, Marzetti L, Romani GL, et al. Natural Scenes Viewing Alters the Dynamics of Functional Connectivity in the Human Brain. Neuron. 2013; 79(4): 782–797. 23891400
40. Cole MW, Bassett DS, Power JD, Braver TS, Petersen SE, Intrinsic and task-evoked network architectures of the human brain. Neuron. 2014; 83(1): 238–251. 24991964
41. Esposito F, Aragri A, Latorre V, Popolizio T, Scarabino T, Cirillo S, et al. Does the default-mode functional connectivity of the brain correlate with working-memory performances? Arch Ital Biol. 2009; 147: 11–20. 19678593
42. DeSalvo MN, Douw L, Takaya S, Liu H, Stufflebeam SM, Task-dependent reorganization of functional connectivity networks during visual semantic decision making. Brain and Behavior. 2014; 4(6): 877–885. doi: 10.1002/brb3.286 25365802
43. Hermundstad AM, Brown KS, Bassett DS, Aminoff EM, Frithsen A, Johnson A, et al. Structurally-Constrained Relationships between Cognitive States in the Human Brain. PLoS Comput Biol. 2014; 10(5): e1003591. doi: 10.1371/journal.pcbi.1003591 24830758
44. Chadderdon GL, Sporns O, A large-scale neurocomputational model of task-oriented behavior selection and working memory in prefrontal cortex. J Cogn Neurosci. 2006: 18(2): 242–257. 16494684
45. Hellyer PJ, Shanahan M, Scott G, Wise RJ, Sharp DJ, Leech R, The control of global brain dynamics: opposing actions of frontoparietal control and default mode networks on attention. J Neurosci. 2014; 34(2): 451–461. doi: 10.1523/JNEUROSCI.1853-13.2014 24403145
46. Fürtinger S, Zinn JC, Simonyan K, A Neural Population Model Incorporating Dopaminergic Neurotransmission during Complex Voluntary Behaviors. PLoS Comput Biol. 2014; 10(11): e1003924. doi: 10.1371/journal.pcbi.1003924 25393005
47. Fox MD, Snyder AZ, Vincent JL, Raichle ME, Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior. Neuron. 2007; 56: 171–184. 17920023
48. Cover TM, Thomas JA, Elements of information theory. Wiley, New York; 1991.
49. Nilsson M, Kleijn WB, On the estimation of differential entropy from data located on embedded manifolds. IEEE Transactions on Information Theory. 2007; 53(7): 2330–2341.
50. Pérez Velázquez JL, Galán RF, Information Gain in the Brain's Resting State: A New Perspective on Autism. Frontiers in Neuroinformatics. 2013; 7(37): 1.