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(Created page with "=== Introduction === An important but outstanding issue in contemporary cognitive neuroscience is understanding the organizational properties of neural activity. For instance, is there a fundamental structure to the spatial–temporal patterns neural brain activity across different conditions? One common approach used to address this question is to examine the brain at “rest”. Measures such as functional connectivity, independent component analysis and graph theoreti...")
 
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  }}</ref><ref>Uddin LQ, Karlsgodt KH. Future directions for examination of brain networks in neurodevelopmental disorders. J. Clin. Child Adolesc. Psychol. 2018;47:483–497. doi: 10.1080/15374416.2018.1443461. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref><ref>Sripada C, et al. Prediction of neurocognition in youth from resting state fMRI. Mol. Psychiatry. 2020;25:3413–3421. doi: 10.1038/s41380-019-0481-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>. These results suggest these networks may be an intrinsic aspect of neural activity.
  }}</ref><ref>Uddin LQ, Karlsgodt KH. Future directions for examination of brain networks in neurodevelopmental disorders. J. Clin. Child Adolesc. Psychol. 2018;47:483–497. doi: 10.1080/15374416.2018.1443461. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref><ref>Sripada C, et al. Prediction of neurocognition in youth from resting state fMRI. Mol. Psychiatry. 2020;25:3413–3421. doi: 10.1038/s41380-019-0481-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>. These results suggest these networks may be an intrinsic aspect of neural activity.
Indeed, the same set of structured patterns of neural activity have been found during "active" states, such as, while completing different tasks<ref>Biswal BB, Eldreth DA, Motes MA, Rypma B. Task-dependent individual differences in prefrontal connectivity. Cereb. Cortex. 2010;20:2188–2197. doi: 10.1093/cercor/bhp284. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref><ref>Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 2007;8:700–711. doi: 10.1038/nrn2201. [PubMed] [CrossRef] [Google Scholar]</ref><ref>Kraus BT, et al. Network variants are similar between task and rest states. Neuroimage. 2021;229:117743. doi: 10.1016/j.neuroimage.2021.117743. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref>. For instance, there is a high degree of correspondence between networks extracted during rest and those extracted during tasks measuring sensorimotor<ref>Kristo G, et al. Task and task-free FMRI reproducibility comparison for motor network identification. Hum. Brain Mapp. 2014;35:340–352. doi: 10.1002/hbm.22180. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref><ref>Sui J, Adali T, Pearlson GD, Calhoun VD. An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques. Neuroimage. 2009;46:73–86. doi: 10.1016/j.neuroimage.2009.01.026.[PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref> and higher-level cognitive abilities (i.e., working memory)<ref>Calhoun VD, Kiehl KA, Pearlson GD. Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Hum. Brain Mapp. 2008;29:828–838. doi: 10.1002/hbm.20581. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref><ref>Xie H, et al. Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study. Neuroimage. 2018;180:495–504. doi: 10.1016/j.neuroimage.2017.05.050. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>.Even completing a task as complicated as following the plot of a movie elicits the same network architecture as observed in the resting brain<ref name=":0">Naci L, Cusack R, Anello M, Owen AM. A common neural code for similar conscious experiences in different individuals. Proc. Natl. Acad. Sci. U. S. A. 2014;111:14277–14282. doi: 10.1073/pnas.1407007111. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>. The correspondence between task and rest-based networks is so strong that task-based fMRI network activity can be predicted from the resting state<ref>Kannurpatti SS, Rypma B, Biswal BB. Prediction of task-related BOLD fMRI with amplitude signatures of resting-state fMRI. Front. Syst. Neurosci. 2012;6:7. doi: 10.3389/fnsys.2012.00007.[PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>, and rest-task network pairs can be identified at the individual level<ref>Elliott ML, et al. General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. Neuroimage. 2019;189:516–532. doi: 10.1016/j.neuroimage.2019.01.068. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref>16. Together, these results suggest that rest and task-based patterns of brain activity likely share a similar underlying neural architecture, despite distinct experiences and cognitive processes<ref>Cole MW, Ito T, Cocuzza C, Sanchez-Romero R. The functional relevance of task-state functional connectivity. J. Neurosci. 2021 doi: 10.1523/JNEUROSCI.1713-20.2021. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref>.
There are, however, important differences between the patterns of brain activity elicited during rest and task-based paradigms, and the set of experiences and cognitive processes associated with each<ref>Zhang S, et al. Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations. Brain Imaging Behav. 2016;10:21–32. doi: 10.1007/s11682-015-9359-7.[PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>. For instance, the presence or absence of a task is accompanied by increases in variability across different scales including neuronal firing rates changes in field potentials<ref>Monier C, Chavane F, Baudot P, Graham LJ, Frégnac Y. Orientation and direction selectivity of synaptic inputs in visual cortical neurons: A diversity of combinations produces spike tuning. Neuron. 2003;37:663–680. doi: 10.1016/S0896-6273(03)00064-3.[PubMed] [CrossRef] [Google Scholar]</ref><ref>Churchland MM, et al. Stimulus onset quenches neural variability: A widespread cortical phenomenon. Nat. Neurosci. 2010;13:369–378. doi: 10.1038/nn.2501. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref>, variation in fMRI blood oxygen level dependent (BOLD signal)<ref name=":1">He BJ. Spontaneous and task-evoked brain activity negatively interact. J. Neurosci. 2013;33:4672–4682. doi: 10.1523/JNEUROSCI.2922-12.2013. [PMC free article][PubMed] [CrossRef] [Google Scholar]</ref> and in EEG frequency bands<ref name=":2">Bonnard M, et al. Resting state brain dynamics and its transients: A combined TMS-EEG study. Sci. Rep. 2016;6:1–9. doi: 10.1038/srep31220. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref> Furthermore, through transcranial direct current stimulation (tDCS) it has been shown that frontal-lobe stimulation increases one’s proclivity to mind wander <ref name=":8">Axelrod V, Zhu X, Qiu J. Transcranial stimulation of the frontal lobes increases propensity of mind-wandering without changing meta-awareness. Sci. Rep. 2018;8:1–14. doi: 10.1038/s41598-018-34098-z. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref><ref name=":3">Axelrod V, Rees G, Lavidor M, Bar M. Increasing propensity to mind-wander with transcranial direct current stimulation. Proc. Natl. Acad. Sci. U. S. A. 2015;112:3314–3319. doi: 10.1073/pnas.1421435112. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>. Importantly, these differences are associated with changes in properties of neural activity but not in changes in the underlying neural architecture.
Is there a way to identify the shared neural architecture underlying the cognitive processes associated with rest and active states while also quantifying how these processes diverge from that shared architecture of neural activity? In this paper, we applied mathematical methods analogous to those of quantum mechanics, and the concept of phase space to EEG recorded during rest and movie-watching to extract spatial and transitional properties of dynamic neural activity. Quantum mechanics was developed to describe the dynamics of the subatomic world in terms of probability amplitudes and densities of states. Quantum systems (in the Schrodinger formulation of quantum mechanics) are described by wavefunctions which square to a probability distribution leading to the loss of local determinism and the Heisenberg uncertainty principle (for an overview/intro to the subject see<ref name=":7">Townsend JS. A Modern Approach to Quantum Mechanics.University Science Books; 2012. [Google Scholar]</ref>. This uncertainty principle places a fundamental limit on the location and the momentum of a point particle <ref>Heisenberg W. Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik. Z. Phys. 1927;43:172–198. doi: 10.1007/BF01397280. [CrossRef] [Google Scholar]</ref>. In essence, if the position of a particle is known there is an underlying uncertainty in its momentum (one cannot precisely say how fast it is going) and vice versa. In addition to the adaptation of the wavefunction approach to quantum mechanics in this paper, we also employed a phase space model. Phase space is a widely used tool in the study of dynamical systems, where the positional variables are paired with their conjugate momenta which establishes a multidimensional space that describes all possible configurations of the given system. This space spans the entire range of states that a system can exist in, each point (in this hyper-space) represents a single state of the system. Phase space and its assorted formalisms are a classical concept, and we simply use it as another tool for analysing the EEG data. Herein, the mathematical methods of quantum mechanics are applied to EEG data to extract a proxy to phase space. This quasi-quantum approach naturally generates the concepts of ‘average’ position, ‘average’ momentum and culminates in an analogous Heisenberg uncertainty principle.
In this paper, we posit that using mathematical tools drawn from quantum mechanics, an underlying pattern representative of task and resting brain activity can be realised, in which differences across conditions are apparent, but culminates in a task independent constant value. It is important to note that we are not claiming that the brain behaves as a quantum object as some believe<ref>Penrose R. The Emperor’s New Mind. Viking Penguin; 1990. [Google Scholar]</ref> <ref>Penrose R. Shadows of the Mind: A Search for the Missing Science of Consciousness. Oxford University Press; 1994. [Google Scholar]</ref> <ref>Atmanspacher H. Quantum Approaches to Consciousness.Stanford Encyclopedia of Philosophy; 2004. [Google Scholar]</ref><ref>Hameroff S. How quantum brain biology can rescue conscious free will. Front. Integr. Neurosci. 2012;6:93. doi: 10.3389/fnint.2012.00093. [PMC free article] [PubMed] [CrossRef] [Google Scholar]</ref>. Rather, we have employed some of the analytical tools from the Schrodinger formulation of quantum mechanics to the brain with the aim of gaining new insight into resting and task-based brain dynamics. Not only does devising this model probe questions into the functions of the brain, but it also provides a novel approach to analysing the myriad of data available in neuroscience.

Revision as of 17:14, 5 November 2022

Introduction

An important but outstanding issue in contemporary cognitive neuroscience is understanding the organizational properties of neural activity. For instance, is there a fundamental structure to the spatial–temporal patterns neural brain activity across different conditions? One common approach used to address this question is to examine the brain at “rest”. Measures such as functional connectivity, independent component analysis and graph theoretic metrics, have been applied to data recorded using different imaging techniques (e.g., functional magnetic resonance imaging (fMRI) and electroencephalography (EEG)), to cluster brain areas that exhibit similar activity patterns. Numerous studies have shown that brain activity during “rest” can be grouped into distinct networks across[1][2]; such as sensory (visual and auditory), default mode, executive, salience, and attentional (ventral and dorsal) networks that have been reliably reproduced across thousands of participants[3], and are predictive of phenotypic measures like cognition and clinical diagnoses[4][5][6]. These results suggest these networks may be an intrinsic aspect of neural activity.


Indeed, the same set of structured patterns of neural activity have been found during "active" states, such as, while completing different tasks[7][8][9]. For instance, there is a high degree of correspondence between networks extracted during rest and those extracted during tasks measuring sensorimotor[10][11] and higher-level cognitive abilities (i.e., working memory)[12][13].Even completing a task as complicated as following the plot of a movie elicits the same network architecture as observed in the resting brain[14]. The correspondence between task and rest-based networks is so strong that task-based fMRI network activity can be predicted from the resting state[15], and rest-task network pairs can be identified at the individual level[16]16. Together, these results suggest that rest and task-based patterns of brain activity likely share a similar underlying neural architecture, despite distinct experiences and cognitive processes[17].

There are, however, important differences between the patterns of brain activity elicited during rest and task-based paradigms, and the set of experiences and cognitive processes associated with each[18]. For instance, the presence or absence of a task is accompanied by increases in variability across different scales including neuronal firing rates changes in field potentials[19][20], variation in fMRI blood oxygen level dependent (BOLD signal)[21] and in EEG frequency bands[22] Furthermore, through transcranial direct current stimulation (tDCS) it has been shown that frontal-lobe stimulation increases one’s proclivity to mind wander [23][24]. Importantly, these differences are associated with changes in properties of neural activity but not in changes in the underlying neural architecture.

Is there a way to identify the shared neural architecture underlying the cognitive processes associated with rest and active states while also quantifying how these processes diverge from that shared architecture of neural activity? In this paper, we applied mathematical methods analogous to those of quantum mechanics, and the concept of phase space to EEG recorded during rest and movie-watching to extract spatial and transitional properties of dynamic neural activity. Quantum mechanics was developed to describe the dynamics of the subatomic world in terms of probability amplitudes and densities of states. Quantum systems (in the Schrodinger formulation of quantum mechanics) are described by wavefunctions which square to a probability distribution leading to the loss of local determinism and the Heisenberg uncertainty principle (for an overview/intro to the subject see[25]. This uncertainty principle places a fundamental limit on the location and the momentum of a point particle [26]. In essence, if the position of a particle is known there is an underlying uncertainty in its momentum (one cannot precisely say how fast it is going) and vice versa. In addition to the adaptation of the wavefunction approach to quantum mechanics in this paper, we also employed a phase space model. Phase space is a widely used tool in the study of dynamical systems, where the positional variables are paired with their conjugate momenta which establishes a multidimensional space that describes all possible configurations of the given system. This space spans the entire range of states that a system can exist in, each point (in this hyper-space) represents a single state of the system. Phase space and its assorted formalisms are a classical concept, and we simply use it as another tool for analysing the EEG data. Herein, the mathematical methods of quantum mechanics are applied to EEG data to extract a proxy to phase space. This quasi-quantum approach naturally generates the concepts of ‘average’ position, ‘average’ momentum and culminates in an analogous Heisenberg uncertainty principle.

In this paper, we posit that using mathematical tools drawn from quantum mechanics, an underlying pattern representative of task and resting brain activity can be realised, in which differences across conditions are apparent, but culminates in a task independent constant value. It is important to note that we are not claiming that the brain behaves as a quantum object as some believe[27] [28] [29][30]. Rather, we have employed some of the analytical tools from the Schrodinger formulation of quantum mechanics to the brain with the aim of gaining new insight into resting and task-based brain dynamics. Not only does devising this model probe questions into the functions of the brain, but it also provides a novel approach to analysing the myriad of data available in neuroscience.

  1. ↑ Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS, «Functional connectivity in the motor cortex of resting human brain using echo-planar MRI», in Magn Reson Med, 1995».
    PMID:8524021
    DOI:10.1002/mrm.1910340409 
  2. ↑ Hutchison RM, Womelsdorfb T, Allenc EA, Bandettinie PA, Calhound VD, Corbetta M, Della Penna S, «Dynamic functional connectivity: Promise, issues, and interpretations», in Neuroimage, 2013».
    PMID:23707587 - PMCID:PMC3807588
    DOI:10.1016/j.neuroimage.2013.05.079 
  3. ↑ Eickhoff SB, Yeo BTT, Genon S, «Imaging-based parcellations of the human brain», in Nat Rev Neurosci, 2018».
    PMID:30305712
    DOI:10.1038/s41583-018-0071-7 
  4. ↑ Dajani DR, Burrows CA, Odriozola P, Baez A, Nebel MB, Mostofsky SH, Uddin LQ, «Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders», in NeuroImage Clin, 2019».
    PMID:30708240 - PMCID:PMC6356009
    DOI:10.1016/j.nicl.2019.101678 
  5. ↑ Uddin LQ, Karlsgodt KH. Future directions for examination of brain networks in neurodevelopmental disorders. J. Clin. Child Adolesc. Psychol. 2018;47:483–497. doi: 10.1080/15374416.2018.1443461. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  6. ↑ Sripada C, et al. Prediction of neurocognition in youth from resting state fMRI. Mol. Psychiatry. 2020;25:3413–3421. doi: 10.1038/s41380-019-0481-6. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  7. ↑ Biswal BB, Eldreth DA, Motes MA, Rypma B. Task-dependent individual differences in prefrontal connectivity. Cereb. Cortex. 2010;20:2188–2197. doi: 10.1093/cercor/bhp284. [PMC free article][PubMed] [CrossRef] [Google Scholar]
  8. ↑ Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 2007;8:700–711. doi: 10.1038/nrn2201. [PubMed] [CrossRef] [Google Scholar]
  9. ↑ Kraus BT, et al. Network variants are similar between task and rest states. Neuroimage. 2021;229:117743. doi: 10.1016/j.neuroimage.2021.117743. [PMC free article][PubMed] [CrossRef] [Google Scholar]
  10. ↑ Kristo G, et al. Task and task-free FMRI reproducibility comparison for motor network identification. Hum. Brain Mapp. 2014;35:340–352. doi: 10.1002/hbm.22180. [PMC free article][PubMed] [CrossRef] [Google Scholar]
  11. ↑ Sui J, Adali T, Pearlson GD, Calhoun VD. An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques. Neuroimage. 2009;46:73–86. doi: 10.1016/j.neuroimage.2009.01.026.[PMC free article] [PubMed] [CrossRef] [Google Scholar]
  12. ↑ Calhoun VD, Kiehl KA, Pearlson GD. Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Hum. Brain Mapp. 2008;29:828–838. doi: 10.1002/hbm.20581. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  13. ↑ Xie H, et al. Whole-brain connectivity dynamics reflect both task-specific and individual-specific modulation: A multitask study. Neuroimage. 2018;180:495–504. doi: 10.1016/j.neuroimage.2017.05.050. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  14. ↑ Naci L, Cusack R, Anello M, Owen AM. A common neural code for similar conscious experiences in different individuals. Proc. Natl. Acad. Sci. U. S. A. 2014;111:14277–14282. doi: 10.1073/pnas.1407007111. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  15. ↑ Kannurpatti SS, Rypma B, Biswal BB. Prediction of task-related BOLD fMRI with amplitude signatures of resting-state fMRI. Front. Syst. Neurosci. 2012;6:7. doi: 10.3389/fnsys.2012.00007.[PMC free article] [PubMed] [CrossRef] [Google Scholar]
  16. ↑ Elliott ML, et al. General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. Neuroimage. 2019;189:516–532. doi: 10.1016/j.neuroimage.2019.01.068. [PMC free article][PubMed] [CrossRef] [Google Scholar]
  17. ↑ Cole MW, Ito T, Cocuzza C, Sanchez-Romero R. The functional relevance of task-state functional connectivity. J. Neurosci. 2021 doi: 10.1523/JNEUROSCI.1713-20.2021. [PMC free article][PubMed] [CrossRef] [Google Scholar]
  18. ↑ Zhang S, et al. Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations. Brain Imaging Behav. 2016;10:21–32. doi: 10.1007/s11682-015-9359-7.[PMC free article] [PubMed] [CrossRef] [Google Scholar]
  19. ↑ Monier C, Chavane F, Baudot P, Graham LJ, Frégnac Y. Orientation and direction selectivity of synaptic inputs in visual cortical neurons: A diversity of combinations produces spike tuning. Neuron. 2003;37:663–680. doi: 10.1016/S0896-6273(03)00064-3.[PubMed] [CrossRef] [Google Scholar]
  20. ↑ Churchland MM, et al. Stimulus onset quenches neural variability: A widespread cortical phenomenon. Nat. Neurosci. 2010;13:369–378. doi: 10.1038/nn.2501. [PMC free article][PubMed] [CrossRef] [Google Scholar]
  21. ↑ He BJ. Spontaneous and task-evoked brain activity negatively interact. J. Neurosci. 2013;33:4672–4682. doi: 10.1523/JNEUROSCI.2922-12.2013. [PMC free article][PubMed] [CrossRef] [Google Scholar]
  22. ↑ Bonnard M, et al. Resting state brain dynamics and its transients: A combined TMS-EEG study. Sci. Rep. 2016;6:1–9. doi: 10.1038/srep31220. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  23. ↑ Axelrod V, Zhu X, Qiu J. Transcranial stimulation of the frontal lobes increases propensity of mind-wandering without changing meta-awareness. Sci. Rep. 2018;8:1–14. doi: 10.1038/s41598-018-34098-z. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  24. ↑ Axelrod V, Rees G, Lavidor M, Bar M. Increasing propensity to mind-wander with transcranial direct current stimulation. Proc. Natl. Acad. Sci. U. S. A. 2015;112:3314–3319. doi: 10.1073/pnas.1421435112. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  25. ↑ Townsend JS. A Modern Approach to Quantum Mechanics.University Science Books; 2012. [Google Scholar]
  26. ↑ Heisenberg W. Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik. Z. Phys. 1927;43:172–198. doi: 10.1007/BF01397280. [CrossRef] [Google Scholar]
  27. ↑ Penrose R. The Emperor’s New Mind. Viking Penguin; 1990. [Google Scholar]
  28. ↑ Penrose R. Shadows of the Mind: A Search for the Missing Science of Consciousness. Oxford University Press; 1994. [Google Scholar]
  29. ↑ Atmanspacher H. Quantum Approaches to Consciousness.Stanford Encyclopedia of Philosophy; 2004. [Google Scholar]
  30. ↑ Hameroff S. How quantum brain biology can rescue conscious free will. Front. Integr. Neurosci. 2012;6:93. doi: 10.3389/fnint.2012.00093. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
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