Open access peer-reviewed chapter

The Neurofunctional Model of Consciousness: The Physiological Interconnectivity of Brain Networks

Written By

Umberto León-Domínguez

Submitted: 25 June 2020 Reviewed: 28 September 2020 Published: 20 October 2020

DOI: 10.5772/intechopen.94221

From the Edited Volume

Connectivity and Functional Specialization in the Brain

Edited by Thomas Heinbockel and Yongxia Zhou

Chapter metrics overview

642 Chapter Downloads

View Full Metrics


The present chapter integrates neural networks’ connectivity into a model that explores consciousness and volitional behavior from a neurofunctional perspective. The model poses a theoretical evidenced-based framework that organizes the brain journey of neural information flow from the ascending reticular activating system and non-specific thalamic nuclei, to cortical networks, such as the default mode network and the fronto-parietal network. These inter-connected brain networks can be divided within three hierarchical and inter-connected “functional neural loops”: (1) the “brainstem-thalamic neural loop” for arousal, (2) the “thalamo-cortical neural loop” for neural information distribution throughout the brain, and (3) the “cortico-cortical neural loop” for transforming neural information into the contents of consciousness that the individual can perceive and manipulate voluntarily. These three neural loops act as a global functional neural system, and its disruption due to brain damage can cause a person to experience catastrophic outcomes, such as a coma, a vegetative state, a minimal conscious state, or other cognitive and behavioral impairments.


  • consciousness
  • cortico-cortical system
  • thalamo-cortical system
  • brainstem
  • fronto-parietal network
  • default mode network

1. Introduction

Consciousness is a complex term to tackle objectively due to its broad epistemological spectrum. From a clinical view, consciousness has been neurophysiologically and behaviorally parameterized for its assessment [1, 2]. It is a central nervous process (reduccionism) that multiple neural long-range connections control (conexionism) and that is teleonomically goal directed. This neurofunctional point of view converges with theories about the emergence of new features in complex systems [3]. Various authors propose that high brain connectivity between distinct and distant neural groups is an elemental characteristic for the emergence of consciousness [3, 4, 5]. In this respect, consciousness is a neurophysiological phenomenon regulated by different brain networks that create qualia, the subjective experience of consciousness [6, 7, 8, 9, 10, 11].

Consciousness should be interpreted as a physiological state of the central nervous system that changes over time and space. This functional mutability allows high-order cognitive functions to take place [6, 12, 13] to produce an overt and/or covert behavior that can be measured via direct observation or neuroimage [14, 15, 16]. All of these intermingled processes are supported via various brain networks that integrate endogenous and exogenous information with the intention of responding effectively to organic and psychological demands [6, 8, 11, 17, 18]. In this regard, acquired brain damage can impair the regular activity of brain networks, disorganizing cognition and behavior (mild, moderate, or severe brain damage), or even inhibiting the experience of consciousness (disorder of consciousness) [14, 19, 20, 21]. Therefore, from a clinical view, the structural and neurophysiological integrity of the neural substrate that underlies consciousness will determine the functional behavior of individuals [6, 22, 23]. Thus, consciousness can be described as a basal, dynamic, and transitive brain state that supports the high-order cognitive processing of information to produce suitable behaviors for environmental demands [24].


2. The neurofunctional model of consciousness

A huge number of theories seem to agree on many assumptions about consciousness, although they diverge regarding the descriptive approach. Some of them, such as the Global Neural Workspace Theory, focus on its neurophysiological components [11]. Meanwhile, others, such as the Global Workspace Theory, focus on its cognitive components [25]. In addition, the Integrated Information Theory focuses on its computational components [8, 26, 27]; the Temporo-Spatial Theory of Consciousness focuses on its inner space and time characteristics [6]; and the PFC-feedback System [28] focuses on its feedforward and feedback components. Crick and Koch introduced one of the first approaches to the study of consciousness [9]. Their approach posits that the experience of consciousness will be determined based on the long-range connectivity between the front and back parts of the brain. All of these authors and theories have shed light on the phenomena of consciousness and have probably contributed to the very first theoretical foundations for the study of consciousness objectively:

  • Consciousness depends on bioelectrical and biochemical brain activity.

  • Some neurophysiological processes are required to experience consciousness as awareness (i.e., the object or event has to trigger a P300 wave on the cortex).

  • These neurophysiological processes are regulated via various neural groups that process information in a rapid, automatic, and stereotypical manner (back brain), as well as via other neural groups that process information in a slow and voluntary manner (front brain).

  • Consciousness needs long-range connectivity between distinct and distant brain areas.

  • These long-range connections (probably in beta bands) assemble distinct and distant neural groups into extended neural networks that regulate various physiological and phenomenological dimensions that are necessary for the experience of consciousness.

One of the main neural models that are emerging currently about neural processing is the “predictive coding model” [29, 30]. This model posits that neural processing occurs within feedforward and feedback loops between upper and lower brain structures and slices. Lower structures/slices send predictions to upper structures and these structures send back error predictions to adjust neural processes to make the ongoing behavior efficient [29, 30, 31, 32, 33]. Llinás has already suggested that consciousness could be more related to a close-loop neural network than to the emergent consequence of a sensory input [34]. In this sense, a functional and preserved consciousness could depend on the predictive codification between inferior (brainstem and thalamus) and superior brain structures (cortex), where the prefrontal cortex (PFC) receives “end-of-the-line” bottom-up predictions and sends top-down error predictions to the thalamus to adjust new top-down projections [24, 35, 36, 37, 38, 39, 40].

Despite all of the theories and experimental evidence about the neural networks involved in consciousness, no global theoretical framework exists to describe how these neural networks operate to produce and maintain consciousness. The present chapter will introduce a neurofunctional model that organizes the interaction and functioning of the neural networks into three neurofunctional loops: (1) the Brainstem-Thalamic neural loop (B-T neural loop), (2) the Thalamo-Cortical neural loop (T-C neural loop), and (3) the Cortico-Cortical neural loop (C-C Neural Loop). Each of these loops are formed via differentiated and semi-independent neural structures that are involved in specific aspects of the phenomenological consciousness.

2.1 B-T neural loop

The brainstem plays a key role in the regulation of consciousness due to the control that it exerts to the Ascending Reticular Activating System (ARAS) and therefore to wakefulness (wakefulness and awareness are the two clinical dimensions typically related to consciousness) [41, 42]. The ARAS is composed of myriad brainstem nuclei (dorsal raphe locus coeruleus, median raphe, pedunculopontine, and parabrachial nuclei), with connections to the thalamus, hypothalamus, and basal forebrain [42, 43, 44, 45, 46, 47, 48], and even with the prefrontal areas [49] and the precuneus (Pcu) [50]. The lower dorsal ARAS connects the pontine reticular formation to the intralaminar thalamic nuclei (ILN), the lower ventral ARAS connects the pontine reticular formation to the hypothalamus, and the upper ARAS connects the intralaminar thalamic nuclei to the cerebral cortex [51, 52, 53, 54]. Whereas hypothalamic-basal forebrain pathways regulate sleep-wakefulness cycles [48, 55, 56], the ILN, as part of the non-specific thalamic nuclei, can block thalamocortical rhythms and therefore the emergence of arousal and awareness [22, 57, 58, 59, 60]. Baars [18] called this circuit the Extended Reticular-Thalamic Activating System, which he considered to be the principal neural assembly in the experience of consciousness.

2.2 T-C neural loop

A significant amount of evidence points out that reciprocal interactions between the thalamus and cortex are a fundamental component of the proper functioning of the thalamo-cortical system [61], which is related to consciousness [62]. This thalamo-cortico-thalamic connectivity starts to develop in the late prenatal and early postnatal stages [61, 63, 64], and the efficient deployment of these developmental processes will determine the functional state of the thalamo-cortical system in the adult stage [65]. The thalamus has been proposed as the main neural structure of the thalamo-cortical system, as it operates as a regulator of cortical functional connectivity, whereby it is involved in the ongoing cognitive processes [66, 67, 68, 69, 70]. The thalamus can be divided into three nuclear groups: first-order thalamic relay nuclei, higher-order thalamic relay nuclei, or non-specific thalamic nuclei. First-order thalamic nuclei send afferent projections to the primary sensory cortical areas, whereas higher-order nuclei receive projections from the primary sensory cortical areas and send these projections back to the higher visual cortical areas forming the cortico-thalamo-cortico circuits. Finally, nonspecific thalamic nuclei are those that receive projections from the ARAS and send diffuse projections throughout the brain [71, 72, 73]. The nonspecific thalamic nuclei are composed of three main nuclear groups: the thalamic reticular nucleus (TRN), the ILN, and the midline thalamic nuclei (MTN). The TRN-ILN-MTN thalamic axis has been related to consciousness [22, 62, 74] with strong implications in the distribution of neural information throughout the brain [24].

The functional extent of each nonspecific thalamic nuclei is related to the control and regulation of a specific cognitive domain [24] . The TRN is one of the main neural nodes that regulates the activity of the thalamus and therefore the activity of the entire thalamo-cortical system [75, 76, 77]. The TRN receives afferent glutamatergic projections from the entire brain, and in turn, it sends only efferent GABAergic projections to the thalamus, thus regulating thalamo-cortical and cortico-cortical activity [28, 78, 79]. On a morphological level, the TRN is divided into sensory and motor regions [80]. Whereas the sensory region modulates attentional processes via connections with the prefrontal cortex [38], the motor region is involved in limbic and motor processes due to high connectivity with the ILN-NMT, the ventrolateral, and the anterior thalamic nuclei [81, 82, 83, 84, 85]. Various authors have referred to the involvement of the TRN in the attentional processes as the “attention spotlight” and “attentional door” that regulate the flow of information between the thalamus and the cortex [35, 86, 87]. The capacity to control neural information throughout the brain is due to the inhibition that it exerts to the thalamic nuclei [37, 76, 86]. This inhibition mechanism underlying the “attention spotlight” selects the information needed to face psychological and physiological demands while suppressing those that are not relevant. Some authors suggest that the TRN is involved in the content of consciousness by controlling selective attentional processes and the thalamus activity [28, 86]. According to Crick [35], the short-term synaptic plasticity of the TRN could influence first-order thalamic relay nuclei in the formation of temporal connections between brain areas related to the content of consciousness [35]. Hence, this capacity to modulate the content of consciousness could be mediated by the control of attentional processes [88, 89, 90].

On the other hand, the functions of the ILN and the MTN are functionally differentiated, but their activity are highly dependent [91, 92, 93, 94, 95]. Regarding consciousness, both nuclei (due to its multiple connections with the ARAS) activate the excitability of the cerebral cortex to maintain vigilance and arousal [42, 58, 59, 60, 76, 91]. For instance, the ILN send and receive projections from the prefrontal, motor, and parietal cortices. Meanwhile, the MTN is connected to the medial prefrontal cortex (mPFC) and the hippocampus (HPC). These diffuse connections spread to the cortex, thus allowing the synchronization of brain activity through the adjustment of the brain waves’ phases. Thus, distinct and distant neural groups assemble into cortico-cortical networks to facilitate the flow of neural information [91]. In addition, The ILN and MTN are also involved in the regulation of the striatal-thalamocortical circuits [96] due to the multiple efferent inhibitory connections that receive from the TRN, the basal ganglia, and the reticular formation of the ARAS [97, 98, 99]. These connections with the striatum, the brainstem, and the cortex highlight the relevance of the ILN and the MTN in the motor, somatic, and visceral functions, which are essential for controlling arousal, perception, and even emotion expression [100].

Specifically, the ILN have been associated with the regulation of cortical activity and the restoration of consciousness [22, 68, 101, 102]. The anterior region of the ILN react to motor inputs [103, 104], whereas the posterior region organizes motor, limbic, and associative information [60, 97, 105, 106]. Projections to limbic structures and sensori-motor areas suggest the relevance of the integration of the affective and motor functions that underly propositional behaviors [107]. In addition, they are involved in tasks that require the focalization of attention and the selection of actions for unexpected events [108, 109]. Kinomura and colleagues pointed out that arousal and attention require the simultaneous activation of the reticular formation of the midbrain and the ILN [110]. This evidence places the ILN as the basic neural nodes for the integration of brain functions, such as arousal, attention, and motor control, to trigger high-level cognitive performance [86, 104, 110, 111, 112, 113]. This functional characteristic of the ILN in the regulation of the arousal has been employed for deep brain stimulation in cases of minimally conscious state. Schiff [22, 114] showed that stimulating the ILN in minimally conscious state patients could improve their motor behavior, but without showing any sign of “real” consciousness [22, 114, 115]. Therefore, although the ILN seems to be involved in consciousness, it cannot produce a constant and fluent stream of consciousness by itself.

Finally, the MTN have been reported as the main “gateway” of information to the HPC and the limbic system, with a high dependence on the individual’s arousal levels [116, 117, 118, 119]. Concretely, the nucleus reuniens and rhomboid of the MTN jointly with the mPFC and the HPC form a specialized neural circuit that contribute to learning and to the cognitive flexibility [120], probably due to its relationship with the working memory [116, 117]. This circuit constituted by the MTN-HPC-mPFC could be modified via the functional state of the TRN [121] and also affect the content of consciousness [122]. Other authors propose that the circuit formed via the orbital and mPFC, the amygdala, the hypothalamus, and the MTN could also be involved in the visceral and emotional control of human behavior [123, 124, 125, 126, 127, 128]. The MTN directly influences the arousal and attentional processes through its involvement in emotional regulation [129]. Thence, it is implicated in the emotional adjustment of behavior in a continuously changing environment [130]. According to these authors, the MTN could mediate the selection of the most suitable behavior depending on the emotional tone inputs received in a specific moment [118, 130]. This evidence places the MTN as a remarkable interface between the diverse structures of the limbic system to integrate memory, emotion, and cognition [100, 119, 129, 131].

All of this evidence points out that the TRN-ILN-MTX thalamic axis and its connections throughout the brain are essential components for being conscious and aware of our surroundings due to the axis’s capacity to place the T-C neural loop in an optimal functional state [24, 35]. In this sense, it is important to distinguish between “be aware” and the “formation of consciousness.” Being aware of something means that our cognitive systems are prepared to receive and manipulate the content of consciousness, but the formation of the content of consciousness depends on other neural processes. The content of consciousness is formed mainly in the posterior cortex [132, 133] through cortico-thalamo-cortico circuits, which facilitate connections among various sensory cortical areas in the “content-specific Neural Correlates of Consciousness (NCC)” [70, 133, 134, 135, 136]. Regardless of the content-specific NCC, when it comes to accessing consciousness, some neurophysiological requirements, such as a late P300 wave, are needed to ignite a global brain activation that will trigger awareness [137]. The conscious perception of the content of consciousness is the end of the concatenation of neurophysiological events that propagate from the back to the front cortex [6, 138]. It would be like a competition among various neural coalitions to access consciousness, and once a winning coalition exists (the first to break neurophysiological requirements), a specific representation or the content of consciousness can be perceived as generating a genuine experience of consciousness [137]. Afterward, this content of consciousness is controlled by high-order cognitive functions and is incorporated into plans, desires, and/or thoughts [6, 139].

2.3 C-C neural loop

Once the content of consciousness is created in the back brain [132, 133], various cortico-cortical networks consciously manipulate the information [140]. One of the main cortico-cortical networks, which is broadly documented, is the Default Mode Network (DMN) [141, 142, 143, 144]. This network is formed by the anterior and posterior cingulate cortex, the mPFC, the orbital PFC, the medial temporal lobe (parahippocampal cortex and HPC), the retrosplenial cortex, and the inferior parietal lobe [145] . The DMN is a rest neural network, whose activity is maximum when the subject is awake and the cognitive demand is low (low-level processing of exogenous information) [146]. Moreover, the DMN is characterized by a high metabolism during rest states [147, 148, 149, 150], a progressive deactivation when more cognitive resources are needed to process information [147], and a high connectivity with other cortico-cortical networks to exchange information [140, 143, 151]. Traditionally, the DMN has been related to internal processes, such as self-reference thoughts and mind-wandering [152, 153, 154], although some studies currently link its activity to extrinsic processes, such as certain attentional processes [155] and the recall of memories [156, 157, 158, 159]. Recently, it has been posed that the DMN could also be involved in the integration of spatial, self-reference, and temporal information, thus generating episodic memories [160]. These authors suggest that, henceforth, the DMN is mostly activated in all of the cognitive processes [160].

One of the key points for understanding the role of the DMN in consciousness is to conceive it as a cognitive system that modulate cortico-cortical activity through its mediation in the transfer of information from resting states or task-negative networks to cognitively active states or task-positive networks [140, 147, 156, 161, 162, 163, 164]. When a subject is resting (with the low-level processing of exogenous information), the DMN controls cortical activity with the posterior cingulate cortex (PCC) and the precuneus (Pcu) as their main neural nodes. However, as long as elaborated processing is required and the load of the working memory increases, the physiological burden of the DMN decreases in favor of task-positive networks: the fronto-parietal central executive network (FPN), the dorsal attention network (DAN), and the salience network (SN). The FPN includes the dorsolateral PFC, the mPFC, the anterior insula (aINS), the Pcu, and the interior parietal lobe [140, 165, 166, 167]. On the other hand, the DAN is formed by the frontal eye field and the intraparietal sulcus [168], and the SN by the aINS, the dorsal anterior cingulate cortex, the amygdala, the ventral striatum, and the ventral tegmental area of the mesencephalon [169]. All of these networks share overlapping regions whereby they can exchange neural information depending on the ongoing cognitive activity [147, 149, 150, 170, 171, 172, 173] . The outcome of the continuous interactions among the cortico-cortical networks will define the functional conscious state of the individual [163].

The FPN, DAN, and SN play a key role in conscious behavior due to its capacity to operate jointly and synchronically in a highly coordinated and temporally accurate manner [140, 165, 174]. For instance, the DAN has been related to focalized attention and working memory, whereas the SN has been related to social communication, social behavior, and self-consciousness [171, 175, 176, 177, 178] . When all of these task-positive networks are operating, the DMN needs to deactivate [179, 180, 181] to facilitate the transition from low-energy cognitive states to high-energy cognitive states [147]. In these high-energy cognitive states, the mPFC takes control of the global brain activity at the expense of the PCC and the Pcu [170, 182]. Therefore, the alteration of structural and functional connectivity “within and between cortico-cortical networks” could cause the individual to experience a broad spectrum of neuropsychiatric and neurocognitive disorders [162, 163, 180, 183, 184].

The FPN and SN, especially in the prefrontal regions, regulate the cognitive processes involved in the achievement of conscious goals through the regulation of the physiological equilibrium between the DMN and the rest of the cortico-cortical networks (cognitive control) [140, 165, 166, 167, 185, 186]. Some studies point out that the mPFC and aINS regulate physiological equilibrium among brain networks [178, 187]. For instance, Crone and colleagues compared the activation/deactivation of the DMN in vegetative states (currently known as “unresponsive wakefulness state”), minimally conscious states, and individuals with preserved and functional consciousness (control subjects) [182]. They suggested that although the deactivation of the DMN was normal in control subjects, the same deactivation was significantly diminished in overlapped areas between the DMN and the FPN in a minimally conscious state, and it was absent in unresponsive wakefulness state patients. In other words, the cohesive and functional integrity between the DMN and the task-positive networks is a crucial factor in the transition between rest states (those with a low cognitive burden) to high-demand cognitive states (those with a high cognitive burden) [147]. Our team conducted an investigation whereby we compared cortical connectivity between minimally conscious states and severe neurocognitive disorders [4]. Our results revealed how the degree of connectivity between the anterior and the posterior cortex in the beta band was essential for maintaining a preserved consciousness. In this investigation, patients with minimally conscious states showed a low connectivity between the posterior and the anterior cortex, which could explain why their consciousness fluctuates over time [4]. In contrast, subjects with preserved consciousness showed a high connectivity between the anterior and the posterior cortex, whereby they can operate continuously without the absence of consciousness [4]. In this sense, in a case study, an unresponsive conscious patient emerged to a minimally conscious state when connectivity between the anterior and the posterior cortex increased [188]. Thus, the integration of the posterior and the anterior cortex into long-distance cortico-cortical networks is one of the principal prerequisites for maintaining functional consciousness [9, 182, 189, 190].


3. Assumptions for the neuroFunctional Model of Consciousness (nFMC)

  1. The nFMC is a theoretical and referential framework from which the study of consciousness can be tackled in all of its operative dimensions: neurophysiological, clinical, neuropharmacological, and phenomenological.

  2. Consciousness is a global neural process that keeps the individual in an optimal and continuous functional state, thus allowing qualia and high-order processes to take place to drive behavior.

  3. The nFMC divides global neural activity into three large systems, or functional loops, that are morphologically differentiated (although they share overlapped areas) and have semi-independent neurophysiological processes: the B-T neural loop, T-C neural loop, and C-C neural loop (see Figure 1).

  4. Cognitive, behavioral, and emotional expression due to brain damage will depend on the location and extension of the lesion within the neural loop, thus leading to clinical outcomes that they may vary from a mild cognitive impairment to a disorder of consciousness, such as a coma, minimally conscious state, or unresponsive wakefulness state.

  5. Each neural loop is activated hierarchically and sequentially by its preceding level, thus extending a representation of the neural processes that took place in the lower level, as well as integrating and transforming this neural representation into new information.

  6. The nFMC is in accordance with predictive coding models that present brain activity as a system in which lower brain structures project predictions/signals via bottom–up processing, and where higher cortical areas send prediction errors back via top-down processes.

  7. Neural processes (both automatic and controlled) related to consciousness (such as P300, brain rhythms, and neurotransmitter discharges) can be localized within either of the neural loops or in their reciprocal interactions.

  8. The nFMC is complementary and comprises several assumptions considered in previous theories and investigations of consciousness:

    • Consciousness can be deemed a Global Neural Workspace in which distinct neural networks compete to access consciousness [11, 25, 192].

    • Consciousness is the result of functional units or complexes that integrate information and that are activated or deactivated depending on the ongoing sensorial/visceral necessities [8, 26, 27].

    • Consciousness is a neurophysiological continuum commanded by inner spatio-temporal brain laws [6].

  9. Regarding the neural mechanisms or processes involved in the formation of the content of consciousness, the nFMC aligns with models and evidence that posit that the contents of consciousness are formed in the back brain via cortico-thalamo-cortical connections [70, 132, 133, 134, 135, 136]. In addition, the nFMC recognizes that PFC top-down connections could modulate the selection and even the formation of the content of consciousness [28].

Figure 1.

Consciousness is the phenomenological quality of human existence that arises from a hierarchical, parallel, and serial activation of long-distance brain networks [7], which operate as neural loops that “inform” upper and lower levels about their own operations [29, 30]. These loops receive input from lower levels (which contains new information/predictions) and input from upper levels (error predictions). The loop will integrate all of this new information, updating its own functional state and, consequently, also the functional state of the rest of the loops and the brain [29, 30, 31, 32, 191]. ARAS: Ascending reticular activating system; TNN: Task-negative networks; TPN: Task-positive networks.


4. Conclusion

Human behavior has to be understood as a global brain activity dominated by complex and hierarchical neural processes that cannot be divided and explained by isolated functional units. Consciousness is the “operating system” running underneath the “interface” of overt and covert human behavior, and it is dominated by the interactions of various neural levels composed of differentiated and semi-independent neural networks. Thence, the nFMC gathers reliable knowledge generated in the study on neural correlates of consciousness, providing a novel theoretical and referential framework that will help clinicians, researchers, and even students to localize the neural processes of interest within a global brain activity model. A further proposal should extend the structures and connectivity involved within and between each neural loop introduced in the nFMC.


Conflict of interest

The authors have no conflict of interest to declare.


  1. 1. Wolff A, Di Giovanni DA, Gómez-Pilar J, et al. The temporal signature of self: Temporal measures of resting-state EEG predict self-consciousness. Hum Brain Mapp. Epub ahead of print 4 October 2018. DOI: 10.1002/hbm.24412
  2. 2. Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet (London, England) 1974; 2: 81-4
  3. 3. Godwin D, Barry RL, Marois R. Breakdown of the brain’s functional network modularity with awareness. Proc Natl Acad Sci U S A 2015; 112: 3799-804
  4. 4. Leon-Carrion J, Leon-Dominguez U, Pollonini L, et al. Synchronization between the anterior and posterior cortex determines consciousness level in patients with traumatic brain injury (TBI). Brain Res 2012; 1476: 22-30
  5. 5. Leon-Dominguez U, Izzetoglu M, Leon-Carrion J, et al. Molecular concentration of deoxyHb in human prefrontal cortex predicts the emergence and suppression of consciousness. Neuroimage 2014; 85 Pt 1: 616-25
  6. 6. Northoff G, Huang Z. How do the brain’s time and space mediate consciousness and its different dimensions? Temporo-spatial theory of consciousness (TTC). Neurosci Biobehav Rev 2017; 80: 630-645
  7. 7. John ER, Prichep LS. The anesthetic cascade: a theory of how anesthesia suppresses consciousness. Anesthesiology 2005; 102: 447-71
  8. 8. Tononi G. An information integration theory of consciousness. BMC Neurosci 2004; 5: 42
  9. 9. Crick F, Koch C. Are we aware of neural activity in primary visual cortex? Nature 1995; 375: 121-123
  10. 10. Edelman GM, Gally JA, Baars BJ. Biology of Consciousness. Front Psychol 2011; 2: 4
  11. 11. Dehaene S, Kerszberg M, Changeux JP. A neuronal model of a global workspace in effortful cognitive tasks. Proc Natl Acad Sci U S A 1998; 95: 14529-34
  12. 12. Northoff G. What the brain’s intrinsic activity can tell us about consciousness? A tri-dimensional view. Neurosci Biobehav Rev 2013; 37: 726-38
  13. 13. Singer W. Consciousness and the binding problem. Ann N Y Acad Sci 2001; 929: 123-46
  14. 14. Giacino JT, Ashwal S, Childs N, et al. The minimally conscious state: definition and diagnostic criteria. Neurology 2002; 58: 349-53
  15. 15. Owen AM, Coleman MR, Boly M, et al. Detecting Awareness in the Vegetative State. Science (80-) 2006; 313: 1402-1402
  16. 16. Bai Y, Xia X, Li X. A Review of Resting-State Electroencephalography Analysis in Disorders of Consciousness. Front Neurol 2017; 8: 471
  17. 17. Crick F, Koch C. A framework for consciousness. Nat Neurosci 2003; 6: 119-126
  18. 18. Baars BJ. A cognitive theory of consciousness. Cambridge University Press, 1993
  19. 19. Laureys S, Faymonville ME, Luxen A, et al. Restoration of thalamocortical connectivity after recovery from persistent vegetative state. Lancet (London, England) 2000; 355: 1790-1
  20. 20. León-Carrión J. Dementia Due to Head Trauma: An obscure name for a clear neurocognitive syndrome. NeuroRehabilitation 2002; 17: 115-22
  21. 21. Riddoch MJ, Humphreys GW. Visual agnosia. Neurol Clin 2003; 21: 501-20
  22. 22. Schiff ND. Central thalamic contributions to arousal regulation and neurological disorders of consciousness. Ann N Y Acad Sci 2008; 1129: 105-18
  23. 23. Fernández-Espejo D, Bekinschtein T, Monti MM, et al. Diffusion weighted imaging distinguishes the vegetative state from the minimally conscious state. Neuroimage 2011; 54: 103-12
  24. 24. León-Domínguez U, Vela-Bueno A, Froufé-Torres M, et al. A chronometric functional sub-network in the thalamo-cortical system regulates the flow of neural information necessary for conscious cognitive processes. Neuropsychologia 2013; 51: 1336-1349
  25. 25. Baars BJ. Global workspace theory of consciousness: toward a cognitive neuroscience of human experience. Prog Brain Res 2005; 150: 45-53
  26. 26. Tononi G, Edelman GM. Consciousness and complexity. Science 1998; 282: 1846-51
  27. 27. Tononi G, Boly M, Massimini M, et al. Integrated information theory: from consciousness to its physical substrate. Nat Rev Neurosci 2016; 17: 450-61
  28. 28. León-Domínguez U, León-Carrión J. Prefrontal neural dynamics in consciousness. Neuropsychologia; 131. Epub ahead of print 2019. DOI: 10.1016/j.neuropsychologia.2019.05.018
  29. 29. Friston K. The free-energy principle: a unified brain theory? Nat Rev Neurosci 2010; 11: 127-138
  30. 30. Friston K. The free-energy principle: a rough guide to the brain? Trends Cogn Sci 2009; 13: 293-301
  31. 31. Rao RPN, Ballard DH. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat Neurosci 1999; 2: 79-87
  32. 32. Lee TS, Mumford D. Hierarchical Bayesian inference in the visual cortex. J Opt Soc Am A Opt Image Sci Vis 2003; 20: 1434-48
  33. 33. Kellermann T, Scholle R, Schneider F, et al. Decreasing predictability of visual motion enhances feed-forward processing in visual cortex when stimuli are behaviorally relevant. Brain Struct Funct 2017; 222: 849-866
  34. 34. Llinás RR, Paré D. Of dreaming and wakefulness. Neuroscience 1991; 44: 521-35
  35. 35. Crick F. Function of the thalamic reticular complex: the searchlight hypothesis. Proc Natl Acad Sci U S A 1984; 81: 4586-90
  36. 36. Barbas H, García-Cabezas MÁ. How the prefrontal executive got its stripes. Curr Opin Neurobiol 2016; 40: 125-134
  37. 37. Zikopoulos B, Barbas H. Pathways for emotions and attention converge on the thalamic reticular nucleus in primates. J Neurosci 2012; 32: 5338-50
  38. 38. Zikopoulos B, Barbas H. Prefrontal projections to the thalamic reticular nucleus form a unique circuit for attentional mechanisms. J Neurosci 2006; 26: 7348-61
  39. 39. Chanes L, Barrett LF. Redefining the Role of Limbic Areas in Cortical Processing. Trends Cogn Sci 2016; 20: 96-106
  40. 40. Alexander WH, Brown JW. Frontal cortex function as derived from hierarchical predictive coding. Sci Rep 2018; 8: 3843
  41. 41. Laureys S. The neural correlate of (un)awareness: lessons from the vegetative state. Trends Cogn Sci 2005; 9: 556-9
  42. 42. Moruzzi G, Magoun HW. Brain stem reticular formation and activation of the EEG. Electroencephalogr Clin Neurophysiol 1949; 1: 455-73
  43. 43. Långsjö JW, Alkire MT, Kaskinoro K, et al. Returning from oblivion: imaging the neural core of consciousness. J Neurosci 2012; 32: 4935-43
  44. 44. Jones BE. Arousal systems. Front Biosci 2003; 8: s438-51
  45. 45. Kolmac CI, Mitrofanis J. Patterns of brainstem projection to the thalamic reticular nucleus. J Comp Neurol 1998; 396: 531-43
  46. 46. Fuller PM, Fuller P, Sherman D, et al. Reassessment of the structural basis of the ascending arousal system. J Comp Neurol 2011; 519: 933-56
  47. 47. Parvizi J, Damasio AR. Neuroanatomical correlates of brainstem coma. Brain 2003; 126: 1524-1536
  48. 48. Jang SH, Kwon HG. The Neural Tract Between the Hypothalamus and Basal Forebrain in the Ascending Reticular Activating System: A Diffusion Tensor Tractography Study. Curr Med Imaging Rev 2019; 15: 369-372
  49. 49. SH J, HG K. The direct pathway from the brainstem reticular formation to the cerebral cortex in the ascending reticular activating system: A diffusion tensor imaging study. Neurosci Lett; 606. Epub ahead of print 2015. DOI: 10.1016/J.NEULET.2015.09.004
  50. 50. Silva S, Alacoque X, Fourcade O, et al. Wakefulness and loss of awareness: Brain and brainstem interaction in the vegetative state. Neurology 2010; 74: 313-320
  51. 51. Jang SH, Kwon HG. The ascending reticular activating system from pontine reticular formation to the hypothalamus in the human brain: a diffusion tensor imaging study. Neurosci Lett 2015; 590: 58-61
  52. 52. Yeo SS, Chang PH, Jang SH. The ascending reticular activating system from pontine reticular formation to the thalamus in the human brain. Front Hum Neurosci 2013; 7: 416
  53. 53. Berry DJ, Ohara PT, Jeffery G, et al. Are there connections between the thalamic reticular nucleus and the brainstem reticular formation? J Comp Neurol 1986; 243: 347-62
  54. 54. Jang SH, Kwon YH. Neuroimaging characterization of recovery of impaired consciousness in patients with disorders of consciousness. Neural Regen Res 2019; 14: 1202-1207
  55. 55. Kostin A, Siegel JM, Alam MN. Lack of Hypocretin Attenuates Behavioral Changes Produced by Glutamatergic Activation of the Perifornical-Lateral Hypothalamic Area. Sleep 2014; 37: 1011-1020
  56. 56. Naganuma F, Bandaru SS, Absi G, et al. Melanin-concentrating hormone neurons contribute to dysregulation of rapid eye movement sleep in narcolepsy. Neurobiol Dis 2018; 120: 12-20
  57. 57. McCormick DA. Cholinergic and noradrenergic modulation of thalamocortical processing. Trends Neurosci 1989; 12: 215-21
  58. 58. Lavoie B, Parent A. Serotoninergic innervation of the thalamus in the primate: An immunohistochemical study. J Comp Neurol 1991; 312: 1-18
  59. 59. Oke AF, Carver LA, Gouvion CM, et al. Three-dimensional mapping of norepinephrine and serotonin in human thalamus. Brain Res 1997; 763: 69-78
  60. 60. Krout KE, Belzer RE, Loewy AD. Brainstem projections to midline and intralaminar thalamic nuclei of the rat. J Comp Neurol 2002; 448: 53-101
  61. 61. Antón-Bolaños N, Espinosa A, López-Bendito G. Developmental interactions between thalamus and cortex: a true love reciprocal story. Curr Opin Neurobiol 2018; 52: 33-41
  62. 62. Jones EG. A new view of specific and nonspecific thalamocortical connections. Adv Neurol 1998; 77: 49-71; discussion 72-3
  63. 63. Pouchelon G, Gambino F, Bellone C, et al. Modality-specific thalamocortical inputs instruct the identity of postsynaptic L4 neurons. Nature 2014; 511: 471-474
  64. 64. Zembrzycki A, Chou S-J, Ashery-Padan R, et al. Sensory cortex limits cortical maps and drives top-down plasticity in thalamocortical circuits. Nat Neurosci 2013; 16: 1060-1067
  65. 65. Mitrofanis J, Guillery RW. New views of the thalamic reticular nucleus in the adult and the developing brain. Trends Neurosci 1993; 16: 240-5
  66. 66. McCormick DA, Bal T. Sensory gating mechanisms of the thalamus. Curr Opin Neurobiol 1994; 4: 550-6
  67. 67. Nakajima M, Halassa MM. Thalamic control of functional cortical connectivity. Curr Opin Neurobiol 2017; 44: 127-131
  68. 68. Sherman SM, Guillery RW, Sherman SM. Exploring the thalamus and its role in cortical function. MIT Press, 2006
  69. 69. Sherman SM. Thalamic relay functions. Prog Brain Res 2001; 134: 51-69
  70. 70. Sherman SM. Functioning of Circuits Connecting Thalamus and Cortex. Compr Physiol 2017; 7: 713-739
  71. 71. Groenewegen HJ, Berendse HW. The specificity of the ‘nonspecific’ midline and intralaminar thalamic nuclei. Trends Neurosci 1994; 17: 52-7
  72. 72. Ramcharan EJ, Gnadt JW, Sherman SM. Higher-order thalamic relays burst more than first-order relays. Proc Natl Acad Sci 2005; 102: 12236-12241
  73. 73. Sherman SM. The thalamus is more than just a relay. Curr Opin Neurobiol 2007; 17: 417-422
  74. 74. Zhou J, Liu X, Song W, et al. Specific and nonspecific thalamocortical functional connectivity in normal and vegetative states. Conscious Cogn 2011; 20: 257-68
  75. 75. Jones EG. Thalamic circuitry and thalamocortical synchrony. Philos Trans R Soc B Biol Sci 2002; 357: 1659-1673
  76. 76. Lam Y-W, Sherman SM. Functional Organization of the Thalamic Input to the Thalamic Reticular Nucleus. J Neurosci 2011; 31: 6791-6799
  77. 77. Viviano JD, Schneider KA. Interhemispheric Interactions of the Human Thalamic Reticular Nucleus. J Neurosci 2015; 35: 2026-2032
  78. 78. Guillery RW, Harting JK. Structure and connections of the thalamic reticular nucleus: Advancing views over half a century. J Comp Neurol 2003; 463: 360-71
  79. 79. Yingling CD, Skinner JE. Selective regulation of thalamic sensory relay nuclei by nucleus reticularis thalami. Electroencephalogr Clin Neurophysiol 1976; 41: 476-82
  80. 80. Guillery RW, Feig SL, Lozsádi DA. Paying attention to the thalamic reticular nucleus. Trends Neurosci 1998; 21: 28-32
  81. 81. Jang SH, Lim HW, Yeo SS. The neural connectivity of the intralaminar thalamic nuclei in the human brain: A diffusion tensor tractography study. Neurosci Lett 2014; 579: 140-144
  82. 82. Cicirata F, Angaut P, Serapide MF, et al. Functional organization of the direct and indirect projection via the reticularis thalami nuclear complex from the motor cortex to the thalamic nucleus ventralis lateralis. Exp brain Res 1990; 79: 325-37
  83. 83. Gonzalo-Ruiz A, Lieberman AR. Topographic organization of projections from the thalamic reticular nucleus to the anterior thalamic nuclei in the rat. Brain Res Bull 1995; 37: 17-35
  84. 84. Lozsádi DA. Organization of cortical afferents to the rostral, limbic sector of the rat thalamic reticular nucleus. J Comp Neurol 1994; 341: 520-33
  85. 85. Tai Y, Yi H, Ilinsky IA, et al. Nucleus reticularis thalami connections with the mediodorsal thalamic nucleus: a light and electron microscopic study in the monkey. Brain Res Bull 1995; 38: 475-88
  86. 86. McAlonan K, Brown VJ. The thalamic reticular nucleus: more than a sensory nucleus? Neuroscientist 2002; 8: 302-5
  87. 87. McAlonan K, Cavanaugh J, Wurtz RH. Attentional modulation of thalamic reticular neurons. J Neurosci 2006; 26: 4444-50
  88. 88. Min B-K. A thalamic reticular networking model of consciousness. Theor Biol Med Model 2010; 7: 10
  89. 89. Phillips JM, Kambi NA, Saalmann YB. A Subcortical Pathway for Rapid, Goal-Driven, Attentional Filtering. Trends Neurosci 2016; 39: 49-51
  90. 90. Wimmer RD, Schmitt LI, Davidson TJ, et al. Thalamic control of sensory selection in divided attention. Nature 2015; 526: 705-709
  91. 91. Saalmann YB. Intralaminar and medial thalamic influence on cortical synchrony, information transmission and cognition. Front Syst Neurosci 2014; 8: 83
  92. 92. de Medeiros Silva A, de Santana MAD, de Góis Morais PLA, et al. Serotonergic fibers distribution in the midline and intralaminar thalamic nuclei in the rock cavy (Kerodon rupestris). Brain Res 2014; 1586: 99-108
  93. 93. Kolaj M, Zhang L, Hermes MLHJ, et al. Intrinsic properties and neuropharmacology of midline paraventricular thalamic nucleus neurons. Front Behav Neurosci 2014; 8: 132
  94. 94. Pelzer EA, Melzer C, Timmermann L, et al. Basal ganglia and cerebellar interconnectivity within the human thalamus. Brain Struct Funct 2017; 222: 381-392
  95. 95. Varela C. Thalamic neuromodulation and its implications for executive networks. Front Neural Circuits 2014; 8: 69
  96. 96. Berendse HW, Groenewegen HJ. Restricted cortical termination fields of the midline and intralaminar thalamic nuclei in the rat. Neuroscience 1991; 42: 73-102
  97. 97. Benarroch EE. The midline and intralaminar thalamic nuclei: anatomic and functional specificity and implications in neurologic disease. Neurology 2008; 71: 944-9
  98. 98. Cornwall J, Phillipson OT. Afferent projections to the parafascicular thalamic nucleus of the rat, as shown by the retrograde transport of wheat germ agglutinin. Brain Res Bull 1988; 20: 139-50
  99. 99. Royce GJ, Bromley S, Gracco C. Subcortical projections to the centromedian and parafascicular thalamic nuclei in the cat. J Comp Neurol 1991; 306: 129-55
  100. 100. Vertes RP, Linley SB, Hoover WB. Limbic circuitry of the midline thalamus. Neurosci Biobehav Rev 2015; 54: 89-107
  101. 101. Gummadavelli A, Motelow JE, Smith N, et al. Thalamic stimulation to improve level of consciousness after seizures: Evaluation of electrophysiology and behavior. Epilepsia 2015; 56: 114-124
  102. 102. Suffczynski P, Kalitzin S, Pfurtscheller G, et al. Computational model of thalamo-cortical networks: dynamical control of alpha rhythms in relation to focal attention. Int J Psychophysiol 2001; 43: 25-40
  103. 103. Crabtree JW, Isaac JTR. New intrathalamic pathways allowing modality-related and cross-modality switching in the dorsal thalamus. J Neurosci 2002; 22: 8754-61
  104. 104. Rodriguez-Sabate C, Llanos C, Morales I, et al. The functional connectivity of intralaminar thalamic nuclei in the human basal ganglia. Hum Brain Mapp 2015; 36: 1335-1347
  105. 105. Sadikot AF, Rymar V V. The primate centromedian-parafascicular complex: anatomical organization with a note on neuromodulation. Brain Res Bull 2009; 78: 122-30
  106. 106. Smith Y, Raju D V, Pare J-F, et al. The thalamostriatal system: a highly specific network of the basal ganglia circuitry. Trends Neurosci 2004; 27: 520-7
  107. 107. Vertes RP, Hoover WB, Rodriguez JJ. Projections of the central medial nucleus of the thalamus in the rat: node in cortical, striatal and limbic forebrain circuitry. Neuroscience 2012; 219: 120-36
  108. 108. Minamimoto T, Hori Y, Kimura M. Roles of the thalamic CM–PF complex—Basal ganglia circuit in externally driven rebias of action. Brain Res Bull 2009; 78: 75-79
  109. 109. Raeva SN. The role of the parafascicular complex (CM-Pf) of the human thalamus in the neuronal mechanisms of selective attention. Neurosci Behav Physiol 2006; 36: 287-95
  110. 110. Kinomura S, Larsson J, Gulyás B, et al. Activation by attention of the human reticular formation and thalamic intralaminar nuclei. Science 1996; 271: 512-5
  111. 111. Schlag-Rey M, Schlag J. Visuomotor functions of central thalamus in monkey. I. Unit activity related to spontaneous eye movements. J Neurophysiol 1984; 51: 1149-1174
  112. 112. Grunwerg BS, Krauthamer GM. Sensory responses of intralaminar thalamic neurons activated by the superior colliculus. Exp brain Res 1992; 88: 541-50
  113. 113. Biane JS, Takashima Y, Scanziani M, et al. Thalamocortical Projections onto Behaviorally Relevant Neurons Exhibit Plasticity during Adult Motor Learning. Neuron 2016; 89: 1173-1179
  114. 114. Schiff ND. Central thalamic deep brain stimulation for support of forebrain arousal regulation in the minimally conscious state. In: Handbook of clinical neurology, pp. 295-306
  115. 115. Schiff ND, Fins JJ. Deep brain stimulation and cognition: moving from animal to patient. Curr Opin Neurol 2007; 20: 638-642
  116. 116. Duan AR, Varela C, Zhang Y, et al. Delta Frequency Optogenetic Stimulation of the Thalamic Nucleus Reuniens Is Sufficient to Produce Working Memory Deficits: Relevance to Schizophrenia. Biol Psychiatry 2015; 77: 1098-1107
  117. 117. Layfield DM, Patel M, Hallock H, et al. Inactivation of the nucleus reuniens/rhomboid causes a delay-dependent impairment of spatial working memory. Neurobiol Learn Mem 2015; 125: 163-167
  118. 118. Vertes RP. Major diencephalic inputs to the hippocampus. In: Progress in brain research, pp. 121-144
  119. 119. Vertes RP. Interactions among the medial prefrontal cortex, hippocampus and midline thalamus in emotional and cognitive processing in the rat. Neuroscience 2006; 142: 1-20
  120. 120. Cassel J-C, Pereira de Vasconcelos A, Loureiro M, et al. The reuniens and rhomboid nuclei: Neuroanatomy, electrophysiological characteristics and behavioral implications. Prog Neurobiol 2013; 111: 34-52
  121. 121. Çavdar S, Onat FY, Çakmak YÖ, et al. The pathways connecting the hippocampal formation, the thalamic reuniens nucleus and the thalamic reticular nucleus in the rat. J Anat 2008; 212: 249-256
  122. 122. Zikopoulos B, Barbas H. Circuits formultisensory integration and attentional modulation through the prefrontal cortex and the thalamic reticular nucleus in primates. Rev Neurosci 2007; 18: 417-38
  123. 123. Hsu DT, Price JL. Midline and intralaminar thalamic connections with the orbital and medial prefrontal networks in macaque monkeys. J Comp Neurol 2007; 504: 89-111
  124. 124. Jurik A, Auffenberg E, Klein S, et al. Roles of prefrontal cortex and paraventricular thalamus in affective and mechanical components of visceral nociception. Pain 2015; 156: 2479-2491
  125. 125. Kirouac GJ. Placing the paraventricular nucleus of the thalamus within the brain circuits that control behavior. Neurosci Biobehav Rev 2015; 56: 315-329
  126. 126. Penzo MA, Robert V, Tucciarone J, et al. The paraventricular thalamus controls a central amygdala fear circuit. Nature 2015; 519: 455-459
  127. 127. Dong X, Li S, Kirouac GJ. Collateralization of projections from the paraventricular nucleus of the thalamus to the nucleus accumbens, bed nucleus of the stria terminalis, and central nucleus of the amygdala. Brain Struct Funct 2017; 222: 3927-3943
  128. 128. Huang H, Ghosh P, van den Pol AN. Prefrontal cortex-projecting glutamatergic thalamic paraventricular nucleus-excited by hypocretin: a feedforward circuit that may enhance cognitive arousal. J Neurophysiol 2006; 95: 1656-68
  129. 129. Li S, Kirouac GJ. Sources of inputs to the anterior and posterior aspects of the paraventricular nucleus of the thalamus. Brain Struct Funct 2012; 217: 257-73
  130. 130. Vertes RP, Hoover WB. Projections of the paraventricular and paratenial nuclei of the dorsal midline thalamus in the rat. J Comp Neurol 2008; 508: 212-37
  131. 131. Rolls ET. Limbic systems for emotion and for memory, but no single limbic system. Cortex 2015; 62: 119-57
  132. 132. Luria A. Human brain and psychological processes
  133. 133. Boly M, Massimini M, Tsuchiya N, et al. Are the Neural Correlates of Consciousness in the Front or in the Back of the Cerebral Cortex? Clinical and Neuroimaging Evidence. J Neurosci 2017; 37: 9603-9613
  134. 134. Kirchgessner MA, Franklin AD, Callaway EM. Context-dependent and dynamic functional influence of corticothalamic pathways to first- and higher-order visual thalamus. Proc Natl Acad Sci U S A 2020; 117: 13066-13077
  135. 135. Storm JF, Boly M, Casali AG, et al. Consciousness Regained: Disentangling Mechanisms, Brain Systems, and Behavioral Responses. J Neurosci 2017; 37: 10882-10893
  136. 136. Mesulam MM. From sensation to cognition. Brain 1998; 121 ( Pt 6): 1013-52
  137. 137. Dehaene S. The signatures of a conscious though. In: Consciousness and the Brain. New York: Penguin Books, 2014, pp. 115-160
  138. 138. Gaillard R, Dehaene S, Adam C, et al. Converging intracranial markers of conscious access. PLoS Biol 2009; 7: e61
  139. 139. Miller EK. The prefontral cortex and cognitive control. Nat Rev Neurosci 2000; 1: 59-65
  140. 140. Cole MW, Reynolds JR, Power JD, et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci 2013; 16: 1348-55
  141. 141. Boly M, Tshibanda L, Vanhaudenhuyse A, et al. Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient. Hum Brain Mapp 2009; 30: 2393-400
  142. 142. Buschman TJ, Miller EK. Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices. Science (80-) 2007; 315: 1860-1862
  143. 143. Greicius MD, Supekar K, Menon V, et al. Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network. Cereb Cortex 2009; 19: 72-78
  144. 144. Herbet G, Lafargue G, de Champfleur NM, et al. Disrupting posterior cingulate connectivity disconnects consciousness from the external environment. Neuropsychologia 2014; 56: 239-44
  145. 145. Raichle ME. The Brain’s Default Mode Network. Annu Rev Neurosci 2015; 38: 433-447
  146. 146. Raichle ME, MacLeod AM, Snyder AZ, et al. A default mode of brain function. Proc Natl Acad Sci 2001; 98: 676-682
  147. 147. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007; 8: 700-711
  148. 148. Fransson P, Marrelec G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis. Neuroimage 2008; 42: 1178-84
  149. 149. Greicius MD, Menon V. Default-mode activity during a passive sensory task: uncoupled from deactivation but impacting activation. J Cogn Neurosci 2004; 16: 1484-92
  150. 150. Shulman GL, Fiez JA, Corbetta M, et al. Common Blood Flow Changes across Visual Tasks: II. Decreases in Cerebral Cortex. J Cogn Neurosci 1997; 9: 648-663
  151. 151. Greicius MD, Krasnow B, Reiss AL, et al. Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proc Natl Acad Sci 2003; 100: 253-258
  152. 152. Mason MF, Norton MI, Van Horn JD, et al. Wandering Minds: The Default Network and Stimulus-Independent Thought. Science (80-) 2007; 315: 393-395
  153. 153. Andrews-Hanna JR, Smallwood J, Spreng RN. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann N Y Acad Sci 2014; 1316: 29-52
  154. 154. Axelrod V, Rees G, Bar M. The default network and the combination of cognitive processes that mediate self-generated thought. Nat Hum Behav 2017; 1: 896-910
  155. 155. Kucyi A, Esterman M, Riley CS, et al. Spontaneous default network activity reflects behavioral variability independent of mind-wandering. Proc Natl Acad Sci 2016; 113: 13899-13904
  156. 156. Bellana B, Liu Z-X, Diamond NB, et al. Similarities and differences in the default mode network across rest, retrieval, and future imagining. Hum Brain Mapp 2017; 38: 1155-1171
  157. 157. Monge ZA, Wing EA, Stokes J, et al. Search and recovery of autobiographical and laboratory memories: Shared and distinct neural components. Neuropsychologia 2018; 110: 44-54
  158. 158. Henry JD, Crawford JR, Phillips LH. Verbal fluency performance in dementia of the Alzheimer’s type: a meta-analysis. Neuropsychologia 2004; 42: 1212-1222
  159. 159. Philippi CL, Tranel D, Duff M, et al. Damage to the default mode network disrupts autobiographical memory retrieval. Soc Cogn Affect Neurosci 2015; 10: 318-326
  160. 160. Smith V, Mitchell DJ, Duncan J. Role of the Default Mode Network in Cognitive Transitions. Cereb Cortex 2018; 28: 3685-3696
  161. 161. Snyder AZ, Raichle ME. A brief history of the resting state: the Washington University perspective. Neuroimage 2012; 62: 902-10
  162. 162. Han K, Chapman SB, Krawczyk DC. Disrupted Intrinsic Connectivity among Default, Dorsal Attention, and Frontoparietal Control Networks in Individuals with Chronic Traumatic Brain Injury. J Int Neuropsychol Soc 2016; 22: 263-279
  163. 163. Long J, Xie Q, Ma Q, et al. Distinct Interactions between Fronto-Parietal and Default Mode Networks in Impaired Consciousness. Sci Rep 2016; 6: 38866
  164. 164. Finc K, Bonna K, Lewandowska M, et al. Transition of the functional brain network related to increasing cognitive demands. Hum Brain Mapp 2017; 38: 3659-3674
  165. 165. Niendam TA, Laird AR, Ray KL, et al. Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn Affect Behav Neurosci 2012; 12: 241-68
  166. 166. Spreng RN, Stevens WD, Chamberlain JP, et al. Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition. Neuroimage 2010; 53: 303-17
  167. 167. Vincent JL, Kahn I, Snyder AZ, et al. Evidence for a Frontoparietal Control System Revealed by Intrinsic Functional Connectivity. J Neurophysiol 2008; 100: 3328-3342
  168. 168. Vossel S, Geng JJ, Fink GR. Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. Neuroscientist 2014; 20: 150-9
  169. 169. Menon V. Salience Network. In: Brain mapping : an encyclopedic reference. Amsterdam: Elsevier Inc, 2015, pp. 597-611
  170. 170. Chen AC, Oathes DJ, Chang C, et al. Causal interactions between fronto-parietal central executive and default-mode networks in humans. Proc Natl Acad Sci 2013; 110: 19944-19949
  171. 171. Corbetta M, Shulman GL. CONTROL OF GOAL-DIRECTED AND STIMULUS-DRIVEN ATTENTION IN THE BRAIN. Nat Rev Neurosci 2002; 3: 215-229
  172. 172. Fox MD, Snyder AZ, Vincent JL, et al. From The Cover: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci 2005; 102: 9673-9678
  173. 173. Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci 2008; 105: 12569-12574
  174. 174. Zhou Y, Friston KJ, Zeidman P, et al. The Hierarchical Organization of the Default, Dorsal Attention and Salience Networks in Adolescents and Young Adults. Cereb Cortex 2018; 28: 726-737
  175. 175. (Bud) Craig AD. How do you feel — now? The anterior insula and human awareness. Nat Rev Neurosci 2009; 10: 59-70
  176. 176. Fox MD, Corbetta M, Snyder AZ, et al. Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A 2006; 103: 10046-51
  177. 177. Gogolla N, Takesian AE, Feng G, et al. Sensory integration in mouse insular cortex reflects GABA circuit maturation. Neuron 2014; 83: 894-905
  178. 178. Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct 2010; 214: 655-67
  179. 179. Sonuga-Barke EJS, Castellanos FX. Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neurosci Biobehav Rev 2007; 31: 977-86
  180. 180. Anticevic A, Cole MW, Murray JD, et al. The role of default network deactivation in cognition and disease. Trends Cogn Sci 2012; 16: 584-592
  181. 181. Dosenbach NUF, Fair DA, Miezin FM, et al. Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci 2007; 104: 11073-11078
  182. 182. Crone JS, Ladurner G, Höller Y, et al. Deactivation of the default mode network as a marker of impaired consciousness: an fMRI study. PLoS One 2011; 6: e26373
  183. 183. Supekar K, Cai W, Krishnadas R, et al. Dysregulated Brain Dynamics in a Triple-Network Saliency Model of Schizophrenia and Its Relation to Psychosis. Biol Psychiatry. Epub ahead of print 1 August 2018. DOI: 10.1016/j.biopsych.2018.07.020
  184. 184. Fan D, Liao F, Wang Q. The pacemaker role of thalamic reticular nucleus in controlling spike-wave discharges and spindles. Chaos An Interdiscip J Nonlinear Sci 2017; 27: 073103
  185. 185. Spreng RN, Schacter DL. Default network modulation and large-scale network interactivity in healthy young and old adults. Cereb Cortex 2012; 22: 2610-21
  186. 186. Spreng RN, Sepulcre J, Turner GR, et al. Intrinsic architecture underlying the relations among the default, dorsal attention, and frontoparietal control networks of the human brain. J Cogn Neurosci 2013; 25: 74-86
  187. 187. Seeley WW, Menon V, Schatzberg AF, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 2007; 27: 2349-56
  188. 188. León-Carrión J, León-Dominguez U, Halper J, et al. Restoring cortical connectivity directionality and synchronization is essential to treating disorder of consciousness. Curr Pharm Des 2014; 20: 4268-74
  189. 189. Amico E, Marinazzo D, Di Perri C, et al. Mapping the functional connectome traits of levels of consciousness. Neuroimage 2017; 148: 201-211
  190. 190. Dietrich A. Functional neuroanatomy of altered states of consciousness: the transient hypofrontality hypothesis. Conscious Cogn 2003; 12: 231-56
  191. 191. Parras GG, Nieto-Diego J, Carbajal G V., et al. Neurons along the auditory pathway exhibit a hierarchical organization of prediction error. Nat Commun 2017; 8: 2148
  192. 192. Dehaene S, Changeux J-P, Naccache L, et al. Conscious, preconscious, and subliminal processing: a testable taxonomy. Trends Cogn Sci 2006; 10: 204-11

Written By

Umberto León-Domínguez

Submitted: 25 June 2020 Reviewed: 28 September 2020 Published: 20 October 2020