Dynamic geometry, brain function modeling, and consciousness

Differential analgesic effects of high-frequency or accelerated intermittent theta burst stimulation of M1 on experimental tonic pain: Correlations with cortical activity changes assessed by TMS-EEG

Accelerated intermittent theta burst stimulation (AiTBS) has attracted much attention in the past few years as a new form of brain stimulation paradigm. However, it is unclear the relative efficacy of AiTBS on cortical excitability compared to conventional high-frequency rTMS. Using concurrent TMS and electroencephalogram (TMS-EEG), this study systematically compared the efficacy on cortical excitability and a typical clinical application (i.e. pain), between AiTBS with different intersession interval (ISIs) and 10-Hz rTMS. Participants received 10-Hz rTMS, AiTBS-15 (3 iTBS sessions with a 15-min ISI), AiTBS-50 (3 iTBS sessions with a 50-min ISI), or Sham stimulation over the primary motor cortex on four separate days. All four protocols included a total of 1800 pulses but with different session durations (10-Hz rTMS ​= ​18, AiTBS-15 ​= ​40, and AiTBS-50 ​= ​110 ​min). AiTBS-50 and 10-Hz rTMS were more effective in pain reduction compared to AiTBS-15. Using single-pulse TMS-induced oscillation, our data revealed low gamma oscillation as a shared cortical excitability change across all three active rTMS protocols but demonstrated completely opposite directions. Changes in low gamma oscillation were further associated with changes in pain perception across the three active conditions. In contrast, a distinct pattern of TMS-evoked potentials (TEPs) was revealed, with 10-Hz rTMS decreasing inhibitory N100 amplitude and AiTBS-15 reducing excitatory P60 amplitude. These changes in TEPs were also covarying with low gamma power changes. Sham stimulation indicated no significant effect on either cortical excitability or pain perception. These results are relevant only for provoked experimental pain, without being predictive for chronic pain, and revealed a change in low gamma oscillation, particularly around the very particular frequency of 40 ​Hz, shared between AiTBS and high-frequency rTMS. Conversely, cortical excitability (balance between excitation and inhibition) assessed by TEP recording was modulated differently by AiTBS and high-frequency rTMS paradigms.

Cortical synaptic basis of consciousness

Consciousness is one of final questions for humans to tackle in neuroscience. Due to a lack of understanding of basic brain networks and mechanisms of functions, our knowledge of consciousness mainly stays at a theoretical level. Recent studies using brain imaging in humans and modern neuroscience techniques in animal studies reveal the basic brain network for consciousness. The projection from the thalamus to different cortical regions forms a network of activities to maintain consciousness in humans and animals. These feedback and feedforward circuits maintain consciousness even in certain brain injury conditions. Pterions and ion channels that contribute to these circuit neural activities are targets for drugs and manipulations that affect consciousness such as anesthetic agents. Synaptic plasticity that trains synapses during learning and information recall modified the circuits and contributes to a high level of consciousness in a certain population.

Replay, the default mode network and the cascaded memory systems model

The spontaneous replay of patterns of activity related to past experiences and memories is a striking feature of brain activity, as is the coherent activation of sets of brain areas - particularly those comprising the default mode network (DMN) - during rest. We propose that these two phenomena are strongly intertwined and that their potential functions overlap. In the 'cascaded memory systems model' that we outline here, we hypothesize that the DMN forms the backbone for the propagation of replay, mediating interactions between the hippocampus and the neocortex that enable the consolidation of new memories. The DMN may also independently ignite replay cascades, which support reactivation of older memories or high-level semantic representations. We suggest that transient cortical activations, inducing long-range correlations across the neocortex, are a key mechanism supporting a hierarchy of representations that progresses from simple percepts to semantic representations of causes and, finally, to whole episodes.

The Mental Maxwell Relations: A Thermodynamic Allegory for Higher Brain Functions

The theoretical framework of classical thermodynamics unifies vastly diverse natural phenomena and captures once-elusive effects in concrete terms. Neuroscience confronts equally varied, equally ineffable phenomena in the mental realm, but has yet to unite or to apprehend them rigorously, perhaps due to an insufficient theoretical framework. The terms for mental phenomena, the mental variables, typically used in neuroscience are overly numerous and imprecise. Unlike in thermodynamics or other branches of physics, in neuroscience, there are no core mental variables from which all others formally derive and it is unclear which variables are distinct and which overlap. This may be due to the nature of mental variables themselves. Unlike the variables of physics, perhaps they cannot be interpreted as composites of a small number of axioms. However, it is well worth exploring if they can, as that would allow more parsimonious theories of higher brain function. Here we offer a theoretical exercise in the spirit of the National Institutes of Health Research Domain Criteria (NIH RDoC) Initiative and the Cognitive Atlas Project, which aim to remedy this state of affairs. Imitating classical thermodynamics, we construct a formal framework for mental variables, an extended analogy - an allegory - between mental and thermodynamic quantities. Starting with mental correlates of the physical indefinables length, time, mass or force, and charge, we pursue the allegory up to mental versions of the thermodynamic Maxwell Relations. The Maxwell Relations interrelate the thermodynamic quantities volume, pressure, temperature, and entropy and were chosen since they are easy to derive, yet capable of generating nontrivial, nonobvious predictions. Our "Mental Maxwell Relations" interlink the mental variables consciousness, salience, arousal, and distraction and make nontrivial, nonobvious statements about mental phenomena. The mental system thus constructed is internally consistent, in harmony with introspection, and respects the RDoC criteria of employing only psychologically valid constructs with some evidence of a brain basis. We briefly apply these concepts to the problem of decision-making and sketch how some of them might be tested empirically.

Methodological aspects of studying the mechanisms of consciousness

There are at least two approaches to the definition of consciousness. In the first case, certain aspects of consciousness, called qualia, are considered inaccessible for research from a third person and can only be described through subjective experience. This approach is inextricably linked with the so-called "hard problem of consciousness", that is, the question of why consciousness has qualia or how any physical changes in the environment can generate subjective experience. With this approach, some aspects of consciousness, by definition, cannot be explained on the basis of external observations and, therefore, are outside the scope of scientific research. In the second case, a priori constraints do not constrain the field of scientific investigation, and the best explanation of the experience in the first person is included as a possible subject of empirical research. Historically, in the study of cause-and-effect relationships in biology, it was customary to distinguish between proximate causation and ultimate causation existing in biological systems. Immediate causes are based on the immediate influencing factors [1]. Proximate causation has evolutionary explanations. When studying biological systems themselves, such an approach is undoubtedly justified, but it often seems insufficient when studying the interaction of consciousness and the brain [2,3]. Current scientific communities proceed from the assumption that the physical substrate for the generation of consciousness is a neural network that unites various types of neurons located in various brain structures. Many neuroscientists attach a key role in this process to the cortical and thalamocortical neural networks. This question is directly related to experimental and clinical research in the field of disorder of consciousness. Progress in this area of medicine depends on advances in neuroscience in this area and is also a powerful source of empirical information. In this area of consciousness research, a large amount of experimental data has been accumulated, and in this review an attempt was made to generalize and systematize.

Does the Cerebellum Implement or Select Geometries? A Speculative Note

During evolution, living systems, actively interacting with their environment, developed the ability, through sensorimotor contingencies, to construct functional spaces shaping their perception and their movements. These geometries were modularly embedded in specific functional neuro-architectures. In particular, human movements were shown to obey several empirical laws, such as the 2/3 power law, isochrony, or jerk minimization principles, which constrain and adapt motor planning and execution. Outstandingly, such laws can be deduced from a combination of Euclidean, affine, and equi-affine geometries, whose neural correlates have been partly detected in several brain areas including the cerebellum and the basal ganglia. Reviving Pellionisz and Llinas general hypothesis regarding the cerebrum and the cerebellum as geometric machines, we speculate that the cerebellum should be involved in implementing and/or selecting task-specific geometries for motor and cognitive skills. More precisely, the cerebellum is assumed to compute forward internal models to help specific cortical and subcortical regions to select appropriate geometries among, at least, Euclidean and affine geometries. We emphasize that the geometrical role of the cerebellum deserves a renewal of interest, which may provide a better understanding of its specific contributions to motor and associative (cognitive) functions.

Sensory-driven and spontaneous gamma oscillations engage distinct cortical circuitry

Gamma oscillations are a robust component of sensory responses but are also part of the background spontaneous activity of the brain. To determine whether the properties of gamma oscillations in cortex are specific to their mechanism of generation, we compared in mouse visual cortex in vivo the laminar geometry and single-neuron rhythmicity of oscillations produced during sensory representation with those occurring spontaneously in the absence of stimulation. In mouse visual cortex under anesthesia (isoflurane and xylazine), visual stimulation triggered oscillations mainly between 20 and 50 Hz, which, because of their similar functional significance to gamma oscillations in higher mammals, we define here as gamma range. Sensory representation in visual cortex specifically increased gamma oscillation amplitude in the supragranular (L2/3) and granular (L4) layers and strongly entrained putative excitatory and inhibitory neurons in infragranular layers, while spontaneous gamma oscillations were distributed evenly through the cortical depth and primarily entrained putative inhibitory neurons in the infragranular (L5/6) cortical layers. The difference in laminar distribution of gamma oscillations during the two different conditions may result from differences in the source of excitatory input to the cortex. In addition, modulation of superficial gamma oscillation amplitude did not result in a corresponding change in deep-layer oscillations, suggesting that superficial and deep layers of cortex may utilize independent but related networks for gamma generation. These results demonstrate that stimulus-driven gamma oscillations engage cortical circuitry in a manner distinct from spontaneous oscillations and suggest multiple networks for the generation of gamma oscillations in cortex.

Altered thalamocortical rhythmicity and connectivity in mice lacking CaV3.1 T-type Ca2+ channels in unconsciousness

In unconscious status (e.g., deep sleep and anesthetic unconsciousness) where cognitive functions are not generated there is still a significant level of brain activity present. Indeed, the electrophysiology of the unconscious brain is characterized by well-defined thalamocortical rhythmicity. Here we address the ionic basis for such thalamocortical rhythms during unconsciousness. In particular, we address the role of CaV3.1 T-type Ca(2+) channels, which are richly expressed in thalamic neurons. Toward this aim, we examined the electrophysiological and behavioral phenotypes of mice lacking CaV3.1 channels (CaV3.1 knockout) during unconsciousness induced by ketamine or ethanol administration. Our findings indicate that CaV3.1 KO mice displayed attenuated low-frequency oscillations in thalamocortical loops, especially in the 1- to 4-Hz delta band, compared with control mice (CaV3.1 WT). Intriguingly, we also found that CaV3.1 KO mice exhibited augmented high-frequency oscillations during unconsciousness. In a behavioral measure of unconsciousness dynamics, CaV3.1 KO mice took longer to fall into the unconscious state than controls. In addition, such unconscious events had a shorter duration than those of control mice. The thalamocortical interaction level between mediodorsal thalamus and frontal cortex in CaV3.1 KO mice was significantly lower, especially for delta band oscillations, compared with that of CaV3.1 WT mice, during unconsciousness. These results suggest that the CaV3.1 channel is required for the generation of a given set of thalamocortical rhythms during unconsciousness. Further, that thalamocortical resonant neuronal activity supported by this channel is important for the control of vigilance states.

Towards an integrative theory of consciousness: part 2 (an anthology of various other models)

The study of consciousness has today moved beyond neurobiology and cognitive models. In the past few years, there has been a surge of research into various newer areas. The present article looks at the non-neurobiological and non-cognitive theories regarding this complex phenomenon, especially ones that self-psychology, self-theory, artificial intelligence, quantum physics, visual cognitive science and philosophy have to offer. Self-psychology has proposed the need to understand the self and its development, and the ramifications of the self for morality and empathy, which will help us understand consciousness better. There have been inroads made from the fields of computer science, machine technology and artificial intelligence, including robotics, into understanding the consciousness of these machines and their implications for human consciousness. These areas are explored. Visual cortex and emotional theories along with their implications are discussed. The phylogeny and evolution of the phenomenon of consciousness is also highlighted, with theories on the emergence of consciousness in fetal and neonatal life. Quantum physics and its insights into the mind, along with the implications of consciousness and physics and their interface are debated. The role of neurophilosophy to understand human consciousness, the functions of such a concept, embodiment, the dark side of consciousness, future research needs and limitations of a scientific theory of consciousness complete the review. The importance and salient features of each theory are discussed along with certain pitfalls, if present. A need for the integration of various theories to understand consciousness from a holistic perspective is stressed.

The mind-brain relationship as a mathematical problem

This paper aims to frame certain fundamental aspects of the human mind (content and meaning of mental states) and foundational elements of brain computation (spatial and temporal patterns of neural activity) so as to enable at least in principle their integration within one and the same quantitative representation. Through the history of science, similar approaches have been instrumental to bridge other seemingly mysterious scientific phenomena, such as thermodynamics and statistical mechanics, optics and electromagnetism, or chemistry and quantum physics, among several other examples. Identifying the relevant levels of analysis is important to define proper mathematical formalisms for describing the brain and the mind, such that they could be mapped onto each other in order to explain their equivalence. Based on these premises, we overview the potential of neural connectivity to provide highly informative constraints on brain computational process. Moreover, we outline approaches for representing cognitive and emotional states geometrically with semantic maps. Next, we summarize leading theoretical framework that might serve as an explanatory bridge between neural connectivity and mental space. Furthermore, we discuss the implications of this framework for human communication and our view of reality. We conclude by analyzing the practical requirements to manage the necessary data for solving the mind-brain problem from this perspective.

Network dynamics in nociceptive pathways assessed by the neuronal avalanche model

Background

Traditional electroencephalography provides a critical assessment of pain responses. The perception of pain, however, may involve a series of signal transmission pathways in higher cortical function. Recent studies have shown that a mathematical method, the neuronal avalanche model, may be applied to evaluate higher-order network dynamics. The neuronal avalanche is a cascade of neuronal activity, the size distribution of which can be approximated by a power law relationship manifested by the slope of a straight line (i.e., the α value). We investigated whether the neuronal avalanche could be a useful index for nociceptive assessment.

Findings

Neuronal activity was recorded with a 4 × 8 multichannel electrode array in the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC). Under light anesthesia, peripheral pinch stimulation increased the slope of the α value in both the ACC and S1, whereas brush stimulation increased the α value only in the S1. The increase in α values was blocked in both regions under deep anesthesia. The increase in α values in the ACC induced by peripheral pinch stimulation was blocked by medial thalamic lesion, but the increase in α values in the S1 induced by brush and pinch stimulation was not affected.

Conclusions

The neuronal avalanche model shows a critical state in the cortical network for noxious-related signal processing. The α value may provide an index of brain network activity that distinguishes the responses to somatic stimuli from the control state. These network dynamics may be valuable for the evaluation of acute nociceptive processes and may be applied to chronic pathological pain conditions.

The cerebellar network: from structure to function and dynamics

Since the discoveries of Camillo Golgi and Ramón y Cajal, the precise cellular organization of the cerebellum has inspired major computational theories, which have then influenced the scientific thought not only on the cerebellar function but also on the brain as a whole. However, six major issues revealing a discrepancy between morphologically inspired hypothesis and function have emerged. (1) The cerebellar granular layer does not simply operate a simple combinatorial decorrelation of the inputs but performs more complex non-linear spatio-temporal transformations and is endowed with synaptic plasticity. (2) Transmission along the ascending axon and parallel fibers does not lead to beam formation but rather to vertical columns of activation. (3) The olivo-cerebellar loop could perform complex timing operations rather than error detection and teaching. (4) Purkinje cell firing dynamics are much more complex than for a linear integrator and include pacemaking, burst-pause discharges, and bistable states in response to mossy and climbing fiber synaptic inputs. (5) Long-term synaptic plasticity is far more complex than traditional parallel fiber LTD and involves also other cerebellar synapses. (6) Oscillation and resonance could set up coherent cycles of activity designing a functional geometry that goes far beyond pre-wired anatomical circuits. These observations clearly show that structure is not sufficient to explain function and that a precise knowledge on dynamics is critical to understand how the cerebellar circuit operates.

The 'prediction imperative' as the basis for self-awareness

Here, we propose that global brain function is geared towards the implementation of intelligent motricity. Motricity is the only possible external manifestation of nervous system function (other than endocrine and exocrine secretion and the control of vascular tone). The intelligence component of motricity requires, for its successful wheeling, a prediction imperative to approximate the consequences of the impending motion. We address how such predictive function may originate from the dynamic properties of neuronal networks.