From abstract topology to real thermodynamic brain activity

How the Brain Becomes the Mind: Can Thermodynamics Explain the Emergence and Nature of Emotions?

The neural systems' electric activities are fundamental for the phenomenology of consciousness. Sensory perception triggers an information/energy exchange with the environment, but the brain's recurrent activations maintain a resting state with constant parameters. Therefore, perception forms a closed thermodynamic cycle. In physics, the Carnot engine is an ideal thermodynamic cycle that converts heat from a hot reservoir into work, or inversely, requires work to transfer heat from a low- to a high-temperature reservoir (the reversed Carnot cycle). We analyze the high entropy brain by the endothermic reversed Carnot cycle. Its irreversible activations provide temporal directionality for future orientation. A flexible transfer between neural states inspires openness and creativity. In contrast, the low entropy resting state parallels reversible activations, which impose past focus via repetitive thinking, remorse, and regret. The exothermic Carnot cycle degrades mental energy. Therefore, the brain's energy/information balance formulates motivation, sensed as position or negative emotions. Our work provides an analytical perspective of positive and negative emotions and spontaneous behavior from the free energy principle. Furthermore, electrical activities, thoughts, and beliefs lend themselves to a temporal organization, an orthogonal condition to physical systems. Here, we suggest that an experimental validation of the thermodynamic origin of emotions might inspire better treatment options for mental diseases.

Global Functional Connectivity Analysis Indicating Dysconnectivity of the Hate Circuit in Major Depressive Disorder

Background

Abnormalities of functional connectivity (FC) in certain brain regions are closely related to the pathophysiology of major depressive disorder (MDD). Findings are inconsistent with different presuppositions in regions of interest. Our research focused on voxel-wise brain-wide FC changes in patients with MDD in an unbiased manner.

Method

We examined resting-state functional MRI in 23 patients with MDD and 26 healthy controls. Imaging data were analyzed by using global-brain FC (GFC) and used to explore the correlation of abnormal GFC values with clinical variables.

Results

Increased GFC values in the left medial superior frontal gyrus (SFGmed) and decreased GFC values in the right supplementary motor area (SMA) were observed in the patients with MDD compared with the controls. The decreased GFC values in the right SMA had a positive correlation with vitamin D and Hamilton Anxiety Scale (HAM-A) scores.

Conclusion

Abnormal GFC in the hate circuit, particularly increased GFC in the left SFGmed and decreased GFC in the right SMA, appears to be a new sight for comprehending the pathological alterations in MDD.

Topology of eeg wave fronts

Whenever one attempts to comb a hairy ball flat, there will always be at least one tuft of hair at one point on the ball. This seemingly worthless sentence is an informal description of the hairy ball theorem, an invaluable mathematical weapon that has been proven useful to describe a variety of physical/biological processes/phenomena in terms of topology, rather than classical cause/effect relationships. In this paper we will focus on the electrical brain field-electroencephalogram (EEG). As a starting point we consider the recently-raised observation that, when electromagnetic oscillations propagate with a spherical wave front, there must be at least one point of the tangential components of the vector fields where the electromagnetic field vanishes. We show how this description holds also for the electric waves produced by the brain and detectable by EEG. Once located these zero-points in EEG traces, we confirm that they are able to modify the electric wave fronts detectable in the brain. This sheds new light on the functional features of a nonlinear, metastable nervous system at the edge of chaos, based on the neuroscientific model of Operational Architectonics of brain-mind functioning. As an example of practical application of this theorem, we provide testable previsions, suggesting the proper location of transcranial magnetic stimulation's coils to improve the clinical outcomes of drug-resistant epilepsy.

Increased Global-Brain Functional Connectivity Is Associated with Dyslipidemia and Cognitive Impairment in First-Episode, Drug-Naive Patients with Bipolar Disorder

Objectives

Previous researches have demonstrated that abnormal functional connectivity (FC) is associated with the pathophysiology of bipolar disorder (BD). However, inconsistent results were obtained due to different selections of regions of interest in previous researches. This study is aimed at examining voxel-wise brain-wide functional connectivity (FC) alterations in the first-episode, drug-naive patient with BD in an unbiased way.

Methods

A total of 35 patients with BD and 37 age-, sex-, and education-matched healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI). Global-brain FC (GFC) was applied to analyze the image data. Support vector machine (SVM) was adopted to probe whether GFC abnormalities could be used to identify the patients from the controls.

Results

Patients with BD exhibited increased GFC in the left inferior frontal gyrus (LIFG), pars triangularis and left precuneus (PCu)/superior occipital gyrus (SOG). The left PCu belongs to the default mode network (DMN). Furthermore, increased GFC in the LIFG, pars triangularis was positively correlated with the triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) and negatively correlated with the scores of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) coding test and Stroop color. Increased GFC values in the left PCu/SOG can be applied to discriminate patients from controls with preferable sensitivity (80.00%), specificity (75.68%), and accuracy (77.78%).

Conclusions

This study found increased GFC in the brain regions of DMN; LIFG, pars triangularis; and LSOG, which was associated with dyslipidemia and cognitive impairment in patients with BD. Moreover, increased GFC values in the left PCu/SOG may be utilized as a potential biomarker to differentiate patients with BD from controls.

Voxel-Wise Brain-Wide Functional Connectivity Abnormalities in Patients with Primary Blepharospasm at Rest

Background

Primary blepharospasm (BSP) is one of the most common focal dystonia and its pathophysiological mechanism remains unclear. An unbiased method was used in patients with BSP at rest to observe voxel-wise brain-wide functional connectivity (FC) changes.

Method

A total of 48 subjects, including 24 untreated patients with BSP and 24 healthy controls, were recruited to undergo functional magnetic resonance imaging (fMRI). The method of global-brain FC (GFC) was adopted to analyze the resting-state fMRI data. We designed the support vector machine (SVM) method to determine whether GFC abnormalities could be utilized to distinguish the patients from the controls.

Results

Relative to healthy controls, patients with BSP showed significantly decreased GFC in the bilateral superior medial prefrontal cortex/anterior cingulate cortex (MPFC/ACC) and increased GFC in the right postcentral gyrus/precentral gyrus/paracentral lobule, right superior frontal gyrus (SFG), and left paracentral lobule/supplement motor area (SMA), which were included in the default mode network (DMN) and sensorimotor network. SVM analysis showed that increased GFC values in the right postcentral gyrus/precentral gyrus/paracentral lobule could discriminate patients from controls with optimal accuracy, specificity, and sensitivity of 83.33%, 83.33%, and 83.33%, respectively.

Conclusion

This study suggested that abnormal GFC in the brain areas associated with sensorimotor network and DMN might underlie the pathophysiology of BSP, which provided a new perspective to understand BSP. GFC in the right postcentral gyrus/precentral gyrus/paracentral lobule might be utilized as a latent biomarker to differentiate patients with BSP from controls.

The thermodynamic brain and the evolution of intellect: the role of mental energy

The living state is low entropy, highly complex organization, yet it is part of the energy cycle of the environment. Due to the recurring presence of the resting state, stimulus and its response form a thermodynamic cycle of perception that can be modeled by the Carnot engine. The endothermic reversed Carnot engine relies on energy from the environment to increase entropy (i.e., the synaptic complexity of the resting state). High entropy relies on mental energy, which represents intrinsic motivation and focuses on the future. It increases freedom of action. The Carnot engine can model exothermic, negative emotional states, which direct the focus on the past. The organism dumps entropy and energy to its environment, in the form of aggravation, anxiety, criticism, and physical violence. The loss of mental energy curtails freedom of action, forming apathy, depression, mental diseases, and immune problems. Our improving intuition about the brain's intelligent computations will allow the development of new treatments for mental disease and novel find applications in robotics and artificial intelligence.

A spiking neural network model of spatial and visual mental imagery

Mental imagery has long been of interest to the cognitive and neurosciences, but how it manifests itself in the mind and brain still remains unresolved. In pursuit of this, we built a spiking neural model that can perform mental rotation and mental map scanning using strategies informed by the psychology and neuroscience literature. Results: When performing mental map scanning, reaction times (RTs) for our model closely match behavioural studies (approx. 50 ms/cm), and replicate the cognitive penetrability of the task. When performing mental rotation, our model's RTs once again closely match behavioural studies (model: 55-65°/s; studies: 60°/s), and performed the task using the same task strategy (whole unit rotation of simple and familiar objects through intermediary points). Overall, our model suggests: (1) vector-based approaches to neuro-cognitive modelling are well equipped to re-produce behavioural findings, and (2) the cognitive (in)penetrability of imagery tasks may depend on whether or not the task makes use of (non)symbolic processing.

Removing uncertainty in neural networks

Neuroscientists draw lines of separation among structures and functions that they judge different, arbitrarily excluding or including issues in our description, to achieve positive demarcations that permits a pragmatic treatment of the nervous activity based on regularity and uniformity. However, uncertainty due to disconnectedness, lack of information and absence of objects' sharp boundaries is a troubling issue that prevents these scientists to select the required proper sets/subsets during their experimental assessment of natural and artificial neural networks. Starting from the detection of metamorphoses of shapes inside a Euclidean manifold, we propose a technique to detect the topological changes that occur during their reciprocal interactions and shape morphing. This method, that allows the detection of topological holes development and disappearance, makes it possible to solve the problem of uncertainty in the assessment of countless dynamical phenomena, such as cognitive processes, protein homeostasis deterioration, fire propagation, wireless sensor networks, migration flows, and cosmic bodies analysis.

Projective mechanisms subtending real world phenomena wipe away cause effect relationships

Causal relationships lie at the very core of scientific description of biophysical phenomena. Nevertheless, observable facts involving changes in system shape, dimension and symmetry may elude simple cause and effect inductive explanations. Here we argue that numerous physical and biological phenomena such as chaotic dynamics, symmetry breaking, long-range collisionless neural interactions, zero-valued energy singularities, and particle/wave duality can be accounted for in terms of purely topological mechanisms devoid of causality. We illustrate how simple topological claims, seemingly far away from scientific inquiry (e.g., "given at least some wind on Earth, there must at all times be a cyclone or anticyclone somewhere"; "if one stirs to dissolve a lump of sugar in a cup of coffee, it appears there is always a point without motion"; "at any moment, there is always a pair of antipodal points on the Earth's surface with equal temperatures and barometric pressures") reflect the action of non-causal topological rules. To do so, we introduce some fundamental topological tools and illustrate how phenomena such as double slit experiments, cellular mechanisms and some aspects of brain function can be explained in terms of geometric projections and mappings, rather than local physical effects. We conclude that unavoidable, passive, spontaneous topological modifications may lead to novel functional biophysical features, independent of exerted physical forces, thermodynamic constraints, temporal correlations and probabilistic a priori knowledge of previous cases.

Decoding disparity categories in 3-dimensional images from fMRI data using functional connectivity patterns

Humans use binocular disparity to extract depth information from two-dimensional retinal images in a process called stereopsis. Previous studies usually introduce the standard univariate analysis to describe the correlation between disparity level and brain activity within a given brain region based on functional magnetic resonance imaging (fMRI) data. Recently, multivariate pattern analysis has been developed to extract activity patterns across multiple voxels for deciphering categories of binocular disparity. However, the functional connectivity (FC) of patterns based on regions of interest or voxels and their mapping onto disparity category perception remain unknown. The present study extracted functional connectivity patterns for three disparity conditions (crossed disparity, uncrossed disparity, and zero disparity) at distinct spatial scales to decode the binocular disparity. Results of 27 subjects' fMRI data demonstrate that FC features are more discriminatory than traditional voxel activity features in binocular disparity classification. The average binary classification of the whole brain and visual areas are respectively 87% and 79% at single subject level, and thus above the chance level (50%). Our research highlights the importance of exploring functional connectivity patterns to achieve a novel understanding of 3D image processing.

The frequent subgraphs of the connectome of the human brain

In mapping the human structural connectome, we are in a very fortunate situation: one can compute and compare graphs, describing the cerebral connections between the very same, anatomically identified small regions of the gray matter among hundreds of human subjects. The comparison of these graphs has led to numerous recent results, as the (1) discovery that women's connectomes have deeper and richer connectivity-related graph parameters like those of men, or (2) the description of more and less conservatively connected lobes and cerebral regions, and (3) the discovery of the phenomenon of the consensus connectome dynamics. Today one of the greatest challenges of brain science is the description and modeling of the circuitry of the human brain. For this goal, we need to identify sub-circuits that are present in almost all human subjects and those, which are much less frequent: the former sub-circuits most probably have functions with general importance, the latter sub-circuits are probably related to the individual variability of the brain structure and function. The present contribution describes the frequent connected subgraphs of at most six edges in the human brain. We analyze these frequent graphs and also examine sex differences in these graphs: we demonstrate numerous connected subgraphs that are more frequent in female or male connectomes. While there is no difference in the number of k edge connected subgraphs in males or females for k = 1 , and for k = 2 males have slightly more frequent subgraphs, for k = 6 there is a very strong advantage in the case of female braingraphs. Our data source is the public release of the Human Connectome Project, and we are applying the data of 426 human subjects in this study.

Points and lines inside human brains

Starting from the tenets of human imagination, i.e., the concepts of lines, points and infinity, we provide a biological demonstration that the skeptical claim "human beings cannot attain knowledge of the world" holds true. We show that the Euclidean account of the point as "that of which there is no part" is just a conceptual device produced by our brain, untenable in our physical/biological realm: currently used terms like "lines, surfaces and volumes" label non-existent, arbitrary properties. We elucidate the psychological and neuroscientific features hardwired in our brain that lead us humans to think to points and lines as truly occurring in our environment. Therefore, our current scientific descriptions of objects' shapes, graphs and biological trajectories in phase spaces need to be revisited, leading to a proper portrayal of the real world's events: miniscule bounded physical surface regions stand for the basic objects in a traversal of spacetime, instead of the usual Euclidean points. Our account makes it possible to erase of a painstaking problem that causes many theories to break down and/or being incapable of describing extreme events: the unwanted occurrence of infinite values in equations. We propose a novel approach, based on point-free geometrical standpoints, that banishes infinitesimals, leads to a tenable physical/biological geometry compatible with human reasoning and provides a region-based topological account of the power laws endowed in nervous activities. We conclude that points, lines, volumes and infinity do not describe the world, rather they are fictions introduced by ancient surveyors of land surfaces.

The multidimensional brain

Brain activity takes place in three spatial-plus time dimensions. This rather obvious claim has been recently questioned by papers that, taking into account the big data outburst and novel available computational tools, are starting to unveil a more intricate state of affairs. Indeed, various brain activities and their correlated mental functions can be assessed in terms of trajectories embedded in phase spaces of dimensions higher than the canonical ones. In this review, I show how further dimensions may not just represent a convenient methodological tool that allows a better mathematical treatment of otherwise elusive cortical activities, but may also reflect genuine functional or anatomical relationships among real nervous functions. I then describe how to extract hidden multidimensional information from real or artificial neurodata series, and make clear how our mind dilutes, rather than concentrates as currently believed, inputs coming from the environment. Finally, I argue that the principle "the higher the dimension, the greater the information" may explain the occurrence of mental activities and elucidate the mechanisms of human diseases associated with dimensionality reduction.

Simulation of retinal ganglion cell response using fast independent component analysis

Advances in neurobiology suggest that neuronal response of the primary visual cortex to natural stimuli may be attributed to sparse approximation of images, encoding stimuli to activate specific neurons although the underlying mechanisms are still unclear. The responses of retinal ganglion cells (RGCs) to natural and random checkerboard stimuli were simulated using fast independent component analysis. The neuronal response to stimuli was measured using kurtosis and Treves-Rolls sparseness, and the kurtosis, lifetime and population sparseness were analyzed. RGCs exhibited significant lifetime sparseness in response to natural stimuli and random checkerboard stimuli. About 65 and 72% of RGCs do not fire all the time in response to natural and random checkerboard stimuli, respectively. Both kurtosis of single neurons and lifetime response of single neurons values were larger in the case of natural than in random checkerboard stimuli. The population of RGCs fire much less in response to random checkerboard stimuli than natural stimuli. However, kurtosis of population sparseness and population response of the entire neurons were larger with natural than random checkerboard stimuli. RGCs fire more sparsely in response to natural stimuli. Individual neurons fire at a low rate, while the occasional "burst" of neuronal population transmits information efficiently.

Comparing advanced graph-theoretical parameters of the connectomes of the lobes of the human brain

Deep, classical graph-theoretical parameters, like the size of the minimum vertex cover, the chromatic number, or the eigengap of the adjacency matrix of the graph were studied widely by mathematicians in the last century. Most researchers today study much simpler parameters of braingraphs or connectomes which were defined in the last twenty years for enormous networks-like the graph of the World Wide Web-with hundreds of millions of nodes. Since the connectomes, describing the connections of the human brain, typically contain several hundred vertices today, one can compute and analyze the much deeper, harder-to-compute classical graph parameters for these, relatively small graphs of the brain. This deeper approach has proven to be very successful in the comparison of the connectomes of the sexes in our earlier works: we have shown that graph parameters, deeply characterizing the graph connectivity are significantly better in women's connectomes than in men's. In the present contribution we compare numerous graph parameters in the three largest lobes-frontal, parietal, temporal-and in both hemispheres of the human brain. We apply the diffusion weighted imaging data of 423 subjects of the NIH-funded Human Connectome Project, and present some findings, never described before, including that the right parietal lobe contains significantly more edges, has higher average degree, density, larger minimum vertex cover and Hoffman bound than the left parietal lobe. Similar advantages in the deep graph connectivity properties are held for the left frontal versus the right frontal and the right temporal versus the left temporal lobes.

Multidimensional brain activity dictated by winner-take-all mechanisms

A novel demon-based architecture is introduced to elucidate brain functions such as pattern recognition during human perception and mental interpretation of visual scenes. Starting from the topological concepts of invariance and persistence, we introduce a Selfridge pandemonium variant of brain activity that takes into account a novel feature, namely, demons that recognize short straight-line segments, curved lines and scene shapes, such as shape interior, density and texture. Low-level representations of objects can be mapped to higher-level views (our mental interpretations): a series of transformations can be gradually applied to a pattern in a visual scene, without affecting its invariant properties. This makes it possible to construct a symbolic multi-dimensional representation of the environment. These representations can be projected continuously to an object that we have seen and continue to see, thanks to the mapping from shapes in our memory to shapes in Euclidean space. Although perceived shapes are 3-dimensional (plus time), the evaluation of shape features (volume, color, contour, closeness, texture, and so on) leads to n-dimensional brain landscapes. Here we discuss the advantages of our parallel, hierarchical model in pattern recognition, computer vision and biological nervous system's evolution.

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

Topodynamics of metastable brains

The brain displays both the anatomical features of a vast amount of interconnected topological mappings as well as the functional features of a nonlinear, metastable system at the edge of chaos, equipped with a phase space where mental random walks tend towards lower energetic basins. Nevertheless, with the exception of some advanced neuro-anatomic descriptions and present-day connectomic research, very few studies have been addressing the topological path of a brain embedded or embodied in its external and internal environment. Herein, by using new formal tools derived from algebraic topology, we provide an account of the metastable brain, based on the neuro-scientific model of Operational Architectonics of brain-mind functioning. We introduce a "topodynamic" description that shows how the relationships among the countless intertwined spatio-temporal levels of brain functioning can be assessed in terms of projections and mappings that take place on abstract structures, equipped with different dimensions, curvatures and energetic constraints. Such a topodynamical approach, apart from providing a biologically plausible model of brain function that can be operationalized, is also able to tackle the issue of a long-standing dichotomy: it throws indeed a bridge between the subjective, immediate datum of the naïve complex of sensations and mentations and the objective, quantitative, data extracted from experimental neuro-scientific procedures. Importantly, it opens the door to a series of new predictions and future directions of advancement for neuroscientific research.