VII. Our Earthuman Ascent: A Major Evolutionary Transition in Individuality
2. Systems Neuroscience: Multiplex Networks and Critical Function
Dumas, Guillaume, et al. Anatomical Connectivity Influences both Intra- and Inter-Brain Synchronizations. PLoS One. 7/5, 2013. As cited by Kelso, et al, 2013 herein, CNRS and Universite Pierre et Marie Curie Paris neuroscientists are able to quantify an extension of the internal coordination dynamics of a human brain to a dyad of interacting individuals. How much then, we ought to wonder, might our proximate and distant communications be taking upon the essence of a true social and global neurosphere.
Recent development in diffusion spectrum brain imaging combined to functional simulation has the potential to further our understanding of how structure and dynamics are intertwined in the human brain. At the intra-individual scale, neurocomputational models have already started to uncover how the human connectome constrains the coordination of brain activity across distributed brain regions. In parallel, at the inter-individual scale, nascent social neuroscience provides a new dynamical vista of the coupling between two embodied cognitive agents. Using EEG hyperscanning to record simultaneously the brain activities of subjects during their ongoing interaction, we have previously demonstrated that behavioral synchrony correlates with the emergence of inter-brain synchronization. Here, we use a biophysical model to quantify to what extent inter-brain synchronizations are related to the anatomical and functional similarity of the two brains in interaction. Results show a potential dynamical property of the human connectome to facilitate inter-individual synchronizations and thus may partly account for our propensity to generate dynamical couplings with others. (Abstract)
Expert, Paul, et al. Self-Similar Correlation Function in Brain Resting-State Functional Magnetic Resonance Imaging. Journal of the Royal Society Interface. Online September 22, 2010. In contrast to 2011 Greg Paperin, et al (2011) in this journal who describe a generic complex system, this contribution explains its iconic presence in human cerebral function. A research team from Imperial College London and Northwestern University that includes Henrik Jensen, Kim Christensen, and Dante Chialvo, as a meld of statistical physics with nonlinear science via self-organized criticalities, report their findings of a spatial and temporal scale invariance across many neural realms. By so doing, as cited next, nature’s essential complementarity is once again revealed. Our brains are equally poised in a mutual balance of local concerns within a global context, the same yang and yin, me and we, entity and environment, as everywhere else.
An important problem in neuroscience is to understand the mechanism by which the human brain’s 100 billion neurons and 100 trillion synapses manage to produce this large repertoire of cortical configurations in a flexible manner. In addition, it is recognized that temporal correlations across such configurations cannot be arbitrary, but they need to meet two conflicting demands: while diverse cortical areas should remain functionally segregated from each other, they must still perform as a collective, i.e. they are functionally integrated. We show that this two-point correlation function extracted from resting-state functional magnetic resonance imaging data exhibits self-similarity in space and time. In space, self-similarity is revealed by considering three successive spatial coarse-graining steps while in time it is revealed by the 1/f frequency behaviour of the power spectrum. The uncovered dynamical self-similarity implies that the brain is spontaneously at a continuously changing (in space and time) intermediate state between two extremes, one of excessive cortical integration and the other of complete segregation. (1)
Fernando, Chrisantha, et al. Selectionist and Evolutionary Approaches to Brain Function. Frontiers in Computational Neuroscience. 6/Art. 24, 2012. With Eors Szathmary and Phil Husbands, another contribution that articulates the deep affinity of neural activities with life’s long iterative development. As Richard Watson, Hava Siegelmann, John Mayfield, Steven Frank, and increasing number contend, this achieves a 21st century appreciation of how “natural selection” actually applies. While a winnowing optimization toward “good enough to survive” goes on, the discovery of dynamic, learning-like, algorithms can now provide a prior genetic-like guidance.
We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman’s theory of neuronal group selection, Changeux’s theory of synaptic selection and selective stabilization of pre-representations, Seung’s Darwinian synapse, Loewenstein’s synaptic melioration, Adam’s selfish synapse, and Calvin’s replicating activity patterns. Except for the last two, the proposed mechanisms are selectionist but not truly Darwinian, because no replicators with information transfer to copies and hereditary variation can be identified in them. Bayesian models and reinforcement learning are formally in agreement with selection dynamics. A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity, and variability) as the most powerful mechanism for search in a sparsely occupied search space. Finally, we review our recent attempts to construct and analyze simple models of true Darwinian evolutionary units in the brain in terms of connectivity and activity copying of neuronal groups. (Abstract)
Fornito, Alex, et al. Bridging the Gap between Connectome and Transcriptome. Trends in Cognitive Sciences. 23/1, 2019. Into the year 2019, advances such as imaging techniques and computational graphics allow Monash University, Australia clinical neuroscientists to discern spatial and temporal relations from DNA nucleotides to protein interactions via innate network paths. The wide use of –omic suffixes implies how important the genetic factors are in neural activity. The article glossary contains a Hierarchical Modularity term as another example of how nature’s universal complexity is so manifest in our own cerebral raiment.
The recent construction of brain-wide gene expression atlases, which measure the transcriptional activity of thousands of genes in multiple anatomical locations, has made it possible to connect spatial variations in gene expression to distributed properties of connectome structure and function. These analyses have revealed that spatial patterning of gene expression and neuronal connectivity are closely linked, following broad spatial gradients that track regional variations in microcircuitry, inter-regional connectivity, and functional specialisation. Superimposed on these gradients are more specific associations between gene expression and connectome topology that appear conserved across diverse species and different resolution scales. (Abstract)
Fornito, Alex, et al. Competitive and Cooperative Dynamics of Large-Scale Brain Functional Networks Supporting Recollection. Proceedings of the National Academy of Sciences. 109/12788, 2012. University of Melbourne and Cambridge University behavioral neuroscientists further verify the presence of nature’s balanced complementarity of a relative agent-like neuronal or network semi-autonomy and a more relational modularity across cerebral anatomy and activities. Consider with Olaf Sporns 2012 herein who uses the phrase “segregation and integration” to similarly describe.
Analyses of functional interactions between large-scale brain networks have identified two broad systems that operate in apparent competition or antagonism with each other. One system, termed the default mode network (DMN), is thought to support internally oriented processing. The other system acts as a generic external attention system (EAS) and mediates attention to exogenous stimuli. Using methods to isolate task-related, context-dependent changes in functional connectivity between these systems, we show that increased cooperation between the DMN and a specific right-lateralized frontoparietal component of the EAS is associated with more rapid memory recollection. We also show that these cooperative dynamics are facilitated by a dynamic reconfiguration of the functional architecture of the DMN into core and transitional modules, with the latter serving to enhance integration with frontoparietal regions. In particular, the right posterior cingulate cortex may act as a critical information-processing hub that provokes these context-dependent reconfigurations from an intrinsic or default state of antagonism. Our findings highlight the dynamic, context-dependent nature of large-scale brain dynamics and shed light on their contribution to individual differences in behavior. (Abstract)
Freeman, Walter. A Neurobiological Theory of Meaning in Perception. International Journal of Bifurcation and Chaos. 13/9, 2003. The first of a five part series, this article is subtitled: “Information and Meaning in Nonconvergent and Nonlocal Brain Dynamics.” Highly technical neuroscience which argues that the current emphasis on information processing misses the main activity going on: an organism’s attempt to make sense of its environment. In this regard, information and meaning might appear as left and right hemisphere complements.
The aim of this tutorial is to document a novel approach to brain function, in which the key to understanding is the capacity of brains for self-organization. (2493)
Friston, Karl. The History of the Future of the Bayesian Brain. NeuroImage. 62/1230, 2012. After being immersed for two decades in British and American neuroscience, the now Scientific Director of the Wellcome Trust Center for Neuroimaging surveys the discovery in those years of a cerebral dynamic self-organization, along with a cognitive faculty distinguished by an interactive responsiveness via hierarchical scales in congruence with its greater environment. Such a “Bayesian brain” is busy with optimizing its “beliefs” about any input or reply, so as to minimize any expense of “free energy.” As Friston speaks for the field, the approach, via “statistical physics and information theory,” can be seen to reveal another means to join human and universe.
Gazzaniga, Michael, ed. The New Cognitive Neurosciences. Cambridge: MIT Press, 2000. A large book by leading authorities covering a wide range of brain development, evolution, and cogitation.
Giusti, Chad, et al. Two’s Company, Three (or More) is a Simplex: Algebraic-Topological Tools for Understanding Higher-Order Structure in Neural Data. arXiv:1601.01704. University of Pennsylvania neuroscientists Giusti and Daniella Bassett, and mathematician Robert Ghrist, combine imaging techniques, network theories, and topological principles to press the frontiers of brain architecture studies. In other project postings this year they are joined by UPs Ann Sizemore (search), Edward Bullmore of Cambridge University, and others: Closures and Cavities in the Human Connectome (1608.03520), Classification of Weighted Networks through Mesoscale Homological Features Journal of Complex Networks (Online August, 2016), Small-World Brain Networks Revisited (1608.05665) and Multi-Scale Brain Networks at (1608.08828). Of further note, the paper advises a use of algebraic topologies, persistent homology, signs of universality, and so on to quantify cerebral faculties. And incredibly these exact phrases also appear in a concurrent, far removed posting by European astronomers about The Topology of the Cosmic Web (1608.0451. search Pranav). What great discovery of a cosmic connectome is arising in our midst?
The language of graph theory, or network science, has proven to be an exceptional tool for addressing myriad problems in neuroscience. Yet, the use of networks is predicated on a critical simplifying assumption: that the quintessential unit of interest in a brain is a dyad -- two nodes (neurons or brain regions) connected by an edge. While rarely mentioned, this fundamental assumption inherently limits the types of neural structure and function that graphs can be used to model. Here, we describe a generalization of graphs that overcomes these limitations, thereby offering a broad range of new possibilities in terms of modeling and measuring neural phenomena. Specifically, we explore the use of simplicial complexes, a theoretical notion developed in the field of mathematics known as algebraic topology, which is now becoming applicable to real data due to a rapidly growing computational toolset. We review the underlying mathematical formalism as well as the budding literature applying simplicial complexes to neural data, from electrophysiological recordings in animal models to hemodynamic fluctuations in humans. (1601.01704 Abstract)
Graham, Daniel, et al. Network Communication in the Brain. Network Neuroscience. 4/4, 2020. Hobart and William Smith Colleges, Indiana University and McGill University researchers introduce a special issue with this topical subject. Among the dozen entries are Communicability Distance Reveals Hidden Patterns of Alzheimer’s Disease by El Lella and E. Estrada, and Network Topology of the Marmoset Connectome by Z-Q. Liu, et al (abstract below).
Communication models describe the flow of signals among nodes of a network. In neural systems, communication models are increasingly applied to investigate network dynamics across the whole brain, with the ultimate aim to understand how signal flow gives rise to brain function. Communication models range from diffusion-like processes to those related to infectious disease transmission and those inspired by engineered communication systems like the internet. This Focus Feature brings together novel investigations of a diverse range of mechanisms and strategies that could shape communication in mammal whole-brain networks. (Abstract)
Grossberg, Stephen. Adaptive Resonance Theory: How a Brain Learns to Consciously attend, Learn, and Recognize a Changing World. Neural Networks. 37/1, 2013. In a lead article for the Twenty-fifth Anniversary Issue (see Kelso also), the Boston University computational neuroscientist, with colleague Gail Carpenter, updates the state of this insightful approach. Our life long neural capacity is seen as much engaged with learning and prediction, by virtue of “complementary cortical streams for attentional recognition and orienting action.” See also in this issue, “Essentials of the Self-Organizing Map” by its founder Teuvo Kohonen, and “Dreaming of Mathematical Neuroscience for a Half a Century” by the pioneer Japanese theorist Shun-ichi Amari.
Grossberg, Stephen. Linking Mind to Brain: The Mathematics of Biological Intelligence. Notices of the American Mathematical Society. 47/11, 2000. The Boston University neural network theoretician considers the deep principles that connect cerebral anatomy and physiology with dynamic streams of thought.
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