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New research charts how little we know about the brain

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Here at New York Times:

Scientists have puzzled out profoundly important insights about how the brain works, like the way the mammalian brain navigates and remembers places, work that won the 2014 Nobel Prize in Physiology or Medicine for a British-American and two Norwegians.

Yet the growing body of data — maps, atlases and so-called connectomes that show linkages between cells and regions of the brain — represents a paradox of progress, with the advances also highlighting great gaps in understanding.

Larry Abbott, at Columbia University, has helped develop theoretical models of how perception works in weakly electric fish.

So many large and small questions remain unanswered. How is information encoded and transferred from cell to cell or from network to network of cells? Science found a genetic code but there is no brain-wide neural code; no electrical or chemical alphabet exists that can be recombined to say “red” or “fear” or “wink” or “run.” And no one knows whether information is encoded differently in various parts of the brain. More.

Sounds like, as long as there is funding, they’ll never want for work.

Hat tip: Stephanie West Allen at Brains on Purpose

48 Replies to “New research charts how little we know about the brain

  1. 1
    Dionisio says:

    “Theory is beautiful and internally consistent. Biology, not so much.” – Cori Bargmann of Rockefeller University, who helped lead the N.I.H. committee that set a plan for future neuroscience research.

  2. 2
    Dionisio says:

    The big data challenges of connectomics


    The structure of the nervous system is extraordinarily complicated because individual neurons are interconnected to hundreds or even thousands of other cells in networks that can extend over large volumes.

    Mapping such networks at the level of synaptic connections, a field called connectomics, began in the 1970s with a the study of the small nervous system of a worm and has recently garnered general interest thanks to technical and computational advances that automate the collection of electron-microscopy data and offer the possibility of mapping even large mammalian brains.

    However, modern connectomics produces ‘big data’, unprecedented quantities of digital information at unprecedented rates, and will require, as with genomics at the time, breakthrough algorithmic and computational solutions.


  3. 3
    Dionisio says:

    Focus on big data


    The number of big data projects in neuroscience, such as the BRAIN initiative in the United States or the Human Brain Mapping initiative in Europe, has been increasing in recent years.

    Will such big data efforts become the modus operandi in neuroscience, replacing smaller scale, hypothesis-driven science?

    How much insight will be gained from such projects?

    What are the best ways to go about conducting such projects and sharing the data that they produce?


  4. 4
    Dionisio says:

    We ain’t seen nothing yet. 🙂

    Unlike collecting molecular data, investigating the connectivity and activity of the nervous system is a challenge unique to the neurosciences.

    Electron microscopy–based connectomics, while still in its infancy, is already producing data sets of staggering size. Data acquisition rates are similarly colossal.

    As the field moves toward the high-resolution reconstructions of entire fly and mouse brains, this torrent of data will keep growing, and it is unclear how it will be accommodated by computer systems or distributed to end-users.

    Focus on big data

  5. 5
    Dionisio says:

    While many of us want to see the huge gaps in biological science shrinking ASAP, apparently some old-guard folks out there might prefer those gaps to remain. Why?

    Hopefully that’s not the case in neuroscience. 🙂

  6. 6
    Dionisio says:

    Although it’s impossible to predict the size of the effect big data will have on the way neuroscience research is done and what progress will be made in understanding the brain, it’s clear that the wave of big data is not only coming, it’s here to stay.

    The ultimate success or failure of such efforts will be determined by their ability to be integrated with other types of data and by the insights that they provide.

    We hope that this focus will provide an overview of the types of big data efforts underway in the neurosciences, including the challenges associated with them, and we look forward to the exciting results that follow.

    Focus on big data

  7. 7
    Mapou says:

    That is the main problem facing the AI research community. The only way to design a truly intelligent machine is to copy what the brain does. Unfortunately, the brain is not giving away its secrets just for the asking. Even if we had a complete and accurate connectome of the brain, we would not be the better for it. It would still take researchers hundreds, if not thousands of years to figure out how it works.

    True AI is indeed coming to the world and it is coming much sooner than most people expect. However, it will come from neither academia nor industry.

  8. 8
    Edward says:

    Holy Smokes! We had better get busy and figure all of this stuff out. We only have 10 to 15 years before the singularity begins.

  9. 9
    Mapou says:

    Here is the latest from Elon Musk, the CEO of Tesla and CTO of SpaceX, the man who, not too long ago, declared that AI research is “like summoning the demon.” Now Musk is saying the threat is closer than we think (although I seriously doubt anybody has anything close to working like the brain). He posted a comment at Edge.org and immediately deleted it but not before others copied it.

    The risk of something seriously dangerous happening is in the five year timeframe. 10 years at most.

  10. 10
    tjguy says:

    Rather than making the bacterial flagellum the subject of debate on ID, isn’t the brain a far better example?

    I guess the fact that we don’t understand it very well makes it difficult to write about clearly and use it as an example, but the flagellum is nothing compared to the brain!

    Perhaps the fact that low life forms do not have a brain like ours is also a factor, but just how did organisms with little to no brain turn into humans?

    How in the world do chemicals turn into consciousness?

    How did the wiring to the brain coincide with the evolution of the new organs?

    Where did the information come from for the software that controls the new bodily systems, organs, abilities, reflexes, etc.?

    “I’m sure one day science will figure it all out some day”

    This statement of belief represents the faith of atheists in science, their worldview, and their understanding of life.

    There just are no guarantees! In this realm, we both need to have faith!

  11. 11
    Robert Byers says:

    I don’t think these nobel winners did anything other then fool around with a road system. They didn’t figure anything out. jUst that stuff is moving around.
    why not just say the encoding is simple memory operation.
    not so simple but its just memory road system.
    we are a hugh memory machine and not a brain with a memory partner.
    encoding is just memorization at a molecular level.
    why not just a simple idea of a soul meshed to a memory machine.?
    no mystery.

  12. 12
    Mapou says:

    Elon Musk:

    The pace of progress in artificial intelligence (I’m not referring to narrow AI) is incredibly fast. Unless you have direct exposure to groups like Deepmind, you have no idea how fast—it is growing at a pace close to exponential. The risk of something seriously dangerous happening is in the five year time frame. 10 years at most. This is not a case of crying wolf about something I don’t understand.
    I am not alone in thinking we should be worried. The leading AI companies have taken great steps to ensure safety. They recognize the danger, but believe that they can shape and control the digital superintelligences and prevent bad ones from escaping into the Internet. That remains to be seen…

  13. 13
    bornagain77 says:

    as to:

    Larry Abbott, 64, a former theoretical physicist,,,
    The question now on his mind, and that of many neuroscientists, is how larger groups, thousands of neurons, work together — whether to produce an action, like reaching for a cup, or to perceive something, like a flower.,,,,,
    His tools are computers and equations,

    The material brain does not, and can not ever, ‘perceive’ anything. The hard problem’ of consciousness proves this:

    David Chalmers on Consciousness (Philosophical Zombies and the Hard Problem) – video

    a bit more in-depth look at the ‘hard problem’ is here:

    The impossible Problem of Consciousness – video

    Moreover, Abbott’s very ‘tools’, computers and equations, prove that the ‘hard problem’ of consciousness will never be solved by computers and equations.
    Alan Turing, who was at the forefront in inventing computers, infamously thought that his brain was merely a ‘Turing Machine’. This following poem humorously teases the ‘I’m merely a machine’ notion of Turing

    Alan’s brain tells his mind, “Don’t you blow it.”
    Listen up! (Even though it’s inchoate.)
    “My claim’s neat and clean.
    I’m a Turing Machine!”
    … ‘Tis somewhat curious how he could know it.

    Yet, in spite of Turing’s irrational belief that he was merely a machine, and although I don’t believe Turing ever actually admitted it, Alan Turing actually succeeded in extending Godel’s incompleteness theorem to material computers, and in doing so, thus undermining his own materialistic belief that he was merely a machine in the process. This point is illustrated in the following video and quote:

    Alan Turing & Kurt Godel – Incompleteness Theorem and Human Intuition – video (with Gregory Chaitin)
    Quote from video: Turing recast incompleteness in terms of computers and showed that since they are logic machines, there would always be some problems they would never solve. A machine fed one of these problems would never stop (halting problem). And worse, Turing proved there was no way of telling beforehand which these problems were.”

    And since ‘“an infinite number of true mathematical theorems exist that cannot be proved from any finite system of axioms.”,,,

    The Limits Of Reason – Gregory Chaitin – 2006
    Excerpt: “an infinite number of true mathematical theorems exist that cannot be proved from any finite system of axioms.”,,,

    Then computers, and the notion that we are merely turing machines, by default, will never be able explain the mathematical intuition of humans. Godel himself put the situation like this:

    “Either mathematics is too big for the human mind or the human mind is more than a machine”
    ~ Kurt Godel

    Gödel’s philosophical challenge (to Turing) – Wilfried Sieg – lecture video
    (“The human mind infinitely surpasses any finite machine.”)

    In fact, 6 months ago this challenge was issued to materialists who believe they are merely a turing machine,

    The mathematical world – James Franklin – 7 April 2014
    Excerpt: “the intellect (is) immaterial and immortal. If today’s naturalists do not wish to agree with that, there is a challenge for them. ‘Don’t tell me, show me’: build an artificial intelligence system that imitates genuine mathematical insight. There seem to be no promising plans on the drawing board.”,,,
    James Franklin is professor of mathematics at the University of New South Wales in Sydney.

    Of related note, deep mathematical insight that leads to a physical discovery is said to be guided by a sense of ‘mathematical beauty’:

    “It appears that the Creator shares the mathematicians sense of beauty”
    Alex Vilenkin – Many Worlds in One: (page 201)

    Yet ‘beauty’ is an argument for theism, and is not an argujment for materialism:

    Aesthetic Arguments for the Existence of God:
    Excerpt: Beauty,,, can be appreciated only by the mind. This would be impossible, if this `idea’ of beauty were not found in the mind in a more perfect form.

    Although the following article is somewhat technical, it is almost comical to read how every approach, in which the materialists tried to reduce the subjective sense of beauty to a mere material mechanism, was thwarted.

    Beauty Evades the Clutches of Materialism – March 27, 2013
    per Evolution News and Views

    Why computers will never have genuine mathematical insight is summed up nicely by Dr. Berlinski in the following interview:

    An Interview with David Berlinski – Jonathan Witt
    Berlinski: There is no argument against religion that is not also an argument against mathematics. Mathematicians are capable of grasping a world of objects that lies beyond space and time ….
    Interviewer:… Come again(?) …
    Berlinski: No need to come again: I got to where I was going the first time. The number four, after all, did not come into existence at a particular time, and it is not going to go out of existence at another time. It is neither here nor there. Nonetheless we are in some sense able to grasp the number by a faculty of our minds. Mathematical intuition is utterly mysterious. So for that matter is the fact that mathematical objects such as a Lie Group or a differentiable manifold have the power to interact with elementary particles or accelerating forces. But these are precisely the claims that theologians have always made as well – that human beings are capable by an exercise of their devotional abilities to come to some understanding of the deity; and the deity, although beyond space and time, is capable of interacting with material objects.

    Of related note, the following paper gives the ‘secret’ away for mathematically defeating the infamous ‘Turing test’ in a fairly simple manner:

    Algorithmic Information Theory, Free Will and the Turing Test – Douglas G. Robertson – 1999
    Excerpt: Chaitin’s Algorithmic Information Theory shows that information is conserved under formal mathematical operations and, equivalently, under computer operations. This conservation law puts a new perspective on many familiar problems related to artificial intelligence. For example, the famous “Turing test” for artificial intelligence could be defeated by simply asking for a new axiom in mathematics. Human mathematicians are able to create axioms, but a computer program cannot do this without violating information conservation. Creating new axioms and free will are shown to be different aspects of the same phenomenon: the creation of new information.
    “… no operation performed by a computer can create new information.”

  14. 14
    Dionisio says:


    The emerging field of neural connectomics is rapidly evolving. New cellular and molecular tools allowing ever more sophisticated characterization of individual cells are paving the way toward complete neural circuit diagrams of model organisms. Human brain imaging is elucidating the functional and structural connectivity maps underlying the human connectome.


  15. 15
    Dionisio says:

    Beyond the Connectome: The Dynome

    The human connectome will provide a detailed mapping of the brain’s connectivity, with fundamental insights for health and disease.

    However, further understanding of brain function and dysfunction will require an integrated framework that links brain connectivity with brain dynamics, as well as the biological details that relate this connectivity more directly to function.

    DOI: http://dx.doi.org/10.1016/j.neuron.2014.08.016

  16. 16
    Dionisio says:

    designed to serve specific functions. ?

    Illuminating the Multifaceted Roles of Neurotransmission in Shaping Neuronal Circuitry

    Across the nervous system, neurons form highly stereotypic patterns of synaptic connections that are designed to serve specific functions.

    Mature wiring patterns are often attained upon the refinement of early, less precise connectivity.

    Much work has led to the prevailing view that many developing circuits are sculpted by activity-dependent competition among converging afferents, which results in the elimination of unwanted synapses and the maintenance and strengthening of desired connections.

    Studies of the vertebrate retina, however, have recently revealed that activity can play a role in shaping developing circuits without engaging competition among converging inputs that differ in their activity levels.

    Such neurotransmission-mediated processes can produce stereotypic wiring patterns by promoting selective synapse formation rather than elimination. [how?]

    We discuss how the influence of transmission may also be limited by circuit design and further highlight the importance of transmission beyond development in maintaining wiring specificity and synaptic organization of neural circuits.

    DOI: http://dx.doi.org/10.1016/j.neuron.2014.08.029

  17. 17
    Dionisio says:

    #16 follow-up

    Across the nervous system, neurons form highly stereotypic patterns of synaptic connections that are designed to serve specific functions.

    DOI: http://dx.doi.org/10.1016/j.neuron.2014.08.029

    Say what?

  18. 18
    Robert Byers says:

    Beauty does not exist. its simply a awareness of accuracy. or symmetry.
    so our perceiving of beauty is just perceiving accuracy.
    I’m not sure its a case against materialism.

  19. 19
    Dionisio says:

    Mind, Powered

    Neuroscientist Eric Leuthardt talks about the science and technology behind brain-computer interfaces.


  20. 20
    Dionisio says:

    Brain Region Mapping Using Global Metabolomics

    DOI: http://dx.doi.org/10.1016/j.chembiol.2014.09.016

    Historically, studies of brain metabolism have been based on targeted analyses of a limited number of metabolites.

    Here we present an untargeted mass spectrometry-based metabolomic strategy that has successfully uncovered differences in a broad array of metabolites across anatomical regions of the mouse brain.

    The NSG immunodeficient mouse model was chosen because of its ability to undergo humanization leading to numerous applications in oncology and infectious disease research.

    Metabolic phenotyping by hydrophilic interaction liquid chromatography and nanostructure imaging mass spectrometry revealed both water-soluble and lipid metabolite patterns across brain regions.

    Neurochemical differences in metabolic phenotypes were mainly defined by various phospholipids and several intriguing metabolites including carnosine, cholesterol sulfate, lipoamino acids, uric acid, and sialic acid, whose physiological roles in brain metabolism are poorly understood.

    This study helps define regional homeostasis for the normal mouse brain to give context to the reaction to pathological events.

  21. 21
    Dionisio says:

    Rhythmic Rewiring

    Circadian neurons in fruit flies form synapses with different, noncircadian brain regions depending on the time of day.


  22. 22
    Dionisio says:

    Brain Structure Rediscovered

    First described in the late 19th century, then lost from the literature for more than 100 years, the vertical occipital fasciculus appears to be important in visual processing.

    “There has to be some way for that dichotomy to merge,” Massachusetts General Hospital and Harvard Medical School’s Jeremy Schmahmann, who was not involved in the new research, told Discovery News, “and the [VOF] is one way for the ‘where’ and the ‘what’ streams in the visual modality to become a unified whole.”

    The study also revealed that the VOF is myelinated differently than other regions of the brain, Yeatman noted. “We don’t know what it means yet, but [the myelination differences are] very consistent across every subject,” he told Discovery. “It opens up some new hypotheses, new directions to study: Why is this structure so different than the other neighboring pathways?”


  23. 23
    Dionisio says:


    Linking the human nervous system to computers is providing unprecedented control of artificial limbs and restoring lost sensory function.


  24. 24
    Dionisio says:

    Probing Starling Sleep

    Birds may provide a new animal model for memory consolidation during sleep, according to research presented at the Society for Neuroscience meeting this week.


  25. 25
    Dionisio says:

    Neurons Regenerate in Rat Spinal Cord

    Researchers at the University of California, San Diego, demonstrate that neural progenitor cells grafted into injured rat spinal cords can grow long axons and connect to host neurons.


  26. 26
    Dionisio says:

    Maternal Inflammation Impacts Offspring

    A mom’s stress could lead to changes in her offspring’s brains that can affect the physiology and behavior of the young, researchers report at the Society for Neuroscience annual meeting.


  27. 27
    Dionisio says:

    Unwinding the Mysteries of the Cellular Clock


  28. 28
    Dionisio says:

    Imagination, reality flow in opposite directions in the brain

    As real as that daydream may seem, its path through your brain runs opposite reality.

    “A really important problem in brain research is understanding how different parts of the brain are functionally connected. What areas are interacting? What is the direction of communication?” says Barry Van Veen, a UW-Madison professor of electrical and computer engineering. “We know that the brain does not function as a set of independent areas, but as a network of specialized areas that collaborate.”

    “There seems to be a lot in our brains and animal brains that is directional, that neural signals move in a particular direction, then stop, and start somewhere else,” says. “I think this is really a new theme that had not been explored.”


  29. 29
    Dionisio says:

    Transiently Increasing cAMP Levels Selectively in Hippocampal Excitatory Neurons during Sleep Deprivation Prevents Memory Deficits Caused by Sleep Loss

    doi: 10.1523/JNEUROSCI.2403-14.2014

    The hippocampus is particularly sensitive to sleep loss. Although previous work has indicated that sleep deprivation impairs hippocampal cAMP signaling, it remains to be determined whether the cognitive deficits associated with sleep deprivation are caused by attenuated cAMP signaling in the hippocampus.

    Further, it is unclear which cell types are responsible for the memory impairments associated with sleep deprivation.

    Transgenic approaches lack the spatial resolution to manipulate specific signaling pathways selectively in the hippocampus, while pharmacological strategies are limited in terms of cell-type specificity.

    Therefore, we used a pharmacogenetic approach based on a virus-mediated expression of a G?s-coupled Drosophila octopamine receptor selectively in mouse hippocampal excitatory neurons in vivo.

    With this approach, a systemic injection with the receptor ligand octopamine leads to increased cAMP levels in this specific set of hippocampal neurons. We assessed whether transiently increasing cAMP levels during sleep deprivation prevents memory consolidation deficits associated with sleep loss in an object–location task.

    Five hours of total sleep deprivation directly following training impaired the formation of object–location memories.

    Transiently increasing cAMP levels in hippocampal neurons during the course of sleep deprivation prevented these memory consolidation deficits.

    These findings demonstrate that attenuated cAMP signaling in hippocampal excitatory neurons is a critical component underlying the memory deficits in hippocampus-dependent learning tasks associated with sleep deprivation.


  30. 30
    Dionisio says:

    Laminar activity in the hippocampus and entorhinal cortex related to novelty and episodic encoding


    The ability to form long-term memories for novel events depends on information processing within the hippocampus (HC) and entorhinal cortex (EC).

    The HC–EC circuitry shows a quantitative segregation of anatomical directionality into different neuronal layers.

    Whereas superficial EC layers mainly project to dentate gyrus (DG), CA3 and apical CA1 layers, HC output is primarily sent from pyramidal CA1 layers and subiculum to deep EC layers.

    Here we utilize this directionality information by measuring encoding activity within HC/EC subregions with 7?T high resolution functional magnetic resonance imaging (fMRI).

    Multivariate Bayes decoding within HC/EC subregions shows that processing of novel information most strongly engages the input structures (superficial EC and DG/CA2–3), whereas subsequent memory is more dependent on activation of output regions (deep EC and pyramidal CA1).

    This suggests that while novelty processing is strongly related to HC–EC input pathways, the memory fate of a novel stimulus depends more on HC–EC output.


  31. 31
    Dionisio says:

    #30 addendum

    Although substantial progress has been made towards demonstrating the involvement of HC subfields and the EC in memory encoding in human imaging studies […], an understanding of circuit-level mechanisms underlying encoding operations in the human HC–EC has not been possible to date.

  32. 32
    Dionisio says:

    ‘Bat-nav’ system enables three-dimensional maneuvers

    Study reveals surprising neural code based on bagel-shaped coordinate system.


    The brains of bats have a neuronal ‘compass’ that enables them to navigate in three dimensions


    Neuroscience: A three-dimensional neural compass


    The discovery that the neural navigation system of the mammalian brain acts in three dimensions sheds light on how mammals orient themselves in complex environments.


    Three-dimensional head-direction coding in the bat brain


    Navigation requires a sense of direction (‘compass’), which in mammals is thought to be provided by head-direction cells, neurons that discharge when the animal’s head points to a specific azimuth.

    However, it remains unclear whether a three-dimensional (3D) compass exists in the brain.

    Here we conducted neural recordings in bats, mammals well-adapted to 3D spatial behaviours, and found head-direction cells tuned to azimuth, pitch or roll, or to conjunctive combinations of 3D angles, in both crawling and flying bats.

    Head-direction cells were organized along a functional–anatomical gradient in the presubiculum, transitioning from 2D to 3D representations.

    In inverted bats, the azimuth-tuning of neurons shifted by 180°, suggesting that 3D head direction is represented in azimuth × pitch toroidal coordinates.

    Consistent with our toroidal model, pitch-cell tuning was unimodal, circular, and continuous within the available 360° of pitch.

    Taken together, these results demonstrate a 3D head-direction mechanism in mammals, which could support navigation in 3D space.


  33. 33
    Dionisio says:

    Brain training with non-action video games enhances aspects of cognition

    doi: 10.3389/fnagi.2014.00277

    Age-related cognitive and brain declines can result in functional deterioration in many cognitive domains, dependency, and dementia.

    A major goal of aging research is to investigate methods that help to maintain brain health, cognition, independent living and wellbeing in older adults.

    This randomized controlled study investigated the effects of 20 1-h non-action video game training sessions with games selected from a commercially available package (Lumosity) on a series of age-declined cognitive functions and subjective wellbeing.

    Two groups of healthy older adults participated in the study, the experimental group who received the training and the control group who attended three meetings with the research team along the study.

    Groups were similar at baseline on demographics, vocabulary, global cognition, and depression status.

    All participants were assessed individually before and after the intervention, or a similar period of time, using neuropsychological tests and laboratory tasks to investigate possible transfer effects.

    The results showed significant improvements in the trained group, and no variation in the control group, in processing speed (choice reaction time), attention (reduction of distraction and increase of alertness), immediate and delayed visual recognition memory, as well as a trend to improve in Affection and Assertivity, two dimensions of the Wellbeing Scale.

    Visuospatial working memory (WM) and executive control (shifting strategy) did not improve.

    Overall, the current results support the idea that training healthy older adults with non-action video games will enhance some cognitive abilities but not others.


  34. 34
    Dionisio says:

    A neural circuit mechanism for regulating vocal variability during song learning

    DOI: http://dx.doi.org/10.7554/eLife.03697

    Motor skill learning is characterized by improved performance and reduced motor variability.

    The neural mechanisms that couple skill level and variability, however, are not known.

    The title issue may have been addressed in this paper, but a number of ‘how’ and ‘why’ questions remain in the details. Work in progress. Just open the link and see how many ‘how’ and ‘why’ questions one could ask about every detail.
    From a software development perspective, no procedural questions can be left unanswered.

  35. 35
    Dionisio says:

    A genuine layer 4 in motor cortex with prototypical synaptic circuit connectivity

    DOI: http://dx.doi.org/10.7554/eLife.05422

    The motor cortex (M1) is classically considered an agranular area, lacking a distinct layer 4 (L4).

    Our findings therefore identify pyramidal neurons in M1 with the expected prototypical circuit properties of excitatory L4 neurons, and question the traditional assumption that motor cortex lacks this layer.

    question the traditional assumption ?

    What else is new? 🙂

  36. 36
    Dionisio says:

    More breakthrough discoveries about the brain:


    This raises new “why, how, when” questions.

  37. 37
    Dionisio says:

    The frustrated brain: from dynamics on motifs to communities and networks

    DOI: 10.1098/rstb.2013.0532

    Cognitive function depends on an adaptive balance between flexible dynamics and integrative processes in distributed cortical networks. Patterns of zero-lag synchrony likely underpin numerous perceptual and cognitive functions. Synchronization fulfils integration by reducing entropy, while adaptive function mandates that a broad variety of stable states be readily accessible. Here, we elucidate two complementary influences on patterns of zero-lag synchrony that derive from basic properties of brain networks. First, mutually coupled pairs of neuronal subsystems—resonance pairs—promote stable zero-lag synchrony among the small motifs in which they are embedded, and whose effects can propagate along connected chains. Second, frustrated closed-loop motifs disrupt synchronous dynamics, enabling metastable configurations of zero-lag synchrony to coexist. We document these two complementary influences in small motifs and illustrate how these effects underpin stable versus metastable phase-synchronization patterns in prototypical modular networks and in large-scale cortical networks of the macaque (CoCoMac). We find that the variability of synchronization patterns depends on the inter-node time delay, increases with the network size and is maximized for intermediate coupling strengths. We hypothesize that the dialectic influences of resonance versus frustration may form a dynamic substrate for flexible neuronal integration, an essential platform across diverse cognitive processes.


  38. 38
    Dionisio says:

    ‘Complex network theory and the brain’


    More articles on this subject:


  39. 39
    Dionisio says:

    An edge-centric perspective on the human connectome: link communities in the brain

    Brain function depends on efficient processing and integration of information within a complex network of neural interactions, known as the connectome. An important aspect of connectome architecture is the existence of community structure, providing an anatomical basis for the occurrence of functional specialization. Typically, communities are defined as groups of densely connected network nodes, representing clusters of brain regions. Looking at the connectome from a different perspective, instead focusing on the interconnecting links or edges, we find that the white matter pathways between brain regions also exhibit community structure. Eleven link communities were identified: five spanning through the midline fissure, three through the left hemisphere and three through the right hemisphere. We show that these link communities are consistently identifiable and investigate the network characteristics of their underlying white matter pathways. Furthermore, examination of the relationship between link communities and brain regions revealed that the majority of brain regions participate in multiple link communities. In particular, the highly connected and central hub regions showed a rich level of community participation, supporting the notion that these hubs play a pivotal role as confluence zones in which neural information from different domains merges.


  40. 40
    Dionisio says:

    Functional brain networks: great expectations, hard times and the big leap forward

    DOI: 10.1098/rstb.2013.0525

    Many physical and biological systems can be studied using complex network theory, a new statistical physics understanding of graph theory.

    The recent application of complex network theory to the study of functional brain networks has generated great enthusiasm as it allows addressing hitherto non-standard issues in the field, such as efficiency of brain functioning or vulnerability to damage.

    However, in spite of its high degree of generality, the theory was originally designed to describe systems profoundly different from the brain.

    We discuss some important caveats in the wholesale application of existing tools and concepts to a field they were not originally designed to describe.

    At the same time, we argue that complex network theory has not yet been taken full advantage of, as many of its important aspects are yet to make their appearance in the neuroscience literature.

    Finally, we propose that, rather than simply borrowing from an existing theory, functional neural networks can inspire a fundamental reformulation of complex network theory, to account for its exquisitely complex functioning mode.


  41. 41
    Dionisio says:

    Lag structure in resting-state fMRI

    DOI: 10.1152/jn.00804.2013

    The discovery that spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals contain information about the functional organization of the brain has caused a paradigm shift in neuroimaging.

    It is now well established that intrinsic brain activity is organized into spatially segregated resting-state networks (RSNs).

    Less is known regarding how spatially segregated networks are integrated by the propagation of intrinsic activity over time.

    To explore this question, we examined the latency structure of spontaneous fluctuations in the fMRI BOLD signal.

    Our data reveal that intrinsic activity propagates through and across networks on a timescale of ?1 s.

    Variations in the latency structure of this activity resulting from sensory state manipulation (eyes open vs. closed), antecedent motor task (button press) performance, and time of day (morning vs. evening) suggest that BOLD signal lags reflect neuronal processes rather than hemodynamic delay.

    Our results emphasize the importance of the temporal structure of the brain’s spontaneous activity


  42. 42
    Dionisio says:

    Mechanisms of Zero-Lag Synchronization in Cortical Motifs

    •DOI: 10.1371/journal.pcbi.1003548

    Zero-lag synchronization between distant cortical areas has been observed in a diversity of experimental data sets and between many different regions of the brain.

    Several computational mechanisms have been proposed to account for such isochronous synchronization in the presence of long conduction delays:

    Of these, the phenomenon of “dynamical relaying” – a mechanism that relies on a specific network motif – has proven to be the most robust with respect to parameter mismatch and system noise.

    Surprisingly, despite a contrary belief in the community, the common driving motif is an unreliable means of establishing zero-lag synchrony.

    Although dynamical relaying has been validated in empirical and computational studies, the deeper dynamical mechanisms and comparison to dynamics on other motifs is lacking.

    By systematically comparing synchronization on a variety of small motifs, we establish that the presence of a single reciprocally connected pair – a “resonance pair” – plays a crucial role in disambiguating those motifs that foster zero-lag synchrony in the presence of conduction delays (such as dynamical relaying) from those that do not (such as the common driving triad).

    Remarkably, minor structural changes to the common driving motif that incorporate a reciprocal pair recover robust zero-lag synchrony.

    The findings are observed in computational models of spiking neurons, populations of spiking neurons and neural mass models, and arise whether the oscillatory systems are periodic, chaotic, noise-free or driven by stochastic inputs.

    The influence of the resonance pair is also robust to parameter mismatch and asymmetrical time delays amongst the elements of the motif.

    We call this manner of facilitating zero-lag synchrony resonance-induced synchronization, outline the conditions for its occurrence, and propose that it may be a general mechanism to promote zero-lag synchrony in the brain.


  43. 43
    Dionisio says:

    Visualizing Whole-Brain Activity and Development at the Single-Cell Level

    DOI: http://dx.doi.org/10.1016/j.neuron.2014.12.039

    The nature of nervous system function and development is inherently global, since all components eventually influence one another.

    Networks communicate through dense synaptic, electric, and modulatory connections and develop through concurrent growth and interlinking of their neurons, processes, glia, and blood vessels.

    These factors drive the development of techniques capable of imaging neural signaling, anatomy, and developmental processes at ever-larger scales.

    Here, we discuss the nature of questions benefitting from large-scale imaging techniques and introduce recent applications.

    We focus on emerging light-sheet microscopy approaches, which are well suited for live imaging of large systems with high spatiotemporal resolution and over long periods of time.

    We also discuss computational methods suitable for extracting biological information from the resulting system-level image data sets.

    Together with new tools for reporting and manipulating neuronal activity and gene expression, these techniques promise new insights into the large-scale function and development of neural systems.


    We ain’t seen nothing yet… can’t wait to see what is going to be revealed to these new technology.

  44. 44
    Dionisio says:

    Molecular Mechanisms of Presynaptic Membrane Retrieval and Synaptic Vesicle Reformation

    DOI: http://dx.doi.org/10.1016/j.neuron.2014.12.016

    The function of the nervous system depends on the exocytotic release of neurotransmitter from synaptic vesicles (SVs).

    To sustain neurotransmission, SV membranes need to be retrieved, and SVs have to be reformed locally within presynaptic nerve terminals.

    In spite of more than 40 years of research, the mechanisms underlying presynaptic membrane retrieval and SV recycling remain controversial.

    Here, we review the current state of knowledge in the field, focusing on the molecular mechanism involved in presynaptic membrane retrieval and SV reformation.

    We discuss the challenges associated with studying these pathways and present perspectives for future research.


  45. 45
    Dionisio says:

    Neural Mechanisms of Incentive Salience in Naturalistic Human Vision

    DOI: http://dx.doi.org/10.1016/j.neuron.2014.12.049

    What role does reward play in real-world human vision?

    Reward coding in the midbrain is thought to cause the rapid prioritization of reward-associated visual stimuli.

    However, existing evidence for this incentive salience hypothesis in vision is equivocal, particularly in naturalistic circumstances, and little is known about underlying neural systems.

    Here we use human fMRI to test whether reward primes perceptual encoding of naturalistic visual stimuli and to identify the neural mechanisms underlying this function.

    Participants detected a cued object category in briefly presented images of city- and landscapes.

    Using multivoxel pattern analysis in visual cortex, we found that the encoding of reward-associated targets was enhanced, whereas encoding of reward-associated distractors was suppressed, with the strength of this effect predicted by activity in the dopaminergic midbrain and a connected cortical network.

    These results identify a novel interaction between neural systems responsible for reward processing and visual perception in the human brain.


  46. 46
    Dionisio says:

    The Glia-Derived Alarmin IL-33 Orchestrates the Immune Response and Promotes Recovery following CNS Injury

    DOI: http://dx.doi.org/10.1016/j.neuron.2015.01.013

    Inflammation is a prominent feature of CNS injury that heavily influences neuronal survival, yet the signals that initiate and control it remain poorly understood.

    Here we identify the nuclear alarmin, interleukin (IL)-33, as an important regulator of the innate immune response after CNS injury.

    IL-33 is expressed widely throughout the healthy brain and is concentrated in white mater due to predominant expression in post-mitotic oligodendrocytes.

    IL-33 is released immediately after CNS injury from damaged oligodendrocytes, acting on local astrocytes and microglia to induce chemokines critical for monocyte recruitment.

    These results demonstrate a novel molecular mediator contributing to immune cell recruitment to the injured CNS and may lead to new therapeutic insights in CNS injury and neurodegenerative diseases.


  47. 47
    Dionisio says:

    “Across the diversity of cortical cell types, transcription factors formed a complex, layered regulatory code, suggesting a mechanism for the maintenance of adult cell type identity.”


  48. 48
    Dionisio says:

    Differences in a FZD8 Enhancer Alter Cell-Cycle Dynamics in the Developing Neocortex

    DOI: http://dx.doi.org/10.1016/j.cub.2015.01.041

    The human neocortex differs from that of other great apes in several notable regards, including altered cell cycle, prolonged corticogenesis, and increased size

    Changes in HARE5 function unique to humans thus alter the cell-cycle dynamics of a critical population of stem cells during corticogenesis and may underlie some distinctive anatomical features of the human brain.


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