Monthly Archive: March 2015

For people who have a few hundred thousand dollars to spend and are willing to take on the risks of an “early adopter” and travel to South America, options are now becoming available that were inconceivable just a few years ago. A new company is leapfrogging over the time-consuming process of testing and regulatory approval, and offering the best-established and most promising experimental anti-aging technologies in the near future. This is a new vision for combining research with treatment, for treating diseases that have no proven therapies, and for aging itself.

(This column begins with a couple of pages of background.  If you want to cut to the chase, scroll down to BioViva.)

You only have to read Time Magazine to notice that this is the year anti-aging medicine is coming of age.  Promising life extension technologies are being debuted, with potential for preventing many diseases at once, adding decades to the human life span, and restoring youthful function to an aging body. These include telomerase therapies, stem cell therapies, epigenetic reprogramming, removal of senescent cells, plasma transfer, and hormonal therapies inspired by gene expression changes between young and old.

Inevitably, this has brought a surge in the number of companies eager to jump the gun and offer treatments to consumers based on early lab research, before the technology has proved  safe and effective in humans.  In an age of wildcat capitalism, we are well-advised to approach all claims with a skeptical eye, and assume that hucksterism is rampant.  Anyone who considers signing on with a new company that is offering a promising but unproven anti-aging technology had best start with a foundation of second opinions and broad considerations of risk and rewards.

But I stop short of saying, “stay away”.  The field is too important, with too much at stake for us individually and as a human community, to sit on the sidelines, to wait for the research to be sorted out.  Political control of medical research has protected us imperfectly, and has held back life-saving treatments, sometimes for decades.  The system serves pharmaceutical profits more effectively than the public of medical consumers.  Too often, the treatments that are approved are not those that offer the best risk/reward ratio, but those that are patentable and owned by someone who can afford to invest hundreds of millions of dollars in scientific advocacy.

The standard path to regulatory approval respects individual human life, and is “conservative” in the Hippocratic sense of “first do no harm”.  But it is far from the most effective way to move science forward, and probably is not the most efficient way to save the most lives, even in the short run.  Many libertarians, anti-aging enthusiasts and ordinary citizens who find themselves with a condition for which there is currently no effective medical treatment want the freedom to participate in experimental medicine, and experimental medicine certainly wants to try to help them and to learn from successes and failures.

For people who see their options for an active and creative life being closed by age-related disabilities, for people who are willing to take personal risks to help move the science forward, for people who are bold and adventure-seeking, the choice to try experimental anti-aging technologies can be a rational decision.

The Promise of Telomerase Therapies

In my opinion, the best-validated and most promising of the experimental therapies is the direct delivery of telomerase through gene therapy.  This is a technology pioneered in mice by Maria Blasco’s lab in Madrid, with stunning results.  In a ground-breaking 2012 paperby Blasco’s student Bruno Bernardes de Jesus, ordinary lab mice were given gene therapy with an “extra” telomerase gene spread to their cells by a genetically-engineered virus.  the mice lived 13-24% longer, and the experimenters reported “remarkable beneficial effects on health and fitness, including insulin sensitivity, osteoporosis, neuromuscular coordination and several molecular biomarkers of aging.”

Some strategies work better in mice than in humans, but there is theoretical reason to believe that this technique should work (even) better in humans than in mice.  Untreated mice already have plenty of telomerase, and the telomeres of lab mice are at least 3 times as long as humans’, with shorter life spans in which to lose their telomeres.  Before the above experiment, it was reasonable to think that telomere length was a primary aging clock in humans, but not in mice.  Mice can live up to six generations after their telomerase gene has been knocked out (no telomerase at all), whereas people exhaust their telomere endowment in a single generation.

I’ve written in the past about telomere length as one of the body’s primary aging clocks.  Very little telomerase is expressed in human adults.  As our stem cells divide during a lifetime, telomeres get progressively shorter with age.  Some results include the most important symptoms of aging:

• fewer functioning stem cells to replenish the stock of blood and skin cells
• more senescent cell, each sending out distress signals that promote the body’s hyper-inflamed state
• decline of the immune system, as new white blood cells form more slowly
• a cascade effect, as cells with short telomeres senesce and then trigger senescence in neighboring cells
• higher cancer rates, as the chromosomes in cells with short telomeres become unstable, and the immune system sentinels that nip cancer in the bud go AWOL.

Yes—higher cancer rates result when telomeres get short.  There is a theory that our bodies withhold telomerase in order to prevent cancer, but it is an idea with no experimental support.  Fear of cancer has held back telomerase therapy, and this is a red herring, based on misunderstanding of evolutionary biology.  All evidence suggests that telomerase therapies will lower cancer risk.

BioViva

BioViva is a new company offering experimental medical services outside US borders.  Their team includes

• a lab that provides genetically modified viruses with a gene payload, made to order.  (This has now become a reliable and predictable technology.)
• A doctor who has experience with experimental gene therapy, and who had the courage to experiment on himself five years ago, with good outcome thus far.
• Sites in Colombia and Mexico where doctors will administer therapies for which there is not yet FDA approval.
• Most important, a Scientific Advisory Board that includes two of the most prominent, senior biochemists who developed the science of telomerase in the 1990s and before.  They are Bill Andrews and Michael Fossel.

What they offer is gene therapy with hTERT and a proprietary myostatin inhibitor “in the same family with GDF-11,” according to CEO Elizabeth Parrish.

Parrish stresses that AAV gene therapy is a mature technology and has already passed FDA tests for safety.  “AAV has become increasingly common as a vector for use in human clinical trials; as of [2008], 38 protocols have been approved by the Recombinant DNA Advisory Committee and the Food and Drug Administration (FDA).” [ref] The uncertainties are no longer about safety, but about whether the virus will be destroyed by the body’s immune system before their payload can be delivered.  The rejuvenation benefit is likely to be systemic, and will have ripple consequences that we can only learn with human subjects.

In a surprise marketing move, Parrish has offered a guarantee for Patient #1 only.  If results for the first patient are disappointing, and Bioviva learns to avoid pitfallss and do a better job over the next 2 years, Patient #1 will be re-treated without cost, using the updated technology.

How Gene Therapy Works with AAV

AAV stands for Adeno-Associated Viruses, and there are several types in use.  This virus makes its living by

• slipping its payload of DNA into a human cell (shedding its protein shell at the cell wall)
• finding its way to the cell nucleus
• copying itself into a specific place on Chromosome 19,
• from where it manufactures copies of its own DNA, and also of the proteins that it needs to replicate, to penetrate other cells.

In therapeutic applications, the AAV DNA strand is modified to include a payload of therapeutic DNA, and to eliminate the genes coding for proteins that AAV needs in order to reproduce.  In this form, the modified virus can infect a cell, but once inside it cannot reproduce, infect more cells, reproduce there, and spread, causing disease.  It becomes a one-trick pony.  Each individual virus can infect one cell only, and then it has shot its wad.  No way this infection can “go viral”.

AAV therapy has been studied for over 25 years, and there is some reason to expect that the payload gene can remain active for a long time.  So this is a permanent change in the DNA of some cells in the body, though it is not a permanent infection.  Though AAVs are common in the environment, 80% of us have a naive immune response, so the treatment can be effective.  (For the other 20%, temporary immune suppression may be necessary.)  Repeat treatments are sometimes possible.  Here is a good semi-technical introduction to the subject.

Adeno-associated viruses, from the parvovirus family, are small viruses with a genome of single stranded DNA. These viruses can insert genetic material at a specific site on chromosome 19 with near 100% certainty. There are a few disadvantages to using AAV, including the small amount of DNA it can carry (low capacity) and the difficulty in producing it. This type of virus is being used, however, because it is non-pathogenic (most people carry this harmless virus). In contrast to adenoviruses, most people treated with AAV will not build an immune response to remove the virus and the cells that have been successfully treated with it.

Different AAV viruses can be customized to infect different cell types, and of course the place where the virus is injected is the most likely place for the virus to take root.  Viruses used in previous generations of gene therapy tended to disrupt the body’s own DNA by inserting at sites that are essential, and cancer rates were raised by some early forms of gene therapy.  AAV is favored because its target site seems to be safe, and its insertion harmless.

Therapies with hTERT and Myostatin Inhibitor

hTERT is only half the telomerase molecule, but it is the half that is in short supply, and hence the bottleneck for production of telomerase.  Of course, the DNA in our every cell contains the hTERT gene, but it is covered up and remains un-expressed almost all the time.  The new copy on Chromosome 19 is active, and in tests in cell cultures and live mice, telomeres have been lengthened.

I believe that telomerase is the closest thing we have at present to a cure for aging.  Bill Andrews and others have a long-term goal of developing drugs that will signal the body to activate its own telomerase gene, but these seem to be a few years off.  For now, adding an extra gene for hTERT may be the most promising generalized anti-aging intervention.  An important issue is that a large viral dose may be needed to saturate the body’s stem cells with the gene payload.  This is because a small minority of cells with the shortest telomeres is the source of some of the body’s biggest problems.  We’ll learn about the body’s response—if we are lucky, a rejuvenated immune system will itself eliminate the residual senescent cells without the need to lengthen telomeres in every senescent cell.

The myostatin strategy grows from (of all things) body-enhancement strategies for muscle-builders.  Myostatin is a member of the TGF-β family, is also called GDF-8*, and is a gene that inhibits muscle growth.  So if myostatin can be tied up, there is less inhibition and more muscle growth.  In the last several years, creatine has become a popular supplement for body-builders, and it works directly at the level of the gene, by inhibiting expression of myostatin=GDF-8.  Later in life, expression of the myostatin gene increases, and it is thought, logically enough, that this is a cause of the loss of muscle mass (sarcopoenia) that is almost universal with aging (though it is mitigated by exercise).  Bioviva offers gene therapy for a myostatin inhibitor (the specific gene is not disclosed), and it has been tried by one of the team members, experimenting on himself 5 years ago, with good results in ayounger man.  Here is an article that offers a balanced view of reasons to believe this might or might not work for age-related sarcopoenia.

Perhaps more important, the same gene has been found to clear blocked arteries, with expected reduction of the risk for heart disease and stroke.  There is rodent data and good theoretical reason to expect this will work, and there has been one heart patient who has received the AAV/myostatin treatment it with excellent results.  Blocking myostatin is also expected to reduce the progression of insulin resistance that is a driver of many age-related diseases.

Alzheimer’s Disease

There is a well-supported theory of AD that it has its roots in the microglial cells of the brain.  These are not nerve cells, but they act as a kind of immune system for the brain, protecting it from inflammation and cleaning up plaques.  Their secretions promote growth and repair.  Unlike nerve cells, microglia are continually replicating, and so they lose telomere length over time.  On the theory that restoring telomeres in the microglia will reverse dementia, Bioviva is offering gene therapy with hTERT in the brain as treatment for AD.  Direct evidence that this might work comes from a 2011 experiment from the de Pinho lab at Harvard Med School, in which brains atrophied in mice deprived of telomerase, and the brains actually regrew when telomerase was provided.

The Bottom Line

Experimental treatments are, by definition, at the wrong end of the learning curve.  But there is so much to be gained, and the people involved are such experts, that I am deeply hopeful about Bioviva’s work, and the prospect of a fast track to meaningful anti-aging therapies.

____________________

* Myostatin is GDF-8, not to be confused with GDF-11, which has also been recently in the news.  Both are in the TGF-ß family.  GDF-8 inhibits muscle cell growth, while GDF-11 inhibits nerve cell growth.  Curiously, Bioviva’s anti-aging strategy is to suppress GDF-8but last year’s headline-making paper from Harvard found benefits in  promoting GDF-11.

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This article originally appeared in Josh’s Aging Matters blog here. Republished with permission of the author.

The post Get Tomorrow’s Anti-Aging Therapy — Available Today Outside the U.S. appeared first on h+ Media.

Mesoscopic brain dynamics usually refers to the neural activity or dynamics at intermediate scales of the nervous system, at levels between neurons and the entire brain. It is commonly considered to relate to the dynamics of cortical neural networks, typically on the spatial order of a few millimeters to centimeters, and temporally on the order of milliseconds to seconds. It is usually the type of dynamics that can be measured by methods such as ECoG (electrocorticography), EEG (electroencephalography), LFP (local field potentials) or MEG (magnetoencephalography). Indeed, the terminology can be used in relative terms, where “meso” just indicates that the scale of interest is in between the “micro” and the “macro”.

Introduction

In science in general, e.g. in physics, chemistry and biology, but also in the social sciences, it is common to distinguish between two levels that may be referred to as “microscopic” and “macroscopic”, respectively. Intermediate or “mesoscopic” levels are less commonly considered, but are becoming increasingly in focus (Ingber, 1992; Imry, 1997; Freeman, 2000; Haken, 2005). For example, in lasers and certain chemical reactions, spatio-temporal patterns emerge at scales much larger than the constituent atoms or molecules. The formation of such patterns, which is highly relevant to mesoscopic physics, chemistry and biology has been intensely studied by Hermann Haken and co-workers within the field of synergetics (Haken, 1983, 2002).

Mesoscopic brain dynamics is intermediate between microscopic and macroscopic neurodynamics. What is microscopic could be considered processes and systems studied with a microscope or microelectrodes. It could refer to ion channels or single neurons. The macroscopic scale, on the other hand, could be considered corresponding to the largest scales possible to measure with regard to brain activity. This would be the dynamics related to maps, or systems, such as cortico-thalamic, or cortico-cortical interactions, usually measured with PET, fMRI, or other brain imaging techniques, capturing the dynamics associated with blood flows and metabolism. Also scalp EEG may capture this type of dynamics (Bressler & Menon, 2010).

Mesoscopic brain dynamics refers typically to the dynamics of neuronal populations, networks or columns within cortical areas. It is characterized by its high complexity, often involving oscillations of different frequencies and amplitudes, perhaps interrupted by chaotic or pseudo-chaotic irregular behaviour. The mesoscopic brain dynamics is affected by the activity at other scales. For example, it is often mixed with noise, generated at a microscopic level by spontaneous activity of neurons and ion channels. It is also affected by macroscopic activity, such as slow rhythms generated by cortico-thalamic circuits or neuromodulatory influx from different brain regions.

The possibility to measure, in greater detail, the electrical part of brain activity with external electrodes was discovered and used by Berger in the early 20th century (Berger, 1929). One of the first experiments to demonstrate stimulus-induced activity in the mammalian central nervous system was made by Adrian (1942), showing oscillatory activity in the olfactory system of hedgehog.

Figure 1: Mesoscopic brain dynamics as EEG from rat olfactory cortex (data courtesy of Leslie Kay). The x-axis shows milliseconds, and the y-axis is in microvolts.

Figure 2: Simulated EEG with a model of olfactory cortex (as seen in Fig. 3).

Generation of Mesoscopic Brain Dynamics

Mesoscopic brain dynamics are partly a result of neuronal thresholds and the summed activity of a large number of neuronal elements interconnected with positive and negative feedback. This kind of neural dynamics is often characterized by oscillatory synchronous neuronal population behaviours, which underlie the rhythmical EEG waves in the cortex. Synchronization among groups of neurons were first discovered in the olfactory system (Adrian, 1942; Freeman, 1959), but has also been demonstrated in other brain structures, such as thehippocampus (Green and Arduini, 1954; Buszaki et al., 1992), thalamus (Steriade and Llinás, 1988), and the visual cortex (Eckhorn et al., 1988; Gray and Singer, 1989), where the oscillations tend to synchronize in phase. Synchronous oscillations can occur in nearby neurons, but also over considerable distances across spatially separate columns (Gray et al., 1989) and even between cortical areas (Eckhorn et al., 1988; Engel et al., 1991).

Neural oscillations may be due to intrinsic properties of certain pacemaker cells (Lllinas, 1988) or due to network effects (Traub & Miles, 1991). A population of neurons might fire together either because it responds to an afferent oscillatory input or because of cellular/synaptic interactions. An understanding for how such synchronous groups of neurons may be formed was first given by Donald Hebb (1949), who proposed that representations of sensory or motor patterns should consist of assemblies of cooperatively interacting neurons. Such assemblies could form according to a so called Hebbian synaptic modification, which depends on the co-occurrence of pre- and postsynaptic activity. The finding and analysis of cooperating neural assemblies is facilitated by triple recording from microelectrodes, which simultaneously can detect single unit spike trains, in addition to neural mass signals, such as multiple unit spike activity (MUA) and local slow-wave field potentials (LFP).

Events and processes at the microscopic level of neurons and molecules have an effect on the meso- and macroscopic levels of networks and systems, through their interactions via synaptic and non-synaptic connections. At the same time, the network or ensemble dynamics constrain the constituent neurons, engaging them in a self-organizing and coordinated activity. The higher level dynamics “enslaves” the lower levels, according to the theory of synergetics, providing an example of circular causality of complex systems (Haken, 1983, 2002).

The Olfactory Cortex as a Model System

While mesoscopic brain dynamics is observed in many brain structures, the mammalian olfactory system (primarily bulb and cortex) is often used as a model system, much due to the pioneering work of Walter Freeman and his co-workers since the 1960s (Freeman, 1959; 1975; 2000). In particular, theta and gamma rhythms are observed, as well as spatiotemporal waves of activity moving across the bulbar and cortical surfaces. Also chaotic-like behaviour has been observed and characterized (Freeman, 1987). Furthermore, the structure of this system is well characterized, and Freeman and others have successfully studied and described how structure, dynamics and function are related in this system.

Computational models have contributed to elucidate these relationships, where simulations have been able to closely mimic the dynamics, as captured by LFPs (local field potentials), ECoG, or intracranial EEG (Freeman, 1987; Li and Hopfield, 1989; Liljenström, 1991; Wilson & Bower, 1992). An intracranial EEG trace from the rat olfactory cortex (Fig. 1) is closely reproduced by a simulated EEG (Fig. 2) generated by a neural network model (Liljenstrom, 1991) of the three-layered olfactory cortex (Fig. 3). In this case, the output of several neighbouring network units, representing the average membrane potential of neuronal populations, was summed and weighted to mimic the electrical activity at an electrode located above the network surface. In Fig. 4 spatio-temporal patterns of activity, representing the neural response to an odorous input signal, is shown as color-coded positive and negative “mean membrane potentials” of the network nodes. A specific odor input results in a specific spatio-temporal pattern in the network activity, resulting in learning or recall (see below).

Figure 3: A neural network model of the three-layered olfactory cortex. The middle layer corresponds to excitatory pyramidal cells, whereas the top and bottom layers correspond to feedforward and feedback inhibitory interneurons, respectively. Nerve bundles (LOT) from the olfactory bulb reaches the two top layers. (Liljenström, 1991)

Figure 4: The spatio-temporal activity pattern in the olfactory cortex model resulting from an artificial odor input. The top frame shows the color-coded simultaneous activity in the three layers, whereas the bottom frame shows a number of snapshots of the middle, excitatory layer activity.

Transitions in Mesoscopic Brain Dynamics

Cortical neurodynamics is constantly changing, due to internal fluctuations, neuromodulation, and sensory input. Many factors influence the dynamical states, such as the excitability of neurons and the synaptic strengths between them. A number of neuromodulators affect these neural properties, including acetylcholine (ACh) and serotonin (5-HT). The concentration of these neuromodulators in the cortex seems to be directly related to the arousal ormotivation of the individual (Freeman, 2000).

The state of arousal or attention may change the macro- and mesoscopic brain dynamics considerably, and even induce phase (state) transitions that could affect the functional efficiency of cognitive processes (Liljenström, 2010). Visual attention has several effects on modulating cortical dynamics, in terms of changes in firing rate (McAdams & Maunsell, 1999), as well as gamma- and beta-band coherence (Fries et al., 2001). With attention, there is a reduction in low-frequency synchronization and an increase in gamma-frequency synchronization. Generally, it is believed that lower frequency bands are generated by global circuits, whereas higher frequency bands are derived from local connections (Gu & Liljenström, 2007).

Electrical stimulation may also induce transitions in cortical dynamics. When studying the dynamical properties of the olfactory cortex, Freeman and coworkers stimulated the lateral olfactory tract (LOT) of cats and rodents with electric shock pulses of varying amplitude and duration, and recorded the neural response via intracranial EEG (Freeman, 1959; 1964). A strong pulse gives a biphasic response with a single fast wave moving across the cortical surface, whereas a weak pulse results in an oscillatory response, showing up as a series of waves with diminishing amplitude. When a short pulse is applied to the LOT input corner of the network model, waves of activity move across the model cortex, consistent with corresponding global dynamic behaviour (c.f. Fig. 4).

Figure 5: Simulated effect of anaesthetics. The dynamics of a network, where K channels are increasingly blocked by anaesthetics. The two upper time series show the activity of single excitatory and inhibitory neurons, respectively, while the lower time series is the network mean activity (Halnes et al., 2007)

Another way of artificially inducing phase (state) transitions in cortical network dynamics is by using neuroactive drugs, such as certain kinds of anesthetics and anti-epileptics, which clearly can induce transitions between mental states, characterized by different oscillatory modes and frequencies (see Fig. 5). An important principle in the action of these drugs is selective blocking or activation of ion channels, which will have different neurodynamical effects, depending on the relative selectivity and the intrinsic network activity (Århem et al., 2003, 2007; Halnes et al., 2007).

Functional relevance and computational models

A fundamental question in neuroscience concerns the functional significance of mesoscopic brain dynamics, including the observed phase transitions between various oscillatory states and chaotic or noisy states. The electrical activity of the brain, as captured with EEG is considered by some to be an epiphenomenon, without any information content or functional significance, but there exists contrary evidence that mesoscopic brain dynamics at least to some degree reflects mental states and processes (Wright & Liley, 1996; Freeman, 2000).

Most cognitive and mental functions presumably involve larger “macroscopic” brain areas or even networks of interconnected cortical areas, but the mesoscopic dynamics of such an area may still reflect some aspect or part of the mental activity. For example, different conscious states, such as drowsiness, sleep or alertness may result in similar type of dynamics, such as alpha or gamma waves across several areas, and detectable at mesoscopic spatial scales. Similarly, some aspects of a “macroscopic” phenomenon such as face recognition or eating behaviour may be reflected in the “microscopic” activity of single neurons (c.f mirror neurons).

Figure 6: The attractor dynamics during recognition of an unknown input pattern. After an initial semi-chaotic period, the system converges to a near-limit-cycle attractor (40 Hz), corresponding to the recognized odor. Cholinergic modulation facilitates learning and increases pattern recognition efficiency. The activity of three arbitrary network nodes are plotted against each other in arbitrary units.

In order to elucidate the significance of mesoscopic brain dynamics, computational methods are used to supplement experimental methods. For example, there is strong computational, as well as experimental support for a population (relational) coding in cortical networks, where mesoscopic brain dynamics apparently play a functional role (Singer, 1994). Such a coding principle implies that information is contained not only in the activation level of individual neurons but also in the relations between the activities of distributed neurons.

Computer simulations with cortical neural network models support the view that complex dynamics makes neural information processing more efficient, providing a fast and accurate response to external stimuli or in associative memory tasks (Liljenström, 1995; Liljenström & Hasselmo, 1995). For example, with an initial chaotic-like state, sensitive to the input signal, the system can rapidly converge to a limit cycle attractor memory state, see Fig.6 (Wu & Liljenström, 1994). Perhaps the most direct effect of cortical oscillations could be to enhance weak signals and speed up information processing, but they may also reflect various cognitive functions, including segmentation of sensory input, learning, perception, and attention. Phase transitions in mesoscopic brain dynamics can reflect transitions between different cognitive and mental levels or states, for example corresponding to various stages of sleep, anesthesia or wake states with different levels of arousal, which in turn could affect the efficiency and rate of information processing (Liljenström, 2010).

Simulations also show that mesoscopic network dynamics can be shifted into, or out of, different oscillatory states by small changes in ion-channel densities or by changing connection strengths in a network model (Halnes et al., 2007). It is demonstrated that the blocking of specific ion channels, as a possible effect of some anesthetics, can change global brain activity from high-frequency (awake) states to low-frequency (anesthetized) states, as apparent in the recorded and simulated EEG (See Fig. 5).

It can further be demonstrated that “microscopic” noise can induce global synchronous oscillations in cortical networks and shift the system dynamics from one dynamical state to another (Liljenström & Wu, 1995; Liljenström & Århem, 1997; Basu & Liljenström, 2001). This in turn can change the efficiency of the information processing of the system, e.g. system performance can be maximized at an optimal noise level, analogous to the case of stochastic resonance (Wiesenfeld & Moss, 1995), and spontaneous activity can facilitate learning and associative memory (Liljenström, 1996).

Mesoscopic brain dynamics and consciousness

As briefly discussed above, low frequencies in mesoscopic brain dynamics correspond to low mental activity, drowsiness or sleep, whereas higher frequencies are associated with alertness and higher conscious activity. In particular, oscillations in the gamma frequency band, around 40 Hz, have long been associated with (visual) attention, initially based on experiments on cats (Eckhorn et al., 1988; Gray & Singer, 1989; Engel et al., 1991). It was this phenomenon that triggered the boost of studies on neural correlates of consciousness, as e.g. suggested by Crick & Koch (1990; Koch, 2004) and contributed to opening the field of neuroscience to consciousness studies.

The complex neurodynamics at a mesoscopic level of the brain seems significant for the macroscopic phenomena of cognition and consciousness (Århem & Liljenström, 2007). It has been related to perception, attention and associative memory, but also to volition and activity in the sensory and motor areas of the brain. Even though many details are still unknown, it is obvious that there is an interplay between the neurodynamics of the sensory and motor systems, essential for the interaction with our environment in a perception-action cycle (Cotterill, 1998; Freeman, 2000).

Associated with the perception-action cycle are the dual aspects of consciousness, attention and intention (Liljenström, 2011). Attention is primarily related to the sensory/perceptual pathways and brain areas, whereas intention is more related to the motor areas and pathways. In particular, the supplementary motor area (SMA), but also the parietal cortex, show early signs of intentional motor activity (Eccles, 1982; Libet 1985; Desmurget et al., 2009). EEG measurements from these areas reflect mesoscopic brain dynamics, apparently correlating to various conscious states and events.

Summary

Mesoscopic neurodynamics can be seen as resulting from the dynamic balance between opposing processes at several scales, from the influx and efflux of ions, inhibition and excitation etc. Such interplay between opposing processes often results in (transient or continuous) oscillatory and chaotic-like behaviour. Indeed, brain activity is constantly changing, due to neuronal information processing, intrinsic fluctuations, neuromodulation, sensory input, and internal state shifts. An essential feature of mesoscopic brain dynamics is spatio-temporal patterns of activity, appearing at the collective level of a large number of neurons. Waves of activity move across the surface of sensory cortices, with oscillations at various frequency bands. This kind of activity is often associated with mental processes and states, which can be characterized by the specific patterns involved.

A combination of mathematical analysis and computational modeling can serve as an essential complement to clinical and experimental methods in furthering our understanding of neural and mental processes. In particular, when concerning the inter-relation between structure, dynamics and function of the brain and its cognitive functions, this method may be the best way to make progress. The study of phase transitions in mesoscopic brain dynamics could be one of the most fruitful approaches in this respect (Steyn-Ross & Steyn-Ross, 2010). In fact, relating different spatial and temporal scales in the nervous system, and linking them to mental processes may be seen as one of the greatest challenges to modern neuroscience.

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• Gray C M & Singer W (1989) Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl. Acad. Sci. USA 86, 1698-1702.
• Green J D & Arduini A A (1954) Hippocampal electrical activity in arousal. J. Neurophysiol. 17, 533-557.
• Gu Y & Liljenström H (2007) A neural network model of attention-modulated neurodynamics. Cognitive Neurodynamics 1, 275-285.
• Haken H (1983)Synergetics, an Introduction: Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology, New York: Springer-Verlag.
• Haken H (2002)Brain Dynamics – An Introduction to Models and Simulations, Berlin: Springer-Verlag.
• Haken H (2005) Mesoscopic levels in science – some comments. In: H. Liljenström, U. Svedin (eds.), Micro – Meso – Macro: Addressing Complex Systems Couplings, pp. 19–24, London: World Scientific.
• Halnes G, Liljenström H & Århem P (2007) Density dependent neurodynamics. BioSystems 89, 126-134.
• Hasselmo M E (1993) Acetylcholine and learning in a cortical associative memory. Neural Comp. 5, 32-44.
• Hebb D O (1949) The Organization of Behavior: A Neurophysiological Theory. New York: Wiley.
• Imry Y (1997) Introduction to Mesoscopic Physics. New York: Oxford University Press.
• Ingber L (1992) Generic mesoscopic neural networks based on statistical mechanics of neocortical interactions. Phys. Rev. A 45, R2183-2186.
• Koch C (2004) The Quest for Consciousness – A Neurobiological Approach. Greenwood Village, CO: Roberts & Co. Publ.
• Li Z & Hopfield J J (1989) Modeling the olfactory bulb and its neural oscillatory processings. Biol. Cybern. 61, 379-392.
• Libet B (1985) Unconscious cerebral initiative and the role of conscious will in voluntary action. Behav. Brain Sc. 8, 529-566.
• Liljenström H (1991) Modeling the dynamics of olfactory cortex using simplified network units and realistic architecture. Int. J. Neural Systems 2, 1-15.
• Liljenström H (1995) Autonomous learning with complex dynamics. Intl. J. Intelligent Systems 10, 119-153.
• Liljenström H (1996) Global effects of fluctuations in neural information processing. Intl. J. Neural Systems 7, 497-505.
• Liljenström H (1997) Cognition and the efficiency of neural processes In Matter Matters? On the Material Basis of the Cognitive Aspects of Mind (P Århem, H Liljenström and U Svedin, Eds.) Heidelberg: Springer, pp. 177-213.
• Liljenström H (2003) Neural stability and flexibility – A computational approach. Neuropsychopharmacology 28: S64-S73.
• Liljenström H (2010) Inducing transitions in mesoscopic brain dynamics. In: Modeling Phase Transitions in the Brain (DA Steyn-Ross & ML Steyn-Ross, eds.) New York: Springer, pp. 149-178.
• Liljenström H (2011) Intention and attention in consciousness dynamics and evolution. J. Cosmology 14, 4848-4858.
• Liljenström H & Århem P (1997) Investigating amplifying and controlling mechanisms for random events in neural systems. In Computational Neuroscience (JM Bower, Ed.) New York: Plenum Press. pp. 711-716.
• Liljenström H & Hasselmo ME (1995) Cholinergic modulation of cortical oscillatory dynamics. J. Neurophysiol. 74, 288-297.
• Liljenström H & Wu X (1995) Noise-enhanced performance in a cortical associative memory model. Int. J. Neural Systems 6, 19-29.
• Llinás R (1988) The intrinsic electrophysiological properties of mammalian neurons: Insight into central nervous system function. Science 242, 1654-1664.
• McAdams C & Maunsell J (1999) Effects of attention on orientation-tuning functions of single neurons in macaque cortical are v4. J. Neurosci. 19, 431–441.
• Singer W (1994) Putative functions of temporal correlations in neocortical processing. In: C Koch & J L Davis (eds) Large-scale neuronal theories of the brain. Cambridge, MA: The MIT Press, pp. 201-237.
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Recommended reading – books

• Arbib M A, Erdi P and Szentagothai J (1998) Neural Organization – Structure, Function and Dynamics. Cambridge: MIT Press.
• Arhem P, Liljenström H & Svedin U, eds. (1997) Matter Matters? – On the Material Basis of the Cognitive Activity of Mind. Heidelberg: Springer.
• Arhem P, Blomberg, C & Liljenström H, eds. (2000) Disorder Versus Order in Brain Function. London: World Scientific.
• Freeman WJ (1975) Mass Action in the Nervous System. New York: Academic Press. © 2004: (online)
• Freeman WJ (2000) Neurodynamics – An Exploration in Mesoscopic Brain Dynamics. London: Springer.
• Haken H (2002, 2008) Brain Dynamics – An Introduction to Models and Simulations. Berlin: Springer.
• Liljenström H & Århem P (2007) Consciousness Transitions – Phylogenetic, Ontogenetic and Physiological Aspects. Amsterdam: Elsevier.
• Liljenstrom H & Svedin U, eds. (2005) Micro – Meso – Macro: Addressing Complex Systems Couplings. London: World Scientific.
• Moss F & Gielen S (2001) Neuroinformatics and Neural Modelling. Handbook of Biological Physics (ed. A J Hoff) Vol 4. Amsterdam: Elsevier.
• Perlovsky L I & Kozma R, eds. (2007) Neurodynamics of Cognition and Consciousness. Berlin: Springer.
• Steyn-Ross DA & Steyn-Ross ML, eds. (2010) Modeling Phase Transitions in the Brain. New York: Springer.

Scholarpedia references

• Bressler, Neurocognitive networks, Scholarpedia, 3(2):1567.
• Freeman, Intentionality, Scholarpedia, 2(2):1337.
• Freeman and Breakspear, Scale-free neocortical dynamics, Scholarpedia, 2(2):1357.
• Kozma, Neuropercolation, Scholarpedia, 2(8):1360.
• Kozma and Freeman, Freeman’s mass action, Scholarpedia

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Pierre Teilhard de Chardin (1881 – 1955), a Jesuit priest trained as a paleontologist and geologist, was one of the most prominent thinkers who tried to reconcile evolutionary theory, religion, and the meaning of life. In his magnum opus,The Phenomenon of Man,  he sets forth a sweeping account of cosmic unfolding.

While Teilhard’s philosophy is notoriously complex, the key notion is that cosmic evolution is directional or teleological. Evolution brings about an increasing complexity of consciousness, leading from an unconscious geosphere, to a semi-conscious biosphere, and eventually to conscious sphere of mind. The arrival of human beings on the cosmic scene is particularly important, signaling that evolution is becoming conscious of itself. As the process continues, the human ability to accumulate and transmit ideas increases along with the depth and complexity of those ideas. This will lead to the emergence of what Teilhard calls the “noosphere,” a thinking layer containing the collective consciousness of humanity which will envelope the earth. (Some contemporary commentators view the World Wide Web as a partial fulfillment of Teilhard’s prophecy.)

Not only does evolution explain how mind arose from matter, it is also the key to all metaphysical understanding, if such understanding is to be based on a firm foundation.

Is evolution a theory, a system or a hypothesis? It is much more: it is a general condition to which all theories, all hypotheses, all systems must bow and which they must satisfy henceforth if they are to be thinkable and true. Evolution is a light illuminating all facts, a curve that all lines must follow.[i]

Teilhard recognized this evolutionary worldview, with its oceans of space and time, as a source of disquiet for minds previously comforted by childlike myths. Anxiety begins when we reflect, and reflection on the nature of the universe clearly discomforts.

Which of us has ever in his life really had the courage to look squarely at and try to ‘live’ [in] a universe formed of galaxies whose distance apart runs into hundreds of thousands of light years? Which of us, having tried, has not emerged from the ordeal shaken in one or other of his beliefs? And who, even when trying to shut his eyes as best he can to what the astronomers implacably put before us, has not had a confused sensation of gigantic shadow passing over the serenity of his joy?[ii]

Yet psychic troubles derives from this evolutionary worldview. “What disconcerts the modern world at its very roots is not being sure, and not seeing how it ever could be sure, that there is an outcome—a suitable outcome—to that evolution.”[iii]But alas the source of our discomfort is also the fount of our salvation. For if the future is open to our further development, then we have the chance to fulfill ourselves, “to progress until we arrive … at the utmost limits of ourselves.”[iv]

The increasing power and influence of the noosphere or world of mind will culminate in the Omega Point—a supreme consciousness or God. At that point all consciousness will converge, although Teilhard argues that individual consciousness will somehow still be preserved. While the Omega point is extraordinarily difficult to describe, it must be a union of love if it is to be a sublimely suitable outcome of evolution. Here Teilhard waxes poetic:

Love alone is capable of uniting living beings in such a way as to complete and fulfill them, for it alone takes them and joins them by what is deepest in themselves. This is a fact of daily experience. At what moment do lovers come into the most complete possession of themselves if not when they say they are lost in each other? In truth, does not love every instant achieve all around us, in the couple or the team, the magic feat, the feat reputed to be contradictory, of personalizing by totalizing? And if that is what it can achieve daily on a small scale, why should it not repeat this one day on world-wide dimensions?[v]

In Teilhard’s vision, all reality evolves toward higher forms of being and consciousness, which includes more intense and satisfying forms of love. Thus spirit or mind, not matter or energy, ground the unity of the universe; they are the inner driving force propelling evolution forward. (This is Teilhard’s god.) Teilhard found meaning and purpose in this sweeping epic of cosmic evolution in which the endpoint of all evolution will be the highest good.

(Note – I do have doubts about some of Teilhard’s esoteric ideas and concepts. A lot of what he says is profound, but some of it is probably nonsense. For the most devastating critique of Teilhard ever penned see the great biologist P. B. Medawar’s “Review of the Phenomenon of Man” (1961).

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