File: Evol brain dynamics2.wpd Rev. 10-07-02
[Based on paper for Brain Dynamics Workshop, May 10-12, 2002, Rancho Santa Fe, CA
"From Microscopic to Macroscopic Brain Dynamics" organized by T. Sejnowski
Have brain dynamics evolved?
Should we look for unique dynamics in the sapient species?
Theodore Holmes Bullock
Department of Neurosciences and Neurobiology Unit, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093-0240
Abstract. Ongoing "spontaneous" electrical field potentials of assemblies of neurons in the brains of diverse animal groups differ widely in character and amplitude without obvious explanation. There may be correlates with other measures of brain complexity, such as histological differentiation but there are so far no known differences between the EEG s of humans and other mammals or between mammals and reptiles, amphibians or fish, apart from amplitude. The proposition is defended that further search for descriptors or statistical, probably non-linear features of the time series will reveal consistent differences - meaning that we have so far missed major features of the natural history of EEGs, just as we have, thus far, relatively neglected the identification of features of the physiology of the brain relevant to its evolution of complexity through major grades of phyla, classes and orders.
The outstanding differences between humans and other animals in behavior - the diversity of dances, the fuss over food preparation, the mess over morals, compulsion to creativity, interest in introspection and passion for the past make me believe, as a physiologist, that we should find differences in brain dynamics - at some level, between the human brain and that of taxa with less complex behavior. All the more when we learn how conserved is the brain anatomy, the cellular physiology and the molecular mechanisms.
Actually, I won't be satisfied to find some uniquely human physiological traits - just as it won't be enough to find a uniquely human type of neuron (Nimchinsky et al. 1999). I am curious how the brain evolved through the drastic stages in complexity between medusae (jellyfish) and corals, flat and round worms, insects and lobsters, clams, snails and squid, plus lampreys, frogs, and lizards, opossums, shrews, lemurs and marmosets - not necessarily in that sequence in time (Bullock 1948, 1958, 1959, 1965, 1986, 1993). This is not anthropocentric - repeated saltations in grade of complexity took place before humans or primates or mammals or vertebrates.
We know a lot about the neurons, including individually identifiable ones in every specimen in many invertebrate taxa (Bullock 2000, Leonard 1999, 2000. and in some fish, but the steps of advance in physiology are more elusive. It doesn't look as though the great leaps in brain develoment are due to sheer numbers or to more kinds of transmitters or modulators (Bullock 1995, Stevens 19xx, Tononi et al. 1994,). What else could it be?
I made a stab some time ago to estimate the numbers of kinds of neurons, not just on morphology, cytology, and kinds of processes or on transmitters, modulators, chemical öreceptors, cytochemistry, molecular inhabitants, immunological criteria and so on - but on all of these and their permutations. Very importantly, one must add their dynamic personality traits - tendency to spontaneity, to bursting, to fast or slow adaptation or both in sequence, to facilitation or its opposite, to regular or irregular firing and each of some forty-odd additional integrative dimensions that show a distribution from high to low, characteristic for types of neurons (Arouz and Gray 1999, arlow 1996, Barrio and Buño 1990, 1991, Buo and Barrio 1990, Baar1983, Bennett 1968, Bernander et al. 1991, Boorman et al. 1994, Borst et al. 1995, Braitenberg 1980, Bullock 1946, 1947, 1948, 1951,a b, 1952, 1957, 1958, 1964, 1965, 1966, 1970. 1976, 1977a, b, 1979, 1981a,b, 1984, 1986, 2000b, Bullock and Terzuolo 1957, Bullock and Turner1950, Burrows 1975, 1978, Buzsaki and Kandel 1998, Carlson 1968, Chernetski 1964, Debanne et al. 1997, Delmas et al. 1997, Ehret 1988, Elston et al. 1999, Engel et al, 1992, Fain, 1981, Gerstner et al. 1997, Goda and Stevens 1998, Gold and Bear 1994, Graf and Meyer 1978, Gray 1993, Hagiwara and Bullock 1955, Hernández et al. 1999, Horridge 1968, Juusola et al. 1996,.Kennedy 1968, Lev-Ram et al 2002, Libet 1988, Lisman 1997, Mann-Metzer and Yarom 1999, Makram et al. 1998, Maynard 1953a,b, Montague 1995,Newman and Hartline 1981, Otani and Bullock 1957, 1959, Perkel and Bullock, 1968, Petersen et al. 1998, Rao et al. 1969, Reinoso-Suarez and Ajmone-Marsan 1984, Richmond et al. 1987, Schmielau 1980, Segundo 1986, Segundo et al. 1963, 1986, Sejnowski 1997, Serrrato et al. 1996, Siegler 1984, Stanford and Hartline 1984, Suga and Yajima 1988, Swenarchuk and Atwood 1975, Tauc 1960, Terzuolo and Bullock 1956, 1957, Vibert et al. 1985, Vizi and Làbos 1991, Watanabe and Bullock 1960, Watanabe et al. 2002, Weckström et al. 1992, Whittington et al. 1992, Wiese et al. 1976, Wilson and Talbot11963, Wu et al. 1998, Zoli et al. 1999, Zucker 1989).
Lastly, one must add the number of significantly different receptive fields and projection fields. Neighboring neurons have some degree of overlap but also some degree of non-overlap that makes it possible to recognize distinct sets of fully equivalent neurons from other such sets (Bullock 1980, Bullock, 1993 p. 127 et seq.). When we speak of the numbers of kinds of neurons, we should mean on all these criteria.
My estimates, based on a few well studied systems, such as the visual system and a lot of educated extrapolation put the number in the hundreds in some simple worms, thousands in Aplysia, tens of thousands in more advanced insects and crustaceans and in a fish like a carp, millions in a rat and billions in humans! (Bullock 1993, p. 131). This may seem out of reason and is certainly at odds with a common view, such as that of Changeux (1985) who said the numbers of categories of pyramidal cells in the cortex is probably tens or hundreds. He neglected to consider their connectivity at the level of receptive fields and projection fields and by "categories" meant classes. I am asking how many species are there, on all criteria, including distinguishably different connectivity fields.
I'm talking about evolution of nervous systems, from simple to complex and looking for relevant measurable variables. Relative brain size or number of cells, or of synapses or of impulses per second in the whole system are doubtless relevant but imperfect and inadequate measures of the machine, (Bullock 1995a) just as numbers of distinct behaviors in the whole ethogram approximates as a measure of accomplishment of the machine - complexity of behavior. But both neglect major variables simply because they are difficult to count.
Stipulating the difficulty, I think it is worthwhile, to recognize what we would like to count in animals at different grades of evolution. In the brain one such desideratum would be the number of species of cells and their synaptic and nonsynaptic connections - revising and correcting my crude estimates. In the consequence domain, the behaviors mediated, one hitherto untouched variable that would make a great difference if we could count them would be the numbers of items of knowledge that an animal can accumulate and use. This means I lump together under the term knowledge the numbers of individual neighbors it can distinguish, of audible signals, of different odors, of levels of social hierarchy, relative values of alternative food sources, kinds of sadness and kinds of sensations, emotions, motivations, goals and memories. Difficult - certainly! Tangible, definable, approachable - in comparable packages from taxa far enough apart - yes. We don't do justice to the results of brain evolution if we don't at least mention such measurable variables in behavioral evolution (much ethological literature is relevant here).
Now, let me turn to another extreme along the spectrum of efforts to measure evolution in terms of its manifestations. What has it wrought? What differences have come about? I call this the natural history of brain traits. The textbook lists - corpus callosum, six-layered cortex, corpora pedunculata, non-spiking neurons, post-tetanic potentiation, kindling and the rest - must be a small fraction of the traits that have evolved. It istime we aimed some frontal assault on the frontier of complexity per se(Bullock 1993, Ch. 13), lest we claim to study evolution while overlooking the saltations in complexity. Eben though most evolution is lateral radation adapting species to different niches within the same general grade of organization, now and then new grades of more complexity appear on some twigs of the tree (Bullock 1992. The overall span bnetween the simplest levels of behavior and the most complex is a span difficult to overstate. We already know it is not due merely to numbers of cells or synapses. Emergent traits of anatomy and physiology have accompanied these unpredictable saltations - far from inevitable or linear and not obviously benefitting survival value.
I want to point here to just one domain of little known physiological raits of central nervous systems. I bring it up because it is neglected. I'm thinking about the information-richest measure of the living, working brain : the electrical activity seen by wide-band amplifiers that record slow as well as fast activity (Bullock 1997, 1999) from extracellular semimicro-electrodes at many loci at the same time, both on the surface and in the depths of the brain, with millisecond and millimeter resolution.
What should we expect if we compare the ongoing, not intentionally stimulated but apparently spontaneous activity of Aplysia and a garden snail, a honey bee, lobster, octopus, shark, bony fish, alligator, opossum and human (Baar et al. 1999a,b). This is not an evolutionary ladder! I pick a list of living species each at the end of some branch or twig, using the criterion of how intricate the brain looks histologically among invertebrates and vertebrates (Bullock and Horridge 1965, Bullock 1984, 1986).
First, let's look at the ongoing, apparently spontaneous background activity, itself a surprising discovery made during my lifetime. Later, if we want to, we can look at the responses to stimuli and situations. Both the spontaneous, ongoing and the stimulus or state-related forms of electrical activity are rich in dynamical variables to measure (Bullock 1984, 1986a,1995c, 1997, 2002, Bullock et al. 1994, Bullock and Terzuolo 1957, Prechtl et al. 1998, Bullock and Achimowicz 1994, Schütt et al. 2000.
The hypothesis we are testing - and rejecting (to spill the beans up front) - is that such background population activity of many or most places we can sample in the brain is either a mixture of rhythms at many different frequencies plus some stochastic, information-poor and entropy-high components. The bottom line as I predict it now - probably wrongly - is that our own EEGs have a lot of stochastic components plus a very few rhythms, now and then when the situation is just so, and even less commonly more than one at a time, plus a significant proportion of the total mix in patterned sequences that are not rhythmic or sustained but spatially and temporally information-rich, like speech in a crowded room.
This description, I think, is a risky and far out extrapolation of meager knowledge, but perfectly compatible with prevailing views except for the common mental model of a wide spectrum of independent rhythms, as though the assumptions of the Fourier analysis were actually true. And I think this kind of EEG has been achieved by evolution from simpler species whose EEGs are indistinguishable from stochastic, pink noise, that is a random mixture of unsynchronized, unpatterned spikes and slow waves of single neurons.
I'm going to give only a little space of that achievement although it is full of surprises, in order to give more space to evidence for the high information content of the most evolved EEGs. To my surprise it has turned out that most invertebrates, including worms, crustaceans, insects, snails and slugs have EEGs dominated by millisecond spikes which do not require microelectrodes or careful positioning. Slow waves, meaning waves below about 50 Hz, are inconspicuous except in some preparations and conditions.
There is little or no synchrony in Aplysia or in arthropods, most of the time, as detected by coherence between recording electrodes only a fraction of a millimeter apart. It looks quite stochastic and very different from our own EEG, dominated by slow waves of high amplitude.
Another surprise is that fish, frogs, and reptiles have EEGs that look just like ours; only the amplitude is lower (Baar et al. 1999). The power spectrum has the same general shape - a maximum between 5 and 15 Hz and falling steeply above this.
So, we have several puzzles: (1) First, why are spikes so much harder to see in all vertebrates than in almost any invertebrate? It is hard to dismiss this as due to cell size or packing density or the extracellular impedance or shunting of high frequencies by the proliferation of cell membranes in myelin and glia.
(2) Second, why are slow waves so weak in the invertebrates most of the time, and so strong in mammals and birds in many parts of the brain! This puzzle is more interesting because a first blush explanation is that synchrony has suddenly become important in the vertebrates. Some evidence supports that - more coherence at a given distance in fish than in Aplysia and still more in reptiles and most in mammals.
(3) Third, isn't there something more than mere amplitude that's different in mammals with a good cortex? My bet is that we can find a measure of activity patterning that, like coherence is higher in reptiles than in fish and highest in mammals - perhaps even graded within mammals with primates having more than insectivores. Could it be that the power spectrum is not a good method for describing our EEG - perhaps losing the real character as it would if we described speech or music with FFTs?
The state of the art and the challenge for the future is that we need to learn how to filter for transient microstructure - like distinguishing babbling from speech. It should be easier to distinguish structure from non-structure in the brain than in speech because in the brain structure is sure to be spatial as well as temporal. Maybe an analog is the Tokyo fish market where at 5:00 A.M. dozens of auctions are taking place simultaneously, a few meters apart, each with 20 or 30 bidders responding in a time-locked fashion to their own auctioneer's clues. As far as my limited understanding goes wavelet analysis is not very promising for this task, or mutual information or measures of entropy.
Now I finish by mentioning three forays or exploratory excursions into measures that, although elementary have not been done heretofore, I believe. These are attempts to find descriptors of wide-band time series that might discriminate among data sets, for example between brain states or brain regions or classes of animals or disease or ontogenetic stages. They are estimates of the distribution and dynamics of coherence, of bicoherence and of periodicity.
The first is coherence - a pairwise measure of correlation at each frequency between two simultaneous time series - and I will only mention it as a function of separation between the pick-up electrodes. This is the best estimator for synchrony, so far. It tells us that the eye is a poor estimator - confusing amplitude with synchrony. As I said earlier, there seems to be a difference between classes, with virtually no synchrony at any frequency in Aplysia, the sea slug, even at less than a millimeter and even for low frequencies. The fish we measured had significant coherence at one or two millimeters; turtles and gecko had a bit more and mammals still more.
We had no theoretical or empirical basis to guess what to expect. We found that picking up EEGs from the brain surface on a smooth brain - the rabbit's - the average of many pairs showed coherence of about 50% when the electrodes were about 3 to 5 mm apart, falling to chance level at about 7 to 10 mm (Bullock and McClune 1998, Bullock et al. 1990, 1995a,b) . Human subdural recordings are about twice those numbers but the electrodes are quite different so we cannot claim that this is a fair comparison. We can emphasize that the distance for 0.5 coherence varies greatly from pair to pair, place to place and moment to moment. The average is much greater in scalp recordings and smaller with intracortical microneedle recordings. All these features underline the main finding - microstructure and widely varying dynamics in time and space. Other puzzles I will skip over today such as the high agreement of different frequencies over the spectrum.
Bicoherence is not related to coherence. It is a higher moment of a form of phase coupling of pairs of frequencies, F1 and F2 and the frequency at their sum, F3 - a special case not found in purely linear systems or detected by purely linear analyses. It is not present above the chance level, for any pair of frequencies, in many EEG samples (Bullock et al. 1996, 1998a). Now and then for brief or longer episodes peaks appear or elevated plateaus or mountain ranges in certain portions of the 3D plot of coupling strength vs the intersections of F1 and F2. Here also we find microstructure and lability in space and time, possibly correlated with some state of the brain at that place and time.
The third measure is the most difficult - how strong is the periodicity at any frequency in the chosen spectral range? There is a tendency by authors to think of the Fourier spectrum of power at each frequency component as a display of rhythms and to think of the brain waves as the sum of many oscillators. But extremely nonperiodic time series, like an irregular succession of transients have FFT peaks.
We have applied a simple, additive method that averages all the segments of the time series, segmented at all the periods between chosen limits - in our usual frequency resolution there are 100 periods per octave or 564 between one and 50 Hz. We plot the variance of the averages as a function of period, normalizing by dividing these values by the values of a stochastic control.
The main findings are that many EEG samples from different times and different places on the scalp or intracranial, subdural or depth electrodes show no significant periodicity; others show one of the classical half dozen rhythms, a few show two non-harmonically related frequencies and still fewer may have 3, 4, or even 5 peaks in the range from 1-50 Hz (Bullock et al. 1998b). I can't list the difficulties and limitations of the method here but one is that, even when we use samples as short as two seconds, we cannot exclude that the rhythms are present only for a fraction of that time and may not be 3, 4, or 5 simultaneous rhythms. Microstructure is also clear by this measure, both in time and space.
A long lasting alpha or theta rhythm over a wide area is a special case. We have not enough experience with this measure to make statements about its correlation with brain states, regions or taxa. It is surely a crude, first-order feature but points to the possibility that EEGs from small electrodes in the brain parenchyma are extremely local, nonstationary and maybe as rich in non-rhythmic, phase-coupled 3D pattern as a political convention.
I want to conclude by underlining the extent of our ignorance about the evolution of the nervous systems of animals, most especially in the intermediate integrative levels, the mesoscopic levels of small or larger assemblages of cells in systems between the subcellular, ionic channel and molecular level and the behavioral level. The tremendous gaps between the complexity of the brain in a lower invertebrate and a higher invertebrate or a lower vertebrate and a higher vertebrate are apparently not principally attributable to sheer numbers of cells or to properties of neurons, synapses, transmitters and modulators but to some organizational features that utilize the permutations of scores of integrative variables and thousands or millions of connectivity variables.
We are seriously lacking in knowledge of what the actual differences are between less complex and more complex brains, particularly in their physiology. Crude, low magnification histology tells us there are marked differences between taxa of several grades of complexity. The latest is a spindle shaped cell type in the fifth layer of cuneate cortex that has just been found to be peculiar to higher primates - humans, chimps, gorillas and orangs.
But in spite of off and on claims of more speech cortex and prefrontal lobes in humans, we have no neural basis for the vast differences in behavioral complexity between humans and other species. If we found a difference in the EEG, it would not be a satisfying answer to the question "What features in the brain account for our level of complexity?" - but it would be a notable advance. It would be a notable advance to find relevant differences between Aplysia and squid, besides numbers of cells and percentages of them without axons or between alligators and cats, besides a corpus callosum and 6-layered cortex.
Biologists, evolutionists and neurophysiologists have too long overlooked one of the major biological, evolutionary and neural aspects of the animal world - namely what, specifically has evolution produced? In our concern for the how questions about evolution, a specialty with a good press and a lot of disciples, we tend to be satisfied with those differences between taxa that diagnose them or obviously concern their reproduction and we ignore the greatest reservoir of differences, the behavior machine, the brain. And that feature of brain evolution, its repeated saltations to more complex grades histologically and in behavior - which are not obviously improving survival value.
I am shamelessly beating the drum for comparative studies, for physiological studies in addition to anatomical and chemical and for study of brain mechanisms of information evaluation, recognition, processing and communication over and beyond spikes and circuits.
The Workshop was supported by the Sloan / Swartz Center for Theoretical Neurobiology, Salk Institute and the Swartz Center for Computational Neuroscience, UCSD, La Jolla
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