Information

At what stage is the nervous system developed enough to interpret neuronal signals as 'pain'?


According to this article in Live Science, one of the reasons the fetus can't feel pain until 19 weeks is because the nervous system isn't fully developed.

But according to this article, the heart starts beating at day 16.

And according to this article, the nervous system controls the rate beating of the heart.

Then my question is, **how can it be assured that the nervous system isn't developed until 19 weeks, when the nervous system controls the heart beating rate since day 16?


First, there is some confusion on your part about heart cells and pain perception. Heart cells generate an action potential intrinsically; they do not need the central nervous system to beat (your second article explains this; read the part about the importance of calcium.) So yes, long before a fetus can feel pain, the heart is beating, because there must be circulation of nutrients throughout the embryo.

Secondly, the vagus nerve and sympathetic nerves can affect heart rate (the former by slowing it down when firing). These nerves start to reach their endpoints late in week 4 of development. So 19 days is not correct.

Cardiac sympathetic system

Although the primitive human heart starts to beat at 21 to 22 d, heart development continues to day 50, and it is near the end of this period, during the fifth week, that thoracic neural crest cells migrate from the neural tube through the somites and form aggregations (ganglia) near the dorsal aorta. [emphasis mine]

To experience pain, however, requires maturation of certain parts of the brain, most importantly, part of the thalamus and the cerebral cortex:

Current theories of pain consider an intact cortical system to be both necessary and sufficient for pain experience. In support are functional imaging studies showing that activation within a network of cortical regions correlate with reported pain experience. Furthermore, cortical activation can generate the experience of pain even in the absence of actual noxious stimulation. These observations suggest thalamic projections into the cortical plate are the minimal necessary anatomy for pain experience. These projections are complete at 23 weeks' gestation. [emphasis mine]

Fetal surgeons and anesthesiologists often use 20 weeks as the time at which a fetus can start to feel pain, because that is when the fetus reacts to stimuli that would be recognized as pain if applied to adults (e.g. recoiling from noxious stimuli.)

One must recognize that recoiling from noxious stimuli does not prove what the fetus is perceiving. But it's a step in the direction of protecting the fetus from unnecessary pain.

Autonomic cardiac innervation: Development and adult plasticity
Can fetuses feel pain?


Development isn't my area of expertise within neuroscience, but without evaluating each of your sources (note: it might be better to read actual scientific articles; live science and especially livestrong are basically blogs; I haven't heard of iflscience but assume it is similar), it is important to recognize that the "nervous system" isn't all one unit, it is a collection of many networks and circuits.

There is no universal "on" switch where one day it is off and the next it is suddenly all working. Different pieces develop at different paces, and many functions are still developing even long after birth, through adolescence. In general, you can assume that the most basic functions necessary to sustain the body will develop first, followed by sensory and motor systems, followed by circuits that interpret sensory information to produce motor outputs (this is where awareness of things like pain would first come up), and lastly, executive functions that control whole patterns of behavior and higher-order decision making.


Pain Pathways

Pain is a somatic and emotional sensation that is unpleasant in nature and associated with actual or potential tissue damage. Physiologically, the function of pain is critical for survival and has a major evolutionary advantage. This is because behaviours that cause pain are often dangerous and harmful, therefore they are generally not reinforced and are unlikely to be repeated.

The classification of pain is complicated and there are many different types of pain, each arising through unique mechanisms. Types of pain include: sharp pain, prickling pain, thermal pain, aching pain. In addition, the origin of pain can be somatic, visceral, thalamic, neuropathic, psychosomatic, referred, or illusionary. Pain can also be acute or chronic in nature.

This article will provide a general overview of a ‘classic’ picture of pain i.e. the pain we feel when we stub our toe or touch something sharp. It will focus on how the pain pathway is initiated and processed within the spinal cord.


From Neurons to Neighborhoods: The Science of Early Childhood Development.

The brain is the ultimate organ of adaptation. It takes in information and orchestrates complex behavioral repertoires that allow human beings to act in sometimes marvelous, sometimes terrible ways. Most of what people think of as the “self”—what we think, what we remember, what we can do, how we feel—is acquired by the brain from the experiences that occur after birth. Some of this information is acquired during critical or sensitive periods of development, when the brain appears uniquely ready to take in certain kinds of information, while other information can be acquired across broad swaths of development that can extend into adulthood. This spectrum of possibilities is well captured by coinciding evidence of both the remarkably rapid brain development that characterizes the early childhood period and the brain's lifelong capacity for growth and change. The balance between the enduring significance of early brain development and its impressive continuing plasticity lies at the heart of the current controversy about the effects on the brain of early experience.

The past 20 years have seen unprecedented progress in understanding how the brain develops and, in particular, the phenomenal changes in both its circuitry and neurochemistry that occur during prenatal and early postnatal development. As discussed in Chapter 2, knowledge of the ways in which genes and the environment interact to affect the maturation of the brain has expanded by leaps and bounds. The years ahead will bring even more breathtaking progress as, for example, knowledge of the human genome is increasingly transformed into knowledge about how genes are expressed in the brain. This promises a dramatic expansion in the ability to understand the interweaving of genetic and environmental influences as they affect both brain and behavioral development (see Nelson and Bloom, 1997).

Growth in brain knowledge naturally leads to questions about what it means for raising children and, specifically, for improving their development. Accordingly, efforts to translate this emerging knowledge for public consumption have proliferated in recent years. Some of this information has been portrayed well and accurately, but some has not. The challenge of deciphering what this information means for what parents, guardians, and teachers of young children should do is enormous. There are actually few neuroscience studies of very young children, and those that exist have not usually focused on the brain regions that affect cognition, emotions, and other complex developmental tasks.

Much of the fundamental knowledge about brain development is based on experimental studies of animals. The translation of this information from basic neuroscience into rules for application to humans can be quite straightforward when the mechanisms involved are very similar in humans and animals, as is the case with the developing visual system. But the interpretation of other data from animals, or even some data from humans (such as estimates of the density of synapses in various brain regions at various ages), can be extraordinarily complex or inappropriate when the brain mechanisms of cognition, language, and social-emotional development are addressed. In this context, it is essential to balance excitement about all the new learning with caution about the limits of what is understood today.

This chapter about the developing brain focuses on the role of experience in early brain development. Following a brief discussion of how to study the developing brain is an overview of early brain development from conception through the early childhood years. We then turn to a discussion of how early experiences contribute to brain development. Four themes run throughout this section:

Developmental neuroscience research says a great deal about the conditions that pose dangers to the developing brain and from which young children need to be protected. It says virtually nothing about what to do to create enhanced or accelerated brain development.

The developing brain is open to influential experiences across broad periods of development. This openness to experience is part of what accounts for the remarkable adaptability of the developing mind. Although there are a few aspects of brain growth that require particular kinds of experience at particular times, as far as we know at present, this is more the exception than the norm for human brain growth.

The kinds of early experiences on which healthy brain development depends are ubiquitous in typical early human experience—just as nature intended. This means, however, that concern should be devoted to children who, for reasons of visual impairment, auditory processing problems, major perceptual-motor delays, and other basic deficits cannot obtain these experiences on which the developing nervous system depends.

Abusive or neglectful care, growing up in a dangerous or toxic environment, and related conditions are manifest risks for healthy brain development. Beyond these extremes, the nature and boundaries of the environmental conditions necessary for healthy brain growth are less well known, partly owing to the complexity and the cumulative achievements of cognitive, language, and socioemotional growth. Exploration in this area is cutting-edge research.


Neuronal plasticity and neurocircuitry

The term “plasticity” refers to the possible significant neuronal changes that occur in the acquisition of new skills. 1 – 3 These skills initiate the process of elaboration and stabilization of synaptic circuitry as part of the learning process. Plasticity permits adolescents to learn and adapt in order to acquire independence however, plasticity also increases an individual’s vulnerability toward making improper decisions because the brain’s region-specific neurocircuitry remains under construction, thus making it difficult to think critically and rationally before making complex decisions. Moreover, the neurocircuitry may be forged, refined or weakened, and damaged during plasticity. Thus, neuronal proliferation, rewiring, dendritic pruning, and environmental exposure are important components of brain plasticity during adolescence. A significant portion of brain growth and development occurring in adolescence is the construction and strengthening of regional neurocircuitry and pathways in particular, the brain stem, cerebellum, occipital lobe, parietal lobe, frontal lobe, and temporal lobe actively mature during adolescence. The frontal lobes are involved in movement control, problem solving, spontaneity, memory, language, initiation, judgment, impulse control, and social and sexual behavior. Furthermore, the prefrontal cortex, which is implicated in drug-seeking behavior, remains in a process of continuous reconstruction, consolidation, and maturation during adolescence.


References

Adrian, E. D. & Zotterman, Y. The impulses produced by sensory nerve endings. II. The responses of a single end-organ. J. Physiol. 61, 151–171 (1926).

Tomko, G. J. & Crapper, D. R. Neuronal variability: non-stationary responses to identical visual stimuli. Brain Res. 79, 405–418 (1974).

Tolhurst, D. J., Movshon, J. A. & Dean, A. F. The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Res. 23, 775–785 (1983).

Shadlen, M. N. & Newsome, W. T. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 18, 3870–3896 (1998).

Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 373–423, 623–656 (1948).

von Neumann, J. Probabilistic logics and the synthesis of reliable organisms from unreliable components. Automata Studies 34, 43–99 (1956).

Berg, H. C. & Purcell, E. M. Physics of chemoreception. Biophys. J. 20, 193–219 (1977).

Bialek, W. & Setayeshgar, S. Physical limits to biochemical signaling. Proc. Natl Acad. Sci. USA 102, 10040–10045 (2005). This paper uses a first-principle approach to show that there are noise limits to biochemical signalling. These noise limits are of importance in olfaction, intracellular and synaptical signalling.

Bialek, W. Physical limits to sensation and perception. Annu. Rev. Biophys. Biophys. Chem. 16, 455–478 (1987).

Lillywhite, P. G. & Laughlin, S. B. Transducer noise in a photoreceptor. Nature 277, 569–572 (1979).

Barlow, H. B., Levick, W. R. & Yoon, M. Responses to single quanta of light in retinal ganglion cells of the cat. Vision Res. 11, 87–101 (1971).

Cover, T. M. & Thomas, J. A. Elements of Information Theory (Wiley-Interscience, New York, 1991).

Laughlin, S. B. A., John, C., O'Carroll, D. C. & de Ruyter van Steveninck, R. R. in Information Theory and the Brain (eds Baddeley, R. H. R. & Foldiak, R.) 46–61 (Cambridge Univ. Press, 2000).

Bryant, H. L. & Segundo, J. P. Spike initiation by transmembrane current: a white-noise analysis. J. Physiol. 260, 279–314 (1976).

Mainen, Z. F. & Sejnowski, T. J. Reliability of spike timing in neocortical neurons. Science 268, 1503–1506 (1995).

Harsch, A. & Robinson, H. P. C. Postsynaptic variability of firing in rat cortical neurons: the roles of input synchronization and synaptic NMDA receptor conductance. J. Neurosci. 20, 6181–6192 (2000).

Schreiber, S., Fellous, J.-M., Tiesinga, P. & Sejnowski, T. J. Influence of ionic conductances on spike timing reliability of cortical neurons for suprathreshold rhythmic inputs. J. Neurophysiol. 91, 194–205 (2004).

de Ruyter van Steveninck, R. R., Lewen, G. D., Strong, S. P., Koberle, R. & Bialek, W. Reproducibility and variability in neural spike trains. Science 275, 1805–1808 (1997).

Berry, M. J., Warland, D. K. & Meister, M. The structure and precision of retinal spike trains. Proc. Natl Acad. Sci. USA 94, 5411–5416 (1997).

Rieke, F., Warland, D., van Steveninck, R. & Bialek, W. Spikes. Exploring the neural code (MIT Press, Cambridge, Massachusetts, 1997).

Bair, W. Spike timing in the mammalian visual system. Curr. Opin. Neurobiol. 9, 447–453 (1999).

Keat, J., Reinagel, P., Reid, R. C. & Meister, M. Predicting every spike: a model for the responses of visual neurons. Neuron 30, 803–817 (2001).

Panzeri, S., Petersen, R. S., Schultz, S. R., Lebedev, M. & Diamond, M. E. The role of spike timing in the coding of stimulus location in rat somatosensory cortex. Neuron 29, 769–777 (2001).

Kara, P., Reinagel, P. & Reid, R. C. Low response variability in simultaneously recorded retinal, thalamic and cortical neurons. Neuron 27, 635–646 (2000).

Billimoria, C. P., DiCaprio, R. A., Birmingham, J. T., Abbott, L. F. & Marder, E. Neuromodulation of spike-timing precision in sensory neurons. J. Neurosci. 26, 5910–5919 (2006).

Hausser, M., Spruston, N. & Stuart, G. J. Diversity and dynamics of dendritic signaling. Science 290, 739–744 (2000).

Stuart, G. & Hausser, M. Dendritic coincidence detection of EPSPs and action potentials. Nature Neurosci. 4, 63–71 (2001).

Carr, C. E. & Konishi, M. A circuit for detection of interaural time differences in the brain stem of the barn owl. J. Neurosci. 10, 3227–3246 (1990).

Fairhall, A. L., Lewen, G. D., Bialek, W. & de Ruyter van Steveninck, R. R. Efficiency and ambiguity in an adaptive neural code. Nature 412, 787–792 (2001).

Zhurov, Y. & Brezina, V. Variability of motor neuron spike timing maintains and shapes contractions of the accessory radula closer muscle of aplysia. J. Neurosci. 26, 7056–7070 (2006).

Bialek, W., Rieke, F., de Ruyter van Steveninck, R. R. & Warland, D. Reading a neural code. Science 252, 1854–1857 (1991).

Softky, W. R. & Koch, C. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334–350 (1993).

Strong, S. P., Koberle, R., de Ruyter van Steveninck, R. R. & Bialek, W. Entropy and information in neural spike trains. Phys. Rev. Lett. 80, 197–200 (1998).

Heggelund, P. & Albus, K. Response variability and orientation discrimination of single cells in striate cortex of cat. Exp. Brain Res. 32, 197–211 (1978).

Gur, M., Beylin, A. & Snodderly, D. M. Response variability of neurons in primary visual cortex ( V 1) of alert monkeys. J. Neurosci. 17, 2914–2920 (1997).

DeWeese, M. R., Wehr, M. & Zador, A. M. Binary spiking in auditory cortex. J. Neurosci. 23, 7940–7949 (2003).

Berry, M. J. & Meister, M. Refractoriness and neural precision. J. Neurosci. 18, 2200–2211 (1998).

Warzecha, A.-K. & Egelhaaf, M. Variability in spike trains during constant and dynamic stimulation. Science 283, 1927–1930 (1999).

Azouz, R. & Gray, C. M. Cellular mechanisms contributing to response variability of cortical neurons in vivo. J. Neurosci. 19, 2209–2223 (1999).

Deweese, M. R. & Zador, A. M. Shared and private variability in the auditory cortex. J. Neurophysiol. 92, 1840–1855 (2004).

Calvin, W. H. & Stevens, C. F. Synaptic noise and other sources of randomness in motoneuron interspike intervals. J. Neurophysiol. 31, 574–587 (1968).

Strassberg, A. F. & DeFelice, L. J. Limitation of the Hodgkin-Huxley formalism: effects of single channel kinetics on transmembrane voltage dynamics. Neural Comput. 5, 843–855 (1993).

Schneidman, E., Freedman, B. & Segev, I. Ion channel stochasticity may be critical in determining the reliability and precision of spike timing. Neural Comput. 10, 1679–1703 (1998). This paper shows that even when large numbers of stochastic ion channels are present in a neuron, fluctuations can become critical near the AP threshold.

Hodgkin, A. L. & Huxley, A. F. Quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952).

Fatt, P. & Katz, B. Some observations on biological noise. Nature 166, 597–598 (1950).

Calvin, W. H. & Stevens, C. F. Synaptic noise as a source of variability in the interval between action potentials. Science 155, 842–844 (1967).

Lou, X., Scheuss, V. & Schneggenburger, R. Allosteric modulation of the presynaptic Ca 2+ sensor for vesicle fusion. Nature 435, 497–501 (2005).

Derksen, H. E. & Verveen, A. A. Fluctuations of resting neural membrane potential. Science 151, 1388–1389 (1966).

Verveen, A. A., Derksen, H. E. & Schick, K. L. Voltage fluctuations of neural membrane. Nature 216, 588–589 (1967).

Blair, E. A. & Erlanger, J. A comparison of the characteristics of axons through their individual electric responses. Am. J. Physiol. 106, 524–564 (1933).

Steinmetz, P. N., Manwani, A., Koch, C., London, M. & Segev, I. Subthreshold voltage noise due to channel fluctuations in active neuronal membranes. J. Comput. Neurosci. 9, 133–148 (2000).

White, J. A., Rubinstein, J. T. & Kay, A. R. Channel noise in neurons. Trends Neurosci. 23, 131–137 (2000).

van Rossum, M. C., O'Brien, B. J. & Smith, R. G. Effects of noise on the spike timing precision of retinal ganglion cells. J. Neurophysiol. 89, 2406–2419 (2003).

Rubinstein, J. T. Threshold fluctuations in an N sodium channel model of the node of Ranvier. Biophys. J. 68, 779–785 (1995).

Skaugen, E. & Wallow, L. Firing behaviour in a stochastic nerve membrane model based upon the Hodgkin-Huxley equations. Acta Physiol. Scand. 107, 343–363 (1979).

Chow, C. C. & White, J. A. Spontaneous action potentials due to channel fluctuations. Biophys. J. 71, 3012–3021 (1996).

Diba, K., Lester, H. A. & Koch, C. Intrinsic noise in cultured hippocampal neurons: experiment and modeling. J. Neurosci. 24, 9723–9733 (2004).

Jacobson, G. A. et al. Subthreshold voltage noise of rat neocortical pyramidal neurones. J. Physiol. 564, 145–160 (2005).

Dorval, A. D. Jr & White, J. A. Channel noise is essential for perithreshold oscillations in entorhinal stellate neurons. J. Neurosci. 25, 10025–10028 (2005). This paper uses an elegant application of the dynamic-clamp technique to show that stochastic effects are required to describe CNS behaviour in vitro.

Kole, M. H., Hallermann, S. & Stuart, G. J. Single Ih channels in pyramidal neuron dendrites: properties, distribution, and impact on action potential output. J. Neurosci. 26, 1677–1687 (2006).

Faisal, A. A., White, J. A. & Laughlin, S. B. Ion-channel noise places limits on the miniaturization of the brain's wiring. Curr. Biol. 15, 1143–1149 (2005). This paper shows that channel noise places a universal lower limit on neuron diameter, matching anatomical data across species.

Rall, W. Time constants and electrotonic length of membrane cylinders and neurons. Biophys. J. 9, 1483–1508 (1969).

Faisal, A. A. & Laughlin, S. B. Stochastic simulations on the reliability of action potential propagation in thin axons. PLoS Comput. Biol. 3, e79 (2007). This paper uses stochastic simulations to show that the many thin axons in the CNS are a source of spike-time variability that had been overlooked experimentally. It demonstrated that noise acts in a context-dependent manner and allows for a previously unknown mode of AP conduction.

Cecchi, G. A. et al. Noise in neurons is message dependent. Proc. Natl Acad. Sci. USA 97, 5557–5561 (2000).

Fellous, J.-M., Tiesinga, P. H. E., Thomas, P. J. & Sejnowski, T. J. Discovering spike patterns in neuronal responses. J. Neurosci. 24, 2989–3001 (2004).

Horikawa, Y. Noise effects on spike propagation in the stochastic Hodgkin-Huxley models. Biol. Cybern. 66, 19–25 (1991).

Kandel, E. R., Schwartz, J. H. & Jessell, T. M. Principles of Neural Science (McGraw–Hill/Appleton & Lange, New York, 2000).

Debanne, D. Information processing in the axon. Nature Rev. Neurosci. 5, 304–316 (2004).

Debanne, D., Guerineau, N. C., Gahwiler, B. H. & Thompson, S. M. Action-potential propagation gated by an axonal IA-like K + conductance in hippocampus. Nature 389, 286–289 (1997).

Lecar, H. & Nossal, R. Theory of threshold fluctuations in nerves. II. Analysis of various sources of membrane noise. Biophys. J. 11, 1068–1084 (1971).

Manwani, A. & Koch, C. Detecting and estimating signals in noisy cable structures. I. Neuronal noise sources. Neural Comput. 11, 1797–1829 (1999).

Jefferys, J. G. Nonsynaptic modulation of neuronal activity in the brain: electric currents and extracellular ions. Physiol. Rev. 75, 689–723 (1995).

Szapiro, G. & Barbour, B. Multiple climbing fibers signal to molecular layer interneurons exclusively via glutamate spillover. Nature Neurosci. 10, 735–742 (2007).

Katz, B. & Miledi, R. Membrane noise produced by acetylcholine. Nature 226, 962–963 (1970).

Kleppe, I. C. & Robinson, H. P. C. Correlation entropy of synaptic input-output dynamics. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 74, 041909 (2006).

Faure, P., Kaplan, D. & Korn, H. Synaptic efficacy and the transmission of complex firing patterns between neurons. J. Neurophysiol. 84, 3010–3025 (2000).

Steriade, M., McCormick, D. A. & Sejnowski, T. J. Thalamocortical oscillations in the sleeping and aroused brain. Science 262, 679–685 (1993).

Cowan, R. L. & Wilson, C. J. Spontaneous firing patterns and axonal projections of single corticostriatal neurons in the rat medial agranular cortex. J. Neurophysiol. 71, 17–32 (1994).

Metherate, R. & Ashe, J. H. Ionic flux contributions to neocortical slow waves and nucleus basalis-mediated activation: whole-cell recordings in vivo. J. Neurosci. 13, 5312–5323 (1993).

Destexhe, A., Rudolph, M., Fellous, J. M. & Sejnowski, T. J. Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience 107, 13–24 (2001).

Fellous, J. M., Rudolph, M., Destexhe, A. & Sejnowski, T. J. Synaptic background noise controls the input/output characteristics of single cells in an in vitro model of in vivo activity. Neuroscience 122, 811–829 (2003).

Stuart, G. J. & Sakmann, B. Active propagation of somatic action potentials into neocortical pyramidal cell dendrites. Nature 367, 69–72 (1994).

Wehr, M. & Zador, A. M. Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature 426, 442–446 (2003).

Stevens, C. F. & Zador, A. M. Input synchrony and the irregular firing of cortical neurons. Nature Neurosci. 1, 210–217 (1998).

Shu, Y., Hasenstaub, A., Badoual, M., Bal, T. & McCormick, D. A. Barrages of synaptic activity control the gain and sensitivity of cortical neurons. J. Neurosci. 23, 10388–10401 (2003).

Conti, R., Tan, Y. & Llano, I. Action potential-evoked and ryanodine-sensitive spontaneous Ca 2+ transients at the presynaptic terminal of a developing CNS inhibitory synapse. J. Neurosci. 24, 6946–6957 (2004).

Wang, S. Q., Song, L. S., Lakatta, E. G. & Cheng, H. Ca 2+ signalling between single L-type Ca 2+ channels and ryanodine receptors in heart cells. Nature 410, 592–596 (2001).

Bekkers, J. M., Richerson, G. B. & Stevens, C. F. Origin of variability in quantal size in cultured hippocampal neurons and hippocampal slices. Proc. Natl Acad. Sci. USA 87, 5359–5362 (1990).

Zucker, R. S. & Regehr, W. G. Short-term synaptic plasticity. Annu. Rev. Physiol. 64, 355–405 (2002).

Sulzer, D. & Edwards, R. Vesicles: equal in neurotransmitter concentration but not in volume. Neuron 28, 5–7 (2000).

Wu, X.-S. et al. The origin of quantal size variation: vesicular glutamate concentration plays a significant role. J. Neurosci. 27, 3046–3056 (2007).

Franks, K. M., Stevens, C. F. & Sejnowski, T. J. Independent sources of quantal variability at single glutamatergic synapses. J. Neurosci. 23, 3186–3195 (2003). This paper uses stochastic simulations to model the effects of random variability in the synapse, down to the level of individual transmitter molecules.

Nimchinsky, E. A., Yasuda, R., Oertner, T. G. & Svoboda, K. The number of glutamate receptors opened by synaptic stimulation in single hippocampal spines. J. Neurosci. 24, 2054–2064 (2004).

Nusser, Z., Cull-Candy, S. & Farrant, M. Differences in synaptic GABAA receptor number underlie variation in GABA mini amplitude. Neuron 19, 697–709 (1997).

Lim, R., Alvarez, F. J. & Walmsley, B. Quantal size is correlated with receptor cluster area at glycinergic synapses in the rat brainstem. J. Physiol. 516, 505–512 (1999).

Paulsson, J. Summing up the noise in gene networks. Nature 427, 415–418 (2004).

Abbott, L. F. & Regehr, W. G. Synaptic computation. Nature 431, 796–803 (2004).

Miller, P., Zhabotinsky, A. M., Lisman, J. E. & Wang, X. J. The stability of a stochastic CaMKII switch: dependence on the number of enzyme molecules and protein turnover. PLoS Biol. 3, e107 (2005).

Henneman, E. Relation between size of neurons and their susceptibility to discharge. Science 126, 1345–1347 (1957).

Schmidt, R., Zelaznik, H., Hawkins, B., Frank, J. & Quinn, J. Jr. Motor-output variability: a theory for the accuracy of rapid motor acts. Psychol. Rev. 47, 415–451 (1979).

Jones, K. E., Hamilton, A. F. & Wolpert, D. M. Sources of signal-dependent noise during isometric force production. J. Neurophysiol. 88, 1533–1544 (2002). This paper provides experimental and theoretical evidence that the linear scaling of force variability (signal-dependent noise) is a natural by-product of the organization of motor neurons and muscle fibres (the size principle).

Hamilton, A. F., Jones, K. E. & Wolpert, D. M. The scaling of motor noise with muscle strength and motor unit number in humans. Exp. Brain Res. 157, 417–430 (2004).

Selen, L. P., Beek, P. J. & van Dieen, J. H. Can co-activation reduce kinematic variability? A simulation study. Biol. Cybern. 93, 373–381 (2005).

Moritz, C. T., Barry, B. K., Pascoe, M. A. & Enoka, R. M. Discharge rate variability influences the variation in force fluctuations across the working range of a hand muscle. J. Neurophysiol. 93, 2449–2459 (2005).

Christakos, C. N., Papadimitriou, N. A. & Erimaki, S. Parallel neuronal mechanisms underlying physiological force tremor in steady muscle contractions of humans. J. Neurophysiol. 95, 53–66 (2006).

Moffitt, M. A., McIntyre, C. C. & Grill, W. M. Prediction of myelinated nerve fiber stimulation thresholds: limitations of linear models. IEEE Trans. Biomed. Eng. 51, 229–236 (2004).

Mino, H. & Grill, W. M. Jr. Effects of stochastic sodium channels on extracellular excitation of myelinated nerve fibers. IEEE Trans. Biomed. Eng. 49, 527–532 (2002).

Frank, T. D., Friedrich, R. & Beek, P. J. Stochastic order parameter equation of isometric force production revealed by drift-diffusion estimates. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 74, 051905 (2006).

Capogrossi, M. C., Stern, M. D., Spurgeon, H. A. & Lakatta, E. G. Spontaneous Ca 2+ release from the sarcoplasmic reticulum limits Ca 2+ -dependent twitch potentiation in individual cardiac myocytes. A mechanism for maximum inotropy in the myocardium. J. Gen. Physiol. 91, 133–155 (1988).

Khatib, M., Hilaire, G. & Monteau, R. Excitatory interactions between phrenic motoneurons in the cat. Exp. Brain Res. 62, 273–280 (1986).

Chen, H., Tourtellotte, W. & Frank, E. Muscle spindle-derived neurotrophin 3 regulates synaptic connectivity between muscle sensory and motor neurons. J. Neurosci. 22, 3512–3519 (2002).

Furness, J. The excitatory input to a single smooth muscle cell. Pflugers Arch. 314, 1–13 (1970).

Morris, L. G. & Hooper, S. L. Muscle response to changing neuronal input in the lobster (Panulirus interruptus) stomatogastric system: spike number- versus spike frequency-dependent domains. J. Neurosci. 17, 5956–5971 (1997).

Lum, C. S., Zhurov, Y., Cropper, E. C., Weiss, K. R. & Brezina, V. Variability of swallowing performance in intact, freely feeding aplysia. J. Neurophysiol. 94, 2427–2446 (2005).

Hooper, S. L., Guschlbauer, C., von Uckermann, G. & Buschges, A. Natural neural output that produces highly variable locomotory movements. J. Neurophysiol. 96, 2072–2088 (2006).

Ludlow, C. L. Central nervous system control of the laryngeal muscles in humans. Respir. Physiol. Neurobiol. 147, 205–222 (2005).

Harris, C. M. & Wolpert, D. M. Signal-dependent noise determines motor planning. Nature 394, 780–784 (1998). This paper assumes that there is signal-dependent noise on the outgoing motor command and predicts the trajectories of goal-directed movements. The model suggests that subjects move in a way that minimizes the negative consequences of this noise.

Harris, C. M. & Wolpert, D. M. The main sequence of saccades optimizes speed-accuracy trade-off. Biol. Cybern. 95, 21–29 (2006).

van Beers, R. J. The sources of variability in saccadic eye movements. J. Neurosci. 27, 8757–8770 (2007).

Hamilton, A. F. & Wolpert, D. M. Controlling the statistics of action: obstacle avoidance. J. Neurophysiol. 87, 2434–2440 (2002).

van Beers, R. J., Haggard, P. & Wolpert, D. M. The role of execution noise in movement variability. J. Neurophysiol. 91, 1050–1063 (2004).

Kozlov, A. S., Risler, T. & Hudspeth, A. J. Coherent motion of stereocilia assures the concerted gating of hair-cell transduction channels. Nature Neurosci. 10, 87–92 (2007).

de Ruyter van Steveninck, R. R. & Laughlin, S. B. The rate of information-transfer at graded-potential synapses. Nature 379, 642–645 (1996).

Glowatzki, E. & Fuchs, P. A. Transmitter release at the hair cell ribbon synapse. Nature Neurosci. 5, 147–154 (2002).

Barlow, H. in Sensory Communication (ed. Rosenblith, W. A.), 217–234 (MIT Press, Cambridge, Massachusetts,1961).

Laughlin, S. A simple coding procedure enhances a neuron's information capacity. Z. Naturforsch. [C] 36, 910–912 (1981).

Field, D. Relations between the statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am. A 4, 2379–2394 (1987).

Adelman, T. L., Bialek, W. & Olberg, R. M. The information content of receptive fields. Neuron 40, 823–833 (2003).

Turin, G. An introduction to matched filters. IEEE Trans. Information Theory 6, 311–329 (1960).

Barlow, H. B. The neurologic of matching filters. J. Opt. Technol. 66, 776–781 (1999).

Ernst, M. O. & Banks, M. S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002).

Alais, D. & Burr, D. The ventriloquist effect results from near-optimal bimodal integration. Curr. Biol. 14, 257–262 (2004).

Jacobs, R. A. Optimal integration of texture and motion cues to depth. Vision Res. 39, 3621–3629 (1999). This was one of the first studies to demonstrate statistically optimal weighting (weighted averaging) of sensory cues.

Knill, D. C. Mixture models and the probabilistic structure of depth cues. Vision Res. 43, 831–854 (2003).

Hillis, J., Watt, S., Landy, M. & Banks, M. Slant from texture and disparity cues: optimal cue combination. J. Vis. 4, 967–992 (2004).

van Beers, R. J., Sittig, A. C. & Denier, J. J. How humans combine simultaneous proprioceptive and visual position information. Exp. Brain Res. 111, 253–261 (1996).

van Beers, R. J., Sittig, A. C. & Denier van der Gon, J. J. Integration of proprioceptive and visual position-information: an experimentally supported model. J. Neurophysiol. 81, 1355–1364 (1999). This paper provided the first evidence of a strategy for the optimal integration of multi-dimensional cues. When integrating proprioception and visual information in the plane (two-dimensional cues), subjects use knowledge of direction-dependent variability to generate an optimal estimate.

Sober, S. & Sabes, P. Flexible strategies for sensory integration during motor planning. Nature Neurosci. 8, 490–497 (2005).

Haruno, M. & Wolpert, D. M. Optimal control of redundant muscles in step-tracking wrist movements. J. Neurophysiol. 94, 4244–4255 (2005).

Georgopoulos, A., Schwartz, A. & Kettner, R. Neuronal population coding of movement direction. Science 233, 1416–1419 (1986).

Lee, C., Rohrer, W. H. & Sparks, D. L. Population coding of saccadic eye movements by neurons in the superior colliculus. Nature 332, 357–360 (1988).

Deneve, S., Latham, P. & Pouget, A. Efficient computation and cue integration with noisy population codes. Nature Neurosci. 4, 826–831 (2001).

Zohary, E., Shadlen, M. N. & Newsome, W. T. Correlated neuronal discharge rate and its implications for psychophysical performance. 370, 140–143 (1994).

Osborne, L. C., Bialek, W. & Lisberger, S. G. Time course of information about motion direction in visual area MT of macaque monkeys. J. Neurosci. 24, 3210–3222 (2004).

Romo, R., Hernandez, A., Zainos, A. & Salinas, E. Correlated neuronal discharges that increase coding efficiency during perceptual discrimination. Neuron 38, 649–657 (2003).

Jaynes, E. T. Probability Theory: The Logic of Science (ed. Bretthorst, G. L.) (Cambridge Univ. Press, 2003).

Kording, K. P. & Wolpert, D. M. Bayesian integration in sensorimotor learning. Nature 427, 244–247 (2004). This was the first paper to demonstrate that subjects use task statistics (prior knowledge) and knowledge of their sensory variability to produce estimates that are Bayes optimal.

Stocker, A. A. & Simoncelli, E. P. Noise characteristics and prior expectations in human visual speed perception. Nature Neurosci. 9, 578–585 (2006). This paper provides a framework for testing how experimental variability can be used to infer prior probabilities as well as the internal noise characteristics directly from experimental data.

Barlow, H. B. Pattern recognition and the responses of sensory neurons. Ann. NY Acad. Sci. 156, 872–881 (1969).

Barlow, H. Single units and sensation: a neuron doctrine for perceptual psychology. Perception 1, 371–394 (1972).

Paradiso, M. A theory for the use of visual orientation information which exploits the columnar structure of striate cortex. Biol. Cybern. 58, 35–49 (1988).

Ma, W. J., Beck, J. M., Latham, P. E. & Pouget, A. Bayesian inference with probabilistic population codes. Nature Neurosci. 9, 1432–1438 (2006).

Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402, C47–C42 (1999).

van Hateren, J. H. & Laughlin, S. B. Membrane parameters, signal transmission, and the design of a graded potential neuron. J. Comp. Physiol. A 166, 437–448 (1990).

Niven, J. E. et al. The contribution of Shaker K + channels to the information capacity of Drosophila photoreceptors. Nature 421, 630–634 (2003).

Gold, J. I. & Shadlen, M. N. Representation of a perceptual decision in developing oculomotor commands. Nature 404, 390–394 (2000).

Goodwin, G. & Sin, K. Adaptive filtering, prediction and control (Prentice-Hall, 1985).

Wolpert, D. M., Ghahramani, Z. & Jordan, M. I. An internal model for sensorimotor integration. Science 269, 1880–1882 (1995).

Kuo, A. An optimal control model for analyzing human postural balance. IEEE Trans. Biomed. Eng. 42, 87–101 (1995).

Todorov, E. & Jordan, M. I. Optimal feedback control as a theory of motor coordination. Nature Neurosci. 5, 1226–1235 (2002). This paper shows that an optimal control theory with signal-dependent noise in both the sensory feedback and the motor commands produces a number of observed motor behaviours.

Todorov, E. Optimality principles in sensorimotor control. Nature Neurosci. 7, 907–915 (2004).

Diedrichsen, J. Optimal task-dependent changes of bimanual feedback control and adaptation. Curr. Biol. 17, 1675–1679 (2007).

Liu, D. & Todorov, E. Evidence for the flexible sensorimotor strategies predicted by optimal feedback control. J. Neurosci. 27, 9354–9368 (2007).

Gribble, P. L., Mullin, L. I., Cothros, N. & Mattar, A. Role of cocontraction in arm movement accuracy. J. Neurophysiol. 89, 2396–2405 (2003).

Selen, L. P., Beek, P. J. & van Dieen, J. H. Impedance is modulated to meet accuracy demands during goal-directed arm movements. Exp. Brain Res. 172, 129–138 (2006). This paper provided the first experimental evidence that subjects increase joint stiffness when confronted with higher accuracy demands in goal-directed movements. The increase in joint stiffness results in less kinematic variability.

Selen, L. P., van Dieen, J. H. & Beek, P. J. Impedance modulation and feedback corrections in tracking targets of variable size and frequency. J. Neurophysiol. 96, 2750–2759 (2006).

Lametti, D. R., Houle, G. & Ostry, D. J. Control of movement variability and the regulation of limb impedance. J. Neurophysiol. 98, 3516–3524 (2007).

Attwell, D. & Laughlin, S. B. An energy budget for signaling in the grey matter of the brain. J. Cereb. Blood Flow Metab. 21, 1133–1145 (2001).

Balasubramanian, V., Kimber, D. & Berry, M. J. Metabolically efficient information processing. Neural Comput. 13, 799–815 (2001).

de Polavieja, G. G. Errors drive the evolution of biological signalling to costly codes. J. Theor. Biol. 214, 657–664 (2002).

Fitts, P. M. The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47, 381–391 (1954).

Maass, W. & Orponen, P. On the effect of analog noise in discrete-time analog computations. Neural Comp. 10, 1071–1095 (1998).

Benzi, R., Sutera, A. & Vulpiani, A. The mechanism of stochastic resonance. J. Phys. A Math. Gen. 14, L453–L457 (1981).

Longtin, A., Bulsara, A. & Moss, F. Time-interval sequences in bistable systems and the noise-induced transmission of information by sensory neurons. Phys. Rev. Lett. 67, 656–659 (1991).

Douglass, J. K., Wilkens, L., Pantazelou, E. & Moss, F. Noise enhancement of information transfer in crayfish mechanoreceptors by stochastic resonance. 365, 337–340 (1993).

Braun, H. A., Wissing, H., Schafer, K. & Hirsch, M. C. Oscillation and noise determine signal transduction in shark multimodal sensory cells. 367, 270–273 (1994).

Levin, J. E. & Miller, J. P. Broadband neural encoding in the cricket cereal sensory system enhanced by stochastic resonance. 380, 165–168 (1996).

Cordo, P. et al. Noise in human muscle spindles. 383, 769–770 (1996).

Russell, D. F., Wilkens, L. A. & Moss, F. Use of behavioural stochastic resonance by paddle fish for feeding. 402, 291–294 (1999). This paper provided the first demonstration of stochastic resonance effects at the behavioural level.

Priplata, A. A., Niemi, J. B., Harry, J. D., Lipsitz, L. A. & Collins, J. J. Vibrating insoles and balance control in elderly people. Lancet 362, 1123–1124 (2003).

Anderson, J. S., Lampl, I., Gillespie, D. C. & Ferster, D. The contribution of noise to contrast invariance of orientation tuning in cat visual cortex. Science 290, 1968–1972 (2000).

Krogh, A. & Hertz, J. A. Generalization in a linear perceptron in the presence of noise. J. Phys. A Math. Gen. 25, 1135–1147 (1992).

Kirkpatrick, S., Gelatt, C. D. Jr & Vecchi, M. P. Optimization by simulated annealing. Science 220, 671–680 (1983).

Tuckwell, H. C. & Rodriguez, R. Analytical and simulation results for stochastic Fitzhugh-Nagumo neurons and neural networks. J. Comput. Neurosci. 5, 91–113 (1998).

Lindner, B., Garcia-Ojalvo, J., Neiman, A. & Schimansky-Geier, L. Effects of noise in excitable systems. Phys. Rep. 392, 321–424 (2004).

Turrigiano, G. G., Leslie, K. R., Desai, N. S., Rutherford, L. C. & Nelson, S. B. Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391, 892–896 (1998).

Prinz, A., Bucher, D. & Marder, E. Similar network activity from disparate circuit parameters. Nature Neurosci. 7, 1345–1352 (2004).

Marsalek, P., Koch, C. & Maunsell, J. On the relationship between synaptic input and spike output jitter in individual neurons. Proc. Natl Acad. Sci. USA 94, 735–740 (1997).

Poliakov, A. V., Powers, R. K. & Binder, M. D. Functional identification of the input-output transforms of motoneurones in the rat and cat. J. Physiol. 504, 401–424 (1997).

Sarpeshkar, R. Analog versus digital: extrapolating from electronics to neurobiology. Neural Comp. 9, 1601–1638 (1998).

Laughlin, S., van Steveninck, R. R. & Anderson, J. The metabolic cost of neural information. Nature Neurosci. 1, 36–41 (1998).

Laughlin, S. B. & Sejnowski, T. J. Communication in neuronal networks. Science 301, 1870–1874 (2003).

Laughlin, S. B. in Complexity in biological information processing (eds Bock, G. & Good, J.) 177–187 (Wiley, Chichester, 2001).


Prenatal and postnatal development of the human nervous system

Almost all nerve cells, or neurons, are generated during prenatal life, and in most cases they are not replaced by new neurons thereafter. Morphologically, the nervous system first appears about 18 days after conception, with the genesis of a neural plate. Functionally, it appears with the first sign of a reflex activity during the second prenatal month, when stimulation by touch of the upper lip evokes a withdrawal response of the head. Many reflexes of the head, trunk, and extremities can be elicited in the third month.

During its development the nervous system undergoes remarkable changes to attain its complex organization. In order to produce the estimated 1 trillion neurons present in the mature brain, an average of 2.5 million neurons must be generated per minute during the entire prenatal life. This includes the formation of neuronal circuits comprising 100 trillion synapses, as each potential neuron is ultimately connected with either a selected set of other neurons or specific targets such as sensory endings. Moreover, synaptic connections with other neurons are made at precise locations on the cell membranes of target neurons. The totality of these events is not thought to be the exclusive product of the genetic code, for there are simply not enough genes to account for such complexity. Rather, the differentiation and subsequent development of embryonic cells into mature neurons and glial cells are achieved by two sets of influences: (1) specific subsets of genes and (2) environmental stimuli from within and outside the embryo. Genetic influences are critical to the development of the nervous system in ordered and temporally timed sequences. Cell differentiation, for example, depends on a series of signals that regulate transcription, the process in which deoxyribonucleic acid (DNA) molecules give rise to ribonucleic acid (RNA) molecules, which in turn express the genetic messages that control cellular activity. Environmental influences derived from the embryo itself include cellular signals that consist of diffusible molecular factors (see below Neuronal development). External environmental factors include nutrition, sensory experience, social interaction, and even learning. All of these are essential for the proper differentiation of individual neurons and for fine-tuning the details of synaptic connections. Thus, the nervous system requires continuous stimulation over an entire lifetime in order to sustain functional activity.


Endocrine System

The endocrine system is a control system of ductless glands that secrete hormones within specific organs. Hormones act as “messengers,” and are carried by the bloodstream to different cells in the body, which interpret these messages and act on them.

It seems like a far fetched idea that a small chemical can enter the bloodstream and cause an action at a distant location in the body. Yet this occurs in our bodies everyday of our lives. The ability to maintain homeostasis and respond to stimuli is largely due to hormones secreted within the body. Without hormones, you could not grow, maintain a constant temperature, produce offspring, or perform the basic actions and functions that are essential for life.

The endocrine system provides an electrochemical connection from the hypothalamus of the brain to all the organs that control the body metabolism, growth and development, and reproduction.

There are two types of hormones secreted in the endocrine system: Steroidal (or lipid based) and non-steroidal, (or protein based) hormones.

The endocrine system regulates its hormones through negative feedback, except in very specific cases like childbirth. Increases in hormone activity decrease the production of that hormone. The immune system and other factors contribute as control factors also, altogether maintaining constant levels of hormones.

Types of Glands

Exocrine Glands are those which release their cellular secretions through a duct which empties to the outside or into the lumen (empty internal space) of an organ. These include certain sweat glands, salivary and pancreatic glands, and mammary glands. They are not considered a part of the endocrine system.

Figure 2. Major endocrine glands. (Male left, female on the right.) 1. Pineal gland 2. Pituitary gland 3. Thyroid gland 4. Thymus 5. Adrenal gland 6. Pancreas 7. Ovary 8. Testis

Endocrine Glands are those glands which have no duct and release their secretions directly into the intercellular fluid or into the blood. The collection of endocrine glands makes up the endocrine system.

  1. The main endocrine glands are the pituitary (anterior and posterior lobes), thyroid, parathyroid, adrenal (cortex and medulla), pancreas and gonads.
  2. The pituitary gland is attached to the hypothalamus of the lower forebrain.
  3. The thyroid gland consists of two lateral masses, connected by a cross bridge, that are attached to the trachea. They are slightly inferior to the larynx.
  4. The parathyroid glands are four masses of tissue, two embedded posterior in each lateral mass of the thyroid gland.
  5. One adrenal gland is located on top of each kidney. The cortex is the outer layer of the adrenal gland. The medulla is the inner core.
  6. The pancreas is along the lower curvature of the stomach, close to where it meets the first region of the small intestine, the duodenum.
  7. The gonads (ovaries and testes) are found in the pelvic cavity.

Hormones and Types

A hormone is a type of chemical signal. They are a means of communication between cells.

The endocrine system produces hormones that are instrumental in maintaining homeostasis and regulating reproduction and development. A hormone is a chemical messenger produced by a cell that effects specific change in the cellular activity of other cells (target cells). Unlike exocrine glands (which produce substances such as saliva, milk, stomach acid and digestive enzymes), endocrine glands do not secrete substances into ducts (tubes). Instead, endocrine glands secrete their hormones directly into the surrounding extra cellular space. The hormones then diffuse into nearby capillaries and are transported throughout the body in the blood.

The endocrine and nervous systems often work toward the same goal. Both influence other cells with chemicals (hormones and neurotransmitters). However, they attain their goals differently. Neurotransmitters act immediately (within milliseconds) on adjacent muscle, gland, or other nervous cells, and their effect is short-lived. In contrast, hormones take longer to produce their intended effect (seconds to days), may affect any cell, nearby or distant, and produce effects that last as long as they remain in the blood, which could be up to several hours.

Table 1 shows the major hormones, their target and their function once in the target cell.

Table 1. Major Horomes
Endocrine Gland Hormone Released Chemical Class Target Tissue/Organ Major Function of Hormone
Hypothalamus Hypothalamic releasing and inhibiting hormones Peptide Anterior pituitary Regulate anterior pituitary hormone
Posterior Pituitary Antidiuretic (ADH) Peptide Kidneys Stimulates water reabsorption by kidneys
Oxytocin Peptide Uterus, mammary glands Stimulates uterine muscle contractions and release of milk by mammary glands
Anterior Pituitary Thyroid stimulating (TSH) Glycoprotein Thyroid Stimulates thyroid
Adrenocorticotropic (ACTH) Peptide Adrenal cortex Stimulates adrenal cortex
Gonadotropic (FSH, LH) Glycoprotein Gonads Egg and sperm production, sex hormone production
Prolactin (PRL) Protein Mammary glands Milk production
Growth (GH) Protein Soft tissue, bones Cell division, protein synthesis and bone growth
Thyroid Thyroxine (T4) and Triiodothyronie (T3) Iodinated amino acid All tissue Increase metabolic rate, regulates growth and development
Calcitonin Peptide Bones, kidneys and intestine Lowers blood calcium level
Parathyroids Parathyroid (PTH) Peptide Bones, kidneys and intestine Raises blood calcium level
Adrenal Cortex Glucocorticoids (cortisol) Steroid All tissue Raise blood glucose level, stimulates breakdown of protein
Mineralocorticoids (aldosterone) Steroid Kidneys Reabsorb sodium and excrete potassium
Sex Hormones Steroid Gonads, skin, muscles and bones Stimulates reproductive organs and brings on sex characteristics
Adrenal Medulla Epinephrine and norepinephrine Modified amino acid Cardiac and other muscles Released in emergency situations, raises blood glucose level, “fight or flight” response
Pancreas Insulin Protein Liver, muscles, adipose tissue Lowers blood glucose levels, promotes formation of glycogen
Glucagon Protein Liver, muscles, adipose tissue Raises blood glucose levels
Testes Androgens (testosterone) Steroid Gonads, skin, muscles and bone Stimulates male sex characteristics
Ovaries Estrogen and progesterone Steroid Gonads, skin, muscles and bones Stimulates female sex characteristics
Thymus Thymosins Peptide T lymphocytes Stimulates production and maturation of T lymphocytes
Pineal Gland Melatonin Modified amino acid Brain Controls circadian and circannual rhythms, possibly involved in maturation of sexual organs


Nervous System

Catherine E. Hagan , . C. Dirk Keene , in Comparative Anatomy and Histology , 2012

• Cell types

Cells of the CNS can be divided into two categories based on their embryonic origin. Cells arising from the neuroectodermal layer include neurons, astrocytes, oligodendrocytes, and ependymal cells. Cells of mesenchymal origin include the meninges, blood vessels, and microglia. The catchall term “glia” refers to astrocytes, oligodendrocytes, ependyma, and choroid plexus. Microglia are sometimes included in this category as well, but they are not true “glia” because they are thought to arise from yolk sac progenitors and/or circulating precursors of the macrophage lineage. In the CNS, each neuron is sustained by approximately 10–50 glial cells. More detailed descriptions of CNS histology and cytoarchitecture, including common lesions and artifacts, may be found in standard histology and neuropathology texts and also reviews listed in Further Reading and Relevant Websites.

Neurons

The functional unit of the nervous system is the neuron. Neurons have one or more dendrites through which they receive input from other neurons and one axon that synapses on other neurons or non-neural tissues, such as the musculature. Within the adult human brain, there are approximately 130 billion neurons forming 150 trillion synapses.

Neurons can be categorized in a number of ways, but the principal features used to distinguish populations of neurons are their neurotransmitter phenotype and their morphologic appearance. Neurons have a high metabolic rate, which makes them extremely vulnerable to certain global toxic insults that impair intracellular energy metabolism. Neurons typically secrete a single small molecule neurotransmitter, most commonly glutamate (in excitatory cells) or γ-aminobutyric acid (GABA inhibitory cells), as well as certain small peptide neurotransmitters such as enkephalin or parvalbumin. There are many sizes and shapes of neurons. Large pyramidal neurons, such as projection neurons of the cerebral cortex, have relatively large cell bodies, nuclei with a single prominent nucleolus, and prominent Nissl substance (rough endoplasmic reticulum) in the peripheral soma. These features may not be apparent in smaller neurons, such as the granule neurons of the cerebellar cortex. Interneurons are usually smaller than projection neurons (so called because their long axons innervate distant central nuclei or peripheral tissues). However, there are clear exceptions to this pattern in the striatum, some interneurons are larger than the prevalent GABAergic medium spiny projection neurons. The variety of neuronal appearances can help pathologists identify specific brain regions and nuclei. Generally, neurons have multiple dendrites surrounding their cell bodies and a single axon. Neurons can also be categorized by the number of processes extending from the cell body. Unipolar neurons have one axon. Bipolar neurons have an axon and one dendrite extending from the cell body toward opposite poles. Multipolar neurons have multiple dendrites and a single axon. Generally, in the nervous system of mice, neurons are smaller and populate the neuropil more densely than is evident in the same structures of human neural tissues ( Figure 10 ). The most common markers for neurons are NFP, neuronal nuclei (NeuN), and protein gene product 9.5 (PGP 9.5) for general detection of cells, as well as neurotransmitters or transmitter-producing enzymes for specific neuronal populations.

Many neurologic insults can affect any part of the CNS, including stroke, infection, and trauma. A subset of neurologic disorders, particularly chronic neurodegenerative diseases, are characterized by specific loss of a given neuron type. Examples include loss of GABAergic medium spiny neurons of the striatum in Huntington’s disease, reduced dopaminergic neurons of the substantia nigra in Parkinson’s disease, decreased cerebellar Purkinje neurons in many spinocerebellar ataxias, and depletion of primary motor neurons of the spinal cord anterior horn in amyotrophic lateral sclerosis. Careful examination of potential murine models of human neurologic diseases is required to assess the relevance of the model to human pathophysiology.

Oligodendrocytes

Oligodendrocytes (oligodendroglia) form and maintain the myelin sheaths that surround processes of CNS neurons. Each oligodendrocyte sheathes multiple axons. Oligodendrocytes have round nuclei with condensed chromatin that stain darker than those of astrocytes and neurons, and they lack basal lamina. These cells are called “satellite cells” when they are found next to neuron cell bodies in gray matter. Immersion-fixed tissue commonly exhibits a clear “halo” artifact of the oligodendrocyte cytoplasm that gives the cells a “fried egg” appearance. This artifact does not occur when tissue is fixed by perfusion. Oligodendrocytes are often seen in linear rows between the nerve fibers of white matter tracts ( Figure 11 ). Oligodendrocytes/myelin are the chief targets in autoimmune white matter disorders such as multiple sclerosis in humans and experimental autoimmune encephalomyelitis (EAE) in rodents. Destruction of myelin in the CNS is essentially permanent. Some common oligodendrocyte markers are carbonic anhydrase II, CNPase (2′,3′-cyclic nucleotide 3′-phosphohydrolase), MBP, and myelin oligodendrocyte glycoprotein (MOG).

Figure 11 . White matter in the adult mouse (A) and human (B).

Oligodendrocytes (arrows) have round, dark nuclei and are arranged in long rows (bracketed by lines) oriented parallel to the axons that they myelinate. In H&ampE-stained sections, white matter is slightly more eosinophilic and less cellular than gray matter, and the neuropil has a more organized, streaming appearance. The human oligodendrocytes often have a slight perinuclear halo (“fried egg” appearance). This feature occurs as an artifact of immersion fixation and is not prominent in the mouse image because the animal was perfused with fixative. Occasional astrocytes (arrowheads) are evident as oblong, irregularly shaped nuclei. The cytoplasm of astrocytes (and microglia) normally blends with the neuropil and typically cannot be observed unless the cells are activated.

In the PNS, axons are myelinated by Schwann cells. These cells have elongate, wavy nuclei (see Peripheral Nerve section for further details) and form the myelin sheath for a single axon. Damaged myelin in the PNS may be repaired by Schwann cell proliferation, although in this event the length of each internode (the distance between Schwann cells) is reduced. Standard Schwann cell markers are CNPase, MBP, peripheral myelin protein 22, and S100β.

Astrocytes

Astrocytes are the most common glial cell, with the number of astrocytes equaling or exceeding that of neurons in most brain areas. These cells support neurons in many ways. Neurons rely on astrocyte-derived thiols to maintain stable glutathione concentrations low glutathione renders neurons more susceptible to injury from oxidative stress. Astrocytes also take up and recycle neurotransmitters (glutamate and GABA), maintain the ionic composition of the extracellular milieu, and preserve the integrity of the blood–brain barrier. The cytoplasmic processes of astrocytes give them their starlike (stellate) shape. These processes extend outward and can make contact with any part of a neuron’s surface. Astrocyte foot processes contact CNS capillaries and induce endothelial cells to form tight junctions during brain development in utero.

The cytoarchitecture of astrocytes is characteristic. Astrocyte nuclei are approximately the same size as many neuronal nuclei but are larger than oligodendrocyte nuclei. They are round to ovoid, have small or indistinct nucleoli, and have pale, vesicular euchromatin ( Figure 11 ). Cytoplasmic processes of nonreactive astrocytes are inconspicuous in H&E-stained sections. However, when astrocytes react to injury, their cytoplasm and processes become distinctly eosinophilic and expand substantially due in part to the accumulation of intermediate filament proteins (e.g., GFAP) so that their cell borders become distinguishable. The culmination of these changes results in “gemistocytes,” which are reactive astrocytes with blunt processes and markedly expanded and eosinophilic cytoplasm usually accompanied by nuclear eccentricity. The immunostain most frequently used to demonstrate astrocytes is GFAP.

Microglia

Microglia are the resident histiocyte-type cell and the key innate immune effector of the CNS. They are often described as either resting (i.e., ramified) or activated, but these terms fail to convey the dynamic remodeling of their fine processes and constitutive immunosurveillance activity. Their origin is highly debated. Whereas some microglia are derived from circulating bone marrow-derived monocytes, particularly in the setting of acute or chronic injury, evidence suggests that early microglia are derived from yolk sac progenitors. Thus, microglia in adult mice and humans are the result of a combination of proliferation of the resident population and migration into the CNS by myeloid progenitors. In H&E-stained sections of normal brain, microglia are relatively few in number. Such “resting” microglia have small, dark, rod-shaped nuclei with condensed chromatin ( Figure 11 ) they are smaller than the nuclei of astrocytes. The cytoplasm of surveying (not activated) microglia is inconspicuous. In contrast, activated microglia that have become distended by phagocytosed material resemble foamy macrophages and are sometimes designated Gitter cells or foam cells. The markers frequently used to demonstrate microglia are CD68 in humans and CD11b or Iba-1 in mice.


What affects your experience of pain?

Severe pain quickly gets your attention and usually produces a stronger physical response than mild pain. The location of your pain can also affect how you perceive it. For example, pain coming from the head is harder to ignore than pain originating elsewhere in the body.

The location of pain in your body does not always indicate where it is coming from. For example, the pain from a heart attack can be felt in the neck, jaws, arms or abdomen. This is known as referred pain and occurs because signals from different parts of the body often converge on the same neurones in the spinal cord.

The gate control theory helps explain how the brain influences your experience of pain. It seems that several factors can affect how you interpret pain:

  • emotional and psychological state
  • memories of previous pain
  • upbringing
  • expectations of and attitudes towards pain
  • beliefs and values
  • age
  • sex and
  • social and cultural influences.

Hence the experience of pain differs from person to person.


Subdivisions of the Nervous System

The CNS includes the brain and spinal cord, while the PNS is a network of nerves linking the body to the brain and spinal cord.

Learning Objectives

Describe the subdivisions of the nervous system

Key Takeaways

Key Points

  • The nervous system is often divided into components called gray matter and white matter. Gray matter contains a relatively high proportion of neuron cell bodies and white matter is composed mainly of axons.
  • The peripheral nervous system is subdivided into nerves, the autonomic system, and the somatic system. The autonomic nervous system is further subdivided into the parasympathetic and sympathetic nervous systems.
  • The enteric nervous system is an independent subsystem of the peripheral nervous system.
  • The central nervous system includes the brain and spinal cord and has various centers that integrate of all the information in the body. These can be subdivided into lower centers that carry out essential body functions and higher centers that control more sophisticated information processing.

Key Terms

  • gray matter: A major component of the central nervous system, consisting of neuronal cell bodies, neuropil (dendrites and unmyelinated axons), glial cells (astroglia and oligodendrocytes), and capillaries.
  • central nervous system: In vertebrates, the part of the nervous system comprising the brain, brainstem, and spinal cord.
  • white matter: A region of the central nervous system containing myelinated nerve fibers and no dendrites.
  • peripheral nervous system: This system consists of the nerves and ganglia outside of the brain and spinal cord.

The nervous system is comprised of two major subdivisions, the central nervous system (CNS) and the peripheral nervous system (PNS).

Central Nervous System

The Central Nervous System: The central nervous system (2) is a combination of the brain (1) and the spinal cord (3).

The CNS includes the brain and spinal cord along with various centers that integrate all the sensory and motor information in the body. These centers can be broadly subdivided into lower centers, including the spinal cord and brain stem, that carry out essential body and organ-control functions and higher centers within the brain that control more sophisticated information processing, including our thoughts and perceptions. Further subdivisions of the brain will be discussed in a later section.

Gray Matter and White Matter

The nervous system is often divided into components called gray matter and white matter. Gray matter, which is gray in preserved tissue but pink or light brown in living tissue, contains a relatively high proportion of neuron cell bodies. Conversely, white matter is composed mainly of axons and is named because of the color of the fatty insulation called myelin that coats many axons. White matter includes all of the nerves of the PNS and much of the interior of the brain and spinal cord. Gray matter is found in clusters of neurons in the brain and spinal cord and in cortical layers that line their surfaces.

By convention, a cluster of neuron cell bodies in the gray matter of the brain or spinal cord is called a nucleus, whereas a cluster of neuron cell bodies in the periphery is called a ganglion. However, there are a few notable exceptions to this rule, including a part of the brain called the basal ganglia, which will be discussed later.

Peripheral Nervous System

The PNS is a vast network of nerves consisting of bundles of axons that link the body to the brain and the spinal cord. Sensory nerves of the PNS contain sensory receptors that detect changes in the internal and external environment. This information is sent to the CNS via afferent sensory nerves. Following information processing in the CNS, signals are relayed back to the PNS by way of efferent peripheral nerves.

Autonomic and Somatic Nervous Systems

The PNS is further subdivided into the autonomic nervous system (ANS) and the somatic nervous system. The autonomic system has involuntary control of internal organs, blood vessels, and smooth and cardiac muscles. The somatic system has voluntary control of our movements via skeletal muscle.

As mentioned, the autonomic nervous system acts as a control system and most functions occur without conscious thought. The ANS affects heart rate, digestion, respiratory rate, salivation, perspiration, pupil diameter, urination, and sexual arousal. While most of its actions are involuntary, some, such as breathing, work in tandem with the conscious mind. The ANS is classically divided into two subsystems: the parasympathetic nervous system (PSNS) and sympathetic nervous system (SNS).

Parasympathetic and Sympathetic Nervous Systems

Broadly, the parasympathetic system is responsible for stimulation of “rest-and-digest” activities that occur when the body is at rest, including sexual arousal, salivation, lacrimation (tears), urination, digestion, and defecation. The sympathetic nervous syste is responsible for stimulating activities associated with the “fight-or-flight” response: mobilizing the systems of the body for escape or attacking sources of danger. In truth, the functions of both the parasympathetic and sympathetic nervous systems are not so straightforward, but this division is a useful rule of thumb.

The enteric nervous system (ENS) controls the gastrointestinal system and is sometimes considered part of the autonomic nervous system. However, it is sometimes considered an independent system because it can operate independently of the brain and the spinal cord.

The Nervous System of a Vertebrate: The brain and the spinal cord are the central nervous system (CNS) (shown in yellow). The left-right pair of cranial nerves, spinal nerves, and ganglia make up the peripheral nervous system (shown in dark gold).


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