Why some neurons are tetraploid

Why some neurons are tetraploid

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Why are some neurons tetraploid, and how does this result from it's ancestor cell ?

Neurons are tetraploid in pathological situations like Alzheimer disease:

Neurons that duplicate their DNA are rarely observed to undergo mitosis, and they remain for long time as tetraploid cells, in accordance with the chronic course of the disease. We have recently shown that cell cycle re-entry and somatic tetraploidization occurs during normal development in a subpopulation of RGCs (retinal ganglion cells), giving rise to enlarged neurons with extensive dendritic trees. Tetraploization in these neurons occurs in response to the activation of the neurotrophin receptor p75NTR by an endogenous source of NGF (nerve growth factor).

Source: Frade JM, López-Sánchez N. A novel hypothesis for Alzheimer disease based on neuronal tetraploidy induced by p75NTR. Cell Cycle 2010; 9:1934 - 1941; PMID: 20436277;

These neurons are generated in response to nerve growth factor (NGF) acting through the neurotrophin receptor p75 (p75NTR), which induces E2F1 activity and cell cycle re-entry in migrating RGC neuroblasts lacking retinoblastoma (Rb) protein. We have also showed that brain-derived neurotrophic factor (BDNF) prevents G2/M transition in the tetraploid RGCs, thus being crucial for the maintenance of the tetraploid status as well as the survival of these neurons. The realization that tetraploid neurons can be readily observed in the vertebrate nervous system has important physiological consequences.

Several eukaryotes are known to undergo endoreduplicative cycles leading to somatic polyploidy, thus increasing cell size in specific tissues. Examples of polyploidy in neurons are known in some invertebrates.

Source: José María Frade. Somatic tetraploidy in vertebrate neurons - Implications in physiology and pathology. Commun Integr Biol. 2010 Mar-Apr; 3(2): 201-203. PMCID: PMC2889987

It's not all pathological. From the same second article cited:

Modern techniques such as flow cytometry, fluorescent in situ hybridization (FISH), slide-based cytometry (SBC) and quantitative PCR analysis of DNA from isolated nuclei can all reliably quantify the amount of nuclear DNA in neurons.11 By using SBC, flow cytometry and FISH, we have recently demonstrated that tetraploid neurons exist in the normal vertebrate retina, representing a subpopulation of RGCs that, in the chick, innervate lamina F in the stratum-griseum-et-fibrosum-superficiale of the tectal cortex.12 These neurons are generated during the early stages of retinal development, soon after they acquire neuronal markers. Indeed, a subset of migrating RGCs expressing the transcription factor E2F1, and lacking Rb protein, was observed to undergo S-phase and remain in a permanent G2-like state. Therefore, endoreduplication, but not alternative mechanisms such as aneuploidy or cell fusion,13,14 represents the mechanism generating tetraploid RGCs in the vertebrate nervous system.

Coronavirus Does Not Infect the Brain, but Still Inflicts Significant Neurological Damage

SARS-CoV-2, the virus that causes COVID-19, likely does not directly infect the brain but can still inflict significant neurological damage, according to a new study from neuropathologists, neurologists, and neuroradiologists at Columbia University Vagelos College of Physicians and Surgeons.

“There’s been considerable debate about whether this virus infects the brain, but we were unable to find any signs of virus inside brain cells of more than 40 COVID-19 patients,” says James E. Goldman, MD, PhD, professor of pathology & cell biology (in psychiatry), who led the study with Peter D. Canoll, MD, PhD, professor of pathology & cell biology, and Kiran T. Thakur, MD, the Winifred Mercer Pitkin Assistant Professor of Neurology.

“At the same time, we observed many pathological changes in these brains, which could explain why severely ill patients experience confusion and delirium and other serious neurological effects — and why those with mild cases may experience ‘brain fog’ for weeks and months.”

The study, published in the journal Brain, is the largest and most detailed COVID-19 brain autopsy report published to date, suggests that the neurological changes often seen in these patients may result from inflammation triggered by the virus in other parts of the body or in the brain’s blood vessels.

No Virus in Brain Cells

The study examined the brains of 41 patients with COVID-19 who succumbed to the disease during their hospitalization. The patients ranged in age from 38 to 97 about half had been intubated and all had lung damage caused by the virus. Many of the patients were of Hispanic ethnicity. There was a wide range of hospital length with some patients dying soon after arrival to the emergency room while others remained in the hospital for months. All of the patients had extensive clinical and laboratory investigations, and some had brain MRI and CT scans.

To detect any virus in the neurons and glia cells of the brain, the researchers used multiple methods including RNA in situ hybridization, which can detect viral RNA within intact cells antibodies that can detect viral proteins within cells and RT-PCR, a sensitive technique for detecting viral RNA.

Despite their intensive search, the researchers found no evidence of the virus in the patients’ brain cells. Though they did detect very low levels of viral RNA by RT-PCR, this was likely due to virus in blood vessels or leptomeninges covering the brain.

“We’ve looked at more brains than other studies, and we’ve used more techniques to search for the virus. The bottom line is that we find no evidence of viral RNA or protein in brain cells,” Goldman says. “Though there are some papers that claim to have found virus in neurons or glia, we think that those result from contamination, and any virus in the brain is contained within the brain’s blood vessels.” “If there’s any virus present in the brain tissue, it has to be in very small amounts and does not correlate with the distribution or abundance of neuropathological findings,” Canoll says.

The tests were conducted on more than two dozen brain regions, including the olfactory bulb, which was searched because some reports have speculated that the coronavirus can travel from the nasal cavity into the brain via the olfactory nerve. “Even there, we didn’t find any viral protein or RNA,” Goldman says, “though we found viral RNA and protein in the patients’ nasal mucosa and in the olfactory mucosa high in the nasal cavity.” (The latter finding appears in an unpublished study, currently on BioRxiv, led by Jonathan Overdevest, MD, PhD, assistant professor of otolaryngology, and Stavros Lomvardas, PhD, professor of biochemistry & molecular biophysics and neuroscience.)

Hypoxic Damage and Signs of Neuronal Death

Despite the absence of virus in the brain, in every patient the researchers found significant brain pathology, which mostly fell into two categories.

“The first thing we noticed was a lot of areas with damage from a lack of oxygen,” Goldman says. “They all had severe lung disease, so it’s not surprising that there’s hypoxic damage in the brain.”

Some of these were large areas caused by strokes, but most were very small and only detectable with a microscope. Based on other features, the researchers believe these small areas of hypoxic damage were caused by blood clots, common in patients with severe COVID-19, that temporarily stopped the supply of oxygen to that area.

A more surprising finding, Goldman says, was the large number of activated microglia they found in the brains of most patients. Microglia are immune cells that reside in the brain and can be activated by pathogens.

“We found clusters of microglia attacking neurons, a process called ‘neuronophagia,'” says Canoll. Since no virus was found in the brain, it’s possible the microglia may have been activated by inflammatory cytokines, such as Interleukin-6, associated with SARS-CoV-2 infection.

“At the same time, hypoxia can induce the expression of ‘eat me’ signals on the surface of neurons, making hypoxic neurons more vulnerable to activated microglia,” Canoll says, “so even without directly infecting brain cells, COVID-19 can cause damage to the brain.”

The group found this pattern of pathology in one of their first autopsies, described by Osama Al-Dalahmah, MD, PhD, instructor in pathology & cell biology, in a case report published last March in Acta Neuropathologica Communications. Over the next few months, as the neuropathologists did many more COVID brain autopsies, they saw similar findings over and over again and realized that this is a prominent and common neuropathological finding in patients who die of COVID.

The activated microglia were found predominantly in the lower brain stem, which regulates heart and breathing rhythms, as well as levels of consciousness, and in the hippocampus, which is involved in memory and mood.

“We know the microglia activity will lead to loss of neurons, and that loss is permanent,” Goldman says. “Is there enough loss of neurons in the hippocampus to cause memory problems? Or in other parts of the brain that help direct our attention? It’s possible, but we really don’t know at this point.”

Persistent Neurological Problems in Survivors

Goldman says that more research is needed to understand the reasons why some post-COVID-19 patients continue to experience symptoms.

The researchers are now examining autopsies on patients who died several months after recovering from COVID-19 to learn more.

They are also examining the brains from patients who were critically ill with acute respiratory distress syndrome (ARDS) before the COVID-19 pandemic to see how much of COVID-19 brain pathology is a result of the severe lung disease.

Reference: “COVID-19 neuropathology at Columbia University Irving Medical Center/New York Presbyterian Hospital” by Kiran T Thakur, Emily Happy Miller, Michael D Glendinning, Osama Al-Dalahmah, Matei A Banu, Amelia K Boehme, Alexandra L Boubour, Samuel S Bruce, Alexander M Chong, Jan Claassen, Phyllis L Faust, Gunnar Hargus, Richard A Hickman, Sachin Jambawalikar, Alexander G Khandji, Carla Y Kim, Robyn S Klein, Angela Lignelli-Dipple, Chun-Chieh Lin, Yang Liu, Michael L Miller, Gul Moonis, Anna S Nordvig, Jonathan B Overdevest, Morgan L Prust, Serge Przedborski, William H Roth, Allison Soung, Kurenai Tanji, Andrew F Teich, Dritan Agalliu, Anne-Catrin Uhlemann, James E Goldman and Peter Canoll, 15 April 2021, Brain.
DOI: 10.1093/brain/awab148

Other contributors (all at Columbia unless otherwise noted): Emily Happy Miller, Michael D. Glendinning, Osama Al-Dalahmah, Matei A. Banu, Amelia K. Boehme, Alexandra L. Boubour, Samuel L. Bruce, Alexander M. Chong, Jan Claassen, Phyllis L. Faust, Gunnar Hargus, Richard Hickman, Sachin Jambawalikar, Alexander G. Khandji, Carla Y. Kim, Robyn S. Klein (Washington University School of Medicine), Angela Lignelli-Dipple, Chun-Chieh Lin (Dartmouth-Hitchcock Medical Center), Yang Liu, Michael L. Miller, Gul Moonis, Anna S. Nordvig, Serge Przedborski, Morgan L. Prust, William H. Roth, Allison Soung (Washington University School of Medicine), Kurenai Tanji, Andrew F. Teich, Dritan Agalliu, and Anne-Catrin Uhlemann.

Main Text


Mirror neurons are a class of neuron that modulate their activity both when an individual executes a specific motor act and when they observe the same or similar act performed by another individual. They were first reported in the macaque monkey ventral premotor area F5 [1] and were named mirror neurons in a subsequent publication from the same group [2]. Ever since their discovery, there has been great interest in mirror neurons and much speculation about their possible functional role with a particular focus on their proposed role in social cognition. As Heyes [3] wrote “[mirror neurons] intrigue both specialists and non-specialists, celebrated as a ‘revolution’ in understanding social behaviour … and ‘the driving force’ behind ‘the great leap forward’ in human evolution…”. Indeed so much has been written in both peer-review literature and elsewhere about mirror neurons and their proposed functional role(s) that they have recently been given the moniker “The most hyped concept in neuroscience” [4].

For us, the discovery of mirror neurons was exciting because it has led to a new way of thinking about how we generate our own actions and how we monitor and interpret the actions of others. This discovery prompted the notion that, from a functional viewpoint, action execution and observation are closely-related processes, and indeed that our ability to interpret the actions of others requires the involvement of our own motor system.

The aim of this article is not to add to this literature on the putative functional role(s) of mirror neurons, but instead to provide a review of the studies that have directly recorded mirror neuron activity. To date, there have been over 800 published papers on mirror neurons (from a PubMed search using: “mirror neuron” OR “mirror neurons”). Here, we restrict our attention to only the primary literature on mirror neurons. Mirror neurons were originally defined as neurons which 𠇍ischarged both during monkey’s active movements and when the monkey observed meaningful hand movements made by the experimenter” [2]. Thus, the key characteristics of mirror neurons are that their activity is modulated both by action execution and action observation, and that this activity shows a degree of action specificity. This distinguishes mirror neurons from other ‘motor’ or ‘sensory’ neurons whose discharge is associated with either execution or observation, but not both. It also distinguishes mirror neuron responses from other types of response to vision of objects or other non-action stimuli. As the activity of mirror neurons cannot yet be unambiguously detected using neuroimaging techniques, we have excluded human and non-human primate imaging studies from this review. We therefore focus on the 25 papers [1,2,5�] that have reported quantitative results of recording mirror neurons or mirror-like neurons in macaque monkeys since 1992 ( Table 1 ).

Table 1

Proportion of neurons recorded in macaque premotor cortex (area F5) and posterior parietal cortex that showed mirror neuron properties.

ReferenceRecording areaNo. neuronsNo. mirror% mirror 1 Action specificityObserved effector
Bonini etਊl.[5]F51543623.4%yHand
Caggiano etਊl.[6]F529914949.8%nHand
Caggiano etਊl.[8]F521910548%nHand
Caggiano etਊl.[7]F522412354.9%nHand (video)
Caggiano etਊl.[9]F578524731.5%nHand (video)
Ferrari etਊl.[11]F548513026.8%yMouth
Ferrari etਊl.[12]F52095224.9%yHand
Gallese etਊl.[2]F55329217.3%yHand
Kohler etਊl.[16] 2 F54976312.7%yAuditory
Kraskov etਊl.[17]F5643148.4%yHand (PTNs)
di Pellegrino etਊl.[1]F5184189.8%yHand
Rizzolatti etਊl.[18]F53006020%yHand
Rochat etਊl.[19]F52829232.6%yHand
Umilta etਊl.[23]F522010346.8%yHand
Bonini etਊl.[5]IPL1202823.3%yHand
Fogassi etਊl.[13]IPL1654124.8%yHand
Rozzi etਊl.[20]IPL4235112%yHand
Shepherd etਊl.[21]LIP1533019.6%nEye-gaze
Dushanova and Donoghue [10]M130310534.6%yReaching
Tkach etਊl.[22]M182958170.1%yTracking arm
Vigneswaran etਊl.[24]M11327758.3%nHand (PTNs)
Tkach etਊl.[22]PMd1287760.1%yTracking arm
Ishida etਊl.[14]VIP541488.9%yBimodal tactile/visual
Fujii etਊl.[27]PM 3 148_3�% 4 nHand
IPS 5 148-10�% 4 n

Mirror neurons were first described in the rostral division of the ventral premotor cortex (area F5) of the macaque brain, and have subsequently been reported in the inferior parietal lobule, including the lateral and ventral intraparietal areas, and in the dorsal premotor and primary motor cortex. But despite the large array of areas in which mirror neurons have been reported, the majority of mirror neuron research has studied the activity of mirror neurons in area F5 (15/25 papers  Figureਁ A).

Number of mirror neurons recorded in areas F5 and in the IPL.

(A) The percentage of mirror neurons as a function of publication year for studies reporting mirror neurons in F5 when observing hand actions. The black line shows the line of best fit. (B) The percentage of mirror neurons in premotor area F5 and in the inferior parietal lobule (IPL). The average percentage of mirror neurons for each region is shown in black and the percentage of total mirror neurons is shown in grey with the total number of mirror neurons and neurons recorded given above.

Mirror Neurons in Ventral Premotor Region F5

Of the 15 papers reporting mirror neuron activity in area F5, 11 provide details of the number of mirror neurons recorded when observing the experimenter (not a video) reaching and grasping objects with their hand. On average, 33.6% of neurons recorded in F5 have been described as mirror neurons when the monkey observed hand actions performed by a human experimenter in front of them (ranging from 9.8�.8% Figureਁ A,B). It is of note that the percentage of mirror neurons reported appears to increase as a function of time. This most likely reflects a sampling bias during data collection.

The first three papers [1,2,18] described the basic properties of mirror neurons, and their percentages are low compared with later studies. The more recent papers, in general, have investigated modulations of mirror neuron activity with some form of task manipulation. The methodological approach of these later papers is to first select neurons based on their motor properties (for example, selectivity for grasping) and then investigate the responses of this neuronal population to observed actions. This subtle change in the experimental strategy might explain the apparent increase in the percentage of mirror neurons in F5 as a function of time. Some investigators have avoided the sampling bias based on mirror properties by studying identified pyramidal tract neurons in area F5, selected on the basis of their antidromic response and not for their properties during action execution or observation [18]. A large proportion of pyramidal tract neurons in F5 and in M1 appear to show mirror-like responses ( Table 1 ).

The three early papers [1,2,18] provided details about the relative selectivity of mirror neuron discharge during action execution and observation. On average, 48.9% of mirror neurons were classified as broadly congruent. Some mirror neurons discharged for only one action type, such as grasping, during both execution and observation, but showed no specificity for the type of grasp, for example precision grip or whole hand prehension. Others discharged for more than one type of observed action, for example grasping and holding. One of the three papers [2] describes a further category of mirror neurons, strictly congruent mirror neurons these are defined as mirror neurons that respond selectively to one action type, such as precision grip, during both action execution and observation, and are reported as constituting 31.5% of mirror neurons recorded. Two of the three papers [2,18] report a further category of neuron in F5 that discharged during action observation but not during action execution on average these neurons, which would not be included as mirror neurons, have been reported as making up 5.1% of the neurons in F5.

Further neuroanatomical studies of area F5 have revealed three interconnected sub-divisions [28]. The sub-division in which mirror neurons are located is suggested to be on the convexity of the precentral gyrus, adjacent to the inferior limb of the arcuate sulcus, and referred to as area F5c. This is distinguished from area F5p (posterior), which is reciprocally connected both with posterior parietal area AIP and primary motor cortex M1, and from area F5a (anterior) in the depth of the sulcus, which has prefrontal connections [29].

Two studies [7,9] have been reported that have shown that F5 mirror neurons discharged both to the observation of an action performed in front of the monkey by the experimenter and to videos of the same action. On average 26.9% of F5 neurons discharged when the monkey observed a video of a grasping action. One of the two studies [7] reported the relative number of mirror neurons that discharged to real and to videoed actions: 46.4% of neurons in F5 that responded to an executed action also responded when observing a real action, whereas only 22.3% responded when observing a videoed action. Although fewer mirror neurons responded when the monkey was observing the video of an action, for those mirror neurons that did discharge, there was no significant difference in the pattern or rate of mirror neuron discharge between real and videoed actions.

Two of the early papers [2,18] on mirror neurons reported that they could not find any neurons that discharged when monkeys observed an object being grasped with a tool. Subsequently, two studies [12,19] showed that mirror neurons did respond to such a tool-based action. In both these latter cases, however, the monkeys had received a high exposure to tool use during the training period prior to the recordings. One study [12] reported that 20% of F5 neurons were tool-responding mirror neurons, whereas the other reported the much higher percentage of 66.6% [20]. This high percentage most likely reflects a combination of a small sample size (n =ꀧ) and strict inclusion criteria.

Two papers [15,16] have reported that neurons in F5 responded to the sound of an action: so-called auditory mirror neurons. On average, 17% of F5 neurons have been reported to have auditory properties (12.7% and 21.3%, respectively, in the two papers). Four papers [6𠄸,23] have reported that mirror neurons not only discharged during action observation but that their firing is further modulated by different factors: occlusion [23], relative distance of observed action [8], reward value [6] and the view point of the observed action [7]. Umilta etਊl. [23] showed that 19/37 mirror neurons discharged even when the observed action was occluded or hidden from the observer, demonstrating that direct vision of the action was not necessary to elicit mirror neuron discharge. Caggiano etਊl. [7] showed that 149/201 mirror neurons discharged preferentially for one or more of three different views of the same action (at 0, 90 and 180 degrees). Sixty of these neurons showed a preference for only one view point.

Caggiano etਊl. [8] also found that F5 mirror neurons have a preference for whether an observed action occurred in peripersonal or extrapersonal space: 27/105 mirror neurons discharged preferentially when the observed action occurred in the monkeys extra-personal space, whereas 28/105 mirror neurons discharged preferentially when the observed action occurred in the monkey's peri-personal space. The remaining 50 mirror neurons showed no preference. Caggiano etਊl. [6] reported that mirror neuron discharge is modulated by the value of the reward associated with the action: they showed that 40/87 mirror neurons responded more when a rewarded object was grasped, while 11/87 responded more when observing an action to a non-rewarded action. The remaining mirror neurons showed no preference.

One study [17] recorded from 64 neurons in F5 that were identified as pyramidal tract neurons. Thirty-one of these neurons were classified as mirror neurons, with 14/31 mirror neurons showing the 𠆌lassic’ facilitation response during the action observation condition. Compared with baseline, the activity of the remaining 17 mirror neurons was significantly suppressed during action observation. The inclusion of these ‘suppression mirror neurons’ [8,17,24,25] clearly changes the overall proportion of neurons responsive during action observation.

In a recent study, Maranesi etਊl. [30] compared multiunit activity responses in areas F5, F4 (premotor regions) and F1 (primary motor cortex, M1). They reported a higher proportion of recording sites showing mirror type responses in area F5 (particularly in area F5c), compared with area F4 (caudal part of the ventral premotor cortex) and with F1. In addition, they reported that in penetration sites where they identified mirror responses, they were rarely able to evoke movement using intracortical microsimulation and argued that this might be due to presence of suppression mirror neurons, as first identified by Kraskov etਊl. [17].

One interesting study [27] looked at activity in premotor and parietal cortex neurons of the left hemisphere of a Japanese macaque monkey, either while it observed another monkey sitting opposite making reach-to-grasp movements for food rewards, or when it performed similar actions itself. Many neurons in both cortical areas were active during the other monkey’s movements, with the proportion varying across different actions ( Table 1 ). Premotor cortex neurons showed a distinct preference for movements involving the observed monkey’s right arm and hand, and showed a similar preference for the monkey’s own right-sided actions.

Mirror Neurons in the Inferior Parietal Lobule

Four papers [5,13,20,25] have reported neuronal activity recorded in the inferior parietal lobule that the authors have described as that of mirror neurons ( Figureਁ B). None of these papers explicitly specifies the percentage of neurons that were classified as mirror neurons for three of these papers, however, we were able to estimate from the numbers in the papers that the average percentage of sampled neurons that were mirror neurons was 20% (41/165 Fogassi etਊl. [13] 28/120 Bonnini etਊl. [5] 51/423 Rozzi etਊl. [20]).

Two papers [5,13] describe the modulation of mirror neuron activity in the inferior parietal lobule by the overall goal of the observed action. Here monkeys observed an experimenter reaching for and grasping an object and either placing it in the mouth (eating) or placing it in a container (placing). On average 53% of mirror neurons had a significantly greater firing rate when the monkey observed the �ting’ compared with the ‘placing’ condition, 17% had a significantly greater firing rate for ‘placing’ compared with �ting’. The remaining 30% showed no difference between the two conditions. Yamazaki etਊl. [25] reported examples of mirror neuron activity in macaque area inferior parietal lobe these neurons responded to the same action carried out in rather different contexts, suggesting that they are involved in encoding the ‘semantic equivalence’ of actions carried out by different agents in different contexts.

Rozzi etਊl. [20] investigated the properties of mirror neurons in the IPL. They reported that 58% of mirror neurons were responsive to only one type of hand action, for example grasping, and 25% were responsive to two different hand actions. The remaining 17% were responsive to either observed mouth actions or mouth and hand actions. Furthermore, they reported that 29% of IPL mirror neurons were strictly congruent and 54% were broadly congruent.

Mirror Neurons in the Primary Motor Cortex

The first few papers [2,18] that described mirror neurons in area F5 also reported that the authors found no evidence of mirror activity in M1. Indeed, Gallese etਊl. [2] argued that, because most neurons in M1 show activity during self-movement, the absence of detectable mirror activity in M1 was evidence against the idea that this activity might actually represent monkey’s making small, covert movements while they watched the experimenter. Similarly, a recent multiunit recording study [29] found only a low level of mirror activity within primary motor cortex. However, three papers [10,22,24] have reported mirror neuron-like responses in M1.

Tkach etਊl. [22] reported that when monkeys either performed a visuomotor tracking task themselves, or watched the same target and cursor being operated by an experimenter, 70% (581/829) of recorded neurons in M1 showed stable preferred direction tuning during both execution and observation. These authors also reported that 60% (77/128) of neurons in dorsal premotor cortex were modulated in the same way.

Dushanova and Donoghue [10] recorded from neurons in M1 whilst the monkey either performed a point-to-point arm-reaching task or observed a human experimenter performing the same action. This study reported that 34.7% (105/303) of the neurons recorded in M1 were directionally tuned during both action execution and action observation. The mean firing rate during the observation condition was on average 46% of that during the execution condition. In addition, 38% of neurons retained the same directional tuning during both execution and observation conditions. It should be noted that these studies differ from those previously described that recorded from F5 and IPL.

All the studies on mirror neurons in F5 and IPL have employed tasks where the macaque monkey observed either a video or the experimenter performing simple reach and grasp actions. The two studies [10,22] described above on mirror-like responses in M1 differed in that they used tasks in which the monkey had been extensively trained on the motor execution task. It is unclear whether the relatively high percentage of these mirror-like responses, compared with those in F5 and IPL, reflects differences between the task or real differences in the number of mirror neurons.

The final paper [24] on M1 mirror neurons recorded from 132 neurons that were identified as pyramidal tract neurons 58% of these neurons (77/132) were classified as mirror neurons. As in F5, these authors found that these pyramidal tract neurons were either facilitation mirror neurons (58.5%) or suppression mirror neurons (41.5%) during the action observation condition. In contrast to F5, facilitation mirror neurons in M1 fired at significantly lower rates during action observation vs execution, with the former reported as “less than half of that when the monkey performed the grip”. It is noteworthy that these authors made simultaneous EMG recordings from up to 11 different arm, hand and digit muscles and confirmed complete absence of activity during action observation.

Mirror Neurons in Other Regions

Above, we have described the results of studies reporting mirror neurons in ventral premotor cortex, dorsal premotor cortex, primary motor cortex and inferior parietal lobule. Three further papers [14,21,26] have reported mirror neuron-like responses in two further areas. The first [14] recorded visuotactile bimodal neurons in the ventral intraparietal area (VIP). These are neurons that exhibit tactile receptive fields for a particular body part (for example, face or head) and also exhibit visual receptive fields in the congruent location. This study demonstrated that 48/541 bimodal neurons also exhibited visual receptive fields when observing the congruent area being touched on the experimenter. These neurons were not called mirror neurons but 𠆋ody-matching bimodal neurons’.

Shepherd etਊl. [21] reported mirror neuron-like responses in the lateral intraparietal (LIP) area. These authors reported that 30/153 neurons in LIP responded not only when monkeys oriented attention towards the receptive field of those neurons, but also when they observed other monkeys orienting in the same direction.

Yoshida etਊl. [26] recently recorded from neurons in the medial frontal cortex, some of which selectively responded to self or observed actions within a social context. The neurons were recorded in one of two monkeys who, on alternate trials, chose a movement in order to earn a reward. Correct (or incorrect) choices rewarded (or punished: no reward) both monkeys. ‘Partner-type’ neurons were selectively responsive to the choices made by the other monkey, signalling the correct or incorrect choice made interestingly around 19% of these ‘partner neurons’ showed decreased activity during self-movement.

Relating Human Neuroimaging Data to Mirror Neuron Activity

Of the over 800 papers returned when searching PubMed for ‘mirror neurons’ or ‘mirror neuron’, the vast majority report the results of experiments on human subjects. Of these, the results of human neuroimaging experiments, specifically fMRI [31], confirm a broad overlap between cortical areas active in humans during action observation and areas where mirror neurons have been reported in macaque monkeys (see above). Thus, changes in the BOLD signal during action observation seem to be consistent with the existence of a mirror neuron system in humans, but they cannot yet furnish conclusive proof. There has, however, also been a report of single neuron activity recorded from human neurosurgical patients that has demonstrated mirror neuron activity [32]. Recordings were focused on medial frontal cortex and temporal lobe structures, and show the extensive nature of the mirror neuron system. Unfortunately, neither of the premotor or posterior parietal areas so heavily investigated in monkeys were available for study in these patients.

Central to being able to interpret human fMRI studies of the mirror neuron system is understanding the relationship between the BOLD signal in human and mirror neuron activity in macaque monkey. To this end, monkey fMRI studies have now demonstrated significant activity during action observation in regions where mirror neurons have been previously reported [33,34]. These monkey imaging studies have taken advantage of enhancing the neurovascular responses with an iron-based (MION) contrast agent. As with the vast majority of human fMRI studies, however, there is difficulty in relating these results to mirror neurons, in that they only employ an action observation condition and have no action execution condition. This makes it difficult to calibrate the activity changes in observation to those in execution, and also raises the possibility that sensory responses other than mirror responses contribute to the neurovascular changes (see Introduction).

One possible way of attributing the fMRI response to a single neuronal population, such as mirror neurons, is to use fMRI adaptation, or repetition suppression. This is a neuroimaging tool that has been adopted to identify neural populations that encode a particular stimulus feature [35]. The logic behind fMRI adaptation is that neurons decrease their firing rate with repeated presentations of the stimulus feature that those neurons encode. By extension it has been argued that the BOLD signal will also decrease with repeated presentations. It has been argued that areas of the cortex that contain mirror neurons should show fMRI adaptation both when an action is executed and subsequently observed, and when an action is observed and subsequently executed. This is because the stimulus feature encoded in mirror neurons is repeated irrespective of whether the action is observed or executed [36].

The results of such studies have produced mixed results. Of the five studies using this technique published to date [36�], only three have demonstrated significant fMRI adaptation consistent with the presence of mirror neurons in the human brain [38�]. One possible explanation for the mixed results is that humans do have mirror neurons, but that they do not alter their pattern of activation when stimuli that evoke their response are repeated. Indeed a recent study [9] has shown some evidence that mirror neurons may not alter their firing rate during repetitions of the same action however, in this work the neuronal activity represented in the local field potential (LFP) did modulate with repetition. Further work is clearly required to determine why the BOLD signal in humans and the LFP in monkeys do adapt with repetition, while the evidence to date suggests that mirror neurons may not.

Great care must be taken when comparing the results from human and monkey studies. Specifically, readers must pay careful attention to the difference in the level of inference between the different modalities. The majority of human neuroimaging studies report significant results at the population level where the variance is estimated across subjects. This is in contrast to the studies reporting mirror neurons in macaque monkeys, where the aim is to test whether individual neurons show a consistent modulation of firing rate during periods of action observation and execution. Here the inference is closer to the analysis of fMRI at the single subject level. Therefore, when it is reported that 30% of neurons in any region were significantly modulated during both action observation and execution this does not mean that the remaining 70% do not modulate at all. Rather, it means there was not sufficient statistical evidence that these neurons displayed mirror activity. Indeed it is quite possible that when tested at the population level, the neurons that are non-significant at the single neuron level could be significantly modulated when observing an action.

The point here is that care must be taken when arguing that ‘only’ X% of neurons in any brain region are mirror neurons. The ‘only’ implies that the remaining neurons are not significantly modulated in any way during action observation. This is not a valid inference as to do so would be to accept the null hypothesis. This may be particularly problematic for cortical regions where responses in individual mirror neurons are relatively weak, such as in M1.

It is often assumed that mirror neuron activity during action observation is driven, bottom-up, by the visual (or auditory) input. The review of mirror neuron discharge presented here provides evidence that this is, at best, an incomplete description of mirror neuron firing. We now know that mirror neuron firing rates are modulated by view point [7], value [6] and that they even discharge in the absence of any visual input [23]. This suggests that mirror neurons can be driven or modulated top-down by backward connectionsਏrom other neuronal populations. Indeed, the requirement for such top-down input to regions containing mirror neurons was realized by Jacob and Jeannerod [41], who argued that it was impossible for a mirror neuron system driven uniquely by the visual input to correctly infer an intention from an observed action if two or more different intentions would generate the same action. The fact that mirror neurons can be driven by backward connections is consistent with recent predictive coding models of mirror neuron function [42�]. Within this framework, mirror neurons discharge during action observation not because they are driven by the visual input but because they are part of a generative model that is predicting the sensory input. This framework provides a theoretical account of mirror neuron activity that resolves the one-to-many mapping problem described by Jacob and Jeannerod [41] and is consistent with top-down modulation of mirror neuron firing rates.

Concluding Remarks

The discovery of mirror neurons has had a profound effect on the field of social cognition. Here we have reviewed what is currently known about mirror neurons in the different cortical areas in which they have been described. There is now evidence that mirror neurons are present throughout the motor system, including ventral and dorsal premotor cortices and primary motor cortex, as well as being present in different regions of the parietal cortex. The functional role(s) of mirror neurons and whether mirror neurons arise as a result of a functional adaptation and/or of associative learning during development are important questions that still remain to be solved. In answering these questions we will need to know more about the connectivity of mirror neurons and their comparative biology across different species.

The Missing Link Between Psychology and Biology

The distinction between mind and body was unfortunately made centuries ago and remains with us today. We label illnesses caused by germs or viruses as "physical." We label other illnesses such as depression and anxiety as "mental." Yet the distinction between mental and physical is often unclear. For example, placebos are substances, such as sugar pills, that are thought to be physically inactive but can produce medical benefits in patients who believe they will work. In my book Cognitive Neuroscience and Psychotherapy, I point out that “Kirsch et al. (2008) reported that placebos are about 80% as effective as antidepressant medications are and 50% as effective as analgesic medications are. Kirsch and Sapirstein (1998) estimated that placebos were 75% as effective as antidepressive medications” (p. 252). The field of psychobiology studies placebo responses and other instances of mind-brain links. The field of biopsychology involves the reverse case it is concerned with ways that the physical body effects mental states. For example, it studies how legal and illegal psychoactive drugs alter the ways that we think, feel, and act.

Most people probably think of transitional fossils or species when they hear the term “missing link,” but another just as important missing link is the one between psychology and biology, between our physical and mental states. The terms psychobiology and biopsychology imply that psychology and biology are connected and interact. The problem with both psychobiology and biopsychology is that there is a missing causal explanatory link between mind and brain.

A very well accepted theoretical orientation in psychological science is the BioPsychoSocial (BPS) model. You might think that this model explains how psychology and biology interact, but you would be wrong. The BPS model is actually just a list of ingredients. It lists important biological, psychological, and social variables and claims that they mutually interact but does not provide any natural science mechanism information that can explain how they interact.

Some authors place these terms in boxes and draw arrows among them to impute causality but never provide any natural science mechanism information that actually explains how they physically interact. In short, the BPS model explains nothing more about how psychology and biology interact than explaining how a car works by listing that it is made of glass, metal, and petroleum. Listing is not explaining. Instead, the missing explanatory link is glossed over in hopes that you will either not notice or ask about it.

The most important task facing psychobiologists and biopsychologists is to provide a natural science explanation that links psychology and biology. This task requires identifying principles that provide mechanism information because mature sciences are organized around principles psychology is currently not. Those of you who have taken an introductory psychology course or who have read about psychology will recognize that psychology is currently organized around famous people, such as Freud and Skinner, or around “isms” such as behaviorism and cognitivism. This organization differs from all other natural sciences. They are organized around physical entities such as the cell in biology and molecules in chemistry. This allows biologists and chemists to explain more about how things work than psychologists can. Imagine how much better our therapies will become once we understand how and why they work.

I provide some of the missing explanatory details in my book entitled Cognitive Neuroscience and Psychotherapy: Network Principles for a Unified Theory. The remainder of this blog briefly presents the general conceptual framework for understanding how psychology and biology interact that my book is based on. I refer to this explanatory approach as a Bio«Psychology Network (BPN) explanatory system because it consists of four core and now nine corollary principles that together can explain a wide variety of well replicated psychological phenomena in ways that are fully consistent with neuroscience.

The first thing to understand is that our brains are made up of neurons that form neural networks. Hence, some form of network theory is required to explain how psychology and biology interact. How can these neural network models explain psychology? To answer that, we must first recognize that learning and memory form the basis of all psychology. Carlson, Miller, Heth, Donahoe, and Martin (2010) stated that: “Learning refers to the process by which experiences change our nervous system and hence our behavior. We refer to these changes as memories” (p. 440 italics in the original). Learning is crucial to human survival. If we could not form memories as infants, we could not learn to do anything. We would not develop language nor could we benefit from experience. In short, we would never develop into the children, adolescents, and adults that we are familiar with.

Rumelhart and McClelland (1986) and McClelland and Rumelhart (1986) provided demonstration proofs that artificial neural networks, called connectionist models, can form memories, can learn, and therefore can do psychology. Connectionist models of many psychological phenomena have been developed. The Psychological Review is a journal that specializes in psychological theory. It has published numerous long articles featuring connectionist neural network models. Many other demonstration proofs have been published in a wide variety of journals and books. Connectionist neural network models now rival traditional cognitive psychology models.

How Psychology Changes Biology

Here I sketch a general explanation that derives from parallel-distributed-processing (PDP) connectionist-neural-network (CNN) models that I collectively refer to as Computational Neuropsychology (CNP). Two major features characterize these models. The first major feature of these models is that they simulate neural architecture by using layers of simulated neurons. The second major feature of these models is that these simulated neurons are connected by simulated synapses. Artificial neural networks learn through training that modifies these synapses. Some synapses become more excitatory while others become more inhibitory of received activations. The difference between what the neural network computes as simulated behavior and the desired response is considered to be an error. These errors are used to modify the simulated synapses. These changes simulate the way that experience-dependent plasticity mechanisms modify real synapses in biological neural networks while they learn by forming memories. And then another learning trial begins. The network’s performance gradually improves through additional synaptic modification. Here we can see that learning is mostly about modifying synaptic connections.

But more brain changes are involved in psychological development. Infants are born with far more synapses than they will need as adults. Neural network pathways that are active while learning language, music, reading, writing, and playing sports, among other skills, are biologically reinforced by modifying synapses. Unused synapses are cannibalized to save precious metabolic energy. Psychological development literally, physically, sculpts the brain in addition to modifying synapses and thereby changes biology! Our brains physically specialize as we develop psychologically. This explains why it is more difficult for older people to learn a new language.

How Biology Changes Psychology

Our neural network understanding of how psychology changes biology prepares us to understand how biology modifies psychology. Understanding that the synapses that connect neurons contain our memories of who we are, the people that we know, the experiences we have had, and our attitudes about everyone and everything along with how we feel enables us to see that directly modifying them with legal or illegal psychoactive substances will change our psychology. Psychology normally changes our synapses by activating internal experience-dependent plasticity mechanisms. Drugs directly modify these same synapses pharmacologically and consequently alters our psychology. Pharmacological psychiatry is a relatively young field. The clinical practice of selecting the right medication to make therapeutic synaptic modifications is, by and large, a trial and error business. It can take several weeks for therapeutic effects to be noticed. Therapeutic effects are often dose dependent, which means that dosage may need to be systematically increased.

Neural network models enable us to see how psychology changes biology because the memory formation process that drives learning and all psychological development modifies synapses through experience-dependent plasticity neuroscience mechanisms. This knowledge enables to understand that modifying synapses pharmacologically will also change our psychology. The causal role of synapses in learning and memory make them the missing link in psychobiology and biopsychology. I predict that psychology will organize around the synapse when it becomes a mature natural science just as biology organized around the cell when it became a mature natural science. Subsequent blogs will present more by way of fascinating new developments—stay tuned.

Carlson, N. R., Miller, H., Heth, C. D., Donahoe, J. W., & Martin, G. N. (2010). Psychology: The science of behavior (7th ed.) (p. 196) Boston: Allyn & Bacon.

Kirsch, I., Deacon, B. J., Huedo-Medina, T. B. H., Scoboria, A., Moore, T. J., & Johnson, B. T. (2008). Initial severity and antidepressant benefits: A meta-analysis of data submitted to the Food and Drug Administration. PLoS Medicine, 5, 260-268.

Kirsch, I., & Sapirstein, G. (1998) Listening to Prozac but hearing placebo: a meta-analysis of antidepressant medication. Prevention and treatment, Vol. I, article 0002a, posted June 26, 1998, available at

McClelland, J. L., Rumelhart, D. E., & the PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 2: Psychological and biological models. Cambridge, MA: MIT Press.

Rumelhart, D. E., McClelland, J. L., & the PDP Research Group (1986). Parallel distributed processing: Explorations in the microstructure of cognition, Vol. 1: Foundations. Cambridge, MA: MIT Press.

Why Do Drunk People Stumble, Fumble, and Slur Their Words?


One of the first signs of being drunk is a lack of bodily coordination that includes the inability to articulate words. A drunk person will stereotypically stumble, fumble, and slur his or her words. What does alcohol do to the brain that causes this loss of motor control? Recently, neuroscientists have identified specific neurons in the cerebellum that are at the root of alcohol's discombobulating effects.

The May 2015 study, "'Simulation of Alcohol Action Upon a Detailed Purkinje Neuron Model and a Simpler Surrogate Model that Runs >400 Times Faster," was published in the journal BMC Neuroscience.

Cerebellum is Latin for "Little Brain"

Although the cerebellum is only 10% of brain volume it holds over 50% of your brain's total neurons. As a neuroscientist, my father was always perplexed and intrigued by this disproportionate distribution of neurons. He would often say, "We don't know exactly what the cerebellum is doing, but whatever it's doing, it's doing a lot of it."

Traditionally, neuroscience credits the cerebellum with being responsible for the relatively simple tasks of coordinating muscle movements, maintaining balance, posture, and proprioception (tracking your body's position in space).

Until recently, neuroscientists haven't given the cerebellum much credit for higher executive functions, cognition, psychiatric disorders, or emotional regulation. Luckily, this outdated viewpoint about the cerebellum is rapidly evolving.

My dad was obsessed with the cerebellum and passed this obsession on to me. Over the years, I've written extensively about the everchanging views on the role our cerebellum plays in brain function and performance both on and off the court.

On a scale of -5 to +5, alcohol takes someone "south of zero" in terms of having a highly functioning cerebellum. On the flip side, regular practice enhances cerebellar (of or pertaining to the cerebellum) function and takes someone "north of zero" when performing any sport, playing a musical intstrument, or performing any skill that requires muscular coordination.

As an athlete, I always knew the cerebellum was why practice, practice, practice improved athletic performance. Through practice, you hammer and forge muscle memory into the Purkinje neurons of the cerebellum. This is why you never forget how to ride a bike or drive a stick shift once you've learned the motor skills involved.

The Purkinje neurons in the cerebellum play a pivotal role in orchestrating motor movements and are the seat of muscle memory. Previous research has identified that alcohol disrupts the firing pattern of cerebellar Purkinje neurons. However, the results were difficult to decipher because individual Purkinje neurons showed very different activity patterns before, during, and after the administration of alcohol.

In his recent study, Dr. Michael D. Forrest was able to use a novel mathematical model of a Purkinje neuron to show that all of this diversity and complexity can be explained by the fact that alcohol alters the firing rate of Purkinje neuron by inhibiting each neuron’s sodium-potassium pump.

Dr. Forrest and co-workers have previously shown that the sodium-potassium pump controls the intrinsic firing mode of Purkinje neurons and that the sodium-potassium pump is a computational element in the cerebellum and the brain. This is a significant breakthrough in the understanding of the role of these pumps, which were previously thought to have no direct role in brain computations.


Field Sobriety Tests Focus on Purkinje Neurons and Cerebellar Function

Alcohol causes Purkinje neurons in the cerebellum to become discombobulated which is why drunk driving is so dangerous. The statistics on drunk driving are alarming: Once every hour, someone in the United States is killed in a drunk driving car crash. Every 90 seconds, someone is injured from a drunk driving incident. Traffic accidents are the leading cause of death for teens, and roughly 1/3 of these accidents involve alcohol or another substance.

Standardized Field Sobriety Tests (SFST) are used to gauge a driver's level of impairment due to alcohol or other drug use. The three tests used to test sobriety are basically testing the Purkinje neurons of someone’s cerebellum. These tests include: Horizontal Gaze Nystagmus (HGN), Walk-and-Turn (WAT), and One-Leg Stand (OLS).

How COVID-19 Worms Its Way Into the Brain – Explaining Baffling Neurological Symptoms

New research offers an up-close view of how SARS-CoV-2, the virus that causes COVID-19, can spread to the brain. The study helps explain the alarming array of neurological symptoms reported in some patients with COVID-19, as well as why some patients suffer severe neurological effects while others experience none at all.

The researchers report evidence that SARS-CoV-2 can infect both the nerve cells that power our brains (neurons) and the cells in the brain and spinal cord that support and protect neurons (astrocytes).

A single astrocyte displaying expression of the SARS-CoV-2 receptor protein ACE2 in red. Credit: Ricardo Costa, LSUHS

“Our findings suggest that astrocytes are a pathway through which COVID-19 causes neurological damage,” said Ricardo Costa, PhD, a postdoctoral fellow at the Louisiana State University (LSU) Health Shreveport and the study’s first author. “This could explain many of the neurologic symptoms we see in COVID-19 patients, which include loss of sense of smell and taste, disorientation, psychosis, and stroke.”

Costa will present the research at the American Physiological Society annual meeting during the Experimental Biology (EB) 2021 meeting, held virtually April 27-30. The study is led by Diana Cruz-Topete, assistant professor of molecular and cellular biology at LSU Health Shreveport, and includes collaborators Oscar Gomez-Torres, PhD, and Emma Burgos-Ramos, PhD, from Universidad de Castilla-La Mancha in Spain.

A group of neurons (blue) and the dendrites that connect them (green). The ACE2 receptor (in red) is present in the neuronal main body. Credit: Ricardo Costa, LSUHS original cells donated by Lynn Harrison, LSUHS

In the respiratory system, SARS-CoV-2 is known to infect a person’s cells by grabbing hold of proteins on the cell surface called angiotensin-converting enzyme-2 (ACE2) receptors. It has been unclear whether brain cells have this receptor.

For the study, Costa and colleagues examined RNA and proteins to determine whether cell cultures of human astrocytes and neurons expressed ACE2. They then exposed the cells to a version of the SARS-CoV-2 virus that had been modified to be safe for researchers to handle. The studies confirmed that both astrocytes and neurons express the ACE2 receptor and that both cell types can become infected with SARS-CoV-2, though astrocytes were less likely to become infected.

A single astrocyte infected with a modified version of SARS-CoV-2. The virus was modified to express a green fluorescent protein upon successful infection. Nearby astrocytes (not visible) were not infected. Credit: Ricardo Costa, LSUHS

Astrocytes are the main gateway to the brain, responsible for shuttling nutrients from the bloodstream to the neurons while keeping harmful particles out. By resisting infection, astrocytes could help keep SARS-CoV-2 out of the brain, but once infected, they could easily pass the virus along to many neurons, according to researchers.

“While astrocytes display a higher resistance to infection, neurons seem to be more susceptible,” said Costa. “This suggests that only few astrocytes getting infected could be sufficient for the infection to quickly spread to neurons and multiply quickly. These observations could explain why while some patients do not have any neurological symptoms, others seem to have severe ones.”


Epithelial cells Edit

Epithelial cells adhere to one another through tight junctions, desmosomes and adherens junctions, forming sheets of cells that line the surface of the animal body and internal cavities (e.g., digestive tract and circulatory system). These cells have an apical-basal polarity defined by the apical membrane facing the outside surface of the body, or the lumen of internal cavities, and the basolateral membrane oriented away from the lumen. The basolateral membrane refers to both the lateral membrane where cell-cell junctions connect neighboring cells and to the basal membrane where cells are attached to the basement membrane, a thin sheet of extracellular matrix proteins that separates the epithelial sheet from underlying cells and connective tissue. Epithelial cells also exhibit planar cell polarity, in which specialized structures are orientated within the plane of the epithelial sheet. Some examples of planar cell polarity include the scales of fish being oriented in the same direction and similarly the feathers of birds, the fur of mammals, and the cuticular projections (sensory hairs, etc.) on the bodies and appendages of flies and other insects. [2]

Neurons Edit

A neuron receives signals from neighboring cells through branched, cellular extensions called dendrites. The neuron then propagates an electrical signal down a specialized axon extension from the basal pole to the synapse, where neurotransmitters are released to propagate the signal to another neuron or effector cell (e.g., muscle or gland). The polarity of the neuron thus facilitates the directional flow of information, which is required for communication between neurons and effector cells. [3]

Migratory cells Edit

Many cell types are capable of migration, such as leukocytes and fibroblasts, and in order for these cells to move in one direction, they must have a defined front and rear. At the front of the cell is the leading edge, which is often defined by a flat ruffling of the cell membrane called the lamellipodium or thin protrusions called filopodia. Here, actin polymerization in the direction of migration allows cells to extend the leading edge of the cell and to attach to the surface. [4] At the rear of the cell, adhesions are disassembled and bundles of actin microfilaments, called stress fibers, contract and pull the trailing edge forward to keep up with the rest of the cell. Without this front-rear polarity, cells would be unable to coordinate directed migration. [5]

Budding yeast Edit

The budding yeast, Saccharomyces cerevisiae, is a model system for eukaryotic biology in which many of the fundamental elements of polarity development have been elucidated. Yeast cells share many features of cell polarity with other organisms, but feature fewer protein components. In yeast, polarity is biased to form at an inherited landmark, a patch of the protein Rsr1 in the case of budding, or a patch of Rax1 in mating projections. [6] In the absence of polarity landmarks (i.e. in gene deletion mutants), cells can perform spontaneous symmetry breaking, [7] in which the location of the polarity site is determined randomly. Spontaneous polarization still generates only a single bud site, which has been explained by positive feedback increasing polarity protein concentrations locally at the largest polarity patch while decreasing polarity proteins globally by depleting them. The master regulator of polarity in yeast is Cdc42, which is a member of the eukaryotic Ras-homologous Rho-family of GTPases, and a member of the super-family of small GTPases, which include Rop GTPases in plants and small GTPases in prokaryotes. For polarity sites to form, Cdc42 must be present and capable of cycling GTP, a process regulated by its guanine nucleotide exchange factor (GEF), Cdc24, and by its GTPase-activating proteins (GAPs). Cdc42 localization is further regulated by cell cycle queues, and a number of binding partners. [8] A recent study to elucidate the connection between cell cycle timing and Cdc42 accumulation in the bud site uses optogenetics to control protein localization using light. [9] During mating, these polarity sites can relocate. Mathematical modeling coupled with imaging experiments suggest the relocation is mediated by actin-driven vesicle delivery. [10] [11]

The bodies of vertebrate animals are asymmetric along three axes: anterior-posterior (head to tail), dorsal-ventral (spine to belly), and left-right (for example, our heart is on the left side of our body). These polarities arise within the developing embryo through a combination of several processes: 1) asymmetric cell division, in which two daughter cells receive different amounts of cellular material (e.g. mRNA, proteins), 2) asymmetric localization of specific proteins or RNAs within cells (which is often mediated by the cytoskeleton), 3) concentration gradients of secreted proteins across the embryo such as Wnt, Nodal, and Bone Morphogenic Proteins (BMPs), and 4) differential expression of membrane receptors and ligands that cause lateral inhibition, in which the receptor-expressing cell adopts one fate and its neighbors another. [12] [13]

In addition to defining asymmetric axes in the adult organism, cell polarity also regulates both individual and collective cell movements during embryonic development such as apical constriction, invagination, and epiboly. These movements are critical for shaping the embryo and creating the complex structures of the adult body.

Cell polarity arises primarily through the localization of specific proteins to specific areas of the cell membrane. This localization often requires both the recruitment of cytoplasmic proteins to the cell membrane and polarized vesicle transport along cytoskeletal filaments to deliver transmembrane proteins from the golgi apparatus. Many of the molecules responsible for regulating cell polarity are conserved across cell types and throughout metazoan species. Examples include the PAR complex (Cdc42, PAR3/ASIP, PAR6, atypical protein kinase C), [14] [15] Crumbs complex (Crb, PALS, PATJ, Lin7), and Scribble complex (Scrib, Dlg, Lgl). [16] These polarity complexes are localized at the cytoplasmic side of the cell membrane, asymmetrically within cells. For example, in epithelial cells the PAR and Crumbs complexes are localized along the apical membrane and the Scribble complex along the lateral membrane. [17] Together with a group of signaling molecules called Rho GTPases, these polarity complexes can regulate vesicle transport and also control the localization of cytoplasmic proteins primarily by regulating the phosphorylation of phospholipids called phosphoinositides. Phosphoinositides serve as docking sites for proteins at the cell membrane, and their state of phosphorylation determines which proteins can bind. [18]

While many of the key polarity proteins are well conserved, different mechanisms exist to establish cell polarity in different cell types. Here, two main classes can be distinguished: (1) cells that are able to polarize spontaneously, and (2) cells that establish polarity based on intrinsic or environmental cues. [19]

Spontaneous symmetry breaking can be explained by amplification of stochastic fluctuations of molecules due to non-linear chemical kinetics. The mathematical basis for this biological phenomenon was established by Alan Turing in his 1953 paper 'The chemical basis of morphogenesis.' [20] While Turing initially attempted to explain pattern formation in a multicellular system, similar mechanisms can also be applied to intracellular pattern formation. [21] Briefly, if a network of at least two interacting chemicals (in this case, proteins) exhibits certain types of reaction kinetics, as well as differential diffusion, stochastic concentration fluctuations can give rise to the formation of large-scale stable patterns, thus bridging from a molecular length scale to a cellular or even tissue scale.

A prime example for the second type of polarity establishment, which relies on extracellular or intracellular cues, is the C. elegans zygote. Here, mutual inhibition between two sets of proteins guides polarity establishment and maintenance. On the one hand, PAR-3, PAR-6 and aPKC (called anterior PAR proteins) occupy both the plasma membrane and cytoplasm prior to symmetry breaking. PAR-1, the C. elegans-specific ring-finger-containing protein PAR-2, and LGL-1 (called posterior PAR proteins) are present mostly in the cytoplasm. [22] The male centrosome provides a cue, which breaks an initially homogenous membrane distribution of anterior PARs by inducing cortical flows. These are thought to advect anterior PARs towards one side of the cell, allowing posterior PARs to bind to other pole (posterior). [23] [24] Anterior and posterior PAR proteins then maintain polarity until cytokinesis by mutually excluding each other from their respective cell membrane areas.


Neurons are the primary components of the nervous system, along with the glial cells that give them structural and metabolic support. The nervous system is made up of the central nervous system, which includes the brain and spinal cord, and the peripheral nervous system, which includes the autonomic and somatic nervous systems. In vertebrates, the majority of neurons belong to the central nervous system, but some reside in peripheral ganglia, and many sensory neurons are situated in sensory organs such as the retina and cochlea.

Axons may bundle into fascicles that make up the nerves in the peripheral nervous system (like strands of wire make up cables). Bundles of axons in the central nervous system are called tracts.

Neurons are highly specialized for the processing and transmission of cellular signals. Given their diversity of functions performed in different parts of the nervous system, there is a wide variety in their shape, size, and electrochemical properties. For instance, the soma of a neuron can vary from 4 to 100 micrometers in diameter. [1]

  • The soma is the body of the neuron. As it contains the nucleus, most protein synthesis occurs here. The nucleus can range from 3 to 18 micrometers in diameter. [2]
  • The dendrites of a neuron are cellular extensions with many branches. This overall shape and structure is referred to metaphorically as a dendritic tree. This is where the majority of input to the neuron occurs via the dendritic spine.
  • The axon is a finer, cable-like projection that can extend tens, hundreds, or even tens of thousands of times the diameter of the soma in length. The axon primarily carries nerve signals away from the soma, and carries some types of information back to it. Many neurons have only one axon, but this axon may—and usually will—undergo extensive branching, enabling communication with many target cells. The part of the axon where it emerges from the soma is called the axon hillock. Besides being an anatomical structure, the axon hillock also has the greatest density of voltage-dependent sodium channels. This makes it the most easily excited part of the neuron and the spike initiation zone for the axon. In electrophysiological terms, it has the most negative threshold potential.
    • While the axon and axon hillock are generally involved in information outflow, this region can also receive input from other neurons.

    The accepted view of the neuron attributes dedicated functions to its various anatomical components however, dendrites and axons often act in ways contrary to their so-called main function. [ citation needed ]

    Axons and dendrites in the central nervous system are typically only about one micrometer thick, while some in the peripheral nervous system are much thicker. The soma is usually about 10–25 micrometers in diameter and often is not much larger than the cell nucleus it contains. The longest axon of a human motor neuron can be over a meter long, reaching from the base of the spine to the toes.

    Sensory neurons can have axons that run from the toes to the posterior column of the spinal cord, over 1.5 meters in adults. Giraffes have single axons several meters in length running along the entire length of their necks. Much of what is known about axonal function comes from studying the squid giant axon, an ideal experimental preparation because of its relatively immense size (0.5–1 millimeters thick, several centimeters long).

    Fully differentiated neurons are permanently postmitotic [3] however, stem cells present in the adult brain may regenerate functional neurons throughout the life of an organism (see neurogenesis). Astrocytes are star-shaped glial cells. They have been observed to turn into neurons by virtue of their stem cell-like characteristic of pluripotency.

    Membrane Edit

    Like all animal cells, the cell body of every neuron is enclosed by a plasma membrane, a bilayer of lipid molecules with many types of protein structures embedded in it. A lipid bilayer is a powerful electrical insulator, but in neurons, many of the protein structures embedded in the membrane are electrically active. These include ion channels that permit electrically charged ions to flow across the membrane and ion pumps that chemically transport ions from one side of the membrane to the other. Most ion channels are permeable only to specific types of ions. Some ion channels are voltage gated, meaning that they can be switched between open and closed states by altering the voltage difference across the membrane. Others are chemically gated, meaning that they can be switched between open and closed states by interactions with chemicals that diffuse through the extracellular fluid. The ion materials include sodium, potassium, chloride, and calcium. The interactions between ion channels and ion pumps produce a voltage difference across the membrane, typically a bit less than 1/10 of a volt at baseline. This voltage has two functions: first, it provides a power source for an assortment of voltage-dependent protein machinery that is embedded in the membrane second, it provides a basis for electrical signal transmission between different parts of the membrane.

    Histology and internal structure Edit

    Numerous microscopic clumps called Nissl bodies (or Nissl substance) are seen when nerve cell bodies are stained with a basophilic ("base-loving") dye. These structures consist of rough endoplasmic reticulum and associated ribosomal RNA. Named after German psychiatrist and neuropathologist Franz Nissl (1860–1919), they are involved in protein synthesis and their prominence can be explained by the fact that nerve cells are very metabolically active. Basophilic dyes such as aniline or (weakly) haematoxylin [4] highlight negatively charged components, and so bind to the phosphate backbone of the ribosomal RNA.

    The cell body of a neuron is supported by a complex mesh of structural proteins called neurofilaments, which together with neurotubules (neuronal microtubules) are assembled into larger neurofibrils. [5] Some neurons also contain pigment granules, such as neuromelanin (a brownish-black pigment that is byproduct of synthesis of catecholamines), and lipofuscin (a yellowish-brown pigment), both of which accumulate with age. [6] [7] [8] Other structural proteins that are important for neuronal function are actin and the tubulin of microtubules. Class III β-tubulin is found almost exclusively in neurons. Actin is predominately found at the tips of axons and dendrites during neuronal development. There the actin dynamics can be modulated via an interplay with microtubule. [9]

    There are different internal structural characteristics between axons and dendrites. Typical axons almost never contain ribosomes, except some in the initial segment. Dendrites contain granular endoplasmic reticulum or ribosomes, in diminishing amounts as the distance from the cell body increases.

    Neurons vary in shape and size and can be classified by their morphology and function. [11] The anatomist Camillo Golgi grouped neurons into two types type I with long axons used to move signals over long distances and type II with short axons, which can often be confused with dendrites. Type I cells can be further classified by the location of the soma. The basic morphology of type I neurons, represented by spinal motor neurons, consists of a cell body called the soma and a long thin axon covered by a myelin sheath. The dendritic tree wraps around the cell body and receives signals from other neurons. The end of the axon has branching axon terminals that release neurotransmitters into a gap called the synaptic cleft between the terminals and the dendrites of the next neuron.

    Structural classification Edit

    Polarity Edit

    Most neurons can be anatomically characterized as:

      : single process : 1 axon and 1 dendrite : 1 axon and 2 or more dendrites
        : neurons with projecting axonal processes examples are pyramidal cells, Purkinje cells, and anterior horn cells : neurons whose axonal process projects locally the best example is the granule cell

      Other Edit

      Some unique neuronal types can be identified according to their location in the nervous system and distinct shape. Some examples are:

        , interneurons that form a dense plexus of terminals around the soma of target cells, found in the cortex and cerebellum , large motor neurons , interneurons of the cerebellum , most neurons in the corpus striatum , huge neurons in the cerebellum, a type of Golgi I multipolar neuron , neurons with triangular soma, a type of Golgi I , neurons with both ends linked to alpha motor neurons , interneurons with unique dendrite ending in a brush-like tuft , a type of Golgi II neuron cells, motoneurons located in the spinal cord , interneurons that connect widely separated areas of the brain

      Functional classification Edit

      Direction Edit

        convey information from tissues and organs into the central nervous system and are also called sensory neurons. (motor neurons) transmit signals from the central nervous system to the effector cells. connect neurons within specific regions of the central nervous system.

      Afferent and efferent also refer generally to neurons that, respectively, bring information to or send information from the brain.

      Action on other neurons Edit

      A neuron affects other neurons by releasing a neurotransmitter that binds to chemical receptors. The effect upon the postsynaptic neuron is determined by the type of receptor that is activated, not by the presynaptic neuron or by the neurotransmitter. A neurotransmitter can be thought of as a key, and a receptor as a lock: the same neurotransmitter can activate multiple types of receptors. Receptors can be classified broadly as excitatory (causing an increase in firing rate), inhibitory (causing a decrease in firing rate), or modulatory (causing long-lasting effects not directly related to firing rate).

      The two most common (90%+) neurotransmitters in the brain, glutamate and GABA, have largely consistent actions. Glutamate acts on several types of receptors, and has effects that are excitatory at ionotropic receptors and a modulatory effect at metabotropic receptors. Similarly, GABA acts on several types of receptors, but all of them have inhibitory effects (in adult animals, at least). Because of this consistency, it is common for neuroscientists to refer to cells that release glutamate as "excitatory neurons", and cells that release GABA as "inhibitory neurons". Some other types of neurons have consistent effects, for example, "excitatory" motor neurons in the spinal cord that release acetylcholine, and "inhibitory" spinal neurons that release glycine.

      The distinction between excitatory and inhibitory neurotransmitters is not absolute. Rather, it depends on the class of chemical receptors present on the postsynaptic neuron. In principle, a single neuron, releasing a single neurotransmitter, can have excitatory effects on some targets, inhibitory effects on others, and modulatory effects on others still. For example, photoreceptor cells in the retina constantly release the neurotransmitter glutamate in the absence of light. So-called OFF bipolar cells are, like most neurons, excited by the released glutamate. However, neighboring target neurons called ON bipolar cells are instead inhibited by glutamate, because they lack typical ionotropic glutamate receptors and instead express a class of inhibitory metabotropic glutamate receptors. [12] When light is present, the photoreceptors cease releasing glutamate, which relieves the ON bipolar cells from inhibition, activating them this simultaneously removes the excitation from the OFF bipolar cells, silencing them.

      It is possible to identify the type of inhibitory effect a presynaptic neuron will have on a postsynaptic neuron, based on the proteins the presynaptic neuron expresses. Parvalbumin-expressing neurons typically dampen the output signal of the postsynaptic neuron in the visual cortex, whereas somatostatin-expressing neurons typically block dendritic inputs to the postsynaptic neuron. [13]

      Discharge patterns Edit

      Neurons have intrinsic electroresponsive properties like intrinsic transmembrane voltage oscillatory patterns. [14] So neurons can be classified according to their electrophysiological characteristics:

      • Tonic or regular spiking. Some neurons are typically constantly (tonically) active, typically firing at a constant frequency. Example: interneurons in neurostriatum.
      • Phasic or bursting. Neurons that fire in bursts are called phasic.
      • Fast spiking. Some neurons are notable for their high firing rates, for example some types of cortical inhibitory interneurons, cells in globus pallidus, retinal ganglion cells. [15][16]

      Neurotransmitter Edit

      • Cholinergic neurons—acetylcholine. Acetylcholine is released from presynaptic neurons into the synaptic cleft. It acts as a ligand for both ligand-gated ion channels and metabotropic (GPCRs) muscarinic receptors. Nicotinic receptors are pentameric ligand-gated ion channels composed of alpha and beta subunits that bind nicotine. Ligand binding opens the channel causing influx of Na + depolarization and increases the probability of presynaptic neurotransmitter release. Acetylcholine is synthesized from choline and acetyl coenzyme A.
      • Adrenergic neurons—noradrenaline. Noradrenaline (norepinephrine) is release from most postganglionic neurons in the sympathetic nervous system onto two sets of GPCRs: alpha adrenoceptors and beta adrenoceptors. Noradrenaline is one of the three common catecholamine neurotransmitter, and the most prevalent of them in the peripheral nervous system as with other catecholamines, it is synthesised from tyrosine.
      • GABAergic neurons—gamma aminobutyric acid. GABA is one of two neuroinhibitors in the central nervous system (CNS), along with glycine. GABA has a homologous function to ACh, gating anion channels that allow Cl − ions to enter the post synaptic neuron. Cl − causes hyperpolarization within the neuron, decreasing the probability of an action potential firing as the voltage becomes more negative (for an action potential to fire, a positive voltage threshold must be reached). GABA is synthesized from glutamate neurotransmitters by the enzyme glutamate decarboxylase.
      • Glutamatergic neurons—glutamate. Glutamate is one of two primary excitatory amino acid neurotransmitters, along with aspartate. Glutamate receptors are one of four categories, three of which are ligand-gated ion channels and one of which is a G-protein coupled receptor (often referred to as GPCR).
        and Kainate receptors function as cation channels permeable to Na + cation channels mediating fast excitatory synaptic transmission. receptors are another cation channel that is more permeable to Ca 2+ . The function of NMDA receptors depend on glycine receptor binding as a co-agonist within the channel pore. NMDA receptors do not function without both ligands present.
  • Metabotropic receptors, GPCRs modulate synaptic transmission and postsynaptic excitability.
    • Dopaminergic neurons—dopamine. Dopamine is a neurotransmitter that acts on D1 type (D1 and D5) Gs-coupled receptors, which increase cAMP and PKA, and D2 type (D2, D3, and D4) receptors, which activate Gi-coupled receptors that decrease cAMP and PKA. Dopamine is connected to mood and behavior and modulates both pre- and post-synaptic neurotransmission. Loss of dopamine neurons in the substantia nigra has been linked to Parkinson's disease. Dopamine is synthesized from the amino acid tyrosine. Tyrosine is catalyzed into levadopa (or L-DOPA) by tyrosine hydroxlase, and levadopa is then converted into dopamine by the aromatic amino acid decarboxylase.
    • Serotonergic neurons—serotonin. Serotonin (5-Hydroxytryptamine, 5-HT) can act as excitatory or inhibitory. Of its four 5-HT receptor classes, 3 are GPCR and 1 is a ligand-gated cation channel. Serotonin is synthesized from tryptophan by tryptophan hydroxylase, and then further by decarboxylase. A lack of 5-HT at postsynaptic neurons has been linked to depression. Drugs that block the presynaptic serotonin transporter are used for treatment, such as Prozac and Zoloft.
    • Purinergic neurons—ATP. ATP is a neurotransmitter acting at both ligand-gated ion channels (P2X receptors) and GPCRs (P2Y) receptors. ATP is, however, best known as a cotransmitter. Such purinergic signalling can also be mediated by other purines like adenosine, which particularly acts at P2Y receptors.
    • Histaminergic neurons—histamine. Histamine is a monoamine neurotransmitter and neuromodulator. Histamine-producing neurons are found in the tuberomammillary nucleus of the hypothalamus. [17] Histamine is involved in arousal and regulating sleep/wake behaviors.

    Multimodel Classification Edit

    Since 2012 there has been a push from the cellular and computational neuroscience community to come up with a universal classification of neurons that will apply to all neurons in the brain as well as across species. this is done by considering the 3 essential qualities of all neurons: electrophysiology, morphology, and the individual transcriptome of the cells. besides being universal this classification has the advantage of being able to classify astrocytes as well. A method called Patch-Seq in which all 3 qualities can be measured at once is used extensively by the Allen Institute for Brain Science. [18]

    Neurons communicate with each other via synapses, where either the axon terminal of one cell contacts another neuron's dendrite, soma or, less commonly, axon. Neurons such as Purkinje cells in the cerebellum can have over 1000 dendritic branches, making connections with tens of thousands of other cells other neurons, such as the magnocellular neurons of the supraoptic nucleus, have only one or two dendrites, each of which receives thousands of synapses.

    Synapses can be excitatory or inhibitory, either increasing or decreasing activity in the target neuron, respectively. Some neurons also communicate via electrical synapses, which are direct, electrically conductive junctions between cells. [19]

    When an action potential reaches the axon terminal, it opens voltage-gated calcium channels, allowing calcium ions to enter the terminal. Calcium causes synaptic vesicles filled with neurotransmitter molecules to fuse with the membrane, releasing their contents into the synaptic cleft. The neurotransmitters diffuse across the synaptic cleft and activate receptors on the postsynaptic neuron. High cytosolic calcium in the axon terminal triggers mitochondrial calcium uptake, which, in turn, activates mitochondrial energy metabolism to produce ATP to support continuous neurotransmission. [20]

    An autapse is a synapse in which a neuron's axon connects to its own dendrites.

    The human brain has some 8.6 x 10 10 (eighty six billion) neurons. [21] Each neuron has on average 7,000 synaptic connections to other neurons. It has been estimated that the brain of a three-year-old child has about 10 15 synapses (1 quadrillion). This number declines with age, stabilizing by adulthood. Estimates vary for an adult, ranging from 10 14 to 5 x 10 14 synapses (100 to 500 trillion). [22]

    In 1937 John Zachary Young suggested that the squid giant axon could be used to study neuronal electrical properties. [23] It is larger than but similar to human neurons, making it easier to study. By inserting electrodes into the squid giant axons, accurate measurements were made of the membrane potential.

    The cell membrane of the axon and soma contain voltage-gated ion channels that allow the neuron to generate and propagate an electrical signal (an action potential). Some neurons also generate subthreshold membrane potential oscillations. These signals are generated and propagated by charge-carrying ions including sodium (Na + ), potassium (K + ), chloride (Cl − ), and calcium (Ca 2+ ).

    Several stimuli can activate a neuron leading to electrical activity, including pressure, stretch, chemical transmitters, and changes of the electric potential across the cell membrane. [24] Stimuli cause specific ion-channels within the cell membrane to open, leading to a flow of ions through the cell membrane, changing the membrane potential. Neurons must maintain the specific electrical properties that define their neuron type. [25]

    Thin neurons and axons require less metabolic expense to produce and carry action potentials, but thicker axons convey impulses more rapidly. To minimize metabolic expense while maintaining rapid conduction, many neurons have insulating sheaths of myelin around their axons. The sheaths are formed by glial cells: oligodendrocytes in the central nervous system and Schwann cells in the peripheral nervous system. The sheath enables action potentials to travel faster than in unmyelinated axons of the same diameter, whilst using less energy. The myelin sheath in peripheral nerves normally runs along the axon in sections about 1 mm long, punctuated by unsheathed nodes of Ranvier, which contain a high density of voltage-gated ion channels. Multiple sclerosis is a neurological disorder that results from demyelination of axons in the central nervous system.

    Some neurons do not generate action potentials, but instead generate a graded electrical signal, which in turn causes graded neurotransmitter release. Such non-spiking neurons tend to be sensory neurons or interneurons, because they cannot carry signals long distances.

    Neural coding is concerned with how sensory and other information is represented in the brain by neurons. The main goal of studying neural coding is to characterize the relationship between the stimulus and the individual or ensemble neuronal responses, and the relationships among the electrical activities of the neurons within the ensemble. [26] It is thought that neurons can encode both digital and analog information. [27]

    The conduction of nerve impulses is an example of an all-or-none response. In other words, if a neuron responds at all, then it must respond completely. Greater intensity of stimulation, like brighter image/louder sound, does not produce a stronger signal, but can increase firing frequency. [28] : 31 Receptors respond in different ways to stimuli. Slowly adapting or tonic receptors respond to steady stimulus and produce a steady rate of firing. Tonic receptors most often respond to increased intensity of stimulus by increasing their firing frequency, usually as a power function of stimulus plotted against impulses per second. This can be likened to an intrinsic property of light where greater intensity of a specific frequency (color) requires more photons, as the photons can't become "stronger" for a specific frequency.

    Other receptor types include quickly adapting or phasic receptors, where firing decreases or stops with steady stimulus examples include skin which, when touched causes neurons to fire, but if the object maintains even pressure, the neurons stop firing. The neurons of the skin and muscles that are responsive to pressure and vibration have filtering accessory structures that aid their function.

    The pacinian corpuscle is one such structure. It has concentric layers like an onion, which form around the axon terminal. When pressure is applied and the corpuscle is deformed, mechanical stimulus is transferred to the axon, which fires. If the pressure is steady, stimulus ends thus, typically these neurons respond with a transient depolarization during the initial deformation and again when the pressure is removed, which causes the corpuscle to change shape again. Other types of adaptation are important in extending the function of a number of other neurons. [29]

    The German anatomist Heinrich Wilhelm Waldeyer introduced the term neuron in 1891, [30] based on the ancient Greek νεῦρον neuron 'sinew, cord, nerve'. [31]

    The word was adopted in French with the spelling neurone. That spelling was also used by many writers in English, [32] but has now become rare in American usage and uncommon in British usage. [33] [31]

    Neuron Structure

    Neurons are the central building blocks of the nervous system, 100 billion strong at birth. Like all cells, neurons consist of several different parts, each serving a specialized function (Figure 1). A neuron’s outer surface is made up of a semipermeable membrane. This membrane allows smaller molecules and molecules without an electrical charge to pass through it, while stopping larger or highly charged molecules.

    Figure 1. This illustration shows a prototypical neuron, which is being myelinated.

    The nucleus of the neuron is located in the soma, or cell body. The soma has branching extensions known as dendrites. The neuron is a small information processor, and dendrites serve as input sites where signals are received from other neurons. These signals are transmitted electrically across the soma and down a major extension from the soma known as the axon, which ends at multiple terminal buttons. The terminal buttons contain synaptic vesicles that house neurotransmitters, the chemical messengers of the nervous system.

    Axons range in length from a fraction of an inch to several feet. In some axons, glial cells form a fatty substance known as the myelin sheath, which coats the axon and acts as an insulator, increasing the speed at which the signal travels. The myelin sheath is crucial for the normal operation of the neurons within the nervous system: the loss of the insulation it provides can be detrimental to normal function. To understand how this works, let’s consider an example. Multiple sclerosis (MS), an autoimmune disorder, involves a large-scale loss of the myelin sheath on axons throughout the nervous system. The resulting interference in the electrical signal prevents the quick transmittal of information by neurons and can lead to a number of symptoms, such as dizziness, fatigue, loss of motor control, and sexual dysfunction. While some treatments may help to modify the course of the disease and manage certain symptoms, there is currently no known cure for multiple sclerosis.

    In healthy individuals, the neuronal signal moves rapidly down the axon to the terminal buttons, where synaptic vesicles release neurotransmitters into the synapse (Figure 2). The synapse is a very small space between two neurons and is an important site where communication between neurons occurs. Once neurotransmitters are released into the synapse, they travel across the small space and bind with corresponding receptors on the dendrite of an adjacent neuron. Receptors, proteins on the cell surface where neurotransmitters attach, vary in shape, with different shapes “matching” different neurotransmitters.

    How does a neurotransmitter “know” which receptor to bind to? The neurotransmitter and the receptor have what is referred to as a lock-and-key relationship—specific neurotransmitters fit specific receptors similar to how a key fits a lock. The neurotransmitter binds to any receptor that it fits.

    Figure 2. (a) The synapse is the space between the terminal button of one neuron and the dendrite of another neuron. (b) In this pseudo-colored image from a scanning electron microscope, a terminal button (green) has been opened to reveal the synaptic vesicles (orange and blue) inside. Each vesicle contains about 10,000 neurotransmitter molecules. (credit b: modification of work by Tina Carvalho, NIH-NIGMS scale-bar data from Matt Russell)

    Link to Learning

    Click through the links at the top of this interactive simulation to review the parts of a nerve cell and to take a closer look at how neurons communicate.

    Electrical Signals in Neurons

    All living cells have a separation of charges across the cell membrane. This separation of charges gives rise to the resting membrane potential .

    Myelin, a fatty insulating material derived from the cell membranes of glial cells, covers the axons of many vertebrate neurons and speeds the conduction of action potentials. The importance of this myelin covering to normal nervous system function is made painfully obvious in individuals with demyelinating diseases in which the myelin covering of the axons is destroyed. Among these diseases is multiple sclerosis, a demyelinating disease of the central nervous system that can have devastating consequences, including visual, sensory, and motor disturbances.

    Although neurons share many of the features found in other cell types, they have some special characteristics. For example, neurons have a very high metabolic rate and must have a constant supply of oxygen and glucose to survive. Also, mature neurons lose the ability to divide by mitosis . Until the late twentieth century it was thought that no new neurons were produced in the adult human brain. However, there is evidence that, at least in some brain areas, new neurons are produced in adulthood. This finding suggests an exciting avenue for possible approaches to treating such common neurological diseases as Parkinson's disease and Alzheimer Disease, which are characterized by the loss of neurons in certain brain areas.

    Watch the video: You can grow new brain cells. Heres how. Sandrine Thuret (January 2023).