Psilocybin mushroom: a possible reboot to get away from depression

A recent study from the Imperial College of London has shown that the use of a component of the so called magic mushroom may be a therapy against depression. The patients described themselves as feeling “reset” after it’s use.

fMRI before and after the treatment. – Scientific Reports 7, Article number: 13187

A possible explanation of the results of this experiment is that the psychedelic effect of the psilocybin, while altering the brain functionality and the modulation of the neuron trasmission, seem also to improve the general connectivity of the neuronal networks.

Psilocybin may be giving these individuals the temporary ‘kick start’ they need to break out of their depressive states and these imaging results do tentatively support a ‘reset’ analogy. Similar brain effects to these have been seen with electroconvulsive therapy.

The psilocybin molecule is a prodrug, that gets dephosphorylated to become psilocin, which is a substituted tryptamine alkaloid and a serotonergic psychedelic substance. It gives its effects by interacting with various serotonine receptors (it’s a partial serotonine antagonist), binding in particular the 5-HT2A . Psilocin is also linked to indirect effects on the regulation of dopamine.

[…] psilocin indirectly increases the concentration of the neurotransmitter dopamine in the basal ganglia, and some psychotomimetic symptoms of psilocin are reduced by haloperidol, a non-selective dopamine receptor antagonist. Taken together, these suggest that there may be an indirect dopaminergic contribution to psilocin’s psychotomimetic effects.

The study analysed the fMRI of 16 patients before and after the treatment with psilocybin and it showed a lower blood flow in areas of the brain linked to stress and anxiety, like the amygdala. At the same time areas which were active under the depression and the psilocybin effects shown to be more stable.

Based on what we know from various brain imaging studies with psychedelics, as well as taking heed of what people say about their experiences, it may be that psychedelics do indeed ‘reset’ the brain networks associated with depression, effectively enabling them to be lifted from the depressed state.

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A recent discovery opens new hopes for Fibromyalgia patients

A typical symptom that the patients affected by fibromyalgia, it the pain. This is due to the high levels of excitatory amminoacids that act on NMDA(n-methyl d-aspartate) receptors lowering the level necessary to trigger the signal. The hard consequence of this is that stimuli which didn’t cause pain before the disease appeared, might cause pain in patients with fibromyalgia. Also, pain stimuli are in in general more painful. These two phenomena are called respectively hyperalesia and allodynia.

A common condition which affects the subjects with fibromyalgia is difficulty to sleep. Chronical insomnia is associated with increased levels of GABA (gamma amino butyric acid).

A recent study from the University of Florida shown how moderate alcohol consumption might help on both symptoms. The underlying mechanism is not yet clear, but it could be due to the capacity of alcohol of inhibiting GABA and NMDA receptors.

Fibromyalgia and chronic insomnia are frequently comorbid conditions with heightened sensitivity to painful stimuli, potentially subserved by the hippocampus. Recent evidence suggests moderate alcohol consumption is associated with reduced fibromyalgia symptom severity.

Another typical symptom associated with fibromyalgia is a reduced volume of hyppocampus, which after the treatment with the moderate alcohol consumption was improved and displayed an increased volume in MRI. Nevertheless, the correlation between pain relieve and increment of hyppocampus volume was not significant enough.

The results are promising and may lead to better treatment of this disease alleviating the suffering of the people affected.

Based on these findings, systematic prospective and longitudinal work examining the relationship between drinking pattern and FM/FMI symptomatology is warranted.

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Alzheimer disease and sleep habits

It is known how the formation of brain plaques can bring people to being affected by Alzheimer’s Disease(AD). This devastating neurodegenerative disease in an initial phase can cause memory problems, but at more advanced level can cause even behavioural changes, such as mood swing, or problems with language.

The main component of these plaques is the Amyloid ꞵ-peptide(Aꞵ), and so the accumulation of Aꞵ has been accounted as primary cause in AD pathogenesis. Furthermore, particular genetic mutations can increase the Aꞵ deposition. As a result, the so called “amyloid hypothesis” has become central in the study of AD. More interested reader can find all the information in the genetic modification and the different trials that have been done to access this hypothesis in the article: Hardy, John, and Dennis J. Selkoe. “The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics.” science 297.5580 (2002): 353-356. What seems to be the primary influence and the rest of the disease process is the result of an imbalance between Aꞵ production and Aꞵ clearance.

Anyway, looking at the several different studies that has been conducted on the Aꞵ, brings to the controversial results. Many correlations between Aꞵ depositions and AD symptoms have been discovered, but not every time they the relationship is so close and clear. Even though, since no alternative hypothesis explaining the cause and early pathogenesis of AD with a so strong experimental support has come out, the work has to continue in this direction in order to have a better understanding of AD. Among them two correlations are for example the one with the inhibition of long-term potentiation in the hippocampus, that is required for memory formation (D.Games et al., Nature 373, 523 (1995)) and the one with the interference with synaptic plasticity.

A particular correlation that we want to report here is the one connected with the sleep. Indeed, it has been shown that poor sleep habit can increase the deposit of Aꞵ, and so the probability to be affected later on by AD goes up. A very clear explanation of most of the information reported here, the correlation with sleep, and a speech in favour of the first and better solution to this neurodegenerative disease, that is prevention, are reported in this TedXTalk speech by Lisa Genova: https://youtu.be/twG4mr6Jov0

In conclusion, in our opinion, one of the most beautiful activities that can help you to smooth the effects of AD, as explained by Lisa Genova, is to keep learning and make you brain engaged and active, so that to increase the number of neuronal connections. In this way you create sort of rescue connections to maintain the access to all the information in your brain.

The computer who dreams of becoming a real boy

A scene from the movie A.I. Artificial Intelligence: a robotic child gets activated by a woman whose son is in deep coma.

A scene from the movie A.I. Artificial Intelligence (2001) – © 2001 – Warner Brothers and Dreamworks LLC – All Rights Reserved

Contents at a glance:

  • Real neural networks and Artificial Neural Networks
  • The abilities and the limits of the current Artificial Neural Networks models

The neurons are the cells responsible for the transmission of the signal through the human nervous system. A single neuron cannot achieve much by itself, but groups of them can create highly complex networks, with billions of connections. Neural networks can be considered the analogue of what processors are for computers: a powerful calculation machine.

Scheme visualisation of a mulipolar neuron.

Scheme visualisation of a mulipolar neuron.

Artificial Neural Networks (ANN) are mathematical models which mimic the functioning of human neural networks, and it is thanks to them that today we are experiencing a new spring in the artificial intelligence research field. The artificial neuron is represented as simply as a summation unit, which makes a weighted sum of the inputs of its afferent neurons. The entity of its output is related to the value of the inputs sum and to the activation function.

An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another.

An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. – Wikipedia

What makes neurons especially powerful, is their ability to learn. This has been discovered to be determined by the establishment, the strengthening or loosening of connections between neurons. In the model of artificial neural networks such a mechanism has been mimicked leveraging the values of the weights which represent the connections. As such, the lower (or higher) the value the loosen (or stronger) the connection will be.

Error surface of a linear neuron with two input weights.

Error surface of a linear neuron with two input weights. – By AI456 – Graphed with MatLab – This graphic was created with MATLAB, CC BY-SA 3.0

The algorithm responsible for this learning operations, in the ANN, is the backpropagation. It’s a technique which uses derivatives to find the connection pattern which minimises the error given a certain task. It was discovered in the 80s, but only recently computers have become powerful enough to compute it in complex multilayered networks. This lead to the deep learning revolution that we are experiencing today, and to very complex artificial intelligences, which can beat the strongest human players at Go, drive cars and become everyone’s personal assistant.

Geoffrey Hinton giving a lecture about deep neural networks at the University of British Columbia. He was one of the first researchers who demonstrated the use of generalized backpropagation algorithm for training multi-layer neural nets and is an important figure in the deep learning community. - By Eviatar Bach - Own work, CC BY-SA 3.0

Geoffrey Hinton giving a lecture about deep neural networks at the University of British Columbia. He was one of the first researchers who demonstrated the use of generalized backpropagation algorithm for training multi-layer neural nets and is an important figure in the deep learning community. – By Eviatar Bach – Own work, CC BY-SA 3.0

However ANNs, as they’re currently modelled, require a very hard and long training before they can do all their marvelous things, which is the reason behind the immense growth of value of large collections of data, usually known as Big Data. This limits upset even one or the pioneers of the backpropagation algorithm, Geoffrey Hinton, who recently suggested to get rid of the current learning method, to try to discover something totally new. Clearly backpropagation cannot be trashed from one day to another, but the development of new means for modelling learning is a major interest of the artificial intelligence researchers.

Researcher checking fMRI images - By NIMH - US Department of Health and Human Services: National Institute of Mental Health, Public Domain

Researcher checking fMRI images – By NIMH – US Department of Health and Human Services: National Institute of Mental Health, Public Domain



The way to discover more powerful techniques to mimic learning is long and perilous but new discoveries in neuroscience may unravel and lead to new and unexpected fields. Studying the behaviour and inner working of the brain can open paths to new state of the art algorithms and techniques for machine learning.

By Fabio Colella and Michele Vantini