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I am not a brain scientist but the following question seems reasonable to me.

Prof. Sebastian Seung has given a TED talk named "I Am My Connectome". If I understand correctly, connectome is basically the graph whose vertices are neurons and whose edges are synapses (strictly speaking, there are many different kinds of neurons so the vertices of the graph should be colored, but let us forget it for now).

The title then appears to be a (probably intentional) oversimplification, since even though we do not understand how exactly memories are stored in the brain (and there are many different kinds of memories), things I will roughy call "electrical and chemical variables" play some role in it (so simply remembering whether there is a link or not between two neurons is not going to fly). I understand that studying these "electrical and chemical variables" is like over a half of the brain science but the exact definition is not particularly important for this question. If my understanding is correct, then one should not get overly excited about the present achievements of the Brain Preservation Foundation (the most promising one is, I think, preserving the connectome of a mammal for some time after its death using a chemical solution/freezing), since they have not shown that they can preserve any of the "electrical and chemical variables".

The question is: what information can you actually extract out of a connectome, assuming you do not know anything else about the brain? Like something about a person's health, personality, anything? The chance of them having schizophrenia? Their most likely age?

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Well, regarding this short definition of a connectome from Wiki:

A connectome (/kəˈnɛktoʊm/) is a comprehensive map of neural connections in the brain, and may be thought of as its "wiring diagram". More broadly, a connectome would include the mapping of all neural connections within an organism's nervous system.

Therefore you could read out all information, assuming you have enough computational power. (A perfect connectome would represent all information processes in the brain. Because processing information through connections is all the brain does, right? :D)

In the real world however, we need 'models' as simplifications to deal with these phenomena. A connectome is nothing else than such a model. The classical model as vertices and nodes approach (which leads to solving differential equations in the end) is mainly based on the mathematical field of graph theory and probabilistics. Also measuring brain properties has always to do with oscillations and signal theory. (Note on simplification and prediction: The fact that a model is simplifying should be independent of the quality of prediction of outcomes. The model might me accurate, even if it is a simple one.)

Some thoughts on the limits of obtainable information

  1. To make predictions of real-world constructs, you need to assess real-world phenomena. F.i. you need to have postulated some outcome-variables (for lets say Schizophrenia) that need to be quantified in the real-world (external validity) to be related to inner-brain processes. These real-world concepts of psychological processes, speaking of the behavioral, emotional and cognitive aspects (which can be measured), are inherently fuzzy, which raises the question, to which certainty we will be able to predict psycho-physiological correlates.

  2. Not to forget, memories are mainly coded as 'processes over-time', something like chains of activation (other than distinct anatomy and brain-areas who would define thoughts). Also noisyness and randomness is always a variable you need to consider. (I think the Hippocampus functioning of neurons induces randomness by design, but I have no source right now.)

  3. Also to mention: The individuality of brains leads to decreased prediction of the model or a general model that would hold for every individual

  4. The plasticity of the brain states states the ability to change and evolve over time. (Concepts like Imprinting and so on). A static model might also not be sufficient to represent these (also highly individualized) processes.

  5. Situational- or environmental dependency also influences your current state of mind. (It is not possible to 'control' all environmental factors, unless you had infinite computational power.)

  6. Also the brain and toughts are highly self-/auto-activating, recurring and in parallel. Millions of neurons firing at the same time and oscillating in rhythms. Thoughts that become conscious might only be the tip of the iceberg in a sea of thoughts that are below a certain threshold.

My proposal to your question: "What exactly can you get from a connectome?"

In principle everything is possible, but practically we might never truely decode everything from a philosopical and mathematical point-of-view (we needed to create a perfect connectome, which would be a perfect simulation of the entire brain). We might however be able to find very accurate brain-markers that help in predicting real-world concepts that seem relevant for humans :)

Resources

Because it is great: Video of synchronizing fireflies

Buszaki: Rhythms of the Brain is a nice read on some how information is created in the brain and different processes

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  • $\begingroup$ I do not understand your answer. What is "wiring diagram" supposed to mean? In each synapse, you have thousands and thousands of dynamical variables (concentration of this neutrotransmitter, of that neurotransmitter). Are they considered a part of the connectome or not? If not, then how can we extract all the information (you talk about computational power, so you probably have an algorithm)? If yes, then does the definition of the connectome simply mean "the entire brain"? $\endgroup$ – mrd May 15 at 10:10
  • $\begingroup$ Each synapse and each neuron surely plays a part of the communication processes. But I guess more implicitely in the case of a Connectome. like any communication/information process in the world is using atoms, though you would usually not describe these processes on an atom level. (Maybe (Quantum) Information Theory does). A Connectome would probably focus on whole brain communication processes. $\endgroup$ – bambamfox May 16 at 14:20
  • $\begingroup$ But then it is unclear how is connectome a useful definition. If the definition was something like "get only these precise pieces on information from the brain, then the rest can be computed from them", it could have been useful. But apparently, the definition is just "all the information in the brain is considered a part of the connectome too". Then connectome is just a fancy word for brain. And then it is not surprising that you can read out all information from the connectome. $\endgroup$ – mrd May 16 at 14:24
  • $\begingroup$ and I believe there has been some work on determining whether a person has schizophrenia (a fuzzy notion, as described in your answer) by eye movement (something fairly objective). So while your general point is reasonable, there are apparently some ways to extract something concrete out this fuzzy notion. nhs.uk/news/mental-health/… $\endgroup$ – mrd May 16 at 14:26
  • $\begingroup$ It is the job of scientists to extract information out of a fuzzy world. Didn't say otherwise. And because everything is interconnected, you cannot ultimately explain the brain by either a large scale communcation model (connectome as 'one' out of many models for large-scale dynamics), or a synapse level or a chemical level, and so on, all alone. The Connectome just focusses on the larger scale communication. $\endgroup$ – bambamfox May 16 at 14:29

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