One of the most persistently frustrating things for me is how poorly we understand what “normal” or “healthy” brain function looks like, despite the fixation on what is “unhealthy” or “disordered”. Not to get too Maslow here (or maybe), the obsession with crippled brains is resulting in crippled philosophy.
I started researching brains in general after some pandemic induced work slow downs, I tend to wander without something significant to bind my attention to. One of my girls received an “ASD” diagnosis around this time and reading the report it stuck me how bizarre the process was. A neurological condition without a known neurological basis? A genetic condition with no specific genetic basis? A behavioral condition with no distinct behavioral presentation?
It stuck me as odd that there was literally nothing anyone could actually tell me about this diagnosis that would be useful in anyway, not to describe, not to treat, not even to understand. It’s such a bizarre thing that there’s common tropes about “every autist is different!”. Which is exactly the opposite of what defining criteria is supposed to do.
Many crises of faith later regarding the philosophy of psychiatry, it finally clicked that “autism” itself literally means nothing. The heterogeneity allowable by it’s definition is a feature allows it to work as a diagnostic fall back for individuals who exhibit behavior that’s non socially conformant, yet fails to fit existing diagnostic criteria.
This is a powerful feature in that it hijacks the obsession with “disordered” and “unhealthy” to generate information which is easily generalizable into how brains work in general. It gives the impetus to do intensive study on various systems which simply don’t exist for “healthy” brains, and allows researchers to ask questions about brain function they aren’t really willing/able to regarding “healthy” brains. The massive mash of heterogeneity in autism research has inadvertently produced one of the best maps of general brain function available.
One of my common research methods is to quickly determine what the “min/max” properties of a system are. By determining this first, it allows a a way to determine the distribution rate of other properties, and this allows a much more consistent way to determine “normal”. Once this “normal” has been determined, I can go on to infer other properties of data by leveraging “normal”.
Autism itself includes lots of data about “min/max” conditions. The inclusion of extreme dorsal dominant presentations and extremely ventral presentations (like Asperger’s) was pretty key to understanding that most humans ventrally bias data pretty heavily, and allowed follow up research to understand where “normal” in that processing was. It allowed asking questions about the core mechanics of brains due to glial research performed to explore autism that wouldn’t be on the radar at all in “normal” brains. Autism has provided a way to explore out past our basic assumptions of functions, ironically by being so heavily laden with assumptions.
Rather than the map of autism related etiologies being contradictory, they provide the most complete map of function available and give us room to ask questions we couldn’t otherwise. Reconciling these etiologies provides insights simply not available through data in other areas.
Autism is a fantastic model for not just healthy brain function, but all brain function as a happy artifact of it’s uselessness in it’s primary purpose.