Lithium is a great example - very effective treatment for bipolar. No one really knows why. Prescribed for decades as they've tried to figure it out because tests showed it was effective and relatively safe, just no one knew exactly what it was doing in there.
It's sort of how you can be a woodworker without knowing the cellular biology of trees, and without being an electrical or mechanical engineer who can build a table saw from scratch.
Lithium-rich mineral springs have historically been touted for their healing properties. It was first used for mania in the late 1800s, with Denmark leading the way, but little was published about the medication for more than half a century.
https://www.verywellmind.com/lithium-the-first-mood-stabiliz...
I'm a believer in modern scientific medicine, but think we often have it backwards. Before reinventing the wheel we should exhaustively test what we used traditionally. Maybe the reason we don't do much of that is that it's not possible to patent, and so there's no financial incentive to do so?
https://pubmed.ncbi.nlm.nih.gov/?linkname=pubmed_pubmed&from...
It also doesn't make its way into mainstream practice among GPs and psychiatrists. As an example, which mainstream practitioner would ever prescribe or recommend curcurmin with piperine for any condition, aside from alternative medicine practitioners? Which psychiatrist would recommend EPA fish oil for depression? I could go on. The research that does exist is largely ignored.
Also, the many dead ends thing is true in general for pharma, but at some point there are too many dead ends for it to be profitable even given their bankroll. This is happening a lot lately with neuro-related drug development. In the last 10 years I know Amgen, Pfizer, Novartis, Eli Lilly have all had shut downs/lay offs in their neuroscience research divisions.
Subtle, but I get it :D
Link for those curious about this comment:
(Neural Correlates of Interspecies Perspective Taking in the Post-Mortem Atlantic Salmon: An Argument For Proper Multiple Comparisons Correction)[https://teenspecies.github.io/pdfs/NeuralCorrelates.pdf]
We tend to give the impression that we’ve figured almost everything out, and so you’re just learning the facts of it all.
Really, we’ve carved out a little island in a sea of ignorance, and the foundations of the island are just our current best rough approximations that might collapse.
An example from the field I’m in — biology. It’s quite likely that if you worked at it you can describe a new species in your backyard. You don’t have to go to the Amazon rainforest, there’s scientific unknowns all around you all the time.
We also have some grand principles that further unite the field, like the cell as the basic unit of organismal life, emergent properties of higher order systems, &c.
The remaining work is mostly 'stamp collecting' - looking at the details of the output of that algorithm. Of course those details are enormously complex. Kind of like mapping out the Mandlebrot Set after you already know the algorithm.
If you wanna cure cancer, resolve chronic diseases, conserve ecosystems under climate change, etc.
It’s cool to know that DNA exists and that life evolves by natural selection, but getting into the complex weeds of that fractal is where we discover that we know so little.
There is plenty, more than enough, data to work with, and many promising/proven models to build upon, need more good theorists in the field.
Saying "but we don't know much" is just being lazy.
This is where science communication fails, the general population and even the most curious people still think that there is such a thing as "Jennifer Aniston neuron", but in reality there is "Rachel from Friends neuron" that sits at the top of hierarchy where the signalling converges and then it's highly likely it isn't the only thing it's responsible for (OR gate), but finding what is the hard question. This is speculation stated at about 20min in this talk: https://www.youtube.com/watch?v=Y1ID0FQN9tg
Interesting that you had this takeaway, I didn’t get a sense of this at all. My takeaway was that the author presented modern findings (which constitute our present model for how the brain works, speaking nothing to a notion of some absolute correctness) to dispel older hypotheses that are disprovable based on the latest research. As presented, IMO, the author captures well that these modern findings are just a reference point against which to refute the stubborn tropes.
> So why does the myth of a compartmentalized brain persist? One reason is that brain-scanning studies are expensive. As a compromise, typical studies include only enough scanning to show the strongest, most robust brain activity. These underpowered studies produce pretty pictures that appear to show little islands of activity in a calm-looking brain. But they miss plenty of other, less robust activity that may still be psychologically and biologically meaningful. In contrast, when studies are run with enough power, they show activity in the majority of the brain.
Another, more relevant reason, is that brain-scanning studies are in their infancy.
It’s expensive to run brain scanning studies, so studies have smaller populations or lower resolution data collection in response. Yes, brain scanning studies are new, but we’ve been doing fMRI studies for nearly 3 decades and it’s still expensive. Had that cost been scaled back, we’d have more and/or better data because grants are (very) finite.
As such it’s impossible to say what subset of brain activity is directly related to some activity rather than some related mental processes.
There's a lot of signal processing theory, regression analysis, etc. that goes in to it. With that said, the issue that arises is that only the strongest correlates may surface or be observed. There may be a lot of brain activity that overlaps or looks identical between that resting state (baseline) and the stimulation state, but can't really be included because a distinction can't be made, which goes back to your final point. That, however, doesn't mean that there aren't actual subsets of brain activity that we can attribute to a specific activity.
For example, if you're in an fMRI and I show you nothing but a fixation cross, then show you a sad image at a specific time for a specific interval, then went back to the fixation cross, and also showed you a scrambled version of that same image, I can (oversimplified) run a diff check to see what's different between the three. From there, I can remove the overlap between the three from normal baseline activity and activity specifically related to visual processing (scrambled image) to reveal what's specifically different with that sad image. If I see that the amygdala has much higher activity during that sad image compared to the fixation cross and the scrambled image, I can conclude that it has some role in emotional processing (well not really in this example because you'd also need a ton of images, including neutral images, with different orderings of each to really be confident; also really couldn't make that conclusion, just present the correlation because...science).
The point is that there are methods to isolate activity related to a specific activity/stimuli/response/etc. However, it's currently very difficult, if not impossible, to make distinctions in those overlapped areas.
Note: this is obviously an oversimplification of neuroimaging research and analysis
The simplest thing you can do in a scanner is have them sit there doing nothing while you acquire scans. In the end, you just have a 4d matrix of unsigned integers. For each voxel you can acquire an average over the scan and check whether it is significantly above zero using a t-test. Given enough data everything will be significantly greater than zero, including parts outside the head. Or you can compute a global mean to center all the voxles, and check which parts of the brain are significantly above the average, or significantly below the average. Extremely simple analyses (and so yes, there are lots more you could do).
In task fMRI, you have them do a task, and you use events in your task design as predictors of the BOLD, and then display a voxel map of either the beta values, or, more commonly, the T-values of those regression betas (or a contrast of those regression betas). In this case, you really aren't looking at activity. You are looking at correlations.
Those islands of activity in whole-brain analysis images in figures in papers happen because the result images are thresholded, e.g. at p < .05 false-discovery-rate correction for multiple comparisons. Personally, I think unthresholded images are better because they are more informative.
Let's take a concrete example. You have a subject do a task where they have to choose between two gambles varying in risk and reward. Then, for each voxel, you predict the BOLD time course using a series of events (time of presentation of gamble options) with magnitude equal to the coefficient of variation between the two gambles. So now, for each voxel you have a beta value showing how CoV predicts BOLD. You notice that anterior insula on both cases has the highest beta values. You threshold at conventional statistical signficance, after correcting for multiple comparions, and all the spurious, or less important, correlations drop out of the image, and you are left with two bright spots on a map pin-pointed on the left and right anterior insula. See: in this anaylysis, not all "psychologically and biolotically meaninful activity" is being examined or looked at. For example, button presses events show up localized in the motor areas too, but they weren't looking at those. But they could have, if we were interested.
Edit for clarification: I was not expecting folks to respond to this by linking split-brain experiments; I am specifically referring to split-brain experiments showing us that our ideas about left brain/right brain were correct. This was in reply to "I'd agree with this author that they are oversimplified and generally incorrect - I'd add the left brain right brain divide to the list" -- I want to know what this person thinks, thanks.
> McGilchrist digests study after study, replacing the popular and superficial notion of the hemispheres as respectively logical and creative in nature with the idea that they pay attention in fundamentally different ways, the left being detail-oriented, the right being whole-oriented.
[1] https://en.wikipedia.org/wiki/The_Master_and_His_Emissary
Each time I lose feeling in right side of body, and lose the ability to write. Not 100%, but close enough. Typing is fine however, if slow. Critical thinking goes to crap. Talking is fine. I can still draw, though my motivation goes to crap.
Is weird what stays and what goes.
Last time I looked writing is a left brain activity. Which controls right side.
I can read and speak well enough. Thinking is harder, so I tend to stay quiet.
However, there's left-brain/right-brain in terms of "some functions of the brain tend to be more on one side or another", and then there's left-brain/right-brain in terms of a pseudoscientific personality test about whether you're more of a "logical" thinker or "artistic" thinker, blah blah blah. There's no evidence for that. It's just mumbo-jumbo Buzzfeed-style quizzes to make you feel good about yourself.
The brain is complex.
Edit: https://en.wikipedia.org/wiki/Agenesis_of_the_corpus_callosu...
They may be bad abstractions at a lower level of detail, but for general purposes they seem to be useful enough.
Solutions offered based on bad simple metaphorical models have the benefit of being intuitive, so they stick around.
Theory-theory is the Efficient Market Hypothesis applied to common sense.
The mirror neuron system is also a myth-myth. While there aren't "mirror neurons", per se (e.g., biochemically different), there is a system of mirroring and it is very important
Speaking specifically to left vs right brain, in popular media I've mostly seen it presented as logic vs creativity, which completely disregards that people can be creative verbally, and that certain quantitative/technical things are very non-verbal.
https://en.wikipedia.org/wiki/Wernicke%27s_area https://en.wikipedia.org/wiki/Broca%27s_area
The evidence for more advanced brains in higher mammals is also plainly there.
The author seems to be over-stating all this stuff in order to reject it.