Nowadays there are broadly two sorts of computational neuroscientist: those who analyze experimental data using statistical methods, and those who propose computational models aimed at a theoretical understanding. I belong to the latter category, so what I say doesn’t really apply to data analysis issues.
Computational or mathematical modeling/theory is still at a very rudimentary stage when it comes to addressing clinical neuroscientific questions. The sheer complexity of the brain’s neurons, connections, and dynamics prevents a direct “brute force” modeling of how the cells interact to generate behaviors and disorders.
But highly simplified models can be used to explore hypotheses, or the implications of experimental findings. So a schematic computational model can test out how a particular brain circuit might work, and how “breaking” the circuit in various ways could correspond with particular disorders.
For instance, depression could be modeled as a weakening of cells in the hippocampus that help a person discover new possibilities in life. Depression is often described as an inability to move out of the apathetic “place” you find yourself in. Metaphorically speaking, discovering new “places” to go to may involve the formation of new neurons (neurogenesis) in the hippocampal circuit — a process that may be disrupted in some depressed patients. (For more on the evidence read this excellent New York Times article on depression The Science and History of Treating Depression)
The (ideal!) steps involved in computational modeling of disorders might go something like this:
- A network of artificial neurons can be arranged in a circuit inspired by anatomical data.
- The dynamic properties can then be derived from physiological data. (Cell firing, fMRI, EEG etc.)
- The global activity or output of the network is related to some behavior, such as goal-directed decision making
- Changing parameters in the network or in the artificial neurons can be investigated. Weakening some parameters — such as inhibitory or excitory strength — may make the system resemble a “depressed” person or animal who is incapable of making a decision. Enhancing some other parameter may appear equivalent to the action of a drug.
- The modeling results can be discussed with experimentalists, and ways to verify or falsify the results can be invented.
- In the event of a falsification (which is pretty much all the time!) you return to step 1!
- If all goes well, a well-tested model can be introduced to clinicians and the general public, and can inform treatment strategies. As far as I know this has yet to happen! đŸ™‚
A computational model I have developed in conjunction with anatomists and another modeler looks at a circuit centering on the amygdala, which is believed to play a major role in emotion, and in emotional disorders. The model suggests that prefrontal cortical modulation of the amygdala can push the system into a “cautious” state, or a more “reckless” state. And this modulation may be related to pathological brain states. We also show that weak attention could relate to overgeneralization, a problem that seems to occur in phobic patients.
Our model is highly simplified, but hopefully it will throw up some ideas for experimentalists to take further.
You can read more about our model here: Anatomy and computational modeling of networks underlying cognitive-emotional interaction | Frontiers in Human Neuroscience
(Pardon the self-promotion!)
