The model proposed here. Mania is too many mitochondria; depression too few. We predict it is possible to detect mood state from optical images of certain cells and counting stained mitochondria.
Mania is too many mitochondria; depression too few.
Mitolevel is mitochondrial count divided by cell count (ML = M/C).
Mitolevel varies by cell type.
The mitolevels in certain cell types will be strong predictors of current system wide mood state.
Mania is elevated mitolevels and depression is depressed mitolevels.
Individuals who have experienced mania frequently report a feeling of quickening or acceleration, which matches this model as self-reproducing mitochondria change population exponentially over time.
Individuals who experience severe depression take a long time to recover, which matches a model where the cell is filled with debris (likely from dead mitochondria from a manic episode) preventing the restoration of a healthy mitolevel.
Mitochondrial populations change much more gradually than substance levels in the bloodstream, which explains why mania is described as a "sustained high" and why depression can't be immediately "snapped out of".
Predictions
- Abundant lab evidence. Someone will discover a human cell type whereby one can reliably and abundantly predict mood-energy state of the whole person simply by measuring the mitolevels of a cell sample.
- At-home mitolevel tests. Someone will organize a large number of people in different mood-energy states to contribute to a large dataset of microscopic images of mitochondria from various cell types and then, using this reference, cheap at-home tests of mitolevels using simple staining, consumer microscopes, and machine assisted counting will be developed.
- Millions cured. High fat, low glucose diets will cause stabler mitolevels and radically reduce episodes of mania and severe depression
- A Universal Condition. Any human being can experience mania and depression through manipulation of their diet and/or environment to force high or low mitolevels.
References
Because of the importance of this model I am publishing the first draft to hopefully disprove/evolve it quickly and so I am publishing before adding a complete list of references.
As one can assume, hundreds of works directly contributed to the model above.
If this model proves correct, all credit is due to the breakthrough work of Dr. Iain Campbell at University of Edinburgh.
Having observed this field for a long time, even if this model proves inadequate, I feel an accurate model is in sight and it is now a race between Dr. Campbell and his team versus everyone else.