Fig. 1: Schematic of study methodology: from human participants to computational modelling.
A Blood samples were first collected from individuals with bipolar disorder (BD) (both lithium responders, LRs, and non-responders, NRs) and healthy controls (HCs), and cells were reprogrammed into granule cell (GC)-like neurons. Half of the GCs were exposed to lithium, and the electrophysiological properties of these neurons were studied. These results have been previously reported by Khayachi et al. [41]. B We used these electrophysiological data (frequency-current and current-voltage curves specifically) to fit the parameters of a model GC such that the model generated the same electrophysiological behaviour as the in-vitro GCs. Note: spike trains shown here are for illustrative purposes only, and are not real GC spike trains. C These model GCs were then incorporated into a biophysical dentate gyrus (DG) network, to form model DGs for LRs, NRs and HCs. Abbreviations are as follows: PP perforant path, BC basket cell, HIPP hilar perforant path cells, MC mossy cell. Solid lines indicate excitatory connections, and dashed lines indicate inhibitory connections. N indicates the number of cells per population. This circuit diagram was adapted from our previous paper [37]. D The pattern separation (PS) performance of these networks were then assessed, by presenting the network with a series of partially overlapping PP input patterns, and assessing whether the resulting output patterns were less correlated. Plotting the correlation between pairs of input patterns and resulting output patterns against each other generated a PS curve. The area between the diagonal and this pattern separation curve (AUCPS) summarised the network’s PS abilities, with larger AUCPS values representing better PS.
