One such action circuitry is the loop between prefrontal cortex and basal ganglia which is involved in gaze control 8, 9. Prefrontal cortex is known to be sensitive to object novelty 10, 11 even from childhood 12. Prefrontal cortex is also implicated in processing rewarding and aversive stimuli 13, 14, 15. Likewise selective coding of rewarding and/or punishing stimuli is observed in basal ganglia 3, 16, 17. In addition, novel objects are also known to invoke enhanced responses in areas within basal ganglia such as in caudate 18. We have previously shown that value memories shape the visual responses of objects similarly and with a remarkable granularity in vlPFC and the basal ganglia output, SNr. 19. However, it is not known whether the visual responses to objects in the corticobasal circuitry are also affected by other past experiential dimensions such as perceptual exposure (familiarity vs novelty) or aversive associations and if so whether the same neurons encode such disparate object memories across different domains. Olds, J., and Milner, P. (1954). Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain. J. Comp. Physiol. Psychol. 47, 419–427. doi: 10.1037/h0058775

Circuitry Frontiers | The Convergence Model of Brain Reward Circuitry

Miliaressis, E., Rompré, P. P., Laviolette, P., Philippe, L., and Coulombe, D. (1986). The curve-shift paradigm in self-stimulation. Physiol. Behav. 37, 85–91. doi: 10.1016/0031-9384(86)90388-4 In his pioneering work in affective neuroscience, Panksepp proposed a set of highly conserved, “primal emotional systems” ( Panksepp and Yovell, 2014). He used the label “SEEKING” to refer to the system mediating investigative behaviors, approach, and “appetitive eagerness:” the highly motivating anticipation of hedonically positive events. He strongly emphasized the anticipatory quality of the emotion generated by activation of the SEEKING system and proposed that its neural substrate differs from that of the coveted hedonic experience.Fouriezos, G., Bielajew, C., and Pagotto, W. (1990). Task difficulty increases thresholds of rewarding brain stimulation. Behav. Brain Res. 37, 1–7. doi: 10.1016/0166-4328(90)90066-n Breton, Y.-A., Marcus, J. C., and Shizgal, P. (2009). Rattus Psychologicus: construction of preferences by self-stimulating rats. Behav. Brain Res. 202, 77–91. doi: 10.1016/j.bbr.2009.03.019

Trigger-Action-Circuits | Proceedings of the 30th Annual ACM

Bernal, M., Haro, J. M., Bernert, S., Brugha, T., Graaf, R., de, et al. (2007). Risk factors for suicidality in Europe: Results from the ESEMED study. J. Affect. Disord. 101, 27–34. doi: 10.1016/J.JAD.2006.09.018 Ekers, D., Webster, L., Straten, A. V., Cuijpers, P., Richards, D., and Gilbody, S. (2014). Behavioural Activation for Depression; An Update of Meta-Analysis of Effectiveness and Sub Group Analysis. PLoS One 9:e100100. doi: 10.1371/JOURNAL.PONE.0100100 The failure of psychomotor stimulants to serve as an effective monotherapy for depression invites reconsideration of a series-circuit model of the antidepressant effect of MFB stimulation. An alternative, analogous to the convergence model of intracranial MFB self-stimulation, would include multiple, convergent pathways. On that view, non-dopaminergic MFB components may contribute to the therapeutic effect in parallel to, in synergy with, or even instead of, a dopaminergic component. To assess those possibilities, we must look in more detail at the neuroanatomical complexity of the region where MFB stimulation is effective in relieving treatment-resistant depression and at the methods that have been used to link that effect to particular fiber bundles. Which Neurons Are Activated Directly by Therapeutically Effective Stimulation of the Medial Forebrain Bundle, and Which Are Responsible for the Antidepressant Effect? Yim, C. Y., and Mogenson, G. J. (1980). Electrophysiological studies of neurons in the ventral tegmental area of Tsai. Brain Res. 181, 301–313. doi: 10.1016/0006-8993(80)90614-9

Gaynes, B. N., Lux, L., Gartlehner, G., Asher, G., Forman-Hoffman, V., Green, J., et al. (2020). Defining treatment-resistant depression. Depress. Anxiety 37, 134–145. doi: 10.1002/DA.22968 Grisot, G., Haber, S. N., and Yendiki, A. (2021). Diffusion MRI and anatomic tracing in the same brain reveal common failure modes of tractography. NeuroImage 239:118300. doi: 10.1016/j.neuroimage.2021.118300 Harmer, C. J., Duman, R. S., and Cowen, P. J. (2017). How do antidepressants work? New perspectives for refining future treatment approaches. Lancet Psychiat. 4, 409–418. doi: 10.1016/S2215-0366(17)30015-9 Haber, S. N., Liu, H., Seidlitz, J., and Bullmore, E. (2022). Prefrontal connectomics: from anatomy to human imaging. Neuropsychopharmacol 47, 20–40. doi: 10.1038/s41386-021-01156-6 Scangos, K. W., Khambhati, A. N., Daly, P. M., Makhoul, G. S., Sugrue, L. P., Zamanian, H., et al. (2021). Closed-loop neuromodulation in an individual with treatment-resistant depression. Nat. Med. 10, 1696–1700. doi: 10.1038/s41591-021-01480-w

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Figure 1c, d show average population responses of vlPFC and cdlSNr neurons to good/bad and novel/familiar objects (average PSTHs). As reported previously vlPFC neurons showed on average stronger excitation to good objects (for average AUC see Fig. 1g, h). SNr showed robust excitation to bad objects and an equally robust inhibition to good objects with drastic differential response to good/bad objects (significantly stronger than vlPFC, t 233 = 12, p< 1e-3). Interestingly, responses of the same neurons recorded with novel/familiar object revealed a somewhat different pattern. While vlPFC neurons on average showed stronger excitation to novel compared to familiar objects, paralleling responses to good and bad objects, for cdlSNr the response difference to novel vs familiar objects was muted when compared to differential activations seen to good and bad objects (see Supplementary Fig. 1, for example rasters). The difference in sensitivity to value vs novelty can be further examined by comparing value (good minus bad response) and novelty (novel minus familiar response) signals within each region (Fig. 1e, f). In vlPFC the novelty signal peak was about 55% of value signal peak (5.6 ± 0.8 Hz vs 10.1 ± 1.5 Hz). In cdlSNr the novelty signal peak was only about 13% of value signal peak (7.7 ± 1.7 Hz vs 56 ± 4.4 Hz). Furthermore, while the distribution of onset times of value and novelty signal were comparable in vlPFC, in cdlSNr, the onset of novelty signal was significantly later than the value signal (Fig. 1e, f). These value and novelty signals and their onsets in vlPFC and cdlSNr were consistent in both monkeys (Supplementary Fig. 2). Fenoy, A. J., Quevedo, J., and Soares, J. C. (2021). Deep brain stimulation of the “medial forebrain bundle”: a strategy to modulate the reward system and manage treatment-resistant depression. Mol. Psychiat. 2021:6. doi: 10.1038/s41380-021-01100-6Arvanitogiannis, A., and Shizgal, P. (2008). The reinforcement mountain: allocation of behavior as a function of the rate and intensity of rewarding brain stimulation. Behav. Neurosci. 122, 1126–1138. doi: 10.1037/a0012679 Solomon, R. B., Conover, K., and Shizgal, P. (2017). Valuation of opportunity costs by rats working for rewarding electrical brain stimulation. PLoS One 12:e0182120. doi: 10.1371/journal.pone.0182120 Kringelbach, M. L., Jenkinson, N., Owen, S. L. F., and Aziz, T. Z. (2007). Translational principles of deep brain stimulation. Nat. Rev. Neurosci. 8, 623–635. doi: 10.1038/nrn2196