The spacing of training experiences is known to lead to more robust memory formation as compared to training experiences with short time intervals. However, the neurological mechanisms underlying this rather counterintuitive phenomenon are still unknown. Here, we propose that the benefit of spaced training experience is supported by distinctive cellular properties within specific neuronal circuits.
To deepen this hypothesis, based on preliminary results demonstrating differential involvement of distinct striatal domain in the encoding and retrieval of information acquired through massed and spaced training, we will first try to understand pre- and post-synaptic contributions of corticostriatal parallel loops to this process. Next we will build a whole brain functional wiring diagram of spatial memory acquired through spaced and massed training. This should provide a holistic view of brain circuits activity revealing the network logic sustaining differences in training efficacy. Network analysis by graph theory tools will determine crucial nodes in the circuit and will put us in a position to selectively intervene on memory processing, by loss and gain of function manipulations. In particular, once identified hub regions essential in the spaced learning circuit by artificially priming neuronal ensemble in the region, by means of optogenetic stimulation, we will try to favour the formation of longer lasting memories after massed training.
This proposal will impact at different levels: 1. The network level approach will help identify processing-based differences at a circuit level in the striatum, and in other regions of interest, that may sustain better efficacy of spaced as compared to massed training; 2. From a theoretical point of view the findings will drive refinement of current models, challenging the view of multiple segregated memory systems; 3. It will provide insight to understand the neural determinant of the cognitive deficits observed in patients with striatal dysfunctions but also suggest possible therapeutic avenue to treat these symptoms from a behavioral and pharmaceutical point of view. Indeed, enhancing normal learning with optimized learning protocols is a therapeutic strategy already exploited in clinical practice.
BIBLIOGRAPHY
1. Toppino, T. C. & Gerbier, E. About Practice. in Psychology of Learning and Motivation 113¿189 (2014). doi:10.1016/B978-0-12-800090-8.00004-4.
2. Cermak, L. S., Hill, R. & Wong, B. Effects of spacing and repetition on amnesic patients¿ performance during perceptual identification, stem completion, and category exemplar production. Neuropsychology 12, 65¿77 (1998).
3. Carpenter, S. K., Cepeda, N. J., Rohrer, D., Kang, S. H. K. & Pashler, H. Using Spacing to Enhance Diverse Forms of Learning: Review of Recent Research and Implications for Instruction. Educ. Psychol. Rev. 24, 369¿378 (2012).
4. Bolam, J. P., Hanley, J. J., Booth, P. A. & Bevan, M. D. Synaptic organisation of the basal ganglia. J. Anat. 196 ( Pt 4, 527¿42 (2000).
5. Hintiryan, H. et al. The mouse cortico-striatal projectome. Nat. Neurosci. 19, 1100¿14 (2016).
6. Miyachi, S., Hikosaka, O. & Lu, X. Differential activation of monkey striatal neurons in the early and late stages of procedural learning. Exp. brain Res. 146, 122¿6 (2002).
7. Yin, H. H., Knowlton, B. J. & Balleine, B. W. Blockade of NMDA receptors in the dorsomedial striatum prevents action-outcome learning in instrumental conditioning. Eur. J. Neurosci. 22, 505¿12 (2005).
8. Dias-Ferreira, E. et al. Chronic stress causes frontostriatal reorganization and affects decision-making. Science 325, 621¿5 (2009).
9. Yin, H. H. & Knowlton, B. J. Contributions of striatal subregions to place and response learning. Learn. Mem. 11, 459¿63 (2004).
10. Woolley, D. G. et al. Homologous involvement of striatum and prefrontal cortex in rodent and human water maze learning. Proc. Natl. Acad. Sci. U. S. A. 110, 3131¿6 (2013).
11. Henke, K. A model for memory systems based on processing modes rather than consciousness. Nat. Rev. Neurosci. 11, 523¿532 (2010).
12. Dudai, Y., Karni, A. & Born, J. The Consolidation and Transformation of Memory. Neuron 88, 20¿32 (2015).
13. Vetere, G. et al. Chemogenetic Interrogation of a Brain-wide Fear Memory Network in Mice. Neuron 94, 363-374.e4 (2017).
14. Wheeler, A. L. et al. Identification of a functional connectome for long-term fear memory in mice. PLoS Comput. Biol. 9, e1002853 (2013).
15. Torromino, G. et al. Offline ventral subiculum-ventral striatum serial communication is required for spatial memory consolidation. Nat. Commun. 10, 5721 (2019).
16. Yin, H. H. et al. Dynamic reorganization of striatal circuits during the acquisition and consolidation of a skill. Nat. Neurosci. 12, 333¿41 (2009).
17. Bjerke, I. E. et al. Navigating the Murine Brain: Toward Best Practices for Determining and Documenting Neuroanatomical Locations in Experimental Studies. Front. Neuroanat. 12, 82 (2018).
18. Kupferschmidt, D. A., Juczewski, K., Cui, G., Johnson, K. A. & Lovinger, D. M. Parallel, but Dissociable, Processing in Discrete Corticostriatal Inputs Encodes Skill Learning. Neuron 96, 476-489.e5 (2017).
19. Jimenez, J. C. et al. Anxiety Cells in a Hippocampal-Hypothalamic Circuit. Neuron 97, 670-683.e6 (2018).
20. Gerfen, C. et al. D1 and D2 dopamine receptor-regulated gene expression of striatonigral and striatopallidal neurons. Science (80-. ). 250, 1429¿1432 (1990).
21. Shen, W., Flajolet, M., Greengard, P. & Surmeier, D. J. Dichotomous dopaminergic control of striatal synaptic plasticity. Science 321, 848¿51 (2008).
22. Kravitz, A. V et al. Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry. Nature 466, 622¿6 (2010).
23. Bergstrom, H. C. et al. Dorsolateral Striatum Engagement Interferes with Early Discrimination Learning. Cell Rep. 23, 2264¿2272 (2018).
24. Furman, O., Mendelsohn, A. & Dudai, Y. The episodic engram transformed: Time reduces retrieval-related brain activity but correlates it with memory accuracy. Learn. Mem. 19, 575¿87 (2012).
25. Kitamura, T. et al. Engrams and circuits crucial for systems consolidation of a memory. Science (80-. ). 356, 73¿78 (2017).
26. Del Ferraro, G. et al. Finding influential nodes for integration in brain networks using optimal percolation theory. Nat. Commun. 9, 2274 (2018).