P058 Homeostatic memory engrams in mesoscopic structural connectomes
Fabian Czappa(1, *)
Marvin Kaster (1)
Marcus Kaiser (2)
Markus Butz-Ostendorf (1,3)
Felix Wolf (1)
(1) Laboratory for Parallel Programming, Department of Computer
Science, Technical University of Darmstadt, Hochschulstraße. 10,
Darmstadt, 64285, Hesse, Germany
(2) Translational Neuroimaging, Faculty of Medicine & Health Sciences,
University of Nottingham, NG7 2RD, Nottingham, United Kingdom
(3) Translational Medicine and Clinical Pharmacology, Boehringer Ingelheim Pharma GmbH & Co. KG Birkendorfer Straße 65 88397 Biberach/ Riss, Baden-Wuerttemberg, Germany
(*)fabian.czappa@tu-darmstadt.de
Introduction
Memory engrams are defined as physical traces of memory [1]. However, their actual representation in the brain is still unknown. To shed light on the underlying mechanisms, we simulate the formation of memories in healthy human subjects based on connectomes extracted from their DT-MRI brain scans. To prepare the networks for learning, we first bring them into a state of homeostatic equilibrium, leaving their topology largely intact [2]. Once this homestatization is complete, we perform a memory experiment by stimulating groups of neurons and observing memory formation as the network changes its structure to maintain equilibrium [3]. After our "thought experiment", we can precisely locate the memory engram in the connectome.
Methods
We use the Model of Structural Plasticity (MSP) [4], which grows and retracts synaptic elements based on a homeostatic rule. When a neuron searches for a partner, it chooses one based on the number of free synaptic elements and a distance-dependent probability kernel. We augment the original kernel at longer distances, giving preference to the vicinity of established synapses. After homeostatizing the structural connectome with the augmented MSP, we select a group of concept cells (CC) from the middle temporal lobe and two groups of neurons C1 and C2 scattered outside this region. We then perform a Hebbian learning experiment, associating CC with C1. We perform our experiments using data from n=7 healthy human subjects [5].
Results
Homeostatizing the connectome brings the node-degree distribution from a power-law to a normal distribution, yet we keep many distinguishing features of the network. The (geometric) axon-length histogram, the small-worldness, and the assortativity – among others – are comparable between the scanned connectome and the homeostatized one. Furthermore, we see that we form a memory engram after picking neurons for CC, C1, and C2 and stimulating CC and C1 together. Testing with n=7 high-resolution connectomes, we see that the memory engram is located in specific brain areas such as the inferior parietal lobule (7 times), the superior temporal lobe (7 times), but only sometimes in the fusiform gyrus (4 times); see Figure 1 for details.
Discussion
For the first time, it is now possible to conduct brain simulations based on individual brain scans without parameter fitting. Using MSP-generated avatar connectomes of healthy subjects that were topologically similar to the original tractograms, our method ensured the functioning of model neurons in a physiological regime, which was the necessary precondition for the learning experiments. The proposed approach is the starting point of various testable and personalized brain simulations, from designing novel stimulation protocols for transcranial stimulations (TMS, tDCS) to innovative AD models exploring the causal relationship between homeostatic imbalance, network decay, and cognitive decline.
Figure 1. Caption: We evaluate our model on n=7 high-resolution structural connectomes of healthy adults. Shown here are the number of connectomes in which US created an engram within the C1/C2 group within the area. Our criterion is that the firing frequency of the readout neuron is larger than three times the standard deviation of its usual firing frequency.
AcknowledgementsThe authors thank the German Federal Ministry of Education and Research and the Hessian Ministry of Science and Research, Art and Culture for supporting this work as part of the NHR funding. Moreover, the authors acknowledge the computing time provided to them on the HPC Lichtenberg II at TU Darmstadt, funded by the German Federal Ministry of Education and Research and the State of Hesse.
References
[1]https://doi.org/10.1016/s0361-9230(99)00182-3
[2] https://doi.org/10.1016/j.neuroimage.2009.10.003
[3]https://doi.org/10.3389/fninf.2024.1323203
[4]https://doi.org/10.1371/journal.pcbi.1003259
[5] https://doi.org/10.1002/hbm.25464
