Coupling Realistic Computational Neural Model and Human Experimental Data: the Cerebellar Role in Eye Blink Conditioning and its Alteration due to Transcranial Magnetic Stimulation
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1
NearLab; Department of Electronics, Information and Bioengineering; Politecnico di Milano, Italy
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2
University of Pavia, Department of Brain and Behavioral Sciences, Italy
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3
UCL, Institute of Neurology, United Kingdom
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4
C. Mondino National Neurological Institute, Brain Connectivity Center, Italy
The cerebellum has a fundamental role in motor control; it implements three fundamental operations: prediction, timing and learning of motor commands, through complex and distributed plasticity mechanisms [1]. However, how plasticity is engaged in dynamic processing during behavior is still unclear. We tackle this issue by fitting a realistic modeling reconstruction of the cerebellar microcircuit on an experimental dataset coming from human subjects, which have performed a cerebellar-driven associative sensorimotor paradigm, the Eye Blink Classical Conditioning task (EBCC). We developed a realistic Spiking cerebellar Neural Network (SNN), with plasticity at multiple sites (cortical and nuclear), embedded within a loop able to reproduce the EBCC. The SNN included 6480 Leaky Integrate and Fire neurons with different sets of parameters to represent specific neuronal types and organized to reproduce the multi-layered structure of the cerebellum [2][3]. The parameterization of the network plasticity mechanisms allows the model to specifically fit, by evolutionary algorithms, on the acquired dataset.
The experimental data comes from 36 subjects performing two sessions of an EBCC protocol, with a very short (5 minutes) washout period in between. Both sessions included an acquisition phase (repeated presentations of paired stimuli) and an extinction phase (presentation of only the unconditioned stimulus). During the washout pause, Transcranial Magnetic Stimulation (TMS) was applied on the cerebellum to alter circuit function and plasticity. Specifically, the subjects were blindly divided into three groups: 12 subjects (control group) received a sham TMS, 12 subjects were perturbed by inhibitory TMS applied on the right cerebellar hemisphere, and 12 subjects were perturbed by TMS on the left cerebellar hemisphere. The measured behavioral outcome was the number of generated Conditioned Responses (CR, i.e. anticipated blink), along all trials. In a previous work [4][5], a similar computational approach was applied on another EBCC dataset with a very long washout (1 week) after TMS delivery.
First of all, a set of properties typical of human behavior was well reproduced by our near-optimal models (one model family for each condition): rapid acquisition, consolidation, extinction, and fast reacquisition following extinction.
It was found out that TMS can dissociate EBCC extinction (related to the fast learning process) from consolidation (related to the slow learning process), probably by acting through a selective alteration of cerebellar plasticity. The model fittings to TMS data suggested that TMS shall mostly alter plasticity in the cerebellar cortex, i.e., in most superficial layers directly affected by the neurostimulation. That would affect the memory consolidation process occurring, along trials themselves, but also during the washout period by an interactive transfer between cortical and deeper nuclear sites. Indeed, the findings on the dataset with very short washout between the two EBCC sessions highlight how the reacquisition speed was decreased after the TMS administration, compared to the control group reacquisition. While, whether a week passed after the TMS, the response in the reacquisition phase of the TMS group was identical to the control group.
By ad-hoc simulations, we showed that the altered behavior in the fast reacquisition trials yielded to a reinforcement of the plasticity at the nuclear sites; they partially compensated the reduced learning, thorough neural states able to facilitate the CR generation. The nuclear sites worked on a slower time scale, hence their effect on behavioral responses was detectable only in late acquisition trials. The higher involvement of the deep nuclei in the CR generations explains the slower extinction phase in the TMS groups with respect to the control group: the learnt association is placed in a more conservative site, slower to be reversed.
Realistic computational models of neural circuits can be a powerful tool for simulating real data sets rather than formulating pure theoretical predictions, and for testing hypotheses that correlate specific local lesions to altered behavioral outcomes. Future work will have to consider extra cerebellar connections, more complex nonlinear dynamic properties of the neuron models and additive plasticity sites.
The current method may help developing new tools for medicine, by exploiting the bidirectional coupling between non-invasive computational and experimental worlds in order to verify new pathogenetic hypotheses and define appropriate corrective strategies, addressing the concepts of personalized medicine in neurorehabilitation and in the evaluation of treatment efficacy.
Figure 1. A) Experimental and modeling approaches. Left: schematic representation of the EBCC experimental setup. The subject is stimulated with appropriate combinations of CS (conditioned stimulus, tone) and US (unconditioned stimulus, electrical stimulation) organized in two sessions comprising acquisition and extinction phases; the CR (conditioned response, eye-blink) is detected by EMG on the orbicularis oculi muscle. Between the two sessions, two group of subjects receives TMS on the posterior lobules of the lateral cerebellum (right or left). The model is endowed with three plasticity sites (PF-PC, MF-DCN and PC-DCN), each bidirectional (LTP and LTD). B) The CR values obtained from the models are compared with those obtained from human subjects and used for optimal tuning of the model parameters through genetic algorithms. The optimal models are able to reproduce the real EBCC behaviour in each condition.
Acknowledgements
The work was supported by grants of the European Union (CEREBNET FP7-ITN238686, REALNET P7-ICT270434, Human Brain Project HBP-604102)
References
[1] D‘Angelo E., Antonietti A., Casali S., Casellato C., et al. Modelling the cerebellar microcircuit: new strategies for a long-standing issue. Frontiers in Cellular Neuroscience 2016; doi: 10.3389/fncel.2016.00176
[2] Casellato C., Antonietti A., Garrido J.A., Carrillo R.R., Luque N.R., Ros E., Pedrocchi A., D’Angelo E. Adaptive robotic control driven by a versatile spiking cerebellar network. PLoS ONE 2014, 9(11): e112265. doi:10.1371/journal.pone.0112265
[3] Antonietti A., Casellato C., Garrido J.A., Luque N.R., Naveros F., Ros E., D’Angelo E. and Pedrocchi A. Spiking Neural Network with Distributed Plasticity Reproduces Cerebellar Learning in Eye Blink Conditioning Paradigms. IEEE Transactions on Biomedical Engineering, 2016, 63: 210-219. doi: 10.1109/TBME.2015.2485301
[4] Antonietti A., Casellato C., D’Angelo E. and Pedrocchi A. Model-driven Analysis of Eyeblink Classical Conditioning Reveals the Underlying Structure of Cerebellar Plasticity and Neuronal Activity. IEEE Transactions on Neural Networks and Learning Systems 2016; doi: 10.1109/TNNLS.2016.2598190
[5] Monaco J., Casellato C., Koch G., D'Angelo E. Cerebellar theta burst stimulation dissociates memory components in eyeblink classical conditioning. European Journal of Neuroscience 2014 Nov;40(9):3363-3370. doi: 10.1111/ejn.12700
Keywords:
Cerebellum,
Distributed plasticity,
Eyeblink Classical Conditioning,
spiking network model,
Transcranial magnetic stimulation.,
Learning,
Model tuning
Conference:
The Cerebellum inside out: cells, circuits and functions
, ERICE (Trapani), Italy, 1 Dec - 5 Dec, 2016.
Presentation Type:
poster
Topic:
Integrative nuroscience and MRI
Citation:
Antonietti
A,
Casellato
C,
Monaco
J,
D‘Angelo
E and
Pedrocchi
A
(2019). Coupling Realistic Computational Neural Model and Human Experimental Data: the Cerebellar Role in Eye Blink Conditioning and its Alteration due to Transcranial Magnetic Stimulation.
Conference Abstract:
The Cerebellum inside out: cells, circuits and functions
.
doi: 10.3389/conf.fncel.2017.37.000014
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Received:
25 Nov 2016;
Published Online:
25 Jan 2019.
*
Correspondence:
PhD. Claudia Casellato, NearLab; Department of Electronics, Information and Bioengineering; Politecnico di Milano, Milan, Italy, claudia.casellato@unipv.it