Event Abstract

Back to Event

Computational Neurosciences of Cerebellar Circuit Disorders

Shyam Diwakar1
  • 1 Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham (Amrita University), India

Spinocerebellar ataxias, Parkinson’s disease, Alzheimer’s dementia and other neurological disorders are associated with dysfunction attributed to various brain regions, including the cerebellum. Neuronal dynamics of the cerebellum correlate with learning and memory processes, via interactions at the synaptic levels forming neuronal microcircuits. Brain regions, including the cerebellum, have been known to include representations of internal models, transferring relevant information via their inputs and outputs [1]. The cerebellum, also known as the little brain, previously known for its role in motor coordination and timing [2], is now being implicated in autism [3], ataxias [4], dyskinesia [5], Alzheimer’s disease [6] and Parkinson’s disease [7]. As part of this study, we will discuss perspectives of cerebellum function as well as its interaction with basal ganglia and thalamo-cortical-thalamic circuitry. Computational models of individual circuits within the cerebellum allow validating and predicting behavioral functions and disease conditions observed during neurological disorders. The study will also look into multiple levels of analysis as this is important for determining physiological function and upstream and downstream roles during disease states and dysfunction. A modeling study of cerebellar function [8] during impaired motor control involved interlinking ion channel mutations to function at cellular and circuit level computations [9]. A study had looked into sodium channel excitability disruptions caused by fibroblast growth factor homologous factor mutations where an ataxia-like condition were observed in adult Wistar rats [10]. Sodium excitability has also been noted and mathematical modeling show spike suppression roles in juvenile prion protein knock-out mice with impaired motor control [9]. Epileptic seizure-like symptoms observed in mutant animals’ granule neurons suggest that sparse and asynchronous neuronal activity evolves into a single hyper-synchronous cluster with elevated spiking rates at seizure initiation [10]. In another study, blocking NMDA receptors in granule neurons showed reduced excitation. A selective blocking of NMDA receptors is seen during NR2A/NR2B mutations. Such simulations implicate a decreased number of spikes as seen via a change in N2A amplitude compared to controls in the generated local field response. A similar selective loss of neural activity in thalamo-cortical circuitry had resulted in glaucoma in human subjects [11]. Computational neuroscience of cerebellar [12] and cortical circuits [13] allowed reconstructing local field potentials[14] and fMRI Blood Oxygen-Level Dependent (BOLD) [15] signals via approximations of intercellular [16] and extracellular spiking activity [17]. We were also able to model local field potentials, cortical EEG and activity-dependent fMRI BOLD signals to correlate neural activity and dysfunction to population behavior.

Acknowledgements

This work derives direction and ideas from the Chancellor of Amrita University, Sri Mata Amritanandamayi Devi. This work was partially funded by Grants SR/CSI/49/2010, SR/CSI/60/2011, SR/CSRI/60/2013, SR/CSRI/61/2014 and Indo-Italy POC 2012-2013 from DST and BT/PR5142/MED/30/764/2012 from DBT, Government of India and by Embracing The World.

References


1. D’Angelo, E., Solinas, S., Garrido, J., et al. (2013) Realistic modeling of neurons and networks: towards brain simulation. Funct. Neurol., 28 (3), 153–66.
2. D’Angelo, E., and Zeeuw, C.I. De (2008) Timing and plasticity in the cerebellum: focus on the granular layer. Trends Neurosci., 32 (1), 30–40.
3. Fatemi, S.H., Halt, A.R., Realmuto, G., et al. (2002) Purkinje cell size is reduced in cerebellum of patients with autism. Cell. Mol. Neurobiol., 22 (2), 171–175.
4. Tempia, F., Hoxha, E., Negro, G., et al. (2015) Parallel fiber to Purkinje cell synaptic impairment in a mouse model of spinocerebellar ataxia type 27. Front. Cell. Neurosci., 9 (June), 1–10.
5. Narabayashi, H., Maeda, T., and Yokochi, F. (1987) Long-term follow-up study of nucleus ventralis intermedius and ventrolateralis thalamotomy using a microelectrode technique in parkinsonism. Appl. Neurophysiol., 50 (1–6), 330–7.
6. Renoux, A.J., Carducci, N.M., Ahmady, A.A., and Todd, P.K. (2014) Fragile X mental retardation protein expression in Alzheimer’s disease. Front. Genet., 5, 360.
7. Wu, T., and Hallett, M. (2013) The cerebellum in Parkinson’s disease. Brain, 136 (Pt 3), 696–709.
8. Eccles, J.C., Ito, M., Szentagothai, J., and Szentágothai, J. (1967) The Cerebellum as a Neuronal Machine, Springer-Verlag, Berlin, Heidelberg.
9. Prestori, F., Rossi, P., Bearzatto, B., et al. (2008) Altered Neuron Excitability and Synaptic Plasticity in the Cerebellar Granular Layer of Juvenile Prion Protein Knock-Out Mice with Impaired Motor Control. J. Neurosci., 28 (28), 7091–7103.
10. Goldfarb, M., Schoorlemmer, J., Williams, A., et al. (2007) Fibroblast growth factor homologous factors control neuronal excitability through modulation of voltage-gated sodium channels. Neuron, 55 (3), 449–463.
11. Yücel, Y.H., Zhang, Q., Weinreb, R.N., et al. (2001) Atrophy of Relay Neurons in Magno- and Parvocellular Layers in the Lateral Geniculate Nucleus in Experimental Glaucoma. Invest. Ophthalmol. Vis. Sci., 42 (13), 3216–3222.
12. Solinas, S., Nieus, T., and D’Angelo, E. (2010) A realistic large-scale model of the cerebellum granular layer predicts circuit spatio-temporal filtering properties. Front. Cell. Neurosci., 4, 12.
13. Einevoll, G.T., Kayser, C., Logothetis, N.K., and Panzeri, S. (2013) Modelling and analysis of local field potentials for studying the function of cortical circuits. Nat. Rev. Neurosci., 14 (11), 770–85.
14. Diwakar, S., Lombardo, P., Solinas, S., et al. (2011) Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD control. PLoS One, 6 (7), e21928.
15. Logothetis, N.K. (2002) The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal. (August), 1003–1037.
16. Reimann, M.W., Anastassiou, C.A., Perin, R., et al. (2013) A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron, 79 (2), 375–90.
17. Bédard, C., Rodrigues, S., Roy, N., et al. (2010) Evidence for frequency-dependent extracellular impedance from the transfer function between extracellular and intracellular potentials Intracellular-LFP transfer function. 389–403.

Keywords: Cerebellum, computational neuroscience, neurological disorders, neuronal circuits, Neurons

Conference: The Cerebellum inside out: cells, circuits and functions , ERICE (Trapani), Italy, 1 Dec - 5 Dec, 2016.

Presentation Type: poster

Topic: Cellular & Molecular Neuroscience

Citation: Diwakar S (2019). Computational Neurosciences of Cerebellar Circuit Disorders. Conference Abstract: The Cerebellum inside out: cells, circuits and functions . doi: 10.3389/conf.fncel.2017.37.00006

Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.

The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.

Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.

For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.

Received: 28 Nov 2016; Published Online: 25 Jan 2019.