Abstract
To understand brain activity relating neurons to circuits to learning and behavior, we explored a bottom-up computational reconstruction of population signals arising from cerebellum granular layer. As a first implementation, using bio-realistic computational models of cerebellum granule cell, in vivo spike train patterns were computed and then translated into functional Magnetic Resonance Imaging, Blood Oxygen-Level Dependent (BOLD) signals. The BOLD response was generated from averaged activity arising from center-surround organization modeled by using excitatory-inhibitory ratios related to experimental data. The averaged responses were converted to BOLD signals using the balloon and modified Windkessel models. Although both models generated BOLD responses corresponding to neural activity, the temporal mismatch was attributed to the response by the delayed compliance parameter in the Windkessel model. The modeling suggests that experimental variability observed in the cerebellar micro-zones could be related to compliance chances, activation patterns and number of neurons. Although detailed neuro-vasculature information was not modeled, the advantage in this methodology is that cerebellar cortex may allow seemingly linear transformations of underlying spiking that could be then used to validate network reconstructions.
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References
Buxton RB, Wong EC, Frank LR (1998) Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magn Reson Med 39(6):855–864
Ekstrom A (2010) How and when the fMRI BOLD signal relates to underlying neural activity: the danger in dissociation. Brain Res Rev 62(2):233–244
Shen Q, Ren H, Duong TQ (2008) CBF, BOLD, CBV, and CMRO2 fMRI signal temporal dynamics at 500-msec resolution. J Magn Reson Imaging 27(3):599–606
Nutakki C, Nair A, Medini C, Nair M, Nair B, Diwakar S (2016) Computational reconstruction of fMRI-BOLD from neural activity. In: 2016 International conference on advances in computing, communications and informatics (ICACCI), pp. 922–926
Kwong KK et al (1992) Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci 89(12):5675–5679
Buxton RB (2001) The elusive initial dip. Neuroimage 13(6):953–958
Boxerman JL et al (1995) The intravascular contribution to fMRI signal change: Monte Carlo modeling and diffusion-weighted studies in vivo. Magn Reson Med 34(1):4–10
Buxton RB, Frank LR (1997) A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J Cereb Blood Flow Metab 17(1):64–72
Ogawa S, Menon RS, Kim S-G, Ugurbil K (1998) On the characteristics of functional magnetic resonance imaging of the brain. Annu Rev Biophys Biomol Struct 27(1):447–474
Koziol LF et al (2014) Consensus paper: the cerebellum’s role in movement and cognition. Cerebellum 13(1):151–177
D’Angelo E, De Zeeuw CI (2008) Timing and plasticity in the cerebellum: focus on the granular layer. Trends Neurosci 32(1):30–40
Mauk MD, Medina JF, Nores WL, Ohyama T (2000) Cerebellar function: coordination, learning or timing? Curr Biol 14:522–525
Ivry RB, Spencer RM, Zelaznik HN, Diedrichsen J (2002) The cerebellum and event timing. Ann N Y Acad Sci 978:302–317
Chen S, Augustine GJ, Chadderton P (2016) The cerebellum linearly encodes whisker position during voluntary movement. Elife 5(JANUARY2016):1–16
Proville RD et al (2014) Cerebellum involvement in cortical sensorimotor circuits for the control of voluntary movements. Nat Neurosci 17(9):1233–1239
Rajendran A, Nutakki C, Sasidharakurup H, Bodda S, Nair B, Diwakar S (2017) Cerebellum in neurological disorders: a review on the role of inter-connected neural circuits. J Neurol Stroke 6(2):1–4
Rinaldo L, Hansel C (2010) Ataxias and cerebellar dysfunction: involvement of synaptic plasticity deficits? Funct Neurol 25(3):135–139
Castellazzi G et al (2014) A comprehensive assessment of resting state networks: bidirectional modification of functional integrity in cerebro-cerebellar networks in dementia. Front Neurosci 8:223
Yu H, Sternad D, Corcos DM, Vaillancourt DE (2007) Role of hyperactive cerebellum and motor cortex in Parkinson’s disease. Neuroimage 35(1):222–233
Schmahmann JD (1998) Dysmetria of thought: clinical consequences of cerebellar dysfunction on cognition and affect. Trends Cogn Sci 2(9):362–371
Diwakar S, Magistretti J, Goldfarb M, Naldi G, D’Angelo E (2009) Axonal Na + channels ensure fast spike activation and back-propagation in cerebellar granule cells. J Neurophysiol 101(2):519–532
Medini C, Vijayan A, D’Angelo E, Nair B, Diwakar S (2014) Computationally efficient bio-realistic reconstructions of cerebellar neuron spiking patterns. In: International conference on interdisciplinary advances in applied computing, Amrita University, Coimbatore, India
Howarth C, Peppiatt-Wildman CM, Attwell D (2010) The energy use associated with neural computation in the cerebellum. J Cereb Blood Flow Metab 30(2):403–414
Snyder SH (1992) Nitric oxide: first in a new class of neurotransmitters. Science 257(5069):494–496
Garthwaite J, Garthwaite G (1987) Cellular origins of cyclic GMP responses to excitatory amino acid receptor agonists in rat cerebellum in vitro. J Neurochem 48(1):29–39
Garthwaite J, Garthwaite G, Palmer RMJ, Moncada S (1989) NMDA receptor activation induces nitric oxide synthesis from arginine in rat brain slices. Eur J Pharmacol Mol Pharmacol 172(4–5):413–416
Mapelli L, Gagliano G, Soda T, Laforenza U, Moccia F, D’Angelo EU (2017) Granular layer neurons control cerebellar neurovascular coupling through an NMDA receptor/NO-dependent system. J Neurosci 37(5):1340–1351
K. J. Friston, A. Mechelli, R. Turner, and C. J. Price (2000) Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. NeuroImage, [Online]. http://www.ncbi.nlm.nih.gov/pubmed/10988040. Accessed 07 Mar 2013
Kong Y et al (2004) A model of the dynamic relationship between blood flow and volume changes during brain activation. J Cereb Blood Flow Metab 24(12):1382–1392
Stephan KE, Penny WD, Moran RJ, den Ouden HEM, Daunizeau J, Friston KJ (2010) Ten simple rules for dynamic causal modeling. Neuroimage 49(4):3099–3109
Friston KJ, Harrison L, Penny W (2003) Dynamic causal modelling. Neuroimage 19(4):1273–1302
Friston K (2009) Causal modelling and brain connectivity in functional magnetic resonance imaging. PLoS Biol 7(2):e1000033
Marreiros AC, Kiebel SJ, Friston KJ (2008) Dynamic causal modelling for fMRI: a two-state model. Neuroimage 39(1):269–278
D’Angelo E, De Zeeuw CI (2009) Timing and plasticity in the cerebellum: focus on the granular layer. Trends Neurosci 32:30–40
Solinas S, Nieus T, 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
Medini C, Nair B, D’Angelo E, Naldi G, Diwakar S (2012) Modeling spike-train processing in the cerebellum granular layer and changes in plasticity reveal single neuron effects in neural ensembles. Comput Intell Neurosci 2012:359529
Diwakar S, Lombardo P, Solinas S, Naldi G, D’Angelo E (2011) Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD control. PLoS One 6(7):e21928
D’angelo E et al (2011) The cerebellar network: from structure to function and dynamics. Brain Res 66(1–2):1–11
Medini C, Vijayan A, D’Angelo E, Nair B, Diwakar S (2014) Computationally efficient biorealistic reconstructions of cerebellar neuron spiking patterns. In: International Conference on Interdisciplinary Advances in Applied Computing—ICONIAAC’14, pp. 1–6
Acknowledgements
This work derives direction and ideas from the Chancellor of Amrita Vishwa Vidyapeetham, Sri Mata Amritanandamayi Devi. This work was supported by Visvesvaraya Young Faculty Research Fellowship from Digital India Corporation, Ministry of Electronics and IT, Government of India and partially by grant DST/CSRI/2017/31 from the Department of Science and Technology, Government of India and by Embracing The World.
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Nutakki, C., Radhakrishnan, S., Nair, B. et al. Modeling fMRI BOLD signals and temporal mismatches in the cerebellar cortex. CSIT 7, 191–198 (2019). https://doi.org/10.1007/s40012-019-00229-8
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DOI: https://doi.org/10.1007/s40012-019-00229-8