Publications

In this note, we assess the predictive validity of stochastic dynamic causal modeling (sDCM) of functional magnetic resonance imaging (fMRI) data, in terms of its ability to explain changes in the frequency spectrum of concurrently acquired electroencephalography (EEG) signal. We first revisit the heuristic model proposed in Kilner et al. (2005), which suggests that fMRI activation is associated with a frequency modulation of the EEG signal (rather than an amplitude modulation within frequency bands). We propose a quantitative derivation of the underlying idea, based upon a neural field formulation of cortical activity. In brief, dense lateral connections induce a separation of time scales, whereby fast (and high spatial frequency) modes are enslaved by slow (low spatial frequency) modes. This slaving effect is such that the frequency spectrum of fast modes (which dominate EEG signals) is controlled by the amplitude of slow modes (which dominate fMRI signals). We then use conjoint empirical EEG-fMRI data-acquired in epilepsy patients-to demonstrate the electrophysiological underpinning of neural fluctuations inferred from sDCM for fMRI.

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. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Shyam Diwakar Google Shyam Diwakar Google Scholar Shyam Diwakar PubMed Shyam Diwakar Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. 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The effects of striatal dopamine (DA) on behavior have been widely investigated over the past decades, with ``phasic'' burst firings considered as the key expression of a reward prediction error responsible for reinforcement learning. Less well studied is tonic DA, where putative functions include the idea that it is a regulator of vigor, incentive salience, disposition to exert an effort and a modulator of approach strategies. We present a preliminary model combining tonic and phasic DA to show how different outflows triggered by either intrinsically or extrinsically motivating stimuli dynamically affect the basal ganglia by impacting on a selection process that this system performs on the inputs provided by the targeted cortex.The model, which has been tested on the simulated humanoid robot iCub in the interaction with a mechatronic board, shows the putative functions ascribed to DA emerging from the combination of a standard computational mechanism coupled to a differential sensitivity to the presence of DA across the striatum.

The mismatch negativity (MMN) evoked potential, a preattentive brain response to a discriminable change in auditory stimulation, is significantly reduced in psychosis. Glutamatergic theories of psychosis propose that hypofunction of NMDA receptors (on pyramidal cells and inhibitory interneurons) causes a loss of synaptic gain control. We measured changes in neuronal effective connectivity underlying the MMN using dynamic causal modeling (DCM), where the gain (excitability) of superficial pyramidal cells is explicitly parameterised. EEG data were obtained during a MMN task—for 24 patients with psychosis, 25 of their first-degree unaffected relatives, and 35 controls—and DCM was used to estimate the excitability (modeled as self-inhibition) of (source-specific) superficial pyramidal populations. The MMN sources, based on previous research, included primary and secondary auditory cortices, and the right inferior frontal gyrus. Both patients with psychosis and unaffected relatives (to a lesser degree) showed increased excitability in right inferior frontal gyrus across task conditions, compared to controls. Furthermore, in the same region, both patients and their relatives showed a reversal of the normal response to deviant stimuli; that is, a decrease in excitability in comparison to standard conditions. Our results suggest that psychosis and genetic risk for the illness are associated with both context-dependent (condition-specific) and context-independent abnormalities of the excitability of superficial pyramidal cell populations in the MMN paradigm. These abnormalities could relate to NMDA receptor hypofunction on both pyramidal cells and inhibitory interneurons, and appear to be linked to the genetic aetiology of the illness, thereby constituting potential endophenotypes for psychosis. Hum Brain Mapp 37:351–365, 2016. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Subthalamic nucleus deep brain stimulation is an effective treatment for advanced Parkinson's disease; however, its therapeutic mechanism is unclear. Previous modelling of functional MRI data has suggested that deep brain stimulation has modulatory effects on a number of basal ganglia pathways. This work uses an enhanced data collection protocol to collect rare functional MRI data in patients with subthalamic nucleus deep brain stimulation. Eleven patients with Parkinson's disease and subthalamic nucleus deep brain stimulation underwent functional MRI at rest and during a movement task; once with active deep brain stimulation, and once with deep brain stimulation switched off. Dynamic causal modelling and Bayesian model selection were first used to compare a series of plausible biophysical models of the cortico-basal ganglia circuit that could explain the functional MRI activity at rest in an attempt to reproduce and extend the findings from our previous work. General linear modelling of the movement task functional MRI data revealed deep brain stimulation-associated signal increases in the primary motor and cerebellar cortices. Given the significance of the cerebellum in voluntary movement, we then built a more complete model of the motor system by including cerebellar-basal ganglia interactions, and compared the modulatory effects deep brain stimulation had on different circuit components during the movement task and again using the resting state data. Consistent with previous results from our independent cohort, model comparison found that the rest data were best explained by deep brain stimulation-induced increased (effective) connectivity of the cortico-striatal, thalamo-cortical and direct pathway and reduced coupling of subthalamic nucleus afferent and efferent connections. No changes in cerebellar connectivity were identified at rest. In contrast, during the movement task, there was functional recruitment of subcortical-cerebellar pathways, which were additionally modulated by deep brain stimulation, as well as modulation of local (intrinsic) cortical and cerebellar circuits. This work provides in vivo evidence for the modulatory effects of subthalamic nucleus deep brain stimulation on effective connectivity within the cortico-basal ganglia loops at rest, as well as further modulations in the cortico-cerebellar motor system during voluntary movement. We propose that deep brain stimulation has both behaviour-independent effects on basal ganglia connectivity, as well as behaviour-dependent modulatory effects.

This tutorial provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity). This involves specifying a hierarchical model with two or more levels. At the first level, state space models (DCMs) are used to infer the effective connectivity that best explains a subject's neuroimaging timeseries (e.g. fMRI, MEG, EEG). Subject-specific connectivity parameters are then taken to the group level, where they are modelled using a General Linear Model (GLM) that partitions between-subject variability into designed effects and additive random effects. The ensuing (Bayesian) hierarchical model conveys both the estimated connection strengths and their uncertainty (i.e., posterior covariance) from the subject to the group level; enabling hypotheses to be tested about the commonalities and differences across subjects. This approach can also finesse parameter estimation at the subject level, by using the group-level parameters as empirical priors. We walk through this approach in detail, using data from a published fMRI experiment that characterised individual differences in hemispheric lateralization in a semantic processing task. The preliminary subject specific DCM analysis is covered in detail in a companion paper. This tutorial is accompanied by the example dataset and step-by-step instructions to reproduce the analyses.