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Computational Dissection of the Basal Ganglia: functions and dynamics in health and disease
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The basal ganglia (BG), a group of nuclei in the forebrain of all vertebrates, are important for behavioral selection. BG receive contextual input from most cortical areas as well as from parts of the thalamus, and provide output to brain systems that are involved in the generation of behavior, i.e. the thalamus and the brain stem. Many neurological disorders such as Parkinson’s disease and Huntington’s disease, and several neuropsychiatric disorders, are related to BG. Studying BG enhances the understanding as to how behaviors are learned and modified. These insights can be used to improve treatments for several BG disorders, and to develop brain-inspired algorithms for solving special information-processing tasks.

 

In this thesis modeling and simulations have been used to investigate function and dynamics of BG. In the first project a model was developed to explore a new hypothesis about how conflicts between competing actions are resolved in BG. It was proposed that a subsystem named the arbitration system, composed of the subthalamic nucleus (STN), pedunculopontine nucleus (PPN), the brain stem, central medial nucleus of thalamus (CM), globus pallidus interna (GPi) and globus pallidus externa (GPe), resolve basic conflicts between alternative motor programs. On top of the arbitration system there is a second subsystem named the extension systems, which involves the direct and indirect pathway of the striatum. This system can modify the output of the arbitration system to bias action selection towards outcomes dependent on contextual information.

 

In the second project a model framework was developed in two steps, with the aim to gain a deeper understanding of how synapse dynamics, connectivity and neural excitability in the BG relate to function and dynamics in health and disease. First a spiking model of STN, GPe and substantia nigra pars reticulata (SNr), with emulated inputs from striatal medium spiny neurons (MSNs) and the cortex, was built and used to study how synaptic short-term plasticity affected action selection signaling in the direct-, hyperdirect- and indirect pathways. It was found that the functional consequences of facilitatory synapses onto SNr neurons are substantial, and only a few presynaptic MSNs can suppress postsynaptic SNr neurons. The model also predicted that STN signaling in SNr is mainly effective in a transient manner. The model was later extended with a striatal network, containing MSNs and fast spiking interneurons (FSNs), and modified to represent GPe with two types of neurons: type I, which projects downstream in BG, and type A, which have a back-projection to striatum. Furthermore, dopamine depletion dependent modification of connectivity and neuron excitability were added to the model. Using this extended BG model, it was found that FSNs and GPe type A neurons controlled excitability of striatal neurons during low cortical drive, whereas MSN collaterals have a greater impact at higher cortical drive. The indirect pathway increased the dynamical range over which two possible action commands were competing, while removing intrastriatal inhibition deteriorated action selection capabilities. Dopamine-depletion induced effects on spike synchronization and oscillations in the BG were also investigated here.

 

For the final project, an abstract spiking BG model which included a hypothesized control of the reward signaling dopamine system was developed. This model incorporated dopamine-dependent synaptic plasticity, and used a plasticity rule based on probabilistic inference called Bayesian Confidence Propagation Neural Network (BCPNN). In this paradigm synaptic connections were controlled by gathering statistics about neural input and output activity. Synaptic weights were inferred using Bayes’ rule to estimate the confidence of future observations from the input. The model exhibits successful performance, measured as a moving average of correct selected actions, in a multiple-choice learning task with a changeable reward schedule. Furthermore, the model predicts a decreased performance upon dopamine lesioning, and suggests that removing the indirect pathway may disrupt learning in profound ways.

Place, publisher, year, edition, pages
Stockholm: Kungliga Tekniska högskolan, 2016. , 47 p.
Series
TRITA-CSC-A, ISSN 1653-5723 ; 1653-5723
National Category
Neurosciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-185160ISBN: 978-91-7595-925-2 (print)OAI: oai:DiVA.org:kth-185160DiVA: diva2:918750
Public defence
2016-05-04, FS, Lindstedtsvägen 26, KTH Campus, Stockholm, 13:00
Opponent
Supervisors
Note

QC 20160412

Available from: 2016-04-12 Created: 2016-04-11 Last updated: 2016-04-12Bibliographically approved
List of papers
1. Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking 1 Model with Reward Dependent Plasticity
Open this publication in new window or tab >>Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking 1 Model with Reward Dependent Plasticity
Show others...
(English)Manuscript (preprint) (Other academic)
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-185159 (URN)
Note

QS 2016

Available from: 2016-04-11 Created: 2016-04-11 Last updated: 2017-04-19Bibliographically approved
2. Signal enhancement in the output stage of the basal ganglia by synaptic short-term plasticity in the direct, indirect, and hyperdirect pathways
Open this publication in new window or tab >>Signal enhancement in the output stage of the basal ganglia by synaptic short-term plasticity in the direct, indirect, and hyperdirect pathways
2013 (English)In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 7, UNSP 76- p.Article in journal (Refereed) Published
Abstract [en]

Many of the synapses in the basal ganglia display short-term plasticity. Still, computational models have not yet been used to investigate how this affects signaling. Here we use a model of the basal ganglia network, constrained by available data, to quantitatively investigate how synaptic short-term plasticity affects the substantia nigra reticulata (SNr), the basal ganglia output nucleus. We find that SNr becomes particularly responsive to the characteristic burst-like activity seen in both direct and indirect pathway striatal medium spiny neurons (MSN). As expected by the standard model, direct pathway MSNs are responsible for decreasing the activity in SNr. In particular, our simulations indicate that bursting in only a few percent of the direct pathway MSNs is sufficient for completely inhibiting SNr neuron activity. The standard model also suggests that SNr activity in the indirect pathway is controlled by MSNs disinhibiting the subthalamic nucleus (STN) via the globus pallidus externa (GPe). Our model rather indicates that SNr activity is controlled by the direct GPe-SNr projections. This is partly because GPe strongly inhibits SNr but also due to depressing STN-SNr synapses. Furthermore, depressing GPe-SNr synapses allow the system to become sensitive to irregularly firing GPe subpopulations, as seen in dopamine depleted conditions, even when the GPe mean firing rate does not change. Similar to the direct pathway, simulations indicate that only a few percent of bursting indirect pathway MSNs can significantly increase the activity in SNr. Finally, the model predicts depressing STN-SNr synapses, since such an assumption explains experiments showing that a brief transient activation of the hyperdirect pathway generates a tri-phasic response in SNr, while a sustained STN activation has minor effects. This can be explained if STN-SNr synapses are depressing such that their effects are counteracted by the (known) depressing GPe-SNr inputs.

Place, publisher, year, edition, pages
Frontiers Research Foundation, 2013
Keyword
substantia nigra pars reticulata, short-term plasticity, basal ganglia, network model, subthalamic nucleus, globus pallidus, facilitation, depression
National Category
Neurosciences Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-125577 (URN)10.3389/fncom.2013.00076 (DOI)000320851300001 ()2-s2.0-84879713273 (Scopus ID)
Funder
Swedish Research Council
Note

QC 20130809

Available from: 2013-08-09 Created: 2013-08-09 Last updated: 2017-12-06Bibliographically approved
3. Untangling basal ganglia network dynamics and function: role of dopamine depletion and inhibition investigated in a spiking network model
Open this publication in new window or tab >>Untangling basal ganglia network dynamics and function: role of dopamine depletion and inhibition investigated in a spiking network model
(English)Manuscript (preprint) (Other academic)
National Category
Neurosciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kth:diva-185157 (URN)
Note

QC 20160412

Available from: 2016-04-11 Created: 2016-04-11 Last updated: 2016-04-12Bibliographically approved
4. The arbitration-extension hypothesis: A hierarchical interpretation of the functional organization of the basal ganglia
Open this publication in new window or tab >>The arbitration-extension hypothesis: A hierarchical interpretation of the functional organization of the basal ganglia
2011 (English)In: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 5, 13- p.Article in journal (Refereed) Published
Abstract [en]

Based on known anatomy and physiology, we present a hypothesis where the basal ganglia motor loop is hierarchically organized in two main subsystems: the arbitration system and the extension system. The arbitration system, comprised of the subthalamic nucleus, globus pallidus, and pedunculopontine nucleus, serves the role of selecting one out of several candidate actions as they are ascending from various brain stem motor regions and aggregated in the centromedian thalamus or descending from the extension system or from the cerebral cortex. This system is an action-input/action-output system whose winner-take-all mechanism finds the strongest response among several candidates to execute. This decision is communicated back to the brain stem by facilitating the desired action via cholinergic/glutamatergic projections and suppressing conflicting alternatives via GABAergic connections. The extension system, comprised of the striatum and, again, globus pallidus, can extend the repertoire of responses by learning to associate novel complex states to certain actions. This system is a state-input/action-output system, whose organization enables it to encode arbitrarily complex Boolean logic rules using striatal neurons that only fire given specific constellations of inputs (Boolean AND) and pallidal neurons that are silenced by any striatal input (Boolean OR). We demonstrate the capabilities of this hierarchical system by a computational model where a simulated generic "animal" interacts with an environment by selecting direction of movement based on combinations of sensory stimuli, some being appetitive, others aversive or neutral. While the arbitration system can autonomously handle conflicting actions proposed by brain stem motor nuclei, the extension system is required to execute learned actions not suggested by external motor centers. Being precise in the functional role of each component of the system, this hypothesis generates several readily testable predictions.

Keyword
Action selection, Basal ganglia, Boolean logic, Brain stem, Centromedian parafascicular thalamus, Motor synergies, Pedunculopontine nucleus, Winner-take-all
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:kth:diva-39201 (URN)10.3389/fnsys.2011.00013 (DOI)21441994 (PubMedID)2-s2.0-84855971284 (Scopus ID)
Note
QC 20111004Available from: 2011-09-08 Created: 2011-09-08 Last updated: 2017-12-08Bibliographically approved

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