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Interactions in the Striatal Network with Different Oscillation Frequencies
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg Germany.
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg Germany.ORCID iD: 0000-0002-8044-9195
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.ORCID iD: 0000-0002-0550-0739
2017 (English)In: Artificial Neural Networks and Machine Learning – ICANN. Lecture Notes in Computer Science, Springer, 2017, Vol. 10613, p. 129-136Conference paper, Published paper (Refereed)
Abstract [en]

Simultaneous oscillations in different frequency bands are implicated in the striatum, and understanding their interactions will bring us one step closer to restoring the spectral characteristics of striatal activity that correspond to the healthy state. We constructed a computational model of the striatum in order to investigate how different, simultaneously present, and externally induced oscillations propagate through striatal circuitry and which stimulation parameters have a significant contribution. Our results show that features of these oscillations and their interactions can be influenced via amplitude, input frequencies, and the phase offset between different external inputs. Our findings provide further untangling of the oscillatory activity that can be seen within the striatal network.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 10613, p. 129-136
Keywords [en]
Corticostriatal network, Network oscillations, GABAergic transmission, Basal ganglia, Cortex
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-217110DOI: 10.1007/978-3-319-68600-4_16Scopus ID: 2-s2.0-85034229782ISBN: 978-3-319-68599-1 (print)OAI: oai:DiVA.org:kth-217110DiVA, id: diva2:1153754
Conference
Artificial Neural Networks and Machine Learning – ICANN 2017.
Note

QC 20171101

Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2018-02-09Bibliographically approved
In thesis
1. Untangling Cortico-Striatal Circuitry and its Role in Health and Disease - A computational investigation
Open this publication in new window or tab >>Untangling Cortico-Striatal Circuitry and its Role in Health and Disease - A computational investigation
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The basal ganglia (BG) play a critical role in a variety of regular motor and cognitive functions. Many brain diseases, such as Parkinson’s diseases, Huntington’s disease and dyskinesia, are directly related to malfunctions of the BG nuclei. One of those nuclei, the input nucleus called the striatum, is heavily connected to the cortex and receives afferents from nearly all cortical areas. The striatum is a recurrent inhibitory network that contains several distinct cell types. About 95% of neurons in the striatum are medium spiny neurons (MSNs) that form the only output from the striatum. Two of the most examined sources of GABAergic inhibition into MSNs are the feedback inhibition (FB) from the axon collaterals of the MSNs themselves, and the feedforward inhibition (FF) via the small population (1-2% of striatal neurons) of fast spiking interneurons (FSIs). The cortex sends direct projections to the striatum, while the striatum can affect the cortex only indirectly through other BG nuclei and the thalamus. Understanding how different components of the striatal network interact with each other and influence the striatal response to cortical inputs has crucial importance for clarifying the overall functions and dysfunctions of the BG.

    In this thesis I have employed advanced experimental data analysis techniques as well as computational modelling, to study the complex nature of cortico-striatal interactions. I found that for pathological states, such as Parkinson’s disease and L-DOPA-induced dyskinesia, effective connectivity is bidirectional with an accent on the striatal influence on the cortex. Interestingly, in the case of L-DOPA-induced dyskinesia, there was a high increase in effective connectivity at ~80 Hz and the results also showed a large relative decrease in the modulation of the local field potential amplitude (recorded in the primary motor cortex and sensorimotor striatum in awake, freely behaving, 6-OHDA lesioned hemi-parkinsonian rats) at ~80 Hz by the phase of low frequency oscillations. These results suggest a lack of coupling between the low frequency activity of a presumably larger neuronal population and the synchronized activity of a presumably smaller group of neurons active at 80 Hz.

    Next, I used a spiking neuron network model of the striatum to isolate the mechanisms underlying the transmission of cortical oscillations to the MSN population. I showed that FSIs play a crucial role in efficient propagation of cortical oscillations to the MSNs that did not receive direct cortical oscillations. Further, I have identified multiple factors such as the number of activated neurons, ongoing activity, connectivity, and synchronicity of inputs that influenced the transfer of oscillations by modifying the levels of FB and FF inhibitions. Overall, these findings reveal a new role of FSIs in modulating the transfer of information from the cortex to striatum. By modulating the activity and properties of the FSIs, striatal oscillations can be controlled very efficiently. Finally, I explored the interactions in the striatal network with different oscillation frequencies and showed that the features of those oscillations, such as amplitude and frequency fluctuations, can be influenced by a change in the input intensities into MSNs and FSIs and that these fluctuations are also highly dependent on the selected frequencies in addition to the phase offset between different cortical inputs.

    Lastly, I investigated how the striatum responds to cortical neuronal avalanches. Recordings in the striatum revealed that striatal activity was also characterized by spatiotemporal clusters that followed a power law distribution albeit, with significantly steeper slope. In this study, an abstract computational model was developed to elucidate the influence of intrastriatal inhibition and cortico-striatal interplay as important factors to understand the experimental findings. I showed that one particularly high activation threshold of striatal nodes can reproduce a power law-like distribution with a coefficient similar to the one found experimentally. By changing the ratio of excitation and inhibition in the cortical model, I saw that increased activity in the cortex strongly influenced striatal dynamics, which was reflected in a less negative slope of cluster size distributions in the striatum.  Finally, when inhibition was added to the model, cluster size distributions had a prominently earlier deviation from the power law distribution compared to the case when inhibition was not present. 

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 88
Series
TRITA-EECS-AVL ; 2018:9
Keywords
cortico-striatal circuits, levodopa-induced dyskinesia, Parkinson’s disease, effective connectivity, cross-frequency coupling, corticostriatal network, network oscillations, GABAergic transmission. basal ganglia, striatum, cortex, fast spiking interneurons, medium spiny neurons, neuronal avalanches
National Category
Neurosciences
Identifiers
urn:nbn:se:kth:diva-222467 (URN)978-91-7729-676-8 (ISBN)
Public defence
2018-03-05, F3, Lindstedtsvägen 26, KTH Campus, Stockholm, 13:15 (English)
Opponent
Supervisors
Note

QC 20180209

Available from: 2018-02-09 Created: 2018-02-09 Last updated: 2018-06-12Bibliographically approved

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Publisher's full textScopushttps://link.springer.com/chapter/10.1007/978-3-319-68600-4_16

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Kumar, ArvindHellgren Kotaleski, Jeanette

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