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  • 1.
    Belic, Jovana
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Untangling Cortico-Striatal Circuitry and its Role in Health and Disease - A computational investigation2018Doctoral 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. 

  • 2.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Freiburg, Germany; Imperial College London.
    Faisal, Aldo
    Imperial College London.
    Bayesian approach to handle missing limbs in Neuroprosthetics2014Conference paper (Refereed)
    Abstract [en]

    Motor synergies have been supposed to simplify motor control [1]-[5]. In order to test it, we exploit the correlations of our hand's joints to discover some underlying simplicity in a complex stream of behavioral actions. Instead of averaging variability out, we take the view that the structure of variability may contain valuable information about the task being performed. Therefore, we asked 7 subjects to interact in 17 daily-life situations and quantified behavior in principled manner using cyber glove technology. We combined Probabilistic Principal Component Analysis (PPCA) with a Bayesian classifier to analyze the data. Our key findings are: 1. we confirmed that hand control is low-dimensional, where 4-5 dimensions were sufficient to explain 80-90% of the variability in the movement data [6]. 2. We established a universally applicable measure of manipulative complexity that allowed us to measure this quantity across vastly different tasks. 3. We discovered that within the first 1000 ms of an action the hand shape already configures itself to vastly different tasks, enabling us to reliable predict the action intention [6]. 4. We suggest how using the statistics of natural finger movements paired with Bayesian latent variable model can be used to infer the movements of missing limbs from existing limbs to control e.g. a prosthetic device. Overall, these predictabilities could be used to build intelligent Neuroprosthetics for lost fingers that implement the task from the movement of the remaining limbs.

    References

    1. Santello, M., Flanders, M., Soechting, J.F. (1998). Postural hand synergies for tool use. J Neurosci. 18, 10105–10115.
    2. Todorov, E., Ghahramani, Z. (2004). Analysis of the synergies underlying complex hand manipulation. Conf Proc. IEEE Eng. Med. Biol. Soc. 6, 4637-4640.
    3. Weiss, E., Flanders, M. (2004). Muscular and Postural Synergies of the Human Hand. J. Neurophysiol. 92, 523-535.
    4. Tresch, M.C., Cheung, V.C.K., d’Avella, A. (2006). Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. J Neurophysiol. 95, 2199–2212.
    5. Faisal, A., Stout D., Apel, J., Bradley, B. (2010). The Manipulative Complexity of Lower Paleolithic Stone Toolmaking. PloS ONE 5(11): e13718.
    6. Belić, J.J., Faisal, A.A. (2011). The structured variability of finger motor coordination in daily tasks. BMC Neuroscience, doi:10.1186/1471-2202-12-S1-P102.
  • 3.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Imperial College London, United Kingdom; University of Belgrade, Serbia.
    Faisal, Aldo
    Imperial College London.
    Decoding of human hand actions to handle missing limbs in neuroprosthetics2015In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 9, no 27, p. 1-11Article in journal (Refereed)
    Abstract [en]

    The only way we can interact with the world is through movements, and our primary interactions are via the hands, thus any loss of hand function has immediate impact on our quality of life. However, to date it has not been systematically assessed how coordination in the hand's joints affects every day actions. This is important for two fundamental reasons. Firstly, to understand the representations and computations underlying motor control “in-the-wild” situations, and secondly to develop smarter controllers for prosthetic hands that have the same functionality as natural limbs. In this work we exploit the correlation structure of our hand and finger movements in daily-life. The novelty of our idea is that instead of averaging variability out, we take the view that the structure of variability may contain valuable information about the task being performed. We asked seven subjects to interact in 17 daily-life situations, and quantified behavior in a principled manner using CyberGlove body sensor networks that, after accurate calibration, track all major joints of the hand. Our key findings are: (1) We confirmed that hand control in daily-life tasks is very low-dimensional, with four to five dimensions being sufficient to explain 80–90% of the variability in the natural movement data. (2) We established a universally applicable measure of manipulative complexity that allowed us to measure and compare limb movements across tasks. We used Bayesian latent variable models to model the low-dimensional structure of finger joint angles in natural actions. (3) This allowed us to build a naïve classifier that within the first 1000 ms of action initiation (from a flat hand start configuration) predicted which of the 17 actions was going to be executed—enabling us to reliably predict the action intention from very short-time-scale initial data, further revealing the foreseeable nature of hand movements for control of neuroprosthetics and tele operation purposes. (4) Using the Expectation-Maximization algorithm on our latent variable model permitted us to reconstruct with high accuracy (<56° MAE) the movement trajectory of missing fingers by simply tracking the remaining fingers. Overall, our results suggest the hypothesis that specific hand actions are orchestrated by the brain in such a way that in the natural tasks of daily-life there is sufficient redundancy and predictability to be directly exploitable for neuroprosthetics.

  • 4.
    Belic, Jovana
    et al.
    University of Belgrade, Serbia.
    Faisal, Aldo
    Imperial College London.
    The structured variability of finger coordination in daily tasks2011In: BMC neuroscience (Online), ISSN 1471-2202, E-ISSN 1471-2202, Vol. 12, p. 102-Article in journal (Refereed)
  • 5.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab. Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany.
    Halje, Pär
    Lund University.
    Richter, Ulrike
    Lund University.
    Per, Petersson
    Lund University.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.
    Untangling cortico-striatal connectivity and cross-frequency coupling in L-DOPA-induced dyskinesia2016In: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. 10, no 26, p. 1-12Article in journal (Refereed)
    Abstract [en]

    We simultaneously recorded local field potentials in the primary motor cortex and sensorimotor striatum in awake, freely behaving, 6-OHDA lesioned hemi-parkinsonian rats in order to study the features directly related to pathological states such as parkinsonian state and levodopa-induced dyskinesia. We analysed the spectral characteristics of the obtained signals and observed that during dyskinesia the most prominent feature was a relative power increase in the high gamma frequency range at around 80 Hz, while for the parkinsonian state it was in the beta frequency range. Here we show that during both pathological states effective connectivity in terms of Granger causality is bidirectional with an accent on the striatal influence on the cortex. In the case of dyskinesia, we also found a high increase in effective connectivity at 80 Hz. In order to further understand the 80- Hz phenomenon, we performed cross-frequency analysis and observed characteristic patterns in the case of dyskinesia but not in the case of the parkinsonian state or the healthy state. We noted a large decrease in the modulation of the amplitude at 80 Hz by the phase of low frequency oscillations (up to ~10 Hz) across both structures in the case of dyskinesia. This may suggest a lack of coupling between the low frequency activity of the recorded network and the group of neurons active at ~80 Hz.

  • 6.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Freiburg, Germany.
    Halje, Pär
    Lund University.
    Richter, Ulrike
    Lund University.
    Petersson, Per
    Lund Unversity.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Karolinska Institutet, Sweden.
    Corticostriatal circuits and their role in disease2015In: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 8, p. 31-Article in journal (Refereed)
    Abstract [en]

    The basal ganglia (BG) represent subcortical structures considered to be involved in action selection and decision making [1]. Dysfunction of the BG circuitry leads to many motor and cognitive disorders such as Parkinson’s disease (PD), Tourette syndrome, Huntington’s disease, obsessive compulsive disorder and many others. Therefore, we simultaneously recorded local field potentials (LFPs) in primary motor cortex and sensorimotor striatum to study features directly related to healthy versus pathological states such as Parkinson disease and levodopa-induced dyskinesia [2], [3]. The striatum, the input stage of the basal ganglia (BG), is an inhibitory network that contains several distinct cell types and receives massive excitatory inputs from the cortex. Cortex sends direct projections to the striatum, while striatum can affect cortex only indirectly through other BG nuclei and thalamus. Firstly we analyzed spectral characteristics of the obtained signals and observed that during dyskinesia, the most prominent feature was a relative power increase in the high gamma frequency range around 80 Hz, while for PD it was the beta frequency range. Secondly our preliminary results have shown that during both pathological states effective connectivity in terms of Granger causality is bidirectional with an accent on striatal influence on cortex. In the case of dyskinesia we have also found a specifically high increase in effective connectivity at 80 Hz. In order to further understand the 80-Hz phenomenon we have performed cross-frequency analysis across all states and both structures and observed characteristic patterns in the case of dyskinesia in both structures but not in the case of PD and healthy state. We have seen a large relative decrease in the modulation of the amplitude at 80Hz by the phase of low frequency oscillations (up to ~10Hz). It has been suggested that the activity of local neural populations is modulated according to the global neuronal dynamics in the way that populations oscillate and synchronize at lower frequencies and smaller ensembles are active at higher frequencies Our results suggest unexpectedly a lack of coupling between the low frequency activity of a larger population and the synchronized activity of a smaller group of neurons active at 80Hz.

  • 7.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Halje, Pär
    Lund University.
    Richter, Ulrike
    Lund University.
    Petersson, Per
    Lund University.
    Hällgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Behavior Discrimination Using a Discrete Wavelet Based Approach for Feature Extraction on Local Field Potentials in the Cortex and Striatum2015In: 7th International IEEE/EMBS Conference on Neural Engineering (NER), IEEE conference proceedings, 2015, Vol. 7, p. 964-967Conference paper (Refereed)
    Abstract [en]

    Linkage between behavioral states and neural activity is one of the most important challenges in neuroscience. The network activity patterns in the awake resting state and in the actively behaving state in rodents are not well understood, and a better tool for differentiating these states can provide insights on healthy brain functions and its alteration with disease. Therefore, we simultaneously recorded local field potentials (LFPs) bilaterally in motor cortex and striatum, and measured locomotion from healthy, freely behaving rats. Here we analyze spectral characteristics of the obtained signals and present an algorithm for automatic discrimination of the awake resting and the behavioral states. We used the Support Vector Machine (SVM) classifier and utilized features obtained by applying discrete wavelet transform (DWT) on LFPs, which arose as a solution with high accuracy.

  • 8.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). University of Freiburg, Germany.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Karolinska Institute, Sweden.
    Striatal processing of cortical neuronal avalanches – A computational investigation2016In: International Conference on Artificial Neural Networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2016, Vol. 9886, p. 72-79Conference paper (Refereed)
    Abstract [en]

    In the cortex, spontaneous neuronal avalanches can be characterized by spatiotemporal activity clusters with a cluster size distribution that follows a power law with exponent –1.5. 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, i.e., they lacked the large spatial clusters that are commonly expected for avalanche dynamics. In this study, we used computational modeling to investigate the influence of intrastriatal inhibition and corticostriatal interplay as important factors to understand the experimental findings and overall information transmission among these circuits.

  • 9.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Freiburg, Germany.
    Klaus, Andreas
    National Institute of Mental Health, Bethesda, USA.
    Plenz, Dietmar
    National Institute of Mental Health, Bethesda, USA..
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Karolinska Institutet (KI), Sweden.
    Impact of inhibition in striatal decorrelation of cortical neuronal avalanches2013In: BMC neuroscience (Online), ISSN 1471-2202, E-ISSN 1471-2202, Vol. 14, p. 165-Article in journal (Refereed)
  • 10.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). University of Freiburg, Germany.
    Klaus, Andreas
    National Institute of Mental Health, Bethesda, USA.
    Plenz, Dietmar
    National Institute of Mental Health, Bethesda, USA.
    Hällgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC).
    Mapping of Cortical Avalanches to the Striatum2015In: Advances in Cognitive Neurodynamics, Springer Netherlands, 2015, 4, p. 291-297Chapter in book (Refereed)
    Abstract [en]

    Neuronal avalanches are found in the resting state activity of the mammaliancortex. Here we studied whether and how cortical avalanches are mappedonto the striatal circuitry, the first stage of the basal ganglia. We first demonstrate using organotypic cortex-striatum-substantia nigra cultures from rat that indeed striatal neurons respond to cortical avalanches originating in superficial layers. We simultaneously recorded spontaneous local field potentials (LFPs) in the cortical and striatal tissue using high-density microelectrode arrays. In the cortex, spontaneous neuronal avalanches were characterized by intermittent spatiotemporal activity clusters with a cluster size distribution that followed a power law with exponent 1.5. In the striatum, intermittent spatiotemporal activity was found to correlate with cortical avalanches. However, striatal negative LFP peaks (nLFPs) did not showavalanche signatures, but formed a cluster size distribution that had a much steeper drop-off, i.e., lacked large spatial clusters that are commonly expected for avalanche dynamics. The underlying de-correlation of striatal activity could have its origin in the striatum through local inhibition and/or could result from a particular mapping in the corticostriatal pathway. Here we show, using modeling, that highly convergent corticostriatal projections can map spatially extended cortical activity into spatially restricted striatal regimes.

  • 11.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg Germany.
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg Germany.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.
    Interactions in the Striatal Network with Different Oscillation Frequencies2017In: Artificial Neural Networks and Machine Learning – ICANN. Lecture Notes in Computer Science, Springer, 2017, Vol. 10613, p. 129-136Conference 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.

  • 12.
    Belic, Jovana
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab. Bernstein Center Freiburg, University of Freiburg, Freiburg, 79104, Germany.
    Kumar, Arvind
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg, 79104, Germany.
    Hellgren Kotaleski, Jeanette
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Neuroscience, Karolinska Institute, Solna, 17177, Sweden.
    The role of striatal feedforward inhibition in propagation of cortical oscillations2017In: BMC neuroscience (Online), ISSN 1471-2202, E-ISSN 1471-2202, Vol. 18, p. 91-91Article in journal (Refereed)
    Abstract [en]

    Fast spiking interneurons (FSIs) and feedforward (FF) inhibition are a common property of neuronal networks throughout the brain and play crucial role in neural computations. For instance, in the cortex FF inhibition sets the window of temporal integration and spiking and thereby contributes to the control of firing rate and correlations [1]. In the striatum (the main input structure of the basal ganglia) despite their high firing rates and strong synapses, FSIs (comprise 1–2% of striatal neurons) do not seem to play a major role in controlling the firing of medium spiny neurons (MSNs; comprise 95% of striatal neurons) [2] and so far, it has not been possible to attribute a functional role to FSIs in the striatum. Here we use a spiking neuron network model in order to investigate how externally induced oscillations propagate through striatal circuitry. Recordings in the striatum have shown robust oscillatory activity that might be in fact cortical oscillations transmitted by the corticostriatal projections [3–5]. We propose that FSIs can perform an important role in transferring cortical oscillations to the striatum especially to those MSNs that are not directly driven by the cortical oscillations. Strong and divergent connectivity of FSIs implies that even weak oscillations in FSI population activity can be spread to the whole MSN population [6]. Further, we have identified multiple factors that influence the transfer of oscillations to MSNs. The variables such as the number of activated neurons, ongoing activity, connectivity, and synchronicity of inputs influence the transfer of oscillations by modifying the levels of feedforward and feedback inhibitions suggesting that the striatum can exploit different parameters to impact the transfer of oscillatory signals.

    References

    1. Isaacson, J. S., & Scanziani, M. (2011). How inhibition shapes cortical activity. Neuron, 72(2), 231–243. 

    2. Berke, J. D. (2011). Functional properties of striatal fast-spiking interneurons. Frontiers in systems neuroscience, 5.

    3. Belić, J. J., Halje, P., Richter, U., Petersson, P., & Kotaleski, J. H. (2016). Untangling cortico-striatal connectivity and cross-frequency coupling in L-DOPA-induced dyskinesia. Frontiers in systems neuroscience, 10.

    4. Berke, J. D. (2009). Fast oscillations in cortical‐striatal networks switch frequency following rewarding events and stimulant drugs. European Journal of Neuroscience, 30(5), 848–859.

    5. Boraud, T., Brown, P., Goldberg, J. A., Graybiel, A. M., & Magill, P. J. (2005). Oscillations in the basal ganglia: the good, the bad, and the unexpected. In The basal ganglia VIII (pp. 1–24). Springer US.

    6. Belić, J. J., Kumar, A., & Kotaleski, J. H. (2017). Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations. PloS one, 12(4), e0175135.

  • 13.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Savic, Andrej
    University of Belgrade, Sebia.
    Brain Computer Interface-Based Algorithm For The Detection Of Finger Movement2012Conference paper (Refereed)
    Abstract [en]

    User of Brain Computer Interface system stays in “idle state” between executions of motor task or imagining one. Movement-based BCIs can operate in synchronous and asynchronous mode and in both cases in order to make the system robust, it is necessary that the system is able to distinguish with certainty idle state from the initiation of the movement. We propose computing method that determines the probability of subject's intention to make movement in comparison to idle state. We therefore asked 4 subjects between 20-30 years of age to perform the task of pressing the taster button by thumb while their EEG recordings were obtained. First, the subjects performed motor task at instants defined with the animation shown on screen and second, subjects performed self-initiated movement. Movement onsets were identified by voltage change when taster sensor was pressed while analysis was based on the Event Related Desynchronisation (ERD). This neurophysiological phenomenon refers to the decrease of the EEG signal power just before the voluntary movement onset (pre-movement state). Features of the extracted signals were determined by applying one of the following methods: Welch's method, Burg's algorithm or wavelet transform. In order to distinguish data in the two states, we performed classification by using Support Vector Machine (SVM) method. Results showed that SVM classifier was able to anticipate up to 78% of the movements executed.

  • 14.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Savic, Andrej
    University of Belgrade.
    Detecting and comparing the onset of self-paced and cue-based finger movements from EEG signals2015In: 7th Annual International IEEE Conference on Computer Science and Electronic Engineering (CEEC), Colchester, UK: IEEE conference proceedings, 2015, Vol. 7, p. 157-160Conference paper (Refereed)
    Abstract [en]

    We asked four subjects to perform the task of pressing a taster button with their thumbs, while their EEG recordings were obtained, in order to determine the probability of the subjects' intention to make the movement in comparison to the idle state. Humans usually spontaneously decide when to initiate movements to complete daily-life tasks, but sometimes our movements can also be externally triggered. Thus, the subjects first performed motor tasks at the instants defined by the animation shown on the screen and second, the subjects performed self-initiated movements. In this paper, we study if there is a difference in the classification results and coherence measures of EEG signals in these two paradigms. We used the Support Vector Machine (SVM) classifier on features extracted by applying Burg's algorithm to EEG signals, which arose as a solution with high accuracy.

  • 15.
    Belić, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 4, p. 1-17Article in journal (Refereed)
    Abstract [en]

    Network oscillations are ubiquitous across many brain regions. In the basal ganglia, oscillations are also present at many levels and a wide range of characteristic frequencies have been reported to occur during both health and disease. The striatum, the main input nucleus of the basal ganglia, receives massive glutamatergic inputs from the cortex and is highly susceptible to external oscillations. However, there is limited knowledge about the exact nature of this routing process and therefore, it is of key importance to understand how time-dependent, external stimuli propagate through the striatal circuitry. Using a network model of the striatum and corticostriatal projections, we try to elucidate the importance of specific GABAergic neurons and their interactions in shaping striatal oscillatory activity. Here, we propose that fast-spiking interneurons can perform an important role in transferring cortical oscillations to the striatum especially to those medium spiny neurons that are not directly driven by the cortical oscillations. We show how the activity levels of different populations, the strengths of different inhibitory synapses, degree of outgoing projections of striatal cells, ongoing activity and synchronicity of inputs can influence network activity. These results suggest that the propagation of oscillatory inputs into the medium spiny neuron population is most efficient, if conveyed via the fast-spiking interneurons. Therefore, pharmaceuticals that target fast-spiking interneurons may provide a novel treatment for regaining the spectral characteristics of striatal activity that correspond to the healthy state.

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