Jovana J. Belić

Untangling Cortico-Striatal Connectivity and Cross-Frequency Coupling in L-DOPA-Induced Dyskinesia

We simultaneously recorded local field potentials (LFPs) 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 analyzed 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 control 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.

Mapping of Cortical Avalanches to the Striatum

Neuronal avalanches are found in the resting state activity of the mammalian cortex. Here we studied whether and how cortical avalanches are mapped onto 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 show avalanche 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.

Neuronal avalanches and the cortico-striatal network

Neuronal avalanches are spontaneous activity cascades observed in superficial cortical layers with statistical properties expected from the network operating near a critical point [1]. In such a network, neuronal activity on one active site triggers, on average, similar activity at other site and therefore the overall activity does not increase or die out over time. Neuronal avalanches have been found in vitro [1] and in vivo [2], and display long-term stability, diversity, and fast propagation of local synchrony. They characterize networks that have a maximum dynamic range [3] and might play a central role in information transmission [1] and storage [4]. Their activity is characterized by brief bursts lasting tens of milliseconds, separated by periods of quiescence lasting several seconds and when observed with multi-electrode arrays, the number of electrodes activated is well described by a power law with exponent close to -1.5 [5]. Here we study neuronal avalanches in an open-loop system of cortex and striatum. The striatum is the main input structure of the basal ganglia and plays an important role in motor and cognitive functions. Understanding how the striatum responds to cortical inputs has crucial importance for clarifying the overall functions of the basal ganglia. The projection neurons of the striatum have a high threshold for activation and receive excitatory input from different regions of the cerebral cortex. Although the striatum contains several distinct cell types, 90-95% are GABAergic medium spiny projection neurons (MSNs). These cells are major targets of cortical inputs, the recurrent connection among them mediate weak feedback inhibition and neighboring MSNs are not likely to share cortical inputs [6]. Fast-spiking interneurons are relatively sparse elements of striatal networks. They project extensively to nearby MSNs and provide strong feedforward inhibition and seem to be critical nodes governing striatal output [7]. Preliminary experiments indicate that activity bursts in the striatum do not follow a power law with characteristic exponent of -1.5. Here we developed an abstract model of the cortico-striatal network that reproduces statistics observed in experimental data. We discuss which kind of connectivity between cortex and striatum, as well as connectivity and strength of inhibition within striatum can lead to results that are in line with experimental data.

Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations

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.

Behavior discrimination using a discrete wavelet based approach for feature extraction on local field potentials in the cortex and striatum

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.

Interactions in the Striatal Network with Different Oscillation Frequencies

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.

Striatal Processing of Cortical Neuronal Avalanches – A Computational Investigation

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.

Impact of inhibition in striatal decorrelation of cortical neuronal avalanches

The Basal Ganglia represent subcortical structures that have a crucial role in determining when a given motor program should be selected and called into action [1]. The input region of the Basal Ganglia (the striatum) contains several distinct cell types. 90-95% of them are medium spiny projection neurons (MSNs) that have high threshold for activation and represent the sole source of the output. There is also a small population of fast-spiking interneurons (FSIs) that receive inputs from a wider range of distinct cortical regions compared to projection neurons [2]. Two sources of GABAergic inhibition onto MSNs are the feedforward inhibition via the FSIs and the feedback inhibition from the axon collaterals of the MSNs themselves. Feedforward inhibition is very powerful and may filter cortical information transmitted by striatal projection neurons [3]. In contrast, feedback inhibition between pairs of MSNs acts predominantly at the distal dendrites, but may still significantly control the overall level of activity of the spiny neurons [4]. We simultaneously recorded local field potentials (LFPs) in the cortex and striatum in order to determine how striatum processes cortical neuronal avalanches. Cortical neuronal avalanches represent activity clusters with a cluster size distribution that follows a power law with exponent -1.5 [5]. Analysis of experimental data revealed that activity clusters in striatum also follow power law distributions, but with an exponent significantly lower than what is observed in the cortex [6]. To understand what controls the LFP statistics observed in experiments, we developed an abstract model of the cortico-striatal network. We investigated to what extent the connectivity pattern between cortex and striatum as well as the inhibition within striatum can explain the experimental results [7]. Our model predicts that striatal inhibition plays a prominent role in shaping the observed striatal dynamics and decorrelating the striatal responses to cortical neuronal avalanches. To understand the contribution of feedforward vs feedback inhibition to the dynamics, we extended our abstract model to spiking networks. We used the model to quantify the role of feedback and feedforward inhibition for decorrelating MSNs, and preliminary results suggest that FSIs play a significant role.