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A dendritic mechanism for decoding traveling waves: principles and applications to motor cortex.

Heitmann S, Boonstra T, Breakspear M - PLoS Comput. Biol. (2013)

Bottom Line: We validate this proposal in the descending motor system, where we model the large receptor fields of the pyramidal tract neurons - the principle outputs of the motor cortex - decoding motor commands encoded in the direction of traveling wave patterns in motor cortex.The model replicates key findings of the descending motor system during simple motor tasks, including variable interspike intervals and weak corticospinal coherence.By additionally showing how the nature of the wave patterns can be controlled by modulating the topology of local intra-cortical connections, we hence propose a novel integrated neuronal model of encoding and decoding motor commands.

View Article: PubMed Central - PubMed

Affiliation: School of Psychiatry, The University of New South Wales, Sydney, Australia ; The Black Dog Institute, Sydney, Australia.

ABSTRACT
Traveling waves of neuronal oscillations have been observed in many cortical regions, including the motor and sensory cortex. Such waves are often modulated in a task-dependent fashion although their precise functional role remains a matter of debate. Here we conjecture that the cortex can utilize the direction and wavelength of traveling waves to encode information. We present a novel neural mechanism by which such information may be decoded by the spatial arrangement of receptors within the dendritic receptor field. In particular, we show how the density distributions of excitatory and inhibitory receptors can combine to act as a spatial filter of wave patterns. The proposed dendritic mechanism ensures that the neuron selectively responds to specific wave patterns, thus constituting a neural basis of pattern decoding. We validate this proposal in the descending motor system, where we model the large receptor fields of the pyramidal tract neurons - the principle outputs of the motor cortex - decoding motor commands encoded in the direction of traveling wave patterns in motor cortex. We use an existing model of field oscillations in motor cortex to investigate how the topology of the pyramidal cell receptor field acts to tune the cells responses to specific oscillatory wave patterns, even when those patterns are highly degraded. The model replicates key findings of the descending motor system during simple motor tasks, including variable interspike intervals and weak corticospinal coherence. By additionally showing how the nature of the wave patterns can be controlled by modulating the topology of local intra-cortical connections, we hence propose a novel integrated neuronal model of encoding and decoding motor commands.

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The effect of wave orientation on the output of the descending motor system.Each column presents the responses of the descending motor system for pyramidal neurons with a given dendritic orientation ( and ) relative to the cortical pattern. (A) Orientation of the dendritic kernels. The cortical pattern is the same in all cases. (B) Firing rate distribution of the pyramidal tract neurons. (C) Firing rate distribution of the motor neurons. (D) Time course of the simulated EMG. (E) Magnitude squared coherence between LFP and EMG. Light gray lines represent individual trials (n = 100). Black line shows the trial average. In red, average MEG-EMG coherence in 16 subjects while they perform a precision grip task at different force levels (2.0 N, 1.65 N, 0.95 N, 0.0 N). Dashed horizontal line indicates the 95% confidence level for the coherence distribution in each frequency bin. Peaks above that line are statistically significant at p = 0.05.
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pcbi-1003260-g009: The effect of wave orientation on the output of the descending motor system.Each column presents the responses of the descending motor system for pyramidal neurons with a given dendritic orientation ( and ) relative to the cortical pattern. (A) Orientation of the dendritic kernels. The cortical pattern is the same in all cases. (B) Firing rate distribution of the pyramidal tract neurons. (C) Firing rate distribution of the motor neurons. (D) Time course of the simulated EMG. (E) Magnitude squared coherence between LFP and EMG. Light gray lines represent individual trials (n = 100). Black line shows the trial average. In red, average MEG-EMG coherence in 16 subjects while they perform a precision grip task at different force levels (2.0 N, 1.65 N, 0.95 N, 0.0 N). Dashed horizontal line indicates the 95% confidence level for the coherence distribution in each frequency bin. Peaks above that line are statistically significant at p = 0.05.

Mentions: Thirty seconds of cortical traveling wave activity was simulated using a fixed cortical coupling kernel that was oriented at 60 degrees from the horizontal (as in Figure 2). This wave sequence was then decoded by PTNs with dendritic filters that were rotated away from the dominant wave orientation by and respectively in each condition. The results are rotationally equivalent to holding the orientation of the dendritic filters fixed while manipulating the orientation of the cortical coupling except in this case there are no confounds with between-trial differences in the self-organized wave patterns. Figure 9 shows various aspects of the descending motor drive for each orientation offset condition. Each column pertains to one condition. The panels in row A show the orientation of each of the dendritic filters in relation to the cortical wave pattern. The panels in row B show the distribution of firing rates exhibited by the 200 PTNs embedded in the cortex. The mean firing rates of the PTN population (22.4 Hz, 18.0 Hz, 9.7 Hz, 2.4 Hz) are seen to diminish as orientation offset increases from to which confirms that PTN responses are selective to wave orientation. The maximum responses occur when the waves are perfectly aligned with the dendritic filter ( offset) whereas the bulk of the PTNs barely fire at all in the case of offset. A persistent spread in the PTN response rates is observed for all orientation offsets. This variation is due to local defects in the wave pattern, as will be discussed later.


A dendritic mechanism for decoding traveling waves: principles and applications to motor cortex.

Heitmann S, Boonstra T, Breakspear M - PLoS Comput. Biol. (2013)

The effect of wave orientation on the output of the descending motor system.Each column presents the responses of the descending motor system for pyramidal neurons with a given dendritic orientation ( and ) relative to the cortical pattern. (A) Orientation of the dendritic kernels. The cortical pattern is the same in all cases. (B) Firing rate distribution of the pyramidal tract neurons. (C) Firing rate distribution of the motor neurons. (D) Time course of the simulated EMG. (E) Magnitude squared coherence between LFP and EMG. Light gray lines represent individual trials (n = 100). Black line shows the trial average. In red, average MEG-EMG coherence in 16 subjects while they perform a precision grip task at different force levels (2.0 N, 1.65 N, 0.95 N, 0.0 N). Dashed horizontal line indicates the 95% confidence level for the coherence distribution in each frequency bin. Peaks above that line are statistically significant at p = 0.05.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC3814333&req=5

pcbi-1003260-g009: The effect of wave orientation on the output of the descending motor system.Each column presents the responses of the descending motor system for pyramidal neurons with a given dendritic orientation ( and ) relative to the cortical pattern. (A) Orientation of the dendritic kernels. The cortical pattern is the same in all cases. (B) Firing rate distribution of the pyramidal tract neurons. (C) Firing rate distribution of the motor neurons. (D) Time course of the simulated EMG. (E) Magnitude squared coherence between LFP and EMG. Light gray lines represent individual trials (n = 100). Black line shows the trial average. In red, average MEG-EMG coherence in 16 subjects while they perform a precision grip task at different force levels (2.0 N, 1.65 N, 0.95 N, 0.0 N). Dashed horizontal line indicates the 95% confidence level for the coherence distribution in each frequency bin. Peaks above that line are statistically significant at p = 0.05.
Mentions: Thirty seconds of cortical traveling wave activity was simulated using a fixed cortical coupling kernel that was oriented at 60 degrees from the horizontal (as in Figure 2). This wave sequence was then decoded by PTNs with dendritic filters that were rotated away from the dominant wave orientation by and respectively in each condition. The results are rotationally equivalent to holding the orientation of the dendritic filters fixed while manipulating the orientation of the cortical coupling except in this case there are no confounds with between-trial differences in the self-organized wave patterns. Figure 9 shows various aspects of the descending motor drive for each orientation offset condition. Each column pertains to one condition. The panels in row A show the orientation of each of the dendritic filters in relation to the cortical wave pattern. The panels in row B show the distribution of firing rates exhibited by the 200 PTNs embedded in the cortex. The mean firing rates of the PTN population (22.4 Hz, 18.0 Hz, 9.7 Hz, 2.4 Hz) are seen to diminish as orientation offset increases from to which confirms that PTN responses are selective to wave orientation. The maximum responses occur when the waves are perfectly aligned with the dendritic filter ( offset) whereas the bulk of the PTNs barely fire at all in the case of offset. A persistent spread in the PTN response rates is observed for all orientation offsets. This variation is due to local defects in the wave pattern, as will be discussed later.

Bottom Line: We validate this proposal in the descending motor system, where we model the large receptor fields of the pyramidal tract neurons - the principle outputs of the motor cortex - decoding motor commands encoded in the direction of traveling wave patterns in motor cortex.The model replicates key findings of the descending motor system during simple motor tasks, including variable interspike intervals and weak corticospinal coherence.By additionally showing how the nature of the wave patterns can be controlled by modulating the topology of local intra-cortical connections, we hence propose a novel integrated neuronal model of encoding and decoding motor commands.

View Article: PubMed Central - PubMed

Affiliation: School of Psychiatry, The University of New South Wales, Sydney, Australia ; The Black Dog Institute, Sydney, Australia.

ABSTRACT
Traveling waves of neuronal oscillations have been observed in many cortical regions, including the motor and sensory cortex. Such waves are often modulated in a task-dependent fashion although their precise functional role remains a matter of debate. Here we conjecture that the cortex can utilize the direction and wavelength of traveling waves to encode information. We present a novel neural mechanism by which such information may be decoded by the spatial arrangement of receptors within the dendritic receptor field. In particular, we show how the density distributions of excitatory and inhibitory receptors can combine to act as a spatial filter of wave patterns. The proposed dendritic mechanism ensures that the neuron selectively responds to specific wave patterns, thus constituting a neural basis of pattern decoding. We validate this proposal in the descending motor system, where we model the large receptor fields of the pyramidal tract neurons - the principle outputs of the motor cortex - decoding motor commands encoded in the direction of traveling wave patterns in motor cortex. We use an existing model of field oscillations in motor cortex to investigate how the topology of the pyramidal cell receptor field acts to tune the cells responses to specific oscillatory wave patterns, even when those patterns are highly degraded. The model replicates key findings of the descending motor system during simple motor tasks, including variable interspike intervals and weak corticospinal coherence. By additionally showing how the nature of the wave patterns can be controlled by modulating the topology of local intra-cortical connections, we hence propose a novel integrated neuronal model of encoding and decoding motor commands.

Show MeSH
Related in: MedlinePlus