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On the similarity of functional connectivity between neurons estimated across timescales.

Stevenson IH, Körding KP - PLoS ONE (2010)

Bottom Line: We address the origin of these correlations using simulation techniques and find evidence that the similarity between functional connectivity estimated across timescales is due to processes that do not depend on fast pair-wise interactions alone.Rather, it appears that connectivity on multiple timescales or common-input related to stimuli or movement drives the observed correlations.Despite this qualification, our results suggest that techniques with intermediate temporal resolution may yield good estimates of the functional connections between individual neurons.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America. i-stevenson@northwestern.edu

ABSTRACT
A central objective in neuroscience is to understand how neurons interact. Such functional interactions have been estimated using signals recorded with different techniques and, consequently, different temporal resolutions. For example, spike data often have sub-millisecond resolution while some imaging techniques may have a resolution of many seconds. Here we use multi-electrode spike recordings to ask how similar functional connectivity inferred from slower timescale signals is to the one inferred from fast timescale signals. We find that functional connectivity is relatively robust to low-pass filtering--dropping by about 10% when low pass filtering at 10 hz and about 50% when low pass filtering down to about 1 Hz--and that estimates are robust to high levels of additive noise. Moreover, there is a weak correlation for physiological filters such as hemodynamic or Ca2+ impulse responses and filters based on local field potentials. We address the origin of these correlations using simulation techniques and find evidence that the similarity between functional connectivity estimated across timescales is due to processes that do not depend on fast pair-wise interactions alone. Rather, it appears that connectivity on multiple timescales or common-input related to stimuli or movement drives the observed correlations. Despite this qualification, our results suggest that techniques with intermediate temporal resolution may yield good estimates of the functional connections between individual neurons.

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Filtering analysis.The Granger causality between each pair of neural signals ( and ) is calculated at different levels of smoothing () and down-sampling. This provides measures of functional connectivity from  to  () and from  to  () for each timescale. By comparing these measures across timescales we can examine how robust functional connectivity is to temporal filtering.
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pone-0009206-g001: Filtering analysis.The Granger causality between each pair of neural signals ( and ) is calculated at different levels of smoothing () and down-sampling. This provides measures of functional connectivity from to () and from to () for each timescale. By comparing these measures across timescales we can examine how robust functional connectivity is to temporal filtering.

Mentions: We analyze multi-electrode, single unit, spike data recorded from the motor cortices of two macaque monkeys (Macaca mulatta). After filtering and down-sampling the spike signals, we compute a measure of functional connectivity, the pair-wise Granger causalities from each pair of channels [23] (Fig. 1). Granger causality provides a metric for how much one signal improves prediction of another. It specifically measures the improvement given by adding a second signal to an auto-regressive linear model. Granger causality has been used to estimate interactions with a variety of signals, and here it provides an estimate of the strength of functional connectivity between neurons. Our goal is to compare functional connectivity estimated from different signal types. As a first step, we compare Granger causality estimated from the highest frequency spike signals with Granger causality calculated from filtered spike signals. Using this strategy we can examine how functional connectivity estimated from slow timescale signals relates to fast timescale connectivity. Correlations between these two functional connectivity estimates imply that functional connectivity calculated from filtered signals is predictive of functional connectivity at fast timescales.


On the similarity of functional connectivity between neurons estimated across timescales.

Stevenson IH, Körding KP - PLoS ONE (2010)

Filtering analysis.The Granger causality between each pair of neural signals ( and ) is calculated at different levels of smoothing () and down-sampling. This provides measures of functional connectivity from  to  () and from  to  () for each timescale. By comparing these measures across timescales we can examine how robust functional connectivity is to temporal filtering.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2823767&req=5

pone-0009206-g001: Filtering analysis.The Granger causality between each pair of neural signals ( and ) is calculated at different levels of smoothing () and down-sampling. This provides measures of functional connectivity from to () and from to () for each timescale. By comparing these measures across timescales we can examine how robust functional connectivity is to temporal filtering.
Mentions: We analyze multi-electrode, single unit, spike data recorded from the motor cortices of two macaque monkeys (Macaca mulatta). After filtering and down-sampling the spike signals, we compute a measure of functional connectivity, the pair-wise Granger causalities from each pair of channels [23] (Fig. 1). Granger causality provides a metric for how much one signal improves prediction of another. It specifically measures the improvement given by adding a second signal to an auto-regressive linear model. Granger causality has been used to estimate interactions with a variety of signals, and here it provides an estimate of the strength of functional connectivity between neurons. Our goal is to compare functional connectivity estimated from different signal types. As a first step, we compare Granger causality estimated from the highest frequency spike signals with Granger causality calculated from filtered spike signals. Using this strategy we can examine how functional connectivity estimated from slow timescale signals relates to fast timescale connectivity. Correlations between these two functional connectivity estimates imply that functional connectivity calculated from filtered signals is predictive of functional connectivity at fast timescales.

Bottom Line: We address the origin of these correlations using simulation techniques and find evidence that the similarity between functional connectivity estimated across timescales is due to processes that do not depend on fast pair-wise interactions alone.Rather, it appears that connectivity on multiple timescales or common-input related to stimuli or movement drives the observed correlations.Despite this qualification, our results suggest that techniques with intermediate temporal resolution may yield good estimates of the functional connections between individual neurons.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America. i-stevenson@northwestern.edu

ABSTRACT
A central objective in neuroscience is to understand how neurons interact. Such functional interactions have been estimated using signals recorded with different techniques and, consequently, different temporal resolutions. For example, spike data often have sub-millisecond resolution while some imaging techniques may have a resolution of many seconds. Here we use multi-electrode spike recordings to ask how similar functional connectivity inferred from slower timescale signals is to the one inferred from fast timescale signals. We find that functional connectivity is relatively robust to low-pass filtering--dropping by about 10% when low pass filtering at 10 hz and about 50% when low pass filtering down to about 1 Hz--and that estimates are robust to high levels of additive noise. Moreover, there is a weak correlation for physiological filters such as hemodynamic or Ca2+ impulse responses and filters based on local field potentials. We address the origin of these correlations using simulation techniques and find evidence that the similarity between functional connectivity estimated across timescales is due to processes that do not depend on fast pair-wise interactions alone. Rather, it appears that connectivity on multiple timescales or common-input related to stimuli or movement drives the observed correlations. Despite this qualification, our results suggest that techniques with intermediate temporal resolution may yield good estimates of the functional connections between individual neurons.

Show MeSH