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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.

Show MeSH
The correlation between functional connectivity across timescales is robust for all datasets down to ∼0.25 Hz.Correlation coefficients are calculated across folds (10 min segments). Error-bars denote standard error (SEM) across segments (N = 5).
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pone-0009206-g003: The correlation between functional connectivity across timescales is robust for all datasets down to ∼0.25 Hz.Correlation coefficients are calculated across folds (10 min segments). Error-bars denote standard error (SEM) across segments (N = 5).

Mentions: We examine the full population of neurons from each of our datasets (143 for monkey R, 183 for monkey B) and divide the data into non-overlapping blocks of 10 minute duration. We then compute correlation coefficients between functional connectivity (Granger causality) estimated from different segments at varying levels of low-pass filtering (Fig. 2A and B). Cross-validation ensures that model comparisons are relevant and not due to over-fitting. We find that connectivity estimates are fairly robust to temporal filtering (Fig. 3). For instance, functional connectivity estimated after low-pass filtering at 1 Hz (Gaussian filter, σ = 1 s) is still significantly correlated with the fast timescale functional connectivity (R = 0.4). Moreover, the rate at which this correlation decays as a function of temporal resolution is conserved across animals and tasks. Dataset R was recorded while the animal performed center-out reaches, and dataset B was recorded while the animal was sleeping (non-REM, slow-wave sleep). This invariance suggests that, while functional connectivity itself is task dependent, there may be consistent relationships between functional connectivity across different timescales.


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

Stevenson IH, Körding KP - PLoS ONE (2010)

The correlation between functional connectivity across timescales is robust for all datasets down to ∼0.25 Hz.Correlation coefficients are calculated across folds (10 min segments). Error-bars denote standard error (SEM) across segments (N = 5).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2823767&req=5

pone-0009206-g003: The correlation between functional connectivity across timescales is robust for all datasets down to ∼0.25 Hz.Correlation coefficients are calculated across folds (10 min segments). Error-bars denote standard error (SEM) across segments (N = 5).
Mentions: We examine the full population of neurons from each of our datasets (143 for monkey R, 183 for monkey B) and divide the data into non-overlapping blocks of 10 minute duration. We then compute correlation coefficients between functional connectivity (Granger causality) estimated from different segments at varying levels of low-pass filtering (Fig. 2A and B). Cross-validation ensures that model comparisons are relevant and not due to over-fitting. We find that connectivity estimates are fairly robust to temporal filtering (Fig. 3). For instance, functional connectivity estimated after low-pass filtering at 1 Hz (Gaussian filter, σ = 1 s) is still significantly correlated with the fast timescale functional connectivity (R = 0.4). Moreover, the rate at which this correlation decays as a function of temporal resolution is conserved across animals and tasks. Dataset R was recorded while the animal performed center-out reaches, and dataset B was recorded while the animal was sleeping (non-REM, slow-wave sleep). This invariance suggests that, while functional connectivity itself is task dependent, there may be consistent relationships between functional connectivity across different 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