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Improving spatial localization in MEG inverse imaging by leveraging intersubject anatomical differences.

Larson E, Maddox RK, Lee AK - Front Neurosci (2014)

Bottom Line: Specifically, we argue that differences in subject brain geometry yield differences in point-spread functions, resulting in improved spatial localization across subjects.Using a linear minimum-norm inverse to localize this brain activity, we demonstrate that a substantial increase in the spatial accuracy of MEG source localization can result from combining data from subjects with differing brain geometry.Finally, we use a simple auditory N100(m) localization task to show how this effect can influence localization using a recorded neural dataset.

View Article: PubMed Central - PubMed

Affiliation: Institute for Learning and Brain Sciences, University of Washington Seattle, WA, USA.

ABSTRACT
Modern neuroimaging techniques enable non-invasive observation of ongoing neural processing, with magnetoencephalography (MEG) in particular providing direct measurement of neural activity with millisecond time resolution. However, accurately mapping measured MEG sensor readings onto the underlying source neural structures remains an active area of research. This so-called "inverse problem" is ill posed, and poses a challenge for source estimation that is often cited as a drawback limiting MEG data interpretation. However, anatomically constrained MEG localization estimates may be more accurate than commonly believed. Here we hypothesize that, by combining anatomically constrained inverse estimates across subjects, the spatial uncertainty of MEG source localization can be mitigated. Specifically, we argue that differences in subject brain geometry yield differences in point-spread functions, resulting in improved spatial localization across subjects. To test this, we use standard methods to combine subject anatomical MRI scans with coregistration information to obtain an accurate forward (physical) solution, modeling the MEG sensor data resulting from brain activity originating from different cortical locations. Using a linear minimum-norm inverse to localize this brain activity, we demonstrate that a substantial increase in the spatial accuracy of MEG source localization can result from combining data from subjects with differing brain geometry. This improvement may be enabled by an increase in the amount of available spatial information in MEG data as measurements from different subjects are combined. This approach becomes more important in the face of practical issues of coregistration errors and potential noise sources, where we observe even larger improvements in localization when combining data across subjects. Finally, we use a simple auditory N100(m) localization task to show how this effect can influence localization using a recorded neural dataset.

No MeSH data available.


Combining information across subjects compensates for inaccuracies in localization due to multiple experimental factors. Simulations were run covering different regulation parameters λ (A), noise covariances used in inverse estimation (B), errors in coregistration alignment (C), and a range of signal-to-noise ratios (D). The centroid error (solid lines) and the point spread (dashed lines) were both reduced as information across subjects was combined in all of these scenarios, suggesting that the simulation results from Figure 2 generalize to many situations. Mean ± 2 s.e.m. (across 20,424 cortical locations) is shown by the lines and shaded backgrounds. Note that s.e.m. are mostly small enough to be masked by the mean lines; standard deviation values across cortex are provided in Table 1. Here for simplicity only the V = 25 point case is shown, the V = 25 lines in Figures 2A,D equivalent here to the  case in A, the ERM case in B, and the 0 mm case in C. The infinite SNR case in D corresponds to the task-based covariance case from B, since the evoked covariance was used in the inverse solutions for that simulation.
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Figure 3: Combining information across subjects compensates for inaccuracies in localization due to multiple experimental factors. Simulations were run covering different regulation parameters λ (A), noise covariances used in inverse estimation (B), errors in coregistration alignment (C), and a range of signal-to-noise ratios (D). The centroid error (solid lines) and the point spread (dashed lines) were both reduced as information across subjects was combined in all of these scenarios, suggesting that the simulation results from Figure 2 generalize to many situations. Mean ± 2 s.e.m. (across 20,424 cortical locations) is shown by the lines and shaded backgrounds. Note that s.e.m. are mostly small enough to be masked by the mean lines; standard deviation values across cortex are provided in Table 1. Here for simplicity only the V = 25 point case is shown, the V = 25 lines in Figures 2A,D equivalent here to the case in A, the ERM case in B, and the 0 mm case in C. The infinite SNR case in D corresponds to the task-based covariance case from B, since the evoked covariance was used in the inverse solutions for that simulation.

Mentions: Varying the regularization parameter λ (related to the experimenter-estimated SNR as λ = SNR−2; Figure 3A).


Improving spatial localization in MEG inverse imaging by leveraging intersubject anatomical differences.

Larson E, Maddox RK, Lee AK - Front Neurosci (2014)

Combining information across subjects compensates for inaccuracies in localization due to multiple experimental factors. Simulations were run covering different regulation parameters λ (A), noise covariances used in inverse estimation (B), errors in coregistration alignment (C), and a range of signal-to-noise ratios (D). The centroid error (solid lines) and the point spread (dashed lines) were both reduced as information across subjects was combined in all of these scenarios, suggesting that the simulation results from Figure 2 generalize to many situations. Mean ± 2 s.e.m. (across 20,424 cortical locations) is shown by the lines and shaded backgrounds. Note that s.e.m. are mostly small enough to be masked by the mean lines; standard deviation values across cortex are provided in Table 1. Here for simplicity only the V = 25 point case is shown, the V = 25 lines in Figures 2A,D equivalent here to the  case in A, the ERM case in B, and the 0 mm case in C. The infinite SNR case in D corresponds to the task-based covariance case from B, since the evoked covariance was used in the inverse solutions for that simulation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4202703&req=5

Figure 3: Combining information across subjects compensates for inaccuracies in localization due to multiple experimental factors. Simulations were run covering different regulation parameters λ (A), noise covariances used in inverse estimation (B), errors in coregistration alignment (C), and a range of signal-to-noise ratios (D). The centroid error (solid lines) and the point spread (dashed lines) were both reduced as information across subjects was combined in all of these scenarios, suggesting that the simulation results from Figure 2 generalize to many situations. Mean ± 2 s.e.m. (across 20,424 cortical locations) is shown by the lines and shaded backgrounds. Note that s.e.m. are mostly small enough to be masked by the mean lines; standard deviation values across cortex are provided in Table 1. Here for simplicity only the V = 25 point case is shown, the V = 25 lines in Figures 2A,D equivalent here to the case in A, the ERM case in B, and the 0 mm case in C. The infinite SNR case in D corresponds to the task-based covariance case from B, since the evoked covariance was used in the inverse solutions for that simulation.
Mentions: Varying the regularization parameter λ (related to the experimenter-estimated SNR as λ = SNR−2; Figure 3A).

Bottom Line: Specifically, we argue that differences in subject brain geometry yield differences in point-spread functions, resulting in improved spatial localization across subjects.Using a linear minimum-norm inverse to localize this brain activity, we demonstrate that a substantial increase in the spatial accuracy of MEG source localization can result from combining data from subjects with differing brain geometry.Finally, we use a simple auditory N100(m) localization task to show how this effect can influence localization using a recorded neural dataset.

View Article: PubMed Central - PubMed

Affiliation: Institute for Learning and Brain Sciences, University of Washington Seattle, WA, USA.

ABSTRACT
Modern neuroimaging techniques enable non-invasive observation of ongoing neural processing, with magnetoencephalography (MEG) in particular providing direct measurement of neural activity with millisecond time resolution. However, accurately mapping measured MEG sensor readings onto the underlying source neural structures remains an active area of research. This so-called "inverse problem" is ill posed, and poses a challenge for source estimation that is often cited as a drawback limiting MEG data interpretation. However, anatomically constrained MEG localization estimates may be more accurate than commonly believed. Here we hypothesize that, by combining anatomically constrained inverse estimates across subjects, the spatial uncertainty of MEG source localization can be mitigated. Specifically, we argue that differences in subject brain geometry yield differences in point-spread functions, resulting in improved spatial localization across subjects. To test this, we use standard methods to combine subject anatomical MRI scans with coregistration information to obtain an accurate forward (physical) solution, modeling the MEG sensor data resulting from brain activity originating from different cortical locations. Using a linear minimum-norm inverse to localize this brain activity, we demonstrate that a substantial increase in the spatial accuracy of MEG source localization can result from combining data from subjects with differing brain geometry. This improvement may be enabled by an increase in the amount of available spatial information in MEG data as measurements from different subjects are combined. This approach becomes more important in the face of practical issues of coregistration errors and potential noise sources, where we observe even larger improvements in localization when combining data across subjects. Finally, we use a simple auditory N100(m) localization task to show how this effect can influence localization using a recorded neural dataset.

No MeSH data available.