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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.


Observed activity patterns resulting from simulated activation in representative locations in language, motor, auditory, and visual areas. The location of each simulated active dipole is shown (white spheres in A) as well as individual activation patterns (B–E). For each location, the source activation was projected into the MEG sensor space (as potentially observable data) and then the minimum-norm inverse solution with spherical morphing was used to map the activity to a common brain. In the plot for each region (language, motor, auditory, visual), the top 25 most active points for three different sample subjects are shown in red, green, and blue, alongside the top 25 most active points for the average across all subjects (black outline). Note that the point-spread functions—indicated by activity that has moved or even jumped sulci or gyri away from the location of underlying true activity—differ between subjects, and that the average across subjects is converging toward the point of original activation.
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Figure 1: Observed activity patterns resulting from simulated activation in representative locations in language, motor, auditory, and visual areas. The location of each simulated active dipole is shown (white spheres in A) as well as individual activation patterns (B–E). For each location, the source activation was projected into the MEG sensor space (as potentially observable data) and then the minimum-norm inverse solution with spherical morphing was used to map the activity to a common brain. In the plot for each region (language, motor, auditory, visual), the top 25 most active points for three different sample subjects are shown in red, green, and blue, alongside the top 25 most active points for the average across all subjects (black outline). Note that the point-spread functions—indicated by activity that has moved or even jumped sulci or gyri away from the location of underlying true activity—differ between subjects, and that the average across subjects is converging toward the point of original activation.

Mentions: Anatomical MRI, MEG coregistration information, task-related and empty-room recordings were obtained from 20 subjects in order to examine how combining MEG inverse imaging estimates across subjects can affect localization of the neural activity. First, we simulated point-source activity from the cortical surface of each subject, with the location of activation conserved across subjects, and examined how precisely and accurately the inverse solution localized the average activity across subjects. Figure 1 shows an example of the activity simulation for four different point-source dipole activations. Note that while the individual subjects' point-spread patterns differ for each region, they do share some common spatial overlap which, crucially, includes the location of the true activation.


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

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

Observed activity patterns resulting from simulated activation in representative locations in language, motor, auditory, and visual areas. The location of each simulated active dipole is shown (white spheres in A) as well as individual activation patterns (B–E). For each location, the source activation was projected into the MEG sensor space (as potentially observable data) and then the minimum-norm inverse solution with spherical morphing was used to map the activity to a common brain. In the plot for each region (language, motor, auditory, visual), the top 25 most active points for three different sample subjects are shown in red, green, and blue, alongside the top 25 most active points for the average across all subjects (black outline). Note that the point-spread functions—indicated by activity that has moved or even jumped sulci or gyri away from the location of underlying true activity—differ between subjects, and that the average across subjects is converging toward the point of original activation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Observed activity patterns resulting from simulated activation in representative locations in language, motor, auditory, and visual areas. The location of each simulated active dipole is shown (white spheres in A) as well as individual activation patterns (B–E). For each location, the source activation was projected into the MEG sensor space (as potentially observable data) and then the minimum-norm inverse solution with spherical morphing was used to map the activity to a common brain. In the plot for each region (language, motor, auditory, visual), the top 25 most active points for three different sample subjects are shown in red, green, and blue, alongside the top 25 most active points for the average across all subjects (black outline). Note that the point-spread functions—indicated by activity that has moved or even jumped sulci or gyri away from the location of underlying true activity—differ between subjects, and that the average across subjects is converging toward the point of original activation.
Mentions: Anatomical MRI, MEG coregistration information, task-related and empty-room recordings were obtained from 20 subjects in order to examine how combining MEG inverse imaging estimates across subjects can affect localization of the neural activity. First, we simulated point-source activity from the cortical surface of each subject, with the location of activation conserved across subjects, and examined how precisely and accurately the inverse solution localized the average activity across subjects. Figure 1 shows an example of the activity simulation for four different point-source dipole activations. Note that while the individual subjects' point-spread patterns differ for each region, they do share some common spatial overlap which, crucially, includes the location of the true activation.

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.