Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI
EEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI. Fi...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 35; no. 2; pp. 598 - 608 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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United States
Elsevier Inc
01.04.2007
Elsevier Limited |
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| Abstract | EEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI.
Five source imaging algorithms, including the minimum norm least square (MNLS), LORETA with Lp-norm (p equal to 1, 1.5 and 2), sLORETA, the minimum Lp-norm (p equal to 1 and 1.5; when p=2, the MNLS method is mathematically equivalent to the minimum Lp-norm) and L1-norm (the linear programming) methods, were evaluated in a group of 10 human subjects, in a paradigm with somatosensory stimulation. Cortical current density (CCD) distributions were estimated from the scalp somatosensory evoked potentials (SEPs), at approximately 30 ms following electrical stimulation of median nerve at the wrist. Realistic geometry boundary element head models were constructed from the MRIs of each subject and used in the CCD analysis. Functional MRI results obtained from a motor task and sensory stimulation in all subjects were used to identify the central sulcus, motor and sensory areas. In three patients undergoing neurosurgical evaluation, ECoGs were recorded in response to the somatosensory stimulation, and were used to help determine the central sulcus and the sensory cortex.
The CCD distributions estimated by the Lp-norm and LORETA-Lp methods were smoother when the p values were high. The LORETA based on the L1-norm performed better than the LORETA-L2 method for imaging well localized sources such as the P30 component of the SEP. The mean and standard deviation of the distance between the location of maximum CCD value and the central sulcus, estimated by the minimum Lp-norm (with p equal to 1), L1-norm (the Linear programming) and LORETA-Lp (with p equal to 1) methods, were 4, 7, 7 mm and 3, 4, 2 mm, respectively (after converting into Talairach coordinates). The mean and standard deviation of the aforementioned distance, estimated by the MNLS, LORETA with Lp-norm (p equal to 1.5 and 2.0), sLORETA and the minimum Lp-norm (p equal to 1.5) methods, were over 11 mm and 6 mm, respectively.
The present experimental study suggests that L1-norm-based algorithms provide better performance than L2 and L1.5-norm-based algorithms, in the context of CCD imaging of well localized sources induced by somatosensory electrical stimulation of median nerve at the wrist. |
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| AbstractList | EEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI.
Five source imaging algorithms, including the minimum norm least square (MNLS), LORETA with Lp-norm (p equal to 1, 1.5 and 2), sLORETA, the minimum Lp-norm (p equal to 1 and 1.5; when p=2, the MNLS method is mathematically equivalent to the minimum Lp-norm) and L1-norm (the linear programming) methods, were evaluated in a group of 10 human subjects, in a paradigm with somatosensory stimulation. Cortical current density (CCD) distributions were estimated from the scalp somatosensory evoked potentials (SEPs), at approximately 30 ms following electrical stimulation of median nerve at the wrist. Realistic geometry boundary element head models were constructed from the MRIs of each subject and used in the CCD analysis. Functional MRI results obtained from a motor task and sensory stimulation in all subjects were used to identify the central sulcus, motor and sensory areas. In three patients undergoing neurosurgical evaluation, ECoGs were recorded in response to the somatosensory stimulation, and were used to help determine the central sulcus and the sensory cortex.
The CCD distributions estimated by the Lp-norm and LORETA-Lp methods were smoother when the p values were high. The LORETA based on the L1-norm performed better than the LORETA-L2 method for imaging well localized sources such as the P30 component of the SEP. The mean and standard deviation of the distance between the location of maximum CCD value and the central sulcus, estimated by the minimum Lp-norm (with p equal to 1), L1-norm (the Linear programming) and LORETA-Lp (with p equal to 1) methods, were 4, 7, 7 mm and 3, 4, 2 mm, respectively (after converting into Talairach coordinates). The mean and standard deviation of the aforementioned distance, estimated by the MNLS, LORETA with Lp-norm (p equal to 1.5 and 2.0), sLORETA and the minimum Lp-norm (p equal to 1.5) methods, were over 11 mm and 6 mm, respectively.
The present experimental study suggests that L1-norm-based algorithms provide better performance than L2 and L1.5-norm-based algorithms, in the context of CCD imaging of well localized sources induced by somatosensory electrical stimulation of median nerve at the wrist. Objective: EEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI. Methods: Five source imaging algorithms, including the minimum norm least square (MNLS), LORETA withLp-norm (pequal to 1, 1.5 and 2), sLORETA, the minimumLp-norm (pequal to 1 and 1.5; whenp=2, the MNLS method is mathematically equivalent to the minimumLp-norm) andL1-norm (the linear programming) methods, were evaluated in a group of 10 human subjects, in a paradigm with somatosensory stimulation. Cortical current density (CCD) distributions were estimated from the scalp somatosensory evoked potentials (SEPs), at approximately 30 ms following electrical stimulation of median nerve at the wrist. Realistic geometry boundary element head models were constructed from the MRIs of each subject and used in the CCD analysis. Functional MRI results obtained from a motor task and sensory stimulation in all subjects were used to identify the central sulcus, motor and sensory areas. In three patients undergoing neurosurgical evaluation, ECoGs were recorded in response to the somatosensory stimulation, and were used to help determine the central sulcus and the sensory cortex. Results: The CCD distributions estimated by theLp-norm and LORETA-Lpmethods were smoother when thepvalues were high. The LORETA based on theL1-norm performed better than the LORETA-L2method for imaging well localized sources such as the P30 component of the SEP. The mean and standard deviation of the distance between the location of maximum CCD value and the central sulcus, estimated by the minimumLp-norm (withpequal to 1),L1-norm (the Linear programming) and LORETA-Lp(withpequal to 1) methods, were 4, 7, 7 mm and 3, 4, 2 mm, respectively (after converting into Talairach coordinates). The mean and standard deviation of the aforementioned distance, estimated by the MNLS, LORETA withLp-norm (pequal to 1.5 and 2.0), sLORETA and the minimumLp-norm (pequal to 1.5) methods, were over 11 mm and 6 mm, respectively. Conclusions: The present experimental study suggests thatL1-norm-based algorithms provide better performance thanL2andL1.5-norm-based algorithms, in the context of CCD imaging of well localized sources induced by somatosensory electrical stimulation of median nerve at the wrist. EEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI.OBJECTIVEEEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI.Five source imaging algorithms, including the minimum norm least square (MNLS), LORETA with L(p)-norm (p equal to 1, 1.5 and 2), sLORETA, the minimum L(p)-norm (p equal to 1 and 1.5; when p=2, the MNLS method is mathematically equivalent to the minimum L(p)-norm) and L(1)-norm (the linear programming) methods, were evaluated in a group of 10 human subjects, in a paradigm with somatosensory stimulation. Cortical current density (CCD) distributions were estimated from the scalp somatosensory evoked potentials (SEPs), at approximately 30 ms following electrical stimulation of median nerve at the wrist. Realistic geometry boundary element head models were constructed from the MRIs of each subject and used in the CCD analysis. Functional MRI results obtained from a motor task and sensory stimulation in all subjects were used to identify the central sulcus, motor and sensory areas. In three patients undergoing neurosurgical evaluation, ECoGs were recorded in response to the somatosensory stimulation, and were used to help determine the central sulcus and the sensory cortex.METHODSFive source imaging algorithms, including the minimum norm least square (MNLS), LORETA with L(p)-norm (p equal to 1, 1.5 and 2), sLORETA, the minimum L(p)-norm (p equal to 1 and 1.5; when p=2, the MNLS method is mathematically equivalent to the minimum L(p)-norm) and L(1)-norm (the linear programming) methods, were evaluated in a group of 10 human subjects, in a paradigm with somatosensory stimulation. Cortical current density (CCD) distributions were estimated from the scalp somatosensory evoked potentials (SEPs), at approximately 30 ms following electrical stimulation of median nerve at the wrist. Realistic geometry boundary element head models were constructed from the MRIs of each subject and used in the CCD analysis. Functional MRI results obtained from a motor task and sensory stimulation in all subjects were used to identify the central sulcus, motor and sensory areas. In three patients undergoing neurosurgical evaluation, ECoGs were recorded in response to the somatosensory stimulation, and were used to help determine the central sulcus and the sensory cortex.The CCD distributions estimated by the L(p)-norm and LORETA-L(p) methods were smoother when the p values were high. The LORETA based on the L(1)-norm performed better than the LORETA-L(2) method for imaging well localized sources such as the P30 component of the SEP. The mean and standard deviation of the distance between the location of maximum CCD value and the central sulcus, estimated by the minimum L(p)-norm (with p equal to 1), L(1)-norm (the Linear programming) and LORETA-L(p) (with p equal to 1) methods, were 4, 7, 7 mm and 3, 4, 2 mm, respectively (after converting into Talairach coordinates). The mean and standard deviation of the aforementioned distance, estimated by the MNLS, LORETA with L(p)-norm (p equal to 1.5 and 2.0), sLORETA and the minimum L(p)-norm (p equal to 1.5) methods, were over 11 mm and 6 mm, respectively.RESULTSThe CCD distributions estimated by the L(p)-norm and LORETA-L(p) methods were smoother when the p values were high. The LORETA based on the L(1)-norm performed better than the LORETA-L(2) method for imaging well localized sources such as the P30 component of the SEP. The mean and standard deviation of the distance between the location of maximum CCD value and the central sulcus, estimated by the minimum L(p)-norm (with p equal to 1), L(1)-norm (the Linear programming) and LORETA-L(p) (with p equal to 1) methods, were 4, 7, 7 mm and 3, 4, 2 mm, respectively (after converting into Talairach coordinates). The mean and standard deviation of the aforementioned distance, estimated by the MNLS, LORETA with L(p)-norm (p equal to 1.5 and 2.0), sLORETA and the minimum L(p)-norm (p equal to 1.5) methods, were over 11 mm and 6 mm, respectively.The present experimental study suggests that L(1)-norm-based algorithms provide better performance than L(2) and L(1.5)-norm-based algorithms, in the context of CCD imaging of well localized sources induced by somatosensory electrical stimulation of median nerve at the wrist.CONCLUSIONSThe present experimental study suggests that L(1)-norm-based algorithms provide better performance than L(2) and L(1.5)-norm-based algorithms, in the context of CCD imaging of well localized sources induced by somatosensory electrical stimulation of median nerve at the wrist. EEG source imaging provides important information regarding the underlying neural activity from noninvasive electrophysiological measurements. The aim of the present study was to evaluate source reconstruction techniques by means of the intracranial electrocorticograms (ECoGs) and functional MRI. Five source imaging algorithms, including the minimum norm least square (MNLS), LORETA with L(p)-norm (p equal to 1, 1.5 and 2), sLORETA, the minimum L(p)-norm (p equal to 1 and 1.5; when p=2, the MNLS method is mathematically equivalent to the minimum L(p)-norm) and L(1)-norm (the linear programming) methods, were evaluated in a group of 10 human subjects, in a paradigm with somatosensory stimulation. Cortical current density (CCD) distributions were estimated from the scalp somatosensory evoked potentials (SEPs), at approximately 30 ms following electrical stimulation of median nerve at the wrist. Realistic geometry boundary element head models were constructed from the MRIs of each subject and used in the CCD analysis. Functional MRI results obtained from a motor task and sensory stimulation in all subjects were used to identify the central sulcus, motor and sensory areas. In three patients undergoing neurosurgical evaluation, ECoGs were recorded in response to the somatosensory stimulation, and were used to help determine the central sulcus and the sensory cortex. The CCD distributions estimated by the L(p)-norm and LORETA-L(p) methods were smoother when the p values were high. The LORETA based on the L(1)-norm performed better than the LORETA-L(2) method for imaging well localized sources such as the P30 component of the SEP. The mean and standard deviation of the distance between the location of maximum CCD value and the central sulcus, estimated by the minimum L(p)-norm (with p equal to 1), L(1)-norm (the Linear programming) and LORETA-L(p) (with p equal to 1) methods, were 4, 7, 7 mm and 3, 4, 2 mm, respectively (after converting into Talairach coordinates). The mean and standard deviation of the aforementioned distance, estimated by the MNLS, LORETA with L(p)-norm (p equal to 1.5 and 2.0), sLORETA and the minimum L(p)-norm (p equal to 1.5) methods, were over 11 mm and 6 mm, respectively. The present experimental study suggests that L(1)-norm-based algorithms provide better performance than L(2) and L(1.5)-norm-based algorithms, in the context of CCD imaging of well localized sources induced by somatosensory electrical stimulation of median nerve at the wrist. |
| Author | He, Bin He, Eric J. Bai, Xiaoxiao Towle, Vernon L. |
| AuthorAffiliation | 1 University of Minnesota, Department of Biomedical Engineering 2 University of Chicago, Department of Neurology |
| AuthorAffiliation_xml | – name: 1 University of Minnesota, Department of Biomedical Engineering – name: 2 University of Chicago, Department of Neurology |
| Author_xml | – sequence: 1 givenname: Xiaoxiao surname: Bai fullname: Bai, Xiaoxiao organization: University of Minnesota, Department of Biomedical Engineering, 7-105 NHH, 312 Church Street, Minneapolis, MN 55455, USA – sequence: 2 givenname: Vernon L. surname: Towle fullname: Towle, Vernon L. organization: University of Chicago, Department of Neurology, USA – sequence: 3 givenname: Eric J. surname: He fullname: He, Eric J. organization: University of Minnesota, Department of Biomedical Engineering, 7-105 NHH, 312 Church Street, Minneapolis, MN 55455, USA – sequence: 4 givenname: Bin surname: He fullname: He, Bin email: binhe@umn.edu organization: University of Minnesota, Department of Biomedical Engineering, 7-105 NHH, 312 Church Street, Minneapolis, MN 55455, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/17303438$$D View this record in MEDLINE/PubMed |
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| Keywords | Source imaging Sensory cortex Brain imaging Cortical current density High resolution EEG Somatosensory evoked potentials Inverse Problem Central sulcus |
| Language | English |
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| SubjectTerms | Accuracy Algorithms Brain imaging Central sulcus Cerebral Cortex - physiology Computer simulation Cortical current density Electroencephalography Electroencephalography - methods Electrophysiology Evoked Potentials, Somatosensory High resolution EEG Human subjects Humans Inverse Problem Inverse problems Linear programming Magnetic Resonance Imaging NMR Nuclear magnetic resonance Regularization methods Sensory cortex Somatosensory evoked potentials Source imaging Studies Tomography |
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| Title | Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI |
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