Noninvasive identification of human central sulcus: a comparison of gyral morphology, functional MRI, dipole localization, and direct cortical mapping

Towle, Vernon L.; Khorasani, Leila; Uftring, S. J.; Pelizzari, Charles A.; Erickson, Robert K.; Spire, Jean-Paul; Hoffmann, Kenneth R.; Chu, David; Scherg, Michael

doi:10.1016/s1053-8119(03)00147-2

Cited by 55 publications

(43 citation statements)

References 97 publications

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“…The P/N20 components of this data set were previously studied using a spherical dipole localization model (Towle et al, 2003 (He et al, 2002a;Waberski et al, 2002a) indicated that P30 component around 30 ms after stimulation provided similar results for localizing the sensory area and identifying the central sulcus as the N20 component. In the present study, the P30 component was used for all patients and normal subjects.…”

Section: Experimental Studiesmentioning

confidence: 70%

“…One-hundred and fifty points on the scalp were also digitized along with the electrode locations and fiducial points to provide a patient-specific coordinate reference for registering the electrode locations with the MR images. A surface-matching algorithm was used to fit the digitized scalp to the MR images segmented scalp (Pelizzari et al, 1989;Towle et al, 2003).…”

Section: Registration Of Scalp Electrodes-mentioning

confidence: 99%

“…The electrode arrays and radio-opaque markers placed on the contralateral scalp were determined from a 3-dimensional reconstruction of skull films (Metz and Fencil, 1989). They were then located on a hybrid skin/brain segmented surface for each patient using the surface-fitting algorithm (Towle et al, 2003). The fitting process was finalized by ensuring that the distances to the anterior and posterior poles and the inferior surface of the brain were proportional in the lateral skull film and the MR images.…”

Section: 26mentioning

confidence: 99%

“…This required the extrapolation of electrode locations, which were not visible in the photographs. Neither technique appeared clearly superior to the other, so the results of the two strategies were averaged, yielding an error term for the grid registration process (Towle et al, 2003).…”

Section: 26mentioning

confidence: 99%

“…MRI results for three patients and seven normal subjects were obtained from the motor task and sensory stimulation (Towle et al, 2003). The motor task was performed by a finger opposition movement.…”

Section: Identification Of the Central Sulcus Based On Functional Mrimentioning

confidence: 99%

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Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Xiang

Towle

et al. 2007

NeuroImage

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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 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.Results-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.Conclusions-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.

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Section: Experimental Studiesmentioning

confidence: 70%

Section: Registration Of Scalp Electrodes-mentioning

confidence: 99%

Section: 26mentioning

confidence: 99%

Section: 26mentioning

confidence: 99%

Section: Identification Of the Central Sulcus Based On Functional Mrimentioning

confidence: 99%

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Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Xiang

Towle

et al. 2007

NeuroImage

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Optimized Multilayer Perceptron for Sensorimotor Functional Mapping Based on a Few Minutes of Intracranial Electroencephalogram Data

Iktimal,

Spencer,

Alkawadri

2024

Annals of Neurology

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Using 6‐minute free‐running intracranial‐electroencephalogram (icEEG) during sleep, an optimized multilayer perceptron (MLP) neural network accurately maps the sensorimotor cortex (SM) and identifies the anterior lip of the central sulcus (CS) in intractable epilepsy patients. We calculated 6 performance metrics to evaluate the MLP's efficacy: accuracy, area under the curve (AUC), recall, precision, F1‐scores, and specificity. Each layer had 4 neurons with hyperbolic TanH activation function and 4 with Gaussian distribution function. Conventional 10‐fold cross‐validation was used. Feature extension (ε) and weighted imbalanced data (w) improved MLP performance. ANN NEUROL 2024

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Sparse source imaging in electroencephalography with accurate field modeling

Ding¹,

He²

2007

Human Brain Mapping

115

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We have developed a new L1-norm based generalized minimum norm estimate (GMNE) and have fully characterized the concept of sparseness regularization inherited in the proposed algorithm, which is termed as sparse source imaging (SSI). The new SSI algorithm corrects inaccurate source field modeling in previously reported L1-norm GMNEs and proposes that sparseness a priori should only be applied to the regularization term, not to the data term in the formulation of the regularized inverse problem. A new solver to the newly developed SSI has been adopted and known as the second order cone programming (SOCP). The new SSI is assessed by a series of simulations and then evaluated using somatosensory evoked potential (SEP) data with both scalp and subdural recordings in a human subject. The performance of SSI is compared with other L1-norm GMNEs and L2-norm GMNEs using three evaluation metrics, i.e. localization error, orientation error, and strength percentage. The present simulation results indicate that the new SSI has significantly improved performance in all evaluation metrics, especially in the metric of orientation error. L2-norm GMNEs show large orientation errors because of the smooth regularization. The previously reported L1-norm GMNEs show large orientation errors due to the inaccurate source field modeling. The SEP source imaging results indicate that SSI has the best accuracy in the prediction of subdural potential field as validated by direct subdural recordings. The new SSI algorithm is also applicable to MEG source imaging.

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Noninvasive identification of human central sulcus: a comparison of gyral morphology, functional MRI, dipole localization, and direct cortical mapping

Cited by 55 publications

References 97 publications

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Evaluation of cortical current density imaging methods using intracranial electrocorticograms and functional MRI

Optimized Multilayer Perceptron for Sensorimotor Functional Mapping Based on a Few Minutes of Intracranial Electroencephalogram Data

Sparse source imaging in electroencephalography with accurate field modeling

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