Publications by authors named "Matti Stenroos"

32 Publications

Spatial sampling of MEG and EEG based on generalized spatial-frequency analysis and optimal design.

Neuroimage 2021 Nov 28;245:118747. Epub 2021 Nov 28.

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Aalto FI-00076, Finland.

In this paper, we analyze spatial sampling of electro- (EEG) and magnetoencephalography (MEG), where the electric or magnetic field is typically sampled on a curved surface such as the scalp. By simulating fields originating from a representative adult-male head, we study the spatial-frequency content in EEG as well as in on- and off-scalp MEG. This analysis suggests that on-scalp MEG, off-scalp MEG and EEG can benefit from up to 280, 90 and 110 spatial samples, respectively. In addition, we suggest a new approach to obtain sensor locations that are optimal with respect to prior assumptions. The approach also allows to control, e.g., the uniformity of the sensor locations. Based on our simulations, we argue that for a low number of spatial samples, model-informed non-uniform sampling can be beneficial. For a large number of samples, uniform sampling grids yield nearly the same total information as the model-informed grids.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118747DOI Listing
November 2021

Multi-locus transcranial magnetic stimulation system for electronically targeted brain stimulation.

Brain Stimul 2021 Nov 21;15(1):116-124. Epub 2021 Nov 21.

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

Background: Transcranial magnetic stimulation (TMS) allows non-invasive stimulation of the cortex. In multi-locus TMS (mTMS), the stimulating electric field (E-field) is controlled electronically without coil movement by adjusting currents in the coils of a transducer.

Objective: To develop an mTMS system that allows adjusting the location and orientation of the E-field maximum within a cortical region.

Methods: We designed and manufactured a planar 5-coil mTMS transducer to allow controlling the maximum of the induced E-field within a cortical region approximately 30 mm in diameter. We developed electronics with a design consisting of independently controlled H-bridge circuits to drive up to six TMS coils. To control the hardware, we programmed software that runs on a field-programmable gate array and a computer. To induce the desired E-field in the cortex, we developed an optimization method to calculate the currents needed in the coils. We characterized the mTMS system and conducted a proof-of-concept motor-mapping experiment on a healthy volunteer. In the motor mapping, we kept the transducer placement fixed while electronically shifting the E-field maximum on the precentral gyrus and measuring electromyography from the contralateral hand.

Results: The transducer consists of an oval coil, two figure-of-eight coils, and two four-leaf-clover coils stacked on top of each other. The technical characterization indicated that the mTMS system performs as designed. The measured motor evoked potential amplitudes varied consistently as a function of the location of the E-field maximum.

Conclusion: The developed mTMS system enables electronically targeted brain stimulation within a cortical region.
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http://dx.doi.org/10.1016/j.brs.2021.11.014DOI Listing
November 2021

Prefrontal Theta-Phase Synchronized Brain Stimulation With Real-Time EEG-Triggered TMS.

Front Hum Neurosci 2021 21;15:691821. Epub 2021 Jun 21.

Department of Neurology and Stroke, University of Tübingen, Tübingen, Germany.

Background: Theta-band neuronal oscillations in the prefrontal cortex are associated with several cognitive functions. Oscillatory phase is an important correlate of excitability and phase synchrony mediates information transfer between neuronal populations oscillating at that frequency. The ability to extract and exploit the prefrontal theta rhythm in real time in humans would facilitate insight into neurophysiological mechanisms of cognitive processes involving the prefrontal cortex, and development of brain-state-dependent stimulation for therapeutic applications.

Objectives: We investigate individual source-space beamforming-based estimation of the prefrontal theta oscillation as a method to target specific phases of the ongoing theta oscillations in the human dorsomedial prefrontal cortex (DMPFC) with real-time EEG-triggered transcranial magnetic stimulation (TMS). Different spatial filters for extracting the prefrontal theta oscillation from EEG signals are compared and additional signal quality criteria are assessed to take into account the dynamics of this cortical oscillation.

Methods: Twenty two healthy participants were recruited for anatomical MRI scans and EEG recordings with 18 composing the final analysis. We calculated individual spatial filters based on EEG beamforming in source space. The extracted EEG signal was then used to simulate real-time phase-detection and quantify the accuracy as compared to post-hoc phase estimates. Different spatial filters and triggering parameters were compared. Finally, we validated the feasibility of this approach by actual real-time triggering of TMS pulses at different phases of the prefrontal theta oscillation.

Results: Higher phase-detection accuracy was achieved using individualized source-based spatial filters, as compared to an average or standard Laplacian filter, and also by detecting and avoiding periods of low theta amplitude and periods containing a phase reset. Using optimized parameters, prefrontal theta-phase synchronized TMS of DMPFC was achieved with an accuracy of ±55°.

Conclusion: This study demonstrates the feasibility of triggering TMS pulses during different phases of the ongoing prefrontal theta oscillation in real time. This method is relevant for brain state-dependent stimulation in human studies of cognition. It will also enable new personalized therapeutic repetitive TMS protocols for more effective treatment of neuropsychiatric disorders.
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http://dx.doi.org/10.3389/fnhum.2021.691821DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8255809PMC
June 2021

Individual head models for estimating the TMS-induced electric field in rat brain.

Sci Rep 2020 10 15;10(1):17397. Epub 2020 Oct 15.

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.

In transcranial magnetic stimulation (TMS), the initial cortical activation due to stimulation is determined by the state of the brain and the magnitude, waveform, and direction of the induced electric field (E-field) in the cortex. The E-field distribution depends on the conductivity geometry of the head. The effects of deviations from a spherically symmetric conductivity profile have been studied in detail in humans. In small mammals, such as rats, these effects are more pronounced due to their less spherical head, proportionally much thicker neck region, and overall much smaller size compared to the TMS coils. In this study, we describe a simple method for building individual realistically shaped head models for rats from high-resolution X-ray tomography images. We computed the TMS-induced E-field with the boundary element method and assessed the effect of head-model simplifications on the estimated E-field. The deviations from spherical symmetry have large, non-trivial effects on the E-field distribution: for some coil orientations, the strongest stimulation is in the brainstem even when the coil is over the motor cortex. With modelling prior to an experiment, such problematic coil orientations can be avoided for more accurate targeting.
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http://dx.doi.org/10.1038/s41598-020-74431-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567095PMC
October 2020

Concurrent electrophysiological and hemodynamic measurements of evoked neural oscillations in human visual cortex using sparsely interleaved fast fMRI and EEG.

Neuroimage 2020 08 7;217:116910. Epub 2020 May 7.

Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland. Electronic address:

Electroencephalography (EEG) concurrently collected with functional magnetic resonance imaging (fMRI) is heavily distorted by the repetitive gradient coil switching during the fMRI acquisition. The performance of the typical template-based gradient artifact suppression method can be suboptimal because the artifact changes over time. Gradient artifact residuals also impede the subsequent suppression of ballistocardiography artifacts. Here we propose recording continuous EEG with temporally sparse fast fMRI (fast fMRI-EEG) to minimize the EEG artifacts caused by MRI gradient coil switching without significantly compromising the field-of-view and spatiotemporal resolution of fMRI. Using simultaneous multi-slice inverse imaging to achieve whole-brain fMRI with isotropic 5-mm resolution in 0.1 ​s, and performing these acquisitions once every 2 ​s, we have 95% of the duty cycle available to record EEG with substantially less gradient artifact. We found that the standard deviation of EEG signals over the entire acquisition period in fast fMRI-EEG was reduced to 54% of that in conventional concurrent echo-planar imaging (EPI) and EEG recordings (EPI-EEG) across participants. When measuring 15-Hz steady-state visual evoked potentials (SSVEPs), the baseline-normalized oscillatory neural response in fast fMRI-EEG was 2.5-fold of that in EPI-EEG. The functional MRI responses associated with the SSVEP delineated by EPI and fast fMRI were similar in the spatial distribution, the elicited waveform, and detection power. Sparsely interleaved fast fMRI-EEG provides high-quality EEG without substantially compromising the quality of fMRI in evoked response measurements, and has the potential utility for applications where the onset of the target stimulus cannot be precisely determined, such as epilepsy.
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http://dx.doi.org/10.1016/j.neuroimage.2020.116910DOI Listing
August 2020

Comparison of beamformer implementations for MEG source localization.

Neuroimage 2020 08 8;216:116797. Epub 2020 Apr 8.

Megin Oy, Helsinki, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki, Finland.

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3-15 ​dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization.
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http://dx.doi.org/10.1016/j.neuroimage.2020.116797DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7322560PMC
August 2020

Short-interval intracortical inhibition in human primary motor cortex: A multi-locus transcranial magnetic stimulation study.

Neuroimage 2019 12 13;203:116194. Epub 2019 Sep 13.

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

Short-interval intracortical inhibition (SICI) has been studied with paired-pulse transcranial magnetic stimulation (TMS) by administering two pulses at a millisecond-scale interstimulus interval (ISI) to a single cortical target. It has, however, been difficult to study the interaction of nearby cortical targets with paired-pulse TMS. To overcome this limitation, we have developed a multi-locus TMS (mTMS) device, which allows controlling the stimulus location electronically. Here, we applied mTMS to study SICI in primary motor cortex with paired pulses targeted to adjacent locations, aiming to quantify the extent of the cortical region producing SICI in the location of a test stimulus. We varied the location and timing of the conditioning stimulus with respect to a test stimulus targeted to the cortical hotspot of the abductor pollicis brevis (APB) in order to study their effects on motor evoked potentials. We further applied a two-coil protocol with the conditioning stimulus given by an oval coil only to the surroundings of the APB hotspot, to which a subsequent test stimulus was administered with a figure-of-eight coil. The strongest SICI occurred at ISIs below 1 ms and at ISIs around 2.5 ms. These ISIs increased when the conditioning stimulus receded from the APB hotspot. Our two-coil paired-pulse TMS study suggests that SICI at ISIs of 0.5 and 2.5 ms originate from different mechanisms or neuronal elements.
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http://dx.doi.org/10.1016/j.neuroimage.2019.116194DOI Listing
December 2019

Real-time computation of the TMS-induced electric field in a realistic head model.

Neuroimage 2019 12 5;203:116159. Epub 2019 Sep 5.

Aalto University, Department of Neuroscience and Biomedical Engineering, P.O. Box 12200, FI-00076, Aalto, Finland.

Transcranial magnetic stimulation (TMS) is often targeted using a model of TMS-induced electric field (E). In such navigated TMS, the E-field models have been based on spherical approximation of the head. Such models omit the effects of cerebrospinal fluid (CSF) and gyral folding, leading to potentially large errors in the computed E-field. So far, realistic models have been too slow for interactive TMS navigation. We present computational methods that enable real-time solving of the E-field in a realistic five-compartment (5-C) head model that contains isotropic white matter, gray matter, CSF, skull and scalp. Using reciprocity and Geselowitz integral equation, we separate the computations to coil-dependent and -independent parts. For the Geselowitz integrals, we present a fast numerical quadrature. Further, we present a moment-matching approach for optimizing dipole-based coil models. We verified and benchmarked the new methods using simulations with over 100 coil locations. The new quadrature introduced a relative error (RE) of 0.3-0.6%. For a coil model with 42 dipoles, the total RE of the quadrature and coil model was 0.44-0.72%. Taking also other model errors into account, the contribution of the new approximations to the RE was 0.1%. For comparison, the RE due to omitting the separation of white and gray matter was >11%, and the RE due to omitting also the CSF was >23%. After the coil-independent part of the model has been built, E-fields can be computed very quickly: Using a standard PC and basic GPU, our solver computed the full E-field in a 5-C model in 9000 points on the cortex in 27 coil positions per second (cps). When the separation of white and gray matter was omitted, the speed was 43-65 cps. Solving only one component of the E-field tripled the speed. The presented methods enable real-time solving of the TMS-induced E-field in a realistic head model that contains the CSF and gyral folding. The new methodology allows more accurate targeting and precise adjustment of stimulation intensity during experimental or clinical TMS mapping.
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http://dx.doi.org/10.1016/j.neuroimage.2019.116159DOI Listing
December 2019

The impact of improved MEG-MRI co-registration on MEG connectivity analysis.

Neuroimage 2019 08 25;197:354-367. Epub 2019 Apr 25.

Department of Neuroscience, Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy; Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, via dei Vestini 31, 66100 Chieti, Italy.

Co-registration between structural head images and functional MEG data is needed for anatomically-informed MEG data analysis. Despite the efforts to minimize the co-registration error, conventional landmark- and surface-based strategies for co-registering head and MEG device coordinates achieve an accuracy of typically 5-10 mm. Recent advances in instrumentation and technical solutions, such as the development of hybrid ultra-low-field (ULF) MRI-MEG devices or the use of 3D-printed individualized foam head-casts, promise unprecedented co-registration accuracy, i.e., 2 mm or better. In the present study, we assess through simulations the impact of such an improved co-registration on MEG connectivity analysis. We generated synthetic MEG recordings for pairs of connected cortical sources with variable locations. We then assessed the capability to reconstruct source-level connectivity from these recordings for 0-15-mm co-registration error, three levels of head modeling detail (one-, three- and four-compartment models), two source estimation techniques (linearly constrained minimum-variance beamforming and minimum-norm estimation MNE) and five separate connectivity metrics (imaginary coherency, phase-locking value, amplitude-envelope correlation, phase-slope index and frequency-domain Granger causality). We found that beamforming can better take advantage of an accurate co-registration than MNE. Specifically, when the co-registration error was smaller than 3 mm, the relative error in connectivity estimates was down to one-third of that observed with typical co-registration errors. MNE provided stable results for a wide range of co-registration errors, while the performance of beamforming rapidly degraded as the co-registration error increased. Furthermore, we found that even moderate co-registration errors (>6 mm, on average) essentially decrease the difference of four- and three- or one-compartment models. Hence, a precise co-registration is important if one wants to take full advantage of highly accurate head models for connectivity analysis. We conclude that an improved co-registration will be beneficial for reliable connectivity analysis and effective use of highly accurate head models in future MEG connectivity studies.
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http://dx.doi.org/10.1016/j.neuroimage.2019.04.061DOI Listing
August 2019

Individual Activation Patterns After the Stimulation of Different Motor Areas: A Transcranial Magnetic Stimulation-Electroencephalography Study.

Brain Connect 2018 Sep;8(7):420-428

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) enables one to study effective connectivity and activation order in neuronal networks. To characterize effective connectivity originating from the primary motor cortex (M1), dorsal premotor area (PMd), and supplementary motor area (SMA). Three right-handed volunteers (two men, aged 25-30 years) participated in a navigated TMS-EEG experiment. M1, PMd, and SMA over the nondominant hemisphere were stimulated with 150 TMS pulses. Minimum-norm estimates were derived from the EEG data to estimate the spatial spreading of TMS-elicited neuronal activation on an individual level. The activation order of the cortical areas varied depending on the stimulated area. There were similarities and differences in the spatial distribution of the TMS-evoked potentials between subjects. Similarities in cortical activation patterns were seen at short poststimulus latencies and the differences at long latencies. This pilot study suggests that cortical activation patterns and the activation order of motor areas differ interindividually and depend on the stimulated motor area. It further indicates that TMS-activated effective connections or underlying structural connections vary between subjects. The spatial patterns of TMS-evoked potentials differ between subjects especially at long latencies, when probably more complex neuronal networks are active.
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http://dx.doi.org/10.1089/brain.2018.0593DOI Listing
September 2018

Requirements for Coregistration Accuracy in On-Scalp MEG.

Brain Topogr 2018 11 22;31(6):931-948. Epub 2018 Jun 22.

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, 00076, Aalto, Finland.

Recent advances in magnetic sensing has made on-scalp magnetoencephalography (MEG) possible. In particular, optically-pumped magnetometers (OPMs) have reached sensitivity levels that enable their use in MEG. In contrast to the SQUID sensors used in current MEG systems, OPMs do not require cryogenic cooling and can thus be placed within millimetres from the head, enabling the construction of sensor arrays that conform to the shape of an individual's head. To properly estimate the location of neural sources within the brain, one must accurately know the position and orientation of sensors in relation to the head. With the adaptable on-scalp MEG sensor arrays, this coregistration becomes more challenging than in current SQUID-based MEG systems that use rigid sensor arrays. Here, we used simulations to quantify how accurately one needs to know the position and orientation of sensors in an on-scalp MEG system. The effects that different types of localisation errors have on forward modelling and source estimates obtained by minimum-norm estimation, dipole fitting, and beamforming are detailed. We found that sensor position errors generally have a larger effect than orientation errors and that these errors affect the localisation accuracy of superficial sources the most. To obtain similar or higher accuracy than with current SQUID-based MEG systems, RMS sensor position and orientation errors should be [Formula: see text] and [Formula: see text], respectively.
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http://dx.doi.org/10.1007/s10548-018-0656-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6182446PMC
November 2018

Truncated RAP-MUSIC (TRAP-MUSIC) for MEG and EEG source localization.

Neuroimage 2018 02 8;167:73-83. Epub 2017 Nov 8.

Department of Neuroscience and Biomedical Engineering (NBE), Aalto University School of Science, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital (HUH), Helsinki, Finland.

Electrically active brain regions can be located applying MUltiple SIgnal Classification (MUSIC) on magneto- or electroencephalographic (MEG; EEG) data. We introduce a new MUSIC method, called truncated recursively-applied-and-projected MUSIC (TRAP-MUSIC). It corrects a hidden deficiency of the conventional RAP-MUSIC algorithm, which prevents estimation of the true number of brain-signal sources accurately. The correction is done by applying a sequential dimension reduction to the signal-subspace projection. We show that TRAP-MUSIC significantly improves the performance of MUSIC-type localization; in particular, it successfully and robustly locates active brain regions and estimates their number. We compare TRAP-MUSIC and RAP-MUSIC in simulations with varying key parameters, e.g., signal-to-noise ratio, correlation between source time-courses, and initial estimate for the dimension of the signal space. In addition, we validate TRAP-MUSIC with measured MEG data. We suggest that with the proposed TRAP-MUSIC method, MUSIC-type localization could become more reliable and suitable for various online and offline MEG and EEG applications.
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http://dx.doi.org/10.1016/j.neuroimage.2017.11.013DOI Listing
February 2018

Coil optimisation for transcranial magnetic stimulation in realistic head geometry.

Brain Stimul 2017 Jul - Aug;10(4):795-805. Epub 2017 Apr 15.

Department of Neuroscience and Biomedical Engineering, Aalto University, P.O. Box 12200, FI-00076 AALTO, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00029 HUS, Helsinki, Finland.

Background: Transcranial magnetic stimulation (TMS) allows focal, non-invasive stimulation of the cortex. A TMS pulse is inherently weakly coupled to the cortex; thus, magnetic stimulation requires both high current and high voltage to reach sufficient intensity. These requirements limit, for example, the maximum repetition rate and the maximum number of consecutive pulses with the same coil due to the rise of its temperature.

Objective: To develop methods to optimise, design, and manufacture energy-efficient TMS coils in realistic head geometry with an arbitrary overall coil shape.

Methods: We derive a semi-analytical integration scheme for computing the magnetic field energy of an arbitrary surface current distribution, compute the electric field induced by this distribution with a boundary element method, and optimise a TMS coil for focal stimulation. Additionally, we introduce a method for manufacturing such a coil by using Litz wire and a coil former machined from polyvinyl chloride.

Results: We designed, manufactured, and validated an optimised TMS coil and applied it to brain stimulation. Our simulations indicate that this coil requires less than half the power of a commercial figure-of-eight coil, with a 41% reduction due to the optimised winding geometry and a partial contribution due to our thinner coil former and reduced conductor height. With the optimised coil, the resting motor threshold of abductor pollicis brevis was reached with the capacitor voltage below 600 V and peak current below 3000 A.

Conclusion: The described method allows designing practical TMS coils that have considerably higher efficiency than conventional figure-of-eight coils.
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http://dx.doi.org/10.1016/j.brs.2017.04.001DOI Listing
February 2018

Measuring MEG closer to the brain: Performance of on-scalp sensor arrays.

Neuroimage 2017 02 19;147:542-553. Epub 2016 Dec 19.

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, FI-00076 Aalto, Finland; Aalto NeuroImaging, Aalto University, FI-00076 Aalto, Finland.

Optically-pumped magnetometers (OPMs) have recently reached sensitivity levels required for magnetoencephalography (MEG). OPMs do not need cryogenics and can thus be placed within millimetres from the scalp into an array that adapts to the individual head size and shape, thereby reducing the distance from cortical sources to the sensors. Here, we quantified the improvement in recording MEG with hypothetical on-scalp OPM arrays compared to a 306-channel state-of-the-art SQUID array (102 magnetometers and 204 planar gradiometers). We simulated OPM arrays that measured either normal (nOPM; 102 sensors), tangential (tOPM; 204 sensors), or all components (aOPM; 306 sensors) of the magnetic field. We built forward models based on magnetic resonance images of 10 adult heads; we employed a three-compartment boundary element model and distributed current dipoles evenly across the cortical mantle. Compared to the SQUID magnetometers, nOPM and tOPM yielded 7.5 and 5.3 times higher signal power, while the correlations between the field patterns of source dipoles were reduced by factors of 2.8 and 3.6, respectively. Values of the field-pattern correlations were similar across nOPM, tOPM and SQUID gradiometers. Volume currents reduced the signals of primary currents on average by 10%, 72% and 15% in nOPM, tOPM and SQUID magnetometers, respectively. The information capacities of the OPM arrays were clearly higher than that of the SQUID array. The dipole-localization accuracies of the arrays were similar while the minimum-norm-based point-spread functions were on average 2.4 and 2.5 times more spread for the SQUID array compared to nOPM and tOPM arrays, respectively.
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http://dx.doi.org/10.1016/j.neuroimage.2016.12.048DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5432137PMC
February 2017

Integral equations and boundary-element solution for static potential in a general piece-wise homogeneous volume conductor.

Authors:
Matti Stenroos

Phys Med Biol 2016 11 25;61(22):N606-N617. Epub 2016 Oct 25.

Department of Neuroscience and Biomedical Engineering, Aalto University, PO Box 12200, FI-00076 Aalto, Finland.

Boundary element methods (BEM) are used for forward computation of bioelectromagnetic fields in multi-compartment volume conductor models. Most BEM approaches assume that each compartment is in contact with at most one external compartment. In this work, I present a general surface integral equation and BEM discretization that remove this limitation and allow BEM modeling of general piecewise-homogeneous medium. The new integral equation allows positioning of field points at junctioned boundary of more than two compartments, enabling the use of linear collocation BEM in such a complex geometry. A modular BEM implementation is presented for linear collocation and Galerkin approaches, starting from the standard formulation. The approach and resulting solver are verified in four ways, including comparisons of volume and surface potentials to those obtained using the finite element method (FEM), and the effect of a hole in skull on electroencephalographic scalp potentials is demonstrated.
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http://dx.doi.org/10.1088/0031-9155/61/22/N606DOI Listing
November 2016

Incorporating and Compensating Cerebrospinal Fluid in Surface-Based Forward Models of Magneto- and Electroencephalography.

PLoS One 2016 29;11(7):e0159595. Epub 2016 Jul 29.

Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02129, United States of America.

MEG/EEG source imaging is usually done using a three-shell (3-S) or a simpler head model. Such models omit cerebrospinal fluid (CSF) that strongly affects the volume currents. We present a four-compartment (4-C) boundary-element (BEM) model that incorporates the CSF and is computationally efficient and straightforward to build using freely available software. We propose a way for compensating the omission of CSF by decreasing the skull conductivity of the 3-S model, and study the robustness of the 4-C and 3-S models to errors in skull conductivity. We generated dense boundary meshes using MRI datasets and automated SimNIBS pipeline. Then, we built a dense 4-C reference model using Galerkin BEM, and 4-C and 3-S test models using coarser meshes and both Galerkin and collocation BEMs. We compared field topographies of cortical sources, applying various skull conductivities and fitting conductivities that minimized the relative error in 4-C and 3-S models. When the CSF was left out from the EEG model, our compensated, unbiased approach improved the accuracy of the 3-S model considerably compared to the conventional approach, where CSF is neglected without any compensation (mean relative error < 20% vs. > 40%). The error due to the omission of CSF was of the same order in MEG and compensated EEG. EEG has, however, large overall error due to uncertain skull conductivity. Our results show that a realistic 4-C MEG/EEG model can be implemented using standard tools and basic BEM, without excessive workload or computational burden. If the CSF is omitted, compensated skull conductivity should be used in EEG.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0159595PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4966911PMC
August 2017

Recovering TMS-evoked EEG responses masked by muscle artifacts.

Neuroimage 2016 Oct 9;139:157-166. Epub 2016 Jun 9.

Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, FI-00076 AALTO, Finland; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, P.O. Box 340, FI-00029 HUS, Finland.

Combined transcranial magnetic stimulation (TMS) and electroencephalography (EEG) often suffers from large muscle artifacts. Muscle artifacts can be removed using signal-space projection (SSP), but this can make the visual interpretation of the remaining EEG data difficult. We suggest to use an additional step after SSP that we call source-informed reconstruction (SIR). SSP-SIR improves substantially the signal quality of artifactual TMS-EEG data, causing minimal distortion in the neuronal signal components. In the SSP-SIR approach, we first project out the muscle artifact using SSP. Utilizing an anatomical model and the remaining signal, we estimate an equivalent source distribution in the brain. Finally, we map the obtained source estimate onto the original signal space, again using anatomical information. This approach restores the neuronal signals in the sensor space and interpolates EEG traces onto the completely rejected channels. The introduced algorithm efficiently suppresses TMS-related muscle artifacts in EEG while retaining well the neuronal EEG topographies and signals. With the presented method, we can remove muscle artifacts from TMS-EEG data and recover the underlying brain responses without compromising the readability of the signals of interest.
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http://dx.doi.org/10.1016/j.neuroimage.2016.05.028DOI Listing
October 2016

Dealing with artifacts in TMS-evoked EEG.

Annu Int Conf IEEE Eng Med Biol Soc 2015 ;2015:230-3

The artifact problem in TMS-evoked EEG is analyzed in an attempt to clarify the nature of the problem and to present solutions. The best way to deal with artifacts is to avoid them; the removal or suppression of the unavoidable artifacts should be based on accurate information about their characteristics and the properties of the signal of interest.
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http://dx.doi.org/10.1109/EMBC.2015.7318342DOI Listing
October 2016

Investigations of sensitivity and resolution of ECG and MCG in a realistically shaped thorax model.

Phys Med Biol 2014 Dec 3;59(23):7141-58. Epub 2014 Nov 3.

Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, PO Box 12200, FI-00076, AALTO, Finland. BioMag Laboratory, HUS Medical Imaging Center, Helsinki, PO Box 340, FI-00029, HUS, Finland.

Solving the inverse problem of electrocardiography (ECG) and magnetocardiography (MCG) is often referred to as cardiac source imaging. Spatial properties of ECG and MCG as imaging systems are, however, not well known. In this modelling study, we investigate the sensitivity and point-spread function (PSF) of ECG, MCG, and combined ECG+MCG as a function of source position and orientation, globally around the ventricles: signal topographies are modelled using a realistically-shaped volume conductor model, and the inverse problem is solved using a distributed source model and linear source estimation with minimal use of prior information. The results show that the sensitivity depends not only on the modality but also on the location and orientation of the source and that the sensitivity distribution is clearly reflected in the PSF. MCG can better characterize tangential anterior sources (with respect to the heart surface), while ECG excels with normally-oriented and posterior sources. Compared to either modality used alone, the sensitivity of combined ECG+MCG is less dependent on source orientation per source location, leading to better source estimates. Thus, for maximal sensitivity and optimal source estimation, the electric and magnetic measurements should be combined.
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http://dx.doi.org/10.1088/0031-9155/59/23/7141DOI Listing
December 2014

Assessment of myocardial infarct size with body surface potential mapping: validation against contrast-enhanced cardiac magnetic resonance imaging.

Ann Noninvasive Electrocardiol 2015 May 18;20(3):240-52. Epub 2014 Sep 18.

Division of Cardiology, Heart and Lung Center, Helsinki University Central Hospital, Helsinki, Finland.

Background: Assessment of myocardial infarct (MI) size is important for therapeutic and prognostic reasons. We used body surface potential mapping (BSPM) to evaluate whether single-lead electrocardiographic variables can assess MI size.

Methods: We performed BSPM with 120 leads covering the front and back chest (plus limb leads) on 57 patients at different phases of MI: acutely, during healing, and in the chronic phase. Final MI size was determined by contrast-enhanced cardiac magnetic resonance imaging (DE-CMR) and correlated with various computed depolarization- and repolarization-phase BSPM variables. We also calculated correlations between BSPM variables and enzymatic MI size (peak CK-MBm).

Results: BSPM variables reflecting the Q- and R wave showed strong correlations with MI size at all stages of MI. R width performed the best, showing its strongest correlation with MI size on the upper right back, there representing the width of the "reciprocal Q wave" (r = 0.64-0.71 for DE-CMR, r = 0.57-0.64 for CK-MBm, P < 0.0001). Repolarization-phase variables showed only weak correlations with MI size in the acute phase, but these correlations improved during MI healing. T-wave variables and the QRSSTT integral showed their best correlations with DE-CMR defined MI size on the precordial area, at best r = -0.57, P < 0.0001 in the chronic phase. The best performing BSPM variables could differentiate between large and small infarcts at all stages of MI.

Conclusions: Computed, single-lead electrocardiographic variables can estimate the final infarct size at all stages of MI, and differentiate large infarcts from small.
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http://dx.doi.org/10.1111/anec.12198DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6931515PMC
May 2015

Comparison of minimum-norm estimation and beamforming in electrocardiography with acute ischemia.

Physiol Meas 2014 Apr 12;35(4):623-38. Epub 2014 Mar 12.

Department of Biomedical Engineering and Computational Science, Aalto University, Espoo, Finland.

In the electrocardiographic (ECG) inverse problem, the electrical activity of the heart is estimated from measured electrocardiogram. A model of thorax conductivities and a model of the cardiac generator is required for the ECG inverse problem. Limitations and errors in methods, models, and data will lead to errors in the estimates. However, in experimental applications, the use of limited or erroneous models is often inevitable due to necessary model simplifications and the difficulty of obtaining accurate 3D anatomical imaging data. In this work, we focus on two methods for solving the inverse problem of ECG in the case of acute ischemia: minimum-norm (MN) estimation and linearly constrained minimum-variance beamforming. We study how these methods perform with different sizes of ischemia and with erroneous conductivity models. The results indicate that the beamformer can localize small ischemia given an accurate model, but it cannot be used for estimating the size of ischemia. The MN estimator is tolerant to geometry errors and excels in estimating the size of ischemia, although the beamformer performs better with accurate model and small ischemia.
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http://dx.doi.org/10.1088/0967-3334/35/4/623DOI Listing
April 2014

Comparison of three-shell and simplified volume conductor models in magnetoencephalography.

Neuroimage 2014 Jul 14;94:337-348. Epub 2014 Jan 14.

Ilmenau University of Technology, Institute of Biomedical Engineering and Informatics, P.O. Box 100565, D-98684 Ilmenau, Germany.

Experimental MEG source imaging studies have typically been carried out with either a spherically symmetric head model or a single-shell boundary-element (BEM) model that is shaped according to the inner skull surface. The concepts and comparisons behind these simplified models have led to misunderstandings regarding the role of skull and scalp in MEG. In this work, we assess the forward-model errors due to different skull/scalp approximations and due to differences and errors in model geometries. We built five anatomical models of a volunteer using a set of T1-weighted MR scans and three common toolboxes. Three of the models represented typical models in experimental MEG, one was manually constructed, and one contained a major segmentation error at the skull base. For these anatomical models, we built forward models using four simplified approaches and a three-shell BEM approach that has been used as reference in previous studies. Our reference model contained in addition the skull fine-structure (spongy bone). We computed signal topographies for cortically constrained sources in the left hemisphere and compared the topographies using relative error and correlation metrics. The results show that the spongy bone has a minimal effect on MEG topographies, and thus the skull approximation of the three-shell model is justified. The three-shell model performed best, followed by the corrected-sphere and single-shell models, whereas the local-spheres and single-sphere models were clearly worse. The three-shell model was the most robust against the introduced segmentation error. In contrast to earlier claims, there was no noteworthy difference in the computation times between the realistically-shaped and sphere-based models, and the manual effort of building a three-shell model and a simplified model is comparable. We thus recommend the realistically-shaped three-shell model for experimental MEG work. In cases where this is not possible, we recommend a realistically-shaped corrected-sphere or single-shell model.
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http://dx.doi.org/10.1016/j.neuroimage.2014.01.006DOI Listing
July 2014

Comparison of spherical and realistically shaped boundary element head models for transcranial magnetic stimulation navigation.

Clin Neurophysiol 2013 Oct 25;124(10):1995-2007. Epub 2013 Jul 25.

Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA.

Objective: MRI-guided real-time transcranial magnetic stimulation (TMS) navigators that apply electromagnetic modeling have improved the utility of TMS. However, their accuracy and speed depends on the assumed volume conductor geometry. Spherical models found in present navigators are computationally fast but may be inaccurate in some areas. Realistically shaped boundary-element models (BEMs) could increase accuracy at a moderate computational cost, but it is unknown which model features have the largest influence on accuracy. Thus, we compared different types of spherical models and BEMs.

Methods: Globally and locally fitted spherical models and different BEMs with either one or three compartments and with different skull-to-brain conductivity ratios (1/1-1/80) were compared against a reference BEM.

Results: The one-compartment BEM at inner skull surface was almost as accurate as the reference BEM. Skull/brain conductivity ratio in the range 1/10-1/80 had only a minor influence. BEMs were superior to spherical models especially in frontal and temporal areas (up to 20mm localization and 40% intensity improvement); in motor cortex all models provided similar results.

Conclusions: One-compartment BEMs offer a good balance between accuracy and computational cost.

Significance: Realistically shaped BEMs may increase TMS navigation accuracy in several brain areas, such as in prefrontal regions often targeted in clinical applications.
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http://dx.doi.org/10.1016/j.clinph.2013.04.019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3790855PMC
October 2013

Predicting recovery of myocardial function by electrocardiography after acute infarction.

Ann Noninvasive Electrocardiol 2013 May 22;18(3):230-9. Epub 2012 Nov 22.

Division of Cardiology, Helsinki University Central Hospital, Helsinki, Finland.

Background: In acute ischemic left ventricular (LV) dysfunction, distinguishing viable myocardium is clinically important.

Methods: Body surface potential mapping (Electrocardiography [ECG] with 123 leads), was recorded in 62 patients with acute coronary syndrome (ACS). ECG variables were computed from de- and repolarization phases. LV segmental wall motion was assessed by echocardiography acutely and after 1 year.

Results: The number of dysfunctional segments (DFS) diminished during follow-up in 37 patients (recovery group) and remained the same or increased in 25 patients (nonrecovery group). Acutely, DFS was 5.7 ± 2.1 versus 4.4 ± 2.4 (P = 0.02), and peak CK-MBm 141 ± 157 versus 156 ± 167 μg/L (P = 0.78) in the recovery versus nonrecovery group. At follow-up, DFS was 1.9 ± 1.7 versus 6.5 ± 2.6 (P < 0.001). The best ECG variable to predict decrease in DFS depended on the region of acute LV dysfunction: The best variable in the left anterior descending region was the integral of the first QRS integral (area under the curve [AUC] 0.82, P = 0.002); in the right coronary artery region, this was the integral of the ST segment (AUC 0.98, P = 0.003); and in the left circumflex region, the area including the ST segment and the T wave (AUC 0.97, P = 0.006).

Conclusions: In ACS patients, computed ECG variables predict recovery of LV function from ischemic myocardial injury, even in the presence of comparable CK-MBm release and LV dysfunction.
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http://dx.doi.org/10.1111/anec.12015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6932276PMC
May 2013

Minimum-norm cortical source estimation in layered head models is robust against skull conductivity error.

Neuroimage 2013 Nov 29;81:265-272. Epub 2013 Apr 29.

MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK.

The conductivity profile of the head has a major effect on EEG signals, but unfortunately the conductivity for the most important compartment, skull, is only poorly known. In dipole modeling studies, errors in modeled skull conductivity have been considered to have a detrimental effect on EEG source estimation. However, as dipole models are very restrictive, those results cannot be generalized to other source estimation methods. In this work, we studied the sensitivity of EEG and combined MEG+EEG source estimation to errors in skull conductivity using a distributed source model and minimum-norm (MN) estimation. We used a MEG/EEG modeling set-up that reflected state-of-the-art practices of experimental research. Cortical surfaces were segmented and realistically-shaped three-layer anatomical head models were constructed, and forward models were built with Galerkin boundary element method while varying the skull conductivity. Lead-field topographies and MN spatial filter vectors were compared across conductivities, and the localization and spatial spread of the MN estimators were assessed using intuitive resolution metrics. The results showed that the MN estimator is robust against errors in skull conductivity: the conductivity had a moderate effect on amplitudes of lead fields and spatial filter vectors, but the effect on corresponding morphologies was small. The localization performance of the EEG or combined MEG+EEG MN estimator was only minimally affected by the conductivity error, while the spread of the estimate varied slightly. Thus, the uncertainty with respect to skull conductivity should not prevent researchers from applying minimum norm estimation to EEG or combined MEG+EEG data. Comparing our results to those obtained earlier with dipole models shows that general judgment on the performance of an imaging modality should not be based on analysis with one source estimation method only.
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http://dx.doi.org/10.1016/j.neuroimage.2013.04.086DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3915841PMC
November 2013

A framework for the design of flexible cross-talk functions for spatial filtering of EEG/MEG data: DeFleCT.

Hum Brain Mapp 2014 Apr 24;35(4):1642-53. Epub 2013 Apr 24.

MRC Cognition and Brain Sciences Unit, Cambridge, United Kingdom.

Brain activation estimated from EEG and MEG data is the basis for a number of time-series analyses. In these applications, it is essential to minimize "leakage" or "cross-talk" of the estimates among brain areas. Here, we present a novel framework that allows the design of flexible cross-talk functions (DeFleCT), combining three types of constraints: (1) full separation of multiple discrete brain sources, (2) minimization of contributions from other (distributed) brain sources, and (3) minimization of the contribution from measurement noise. Our framework allows the design of novel estimators by combining knowledge about discrete sources with constraints on distributed source activity and knowledge about noise covariance. These estimators will be useful in situations where assumptions about sources of interest need to be combined with uncertain information about additional sources that may contaminate the signal (e.g. distributed sources), and for which existing methods may not yield optimal solutions. We also show how existing estimators, such as maximum-likelihood dipole estimation, L2 minimum-norm estimation, and linearly-constrained minimum variance as well as null-beamformers, can be derived as special cases from this general formalism. The performance of the resulting estimators is demonstrated for the estimation of discrete sources and regions-of-interest in simulations of combined EEG/MEG data. Our framework will be useful for EEG/MEG studies applying time-series analysis in source space as well as for the evaluation and comparison of linear estimators.
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http://dx.doi.org/10.1002/hbm.22279DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6869144PMC
April 2014

Uncovering neural independent components from highly artifactual TMS-evoked EEG data.

J Neurosci Methods 2012 Jul 9;209(1):144-57. Epub 2012 Jun 9.

Department of Biomedical Engineering and Computational Science (BECS), Aalto University, School of Science, P.O. Box 12200, FI-00076 Aalto, Espoo, Finland.

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) is a powerful tool for studying cortical excitability and connectivity. To enhance the EEG interpretation, independent component analysis (ICA) has been used to separate the data into independent components (ICs). However, TMS can evoke large artifacts in EEG, which may greatly distort the ICA separation. The removal of such artifactual EEG from the data is a difficult task. In this paper we study how badly the large artifacts distort the ICA separation, and whether the distortions could be avoided without removing the artifacts. We first show that, in the ICA separation, the time courses of the ICs are not affected by the large artifacts, but their topographies could be greatly distorted. Next, we show how this distortion can be circumvented. We introduce a novel technique of suppression, by which the EEG data are modified so that the ICA separation of the suppressed data becomes reliable. The suppression, instead of removing the artifactual EEG, rescales all the data to about the same magnitude as the neural EEG. For the suppressed data, ICA returns the original time courses, but instead of the original topographies, it returns modified ones, which can be used, e.g., for the source localization. We present three suppression methods based on principal component analysis, wavelet analysis, and whitening of the data matrix, respectively. We test the methods with numerical simulations. The results show that the suppression improves the source localization.
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http://dx.doi.org/10.1016/j.jneumeth.2012.05.029DOI Listing
July 2012

Electrocardiographic inverse problem: spatial characterization of the left ventricle potential.

Annu Int Conf IEEE Eng Med Biol Soc 2009 ;2009:3274-7

Department of Biomedical Engineering and Computational Science, Helsinki University of Tehchnology, P.O. Box 2200, FI-02015 TKK, Finland.

In this work, we present a method for spatial characterization of the electrical activity of the left ventricle (LV). The presented method, electrocardiographic LV imaging, aims at characterization of main morphological features of the LV electrical activity via simple inverse reconstruction of the electrocardiogram on a standard LV segment model. The method is demonstrated with a case study dealing with the spatial characterization of an old myocardial infarction (MI). The results are encouraging: the centroid of the MI region is localized correctly, and the shape of the reconstructed infarcted region is similar to that in the golden standard solution, even though a patient-specific thorax model was not used.
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http://dx.doi.org/10.1109/IEMBS.2009.5333513DOI Listing
April 2010

Boundary element computations in the forward and inverse problems of electrocardiography: comparison of collocation and Galerkin weightings.

IEEE Trans Biomed Eng 2008 Sep;55(9):2124-33

Department of Biomedical Engineering and Computational Science, Helsinki University of Technology, Helsinki FI-02015 TKK, Finland.

In electrocardiographic imaging, epicardial potentials are reconstructed computationally from electrocardiographic measurements. The reconstruction is typically done with the help of the boundary element method (BEM), using the point collocation weighting and constant or linear basis functions. In this paper, we evaluated the performance of constant and linear point collocation and Galerkin BEMs in the epicardial potential problem. The integral equations and discretizations were formulated in terms of the single- and double-layer operators. All inner element integrals were calculated analytically. The computational methods were validated against analytical solutions in a simplified geometry. On the basis of the validation, no method was optimal in all testing scenarios. In the forward computation of the epicardial potential, the linear Galerkin (LG) method produced the smallest errors. The LG method also produced the smallest discretization error on the epicardial surface. In the inverse computation of epicardial potential, the electrode-specific transfer matrix performed better than the full transfer matrix. The Tikhonov 2 regularization outperformed the Tikhonov 0. In the optimal modeling conditions, the best BEM technique depended on electrode positions and chosen error measure. When large modeling errors such as omission of the lungs were present, the choice of the basis and weighting functions was not significant.
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http://dx.doi.org/10.1109/TBME.2008.923913DOI Listing
September 2008

Localization of prior myocardial infarction by repolarization variables.

Int J Cardiol 2008 Feb 26;124(1):100-6. Epub 2007 Mar 26.

Division of Cardiology, Helsinki University Central Hospital, Helsinki, Finland.

Background: To find quantitative, automatically applicable electrocardiographic (ECG) variables for detecting prior myocardial infarction (MI) in different myocardial regions.

Methods: Observational study. Body surface potential mapping (BSPM) was recorded at rest, and automatically analyzed with regard to ECG parameters, blinded to the clinical characteristics of the study subjects, 144 patients with prior MI and 75 healthy controls. MI location was determined by cine angiography or echocardiography as anterior (66 patients), inferoposterior (89 patients), and lateral (15 patients). Patients' 12-lead ECG was interpreted according to Minnesota code (Q-wave MI in 97 patients). The QRSSTT, QRS, and STT integrals, and the T-apex amplitude in detecting prior anterior and inferoposterior MI were analyzed.

Results: The T-apex amplitude, QRSSTT integral, and STT integral were functional in detecting MI in all tested locations on a single-lead basis, with areas under receiver operating characteristic curves (AUC) of over 90% (p<0.001) in optimal sites. In the best leads AUC for the QRSSTT integral in anterior MI was 93% (CI 87-99%) and for the inferoposterior MI 92% (CI 88-97%). These repolarization variables outperformed the Minnesota code in all tested MI locations. They were also able to distinguish between anterior and inferoposterior MI with an AUC of >85% (p<0.001).

Conclusions: Quantitative, automatically applicable single-lead repolarization variables detect prior MI irrespective of its location. They may simplify the screening for and localization of old infarctions as compared to the conventional ECG methods.
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http://dx.doi.org/10.1016/j.ijcard.2006.12.029DOI Listing
February 2008
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