Optimization of a Third-Order Gradiometer for Operation in Unshielded Environments

Uzunbajakau, S.; Rijpma, A.P.; Brake, H.J.M. ter; Peters, M.J.

doi:10.1109/tasc.2005.854300

Cited by 8 publications

(9 citation statements)

References 7 publications

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“…Nevertheless, it is possible that the hardware configuration of the device of interest could include other sensor types of which many exist, including vector magnetometers, axial gradiometers, second-order or even third-order gradiometers, etc. (Seki and Kandori, 2007, Uzunbajakau et al, 2005). The latter two types of sensors may be based on an appreciable variety of coil arrangements, and therefore many varieties of MEG systems are used in neuroimaging (Lima et al, 2004).…”

Section: Discussionmentioning

confidence: 99%

Variability of magnetoencephalographic sensor sensitivity measures as a function of age, brain volume and cortical area

Irimia

Erhart

Brown

2014

Clinical Neurophysiology

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Objective To assess the feasibility and appropriateness of magnetoencephalography (MEG) for both adult and pediatric studies, as well as for the developmental comparison of these factors across a wide range of ages. Methods For 45 subjects with ages from 1 to 24 years (infants, toddlers, school-age children and young adults), lead fields (LFs) of MEG sensors are computed using anatomically realistic boundary element models (BEMs) and individually-reconstructed cortical surfaces. Novel metrics are introduced to quantify MEG sensor focality. Results The variability of MEG focality is graphed as a function of brain volume and cortical area. Statistically significant differences in total cerebral volume, cortical area, MEG global sensitivity and LF focality are found between age groups. Conclusions Because MEG focality and sensitivity differ substantially across the age groups studied, the cortical LF maps explored here can provide important insights for the examination and interpretation of MEG signals from early childhood to young adulthood. Significance This is the first study to (1) investigate the relationship between MEG cortical LFs and brain volume as well as cortical area across development, and (2) compare LFs between subjects with different head sizes using detailed cortical reconstructions.

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Section: Discussionmentioning

confidence: 99%

Variability of magnetoencephalographic sensor sensitivity measures as a function of age, brain volume and cortical area

Irimia

Erhart

Brown

2014

Clinical Neurophysiology

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“…It is possible, however, that the hardware configuration of the MEG device that is of interest to a particular investigator might include some other type of sensors, of which a wide variety exist: vector magnetometers, axial gradiometers, second-order or even third-order gradiometers, etc. (Seki and Kandori, 2007; Uzunbajakau et al, 2005). The latter two categories of sensors can include a wide variety of coil arrangements, and consequently many types of MEG systems are used in neuroimaging (Clarke and Braginski, 2004).…”

Section: Discussionmentioning

confidence: 99%

Source cancellation profiles of electroencephalography and magnetoencephalography

2012

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Recorded electric potentials and magnetic fields due to cortical electrical activity have spatial spread even if their underlying brain sources are focal. Consequently, as a result of source cancellation, loss in signal amplitude and reduction in the effective signal-to-noise ratio can be expected when distributed sources are active simultaneously. Here we investigate the cancellation effects of EEG and MEG through the use of an anatomically correct forward model based on structural MRI acquired from 7 healthy adults. A boundary element model (BEM) with four compartments (brain, cerebrospinal fluid, skull and scalp) and highly accurate cortical meshes (~300,000 vertices) were generated. Distributed source activations were simulated using contiguous patches of active dipoles. To investigate cancellation effects in both EEG and MEG, quantitative indices were defined (source enhancement, cortical orientation disparity) and computed for varying values of the patch radius as well as for automatically parcellated gyri and sulci. Results were calculated for each cortical location, averaged over all subjects using a probabilistic atlas, and quantitatively compared between MEG and EEG. As expected, MEG sensors were found to be maximally sensitive to signals due to sources tangential to the scalp, and minimally sensitive to radial sources. Compared to EEG, however, MEG was found to be much more sensitive to signals generated antero-medially, notably in the anterior cingulate gyrus. Given that sources of activation cancel each other according to the orientation disparity of the cortex, this study provides useful methods and results for quantifying the effect of source orientation disparity upon source cancellation.

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“…If geometry of the openings is known, the imbalance can be calculated as discussed in the following sections. In [17], we optimized the geometrical parameters of this gradiometer, and in the present paper, we use the resulting geometrical parameters. That is m, m, m, m [see Fig.…”

Section: A Measuring Setupmentioning

confidence: 99%

On Gradiometer Imbalance

Uzunbajakau

Rijpma

Brake

et al. 2006

IEEE Trans. Appl. Supercond.

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We present methods to compute the imbalance in a gradiometer of arbitrary shape due to imperfections in its geometry, eddy currents induced in the radio-frequency interference shield, and screening currents induced in the modules of the superconducting quantum interference devices (SQUIDs). As an example, the methods are applied to evaluate the maximum expected initial imbalance of second-and third-order axial gradiometers in a measuring setup designed for fetal magnetocardiography. Mechanical imperfections in this specific setup appear to have the largest effect: the field imbalance is 2 10 2 ; the first-order gradient imbalance is 10 3 m; the second-order gradient imbalance is 10 4 m 2 . In the example, the imbalance caused by the other effects is one order smaller.

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Optimization of a Third-Order Gradiometer for Operation in Unshielded Environments

Cited by 8 publications

References 7 publications

Variability of magnetoencephalographic sensor sensitivity measures as a function of age, brain volume and cortical area

Variability of magnetoencephalographic sensor sensitivity measures as a function of age, brain volume and cortical area

Source cancellation profiles of electroencephalography and magnetoencephalography

On Gradiometer Imbalance

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