Dynamic functional neuroimaging integrating multiple modalities

George, John; Schmidt, D.; Rector, David M.; Wood, Chris

doi:10.1093/acprof:oso/9780192630711.003.0019

Cited by 6 publications

(4 citation statements)

References 72 publications

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“…Combining measurement from these modalities has been of great interest because the time resolution of fMRI and PET is poor, whereas that of MEG and EEG is excellent. Combining fMRI and EEG/MEG might provide a noninvasive unified view of human brain activity with high spatial and temporal resolution [42,43].…”

Section: Related Measurement Methods: Pet Meg Eeg and Tmsmentioning

confidence: 99%

Functional imaging of the visual pathways

Wandell¹,

Wade²

2003

Neurologic Clinics

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Functional neuroimaging has provided a new view of activity in human visual cortex. There have been a series of interesting developments in understanding the relationship between the functional signals, particularly functional MRI, and basic measurements of action potentials and local field potentials. The new human neuro-imaging measurements have clarified some of the similarities and differences between the general organization of visual areas in human and macaque visual cortex, and there have been some interesting new results concerning cortical visual plasticity and dysfunction. The new fMRI focus on measurements of the human brain will drive new relationships between neurology and visual neuroscience that should help us learn much more about the neural basis of perception.

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Section: Related Measurement Methods: Pet Meg Eeg and Tmsmentioning

confidence: 99%

Functional imaging of the visual pathways

Wandell¹,

Wade²

2003

Neurologic Clinics

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“…Initial attempts at EEG-fMRI fusion were mostly asymmetric methods where the spatial information provided by an fMRI study was used to constrain the localization of EEG/MEG (Dale & Sereno, 1993;Baillet & Garnero, 1997;Liu, Belliveau, & Dale, 1998;George et al, 1995;George, Schmidt, Rector, & Wood, 2001;Ahlfors & Simpson, 2004;Babiloni et al, 2004Babiloni et al, , 2005. Daunizeau et al (2007) proposed Bayesian frameworks in conjunction with a parcellation of the cortical surface in order to localize the neuronal activity common to EEG and fMRI in a symmetric manner, but where the electrical and hemodynamic temporal patterns were supposed to be independent from each other.…”

Section: Introductionmentioning

confidence: 99%

EEG-fMRI Fusion of Paradigm-Free Activity Using Kalman Filtering

Deneux

Faugeras

2010

Neural Computation

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We address here the use of EEG and fMRI, and their combination, in order to estimate the full spatiotemporal patterns of activity on the cortical surface in the absence of any particular assumptions on this activity such as stimulation times. For handling such a high-dimension inverse problem, we propose the use of (1) a global forward model of how these measures are functions of the "neural activity" of a large number of sources distributed on the cortical surface, formalized as a dynamical system, and (2) adaptive filters, as a natural solution to solve this inverse problem iteratively along the temporal dimension. This estimation framework relies on realistic physiological models, uses EEG and fMRI in a symmetric manner, and takes into account both their temporal and spatial information. We use the Kalman filter and smoother to perform such an estimation on realistic artificial data and demonstrate that the algorithm can handle the high dimensionality of these data and that it succeeds in solving this inverse problem, combining efficiently the information provided by the two modalities (this information being naturally predominantly temporal for EEG and spatial for fMRI). It performs particularly well in reconstructing a random temporally and spatially smooth activity spread over the cortex. The Kalman filter and smoother show some limitations, however, which call for the development of more specific adaptive filters. First, they do not cope well with the strong nonlinearity in the model that is necessary for an adequate description of the relation between cortical electric activities and the metabolic demand responsible for fMRI signals. Second, they fail to estimate a sparse activity (i.e., presenting sharp peaks at specific locations and times). Finally their computational cost remains high. We use schematic examples to explain these limitations and propose further developments of our method to overcome them.

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“…Alternatively, reliable location information from neural current MRI might be coupled with accu-rate timing information obtained from EEG or MEG [Dale et al, 2000;George et al, 2001;George et al, 1995] to transcend the limitations of each technique in isolation.…”

Section: Introductionmentioning

confidence: 99%

Modeling direct effects of neural current on MRI

Heller

Barrowes

George

2007

Human Brain Mapping

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Abstract:We investigate the effect of the magnetic field generated by neural activity on the magnitude and phase of the MRI signal in terms of a phenomenological parameter with the dimensions of length; it involves the product of the strength and duration of these currents. We obtain an analytic approximation to the MRI signal when the neuromagnetically induced phase is small inside the MRI voxel. The phase shift is the average of the MRI phase over the voxel, and therefore first order in that phase; and the reduction in the signal magnitude is one half the square of the standard deviation of the MRI phase, which is second order. The analytic approximation is compared with numerical simulations. For weak currents the agreement is excellent, and the magnitude change is generally much smaller than the phase shift. Using MEG data as a weak constraint on the current strength we find that for a net dipole moment of 10 nAm, a typical value for an evoked response, the reduction in the magnitude of the MRI signal is two parts in 10 5 , and the maximum value of the overall phase shift is % 4 Á 10 À3 , obtained when the MRI voxel is displaced 2/3 the size of the neuronal activity. We also show signal changes over a large range of values of the net dipole moment. We compare these results with others in the literature.

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Dynamic functional neuroimaging integrating multiple modalities

Cited by 6 publications

References 72 publications

Functional imaging of the visual pathways

Functional imaging of the visual pathways

EEG-fMRI Fusion of Paradigm-Free Activity Using Kalman Filtering

Modeling direct effects of neural current on MRI

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