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84
Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area
, 2006
"... decreases in neuronal activity in monkey visual area V1 ..."
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Cited by 50 (1 self)
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decreases in neuronal activity in monkey visual area V1
Comparing hemodynamic models with DCM
- NeuroImage
, 2007
"... The classical model of blood oxygen level-dependent (BOLD) responses by Buxton et al. [Buxton, R.B., Wong, E.C., Frank, L.R., 1998. Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model. Magn. Reson. Med. 39, 855–864] has been very important in providing a biophys ..."
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Cited by 33 (3 self)
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The classical model of blood oxygen level-dependent (BOLD) responses by Buxton et al. [Buxton, R.B., Wong, E.C., Frank, L.R., 1998. Dynamics of blood flow and oxygenation changes during brain activation: the Balloon model. Magn. Reson. Med. 39, 855–864] has been very important in providing a biophysically plausible framework for explaining different aspects of hemodynamic responses. It also plays an important role in the hemodynamic forward model for dynamic causal modeling (DCM) of fMRI data. A recent study by Obata et al. [Obata, T., Liu, T.T., Miller, K.L., Luh, W.M., Wong, E.C., Frank, L.R., Buxton, R.B., 2004. Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the Balloon model to the interpretation of BOLD transients. NeuroImage 21, 144–153] linearized the BOLD signal equation and suggested a revised form for the model coefficients. In this paper, we show that the classical and revised models are special
Variational filtering
, 2008
"... This note presents a simple Bayesian filtering scheme, using variational calculus, for inference on the hidden states of dynamic systems. Variational filtering is a stochastic scheme that propagates particles over a changing variational energy landscape, such that their sample density approximates t ..."
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Cited by 18 (7 self)
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This note presents a simple Bayesian filtering scheme, using variational calculus, for inference on the hidden states of dynamic systems. Variational filtering is a stochastic scheme that propagates particles over a changing variational energy landscape, such that their sample density approximates the conditional density of hidden and states and inputs. The key innovation, on which variational filtering rests, is a formulation in generalised coordinates of motion. This renders the scheme much simpler and more versatile than existing approaches, such as those based on particle filtering. We demonstrate variational filtering using simulated and real data from hemodynamic systems studied in neuroimaging and provide comparative evaluations using particle filtering and the fixed-form homologue of variational filtering, namely dynamic expectation maximisation.
Comparing dynamic causal models using AIC, BIC and free energy
- NeuroImage
, 2012
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Electrophysiological imaging of brain activity and connectivity-challenges and opportunities
- IEEE Transactions on Bio-Medical Engineering
, 2011
"... Abstract—Unlocking the dynamic inner workings of the brain continues to remain a grand challenge of the 21st century. To this end, functional neuroimaging modalities represent an outstanding approach to better understand the mechanisms of both normal and abnormal brain functions. The ability to imag ..."
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Cited by 16 (1 self)
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Abstract—Unlocking the dynamic inner workings of the brain continues to remain a grand challenge of the 21st century. To this end, functional neuroimaging modalities represent an outstanding approach to better understand the mechanisms of both normal and abnormal brain functions. The ability to image brain function with ever increasing spatial and temporal resolution has made a significant leap over the past several decades. Further delineation of functional networks could lead to improved understanding of brain function in both normal and diseased states. This paper reviews recent advancements and current challenges in dynamic functional neuroimaging techniques, including electrophysiologi-cal source imaging, multimodal neuroimaging integrating fMRI with EEG/MEG, and functional connectivity imaging. Index Terms—Electroencephalography (EEG), electrophysio-logical imaging, functional connectivity, functional magnetic res-onance imaging (fMRI), magnetoencephalography (MEG), source imaging. I.
Differential activation of frontal and parietal regions during visual word recognition: an optical topography study
- NeuroImage
, 2008
"... The present study examined cortical oxygenation changes during lexical decision on words and pseudowords using functional Near-Infrared Spectroscopy (fNIRS). Focal hyperoxygenation as an indi-cator of functional activation was compared over three target areas over the left hemisphere. A 52-channel H ..."
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Cited by 10 (2 self)
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The present study examined cortical oxygenation changes during lexical decision on words and pseudowords using functional Near-Infrared Spectroscopy (fNIRS). Focal hyperoxygenation as an indi-cator of functional activation was compared over three target areas over the left hemisphere. A 52-channel Hitachi ETG-4000 was used covering the superior frontal gyrus (SFG), the left inferior parietal gyrus (IPG) and the left inferior frontal gyrus (IFG). To allow for anatomical inference a recently developed probabilistic mapping method was used to determine the most likely anatomic locations of
Review of P
- Krugman, Geography and Trade, Environment and Planning A
, 1992
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Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models. The Annals of Applied Statistics
, 2011
"... Functional MRI (fMRI) has become the most common method for in-vestigating the human brain. However, fMRI data present some complica-tions for statistical analysis and modeling. One recently developed approach to these data focuses on estimation of computational encoding models that describe how sti ..."
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Cited by 4 (0 self)
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Functional MRI (fMRI) has become the most common method for in-vestigating the human brain. However, fMRI data present some complica-tions for statistical analysis and modeling. One recently developed approach to these data focuses on estimation of computational encoding models that describe how stimuli are transformed into brain activity measured in indi-vidual voxels. Here we aim at building encoding models for fMRI signals recorded in the primary visual cortex of the human brain. We use residual analyses to reveal systematic nonlinearity across voxels not taken into ac-count by previous models. We then show how a sparse nonparametric method [J. Roy. Statist. Soc. Ser. B 71 (2009b) 1009–1030] can be used together with correlation screening to estimate nonlinear encoding models effectively. Our approach produces encoding models that predict about 25 % more accurately than models estimated using other methods [Nature 452 (2008a) 352–355]. The estimated nonlinearity impacts the inferred properties of individual vox-els, and it has a plausible biological interpretation. One benefit of quantitative encoding models is that estimated models can be used to decode brain activ-ity, in order to identify which specific image was seen by an observer. Encod-ing models estimated by our approach also improve such image identification by about 12 % when the correct image is one of 11,500 possible images. 1. Introduction. One
Assessment of Stimulus-Induced Changes in Human V1 Visual Field Maps
, 2006
"... You might find this additional information useful... This article cites 33 articles, 14 of which you can access free at: ..."
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Cited by 4 (0 self)
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You might find this additional information useful... This article cites 33 articles, 14 of which you can access free at:
