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Functional MRI studies are frequently based on the BOLD (blood oxygen level-dependent) effect.
Early childhood program research that began in the 1960s was based on the bold hypothesis that early childhood programs could affect lives.
Since it was known that changes in neuronal activity were accompanied by local changes in brain oxygen content [14], it became evident that a technique based on the BOLD effect could potentially be used to investigate neuronal activation through changes induced in tissue oxygenation.
It is then clear that the difference between our observations and the results of transient inactivation studies based on electrophysiology [1], [2], [3] cannot be discounted based on the BOLD signal's sensitivity to subthreshold synaptic activity.
We have addressed the problem of spatial EPI distortions, which are particularly prominent in the medial temporal lobe [51], by applying a new distortion correction method based on the BOLD point spread function [39].
Therefore, the single layer and double layer groups may be combined and the analysis is based on the (bold) marginal totals (25% vs 12.5% gives risk ratio = 2).
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The MVPA can be modeled as a high-dimensional pattern classification problem to train a classification (or prediction) model based on the fMRI BOLD signals, in which voxels (as features) are identified in response to stimulus or diagnostic conditions (as class labels).
> Based on the differential BOLD results discussed fully in the next section, we formed two hypotheses concerning functional connectivity between ROI.
Permeability estimates based on the general model (bold line) are therefore somewhat higher than permeability estimates obtained from the initial model (thin line) k_{text{ini}} = { 3}. 4 7cdot 10^{ - 4} cdot varphi^{ 4. 38}, (1 where k ini is the initial permeability (in mD) and (varphi) is the porosity (in %).
Confirmation of this hypothesis will require further investigation based on the comparison of BOLD and invasive EEG investigation.
The group showed that single-trial correlations can be particularly helpful to separate different aspects of the BOLD signal based on their specific correlation to different ERP features (e.g., N1 potential fluctuations due to the high-effort condition).
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